cxx11_tensor_image_op_sycl.cpp
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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 #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 using Eigen::Tensor;
29 using Eigen::RowMajor;
30 template <typename DataType, int DataLayout, typename IndexType>
31 static void test_image_op_sycl(const Eigen::SyclDevice &sycl_device)
32 {
33  IndexType sizeDim1 = 245;
34  IndexType sizeDim2 = 343;
35  IndexType sizeDim3 = 577;
36 
37  array<IndexType, 3> input_range ={{sizeDim1, sizeDim2, sizeDim3}};
38  array<IndexType, 3> slice_range ={{sizeDim1-1, sizeDim2, sizeDim3}};
39 
40  Tensor<DataType, 3,DataLayout, IndexType> tensor1(input_range);
41  Tensor<DataType, 3,DataLayout, IndexType> tensor2(input_range);
42  Tensor<DataType, 3, DataLayout, IndexType> tensor3(slice_range);
43  Tensor<DataType, 3, DataLayout, IndexType> tensor3_cpu(slice_range);
44 
45 
46 
47  typedef Eigen::DSizes<IndexType, 3> Index3;
48  Index3 strides1(1L,1L, 1L);
49  Index3 indicesStart1(1L, 0L, 0L);
50  Index3 indicesStop1(sizeDim1, sizeDim2, sizeDim3);
51 
52  Index3 strides2(1L,1L, 1L);
53  Index3 indicesStart2(0L, 0L, 0L);
54  Index3 indicesStop2(sizeDim1-1, sizeDim2, sizeDim3);
55  Eigen::DSizes<IndexType, 3> sizes(sizeDim1-1,sizeDim2,sizeDim3);
56 
57  tensor1.setRandom();
58  tensor2.setRandom();
59 
60 
61  DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor1.size()*sizeof(DataType)));
62  DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(tensor2.size()*sizeof(DataType)));
63  DataType* gpu_data3 = static_cast<DataType*>(sycl_device.allocate(tensor3.size()*sizeof(DataType)));
64 
65  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu1(gpu_data1, input_range);
66  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu2(gpu_data2, input_range);
67  TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu3(gpu_data3, slice_range);
68 
69  sycl_device.memcpyHostToDevice(gpu_data1, tensor1.data(),(tensor1.size())*sizeof(DataType));
70  sycl_device.memcpyHostToDevice(gpu_data2, tensor2.data(),(tensor2.size())*sizeof(DataType));
71  gpu3.device(sycl_device)= gpu1.slice(indicesStart1, sizes) - gpu2.slice(indicesStart2, sizes);
72  sycl_device.memcpyDeviceToHost(tensor3.data(), gpu_data3,(tensor3.size())*sizeof(DataType));
73 
74  tensor3_cpu = tensor1.stridedSlice(indicesStart1,indicesStop1,strides1) - tensor2.stridedSlice(indicesStart2,indicesStop2,strides2);
75 
76 
77  for (IndexType i = 0; i <slice_range[0] ; ++i) {
78  for (IndexType j = 0; j < slice_range[1]; ++j) {
79  for (IndexType k = 0; k < slice_range[2]; ++k) {
80  VERIFY_IS_EQUAL(tensor3_cpu(i,j,k), tensor3(i,j,k));
81  }
82  }
83  }
84  sycl_device.deallocate(gpu_data1);
85  sycl_device.deallocate(gpu_data2);
86  sycl_device.deallocate(gpu_data3);
87 }
88 
89 
90 template<typename DataType, typename dev_Selector> void sycl_computing_test_per_device(dev_Selector s){
91  QueueInterface queueInterface(s);
92  auto sycl_device = Eigen::SyclDevice(&queueInterface);
93  test_image_op_sycl<DataType, RowMajor, int64_t>(sycl_device);
94 }
95 
96 EIGEN_DECLARE_TEST(cxx11_tensor_image_op_sycl) {
97  for (const auto& device :Eigen::get_sycl_supported_devices()) {
98  CALL_SUBTEST(sycl_computing_test_per_device<float>(device));
99 #ifdef EIGEN_SYCL_DOUBLE_SUPPORT
100  CALL_SUBTEST(sycl_computing_test_per_device<double>(device));
101 #endif
102  }
103 }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index size() const
Definition: Tensor.h:103
int array[24]
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const TensorStridingSlicingOp< const StartIndices, const StopIndices, const Strides, const Tensor< Scalar_, NumIndices_, Options_, IndexType_ > > stridedSlice(const StartIndices &startIndices, const StopIndices &stopIndices, const Strides &strides) const
Definition: TensorBase.h:1077
EIGEN_DECLARE_TEST(cxx11_tensor_image_op_sycl)
std::vector< Array2i > sizes
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor< Scalar_, NumIndices_, Options_, IndexType_ > & setRandom()
Definition: TensorBase.h:996
MatrixXd L
Definition: LLT_example.cpp:6
void sycl_computing_test_per_device(dev_Selector s)
#define VERIFY_IS_EQUAL(a, b)
Definition: main.h:386
A tensor expression mapping an existing array of data.
RealScalar s
TensorDevice< TensorMap< PlainObjectType, Options_, MakePointer_ >, DeviceType > device(const DeviceType &dev)
Definition: TensorBase.h:1145
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const TensorSlicingOp< const StartIndices, const Sizes, const TensorMap< PlainObjectType, Options_, MakePointer_ > > slice(const StartIndices &startIndices, const Sizes &sizes) const
Definition: TensorBase.h:1066
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar * data()
Definition: Tensor.h:104
#define CALL_SUBTEST(FUNC)
Definition: main.h:399
static void test_image_op_sycl(const Eigen::SyclDevice &sycl_device)
std::ptrdiff_t j
The tensor class.
Definition: Tensor.h:63


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autogenerated on Tue Jul 4 2023 02:34:07