/******************************************************************************* * 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 #include #include #include #include #include #include #include #include #include #include using namespace sd; using namespace sd::graph; class NDArrayCudaBasicsTests : public testing::Test { public: }; ////////////////////////////////////////////////////////////////////////// static cudaError_t allocateDeviceMem(LaunchContext& lc, std::vector& devicePtrs, const std::vector>& 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(&reductionPointer), 1024*1024); if(cudaResult != 0) return cudaResult; int* allocationPointer; cudaResult = cudaMalloc(reinterpret_cast(&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(&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('c', {5}, {1, 2, 3, 4, 5}); auto y = NDArrayFactory::create('c', {5}, {5, 4, 3, 2, 1}); ASSERT_TRUE(x.isActualOnDeviceSide()); ASSERT_FALSE(x.isActualOnHostSide()); } TEST_F(NDArrayCudaBasicsTests, Test_Registration_2) { auto x = NDArrayFactory::create('c', {5}); auto y = NDArrayFactory::create('c', {5}); ASSERT_TRUE(x.isActualOnDeviceSide()); ASSERT_FALSE(x.isActualOnHostSide()); } TEST_F(NDArrayCudaBasicsTests, Test_Registration_3) { auto x = NDArrayFactory::create('c', {5}, {1, 2, 3, 4, 5}); auto y = NDArrayFactory::create('c', {5}, {5, 4, 3, 2, 1}); ASSERT_TRUE(x.isActualOnDeviceSide()); ASSERT_FALSE(x.isActualOnHostSide()); NDArray::registerSpecialUse({&x}, {&y}); ASSERT_TRUE(x.isActualOnDeviceSide()); ASSERT_FALSE(x.isActualOnHostSide()); ASSERT_TRUE(y.isActualOnDeviceSide()); ASSERT_FALSE(y.isActualOnHostSide()); } TEST_F(NDArrayCudaBasicsTests, Test_Registration_01) { auto x = NDArrayFactory::create_('c', {5}, {1, 2, 3, 4, 5}); auto y = NDArrayFactory::create_('c', {5}, {5, 4, 3, 2, 1}); ASSERT_TRUE(x->isActualOnDeviceSide()); ASSERT_FALSE(x->isActualOnHostSide()); delete x; delete y; } TEST_F(NDArrayCudaBasicsTests, Test_Registration_02) { auto x = NDArrayFactory::create_('c', {5}); auto y = NDArrayFactory::create_('c', {5}); ASSERT_TRUE(x->isActualOnDeviceSide()); ASSERT_FALSE(x->isActualOnHostSide()); delete x; delete y; } TEST_F(NDArrayCudaBasicsTests, Test_Registration_03) { auto x = NDArrayFactory::create_('c', {5}, {1, 2, 3, 4, 5}); auto y = NDArrayFactory::create_('c', {5}, {5, 4, 3, 2, 1}); ASSERT_TRUE(x->isActualOnDeviceSide()); ASSERT_FALSE(x->isActualOnHostSide()); NDArray::registerSpecialUse({y}, {x}); x->applyTransform(transform::Neg, *y); //ASSERT_TRUE(x->isActualOnDeviceSide()); //ASSERT_FALSE(x->isActualOnHostSide()); //ASSERT_TRUE(y->isActualOnDeviceSide()); //ASSERT_TRUE(y->isActualOnHostSide()); //y->syncToHost(); // y->printBuffer("Negatives"); delete x; delete y; } TEST_F(NDArrayCudaBasicsTests, Test_Cosine_1) { auto x = NDArrayFactory::create_('c', {5}, {1, 2, 3, 4, 5}); auto y = NDArrayFactory::create_('c', {5}, {5, 4, 3, 2, 1}); ASSERT_TRUE(x->isActualOnDeviceSide()); ASSERT_FALSE(x->isActualOnHostSide()); NDArray::registerSpecialUse({y}, {x}); x->applyTransform(transform::Cosine, *y); //ASSERT_TRUE(x->isActualOnDeviceSide()); //ASSERT_FALSE(x->isActualOnHostSide()); //ASSERT_TRUE(y->isActualOnDeviceSide()); //ASSERT_TRUE(y->isActualOnHostSide()); //y->syncToHost(); delete x; delete y; } ////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, TestAdd_1) { // allocating host-side arrays auto x = NDArrayFactory::create('c', { 5 }, { 1, 2, 3, 4, 5}); auto y = NDArrayFactory::create('c', { 5 }, { 1, 2, 3, 4, 5}); auto z = NDArrayFactory::create('c', { 5 }, {10, 10, 10, 10, 10}); auto exp = NDArrayFactory::create('c', { 5 }, { 2, 4, 6, 8, 10 }); // making raw buffers //Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX; //cudaError_t res = cudaMalloc(reinterpret_cast(&devBufferPtrX), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&devBufferPtrZ), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&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(&nativeStream)); auto stream = reinterpret_cast(&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(e), z.e(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 }, sd::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(&nativeStream)); auto stream = reinterpret_cast(&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(e), z.e(e), 1e-5); } ////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, TestAdd_3) { // allocating host-side arrays auto x = NDArrayFactory::create('c', { 5 }, { 1, 2, 3, 4, 5}); auto y = NDArrayFactory::create('c', { 5 }, { 1, 2, 3, 4, 5}); auto z = NDArrayFactory::create('c', { 5 }, {10, 10, 10, 10, 10}); auto exp = NDArrayFactory::create('c', { 5 }, { 2, 4, 6, 8, 10 }); // making raw buffers //Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX; //cudaError_t res = cudaMalloc(reinterpret_cast(&devBufferPtrX), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&devBufferPtrZ), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&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(&nativeStream)); auto stream = reinterpret_cast(&nativeStream); //cudaMemcpyAsync(devBufferPtrX, x.buffer(), x.lengthOf() * x.sizeOfT(), cudaMemcpyHostToDevice, *stream); //cudaMemcpyAsync(devShapePtrX, x.shapeInfo(), shape::shapeInfoByteLength(x.shapeInfo()), cudaMemcpyHostToDevice, *stream); LaunchContext lc(stream, *stream, nullptr, nullptr); NativeOpExecutioner::execPairwiseTransform(&lc, pairwise::Add, x.buffer(), x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo(), y.buffer(), y.shapeInfo(), y.specialBuffer(), y.specialShapeInfo(), z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr); z.tickWriteDevice(); auto res = cudaStreamSynchronize(*stream); ASSERT_EQ(0, res); //double* localBuffer = ; z.syncToHost(); cudaMemcpy(z.buffer(), z.specialBuffer(), z.lengthOf() * z.sizeOfT(), cudaMemcpyDeviceToHost); res = cudaStreamSynchronize(*stream); z.tickWriteHost(); ASSERT_EQ(0, res); // // cudaFree(devBufferPtrX); //cudaFree(devBufferPtrZ); //cudaFree(devShapePtrX); for (int e = 0; e < z.lengthOf(); e++) { ASSERT_NEAR(exp.e(e), z.e(e), 1e-5); } } ////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, TestAdd_4) { // allocating host-side arrays auto x = NDArrayFactory::create('c', { 5 }, { 1, 2, 3, 4, 5}); auto y = NDArrayFactory::create('c', { 5 }, { 1, 2, 3, 4, 5}); auto z = NDArrayFactory::create('c', { 5 }); auto exp = NDArrayFactory::create('c', { 5 }, { 2, 4, 6, 8, 10 }); // making raw buffers //Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX; //cudaError_t res = cudaMalloc(reinterpret_cast(&devBufferPtrX), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&devBufferPtrZ), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&devShapePtrX), shape::shapeInfoByteLength(x.shapeInfo())); //ASSERT_EQ(0, res); x.applyPairwiseTransform(pairwise::Add, y, z); // // cudaFree(devBufferPtrX); //cudaFree(devBufferPtrZ); //cudaFree(devShapePtrX); for (int e = 0; e < z.lengthOf(); e++) { ASSERT_NEAR(exp.e(e), z.e(e), 1e-5); } } ////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, TestAdd_5) { // allocating host-side arrays auto x = NDArrayFactory::create('c', { 5 }, { 1, 2, 3, 4, 5}); auto y = NDArrayFactory::create('c', { 5 }, { 1, 2, 3, 4, 5}); //auto z = NDArrayFactory::create('c', { 5 }); auto exp = NDArrayFactory::create('c', { 5 }, { 2, 4, 6, 8, 10 }); // making raw buffers //Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX; //cudaError_t res = cudaMalloc(reinterpret_cast(&devBufferPtrX), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&devBufferPtrZ), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&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(e), x.e(e), 1e-5); } } ////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, TestAdd_6) { // allocating host-side arrays auto x = NDArrayFactory::create('c', { 5 }, { 1, 2, 3, 4, 5}); auto y = NDArrayFactory::create(2); //.'c', { 5 }, { 1, 2, 3, 4, 5}); //auto z = NDArrayFactory::create('c', { 5 }); auto exp = NDArrayFactory::create('c', { 5 }, { 3, 4, 5, 6, 7 }); // making raw buffers //Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX; //cudaError_t res = cudaMalloc(reinterpret_cast(&devBufferPtrX), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&devBufferPtrZ), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&devShapePtrX), shape::shapeInfoByteLength(x.shapeInfo())); //ASSERT_EQ(0, res); x += y; //x.applyPairwiseTransform(pairwise::Add, &y, &z, nullptr); x.syncToHost(); // cudaFree(devBufferPtrX); //cudaFree(devBufferPtrZ); //cudaFree(devShapePtrX); for (int e = 0; e < x.lengthOf(); e++) { ASSERT_NEAR(exp.e(e), x.e(e), 1e-5); } } ////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, TestAdd_7) { // allocating host-side arrays auto x = NDArrayFactory::create('c', { 5 }, { 1, 2, 3, 4, 5}); //auto y = NDArrayFactory::create(2); //.'c', { 5 }, { 1, 2, 3, 4, 5}); //auto z = NDArrayFactory::create('c', { 5 }); auto exp = NDArrayFactory::create('c', { 5 }, { 3, 4, 5, 6, 7 }); // making raw buffers //Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX; //cudaError_t res = cudaMalloc(reinterpret_cast(&devBufferPtrX), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&devBufferPtrZ), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&devShapePtrX), shape::shapeInfoByteLength(x.shapeInfo())); //ASSERT_EQ(0, res); x += 2.; //x.applyPairwiseTransform(pairwise::Add, &y, &z, nullptr); x.syncToHost(); // cudaFree(devBufferPtrX); //cudaFree(devBufferPtrZ); //cudaFree(devShapePtrX); for (int e = 0; e < x.lengthOf(); e++) { ASSERT_NEAR(exp.e(e), x.e(e), 1e-5); } } ////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, TestMultiply_1) { // allocating host-side arrays auto x = NDArrayFactory::create('c', { 5 }, { 1, 2, 3, 4, 5}); auto y = NDArrayFactory::create('c', { 5 }, { 1, 2, 3, 4, 5}); auto z = NDArrayFactory::create('c', { 5 }); auto exp = NDArrayFactory::create('c', { 5 }, { 1, 4, 9, 16, 25 }); // making raw buffers //Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX; //cudaError_t res = cudaMalloc(reinterpret_cast(&devBufferPtrX), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&devBufferPtrZ), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&devShapePtrX), shape::shapeInfoByteLength(x.shapeInfo())); //ASSERT_EQ(0, res); x.applyPairwiseTransform(pairwise::Multiply, y, z); // x.printBuffer("3X = "); // y.printBuffer("3Y = "); // z.printBuffer("3Result out"); // // cudaFree(devBufferPtrX); //cudaFree(devBufferPtrZ); //cudaFree(devShapePtrX); for (int e = 0; e < z.lengthOf(); e++) { ASSERT_NEAR(exp.e(e), z.e(e), 1e-5); } } ////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, TestMultiply_2) { // allocating host-side arrays auto x = NDArrayFactory::create('c', { 5 }, { 1, 2, 3, 4, 5}); auto y = NDArrayFactory::create('c', { 5 }, { 1, 2, 3, 4, 5}); NDArray z('c', { 5 }, sd::DataType::DOUBLE); auto exp = NDArrayFactory::create('c', { 5 }, { 1, 4, 9, 16, 25 }); // making raw buffers //Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX; //cudaError_t res = cudaMalloc(reinterpret_cast(&devBufferPtrX), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&devBufferPtrZ), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&devShapePtrX), shape::shapeInfoByteLength(x.shapeInfo())); //ASSERT_EQ(0, res); x.applyPairwiseTransform(pairwise::Multiply, y, z); // // cudaFree(devBufferPtrX); //cudaFree(devBufferPtrZ); //cudaFree(devShapePtrX); for (int e = 0; e < z.lengthOf(); e++) { ASSERT_NEAR(exp.e(e), z.e(e), 1e-5); } } ////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, TestMultiply_3) { // allocating host-side arrays NDArray x('c', { 5 }, { 1, 2, 3, 4, 5}, sd::DataType::DOUBLE); NDArray y('c', { 5 }, { 1., 2., 3., 4., 5.}, sd::DataType::DOUBLE); auto z = NDArrayFactory::create('c', { 5 }); auto exp = NDArrayFactory::create('c', { 5 }, { 1, 4, 9, 16, 25 }); // making raw buffers //Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX; //cudaError_t res = cudaMalloc(reinterpret_cast(&devBufferPtrX), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&devBufferPtrZ), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&devShapePtrX), shape::shapeInfoByteLength(x.shapeInfo())); //ASSERT_EQ(0, res); x.applyPairwiseTransform(pairwise::Multiply, y, z); //x.printBuffer("23X = "); //y.printBuffer("23Y = "); // z.printBuffer("23Result out"); // // cudaFree(devBufferPtrX); //cudaFree(devBufferPtrZ); //cudaFree(devShapePtrX); for (int e = 0; e < z.lengthOf(); e++) { ASSERT_NEAR(exp.e(e), z.e(e), 1e-5); } } ////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, TestMultiply_4) { // allocating host-side arrays NDArray x('c', { 5 }, { 1, 2, 3, 4, 5}, sd::DataType::DOUBLE); NDArray y('c', { 5 }, { 1., 2., 3., 4., 5.}, sd::DataType::DOUBLE); //auto z = NDArrayFactory::create('c', { 5 }); auto exp = NDArrayFactory::create('c', { 5 }, { 1, 4, 9, 16, 25 }); // making raw buffers //Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX; //cudaError_t res = cudaMalloc(reinterpret_cast(&devBufferPtrX), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&devBufferPtrZ), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&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(e), x.e(e), 1e-5); } } ////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, TestPrimitiveNeg_01) { // allocating host-side arrays auto x = NDArrayFactory::create('c', { 5 }, { 1, 2, 3, 4, 5}); auto y = NDArrayFactory::create('c', { 5 }, { 1, 2, 3, 4, 5}); auto exp = NDArrayFactory::create('c', { 5 }, { -1, -2, -3, -4, -5 }); auto stream = x.getContext()->getCudaStream();//reinterpret_cast(&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(e), y.e(e), 1e-5); } } TEST_F(NDArrayCudaBasicsTests, Test_PrimitiveNeg_2) { auto x = NDArrayFactory::create('c', {5}, {1, 2, 3, 4, 5}); auto y = NDArrayFactory::create('c', {5}); ASSERT_TRUE(x.isActualOnDeviceSide()); ASSERT_FALSE(x.isActualOnHostSide()); x.applyTransform(transform::Neg, y); //ASSERT_TRUE(x->isActualOnDeviceSide()); //ASSERT_FALSE(x->isActualOnHostSide()); //ASSERT_TRUE(y->isActualOnDeviceSide()); //ASSERT_TRUE(y->isActualOnHostSide()); //auto res = cudaStreamSynchronize(*y.getContext()->getCudaStream()); //ASSERT_EQ(0, res); // y.printBuffer("Negatives2"); //delete x; //delete y; } TEST_F(NDArrayCudaBasicsTests, Test_PrimitiveSqrt_1) { // strict auto x = NDArrayFactory::create('c', {5}, {1, 2, 3, 4, 5}); auto y = NDArrayFactory::create('c', {5}); auto exp = NDArrayFactory::create({1.000000, 1.414214, 1.732051, 2.000000, 2.236068}); ASSERT_TRUE(x.isActualOnDeviceSide()); ASSERT_FALSE(x.isActualOnHostSide()); x.applyTransform(transform::Sqrt, y); //ASSERT_TRUE(x->isActualOnDeviceSide()); //ASSERT_FALSE(x->isActualOnHostSide()); //ASSERT_TRUE(y->isActualOnDeviceSide()); //ASSERT_TRUE(y->isActualOnHostSide()); //auto res = cudaStreamSynchronize(*y.getContext()->getCudaStream()); //ASSERT_EQ(0, res); ASSERT_TRUE(y.equalsTo(exp)); //y.printBuffer("SQRT output"); //delete x; //delete y; } TEST_F(NDArrayCudaBasicsTests, Test_PrimitiveAssign_1) { // strict auto x = NDArrayFactory::create('c', {5}, {1, 2, 3, 4, 5}); auto y = NDArrayFactory::create('c', {5}); //auto exp = NDArrayFactory::create({1.000000, 1.414214, 1.732051, 2.000000, 2.236068}); //ASSERT_TRUE(x.isActualOnDeviceSide()); //ASSERT_TRUE(x.isActualOnHostSide()); x.applyTransform(transform::Assign, y); //ASSERT_TRUE(x->isActualOnDeviceSide()); //ASSERT_FALSE(x->isActualOnHostSide()); //ASSERT_TRUE(y->isActualOnDeviceSide()); //ASSERT_TRUE(y->isActualOnHostSide()); //auto res = cudaStreamSynchronize(*y.getContext()->getCudaStream()); //ASSERT_EQ(0, res); // printf("Assigned to another array\n"); // y.printBuffer("OUput"); ASSERT_TRUE(y.equalsTo(x)); //y.syncToHost(); //y.printBuffer("IsMax output"); //delete x; //delete y; } TEST_F(NDArrayCudaBasicsTests, Test_PrimitiveCosine_1) { // strict auto x = NDArrayFactory::create('c', {5}, {1, 2, 3, 4, 5}); auto y = NDArrayFactory::create('c', {5}); auto exp = NDArrayFactory::create('c', {5}, {0.540302, -0.416147, -0.989992, -0.653644, 0.283662}); ASSERT_TRUE(x.isActualOnDeviceSide()); ASSERT_FALSE(x.isActualOnHostSide()); x.applyTransform(transform::Cosine, y); //ASSERT_TRUE(x->isActualOnDeviceSide()); //ASSERT_FALSE(x->isActualOnHostSide()); //ASSERT_TRUE(y->isActualOnDeviceSide()); //ASSERT_TRUE(y->isActualOnHostSide()); //auto res = cudaStreamSynchronize(*y.getContext()->getCudaStream()); //ASSERT_EQ(0, res); ASSERT_TRUE(exp.isSameShape(y)); ASSERT_TRUE(exp.dataType() == y.dataType()); //y.printBuffer("Cosine2"); //delete x; //delete y; } TEST_F(NDArrayCudaBasicsTests, Test_PrimitiveCosine_2) { auto x = NDArrayFactory::create('c', {5}, {1, 2, 3, 4, 5}); auto y = NDArrayFactory::create('c', {5}); auto exp = NDArrayFactory::create('c', {5}, {0.540302, -0.416147, -0.989992, -0.653644, 0.283662}); ASSERT_TRUE(x.isActualOnDeviceSide()); ASSERT_FALSE(x.isActualOnHostSide()); x.applyTransform(transform::Cosine, y); //ASSERT_TRUE(x->isActualOnDeviceSide()); //ASSERT_FALSE(x->isActualOnHostSide()); //ASSERT_TRUE(y->isActualOnDeviceSide()); //ASSERT_TRUE(y->isActualOnHostSide()); //auto res = cudaStreamSynchronize(*y.getContext()->getCudaStream()); //ASSERT_EQ(0, res); //exp.syncToHost(); //y.printBuffer("PrimitiveCosine2"); //exp.printBuffer("Primitive Cosine exp"); ASSERT_TRUE(exp.isSameShape(y)); ASSERT_TRUE(exp.dataType() == y.dataType()); //for (int e = 0; e < y.lengthOf(); e++) { // ASSERT_NEAR(exp.e(e), y.e(e), 1e-5); //} ASSERT_TRUE(exp.equalsTo(y)); //delete x; //delete y; } TEST_F(NDArrayCudaBasicsTests, Test_PrimitiveCosine_3) { auto x = NDArrayFactory::create('c', {5}, {1, 2, 3, 4, 5}); auto y = NDArrayFactory::create('c', {5}); auto exp = NDArrayFactory::create({0.540302, -0.416147, -0.989992, -0.653644, 0.283662}); ASSERT_TRUE(x.isActualOnDeviceSide()); ASSERT_FALSE(x.isActualOnHostSide()); x.applyTransform(transform::Cosine, y); //ASSERT_TRUE(x->isActualOnDeviceSide()); //ASSERT_FALSE(x->isActualOnHostSide()); //ASSERT_TRUE(y->isActualOnDeviceSide()); //ASSERT_TRUE(y->isActualOnHostSide()); //auto res = cudaStreamSynchronize(*y.getContext()->getCudaStream()); //ASSERT_EQ(0, res); //exp.syncToHost(); // y.printBuffer("PrimitiveCosine3"); // exp.printBuffer("Primitive Cosine3 exp"); // y.printShapeInfo("Y shape"); // exp.printShapeInfo("Exp Shape"); ASSERT_TRUE(exp.isSameShape(y)); // // for (int e = 0; e < y.lengthOf(); e++) { // printf("%lf == %lf\n", exp.e(e), y.e(e)); //// ASSERT_NEAR(exp.e(e), y.e(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('c', {2,3,4}); NDArray y('c', {2,4}, {10,20,30,40,50,60,70,80}, sd::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}, sd::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}, sd::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.}, sd::DataType::DOUBLE); x.linspace(1); x.syncToDevice(); std::vector dimensions = {0,2}; // evaluate xTad data shape::TAD xTad; xTad.init(x.shapeInfo(), dimensions.data(), dimensions.size()); xTad.createTadOnlyShapeInfo(); xTad.createOffsets(); // prepare input arrays for prepareDataForCuda function std::vector> 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 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, sd::broadcast::Multiply, nullptr, x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, y.shapeInfo(), y.specialBuffer(), y.specialShapeInfo(), nullptr, z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(), (int*)devicePtrs[0], dimensions.size(), (Nd4jLong*)devicePtrs[1], (Nd4jLong*)devicePtrs[2], nullptr, nullptr); cudaResult = cudaStreamSynchronize(stream); ASSERT_EQ(0, cudaResult); z.tickWriteDevice(); // verify results for (int e = 0; e < z.lengthOf(); e++) ASSERT_NEAR(exp.e(e), z.e(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}, sd::DataType::DOUBLE); NDArray y('c', {2,4}, {10,20,30,40,50,60,70,80}, sd::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}, sd::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}, sd::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.}, sd::DataType::DOUBLE); x.linspace(1); x.syncToDevice(); std::vector dimensions = {0,2}; // evaluate xTad data shape::TAD xTad; xTad.init(x.shapeInfo(), dimensions.data(), dimensions.size()); xTad.createTadOnlyShapeInfo(); xTad.createOffsets(); // prepare input arrays for prepareDataForCuda function std::vector> 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 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(&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, sd::broadcast::Multiply, nullptr, x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, y.shapeInfo(), y.specialBuffer(), y.specialShapeInfo(), nullptr, z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(), (int*)devicePtrs[0], dimensions.size(), (Nd4jLong*)devicePtrs[1], (Nd4jLong*)devicePtrs[2], nullptr, nullptr); //cudaResult = cudaStreamSynchronize(stream); ASSERT_EQ(0, cudaResult); //z.syncToHost(); // verify results for (int e = 0; e < z.lengthOf(); e++) ASSERT_NEAR(exp.e(e), z.e(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}, sd::DataType::DOUBLE); NDArray y = NDArrayFactory::create(3.); //'c', { 3 }, { 2., 3., 4.}, sd::DataType::DOUBLE); //auto z = NDArrayFactory::create('c', { 5 }); auto exp = NDArrayFactory::create('c', { 2, 3 }, { 3, 6, 9, 12, 15, 18 }); // making raw buffers //Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX; //cudaError_t res = cudaMalloc(reinterpret_cast(&devBufferPtrX), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&devBufferPtrZ), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&devShapePtrX), shape::shapeInfoByteLength(x.shapeInfo())); //ASSERT_EQ(0, res); //x.applyPairwiseTransform(pairwise::Multiply, &y, &z, nullptr); x *= y; //x.syncToHost(); // // cudaFree(devBufferPtrX); //cudaFree(devBufferPtrZ); //cudaFree(devShapePtrX); ASSERT_TRUE(exp.equalsTo(x)); // for (int e = 0; e < x.lengthOf(); e++) { // ASSERT_NEAR(exp.e(e), x.e(e), 1e-5); // } } TEST_F(NDArrayCudaBasicsTests, TestBroadcastMultiply_01) { // allocating host-side arrays NDArray x('c', { 2, 3 }, { 1, 2, 3, 4, 5, 6}, sd::DataType::DOUBLE); NDArray y = NDArrayFactory::create(3.); //'c', { 3 }, { 2., 3., 4.}, sd::DataType::DOUBLE); auto z = NDArrayFactory::create('c', { 2, 3 }); auto exp = NDArrayFactory::create('c', { 2, 3 }, { 3, 6, 9, 12, 15, 18 }); // making raw buffers //Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX; //cudaError_t res = cudaMalloc(reinterpret_cast(&devBufferPtrX), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&devBufferPtrZ), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&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(e), z.e(e), 1e-5); // } } TEST_F(NDArrayCudaBasicsTests, TestBroadcastMultiply_02) { // allocating host-side arrays auto x = NDArrayFactory::create('c', { 2, 3 }, { 1, 2, 3, 4, 5, 6}); //, sd::DataType::DOUBLE); auto y = NDArrayFactory::create('c', {2,3}, {3, 3, 3, 3, 3, 3}); //'c', { 3 }, { 2., 3., 4.}, sd::DataType::DOUBLE); auto z = NDArrayFactory::create('c', { 2, 3 }); auto exp = NDArrayFactory::create('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(&devBufferPtrX), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&devBufferPtrZ), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&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(e), z.e(e), 1e-5); // } } TEST_F(NDArrayCudaBasicsTests, TestBroadcastMultiply_002) { // allocating host-side arrays auto x = NDArrayFactory::create('c', { 2, 3 }, { 1, 2, 3, 4, 5, 6}); //, sd::DataType::DOUBLE); auto y = NDArrayFactory::create('c', {2, 3}, {2., 3., 3., 3., 3., 3.}); //'c', { 3 }, { 2., 3., 4.}, sd::DataType::DOUBLE); auto z = NDArrayFactory::create('c', { 2, 3 }); auto exp = NDArrayFactory::create('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(&devBufferPtrX), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&devBufferPtrZ), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&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(e), z.e(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}, sd::DataType::INT32); NDArray y('c', {3}, {10, 20, 30}, sd::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}, sd::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}, sd::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 dimensions = {1}; // evaluate xTad data shape::TAD xTad; xTad.init(x.shapeInfo(), dimensions.data(), dimensions.size()); xTad.createTadOnlyShapeInfo(); xTad.createOffsets(); // prepare input arrays for prepareDataForCuda function std::vector> 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 devicePtrs(hostData.size(), nullptr); // create cuda stream and LaunchContext cudaError_t cudaResult; cudaStream_t* stream = x.getContext()->getCudaStream(); LaunchContext* pLc = x.getContext(); // allocate required amount of global device memory and copy host data to it //cudaResult = allocateDeviceMem(*pLc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); for(size_t i = 0; i < devicePtrs.size(); ++i) { cudaResult = cudaMalloc(&devicePtrs[i], hostData[i].second); //if(cudaResult != 0) return cudaResult; ASSERT_EQ(cudaResult, 0); cudaMemcpy(devicePtrs[i], hostData[i].first, hostData[i].second, cudaMemcpyHostToDevice); } // call cuda kernel which calculates result NativeOpExecutioner::execBroadcast(pLc, sd::broadcast::Add, nullptr, x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, y.shapeInfo(), y.specialBuffer(), y.specialShapeInfo(), nullptr, z.shapeInfo(), 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(e), z.e(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}, sd::DataType::DOUBLE); NDArray y('c', { 3 }, { 2., 3., 4.}, sd::DataType::DOUBLE); //auto z = NDArrayFactory::create('c', { 5 }); auto exp = NDArrayFactory::create('c', { 2, 3 }, { 2, 6, 12, 8, 15, 24 }); // making raw buffers //Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX; //cudaError_t res = cudaMalloc(reinterpret_cast(&devBufferPtrX), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&devBufferPtrZ), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&devShapePtrX), shape::shapeInfoByteLength(x.shapeInfo())); //ASSERT_EQ(0, res); //x.applyPairwiseTransform(pairwise::Multiply, &y, &z, nullptr); //x.printBuffer("23X = "); //y.printBuffer("23Y = "); x *= y; // // cudaFree(devBufferPtrX); //cudaFree(devBufferPtrZ); //cudaFree(devShapePtrX); //for (int e = 0; e < x.lengthOf(); e++) { // ASSERT_NEAR(exp.e(e), x.e(e), 1e-5); //} } TEST_F(NDArrayCudaBasicsTests, TestBroadcastMultiply_2) { // allocating host-side arrays NDArray x('c', { 2, 3 }, { 1, 2, 3, 4, 5, 6}, sd::DataType::DOUBLE); NDArray y('c', { 3 }, { 2., 3., 4.}, sd::DataType::DOUBLE); //auto z = NDArrayFactory::create('c', { 5 }); auto exp = NDArrayFactory::create('c', { 2, 3 }, { 11,12, 13,14, 15, 16 }); auto expZ = NDArrayFactory::create('c', { 2, 3 }, { 2, 6, 12, 8, 15, 24 }); // making raw buffers //Nd4jPointer devBufferPtrX, devBufferPtrZ, devShapePtrX; //cudaError_t res = cudaMalloc(reinterpret_cast(&devBufferPtrX), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&devBufferPtrZ), x.lengthOf() * x.sizeOfT()); //ASSERT_EQ(0, res); //res = cudaMalloc(reinterpret_cast(&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(sd::BroadcastOpsTuple op, const NDArray* other, NDArray* target, const bool checkTargetShape, ExtraArguments *extraArgs) x.applyTrueBroadcast(BroadcastOpsTuple::Multiply(), y, exp); // // cudaFree(devBufferPtrX); //cudaFree(devBufferPtrZ); //cudaFree(devShapePtrX); //for (int e = 0; e < x.lengthOf(); e++) { // ASSERT_NEAR(exp.e(e), x.e(e), 1e-5); //} ASSERT_TRUE(exp.equalsTo(expZ)); } ////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, TestReduceSum_1) { // allocating host-side arrays auto x = NDArrayFactory::create('c', { 5 }, { 1, 2, 3, 4, 5}); auto y = NDArrayFactory::create(15); auto exp = NDArrayFactory::create(15); auto stream = x.getContext()->getCudaStream();//reinterpret_cast(&nativeStream); NativeOpExecutioner::execReduceSameScalar(x.getContext(), reduce::Sum, x.buffer(), x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, y.buffer(), y.shapeInfo(), y.specialBuffer(), y.specialShapeInfo()); auto res = cudaStreamSynchronize(*stream); ASSERT_EQ(0, res); y.syncToHost(); ASSERT_NEAR(y.e(0), 15, 1e-5); } ////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, TestDup1) { NDArray array('c', {2,3}, {1,2,3,4,5,6}); auto arrC = array.dup('c'); auto arrF = array.dup('f'); // arrC->printBuffer("arrC"); // arrF->printBuffer("arrF"); //arrC->printShapeInfo("C shape"); //arrF->printShapeInfo("F shape"); ASSERT_TRUE(array.equalsTo(arrF)); ASSERT_TRUE(array.equalsTo(arrC)); ASSERT_TRUE(arrF.equalsTo(arrC)); } ////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, equalsTo_1) { NDArray x('c', {2,5}, {1,2,3,4,5,6,7,8,9,10}, sd::DataType::DOUBLE); NDArray y('c', {2,5}, {1,2,3,4,5,6,7,8,9,10}, sd::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}, sd::DataType::DOUBLE); NDArray y('c', {2,5}, {1,2,5,4,5,6,7,8,9,10}, sd::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}, sd::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}, sd::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}, sd::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}, sd::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}, sd::DataType::INT32); NDArray k('c', {2,3}, {-2,3,-4,5,-2,3}, sd::DataType::INT32); NDArray k2('c', {3,2}, {-2,3,-4,5,-2,3}, sd::DataType::INT32); NDArray exp1('c', {3}, {4.f, 20.f, 36.f}, sd::DataType::FLOAT32); NDArray exp2('c', {2,3}, {-10.f, -2.f, 6.f,14.f, 22.f, 30.f}, sd::DataType::FLOAT32); NDArray exp3('c', {4}, {38.f, 41.f, 44.f, 47.f}, sd::DataType::FLOAT32); NDArray exp4('c', {4}, {114.f, 117.f, 120.f, 123.f}, sd::DataType::FLOAT32); NDArray z = x.applyReduce3(sd::reduce3::Dot, y, {0,2}); ASSERT_TRUE(z.equalsTo(&exp1)); z = x.applyReduce3(sd::reduce3::Dot, k, {0,1}); ASSERT_TRUE(z.equalsTo(&exp3)); x.permutei({0,2,1}); y.permutei({0,2,1}); z = y.applyReduce3(sd::reduce3::Dot, x, {1}); ASSERT_TRUE(z.equalsTo(&exp2)); x2.permutei({1,0,2}); z = x2.applyReduce3(sd::reduce3::Dot, k2, {0,1}); ASSERT_TRUE(z.equalsTo(&exp4)); } //////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, applyReduce3_2) { NDArray x('c', {2,3,4}, {-10,-9,-8.5,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12,13}, sd::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}, sd::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}, sd::DataType::DOUBLE); NDArray k('c', {2,3}, {-2,3,-4,5.5,-2,3}, sd::DataType::DOUBLE); NDArray k2('c', {3,2}, {-2,3,-4,5,-2,3.5}, sd::DataType::DOUBLE); NDArray exp1('c', {3}, {5., 20., 36.}, sd::DataType::DOUBLE); NDArray exp2('c', {2,3}, {-8., -2., 6., 13., 22., 30.}, sd::DataType::DOUBLE); NDArray exp3('c', {4}, {39., 42.5, 47., 49.5}, sd::DataType::DOUBLE); NDArray exp4('c', {4}, {119., 122.5, 125., 129.5}, sd::DataType::DOUBLE); NDArray z = x.applyReduce3(sd::reduce3::Dot, y, {0,2}); ASSERT_TRUE(z.equalsTo(&exp1)); z = x.applyReduce3(sd::reduce3::Dot, k, {0,1}); ASSERT_TRUE(z.equalsTo(&exp3)); x.permutei({0,2,1}); y.permutei({0,2,1}); z = y.applyReduce3(sd::reduce3::Dot, x, {1}); ASSERT_TRUE(z.equalsTo(&exp2)); x2.permutei({1,0,2}); z = x2.applyReduce3(sd::reduce3::Dot, k2, {0,1}); ASSERT_TRUE(z.equalsTo(&exp4)); } //////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, applyReduce3_3) { NDArray x1('c', {2,2,2}, {1,2,3,4,5,6,7,8}, sd::DataType::INT32); NDArray x2('c', {2,2,2}, {-1,-2,-3,-4,-5,-6,-7,-8}, sd::DataType::INT32); NDArray x3('c', {3,2}, {1.5,1.5,1.5,1.5,1.5,1.5}, sd::DataType::DOUBLE); NDArray x4('c', {3,2}, {1,2,3,4,5,6}, sd::DataType::DOUBLE); NDArray exp1('c', {}, std::vector{-204}, sd::DataType::FLOAT32); NDArray exp2('c', {}, std::vector{31.5}, sd::DataType::DOUBLE); auto z = x1.applyReduce3(reduce3::Dot, x2); ASSERT_TRUE(z.equalsTo(&exp1)); z = x3.applyReduce3(reduce3::Dot, x4); ASSERT_TRUE(z.equalsTo(&exp2)); x1.permutei({2,1,0}); x2.permutei({2,1,0}); x3.permutei({1,0}); x4.permutei({1,0}); z = x1.applyReduce3(reduce3::Dot, x2); ASSERT_TRUE(z.equalsTo(&exp1)); z = x3.applyReduce3(reduce3::Dot, x4); ASSERT_TRUE(z.equalsTo(&exp2)); } //////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, applyAllReduce3_1) { NDArray x1('c', {2,3,2}, {1,2,3,4,5,6,7,8,-1,-2,-3,-4,}, sd::DataType::INT32); NDArray x2('c', {2,2,2}, {-1,-2,-3,-4,-5,-6,-7,-8}, sd::DataType::INT32); NDArray x3('c', {3,2}, {1.5,1.5,1.5,1.5,1.5,1.5}, sd::DataType::DOUBLE); NDArray x4('c', {3,2}, {1,2,3,4,5,6}, sd::DataType::DOUBLE); NDArray exp1('c', {3,2}, {-88.f, -124.f, 6.f, -2.f, 22.f, 14.f}, sd::DataType::FLOAT32); NDArray exp2('c', {6,4}, {-36.f, -44.f, -52.f, -60.f,-42.f, -52.f, -62.f, -72.f, 2.f, 0.f, -2.f, -4.f, 6.f, 4.f, 2.f, 0.f, 10.f, 8.f, 6.f, 4.f, 14.f, 12.f, 10.f, 8.f}, sd::DataType::FLOAT32); NDArray exp3('c', {1,1}, std::vector{31.5}, sd::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}, sd::DataType::DOUBLE); auto z = x1.applyAllReduce3(reduce3::Dot, x2, {0,2}); ASSERT_TRUE(z.equalsTo(&exp1)); z = x1.applyAllReduce3(reduce3::Dot, x2, {0}); ASSERT_TRUE(z.equalsTo(&exp2)); z = x3.applyAllReduce3(reduce3::Dot, x4, {0,1}); ASSERT_TRUE(z.equalsTo(&exp3)); z = x3.applyAllReduce3(reduce3::Dot, x4, {1}); ASSERT_TRUE(z.equalsTo(&exp4)); x1.permutei({2,1,0}); x2.permutei({2,1,0}); x3.permutei({1,0}); x4.permutei({1,0}); z = x1.applyAllReduce3(reduce3::Dot, x2, {0,2}); ASSERT_TRUE(z.equalsTo(&exp1)); z = x3.applyAllReduce3(reduce3::Dot, x4, {0}); ASSERT_TRUE(z.equalsTo(&exp4)); } ////////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, applyIndexReduce_test1) { NDArray x('c', {2,3}, {0, 10, 1, 2, 2.5,-4}, sd::DataType::DOUBLE); NDArray scalar('c', {}, std::vector{100}, sd::DataType::INT64); NDArray vec1('c', {2}, {100,100}, sd::DataType::INT64); NDArray vec2('c', {3}, {100,100,100}, sd::DataType::INT64); NDArray exp1('c', {}, std::vector{1}, sd::DataType::INT64); NDArray exp2('c', {2}, {1,1}, sd::DataType::INT64); NDArray exp3('c', {3}, {1,0,0}, sd::DataType::INT64); NDArray exp4('c', {}, std::vector{2}, sd::DataType::INT64); NDArray exp5('c', {2}, {1,1}, sd::DataType::INT64); NDArray exp6('c', {3}, {1,0,0}, sd::DataType::INT64); x.applyIndexReduce(sd::indexreduce::IndexMax, scalar, {0,1}); ASSERT_TRUE(scalar.equalsTo(&exp1)); x.applyIndexReduce(sd::indexreduce::IndexMax, vec1, {1}); ASSERT_TRUE(vec1.equalsTo(&exp2)); x.applyIndexReduce(sd::indexreduce::IndexMax, vec2, {0}); ASSERT_TRUE(vec2.equalsTo(&exp3)); x.permutei({1,0}); x.applyIndexReduce(sd::indexreduce::IndexMax, scalar, {0,1}); ASSERT_TRUE(scalar.equalsTo(&exp4)); x.applyIndexReduce(sd::indexreduce::IndexMax, vec1, {0}); ASSERT_TRUE(vec1.equalsTo(&exp5)); x.applyIndexReduce(sd::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}, sd::DataType::DOUBLE); NDArray exp1('c', {}, std::vector{1}, sd::DataType::INT64); NDArray exp2('c', {2}, {1,1}, sd::DataType::INT64); NDArray exp3('c', {3}, {1,0,0}, sd::DataType::INT64); NDArray exp4('c', {}, std::vector{2}, sd::DataType::INT64); NDArray exp5('c', {2}, {1,1}, sd::DataType::INT64); NDArray exp6('c', {3}, {1,0,0}, sd::DataType::INT64); auto z = x.applyIndexReduce(sd::indexreduce::IndexMax, {0,1}); ASSERT_TRUE(z.equalsTo(&exp1)); z = x.applyIndexReduce(sd::indexreduce::IndexMax, {1}); ASSERT_TRUE(z.equalsTo(&exp2)); z = x.applyIndexReduce(sd::indexreduce::IndexMax, {0}); ASSERT_TRUE(z.equalsTo(&exp3)); x.permutei({1,0}); z = x.applyIndexReduce(sd::indexreduce::IndexMax, {0,1}); ASSERT_TRUE(z.equalsTo(&exp4)); z = x.applyIndexReduce(sd::indexreduce::IndexMax, {0}); ASSERT_TRUE(z.equalsTo(&exp5)); z = x.applyIndexReduce(sd::indexreduce::IndexMax, {1}); ASSERT_TRUE(z.equalsTo(&exp6)); } //////////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, reduceAlongDimension_float_test1) { NDArray x('c', {2,3,2}, {1,2,3,4,5,6,7,8,-1,-2,-3,-4,}, sd::DataType::INT32); NDArray z1('c', {}, std::vector{100}, sd::DataType::DOUBLE); NDArray z2('c', {2,2}, {100,100,100,100}, sd::DataType::FLOAT32); NDArray z3('c', {3}, {100,100,100}, sd::DataType::DOUBLE); NDArray z4('c', {3,2}, {100,100,100,100,100,100}, sd::DataType::FLOAT32); NDArray z5('c', {2}, {100,100}, sd::DataType::FLOAT32); NDArray exp1('c', {}, std::vector{2.166667}, sd::DataType::DOUBLE); NDArray exp2('c', {2,2}, {3.f,4.f,1.f,0.666667f}, sd::DataType::FLOAT32); NDArray exp3('c', {3}, {4.5,1,1}, sd::DataType::DOUBLE); NDArray exp4('c', {3,2}, {4,5,1,1,1,1}, sd::DataType::FLOAT32); NDArray exp5('c', {2}, {3.5f,0.833333f}, sd::DataType::FLOAT32); x.reduceAlongDimension(sd::reduce::Mean, z1, {0,1,2}); ASSERT_TRUE(z1.equalsTo(&exp1)); x.reduceAlongDimension(sd::reduce::Mean, z2, {1}); ASSERT_TRUE(z2.equalsTo(&exp2)); x.reduceAlongDimension(sd::reduce::Mean, z3, {0,2}); ASSERT_TRUE(z3.equalsTo(&exp3)); x.permutei({1,0,2}); // 3x2x2 x.reduceAlongDimension(sd::reduce::Mean, z1, {0,1,2}); ASSERT_TRUE(z1.equalsTo(&exp1)); x.reduceAlongDimension(sd::reduce::Mean, z4, {1}); ASSERT_TRUE(z4.equalsTo(&exp4)); x.reduceAlongDimension(sd::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,}, sd::DataType::DOUBLE); NDArray exp1('c', {}, std::vector{2.166667}, sd::DataType::DOUBLE); NDArray exp2('c', {2,2}, {3,4,1,0.666667}, sd::DataType::DOUBLE); NDArray exp3('c', {3}, {4.5,1,1}, sd::DataType::DOUBLE); NDArray exp4('c', {3,2}, {4,5,1,1,1,1}, sd::DataType::DOUBLE); NDArray exp5('c', {2}, {3.5,0.833333}, sd::DataType::DOUBLE); NDArray z1 = x.reduceAlongDimension(sd::reduce::Mean, {0,1,2}); ASSERT_TRUE(z1.equalsTo(&exp1)); NDArray z2 = x.reduceAlongDimension(sd::reduce::Mean, {1}); ASSERT_TRUE(z2.equalsTo(&exp2)); NDArray z3 = x.reduceAlongDimension(sd::reduce::Mean, {0,2}); ASSERT_TRUE(z3.equalsTo(&exp3)); x.permutei({1,0,2}); // 3x2x2 NDArray z4 = x.reduceAlongDimension(sd::reduce::Mean, {0,1,2}); ASSERT_TRUE(z4.equalsTo(&exp1)); NDArray z5 = x.reduceAlongDimension(sd::reduce::Mean, {1}); ASSERT_TRUE(z5.equalsTo(&exp4)); NDArray z6 = x.reduceAlongDimension(sd::reduce::Mean, {0,2}); ASSERT_TRUE(z6.equalsTo(&exp5)); } ////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, EqualityTest1) { auto arrayA = NDArrayFactory::create_('f', {3, 5}); auto arrayB = NDArrayFactory::create_('f', {3, 5}); auto arrayC = NDArrayFactory::create_('f', {3, 5}); auto arrayD = NDArrayFactory::create_('f', {2, 4}); auto arrayE = NDArrayFactory::create_('f', {1, 15}); for (int i = 0; i < arrayA->rows(); i++) { for (int k = 0; k < arrayA->columns(); k++) { arrayA->p(i, k, (float) i); } } for (int i = 0; i < arrayB->rows(); i++) { for (int k = 0; k < arrayB->columns(); k++) { arrayB->p(i, k, (float) i); } } for (int i = 0; i < arrayC->rows(); i++) { for (int k = 0; k < arrayC->columns(); k++) { arrayC->p(i, k, (float) i+1); } } ASSERT_TRUE(arrayA->equalsTo(arrayB, 1e-5)); ASSERT_FALSE(arrayC->equalsTo(arrayB, 1e-5)); ASSERT_FALSE(arrayD->equalsTo(arrayB, 1e-5)); ASSERT_FALSE(arrayE->equalsTo(arrayB, 1e-5)); delete arrayA; delete arrayB; delete arrayC; delete arrayD; delete arrayE; } //////////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, reduceAlongDimension_same_test1) { NDArray x('c', {2,3,2}, {1.5f,2.f,3.f,4.f,5.f,6.f,7.5f,8.f,-1.f,-2.f,-3.5f,-4.f}, sd::DataType::FLOAT32); NDArray z1('c', {}, std::vector{100}, sd::DataType::FLOAT32); NDArray z2('c', {2,2}, {100,100,100,100}, sd::DataType::FLOAT32); NDArray z3('c', {3}, {100,100,100}, sd::DataType::FLOAT32); NDArray z4('c', {3,2}, {100,100,100,100,100,100}, sd::DataType::FLOAT32); NDArray z5('c', {2}, {100,100}, sd::DataType::FLOAT32); NDArray exp1('c', {}, std::vector{26.5f}, sd::DataType::FLOAT32); NDArray exp2('c', {2,2}, {9.5f,12.f,3.f,2.f}, sd::DataType::FLOAT32); NDArray exp3('c', {3}, {19.f,4.f,3.5f}, sd::DataType::FLOAT32); NDArray exp4('c', {3,2}, {9.f,10.f,2.f,2.f,1.5f,2.f}, sd::DataType::FLOAT32); NDArray exp5('c', {2}, {21.5f,5.f}, sd::DataType::FLOAT32); x.reduceAlongDimension(sd::reduce::Sum, z1, {0,1,2}); ASSERT_TRUE(z1.equalsTo(&exp1)); x.reduceAlongDimension(sd::reduce::Sum, z2, {1}); ASSERT_TRUE(z2.equalsTo(&exp2)); x.reduceAlongDimension(sd::reduce::Sum, z3, {0,2}); ASSERT_TRUE(z3.equalsTo(&exp3)); x.permutei({1,0,2}); // 3x2x2 x.reduceAlongDimension(sd::reduce::Sum, z1, {0,1,2}); ASSERT_TRUE(z1.equalsTo(&exp1)); x.reduceAlongDimension(sd::reduce::Sum, z4, {1}); ASSERT_TRUE(z4.equalsTo(&exp4)); x.reduceAlongDimension(sd::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,}, sd::DataType::INT64); NDArray exp1('c', {}, std::vector{26}, sd::DataType::INT64); NDArray exp2('c', {2,2}, {9,12,3,2}, sd::DataType::INT64); NDArray exp3('c', {3}, {18,4,4}, sd::DataType::INT64); NDArray exp4('c', {3,2}, {8,10,2,2,2,2}, sd::DataType::INT64); NDArray exp5('c', {2}, {21,5}, sd::DataType::INT64); NDArray z1 = x.reduceAlongDimension(sd::reduce::Sum, {0,1,2}); ASSERT_TRUE(z1.equalsTo(&exp1)); NDArray z2 = x.reduceAlongDimension(sd::reduce::Sum, {1}); ASSERT_TRUE(z2.equalsTo(&exp2)); NDArray z3 = x.reduceAlongDimension(sd::reduce::Sum, {0,2}); ASSERT_TRUE(z3.equalsTo(&exp3)); x.permutei({1,0,2}); // 3x2x2 NDArray z4 = x.reduceAlongDimension(sd::reduce::Sum, {0,1,2}); ASSERT_TRUE(z4.equalsTo(&exp1)); NDArray z5 = x.reduceAlongDimension(sd::reduce::Sum, {1}); ASSERT_TRUE(z5.equalsTo(&exp4)); NDArray z6 = x.reduceAlongDimension(sd::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}, sd::DataType::DOUBLE); NDArray z1('c', {}, std::vector{true}, sd::DataType::BOOL); NDArray z2('c', {2,2}, {true,true,true,true}, sd::DataType::BOOL); NDArray z3('c', {3}, {true,true,true}, sd::DataType::BOOL); NDArray z4('c', {3,2}, {true,true,true,true,true,true}, sd::DataType::BOOL); NDArray z5('c', {2}, {true,true}, sd::DataType::BOOL); NDArray exp1('c', {}, std::vector{true}, sd::DataType::BOOL); NDArray exp2('c', {2,2}, {true,true,false,true}, sd::DataType::BOOL); NDArray exp3('c', {3}, {true,true,true}, sd::DataType::BOOL); NDArray exp4('c', {3,2}, {true,true,true,false,true,true}, sd::DataType::BOOL); NDArray exp5('c', {2}, {true,true}, sd::DataType::BOOL); x.reduceAlongDimension(sd::reduce::IsPositive, z1, {0,1,2}); ASSERT_TRUE(z1.equalsTo(&exp1)); x.reduceAlongDimension(sd::reduce::IsPositive, z2, {1}); ASSERT_TRUE(z2.equalsTo(&exp2)); x.reduceAlongDimension(sd::reduce::IsPositive, z3, {0,2}); ASSERT_TRUE(z3.equalsTo(&exp3)); x.permutei({1,0,2}); // 3x2x2 x.reduceAlongDimension(sd::reduce::IsPositive, z1, {0,1,2}); ASSERT_TRUE(z1.equalsTo(&exp1)); x.reduceAlongDimension(sd::reduce::IsPositive, z4, {1}); ASSERT_TRUE(z4.equalsTo(&exp4)); x.reduceAlongDimension(sd::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}, sd::DataType::INT32); NDArray exp1('c', {}, std::vector{1}, sd::DataType::BOOL); NDArray exp2('c', {2,2}, {1,1,0,1}, sd::DataType::BOOL); NDArray exp3('c', {3}, {1,1,1}, sd::DataType::BOOL); NDArray exp4('c', {3,2}, {0,1,1,0,1,1}, sd::DataType::BOOL); NDArray exp5('c', {2}, {1,1}, sd::DataType::BOOL); NDArray z1 = x.reduceAlongDimension(sd::reduce::IsPositive, {0,1,2}); ASSERT_TRUE(z1.equalsTo(&exp1)); NDArray z2 = x.reduceAlongDimension(sd::reduce::IsPositive, {1}); ASSERT_TRUE(z2.equalsTo(&exp2)); NDArray z3 = x.reduceAlongDimension(sd::reduce::IsPositive, {0,2}); ASSERT_TRUE(z3.equalsTo(&exp3)); x.permutei({1,0,2}); // 3x2x2 NDArray z4 = x.reduceAlongDimension(sd::reduce::IsPositive, {0,1,2}); ASSERT_TRUE(z4.equalsTo(&exp1)); NDArray z5 = x.reduceAlongDimension(sd::reduce::IsPositive, {1}); ASSERT_TRUE(z5.equalsTo(&exp4)); NDArray z6 = x.reduceAlongDimension(sd::reduce::IsPositive, {0,2}); ASSERT_TRUE(z6.equalsTo(&exp5)); } //////////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, reduceAlongDimension_long_test1) { NDArray x('c', {2,3,2}, {0.5f,2.f,3.f,-0.f,5.f,6.f,-7.5f,0.f,-1.f,-0.5f,-3.5f,4.f}, sd::DataType::FLOAT32); NDArray z1('c', {}, std::vector{100}, sd::DataType::INT64); NDArray z2('c', {2,2}, {100,100,100,100}, sd::DataType::INT64); NDArray z3('c', {3}, {100,100,100}, sd::DataType::INT64); NDArray z4('c', {3,2}, {100,100,100,100,100,100}, sd::DataType::INT64); NDArray z5('c', {2}, {100,100}, sd::DataType::INT64); NDArray exp1('c', {}, std::vector{2}, sd::DataType::INT64); NDArray exp2('c', {2,2}, {0,1,0,1}, sd::DataType::INT64); NDArray exp3('c', {3}, {1,1,0}, sd::DataType::INT64); NDArray exp4('c', {3,2}, {0,1,0,1,0,0}, sd::DataType::INT64); NDArray exp5('c', {2}, {1,1}, sd::DataType::INT64); x.reduceAlongDimension(sd::reduce::CountZero, z1, {0,1,2}); ASSERT_TRUE(z1.equalsTo(&exp1)); x.reduceAlongDimension(sd::reduce::CountZero, z2, {1}); ASSERT_TRUE(z2.equalsTo(&exp2)); x.reduceAlongDimension(sd::reduce::CountZero, z3, {0,2}); ASSERT_TRUE(z3.equalsTo(&exp3)); x.permutei({1,0,2}); // 3x2x2 x.reduceAlongDimension(sd::reduce::CountZero, z1, {0,1,2}); ASSERT_TRUE(z1.equalsTo(&exp1)); x.reduceAlongDimension(sd::reduce::CountZero, z4, {1}); ASSERT_TRUE(z4.equalsTo(&exp4)); x.reduceAlongDimension(sd::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}, sd::DataType::INT32); NDArray exp1('c', {}, std::vector{4}, sd::DataType::INT64); NDArray exp2('c', {2,2}, {1,1,0,2}, sd::DataType::INT64); NDArray exp3('c', {3}, {2,2,0}, sd::DataType::INT64); NDArray exp4('c', {3,2}, {1,1,0,2,0,0}, sd::DataType::INT64); NDArray exp5('c', {2}, {2,2}, sd::DataType::INT64); NDArray z1 = x.reduceAlongDimension(sd::reduce::CountZero, {0,1,2}); ASSERT_TRUE(z1.equalsTo(&exp1)); NDArray z2 = x.reduceAlongDimension(sd::reduce::CountZero, {1}); ASSERT_TRUE(z2.equalsTo(&exp2)); NDArray z3 = x.reduceAlongDimension(sd::reduce::CountZero, {0,2}); ASSERT_TRUE(z3.equalsTo(&exp3)); x.permutei({1,0,2}); // 3x2x2 NDArray z4 = x.reduceAlongDimension(sd::reduce::CountZero, {0,1,2}); ASSERT_TRUE(z4.equalsTo(&exp1)); NDArray z5 = x.reduceAlongDimension(sd::reduce::CountZero, {1}); ASSERT_TRUE(z5.equalsTo(&exp4)); NDArray z6 = x.reduceAlongDimension(sd::reduce::CountZero, {0,2}); ASSERT_TRUE(z6.equalsTo(&exp5)); } TEST_F(NDArrayCudaBasicsTests, BroadcastOpsTest1) { auto x = NDArrayFactory::create('c', {5, 5}); auto z = NDArrayFactory::create('c', {5, 5}); auto row = NDArrayFactory::linspace(1.0f, 5.0f, 5); NDArray expRow('c', {1, 5,}, {1,2,3,4,5}, sd::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}, sd::DataType::FLOAT32); ASSERT_TRUE(row->equalsTo(&expRow)); x.applyBroadcast(broadcast::Add, {1}, *row, z); x += *row; ASSERT_TRUE(x.equalsTo(z)); //ASSERT_TRUE(z.equalsTo(&exp)); delete row; } TEST_F(NDArrayCudaBasicsTests, BroadcastOpsTest2) { auto x = NDArrayFactory::create('c', {5, 5}); //auto z = NDArrayFactory::create('c', {5, 5}); auto row = NDArrayFactory::linspace(1.0f, 5.0f, 5); NDArray expRow('c', {1, 5,}, {1,2,3,4,5}, sd::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}, sd::DataType::FLOAT32); ASSERT_TRUE(row->equalsTo(&expRow)); x.applyBroadcast(broadcast::Add, {1}, *row, x); ASSERT_TRUE(x.equalsTo(&exp)); } ////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, TestBroadcast_1) { NDArray exp('c', {2, 3, 2, 2}, {1., 1., 1., 1., 2., 2., 2., 2., 3., 3., 3., 3., 1., 1., 1., 1., 2., 2., 2., 2., 3., 3., 3., 3.}, sd::DataType::DOUBLE); auto input = NDArrayFactory::create('c',{ 2, 3, 2, 2}); auto bias = NDArrayFactory::create('c', {1, 3}); bias.linspace(1); input.applyBroadcast(broadcast::Add, {1}, bias, input); ASSERT_TRUE(exp.equalsTo(&input)); } TEST_F(NDArrayCudaBasicsTests, TestFloat16_1) { auto x = NDArrayFactory::create({1,2,3,4,5,7,8,9}); auto y = NDArrayFactory::create({1,2,3,4,5,7,8,9}); ASSERT_TRUE(x.equalsTo(&y)); } TEST_F(NDArrayCudaBasicsTests, TestFloat16_2) { auto x = NDArrayFactory::create('c', {9}, {1,2,3,4,5,6,7,8,9}); auto y = NDArrayFactory::create('c', {9}, {1,2,3,4,5,6,7,8,9}); ASSERT_TRUE(x.equalsTo(y)); //for (int e = 0; e < x.lengthOf(); e++) // ASSERT_NEAR(x.e(e), y.e(e), 1.e-5f); } TEST_F(NDArrayCudaBasicsTests, TestFloat16_3) { auto x = NDArrayFactory::create({1,2,3,4,5,7,8,9}); auto y = NDArrayFactory::create({1,2,3,4,5,7,8,9}); ASSERT_TRUE(x.equalsTo(&y)); } TEST_F(NDArrayCudaBasicsTests, TestFloat_4) { auto x = NDArrayFactory::create({1,2,3,4,5,7,8,9}); auto y = NDArrayFactory::create({2,4,5,5,6,7,8,9}); ASSERT_FALSE(x.equalsTo(&y)); } TEST_F(NDArrayCudaBasicsTests, TestFloat_5) { auto x = NDArrayFactory::create('c', {3,3}, {1,2,3,4,5,6,7,8,9}); auto y = NDArrayFactory::create('c', {3,3}, {2,4,5,5,6,7,8,9, 10}); ASSERT_FALSE(x.equalsTo(&y)); } TEST_F(NDArrayCudaBasicsTests, TestFloat_6) { auto x = NDArrayFactory::create('f', {3,3}, {1,2,3,4,5,6,7,8,9}); auto y = NDArrayFactory::create('f', {3,3}, {2,4,5,5,6,7,8,9,10}); ASSERT_FALSE(x.equalsTo(&y)); } ////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, Operator_Plus_Test_05) { auto x = NDArrayFactory::create('c', {8, 8, 8}); auto y = NDArrayFactory::create('c', {1, 8, 8}); auto expected = NDArrayFactory::create('c', {8, 8, 8}); NDArray res2 = NDArrayFactory::create(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('c', {8, 8, 8}); auto y = NDArrayFactory::create('c', {8, 1, 8}); auto expected = NDArrayFactory::create('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('c', {8, 8, 8}); auto y = NDArrayFactory::create('c', {8, 8}); auto expected = NDArrayFactory::create('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('c', {2, 1, 2}); x = 10.; auto y = x.tile({1,2,1}); auto exp = NDArrayFactory::create('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('f', {2, 1, 2}); x = 10.; auto y = x.tile({1,2,1}); auto exp = NDArrayFactory::create('f', {2, 2, 2}); exp = 10.; ASSERT_TRUE(exp.equalsTo(y)); } TEST_F(NDArrayCudaBasicsTests, Tile_Test_2_3) { auto x = NDArrayFactory::create('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('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}, sd::DataType::FLOAT32); auto x = NDArrayFactory::create('c', {3, 2, 1}); auto y = NDArrayFactory::create('c', {1, 2}); auto expected = NDArrayFactory::create(expBuff, 'c', {3, 2, 2}); x.linspace(1); y.linspace(1); auto result = x + y; ASSERT_TRUE(expected.isSameShape(&result)); ASSERT_TRUE(expected.equalsTo(&result)); } ////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, assign_2) { NDArray x('c', {4}, {1.5f,2.5f,3.5f,4.5f}, sd::DataType::FLOAT32); NDArray y('c', {4}, sd::DataType::INT32); NDArray expected('c', {4}, {1,2,3,4}, sd::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}, sd::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}, sd::DataType::FLOAT32); Nd4jLong shapeExpX0[] = {1, 2, 12, 8192, 1, 99}; float buffExpX0[] = {1.f, 13.f}; Nd4jLong shapeExpX1[] = {1, 2, 12, 8192, 1, 99}; float buffExpX1[] = {2.f, 14.f}; Nd4jLong shapeExpX2[] = {3, 2, 1, 1, 12, 4, 1, 8192, 1, 99}; float buffExpX2[] = {1.f, 13.f}; Nd4jLong shapeExpX3[] = {2, 2, 4, 12, 1, 8192, 1, 99}; float buffExpX3[] = {9.f, 10.f, 11.f, 12.f, 21.f, 22.f, 23.f, 24.f}; Nd4jLong shapeExpX4[] = {3, 2, 1, 4, 12, 4, 1, 8192, 1, 99}; float buffExpX4[] = {9.f, 10.f, 11.f, 12.f, 21.f, 22.f, 23.f, 24.f}; Nd4jLong shapeExpX5[] = {2, 2, 3, 12, 4, 8192, 1, 99}; float buffExpX5[] = {4.f, 8.f, 12.f, 16.f, 20.f, 24.f}; Nd4jLong shapeExpY0[] = {1, 2, 1, 8192, 1, 99}; float buffExpY0[] = {1.f, 2.f}; Nd4jLong shapeExpY1[] = {1, 2, 1, 8192, 1, 99}; float buffExpY1[] = {7.f, 8.f}; Nd4jLong shapeExpY2[] = {3, 2, 1, 1, 1, 2, 6, 8192, 1, 102}; float buffExpY2[] = {1.f, 2.f}; Nd4jLong shapeExpY3[] = {2, 2, 4, 1, 6, 8192, 1, 99}; float buffExpY3[] = {5.f, 11.f, 17.f, 23.f, 6.f, 12.f, 18.f, 24.f}; Nd4jLong shapeExpY4[] = {3, 2, 1, 4, 1, 2, 6, 8192, 1, 102}; float buffExpY4[] = {5.f, 11.f, 17.f, 23.f, 6.f, 12.f, 18.f, 24.f}; Nd4jLong shapeExpY5[] = {2, 2, 3, 1, 2, 8192, 1, 99}; float buffExpY5[] = {19.f, 21.f, 23.f, 20.f, 22.f, 24.f}; NDArray x0 = x(0, {1,2}); NDArray xExp(buffExpX0, shapeExpX0); ASSERT_TRUE(xExp.isSameShape(x0)); ASSERT_TRUE(xExp.equalsTo(x0)); // for(int i = 0; i < shape::shapeInfoLength(x0.rankOf()); ++i) // ASSERT_TRUE(x0.shapeInfo()[i] == shapeExpX0[i]); // for(int i = 0; i < x0.lengthOf(); ++i) // ASSERT_TRUE(x0.e(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.shapeInfo()[i] == shapeExpX1[i]); // for(int i = 0; i < x1.lengthOf(); ++i) // ASSERT_TRUE(x1.e(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.shapeInfo()[i] == shapeExpX2[i]); // for(int i = 0; i < x2.lengthOf(); ++i) // ASSERT_TRUE(x2.e(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.shapeInfo()[i] == shapeExpX3[i]); // for(int i = 0; i < x3.lengthOf(); ++i) // ASSERT_TRUE(x3.e(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.shapeInfo()[i] == shapeExpX4[i]); // for(int i = 0; i < x4.lengthOf(); ++i) // ASSERT_TRUE(x4.e(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.shapeInfo()[i] == shapeExpX5[i]); // for(int i = 0; i < x5.lengthOf(); ++i) // ASSERT_TRUE(x5.e(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.shapeInfo()[i] == shapeExpY0[i]); // for(int i = 0; i < y0.lengthOf(); ++i) // ASSERT_TRUE(y0.e(i) == buffExpY0[i]); NDArray y1 = y(1, {1,2}); NDArray y1Exp(buffExpY1, shapeExpY1); ASSERT_TRUE(y1Exp.isSameShape(y1)); ASSERT_TRUE(y1Exp.equalsTo(y1)); // for(int i = 0; i < shape::shapeInfoLength(y1.rankOf()); ++i) // ASSERT_TRUE(y1.shapeInfo()[i] == shapeExpY1[i]); // for(int i = 0; i < y1.lengthOf(); ++i) // ASSERT_TRUE(y1.e(i) == buffExpY1[i]); NDArray y2 = y(0, {1,2}, true); NDArray y2Exp(buffExpY2, shapeExpY2); ASSERT_TRUE(y2Exp.isSameShape(y2)); ASSERT_TRUE(y2Exp.equalsTo(y2)); // for(int i = 0; i < shape::shapeInfoLength(y2.rankOf()); ++i) // ASSERT_TRUE(y2.shapeInfo()[i] == shapeExpY2[i]); // for(int i = 0; i < y2.lengthOf(); ++i) // ASSERT_TRUE(y2.e(i) == buffExpY2[i]); NDArray y3 = y(2, {1}); NDArray y3Exp(buffExpY3, shapeExpY3); ASSERT_TRUE(y3Exp.isSameShape(y3)); ASSERT_TRUE(y3Exp.equalsTo(y3)); // for(int i = 0; i < shape::shapeInfoLength(y3.rankOf()); ++i) // ASSERT_TRUE(y3.shapeInfo()[i] == shapeExpY3[i]); // for(int i = 0; i < y3.lengthOf(); ++i) // ASSERT_TRUE(y3.e(i) == buffExpY3[i]); NDArray y4 = y(2, {1}, true); NDArray y4Exp = NDArrayFactory::create('f', {2,1,4}, {5, 6, 11, 12, 17, 18, 23, 24}); ASSERT_TRUE(y4Exp.isSameShape(y4)); ASSERT_TRUE(y4Exp.equalsTo(y4)); // for(int i = 0; i < shape::shapeInfoLength(y4.rankOf()); ++i) // ASSERT_TRUE(y4.shapeInfo()[i] == shapeExpY4[i]); // for(int i = 0; i < y4.lengthOf(); ++i) // ASSERT_TRUE(y4.e(i) == buffExpY4[i]); NDArray y5 = y(3, {2}); NDArray y5Exp(buffExpY5, shapeExpY5); ASSERT_TRUE(y5Exp.isSameShape(y5)); ASSERT_TRUE(y5Exp.equalsTo(y5)); // for(int i = 0; i < shape::shapeInfoLength(y5.rankOf()); ++i) // ASSERT_TRUE(y5.shapeInfo()[i] == shapeExpY5[i]); // for(int i = 0; i < y5.lengthOf(); ++i) // ASSERT_TRUE(y5.e(i) == buffExpY5[i]); } ////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, Test_diagonal_1) { auto x = NDArrayFactory::create('c', {2, 3}, {1, 2, 3, 4, 5, 6}); auto exp = NDArrayFactory::create('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(e)); //, exp.e(e), 1.e-5); } for (Nd4jLong e = 0; e < exp.lengthOf(); ++e) { ASSERT_NEAR(diag.e(e), exp.e(e), 1.e-5); } double eps(1.e-5); NDArray tmp(sd::DataType::FLOAT32, x.getContext()); // scalar = 0 ExtraArguments extras({eps}); NativeOpExecutioner::execReduce3Scalar(diag.getContext(), reduce3::EqualsWithEps, diag.buffer(), diag.shapeInfo(), diag.specialBuffer(), diag.specialShapeInfo(), extras.argumentsAsT(sd::DataType::FLOAT32), exp.buffer(), exp.shapeInfo(), exp.specialBuffer(), exp.specialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo()); cudaStream_t* stream = x.getContext()->getCudaStream(); auto res = cudaStreamSynchronize(*stream); // tmp.printBuffer("Compare result is (expected 0)"); ASSERT_TRUE(exp.isSameShape(diag)); ASSERT_TRUE(exp.equalsTo(diag)); } TEST_F(NDArrayCudaBasicsTests, Test_PermuteEquality_02) { auto x = NDArrayFactory::linspace(1.f, 60.f, 60); //('c', {1, 60}); //x.linspace(1); auto exp = NDArrayFactory::create('c', {3, 4, 5}, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f, 25.0f, 26.0f, 27.0f, 28.0f, 29.0f, 30.0f, 31.0f, 32.0f, 33.0f, 34.0f, 35.0f, 36.0f, 37.0f, 38.0f, 39.0f, 40.0f, 41.0f, 42.0f, 43.0f, 44.0f, 45.0f, 46.0f, 47.0f, 48.0f, 49.0f, 50.0f, 51.0f, 52.0f, 53.0f, 54.0f, 55.0f, 56.0f, 57.0f, 58.0f, 59.0f, 60.0}); x->reshapei('c', {3, 4, 5}); x->permutei({0, 1, 2}); x->streamline(); // x.printShapeInfo("{0, 1, 2} shape"); // x.printBuffer("{0, 1, 2} data"); ASSERT_TRUE(exp.isSameShape(x)); ASSERT_TRUE(exp.equalsTo(x)); delete x; } TEST_F(NDArrayCudaBasicsTests, Test_PermuteEquality_0) { auto x = NDArrayFactory::create('c', {1, 60}); x.linspace(1); auto exp = NDArrayFactory::create('c', {3, 4, 5}, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f, 25.0f, 26.0f, 27.0f, 28.0f, 29.0f, 30.0f, 31.0f, 32.0f, 33.0f, 34.0f, 35.0f, 36.0f, 37.0f, 38.0f, 39.0f, 40.0f, 41.0f, 42.0f, 43.0f, 44.0f, 45.0f, 46.0f, 47.0f, 48.0f, 49.0f, 50.0f, 51.0f, 52.0f, 53.0f, 54.0f, 55.0f, 56.0f, 57.0f, 58.0f, 59.0f, 60.0}); x.reshapei('c', {3, 4, 5}); x.permutei({0, 1, 2}); x.streamline(); // x.printShapeInfo("{0, 1, 2} shape"); // x.printBuffer("{0, 1, 2} data"); ASSERT_TRUE(exp.isSameShape(&x)); ASSERT_TRUE(exp.equalsTo(&x)); } TEST_F(NDArrayCudaBasicsTests, Test_PermuteEquality_1) { auto x = NDArrayFactory::create('c', {1, 60}); x.linspace(1); auto exp = NDArrayFactory::create('c', {3, 4, 5}, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f, 25.0f, 26.0f, 27.0f, 28.0f, 29.0f, 30.0f, 31.0f, 32.0f, 33.0f, 34.0f, 35.0f, 36.0f, 37.0f, 38.0f, 39.0f, 40.0f, 41.0f, 42.0f, 43.0f, 44.0f, 45.0f, 46.0f, 47.0f, 48.0f, 49.0f, 50.0f, 51.0f, 52.0f, 53.0f, 54.0f, 55.0f, 56.0f, 57.0f, 58.0f, 59.0f, 60.0}); x.reshapei('c', {3, 4, 5}); x.permutei({0, 1, 2}); x.streamline(); // x.printShapeInfo("{0, 1, 2} shape"); // x.printBuffer("{0, 1, 2} data"); ASSERT_TRUE(exp.isSameShape(&x)); ASSERT_TRUE(exp.equalsTo(&x)); } TEST_F(NDArrayCudaBasicsTests, Test_PermuteEquality_2) { //auto x = NDArrayFactory::create('c', {1, 60}); auto xx = NDArrayFactory::linspace(1.f, 60.f, 60); //('c', {1, 60}); // auto x = *xx; //x.linspace(1); // auto exp = NDArrayFactory::create('c', {3, 4, 5}, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f, 25.0f, 26.0f, 27.0f, 28.0f, 29.0f, 30.0f, 31.0f, 32.0f, 33.0f, 34.0f, 35.0f, 36.0f, 37.0f, 38.0f, 39.0f, 40.0f, 41.0f, 42.0f, 43.0f, 44.0f, 45.0f, 46.0f, 47.0f, 48.0f, 49.0f, 50.0f, 51.0f, 52.0f, 53.0f, 54.0f, 55.0f, 56.0f, 57.0f, 58.0f, 59.0f, 60.0}); // x.reshapei('c', {3, 4, 5}); // x.permutei({0, 1, 2}); // x.streamline(); // x.printShapeInfo("{0, 1, 2} shape"); // x.printBuffer("{0, 1, 2} data"); // ASSERT_TRUE(exp.isSameShape(&x)); // ASSERT_TRUE(exp.equalsTo(&x)); delete xx; } TEST_F(NDArrayCudaBasicsTests, Test_PermuteEquality_3) { auto x = NDArrayFactory::create('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('c', {3, 4, 5}, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f, 25.0f, 26.0f, 27.0f, 28.0f, 29.0f, 30.0f, 31.0f, 32.0f, 33.0f, 34.0f, 35.0f, 36.0f, 37.0f, 38.0f, 39.0f, 40.0f, 41.0f, 42.0f, 43.0f, 44.0f, 45.0f, 46.0f, 47.0f, 48.0f, 49.0f, 50.0f, 51.0f, 52.0f, 53.0f, 54.0f, 55.0f, 56.0f, 57.0f, 58.0f, 59.0f, 60.0}); x.reshapei('c', {3, 4, 5}); x.permutei({0, 1, 2}); x.streamline(); // x.printShapeInfo("{0, 1, 2} shape"); // x.printBuffer("{0, 1, 2} data"); ASSERT_TRUE(exp.isSameShape(&x)); ASSERT_TRUE(exp.equalsTo(&x)); } TEST_F(NDArrayCudaBasicsTests, Test_Empty_1) { auto x = NDArrayFactory::empty(); ASSERT_TRUE(x.isActualOnHostSide()); ASSERT_TRUE(x.isEmpty()); } TEST_F(NDArrayCudaBasicsTests, Test_Empty_2) { auto x = NDArrayFactory::empty_(); ASSERT_TRUE(x->isEmpty()); delete x; } TEST_F(NDArrayCudaBasicsTests, Test_Empty_3) { auto x = NDArrayFactory::empty(sd::DataType::FLOAT32); ASSERT_TRUE(x.isEmpty()); } TEST_F(NDArrayCudaBasicsTests, Test_Empty_4) { auto x = NDArrayFactory::empty_(sd::DataType::FLOAT32); ASSERT_TRUE(x->isEmpty()); delete x; }