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 #define EIGEN_TEST_NO_LONGDOUBLE
16 #define EIGEN_TEST_NO_COMPLEX
17
18 #define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
19 #define EIGEN_USE_SYCL
20
21 #include "main.h"
22
23 #include <Eigen/CXX11/Tensor>
24
25 using Eigen::Tensor;
26
27 template <typename DataType, int DataLayout, typename IndexType>
test_simple_patch_sycl(const Eigen::SyclDevice & sycl_device)28 static void test_simple_patch_sycl(const Eigen::SyclDevice& sycl_device){
29
30 IndexType sizeDim1 = 2;
31 IndexType sizeDim2 = 3;
32 IndexType sizeDim3 = 5;
33 IndexType sizeDim4 = 7;
34 array<IndexType, 4> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
35 array<IndexType, 5> patchTensorRange;
36 if (DataLayout == ColMajor) {
37 patchTensorRange = {{1, 1, 1, 1, sizeDim1*sizeDim2*sizeDim3*sizeDim4}};
38 }else{
39 patchTensorRange = {{sizeDim1*sizeDim2*sizeDim3*sizeDim4,1, 1, 1, 1}};
40 }
41
42 Tensor<DataType, 4, DataLayout,IndexType> tensor(tensorRange);
43 Tensor<DataType, 5, DataLayout,IndexType> no_patch(patchTensorRange);
44
45 tensor.setRandom();
46
47 array<ptrdiff_t, 4> patch_dims;
48 patch_dims[0] = 1;
49 patch_dims[1] = 1;
50 patch_dims[2] = 1;
51 patch_dims[3] = 1;
52
53 const size_t tensorBuffSize =tensor.size()*sizeof(DataType);
54 size_t patchTensorBuffSize =no_patch.size()*sizeof(DataType);
55 DataType* gpu_data_tensor = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
56 DataType* gpu_data_no_patch = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
57
58 TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_tensor(gpu_data_tensor, tensorRange);
59 TensorMap<Tensor<DataType, 5, DataLayout,IndexType>> gpu_no_patch(gpu_data_no_patch, patchTensorRange);
60
61 sycl_device.memcpyHostToDevice(gpu_data_tensor, tensor.data(), tensorBuffSize);
62 gpu_no_patch.device(sycl_device)=gpu_tensor.extract_patches(patch_dims);
63 sycl_device.memcpyDeviceToHost(no_patch.data(), gpu_data_no_patch, patchTensorBuffSize);
64
65 if (DataLayout == ColMajor) {
66 VERIFY_IS_EQUAL(no_patch.dimension(0), 1);
67 VERIFY_IS_EQUAL(no_patch.dimension(1), 1);
68 VERIFY_IS_EQUAL(no_patch.dimension(2), 1);
69 VERIFY_IS_EQUAL(no_patch.dimension(3), 1);
70 VERIFY_IS_EQUAL(no_patch.dimension(4), tensor.size());
71 } else {
72 VERIFY_IS_EQUAL(no_patch.dimension(0), tensor.size());
73 VERIFY_IS_EQUAL(no_patch.dimension(1), 1);
74 VERIFY_IS_EQUAL(no_patch.dimension(2), 1);
75 VERIFY_IS_EQUAL(no_patch.dimension(3), 1);
76 VERIFY_IS_EQUAL(no_patch.dimension(4), 1);
77 }
78
79 for (int i = 0; i < tensor.size(); ++i) {
80 VERIFY_IS_EQUAL(tensor.data()[i], no_patch.data()[i]);
81 }
82
83 patch_dims[0] = 2;
84 patch_dims[1] = 3;
85 patch_dims[2] = 5;
86 patch_dims[3] = 7;
87
88 if (DataLayout == ColMajor) {
89 patchTensorRange = {{sizeDim1,sizeDim2,sizeDim3,sizeDim4,1}};
90 }else{
91 patchTensorRange = {{1,sizeDim1,sizeDim2,sizeDim3,sizeDim4}};
92 }
93 Tensor<DataType, 5, DataLayout,IndexType> single_patch(patchTensorRange);
94 patchTensorBuffSize =single_patch.size()*sizeof(DataType);
95 DataType* gpu_data_single_patch = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
96 TensorMap<Tensor<DataType, 5, DataLayout,IndexType>> gpu_single_patch(gpu_data_single_patch, patchTensorRange);
97
98 gpu_single_patch.device(sycl_device)=gpu_tensor.extract_patches(patch_dims);
99 sycl_device.memcpyDeviceToHost(single_patch.data(), gpu_data_single_patch, patchTensorBuffSize);
100
101 if (DataLayout == ColMajor) {
102 VERIFY_IS_EQUAL(single_patch.dimension(0), 2);
103 VERIFY_IS_EQUAL(single_patch.dimension(1), 3);
104 VERIFY_IS_EQUAL(single_patch.dimension(2), 5);
105 VERIFY_IS_EQUAL(single_patch.dimension(3), 7);
106 VERIFY_IS_EQUAL(single_patch.dimension(4), 1);
107 } else {
108 VERIFY_IS_EQUAL(single_patch.dimension(0), 1);
109 VERIFY_IS_EQUAL(single_patch.dimension(1), 2);
110 VERIFY_IS_EQUAL(single_patch.dimension(2), 3);
111 VERIFY_IS_EQUAL(single_patch.dimension(3), 5);
112 VERIFY_IS_EQUAL(single_patch.dimension(4), 7);
113 }
114
115 for (int i = 0; i < tensor.size(); ++i) {
116 VERIFY_IS_EQUAL(tensor.data()[i], single_patch.data()[i]);
117 }
118 patch_dims[0] = 1;
119 patch_dims[1] = 2;
120 patch_dims[2] = 2;
121 patch_dims[3] = 1;
122
123 if (DataLayout == ColMajor) {
124 patchTensorRange = {{1,2,2,1,2*2*4*7}};
125 }else{
126 patchTensorRange = {{2*2*4*7, 1, 2,2,1}};
127 }
128 Tensor<DataType, 5, DataLayout,IndexType> twod_patch(patchTensorRange);
129 patchTensorBuffSize =twod_patch.size()*sizeof(DataType);
130 DataType* gpu_data_twod_patch = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
131 TensorMap<Tensor<DataType, 5, DataLayout,IndexType>> gpu_twod_patch(gpu_data_twod_patch, patchTensorRange);
132
133 gpu_twod_patch.device(sycl_device)=gpu_tensor.extract_patches(patch_dims);
134 sycl_device.memcpyDeviceToHost(twod_patch.data(), gpu_data_twod_patch, patchTensorBuffSize);
135
136 if (DataLayout == ColMajor) {
137 VERIFY_IS_EQUAL(twod_patch.dimension(0), 1);
138 VERIFY_IS_EQUAL(twod_patch.dimension(1), 2);
139 VERIFY_IS_EQUAL(twod_patch.dimension(2), 2);
140 VERIFY_IS_EQUAL(twod_patch.dimension(3), 1);
141 VERIFY_IS_EQUAL(twod_patch.dimension(4), 2*2*4*7);
142 } else {
143 VERIFY_IS_EQUAL(twod_patch.dimension(0), 2*2*4*7);
144 VERIFY_IS_EQUAL(twod_patch.dimension(1), 1);
145 VERIFY_IS_EQUAL(twod_patch.dimension(2), 2);
146 VERIFY_IS_EQUAL(twod_patch.dimension(3), 2);
147 VERIFY_IS_EQUAL(twod_patch.dimension(4), 1);
148 }
149
150 for (int i = 0; i < 2; ++i) {
151 for (int j = 0; j < 2; ++j) {
152 for (int k = 0; k < 4; ++k) {
153 for (int l = 0; l < 7; ++l) {
154 int patch_loc;
155 if (DataLayout == ColMajor) {
156 patch_loc = i + 2 * (j + 2 * (k + 4 * l));
157 } else {
158 patch_loc = l + 7 * (k + 4 * (j + 2 * i));
159 }
160 for (int x = 0; x < 2; ++x) {
161 for (int y = 0; y < 2; ++y) {
162 if (DataLayout == ColMajor) {
163 VERIFY_IS_EQUAL(tensor(i,j+x,k+y,l), twod_patch(0,x,y,0,patch_loc));
164 } else {
165 VERIFY_IS_EQUAL(tensor(i,j+x,k+y,l), twod_patch(patch_loc,0,x,y,0));
166 }
167 }
168 }
169 }
170 }
171 }
172 }
173
174 patch_dims[0] = 1;
175 patch_dims[1] = 2;
176 patch_dims[2] = 3;
177 patch_dims[3] = 5;
178
179 if (DataLayout == ColMajor) {
180 patchTensorRange = {{1,2,3,5,2*2*3*3}};
181 }else{
182 patchTensorRange = {{2*2*3*3, 1, 2,3,5}};
183 }
184 Tensor<DataType, 5, DataLayout,IndexType> threed_patch(patchTensorRange);
185 patchTensorBuffSize =threed_patch.size()*sizeof(DataType);
186 DataType* gpu_data_threed_patch = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
187 TensorMap<Tensor<DataType, 5, DataLayout,IndexType>> gpu_threed_patch(gpu_data_threed_patch, patchTensorRange);
188
189 gpu_threed_patch.device(sycl_device)=gpu_tensor.extract_patches(patch_dims);
190 sycl_device.memcpyDeviceToHost(threed_patch.data(), gpu_data_threed_patch, patchTensorBuffSize);
191
192 if (DataLayout == ColMajor) {
193 VERIFY_IS_EQUAL(threed_patch.dimension(0), 1);
194 VERIFY_IS_EQUAL(threed_patch.dimension(1), 2);
195 VERIFY_IS_EQUAL(threed_patch.dimension(2), 3);
196 VERIFY_IS_EQUAL(threed_patch.dimension(3), 5);
197 VERIFY_IS_EQUAL(threed_patch.dimension(4), 2*2*3*3);
198 } else {
199 VERIFY_IS_EQUAL(threed_patch.dimension(0), 2*2*3*3);
200 VERIFY_IS_EQUAL(threed_patch.dimension(1), 1);
201 VERIFY_IS_EQUAL(threed_patch.dimension(2), 2);
202 VERIFY_IS_EQUAL(threed_patch.dimension(3), 3);
203 VERIFY_IS_EQUAL(threed_patch.dimension(4), 5);
204 }
205
206 for (int i = 0; i < 2; ++i) {
207 for (int j = 0; j < 2; ++j) {
208 for (int k = 0; k < 3; ++k) {
209 for (int l = 0; l < 3; ++l) {
210 int patch_loc;
211 if (DataLayout == ColMajor) {
212 patch_loc = i + 2 * (j + 2 * (k + 3 * l));
213 } else {
214 patch_loc = l + 3 * (k + 3 * (j + 2 * i));
215 }
216 for (int x = 0; x < 2; ++x) {
217 for (int y = 0; y < 3; ++y) {
218 for (int z = 0; z < 5; ++z) {
219 if (DataLayout == ColMajor) {
220 VERIFY_IS_EQUAL(tensor(i,j+x,k+y,l+z), threed_patch(0,x,y,z,patch_loc));
221 } else {
222 VERIFY_IS_EQUAL(tensor(i,j+x,k+y,l+z), threed_patch(patch_loc,0,x,y,z));
223 }
224 }
225 }
226 }
227 }
228 }
229 }
230 }
231 sycl_device.deallocate(gpu_data_tensor);
232 sycl_device.deallocate(gpu_data_no_patch);
233 sycl_device.deallocate(gpu_data_single_patch);
234 sycl_device.deallocate(gpu_data_twod_patch);
235 sycl_device.deallocate(gpu_data_threed_patch);
236 }
237
sycl_tensor_patch_test_per_device(dev_Selector s)238 template<typename DataType, typename dev_Selector> void sycl_tensor_patch_test_per_device(dev_Selector s){
239 QueueInterface queueInterface(s);
240 auto sycl_device = Eigen::SyclDevice(&queueInterface);
241 test_simple_patch_sycl<DataType, RowMajor, int64_t>(sycl_device);
242 test_simple_patch_sycl<DataType, ColMajor, int64_t>(sycl_device);
243 }
EIGEN_DECLARE_TEST(cxx11_tensor_patch_sycl)244 EIGEN_DECLARE_TEST(cxx11_tensor_patch_sycl)
245 {
246 for (const auto& device :Eigen::get_sycl_supported_devices()) {
247 CALL_SUBTEST(sycl_tensor_patch_test_per_device<float>(device));
248 }
249 }
250