// 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: // Benoit Steiner // // 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_shuffling_sycl #define EIGEN_DEFAULT_DENSE_INDEX_TYPE int #define EIGEN_USE_SYCL #include "main.h" #include using Eigen::array; using Eigen::SyclDevice; using Eigen::Tensor; using Eigen::TensorMap; template static void test_simple_shuffling_sycl(const Eigen::SyclDevice& sycl_device) { IndexTypes sizeDim1 = 2; IndexTypes sizeDim2 = 3; IndexTypes sizeDim3 = 5; IndexTypes sizeDim4 = 7; array tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}}; Tensor tensor(tensorRange); Tensor no_shuffle(tensorRange); tensor.setRandom(); const size_t buffSize =tensor.size()*sizeof(DataType); array shuffles; shuffles[0] = 0; shuffles[1] = 1; shuffles[2] = 2; shuffles[3] = 3; DataType* gpu_data1 = static_cast(sycl_device.allocate(buffSize)); DataType* gpu_data2 = static_cast(sycl_device.allocate(buffSize)); TensorMap> gpu1(gpu_data1, tensorRange); TensorMap> gpu2(gpu_data2, tensorRange); sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(), buffSize); gpu2.device(sycl_device)=gpu1.shuffle(shuffles); sycl_device.memcpyDeviceToHost(no_shuffle.data(), gpu_data2, buffSize); VERIFY_IS_EQUAL(no_shuffle.dimension(0), sizeDim1); VERIFY_IS_EQUAL(no_shuffle.dimension(1), sizeDim2); VERIFY_IS_EQUAL(no_shuffle.dimension(2), sizeDim3); VERIFY_IS_EQUAL(no_shuffle.dimension(3), sizeDim4); for (int i = 0; i < sizeDim1; ++i) { for (int j = 0; j < sizeDim2; ++j) { for (int k = 0; k < sizeDim3; ++k) { for (int l = 0; l < sizeDim4; ++l) { VERIFY_IS_EQUAL(tensor(i,j,k,l), no_shuffle(i,j,k,l)); } } } } shuffles[0] = 2; shuffles[1] = 3; shuffles[2] = 1; shuffles[3] = 0; array tensorrangeShuffle = {{sizeDim3, sizeDim4, sizeDim2, sizeDim1}}; Tensor shuffle(tensorrangeShuffle); DataType* gpu_data3 = static_cast(sycl_device.allocate(buffSize)); TensorMap> gpu3(gpu_data3, tensorrangeShuffle); gpu3.device(sycl_device)=gpu1.shuffle(shuffles); sycl_device.memcpyDeviceToHost(shuffle.data(), gpu_data3, buffSize); VERIFY_IS_EQUAL(shuffle.dimension(0), sizeDim3); VERIFY_IS_EQUAL(shuffle.dimension(1), sizeDim4); VERIFY_IS_EQUAL(shuffle.dimension(2), sizeDim2); VERIFY_IS_EQUAL(shuffle.dimension(3), sizeDim1); for (int i = 0; i < sizeDim1; ++i) { for (int j = 0; j < sizeDim2; ++j) { for (int k = 0; k < sizeDim3; ++k) { for (int l = 0; l < sizeDim4; ++l) { VERIFY_IS_EQUAL(tensor(i,j,k,l), shuffle(k,l,j,i)); } } } } } template void sycl_shuffling_test_per_device(dev_Selector s){ QueueInterface queueInterface(s); auto sycl_device = Eigen::SyclDevice(&queueInterface); test_simple_shuffling_sycl(sycl_device); test_simple_shuffling_sycl(sycl_device); test_simple_shuffling_sycl(sycl_device); test_simple_shuffling_sycl(sycl_device); } void test_cxx11_tensor_shuffling_sycl() { for (const auto& device :Eigen::get_sycl_supported_devices()) { CALL_SUBTEST(sycl_shuffling_test_per_device(device)); } }