cxx11_tensor_padding_sycl.cpp (5677B)
1 // This file is part of Eigen, a lightweight C++ template library 2 // for linear algebra. 3 // 4 // Copyright (C) 2016 5 // Mehdi Goli Codeplay Software Ltd. 6 // Ralph Potter Codeplay Software Ltd. 7 // Luke Iwanski Codeplay Software Ltd. 8 // Contact: <eigen@codeplay.com> 9 // Benoit Steiner <benoit.steiner.goog@gmail.com> 10 // 11 // This Source Code Form is subject to the terms of the Mozilla 12 // Public License v. 2.0. If a copy of the MPL was not distributed 13 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 14 15 16 #define EIGEN_TEST_NO_LONGDOUBLE 17 #define EIGEN_TEST_NO_COMPLEX 18 19 #define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t 20 #define EIGEN_USE_SYCL 21 22 23 #include "main.h" 24 #include <unsupported/Eigen/CXX11/Tensor> 25 26 using Eigen::array; 27 using Eigen::SyclDevice; 28 using Eigen::Tensor; 29 using Eigen::TensorMap; 30 31 32 template<typename DataType, int DataLayout, typename IndexType> 33 static void test_simple_padding(const Eigen::SyclDevice& sycl_device) 34 { 35 36 IndexType sizeDim1 = 2; 37 IndexType sizeDim2 = 3; 38 IndexType sizeDim3 = 5; 39 IndexType sizeDim4 = 7; 40 array<IndexType, 4> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}}; 41 42 Tensor<DataType, 4, DataLayout, IndexType> tensor(tensorRange); 43 tensor.setRandom(); 44 45 array<std::pair<IndexType, IndexType>, 4> paddings; 46 paddings[0] = std::make_pair(0, 0); 47 paddings[1] = std::make_pair(2, 1); 48 paddings[2] = std::make_pair(3, 4); 49 paddings[3] = std::make_pair(0, 0); 50 51 IndexType padedSizeDim1 = 2; 52 IndexType padedSizeDim2 = 6; 53 IndexType padedSizeDim3 = 12; 54 IndexType padedSizeDim4 = 7; 55 array<IndexType, 4> padedtensorRange = {{padedSizeDim1, padedSizeDim2, padedSizeDim3, padedSizeDim4}}; 56 57 Tensor<DataType, 4, DataLayout, IndexType> padded(padedtensorRange); 58 59 60 DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor.size()*sizeof(DataType))); 61 DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(padded.size()*sizeof(DataType))); 62 TensorMap<Tensor<DataType, 4,DataLayout,IndexType>> gpu1(gpu_data1, tensorRange); 63 TensorMap<Tensor<DataType, 4,DataLayout,IndexType>> gpu2(gpu_data2, padedtensorRange); 64 65 VERIFY_IS_EQUAL(padded.dimension(0), 2+0); 66 VERIFY_IS_EQUAL(padded.dimension(1), 3+3); 67 VERIFY_IS_EQUAL(padded.dimension(2), 5+7); 68 VERIFY_IS_EQUAL(padded.dimension(3), 7+0); 69 sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(),(tensor.size())*sizeof(DataType)); 70 gpu2.device(sycl_device)=gpu1.pad(paddings); 71 sycl_device.memcpyDeviceToHost(padded.data(), gpu_data2,(padded.size())*sizeof(DataType)); 72 for (IndexType i = 0; i < padedSizeDim1; ++i) { 73 for (IndexType j = 0; j < padedSizeDim2; ++j) { 74 for (IndexType k = 0; k < padedSizeDim3; ++k) { 75 for (IndexType l = 0; l < padedSizeDim4; ++l) { 76 if (j >= 2 && j < 5 && k >= 3 && k < 8) { 77 VERIFY_IS_EQUAL(padded(i,j,k,l), tensor(i,j-2,k-3,l)); 78 } else { 79 VERIFY_IS_EQUAL(padded(i,j,k,l), 0.0f); 80 } 81 } 82 } 83 } 84 } 85 sycl_device.deallocate(gpu_data1); 86 sycl_device.deallocate(gpu_data2); 87 } 88 89 template<typename DataType, int DataLayout, typename IndexType> 90 static void test_padded_expr(const Eigen::SyclDevice& sycl_device) 91 { 92 IndexType sizeDim1 = 2; 93 IndexType sizeDim2 = 3; 94 IndexType sizeDim3 = 5; 95 IndexType sizeDim4 = 7; 96 array<IndexType, 4> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}}; 97 98 Tensor<DataType, 4, DataLayout, IndexType> tensor(tensorRange); 99 tensor.setRandom(); 100 101 array<std::pair<IndexType, IndexType>, 4> paddings; 102 paddings[0] = std::make_pair(0, 0); 103 paddings[1] = std::make_pair(2, 1); 104 paddings[2] = std::make_pair(3, 4); 105 paddings[3] = std::make_pair(0, 0); 106 107 Eigen::DSizes<IndexType, 2> reshape_dims; 108 reshape_dims[0] = 12; 109 reshape_dims[1] = 84; 110 111 112 Tensor<DataType, 2, DataLayout, IndexType> result(reshape_dims); 113 114 DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor.size()*sizeof(DataType))); 115 DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(result.size()*sizeof(DataType))); 116 TensorMap<Tensor<DataType, 4,DataLayout,IndexType>> gpu1(gpu_data1, tensorRange); 117 TensorMap<Tensor<DataType, 2,DataLayout,IndexType>> gpu2(gpu_data2, reshape_dims); 118 119 120 sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(),(tensor.size())*sizeof(DataType)); 121 gpu2.device(sycl_device)=gpu1.pad(paddings).reshape(reshape_dims); 122 sycl_device.memcpyDeviceToHost(result.data(), gpu_data2,(result.size())*sizeof(DataType)); 123 124 for (IndexType i = 0; i < 2; ++i) { 125 for (IndexType j = 0; j < 6; ++j) { 126 for (IndexType k = 0; k < 12; ++k) { 127 for (IndexType l = 0; l < 7; ++l) { 128 const float result_value = DataLayout == ColMajor ? 129 result(i+2*j,k+12*l) : result(j+6*i,l+7*k); 130 if (j >= 2 && j < 5 && k >= 3 && k < 8) { 131 VERIFY_IS_EQUAL(result_value, tensor(i,j-2,k-3,l)); 132 } else { 133 VERIFY_IS_EQUAL(result_value, 0.0f); 134 } 135 } 136 } 137 } 138 } 139 sycl_device.deallocate(gpu_data1); 140 sycl_device.deallocate(gpu_data2); 141 } 142 143 template<typename DataType, typename dev_Selector> void sycl_padding_test_per_device(dev_Selector s){ 144 QueueInterface queueInterface(s); 145 auto sycl_device = Eigen::SyclDevice(&queueInterface); 146 test_simple_padding<DataType, RowMajor, int64_t>(sycl_device); 147 test_simple_padding<DataType, ColMajor, int64_t>(sycl_device); 148 test_padded_expr<DataType, RowMajor, int64_t>(sycl_device); 149 test_padded_expr<DataType, ColMajor, int64_t>(sycl_device); 150 151 } 152 EIGEN_DECLARE_TEST(cxx11_tensor_padding_sycl) 153 { 154 for (const auto& device :Eigen::get_sycl_supported_devices()) { 155 CALL_SUBTEST(sycl_padding_test_per_device<float>(device)); 156 } 157 }