| // This file is part of Eigen, a lightweight C++ template library |
| // for linear algebra. |
| // |
| // Copyright (C) 2016 |
| // 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_broadcast_sycl |
| #define EIGEN_DEFAULT_DENSE_INDEX_TYPE int |
| #define EIGEN_USE_SYCL |
| |
| #include "main.h" |
| #include <unsupported/Eigen/CXX11/Tensor> |
| |
| using Eigen::array; |
| using Eigen::SyclDevice; |
| using Eigen::Tensor; |
| using Eigen::TensorMap; |
| |
| static void test_broadcast_sycl(const Eigen::SyclDevice &sycl_device){ |
| |
| // BROADCAST test: |
| array<int, 4> in_range = {{2, 3, 5, 7}}; |
| array<int, 4> broadcasts = {{2, 3, 1, 4}}; |
| array<int, 4> out_range; // = in_range * broadcasts |
| for (size_t i = 0; i < out_range.size(); ++i) |
| out_range[i] = in_range[i] * broadcasts[i]; |
| |
| Tensor<float, 4> input(in_range); |
| Tensor<float, 4> out(out_range); |
| |
| for (size_t i = 0; i < in_range.size(); ++i) |
| VERIFY_IS_EQUAL(out.dimension(i), out_range[i]); |
| |
| |
| for (int i = 0; i < input.size(); ++i) |
| input(i) = static_cast<float>(i); |
| |
| float * gpu_in_data = static_cast<float*>(sycl_device.allocate(input.dimensions().TotalSize()*sizeof(float))); |
| float * gpu_out_data = static_cast<float*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(float))); |
| |
| TensorMap<Tensor<float, 4>> gpu_in(gpu_in_data, in_range); |
| TensorMap<Tensor<float, 4>> gpu_out(gpu_out_data, out_range); |
| sycl_device.memcpyHostToDevice(gpu_in_data, input.data(),(input.dimensions().TotalSize())*sizeof(float)); |
| gpu_out.device(sycl_device) = gpu_in.broadcast(broadcasts); |
| sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float)); |
| |
| for (int i = 0; i < 4; ++i) { |
| for (int j = 0; j < 9; ++j) { |
| for (int k = 0; k < 5; ++k) { |
| for (int l = 0; l < 28; ++l) { |
| VERIFY_IS_APPROX(input(i%2,j%3,k%5,l%7), out(i,j,k,l)); |
| } |
| } |
| } |
| } |
| printf("Broadcast Test Passed\n"); |
| sycl_device.deallocate(gpu_in_data); |
| sycl_device.deallocate(gpu_out_data); |
| } |
| |
| void test_cxx11_tensor_broadcast_sycl() { |
| cl::sycl::gpu_selector s; |
| Eigen::SyclDevice sycl_device(s); |
| CALL_SUBTEST(test_broadcast_sycl(sycl_device)); |
| } |