* - specifying template instantiation for certain types in float16 and bloat16 Signed-off-by: Yurii <iuriish@yahoo.com> * - polishing bfloat16 and float16 member functions template specialization Signed-off-by: Yurii <iuriish@yahoo.com> * - rewrite and overload array +-*/ scalar and scalar +-*/ arr in NDAray class Signed-off-by: Yurii <iuriish@yahoo.com> * - make corrections which have to do with and rvalue lvalue conversions Signed-off-by: Yurii <iuriish@yahoo.com> * - provide move semantic in NDArray operators array +-/* array Signed-off-by: Yurii <iuriish@yahoo.com> * float16/bfloat16 tweaks Signed-off-by: raver119 <raver119@gmail.com> * one more tweak Signed-off-by: raver119 <raver119@gmail.com> * - make float16 and bfloat16 to compile successfully on cuda Signed-off-by: Yurii <iuriish@yahoo.com> * - do not use resources of view-like arrays when move semantics is applied Signed-off-by: Yurii <iuriish@yahoo.com> * - get rid of pointers in signatures NDArray methods 1 Signed-off-by: Yurii <iuriish@yahoo.com> * - correction of signature of NDArray::dup method Signed-off-by: Yurii <iuriish@yahoo.com> * - correction of signature of NDArray::reduceAlongDimension method Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::applyIndexReduce and applyTrueBroadcast methods Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::applyReduce3 and varianceAlongDimension methods Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::tensorsAlongDimension and diagonal methods Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::allTensorsAlongDimension Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::reduceAlongDimension 2 Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::applyTransform 2 Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::applyPairwiseTransform 2 Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::applyBroadcast 2 Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::applyTrueBroadcast 2 Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::applyScalar and applyScalarArr Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::lambda methods Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::reduce3 methods 2 Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of following NDArray methods: add/sub/mul/div row/column and fillAsTriangular Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::tileToShape methods Signed-off-by: Yurii <iuriish@yahoo.com> * - signature correction of NDArray::isShapeSameStrict method Signed-off-by: Yurii <iuriish@yahoo.com> * minor corrections in tests Signed-off-by: Yurii <iuriish@yahoo.com> * - replace reduce op in batchnorm mkldnn Signed-off-by: Yurii <iuriish@yahoo.com> * - add explicit templates instantiations for operator+(NDArray&&. const scalar) Signed-off-by: Yurii <iuriish@yahoo.com> * - corrections of casts in float16/bfloat16 Signed-off-by: Yurii <iuriish@yahoo.com> * - provide move semantics in following NDArray methods: transform, applyTrueBroadcast, transpose, reshape, permute Signed-off-by: Yurii <iuriish@yahoo.com> * - get rid of input array A duplicate in svd cuda op Signed-off-by: Yurii <iuriish@yahoo.com> * - avoid available bug in svd cuda API Signed-off-by: Yurii <iuriish@yahoo.com> * - add temporary global memory buffer in svd cuda when calcUV = false and m != n Signed-off-by: Yurii <iuriish@yahoo.com> * - remove test with blfoat16 type for betainC Signed-off-by: Yurii <iuriish@yahoo.com> * - resolve conflicts after master has been merged in Signed-off-by: Yurii <iuriish@yahoo.com> * - changed type of affected input array in fused_batch_norm Signed-off-by: Yurii <iuriish@yahoo.com> * - add several explicit type castings Signed-off-by: Yurii <iuriish@yahoo.com> * - add ND4J_EXPORT to operators Signed-off-by: Yurii <iuriish@yahoo.com> * - add explicit template types in instantiations of template arithm operators of NDArray class Signed-off-by: Yurii <iuriish@yahoo.com> * - one more test fix Signed-off-by: Yurii <iuriish@yahoo.com> Co-authored-by: raver119 <raver119@gmail.com>
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 = new NDArray(_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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} |