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 template<typename DataType, int DataLayout, typename IndexType>
test_simple_concatenation(const Eigen::SyclDevice & sycl_device)26 static void test_simple_concatenation(const Eigen::SyclDevice& sycl_device)
27 {
28 IndexType leftDim1 = 2;
29 IndexType leftDim2 = 3;
30 IndexType leftDim3 = 1;
31 Eigen::array<IndexType, 3> leftRange = {{leftDim1, leftDim2, leftDim3}};
32 IndexType rightDim1 = 2;
33 IndexType rightDim2 = 3;
34 IndexType rightDim3 = 1;
35 Eigen::array<IndexType, 3> rightRange = {{rightDim1, rightDim2, rightDim3}};
36
37 //IndexType concatDim1 = 3;
38 // IndexType concatDim2 = 3;
39 // IndexType concatDim3 = 1;
40 //Eigen::array<IndexType, 3> concatRange = {{concatDim1, concatDim2, concatDim3}};
41
42 Tensor<DataType, 3, DataLayout, IndexType> left(leftRange);
43 Tensor<DataType, 3, DataLayout, IndexType> right(rightRange);
44 left.setRandom();
45 right.setRandom();
46
47 DataType * gpu_in1_data = static_cast<DataType*>(sycl_device.allocate(left.dimensions().TotalSize()*sizeof(DataType)));
48 DataType * gpu_in2_data = static_cast<DataType*>(sycl_device.allocate(right.dimensions().TotalSize()*sizeof(DataType)));
49
50 Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_in1(gpu_in1_data, leftRange);
51 Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_in2(gpu_in2_data, rightRange);
52 sycl_device.memcpyHostToDevice(gpu_in1_data, left.data(),(left.dimensions().TotalSize())*sizeof(DataType));
53 sycl_device.memcpyHostToDevice(gpu_in2_data, right.data(),(right.dimensions().TotalSize())*sizeof(DataType));
54 ///
55 Tensor<DataType, 3, DataLayout, IndexType> concatenation1(leftDim1+rightDim1, leftDim2, leftDim3);
56 DataType * gpu_out_data1 = static_cast<DataType*>(sycl_device.allocate(concatenation1.dimensions().TotalSize()*sizeof(DataType)));
57 Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_out1(gpu_out_data1, concatenation1.dimensions());
58
59 //concatenation = left.concatenate(right, 0);
60 gpu_out1.device(sycl_device) =gpu_in1.concatenate(gpu_in2, 0);
61 sycl_device.memcpyDeviceToHost(concatenation1.data(), gpu_out_data1,(concatenation1.dimensions().TotalSize())*sizeof(DataType));
62
63 VERIFY_IS_EQUAL(concatenation1.dimension(0), 4);
64 VERIFY_IS_EQUAL(concatenation1.dimension(1), 3);
65 VERIFY_IS_EQUAL(concatenation1.dimension(2), 1);
66 for (IndexType j = 0; j < 3; ++j) {
67 for (IndexType i = 0; i < 2; ++i) {
68 VERIFY_IS_EQUAL(concatenation1(i, j, 0), left(i, j, 0));
69 }
70 for (IndexType i = 2; i < 4; ++i) {
71 VERIFY_IS_EQUAL(concatenation1(i, j, 0), right(i - 2, j, 0));
72 }
73 }
74
75 sycl_device.deallocate(gpu_out_data1);
76 Tensor<DataType, 3, DataLayout, IndexType> concatenation2(leftDim1, leftDim2 +rightDim2, leftDim3);
77 DataType * gpu_out_data2 = static_cast<DataType*>(sycl_device.allocate(concatenation2.dimensions().TotalSize()*sizeof(DataType)));
78 Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_out2(gpu_out_data2, concatenation2.dimensions());
79 gpu_out2.device(sycl_device) =gpu_in1.concatenate(gpu_in2, 1);
80 sycl_device.memcpyDeviceToHost(concatenation2.data(), gpu_out_data2,(concatenation2.dimensions().TotalSize())*sizeof(DataType));
81
82 //concatenation = left.concatenate(right, 1);
83 VERIFY_IS_EQUAL(concatenation2.dimension(0), 2);
84 VERIFY_IS_EQUAL(concatenation2.dimension(1), 6);
85 VERIFY_IS_EQUAL(concatenation2.dimension(2), 1);
86 for (IndexType i = 0; i < 2; ++i) {
87 for (IndexType j = 0; j < 3; ++j) {
88 VERIFY_IS_EQUAL(concatenation2(i, j, 0), left(i, j, 0));
89 }
90 for (IndexType j = 3; j < 6; ++j) {
91 VERIFY_IS_EQUAL(concatenation2(i, j, 0), right(i, j - 3, 0));
92 }
93 }
94 sycl_device.deallocate(gpu_out_data2);
95 Tensor<DataType, 3, DataLayout, IndexType> concatenation3(leftDim1, leftDim2, leftDim3+rightDim3);
96 DataType * gpu_out_data3 = static_cast<DataType*>(sycl_device.allocate(concatenation3.dimensions().TotalSize()*sizeof(DataType)));
97 Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_out3(gpu_out_data3, concatenation3.dimensions());
98 gpu_out3.device(sycl_device) =gpu_in1.concatenate(gpu_in2, 2);
99 sycl_device.memcpyDeviceToHost(concatenation3.data(), gpu_out_data3,(concatenation3.dimensions().TotalSize())*sizeof(DataType));
100
101 //concatenation = left.concatenate(right, 2);
102 VERIFY_IS_EQUAL(concatenation3.dimension(0), 2);
103 VERIFY_IS_EQUAL(concatenation3.dimension(1), 3);
104 VERIFY_IS_EQUAL(concatenation3.dimension(2), 2);
105 for (IndexType i = 0; i < 2; ++i) {
106 for (IndexType j = 0; j < 3; ++j) {
107 VERIFY_IS_EQUAL(concatenation3(i, j, 0), left(i, j, 0));
108 VERIFY_IS_EQUAL(concatenation3(i, j, 1), right(i, j, 0));
109 }
110 }
111 sycl_device.deallocate(gpu_out_data3);
112 sycl_device.deallocate(gpu_in1_data);
113 sycl_device.deallocate(gpu_in2_data);
114 }
115 template<typename DataType, int DataLayout, typename IndexType>
test_concatenation_as_lvalue(const Eigen::SyclDevice & sycl_device)116 static void test_concatenation_as_lvalue(const Eigen::SyclDevice& sycl_device)
117 {
118
119 IndexType leftDim1 = 2;
120 IndexType leftDim2 = 3;
121 Eigen::array<IndexType, 2> leftRange = {{leftDim1, leftDim2}};
122
123 IndexType rightDim1 = 2;
124 IndexType rightDim2 = 3;
125 Eigen::array<IndexType, 2> rightRange = {{rightDim1, rightDim2}};
126
127 IndexType concatDim1 = 4;
128 IndexType concatDim2 = 3;
129 Eigen::array<IndexType, 2> resRange = {{concatDim1, concatDim2}};
130
131 Tensor<DataType, 2, DataLayout, IndexType> left(leftRange);
132 Tensor<DataType, 2, DataLayout, IndexType> right(rightRange);
133 Tensor<DataType, 2, DataLayout, IndexType> result(resRange);
134
135 left.setRandom();
136 right.setRandom();
137 result.setRandom();
138
139 DataType * gpu_in1_data = static_cast<DataType*>(sycl_device.allocate(left.dimensions().TotalSize()*sizeof(DataType)));
140 DataType * gpu_in2_data = static_cast<DataType*>(sycl_device.allocate(right.dimensions().TotalSize()*sizeof(DataType)));
141 DataType * gpu_out_data = static_cast<DataType*>(sycl_device.allocate(result.dimensions().TotalSize()*sizeof(DataType)));
142
143
144 Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_in1(gpu_in1_data, leftRange);
145 Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_in2(gpu_in2_data, rightRange);
146 Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_out(gpu_out_data, resRange);
147
148 sycl_device.memcpyHostToDevice(gpu_in1_data, left.data(),(left.dimensions().TotalSize())*sizeof(DataType));
149 sycl_device.memcpyHostToDevice(gpu_in2_data, right.data(),(right.dimensions().TotalSize())*sizeof(DataType));
150 sycl_device.memcpyHostToDevice(gpu_out_data, result.data(),(result.dimensions().TotalSize())*sizeof(DataType));
151
152 // t1.concatenate(t2, 0) = result;
153 gpu_in1.concatenate(gpu_in2, 0).device(sycl_device) =gpu_out;
154 sycl_device.memcpyDeviceToHost(left.data(), gpu_in1_data,(left.dimensions().TotalSize())*sizeof(DataType));
155 sycl_device.memcpyDeviceToHost(right.data(), gpu_in2_data,(right.dimensions().TotalSize())*sizeof(DataType));
156
157 for (IndexType i = 0; i < 2; ++i) {
158 for (IndexType j = 0; j < 3; ++j) {
159 VERIFY_IS_EQUAL(left(i, j), result(i, j));
160 VERIFY_IS_EQUAL(right(i, j), result(i+2, j));
161 }
162 }
163 sycl_device.deallocate(gpu_in1_data);
164 sycl_device.deallocate(gpu_in2_data);
165 sycl_device.deallocate(gpu_out_data);
166 }
167
168
tensorConcat_perDevice(Dev_selector s)169 template <typename DataType, typename Dev_selector> void tensorConcat_perDevice(Dev_selector s){
170 QueueInterface queueInterface(s);
171 auto sycl_device = Eigen::SyclDevice(&queueInterface);
172 test_simple_concatenation<DataType, RowMajor, int64_t>(sycl_device);
173 test_simple_concatenation<DataType, ColMajor, int64_t>(sycl_device);
174 test_concatenation_as_lvalue<DataType, ColMajor, int64_t>(sycl_device);
175 }
EIGEN_DECLARE_TEST(cxx11_tensor_concatenation_sycl)176 EIGEN_DECLARE_TEST(cxx11_tensor_concatenation_sycl) {
177 for (const auto& device :Eigen::get_sycl_supported_devices()) {
178 CALL_SUBTEST(tensorConcat_perDevice<float>(device));
179 }
180 }
181