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 #define EIGEN_TEST_FUNC cxx11_tensor_sycl
19 #define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
20 #define EIGEN_USE_SYCL
21
22 #include "main.h"
23 #include <unsupported/Eigen/CXX11/Tensor>
24
25 using Eigen::array;
26 using Eigen::SyclDevice;
27 using Eigen::Tensor;
28 using Eigen::TensorMap;
29
test_sycl_cpu(const Eigen::SyclDevice & sycl_device)30 void test_sycl_cpu(const Eigen::SyclDevice &sycl_device) {
31
32 int sizeDim1 = 100;
33 int sizeDim2 = 100;
34 int sizeDim3 = 100;
35 array<int, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
36 Tensor<float, 3> in1(tensorRange);
37 Tensor<float, 3> in2(tensorRange);
38 Tensor<float, 3> in3(tensorRange);
39 Tensor<float, 3> out(tensorRange);
40
41 in2 = in2.random();
42 in3 = in3.random();
43
44 float * gpu_in1_data = static_cast<float*>(sycl_device.allocate(in1.dimensions().TotalSize()*sizeof(float)));
45 float * gpu_in2_data = static_cast<float*>(sycl_device.allocate(in2.dimensions().TotalSize()*sizeof(float)));
46 float * gpu_in3_data = static_cast<float*>(sycl_device.allocate(in3.dimensions().TotalSize()*sizeof(float)));
47 float * gpu_out_data = static_cast<float*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(float)));
48
49 TensorMap<Tensor<float, 3>> gpu_in1(gpu_in1_data, tensorRange);
50 TensorMap<Tensor<float, 3>> gpu_in2(gpu_in2_data, tensorRange);
51 TensorMap<Tensor<float, 3>> gpu_in3(gpu_in3_data, tensorRange);
52 TensorMap<Tensor<float, 3>> gpu_out(gpu_out_data, tensorRange);
53
54 /// a=1.2f
55 gpu_in1.device(sycl_device) = gpu_in1.constant(1.2f);
56 sycl_device.memcpyDeviceToHost(in1.data(), gpu_in1_data ,(in1.dimensions().TotalSize())*sizeof(float));
57 for (int i = 0; i < sizeDim1; ++i) {
58 for (int j = 0; j < sizeDim2; ++j) {
59 for (int k = 0; k < sizeDim3; ++k) {
60 VERIFY_IS_APPROX(in1(i,j,k), 1.2f);
61 }
62 }
63 }
64 printf("a=1.2f Test passed\n");
65
66 /// a=b*1.2f
67 gpu_out.device(sycl_device) = gpu_in1 * 1.2f;
68 sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data ,(out.dimensions().TotalSize())*sizeof(float));
69 for (int i = 0; i < sizeDim1; ++i) {
70 for (int j = 0; j < sizeDim2; ++j) {
71 for (int k = 0; k < sizeDim3; ++k) {
72 VERIFY_IS_APPROX(out(i,j,k),
73 in1(i,j,k) * 1.2f);
74 }
75 }
76 }
77 printf("a=b*1.2f Test Passed\n");
78
79 /// c=a*b
80 sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(),(in2.dimensions().TotalSize())*sizeof(float));
81 gpu_out.device(sycl_device) = gpu_in1 * gpu_in2;
82 sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float));
83 for (int i = 0; i < sizeDim1; ++i) {
84 for (int j = 0; j < sizeDim2; ++j) {
85 for (int k = 0; k < sizeDim3; ++k) {
86 VERIFY_IS_APPROX(out(i,j,k),
87 in1(i,j,k) *
88 in2(i,j,k));
89 }
90 }
91 }
92 printf("c=a*b Test Passed\n");
93
94 /// c=a+b
95 gpu_out.device(sycl_device) = gpu_in1 + gpu_in2;
96 sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float));
97 for (int i = 0; i < sizeDim1; ++i) {
98 for (int j = 0; j < sizeDim2; ++j) {
99 for (int k = 0; k < sizeDim3; ++k) {
100 VERIFY_IS_APPROX(out(i,j,k),
101 in1(i,j,k) +
102 in2(i,j,k));
103 }
104 }
105 }
106 printf("c=a+b Test Passed\n");
107
108 /// c=a*a
109 gpu_out.device(sycl_device) = gpu_in1 * gpu_in1;
110 sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float));
111 for (int i = 0; i < sizeDim1; ++i) {
112 for (int j = 0; j < sizeDim2; ++j) {
113 for (int k = 0; k < sizeDim3; ++k) {
114 VERIFY_IS_APPROX(out(i,j,k),
115 in1(i,j,k) *
116 in1(i,j,k));
117 }
118 }
119 }
120 printf("c= a*a Test Passed\n");
121
122 //a*3.14f + b*2.7f
123 gpu_out.device(sycl_device) = gpu_in1 * gpu_in1.constant(3.14f) + gpu_in2 * gpu_in2.constant(2.7f);
124 sycl_device.memcpyDeviceToHost(out.data(),gpu_out_data,(out.dimensions().TotalSize())*sizeof(float));
125 for (int i = 0; i < sizeDim1; ++i) {
126 for (int j = 0; j < sizeDim2; ++j) {
127 for (int k = 0; k < sizeDim3; ++k) {
128 VERIFY_IS_APPROX(out(i,j,k),
129 in1(i,j,k) * 3.14f
130 + in2(i,j,k) * 2.7f);
131 }
132 }
133 }
134 printf("a*3.14f + b*2.7f Test Passed\n");
135
136 ///d= (a>0.5? b:c)
137 sycl_device.memcpyHostToDevice(gpu_in3_data, in3.data(),(in3.dimensions().TotalSize())*sizeof(float));
138 gpu_out.device(sycl_device) =(gpu_in1 > gpu_in1.constant(0.5f)).select(gpu_in2, gpu_in3);
139 sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float));
140 for (int i = 0; i < sizeDim1; ++i) {
141 for (int j = 0; j < sizeDim2; ++j) {
142 for (int k = 0; k < sizeDim3; ++k) {
143 VERIFY_IS_APPROX(out(i, j, k), (in1(i, j, k) > 0.5f)
144 ? in2(i, j, k)
145 : in3(i, j, k));
146 }
147 }
148 }
149 printf("d= (a>0.5? b:c) Test Passed\n");
150 sycl_device.deallocate(gpu_in1_data);
151 sycl_device.deallocate(gpu_in2_data);
152 sycl_device.deallocate(gpu_in3_data);
153 sycl_device.deallocate(gpu_out_data);
154 }
test_cxx11_tensor_sycl()155 void test_cxx11_tensor_sycl() {
156 cl::sycl::gpu_selector s;
157 Eigen::SyclDevice sycl_device(s);
158 CALL_SUBTEST(test_sycl_cpu(sycl_device));
159 }
160