1 // This file is part of Eigen, a lightweight C++ template library
2 // for linear algebra.
3 //
4 // Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
5 //
6 // This Source Code Form is subject to the terms of the Mozilla
7 // Public License v. 2.0. If a copy of the MPL was not distributed
8 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
9
10
11 #define EIGEN_TEST_NO_LONGDOUBLE
12 #define EIGEN_TEST_FUNC cxx11_tensor_cuda
13 #define EIGEN_USE_GPU
14
15 #if defined __CUDACC_VER__ && __CUDACC_VER__ >= 70500
16 #include <cuda_fp16.h>
17 #endif
18 #include "main.h"
19 #include <unsupported/Eigen/CXX11/Tensor>
20
21 using Eigen::Tensor;
22
23 template <int Layout>
test_cuda_simple_argmax()24 void test_cuda_simple_argmax()
25 {
26 Tensor<double, 3, Layout> in(Eigen::array<DenseIndex, 3>(72,53,97));
27 Tensor<DenseIndex, 1, Layout> out_max(Eigen::array<DenseIndex, 1>(1));
28 Tensor<DenseIndex, 1, Layout> out_min(Eigen::array<DenseIndex, 1>(1));
29 in.setRandom();
30 in *= in.constant(100.0);
31 in(0, 0, 0) = -1000.0;
32 in(71, 52, 96) = 1000.0;
33
34 std::size_t in_bytes = in.size() * sizeof(double);
35 std::size_t out_bytes = out_max.size() * sizeof(DenseIndex);
36
37 double* d_in;
38 DenseIndex* d_out_max;
39 DenseIndex* d_out_min;
40 cudaMalloc((void**)(&d_in), in_bytes);
41 cudaMalloc((void**)(&d_out_max), out_bytes);
42 cudaMalloc((void**)(&d_out_min), out_bytes);
43
44 cudaMemcpy(d_in, in.data(), in_bytes, cudaMemcpyHostToDevice);
45
46 Eigen::CudaStreamDevice stream;
47 Eigen::GpuDevice gpu_device(&stream);
48
49 Eigen::TensorMap<Eigen::Tensor<double, 3, Layout>, Aligned > gpu_in(d_in, Eigen::array<DenseIndex, 3>(72,53,97));
50 Eigen::TensorMap<Eigen::Tensor<DenseIndex, 1, Layout>, Aligned > gpu_out_max(d_out_max, Eigen::array<DenseIndex, 1>(1));
51 Eigen::TensorMap<Eigen::Tensor<DenseIndex, 1, Layout>, Aligned > gpu_out_min(d_out_min, Eigen::array<DenseIndex, 1>(1));
52
53 gpu_out_max.device(gpu_device) = gpu_in.argmax();
54 gpu_out_min.device(gpu_device) = gpu_in.argmin();
55
56 assert(cudaMemcpyAsync(out_max.data(), d_out_max, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess);
57 assert(cudaMemcpyAsync(out_min.data(), d_out_min, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess);
58 assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess);
59
60 VERIFY_IS_EQUAL(out_max(Eigen::array<DenseIndex, 1>(0)), 72*53*97 - 1);
61 VERIFY_IS_EQUAL(out_min(Eigen::array<DenseIndex, 1>(0)), 0);
62
63 cudaFree(d_in);
64 cudaFree(d_out_max);
65 cudaFree(d_out_min);
66 }
67
68 template <int DataLayout>
test_cuda_argmax_dim()69 void test_cuda_argmax_dim()
70 {
71 Tensor<float, 4, DataLayout> tensor(2,3,5,7);
72 std::vector<int> dims;
73 dims.push_back(2); dims.push_back(3); dims.push_back(5); dims.push_back(7);
74
75 for (int dim = 0; dim < 4; ++dim) {
76 tensor.setRandom();
77 tensor = (tensor + tensor.constant(0.5)).log();
78
79 array<DenseIndex, 3> out_shape;
80 for (int d = 0; d < 3; ++d) out_shape[d] = (d < dim) ? dims[d] : dims[d+1];
81
82 Tensor<DenseIndex, 3, DataLayout> tensor_arg(out_shape);
83
84 array<DenseIndex, 4> ix;
85 for (int i = 0; i < 2; ++i) {
86 for (int j = 0; j < 3; ++j) {
87 for (int k = 0; k < 5; ++k) {
88 for (int l = 0; l < 7; ++l) {
89 ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l;
90 if (ix[dim] != 0) continue;
91 // suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l) = 10.0
92 tensor(ix) = 10.0;
93 }
94 }
95 }
96 }
97
98 std::size_t in_bytes = tensor.size() * sizeof(float);
99 std::size_t out_bytes = tensor_arg.size() * sizeof(DenseIndex);
100
101 float* d_in;
102 DenseIndex* d_out;
103 cudaMalloc((void**)(&d_in), in_bytes);
104 cudaMalloc((void**)(&d_out), out_bytes);
105
106 cudaMemcpy(d_in, tensor.data(), in_bytes, cudaMemcpyHostToDevice);
107
108 Eigen::CudaStreamDevice stream;
109 Eigen::GpuDevice gpu_device(&stream);
110
111 Eigen::TensorMap<Eigen::Tensor<float, 4, DataLayout>, Aligned > gpu_in(d_in, Eigen::array<DenseIndex, 4>(2, 3, 5, 7));
112 Eigen::TensorMap<Eigen::Tensor<DenseIndex, 3, DataLayout>, Aligned > gpu_out(d_out, out_shape);
113
114 gpu_out.device(gpu_device) = gpu_in.argmax(dim);
115
116 assert(cudaMemcpyAsync(tensor_arg.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess);
117 assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess);
118
119 VERIFY_IS_EQUAL(tensor_arg.size(),
120 size_t(2*3*5*7 / tensor.dimension(dim)));
121
122 for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
123 // Expect max to be in the first index of the reduced dimension
124 VERIFY_IS_EQUAL(tensor_arg.data()[n], 0);
125 }
126
127 for (int i = 0; i < 2; ++i) {
128 for (int j = 0; j < 3; ++j) {
129 for (int k = 0; k < 5; ++k) {
130 for (int l = 0; l < 7; ++l) {
131 ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l;
132 if (ix[dim] != tensor.dimension(dim) - 1) continue;
133 // suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = 20.0
134 tensor(ix) = 20.0;
135 }
136 }
137 }
138 }
139
140 cudaMemcpy(d_in, tensor.data(), in_bytes, cudaMemcpyHostToDevice);
141
142 gpu_out.device(gpu_device) = gpu_in.argmax(dim);
143
144 assert(cudaMemcpyAsync(tensor_arg.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess);
145 assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess);
146
147 for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
148 // Expect max to be in the last index of the reduced dimension
149 VERIFY_IS_EQUAL(tensor_arg.data()[n], tensor.dimension(dim) - 1);
150 }
151
152 cudaFree(d_in);
153 cudaFree(d_out);
154 }
155 }
156
157 template <int DataLayout>
test_cuda_argmin_dim()158 void test_cuda_argmin_dim()
159 {
160 Tensor<float, 4, DataLayout> tensor(2,3,5,7);
161 std::vector<int> dims;
162 dims.push_back(2); dims.push_back(3); dims.push_back(5); dims.push_back(7);
163
164 for (int dim = 0; dim < 4; ++dim) {
165 tensor.setRandom();
166 tensor = (tensor + tensor.constant(0.5)).log();
167
168 array<DenseIndex, 3> out_shape;
169 for (int d = 0; d < 3; ++d) out_shape[d] = (d < dim) ? dims[d] : dims[d+1];
170
171 Tensor<DenseIndex, 3, DataLayout> tensor_arg(out_shape);
172
173 array<DenseIndex, 4> ix;
174 for (int i = 0; i < 2; ++i) {
175 for (int j = 0; j < 3; ++j) {
176 for (int k = 0; k < 5; ++k) {
177 for (int l = 0; l < 7; ++l) {
178 ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l;
179 if (ix[dim] != 0) continue;
180 // suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l) = 10.0
181 tensor(ix) = -10.0;
182 }
183 }
184 }
185 }
186
187 std::size_t in_bytes = tensor.size() * sizeof(float);
188 std::size_t out_bytes = tensor_arg.size() * sizeof(DenseIndex);
189
190 float* d_in;
191 DenseIndex* d_out;
192 cudaMalloc((void**)(&d_in), in_bytes);
193 cudaMalloc((void**)(&d_out), out_bytes);
194
195 cudaMemcpy(d_in, tensor.data(), in_bytes, cudaMemcpyHostToDevice);
196
197 Eigen::CudaStreamDevice stream;
198 Eigen::GpuDevice gpu_device(&stream);
199
200 Eigen::TensorMap<Eigen::Tensor<float, 4, DataLayout>, Aligned > gpu_in(d_in, Eigen::array<DenseIndex, 4>(2, 3, 5, 7));
201 Eigen::TensorMap<Eigen::Tensor<DenseIndex, 3, DataLayout>, Aligned > gpu_out(d_out, out_shape);
202
203 gpu_out.device(gpu_device) = gpu_in.argmin(dim);
204
205 assert(cudaMemcpyAsync(tensor_arg.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess);
206 assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess);
207
208 VERIFY_IS_EQUAL(tensor_arg.size(),
209 2*3*5*7 / tensor.dimension(dim));
210
211 for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
212 // Expect min to be in the first index of the reduced dimension
213 VERIFY_IS_EQUAL(tensor_arg.data()[n], 0);
214 }
215
216 for (int i = 0; i < 2; ++i) {
217 for (int j = 0; j < 3; ++j) {
218 for (int k = 0; k < 5; ++k) {
219 for (int l = 0; l < 7; ++l) {
220 ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l;
221 if (ix[dim] != tensor.dimension(dim) - 1) continue;
222 // suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = 20.0
223 tensor(ix) = -20.0;
224 }
225 }
226 }
227 }
228
229 cudaMemcpy(d_in, tensor.data(), in_bytes, cudaMemcpyHostToDevice);
230
231 gpu_out.device(gpu_device) = gpu_in.argmin(dim);
232
233 assert(cudaMemcpyAsync(tensor_arg.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess);
234 assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess);
235
236 for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
237 // Expect max to be in the last index of the reduced dimension
238 VERIFY_IS_EQUAL(tensor_arg.data()[n], tensor.dimension(dim) - 1);
239 }
240
241 cudaFree(d_in);
242 cudaFree(d_out);
243 }
244 }
245
test_cxx11_tensor_cuda()246 void test_cxx11_tensor_cuda()
247 {
248 CALL_SUBTEST_1(test_cuda_simple_argmax<RowMajor>());
249 CALL_SUBTEST_1(test_cuda_simple_argmax<ColMajor>());
250 CALL_SUBTEST_2(test_cuda_argmax_dim<RowMajor>());
251 CALL_SUBTEST_2(test_cuda_argmax_dim<ColMajor>());
252 CALL_SUBTEST_3(test_cuda_argmin_dim<RowMajor>());
253 CALL_SUBTEST_3(test_cuda_argmin_dim<ColMajor>());
254 }
255