| // 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 <Eigen/CXX11/Tensor> |
| |
| using Eigen::Tensor; |
| |
| template <typename DataType, int DataLayout, typename IndexType> |
| static void test_simple_patch_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}}; |
| array<IndexType, 5> patchTensorRange; |
| if (DataLayout == ColMajor) { |
| patchTensorRange = {{1, 1, 1, 1, sizeDim1 * sizeDim2 * sizeDim3 * sizeDim4}}; |
| } else { |
| patchTensorRange = {{sizeDim1 * sizeDim2 * sizeDim3 * sizeDim4, 1, 1, 1, 1}}; |
| } |
| |
| Tensor<DataType, 4, DataLayout, IndexType> tensor(tensorRange); |
| Tensor<DataType, 5, DataLayout, IndexType> no_patch(patchTensorRange); |
| |
| tensor.setRandom(); |
| |
| array<ptrdiff_t, 4> patch_dims; |
| patch_dims[0] = 1; |
| patch_dims[1] = 1; |
| patch_dims[2] = 1; |
| patch_dims[3] = 1; |
| |
| const size_t tensorBuffSize = tensor.size() * sizeof(DataType); |
| size_t patchTensorBuffSize = no_patch.size() * sizeof(DataType); |
| DataType* gpu_data_tensor = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize)); |
| DataType* gpu_data_no_patch = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| |
| TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_tensor(gpu_data_tensor, tensorRange); |
| TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_no_patch(gpu_data_no_patch, patchTensorRange); |
| |
| sycl_device.memcpyHostToDevice(gpu_data_tensor, tensor.data(), tensorBuffSize); |
| gpu_no_patch.device(sycl_device) = gpu_tensor.extract_patches(patch_dims); |
| sycl_device.memcpyDeviceToHost(no_patch.data(), gpu_data_no_patch, patchTensorBuffSize); |
| |
| if (DataLayout == ColMajor) { |
| VERIFY_IS_EQUAL(no_patch.dimension(0), 1); |
| VERIFY_IS_EQUAL(no_patch.dimension(1), 1); |
| VERIFY_IS_EQUAL(no_patch.dimension(2), 1); |
| VERIFY_IS_EQUAL(no_patch.dimension(3), 1); |
| VERIFY_IS_EQUAL(no_patch.dimension(4), tensor.size()); |
| } else { |
| VERIFY_IS_EQUAL(no_patch.dimension(0), tensor.size()); |
| VERIFY_IS_EQUAL(no_patch.dimension(1), 1); |
| VERIFY_IS_EQUAL(no_patch.dimension(2), 1); |
| VERIFY_IS_EQUAL(no_patch.dimension(3), 1); |
| VERIFY_IS_EQUAL(no_patch.dimension(4), 1); |
| } |
| |
| for (int i = 0; i < tensor.size(); ++i) { |
| VERIFY_IS_EQUAL(tensor.data()[i], no_patch.data()[i]); |
| } |
| |
| patch_dims[0] = 2; |
| patch_dims[1] = 3; |
| patch_dims[2] = 5; |
| patch_dims[3] = 7; |
| |
| if (DataLayout == ColMajor) { |
| patchTensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4, 1}}; |
| } else { |
| patchTensorRange = {{1, sizeDim1, sizeDim2, sizeDim3, sizeDim4}}; |
| } |
| Tensor<DataType, 5, DataLayout, IndexType> single_patch(patchTensorRange); |
| patchTensorBuffSize = single_patch.size() * sizeof(DataType); |
| DataType* gpu_data_single_patch = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_single_patch(gpu_data_single_patch, patchTensorRange); |
| |
| gpu_single_patch.device(sycl_device) = gpu_tensor.extract_patches(patch_dims); |
| sycl_device.memcpyDeviceToHost(single_patch.data(), gpu_data_single_patch, patchTensorBuffSize); |
| |
| if (DataLayout == ColMajor) { |
| VERIFY_IS_EQUAL(single_patch.dimension(0), 2); |
| VERIFY_IS_EQUAL(single_patch.dimension(1), 3); |
| VERIFY_IS_EQUAL(single_patch.dimension(2), 5); |
| VERIFY_IS_EQUAL(single_patch.dimension(3), 7); |
| VERIFY_IS_EQUAL(single_patch.dimension(4), 1); |
| } else { |
| VERIFY_IS_EQUAL(single_patch.dimension(0), 1); |
| VERIFY_IS_EQUAL(single_patch.dimension(1), 2); |
| VERIFY_IS_EQUAL(single_patch.dimension(2), 3); |
| VERIFY_IS_EQUAL(single_patch.dimension(3), 5); |
| VERIFY_IS_EQUAL(single_patch.dimension(4), 7); |
| } |
| |
| for (int i = 0; i < tensor.size(); ++i) { |
| VERIFY_IS_EQUAL(tensor.data()[i], single_patch.data()[i]); |
| } |
| patch_dims[0] = 1; |
| patch_dims[1] = 2; |
| patch_dims[2] = 2; |
| patch_dims[3] = 1; |
| |
| if (DataLayout == ColMajor) { |
| patchTensorRange = {{1, 2, 2, 1, 2 * 2 * 4 * 7}}; |
| } else { |
| patchTensorRange = {{2 * 2 * 4 * 7, 1, 2, 2, 1}}; |
| } |
| Tensor<DataType, 5, DataLayout, IndexType> twod_patch(patchTensorRange); |
| patchTensorBuffSize = twod_patch.size() * sizeof(DataType); |
| DataType* gpu_data_twod_patch = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_twod_patch(gpu_data_twod_patch, patchTensorRange); |
| |
| gpu_twod_patch.device(sycl_device) = gpu_tensor.extract_patches(patch_dims); |
| sycl_device.memcpyDeviceToHost(twod_patch.data(), gpu_data_twod_patch, patchTensorBuffSize); |
| |
| if (DataLayout == ColMajor) { |
| VERIFY_IS_EQUAL(twod_patch.dimension(0), 1); |
| VERIFY_IS_EQUAL(twod_patch.dimension(1), 2); |
| VERIFY_IS_EQUAL(twod_patch.dimension(2), 2); |
| VERIFY_IS_EQUAL(twod_patch.dimension(3), 1); |
| VERIFY_IS_EQUAL(twod_patch.dimension(4), 2 * 2 * 4 * 7); |
| } else { |
| VERIFY_IS_EQUAL(twod_patch.dimension(0), 2 * 2 * 4 * 7); |
| VERIFY_IS_EQUAL(twod_patch.dimension(1), 1); |
| VERIFY_IS_EQUAL(twod_patch.dimension(2), 2); |
| VERIFY_IS_EQUAL(twod_patch.dimension(3), 2); |
| VERIFY_IS_EQUAL(twod_patch.dimension(4), 1); |
| } |
| |
| for (int i = 0; i < 2; ++i) { |
| for (int j = 0; j < 2; ++j) { |
| for (int k = 0; k < 4; ++k) { |
| for (int l = 0; l < 7; ++l) { |
| int patch_loc; |
| if (DataLayout == ColMajor) { |
| patch_loc = i + 2 * (j + 2 * (k + 4 * l)); |
| } else { |
| patch_loc = l + 7 * (k + 4 * (j + 2 * i)); |
| } |
| for (int x = 0; x < 2; ++x) { |
| for (int y = 0; y < 2; ++y) { |
| if (DataLayout == ColMajor) { |
| VERIFY_IS_EQUAL(tensor(i, j + x, k + y, l), twod_patch(0, x, y, 0, patch_loc)); |
| } else { |
| VERIFY_IS_EQUAL(tensor(i, j + x, k + y, l), twod_patch(patch_loc, 0, x, y, 0)); |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| patch_dims[0] = 1; |
| patch_dims[1] = 2; |
| patch_dims[2] = 3; |
| patch_dims[3] = 5; |
| |
| if (DataLayout == ColMajor) { |
| patchTensorRange = {{1, 2, 3, 5, 2 * 2 * 3 * 3}}; |
| } else { |
| patchTensorRange = {{2 * 2 * 3 * 3, 1, 2, 3, 5}}; |
| } |
| Tensor<DataType, 5, DataLayout, IndexType> threed_patch(patchTensorRange); |
| patchTensorBuffSize = threed_patch.size() * sizeof(DataType); |
| DataType* gpu_data_threed_patch = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize)); |
| TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_threed_patch(gpu_data_threed_patch, patchTensorRange); |
| |
| gpu_threed_patch.device(sycl_device) = gpu_tensor.extract_patches(patch_dims); |
| sycl_device.memcpyDeviceToHost(threed_patch.data(), gpu_data_threed_patch, patchTensorBuffSize); |
| |
| if (DataLayout == ColMajor) { |
| VERIFY_IS_EQUAL(threed_patch.dimension(0), 1); |
| VERIFY_IS_EQUAL(threed_patch.dimension(1), 2); |
| VERIFY_IS_EQUAL(threed_patch.dimension(2), 3); |
| VERIFY_IS_EQUAL(threed_patch.dimension(3), 5); |
| VERIFY_IS_EQUAL(threed_patch.dimension(4), 2 * 2 * 3 * 3); |
| } else { |
| VERIFY_IS_EQUAL(threed_patch.dimension(0), 2 * 2 * 3 * 3); |
| VERIFY_IS_EQUAL(threed_patch.dimension(1), 1); |
| VERIFY_IS_EQUAL(threed_patch.dimension(2), 2); |
| VERIFY_IS_EQUAL(threed_patch.dimension(3), 3); |
| VERIFY_IS_EQUAL(threed_patch.dimension(4), 5); |
| } |
| |
| for (int i = 0; i < 2; ++i) { |
| for (int j = 0; j < 2; ++j) { |
| for (int k = 0; k < 3; ++k) { |
| for (int l = 0; l < 3; ++l) { |
| int patch_loc; |
| if (DataLayout == ColMajor) { |
| patch_loc = i + 2 * (j + 2 * (k + 3 * l)); |
| } else { |
| patch_loc = l + 3 * (k + 3 * (j + 2 * i)); |
| } |
| for (int x = 0; x < 2; ++x) { |
| for (int y = 0; y < 3; ++y) { |
| for (int z = 0; z < 5; ++z) { |
| if (DataLayout == ColMajor) { |
| VERIFY_IS_EQUAL(tensor(i, j + x, k + y, l + z), threed_patch(0, x, y, z, patch_loc)); |
| } else { |
| VERIFY_IS_EQUAL(tensor(i, j + x, k + y, l + z), threed_patch(patch_loc, 0, x, y, z)); |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| sycl_device.deallocate(gpu_data_tensor); |
| sycl_device.deallocate(gpu_data_no_patch); |
| sycl_device.deallocate(gpu_data_single_patch); |
| sycl_device.deallocate(gpu_data_twod_patch); |
| sycl_device.deallocate(gpu_data_threed_patch); |
| } |
| |
| template <typename DataType, typename dev_Selector> |
| void sycl_tensor_patch_test_per_device(dev_Selector s) { |
| QueueInterface queueInterface(s); |
| auto sycl_device = Eigen::SyclDevice(&queueInterface); |
| test_simple_patch_sycl<DataType, RowMajor, int64_t>(sycl_device); |
| test_simple_patch_sycl<DataType, ColMajor, int64_t>(sycl_device); |
| } |
| EIGEN_DECLARE_TEST(cxx11_tensor_patch_sycl) { |
| for (const auto& device : Eigen::get_sycl_supported_devices()) { |
| CALL_SUBTEST(sycl_tensor_patch_test_per_device<half>(device)); |
| CALL_SUBTEST(sycl_tensor_patch_test_per_device<float>(device)); |
| } |
| } |
