cxx11_tensor_inflation_sycl.cpp (5101B)
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 // 10 // This Source Code Form is subject to the terms of the Mozilla 11 // Public License v. 2.0. If a copy of the MPL was not distributed 12 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 13 14 #define EIGEN_TEST_NO_LONGDOUBLE 15 #define EIGEN_TEST_NO_COMPLEX 16 17 #define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t 18 #define EIGEN_USE_SYCL 19 20 #include "main.h" 21 #include <unsupported/Eigen/CXX11/Tensor> 22 23 using Eigen::Tensor; 24 25 // Inflation Definition for each dimension the inflated val would be 26 //((dim-1)*strid[dim] +1) 27 28 // for 1 dimension vector of size 3 with value (4,4,4) with the inflated stride value of 3 would be changed to 29 // tensor of size (2*3) +1 = 7 with the value of 30 // (4, 0, 0, 4, 0, 0, 4). 31 32 template <typename DataType, int DataLayout, typename IndexType> 33 void test_simple_inflation_sycl(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 Tensor<DataType, 4, DataLayout,IndexType> tensor(tensorRange); 42 Tensor<DataType, 4, DataLayout,IndexType> no_stride(tensorRange); 43 tensor.setRandom(); 44 45 array<IndexType, 4> strides; 46 strides[0] = 1; 47 strides[1] = 1; 48 strides[2] = 1; 49 strides[3] = 1; 50 51 52 const size_t tensorBuffSize =tensor.size()*sizeof(DataType); 53 DataType* gpu_data_tensor = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize)); 54 DataType* gpu_data_no_stride = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize)); 55 56 TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_tensor(gpu_data_tensor, tensorRange); 57 TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_no_stride(gpu_data_no_stride, tensorRange); 58 59 sycl_device.memcpyHostToDevice(gpu_data_tensor, tensor.data(), tensorBuffSize); 60 gpu_no_stride.device(sycl_device)=gpu_tensor.inflate(strides); 61 sycl_device.memcpyDeviceToHost(no_stride.data(), gpu_data_no_stride, tensorBuffSize); 62 63 VERIFY_IS_EQUAL(no_stride.dimension(0), sizeDim1); 64 VERIFY_IS_EQUAL(no_stride.dimension(1), sizeDim2); 65 VERIFY_IS_EQUAL(no_stride.dimension(2), sizeDim3); 66 VERIFY_IS_EQUAL(no_stride.dimension(3), sizeDim4); 67 68 for (IndexType i = 0; i < 2; ++i) { 69 for (IndexType j = 0; j < 3; ++j) { 70 for (IndexType k = 0; k < 5; ++k) { 71 for (IndexType l = 0; l < 7; ++l) { 72 VERIFY_IS_EQUAL(tensor(i,j,k,l), no_stride(i,j,k,l)); 73 } 74 } 75 } 76 } 77 78 79 strides[0] = 2; 80 strides[1] = 4; 81 strides[2] = 2; 82 strides[3] = 3; 83 84 IndexType inflatedSizeDim1 = 3; 85 IndexType inflatedSizeDim2 = 9; 86 IndexType inflatedSizeDim3 = 9; 87 IndexType inflatedSizeDim4 = 19; 88 array<IndexType, 4> inflatedTensorRange = {{inflatedSizeDim1, inflatedSizeDim2, inflatedSizeDim3, inflatedSizeDim4}}; 89 90 Tensor<DataType, 4, DataLayout, IndexType> inflated(inflatedTensorRange); 91 92 const size_t inflatedTensorBuffSize =inflated.size()*sizeof(DataType); 93 DataType* gpu_data_inflated = static_cast<DataType*>(sycl_device.allocate(inflatedTensorBuffSize)); 94 TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_inflated(gpu_data_inflated, inflatedTensorRange); 95 gpu_inflated.device(sycl_device)=gpu_tensor.inflate(strides); 96 sycl_device.memcpyDeviceToHost(inflated.data(), gpu_data_inflated, inflatedTensorBuffSize); 97 98 VERIFY_IS_EQUAL(inflated.dimension(0), inflatedSizeDim1); 99 VERIFY_IS_EQUAL(inflated.dimension(1), inflatedSizeDim2); 100 VERIFY_IS_EQUAL(inflated.dimension(2), inflatedSizeDim3); 101 VERIFY_IS_EQUAL(inflated.dimension(3), inflatedSizeDim4); 102 103 for (IndexType i = 0; i < inflatedSizeDim1; ++i) { 104 for (IndexType j = 0; j < inflatedSizeDim2; ++j) { 105 for (IndexType k = 0; k < inflatedSizeDim3; ++k) { 106 for (IndexType l = 0; l < inflatedSizeDim4; ++l) { 107 if (i % strides[0] == 0 && 108 j % strides[1] == 0 && 109 k % strides[2] == 0 && 110 l % strides[3] == 0) { 111 VERIFY_IS_EQUAL(inflated(i,j,k,l), 112 tensor(i/strides[0], j/strides[1], k/strides[2], l/strides[3])); 113 } else { 114 VERIFY_IS_EQUAL(0, inflated(i,j,k,l)); 115 } 116 } 117 } 118 } 119 } 120 sycl_device.deallocate(gpu_data_tensor); 121 sycl_device.deallocate(gpu_data_no_stride); 122 sycl_device.deallocate(gpu_data_inflated); 123 } 124 125 template<typename DataType, typename dev_Selector> void sycl_inflation_test_per_device(dev_Selector s){ 126 QueueInterface queueInterface(s); 127 auto sycl_device = Eigen::SyclDevice(&queueInterface); 128 test_simple_inflation_sycl<DataType, RowMajor, int64_t>(sycl_device); 129 test_simple_inflation_sycl<DataType, ColMajor, int64_t>(sycl_device); 130 } 131 EIGEN_DECLARE_TEST(cxx11_tensor_inflation_sycl) 132 { 133 for (const auto& device :Eigen::get_sycl_supported_devices()) { 134 CALL_SUBTEST(sycl_inflation_test_per_device<float>(device)); 135 } 136 }