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Merge remote-tracking branch 'fork/master'

master
AlexDBlack 2019-09-05 22:04:25 +10:00
parent d1e9b34982 fc222d3325
commit a76a44e198
60 changed files with 1219 additions and 485 deletions
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13
arbiter/arbiter-core/src/main/java/org/deeplearning4j/arbiter/optimize/parameter/FixedValue.java
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@ -17,13 +17,14 @@
package org.deeplearning4j.arbiter.optimize.parameter;
import lombok.EqualsAndHashCode;
import lombok.Getter;
import org.deeplearning4j.arbiter.optimize.api.ParameterSpace;
import org.deeplearning4j.arbiter.optimize.serde.jackson.GenericDeserializer;
import org.deeplearning4j.arbiter.optimize.serde.jackson.GenericSerializer;
import org.deeplearning4j.arbiter.optimize.serde.jackson.FixedValueDeserializer;
import org.deeplearning4j.arbiter.optimize.serde.jackson.FixedValueSerializer;
import org.deeplearning4j.arbiter.util.ObjectUtils;
import org.nd4j.shade.jackson.annotation.JsonCreator;
import org.nd4j.shade.jackson.annotation.JsonIgnoreProperties;
import org.nd4j.shade.jackson.annotation.JsonProperty;
import org.nd4j.shade.jackson.annotation.JsonTypeInfo;
import org.nd4j.shade.jackson.databind.annotation.JsonDeserialize;
import org.nd4j.shade.jackson.databind.annotation.JsonSerialize;
@ -37,9 +38,11 @@ import java.util.Map;
* @param <T> Type of (fixed) value
*/
@EqualsAndHashCode
@JsonSerialize(using = FixedValueSerializer.class)
@JsonDeserialize(using = FixedValueDeserializer.class)
@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
public class FixedValue<T> implements ParameterSpace<T> {
@JsonSerialize(using = GenericSerializer.class)
@JsonDeserialize(using = GenericDeserializer.class)
@Getter
private Object value;
private int index;

52
arbiter/arbiter-core/src/main/java/org/deeplearning4j/arbiter/optimize/serde/jackson/FixedValueDeserializer.java Normal file
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@ -0,0 +1,52 @@
package org.deeplearning4j.arbiter.optimize.serde.jackson;
import org.apache.commons.codec.binary.Base64;
import org.deeplearning4j.arbiter.optimize.parameter.FixedValue;
import org.nd4j.shade.jackson.core.JsonParser;
import org.nd4j.shade.jackson.core.JsonProcessingException;
import org.nd4j.shade.jackson.databind.DeserializationContext;
import org.nd4j.shade.jackson.databind.JsonDeserializer;
import org.nd4j.shade.jackson.databind.JsonNode;
import org.nd4j.shade.jackson.databind.ObjectMapper;
import java.io.ByteArrayInputStream;
import java.io.IOException;
import java.io.ObjectInputStream;
/**
* A custom deserializer to be used in conjunction with {@link FixedValueSerializer}
* @author Alex Black
*/
public class FixedValueDeserializer extends JsonDeserializer<FixedValue> {
@Override
public FixedValue deserialize(JsonParser p, DeserializationContext deserializationContext) throws IOException, JsonProcessingException {
JsonNode node = p.getCodec().readTree(p);
String className = node.get("@valueclass").asText();
Class<?> c;
try {
c = Class.forName(className);
} catch (Exception e) {
throw new RuntimeException(e);
}
if(node.has("value")){
//Number, String, Enum
JsonNode valueNode = node.get("value");
Object o = new ObjectMapper().treeToValue(valueNode, c);
return new FixedValue<>(o);
} else {
//Everything else
JsonNode valueNode = node.get("data");
String data = valueNode.asText();
byte[] b = new Base64().decode(data);
ObjectInputStream ois = new ObjectInputStream(new ByteArrayInputStream(b));
try {
Object o = ois.readObject();
return new FixedValue<>(o);
} catch (Throwable t) {
throw new RuntimeException(t);
}
}
}
}

51
arbiter/arbiter-core/src/main/java/org/deeplearning4j/arbiter/optimize/serde/jackson/FixedValueSerializer.java Normal file
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@ -0,0 +1,51 @@
package org.deeplearning4j.arbiter.optimize.serde.jackson;
import org.apache.commons.net.util.Base64;
import org.deeplearning4j.arbiter.optimize.parameter.FixedValue;
import org.nd4j.shade.jackson.core.JsonGenerator;
import org.nd4j.shade.jackson.core.type.WritableTypeId;
import org.nd4j.shade.jackson.databind.JsonSerializer;
import org.nd4j.shade.jackson.databind.SerializerProvider;
import org.nd4j.shade.jackson.databind.jsontype.TypeSerializer;
import java.io.ByteArrayOutputStream;
import java.io.IOException;
import java.io.ObjectOutputStream;
import static org.nd4j.shade.jackson.core.JsonToken.START_OBJECT;
/**
* A custom serializer to handle arbitrary object types
* Uses standard JSON where safe (number, string, enumerations) or Java object serialization (bytes -> base64)
* The latter is not an ideal approach, but Jackson doesn't support serialization/deserialization of arbitrary
* objects very well
*
* @author Alex Black
*/
public class FixedValueSerializer extends JsonSerializer<FixedValue> {
@Override
public void serialize(FixedValue fixedValue, JsonGenerator j, SerializerProvider serializerProvider) throws IOException {
Object o = fixedValue.getValue();
j.writeStringField("@valueclass", o.getClass().getName());
if(o instanceof Number || o instanceof String || o instanceof Enum){
j.writeObjectField("value", o);
} else {
ByteArrayOutputStream baos = new ByteArrayOutputStream();
ObjectOutputStream oos = new ObjectOutputStream(baos);
oos.writeObject(o);
baos.close();
byte[] b = baos.toByteArray();
String base64 = new Base64().encodeToString(b);
j.writeStringField("data", base64);
}
}
@Override
public void serializeWithType(FixedValue value, JsonGenerator gen, SerializerProvider serializers, TypeSerializer typeSer) throws IOException {
WritableTypeId typeId = typeSer.typeId(value, START_OBJECT);
typeSer.writeTypePrefix(gen, typeId);
serialize(value, gen, serializers);
typeSer.writeTypeSuffix(gen, typeId);
}
}

46
arbiter/arbiter-core/src/main/java/org/deeplearning4j/arbiter/optimize/serde/jackson/GenericDeserializer.java
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@ -1,46 +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.arbiter.optimize.serde.jackson;
import org.nd4j.shade.jackson.core.JsonParser;
import org.nd4j.shade.jackson.databind.DeserializationContext;
import org.nd4j.shade.jackson.databind.JsonDeserializer;
import org.nd4j.shade.jackson.databind.JsonNode;
import org.nd4j.shade.jackson.databind.ObjectMapper;
import java.io.IOException;
/**
* Created by Alex on 15/02/2017.
*/
public class GenericDeserializer extends JsonDeserializer<Object> {
@Override
public Object deserialize(JsonParser p, DeserializationContext ctxt) throws IOException {
JsonNode node = p.getCodec().readTree(p);
String className = node.get("@class").asText();
Class<?> c;
try {
c = Class.forName(className);
} catch (Exception e) {
throw new RuntimeException(e);
}
JsonNode valueNode = node.get("value");
Object o = new ObjectMapper().treeToValue(valueNode, c);
return o;
}
}

38
arbiter/arbiter-core/src/main/java/org/deeplearning4j/arbiter/optimize/serde/jackson/GenericSerializer.java
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@ -1,38 +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.arbiter.optimize.serde.jackson;
import org.nd4j.shade.jackson.core.JsonGenerator;
import org.nd4j.shade.jackson.core.JsonProcessingException;
import org.nd4j.shade.jackson.databind.JsonSerializer;
import org.nd4j.shade.jackson.databind.SerializerProvider;
import java.io.IOException;
/**
* Created by Alex on 15/02/2017.
*/
public class GenericSerializer extends JsonSerializer<Object> {
@Override
public void serialize(Object o, JsonGenerator j, SerializerProvider serializerProvider)
throws IOException, JsonProcessingException {
j.writeStartObject();
j.writeStringField("@class", o.getClass().getName());
j.writeObjectField("value", o);
j.writeEndObject();
}
}

4
arbiter/arbiter-core/src/main/java/org/deeplearning4j/arbiter/optimize/serde/jackson/JsonMapper.java
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@ -24,9 +24,6 @@ import org.nd4j.shade.jackson.databind.SerializationFeature;
import org.nd4j.shade.jackson.dataformat.yaml.YAMLFactory;
import org.nd4j.shade.jackson.datatype.joda.JodaModule;
import java.util.Collections;
import java.util.Map;
/**
* Created by Alex on 16/11/2016.
*/
@ -44,6 +41,7 @@ public class JsonMapper {
mapper.setVisibility(PropertyAccessor.ALL, JsonAutoDetect.Visibility.NONE);
mapper.setVisibility(PropertyAccessor.FIELD, JsonAutoDetect.Visibility.ANY);
mapper.setVisibility(PropertyAccessor.CREATOR, JsonAutoDetect.Visibility.ANY);
mapper.setVisibility(PropertyAccessor.SETTER, JsonAutoDetect.Visibility.ANY);
yamlMapper = new ObjectMapper(new YAMLFactory());
yamlMapper.registerModule(new JodaModule());
yamlMapper.configure(DeserializationFeature.FAIL_ON_UNKNOWN_PROPERTIES, false);

2
arbiter/arbiter-deeplearning4j/src/main/java/org/deeplearning4j/arbiter/layers/BaseOutputLayerSpace.java
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@ -32,7 +32,7 @@ import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction;
*/
@Data
@EqualsAndHashCode(callSuper = true)
@NoArgsConstructor(access = AccessLevel.PROTECTED) //For Jackson JSON/YAML deserialization
@NoArgsConstructor(access = AccessLevel.PUBLIC) //For Jackson JSON/YAML deserialization
public abstract class BaseOutputLayerSpace<L extends BaseOutputLayer> extends FeedForwardLayerSpace<L> {
protected ParameterSpace<ILossFunction> lossFunction;

11
arbiter/arbiter-ui/src/main/java/org/deeplearning4j/arbiter/ui/data/GlobalConfigPersistable.java
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@ -18,8 +18,11 @@ package org.deeplearning4j.arbiter.ui.data;
import lombok.Getter;
import org.deeplearning4j.arbiter.optimize.config.OptimizationConfiguration;
import org.deeplearning4j.arbiter.ui.misc.JsonMapper;
import org.deeplearning4j.arbiter.optimize.serde.jackson.JsonMapper;
import org.deeplearning4j.arbiter.ui.module.ArbiterModule;
import org.deeplearning4j.nn.conf.serde.JsonMappers;
import java.io.IOException;
/**
*
@ -64,7 +67,11 @@ public class GlobalConfigPersistable extends BaseJavaPersistable {
public OptimizationConfiguration getOptimizationConfiguration(){
return JsonMapper.fromJson(optimizationConfigJson, OptimizationConfiguration.class);
try {
return JsonMapper.getMapper().readValue(optimizationConfigJson, OptimizationConfiguration.class);
} catch (IOException e){
throw new RuntimeException(e);
}
}
public int getCandidatesQueued(){

9
arbiter/arbiter-ui/src/main/java/org/deeplearning4j/arbiter/ui/listener/ArbiterStatusListener.java
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@ -26,13 +26,14 @@ import org.deeplearning4j.arbiter.optimize.api.OptimizationResult;
import org.deeplearning4j.arbiter.optimize.runner.CandidateInfo;
import org.deeplearning4j.arbiter.optimize.runner.IOptimizationRunner;
import org.deeplearning4j.arbiter.optimize.runner.listener.StatusListener;
import org.deeplearning4j.arbiter.optimize.serde.jackson.JsonMapper;
import org.deeplearning4j.arbiter.ui.data.GlobalConfigPersistable;
import org.deeplearning4j.arbiter.ui.data.ModelInfoPersistable;
import org.deeplearning4j.arbiter.ui.misc.JsonMapper;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.nd4j.linalg.primitives.Pair;
import java.io.IOException;
import java.util.Map;
import java.util.UUID;
import java.util.concurrent.ConcurrentHashMap;
@ -217,7 +218,11 @@ public class ArbiterStatusListener implements StatusListener {
// }
//TODO: cache global config, but we don't want to have outdated info (like uninitialized termination conditions)
ocJson = JsonMapper.asJson(r.getConfiguration());
try {
ocJson = JsonMapper.getMapper().writeValueAsString(r.getConfiguration());
} catch (IOException e){
throw new RuntimeException(e);
}
GlobalConfigPersistable p = new GlobalConfigPersistable.Builder()
.sessionId(sessionId)

11
arbiter/arbiter-ui/src/main/scala/org/deeplearning4j/arbiter/ui/views/html/ArbiterUI.template.scala
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File diff suppressed because one or more lines are too long

4
arbiter/arbiter-ui/src/main/views/org/deeplearning4j/arbiter/ui/views/ArbiterUI.scala.html
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@ -183,12 +183,12 @@
</head>
<body class="bgcolor">
<meta name="viewport" content="width=device-width, initial-scale=1">
<link id="bootstrap-style" href="/assets/webjars/bootstrap/2.3.2/css/bootstrap.min.css" rel="stylesheet">
<link id="bootstrap-style" href="/assets/webjars/bootstrap/4.3.1/dist/css/bootstrap.min.css" rel="stylesheet">
<script src="/assets/webjars/jquery/2.2.0/jquery.min.js"></script>
<link href="/assets/webjars/jquery-ui/1.10.2/themes/base/jquery-ui.css" rel="stylesheet">
<script src="/assets/webjars/jquery-ui/1.10.2/ui/minified/jquery-ui.min.js"></script>
<script src="/assets/webjars/d3js/3.3.5/d3.min.js" charset="utf-8"></script>
<script src="/assets/webjars/bootstrap/2.3.2/js/bootstrap.min.js"></script>
<script src="/assets/webjars/bootstrap/4.3.1/dist/js/bootstrap.min.js"></script>
<script src="/assets/dl4j-ui.js"></script>
<script>

2
datavec/datavec-spark/src/test/java/org/datavec/spark/transform/analysis/TestAnalysis.java
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@ -35,7 +35,7 @@ import org.datavec.spark.BaseSparkTest;
import org.datavec.spark.transform.AnalyzeSpark;
import org.joda.time.DateTimeZone;
import org.junit.Test;
import org.nd4j.graph.DataType;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.io.ClassPathResource;

10
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/solvers/accumulation/EncodingHandler.java
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@ -135,7 +135,7 @@ public class EncodingHandler implements MessageHandler {
iterations.get().incrementAndGet();
if (boundary != null && atomicBoundary.get() < 0)
atomicBoundary.compareAndSet(-1, (int) (updates.lengthLong() * boundary));
atomicBoundary.compareAndSet(-1, (int) (updates.length() * boundary));
INDArray encoded;
@ -160,11 +160,11 @@ public class EncodingHandler implements MessageHandler {
double encLen = encoded.data().getInt(0);
// if updates are too dense - we fallback to bitmap encoding
if (encLen >= (updates.lengthLong() / 16)) {
if (encLen >= (updates.length() / 16)) {
log.debug("Switching back to bitmapEncoding: iteration {}, epoch {}, threshold {}, encoded length {}", iteration, epoch, currThreshold, encLen);
bitmapMode.get().set(true);
DataBuffer buffer = Nd4j.getDataBufferFactory().createInt(updates.lengthLong() / 16 + 5);
DataBuffer buffer = Nd4j.getDataBufferFactory().createInt(updates.length() / 16 + 5);
encoded = Nd4j.createArrayFromShapeBuffer(buffer, updates.shapeInfoDataBuffer());
Nd4j.getExecutioner().bitmapEncode(updates, encoded, currentThreshold.get().get());
@ -186,12 +186,12 @@ public class EncodingHandler implements MessageHandler {
}
} else {
//Dense bitmap updates
DataBuffer buffer = Nd4j.getDataBufferFactory().createInt(updates.lengthLong() / 16 + 5);
DataBuffer buffer = Nd4j.getDataBufferFactory().createInt(updates.length() / 16 + 5);
encoded = Nd4j.createArrayFromShapeBuffer(buffer, updates.shapeInfoDataBuffer());
long values = Nd4j.getExecutioner().bitmapEncode(updates, encoded, currentThreshold.get().get());
if (values < (updates.lengthLong() / 16 + 5) / 2) {
if (values < (updates.length() / 16 + 5) / 2) {
boolean current = bitmapMode.get().get();
bitmapMode.get().set(false);
if(!current) {

2
deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark-nlp/pom.xml
View File

@ -63,7 +63,7 @@
<dependency>
<groupId>com.fasterxml.jackson.module</groupId>
<artifactId>jackson-module-scala_2.11</artifactId>
<version>${jackson.version}</version>
<version>2.6.7</version>
</dependency>
</dependencies>

31
deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark-nlp/src/test/java/org/deeplearning4j/spark/text/BaseSparkTest.java
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@ -23,6 +23,9 @@ import org.junit.After;
import org.junit.Before;
import java.io.Serializable;
import java.lang.reflect.Field;
import java.util.Collections;
import java.util.Map;
/**
* Created by agibsonccc on 1/23/15.
@ -37,7 +40,9 @@ public abstract class BaseSparkTest implements Serializable {
@After
public void after() {
sc.close();
if(sc != null) {
sc.close();
}
sc = null;
}
@ -48,6 +53,30 @@ public abstract class BaseSparkTest implements Serializable {
public JavaSparkContext getContext() {
if (sc != null)
return sc;
//Ensure SPARK_USER environment variable is set for Spark tests
String u = System.getenv("SPARK_USER");
Map<String, String> env = System.getenv();
if(u == null || u.isEmpty()) {
try {
Class[] classes = Collections.class.getDeclaredClasses();
for (Class cl : classes) {
if ("java.util.Collections$UnmodifiableMap".equals(cl.getName())) {
Field field = cl.getDeclaredField("m");
field.setAccessible(true);
Object obj = field.get(env);
Map<String, String> map = (Map<String, String>) obj;
String user = System.getProperty("user.name");
if (user == null || user.isEmpty())
user = "user";
map.put("SPARK_USER", user);
}
}
} catch (Exception e) {
throw new RuntimeException(e);
}
}
// set to test mode
SparkConf sparkConf = new SparkConf().setMaster("local[4]").set("spark.driver.host", "localhost")
.setAppName("sparktest")

29
deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/BaseSparkKryoTest.java
View File

@ -19,6 +19,10 @@ package org.deeplearning4j.spark;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaSparkContext;
import java.lang.reflect.Field;
import java.util.Collections;
import java.util.Map;
/**
* Created by Alex on 04/07/2017.
*/
@ -30,6 +34,31 @@ public class BaseSparkKryoTest extends BaseSparkTest {
return sc;
}
//Ensure SPARK_USER environment variable is set for Spark Kryo tests
String u = System.getenv("SPARK_USER");
if(u == null || u.isEmpty()){
try {
Class[] classes = Collections.class.getDeclaredClasses();
Map<String, String> env = System.getenv();
for (Class cl : classes) {
if ("java.util.Collections$UnmodifiableMap".equals(cl.getName())) {
Field field = cl.getDeclaredField("m");
field.setAccessible(true);
Object obj = field.get(env);
Map<String, String> map = (Map<String, String>) obj;
String user = System.getProperty("user.name");
if(user == null || user.isEmpty())
user = "user";
map.put("SPARK_USER", user);
}
}
} catch (Exception e){
throw new RuntimeException(e);
}
}
SparkConf sparkConf = new SparkConf().setMaster("local[" + numExecutors() + "]").setAppName("sparktest");
sparkConf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer");

4
deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/BaseSparkTest.java
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@ -74,7 +74,9 @@ public abstract class BaseSparkTest implements Serializable {
@After
public void after() {
sc.close();
if(sc != null) {
sc.close();
}
sc = null;
}

11
deeplearning4j/deeplearning4j-ui-parent/deeplearning4j-play/src/main/java/org/deeplearning4j/ui/module/remote/RemoteReceiverModule.java
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@ -19,23 +19,20 @@ package org.deeplearning4j.ui.module.remote;
import com.fasterxml.jackson.databind.JsonNode;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.api.storage.*;
import org.deeplearning4j.ui.api.FunctionType;
import org.deeplearning4j.ui.api.HttpMethod;
import org.deeplearning4j.ui.api.Route;
import org.deeplearning4j.ui.api.UIModule;
import org.deeplearning4j.ui.i18n.I18NResource;
import play.mvc.Http;
import play.mvc.Result;
import play.mvc.Results;
import javax.xml.bind.DatatypeConverter;
import java.io.File;
import java.util.Collection;
import java.util.Collections;
import java.util.List;
import java.util.concurrent.atomic.AtomicBoolean;
import static play.mvc.Http.Context.Implicit.request;
/**
*
* Used to receive UI updates remotely.
@ -73,7 +70,7 @@ public class RemoteReceiverModule implements UIModule {
@Override
public List<Route> getRoutes() {
Route r = new Route("/remoteReceive", HttpMethod.POST, FunctionType.Supplier, this::receiveData);
Route r = Route.request0Function("/remoteReceive", HttpMethod.POST, this::receiveData);
return Collections.singletonList(r);
}
@ -98,7 +95,7 @@ public class RemoteReceiverModule implements UIModule {
return Collections.emptyList();
}
private Result receiveData() {
private Result receiveData(Http.Request request) {
if (!enabled.get()) {
return Results.forbidden(
"UI server remote listening is currently disabled. Use UIServer.getInstance().enableRemoteListener()");
@ -109,7 +106,7 @@ public class RemoteReceiverModule implements UIModule {
"UI Server remote listener: no StatsStorage instance is set/available to store results");
}
JsonNode jn = request().body().asJson();
JsonNode jn = request.body().asJson();
JsonNode type = jn.get("type");
JsonNode dataClass = jn.get("class");
JsonNode data = jn.get("data");

6
deeplearning4j/deeplearning4j-ui-parent/deeplearning4j-play/src/main/resources/deeplearning4jUiAssets/js/train/overview.js
View File

@ -21,16 +21,16 @@ function selectStdevChart(fieldName) {
$("#stdevUpdates").attr("class", "active");
}
renderOverviewPage(false);
renderOverviewPage(true);
}
/* ---------- Render page ---------- */
var lastUpdateTime = -1;
var lastUpdateSession = "";
function renderOverviewPage(firstLoad) {
function renderOverviewPage(forceupdate) {
updateSessionWorkerSelect();
if(firstLoad || !lastUpdateSession || lastUpdateSession == "" || lastUpdateSession != currSession){
if(forceupdate || !lastUpdateSession || lastUpdateSession == "" || lastUpdateSession != currSession){
executeOverviewUpdate();
} else {
//Check last update time first - see if data has actually changed...

2
libnd4j/include/execution/cuda/AffinityManager.cu
View File

@ -109,7 +109,7 @@ namespace nd4j {
if (deviceId != previousDeviceId) {
// discard existing stuff
nd4j_printf("AffinityManager::setCurrentDevice() was invoked, releasing buffers\n", "");
//nd4j_printf("AffinityManager::setCurrentDevice() was invoked, releasing buffers\n", "");
LaunchContext::releaseBuffers();
}
}

2
libnd4j/include/execution/cuda/LaunchContext.cu
View File

@ -158,7 +158,7 @@ LaunchContext::LaunchContext() {
};
void LaunchContext::releaseBuffers() {
nd4j_printf("LaunchContext::releaseBuffers() was invoked\n", "");
//nd4j_printf("LaunchContext::releaseBuffers() was invoked\n", "");
contextBuffers.release();
}

2
libnd4j/include/ops/declarable/generic/recurrent/sruCell.cpp
View File

@ -34,7 +34,7 @@ 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 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 [1 x 2*inSize]
auto b = INPUT_VARIABLE(3); // biases [2*inSize]
auto ht = OUTPUT_VARIABLE(0); // current cell output [bS x inSize], that is at current time step t
auto ct = OUTPUT_VARIABLE(1); // current cell state [bS x inSize], that is at current time step t

18
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/SameDiff.java
View File

@ -6511,4 +6511,22 @@ public class SameDiff extends SDBaseOps {
public String generateNewVarName(String base, int argIndex) {
return generateNewVarName(base, argIndex, true);
}
/**
* Returns an unused variable name of the format &lt;base&gt;_#.
*
* Intended to be used for custom variables (like weights), arguments and op outputs should use {@link #generateNewVarName(String, int)}.
*/
public String generateDistinctCustomVariableName(String base){
if(!variables.containsKey(base))
return base;
int inc = 1;
while(variables.containsKey(base + "_" + inc)){
inc++;
}
return base + "_" + inc;
}
}

205
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDRNN.java
View File

@ -16,6 +16,7 @@
package org.nd4j.autodiff.samediff.ops;
import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.*;
@ -23,6 +24,15 @@ import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.*;
import java.util.Arrays;
import java.util.List;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.outputs.GRUCellOutputs;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.outputs.LSTMCellOutputs;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.outputs.LSTMLayerOutputs;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.outputs.SRUCellOutputs;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.outputs.SRULayerOutputs;
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.primitives.Pair;
/**
* SameDiff Recurrent Neural Network operations<br>
@ -39,90 +49,163 @@ public class SDRNN extends SDOps {
/**
* The gru cell
*
* @param configuration the configuration to use
* @return
* See {@link #gru(String, SDVariable, SDVariable, GRUWeights)}.
*/
public List<SDVariable> gru(GRUCellConfiguration configuration) {
GRUCell c = new GRUCell(sd, configuration);
return Arrays.asList(c.outputVariables());
public GRUCellOutputs gru(@NonNull SDVariable x, @NonNull SDVariable hLast, @NonNull GRUWeights weights) {
GRUCell c = new GRUCell(sd, x, hLast, weights);
return new GRUCellOutputs(c.outputVariables());
}
/**
* The gru cell
* The GRU cell. Does a single time step operation.
*
* @param baseName the base name for the gru cell
* @param configuration the configuration to use
* @return
* @param baseName The base name for the gru cell
* @param x Input, with shape [batchSize, inSize]
* @param hLast Output of the previous cell/time step, with shape [batchSize, numUnits]
* @param weights The cell's weights.
* @return The cell's outputs.
*/
public List<SDVariable> gru(String baseName, GRUCellConfiguration configuration) {
GRUCell c = new GRUCell(sd, configuration);
return Arrays.asList(c.outputVariables(baseName));
}
/**
* LSTM unit
*
* @param baseName the base name for outputs
* @param configuration the configuration to use
* @return
*/
public SDVariable lstmCell(String baseName, LSTMCellConfiguration configuration) {
return new LSTMCell(sd, configuration).outputVariables(baseName)[0];
}
public List<SDVariable> lstmBlockCell(String name, LSTMBlockCellConfiguration configuration){
SDVariable[] v = new LSTMBlockCell(sd, configuration).outputVariables(name);
return Arrays.asList(v);
}
public List<SDVariable> lstmLayer(String name, LSTMConfiguration configuration){
SDVariable[] v = new LSTMLayer(sd, configuration).outputVariables(name);
return Arrays.asList(v);
public GRUCellOutputs gru(String baseName, @NonNull SDVariable x, @NonNull SDVariable hLast, @NonNull GRUWeights weights) {
GRUCell c = new GRUCell(sd, x, hLast, weights);
return new GRUCellOutputs(c.outputVariables(baseName));
}
/**
* Simple recurrent unit
*
* @param configuration the configuration for the sru
* @return
* See {@link #lstmCell(String, SDVariable, SDVariable, SDVariable, LSTMWeights, LSTMConfiguration)}.
*/
public SDVariable sru(SRUConfiguration configuration) {
return new SRU(sd, configuration).outputVariables()[0];
public LSTMCellOutputs lstmCell(@NonNull SDVariable x, @NonNull SDVariable cLast, @NonNull SDVariable yLast,
LSTMWeights weights, LSTMConfiguration config){
LSTMBlockCell c = new LSTMBlockCell(sd, x, cLast, yLast, weights, config);
return new LSTMCellOutputs(c.outputVariables());
}
/**
* Simiple recurrent unit
* The LSTM cell. Does a single time step operation.
*
* @param baseName the base name to use for output variables
* @param configuration the configuration for the sru
* @return
* @param baseName The base name for the lstm cell
* @param x Input, with shape [batchSize, inSize]
* @param cLast Previous cell state, with shape [batchSize, numUnits]
* @param yLast Previous cell output, with shape [batchSize, numUnits]
* @param weights The cell's weights.
* @param config The cell's config.
* @return The cell's outputs.
*/
public SDVariable sru(String baseName, SRUConfiguration configuration) {
return new SRU(sd, configuration).outputVariables(baseName)[0];
public LSTMCellOutputs lstmCell(String baseName, @NonNull SDVariable x, @NonNull SDVariable cLast, @NonNull SDVariable yLast,
@NonNull LSTMWeights weights, @NonNull LSTMConfiguration config){
LSTMBlockCell c = new LSTMBlockCell(sd, x, cLast, yLast, weights, config);
return new LSTMCellOutputs(c.outputVariables(baseName));
}
/**
* An sru cell
*
* @param configuration the configuration for the sru cell
* @return
* See {@link #lstmLayer(String, SDVariable, SDVariable, SDVariable, SDVariable, LSTMWeights, LSTMConfiguration)}
*/
public SDVariable sruCell(SRUCellConfiguration configuration) {
return new SRUCell(sd, configuration).outputVariables()[0];
public LSTMLayerOutputs lstmLayer(@NonNull SDVariable maxTSLength,
@NonNull SDVariable x, @NonNull SDVariable cLast, @NonNull SDVariable yLast,
@NonNull LSTMWeights weights, @NonNull LSTMConfiguration config){
LSTMLayer c = new LSTMLayer(sd, maxTSLength, x, cLast, yLast, weights, config);
return new LSTMLayerOutputs(c.outputVariables(), config.getDataFormat());
}
/**
* An sru cell
*
* @param baseName the base name to use for the output variables
* @param configuration the configuration for the sru cell
* @return
* See {@link #lstmLayer(String, SDVariable, SDVariable, SDVariable, SDVariable, LSTMWeights, LSTMConfiguration)}
*/
public SDVariable sruCell(String baseName, SRUCellConfiguration configuration) {
return new SRUCell(sd, configuration).outputVariables(baseName)[0];
public LSTMLayerOutputs lstmLayer(int maxTSLength, @NonNull SDVariable x, @NonNull SDVariable cLast, @NonNull SDVariable yLast,
@NonNull LSTMWeights weights, @NonNull LSTMConfiguration config){
return lstmLayer(
sd.scalar("lstm_max_ts_length", maxTSLength),
x, cLast, yLast, weights, config);
}
/**
* See {@link #lstmLayer(String, SDVariable, SDVariable, SDVariable, SDVariable, LSTMWeights, LSTMConfiguration)}
*/
public LSTMLayerOutputs lstmLayer(String baseName, int maxTSLength, @NonNull SDVariable x, @NonNull SDVariable cLast, @NonNull SDVariable yLast,
@NonNull LSTMWeights weights, @NonNull LSTMConfiguration config){
if(baseName != null) {
return lstmLayer(baseName,
sd.scalar(sd.generateDistinctCustomVariableName(baseName + "_max_ts_length"), maxTSLength),
x, cLast, yLast, weights, config);
} else {
return lstmLayer(maxTSLength, x, cLast, yLast, weights, config);
}
}
/**
* The LSTM layer. Does multiple time steps.
*
* Input shape depends on data format (in config):<br>
* TNS -> [timeSteps, batchSize, inSize]<br>
* NST -> [batchSize, inSize, timeSteps]<br>
* NTS -> [batchSize, timeSteps, inSize]<br>
*
* @param baseName The base name for the lstm layer
* @param x Input, with shape dependent on the data format (in config).
* @param cLast Previous/initial cell state, with shape [batchSize, numUnits]
* @param yLast Previous/initial cell output, with shape [batchSize, numUnits]
* @param weights The layer's weights.
* @param config The layer's config.
* @return The layer's outputs.
*/
public LSTMLayerOutputs lstmLayer(String baseName, @NonNull SDVariable maxTSLength,
@NonNull SDVariable x, @NonNull SDVariable cLast, @NonNull SDVariable yLast,
@NonNull LSTMWeights weights, @NonNull LSTMConfiguration config){
LSTMLayer c = new LSTMLayer(sd, maxTSLength, x, cLast, yLast, weights, config);
return new LSTMLayerOutputs(c.outputVariables(baseName), config.getDataFormat());
}
/**
* See {@link #sruCell(String, SDVariable, SDVariable, SRUWeights)}.
*/
public SRUCellOutputs sruCell(@NonNull SDVariable x, @NonNull SDVariable cLast, @NonNull SRUWeights weights) {
return new SRUCellOutputs(new SRUCell(sd, x, cLast, weights).outputVariables());
}
/**
* The SRU cell. Does a single time step operation.
*
* @param baseName The base name for the sru cell
* @param x Input, with shape [batchSize, inSize]
* @param cLast Previous cell state, with shape [batchSize, inSize]
* @param weights The cell's weights.
* @return The cell's outputs.
*/
public SRUCellOutputs sruCell(String baseName, @NonNull SDVariable x, @NonNull SDVariable cLast, @NonNull SRUWeights weights) {
return new SRUCellOutputs(new SRUCell(sd, x, cLast, weights).outputVariables(baseName));
}
/**
* See {@link #sru(String, SDVariable, SDVariable, SDVariable, SRUWeights)}
*/
public SRULayerOutputs sru(@NonNull SDVariable x, @NonNull SDVariable initialC, @NonNull SRUWeights weights) {
return sru(x, initialC, null, weights);
}
/**
* See {@link #sru(String, SDVariable, SDVariable, SDVariable, SRUWeights)}
*/
public SRULayerOutputs sru(String baseName, @NonNull SDVariable x, @NonNull SDVariable initialC, @NonNull SRUWeights weights) {
return sru(baseName, x, initialC, null, weights);
}
/**
* See {@link #sru(String, SDVariable, SDVariable, SDVariable, SRUWeights)}
*/
public SRULayerOutputs sru(@NonNull SDVariable x, @NonNull SDVariable initialC, SDVariable mask, @NonNull SRUWeights weights) {
return new SRULayerOutputs(new SRU(sd, x, initialC, mask, weights).outputVariables());
}
/**
* The SRU layer. Does a single time step operation.
*
* @param baseName The base name for the sru layer
* @param x Input, with shape [batchSize, inSize, timeSeriesLength]
* @param initialC Initial cell state, with shape [batchSize, inSize]
* @param mask An optional dropout mask, with shape [batchSize, inSize]
* @param weights The layer's weights.
* @return The layer's outputs.
*/
public SRULayerOutputs sru(String baseName, @NonNull SDVariable x, @NonNull SDVariable initialC, SDVariable mask, @NonNull SRUWeights weights) {
return new SRULayerOutputs(new SRU(sd, x, initialC, mask, weights).outputVariables(baseName));
}
}

6
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/evaluation/classification/ROC.java
View File

@ -75,7 +75,6 @@ import static org.nd4j.linalg.indexing.NDArrayIndex.interval;
@EqualsAndHashCode(callSuper = true,
exclude = {"auc", "auprc", "probAndLabel", "exactAllocBlockSize", "rocCurve", "prCurve", "axis"})
@Data
@ToString(exclude = {"probAndLabel", "exactAllocBlockSize", "rocCurve", "prCurve"})
@JsonIgnoreProperties({"probAndLabel", "exactAllocBlockSize"})
@JsonSerialize(using = ROCSerializer.class)
@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY)
@ -824,6 +823,11 @@ public class ROC extends BaseEvaluation<ROC> {
return sb.toString();
}
@Override
public String toString(){
return stats();
}
public double scoreForMetric(Metric metric){
switch (metric){
case AUROC:

11
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseNDArray.java
View File

@ -4641,17 +4641,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
return jvmShapeInfo.length;
}
/**
* Returns the total number of elements in the ndarray
*
* @return the number of elements in the ndarray
*/
@Override
@Deprecated
public long lengthLong() {
return jvmShapeInfo.length;
}
@Override
public INDArray broadcast(INDArray result) {
Nd4j.getCompressor().autoDecompress(this);

5
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseSparseNDArray.java
View File

@ -279,11 +279,6 @@ public abstract class BaseSparseNDArray implements ISparseNDArray {
return (int) length();
}
@Override
public long lengthLong() {
return length;
}
protected void init(long[] shape) {
if (shape.length == 1) {

10
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/INDArray.java
View File

@ -2378,16 +2378,6 @@ public interface INDArray extends Serializable, AutoCloseable {
*/
long length();
/**
* Returns the total number of elements in the ndarray
*
* @return the number of elements in the ndarray
* @deprecated use {@link #length()}
*/
@Deprecated
long lengthLong();
/**
* Broadcasts this ndarray to be the specified shape
*

11
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/GRUCell.java
View File

@ -16,6 +16,7 @@
package org.nd4j.linalg.api.ops.impl.layers.recurrent;
import lombok.Getter;
import onnx.Onnx;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
@ -23,6 +24,7 @@ import org.nd4j.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.GRUCellConfiguration;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.weights.GRUWeights;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
@ -39,14 +41,15 @@ import java.util.Map;
*/
public class GRUCell extends DynamicCustomOp {
private GRUCellConfiguration configuration;
@Getter
private GRUWeights weights;
public GRUCell() {
}
public GRUCell(SameDiff sameDiff, GRUCellConfiguration configuration) {
super(null, sameDiff, configuration.args());
this.configuration = configuration;
public GRUCell(SameDiff sameDiff, SDVariable x, SDVariable hLast, GRUWeights weights) {
super(null, sameDiff, weights.argsWithInputs(x, hLast));
this.weights = weights;
}
@Override

29
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/LSTMBlockCell.java
View File

@ -16,12 +16,15 @@
package org.nd4j.linalg.api.ops.impl.layers.recurrent;
import lombok.Getter;
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.ops.DynamicCustomOp;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.LSTMBlockCellConfiguration;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.LSTMConfiguration;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.weights.LSTMWeights;
import org.nd4j.linalg.primitives.Pair;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
@ -49,10 +52,12 @@ import java.util.Map;
* 6: weights - cell peephole (t) connections to output gate, [numUnits]<br>
* 7: biases, shape [4*numUnits]<br>
* <br>
* Input integer arguments: set via {@link LSTMBlockCellConfiguration}<br>
* Weights are set via {@link LSTMWeights}.<br>
* <br>
* Input integer arguments: set via {@link LSTMConfiguration}<br>
* 0: if not zero, provide peephole connections<br>
* <br>
* Input float arguments: set via {@link LSTMBlockCellConfiguration}<br>
* Input float arguments: set via {@link LSTMConfiguration}<br>
* 0: the bias added to forget gates in order to reduce the scale of forgetting in the beginning of the training<br>
* 1: clipping value for cell state, if it is not equal to zero, then cell state is clipped<br>
* <br>
@ -69,15 +74,19 @@ import java.util.Map;
*/
public class LSTMBlockCell extends DynamicCustomOp {
private LSTMBlockCellConfiguration configuration;
private LSTMConfiguration configuration;
@Getter
private LSTMWeights weights;
public LSTMBlockCell() {
}
public LSTMBlockCell(SameDiff sameDiff, LSTMBlockCellConfiguration configuration) {
super(null, sameDiff, configuration.args());
public LSTMBlockCell(SameDiff sameDiff, SDVariable x, SDVariable cLast, SDVariable yLast, LSTMWeights weights, LSTMConfiguration configuration) {
super(null, sameDiff, weights.argsWithInputs(x, cLast, yLast));
this.configuration = configuration;
addIArgument(configuration.iArgs());
this.weights = weights;
addIArgument(configuration.iArgs(false));
addTArgument(configuration.tArgs());
}
@ -97,12 +106,12 @@ public class LSTMBlockCell extends DynamicCustomOp {
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
configuration = LSTMBlockCellConfiguration.builder()
configuration = LSTMConfiguration.builder()
.forgetBias(attributesForNode.get("forget_bias").getF())
.clippingCellValue(attributesForNode.get("cell_clip").getF())
.peepHole(attributesForNode.get("use_peephole").getB())
.build();
addIArgument(configuration.iArgs());
addIArgument(configuration.iArgs(false));
addTArgument(configuration.tArgs());
}
@ -113,7 +122,7 @@ public class LSTMBlockCell extends DynamicCustomOp {
@Override
public Map<String, Object> propertiesForFunction() {
return configuration.toProperties();
return configuration.toProperties(false);
}
@Override

16
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/LSTMLayer.java
View File

@ -16,6 +16,7 @@
package org.nd4j.linalg.api.ops.impl.layers.recurrent;
import lombok.Getter;
import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
@ -24,6 +25,7 @@ import org.nd4j.linalg.api.buffer.DataType;
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;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.weights.LSTMWeights;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
@ -75,13 +77,17 @@ public class LSTMLayer extends DynamicCustomOp {
private LSTMConfiguration configuration;
@Getter
private LSTMWeights weights;
public LSTMLayer() {
}
public LSTMLayer(@NonNull SameDiff sameDiff, @NonNull LSTMConfiguration configuration) {
super(null, sameDiff, configuration.args());
public LSTMLayer(@NonNull SameDiff sameDiff, SDVariable maxTSLength, SDVariable x, SDVariable cLast, SDVariable yLast, LSTMWeights weights, LSTMConfiguration configuration) {
super(null, sameDiff, weights.argsWithInputs(maxTSLength, x, cLast, yLast));
this.configuration = configuration;
addIArgument(configuration.iArgs());
this.weights = weights;
addIArgument(configuration.iArgs(true));
addTArgument(configuration.tArgs());
}
@ -107,7 +113,7 @@ public class LSTMLayer extends DynamicCustomOp {
.peepHole(attributesForNode.get("use_peephole").getB())
.dataFormat(RnnDataFormat.TNS) //Always time major for TF BlockLSTM
.build();
addIArgument(configuration.iArgs());
addIArgument(configuration.iArgs(true));
addTArgument(configuration.tArgs());
}
@ -118,7 +124,7 @@ public class LSTMLayer extends DynamicCustomOp {
@Override
public Map<String, Object> propertiesForFunction() {
return configuration.toProperties();
return configuration.toProperties(true);
}
@Override

22
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/SRU.java
View File

@ -16,11 +16,16 @@
package org.nd4j.linalg.api.ops.impl.layers.recurrent;
import java.util.Arrays;
import java.util.List;
import lombok.Getter;
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.ops.DynamicCustomOp;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.SRUConfiguration;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.weights.SRUWeights;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
@ -34,13 +39,18 @@ import java.util.Map;
*/
public class SRU extends DynamicCustomOp {
private SRUConfiguration configuration;
@Getter
private SRUWeights weights;
@Getter
private SDVariable mask;
public SRU() { }
public SRU(SameDiff sameDiff, SRUConfiguration configuration) {
super(null, sameDiff, configuration.args());
this.configuration = configuration;
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;
}
@Override
@ -68,6 +78,4 @@ public class SRU extends DynamicCustomOp {
public void initFromOnnx(Onnx.NodeProto node, SameDiff initWith, Map<String, Onnx.AttributeProto> attributesForNode, Onnx.GraphProto graph) {
super.initFromOnnx(node, initWith, attributesForNode, graph);
}
}

16
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/SRUCell.java
View File

@ -16,17 +16,18 @@
package org.nd4j.linalg.api.ops.impl.layers.recurrent;
import java.util.Map;
import lombok.Getter;
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.ops.DynamicCustomOp;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.SRUCellConfiguration;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.weights.SRUWeights;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.Map;
/**
* A simple recurrent unit cell.
*
@ -34,14 +35,15 @@ import java.util.Map;
*/
public class SRUCell extends DynamicCustomOp {
private SRUCellConfiguration configuration;
@Getter
private SRUWeights weights;
public SRUCell() {
}
public SRUCell(SameDiff sameDiff, SRUCellConfiguration configuration) {
super(null, sameDiff, configuration.args());
this.configuration = configuration;
public SRUCell(SameDiff sameDiff, SDVariable x, SDVariable cLast, SRUWeights weights) {
super(null, sameDiff, weights.argsWithInputs(x, cLast));
this.weights = weights;
}
@Override

57
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/config/LSTMBlockCellConfiguration.java
View File

@ -1,57 +0,0 @@
/*******************************************************************************
* Copyright (c) 2015-2019 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.nd4j.linalg.api.ops.impl.layers.recurrent.config;
import lombok.Builder;
import lombok.Data;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.linalg.util.ArrayUtil;
import java.util.LinkedHashMap;
import java.util.Map;
@Builder
@Data
public class LSTMBlockCellConfiguration {
private boolean peepHole; //IArg(0)
private double forgetBias; //TArg(0)
private double clippingCellValue; //TArg(1)
private SDVariable xt, cLast, yLast, W, Wci, Wcf, Wco, b;
public Map<String,Object> toProperties() {
Map<String,Object> ret = new LinkedHashMap<>();
ret.put("peepHole",peepHole);
ret.put("clippingCellValue",clippingCellValue);
ret.put("forgetBias",forgetBias);
return ret;
}
public SDVariable[] args() {
return new SDVariable[] {xt,cLast, yLast, W, Wci, Wcf, Wco, b};
}
public int[] iArgs() {
return new int[] {ArrayUtil.fromBoolean(peepHole)};
}
public double[] tArgs() {
return new double[] {forgetBias,clippingCellValue};
}
}

38
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/config/LSTMConfiguration.java
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@ -19,13 +19,15 @@ package org.nd4j.linalg.api.ops.impl.layers.recurrent.config;
import lombok.Builder;
import lombok.Data;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.LSTMBlockCell;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.LSTMLayer;
import org.nd4j.linalg.util.ArrayUtil;
import java.util.LinkedHashMap;
import java.util.Map;
/**
* LSTM Configuration - for {@link org.nd4j.linalg.api.ops.impl.layers.recurrent.LSTMLayer}
* LSTM Configuration - for {@link LSTMLayer} and {@link LSTMBlockCell}
*
* @author Alex Black
*/
@ -33,29 +35,41 @@ import java.util.Map;
@Data
public class LSTMConfiguration {
/**
* Whether to provide peephole connections.
*/
private boolean peepHole; //IArg(0)
@Builder.Default private RnnDataFormat dataFormat = RnnDataFormat.TNS; //IArg(1)
/**
* The data format of the input. Only used in {@link LSTMLayer}, ignored in {@link LSTMBlockCell}.
*/
@Builder.Default private RnnDataFormat dataFormat = RnnDataFormat.TNS; //IArg(1) (only for lstmBlock, not lstmBlockCell)
/**
* The bias added to forget gates in order to reduce the scale of forgetting in the beginning of the training.
*/
private double forgetBias; //TArg(0)
/**
* Clipping value for cell state, if it is not equal to zero, then cell state is clipped.
*/
private double clippingCellValue; //TArg(1)
private SDVariable xt, cLast, yLast, W, Wci, Wcf, Wco, b;
public Map<String,Object> toProperties() {
public Map<String,Object> toProperties(boolean includeDataFormat) {
Map<String,Object> ret = new LinkedHashMap<>();
ret.put("peepHole",peepHole);
ret.put("clippingCellValue",clippingCellValue);
ret.put("forgetBias",forgetBias);
ret.put("dataFormat", dataFormat);
if(includeDataFormat)
ret.put("dataFormat", dataFormat);
return ret;
}
public SDVariable[] args() {
return new SDVariable[] {xt,cLast, yLast, W, Wci, Wcf, Wco, b};
}
public int[] iArgs() {
return new int[] {ArrayUtil.fromBoolean(peepHole), dataFormat.ordinal()};
public int[] iArgs(boolean includeDataFormat) {
if(includeDataFormat) {
return new int[]{ArrayUtil.fromBoolean(peepHole), dataFormat.ordinal()};
} else return new int[]{ArrayUtil.fromBoolean(peepHole)};
}
public double[] tArgs() {

44
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@ -1,44 +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.nd4j.linalg.api.ops.impl.layers.recurrent.config;
import lombok.Builder;
import lombok.Data;
import org.nd4j.autodiff.samediff.SDVariable;
@Data
@Builder
public class SRUCellConfiguration {
/**
*
NDArray<T>* xt = INPUT_VARIABLE(0); // input [batchSize x inSize], batchSize - batch size, inSize - number of features
NDArray<T>* ct_1 = INPUT_VARIABLE(1); // previous cell state ct [batchSize x inSize], that is at previous time step t-1
NDArray<T>* w = INPUT_VARIABLE(2); // weights [inSize x 3*inSize]
NDArray<T>* b = INPUT_VARIABLE(3); // biases [1 x 2*inSize]
NDArray<T>* ht = OUTPUT_VARIABLE(0); // current cell output [batchSize x inSize], that is at current time step t
NDArray<T>* ct = OUTPUT_VARIABLE(1); // current cell state [batchSize x inSize], that is at current time step t
*/
private SDVariable xt,ct_1,w,b,h1,ct;
public SDVariable[] args() {
return new SDVariable[] {xt,ct_1,w,b,h1,ct};
}
}

38
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@ -1,38 +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.nd4j.linalg.api.ops.impl.layers.recurrent.config;
import lombok.Builder;
import lombok.Data;
import org.nd4j.autodiff.samediff.SDVariable;
@Data
@Builder
public class SRUConfiguration {
/**
* NDArray<T>* input = INPUT_VARIABLE(0); // X, input 3d tensor [bS x K x N], N - number of time steps, bS - batch size, K - number of features
NDArray<T>* weights = INPUT_VARIABLE(1); // W, 2d tensor of weights [3K x K]
NDArray<T>* bias = INPUT_VARIABLE(2); // B, row of biases with twice length [1 x 2*K]
NDArray<T>* init = INPUT_VARIABLE(3); // C_{0}, 2d tensor of initial state [bS x K] at time t=0
*/
private SDVariable inputs,weights,bias,init;
public SDVariable[] args() {
return new SDVariable[] {inputs,weights,bias,init};
}
}

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@ -0,0 +1,62 @@
package org.nd4j.linalg.api.ops.impl.layers.recurrent.outputs;
import java.util.Arrays;
import java.util.List;
import lombok.Getter;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.GRUCell;
/**
* The outputs of a GRU cell ({@link GRUCell}.
*/
@Getter
public class GRUCellOutputs {
/**
* Reset gate output [batchSize, numUnits].
*/
private SDVariable r;
/**
* Update gate output [batchSize, numUnits].
*/
private SDVariable u;
/**
* Cell gate output [batchSize, numUnits].
*/
private SDVariable c;
/**
* Current cell output [batchSize, numUnits].
*/
private SDVariable h;
public GRUCellOutputs(SDVariable[] outputs){
Preconditions.checkArgument(outputs.length == 4,
"Must have 4 GRU cell outputs, got %s", outputs.length);
r = outputs[0];
u = outputs[1];
c = outputs[2];
h = outputs[3];
}
/**
* Get all outputs returned by the cell.
*/
public List<SDVariable> getAllOutputs(){
return Arrays.asList(r, u, c, h);
}
/**
* Get h, the output of the cell.
*
* Has shape [batchSize, numUnits].
*/
public SDVariable getOutput(){
return h;
}
}

88
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/outputs/LSTMCellOutputs.java Normal file
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@ -0,0 +1,88 @@
package org.nd4j.linalg.api.ops.impl.layers.recurrent.outputs;
import java.util.Arrays;
import java.util.List;
import lombok.Getter;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.LSTMBlockCell;
/**
* The outputs of a LSTM cell ({@link LSTMBlockCell}.
*/
@Getter
public class LSTMCellOutputs {
/**
* Output - input modulation gate activations [batchSize, numUnits].
*/
private SDVariable i;
/**
* Activations, cell state (pre tanh) [batchSize, numUnits].
*/
private SDVariable c;
/**
* Output - forget gate activations [batchSize, numUnits].
*/
private SDVariable f;
/**
* Output - output gate activations [batchSize, numUnits].
*/
private SDVariable o;
/**
* Output - input gate activations [batchSize, numUnits].
*/
private SDVariable z;
/**
* Cell state, post tanh [batchSize, numUnits].
*/
private SDVariable h;
/**
* Current cell output [batchSize, numUnits].
*/
private SDVariable y;
public LSTMCellOutputs(SDVariable[] outputs){
Preconditions.checkArgument(outputs.length == 7,
"Must have 7 LSTM cell outputs, got %s", outputs.length);
i = outputs[0];
c = outputs[1];
f = outputs[2];
o = outputs[3];
z = outputs[4];
h = outputs[5];
y = outputs[6];
}
/**
* Get all outputs returned by the cell.
*/
public List<SDVariable> getAllOutputs(){
return Arrays.asList(i, c, f, o, z, h, y);
}
/**
* Get y, the output of the cell.
*
* Has shape [batchSize, numUnits].
*/
public SDVariable getOutput(){
return y;
}
/**
* Get c, the cell's state.
*
* Has shape [batchSize, numUnits].
*/
public SDVariable getState(){
return c;
}
}

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@ -0,0 +1,180 @@
package org.nd4j.linalg.api.ops.impl.layers.recurrent.outputs;
import java.util.Arrays;
import java.util.List;
import lombok.AccessLevel;
import lombok.Getter;
import org.nd4j.autodiff.samediff.SDIndex;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.LSTMLayer;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.RnnDataFormat;
/**
* The outputs of a LSTM layer ({@link LSTMLayer}.
*/
@Getter
public class LSTMLayerOutputs {
private RnnDataFormat dataFormat;
/**
* Output - input modulation gate activations.
* Shape depends on data format (in layer config):<br>
* TNS -> [timeSteps, batchSize, numUnits]<br>
* NST -> [batchSize, numUnits, timeSteps]<br>
* NTS -> [batchSize, timeSteps, numUnits]<br>
*/
private SDVariable i;
/**
* Activations, cell state (pre tanh).
* Shape depends on data format (in layer config):<br>
* TNS -> [timeSteps, batchSize, numUnits]<br>
* NST -> [batchSize, numUnits, timeSteps]<br>
* NTS -> [batchSize, timeSteps, numUnits]<br>
*/
private SDVariable c;
/**
* Output - forget gate activations.
* Shape depends on data format (in layer config):<br>
* TNS -> [timeSteps, batchSize, numUnits]<br>
* NST -> [batchSize, numUnits, timeSteps]<br>
* NTS -> [batchSize, timeSteps, numUnits]<br>
*/
private SDVariable f;
/**
* Output - output gate activations.
* Shape depends on data format (in layer config):<br>
* TNS -> [timeSteps, batchSize, numUnits]<br>
* NST -> [batchSize, numUnits, timeSteps]<br>
* NTS -> [batchSize, timeSteps, numUnits]<br>
*/
private SDVariable o;
/**
* Output - input gate activations.
* Shape depends on data format (in layer config):<br>
* TNS -> [timeSteps, batchSize, numUnits]<br>
* NST -> [batchSize, numUnits, timeSteps]<br>
* NTS -> [batchSize, timeSteps, numUnits]<br>
*/
private SDVariable z;
/**
* Cell state, post tanh.
* Shape depends on data format (in layer config):<br>
* TNS -> [timeSteps, batchSize, numUnits]<br>
* NST -> [batchSize, numUnits, timeSteps]<br>
* NTS -> [batchSize, timeSteps, numUnits]<br>
*/
private SDVariable h;
/**
* Current cell output.
* Shape depends on data format (in layer config):<br>
* TNS -> [timeSteps, batchSize, numUnits]<br>
* NST -> [batchSize, numUnits, timeSteps]<br>
* NTS -> [batchSize, timeSteps, numUnits]<br>
*/
private SDVariable y;
public LSTMLayerOutputs(SDVariable[] outputs, RnnDataFormat dataFormat){
Preconditions.checkArgument(outputs.length == 7,
"Must have 7 LSTM layer outputs, got %s", outputs.length);
i = outputs[0];
c = outputs[1];
f = outputs[2];
o = outputs[3];
z = outputs[4];
h = outputs[5];
y = outputs[6];
this.dataFormat = dataFormat;
}
/**
* Get all outputs returned by the cell.
*/
public List<SDVariable> getAllOutputs(){
return Arrays.asList(i, c, f, o, z, h, y);
}
/**
* Get y, the output of the cell for all time steps.
*
* Shape depends on data format (in layer config):<br>
* TNS -> [timeSteps, batchSize, numUnits]<br>
* NST -> [batchSize, numUnits, timeSteps]<br>
* NTS -> [batchSize, timeSteps, numUnits]<br>
*/
public SDVariable getOutput(){
return y;
}
/**
* Get c, the cell's state for all time steps.
*
* Shape depends on data format (in layer config):<br>
* TNS -> [timeSteps, batchSize, numUnits]<br>
* NST -> [batchSize, numUnits, timeSteps]<br>
* NTS -> [batchSize, timeSteps, numUnits]<br>
*/
public SDVariable getState(){
return c;
}
private SDVariable lastOutput = null;
/**
* Get y, the output of the cell, for the last time step.
*
* Has shape [batchSize, numUnits].
*/
public SDVariable getLastOutput(){
if(lastOutput != null)
return lastOutput;
switch (dataFormat){
case TNS:
lastOutput = getOutput().get(SDIndex.point(-1), SDIndex.all(), SDIndex.all());
break;
case NST:
lastOutput = getOutput().get(SDIndex.all(), SDIndex.all(), SDIndex.point(-1));
break;
case NTS:
lastOutput = getOutput().get(SDIndex.all(), SDIndex.point(-1), SDIndex.all());
break;
}
return lastOutput;
}
private SDVariable lastState = null;
/**
* Get c, the state of the cell, for the last time step.
*
* Has shape [batchSize, numUnits].
*/
public SDVariable getLastState(){
if(lastState != null)
return lastState;
switch (dataFormat){
case TNS:
lastState = getState().get(SDIndex.point(-1), SDIndex.all(), SDIndex.all());
break;
case NST:
lastState = getState().get(SDIndex.all(), SDIndex.all(), SDIndex.point(-1));
break;
case NTS:
lastState = getState().get(SDIndex.all(), SDIndex.point(-1), SDIndex.all());
break;
}
return lastState;
}
}

60
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@ -0,0 +1,60 @@
package org.nd4j.linalg.api.ops.impl.layers.recurrent.outputs;
import java.util.Arrays;
import java.util.List;
import lombok.Getter;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.GRUCell;
/**
* The outputs of a GRU cell ({@link GRUCell}.
*/
@Getter
public class SRUCellOutputs {
/**
* Current cell output [batchSize, numUnits].
*/
private SDVariable h;
/**
* Current cell state [batchSize, numUnits].
*/
private SDVariable c;
public SRUCellOutputs(SDVariable[] outputs){
Preconditions.checkArgument(outputs.length == 2,
"Must have 2 SRU cell outputs, got %s", outputs.length);
h = outputs[0];
c = outputs[1];
}
/**
* Get all outputs returned by the cell.
*/
public List<SDVariable> getAllOutputs(){
return Arrays.asList(h, c);
}
/**
* Get h, the output of the cell.
*
* Has shape [batchSize, inSize].
*/
public SDVariable getOutput(){
return h;
}
/**
* Get c, the state of the cell.
*
* Has shape [batchSize, inSize].
*/
public SDVariable getState(){
return c;
}
}

92
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@ -0,0 +1,92 @@
package org.nd4j.linalg.api.ops.impl.layers.recurrent.outputs;
import java.util.Arrays;
import java.util.List;
import lombok.AccessLevel;
import lombok.Getter;
import org.nd4j.autodiff.samediff.SDIndex;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.GRUCell;
/**
* The outputs of a GRU cell ({@link GRUCell}.
*/
@Getter
public class SRULayerOutputs {
/**
* Current cell output [batchSize, inSize, timeSeriesLength].
*/
private SDVariable h;
/**
* Current cell state [batchSize, inSize, timeSeriesLength].
*/
private SDVariable c;
public SRULayerOutputs(SDVariable[] outputs){
Preconditions.checkArgument(outputs.length == 2,
"Must have 2 SRU cell outputs, got %s", outputs.length);
h = outputs[0];
c = outputs[1];
}
/**
* Get all outputs returned by the cell.
*/
public List<SDVariable> getAllOutputs(){
return Arrays.asList(h, c);
}
/**
* Get h, the output of the cell.
*
* Has shape [batchSize, inSize, timeSeriesLength].
*/
public SDVariable getOutput(){
return h;
}
/**
* Get c, the state of the cell.
*
* Has shape [batchSize, inSize, timeSeriesLength].
*/
public SDVariable getState(){
return c;
}
private SDVariable lastOutput = null;
/**
* Get y, the output of the cell, for the last time step.
*
* Has shape [batchSize, inSize].
*/
public SDVariable getLastOutput(){
if(lastOutput != null)
return lastOutput;
lastOutput = getOutput().get(SDIndex.all(), SDIndex.all(), SDIndex.point(-1));
return lastOutput;
}
private SDVariable lastState = null;
/**
* Get c, the state of the cell, for the last time step.
*
* Has shape [batchSize, inSize].
*/
public SDVariable getLastState(){
if(lastState != null)
return lastState;
lastOutput = getOutput().get(SDIndex.all(), SDIndex.all(), SDIndex.point(-1));
return lastState;
}
}

51
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/weights/GRUWeights.java Normal file
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@ -0,0 +1,51 @@
package org.nd4j.linalg.api.ops.impl.layers.recurrent.weights;
import lombok.Builder;
import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.GRUCell;
/**
* The weight configuration of a GRU cell. For {@link GRUCell}.
*
*/
@EqualsAndHashCode(callSuper = true)
@Data
@Builder
public class GRUWeights extends RNNWeights {
/**
* Reset and Update gate weights, with a shape of [inSize + numUnits, 2*numUnits].
*
* The reset weights are the [:, 0:numUnits] subset and the update weights are the [:, numUnits:2*numUnits] subset.
*/
@NonNull
private SDVariable ruWeight;
/**
* Cell gate weights, with a shape of [inSize + numUnits, numUnits]
*/
@NonNull
private SDVariable cWeight;
/**
* 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;
/**
* Cell gate bias, with a shape of [numUnits]. May be null.
*/
@NonNull
private SDVariable cBias;
@Override
public SDVariable[] args() {
return filterNonNull(ruWeight, cWeight, ruBias, cBias);
}
}

57
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@ -0,0 +1,57 @@
package org.nd4j.linalg.api.ops.impl.layers.recurrent.weights;
import lombok.Builder;
import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.LSTMBlockCell;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.LSTMLayer;
/**
* The weight configuration of a LSTM layer. For {@link LSTMLayer} and {@link LSTMBlockCell}.
*
*/
@EqualsAndHashCode(callSuper = true)
@Data
@Builder
public class LSTMWeights extends RNNWeights {
/**
* Input to hidden weights and hidden to hidden weights, with a shape of [inSize + numUnits, 4*numUnits].
*
* 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;
/**
* Cell peephole (t-1) connections to input modulation gate, with a shape of [numUnits].
*/
@NonNull
private SDVariable inputPeepholeWeights;
/**
* Cell peephole (t-1) connections to forget gate, with a shape of [numUnits].
*/
@NonNull
private SDVariable forgetPeepholeWeights;
/**
* Cell peephole (t) connections to output gate, with a shape of [numUnits].
*/
@NonNull
private SDVariable outputPeepholeWeights;
/**
* Input to hidden and hidden to hidden biases, with shape [1, 4*numUnits].
*/
@NonNull
private SDVariable bias;
@Override
public SDVariable[] args() {
return filterNonNull(weights, inputPeepholeWeights, forgetPeepholeWeights, outputPeepholeWeights, bias);
}
}

35
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/weights/RNNWeights.java Normal file
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@ -0,0 +1,35 @@
package org.nd4j.linalg.api.ops.impl.layers.recurrent.weights;
import java.util.Arrays;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.linalg.util.ArrayUtil;
public abstract class RNNWeights {
public abstract SDVariable[] args();
protected static SDVariable[] filterNonNull(SDVariable... args){
int count = 0;
for(SDVariable v : args){
if(v != null){
count++;
}
}
SDVariable[] res = new SDVariable[count];
int i = 0;
for(SDVariable v : args){
if(v != null){
res[i] = v;
i++;
}
}
return res;
}
public SDVariable[] argsWithInputs(SDVariable... inputs){
return ArrayUtil.combine(inputs, args());
}
}

37
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/weights/SRUWeights.java Normal file
View File

@ -0,0 +1,37 @@
package org.nd4j.linalg.api.ops.impl.layers.recurrent.weights;
import lombok.Builder;
import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.LSTMBlockCell;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.SRU;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.SRUCell;
/**
* The weight configuration of a SRU layer. For {@link SRU} and {@link SRUCell}.
*
*/
@EqualsAndHashCode(callSuper = true)
@Data
@Builder
public class SRUWeights extends RNNWeights {
/**
* Weights, with shape [inSize, 3*inSize].
*/
@NonNull
private SDVariable weights;
/**
* Biases, with shape [2*inSize].
*/
@NonNull
private SDVariable bias;
@Override
public SDVariable[] args() {
return new SDVariable[]{weights, bias};
}
}

3
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/lossfunctions/impl/LossL2.java
View File

@ -27,6 +27,7 @@ import org.nd4j.linalg.primitives.Pair;
import org.nd4j.serde.jackson.shaded.NDArrayTextDeSerializer;
import org.nd4j.serde.jackson.shaded.NDArrayTextSerializer;
import org.nd4j.shade.jackson.annotation.JsonInclude;
import org.nd4j.shade.jackson.annotation.JsonProperty;
import org.nd4j.shade.jackson.databind.annotation.JsonDeserialize;
import org.nd4j.shade.jackson.databind.annotation.JsonSerialize;
@ -58,7 +59,7 @@ public class LossL2 implements ILossFunction {
*
* @param weights Weights array (row vector). May be null.
*/
public LossL2(INDArray weights) {
public LossL2(@JsonProperty("weights") INDArray weights) {
if (weights != null && !weights.isRowVector()) {
throw new IllegalArgumentException("Weights array must be a row vector");
}

3
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/lossfunctions/impl/LossMSE.java
View File

@ -19,6 +19,7 @@ package org.nd4j.linalg.lossfunctions.impl;
import lombok.EqualsAndHashCode;
import org.nd4j.linalg.activations.IActivation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.shade.jackson.annotation.JsonProperty;
/**
* Mean Squared Error loss function: L = 1/N sum_i (actual_i - predicted)^2
@ -38,7 +39,7 @@ public class LossMSE extends LossL2 {
*
* @param weights Weights array (row vector). May be null.
*/
public LossMSE(INDArray weights) {
public LossMSE(@JsonProperty("weights") INDArray weights) {
super(weights);
}

12
nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/JCublasNDArray.java
View File

@ -664,7 +664,7 @@ public class JCublasNDArray extends BaseNDArray {
//if (1 < 0) {
Nd4j.getExecutioner().commit();
DataBuffer buffer = Nd4j.createBuffer(this.lengthLong(), false);
DataBuffer buffer = Nd4j.createBuffer(this.length(), false);
AllocationPoint pointDst = AtomicAllocator.getInstance().getAllocationPoint(buffer);
AllocationPoint pointSrc = AtomicAllocator.getInstance().getAllocationPoint(this.data);
@ -686,10 +686,10 @@ public class JCublasNDArray extends BaseNDArray {
val perfD = PerformanceTracker.getInstance().helperStartTransaction();
if (pointSrc.isActualOnDeviceSide()) {
if (NativeOpsHolder.getInstance().getDeviceNativeOps().memcpyAsync(pointDst.getDevicePointer(), pointSrc.getDevicePointer(), this.lengthLong() * Nd4j.sizeOfDataType(buffer.dataType()), CudaConstants.cudaMemcpyDeviceToDevice, context.getOldStream()) == 0)
if (NativeOpsHolder.getInstance().getDeviceNativeOps().memcpyAsync(pointDst.getDevicePointer(), pointSrc.getDevicePointer(), this.length() * Nd4j.sizeOfDataType(buffer.dataType()), CudaConstants.cudaMemcpyDeviceToDevice, context.getOldStream()) == 0)
throw new ND4JIllegalStateException("memcpyAsync failed");
} else {
if (NativeOpsHolder.getInstance().getDeviceNativeOps().memcpyAsync(pointDst.getDevicePointer(), pointSrc.getHostPointer(), this.lengthLong() * Nd4j.sizeOfDataType(buffer.dataType()), CudaConstants.cudaMemcpyHostToDevice, context.getOldStream()) == 0)
if (NativeOpsHolder.getInstance().getDeviceNativeOps().memcpyAsync(pointDst.getDevicePointer(), pointSrc.getHostPointer(), this.length() * Nd4j.sizeOfDataType(buffer.dataType()), CudaConstants.cudaMemcpyHostToDevice, context.getOldStream()) == 0)
throw new ND4JIllegalStateException("memcpyAsync failed");
direction = MemcpyDirection.HOST_TO_DEVICE;
@ -738,7 +738,7 @@ public class JCublasNDArray extends BaseNDArray {
if (!this.isView()) {
Nd4j.getExecutioner().commit();
val buffer = Nd4j.createBuffer(this.dataType(), this.lengthLong(), false);
val buffer = Nd4j.createBuffer(this.dataType(), this.length(), false);
val pointDst = AtomicAllocator.getInstance().getAllocationPoint(buffer);
val pointSrc = AtomicAllocator.getInstance().getAllocationPoint(this.data);
@ -749,10 +749,10 @@ public class JCublasNDArray extends BaseNDArray {
val perfD = PerformanceTracker.getInstance().helperStartTransaction();
if (pointSrc.isActualOnDeviceSide()) {
if (NativeOpsHolder.getInstance().getDeviceNativeOps().memcpyAsync(pointDst.getDevicePointer(), pointSrc.getDevicePointer(), this.lengthLong() * Nd4j.sizeOfDataType(buffer.dataType()), CudaConstants.cudaMemcpyDeviceToDevice, context.getOldStream()) == 0)
if (NativeOpsHolder.getInstance().getDeviceNativeOps().memcpyAsync(pointDst.getDevicePointer(), pointSrc.getDevicePointer(), this.length() * Nd4j.sizeOfDataType(buffer.dataType()), CudaConstants.cudaMemcpyDeviceToDevice, context.getOldStream()) == 0)
throw new ND4JIllegalStateException("memcpyAsync failed");
} else {
if (NativeOpsHolder.getInstance().getDeviceNativeOps().memcpyAsync(pointDst.getDevicePointer(), pointSrc.getHostPointer(), this.lengthLong() * Nd4j.sizeOfDataType(buffer.dataType()), CudaConstants.cudaMemcpyHostToDevice, context.getOldStream()) == 0)
if (NativeOpsHolder.getInstance().getDeviceNativeOps().memcpyAsync(pointDst.getDevicePointer(), pointSrc.getHostPointer(), this.length() * Nd4j.sizeOfDataType(buffer.dataType()), CudaConstants.cudaMemcpyHostToDevice, context.getOldStream()) == 0)
throw new ND4JIllegalStateException("memcpyAsync failed");
direction = MemcpyDirection.HOST_TO_DEVICE;

22
nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/JCublasNDArrayFactory.java
View File

@ -424,7 +424,7 @@ public class JCublasNDArrayFactory extends BaseNativeNDArrayFactory {
val perfD = PerformanceTracker.getInstance().helperStartTransaction();
nativeOps.memcpyAsync(point.getDevicePointer(), point.getHostPointer(), ret.lengthLong() * Nd4j.sizeOfDataType(ret.data().dataType()), CudaConstants.cudaMemcpyHostToDevice, context.getSpecialStream());
nativeOps.memcpyAsync(point.getDevicePointer(), point.getHostPointer(), ret.length() * Nd4j.sizeOfDataType(ret.data().dataType()), CudaConstants.cudaMemcpyHostToDevice, context.getSpecialStream());
context.getSpecialStream().synchronize();
if (nativeOps.lastErrorCode() != 0)
@ -580,7 +580,7 @@ public class JCublasNDArrayFactory extends BaseNativeNDArrayFactory {
if (true) {
Nd4j.getExecutioner().push();
long len = target.lengthLong();
long len = target.length();
AtomicAllocator allocator = AtomicAllocator.getInstance();
@ -598,7 +598,7 @@ public class JCublasNDArrayFactory extends BaseNativeNDArrayFactory {
if (arrays[i].elementWiseStride() != 1)
throw new ND4JIllegalStateException("Native averaging is applicable only to continuous INDArrays");
if (arrays[i].lengthLong() != len)
if (arrays[i].length() != len)
throw new ND4JIllegalStateException("All arrays should have equal length for averaging");
AllocationPoint point = allocator.getAllocationPoint(arrays[i]);
@ -621,7 +621,7 @@ public class JCublasNDArrayFactory extends BaseNativeNDArrayFactory {
return target;
} else {
long len = target.lengthLong();
long len = target.length();
Nd4j.getExecutioner().commit();
@ -637,7 +637,7 @@ public class JCublasNDArrayFactory extends BaseNativeNDArrayFactory {
if (arrays[i].elementWiseStride() != 1)
throw new ND4JIllegalStateException("Native averaging is applicable only to continuous INDArrays");
if (arrays[i].lengthLong() != len)
if (arrays[i].length() != len)
throw new ND4JIllegalStateException("All arrays should have equal length for averaging");
((BaseCudaDataBuffer) arrays[i].data()).lazyAllocateHostPointer();
@ -689,7 +689,7 @@ public class JCublasNDArrayFactory extends BaseNativeNDArrayFactory {
Nd4j.getExecutioner().push();
long len = target != null ? target.lengthLong() : arrays[0].lengthLong();
long len = target != null ? target.length() : arrays[0].length();
AtomicAllocator allocator = AtomicAllocator.getInstance();
@ -707,7 +707,7 @@ public class JCublasNDArrayFactory extends BaseNativeNDArrayFactory {
if (arrays[i].elementWiseStride() != 1)
throw new ND4JIllegalStateException("Native averaging is applicable only to continuous INDArrays");
if (arrays[i].lengthLong() != len)
if (arrays[i].length() != len)
throw new ND4JIllegalStateException("All arrays should have equal length for averaging");
AllocationPoint point = allocator.getAllocationPoint(arrays[i]);
@ -744,7 +744,7 @@ public class JCublasNDArrayFactory extends BaseNativeNDArrayFactory {
/**
* We expect all operations are complete at this point
*/
long len = target == null ? arrays[0].lengthLong() : target.lengthLong();
long len = target == null ? arrays[0].length() : target.length();
val context = (CudaContext) AtomicAllocator.getInstance().getDeviceContext();
@ -758,7 +758,7 @@ public class JCublasNDArrayFactory extends BaseNativeNDArrayFactory {
if (arrays[i].elementWiseStride() != 1)
throw new ND4JIllegalStateException("Native averaging is applicable only to continuous INDArrays");
if (arrays[i].lengthLong() != len)
if (arrays[i].length() != len)
throw new ND4JIllegalStateException("All arrays should have equal length for averaging");
((BaseCudaDataBuffer) arrays[i].data()).lazyAllocateHostPointer();
@ -1303,7 +1303,7 @@ public class JCublasNDArrayFactory extends BaseNativeNDArrayFactory {
}
int numTads = (int)(tensor.lengthLong() / tadLength);
int numTads = (int)(tensor.length() / tadLength);
INDArray[] result = new INDArray[numTads];
long[] xPointers = new long[numTads];
@ -1378,7 +1378,7 @@ public class JCublasNDArrayFactory extends BaseNativeNDArrayFactory {
new CudaPointer(0));
// we're sending > 10m elements to radixSort
boolean isRadix = !x.isView() && (x.lengthLong() > 1024 * 1024 * 10);
boolean isRadix = !x.isView() && (x.length() > 1024 * 1024 * 10);
INDArray tmpX = x;
// we need to guarantee all threads are finished here

26
nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/ops/executioner/CudaExecutioner.java
View File

@ -293,9 +293,9 @@ public class CudaExecutioner extends DefaultOpExecutioner {
Pointer yDevTadShapeInfo = null;
if (op.y() != null) {
if (dimension.length == 0 || (dimension.length == 1 && dimension[0] == Integer.MAX_VALUE )|| op.x().tensorAlongDimension(0, dimension).lengthLong() != op.y().lengthLong()) {
if (!op.isComplexAccumulation() && op.x().lengthLong() != op.y().lengthLong())
throw new ND4JIllegalStateException("Op.X [" + op.x().lengthLong() + "] and Op.Y [" + op.y().lengthLong() + "] lengths should match");
if (dimension.length == 0 || (dimension.length == 1 && dimension[0] == Integer.MAX_VALUE )|| op.x().tensorAlongDimension(0, dimension).length() != op.y().length()) {
if (!op.isComplexAccumulation() && op.x().length() != op.y().length())
throw new ND4JIllegalStateException("Op.X [" + op.x().length() + "] and Op.Y [" + op.y().length() + "] lengths should match");
if (!op.z().isScalar()) {
Pair<DataBuffer, DataBuffer> yTadBuffers = tadManager.getTADOnlyShapeInfo(op.y(), dimension);
@ -536,7 +536,7 @@ public class CudaExecutioner extends DefaultOpExecutioner {
} else {
if (op.y() != null) {
//2 options here: either pairwise, equal sizes - OR every X TAD vs. entirety of Y
if (op.x().lengthLong() == op.y().lengthLong()) {
if (op.x().length() == op.y().length()) {
//Pairwise
if (!wholeDims && op.x().tensorsAlongDimension(dimension) != op.y().tensorsAlongDimension(dimension)) {
throw new ND4JIllegalStateException("Number of TADs along dimension don't match: (x shape = " +
@ -548,11 +548,11 @@ public class CudaExecutioner extends DefaultOpExecutioner {
throw new ND4JIllegalStateException("TAD vs TAD comparison requires dimension (or other comparison mode was supposed to be used?)");
//Every X TAD vs. entirety of Y
val xTADSize = op.x().lengthLong() / op.x().tensorsAlongDimension(dimension);
val xTADSize = op.x().length() / op.x().tensorsAlongDimension(dimension);
if (xTADSize != op.y().length()) {
throw new ND4JIllegalStateException("Size of TADs along dimension don't match for pairwise execution:" +
" (x TAD size = " + xTADSize + ", y size = " + op.y().lengthLong());
" (x TAD size = " + xTADSize + ", y size = " + op.y().length());
}
}
}
@ -976,7 +976,7 @@ public class CudaExecutioner extends DefaultOpExecutioner {
if (op.y() != null) {
//2 options here: either pairwise, equal sizes - OR every X TAD vs. entirety of Y
if (op.x().lengthLong() == op.y().lengthLong()) {
if (op.x().length() == op.y().length()) {
//Pairwise
if (op.x().tensorsAlongDimension(dimension) != op.y().tensorsAlongDimension(dimension)) {
throw new ND4JIllegalStateException("Number of TADs along dimension don't match: (x shape = " +
@ -985,11 +985,11 @@ public class CudaExecutioner extends DefaultOpExecutioner {
}
} else {
//Every X TAD vs. entirety of Y
val xTADSize = op.x().lengthLong() / op.x().tensorsAlongDimension(dimension);
val xTADSize = op.x().length() / op.x().tensorsAlongDimension(dimension);
if (xTADSize != op.y().length()) {
throw new ND4JIllegalStateException("Size of TADs along dimension don't match for pairwise execution:" +
" (x TAD size = " + xTADSize + ", y size = " + op.y().lengthLong());
" (x TAD size = " + xTADSize + ", y size = " + op.y().length());
}
}
}
@ -2031,8 +2031,8 @@ public class CudaExecutioner extends DefaultOpExecutioner {
long compressedLength = buffer.getInt(0);
long originalLength = buffer.getInt(1);
if (target.lengthLong() != originalLength)
throw new ND4JIllegalStateException("originalLength ["+ originalLength+"] stored in encoded array doesn't match target length ["+ target.lengthLong()+"]");
if (target.length() != originalLength)
throw new ND4JIllegalStateException("originalLength ["+ originalLength+"] stored in encoded array doesn't match target length ["+ target.length()+"]");
DataBuffer result = target.data();
@ -2056,7 +2056,7 @@ public class CudaExecutioner extends DefaultOpExecutioner {
@Override
public long bitmapEncode(INDArray indArray, INDArray target, double threshold) {
long length = indArray.lengthLong();
long length = indArray.length();
long tLen = target.data().length();
if (tLen != (length / 16 + 5))
@ -2117,7 +2117,7 @@ public class CudaExecutioner extends DefaultOpExecutioner {
context.getBufferScalar(),
context.getBufferReduction());
nativeOps.decodeBitmap(extras, AtomicAllocator.getInstance().getPointer(encoded.data(), context), target.lengthLong(), AtomicAllocator.getInstance().getPointer(target, context), (LongPointer) AtomicAllocator.getInstance().getHostPointer(target.shapeInfoDataBuffer()));
nativeOps.decodeBitmap(extras, AtomicAllocator.getInstance().getPointer(encoded.data(), context), target.length(), AtomicAllocator.getInstance().getPointer(target, context), (LongPointer) AtomicAllocator.getInstance().getHostPointer(target.shapeInfoDataBuffer()));
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());

2
nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/ops/executioner/CudaGridExecutioner.java
View File

@ -655,7 +655,7 @@ public class CudaGridExecutioner extends CudaExecutioner implements GridExecutio
op.setZ(ret);
} else {
// compare length
if (op.z().lengthLong() != ArrayUtil.prodLong(retShape))
if (op.z().length() != ArrayUtil.prodLong(retShape))
throw new ND4JIllegalStateException("Shape of target array for reduction [" + Arrays.toString(op.z().shape()) + "] doesn't match expected [" + Arrays.toString(retShape) + "]");
ret = op.z();

10
nd4j/nd4j-backends/nd4j-backend-impls/nd4j-native/src/main/java/org/nd4j/linalg/cpu/nativecpu/CpuNDArrayFactory.java
View File

@ -514,7 +514,7 @@ public class CpuNDArrayFactory extends BaseNativeNDArrayFactory {
int numTads = (int)(tensor.lengthLong() / tadLength);
int numTads = (int)(tensor.length() / tadLength);
INDArray[] result = new INDArray[numTads];
PointerPointer targets = new PointerPointer(numTads);
@ -693,7 +693,7 @@ public class CpuNDArrayFactory extends BaseNativeNDArrayFactory {
if (arrays.length == 1)
return target.addi(arrays[0]);
long len = target.lengthLong();
long len = target.length();
PointerPointer dataPointers = new PointerPointer(arrays.length);
@ -703,7 +703,7 @@ public class CpuNDArrayFactory extends BaseNativeNDArrayFactory {
if (arrays[i].elementWiseStride() != 1)
throw new ND4JIllegalStateException("Native accumulation is applicable only to continuous INDArrays");
if (arrays[i].lengthLong() != len)
if (arrays[i].length() != len)
throw new ND4JIllegalStateException("All arrays should have equal length for accumulation");
dataPointers.put(i, arrays[i].data().addressPointer());
@ -744,7 +744,7 @@ public class CpuNDArrayFactory extends BaseNativeNDArrayFactory {
return target.assign(arrays[0]);
}
long len = target != null ? target.lengthLong() : arrays[0].length();
long len = target != null ? target.length() : arrays[0].length();
PointerPointer dataPointers = new PointerPointer(arrays.length);
val firstType = arrays[0].dataType();
@ -757,7 +757,7 @@ public class CpuNDArrayFactory extends BaseNativeNDArrayFactory {
if (arrays[i].elementWiseStride() != 1)
throw new ND4JIllegalStateException("Native averaging is applicable only to continuous INDArrays");
if (arrays[i].lengthLong() != len)
if (arrays[i].length() != len)
throw new ND4JIllegalStateException("All arrays should have equal length for averaging");
dataPointers.put(i, arrays[i].data().addressPointer());

22
nd4j/nd4j-backends/nd4j-backend-impls/nd4j-native/src/main/java/org/nd4j/linalg/cpu/nativecpu/ops/NativeOpExecutioner.java
View File

@ -303,11 +303,11 @@ public class NativeOpExecutioner extends DefaultOpExecutioner {
}
} else {
//Every X TAD vs. entirety of Y
val xTADSize = op.x().lengthLong() / op.x().tensorsAlongDimension(dimension);
val xTADSize = op.x().length() / op.x().tensorsAlongDimension(dimension);
if (xTADSize != op.y().length()) {
throw new ND4JIllegalStateException("Size of TADs along dimension don't match for pairwise execution:" +
" (x TAD size = " + xTADSize + ", y size = " + op.y().lengthLong());
" (x TAD size = " + xTADSize + ", y size = " + op.y().length());
}
}
}
@ -329,7 +329,7 @@ public class NativeOpExecutioner extends DefaultOpExecutioner {
long xT = op.x().tensorsAlongDimension(dimension);
long yT = op.y().tensorsAlongDimension(dimension);
if (op.z().lengthLong() != xT * yT)
if (op.z().length() != xT * yT)
throw new ND4JIllegalStateException("Shape of target array for reduction [" + Arrays.toString(op.z().shape()) + "] doesn't match expected [" + (xT * yT) + "]");
}
@ -358,7 +358,7 @@ public class NativeOpExecutioner extends DefaultOpExecutioner {
// we're going to check, if that's TAD vs TAD comparison or TAD vs full array. if later - we're going slightly different route
boolean tvf = false;
if (op.y() != null) {
if (op.x().tensorAlongDimension(0, dimension).lengthLong() == op.y().lengthLong()) {
if (op.x().tensorAlongDimension(0, dimension).length() == op.y().length()) {
tvf = true;
}
}
@ -366,10 +366,10 @@ public class NativeOpExecutioner extends DefaultOpExecutioner {
if (op.isComplexAccumulation()) {
yTadBuffers = tadManager.getTADOnlyShapeInfo(op.y(), dimension);
if (op.x().tensorAlongDimension(0, dimension).lengthLong() != op.y().tensorAlongDimension(0, dimension).lengthLong())
if (op.x().tensorAlongDimension(0, dimension).length() != op.y().tensorAlongDimension(0, dimension).length())
throw new ND4JIllegalStateException("Impossible to issue AllDistances operation: TAD lengths mismatch along given dimension: " +
"x TAD length = " + op.x().tensorAlongDimension(0, dimension).lengthLong() + ", y TAD length " +
op.y().tensorAlongDimension(0, dimension).lengthLong());
"x TAD length = " + op.x().tensorAlongDimension(0, dimension).length() + ", y TAD length " +
op.y().tensorAlongDimension(0, dimension).length());
}
/**
@ -659,7 +659,7 @@ public class NativeOpExecutioner extends DefaultOpExecutioner {
//validateDataType(Nd4j.dataType(), op);
if (op.x().lengthLong() != op.z().lengthLong())
if (op.x().length() != op.z().length())
throw new ND4JIllegalStateException("op.X length should be equal to op.Z length: " +
"x.length()=" + op.x().length() + ", z.length()=" + op.z().length() + " - x shape info = ["
+ Arrays.toString(op.x().shapeInfoDataBuffer().asInt()) + "], z shape info = ["
@ -1449,8 +1449,8 @@ public class NativeOpExecutioner extends DefaultOpExecutioner {
long originalLength = buffer.getInt(1);
float threshold = buffer.getInt(2);
if (target.lengthLong() != originalLength)
throw new ND4JIllegalStateException("originalLength ["+ originalLength+"] stored in encoded array doesn't match target length ["+ target.lengthLong()+"]");
if (target.length() != originalLength)
throw new ND4JIllegalStateException("originalLength ["+ originalLength+"] stored in encoded array doesn't match target length ["+ target.length()+"]");
DataTypeEx typeDst = AbstractCompressor.getBufferTypeEx(target.data());
@ -1465,7 +1465,7 @@ public class NativeOpExecutioner extends DefaultOpExecutioner {
@Override
public long bitmapEncode(INDArray indArray, INDArray target, double threshold) {
long length = indArray.lengthLong();
long length = indArray.length();
long tLen = target.data().length();
if (tLen != (length / 16 + 5))

55
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/RnnOpValidation.java
View File

@ -16,14 +16,19 @@
package org.nd4j.autodiff.opvalidation;
import java.util.Arrays;
import lombok.extern.slf4j.Slf4j;
import org.junit.Test;
import org.nd4j.autodiff.samediff.SDIndex;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.GRUCellConfiguration;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.LSTMBlockCellConfiguration;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.LSTMConfiguration;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.outputs.LSTMCellOutputs;
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.factory.Nd4j;
import org.nd4j.linalg.factory.Nd4jBackend;
import org.nd4j.linalg.indexing.NDArrayIndex;
@ -59,23 +64,18 @@ public class RnnOpValidation extends BaseOpValidation {
SDVariable b = sd.constant(Nd4j.rand(DataType.FLOAT, 4*nOut));
double fb = 1.0;
LSTMBlockCellConfiguration conf = LSTMBlockCellConfiguration.builder()
.xt(x)
.cLast(cLast)
.yLast(yLast)
.W(W)
.Wci(Wci)
.Wcf(Wcf)
.Wco(Wco)
.b(b)
LSTMConfiguration conf = LSTMConfiguration.builder()
.peepHole(true)
.forgetBias(fb)
.clippingCellValue(0.0)
.build();
List<SDVariable> v = sd.rnn().lstmBlockCell("lstm", conf); //Output order: i, c, f, o, z, h, y
LSTMWeights weights = LSTMWeights.builder().weights(W).bias(b)
.inputPeepholeWeights(Wci).forgetPeepholeWeights(Wcf).outputPeepholeWeights(Wco).build();
LSTMCellOutputs v = sd.rnn().lstmCell(x, cLast, yLast, weights, conf); //Output order: i, c, f, o, z, h, y
List<String> toExec = new ArrayList<>();
for(SDVariable sdv : v){
for(SDVariable sdv : v.getAllOutputs()){
toExec.add(sdv.getVarName());
}
@ -167,23 +167,18 @@ public class RnnOpValidation extends BaseOpValidation {
SDVariable b = sd.constant(Nd4j.zeros(DataType.FLOAT, 8));
double fb = 1.0;
LSTMBlockCellConfiguration conf = LSTMBlockCellConfiguration.builder()
.xt(x)
.cLast(cLast)
.yLast(yLast)
.W(W)
.Wci(Wci)
.Wcf(Wcf)
.Wco(Wco)
.b(b)
LSTMConfiguration conf = LSTMConfiguration.builder()
.peepHole(false)
.forgetBias(fb)
.clippingCellValue(0.0)
.build();
List<SDVariable> v = sd.rnn().lstmBlockCell("lstm", conf); //Output order: i, c, f, o, z, h, y
LSTMWeights weights = LSTMWeights.builder().weights(W).bias(b)
.inputPeepholeWeights(Wci).forgetPeepholeWeights(Wcf).outputPeepholeWeights(Wco).build();
LSTMCellOutputs v = sd.rnn().lstmCell(x, cLast, yLast, weights, conf); //Output order: i, c, f, o, z, h, y
List<String> toExec = new ArrayList<>();
for(SDVariable sdv : v){
for(SDVariable sdv : v.getAllOutputs()){
toExec.add(sdv.getVarName());
}
@ -228,16 +223,14 @@ public class RnnOpValidation extends BaseOpValidation {
SDVariable bc = sd.constant(Nd4j.rand(DataType.FLOAT, nOut));
double fb = 1.0;
GRUCellConfiguration conf = GRUCellConfiguration.builder()
.xt(x)
.hLast(hLast)
.Wru(Wru)
.Wc(Wc)
.bru(bru)
.bc(bc)
GRUWeights weights = GRUWeights.builder()
.ruWeight(Wru)
.cWeight(Wc)
.ruBias(bru)
.cBias(bc)
.build();
List<SDVariable> v = sd.rnn().gru("gru", conf);
List<SDVariable> v = sd.rnn().gru("gru", x, hLast, weights).getAllOutputs();
List<String> toExec = new ArrayList<>();
for(SDVariable sdv : v){
toExec.add(sdv.getVarName());

2
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/Nd4jTestsC.java
View File

@ -5155,7 +5155,7 @@ public class Nd4jTestsC extends BaseNd4jTest {
INDArray res = x.entropy(1);
assertEquals(10, res.lengthLong());
assertEquals(10, res.length());
for (int t = 0; t < x.rows(); t++) {
double exp = MathUtils.entropy(x.getRow(t).dup().data().asDouble());

4
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/ShufflesTests.java
View File

@ -415,7 +415,7 @@ public class ShufflesTests extends BaseNd4jTest {
for (int x = 0; x < newData.rows(); x++) {
INDArray row = newData.getRow(x);
for (int y = 0; y < row.lengthLong(); y++) {
for (int y = 0; y < row.length(); y++) {
if (Math.abs(row.getFloat(y) - newMap[x]) > Nd4j.EPS_THRESHOLD) {
System.out.print("Different data in a row");
return false;
@ -442,7 +442,7 @@ public class ShufflesTests extends BaseNd4jTest {
for (int x = 0; x < newData.rows(); x++) {
INDArray column = newData.getColumn(x);
double val = column.getDouble(0);
for (int y = 0; y < column.lengthLong(); y++) {
for (int y = 0; y < column.length(); y++) {
if (Math.abs(column.getFloat(y) - val) > Nd4j.EPS_THRESHOLD) {
System.out.print("Different data in a column: " + column.getFloat(y));
return false;

2
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/api/blas/LapackTest.java
View File

@ -92,7 +92,7 @@ public class LapackTest extends BaseNd4jTest {
@Test
public void testCholeskyU() {
INDArray A = Nd4j.create(new double[] {2, -1, 2, -1, 2, -1, 2, -1, 2,});
INDArray A = Nd4j.create(new double[] {3, -1, 2, -1, 3, -1, 2, -1, 3,});
A = A.reshape('f', 3, 3);
INDArray O = Nd4j.create(A.dataType(), A.shape());
Nd4j.copy(A, O);

2
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/compression/CompressionTests.java
View File

@ -168,7 +168,7 @@ public class CompressionTests extends BaseNd4jTest {
INDArray decompressed = Nd4j.create(1, initial.length());
Nd4j.getExecutioner().thresholdDecode(compressed, decompressed);
log.info("Decompressed length: {}", decompressed.lengthLong());
log.info("Decompressed length: {}", decompressed.length());
assertEquals(exp_d, decompressed);
}

7
nd4j/nd4j-common/src/main/java/org/nd4j/linalg/primitives/Pair.java
View File

@ -23,6 +23,7 @@ import lombok.NoArgsConstructor;
import java.io.Serializable;
import java.util.Arrays;
import org.nd4j.base.Preconditions;
/**
* Simple pair implementation
@ -86,4 +87,10 @@ public class Pair<K, V> implements Serializable {
public static <T, E> Pair<T,E> pairOf(T key, E value) {
return new Pair<T, E>(key, value);
}
public static <T> Pair<T, T> fromArray(T[] arr){
Preconditions.checkArgument(arr.length == 2,
"Can only create a pair from an array with two values, got %s", arr.length);
return new Pair<>(arr[0], arr[1]);
}
}