| // 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_TEST_FUNC cxx11_tensor_reduction_sycl |
| #define EIGEN_DEFAULT_DENSE_INDEX_TYPE int |
| #define EIGEN_USE_SYCL |
| |
| #include "main.h" |
| #include <unsupported/Eigen/CXX11/Tensor> |
| |
| |
| |
| static void test_full_reductions_sycl(const Eigen::SyclDevice& sycl_device) { |
| |
| const int num_rows = 452; |
| const int num_cols = 765; |
| array<int, 2> tensorRange = {{num_rows, num_cols}}; |
| |
| Tensor<float, 2> in(tensorRange); |
| Tensor<float, 0> full_redux; |
| Tensor<float, 0> full_redux_gpu; |
| |
| in.setRandom(); |
| |
| full_redux = in.sum(); |
| |
| float* gpu_in_data = static_cast<float*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(float))); |
| float* gpu_out_data =(float*)sycl_device.allocate(sizeof(float)); |
| |
| TensorMap<Tensor<float, 2> > in_gpu(gpu_in_data, tensorRange); |
| TensorMap<Tensor<float, 0> > out_gpu(gpu_out_data); |
| |
| sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(float)); |
| out_gpu.device(sycl_device) = in_gpu.sum(); |
| sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data, sizeof(float)); |
| // 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); |
| } |
| |
| static void test_first_dim_reductions_sycl(const Eigen::SyclDevice& sycl_device) { |
| |
| int dim_x = 145; |
| int dim_y = 1; |
| int dim_z = 67; |
| |
| array<int, 3> tensorRange = {{dim_x, dim_y, dim_z}}; |
| Eigen::array<int, 1> red_axis; |
| red_axis[0] = 0; |
| array<int, 2> reduced_tensorRange = {{dim_y, dim_z}}; |
| |
| Tensor<float, 3> in(tensorRange); |
| Tensor<float, 2> redux(reduced_tensorRange); |
| Tensor<float, 2> redux_gpu(reduced_tensorRange); |
| |
| in.setRandom(); |
| |
| redux= in.sum(red_axis); |
| |
| float* gpu_in_data = static_cast<float*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(float))); |
| float* gpu_out_data = static_cast<float*>(sycl_device.allocate(redux_gpu.dimensions().TotalSize()*sizeof(float))); |
| |
| TensorMap<Tensor<float, 3> > in_gpu(gpu_in_data, tensorRange); |
| TensorMap<Tensor<float, 2> > out_gpu(gpu_out_data, reduced_tensorRange); |
| |
| sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(float)); |
| out_gpu.device(sycl_device) = in_gpu.sum(red_axis); |
| sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, redux_gpu.dimensions().TotalSize()*sizeof(float)); |
| |
| // Check that the CPU and GPU reductions return the same result. |
| for(int j=0; j<reduced_tensorRange[0]; j++ ) |
| for(int 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); |
| } |
| |
| static void test_last_dim_reductions_sycl(const Eigen::SyclDevice &sycl_device) { |
| |
| int dim_x = 567; |
| int dim_y = 1; |
| int dim_z = 47; |
| |
| array<int, 3> tensorRange = {{dim_x, dim_y, dim_z}}; |
| Eigen::array<int, 1> red_axis; |
| red_axis[0] = 2; |
| array<int, 2> reduced_tensorRange = {{dim_x, dim_y}}; |
| |
| Tensor<float, 3> in(tensorRange); |
| Tensor<float, 2> redux(reduced_tensorRange); |
| Tensor<float, 2> redux_gpu(reduced_tensorRange); |
| |
| in.setRandom(); |
| |
| redux= in.sum(red_axis); |
| |
| float* gpu_in_data = static_cast<float*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(float))); |
| float* gpu_out_data = static_cast<float*>(sycl_device.allocate(redux_gpu.dimensions().TotalSize()*sizeof(float))); |
| |
| TensorMap<Tensor<float, 3> > in_gpu(gpu_in_data, tensorRange); |
| TensorMap<Tensor<float, 2> > out_gpu(gpu_out_data, reduced_tensorRange); |
| |
| sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(float)); |
| out_gpu.device(sycl_device) = in_gpu.sum(red_axis); |
| sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, redux_gpu.dimensions().TotalSize()*sizeof(float)); |
| // Check that the CPU and GPU reductions return the same result. |
| for(int j=0; j<reduced_tensorRange[0]; j++ ) |
| for(int 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); |
| |
| } |
| |
| void test_cxx11_tensor_reduction_sycl() { |
| cl::sycl::gpu_selector s; |
| Eigen::SyclDevice sycl_device(s); |
| CALL_SUBTEST((test_full_reductions_sycl(sycl_device))); |
| CALL_SUBTEST((test_first_dim_reductions_sycl(sycl_device))); |
| CALL_SUBTEST((test_last_dim_reductions_sycl(sycl_device))); |
| |
| } |