cxx11_tensor_shuffling_sycl.cpp
Go to the documentation of this file.
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 #include <unsupported/Eigen/CXX11/Tensor>
23 
24 using Eigen::array;
25 using Eigen::SyclDevice;
26 using Eigen::Tensor;
27 using Eigen::TensorMap;
28 
29 template <typename DataType, int DataLayout, typename IndexType>
30 static void test_simple_shuffling_sycl(const Eigen::SyclDevice& sycl_device) {
31  IndexType sizeDim1 = 2;
32  IndexType sizeDim2 = 3;
33  IndexType sizeDim3 = 5;
34  IndexType sizeDim4 = 7;
35  array<IndexType, 4> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
37  Tensor<DataType, 4, DataLayout, IndexType> no_shuffle(tensorRange);
38  tensor.setRandom();
39 
40  const size_t buffSize = tensor.size() * sizeof(DataType);
41  array<IndexType, 4> shuffles;
42  shuffles[0] = 0;
43  shuffles[1] = 1;
44  shuffles[2] = 2;
45  shuffles[3] = 3;
46  DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(buffSize));
47  DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(buffSize));
48 
50  tensorRange);
52  tensorRange);
53 
54  sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(), buffSize);
55 
56  gpu2.device(sycl_device) = gpu1.shuffle(shuffles);
57  sycl_device.memcpyDeviceToHost(no_shuffle.data(), gpu_data2, buffSize);
58  sycl_device.synchronize();
59 
60  VERIFY_IS_EQUAL(no_shuffle.dimension(0), sizeDim1);
61  VERIFY_IS_EQUAL(no_shuffle.dimension(1), sizeDim2);
62  VERIFY_IS_EQUAL(no_shuffle.dimension(2), sizeDim3);
63  VERIFY_IS_EQUAL(no_shuffle.dimension(3), sizeDim4);
64 
65  for (IndexType i = 0; i < sizeDim1; ++i) {
66  for (IndexType j = 0; j < sizeDim2; ++j) {
67  for (IndexType k = 0; k < sizeDim3; ++k) {
68  for (IndexType l = 0; l < sizeDim4; ++l) {
69  VERIFY_IS_EQUAL(tensor(i, j, k, l), no_shuffle(i, j, k, l));
70  }
71  }
72  }
73  }
74 
75  shuffles[0] = 2;
76  shuffles[1] = 3;
77  shuffles[2] = 1;
78  shuffles[3] = 0;
79  array<IndexType, 4> tensorrangeShuffle = {
80  {sizeDim3, sizeDim4, sizeDim2, sizeDim1}};
81  Tensor<DataType, 4, DataLayout, IndexType> shuffle(tensorrangeShuffle);
82  DataType* gpu_data3 = static_cast<DataType*>(sycl_device.allocate(buffSize));
84  gpu_data3, tensorrangeShuffle);
85 
86  gpu3.device(sycl_device) = gpu1.shuffle(shuffles);
87  sycl_device.memcpyDeviceToHost(shuffle.data(), gpu_data3, buffSize);
88  sycl_device.synchronize();
89 
90  VERIFY_IS_EQUAL(shuffle.dimension(0), sizeDim3);
91  VERIFY_IS_EQUAL(shuffle.dimension(1), sizeDim4);
92  VERIFY_IS_EQUAL(shuffle.dimension(2), sizeDim2);
93  VERIFY_IS_EQUAL(shuffle.dimension(3), sizeDim1);
94 
95  for (IndexType i = 0; i < sizeDim1; ++i) {
96  for (IndexType j = 0; j < sizeDim2; ++j) {
97  for (IndexType k = 0; k < sizeDim3; ++k) {
98  for (IndexType l = 0; l < sizeDim4; ++l) {
99  VERIFY_IS_EQUAL(tensor(i, j, k, l), shuffle(k, l, j, i));
100  }
101  }
102  }
103  }
104 }
105 
106 template <typename DataType, typename dev_Selector>
107 void sycl_shuffling_test_per_device(dev_Selector s) {
108  QueueInterface queueInterface(s);
109  auto sycl_device = Eigen::SyclDevice(&queueInterface);
110  test_simple_shuffling_sycl<DataType, RowMajor, int64_t>(sycl_device);
111  test_simple_shuffling_sycl<DataType, ColMajor, int64_t>(sycl_device);
112 }
113 EIGEN_DECLARE_TEST(cxx11_tensor_shuffling_sycl) {
114  for (const auto& device : Eigen::get_sycl_supported_devices()) {
115  CALL_SUBTEST(sycl_shuffling_test_per_device<float>(device));
116  }
117 }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index size() const
Definition: Tensor.h:103
int array[24]
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor< Scalar_, NumIndices_, Options_, IndexType_ > & setRandom()
Definition: TensorBase.h:996
static void test_simple_shuffling_sycl(const Eigen::SyclDevice &sycl_device)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const TensorShufflingOp< const Shuffle, const TensorMap< PlainObjectType, Options_, MakePointer_ > > shuffle(const Shuffle &shfl) const
Definition: TensorBase.h:1123
static const Line3 l(Rot3(), 1, 1)
#define VERIFY_IS_EQUAL(a, b)
Definition: main.h:386
A tensor expression mapping an existing array of data.
void sycl_shuffling_test_per_device(dev_Selector s)
RealScalar s
TensorDevice< TensorMap< PlainObjectType, Options_, MakePointer_ >, DeviceType > device(const DeviceType &dev)
Definition: TensorBase.h:1145
EIGEN_DECLARE_TEST(cxx11_tensor_shuffling_sycl)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar * data()
Definition: Tensor.h:104
#define CALL_SUBTEST(FUNC)
Definition: main.h:399
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index dimension(std::size_t n) const
Definition: Tensor.h:101
std::ptrdiff_t j
The tensor class.
Definition: Tensor.h:63


gtsam
Author(s):
autogenerated on Tue Jul 4 2023 02:34:08