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::array;
24 using Eigen::SyclDevice;
25 using Eigen::Tensor;
26 using Eigen::TensorMap;
27
28 template <typename DataType, int DataLayout, typename IndexType>
test_broadcast_sycl_fixed(const Eigen::SyclDevice & sycl_device)29 static void test_broadcast_sycl_fixed(const Eigen::SyclDevice &sycl_device){
30
31 // BROADCAST test:
32 IndexType inDim1=2;
33 IndexType inDim2=3;
34 IndexType inDim3=5;
35 IndexType inDim4=7;
36 IndexType bDim1=2;
37 IndexType bDim2=3;
38 IndexType bDim3=1;
39 IndexType bDim4=4;
40 array<IndexType, 4> in_range = {{inDim1, inDim2, inDim3, inDim4}};
41 array<IndexType, 4> broadcasts = {{bDim1, bDim2, bDim3, bDim4}};
42 array<IndexType, 4> out_range; // = in_range * broadcasts
43 for (size_t i = 0; i < out_range.size(); ++i)
44 out_range[i] = in_range[i] * broadcasts[i];
45
46 Tensor<DataType, 4, DataLayout, IndexType> input(in_range);
47 Tensor<DataType, 4, DataLayout, IndexType> out(out_range);
48
49 for (size_t i = 0; i < in_range.size(); ++i)
50 VERIFY_IS_EQUAL(out.dimension(i), out_range[i]);
51
52
53 for (IndexType i = 0; i < input.size(); ++i)
54 input(i) = static_cast<DataType>(i);
55
56 DataType * gpu_in_data = static_cast<DataType*>(sycl_device.allocate(input.dimensions().TotalSize()*sizeof(DataType)));
57 DataType * gpu_out_data = static_cast<DataType*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(DataType)));
58
59 TensorMap<TensorFixedSize<DataType, Sizes<2, 3, 5, 7>, DataLayout, IndexType>> gpu_in(gpu_in_data, in_range);
60 TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_out(gpu_out_data, out_range);
61 sycl_device.memcpyHostToDevice(gpu_in_data, input.data(),(input.dimensions().TotalSize())*sizeof(DataType));
62 gpu_out.device(sycl_device) = gpu_in.broadcast(broadcasts);
63 sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(DataType));
64
65 for (IndexType i = 0; i < inDim1*bDim1; ++i) {
66 for (IndexType j = 0; j < inDim2*bDim2; ++j) {
67 for (IndexType k = 0; k < inDim3*bDim3; ++k) {
68 for (IndexType l = 0; l < inDim4*bDim4; ++l) {
69 VERIFY_IS_APPROX(input(i%2,j%3,k%5,l%7), out(i,j,k,l));
70 }
71 }
72 }
73 }
74 printf("Broadcast Test with fixed size Passed\n");
75 sycl_device.deallocate(gpu_in_data);
76 sycl_device.deallocate(gpu_out_data);
77 }
78
79 template <typename DataType, int DataLayout, typename IndexType>
test_broadcast_sycl(const Eigen::SyclDevice & sycl_device)80 static void test_broadcast_sycl(const Eigen::SyclDevice &sycl_device){
81
82 // BROADCAST test:
83 IndexType inDim1=2;
84 IndexType inDim2=3;
85 IndexType inDim3=5;
86 IndexType inDim4=7;
87 IndexType bDim1=2;
88 IndexType bDim2=3;
89 IndexType bDim3=1;
90 IndexType bDim4=4;
91 array<IndexType, 4> in_range = {{inDim1, inDim2, inDim3, inDim4}};
92 array<IndexType, 4> broadcasts = {{bDim1, bDim2, bDim3, bDim4}};
93 array<IndexType, 4> out_range; // = in_range * broadcasts
94 for (size_t i = 0; i < out_range.size(); ++i)
95 out_range[i] = in_range[i] * broadcasts[i];
96
97 Tensor<DataType, 4, DataLayout, IndexType> input(in_range);
98 Tensor<DataType, 4, DataLayout, IndexType> out(out_range);
99
100 for (size_t i = 0; i < in_range.size(); ++i)
101 VERIFY_IS_EQUAL(out.dimension(i), out_range[i]);
102
103
104 for (IndexType i = 0; i < input.size(); ++i)
105 input(i) = static_cast<DataType>(i);
106
107 DataType * gpu_in_data = static_cast<DataType*>(sycl_device.allocate(input.dimensions().TotalSize()*sizeof(DataType)));
108 DataType * gpu_out_data = static_cast<DataType*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(DataType)));
109
110 TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_in(gpu_in_data, in_range);
111 TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_out(gpu_out_data, out_range);
112 sycl_device.memcpyHostToDevice(gpu_in_data, input.data(),(input.dimensions().TotalSize())*sizeof(DataType));
113 gpu_out.device(sycl_device) = gpu_in.broadcast(broadcasts);
114 sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(DataType));
115
116 for (IndexType i = 0; i < inDim1*bDim1; ++i) {
117 for (IndexType j = 0; j < inDim2*bDim2; ++j) {
118 for (IndexType k = 0; k < inDim3*bDim3; ++k) {
119 for (IndexType l = 0; l < inDim4*bDim4; ++l) {
120 VERIFY_IS_APPROX(input(i%inDim1,j%inDim2,k%inDim3,l%inDim4), out(i,j,k,l));
121 }
122 }
123 }
124 }
125 printf("Broadcast Test Passed\n");
126 sycl_device.deallocate(gpu_in_data);
127 sycl_device.deallocate(gpu_out_data);
128 }
129
sycl_broadcast_test_per_device(const cl::sycl::device & d)130 template<typename DataType> void sycl_broadcast_test_per_device(const cl::sycl::device& d){
131 std::cout << "Running on " << d.template get_info<cl::sycl::info::device::name>() << std::endl;
132 QueueInterface queueInterface(d);
133 auto sycl_device = Eigen::SyclDevice(&queueInterface);
134 test_broadcast_sycl<DataType, RowMajor, int64_t>(sycl_device);
135 test_broadcast_sycl<DataType, ColMajor, int64_t>(sycl_device);
136 test_broadcast_sycl_fixed<DataType, RowMajor, int64_t>(sycl_device);
137 test_broadcast_sycl_fixed<DataType, ColMajor, int64_t>(sycl_device);
138 }
139
EIGEN_DECLARE_TEST(cxx11_tensor_broadcast_sycl)140 EIGEN_DECLARE_TEST(cxx11_tensor_broadcast_sycl) {
141 for (const auto& device :Eigen::get_sycl_supported_devices()) {
142 CALL_SUBTEST(sycl_broadcast_test_per_device<float>(device));
143 }
144 }
145