diff options
Diffstat (limited to 'unsupported/test/cxx11_tensor_sycl.cpp')
| -rw-r--r-- | unsupported/test/cxx11_tensor_sycl.cpp | 308 |
1 files changed, 255 insertions, 53 deletions
diff --git a/unsupported/test/cxx11_tensor_sycl.cpp b/unsupported/test/cxx11_tensor_sycl.cpp index 6a9c33422..e6c5e2378 100644 --- a/unsupported/test/cxx11_tensor_sycl.cpp +++ b/unsupported/test/cxx11_tensor_sycl.cpp @@ -15,8 +15,8 @@ #define EIGEN_TEST_NO_LONGDOUBLE #define EIGEN_TEST_NO_COMPLEX -#define EIGEN_TEST_FUNC cxx11_tensor_sycl -#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int + +#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t #define EIGEN_USE_SYCL #include "main.h" @@ -27,36 +27,188 @@ using Eigen::SyclDevice; using Eigen::Tensor; using Eigen::TensorMap; -void test_sycl_cpu(const Eigen::SyclDevice &sycl_device) { +template <typename DataType, int DataLayout, typename IndexType> +void test_sycl_mem_transfers(const Eigen::SyclDevice &sycl_device) { + IndexType sizeDim1 = 5; + IndexType sizeDim2 = 5; + IndexType sizeDim3 = 1; + array<IndexType, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}}; + Tensor<DataType, 3, DataLayout, IndexType> in1(tensorRange); + Tensor<DataType, 3, DataLayout, IndexType> out1(tensorRange); + Tensor<DataType, 3, DataLayout, IndexType> out2(tensorRange); + Tensor<DataType, 3, DataLayout, IndexType> out3(tensorRange); + + in1 = in1.random(); + + DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(in1.size()*sizeof(DataType))); + DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(out1.size()*sizeof(DataType))); + + TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu1(gpu_data1, tensorRange); + TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu2(gpu_data2, tensorRange); + + sycl_device.memcpyHostToDevice(gpu_data1, in1.data(),(in1.size())*sizeof(DataType)); + sycl_device.memcpyHostToDevice(gpu_data2, in1.data(),(in1.size())*sizeof(DataType)); + gpu1.device(sycl_device) = gpu1 * 3.14f; + gpu2.device(sycl_device) = gpu2 * 2.7f; + sycl_device.memcpyDeviceToHost(out1.data(), gpu_data1,(out1.size())*sizeof(DataType)); + sycl_device.memcpyDeviceToHost(out2.data(), gpu_data1,(out2.size())*sizeof(DataType)); + sycl_device.memcpyDeviceToHost(out3.data(), gpu_data2,(out3.size())*sizeof(DataType)); + sycl_device.synchronize(); + + for (IndexType i = 0; i < in1.size(); ++i) { + // std::cout << "SYCL DATA : " << out1(i) << " vs CPU DATA : " << in1(i) * 3.14f << "\n"; + VERIFY_IS_APPROX(out1(i), in1(i) * 3.14f); + VERIFY_IS_APPROX(out2(i), in1(i) * 3.14f); + VERIFY_IS_APPROX(out3(i), in1(i) * 2.7f); + } + + sycl_device.deallocate(gpu_data1); + sycl_device.deallocate(gpu_data2); +} + +template <typename DataType, int DataLayout, typename IndexType> +void test_sycl_mem_sync(const Eigen::SyclDevice &sycl_device) { + IndexType size = 20; + array<IndexType, 1> tensorRange = {{size}}; + Tensor<DataType, 1, DataLayout, IndexType> in1(tensorRange); + Tensor<DataType, 1, DataLayout, IndexType> in2(tensorRange); + Tensor<DataType, 1, DataLayout, IndexType> out(tensorRange); + + in1 = in1.random(); + in2 = in1; + + DataType* gpu_data = static_cast<DataType*>(sycl_device.allocate(in1.size()*sizeof(DataType))); + + TensorMap<Tensor<DataType, 1, DataLayout, IndexType>> gpu1(gpu_data, tensorRange); + sycl_device.memcpyHostToDevice(gpu_data, in1.data(),(in1.size())*sizeof(DataType)); + sycl_device.synchronize(); + in1.setZero(); + + sycl_device.memcpyDeviceToHost(out.data(), gpu_data, out.size()*sizeof(DataType)); + sycl_device.synchronize(); + + for (IndexType i = 0; i < in1.size(); ++i) { + VERIFY_IS_APPROX(out(i), in2(i)); + } + + sycl_device.deallocate(gpu_data); +} + +template <typename DataType, int DataLayout, typename IndexType> +void test_sycl_mem_sync_offsets(const Eigen::SyclDevice &sycl_device) { + using tensor_type = Tensor<DataType, 1, DataLayout, IndexType>; + IndexType full_size = 32; + IndexType half_size = full_size / 2; + array<IndexType, 1> tensorRange = {{full_size}}; + tensor_type in1(tensorRange); + tensor_type out(tensorRange); + + DataType* gpu_data = static_cast<DataType*>(sycl_device.allocate(full_size * sizeof(DataType))); + TensorMap<tensor_type> gpu1(gpu_data, tensorRange); + + in1 = in1.random(); + // Copy all data to device, then permute on copy back to host + sycl_device.memcpyHostToDevice(gpu_data, in1.data(), full_size * sizeof(DataType)); + sycl_device.memcpyDeviceToHost(out.data(), gpu_data + half_size, half_size * sizeof(DataType)); + sycl_device.memcpyDeviceToHost(out.data() + half_size, gpu_data, half_size * sizeof(DataType)); + + for (IndexType i = 0; i < half_size; ++i) { + VERIFY_IS_APPROX(out(i), in1(i + half_size)); + VERIFY_IS_APPROX(out(i + half_size), in1(i)); + } + + in1 = in1.random(); + out.setZero(); + // Permute copies to device, then copy all back to host + sycl_device.memcpyHostToDevice(gpu_data + half_size, in1.data(), half_size * sizeof(DataType)); + sycl_device.memcpyHostToDevice(gpu_data, in1.data() + half_size, half_size * sizeof(DataType)); + sycl_device.memcpyDeviceToHost(out.data(), gpu_data, full_size * sizeof(DataType)); + + for (IndexType i = 0; i < half_size; ++i) { + VERIFY_IS_APPROX(out(i), in1(i + half_size)); + VERIFY_IS_APPROX(out(i + half_size), in1(i)); + } + + in1 = in1.random(); + out.setZero(); + DataType* gpu_data_out = static_cast<DataType*>(sycl_device.allocate(full_size * sizeof(DataType))); + TensorMap<tensor_type> gpu2(gpu_data_out, tensorRange); + // Copy all to device, permute copies on device, then copy all back to host + sycl_device.memcpyHostToDevice(gpu_data, in1.data(), full_size * sizeof(DataType)); + sycl_device.memcpy(gpu_data_out + half_size, gpu_data, half_size * sizeof(DataType)); + sycl_device.memcpy(gpu_data_out, gpu_data + half_size, half_size * sizeof(DataType)); + sycl_device.memcpyDeviceToHost(out.data(), gpu_data_out, full_size * sizeof(DataType)); + + for (IndexType i = 0; i < half_size; ++i) { + VERIFY_IS_APPROX(out(i), in1(i + half_size)); + VERIFY_IS_APPROX(out(i + half_size), in1(i)); + } + + sycl_device.deallocate(gpu_data_out); + sycl_device.deallocate(gpu_data); +} + +template <typename DataType, int DataLayout, typename IndexType> +void test_sycl_memset_offsets(const Eigen::SyclDevice &sycl_device) { + using tensor_type = Tensor<DataType, 1, DataLayout, IndexType>; + IndexType full_size = 32; + IndexType half_size = full_size / 2; + array<IndexType, 1> tensorRange = {{full_size}}; + tensor_type cpu_out(tensorRange); + tensor_type out(tensorRange); + + cpu_out.setZero(); + + std::memset(cpu_out.data(), 0, half_size * sizeof(DataType)); + std::memset(cpu_out.data() + half_size, 1, half_size * sizeof(DataType)); + + DataType* gpu_data = static_cast<DataType*>(sycl_device.allocate(full_size * sizeof(DataType))); + TensorMap<tensor_type> gpu1(gpu_data, tensorRange); + + sycl_device.memset(gpu_data, 0, half_size * sizeof(DataType)); + sycl_device.memset(gpu_data + half_size, 1, half_size * sizeof(DataType)); + sycl_device.memcpyDeviceToHost(out.data(), gpu_data, full_size * sizeof(DataType)); + + for (IndexType i = 0; i < full_size; ++i) { + VERIFY_IS_APPROX(out(i), cpu_out(i)); + } + + sycl_device.deallocate(gpu_data); +} + +template <typename DataType, int DataLayout, typename IndexType> +void test_sycl_computations(const Eigen::SyclDevice &sycl_device) { - int sizeDim1 = 100; - int sizeDim2 = 100; - int sizeDim3 = 100; - array<int, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}}; - Tensor<float, 3> in1(tensorRange); - Tensor<float, 3> in2(tensorRange); - Tensor<float, 3> in3(tensorRange); - Tensor<float, 3> out(tensorRange); + IndexType sizeDim1 = 100; + IndexType sizeDim2 = 10; + IndexType sizeDim3 = 20; + array<IndexType, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}}; + Tensor<DataType, 3,DataLayout, IndexType> in1(tensorRange); + Tensor<DataType, 3,DataLayout, IndexType> in2(tensorRange); + Tensor<DataType, 3,DataLayout, IndexType> in3(tensorRange); + Tensor<DataType, 3,DataLayout, IndexType> out(tensorRange); in2 = in2.random(); in3 = in3.random(); - float * gpu_in1_data = static_cast<float*>(sycl_device.allocate(in1.dimensions().TotalSize()*sizeof(float))); - float * gpu_in2_data = static_cast<float*>(sycl_device.allocate(in2.dimensions().TotalSize()*sizeof(float))); - float * gpu_in3_data = static_cast<float*>(sycl_device.allocate(in3.dimensions().TotalSize()*sizeof(float))); - float * gpu_out_data = static_cast<float*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(float))); + DataType * gpu_in1_data = static_cast<DataType*>(sycl_device.allocate(in1.size()*sizeof(DataType))); + DataType * gpu_in2_data = static_cast<DataType*>(sycl_device.allocate(in2.size()*sizeof(DataType))); + DataType * gpu_in3_data = static_cast<DataType*>(sycl_device.allocate(in3.size()*sizeof(DataType))); + DataType * gpu_out_data = static_cast<DataType*>(sycl_device.allocate(out.size()*sizeof(DataType))); - TensorMap<Tensor<float, 3>> gpu_in1(gpu_in1_data, tensorRange); - TensorMap<Tensor<float, 3>> gpu_in2(gpu_in2_data, tensorRange); - TensorMap<Tensor<float, 3>> gpu_in3(gpu_in3_data, tensorRange); - TensorMap<Tensor<float, 3>> gpu_out(gpu_out_data, tensorRange); + TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_in1(gpu_in1_data, tensorRange); + TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_in2(gpu_in2_data, tensorRange); + TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_in3(gpu_in3_data, tensorRange); + TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_out(gpu_out_data, tensorRange); /// a=1.2f gpu_in1.device(sycl_device) = gpu_in1.constant(1.2f); - sycl_device.memcpyDeviceToHost(in1.data(), gpu_in1_data ,(in1.dimensions().TotalSize())*sizeof(float)); - for (int i = 0; i < sizeDim1; ++i) { - for (int j = 0; j < sizeDim2; ++j) { - for (int k = 0; k < sizeDim3; ++k) { + sycl_device.memcpyDeviceToHost(in1.data(), gpu_in1_data ,(in1.size())*sizeof(DataType)); + sycl_device.synchronize(); + + for (IndexType i = 0; i < sizeDim1; ++i) { + for (IndexType j = 0; j < sizeDim2; ++j) { + for (IndexType k = 0; k < sizeDim3; ++k) { VERIFY_IS_APPROX(in1(i,j,k), 1.2f); } } @@ -65,10 +217,12 @@ void test_sycl_cpu(const Eigen::SyclDevice &sycl_device) { /// a=b*1.2f gpu_out.device(sycl_device) = gpu_in1 * 1.2f; - sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data ,(out.dimensions().TotalSize())*sizeof(float)); - for (int i = 0; i < sizeDim1; ++i) { - for (int j = 0; j < sizeDim2; ++j) { - for (int k = 0; k < sizeDim3; ++k) { + sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data ,(out.size())*sizeof(DataType)); + sycl_device.synchronize(); + + for (IndexType i = 0; i < sizeDim1; ++i) { + for (IndexType j = 0; j < sizeDim2; ++j) { + for (IndexType k = 0; k < sizeDim3; ++k) { VERIFY_IS_APPROX(out(i,j,k), in1(i,j,k) * 1.2f); } @@ -77,12 +231,14 @@ void test_sycl_cpu(const Eigen::SyclDevice &sycl_device) { printf("a=b*1.2f Test Passed\n"); /// c=a*b - sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(),(in2.dimensions().TotalSize())*sizeof(float)); + sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(),(in2.size())*sizeof(DataType)); gpu_out.device(sycl_device) = gpu_in1 * gpu_in2; - sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float)); - for (int i = 0; i < sizeDim1; ++i) { - for (int j = 0; j < sizeDim2; ++j) { - for (int k = 0; k < sizeDim3; ++k) { + sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.size())*sizeof(DataType)); + sycl_device.synchronize(); + + for (IndexType i = 0; i < sizeDim1; ++i) { + for (IndexType j = 0; j < sizeDim2; ++j) { + for (IndexType k = 0; k < sizeDim3; ++k) { VERIFY_IS_APPROX(out(i,j,k), in1(i,j,k) * in2(i,j,k)); @@ -93,10 +249,11 @@ void test_sycl_cpu(const Eigen::SyclDevice &sycl_device) { /// c=a+b gpu_out.device(sycl_device) = gpu_in1 + gpu_in2; - sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float)); - for (int i = 0; i < sizeDim1; ++i) { - for (int j = 0; j < sizeDim2; ++j) { - for (int k = 0; k < sizeDim3; ++k) { + sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.size())*sizeof(DataType)); + sycl_device.synchronize(); + for (IndexType i = 0; i < sizeDim1; ++i) { + for (IndexType j = 0; j < sizeDim2; ++j) { + for (IndexType k = 0; k < sizeDim3; ++k) { VERIFY_IS_APPROX(out(i,j,k), in1(i,j,k) + in2(i,j,k)); @@ -107,10 +264,11 @@ void test_sycl_cpu(const Eigen::SyclDevice &sycl_device) { /// c=a*a gpu_out.device(sycl_device) = gpu_in1 * gpu_in1; - sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float)); - for (int i = 0; i < sizeDim1; ++i) { - for (int j = 0; j < sizeDim2; ++j) { - for (int k = 0; k < sizeDim3; ++k) { + sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.size())*sizeof(DataType)); + sycl_device.synchronize(); + for (IndexType i = 0; i < sizeDim1; ++i) { + for (IndexType j = 0; j < sizeDim2; ++j) { + for (IndexType k = 0; k < sizeDim3; ++k) { VERIFY_IS_APPROX(out(i,j,k), in1(i,j,k) * in1(i,j,k)); @@ -121,10 +279,11 @@ void test_sycl_cpu(const Eigen::SyclDevice &sycl_device) { //a*3.14f + b*2.7f gpu_out.device(sycl_device) = gpu_in1 * gpu_in1.constant(3.14f) + gpu_in2 * gpu_in2.constant(2.7f); - sycl_device.memcpyDeviceToHost(out.data(),gpu_out_data,(out.dimensions().TotalSize())*sizeof(float)); - for (int i = 0; i < sizeDim1; ++i) { - for (int j = 0; j < sizeDim2; ++j) { - for (int k = 0; k < sizeDim3; ++k) { + sycl_device.memcpyDeviceToHost(out.data(),gpu_out_data,(out.size())*sizeof(DataType)); + sycl_device.synchronize(); + for (IndexType i = 0; i < sizeDim1; ++i) { + for (IndexType j = 0; j < sizeDim2; ++j) { + for (IndexType k = 0; k < sizeDim3; ++k) { VERIFY_IS_APPROX(out(i,j,k), in1(i,j,k) * 3.14f + in2(i,j,k) * 2.7f); @@ -134,12 +293,13 @@ void test_sycl_cpu(const Eigen::SyclDevice &sycl_device) { printf("a*3.14f + b*2.7f Test Passed\n"); ///d= (a>0.5? b:c) - sycl_device.memcpyHostToDevice(gpu_in3_data, in3.data(),(in3.dimensions().TotalSize())*sizeof(float)); + sycl_device.memcpyHostToDevice(gpu_in3_data, in3.data(),(in3.size())*sizeof(DataType)); gpu_out.device(sycl_device) =(gpu_in1 > gpu_in1.constant(0.5f)).select(gpu_in2, gpu_in3); - sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float)); - for (int i = 0; i < sizeDim1; ++i) { - for (int j = 0; j < sizeDim2; ++j) { - for (int k = 0; k < sizeDim3; ++k) { + sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.size())*sizeof(DataType)); + sycl_device.synchronize(); + for (IndexType i = 0; i < sizeDim1; ++i) { + for (IndexType j = 0; j < sizeDim2; ++j) { + for (IndexType k = 0; k < sizeDim3; ++k) { VERIFY_IS_APPROX(out(i, j, k), (in1(i, j, k) > 0.5f) ? in2(i, j, k) : in3(i, j, k)); @@ -152,8 +312,50 @@ void test_sycl_cpu(const Eigen::SyclDevice &sycl_device) { sycl_device.deallocate(gpu_in3_data); sycl_device.deallocate(gpu_out_data); } -void test_cxx11_tensor_sycl() { - cl::sycl::gpu_selector s; - Eigen::SyclDevice sycl_device(s); - CALL_SUBTEST(test_sycl_cpu(sycl_device)); +template<typename Scalar1, typename Scalar2, int DataLayout, typename IndexType> +static void test_sycl_cast(const Eigen::SyclDevice& sycl_device){ + IndexType size = 20; + array<IndexType, 1> tensorRange = {{size}}; + Tensor<Scalar1, 1, DataLayout, IndexType> in(tensorRange); + Tensor<Scalar2, 1, DataLayout, IndexType> out(tensorRange); + Tensor<Scalar2, 1, DataLayout, IndexType> out_host(tensorRange); + + in = in.random(); + + Scalar1* gpu_in_data = static_cast<Scalar1*>(sycl_device.allocate(in.size()*sizeof(Scalar1))); + Scalar2 * gpu_out_data = static_cast<Scalar2*>(sycl_device.allocate(out.size()*sizeof(Scalar2))); + + TensorMap<Tensor<Scalar1, 1, DataLayout, IndexType>> gpu_in(gpu_in_data, tensorRange); + TensorMap<Tensor<Scalar2, 1, DataLayout, IndexType>> gpu_out(gpu_out_data, tensorRange); + sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.size())*sizeof(Scalar1)); + gpu_out.device(sycl_device) = gpu_in. template cast<Scalar2>(); + sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, out.size()*sizeof(Scalar2)); + out_host = in. template cast<Scalar2>(); + for(IndexType i=0; i< size; i++) + { + VERIFY_IS_APPROX(out(i), out_host(i)); + } + printf("cast Test Passed\n"); + sycl_device.deallocate(gpu_in_data); + sycl_device.deallocate(gpu_out_data); +} +template<typename DataType, typename dev_Selector> void sycl_computing_test_per_device(dev_Selector s){ + QueueInterface queueInterface(s); + auto sycl_device = Eigen::SyclDevice(&queueInterface); + test_sycl_mem_transfers<DataType, RowMajor, int64_t>(sycl_device); + test_sycl_computations<DataType, RowMajor, int64_t>(sycl_device); + test_sycl_mem_sync<DataType, RowMajor, int64_t>(sycl_device); + test_sycl_mem_sync_offsets<DataType, RowMajor, int64_t>(sycl_device); + test_sycl_memset_offsets<DataType, RowMajor, int64_t>(sycl_device); + test_sycl_mem_transfers<DataType, ColMajor, int64_t>(sycl_device); + test_sycl_computations<DataType, ColMajor, int64_t>(sycl_device); + test_sycl_mem_sync<DataType, ColMajor, int64_t>(sycl_device); + test_sycl_cast<DataType, int, RowMajor, int64_t>(sycl_device); + test_sycl_cast<DataType, int, ColMajor, int64_t>(sycl_device); +} + +EIGEN_DECLARE_TEST(cxx11_tensor_sycl) { + for (const auto& device :Eigen::get_sycl_supported_devices()) { + CALL_SUBTEST(sycl_computing_test_per_device<float>(device)); + } } |