| // This file is part of Eigen, a lightweight C++ template library |
| // for linear algebra. |
| // |
| // Copyright (C) 2015 |
| // Mehdi Goli Codeplay Software Ltd. |
| // Ralph Potter Codeplay Software Ltd. |
| // Luke Iwanski Codeplay Software Ltd. |
| // Contact: <eigen@codeplay.com> |
| // |
| // This Source Code Form is subject to the terms of the Mozilla |
| // Public License v. 2.0. If a copy of the MPL was not distributed |
| // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. |
| |
| #define EIGEN_TEST_NO_LONGDOUBLE |
| #define EIGEN_TEST_NO_COMPLEX |
| |
| #define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t |
| #define EIGEN_USE_SYCL |
| |
| #include "main.h" |
| #include <unsupported/Eigen/CXX11/Tensor> |
| |
| |
| template <typename DataType, int DataLayout, typename IndexType> |
| static void test_full_reductions_mean_sycl(const Eigen::SyclDevice& sycl_device) { |
| |
| const IndexType num_rows = 452; |
| const IndexType num_cols = 765; |
| array<IndexType, 2> tensorRange = {{num_rows, num_cols}}; |
| |
| Tensor<DataType, 2, DataLayout, IndexType> in(tensorRange); |
| Tensor<DataType, 0, DataLayout, IndexType> full_redux; |
| Tensor<DataType, 0, DataLayout, IndexType> full_redux_gpu; |
| |
| in.setRandom(); |
| |
| full_redux = in.mean(); |
| |
| DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(DataType))); |
| DataType* gpu_out_data =(DataType*)sycl_device.allocate(sizeof(DataType)); |
| |
| TensorMap<Tensor<DataType, 2, DataLayout, IndexType> > in_gpu(gpu_in_data, tensorRange); |
| TensorMap<Tensor<DataType, 0, DataLayout, IndexType> > out_gpu(gpu_out_data); |
| |
| sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(DataType)); |
| out_gpu.device(sycl_device) = in_gpu.mean(); |
| sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data, sizeof(DataType)); |
| // Check that the CPU and GPU reductions return the same result. |
| VERIFY_IS_APPROX(full_redux_gpu(), full_redux()); |
| sycl_device.deallocate(gpu_in_data); |
| sycl_device.deallocate(gpu_out_data); |
| } |
| |
| |
| template <typename DataType, int DataLayout, typename IndexType> |
| static void test_full_reductions_min_sycl(const Eigen::SyclDevice& sycl_device) { |
| |
| const IndexType num_rows = 876; |
| const IndexType num_cols = 953; |
| array<IndexType, 2> tensorRange = {{num_rows, num_cols}}; |
| |
| Tensor<DataType, 2, DataLayout, IndexType> in(tensorRange); |
| Tensor<DataType, 0, DataLayout, IndexType> full_redux; |
| Tensor<DataType, 0, DataLayout, IndexType> full_redux_gpu; |
| |
| in.setRandom(); |
| |
| full_redux = in.minimum(); |
| |
| DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(DataType))); |
| DataType* gpu_out_data =(DataType*)sycl_device.allocate(sizeof(DataType)); |
| |
| TensorMap<Tensor<DataType, 2, DataLayout, IndexType> > in_gpu(gpu_in_data, tensorRange); |
| TensorMap<Tensor<DataType, 0, DataLayout, IndexType> > out_gpu(gpu_out_data); |
| |
| sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(DataType)); |
| out_gpu.device(sycl_device) = in_gpu.minimum(); |
| sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data, sizeof(DataType)); |
| // Check that the CPU and GPU reductions return the same result. |
| VERIFY_IS_APPROX(full_redux_gpu(), full_redux()); |
| sycl_device.deallocate(gpu_in_data); |
| sycl_device.deallocate(gpu_out_data); |
| } |
| |
| |
| template <typename DataType, int DataLayout, typename IndexType> |
| static void test_first_dim_reductions_max_sycl(const Eigen::SyclDevice& sycl_device) { |
| |
| IndexType dim_x = 145; |
| IndexType dim_y = 1; |
| IndexType dim_z = 67; |
| |
| array<IndexType, 3> tensorRange = {{dim_x, dim_y, dim_z}}; |
| Eigen::array<IndexType, 1> red_axis; |
| red_axis[0] = 0; |
| array<IndexType, 2> reduced_tensorRange = {{dim_y, dim_z}}; |
| |
| Tensor<DataType, 3, DataLayout, IndexType> in(tensorRange); |
| Tensor<DataType, 2, DataLayout, IndexType> redux(reduced_tensorRange); |
| Tensor<DataType, 2, DataLayout, IndexType> redux_gpu(reduced_tensorRange); |
| |
| in.setRandom(); |
| |
| redux= in.maximum(red_axis); |
| |
| DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(DataType))); |
| DataType* gpu_out_data = static_cast<DataType*>(sycl_device.allocate(redux_gpu.dimensions().TotalSize()*sizeof(DataType))); |
| |
| TensorMap<Tensor<DataType, 3, DataLayout, IndexType> > in_gpu(gpu_in_data, tensorRange); |
| TensorMap<Tensor<DataType, 2, DataLayout, IndexType> > out_gpu(gpu_out_data, reduced_tensorRange); |
| |
| sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(DataType)); |
| out_gpu.device(sycl_device) = in_gpu.maximum(red_axis); |
| sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, redux_gpu.dimensions().TotalSize()*sizeof(DataType)); |
| |
| // Check that the CPU and GPU reductions return the same result. |
| for(IndexType j=0; j<reduced_tensorRange[0]; j++ ) |
| for(IndexType k=0; k<reduced_tensorRange[1]; k++ ) |
| VERIFY_IS_APPROX(redux_gpu(j,k), redux(j,k)); |
| |
| sycl_device.deallocate(gpu_in_data); |
| sycl_device.deallocate(gpu_out_data); |
| } |
| |
| template <typename DataType, int DataLayout, typename IndexType> |
| static void test_last_dim_reductions_sum_sycl(const Eigen::SyclDevice &sycl_device) { |
| |
| IndexType dim_x = 567; |
| IndexType dim_y = 1; |
| IndexType dim_z = 47; |
| |
| array<IndexType, 3> tensorRange = {{dim_x, dim_y, dim_z}}; |
| Eigen::array<IndexType, 1> red_axis; |
| red_axis[0] = 2; |
| array<IndexType, 2> reduced_tensorRange = {{dim_x, dim_y}}; |
| |
| Tensor<DataType, 3, DataLayout, IndexType> in(tensorRange); |
| Tensor<DataType, 2, DataLayout, IndexType> redux(reduced_tensorRange); |
| Tensor<DataType, 2, DataLayout, IndexType> redux_gpu(reduced_tensorRange); |
| |
| in.setRandom(); |
| |
| redux= in.sum(red_axis); |
| |
| DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(DataType))); |
| DataType* gpu_out_data = static_cast<DataType*>(sycl_device.allocate(redux_gpu.dimensions().TotalSize()*sizeof(DataType))); |
| |
| TensorMap<Tensor<DataType, 3, DataLayout, IndexType> > in_gpu(gpu_in_data, tensorRange); |
| TensorMap<Tensor<DataType, 2, DataLayout, IndexType> > out_gpu(gpu_out_data, reduced_tensorRange); |
| |
| sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(DataType)); |
| out_gpu.device(sycl_device) = in_gpu.sum(red_axis); |
| sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, redux_gpu.dimensions().TotalSize()*sizeof(DataType)); |
| // Check that the CPU and GPU reductions return the same result. |
| for(IndexType j=0; j<reduced_tensorRange[0]; j++ ) |
| for(IndexType k=0; k<reduced_tensorRange[1]; k++ ) |
| VERIFY_IS_APPROX(redux_gpu(j,k), redux(j,k)); |
| |
| sycl_device.deallocate(gpu_in_data); |
| sycl_device.deallocate(gpu_out_data); |
| |
| } |
| template<typename DataType> void sycl_reduction_test_per_device(const cl::sycl::device& d){ |
| std::cout << "Running on " << d.template get_info<cl::sycl::info::device::name>() << std::endl; |
| QueueInterface queueInterface(d); |
| auto sycl_device = Eigen::SyclDevice(&queueInterface); |
| |
| test_full_reductions_mean_sycl<DataType, RowMajor, int64_t>(sycl_device); |
| test_full_reductions_min_sycl<DataType, RowMajor, int64_t>(sycl_device); |
| test_first_dim_reductions_max_sycl<DataType, RowMajor, int64_t>(sycl_device); |
| test_last_dim_reductions_sum_sycl<DataType, RowMajor, int64_t>(sycl_device); |
| test_full_reductions_mean_sycl<DataType, ColMajor, int64_t>(sycl_device); |
| test_full_reductions_min_sycl<DataType, ColMajor, int64_t>(sycl_device); |
| test_first_dim_reductions_max_sycl<DataType, ColMajor, int64_t>(sycl_device); |
| test_last_dim_reductions_sum_sycl<DataType, ColMajor, int64_t>(sycl_device); |
| } |
| EIGEN_DECLARE_TEST(cxx11_tensor_reduction_sycl) { |
| for (const auto& device :Eigen::get_sycl_supported_devices()) { |
| CALL_SUBTEST(sycl_reduction_test_per_device<float>(device)); |
| } |
| } |