| // 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_forced_eval_sycl |
| #define EIGEN_DEFAULT_DENSE_INDEX_TYPE int |
| #define EIGEN_USE_SYCL |
| |
| #include "main.h" |
| #include <unsupported/Eigen/CXX11/Tensor> |
| |
| using Eigen::Tensor; |
| |
| void test_forced_eval_sycl(const Eigen::SyclDevice &sycl_device) { |
| |
| int sizeDim1 = 100; |
| int sizeDim2 = 200; |
| int sizeDim3 = 200; |
| Eigen::array<int, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}}; |
| Eigen::Tensor<float, 3> in1(tensorRange); |
| Eigen::Tensor<float, 3> in2(tensorRange); |
| Eigen::Tensor<float, 3> out(tensorRange); |
| |
| 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_out_data = static_cast<float*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(float))); |
| |
| in1 = in1.random() + in1.constant(10.0f); |
| in2 = in2.random() + in2.constant(10.0f); |
| |
| // creating TensorMap from tensor |
| Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_in1(gpu_in1_data, tensorRange); |
| Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_in2(gpu_in2_data, tensorRange); |
| Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_out(gpu_out_data, tensorRange); |
| sycl_device.memcpyHostToDevice(gpu_in1_data, in1.data(),(in1.dimensions().TotalSize())*sizeof(float)); |
| sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(),(in1.dimensions().TotalSize())*sizeof(float)); |
| /// c=(a+b)*b |
| gpu_out.device(sycl_device) =(gpu_in1 + gpu_in2).eval() * 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) { |
| VERIFY_IS_APPROX(out(i, j, k), |
| (in1(i, j, k) + in2(i, j, k)) * in2(i, j, k)); |
| } |
| } |
| } |
| printf("(a+b)*b Test Passed\n"); |
| sycl_device.deallocate(gpu_in1_data); |
| sycl_device.deallocate(gpu_in2_data); |
| sycl_device.deallocate(gpu_out_data); |
| |
| } |
| |
| void test_cxx11_tensor_forced_eval_sycl() { |
| cl::sycl::gpu_selector s; |
| Eigen::SyclDevice sycl_device(s); |
| CALL_SUBTEST(test_forced_eval_sycl(sycl_device)); |
| } |