cavis/libnd4j/tests_cpu/layers_tests/ConstantShapeHelperTests.cpp

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/* ******************************************************************************
*
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*
* 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.
*
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* See the NOTICE file distributed with this work for additional
* information regarding copyright ownership.
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* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author raver119@gmail.com
//
#include "testlayers.h"
#include <ops/declarable/CustomOperations.h>
#include <helpers/ConstantShapeHelper.h>
#include <array/ShapeDescriptor.h>
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#include <array/ConstantDataBuffer.h>
#include <helpers/PointersManager.h>
using namespace sd;
using namespace sd::ops;
using namespace sd::graph;
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class ConstantShapeHelperTests : public testing::Test {
public:
};
class ConstantHelperTests : public testing::Test {
public:
};
class ConstantTadHelperTests : public testing::Test {
public:
};
TEST_F(ConstantShapeHelperTests, test_cachedAmount_1) {
auto ttlBefore = ConstantShapeHelper::getInstance().totalCachedEntries();
auto arrayA = NDArrayFactory::create<bool>('c', {7, 11, 17, 23, 31, 43});
auto ttlMiddle = ConstantShapeHelper::getInstance().totalCachedEntries();
auto arrayB = NDArrayFactory::create<bool>('c', {7, 11, 17, 23, 31, 43});
auto ttlAfter = ConstantShapeHelper::getInstance().totalCachedEntries();
ASSERT_TRUE(ttlBefore <= ttlMiddle);
ASSERT_EQ(ttlMiddle, ttlAfter);
}
TEST_F(ConstantTadHelperTests, test_cachedAmount_1) {
auto arrayA = NDArrayFactory::create<bool>('c', {7, 11, 17, 23, 31, 43});
auto ttlBefore = ConstantTadHelper::getInstance().totalCachedEntries();
auto packAA = ConstantTadHelper::getInstance().tadForDimensions(arrayA.shapeInfo(), {3, 4});
auto ttlMiddle = ConstantTadHelper::getInstance().totalCachedEntries();
auto packAB = ConstantTadHelper::getInstance().tadForDimensions(arrayA.shapeInfo(), {3, 4});
auto ttlAfter = ConstantTadHelper::getInstance().totalCachedEntries();
ASSERT_TRUE(ttlBefore <= ttlMiddle);
ASSERT_EQ(ttlMiddle, ttlAfter);
}
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TEST_F(ConstantShapeHelperTests, basic_test_1) {
auto ptr = ShapeBuilders::createShapeInfo(sd::DataType::BFLOAT16, 'f', {5, 10, 15});
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ShapeDescriptor descriptor(ptr);
ShapeDescriptor descriptor2(ptr);
ASSERT_EQ(descriptor, descriptor2);
ASSERT_EQ(1, descriptor.ews());
ASSERT_EQ(3, descriptor.rank());
ASSERT_EQ('f', descriptor.order());
ASSERT_EQ(sd::DataType::BFLOAT16, descriptor.dataType());
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ASSERT_FALSE(descriptor.isEmpty());
ASSERT_FALSE(ConstantShapeHelper::getInstance().checkBufferExistenceForShapeInfo(descriptor));
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auto buffer = ConstantShapeHelper::getInstance().bufferForShapeInfo(descriptor);
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ASSERT_TRUE(ConstantShapeHelper::getInstance().checkBufferExistenceForShapeInfo(descriptor));
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auto buffer2 = ConstantShapeHelper::getInstance().bufferForShapeInfo(descriptor2);
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ASSERT_TRUE(buffer.primary() != nullptr);
ASSERT_TRUE(buffer.primary() == buffer2.primary());
ASSERT_TRUE(buffer.special() == buffer2.special());
delete []ptr;
}
TEST_F(ConstantShapeHelperTests, stress_test_1) {
for (auto x = 0; x < 1000; x++) {
auto ptr = ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', {5, x + 10, x + 1});
ShapeDescriptor descriptor(ptr);
ConstantShapeHelper::getInstance().createShapeInfo(descriptor);
delete [] ptr;
}
ShapeDescriptor aShape(sd::DataType::FLOAT32, 'c', {(Nd4jLong)5, (Nd4jLong)382, (Nd4jLong)373});
// nd4j_printf("%d\n", ConstantShapeHelper::getInstance().cachedEntriesForDevice(0));
auto timeStart = std::chrono::system_clock::now();
ASSERT_TRUE(ConstantShapeHelper::getInstance().checkBufferExistenceForShapeInfo(aShape));
auto timeEnd = std::chrono::system_clock::now();
auto outerTime = std::chrono::duration_cast<std::chrono::nanoseconds>(timeEnd - timeStart).count();
nd4j_printf("Total time (us) %lld\n", outerTime);
}
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TEST_F(ConstantShapeHelperTests, basic_test_3) {
auto array = NDArrayFactory::create_<float>('c', {128});
ASSERT_TRUE(array->shapeInfo() != nullptr);
#ifdef __CUDABLAS__
ASSERT_TRUE(array->specialShapeInfo() != nullptr);
#endif
delete array;
}
TEST_F(ConstantShapeHelperTests, basic_test_4) {
auto array = NDArrayFactory::create_<float>('c', {128, 256});
Shyrma temp (#131) * - 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>
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auto dup = new NDArray(array->dup('f'));
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ASSERT_TRUE(dup->shapeInfo() != nullptr);
#ifdef __CUDABLAS__
ASSERT_TRUE(dup->specialShapeInfo() != nullptr);
PointersManager manager(sd::LaunchContext ::defaultContext(), "test");
// manager.printDevContentOnDev<Nd4jLong>(dup->special(), shape::shapeInfoLength(2), 0);
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#endif
delete array;
delete dup;
}
TEST_F(ConstantShapeHelperTests, basic_test_5) {
auto arrayA = NDArrayFactory::create<int>(1);
auto arrayB = NDArrayFactory::create_<float>('c', {128, 256});
//arrayA.printShapeInfo("A");
//arrayB->printShapeInfo("B");
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ASSERT_EQ(0, arrayA.rankOf());
ASSERT_EQ(2, arrayB->rankOf());
ASSERT_NE(arrayA.dataType(), arrayB->dataType());
delete arrayB;
}
TEST_F(ConstantShapeHelperTests, basic_test_6) {
ShapeDescriptor descriptorA(sd::DataType::INT32, 'c', {});
ShapeDescriptor descriptorB(sd::DataType::FLOAT32, 'c', {10, 10});
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// ASSERT_FALSE(descriptorA < descriptorB);
// ASSERT_TRUE(descriptorB < descriptorA);
ASSERT_TRUE(descriptorA < descriptorB);
ASSERT_FALSE(descriptorB < descriptorA);
}
TEST_F(ConstantShapeHelperTests, basic_test_7) {
auto array = NDArrayFactory::create_<float>('c', {32, 256});
IndicesList indices({NDIndex::all(), NDIndex::interval(0,1)});
auto strided = array->subarray(indices);
Shyrma temp (#131) * - 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>
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strided.assign(1.0f);
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//strided->printIndexedBuffer("column");
delete array;
}
TEST_F(ConstantHelperTests, basic_test_1) {
ConstantDescriptor descriptor({1, 2, 3});
ConstantDataBuffer* fBuffer = ConstantHelper::getInstance().constantBuffer(descriptor, sd::DataType::FLOAT32);
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auto fPtr = fBuffer->primaryAsT<float>();
ASSERT_NEAR(1.f, fPtr[0], 1e-5);
ASSERT_NEAR(2.f, fPtr[1], 1e-5);
ASSERT_NEAR(3.f, fPtr[2], 1e-5);
auto iBuffer = ConstantHelper::getInstance().constantBuffer(descriptor, sd::DataType::INT32);
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auto iPtr = iBuffer->primaryAsT<int>();
ASSERT_EQ(1, iPtr[0]);
ASSERT_EQ(2, iPtr[1]);
ASSERT_EQ(3, iPtr[2]);
}
TEST_F(ConstantHelperTests, basic_test_2) {
double array[] = {1., 2., 3.};
ConstantDescriptor descriptor(array, 3);
ConstantDataBuffer* fBuffer = ConstantHelper::getInstance().constantBuffer(descriptor, sd::DataType::FLOAT32);
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auto fPtr = fBuffer->primaryAsT<float>();
ASSERT_NEAR(1.f, fPtr[0], 1e-5);
ASSERT_NEAR(2.f, fPtr[1], 1e-5);
ASSERT_NEAR(3.f, fPtr[2], 1e-5);
auto iBuffer = ConstantHelper::getInstance().constantBuffer(descriptor, sd::DataType::INT32);
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auto iPtr = iBuffer->primaryAsT<int>();
ASSERT_EQ(1, iPtr[0]);
ASSERT_EQ(2, iPtr[1]);
ASSERT_EQ(3, iPtr[2]);
}
//////////////////////////////////////////////////////////////////////
TEST_F(ConstantShapeHelperTests, ShapeDescriptor_1) {
Nd4jLong shapeInfo1[] = {4, 2, 5, 5, 2, 25, 5, 1, 50, 8192, 0, 99};
Nd4jLong shapeInfo2[] = {4, 2, 5, 5, 2, 50, 10, 2, 1, 8192, 1, 99};
ShapeDescriptor descr1(shapeInfo1);
ShapeDescriptor descr2(shapeInfo2);
ASSERT_FALSE(descr1 == descr2);
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}
TEST_F(ConstantShapeHelperTests, ShapeDescriptor_validation) {
//for c order
std::vector<Nd4jLong> shape{ 2,3,4,5 };
std::vector<Nd4jLong> incorrectStride1{ 20,20,5,1 };
std::vector<Nd4jLong> incorrectStride2{ 60,20,5,5 };
std::vector<Nd4jLong> correctStride1{ 60,20,5,1 };
std::vector<Nd4jLong> correctStride2{ 300,100,25,5 };
std::vector<Nd4jLong> correctStride3{ 800, 200, 40, 5 };
auto shapeDesc = ShapeDescriptor(DataType::FLOAT32, 'c', shape, incorrectStride1, 1);
ASSERT_TRUE(shapeDesc.validate() == SHAPE_DESC_INCORRECT_STRIDES);
shapeDesc = ShapeDescriptor(DataType::FLOAT32, 'c', shape, correctStride1, 1);
ASSERT_TRUE(shapeDesc.validate() == SHAPE_DESC_OK);
shapeDesc = ShapeDescriptor(DataType::FLOAT32, 'c', shape, incorrectStride2, 1);
ASSERT_TRUE(shapeDesc.validate() == (SHAPE_DESC_INCORRECT_STRIDES | SHAPE_DESC_INCORRECT_EWS));
shapeDesc = ShapeDescriptor(DataType::FLOAT32, 'c', shape, correctStride2, 1);
ASSERT_TRUE(shapeDesc.validate() == SHAPE_DESC_INCORRECT_EWS);
shapeDesc = ShapeDescriptor(DataType::FLOAT32, 'c', shape, correctStride2, 5);
ASSERT_TRUE(shapeDesc.validate() == SHAPE_DESC_OK);
shapeDesc = ShapeDescriptor(DataType::FLOAT32, 'c', shape, correctStride3, 1);
ASSERT_TRUE(shapeDesc.validate() == SHAPE_DESC_INCORRECT_EWS);
shapeDesc = ShapeDescriptor(DataType::FLOAT32, 'c', shape, correctStride3, 0);
ASSERT_TRUE(shapeDesc.validate() == SHAPE_DESC_OK);
//order f
std::reverse(std::begin(shape), std::end(shape));
std::reverse(std::begin(incorrectStride1), std::end(incorrectStride1));
std::reverse(std::begin(incorrectStride2), std::end(incorrectStride2));
std::reverse(std::begin(correctStride1), std::end(correctStride1));
std::reverse(std::begin(correctStride2), std::end(correctStride2));
std::reverse(std::begin(correctStride3), std::end(correctStride3));
shapeDesc = ShapeDescriptor(DataType::FLOAT32, 'f', shape, incorrectStride1, 1);
ASSERT_TRUE(shapeDesc.validate() == SHAPE_DESC_INCORRECT_STRIDES);
shapeDesc = ShapeDescriptor(DataType::FLOAT32, 'f', shape, correctStride1, 1);
ASSERT_TRUE(shapeDesc.validate() == SHAPE_DESC_OK);
shapeDesc = ShapeDescriptor(DataType::FLOAT32, 'f', shape, incorrectStride2, 1);
ASSERT_TRUE(shapeDesc.validate() == (SHAPE_DESC_INCORRECT_STRIDES | SHAPE_DESC_INCORRECT_EWS));
shapeDesc = ShapeDescriptor(DataType::FLOAT32, 'f', shape, correctStride2, 1);
ASSERT_TRUE(shapeDesc.validate() == SHAPE_DESC_INCORRECT_EWS);
shapeDesc = ShapeDescriptor(DataType::FLOAT32, 'f', shape, correctStride2, 5);
ASSERT_TRUE(shapeDesc.validate() == SHAPE_DESC_OK);
shapeDesc = ShapeDescriptor(DataType::FLOAT32, 'f', shape, correctStride3, 1);
ASSERT_TRUE(shapeDesc.validate() == SHAPE_DESC_INCORRECT_EWS);
shapeDesc = ShapeDescriptor(DataType::FLOAT32, 'f', shape, correctStride3, 0);
ASSERT_TRUE(shapeDesc.validate() == SHAPE_DESC_OK);
std::vector<Nd4jLong> shape1;
shape1.resize(MAX_RANK+1);
shapeDesc = ShapeDescriptor(DataType::FLOAT32, 'f', shape1, correctStride3, 0);
ASSERT_TRUE( (shapeDesc.validate() & SHAPE_DESC_INCORRECT_RANK) == SHAPE_DESC_INCORRECT_RANK);
}
TEST_F(ConstantShapeHelperTests, ShapeDescriptor_paddedBuffer) {
constexpr int n = 2;
constexpr int c = 3;
constexpr int h = 4;
constexpr int w = 5;
constexpr int n_pad = 2;
constexpr int c_pad = 3;
constexpr int h_pad = 4;
constexpr int w_pad = 5;
char orders[] = { 'c', 'f' };
for (auto& order : orders) {
auto shapeDesc1 = ShapeDescriptor::paddedBufferDescriptor(DataType::FLOAT32, order, { n, c, h, w }, { n_pad, c_pad, h_pad, w_pad });
auto shapeDesc2 = ShapeDescriptor(DataType::FLOAT32, order, { n + n_pad, c + c_pad, h + h_pad, w + w_pad });
auto shapeDesc3 = ShapeDescriptor::paddedBufferDescriptor(DataType::FLOAT32, order, { n, c, h, w }, { n_pad, c_pad });
auto shapeDesc4 = ShapeDescriptor(DataType::FLOAT32, order, { n + n_pad, c + c_pad, h, w });
auto shapeDesc5 = ShapeDescriptor::paddedBufferDescriptor(DataType::FLOAT32, order, { n, c, h, w }, { 0, 0, h_pad, w_pad });
auto shapeDesc6 = ShapeDescriptor(DataType::FLOAT32, order, { n, c , h + h_pad, w + w_pad });
ASSERT_TRUE(shapeDesc1.validate() == SHAPE_DESC_OK);
ASSERT_TRUE(shapeDesc2.validate() == SHAPE_DESC_OK);
ASSERT_TRUE(shapeDesc3.validate() == SHAPE_DESC_OK);
ASSERT_TRUE(shapeDesc4.validate() == SHAPE_DESC_OK);
ASSERT_TRUE(shapeDesc5.validate() == SHAPE_DESC_OK);
ASSERT_TRUE(shapeDesc6.validate() == SHAPE_DESC_OK);
ASSERT_TRUE(shapeDesc1.allocLength() == shapeDesc2.allocLength());
ASSERT_TRUE(shapeDesc3.allocLength() == shapeDesc4.allocLength());
ASSERT_TRUE(shapeDesc5.allocLength() == shapeDesc6.allocLength());
const auto& v1 = shapeDesc1.strides();
const auto& v2 = shapeDesc2.strides();
const auto& v3 = shapeDesc3.strides();
const auto& v4 = shapeDesc4.strides();
const auto& v5 = shapeDesc5.strides();
const auto& v6 = shapeDesc6.strides();
for (int i = 0; i < v1.size(); i++) {
ASSERT_TRUE(v1[i] == v2[i]);
}
for (int i = 0; i < v3.size(); i++) {
ASSERT_TRUE(v3[i] == v4[i]);
}
for (int i = 0; i < v5.size(); i++) {
ASSERT_TRUE(v5[i] == v6[i]);
}
}
}