* initial commit Signed-off-by: raver119 <raver119@gmail.com> * Implementation of hashcode cuda helper. Working edition. * Fixed parallel test input arangements. * Fixed tests for hashcode op. * Fixed shape calculation for image:crop_and_resize op and test. * NativeOps tests. Initial test suite. * Added tests for indexReduce methods. * Added test on execBroadcast with NDArray as dimensions. * Added test on execBroadcastBool with NDArray as dimensions. * Added tests on execPairwiseTransform and execPairwiseTransofrmBool. * Added tests for execReduce with scalar results. * Added reduce tests for non-empty dims array. * Added tests for reduce3. * Added tests for execScalar. * Added tests for execSummaryStats. * - provide cpu/cuda code for batch_to_space - testing it Signed-off-by: Yurii <yurii@skymind.io> * - remove old test for batch_to_space (had wrong format and numbers were not checked) Signed-off-by: Yurii <yurii@skymind.io> * Fixed complilation errors with test. * Added test for execTransformFloat. * Added test for execTransformSame. * Added test for execTransformBool. * Added test for execTransformStrict. * Added tests for execScalar/execScalarBool with TADs. * Added test for flatten. * - provide cpu/cuda code for space_to_Batch operaion Signed-off-by: Yurii <yurii@skymind.io> * Added test for concat. * comment unnecessary stuff in s_t_b Signed-off-by: Yurii <yurii@skymind.io> * Added test for specialConcat. * Added tests for memcpy/set routines. * Fixed pullRow cuda test. * Added pullRow test. * Added average test. * - correct typo in NDArray::applyPairwiseTransform(nd4j::pairwise::BoolOps op...) Signed-off-by: Yurii <yurii@skymind.io> * - debugging and fixing cuda tests in JavaInteropTests file Signed-off-by: Yurii <yurii@skymind.io> * - correct some tests Signed-off-by: Yurii <yurii@skymind.io> * Added test for shuffle. * Fixed ops declarations. * Restored omp and added shuffle test. * Added convertTypes test. * Added tests for execRandom. Eliminated usage of RandomBuffer with NativeOps. * Added sort tests. * Added tests for execCustomOp. * - further debuging and fixing tests terminated with crash Signed-off-by: Yurii <yurii@skymind.io> * Added tests for calculateOutputShapes. * Addded Benchmarks test. * Commented benchmark tests. * change assertion Signed-off-by: raver119 <raver119@gmail.com> * Added tests for apply_sgd op. Added cpu helper for that op. * Implement cuda helper for aplly_sgd op. Fixed tests for NativeOps. * Added test for assign broadcastable. * Added tests for assign_bp op. * Added tests for axpy op. * - assign/execScalar/execTransformAny signature change - minor test fix Signed-off-by: raver119 <raver119@gmail.com> * Fixed axpy op. * meh Signed-off-by: raver119 <raver119@gmail.com> * - fix tests for nativeOps::concat Signed-off-by: Yurii <yurii@skymind.io> * sequential transform/scalar Signed-off-by: raver119 <raver119@gmail.com> * allow nested parallelism Signed-off-by: raver119 <raver119@gmail.com> * assign_bp leak fix Signed-off-by: raver119 <raver119@gmail.com> * block setRNG fix Signed-off-by: raver119 <raver119@gmail.com> * enable parallelism by default Signed-off-by: raver119 <raver119@gmail.com> * enable nested parallelism by default Signed-off-by: raver119 <raver119@gmail.com> * Added cuda implementation for row_count helper. * Added implementation for tnse gains op helper. * - take into account possible situations when input arrays are empty in reduce_ cuda stuff Signed-off-by: Yurii <yurii@skymind.io> * Implemented tsne/edge_forces op cuda-based helper. Parallelized cpu-based helper for edge_forces. * Added kernel for tsne/symmetrized op heleper. * Implementation of tsne/symmetrized op cuda helper. Working edition. * Eliminated waste printfs. * Added test for broadcastgradientargs op. * host-only fallback for empty reduce float Signed-off-by: raver119 <raver119@gmail.com> * - some tests fixes Signed-off-by: Yurii <yurii@skymind.io> * - correct the rest of reduce_ stuff Signed-off-by: Yurii <yurii@skymind.io> * - further correction of reduce_ stuff Signed-off-by: Yurii <yurii@skymind.io> * Added test for Cbow op. Also added cuda implementation for cbow helpers. * - improve code of stack operation for scalar case Signed-off-by: Yurii <yurii@skymind.io> * - provide cuda kernel for gatherND operation Signed-off-by: Yurii <yurii@skymind.io> * Implementation of cbow helpers with cuda kernels. * minor tests tweaks Signed-off-by: raver119 <raver119@gmail.com> * minor tests tweaks Signed-off-by: raver119 <raver119@gmail.com> * - further correction of cuda stuff Signed-off-by: Yurii <yurii@skymind.io> * Implementatation of cbow op helper with cuda kernels. Working edition. * Skip random testing for cudablas case. * lstmBlockCell context fix Signed-off-by: raver119 <raver119@gmail.com> * Added tests for ELU and ELU_BP ops. * Added tests for eq_scalar, gt_scalar, gte_scalar and lte_scalar ops. * Added tests for neq_scalar. * Added test for noop. * - further work on clipbynorm_bp Signed-off-by: Yurii <yurii@skymind.io> * - get rid of concat op call, use instead direct concat helper call Signed-off-by: Yurii <yurii@skymind.io> * lstmBlockCell context fix Signed-off-by: raver119 <raver119@gmail.com> * Added tests for lrelu and lrelu_bp. * Added tests for selu and selu_bp. * Fixed lrelu derivative helpers. * - some corrections in lstm Signed-off-by: Yurii <yurii@skymind.io> * operator * result shape fix Signed-off-by: raver119 <raver119@gmail.com> * - correct typo in lstmCell Signed-off-by: Yurii <yurii@skymind.io> * few tests fixed Signed-off-by: raver119 <raver119@gmail.com> * CUDA inverse broadcast bool fix Signed-off-by: raver119 <raver119@gmail.com> * disable MMAP test for CUDA Signed-off-by: raver119 <raver119@gmail.com> * BooleanOp syncToDevice Signed-off-by: raver119 <raver119@gmail.com> * meh Signed-off-by: raver119 <raver119@gmail.com> * additional data types for im2col/col2im Signed-off-by: raver119 <raver119@gmail.com> * Added test for firas_sparse op. * one more RandomBuffer test excluded Signed-off-by: raver119 <raver119@gmail.com> * Added tests for flatten op. * Added test for Floor op. * bunch of tests fixed Signed-off-by: raver119 <raver119@gmail.com> * mmulDot tests fixed Signed-off-by: raver119 <raver119@gmail.com> * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * Implemented floordiv_bp op and tests. * Fixed scalar case with cuda implementation for bds. * - work on cuda kernel for clip_by_norm backprop op is completed Signed-off-by: Yurii <yurii@skymind.io> * Eliminate cbow crach. * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * Eliminated abortion with batched nlp test. * more tests fixed Signed-off-by: raver119 <raver119@gmail.com> * Fixed shared flag initializing. * disabled bunch of cpu workspaces tests Signed-off-by: raver119 <raver119@gmail.com> * scalar operators fix: missing registerSpecialUse call Signed-off-by: raver119 <raver119@gmail.com> * Fixed logdet for cuda and tests. * - correct clipBynorm_bp Signed-off-by: Yurii <yurii@skymind.io> * Fixed crop_and_resize shape datatype. * - correct some mmul tests Signed-off-by: Yurii <yurii@skymind.io>
356 lines
9.9 KiB
C++
356 lines
9.9 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// Created by raver119 on 30.10.2017.
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//
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#include "testlayers.h"
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#include <ops/declarable/CustomOperations.h>
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using namespace nd4j;
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using namespace nd4j::ops;
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using namespace nd4j::graph;
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class ContextTests : public testing::Test {
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public:
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};
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TEST_F(ContextTests, Basic_Test_1) {
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VariableSpace variableSpace;
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auto _20 = NDArrayFactory::create_<float>('c', {2, 2});
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auto _21 = NDArrayFactory::create_<float>('c', {2, 2});
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_20->assign(1.0f);
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_21->assign(2.0f);
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variableSpace.putVariable(2, 0, _20);
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variableSpace.putVariable(2, 1, _21);
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Context block(1, &variableSpace);
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block.pickInput(2, 0);
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block.pickInput(2, 1);
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ASSERT_EQ(2, block.inputs()->size());
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ASSERT_EQ(2, block.width());
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ASSERT_TRUE(variableSpace.hasVariable(2, 0));
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ASSERT_TRUE(variableSpace.hasVariable(2, 1));
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ASSERT_NEAR(1.0f, block.variable(0)->getNDArray()->meanNumber().e<float>(0), 1e-5);
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ASSERT_NEAR(2.0f, block.variable(1)->getNDArray()->meanNumber().e<float>(0), 1e-5);
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}
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TEST_F(ContextTests, Basic_Test_2) {
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VariableSpace variableSpace;
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auto _20 = NDArrayFactory::create_<float>('c', {2, 2});
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auto _21 = NDArrayFactory::create_<float>('c', {2, 2});
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_20->assign(1.0f);
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_21->assign(2.0f);
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variableSpace.putVariable(-1, _20);
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variableSpace.putVariable(-2, _21);
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Context block(1, &variableSpace);
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block.pickInput(-1);
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block.pickInput(-2);
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ASSERT_EQ(2, block.inputs()->size());
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ASSERT_EQ(2, block.width());
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ASSERT_TRUE(variableSpace.hasVariable(-1));
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ASSERT_TRUE(variableSpace.hasVariable(-2));
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ASSERT_NEAR(1.0f, block.variable(0)->getNDArray()->meanNumber().e<float>(0), 1e-5);
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ASSERT_NEAR(2.0f, block.variable(1)->getNDArray()->meanNumber().e<float>(0), 1e-5);
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}
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TEST_F(ContextTests, Basic_Test_3) {
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VariableSpace variableSpace;
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Context ctx(1, &variableSpace);
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auto _20 = NDArrayFactory::create_<float>('c', {2, 2});
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ctx.pushNDArrayToVariableSpace(1, 1, _20);
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ASSERT_TRUE(variableSpace.hasVariable(1, 1));
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}
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TEST_F(ContextTests, Basic_Test_4) {
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VariableSpace variableSpace;
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Context ctx(1, &variableSpace);
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auto _20 = NDArrayFactory::create_<float>('c', {2, 2});
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_20->linspace(1);
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auto _21 = NDArrayFactory::create_<float>('c', {2, 2});
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_21->linspace(10);
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ctx.pushNDArrayToVariableSpace(1, 1, _20);
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ASSERT_TRUE(variableSpace.hasVariable(1, 1));
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ctx.pushNDArrayToVariableSpace(1, 1, _21);
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auto vA = ctx.variable(1, 1);
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ASSERT_TRUE(vA->getNDArray()->equalsTo(_21));
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}
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TEST_F(ContextTests, Basic_Test_5) {
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VariableSpace variableSpace;
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Context ctx(1, &variableSpace);
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auto _20 = NDArrayFactory::create_<float>('c', {2, 2});
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_20->linspace(1);
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auto exp = _20->dup();
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ctx.pushNDArrayToVariableSpace(1, 1, _20);
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ASSERT_TRUE(variableSpace.hasVariable(1, 1));
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ctx.pushNDArrayToVariableSpace(1, 1, _20);
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auto vA = ctx.variable(1, 1);
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ASSERT_TRUE(vA->getNDArray() == _20);
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ASSERT_TRUE(vA->getNDArray()->equalsTo(exp));
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delete exp;
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}
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TEST_F(ContextTests, Basic_Test_6) {
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VariableSpace variableSpace;
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Context ctx(1, &variableSpace);
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auto v0 = ctx.ensureVariable();
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auto v1 = ctx.ensureVariable(1);
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ASSERT_TRUE(variableSpace.hasVariable(1, 0));
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ASSERT_TRUE(variableSpace.hasVariable(1, 1));
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auto var0 = variableSpace.getVariable(1, 0);
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auto var1 = variableSpace.getVariable(1, 1);
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ASSERT_TRUE(v0 == var0);
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ASSERT_TRUE(v1 == var1);
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}
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TEST_F(ContextTests, Basic_Test_7) {
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VariableSpace variableSpace;
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Context ctx(1, &variableSpace);
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auto v0 = ctx.ensureVariable();
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auto v1 = ctx.ensureVariable(1);
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ASSERT_TRUE(variableSpace.hasVariable(1, 0));
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ASSERT_TRUE(variableSpace.hasVariable(1, 1));
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auto var0 = variableSpace.getVariable(1, 0);
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auto var1 = variableSpace.getVariable(1, 1);
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ASSERT_TRUE(v0 == var0);
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ASSERT_TRUE(v1 == var1);
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auto _10 = NDArrayFactory::create_<float>('c', {2, 2});
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_10->linspace(1);
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auto _11 = NDArrayFactory::create_<float>('c', {2, 2});
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_11->linspace(10);
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ctx.pushNDArrayToVariableSpace(1, 0, _10);
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ctx.pushNDArrayToVariableSpace(1, 1, _11);
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auto z0 = variableSpace.getVariable(1, 0);
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auto z1 = variableSpace.getVariable(1, 1);
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ASSERT_TRUE(v0 == z0);
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ASSERT_TRUE(v1 == z1);
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}
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TEST_F(ContextTests, Basic_Test_8) {
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VariableSpace variableSpace;
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Context ctx(1, &variableSpace);
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auto _10 = NDArrayFactory::create_<float>('c', {2, 2});
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_10->linspace(1);
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auto _11 = NDArrayFactory::create_<float>('c', {2, 2});
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_11->linspace(10);
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ctx.pushNDArrayToVariableSpace(1, 0, _10);
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ctx.pushNDArrayToVariableSpace(1, 1, _11);
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auto z0 = variableSpace.getVariable(1, 0);
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auto z1 = variableSpace.getVariable(1, 1);
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auto v0 = ctx.ensureVariable();
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auto v1 = ctx.ensureVariable(1);
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ASSERT_TRUE(v0 == z0);
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ASSERT_TRUE(v1 == z1);
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}
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TEST_F(ContextTests, Basic_Test_9) {
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VariableSpace variableSpace;
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auto in = NDArrayFactory::create<float>('c', {5, 5});
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Context ctx(1, &variableSpace, true);
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ctx.pushNDArrayToVariableSpace(1, 1, &in, false);
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}
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TEST_F(ContextTests, Basic_Test_10) {
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VariableSpace variableSpace;
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Context ctx(119, &variableSpace);
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}
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TEST_F(ContextTests, Prototype_Test_1) {
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ContextPrototype prototype(nullptr, 119, true);
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prototype.pickInput(12, 3);
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prototype.pickInput(12, 4);
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prototype.getTArguments()->push_back(2.0);
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prototype.getTArguments()->push_back(-2.0);
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prototype.getIArguments()->push_back(17);
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prototype.getIArguments()->push_back(119);
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Context ctx(&prototype, nullptr);
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ASSERT_EQ(ctx.nodeId(), prototype.nodeId());
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ASSERT_EQ(ctx.isInplace(), prototype.isInplace());
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ASSERT_EQ(2, ctx.inputs()->size());
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ASSERT_EQ(2, ctx.getTArguments()->size());
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ASSERT_EQ(2, ctx.getIArguments()->size());
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ASSERT_EQ(2.0, ctx.getTArguments()->at(0));
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ASSERT_EQ(-2.0, ctx.getTArguments()->at(1));
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ASSERT_EQ(17, ctx.getIArguments()->at(0));
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ASSERT_EQ(119, ctx.getIArguments()->at(1));
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}
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TEST_F(ContextTests, Prototype_Test_2) {
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ContextPrototype prototype(nullptr, 119, false);
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prototype.setOpNum(179);
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Context ctx(&prototype, nullptr);
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ASSERT_EQ(ctx.isInplace(), prototype.isInplace());
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ASSERT_EQ(ctx.opNum(), prototype.opNum());
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ASSERT_EQ(0, ctx.inputs()->size());
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ASSERT_EQ(0, ctx.getTArguments()->size());
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ASSERT_EQ(0, ctx.getIArguments()->size());
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}
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TEST_F(ContextTests, test_short_context_1) {
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auto array0 = NDArrayFactory::create<float>('c', {3, 2}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f});
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auto array1 = NDArrayFactory::create<float>('c', {3, 2}, {-1.f, -2.f, -3.f, -4.f, -5.f, -6.f});
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Context ctx(1);
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ctx.setInputArray(0, array0.buffer(), array0.shapeInfo(), array0.specialBuffer(), array0.specialShapeInfo());
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ctx.setInputArray(1, array1.buffer(), array1.shapeInfo(), array1.specialBuffer(), array1.specialShapeInfo());
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ASSERT_EQ(2, ctx.width());
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auto input0 = ctx.array(0);
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ASSERT_TRUE(input0 != nullptr);
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auto input1 = ctx.array(1);
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ASSERT_TRUE(input1 != nullptr);
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ASSERT_TRUE(input0->buffer() == array0.buffer());
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ASSERT_TRUE(input0->shapeInfo() == array0.shapeInfo());
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ASSERT_TRUE(input0->specialBuffer() == array0.specialBuffer());
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ASSERT_TRUE(input0->specialShapeInfo() == array0.specialShapeInfo());
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ASSERT_TRUE(input1->buffer() == array1.buffer());
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ASSERT_TRUE(input1->shapeInfo() == array1.shapeInfo());
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ASSERT_TRUE(input1->specialBuffer() == array1.specialBuffer());
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ASSERT_TRUE(input1->specialShapeInfo() == array1.specialShapeInfo());
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}
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TEST_F(ContextTests, test_short_context_2) {
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auto array0 = NDArrayFactory::create<float>('c', {3, 2}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f});
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auto array1 = NDArrayFactory::create<float>('c', {3, 2}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f});
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auto z = NDArrayFactory::create<float>('c', {3, 2});
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auto exp = NDArrayFactory::create<float>('c', {3, 2}, {2.f, 4.f, 6.f, 8.f, 10.f, 12.f});
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Context ctx(1);
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ctx.setInputArray(0, array0.buffer(), array0.shapeInfo(), array0.specialBuffer(), array0.specialShapeInfo());
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ctx.setInputArray(1, array1.buffer(), array1.shapeInfo(), array1.specialBuffer(), array1.specialShapeInfo());
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ctx.setOutputArray(0, z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo());
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ASSERT_EQ(2, ctx.width());
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nd4j::ops::add op;
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op.execute(&ctx);
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ASSERT_EQ(exp, z);
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}
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TEST_F(ContextTests, test_short_context_3) {
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auto array0 = NDArrayFactory::create<float>('c', {3, 2}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f});
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auto array1 = NDArrayFactory::create<float>('c', {3, 2}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f});
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auto exp = NDArrayFactory::create<float>('c', {3, 2}, {2.f, 4.f, 6.f, 8.f, 10.f, 12.f});
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Context ctx(1);
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ctx.setInputArray(0, array0.buffer(), array0.shapeInfo(), array0.specialBuffer(), array0.specialShapeInfo());
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ctx.setInputArray(1, array1.buffer(), array1.shapeInfo(), array1.specialBuffer(), array1.specialShapeInfo());
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ASSERT_EQ(2, ctx.width());
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nd4j::ops::add op;
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op.execute(&ctx);
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ASSERT_EQ(1, ctx.fastpath_out().size());
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auto z = ctx.fastpath_out()[0];
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ASSERT_EQ(exp, *z);
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} |