cxx11_tensor_argmax_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 //
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 #define EIGEN_HAS_CONSTEXPR 1
20 
21 #include "main.h"
22 
23 #include <unsupported/Eigen/CXX11/Tensor>
24 
25 using Eigen::array;
26 using Eigen::SyclDevice;
27 using Eigen::Tensor;
28 using Eigen::TensorMap;
29 
30 template <typename DataType, int Layout, typename DenseIndex>
31 static void test_sycl_simple_argmax(const Eigen::SyclDevice& sycl_device) {
32  Tensor<DataType, 3, Layout, DenseIndex> in(Eigen::array<DenseIndex, 3>{{2, 2, 2}});
33  Tensor<DenseIndex, 0, Layout, DenseIndex> out_max;
34  Tensor<DenseIndex, 0, Layout, DenseIndex> out_min;
35  in.setRandom();
36  in *= in.constant(100.0);
37  in(0, 0, 0) = -1000.0;
38  in(1, 1, 1) = 1000.0;
39 
40  std::size_t in_bytes = in.size() * sizeof(DataType);
41  std::size_t out_bytes = out_max.size() * sizeof(DenseIndex);
42 
43  DataType* d_in = static_cast<DataType*>(sycl_device.allocate(in_bytes));
44  DenseIndex* d_out_max = static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
45  DenseIndex* d_out_min = static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
46 
47  Eigen::TensorMap<Eigen::Tensor<DataType, 3, Layout, DenseIndex> > gpu_in(d_in,
48  Eigen::array<DenseIndex, 3>{{2, 2, 2}});
49  Eigen::TensorMap<Eigen::Tensor<DenseIndex, 0, Layout, DenseIndex> > gpu_out_max(d_out_max);
50  Eigen::TensorMap<Eigen::Tensor<DenseIndex, 0, Layout, DenseIndex> > gpu_out_min(d_out_min);
51  sycl_device.memcpyHostToDevice(d_in, in.data(), in_bytes);
52 
53  gpu_out_max.device(sycl_device) = gpu_in.argmax();
54  gpu_out_min.device(sycl_device) = gpu_in.argmin();
55 
56  sycl_device.memcpyDeviceToHost(out_max.data(), d_out_max, out_bytes);
57  sycl_device.memcpyDeviceToHost(out_min.data(), d_out_min, out_bytes);
58 
59  VERIFY_IS_EQUAL(out_max(), 2 * 2 * 2 - 1);
60  VERIFY_IS_EQUAL(out_min(), 0);
61 
62  sycl_device.deallocate(d_in);
63  sycl_device.deallocate(d_out_max);
64  sycl_device.deallocate(d_out_min);
65 }
66 
67 template <typename DataType, int DataLayout, typename DenseIndex>
68 static void test_sycl_argmax_dim(const Eigen::SyclDevice& sycl_device) {
69  DenseIndex sizeDim0 = 9;
70  DenseIndex sizeDim1 = 3;
71  DenseIndex sizeDim2 = 5;
72  DenseIndex sizeDim3 = 7;
73  Tensor<DataType, 4, DataLayout, DenseIndex> tensor(sizeDim0, sizeDim1, sizeDim2, sizeDim3);
74 
75  std::vector<DenseIndex> dims;
76  dims.push_back(sizeDim0);
77  dims.push_back(sizeDim1);
78  dims.push_back(sizeDim2);
79  dims.push_back(sizeDim3);
80  for (DenseIndex dim = 0; dim < 4; ++dim) {
81  array<DenseIndex, 3> out_shape;
82  for (DenseIndex d = 0; d < 3; ++d) out_shape[d] = (d < dim) ? dims[d] : dims[d + 1];
83 
84  Tensor<DenseIndex, 3, DataLayout, DenseIndex> tensor_arg(out_shape);
85 
86  array<DenseIndex, 4> ix;
87  for (DenseIndex i = 0; i < sizeDim0; ++i) {
88  for (DenseIndex j = 0; j < sizeDim1; ++j) {
89  for (DenseIndex k = 0; k < sizeDim2; ++k) {
90  for (DenseIndex l = 0; l < sizeDim3; ++l) {
91  ix[0] = i;
92  ix[1] = j;
93  ix[2] = k;
94  ix[3] = l;
95  // suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l)
96  // = 10.0
97  tensor(ix) = (ix[dim] != 0) ? -1.0 : 10.0;
98  }
99  }
100  }
101  }
102 
103  std::size_t in_bytes = tensor.size() * sizeof(DataType);
104  std::size_t out_bytes = tensor_arg.size() * sizeof(DenseIndex);
105 
106  DataType* d_in = static_cast<DataType*>(sycl_device.allocate(in_bytes));
107  DenseIndex* d_out = static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
108 
109  Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, DenseIndex> > gpu_in(
110  d_in, Eigen::array<DenseIndex, 4>{{sizeDim0, sizeDim1, sizeDim2, sizeDim3}});
111  Eigen::TensorMap<Eigen::Tensor<DenseIndex, 3, DataLayout, DenseIndex> > gpu_out(d_out, out_shape);
112 
113  sycl_device.memcpyHostToDevice(d_in, tensor.data(), in_bytes);
114  gpu_out.device(sycl_device) = gpu_in.argmax(dim);
115  sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);
116 
117  VERIFY_IS_EQUAL(static_cast<size_t>(tensor_arg.size()),
118  size_t(sizeDim0 * sizeDim1 * sizeDim2 * sizeDim3 / tensor.dimension(dim)));
119 
120  for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
121  // Expect max to be in the first index of the reduced dimension
122  VERIFY_IS_EQUAL(tensor_arg.data()[n], 0);
123  }
124 
125  sycl_device.synchronize();
126 
127  for (DenseIndex i = 0; i < sizeDim0; ++i) {
128  for (DenseIndex j = 0; j < sizeDim1; ++j) {
129  for (DenseIndex k = 0; k < sizeDim2; ++k) {
130  for (DenseIndex l = 0; l < sizeDim3; ++l) {
131  ix[0] = i;
132  ix[1] = j;
133  ix[2] = k;
134  ix[3] = l;
135  // suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = 20.0
136  tensor(ix) = (ix[dim] != tensor.dimension(dim) - 1) ? -1.0 : 20.0;
137  }
138  }
139  }
140  }
141 
142  sycl_device.memcpyHostToDevice(d_in, tensor.data(), in_bytes);
143  gpu_out.device(sycl_device) = gpu_in.argmax(dim);
144  sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);
145 
146  for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
147  // Expect max to be in the last index of the reduced dimension
148  VERIFY_IS_EQUAL(tensor_arg.data()[n], tensor.dimension(dim) - 1);
149  }
150  sycl_device.deallocate(d_in);
151  sycl_device.deallocate(d_out);
152  }
153 }
154 
155 template <typename DataType, int DataLayout, typename DenseIndex>
156 static void test_sycl_argmin_dim(const Eigen::SyclDevice& sycl_device) {
157  DenseIndex sizeDim0 = 9;
158  DenseIndex sizeDim1 = 3;
159  DenseIndex sizeDim2 = 5;
160  DenseIndex sizeDim3 = 7;
161  Tensor<DataType, 4, DataLayout, DenseIndex> tensor(sizeDim0, sizeDim1, sizeDim2, sizeDim3);
162 
163  std::vector<DenseIndex> dims;
164  dims.push_back(sizeDim0);
165  dims.push_back(sizeDim1);
166  dims.push_back(sizeDim2);
167  dims.push_back(sizeDim3);
168  for (DenseIndex dim = 0; dim < 4; ++dim) {
169  array<DenseIndex, 3> out_shape;
170  for (DenseIndex d = 0; d < 3; ++d) out_shape[d] = (d < dim) ? dims[d] : dims[d + 1];
171 
172  Tensor<DenseIndex, 3, DataLayout, DenseIndex> tensor_arg(out_shape);
173 
174  array<DenseIndex, 4> ix;
175  for (DenseIndex i = 0; i < sizeDim0; ++i) {
176  for (DenseIndex j = 0; j < sizeDim1; ++j) {
177  for (DenseIndex k = 0; k < sizeDim2; ++k) {
178  for (DenseIndex l = 0; l < sizeDim3; ++l) {
179  ix[0] = i;
180  ix[1] = j;
181  ix[2] = k;
182  ix[3] = l;
183  // suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l) = -10.0
184  tensor(ix) = (ix[dim] != 0) ? 1.0 : -10.0;
185  }
186  }
187  }
188  }
189 
190  std::size_t in_bytes = tensor.size() * sizeof(DataType);
191  std::size_t out_bytes = tensor_arg.size() * sizeof(DenseIndex);
192 
193  DataType* d_in = static_cast<DataType*>(sycl_device.allocate(in_bytes));
194  DenseIndex* d_out = static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
195 
196  Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, DenseIndex> > gpu_in(
197  d_in, Eigen::array<DenseIndex, 4>{{sizeDim0, sizeDim1, sizeDim2, sizeDim3}});
198  Eigen::TensorMap<Eigen::Tensor<DenseIndex, 3, DataLayout, DenseIndex> > gpu_out(d_out, out_shape);
199 
200  sycl_device.memcpyHostToDevice(d_in, tensor.data(), in_bytes);
201  gpu_out.device(sycl_device) = gpu_in.argmin(dim);
202  sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);
203 
204  VERIFY_IS_EQUAL(static_cast<size_t>(tensor_arg.size()),
205  size_t(sizeDim0 * sizeDim1 * sizeDim2 * sizeDim3 / tensor.dimension(dim)));
206 
207  for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
208  // Expect max to be in the first index of the reduced dimension
209  VERIFY_IS_EQUAL(tensor_arg.data()[n], 0);
210  }
211 
212  sycl_device.synchronize();
213 
214  for (DenseIndex i = 0; i < sizeDim0; ++i) {
215  for (DenseIndex j = 0; j < sizeDim1; ++j) {
216  for (DenseIndex k = 0; k < sizeDim2; ++k) {
217  for (DenseIndex l = 0; l < sizeDim3; ++l) {
218  ix[0] = i;
219  ix[1] = j;
220  ix[2] = k;
221  ix[3] = l;
222  // suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = -20.0
223  tensor(ix) = (ix[dim] != tensor.dimension(dim) - 1) ? 1.0 : -20.0;
224  }
225  }
226  }
227  }
228 
229  sycl_device.memcpyHostToDevice(d_in, tensor.data(), in_bytes);
230  gpu_out.device(sycl_device) = gpu_in.argmin(dim);
231  sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);
232 
233  for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
234  // Expect max to be in the last index of the reduced dimension
235  VERIFY_IS_EQUAL(tensor_arg.data()[n], tensor.dimension(dim) - 1);
236  }
237  sycl_device.deallocate(d_in);
238  sycl_device.deallocate(d_out);
239  }
240 }
241 
242 template <typename DataType, typename Device_Selector>
243 void sycl_argmax_test_per_device(const Device_Selector& d) {
244  QueueInterface queueInterface(d);
245  auto sycl_device = Eigen::SyclDevice(&queueInterface);
246  test_sycl_simple_argmax<DataType, RowMajor, int64_t>(sycl_device);
247  test_sycl_simple_argmax<DataType, ColMajor, int64_t>(sycl_device);
248  test_sycl_argmax_dim<DataType, ColMajor, int64_t>(sycl_device);
249  test_sycl_argmax_dim<DataType, RowMajor, int64_t>(sycl_device);
250  test_sycl_argmin_dim<DataType, ColMajor, int64_t>(sycl_device);
251  test_sycl_argmin_dim<DataType, RowMajor, int64_t>(sycl_device);
252 }
253 
254 EIGEN_DECLARE_TEST(cxx11_tensor_argmax_sycl) {
255  for (const auto& device : Eigen::get_sycl_supported_devices()) {
256  CALL_SUBTEST(sycl_argmax_test_per_device<float>(device));
257  }
258 }
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