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 // Copyright (C) 2014 Navdeep Jaitly <ndjaitly@google.com>
6 //
7 // This Source Code Form is subject to the terms of the Mozilla
8 // Public License v. 2.0. If a copy of the MPL was not distributed
9 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
10
11 #define EIGEN_TEST_NO_LONGDOUBLE
12 #define EIGEN_TEST_NO_COMPLEX
13 #define EIGEN_TEST_FUNC cxx11_tensor_cuda
14 #define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
15 #define EIGEN_USE_GPU
16
17 #if defined __CUDACC_VER__ && __CUDACC_VER__ >= 70500
18 #include <cuda_fp16.h>
19 #endif
20 #include "main.h"
21 #include <unsupported/Eigen/CXX11/Tensor>
22
23 using Eigen::Tensor;
24 typedef Tensor<float, 1>::DimensionPair DimPair;
25
26 template<int DataLayout>
test_cuda_contraction(int m_size,int k_size,int n_size)27 void test_cuda_contraction(int m_size, int k_size, int n_size)
28 {
29 std::cout << "Testing for (" << m_size << "," << k_size << "," << n_size << ")" << std::endl;
30 // with these dimensions, the output has 300 * 140 elements, which is
31 // more than 30 * 1024, which is the number of threads in blocks on
32 // a 15 SM GK110 GPU
33 Tensor<float, 2, DataLayout> t_left(m_size, k_size);
34 Tensor<float, 2, DataLayout> t_right(k_size, n_size);
35 Tensor<float, 2, DataLayout> t_result(m_size, n_size);
36 Tensor<float, 2, DataLayout> t_result_gpu(m_size, n_size);
37 Eigen::array<DimPair, 1> dims(DimPair(1, 0));
38
39 t_left.setRandom();
40 t_right.setRandom();
41
42 std::size_t t_left_bytes = t_left.size() * sizeof(float);
43 std::size_t t_right_bytes = t_right.size() * sizeof(float);
44 std::size_t t_result_bytes = t_result.size() * sizeof(float);
45
46 float* d_t_left;
47 float* d_t_right;
48 float* d_t_result;
49
50 cudaMalloc((void**)(&d_t_left), t_left_bytes);
51 cudaMalloc((void**)(&d_t_right), t_right_bytes);
52 cudaMalloc((void**)(&d_t_result), t_result_bytes);
53
54 cudaMemcpy(d_t_left, t_left.data(), t_left_bytes, cudaMemcpyHostToDevice);
55 cudaMemcpy(d_t_right, t_right.data(), t_right_bytes, cudaMemcpyHostToDevice);
56
57 Eigen::CudaStreamDevice stream;
58 Eigen::GpuDevice gpu_device(&stream);
59
60 Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> >
61 gpu_t_left(d_t_left, Eigen::array<int, 2>(m_size, k_size));
62 Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> >
63 gpu_t_right(d_t_right, Eigen::array<int, 2>(k_size, n_size));
64 Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> >
65 gpu_t_result(d_t_result, Eigen::array<int, 2>(m_size, n_size));
66
67
68 gpu_t_result.device(gpu_device) = gpu_t_left.contract(gpu_t_right, dims);
69 t_result = t_left.contract(t_right, dims);
70
71 cudaMemcpy(t_result_gpu.data(), d_t_result, t_result_bytes, cudaMemcpyDeviceToHost);
72 for (DenseIndex i = 0; i < t_result.size(); i++) {
73 if (fabs(t_result(i) - t_result_gpu(i)) < 1e-4f) {
74 continue;
75 }
76 if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i), 1e-4f)) {
77 continue;
78 }
79 std::cout << "mismatch detected at index " << i << ": " << t_result(i)
80 << " vs " << t_result_gpu(i) << std::endl;
81 assert(false);
82 }
83
84 cudaFree((void*)d_t_left);
85 cudaFree((void*)d_t_right);
86 cudaFree((void*)d_t_result);
87 }
88
89
90 template<int DataLayout>
test_scalar(int m_size,int k_size,int n_size)91 void test_scalar(int m_size, int k_size, int n_size)
92 {
93 std::cout << "Testing for (" << m_size << "," << k_size << "," << n_size << ")" << std::endl;
94 // with these dimensions, the output has 300 * 140 elements, which is
95 // more than 30 * 1024, which is the number of threads in blocks on
96 // a 15 SM GK110 GPU
97 Tensor<float, 2, DataLayout> t_left(m_size, k_size);
98 Tensor<float, 2, DataLayout> t_right(k_size, n_size);
99 Tensor<float, 0, DataLayout> t_result;
100 Tensor<float, 0, DataLayout> t_result_gpu;
101 Eigen::array<DimPair, 2> dims(DimPair(0, 0), DimPair(1, 1));
102
103 t_left.setRandom();
104 t_right.setRandom();
105
106 std::size_t t_left_bytes = t_left.size() * sizeof(float);
107 std::size_t t_right_bytes = t_right.size() * sizeof(float);
108 std::size_t t_result_bytes = sizeof(float);
109
110 float* d_t_left;
111 float* d_t_right;
112 float* d_t_result;
113
114 cudaMalloc((void**)(&d_t_left), t_left_bytes);
115 cudaMalloc((void**)(&d_t_right), t_right_bytes);
116 cudaMalloc((void**)(&d_t_result), t_result_bytes);
117
118 cudaMemcpy(d_t_left, t_left.data(), t_left_bytes, cudaMemcpyHostToDevice);
119 cudaMemcpy(d_t_right, t_right.data(), t_right_bytes, cudaMemcpyHostToDevice);
120
121 Eigen::CudaStreamDevice stream;
122 Eigen::GpuDevice gpu_device(&stream);
123
124 Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> >
125 gpu_t_left(d_t_left, m_size, k_size);
126 Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> >
127 gpu_t_right(d_t_right, k_size, n_size);
128 Eigen::TensorMap<Eigen::Tensor<float, 0, DataLayout> >
129 gpu_t_result(d_t_result);
130
131 gpu_t_result.device(gpu_device) = gpu_t_left.contract(gpu_t_right, dims);
132 t_result = t_left.contract(t_right, dims);
133
134 cudaMemcpy(t_result_gpu.data(), d_t_result, t_result_bytes, cudaMemcpyDeviceToHost);
135 if (fabs(t_result() - t_result_gpu()) > 1e-4f &&
136 !Eigen::internal::isApprox(t_result(), t_result_gpu(), 1e-4f)) {
137 std::cout << "mismatch detected: " << t_result()
138 << " vs " << t_result_gpu() << std::endl;
139 assert(false);
140 }
141
142 cudaFree((void*)d_t_left);
143 cudaFree((void*)d_t_right);
144 cudaFree((void*)d_t_result);
145 }
146
147
148 template<int DataLayout>
test_cuda_contraction_m()149 void test_cuda_contraction_m() {
150 for (int k = 32; k < 256; k++) {
151 test_cuda_contraction<ColMajor>(k, 128, 128);
152 test_cuda_contraction<RowMajor>(k, 128, 128);
153 }
154 }
155
156 template<int DataLayout>
test_cuda_contraction_k()157 void test_cuda_contraction_k() {
158 for (int k = 32; k < 256; k++) {
159 test_cuda_contraction<ColMajor>(128, k, 128);
160 test_cuda_contraction<RowMajor>(128, k, 128);
161 }
162 }
163
164 template<int DataLayout>
test_cuda_contraction_n()165 void test_cuda_contraction_n() {
166 for (int k = 32; k < 256; k++) {
167 test_cuda_contraction<ColMajor>(128, 128, k);
168 test_cuda_contraction<RowMajor>(128, 128, k);
169 }
170 }
171
172
173 template<int DataLayout>
test_cuda_contraction_sizes()174 void test_cuda_contraction_sizes() {
175 int m_sizes[] = { 31, 39, 63, 64, 65,
176 127, 129, 255, 257 , 511,
177 512, 513, 1023, 1024, 1025};
178
179 int n_sizes[] = { 31, 39, 63, 64, 65,
180 127, 129, 255, 257, 511,
181 512, 513, 1023, 1024, 1025};
182
183 int k_sizes[] = { 31, 39, 63, 64, 65,
184 95, 96, 127, 129, 255,
185 257, 511, 512, 513, 1023,
186 1024, 1025};
187
188 for (int i = 0; i < 15; i++) {
189 for (int j = 0; j < 15; j++) {
190 for (int k = 0; k < 17; k++) {
191 test_cuda_contraction<DataLayout>(m_sizes[i], n_sizes[j], k_sizes[k]);
192 }
193 }
194 }
195 }
196
test_cxx11_tensor_cuda()197 void test_cxx11_tensor_cuda()
198 {
199 CALL_SUBTEST_1(test_cuda_contraction<ColMajor>(128, 128, 128));
200 CALL_SUBTEST_1(test_cuda_contraction<RowMajor>(128, 128, 128));
201
202 CALL_SUBTEST_1(test_scalar<ColMajor>(128, 128, 128));
203 CALL_SUBTEST_1(test_scalar<RowMajor>(128, 128, 128));
204
205 CALL_SUBTEST_2(test_cuda_contraction_m<ColMajor>());
206 CALL_SUBTEST_3(test_cuda_contraction_m<RowMajor>());
207
208 CALL_SUBTEST_4(test_cuda_contraction_k<ColMajor>());
209 CALL_SUBTEST_5(test_cuda_contraction_k<RowMajor>());
210
211 CALL_SUBTEST_6(test_cuda_contraction_n<ColMajor>());
212 CALL_SUBTEST_7(test_cuda_contraction_n<RowMajor>());
213
214 CALL_SUBTEST_8(test_cuda_contraction_sizes<ColMajor>());
215 CALL_SUBTEST_9(test_cuda_contraction_sizes<RowMajor>());
216 }
217