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, typename IndexType>
test_simple_swap_sycl(const Eigen::SyclDevice & sycl_device)28 static void test_simple_swap_sycl(const Eigen::SyclDevice& sycl_device)
29 {
30 IndexType sizeDim1 = 2;
31 IndexType sizeDim2 = 3;
32 IndexType sizeDim3 = 7;
33 array<IndexType, 3> tensorColRange = {{sizeDim1, sizeDim2, sizeDim3}};
34 array<IndexType, 3> tensorRowRange = {{sizeDim3, sizeDim2, sizeDim1}};
35
36
37 Tensor<DataType, 3, ColMajor, IndexType> tensor1(tensorColRange);
38 Tensor<DataType, 3, RowMajor, IndexType> tensor2(tensorRowRange);
39 tensor1.setRandom();
40
41 DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor1.size()*sizeof(DataType)));
42 DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(tensor2.size()*sizeof(DataType)));
43 TensorMap<Tensor<DataType, 3, ColMajor, IndexType>> gpu1(gpu_data1, tensorColRange);
44 TensorMap<Tensor<DataType, 3, RowMajor, IndexType>> gpu2(gpu_data2, tensorRowRange);
45
46 sycl_device.memcpyHostToDevice(gpu_data1, tensor1.data(),(tensor1.size())*sizeof(DataType));
47 gpu2.device(sycl_device)=gpu1.swap_layout();
48 sycl_device.memcpyDeviceToHost(tensor2.data(), gpu_data2,(tensor2.size())*sizeof(DataType));
49
50
51 // Tensor<float, 3, ColMajor> tensor(2,3,7);
52 //tensor.setRandom();
53
54 // Tensor<float, 3, RowMajor> tensor2 = tensor.swap_layout();
55 VERIFY_IS_EQUAL(tensor1.dimension(0), tensor2.dimension(2));
56 VERIFY_IS_EQUAL(tensor1.dimension(1), tensor2.dimension(1));
57 VERIFY_IS_EQUAL(tensor1.dimension(2), tensor2.dimension(0));
58
59 for (IndexType i = 0; i < 2; ++i) {
60 for (IndexType j = 0; j < 3; ++j) {
61 for (IndexType k = 0; k < 7; ++k) {
62 VERIFY_IS_EQUAL(tensor1(i,j,k), tensor2(k,j,i));
63 }
64 }
65 }
66 sycl_device.deallocate(gpu_data1);
67 sycl_device.deallocate(gpu_data2);
68 }
69
70 template <typename DataType, typename IndexType>
test_swap_as_lvalue_sycl(const Eigen::SyclDevice & sycl_device)71 static void test_swap_as_lvalue_sycl(const Eigen::SyclDevice& sycl_device)
72 {
73
74 IndexType sizeDim1 = 2;
75 IndexType sizeDim2 = 3;
76 IndexType sizeDim3 = 7;
77 array<IndexType, 3> tensorColRange = {{sizeDim1, sizeDim2, sizeDim3}};
78 array<IndexType, 3> tensorRowRange = {{sizeDim3, sizeDim2, sizeDim1}};
79
80 Tensor<DataType, 3, ColMajor, IndexType> tensor1(tensorColRange);
81 Tensor<DataType, 3, RowMajor, IndexType> tensor2(tensorRowRange);
82 tensor1.setRandom();
83
84 DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor1.size()*sizeof(DataType)));
85 DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(tensor2.size()*sizeof(DataType)));
86 TensorMap<Tensor<DataType, 3, ColMajor, IndexType>> gpu1(gpu_data1, tensorColRange);
87 TensorMap<Tensor<DataType, 3, RowMajor, IndexType>> gpu2(gpu_data2, tensorRowRange);
88
89 sycl_device.memcpyHostToDevice(gpu_data1, tensor1.data(),(tensor1.size())*sizeof(DataType));
90 gpu2.swap_layout().device(sycl_device)=gpu1;
91 sycl_device.memcpyDeviceToHost(tensor2.data(), gpu_data2,(tensor2.size())*sizeof(DataType));
92
93
94 // Tensor<float, 3, ColMajor> tensor(2,3,7);
95 // tensor.setRandom();
96
97 //Tensor<float, 3, RowMajor> tensor2(7,3,2);
98 // tensor2.swap_layout() = tensor;
99 VERIFY_IS_EQUAL(tensor1.dimension(0), tensor2.dimension(2));
100 VERIFY_IS_EQUAL(tensor1.dimension(1), tensor2.dimension(1));
101 VERIFY_IS_EQUAL(tensor1.dimension(2), tensor2.dimension(0));
102
103 for (IndexType i = 0; i < 2; ++i) {
104 for (IndexType j = 0; j < 3; ++j) {
105 for (IndexType k = 0; k < 7; ++k) {
106 VERIFY_IS_EQUAL(tensor1(i,j,k), tensor2(k,j,i));
107 }
108 }
109 }
110 sycl_device.deallocate(gpu_data1);
111 sycl_device.deallocate(gpu_data2);
112 }
113
114
sycl_tensor_layout_swap_test_per_device(dev_Selector s)115 template<typename DataType, typename dev_Selector> void sycl_tensor_layout_swap_test_per_device(dev_Selector s){
116 QueueInterface queueInterface(s);
117 auto sycl_device = Eigen::SyclDevice(&queueInterface);
118 test_simple_swap_sycl<DataType, int64_t>(sycl_device);
119 test_swap_as_lvalue_sycl<DataType, int64_t>(sycl_device);
120 }
EIGEN_DECLARE_TEST(cxx11_tensor_layout_swap_sycl)121 EIGEN_DECLARE_TEST(cxx11_tensor_layout_swap_sycl)
122 {
123 for (const auto& device :Eigen::get_sycl_supported_devices()) {
124 CALL_SUBTEST(sycl_tensor_layout_swap_test_per_device<float>(device));
125 }
126 }
127