| // 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> |
| // Benoit Steiner <benoit.steiner.goog@gmail.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_DEFAULT_DENSE_INDEX_TYPE int64_t |
| #define EIGEN_USE_SYCL |
| |
| #include "main.h" |
| #include <unsupported/Eigen/CXX11/Tensor> |
| |
| using Eigen::array; |
| using Eigen::SyclDevice; |
| using Eigen::Tensor; |
| using Eigen::TensorMap; |
| |
| template <typename DataType, int DataLayout, typename IndexType> |
| static void test_simple_shuffling_sycl(const Eigen::SyclDevice& sycl_device) { |
| IndexType sizeDim1 = 2; |
| IndexType sizeDim2 = 3; |
| IndexType sizeDim3 = 5; |
| IndexType sizeDim4 = 7; |
| array<IndexType, 4> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}}; |
| Tensor<DataType, 4, DataLayout, IndexType> tensor(tensorRange); |
| Tensor<DataType, 4, DataLayout, IndexType> no_shuffle(tensorRange); |
| tensor.setRandom(); |
| |
| const size_t buffSize = tensor.size() * sizeof(DataType); |
| array<IndexType, 4> shuffles; |
| shuffles[0] = 0; |
| shuffles[1] = 1; |
| shuffles[2] = 2; |
| shuffles[3] = 3; |
| DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(buffSize)); |
| DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(buffSize)); |
| |
| TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu1(gpu_data1, tensorRange); |
| TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> 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); |
| sycl_device.synchronize(); |
| |
| 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 (IndexType i = 0; i < sizeDim1; ++i) { |
| for (IndexType j = 0; j < sizeDim2; ++j) { |
| for (IndexType k = 0; k < sizeDim3; ++k) { |
| for (IndexType 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<IndexType, 4> tensorrangeShuffle = {{sizeDim3, sizeDim4, sizeDim2, sizeDim1}}; |
| Tensor<DataType, 4, DataLayout, IndexType> shuffle(tensorrangeShuffle); |
| DataType* gpu_data3 = static_cast<DataType*>(sycl_device.allocate(buffSize)); |
| TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu3(gpu_data3, tensorrangeShuffle); |
| |
| gpu3.device(sycl_device) = gpu1.shuffle(shuffles); |
| sycl_device.memcpyDeviceToHost(shuffle.data(), gpu_data3, buffSize); |
| sycl_device.synchronize(); |
| |
| 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 (IndexType i = 0; i < sizeDim1; ++i) { |
| for (IndexType j = 0; j < sizeDim2; ++j) { |
| for (IndexType k = 0; k < sizeDim3; ++k) { |
| for (IndexType l = 0; l < sizeDim4; ++l) { |
| VERIFY_IS_EQUAL(tensor(i, j, k, l), shuffle(k, l, j, i)); |
| } |
| } |
| } |
| } |
| } |
| |
| template <typename DataType, typename dev_Selector> |
| void sycl_shuffling_test_per_device(dev_Selector s) { |
| QueueInterface queueInterface(s); |
| auto sycl_device = Eigen::SyclDevice(&queueInterface); |
| test_simple_shuffling_sycl<DataType, RowMajor, int64_t>(sycl_device); |
| test_simple_shuffling_sycl<DataType, ColMajor, int64_t>(sycl_device); |
| } |
| EIGEN_DECLARE_TEST(cxx11_tensor_shuffling_sycl) { |
| for (const auto& device : Eigen::get_sycl_supported_devices()) { |
| CALL_SUBTEST(sycl_shuffling_test_per_device<half>(device)); |
| CALL_SUBTEST(sycl_shuffling_test_per_device<float>(device)); |
| } |
| } |