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cavis/libnd4j/tests_cpu/layers_tests/NDArrayCudaBasicsTests.cu

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author raver119@gmail.com
//
#include "testlayers.h"
#include <NDArray.h>
#include <NDArrayFactory.h>
#include <Context.h>
#include <Node.h>
#include <graph/Variable.h>
#include <graph/VariableSpace.h>
#include <execution/LaunchContext.h>
#include <specials_cuda.h>
#include <TAD.h>
#include <ops/declarable/CustomOperations.h>
#include <cuda.h>
using namespace nd4j;
using namespace nd4j::graph;
class NDArrayCudaBasicsTests : public testing::Test {
public:
};
//////////////////////////////////////////////////////////////////////////
static cudaError_t allocateDeviceMem(LaunchContext& lc, std::vector<void*>& devicePtrs, const std::vector<std::pair<void*,size_t>>& hostData) {
if(devicePtrs.size() != hostData.size())
throw std::invalid_argument("prepareDataForCuda: two input sts::vectors should same sizes !");
cudaError_t cudaResult;
void* reductionPointer;
cudaResult = cudaMalloc(reinterpret_cast<void **>(&reductionPointer), 1024*1024); if(cudaResult != 0) return cudaResult;
int* allocationPointer;
cudaResult = cudaMalloc(reinterpret_cast<void **>(&allocationPointer), 1024*1024); if(cudaResult != 0) return cudaResult;
lc.setReductionPointer(reductionPointer);
lc.setAllocationPointer(allocationPointer);
cudaStream_t stream = *lc.getCudaStream();
for(int i = 0; i < devicePtrs.size(); ++i) {
cudaResult = cudaMalloc(reinterpret_cast<void **>(&devicePtrs[i]), hostData[i].second); if(cudaResult != 0) return cudaResult;
cudaMemcpyAsync(devicePtrs[i], hostData[i].first, hostData[i].second, cudaMemcpyHostToDevice, stream);
}
return cudaResult;
}
TEST_F(NDArrayCudaBasicsTests, Test_Registration_1) {
auto x = NDArrayFactory::create<int>('c', {5}, {1, 2, 3, 4, 5});
auto y = NDArrayFactory::create<int>('c', {5}, {5, 4, 3, 2, 1});
ASSERT_TRUE(x.isActualOnDeviceSide());
ASSERT_FALSE(x.isActualOnHostSide());
}
TEST_F(NDArrayCudaBasicsTests, Test_Registration_2) {
auto x = NDArrayFactory::create<int>('c', {5});
auto y = NDArrayFactory::create<int>('c', {5});
ASSERT_TRUE(x.isActualOnDeviceSide());
ASSERT_FALSE(x.isActualOnHostSide());
}
TEST_F(NDArrayCudaBasicsTests, Test_Registration_3) {
auto x = NDArrayFactory::create<int>('c', {5}, {1, 2, 3, 4, 5});
auto y = NDArrayFactory::create<int>('c', {5}, {5, 4, 3, 2, 1});
ASSERT_TRUE(x.isActualOnDeviceSide());
ASSERT_FALSE(x.isActualOnHostSide());
NDArray::registerSpecialUse({&x}, {&y});
ASSERT_TRUE(x.isActualOnDeviceSide());
ASSERT_FALSE(x.isActualOnHostSide());
ASSERT_TRUE(y.isActualOnDeviceSide());
ASSERT_FALSE(y.isActualOnHostSide());
}
TEST_F(NDArrayCudaBasicsTests, Test_Registration_01) {
auto x = NDArrayFactory::create_<int>('c', {5}, {1, 2, 3, 4, 5});
auto y = NDArrayFactory::create_<int>('c', {5}, {5, 4, 3, 2, 1});
ASSERT_TRUE(x->isActualOnDeviceSide());
ASSERT_FALSE(x->isActualOnHostSide());
delete x;
delete y;
}
TEST_F(NDArrayCudaBasicsTests, Test_Registration_02) {
auto x = NDArrayFactory::create_<int>('c', {5});
auto y = NDArrayFactory::create_<int>('c', {5});
ASSERT_TRUE(x->isActualOnDeviceSide());
ASSERT_FALSE(x->isActualOnHostSide());
delete x;
delete y;
}
TEST_F(NDArrayCudaBasicsTests, Test_Registration_03) {
auto x = NDArrayFactory::create_<int>('c', {5}, {1, 2, 3, 4, 5});
auto y = NDArrayFactory::create_<int>('c', {5}, {5, 4, 3, 2, 1});
ASSERT_TRUE(x->isActualOnDeviceSide());
ASSERT_FALSE(x->isActualOnHostSide());
NDArray::registerSpecialUse({y}, {x});
x->applyTransform(transform::Neg, *y);
//ASSERT_TRUE(x->isActualOnDeviceSide());
//ASSERT_FALSE(x->isActualOnHostSide());
//ASSERT_TRUE(y->isActualOnDeviceSide());
//ASSERT_TRUE(y->isActualOnHostSide());
//y->syncToHost();
// y->printBuffer("Negatives");
delete x;
delete y;
}
TEST_F(NDArrayCudaBasicsTests, Test_Cosine_1) {
auto x = NDArrayFactory::create_<double>('c', {5}, {1, 2, 3, 4, 5});
auto y = NDArrayFactory::create_<double>('c', {5}, {5, 4, 3, 2, 1});
ASSERT_TRUE(x->isActualOnDeviceSide());
ASSERT_FALSE(x->isActualOnHostSide());
NDArray::registerSpecialUse({y}, {x});
x->applyTransform(transform::Cosine, *y);
//ASSERT_TRUE(x->isActualOnDeviceSide());
//ASSERT_FALSE(x->isActualOnHostSide());
//ASSERT_TRUE(y->isActualOnDeviceSide());
//ASSERT_TRUE(y->isActualOnHostSide());
//y->syncToHost();
delete x;
delete y;
}
//////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, TestAdd_1) {
// allocating host-side arrays
auto x = NDArrayFactory::create<double>('c', { 5 }, { 1, 2, 3, 4, 5});
auto y = NDArrayFactory::create<double>('c', { 5 }, { 1, 2, 3, 4, 5});
auto z = NDArrayFactory::create<double>('c', { 5 }, {10, 10, 10, 10, 10});
auto exp = NDArrayFactory::create<double>('c', { 5 }, { 2, 4, 6, 8, 10 });
// making raw buffers
//Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX;
//cudaError_t res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrX), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrZ), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devShapePtrX), shape::shapeInfoByteLength(x.shapeInfo()));
//ASSERT_EQ(0, res);
Nd4jPointer nativeStream = (Nd4jPointer)malloc(sizeof(cudaStream_t));
CHECK_ALLOC(nativeStream, "Failed to allocate memory for new CUDA stream", sizeof(cudaStream_t));
cudaError_t dZ = cudaStreamCreate(reinterpret_cast<cudaStream_t *>(&nativeStream));
auto stream = reinterpret_cast<cudaStream_t *>(&nativeStream);
//cudaMemcpyAsync(devBufferPtrX, x.buffer(), x.lengthOf() * x.sizeOfT(), cudaMemcpyHostToDevice, *stream);
//cudaMemcpyAsync(devShapePtrX, x.shapeInfo(), shape::shapeInfoByteLength(x.shapeInfo()), cudaMemcpyHostToDevice, *stream);
LaunchContext lc(stream, nullptr, nullptr);
NativeOpExecutioner::execPairwiseTransform(&lc, pairwise::Add, x.buffer(), x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo(), y.buffer(), y.shapeInfo(), y.specialBuffer(), y.specialShapeInfo(), z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr);
z.tickWriteDevice();
auto res = cudaStreamSynchronize(*stream);
ASSERT_EQ(0, res);
for (int e = 0; e < z.lengthOf(); e++)
ASSERT_NEAR(exp.e<double>(e), z.e<double>(e), 1e-5);
}
//////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, TestAdd_2) {
// allocating host-side arrays
NDArray x('c', { 5 }, { 1, 2, 3, 4, 5});
NDArray y('c', { 5 }, { 1, 2, 3, 4, 5});
NDArray z('c', { 5 }, nd4j::DataType::DOUBLE);
NDArray exp('c', { 5 }, { 2, 4, 6, 8, 10 });
Nd4jPointer nativeStream = (Nd4jPointer)malloc(sizeof(cudaStream_t));
CHECK_ALLOC(nativeStream, "Failed to allocate memory for new CUDA stream", sizeof(cudaStream_t));
cudaError_t dZ = cudaStreamCreate(reinterpret_cast<cudaStream_t *>(&nativeStream));
auto stream = reinterpret_cast<cudaStream_t *>(&nativeStream);
LaunchContext lc(stream, *stream, nullptr, nullptr);
NativeOpExecutioner::execPairwiseTransform(&lc, pairwise::Add, nullptr, x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, y.shapeInfo(), y.specialBuffer(), y.specialShapeInfo(), nullptr, z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr);
auto res = cudaStreamSynchronize(*stream);
ASSERT_EQ(0, res);
for (int e = 0; e < z.lengthOf(); e++)
ASSERT_NEAR(exp.e<double>(e), z.e<double>(e), 1e-5);
}
//////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, TestAdd_3) {
// allocating host-side arrays
auto x = NDArrayFactory::create<double>('c', { 5 }, { 1, 2, 3, 4, 5});
auto y = NDArrayFactory::create<double>('c', { 5 }, { 1, 2, 3, 4, 5});
auto z = NDArrayFactory::create<double>('c', { 5 }, {10, 10, 10, 10, 10});
auto exp = NDArrayFactory::create<double>('c', { 5 }, { 2, 4, 6, 8, 10 });
// making raw buffers
//Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX;
//cudaError_t res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrX), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrZ), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devShapePtrX), shape::shapeInfoByteLength(x.shapeInfo()));
//ASSERT_EQ(0, res);
Nd4jPointer nativeStream = (Nd4jPointer)malloc(sizeof(cudaStream_t));
CHECK_ALLOC(nativeStream, "Failed to allocate memory for new CUDA stream", sizeof(cudaStream_t));
cudaError_t dZ = cudaStreamCreate(reinterpret_cast<cudaStream_t *>(&nativeStream));
auto stream = reinterpret_cast<cudaStream_t *>(&nativeStream);
//cudaMemcpyAsync(devBufferPtrX, x.buffer(), x.lengthOf() * x.sizeOfT(), cudaMemcpyHostToDevice, *stream);
//cudaMemcpyAsync(devShapePtrX, x.shapeInfo(), shape::shapeInfoByteLength(x.shapeInfo()), cudaMemcpyHostToDevice, *stream);
LaunchContext lc(stream, *stream, nullptr, nullptr);
NativeOpExecutioner::execPairwiseTransform(&lc, pairwise::Add, x.buffer(), x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo(), y.buffer(), y.shapeInfo(), y.specialBuffer(), y.specialShapeInfo(), z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr);
z.tickWriteDevice();
auto res = cudaStreamSynchronize(*stream);
ASSERT_EQ(0, res);
//double* localBuffer = ;
z.syncToHost();
cudaMemcpy(z.buffer(), z.specialBuffer(), z.lengthOf() * z.sizeOfT(), cudaMemcpyDeviceToHost);
res = cudaStreamSynchronize(*stream);
z.tickWriteHost();
ASSERT_EQ(0, res);
//
// cudaFree(devBufferPtrX);
//cudaFree(devBufferPtrZ);
//cudaFree(devShapePtrX);
for (int e = 0; e < z.lengthOf(); e++) {
ASSERT_NEAR(exp.e<double>(e), z.e<double>(e), 1e-5);
}
}
//////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, TestAdd_4) {
// allocating host-side arrays
auto x = NDArrayFactory::create<double>('c', { 5 }, { 1, 2, 3, 4, 5});
auto y = NDArrayFactory::create<double>('c', { 5 }, { 1, 2, 3, 4, 5});
auto z = NDArrayFactory::create<double>('c', { 5 });
auto exp = NDArrayFactory::create<double>('c', { 5 }, { 2, 4, 6, 8, 10 });
// making raw buffers
//Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX;
//cudaError_t res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrX), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrZ), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devShapePtrX), shape::shapeInfoByteLength(x.shapeInfo()));
//ASSERT_EQ(0, res);
x.applyPairwiseTransform(pairwise::Add, y, z);
//
// cudaFree(devBufferPtrX);
//cudaFree(devBufferPtrZ);
//cudaFree(devShapePtrX);
for (int e = 0; e < z.lengthOf(); e++) {
ASSERT_NEAR(exp.e<double>(e), z.e<double>(e), 1e-5);
}
}
//////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, TestAdd_5) {
// allocating host-side arrays
auto x = NDArrayFactory::create<double>('c', { 5 }, { 1, 2, 3, 4, 5});
auto y = NDArrayFactory::create<double>('c', { 5 }, { 1, 2, 3, 4, 5});
//auto z = NDArrayFactory::create<double>('c', { 5 });
auto exp = NDArrayFactory::create<double>('c', { 5 }, { 2, 4, 6, 8, 10 });
// making raw buffers
//Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX;
//cudaError_t res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrX), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrZ), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devShapePtrX), shape::shapeInfoByteLength(x.shapeInfo()));
//ASSERT_EQ(0, res);
x += y;
//x.applyPairwiseTransform(pairwise::Add, &y, &z, nullptr);
x.syncToHost();
//y.printBuffer("3Y = ");
//z.printBuffer("3Result out");
//
// cudaFree(devBufferPtrX);
//cudaFree(devBufferPtrZ);
//cudaFree(devShapePtrX);
for (int e = 0; e < x.lengthOf(); e++) {
ASSERT_NEAR(exp.e<double>(e), x.e<double>(e), 1e-5);
}
}
//////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, TestAdd_6) {
// allocating host-side arrays
auto x = NDArrayFactory::create<double>('c', { 5 }, { 1, 2, 3, 4, 5});
auto y = NDArrayFactory::create<double>(2); //.'c', { 5 }, { 1, 2, 3, 4, 5});
//auto z = NDArrayFactory::create<double>('c', { 5 });
auto exp = NDArrayFactory::create<double>('c', { 5 }, { 3, 4, 5, 6, 7 });
// making raw buffers
//Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX;
//cudaError_t res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrX), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrZ), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devShapePtrX), shape::shapeInfoByteLength(x.shapeInfo()));
//ASSERT_EQ(0, res);
x += y;
//x.applyPairwiseTransform(pairwise::Add, &y, &z, nullptr);
x.syncToHost();
// cudaFree(devBufferPtrX);
//cudaFree(devBufferPtrZ);
//cudaFree(devShapePtrX);
for (int e = 0; e < x.lengthOf(); e++) {
ASSERT_NEAR(exp.e<double>(e), x.e<double>(e), 1e-5);
}
}
//////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, TestAdd_7) {
// allocating host-side arrays
auto x = NDArrayFactory::create<double>('c', { 5 }, { 1, 2, 3, 4, 5});
//auto y = NDArrayFactory::create<double>(2); //.'c', { 5 }, { 1, 2, 3, 4, 5});
//auto z = NDArrayFactory::create<double>('c', { 5 });
auto exp = NDArrayFactory::create<double>('c', { 5 }, { 3, 4, 5, 6, 7 });
// making raw buffers
//Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX;
//cudaError_t res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrX), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrZ), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devShapePtrX), shape::shapeInfoByteLength(x.shapeInfo()));
//ASSERT_EQ(0, res);
x += 2.;
//x.applyPairwiseTransform(pairwise::Add, &y, &z, nullptr);
x.syncToHost();
// cudaFree(devBufferPtrX);
//cudaFree(devBufferPtrZ);
//cudaFree(devShapePtrX);
for (int e = 0; e < x.lengthOf(); e++) {
ASSERT_NEAR(exp.e<double>(e), x.e<double>(e), 1e-5);
}
}
//////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, TestMultiply_1) {
// allocating host-side arrays
auto x = NDArrayFactory::create<double>('c', { 5 }, { 1, 2, 3, 4, 5});
auto y = NDArrayFactory::create<double>('c', { 5 }, { 1, 2, 3, 4, 5});
auto z = NDArrayFactory::create<double>('c', { 5 });
auto exp = NDArrayFactory::create<double>('c', { 5 }, { 1, 4, 9, 16, 25 });
// making raw buffers
//Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX;
//cudaError_t res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrX), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrZ), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devShapePtrX), shape::shapeInfoByteLength(x.shapeInfo()));
//ASSERT_EQ(0, res);
x.applyPairwiseTransform(pairwise::Multiply, y, z);
// x.printBuffer("3X = ");
// y.printBuffer("3Y = ");
// z.printBuffer("3Result out");
//
// cudaFree(devBufferPtrX);
//cudaFree(devBufferPtrZ);
//cudaFree(devShapePtrX);
for (int e = 0; e < z.lengthOf(); e++) {
ASSERT_NEAR(exp.e<double>(e), z.e<double>(e), 1e-5);
}
}
//////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, TestMultiply_2) {
// allocating host-side arrays
auto x = NDArrayFactory::create<double>('c', { 5 }, { 1, 2, 3, 4, 5});
auto y = NDArrayFactory::create<double>('c', { 5 }, { 1, 2, 3, 4, 5});
NDArray z('c', { 5 }, nd4j::DataType::DOUBLE);
auto exp = NDArrayFactory::create<double>('c', { 5 }, { 1, 4, 9, 16, 25 });
// making raw buffers
//Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX;
//cudaError_t res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrX), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrZ), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devShapePtrX), shape::shapeInfoByteLength(x.shapeInfo()));
//ASSERT_EQ(0, res);
x.applyPairwiseTransform(pairwise::Multiply, y, z);
//
// cudaFree(devBufferPtrX);
//cudaFree(devBufferPtrZ);
//cudaFree(devShapePtrX);
for (int e = 0; e < z.lengthOf(); e++) {
ASSERT_NEAR(exp.e<double>(e), z.e<double>(e), 1e-5);
}
}
//////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, TestMultiply_3) {
// allocating host-side arrays
NDArray x('c', { 5 }, { 1, 2, 3, 4, 5}, nd4j::DataType::DOUBLE);
NDArray y('c', { 5 }, { 1., 2., 3., 4., 5.}, nd4j::DataType::DOUBLE);
auto z = NDArrayFactory::create<double>('c', { 5 });
auto exp = NDArrayFactory::create<double>('c', { 5 }, { 1, 4, 9, 16, 25 });
// making raw buffers
//Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX;
//cudaError_t res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrX), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrZ), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devShapePtrX), shape::shapeInfoByteLength(x.shapeInfo()));
//ASSERT_EQ(0, res);
x.applyPairwiseTransform(pairwise::Multiply, y, z);
//x.printBuffer("23X = ");
//y.printBuffer("23Y = ");
// z.printBuffer("23Result out");
//
// cudaFree(devBufferPtrX);
//cudaFree(devBufferPtrZ);
//cudaFree(devShapePtrX);
for (int e = 0; e < z.lengthOf(); e++) {
ASSERT_NEAR(exp.e<double>(e), z.e<double>(e), 1e-5);
}
}
//////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, TestMultiply_4) {
// allocating host-side arrays
NDArray x('c', { 5 }, { 1, 2, 3, 4, 5}, nd4j::DataType::DOUBLE);
NDArray y('c', { 5 }, { 1., 2., 3., 4., 5.}, nd4j::DataType::DOUBLE);
//auto z = NDArrayFactory::create<double>('c', { 5 });
auto exp = NDArrayFactory::create<double>('c', { 5 }, { 1, 4, 9, 16, 25 });
// making raw buffers
//Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX;
//cudaError_t res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrX), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrZ), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devShapePtrX), shape::shapeInfoByteLength(x.shapeInfo()));
//ASSERT_EQ(0, res);
//x.applyPairwiseTransform(pairwise::Multiply, &y, &z, nullptr);
//x.printBuffer("23X = ");
//y.printBuffer("23Y = ");
x *= y;
//x.tickWriteDevice();
// x.printBuffer("33Result out");
//
// cudaFree(devBufferPtrX);
//cudaFree(devBufferPtrZ);
//cudaFree(devShapePtrX);
for (int e = 0; e < x.lengthOf(); e++) {
ASSERT_NEAR(exp.e<double>(e), x.e<double>(e), 1e-5);
}
}
//////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, TestPrimitiveNeg_01) {
// allocating host-side arrays
auto x = NDArrayFactory::create<int>('c', { 5 }, { 1, 2, 3, 4, 5});
auto y = NDArrayFactory::create<int>('c', { 5 }, { 1, 2, 3, 4, 5});
auto exp = NDArrayFactory::create<int>('c', { 5 }, { -1, -2, -3, -4, -5 });
auto stream = x.getContext()->getCudaStream();//reinterpret_cast<cudaStream_t *>(&nativeStream);
NativeOpExecutioner::execTransformSame(x.getContext(), transform::Neg, x.buffer(), x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo(), y.buffer(), y.shapeInfo(), y.specialBuffer(), y.specialShapeInfo(), nullptr, nullptr, nullptr);
auto res = cudaStreamSynchronize(*stream);
ASSERT_EQ(0, res);
y.tickWriteDevice();
// x.printBuffer("X = ");
// y.printBuffer("Y = ");
for (int e = 0; e < y.lengthOf(); e++) {
ASSERT_NEAR(exp.e<int>(e), y.e<int>(e), 1e-5);
}
}
TEST_F(NDArrayCudaBasicsTests, Test_PrimitiveNeg_2) {
auto x = NDArrayFactory::create<double>('c', {5}, {1, 2, 3, 4, 5});
auto y = NDArrayFactory::create<double>('c', {5});
ASSERT_TRUE(x.isActualOnDeviceSide());
ASSERT_FALSE(x.isActualOnHostSide());
x.applyTransform(transform::Neg, y);
//ASSERT_TRUE(x->isActualOnDeviceSide());
//ASSERT_FALSE(x->isActualOnHostSide());
//ASSERT_TRUE(y->isActualOnDeviceSide());
//ASSERT_TRUE(y->isActualOnHostSide());
//auto res = cudaStreamSynchronize(*y.getContext()->getCudaStream());
//ASSERT_EQ(0, res);
// y.printBuffer("Negatives2");
//delete x;
//delete y;
}
TEST_F(NDArrayCudaBasicsTests, Test_PrimitiveSqrt_1) { // strict
auto x = NDArrayFactory::create<double>('c', {5}, {1, 2, 3, 4, 5});
auto y = NDArrayFactory::create<double>('c', {5});
auto exp = NDArrayFactory::create<double>({1.000000, 1.414214, 1.732051, 2.000000, 2.236068});
ASSERT_TRUE(x.isActualOnDeviceSide());
ASSERT_FALSE(x.isActualOnHostSide());
x.applyTransform(transform::Sqrt, y);
//ASSERT_TRUE(x->isActualOnDeviceSide());
//ASSERT_FALSE(x->isActualOnHostSide());
//ASSERT_TRUE(y->isActualOnDeviceSide());
//ASSERT_TRUE(y->isActualOnHostSide());
//auto res = cudaStreamSynchronize(*y.getContext()->getCudaStream());
//ASSERT_EQ(0, res);
ASSERT_TRUE(y.equalsTo(exp));
//y.printBuffer("SQRT output");
//delete x;
//delete y;
}
TEST_F(NDArrayCudaBasicsTests, Test_PrimitiveAssign_1) { // strict
auto x = NDArrayFactory::create<double>('c', {5}, {1, 2, 3, 4, 5});
auto y = NDArrayFactory::create<double>('c', {5});
//auto exp = NDArrayFactory::create<double>({1.000000, 1.414214, 1.732051, 2.000000, 2.236068});
//ASSERT_TRUE(x.isActualOnDeviceSide());
//ASSERT_TRUE(x.isActualOnHostSide());
x.applyTransform(transform::Assign, y);
//ASSERT_TRUE(x->isActualOnDeviceSide());
//ASSERT_FALSE(x->isActualOnHostSide());
//ASSERT_TRUE(y->isActualOnDeviceSide());
//ASSERT_TRUE(y->isActualOnHostSide());
//auto res = cudaStreamSynchronize(*y.getContext()->getCudaStream());
//ASSERT_EQ(0, res);
// printf("Assigned to another array\n");
// y.printBuffer("OUput");
ASSERT_TRUE(y.equalsTo(x));
//y.syncToHost();
//y.printBuffer("IsMax output");
//delete x;
//delete y;
}
TEST_F(NDArrayCudaBasicsTests, Test_PrimitiveCosine_1) { // strict
auto x = NDArrayFactory::create<double>('c', {5}, {1, 2, 3, 4, 5});
auto y = NDArrayFactory::create<double>('c', {5});
auto exp = NDArrayFactory::create<double>('c', {5}, {0.540302, -0.416147, -0.989992, -0.653644, 0.283662});
ASSERT_TRUE(x.isActualOnDeviceSide());
ASSERT_FALSE(x.isActualOnHostSide());
x.applyTransform(transform::Cosine, y);
//ASSERT_TRUE(x->isActualOnDeviceSide());
//ASSERT_FALSE(x->isActualOnHostSide());
//ASSERT_TRUE(y->isActualOnDeviceSide());
//ASSERT_TRUE(y->isActualOnHostSide());
//auto res = cudaStreamSynchronize(*y.getContext()->getCudaStream());
//ASSERT_EQ(0, res);
ASSERT_TRUE(exp.isSameShape(y));
ASSERT_TRUE(exp.dataType() == y.dataType());
//y.printBuffer("Cosine2");
//delete x;
//delete y;
}
TEST_F(NDArrayCudaBasicsTests, Test_PrimitiveCosine_2) {
auto x = NDArrayFactory::create<double>('c', {5}, {1, 2, 3, 4, 5});
auto y = NDArrayFactory::create<double>('c', {5});
auto exp = NDArrayFactory::create<double>('c', {5}, {0.540302, -0.416147, -0.989992, -0.653644, 0.283662});
ASSERT_TRUE(x.isActualOnDeviceSide());
ASSERT_FALSE(x.isActualOnHostSide());
x.applyTransform(transform::Cosine, y);
//ASSERT_TRUE(x->isActualOnDeviceSide());
//ASSERT_FALSE(x->isActualOnHostSide());
//ASSERT_TRUE(y->isActualOnDeviceSide());
//ASSERT_TRUE(y->isActualOnHostSide());
//auto res = cudaStreamSynchronize(*y.getContext()->getCudaStream());
//ASSERT_EQ(0, res);
//exp.syncToHost();
//y.printBuffer("PrimitiveCosine2");
//exp.printBuffer("Primitive Cosine exp");
ASSERT_TRUE(exp.isSameShape(y));
ASSERT_TRUE(exp.dataType() == y.dataType());
//for (int e = 0; e < y.lengthOf(); e++) {
// ASSERT_NEAR(exp.e<double>(e), y.e<double>(e), 1e-5);
//}
ASSERT_TRUE(exp.equalsTo(y));
//delete x;
//delete y;
}
TEST_F(NDArrayCudaBasicsTests, Test_PrimitiveCosine_3) {
auto x = NDArrayFactory::create<double>('c', {5}, {1, 2, 3, 4, 5});
auto y = NDArrayFactory::create<double>('c', {5});
auto exp = NDArrayFactory::create<double>({0.540302, -0.416147, -0.989992, -0.653644, 0.283662});
ASSERT_TRUE(x.isActualOnDeviceSide());
ASSERT_FALSE(x.isActualOnHostSide());
x.applyTransform(transform::Cosine, y);
//ASSERT_TRUE(x->isActualOnDeviceSide());
//ASSERT_FALSE(x->isActualOnHostSide());
//ASSERT_TRUE(y->isActualOnDeviceSide());
//ASSERT_TRUE(y->isActualOnHostSide());
//auto res = cudaStreamSynchronize(*y.getContext()->getCudaStream());
//ASSERT_EQ(0, res);
//exp.syncToHost();
// y.printBuffer("PrimitiveCosine3");
// exp.printBuffer("Primitive Cosine3 exp");
// y.printShapeInfo("Y shape");
// exp.printShapeInfo("Exp Shape");
ASSERT_TRUE(exp.isSameShape(y));
//
// for (int e = 0; e < y.lengthOf(); e++) {
// printf("%lf == %lf\n", exp.e<double>(e), y.e<double>(e));
//// ASSERT_NEAR(exp.e<double>(e), y.e<double>(e), 1e-5);
// }
ASSERT_TRUE(exp.equalsTo(y));
//delete x;
//delete y;
}
TEST_F(NDArrayCudaBasicsTests, TestRawBroadcast_2) {
//if (!Environment::getInstance()->isExperimentalBuild())
// return;
NDArray x = NDArrayFactory::create<double>('c', {2,3,4});
NDArray y('c', {2,4}, {10,20,30,40,50,60,70,80}, nd4j::DataType::DOUBLE);
NDArray z('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, nd4j::DataType::DOUBLE);
// NDArray exp('c', {2,3,4}, {10., 21., 32., 43., 14., 25., 36., 47., 18., 29., 40., 51., 62., 73., 84., 95., 66., 77., 88., 99., 70., 81., 92., 103}, nd4j::DataType::DOUBLE);
NDArray exp('c', {2,3,4}, {10., 40., 90., 160., 50., 120., 210., 320., 90., 200., 330., 480., 650., 840., 1050., 1280., 850., 1080., 1330., 1600., 1050., 1320., 1610., 1920.}, nd4j::DataType::DOUBLE);
x.linspace(1); x.syncToDevice();
std::vector<int> dimensions = {0,2};
// evaluate xTad data
shape::TAD xTad;
xTad.init(x.getShapeInfo(), dimensions.data(), dimensions.size());
xTad.createTadOnlyShapeInfo();
xTad.createOffsets();
// prepare input arrays for prepareDataForCuda function
std::vector<std::pair<void*,size_t>> hostData;
hostData.emplace_back(dimensions.data(), dimensions.size() * sizeof(int)); // 0 -- dimensions
hostData.emplace_back(xTad.tadOnlyShapeInfo, shape::shapeInfoByteLength(xTad.tadOnlyShapeInfo)); // 1 -- xTadShapeInfo
hostData.emplace_back(xTad.tadOffsets, xTad.numTads * sizeof(Nd4jLong)); // 2 -- xTadOffsets
std::vector<void*> devicePtrs(hostData.size(), nullptr);
// create cuda stream and LaunchContext
cudaError_t cudaResult;
cudaStream_t stream;
cudaResult = cudaStreamCreate(&stream); ASSERT_EQ(0, cudaResult);
LaunchContext lc(&stream);
// allocate required amount of global device memory and copy host data to it
cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult);
// call cuda kernel which calculates result
NativeOpExecutioner::execBroadcast(&lc, nd4j::broadcast::Multiply,
nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(),
nullptr, y.getShapeInfo(), y.specialBuffer(), y.specialShapeInfo(),
nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(),
(int*)devicePtrs[0], dimensions.size(),
(Nd4jLong*)devicePtrs[1], (Nd4jLong*)devicePtrs[2],
nullptr, nullptr);
cudaResult = cudaStreamSynchronize(stream); ASSERT_EQ(0, cudaResult);
z.tickWriteDevice();
// verify results
for (int e = 0; e < z.lengthOf(); e++)
ASSERT_NEAR(exp.e<double>(e), z.e<double>(e), 1e-5);
// free allocated global device memory
for(int i = 0; i < devicePtrs.size(); ++i)
cudaFree(devicePtrs[i]);
// delete cuda stream
cudaResult = cudaStreamDestroy(stream); ASSERT_EQ(0, cudaResult);
}
TEST_F(NDArrayCudaBasicsTests, TestRawBroadcast_3) {
//if (!Environment::getInstance()->isExperimentalBuild())
// return;
NDArray x('c', {2,3,4}, nd4j::DataType::DOUBLE);
NDArray y('c', {2,4}, {10,20,30,40,50,60,70,80}, nd4j::DataType::DOUBLE);
NDArray z('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, nd4j::DataType::DOUBLE);
// NDArray exp('c', {2,3,4}, {10., 21., 32., 43., 14., 25., 36., 47., 18., 29., 40., 51., 62., 73., 84., 95., 66., 77., 88., 99., 70., 81., 92., 103}, nd4j::DataType::DOUBLE);
NDArray exp('c', {2,3,4}, {10., 40., 90., 160., 50., 120., 210., 320., 90., 200., 330., 480., 650., 840., 1050., 1280., 850., 1080., 1330., 1600., 1050., 1320., 1610., 1920.}, nd4j::DataType::DOUBLE);
x.linspace(1); x.syncToDevice();
std::vector<int> dimensions = {0,2};
// evaluate xTad data
shape::TAD xTad;
xTad.init(x.getShapeInfo(), dimensions.data(), dimensions.size());
xTad.createTadOnlyShapeInfo();
xTad.createOffsets();
// prepare input arrays for prepareDataForCuda function
std::vector<std::pair<void*,size_t>> hostData;
hostData.emplace_back(dimensions.data(), dimensions.size() * sizeof(int)); // 0 -- dimensions
hostData.emplace_back(xTad.tadOnlyShapeInfo, shape::shapeInfoByteLength(xTad.tadOnlyShapeInfo)); // 1 -- xTadShapeInfo
hostData.emplace_back(xTad.tadOffsets, xTad.numTads * sizeof(Nd4jLong)); // 2 -- xTadOffsets
std::vector<void*> devicePtrs(hostData.size(), nullptr);
// create cuda stream and LaunchContext
cudaError_t cudaResult;
//cudaStream_t stream;
//cudaResult = cudaStreamCreate(&stream); ASSERT_EQ(0, cudaResult);
LaunchContext* pLc = x.getContext();//(&stream);
cudaStream_t* stream = pLc->getCudaStream();
// allocate required amount of global device memory and copy host data to it
// cudaResult = allocateDeviceMem(*pLc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult);
for(int i = 0; i < devicePtrs.size(); ++i) {
cudaResult = cudaMalloc(reinterpret_cast<void **>(&devicePtrs[i]), hostData[i].second); ASSERT_EQ(0, cudaResult);
cudaMemcpyAsync(devicePtrs[i], hostData[i].first, hostData[i].second, cudaMemcpyHostToDevice, *stream);
}
NDArray::registerSpecialUse({&z}, {&x, &y});
// call cuda kernel which calculates result
NativeOpExecutioner::execBroadcast(pLc, nd4j::broadcast::Multiply,
nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(),
nullptr, y.getShapeInfo(), y.specialBuffer(), y.specialShapeInfo(),
nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(),
(int*)devicePtrs[0], dimensions.size(),
(Nd4jLong*)devicePtrs[1], (Nd4jLong*)devicePtrs[2],
nullptr, nullptr);
//cudaResult = cudaStreamSynchronize(stream); ASSERT_EQ(0, cudaResult);
//z.syncToHost();
// verify results
for (int e = 0; e < z.lengthOf(); e++)
ASSERT_NEAR(exp.e<double>(e), z.e<double>(e), 1e-5);
// free allocated global device memory
for(int i = 0; i < devicePtrs.size(); ++i)
cudaFree(devicePtrs[i]);
ASSERT_TRUE(exp.equalsTo(z));
// delete cuda stream
//cudaResult = cudaStreamDestroy(stream); ASSERT_EQ(0, cudaResult);
}
TEST_F(NDArrayCudaBasicsTests, TestBroadcastMultiply_1) {
// allocating host-side arrays
NDArray x('c', { 2, 3 }, { 1, 2, 3, 4, 5, 6}, nd4j::DataType::DOUBLE);
NDArray y = NDArrayFactory::create<double>(3.); //'c', { 3 }, { 2., 3., 4.}, nd4j::DataType::DOUBLE);
//auto z = NDArrayFactory::create<double>('c', { 5 });
auto exp = NDArrayFactory::create<double>('c', { 2, 3 }, { 3, 6, 9, 12, 15, 18 });
// making raw buffers
//Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX;
//cudaError_t res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrX), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrZ), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devShapePtrX), shape::shapeInfoByteLength(x.shapeInfo()));
//ASSERT_EQ(0, res);
//x.applyPairwiseTransform(pairwise::Multiply, &y, &z, nullptr);
x *= y;
//x.syncToHost();
//
// cudaFree(devBufferPtrX);
//cudaFree(devBufferPtrZ);
//cudaFree(devShapePtrX);
ASSERT_TRUE(exp.equalsTo(x));
// for (int e = 0; e < x.lengthOf(); e++) {
// ASSERT_NEAR(exp.e<double>(e), x.e<double>(e), 1e-5);
// }
}
TEST_F(NDArrayCudaBasicsTests, TestBroadcastMultiply_01) {
// allocating host-side arrays
NDArray x('c', { 2, 3 }, { 1, 2, 3, 4, 5, 6}, nd4j::DataType::DOUBLE);
NDArray y = NDArrayFactory::create<double>(3.); //'c', { 3 }, { 2., 3., 4.}, nd4j::DataType::DOUBLE);
auto z = NDArrayFactory::create<double>('c', { 2, 3 });
auto exp = NDArrayFactory::create<double>('c', { 2, 3 }, { 3, 6, 9, 12, 15, 18 });
// making raw buffers
//Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX;
//cudaError_t res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrX), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrZ), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devShapePtrX), shape::shapeInfoByteLength(x.shapeInfo()));
//ASSERT_EQ(0, res);
//x.applyPairwiseTransform(pairwise::Multiply, &y, &z, nullptr);
//x.printBuffer("23X = ");
//y.printBuffer("23Y = ");
x.applyTrueBroadcast(BroadcastOpsTuple::Multiply(), y, z);// *= y;
// z.printBuffer("53Result out");
//
// cudaFree(devBufferPtrX);
//cudaFree(devBufferPtrZ);
//cudaFree(devShapePtrX);
ASSERT_TRUE(exp.equalsTo(z));
// for (int e = 0; e < x.lengthOf(); e++) {
// ASSERT_NEAR(exp.e<double>(e), z.e<double>(e), 1e-5);
// }
}
TEST_F(NDArrayCudaBasicsTests, TestBroadcastMultiply_02) {
// allocating host-side arrays
auto x = NDArrayFactory::create<double>('c', { 2, 3 }, { 1, 2, 3, 4, 5, 6}); //, nd4j::DataType::DOUBLE);
auto y = NDArrayFactory::create<double>('c', {2,3}, {3, 3, 3, 3, 3, 3}); //'c', { 3 }, { 2., 3., 4.}, nd4j::DataType::DOUBLE);
auto z = NDArrayFactory::create<double>('c', { 2, 3 });
auto exp = NDArrayFactory::create<double>('c', { 2, 3 }, { 3, 6, 9, 12, 15, 18 });
//if (x.isActualOnHostSide() && !x.isActualOnDeviceSide())
// making raw buffers
//Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX;
//cudaError_t res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrX), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrZ), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devShapePtrX), shape::shapeInfoByteLength(x.shapeInfo()));
//ASSERT_EQ(0, res);
//x.applyPairwiseTransform(pairwise::Multiply, &y, &z, nullptr);
//x.printBuffer("23X = ");
//y.printBuffer("23Y = ");
x.applyTrueBroadcast(BroadcastOpsTuple::Multiply(), y, z);// *= y;
// z.printBuffer("52Result out");
//
// cudaFree(devBufferPtrX);
//cudaFree(devBufferPtrZ);
//cudaFree(devShapePtrX);
ASSERT_TRUE(exp.equalsTo(z));
// for (int e = 0; e < x.lengthOf(); e++) {
// ASSERT_NEAR(exp.e<double>(e), z.e<double>(e), 1e-5);
// }
}
TEST_F(NDArrayCudaBasicsTests, TestBroadcastMultiply_002) {
// allocating host-side arrays
auto x = NDArrayFactory::create<double>('c', { 2, 3 }, { 1, 2, 3, 4, 5, 6}); //, nd4j::DataType::DOUBLE);
auto y = NDArrayFactory::create<double>('c', {2, 3}, {2., 3., 3., 3., 3., 3.}); //'c', { 3 }, { 2., 3., 4.}, nd4j::DataType::DOUBLE);
auto z = NDArrayFactory::create<double>('c', { 2, 3 });
auto exp = NDArrayFactory::create<double>('c', { 2, 3 }, { 2, 6, 9, 12, 15, 18 });
//if (x.isActualOnHostSide() && !x.isActualOnDeviceSide())
// making raw buffers
//Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX;
//cudaError_t res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrX), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrZ), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devShapePtrX), shape::shapeInfoByteLength(x.shapeInfo()));
//ASSERT_EQ(0, res);
//x.applyPairwiseTransform(pairwise::Multiply, &y, &z, nullptr);
//x.printBuffer("23X = ");
//y.printBuffer("23Y = ");
x.applyPairwiseTransform(pairwise::Multiply, y, z);// *= y;
// z.printBuffer("51Result out");
//
// cudaFree(devBufferPtrX);
//cudaFree(devBufferPtrZ);
//cudaFree(devShapePtrX);
ASSERT_TRUE(exp.equalsTo(z));
// for (int e = 0; e < x.lengthOf(); e++) {
// ASSERT_NEAR(exp.e<double>(e), z.e<double>(e), 1e-5);
// }
}
////////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, TestBroadcastRaw_1) {
//if (!Environment::getInstance()->isExperimentalBuild())
// return;
NDArray x('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, nd4j::DataType::INT32);
NDArray y('c', {3}, {10, 20, 30}, nd4j::DataType::INT64);
NDArray z('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, nd4j::DataType::INT32);
NDArray exp('c', {2,3,4}, {10, 11, 12, 13,24, 25, 26, 27,38, 39, 40, 41,22, 23, 24, 25,36, 37, 38, 39,50, 51, 52, 53}, nd4j::DataType::INT32);
//real output [10, 11, 12, 13, 4, 5, 6, 7, 28, 29, 30, 31, 22, 23, 24, 25, 16, 17, 18, 19, 40, 41, 42, 43]
x.linspace(0); x.syncToDevice();
std::vector<int> dimensions = {1};
// evaluate xTad data
shape::TAD xTad;
xTad.init(x.getShapeInfo(), dimensions.data(), dimensions.size());
xTad.createTadOnlyShapeInfo();
xTad.createOffsets();
// prepare input arrays for prepareDataForCuda function
std::vector<std::pair<void*,size_t>> hostData;
hostData.emplace_back(dimensions.data(), dimensions.size() * sizeof(Nd4jLong)); // 0 -- dimensions
hostData.emplace_back(xTad.tadOnlyShapeInfo, shape::shapeInfoByteLength(xTad.tadOnlyShapeInfo)); // 1 -- xTadShapeInfo
hostData.emplace_back(xTad.tadOffsets, xTad.numTads * sizeof(Nd4jLong)); // 2 -- xTadOffsets
std::vector<void*> devicePtrs(hostData.size(), nullptr);
// create cuda stream and LaunchContext
cudaError_t cudaResult;
cudaStream_t* stream = x.getContext()->getCudaStream();
LaunchContext* pLc = x.getContext();
// allocate required amount of global device memory and copy host data to it
//cudaResult = allocateDeviceMem(*pLc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult);
for(size_t i = 0; i < devicePtrs.size(); ++i) {
cudaResult = cudaMalloc(&devicePtrs[i], hostData[i].second); //if(cudaResult != 0) return cudaResult;
ASSERT_EQ(cudaResult, 0);
cudaMemcpy(devicePtrs[i], hostData[i].first, hostData[i].second, cudaMemcpyHostToDevice);
}
// call cuda kernel which calculates result
NativeOpExecutioner::execBroadcast(pLc, nd4j::broadcast::Add,
nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(),
nullptr, y.getShapeInfo(), y.specialBuffer(), y.specialShapeInfo(),
nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(),
(int*)devicePtrs[0], dimensions.size(),
(Nd4jLong*)devicePtrs[1], (Nd4jLong*)devicePtrs[2],
nullptr, nullptr);
cudaResult = cudaStreamSynchronize(*stream); ASSERT_EQ(0, cudaResult);
// x.printIndexedBuffer(" X");
// y.printIndexedBuffer("+Y");
// z.printBuffer("ADD broadcasted output");
// verify results
// for (int e = 0; e < z.lengthOf(); e++)
// ASSERT_NEAR(exp.e<double>(e), z.e<double>(e), 1e-5);
// free allocated global device memory
for(int i = 0; i < devicePtrs.size(); ++i)
cudaFree(devicePtrs[i]);
// delete cuda stream
//cudaResult = cudaStreamDestroy(stream); ASSERT_EQ(0, cudaResult);
}
TEST_F(NDArrayCudaBasicsTests, TestBroadcastMultiply) {
// allocating host-side arrays
NDArray x('c', { 2, 3 }, { 1, 2, 3, 4, 5, 6}, nd4j::DataType::DOUBLE);
NDArray y('c', { 3 }, { 2., 3., 4.}, nd4j::DataType::DOUBLE);
//auto z = NDArrayFactory::create<double>('c', { 5 });
auto exp = NDArrayFactory::create<double>('c', { 2, 3 }, { 2, 6, 12, 8, 15, 24 });
// making raw buffers
//Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX;
//cudaError_t res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrX), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrZ), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devShapePtrX), shape::shapeInfoByteLength(x.shapeInfo()));
//ASSERT_EQ(0, res);
//x.applyPairwiseTransform(pairwise::Multiply, &y, &z, nullptr);
//x.printBuffer("23X = ");
//y.printBuffer("23Y = ");
x *= y;
//
// cudaFree(devBufferPtrX);
//cudaFree(devBufferPtrZ);
//cudaFree(devShapePtrX);
//for (int e = 0; e < x.lengthOf(); e++) {
// ASSERT_NEAR(exp.e<double>(e), x.e<double>(e), 1e-5);
//}
}
TEST_F(NDArrayCudaBasicsTests, TestBroadcastMultiply_2) {
// allocating host-side arrays
NDArray x('c', { 2, 3 }, { 1, 2, 3, 4, 5, 6}, nd4j::DataType::DOUBLE);
NDArray y('c', { 3 }, { 2., 3., 4.}, nd4j::DataType::DOUBLE);
//auto z = NDArrayFactory::create<double>('c', { 5 });
auto exp = NDArrayFactory::create<double>('c', { 2, 3 }, { 11,12, 13,14, 15, 16 });
auto expZ = NDArrayFactory::create<double>('c', { 2, 3 }, { 2, 6, 12, 8, 15, 24 });
// making raw buffers
//Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX;
//cudaError_t res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrX), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devBufferPtrZ), x.lengthOf() * x.sizeOfT());
//ASSERT_EQ(0, res);
//res = cudaMalloc(reinterpret_cast<void **>(&devShapePtrX), shape::shapeInfoByteLength(x.shapeInfo()));
//ASSERT_EQ(0, res);
//x.applyPairwiseTransform(pairwise::Multiply, &y, &z, nullptr);
//x.printBuffer("23X = ");
//y.printBuffer("23Y = ");
//void NDArray::applyTrueBroadcast(nd4j::BroadcastOpsTuple op, const NDArray* other, NDArray* target, const bool checkTargetShape, ExtraArguments *extraArgs)
x.applyTrueBroadcast(BroadcastOpsTuple::Multiply(), y, exp);
//
// cudaFree(devBufferPtrX);
//cudaFree(devBufferPtrZ);
//cudaFree(devShapePtrX);
//for (int e = 0; e < x.lengthOf(); e++) {
// ASSERT_NEAR(exp.e<double>(e), x.e<double>(e), 1e-5);
//}
ASSERT_TRUE(exp.equalsTo(expZ));
}
//////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, TestReduceSum_1) {
// allocating host-side arrays
auto x = NDArrayFactory::create<double>('c', { 5 }, { 1, 2, 3, 4, 5});
auto y = NDArrayFactory::create<double>(15);
auto exp = NDArrayFactory::create<double>(15);
auto stream = x.getContext()->getCudaStream();//reinterpret_cast<cudaStream_t *>(&nativeStream);
NativeOpExecutioner::execReduceSameScalar(x.getContext(), reduce::Sum, x.buffer(), x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, y.buffer(), y.shapeInfo(), y.specialBuffer(), y.specialShapeInfo());
auto res = cudaStreamSynchronize(*stream);
ASSERT_EQ(0, res);
y.syncToHost();
ASSERT_NEAR(y.e<double>(0), 15, 1e-5);
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, TestDup1) {
NDArray array('c', {2,3}, {1,2,3,4,5,6});
auto arrC = array.dup('c');
auto arrF = array.dup('f');
// arrC->printBuffer("arrC");
// arrF->printBuffer("arrF");
//arrC->printShapeInfo("C shape");
//arrF->printShapeInfo("F shape");
ASSERT_TRUE(array.equalsTo(arrF));
ASSERT_TRUE(array.equalsTo(arrC));
ASSERT_TRUE(arrF.equalsTo(arrC));
}
//////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, equalsTo_1) {
NDArray x('c', {2,5}, {1,2,3,4,5,6,7,8,9,10}, nd4j::DataType::DOUBLE);
NDArray y('c', {2,5}, {1,2,3,4,5,6,7,8,9,10}, nd4j::DataType::DOUBLE);
ASSERT_TRUE(x.equalsTo(y));
x.permutei({1,0});
y.permutei({1,0});
ASSERT_TRUE(x.equalsTo(y));
}
//////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, equalsTo_2) {
NDArray x('c', {2,5}, {1,2,3,4,5,6,7,8,10,10}, nd4j::DataType::DOUBLE);
NDArray y('c', {2,5}, {1,2,5,4,5,6,7,8,9,10}, nd4j::DataType::DOUBLE);
ASSERT_FALSE(x.equalsTo(y));
x.permutei({1,0});
y.permutei({1,0});
ASSERT_FALSE(x.equalsTo(y));
}
//////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, equalsTo_3) {
NDArray x('c', {2,5}, {1,2,3,4,5,6,7,8,9,10}, nd4j::DataType::DOUBLE);
NDArray y('c', {2,5}, {1.f,2.f,3.f,4.f,5.f,6.f,7.f,8.f,9.f,10.f}, nd4j::DataType::FLOAT32);
ASSERT_FALSE(x.equalsTo(y));
x.permutei({1,0});
y.permutei({1,0});
ASSERT_FALSE(x.equalsTo(y));
}
////////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, applyReduce3_1) {
NDArray x('c', {2,3,4}, {-10,-9,-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12,13}, nd4j::DataType::INT32);
NDArray x2('c', {2,3,4}, {-10,-9,-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12,13}, nd4j::DataType::INT32);
NDArray y('c', {2,3,4}, {-2,3,-4,5,-2,3,-4,5,-2,3,-4,5,-2,3,-4,5,-2,3,-4,5,-2,3,-4,5}, nd4j::DataType::INT32);
NDArray k('c', {2,3}, {-2,3,-4,5,-2,3}, nd4j::DataType::INT32);
NDArray k2('c', {3,2}, {-2,3,-4,5,-2,3}, nd4j::DataType::INT32);
NDArray exp1('c', {3}, {4.f, 20.f, 36.f}, nd4j::DataType::FLOAT32);
NDArray exp2('c', {2,3}, {-10.f, -2.f, 6.f,14.f, 22.f, 30.f}, nd4j::DataType::FLOAT32);
NDArray exp3('c', {4}, {38.f, 41.f, 44.f, 47.f}, nd4j::DataType::FLOAT32);
NDArray exp4('c', {4}, {114.f, 117.f, 120.f, 123.f}, nd4j::DataType::FLOAT32);
NDArray z = x.applyReduce3(nd4j::reduce3::Dot, y, {0,2});
ASSERT_TRUE(z.equalsTo(&exp1));
z = x.applyReduce3(nd4j::reduce3::Dot, k, {0,1});
ASSERT_TRUE(z.equalsTo(&exp3));
x.permutei({0,2,1});
y.permutei({0,2,1});
z = y.applyReduce3(nd4j::reduce3::Dot, x, {1});
ASSERT_TRUE(z.equalsTo(&exp2));
x2.permutei({1,0,2});
z = x2.applyReduce3(nd4j::reduce3::Dot, k2, {0,1});
ASSERT_TRUE(z.equalsTo(&exp4));
}
////////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, applyReduce3_2) {
NDArray x('c', {2,3,4}, {-10,-9,-8.5,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12,13}, nd4j::DataType::DOUBLE);
NDArray x2('c', {2,3,4}, {-10,-9,-8,-7,-6,-5,-4,-3,-2,-1,0.5,1,2,3,4,5,6,7,8,9,10,11,12,13}, nd4j::DataType::DOUBLE);
NDArray y('c', {2,3,4}, {-2,3,-4,5,-2,3,-4,5,-2,3,-4,5,-2.5,3,-4,5,-2,3,-4,5,-2,3,-4,5}, nd4j::DataType::DOUBLE);
NDArray k('c', {2,3}, {-2,3,-4,5.5,-2,3}, nd4j::DataType::DOUBLE);
NDArray k2('c', {3,2}, {-2,3,-4,5,-2,3.5}, nd4j::DataType::DOUBLE);
NDArray exp1('c', {3}, {5., 20., 36.}, nd4j::DataType::DOUBLE);
NDArray exp2('c', {2,3}, {-8., -2., 6., 13., 22., 30.}, nd4j::DataType::DOUBLE);
NDArray exp3('c', {4}, {39., 42.5, 47., 49.5}, nd4j::DataType::DOUBLE);
NDArray exp4('c', {4}, {119., 122.5, 125., 129.5}, nd4j::DataType::DOUBLE);
NDArray z = x.applyReduce3(nd4j::reduce3::Dot, y, {0,2});
ASSERT_TRUE(z.equalsTo(&exp1));
z = x.applyReduce3(nd4j::reduce3::Dot, k, {0,1});
ASSERT_TRUE(z.equalsTo(&exp3));
x.permutei({0,2,1});
y.permutei({0,2,1});
z = y.applyReduce3(nd4j::reduce3::Dot, x, {1});
ASSERT_TRUE(z.equalsTo(&exp2));
x2.permutei({1,0,2});
z = x2.applyReduce3(nd4j::reduce3::Dot, k2, {0,1});
ASSERT_TRUE(z.equalsTo(&exp4));
}
////////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, applyReduce3_3) {
NDArray x1('c', {2,2,2}, {1,2,3,4,5,6,7,8}, nd4j::DataType::INT32);
NDArray x2('c', {2,2,2}, {-1,-2,-3,-4,-5,-6,-7,-8}, nd4j::DataType::INT32);
NDArray x3('c', {3,2}, {1.5,1.5,1.5,1.5,1.5,1.5}, nd4j::DataType::DOUBLE);
NDArray x4('c', {3,2}, {1,2,3,4,5,6}, nd4j::DataType::DOUBLE);
NDArray exp1('c', {}, {-204}, nd4j::DataType::FLOAT32);
NDArray exp2('c', {}, {31.5}, nd4j::DataType::DOUBLE);
auto z = x1.applyReduce3(reduce3::Dot, x2);
ASSERT_TRUE(z.equalsTo(&exp1));
z = x3.applyReduce3(reduce3::Dot, x4);
ASSERT_TRUE(z.equalsTo(&exp2));
x1.permutei({2,1,0});
x2.permutei({2,1,0});
x3.permutei({1,0});
x4.permutei({1,0});
z = x1.applyReduce3(reduce3::Dot, x2);
ASSERT_TRUE(z.equalsTo(&exp1));
z = x3.applyReduce3(reduce3::Dot, x4);
ASSERT_TRUE(z.equalsTo(&exp2));
}
////////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, applyAllReduce3_1) {
NDArray x1('c', {2,3,2}, {1,2,3,4,5,6,7,8,-1,-2,-3,-4,}, nd4j::DataType::INT32);
NDArray x2('c', {2,2,2}, {-1,-2,-3,-4,-5,-6,-7,-8}, nd4j::DataType::INT32);
NDArray x3('c', {3,2}, {1.5,1.5,1.5,1.5,1.5,1.5}, nd4j::DataType::DOUBLE);
NDArray x4('c', {3,2}, {1,2,3,4,5,6}, nd4j::DataType::DOUBLE);
NDArray exp1('c', {3,2}, {-88.f, -124.f, 6.f, -2.f, 22.f, 14.f}, nd4j::DataType::FLOAT32);
NDArray exp2('c', {6,4}, {-36.f, -44.f, -52.f, -60.f,-42.f, -52.f, -62.f, -72.f, 2.f, 0.f, -2.f,
-4.f, 6.f, 4.f, 2.f, 0.f, 10.f, 8.f, 6.f, 4.f, 14.f, 12.f, 10.f, 8.f},
nd4j::DataType::FLOAT32);
NDArray exp3('c', {1,1}, {31.5}, nd4j::DataType::DOUBLE);
NDArray exp4('c', {3,3}, {4.5, 10.5, 16.5,4.5, 10.5, 16.5,4.5, 10.5, 16.5}, nd4j::DataType::DOUBLE);
auto z = x1.applyAllReduce3(reduce3::Dot, x2, {0,2});
ASSERT_TRUE(z.equalsTo(&exp1));
z = x1.applyAllReduce3(reduce3::Dot, x2, {0});
ASSERT_TRUE(z.equalsTo(&exp2));
z = x3.applyAllReduce3(reduce3::Dot, x4, {0,1});
ASSERT_TRUE(z.equalsTo(&exp3));
z = x3.applyAllReduce3(reduce3::Dot, x4, {1});
ASSERT_TRUE(z.equalsTo(&exp4));
x1.permutei({2,1,0});
x2.permutei({2,1,0});
x3.permutei({1,0});
x4.permutei({1,0});
z = x1.applyAllReduce3(reduce3::Dot, x2, {0,2});
ASSERT_TRUE(z.equalsTo(&exp1));
z = x3.applyAllReduce3(reduce3::Dot, x4, {0});
ASSERT_TRUE(z.equalsTo(&exp4));
}
//////////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, applyIndexReduce_test1) {
NDArray x('c', {2,3}, {0, 10, 1, 2, 2.5,-4}, nd4j::DataType::DOUBLE);
NDArray scalar('c', {}, {100}, nd4j::DataType::INT64);
NDArray vec1('c', {2}, {100,100}, nd4j::DataType::INT64);
NDArray vec2('c', {3}, {100,100,100}, nd4j::DataType::INT64);
NDArray exp1('c', {}, {1}, nd4j::DataType::INT64);
NDArray exp2('c', {2}, {1,1}, nd4j::DataType::INT64);
NDArray exp3('c', {3}, {1,0,0}, nd4j::DataType::INT64);
NDArray exp4('c', {}, {2}, nd4j::DataType::INT64);
NDArray exp5('c', {2}, {1,1}, nd4j::DataType::INT64);
NDArray exp6('c', {3}, {1,0,0}, nd4j::DataType::INT64);
x.applyIndexReduce(nd4j::indexreduce::IndexMax, scalar, {0,1});
ASSERT_TRUE(scalar.equalsTo(&exp1));
x.applyIndexReduce(nd4j::indexreduce::IndexMax, vec1, {1});
ASSERT_TRUE(vec1.equalsTo(&exp2));
x.applyIndexReduce(nd4j::indexreduce::IndexMax, vec2, {0});
ASSERT_TRUE(vec2.equalsTo(&exp3));
x.permutei({1,0});
x.applyIndexReduce(nd4j::indexreduce::IndexMax, scalar, {0,1});
ASSERT_TRUE(scalar.equalsTo(&exp4));
x.applyIndexReduce(nd4j::indexreduce::IndexMax, vec1, {0});
ASSERT_TRUE(vec1.equalsTo(&exp5));
x.applyIndexReduce(nd4j::indexreduce::IndexMax, vec2, {1});
ASSERT_TRUE(vec2.equalsTo(&exp6));
}
//////////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, applyIndexReduce_test2) {
NDArray x('c', {2,3}, {0, 10, 1, 2, 2.5,-4}, nd4j::DataType::DOUBLE);
NDArray exp1('c', {}, {1}, nd4j::DataType::INT64);
NDArray exp2('c', {2}, {1,1}, nd4j::DataType::INT64);
NDArray exp3('c', {3}, {1,0,0}, nd4j::DataType::INT64);
NDArray exp4('c', {}, {2}, nd4j::DataType::INT64);
NDArray exp5('c', {2}, {1,1}, nd4j::DataType::INT64);
NDArray exp6('c', {3}, {1,0,0}, nd4j::DataType::INT64);
auto z = x.applyIndexReduce(nd4j::indexreduce::IndexMax, {0,1});
ASSERT_TRUE(z.equalsTo(&exp1));
z = x.applyIndexReduce(nd4j::indexreduce::IndexMax, {1});
ASSERT_TRUE(z.equalsTo(&exp2));
z = x.applyIndexReduce(nd4j::indexreduce::IndexMax, {0});
ASSERT_TRUE(z.equalsTo(&exp3));
x.permutei({1,0});
z = x.applyIndexReduce(nd4j::indexreduce::IndexMax, {0,1});
ASSERT_TRUE(z.equalsTo(&exp4));
z = x.applyIndexReduce(nd4j::indexreduce::IndexMax, {0});
ASSERT_TRUE(z.equalsTo(&exp5));
z = x.applyIndexReduce(nd4j::indexreduce::IndexMax, {1});
ASSERT_TRUE(z.equalsTo(&exp6));
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, reduceAlongDimension_float_test1) {
NDArray x('c', {2,3,2}, {1,2,3,4,5,6,7,8,-1,-2,-3,-4,}, nd4j::DataType::INT32);
NDArray z1('c', {}, {100}, nd4j::DataType::DOUBLE);
NDArray z2('c', {2,2}, {100,100,100,100}, nd4j::DataType::FLOAT32);
NDArray z3('c', {3}, {100,100,100}, nd4j::DataType::DOUBLE);
NDArray z4('c', {3,2}, {100,100,100,100,100,100}, nd4j::DataType::FLOAT32);
NDArray z5('c', {2}, {100,100}, nd4j::DataType::FLOAT32);
NDArray exp1('c', {}, {2.166667}, nd4j::DataType::DOUBLE);
NDArray exp2('c', {2,2}, {3.f,4.f,1.f,0.666667f}, nd4j::DataType::FLOAT32);
NDArray exp3('c', {3}, {4.5,1,1}, nd4j::DataType::DOUBLE);
NDArray exp4('c', {3,2}, {4,5,1,1,1,1}, nd4j::DataType::FLOAT32);
NDArray exp5('c', {2}, {3.5f,0.833333f}, nd4j::DataType::FLOAT32);
x.reduceAlongDimension(nd4j::reduce::Mean, z1, {0,1,2});
ASSERT_TRUE(z1.equalsTo(&exp1));
x.reduceAlongDimension(nd4j::reduce::Mean, z2, {1});
ASSERT_TRUE(z2.equalsTo(&exp2));
x.reduceAlongDimension(nd4j::reduce::Mean, z3, {0,2});
ASSERT_TRUE(z3.equalsTo(&exp3));
x.permutei({1,0,2}); // 3x2x2
x.reduceAlongDimension(nd4j::reduce::Mean, z1, {0,1,2});
ASSERT_TRUE(z1.equalsTo(&exp1));
x.reduceAlongDimension(nd4j::reduce::Mean, z4, {1});
ASSERT_TRUE(z4.equalsTo(&exp4));
x.reduceAlongDimension(nd4j::reduce::Mean, z5, {0,2});
ASSERT_TRUE(z5.equalsTo(&exp5));
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, reduceAlongDimension_float_test2) {
NDArray x('c', {2,3,2}, {1,2,3,4,5,6,7,8,-1,-2,-3,-4,}, nd4j::DataType::DOUBLE);
NDArray exp1('c', {}, {2.166667}, nd4j::DataType::DOUBLE);
NDArray exp2('c', {2,2}, {3,4,1,0.666667}, nd4j::DataType::DOUBLE);
NDArray exp3('c', {3}, {4.5,1,1}, nd4j::DataType::DOUBLE);
NDArray exp4('c', {3,2}, {4,5,1,1,1,1}, nd4j::DataType::DOUBLE);
NDArray exp5('c', {2}, {3.5,0.833333}, nd4j::DataType::DOUBLE);
NDArray z1 = x.reduceAlongDimension(nd4j::reduce::Mean, {0,1,2});
ASSERT_TRUE(z1.equalsTo(&exp1));
NDArray z2 = x.reduceAlongDimension(nd4j::reduce::Mean, {1});
ASSERT_TRUE(z2.equalsTo(&exp2));
NDArray z3 = x.reduceAlongDimension(nd4j::reduce::Mean, {0,2});
ASSERT_TRUE(z3.equalsTo(&exp3));
x.permutei({1,0,2}); // 3x2x2
NDArray z4 = x.reduceAlongDimension(nd4j::reduce::Mean, {0,1,2});
ASSERT_TRUE(z4.equalsTo(&exp1));
NDArray z5 = x.reduceAlongDimension(nd4j::reduce::Mean, {1});
ASSERT_TRUE(z5.equalsTo(&exp4));
NDArray z6 = x.reduceAlongDimension(nd4j::reduce::Mean, {0,2});
ASSERT_TRUE(z6.equalsTo(&exp5));
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, EqualityTest1) {
auto arrayA = NDArrayFactory::create_<float>('f', {3, 5});
auto arrayB = NDArrayFactory::create_<float>('f', {3, 5});
auto arrayC = NDArrayFactory::create_<float>('f', {3, 5});
auto arrayD = NDArrayFactory::create_<float>('f', {2, 4});
auto arrayE = NDArrayFactory::create_<float>('f', {1, 15});
for (int i = 0; i < arrayA->rows(); i++) {
for (int k = 0; k < arrayA->columns(); k++) {
arrayA->p(i, k, (float) i);
}
}
for (int i = 0; i < arrayB->rows(); i++) {
for (int k = 0; k < arrayB->columns(); k++) {
arrayB->p(i, k, (float) i);
}
}
for (int i = 0; i < arrayC->rows(); i++) {
for (int k = 0; k < arrayC->columns(); k++) {
arrayC->p(i, k, (float) i+1);
}
}
ASSERT_TRUE(arrayA->equalsTo(arrayB, 1e-5));
ASSERT_FALSE(arrayC->equalsTo(arrayB, 1e-5));
ASSERT_FALSE(arrayD->equalsTo(arrayB, 1e-5));
ASSERT_FALSE(arrayE->equalsTo(arrayB, 1e-5));
delete arrayA;
delete arrayB;
delete arrayC;
delete arrayD;
delete arrayE;
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, reduceAlongDimension_same_test1) {
NDArray x('c', {2,3,2}, {1.5f,2.f,3.f,4.f,5.f,6.f,7.5f,8.f,-1.f,-2.f,-3.5f,-4.f}, nd4j::DataType::FLOAT32);
NDArray z1('c', {}, {100}, nd4j::DataType::FLOAT32);
NDArray z2('c', {2,2}, {100,100,100,100}, nd4j::DataType::FLOAT32);
NDArray z3('c', {3}, {100,100,100}, nd4j::DataType::FLOAT32);
NDArray z4('c', {3,2}, {100,100,100,100,100,100}, nd4j::DataType::FLOAT32);
NDArray z5('c', {2}, {100,100}, nd4j::DataType::FLOAT32);
NDArray exp1('c', {}, {26.5f}, nd4j::DataType::FLOAT32);
NDArray exp2('c', {2,2}, {9.5f,12.f,3.f,2.f}, nd4j::DataType::FLOAT32);
NDArray exp3('c', {3}, {19.f,4.f,3.5f}, nd4j::DataType::FLOAT32);
NDArray exp4('c', {3,2}, {9.f,10.f,2.f,2.f,1.5f,2.f}, nd4j::DataType::FLOAT32);
NDArray exp5('c', {2}, {21.5f,5.f}, nd4j::DataType::FLOAT32);
x.reduceAlongDimension(nd4j::reduce::Sum, z1, {0,1,2});
ASSERT_TRUE(z1.equalsTo(&exp1));
x.reduceAlongDimension(nd4j::reduce::Sum, z2, {1});
ASSERT_TRUE(z2.equalsTo(&exp2));
x.reduceAlongDimension(nd4j::reduce::Sum, z3, {0,2});
ASSERT_TRUE(z3.equalsTo(&exp3));
x.permutei({1,0,2}); // 3x2x2
x.reduceAlongDimension(nd4j::reduce::Sum, z1, {0,1,2});
ASSERT_TRUE(z1.equalsTo(&exp1));
x.reduceAlongDimension(nd4j::reduce::Sum, z4, {1});
ASSERT_TRUE(z4.equalsTo(&exp4));
x.reduceAlongDimension(nd4j::reduce::Sum, z5, {0,2});
ASSERT_TRUE(z5.equalsTo(&exp5));
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, reduceAlongDimension_same_test2) {
NDArray x('c', {2,3,2}, {1.5,2,3,4,5,6,7.5,8,-1,-2,-3.5,-4,}, nd4j::DataType::INT64);
NDArray exp1('c', {}, {26}, nd4j::DataType::INT64);
NDArray exp2('c', {2,2}, {9,12,3,2}, nd4j::DataType::INT64);
NDArray exp3('c', {3}, {18,4,4}, nd4j::DataType::INT64);
NDArray exp4('c', {3,2}, {8,10,2,2,2,2}, nd4j::DataType::INT64);
NDArray exp5('c', {2}, {21,5}, nd4j::DataType::INT64);
NDArray z1 = x.reduceAlongDimension(nd4j::reduce::Sum, {0,1,2});
ASSERT_TRUE(z1.equalsTo(&exp1));
NDArray z2 = x.reduceAlongDimension(nd4j::reduce::Sum, {1});
ASSERT_TRUE(z2.equalsTo(&exp2));
NDArray z3 = x.reduceAlongDimension(nd4j::reduce::Sum, {0,2});
ASSERT_TRUE(z3.equalsTo(&exp3));
x.permutei({1,0,2}); // 3x2x2
NDArray z4 = x.reduceAlongDimension(nd4j::reduce::Sum, {0,1,2});
ASSERT_TRUE(z4.equalsTo(&exp1));
NDArray z5 = x.reduceAlongDimension(nd4j::reduce::Sum, {1});
ASSERT_TRUE(z5.equalsTo(&exp4));
NDArray z6 = x.reduceAlongDimension(nd4j::reduce::Sum, {0,2});
ASSERT_TRUE(z6.equalsTo(&exp5));
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, reduceAlongDimension_bool_test1) {
NDArray x('c', {2,3,2}, {0.5,2,3,-4,5,6,-7.5,8,-1,-0.5,-3.5,4}, nd4j::DataType::DOUBLE);
NDArray z1('c', {}, {true}, nd4j::DataType::BOOL);
NDArray z2('c', {2,2}, {true,true,true,true}, nd4j::DataType::BOOL);
NDArray z3('c', {3}, {true,true,true}, nd4j::DataType::BOOL);
NDArray z4('c', {3,2}, {true,true,true,true,true,true}, nd4j::DataType::BOOL);
NDArray z5('c', {2}, {true,true}, nd4j::DataType::BOOL);
NDArray exp1('c', {}, {true}, nd4j::DataType::BOOL);
NDArray exp2('c', {2,2}, {true,true,false,true}, nd4j::DataType::BOOL);
NDArray exp3('c', {3}, {true,true,true}, nd4j::DataType::BOOL);
NDArray exp4('c', {3,2}, {true,true,true,false,true,true}, nd4j::DataType::BOOL);
NDArray exp5('c', {2}, {true,true}, nd4j::DataType::BOOL);
x.reduceAlongDimension(nd4j::reduce::IsPositive, z1, {0,1,2});
ASSERT_TRUE(z1.equalsTo(&exp1));
x.reduceAlongDimension(nd4j::reduce::IsPositive, z2, {1});
ASSERT_TRUE(z2.equalsTo(&exp2));
x.reduceAlongDimension(nd4j::reduce::IsPositive, z3, {0,2});
ASSERT_TRUE(z3.equalsTo(&exp3));
x.permutei({1,0,2}); // 3x2x2
x.reduceAlongDimension(nd4j::reduce::IsPositive, z1, {0,1,2});
ASSERT_TRUE(z1.equalsTo(&exp1));
x.reduceAlongDimension(nd4j::reduce::IsPositive, z4, {1});
ASSERT_TRUE(z4.equalsTo(&exp4));
x.reduceAlongDimension(nd4j::reduce::IsPositive, z5, {0,2});
ASSERT_TRUE(z5.equalsTo(&exp5));
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, reduceAlongDimension_bool_test2) {
NDArray x('c', {2,3,2}, {0.5,2,3,-4,5,6,-7.5,8,-1,-0.5,-3.5,4}, nd4j::DataType::INT32);
NDArray exp1('c', {}, {1}, nd4j::DataType::BOOL);
NDArray exp2('c', {2,2}, {1,1,0,1}, nd4j::DataType::BOOL);
NDArray exp3('c', {3}, {1,1,1}, nd4j::DataType::BOOL);
NDArray exp4('c', {3,2}, {0,1,1,0,1,1}, nd4j::DataType::BOOL);
NDArray exp5('c', {2}, {1,1}, nd4j::DataType::BOOL);
NDArray z1 = x.reduceAlongDimension(nd4j::reduce::IsPositive, {0,1,2});
ASSERT_TRUE(z1.equalsTo(&exp1));
NDArray z2 = x.reduceAlongDimension(nd4j::reduce::IsPositive, {1});
ASSERT_TRUE(z2.equalsTo(&exp2));
NDArray z3 = x.reduceAlongDimension(nd4j::reduce::IsPositive, {0,2});
ASSERT_TRUE(z3.equalsTo(&exp3));
x.permutei({1,0,2}); // 3x2x2
NDArray z4 = x.reduceAlongDimension(nd4j::reduce::IsPositive, {0,1,2});
ASSERT_TRUE(z4.equalsTo(&exp1));
NDArray z5 = x.reduceAlongDimension(nd4j::reduce::IsPositive, {1});
ASSERT_TRUE(z5.equalsTo(&exp4));
NDArray z6 = x.reduceAlongDimension(nd4j::reduce::IsPositive, {0,2});
ASSERT_TRUE(z6.equalsTo(&exp5));
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, reduceAlongDimension_long_test1) {
NDArray x('c', {2,3,2}, {0.5f,2.f,3.f,-0.f,5.f,6.f,-7.5f,0.f,-1.f,-0.5f,-3.5f,4.f}, nd4j::DataType::FLOAT32);
NDArray z1('c', {}, {100}, nd4j::DataType::INT64);
NDArray z2('c', {2,2}, {100,100,100,100}, nd4j::DataType::INT64);
NDArray z3('c', {3}, {100,100,100}, nd4j::DataType::INT64);
NDArray z4('c', {3,2}, {100,100,100,100,100,100}, nd4j::DataType::INT64);
NDArray z5('c', {2}, {100,100}, nd4j::DataType::INT64);
NDArray exp1('c', {}, {2}, nd4j::DataType::INT64);
NDArray exp2('c', {2,2}, {0,1,0,1}, nd4j::DataType::INT64);
NDArray exp3('c', {3}, {1,1,0}, nd4j::DataType::INT64);
NDArray exp4('c', {3,2}, {0,1,0,1,0,0}, nd4j::DataType::INT64);
NDArray exp5('c', {2}, {1,1}, nd4j::DataType::INT64);
x.reduceAlongDimension(nd4j::reduce::CountZero, z1, {0,1,2});
ASSERT_TRUE(z1.equalsTo(&exp1));
x.reduceAlongDimension(nd4j::reduce::CountZero, z2, {1});
ASSERT_TRUE(z2.equalsTo(&exp2));
x.reduceAlongDimension(nd4j::reduce::CountZero, z3, {0,2});
ASSERT_TRUE(z3.equalsTo(&exp3));
x.permutei({1,0,2}); // 3x2x2
x.reduceAlongDimension(nd4j::reduce::CountZero, z1, {0,1,2});
ASSERT_TRUE(z1.equalsTo(&exp1));
x.reduceAlongDimension(nd4j::reduce::CountZero, z4, {1});
ASSERT_TRUE(z4.equalsTo(&exp4));
x.reduceAlongDimension(nd4j::reduce::CountZero, z5, {0,2});
ASSERT_TRUE(z5.equalsTo(&exp5));
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, reduceAlongDimension_long_test2) {
NDArray x('c', {2,3,2}, {0.5,2,3,-0,5,6,-7.5,0,-1,-0.5,-3.5,4}, nd4j::DataType::INT32);
NDArray exp1('c', {}, {4}, nd4j::DataType::INT64);
NDArray exp2('c', {2,2}, {1,1,0,2}, nd4j::DataType::INT64);
NDArray exp3('c', {3}, {2,2,0}, nd4j::DataType::INT64);
NDArray exp4('c', {3,2}, {1,1,0,2,0,0}, nd4j::DataType::INT64);
NDArray exp5('c', {2}, {2,2}, nd4j::DataType::INT64);
NDArray z1 = x.reduceAlongDimension(nd4j::reduce::CountZero, {0,1,2});
ASSERT_TRUE(z1.equalsTo(&exp1));
NDArray z2 = x.reduceAlongDimension(nd4j::reduce::CountZero, {1});
ASSERT_TRUE(z2.equalsTo(&exp2));
NDArray z3 = x.reduceAlongDimension(nd4j::reduce::CountZero, {0,2});
ASSERT_TRUE(z3.equalsTo(&exp3));
x.permutei({1,0,2}); // 3x2x2
NDArray z4 = x.reduceAlongDimension(nd4j::reduce::CountZero, {0,1,2});
ASSERT_TRUE(z4.equalsTo(&exp1));
NDArray z5 = x.reduceAlongDimension(nd4j::reduce::CountZero, {1});
ASSERT_TRUE(z5.equalsTo(&exp4));
NDArray z6 = x.reduceAlongDimension(nd4j::reduce::CountZero, {0,2});
ASSERT_TRUE(z6.equalsTo(&exp5));
}
TEST_F(NDArrayCudaBasicsTests, BroadcastOpsTest1) {
auto x = NDArrayFactory::create<float>('c', {5, 5});
auto z = NDArrayFactory::create<float>('c', {5, 5});
auto row = NDArrayFactory::linspace(1.0f, 5.0f, 5);
NDArray expRow('c', {1, 5,}, {1,2,3,4,5}, nd4j::DataType::FLOAT32);
NDArray exp('c', {5,5}, {1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5}, nd4j::DataType::FLOAT32);
ASSERT_TRUE(row->equalsTo(&expRow));
x.applyBroadcast(broadcast::Add, {1}, *row, z);
x += *row;
ASSERT_TRUE(x.equalsTo(z));
//ASSERT_TRUE(z.equalsTo(&exp));
delete row;
}
TEST_F(NDArrayCudaBasicsTests, BroadcastOpsTest2) {
auto x = NDArrayFactory::create<float>('c', {5, 5});
//auto z = NDArrayFactory::create<float>('c', {5, 5});
auto row = NDArrayFactory::linspace(1.0f, 5.0f, 5);
NDArray expRow('c', {1, 5,}, {1,2,3,4,5}, nd4j::DataType::FLOAT32);
NDArray exp('c', {5,5}, {1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5}, nd4j::DataType::FLOAT32);
ASSERT_TRUE(row->equalsTo(&expRow));
x.applyBroadcast(broadcast::Add, {1}, *row, x);
ASSERT_TRUE(x.equalsTo(&exp));
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, TestBroadcast_1) {
NDArray exp('c', {2, 3, 2, 2}, {1., 1., 1., 1., 2., 2., 2., 2., 3., 3., 3., 3., 1., 1., 1., 1., 2., 2., 2., 2., 3., 3., 3., 3.}, nd4j::DataType::DOUBLE);
auto input = NDArrayFactory::create<double>('c',{ 2, 3, 2, 2});
auto bias = NDArrayFactory::create<double>('c', {1, 3});
bias.linspace(1);
input.applyBroadcast(broadcast::Add, {1}, bias, input);
ASSERT_TRUE(exp.equalsTo(&input));
}
TEST_F(NDArrayCudaBasicsTests, TestFloat16_1) {
auto x = NDArrayFactory::create<float>({1,2,3,4,5,7,8,9});
auto y = NDArrayFactory::create<float>({1,2,3,4,5,7,8,9});
ASSERT_TRUE(x.equalsTo(&y));
}
TEST_F(NDArrayCudaBasicsTests, TestFloat16_2) {
auto x = NDArrayFactory::create<float16>('c', {9}, {1,2,3,4,5,6,7,8,9});
auto y = NDArrayFactory::create<float16>('c', {9}, {1,2,3,4,5,6,7,8,9});
ASSERT_TRUE(x.equalsTo(y));
//for (int e = 0; e < x.lengthOf(); e++)
// ASSERT_NEAR(x.e<float16>(e), y.e<float16>(e), 1.e-5f);
}
TEST_F(NDArrayCudaBasicsTests, TestFloat16_3) {
auto x = NDArrayFactory::create<bfloat16>({1,2,3,4,5,7,8,9});
auto y = NDArrayFactory::create<bfloat16>({1,2,3,4,5,7,8,9});
ASSERT_TRUE(x.equalsTo(&y));
}
TEST_F(NDArrayCudaBasicsTests, TestFloat_4) {
auto x = NDArrayFactory::create<float>({1,2,3,4,5,7,8,9});
auto y = NDArrayFactory::create<float>({2,4,5,5,6,7,8,9});
ASSERT_FALSE(x.equalsTo(&y));
}
TEST_F(NDArrayCudaBasicsTests, TestFloat_5) {
auto x = NDArrayFactory::create<float>('c', {3,3}, {1,2,3,4,5,6,7,8,9});
auto y = NDArrayFactory::create<float>('c', {3,3}, {2,4,5,5,6,7,8,9, 10});
ASSERT_FALSE(x.equalsTo(&y));
}
TEST_F(NDArrayCudaBasicsTests, TestFloat_6) {
auto x = NDArrayFactory::create<float>('f', {3,3}, {1,2,3,4,5,6,7,8,9});
auto y = NDArrayFactory::create<float>('f', {3,3}, {2,4,5,5,6,7,8,9,10});
ASSERT_FALSE(x.equalsTo(&y));
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, Operator_Plus_Test_05)
{
auto x = NDArrayFactory::create<float>('c', {8, 8, 8});
auto y = NDArrayFactory::create<float>('c', {1, 8, 8});
auto expected = NDArrayFactory::create<float>('c', {8, 8, 8});
NDArray res2 = NDArrayFactory::create<float>(expected.ordering(), expected.getShapeAsVector());
x = 1.;
y = 2.;
expected = 3.;
res2 = 0.f;
x.applyTrueBroadcast(BroadcastOpsTuple::Add(), y, res2);// *= y;
ASSERT_TRUE(expected.isSameShape(&res2));
ASSERT_TRUE(expected.equalsTo(&res2));
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, Operator_Plus_Test_5)
{
auto x = NDArrayFactory::create<float>('c', {8, 8, 8});
auto y = NDArrayFactory::create<float>('c', {8, 1, 8});
auto expected = NDArrayFactory::create<float>('c', {8, 8, 8});
NDArray res2(expected);
x = 1.;
y = 2.;
expected = 3.;
//x.printBuffer("X=");
//y.printBuffer("Y=");
//expected.printBuffer("EXPECTED");
auto result = x + y;
//result.printBuffer("1 + 2 =");
//res2.assign(x + y);
//x.applyTrueBroadcast(BroadcastOpsTuple::Add(), &y, &res2);
//res2.printBuffer("Z=");
//x.applyTrueBroadcast(BroadcastOpsTuple::Add(), &y, &res2);// *= y;
// x += y;
//x.printBuffer("OutputX");
//res2.syncToHost();
//res2.printBuffer("OUputZ");
//x.printIndexedBuffer("OUtputX");
ASSERT_TRUE(expected.isSameShape(&result));
ASSERT_TRUE(expected.equalsTo(&result));
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, Operator_Plus_Test_51)
{
auto x = NDArrayFactory::create<float>('c', {8, 8, 8});
auto y = NDArrayFactory::create<float>('c', {8, 8});
auto expected = NDArrayFactory::create<float>('c', {8, 8, 8});
NDArray res2(expected);
x = 1.;
y = 2.;
expected = 3.;
//x.printBuffer("X=");
//y.printBuffer("Y=");
//expected.printBuffer("EXPECTED");
auto result = x + y;
//result.printBuffer("1 + 2 =");
//res2.assign(x + y);
//x.applyTrueBroadcast(BroadcastOpsTuple::Add(), &y, &res2);
//res2.printBuffer("Z=");
//x.applyTrueBroadcast(BroadcastOpsTuple::Add(), &y, &res2);// *= y;
// x += y;
//x.printBuffer("OutputX");
//res2.syncToHost();
//res2.printBuffer("OUputZ");
//x.printIndexedBuffer("OUtputX");
ASSERT_TRUE(expected.isSameShape(&result));
ASSERT_TRUE(expected.equalsTo(&result));
}
TEST_F(NDArrayCudaBasicsTests, Tile_Test_2_1)
{
auto x = NDArrayFactory::create<float>('c', {2, 1, 2});
x = 10.;
auto y = x.tile({1,2,1});
auto exp = NDArrayFactory::create<float>('c', {2, 2, 2});
exp = 10.;
// y.printShapeInfo("Output SHAPE");
// y.printBuffer("Output TILE");
// exp.printBuffer("Expect TILE");
ASSERT_TRUE(exp.equalsTo(y));
}
TEST_F(NDArrayCudaBasicsTests, Tile_Test_2_2)
{
auto x = NDArrayFactory::create<float>('f', {2, 1, 2});
x = 10.;
auto y = x.tile({1,2,1});
auto exp = NDArrayFactory::create<float>('f', {2, 2, 2});
exp = 10.;
ASSERT_TRUE(exp.equalsTo(y));
}
TEST_F(NDArrayCudaBasicsTests, Tile_Test_2_3)
{
auto x = NDArrayFactory::create<float>('f', {2, 1, 2});
x = 10.;
x.p(1,0,1, 20);
x.syncToDevice();
auto y = x.tile({1,2,1});
auto exp = NDArrayFactory::create<float>('f', {2, 2, 2});
exp = 10.;
exp.p(1,0,1, 20.);
exp.p(1, 1, 1, 20.);
exp.syncToDevice();
ASSERT_TRUE(exp.equalsTo(y));
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, Operator_Plus_Test_2)
{
double expBuff[] = {2., 3, 3., 4., 4., 5, 5., 6., 6., 7, 7., 8.};
NDArray a('c', {4,4}, {1,2,3,4,5,6,7,8,9,2,3,2,1,0,4,7}, nd4j::DataType::FLOAT32);
auto x = NDArrayFactory::create<double>('c', {3, 2, 1});
auto y = NDArrayFactory::create<double>('c', {1, 2});
auto expected = NDArrayFactory::create<double>(expBuff, 'c', {3, 2, 2});
x.linspace(1);
y.linspace(1);
auto result = x + y;
ASSERT_TRUE(expected.isSameShape(&result));
ASSERT_TRUE(expected.equalsTo(&result));
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, assign_2)
{
NDArray x('c', {4}, {1.5f,2.5f,3.5f,4.5f}, nd4j::DataType::FLOAT32);
NDArray y('c', {4}, nd4j::DataType::INT32);
NDArray expected('c', {4}, {1,2,3,4}, nd4j::DataType::INT32);
y.assign(x);
// y.printBuffer("ASSIGN VECTOR");
ASSERT_TRUE(expected.equalsTo(&y));
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, subarray_1)
{
NDArray x('c', {2,3,4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}, nd4j::DataType::FLOAT32);
NDArray y('f', {2,3,4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}, nd4j::DataType::FLOAT32);
Nd4jLong shapeExpX0[] = {1, 2, 12, 8192, 1, 99};
float buffExpX0[] = {1.f, 13.f};
Nd4jLong shapeExpX1[] = {1, 2, 12, 8192, 1, 99};
float buffExpX1[] = {2.f, 14.f};
Nd4jLong shapeExpX2[] = {3, 2, 1, 1, 12, 4, 1, 8192, 1, 99};
float buffExpX2[] = {1.f, 13.f};
Nd4jLong shapeExpX3[] = {2, 2, 4, 12, 1, 8192, 1, 99};
float buffExpX3[] = {9.f, 10.f, 11.f, 12.f, 21.f, 22.f, 23.f, 24.f};
Nd4jLong shapeExpX4[] = {3, 2, 1, 4, 12, 4, 1, 8192, 1, 99};
float buffExpX4[] = {9.f, 10.f, 11.f, 12.f, 21.f, 22.f, 23.f, 24.f};
Nd4jLong shapeExpX5[] = {2, 2, 3, 12, 4, 8192, 1, 99};
float buffExpX5[] = {4.f, 8.f, 12.f, 16.f, 20.f, 24.f};
Nd4jLong shapeExpY0[] = {1, 2, 1, 8192, 1, 99};
float buffExpY0[] = {1.f, 2.f};
Nd4jLong shapeExpY1[] = {1, 2, 1, 8192, 1, 99};
float buffExpY1[] = {7.f, 8.f};
Nd4jLong shapeExpY2[] = {3, 2, 1, 1, 1, 2, 6, 8192, 1, 102};
float buffExpY2[] = {1.f, 2.f};
Nd4jLong shapeExpY3[] = {2, 2, 4, 1, 6, 8192, 1, 99};
float buffExpY3[] = {5.f, 11.f, 17.f, 23.f, 6.f, 12.f, 18.f, 24.f};
Nd4jLong shapeExpY4[] = {3, 2, 1, 4, 1, 2, 6, 8192, 1, 102};
float buffExpY4[] = {5.f, 11.f, 17.f, 23.f, 6.f, 12.f, 18.f, 24.f};
Nd4jLong shapeExpY5[] = {2, 2, 3, 1, 2, 8192, 1, 99};
float buffExpY5[] = {19.f, 21.f, 23.f, 20.f, 22.f, 24.f};
NDArray x0 = x(0, {1,2});
NDArray xExp(buffExpX0, shapeExpX0);
ASSERT_TRUE(xExp.isSameShape(x0));
ASSERT_TRUE(xExp.equalsTo(x0));
// for(int i = 0; i < shape::shapeInfoLength(x0.rankOf()); ++i)
// ASSERT_TRUE(x0.getShapeInfo()[i] == shapeExpX0[i]);
// for(int i = 0; i < x0.lengthOf(); ++i)
// ASSERT_TRUE(x0.e<float>(i) == buffExpX0[i]);
NDArray x1 = x(1, {1,2});
NDArray x1Exp(buffExpX1, shapeExpX1);
ASSERT_TRUE(x1Exp.isSameShape(x1));
ASSERT_TRUE(x1Exp.equalsTo(x1));
// for(int i = 0; i < shape::shapeInfoLength(x1.rankOf()); ++i)
// ASSERT_TRUE(x1.getShapeInfo()[i] == shapeExpX1[i]);
// for(int i = 0; i < x1.lengthOf(); ++i)
// ASSERT_TRUE(x1.e<float>(i) == buffExpX1[i]);
NDArray x2 = x(0, {1,2}, true);
NDArray x2Exp(buffExpX2, shapeExpX2);
ASSERT_TRUE(x2Exp.isSameShape(x2));
// x2.printBuffer("X2");
// x2Exp.printBuffer("X2 EXPECT");
ASSERT_TRUE(x2Exp.equalsTo(x2));
// for(int i = 0; i < shape::shapeInfoLength(x2.rankOf()); ++i)
// ASSERT_TRUE(x2.getShapeInfo()[i] == shapeExpX2[i]);
// for(int i = 0; i < x2.lengthOf(); ++i)
// ASSERT_TRUE(x2.e<float>(i) == buffExpX2[i]);
NDArray x3 = x(2, {1});
NDArray x3Exp(buffExpX3, shapeExpX3);
ASSERT_TRUE(x3Exp.isSameShape(x3));
ASSERT_TRUE(x3Exp.equalsTo(x3));
// for(int i = 0; i < shape::shapeInfoLength(x3.rankOf()); ++i)
// ASSERT_TRUE(x3.getShapeInfo()[i] == shapeExpX3[i]);
// for(int i = 0; i < x3.lengthOf(); ++i)
// ASSERT_TRUE(x3.e<float>(i) == buffExpX3[i]);
NDArray x4 = x(2, {1}, true);
NDArray x4Exp(buffExpX4, shapeExpX4);
ASSERT_TRUE(x4Exp.isSameShape(x4));
ASSERT_TRUE(x4Exp.equalsTo(x4));
// for(int i = 0; i < shape::shapeInfoLength(x4.rankOf()); ++i)
// ASSERT_TRUE(x4.getShapeInfo()[i] == shapeExpX4[i]);
// for(int i = 0; i < x4.lengthOf(); ++i)
// ASSERT_TRUE(x4.e<float>(i) == buffExpX4[i]);
NDArray x5 = x(3, {2});
NDArray x5Exp(buffExpX5, shapeExpX5);
ASSERT_TRUE(x5Exp.isSameShape(x5));
ASSERT_TRUE(x5Exp.equalsTo(x5));
// for(int i = 0; i < shape::shapeInfoLength(x5.rankOf()); ++i)
// ASSERT_TRUE(x5.getShapeInfo()[i] == shapeExpX5[i]);
// for(int i = 0; i < x5.lengthOf(); ++i)
// ASSERT_TRUE(x5.e<float>(i) == buffExpX5[i]);
// ******************* //
NDArray y0 = y(0, {1,2});
NDArray y0Exp(buffExpY0, shapeExpY0);
ASSERT_TRUE(y0Exp.isSameShape(y0));
ASSERT_TRUE(y0Exp.equalsTo(y0));
// for(int i = 0; i < shape::shapeInfoLength(y0.rankOf()); ++i)
// ASSERT_TRUE(y0.getShapeInfo()[i] == shapeExpY0[i]);
// for(int i = 0; i < y0.lengthOf(); ++i)
// ASSERT_TRUE(y0.e<float>(i) == buffExpY0[i]);
NDArray y1 = y(1, {1,2});
NDArray y1Exp(buffExpY1, shapeExpY1);
ASSERT_TRUE(y1Exp.isSameShape(y1));
ASSERT_TRUE(y1Exp.equalsTo(y1));
// for(int i = 0; i < shape::shapeInfoLength(y1.rankOf()); ++i)
// ASSERT_TRUE(y1.getShapeInfo()[i] == shapeExpY1[i]);
// for(int i = 0; i < y1.lengthOf(); ++i)
// ASSERT_TRUE(y1.e<float>(i) == buffExpY1[i]);
NDArray y2 = y(0, {1,2}, true);
NDArray y2Exp(buffExpY2, shapeExpY2);
ASSERT_TRUE(y2Exp.isSameShape(y2));
ASSERT_TRUE(y2Exp.equalsTo(y2));
// for(int i = 0; i < shape::shapeInfoLength(y2.rankOf()); ++i)
// ASSERT_TRUE(y2.getShapeInfo()[i] == shapeExpY2[i]);
// for(int i = 0; i < y2.lengthOf(); ++i)
// ASSERT_TRUE(y2.e<float>(i) == buffExpY2[i]);
NDArray y3 = y(2, {1});
NDArray y3Exp(buffExpY3, shapeExpY3);
ASSERT_TRUE(y3Exp.isSameShape(y3));
ASSERT_TRUE(y3Exp.equalsTo(y3));
// for(int i = 0; i < shape::shapeInfoLength(y3.rankOf()); ++i)
// ASSERT_TRUE(y3.getShapeInfo()[i] == shapeExpY3[i]);
// for(int i = 0; i < y3.lengthOf(); ++i)
// ASSERT_TRUE(y3.e<float>(i) == buffExpY3[i]);
NDArray y4 = y(2, {1}, true);
NDArray y4Exp = NDArrayFactory::create<float>('f', {2,1,4}, {5, 6, 11, 12, 17, 18, 23, 24});
ASSERT_TRUE(y4Exp.isSameShape(y4));
ASSERT_TRUE(y4Exp.equalsTo(y4));
// for(int i = 0; i < shape::shapeInfoLength(y4.rankOf()); ++i)
// ASSERT_TRUE(y4.getShapeInfo()[i] == shapeExpY4[i]);
// for(int i = 0; i < y4.lengthOf(); ++i)
// ASSERT_TRUE(y4.e<float>(i) == buffExpY4[i]);
NDArray y5 = y(3, {2});
NDArray y5Exp(buffExpY5, shapeExpY5);
ASSERT_TRUE(y5Exp.isSameShape(y5));
ASSERT_TRUE(y5Exp.equalsTo(y5));
// for(int i = 0; i < shape::shapeInfoLength(y5.rankOf()); ++i)
// ASSERT_TRUE(y5.getShapeInfo()[i] == shapeExpY5[i]);
// for(int i = 0; i < y5.lengthOf(); ++i)
// ASSERT_TRUE(y5.e<float>(i) == buffExpY5[i]);
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, Test_diagonal_1) {
auto x = NDArrayFactory::create<float>('c', {2, 3}, {1, 2, 3, 4, 5, 6});
auto exp = NDArrayFactory::create<float>('c', {2, 1}, {1, 5});
auto diag = x.diagonal('c');
//diag.syncToDevice();
for (Nd4jLong e = 0; e < exp.lengthOf(); ++e) {
printf("VAL[%ld] = %f\n", e, diag.e<float>(e)); //, exp.e<float>(e), 1.e-5);
}
for (Nd4jLong e = 0; e < exp.lengthOf(); ++e) {
ASSERT_NEAR(diag.e<float>(e), exp.e<float>(e), 1.e-5);
}
double eps(1.e-5);
NDArray tmp(nd4j::DataType::FLOAT32, x.getContext()); // scalar = 0
ExtraArguments extras({eps});
NativeOpExecutioner::execReduce3Scalar(diag.getContext(), reduce3::EqualsWithEps, diag.getBuffer(),
diag.getShapeInfo(), diag.getSpecialBuffer(), diag.getSpecialShapeInfo(), extras.argumentsAsT(nd4j::DataType::FLOAT32),
exp.getBuffer(), exp.getShapeInfo(), exp.getSpecialBuffer(), exp.getSpecialShapeInfo(),
tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo());
cudaStream_t* stream = x.getContext()->getCudaStream();
auto res = cudaStreamSynchronize(*stream);
// tmp.printBuffer("Compare result is (expected 0)");
ASSERT_TRUE(exp.isSameShape(diag));
ASSERT_TRUE(exp.equalsTo(diag));
}
TEST_F(NDArrayCudaBasicsTests, Test_PermuteEquality_02) {
auto x = NDArrayFactory::linspace<float>(1.f, 60.f, 60); //('c', {1, 60});
//x.linspace(1);
auto exp = NDArrayFactory::create<float>('c', {3, 4, 5}, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f, 25.0f, 26.0f, 27.0f, 28.0f, 29.0f, 30.0f, 31.0f, 32.0f, 33.0f, 34.0f, 35.0f, 36.0f, 37.0f, 38.0f, 39.0f, 40.0f, 41.0f, 42.0f, 43.0f, 44.0f, 45.0f, 46.0f, 47.0f, 48.0f, 49.0f, 50.0f, 51.0f, 52.0f, 53.0f, 54.0f, 55.0f, 56.0f, 57.0f, 58.0f, 59.0f, 60.0});
x->reshapei('c', {3, 4, 5});
x->permutei({0, 1, 2});
x->streamline();
// x.printShapeInfo("{0, 1, 2} shape");
// x.printBuffer("{0, 1, 2} data");
ASSERT_TRUE(exp.isSameShape(x));
ASSERT_TRUE(exp.equalsTo(x));
delete x;
}
TEST_F(NDArrayCudaBasicsTests, Test_PermuteEquality_0) {
auto x = NDArrayFactory::create<float>('c', {1, 60});
x.linspace(1);
auto exp = NDArrayFactory::create<float>('c', {3, 4, 5}, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f, 25.0f, 26.0f, 27.0f, 28.0f, 29.0f, 30.0f, 31.0f, 32.0f, 33.0f, 34.0f, 35.0f, 36.0f, 37.0f, 38.0f, 39.0f, 40.0f, 41.0f, 42.0f, 43.0f, 44.0f, 45.0f, 46.0f, 47.0f, 48.0f, 49.0f, 50.0f, 51.0f, 52.0f, 53.0f, 54.0f, 55.0f, 56.0f, 57.0f, 58.0f, 59.0f, 60.0});
x.reshapei('c', {3, 4, 5});
x.permutei({0, 1, 2});
x.streamline();
// x.printShapeInfo("{0, 1, 2} shape");
// x.printBuffer("{0, 1, 2} data");
ASSERT_TRUE(exp.isSameShape(&x));
ASSERT_TRUE(exp.equalsTo(&x));
}
TEST_F(NDArrayCudaBasicsTests, Test_PermuteEquality_1) {
auto x = NDArrayFactory::create<float>('c', {1, 60});
x.linspace(1);
auto exp = NDArrayFactory::create<float>('c', {3, 4, 5}, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f, 25.0f, 26.0f, 27.0f, 28.0f, 29.0f, 30.0f, 31.0f, 32.0f, 33.0f, 34.0f, 35.0f, 36.0f, 37.0f, 38.0f, 39.0f, 40.0f, 41.0f, 42.0f, 43.0f, 44.0f, 45.0f, 46.0f, 47.0f, 48.0f, 49.0f, 50.0f, 51.0f, 52.0f, 53.0f, 54.0f, 55.0f, 56.0f, 57.0f, 58.0f, 59.0f, 60.0});
x.reshapei('c', {3, 4, 5});
x.permutei({0, 1, 2});
x.streamline();
// x.printShapeInfo("{0, 1, 2} shape");
// x.printBuffer("{0, 1, 2} data");
ASSERT_TRUE(exp.isSameShape(&x));
ASSERT_TRUE(exp.equalsTo(&x));
}
TEST_F(NDArrayCudaBasicsTests, Test_PermuteEquality_2) {
//auto x = NDArrayFactory::create<float>('c', {1, 60});
auto xx = NDArrayFactory::linspace<float>(1.f, 60.f, 60); //('c', {1, 60});
// auto x = *xx;
//x.linspace(1);
// auto exp = NDArrayFactory::create<float>('c', {3, 4, 5}, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f, 25.0f, 26.0f, 27.0f, 28.0f, 29.0f, 30.0f, 31.0f, 32.0f, 33.0f, 34.0f, 35.0f, 36.0f, 37.0f, 38.0f, 39.0f, 40.0f, 41.0f, 42.0f, 43.0f, 44.0f, 45.0f, 46.0f, 47.0f, 48.0f, 49.0f, 50.0f, 51.0f, 52.0f, 53.0f, 54.0f, 55.0f, 56.0f, 57.0f, 58.0f, 59.0f, 60.0});
// x.reshapei('c', {3, 4, 5});
// x.permutei({0, 1, 2});
// x.streamline();
// x.printShapeInfo("{0, 1, 2} shape");
// x.printBuffer("{0, 1, 2} data");
// ASSERT_TRUE(exp.isSameShape(&x));
// ASSERT_TRUE(exp.equalsTo(&x));
delete xx;
}
TEST_F(NDArrayCudaBasicsTests, Test_PermuteEquality_3) {
auto x = NDArrayFactory::create<float>('c', {1, 60});
//x.linspace(1);
for (int l = 0; l < x.lengthOf(); l++)
x.p(l, float(l + 1.f));
auto exp = NDArrayFactory::create<float>('c', {3, 4, 5}, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f, 25.0f, 26.0f, 27.0f, 28.0f, 29.0f, 30.0f, 31.0f, 32.0f, 33.0f, 34.0f, 35.0f, 36.0f, 37.0f, 38.0f, 39.0f, 40.0f, 41.0f, 42.0f, 43.0f, 44.0f, 45.0f, 46.0f, 47.0f, 48.0f, 49.0f, 50.0f, 51.0f, 52.0f, 53.0f, 54.0f, 55.0f, 56.0f, 57.0f, 58.0f, 59.0f, 60.0});
x.reshapei('c', {3, 4, 5});
x.permutei({0, 1, 2});
x.streamline();
// x.printShapeInfo("{0, 1, 2} shape");
// x.printBuffer("{0, 1, 2} data");
ASSERT_TRUE(exp.isSameShape(&x));
ASSERT_TRUE(exp.equalsTo(&x));
}
TEST_F(NDArrayCudaBasicsTests, Test_Empty_1) {
auto x = NDArrayFactory::empty<float>();
ASSERT_TRUE(x.isActualOnHostSide());
ASSERT_TRUE(x.isEmpty());
}
TEST_F(NDArrayCudaBasicsTests, Test_Empty_2) {
auto x = NDArrayFactory::empty_<float>();
ASSERT_TRUE(x->isEmpty());
delete x;
}
TEST_F(NDArrayCudaBasicsTests, Test_Empty_3) {
auto x = NDArrayFactory::empty(nd4j::DataType::FLOAT32);
ASSERT_TRUE(x.isEmpty());
}
TEST_F(NDArrayCudaBasicsTests, Test_Empty_4) {
auto x = NDArrayFactory::empty_(nd4j::DataType::FLOAT32);
ASSERT_TRUE(x->isEmpty());
delete x;
}
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