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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_TRUE(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_TRUE(x.isActualOnHostSide());
NDArray::registerSpecialUse({&x}, {&y});
ASSERT_TRUE(x.isActualOnDeviceSide());
ASSERT_FALSE(x.isActualOnHostSide());
ASSERT_TRUE(y.isActualOnDeviceSide());
ASSERT_TRUE(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_TRUE(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_TRUE(x->isActualOnHostSide());
NDArray::registerSpecialUse({y}, {x});
x->applyTransform(transform::Neg, y, nullptr);
//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_TRUE(x->isActualOnHostSide());
NDArray::registerSpecialUse({y}, {x});
x->applyTransform(transform::Cosine, y, nullptr);
//ASSERT_TRUE(x->isActualOnDeviceSide());
//ASSERT_FALSE(x->isActualOnHostSide());
//ASSERT_TRUE(y->isActualOnDeviceSide());
//ASSERT_TRUE(y->isActualOnHostSide());
//y->syncToHost();
y->printBuffer("Cosine");
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 = ;
cudaMemcpy(z.buffer(), z.specialBuffer(), z.lengthOf() * z.sizeOfT(), cudaMemcpyDeviceToHost);
res = cudaStreamSynchronize(*stream);
ASSERT_EQ(0, res);
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, 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, nullptr);
//
// 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();
x.printBuffer("6X = ");
//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_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();
x.printBuffer("7X = ");
//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, 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, nullptr);
// 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, nullptr);
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_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, nullptr);
//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_TRUE(x.isActualOnHostSide());
x.applyTransform(transform::Neg, &y, nullptr);
//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_TRUE(x.isActualOnHostSide());
x.applyTransform(transform::Sqrt, &y, nullptr);
//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, nullptr);
//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_TRUE(x.isActualOnHostSide());
x.applyTransform(transform::Cosine, &y, nullptr);
//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_TRUE(x.isActualOnHostSide());
x.applyTransform(transform::Cosine, &y, nullptr);
//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_TRUE(x.isActualOnHostSide());
x.applyTransform(transform::Cosine, &y, nullptr);
//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();
z.printBuffer("Result with Broadcast2 (multiply)");
exp.printBuffer("Expect with Broadcast2 (multiply)");
// 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();
z.printBuffer("Result with Broadcast3 (multiply)");
// 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.printBuffer("23X = ");
//y.printBuffer("23Y = ");
x *= y;
//x.syncToHost();
x.printBuffer("54Result out");
//
// 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) {
nd4j_printf("Allocation of %i bytes with device\n", hostData[i].second)
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;
x.printBuffer("55Result 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, 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);
exp.printBuffer("56Result 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);
//}
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();
x.printBuffer("X = ");
y.printBuffer("Y = ");
ASSERT_NEAR(y.e<double>(0), 15, 1e-5);
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, TestDup1) {
NDArray array('c', {2,3}, {1,2,3,4,5,6});
array.printBuffer("Array at start");
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));
delete arrC;
delete arrF;
}
//////////////////////////////////////////////////////////////////////////
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., 20., 36.}, nd4j::DataType::FLOAT32);
NDArray exp2('c', {2,3}, {-10., -2., 6.,14., 22., 30.}, nd4j::DataType::FLOAT32);
NDArray exp3('c', {4}, {38., 41., 44., 47.}, nd4j::DataType::FLOAT32);
NDArray exp4('c', {4}, {114., 117., 120., 123.}, nd4j::DataType::FLOAT32);
NDArray* z = x.applyReduce3(nd4j::reduce3::Dot, &y, {0,2});
ASSERT_TRUE(z->equalsTo(&exp1));
delete z;
z = x.applyReduce3(nd4j::reduce3::Dot, &k, {0,1});
ASSERT_TRUE(z->equalsTo(&exp3));
delete z;
x.permutei({0,2,1});
y.permutei({0,2,1});
z = y.applyReduce3(nd4j::reduce3::Dot, &x, {1});
ASSERT_TRUE(z->equalsTo(&exp2));
// printCudaGlobal<float><<<1,1,0, *y.getContext()->getCudaStream()>>>(z->specialBuffer(), 6);
delete z;
x2.permutei({1,0,2});
z = x2.applyReduce3(nd4j::reduce3::Dot, &k2, {0,1});
ASSERT_TRUE(z->equalsTo(&exp4));
delete z;
}
////////////////////////////////////////////////////////////////////////////
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));
delete z;
z = x.applyReduce3(nd4j::reduce3::Dot, &k, {0,1});
ASSERT_TRUE(z->equalsTo(&exp3));
delete z;
x.permutei({0,2,1});
y.permutei({0,2,1});
z = y.applyReduce3(nd4j::reduce3::Dot, &x, {1});
ASSERT_TRUE(z->equalsTo(&exp2));
// printCudaGlobal<float><<<1,1,0, *y.getContext()->getCudaStream()>>>(z->specialBuffer(), 6);
delete z;
x2.permutei({1,0,2});
z = x2.applyReduce3(nd4j::reduce3::Dot, &k2, {0,1});
ASSERT_TRUE(z->equalsTo(&exp4));
delete z;
}
////////////////////////////////////////////////////////////////////////////
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', {0}, {-204}, nd4j::DataType::FLOAT32);
NDArray exp2('c', {0}, {31.5}, nd4j::DataType::DOUBLE);
auto z = x1.applyReduce3(reduce3::Dot, &x2);
ASSERT_TRUE(z->equalsTo(&exp1));
delete z;
z = x3.applyReduce3(reduce3::Dot, &x4);
ASSERT_TRUE(z->equalsTo(&exp2));
delete z;
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));
delete z;
z = x3.applyReduce3(reduce3::Dot, &x4);
ASSERT_TRUE(z->equalsTo(&exp2));
delete z;
}
////////////////////////////////////////////////////////////////////////////
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., -124., 6., -2., 22., 14.}, nd4j::DataType::FLOAT32);
NDArray exp2('c', {6,4}, {-36., -44., -52., -60.,-42., -52., -62., -72.,2., 0., -2., -4.,6., 4., 2., 0.,10., 8., 6., 4.,14., 12., 10., 8.}, 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));
delete z;
z = x1.applyAllReduce3(reduce3::Dot, &x2, {0});
ASSERT_TRUE(z->equalsTo(&exp2));
delete z;
z = x3.applyAllReduce3(reduce3::Dot, &x4, {0,1});
ASSERT_TRUE(z->equalsTo(&exp3));
delete z;
z = x3.applyAllReduce3(reduce3::Dot, &x4, {1});
// z->syncToHost();
// z->printShapeInfo();
// z->printIndexedBuffer();
ASSERT_TRUE(z->equalsTo(&exp4));
delete z;
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));
delete z;
z = x3.applyAllReduce3(reduce3::Dot, &x4, {0});
ASSERT_TRUE(z->equalsTo(&exp4));
delete z;
}
//////////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, applyIndexReduce_test1) {
NDArray x('c', {2,3}, {0, 10, 1, 2, 2.5,-4}, nd4j::DataType::DOUBLE);
NDArray scalar('c', {0}, {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', {0}, {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', {0}, {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', {0}, {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', {0}, {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));
delete z;
z = x.applyIndexReduce(nd4j::indexreduce::IndexMax, {1});
ASSERT_TRUE(z->equalsTo(&exp2));
delete z;
z = x.applyIndexReduce(nd4j::indexreduce::IndexMax, {0});
ASSERT_TRUE(z->equalsTo(&exp3));
delete z;
x.permutei({1,0});
z = x.applyIndexReduce(nd4j::indexreduce::IndexMax, {0,1});
ASSERT_TRUE(z->equalsTo(&exp4));
delete z;
z = x.applyIndexReduce(nd4j::indexreduce::IndexMax, {0});
ASSERT_TRUE(z->equalsTo(&exp5));
delete z;
z = x.applyIndexReduce(nd4j::indexreduce::IndexMax, {1});
ASSERT_TRUE(z->equalsTo(&exp6));
delete z;
}
////////////////////////////////////////////////////////////////////////////////
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', {0}, {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', {0}, {2.166667}, nd4j::DataType::DOUBLE);
NDArray exp2('c', {2,2}, {3,4,1,0.666667}, 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}, {3.5,0.833333}, 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', {0}, {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.reduceAlongDims(nd4j::reduce::Mean, {0,1,2});
ASSERT_TRUE(z1.equalsTo(&exp1));
NDArray z2 = x.reduceAlongDims(nd4j::reduce::Mean, {1});
ASSERT_TRUE(z2.equalsTo(&exp2));
NDArray z3 = x.reduceAlongDims(nd4j::reduce::Mean, {0,2});
ASSERT_TRUE(z3.equalsTo(&exp3));
x.permutei({1,0,2}); // 3x2x2
NDArray z4 = x.reduceAlongDims(nd4j::reduce::Mean, {0,1,2});
ASSERT_TRUE(z4.equalsTo(&exp1));
NDArray z5 = x.reduceAlongDims(nd4j::reduce::Mean, {1});
ASSERT_TRUE(z5.equalsTo(&exp4));
NDArray z6 = x.reduceAlongDims(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);
}
}
arrayA->printBuffer("arrayA is ");
for (int i = 0; i < arrayB->rows(); i++) {
for (int k = 0; k < arrayB->columns(); k++) {
arrayB->p(i, k, (float) i);
}
}
arrayB->printBuffer("arrayB is ");
for (int i = 0; i < arrayC->rows(); i++) {
for (int k = 0; k < arrayC->columns(); k++) {
arrayC->p(i, k, (float) i+1);
}
}
arrayC->printBuffer("arrayC is ");
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}, {1.5,2,3,4,5,6,7.5,8,-1,-2,-3.5,-4,}, nd4j::DataType::FLOAT32);
NDArray z1('c', {0}, {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', {0}, {26.5}, nd4j::DataType::FLOAT32);
NDArray exp2('c', {2,2}, {9.5,12,3,2}, nd4j::DataType::FLOAT32);
NDArray exp3('c', {3}, {19,4,3.5}, nd4j::DataType::FLOAT32);
NDArray exp4('c', {3,2}, {9,10,2,2,1.5,2}, nd4j::DataType::FLOAT32);
NDArray exp5('c', {2}, {21.5,5}, 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', {0}, {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.reduceAlongDims(nd4j::reduce::Sum, {0,1,2});
ASSERT_TRUE(z1.equalsTo(&exp1));
NDArray z2 = x.reduceAlongDims(nd4j::reduce::Sum, {1});
ASSERT_TRUE(z2.equalsTo(&exp2));
NDArray z3 = x.reduceAlongDims(nd4j::reduce::Sum, {0,2});
ASSERT_TRUE(z3.equalsTo(&exp3));
x.permutei({1,0,2}); // 3x2x2
NDArray z4 = x.reduceAlongDims(nd4j::reduce::Sum, {0,1,2});
ASSERT_TRUE(z4.equalsTo(&exp1));
NDArray z5 = x.reduceAlongDims(nd4j::reduce::Sum, {1});
ASSERT_TRUE(z5.equalsTo(&exp4));
NDArray z6 = x.reduceAlongDims(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', {0}, {100}, nd4j::DataType::BOOL);
NDArray z2('c', {2,2}, {100,100,100,100}, nd4j::DataType::BOOL);
NDArray z3('c', {3}, {100,100,100}, nd4j::DataType::BOOL);
NDArray z4('c', {3,2}, {100,100,100,100,100,100}, nd4j::DataType::BOOL);
NDArray z5('c', {2}, {100,100}, nd4j::DataType::BOOL);
NDArray exp1('c', {0}, {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}, {1,1,1,0,1,1}, nd4j::DataType::BOOL);
NDArray exp5('c', {2}, {1,1}, 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', {0}, {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.reduceAlongDims(nd4j::reduce::IsPositive, {0,1,2});
ASSERT_TRUE(z1.equalsTo(&exp1));
NDArray z2 = x.reduceAlongDims(nd4j::reduce::IsPositive, {1});
ASSERT_TRUE(z2.equalsTo(&exp2));
NDArray z3 = x.reduceAlongDims(nd4j::reduce::IsPositive, {0,2});
ASSERT_TRUE(z3.equalsTo(&exp3));
x.permutei({1,0,2}); // 3x2x2
NDArray z4 = x.reduceAlongDims(nd4j::reduce::IsPositive, {0,1,2});
ASSERT_TRUE(z4.equalsTo(&exp1));
NDArray z5 = x.reduceAlongDims(nd4j::reduce::IsPositive, {1});
ASSERT_TRUE(z5.equalsTo(&exp4));
NDArray z6 = x.reduceAlongDims(nd4j::reduce::IsPositive, {0,2});
ASSERT_TRUE(z6.equalsTo(&exp5));
}
////////////////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, reduceAlongDimension_long_test1) {
NDArray x('c', {2,3,2}, {0.5,2,3,-0,5,6,-7.5,0,-1,-0.5,-3.5,4}, nd4j::DataType::FLOAT32);
NDArray z1('c', {0}, {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', {0}, {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', {0}, {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.reduceAlongDims(nd4j::reduce::CountZero, {0,1,2});
ASSERT_TRUE(z1.equalsTo(&exp1));
NDArray z2 = x.reduceAlongDims(nd4j::reduce::CountZero, {1});
ASSERT_TRUE(z2.equalsTo(&exp2));
NDArray z3 = x.reduceAlongDims(nd4j::reduce::CountZero, {0,2});
ASSERT_TRUE(z3.equalsTo(&exp3));
x.permutei({1,0,2}); // 3x2x2
NDArray z4 = x.reduceAlongDims(nd4j::reduce::CountZero, {0,1,2});
ASSERT_TRUE(z4.equalsTo(&exp1));
NDArray z5 = x.reduceAlongDims(nd4j::reduce::CountZero, {1});
ASSERT_TRUE(z5.equalsTo(&exp4));
NDArray z6 = x.reduceAlongDims(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, nullptr);
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);
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);
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}, {1,2,3,4,5,7,8,9});
auto y = NDArrayFactory::create<float16>('c', {9}, {1,2,3,4,5,7,8,9});
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}, {1,2,3,4,5,7,8,9});
auto y = NDArrayFactory::create<float>('c', {3,3}, {2,4,5,5,6,7,8,9});
ASSERT_FALSE(x.equalsTo(&y));
}
TEST_F(NDArrayCudaBasicsTests, TestFloat_6) {
auto x = NDArrayFactory::create<float>('f', {3,3}, {1,2,3,4,5,7,8,9});
auto y = NDArrayFactory::create<float>('f', {3,3}, {2,4,5,5,6,7,8,9});
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.;
y.printShapeInfo("Output SHAPE");
y.printBuffer("Output TILE");
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);
a.printBuffer();
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);
x.printBuffer("X=");
y.printBuffer("Y=");
auto result = x + y;
result.printIndexedBuffer("Result");
ASSERT_TRUE(expected.isSameShape(&result));
ASSERT_TRUE(expected.equalsTo(&result));
}
//////////////////////////////////////////////////////////////////////
TEST_F(NDArrayCudaBasicsTests, assign_2)
{
NDArray x('c', {4}, {1.5,2.5,3.5,4.5}, 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[] = {1.000000, 13.000000};
Nd4jLong shapeExpX1[] = {1, 2, 12, 8192, -1, 99};
float buffExpX1[] = {2.000000, 14.000000};
Nd4jLong shapeExpX2[] = {3, 2, 1, 1, 12, 4, 1, 8192, -1, 99};
float buffExpX2[] = {1.000000, 13.000000};
Nd4jLong shapeExpX3[] = {2, 2, 4, 12, 1, 8192, -1, 99};
float buffExpX3[] = {9.000000, 10.000000, 11.000000, 12.000000, 21.000000, 22.000000, 23.000000, 24.000000};
Nd4jLong shapeExpX4[] = {3, 2, 1, 4, 12, 4, 1, 8192, -1, 99};
float buffExpX4[] = {9.000000, 10.000000, 11.000000, 12.000000, 21.000000, 22.000000, 23.000000, 24.000000};
Nd4jLong shapeExpX5[] = {2, 2, 3, 12, 4, 8192, -1, 99};
float buffExpX5[] = {4.000000, 8.000000, 12.000000, 16.000000, 20.000000, 24.000000};
Nd4jLong shapeExpY0[] = {1, 2, 1, 8192, -1, 99};
float buffExpY0[] = {1.000000, 2.000000};
Nd4jLong shapeExpY1[] = {1, 2, 1, 8192, -1, 99};
float buffExpY1[] = {7.000000, 8.000000};
Nd4jLong shapeExpY2[] = {3, 2, 1, 1, 1, 2, 6, 8192, -1, 102};
float buffExpY2[] = {1.000000, 2.000000};
Nd4jLong shapeExpY3[] = {2, 2, 4, 1, 6, 8192, -1, 99};
float buffExpY3[] = {5.000000, 11.000000, 17.000000, 23.000000, 6.000000, 12.000000, 18.000000, 24.000000};
Nd4jLong shapeExpY4[] = {3, 2, 1, 4, 1, 2, 6, 8192, -1, 102};
float buffExpY4[] = {5.000000, 11.000000, 17.000000, 23.000000, 6.000000, 12.000000, 18.000000, 24.000000};
Nd4jLong shapeExpY5[] = {2, 2, 3, 1, 2, 8192, -1, 99};
float buffExpY5[] = {19.000000, 21.000000, 23.000000, 20.000000, 22.000000, 24.000000};
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});
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);
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});
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);
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});
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);
}
diag->printIndexedBuffer("DIAGONAL");
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));
delete 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}, {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 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}, {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 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}, {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 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}, {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 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}, {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 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;
}
// printCudaGlobal<double><<<1,1,0,*stream>>>(dX, 6);
// printCudaGlobal<Nd4jLong><<<1,1,0,*stream>>>(dXShapeInfo, 8);
// printCudaGlobal<double><<<1,1,0,*stream>>>(dZ, 2);
// printCudaGlobal<Nd4jLong><<<1,1,0,*stream>>>(dZShapeInfo, 6);
// printCudaGlobal<int><<<1,1,0,*stream>>>(dimension, 1);
// printCudaGlobal<Nd4jLong><<<1,1,0,*stream>>>(tadShapeInfo, 6);
// printCudaGlobal<Nd4jLong><<<1,1,0,*stream>>>(tadOffsets, 2);
// cudaStreamSynchronize(*stream);
TEST_F(NDArrayCudaBasicsTests, Test_ConcatNative_1) {
auto x = NDArrayFactory::create<float>('c', {5,2}, {0,1,2,3,4,5,6,7,8,9});
NativeOps native;
auto z = NDArrayFactory::create<float>('c', {5, 8});
auto stream = x.getContext()->getCudaStream();//reinterpret_cast<cudaStream_t *>(&nativeStream);
std::vector<void*> buffers(4);
std::vector<Nd4jLong*> shapes(4);
std::vector<Nd4jLong*> hostShapes(4);
for (size_t i = 0; i < buffers.size(); i++) {
buffers[i] = x.specialBuffer();
shapes[i] = x.specialShapeInfo();
hostShapes[i] = x.shapeInfo();
}
Nd4jPointer extra[2];
extra[1] = *stream;
native.concat(extra, 1, 4, nullptr, (Nd4jPointer*)hostShapes.data(), (Nd4jPointer*)buffers.data(), (Nd4jPointer*)shapes.data(), nullptr, z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr, nullptr);
z.syncToHost();
z.printIndexedBuffer("Concat result");
z.printBuffer("C Concat result linear");
}
TEST_F(NDArrayCudaBasicsTests, Test_ConcatNative_2) {
auto x = NDArrayFactory::create<float>('c', {5,2}, {0,1,2,3,4,5,6,7,8,9});
NativeOps native;
auto z = NDArrayFactory::create<float>('f', {5, 8});
auto stream = x.getContext()->getCudaStream();//reinterpret_cast<cudaStream_t *>(&nativeStream);
std::vector<void*> buffers(4);
std::vector<Nd4jLong*> shapes(4);
std::vector<Nd4jLong*> hostShapes(4);
for (size_t i = 0; i < buffers.size(); i++) {
buffers[i] = x.specialBuffer();
shapes[i] = x.specialShapeInfo();
hostShapes[i] = x.shapeInfo();
}
Nd4jPointer extra[2];
extra[1] = *stream;
native.concat(extra, 1, 4, nullptr, (Nd4jPointer*)hostShapes.data(), (Nd4jPointer*)buffers.data(), (Nd4jPointer*)shapes.data(), nullptr, z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr, nullptr);
z.syncToHost();
z.printIndexedBuffer("Concat result");
z.printBuffer("F Concat result linear");
}
TEST_F(NDArrayCudaBasicsTests, Test_ConcatNative_3) {
auto x = NDArrayFactory::create<float>('c', {2,3}, {1,2,3,4,5,6});
auto y = NDArrayFactory::create<float>('c', {1,3}, {7,8,9});
NativeOps native;
auto z = NDArrayFactory::create<float>('f', {3, 3});
auto stream = x.getContext()->getCudaStream();//reinterpret_cast<cudaStream_t *>(&nativeStream);
std::vector<void*> buffers(2);
std::vector<Nd4jLong*> shapes(2);
std::vector<Nd4jLong*> hostShapes(2);
//for (size_t i = 0; i < buffers.size(); i++) {
buffers[0] = x.specialBuffer();
shapes[0] = x.specialShapeInfo();
hostShapes[0] = x.shapeInfo();
buffers[1] = y.specialBuffer();
shapes[1] = y.specialShapeInfo();
hostShapes[1] = y.shapeInfo();
//}
Nd4jPointer extra[2];
extra[1] = *stream;
native.concat(extra, 0, 2, nullptr, (Nd4jPointer*)hostShapes.data(), (Nd4jPointer*)buffers.data(), (Nd4jPointer*)shapes.data(), nullptr, z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr, nullptr);
z.syncToHost();
z.printIndexedBuffer("Concat result");
z.printBuffer("F Concat result linear");
}
TEST_F(NDArrayCudaBasicsTests, Test_ConcatNative_4) {
auto x = NDArrayFactory::create<float>('c', {2,3}, {1,2,3,4,5,6});
auto y = NDArrayFactory::create<float>('c', {1,3}, {7,8,9});
NativeOps native;
auto z = NDArrayFactory::create<float>('c', {3, 3});
auto stream = x.getContext()->getCudaStream();//reinterpret_cast<cudaStream_t *>(&nativeStream);
std::vector<void*> buffers(2);
std::vector<Nd4jLong*> shapes(2);
std::vector<Nd4jLong*> hostShapes(2);
//for (size_t i = 0; i < buffers.size(); i++) {
buffers[0] = x.specialBuffer();
shapes[0] = x.specialShapeInfo();
hostShapes[0] = x.shapeInfo();
buffers[1] = y.specialBuffer();
shapes[1] = y.specialShapeInfo();
hostShapes[1] = y.shapeInfo();
//}
Nd4jPointer extra[2];
extra[1] = *stream;
native.concat(extra, 0, 2, nullptr, (Nd4jPointer*)hostShapes.data(), (Nd4jPointer*)buffers.data(), (Nd4jPointer*)shapes.data(), nullptr, z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr, nullptr);
z.syncToHost();
z.printIndexedBuffer("Concat result");
z.printBuffer("C Concat result linear");
}
TEST_F(NDArrayCudaBasicsTests, Test_ConcatNative_5) {
auto x = NDArrayFactory::create<float>('c', {1,2,3}, {1,2,3,4,5,6});
auto y = NDArrayFactory::create<float>('c', {1,2,3}, {7,8,9,10,11, 12});
NativeOps native;
auto z = NDArrayFactory::create<float>('c', {2, 2, 3});
auto stream = x.getContext()->getCudaStream();//reinterpret_cast<cudaStream_t *>(&nativeStream);
std::vector<void*> buffers(2);
std::vector<Nd4jLong*> shapes(2);
std::vector<Nd4jLong*> hostShapes(2);
//for (size_t i = 0; i < buffers.size(); i++) {
buffers[0] = x.specialBuffer();
shapes[0] = x.specialShapeInfo();
hostShapes[0] = x.shapeInfo();
buffers[1] = y.specialBuffer();
shapes[1] = y.specialShapeInfo();
hostShapes[1] = y.shapeInfo();
//}
Nd4jPointer extra[2];
extra[1] = *stream;
native.concat(extra, 0, 2, nullptr, (Nd4jPointer*)hostShapes.data(), (Nd4jPointer*)buffers.data(), (Nd4jPointer*)shapes.data(), nullptr, z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr, nullptr);
z.syncToHost();
z.printIndexedBuffer("Concat result");
z.printBuffer("C Concat result linear");
}
TEST_F(NDArrayCudaBasicsTests, Test_ConcatNative_6) {
auto x1 = NDArrayFactory::create<float>('c', {2,2,3}, {1,2,3,4,5,6,7,8, 9, 10,11,12});
auto x2 = NDArrayFactory::create<float>('c', {1,2,3}, {13,14,15,16,17, 18});
auto x3 = NDArrayFactory::create<float>('c', {1,2,3}, {19,20,21,22,23, 24});
NativeOps native;
auto z = NDArrayFactory::create<float>('c', {4, 2, 3});
auto stream = x1.getContext()->getCudaStream();//reinterpret_cast<cudaStream_t *>(&nativeStream);
std::vector<void*> buffers(3);
std::vector<Nd4jLong*> shapes(3);
std::vector<Nd4jLong*> hostShapes(3);
//for (size_t i = 0; i < buffers.size(); i++) {
buffers[0] = x1.specialBuffer();
shapes[0] = x1.specialShapeInfo();
hostShapes[0] = x1.shapeInfo();
buffers[1] = x2.specialBuffer();
shapes[1] = x2.specialShapeInfo();
hostShapes[1] = x2.shapeInfo();
buffers[2] = x3.specialBuffer();
shapes[2] = x3.specialShapeInfo();
hostShapes[2] = x3.shapeInfo();
//}
printf("The third array is %p\n", buffers[2]);
Nd4jPointer extra[2];
extra[1] = *stream;
native.concat(extra, 0, 3, nullptr, (Nd4jPointer*)hostShapes.data(), (Nd4jPointer*)buffers.data(), (Nd4jPointer*)shapes.data(), nullptr, z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr, nullptr);
z.syncToHost();
z.printIndexedBuffer("Concat result");
z.printBuffer("C Concat3D result linear");
}
TEST_F(NDArrayCudaBasicsTests, Test_ConcatNative_7) {
auto x1 = NDArrayFactory::create<float>(1);
auto x2 = NDArrayFactory::create<float>(2);
auto x3 = NDArrayFactory::create<float>(3);
NativeOps native;
auto z = NDArrayFactory::create<float>('c', {3}, {1,2,3});
auto stream = x1.getContext()->getCudaStream();//reinterpret_cast<cudaStream_t *>(&nativeStream);
std::vector<void*> buffers(3);
std::vector<Nd4jLong*> shapes(3);
std::vector<Nd4jLong*> hostShapes(3);
//for (size_t i = 0; i < buffers.size(); i++) {
buffers[0] = x1.specialBuffer();
shapes[0] = x1.specialShapeInfo();
hostShapes[0] = x1.shapeInfo();
buffers[1] = x2.specialBuffer();
shapes[1] = x2.specialShapeInfo();
hostShapes[1] = x2.shapeInfo();
buffers[2] = x3.specialBuffer();
shapes[2] = x3.specialShapeInfo();
hostShapes[2] = x3.shapeInfo();
//}
printf("The third array is %p\n", buffers[2]);
Nd4jPointer extra[2];
extra[1] = *stream;
native.concat(extra, 0, 3, nullptr, (Nd4jPointer*)hostShapes.data(), (Nd4jPointer*)buffers.data(), (Nd4jPointer*)shapes.data(), nullptr, z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr, nullptr);
z.syncToHost();
z.printIndexedBuffer("Concat result");
z.printBuffer("C Concat scalar result linear");
}
TEST_F(NDArrayCudaBasicsTests, Test_ConcatNative_8) {
// public void testLargeConcat() {
// val list = new ArrayList<INDArray>();
//
// for (int e = 0; e < 100000; e++)
// list.add(Nd4j.create(1, 300));
//
// val result = Nd4j.concat(0, list.toArray(new INDArray[list.size()]));
// }
auto totalCount = 1000;
auto width = 300;
std::vector<NDArray> lx;//(totalCount);
for (int i = 0; i < totalCount; i++) {
lx.emplace_back(NDArrayFactory::create<float>('c', {1, width}));
lx[i].assign(i);
}
NativeOps native;
auto z = NDArrayFactory::create<float>('c', {totalCount, width});
auto stream = nd4j::LaunchContext ::defaultContext()->getCudaStream();//reinterpret_cast<cudaStream_t *>(&nativeStream);
std::vector<void*> buffers(totalCount);
std::vector<Nd4jLong*> shapes(totalCount);
std::vector<Nd4jLong*> hostShapes(totalCount);
for (size_t i = 0; i < lx.size(); i++) {
buffers[i] = lx[i].specialBuffer();
shapes[i] = lx[i].specialShapeInfo();
hostShapes[i] = lx[i].shapeInfo();
}
printf("The third array is %p\n", buffers[2]);
Nd4jPointer extra[2];
extra[1] = *stream;
native.concat(extra, 0, totalCount, nullptr, (Nd4jPointer*)hostShapes.data(), (Nd4jPointer*)buffers.data(), (Nd4jPointer*)shapes.data(), nullptr, z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr, nullptr);
z.syncToHost();
nd4j_printf("%f %f %f\n", z.e<float>(0), z.e<float>(width * totalCount / 2), z.e<float>(width * (totalCount - 1)));
//z.printIndexedBuffer("Concat result");
//z.printBuffer("C Concat scalar result linear");
}
TEST_F(NDArrayCudaBasicsTests, TestTear_1) {
auto input = NDArrayFactory::create<float>('c', {1, 10, 10});
std::vector<NDArray> arrays; // = {NDArrayFactory::create<float>('c', {1, 10, 10}), NDArrayFactory::create<float>('c', {1, 10, 10}), NDArrayFactory::create<float>('c', {1, 10, 10}), NDArrayFactory::create<float>('c', {1, 10, 10}), NDArrayFactory::create<float>('c', {1, 10, 10})};
int total = 151;
for (int e = 0; e < total; e++) {
input.assign(e);
arrays.emplace_back(input);
}
auto z = NDArrayFactory::create<float>('c', {total, 10, 10});
NativeOps native;
auto stream = input.getContext()->getCudaStream();//reinterpret_cast<cudaStream_t *>(&nativeStream);
Nd4jPointer extra[2];
extra[1] = *stream;
std::vector<void*> buffers(total);
std::vector<Nd4jLong*> shapes(total);
std::vector<Nd4jLong*> hostShapes(total);
for (size_t i = 0; i < buffers.size(); i++) {
buffers[i] = arrays[i].specialBuffer();
shapes[i] = arrays[i].specialShapeInfo();
hostShapes[i] = arrays[i].shapeInfo();
}
native.concat(extra, 0, total, nullptr, (Nd4jPointer*)hostShapes.data(), (Nd4jPointer*)buffers.data(), (Nd4jPointer*)shapes.data(), nullptr, z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr, nullptr);
nd4j::ops::tear op;
auto result = op.execute({&z}, {}, {1, 2});
//ASSERT_EQ(10, result->size());
auto e = result->size() - 1;
//for (size_t e = 0; e < result->size(); e++) {
// arrays[e].printIndexedBuffer("Input list at 40");
// result->at(e)->printIndexedBuffer("OUtput TEAR at 40");
//}
// ASSERT_TRUE(tads->at(e)->equalsTo(result->at(e)));
delete result;
// delete tads;
}
TEST_F(NDArrayCudaBasicsTests, TestTear_2) {
auto input = NDArrayFactory::create<float>('c', {1, 10, 10});
std::vector<NDArray> arrays; // = {NDArrayFactory::create<float>('c', {1, 10, 10}), NDArrayFactory::create<float>('c', {1, 10, 10}), NDArrayFactory::create<float>('c', {1, 10, 10}), NDArrayFactory::create<float>('c', {1, 10, 10}), NDArrayFactory::create<float>('c', {1, 10, 10})};
for (int e = 0; e < 10; e++) {
input.assign(e);
arrays.emplace_back(input);
}
auto z = NDArrayFactory::create<float>('c', {10, 10, 10});
NativeOps native;
auto stream = input.getContext()->getCudaStream();//reinterpret_cast<cudaStream_t *>(&nativeStream);
Nd4jPointer extra[2];
extra[1] = *stream;
std::vector<void*> buffers(10);
std::vector<Nd4jLong*> shapes(10);
std::vector<Nd4jLong*> hostShapes(10);
for (size_t i = 0; i < buffers.size(); i++) {
buffers[i] = arrays[i].specialBuffer();
shapes[i] = arrays[i].specialShapeInfo();
hostShapes[i] = arrays[i].shapeInfo();
}
std::vector<int> dimsToExclude({1,2});
native.concat(extra, 0, 10, nullptr, (Nd4jPointer*)hostShapes.data(), (Nd4jPointer*)buffers.data(), (Nd4jPointer*)shapes.data(), nullptr, z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr, nullptr);
// z.syncToHost();
// z.printBuffer("Pile OK");
// z.printIndexedBuffer("Pile 10x10");
// z.printIndexedBuffer("Pile 10x10");
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dimsToExclude);
//std::vector<void*> arraysData(arrays.size());
Nd4jPointer* arraysData;
cudaError_t err = cudaMalloc(&arraysData, arrays.size() * sizeof(void*));
if (err != 0) {
printf("Cannot allocate device memory for targets due error %d\n", err);
ASSERT_TRUE(false);
}
for (size_t i = 0; i < arrays.size(); i++) {
Nd4jPointer target = arrays[i].specialBuffer();
cudaMemcpy(&arraysData[i], &target, sizeof(Nd4jPointer), cudaMemcpyHostToDevice);
}
native.tear(extra, z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(), arraysData, input.specialShapeInfo(), packX.specialShapeInfo(), packX.specialOffsets());
// auto result = op.execute({&z}, {}, {1, 2});
// nd4j_printf("Result count is %lu\n", result->size());
//ASSERT_EQ(10, result->size());
err = cudaFree(arraysData);
if (err != 0) {
printf("Cannot deallocate device memory for targets due error %d\n", err);
ASSERT_TRUE(false);
}
for (size_t e = 0; e < arrays.size(); e++) {
arrays[e].syncToHost();
arrays[e].printBuffer("Output list at");
//result->at(e)->printBuffer("OUtput TEAR at");
}
// ASSERT_TRUE(tads->at(e)->equalsTo(result->at(e)));
// delete result;
// delete tads;
}
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