12b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang// This file is part of Eigen, a lightweight C++ template library 22b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang// for linear algebra. 32b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang// 42b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang// Copyright (C) 2015 52b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang// Mehdi Goli Codeplay Software Ltd. 62b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang// Ralph Potter Codeplay Software Ltd. 72b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang// Luke Iwanski Codeplay Software Ltd. 82b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang// Contact: <eigen@codeplay.com> 92b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang// 102b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang// This Source Code Form is subject to the terms of the Mozilla 112b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang// Public License v. 2.0. If a copy of the MPL was not distributed 122b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 132b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 142b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang#define EIGEN_TEST_NO_LONGDOUBLE 152b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang#define EIGEN_TEST_NO_COMPLEX 162b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang#define EIGEN_TEST_FUNC cxx11_tensor_reduction_sycl 172b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int 182b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang#define EIGEN_USE_SYCL 192b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 202b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang#include "main.h" 212b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang#include <unsupported/Eigen/CXX11/Tensor> 222b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 232b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 242b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 252b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wangstatic void test_full_reductions_sycl(const Eigen::SyclDevice& sycl_device) { 262b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 272b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang const int num_rows = 452; 282b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang const int num_cols = 765; 292b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang array<int, 2> tensorRange = {{num_rows, num_cols}}; 302b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 312b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang Tensor<float, 2> in(tensorRange); 322b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang Tensor<float, 0> full_redux; 332b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang Tensor<float, 0> full_redux_gpu; 342b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 352b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang in.setRandom(); 362b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 372b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang full_redux = in.sum(); 382b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 392b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang float* gpu_in_data = static_cast<float*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(float))); 402b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang float* gpu_out_data =(float*)sycl_device.allocate(sizeof(float)); 412b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 422b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang TensorMap<Tensor<float, 2> > in_gpu(gpu_in_data, tensorRange); 432b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang TensorMap<Tensor<float, 0> > out_gpu(gpu_out_data); 442b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 452b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(float)); 462b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang out_gpu.device(sycl_device) = in_gpu.sum(); 472b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data, sizeof(float)); 482b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang // Check that the CPU and GPU reductions return the same result. 492b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang VERIFY_IS_APPROX(full_redux_gpu(), full_redux()); 502b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 512b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang sycl_device.deallocate(gpu_in_data); 522b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang sycl_device.deallocate(gpu_out_data); 532b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang} 542b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 552b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wangstatic void test_first_dim_reductions_sycl(const Eigen::SyclDevice& sycl_device) { 562b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 572b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang int dim_x = 145; 582b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang int dim_y = 1; 592b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang int dim_z = 67; 602b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 612b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang array<int, 3> tensorRange = {{dim_x, dim_y, dim_z}}; 622b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang Eigen::array<int, 1> red_axis; 632b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang red_axis[0] = 0; 642b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang array<int, 2> reduced_tensorRange = {{dim_y, dim_z}}; 652b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 662b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang Tensor<float, 3> in(tensorRange); 672b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang Tensor<float, 2> redux(reduced_tensorRange); 682b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang Tensor<float, 2> redux_gpu(reduced_tensorRange); 692b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 702b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang in.setRandom(); 712b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 722b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang redux= in.sum(red_axis); 732b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 742b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang float* gpu_in_data = static_cast<float*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(float))); 752b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang float* gpu_out_data = static_cast<float*>(sycl_device.allocate(redux_gpu.dimensions().TotalSize()*sizeof(float))); 762b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 772b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang TensorMap<Tensor<float, 3> > in_gpu(gpu_in_data, tensorRange); 782b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang TensorMap<Tensor<float, 2> > out_gpu(gpu_out_data, reduced_tensorRange); 792b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 802b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(float)); 812b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang out_gpu.device(sycl_device) = in_gpu.sum(red_axis); 822b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, redux_gpu.dimensions().TotalSize()*sizeof(float)); 832b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 842b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang // Check that the CPU and GPU reductions return the same result. 852b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang for(int j=0; j<reduced_tensorRange[0]; j++ ) 862b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang for(int k=0; k<reduced_tensorRange[1]; k++ ) 872b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang VERIFY_IS_APPROX(redux_gpu(j,k), redux(j,k)); 882b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 892b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang sycl_device.deallocate(gpu_in_data); 902b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang sycl_device.deallocate(gpu_out_data); 912b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang} 922b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 932b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wangstatic void test_last_dim_reductions_sycl(const Eigen::SyclDevice &sycl_device) { 942b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 952b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang int dim_x = 567; 962b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang int dim_y = 1; 972b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang int dim_z = 47; 982b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 992b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang array<int, 3> tensorRange = {{dim_x, dim_y, dim_z}}; 1002b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang Eigen::array<int, 1> red_axis; 1012b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang red_axis[0] = 2; 1022b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang array<int, 2> reduced_tensorRange = {{dim_x, dim_y}}; 1032b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 1042b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang Tensor<float, 3> in(tensorRange); 1052b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang Tensor<float, 2> redux(reduced_tensorRange); 1062b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang Tensor<float, 2> redux_gpu(reduced_tensorRange); 1072b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 1082b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang in.setRandom(); 1092b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 1102b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang redux= in.sum(red_axis); 1112b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 1122b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang float* gpu_in_data = static_cast<float*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(float))); 1132b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang float* gpu_out_data = static_cast<float*>(sycl_device.allocate(redux_gpu.dimensions().TotalSize()*sizeof(float))); 1142b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 1152b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang TensorMap<Tensor<float, 3> > in_gpu(gpu_in_data, tensorRange); 1162b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang TensorMap<Tensor<float, 2> > out_gpu(gpu_out_data, reduced_tensorRange); 1172b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 1182b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(float)); 1192b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang out_gpu.device(sycl_device) = in_gpu.sum(red_axis); 1202b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, redux_gpu.dimensions().TotalSize()*sizeof(float)); 1212b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang // Check that the CPU and GPU reductions return the same result. 1222b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang for(int j=0; j<reduced_tensorRange[0]; j++ ) 1232b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang for(int k=0; k<reduced_tensorRange[1]; k++ ) 1242b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang VERIFY_IS_APPROX(redux_gpu(j,k), redux(j,k)); 1252b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 1262b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang sycl_device.deallocate(gpu_in_data); 1272b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang sycl_device.deallocate(gpu_out_data); 1282b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 1292b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang} 1302b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 1312b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wangvoid test_cxx11_tensor_reduction_sycl() { 1322b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang cl::sycl::gpu_selector s; 1332b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang Eigen::SyclDevice sycl_device(s); 1342b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang CALL_SUBTEST((test_full_reductions_sycl(sycl_device))); 1352b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang CALL_SUBTEST((test_first_dim_reductions_sycl(sycl_device))); 1362b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang CALL_SUBTEST((test_last_dim_reductions_sycl(sycl_device))); 1372b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 1382b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang} 139