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>
test_simple_padding(const Eigen::SyclDevice & sycl_device)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>
test_padded_expr(const Eigen::SyclDevice & sycl_device)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
sycl_padding_test_per_device(dev_Selector s)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 }
EIGEN_DECLARE_TEST(cxx11_tensor_padding_sycl)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 }
158