cxx11_tensor_of_float16_gpu.cu (21223B)
1 // This file is part of Eigen, a lightweight C++ template library 2 // for linear algebra. 3 // 4 // Copyright (C) 2016 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 #define EIGEN_TEST_NO_LONGDOUBLE 11 #define EIGEN_TEST_NO_COMPLEX 12 13 #define EIGEN_DEFAULT_DENSE_INDEX_TYPE int 14 #define EIGEN_USE_GPU 15 16 #include "main.h" 17 #include <unsupported/Eigen/CXX11/Tensor> 18 19 20 using Eigen::Tensor; 21 22 template<typename> 23 void test_gpu_numext() { 24 Eigen::GpuStreamDevice stream; 25 Eigen::GpuDevice gpu_device(&stream); 26 int num_elem = 101; 27 28 float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); 29 bool* d_res_half = (bool*)gpu_device.allocate(num_elem * sizeof(bool)); 30 bool* d_res_float = (bool*)gpu_device.allocate(num_elem * sizeof(bool)); 31 32 Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float( 33 d_float, num_elem); 34 Eigen::TensorMap<Eigen::Tensor<bool, 1>, Eigen::Aligned> gpu_res_half( 35 d_res_half, num_elem); 36 Eigen::TensorMap<Eigen::Tensor<bool, 1>, Eigen::Aligned> gpu_res_float( 37 d_res_float, num_elem); 38 39 gpu_float.device(gpu_device) = gpu_float.random() - gpu_float.constant(0.5f); 40 gpu_res_float.device(gpu_device) = gpu_float.unaryExpr(Eigen::internal::scalar_isnan_op<float>()); 41 gpu_res_half.device(gpu_device) = gpu_float.cast<Eigen::half>().unaryExpr(Eigen::internal::scalar_isnan_op<Eigen::half>()); 42 43 Tensor<bool, 1> half_prec(num_elem); 44 Tensor<bool, 1> full_prec(num_elem); 45 gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem*sizeof(bool)); 46 gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem*sizeof(bool)); 47 gpu_device.synchronize(); 48 49 for (int i = 0; i < num_elem; ++i) { 50 std::cout << "Checking numext " << i << std::endl; 51 VERIFY_IS_EQUAL(full_prec(i), half_prec(i)); 52 } 53 54 gpu_device.deallocate(d_float); 55 gpu_device.deallocate(d_res_half); 56 gpu_device.deallocate(d_res_float); 57 } 58 59 60 #ifdef EIGEN_HAS_GPU_FP16 61 62 template<typename> 63 void test_gpu_conversion() { 64 Eigen::GpuStreamDevice stream; 65 Eigen::GpuDevice gpu_device(&stream); 66 int num_elem = 101; 67 68 float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); 69 Eigen::half* d_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); 70 float* d_conv = (float*)gpu_device.allocate(num_elem * sizeof(float)); 71 72 Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float( 73 d_float, num_elem); 74 Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_half( 75 d_half, num_elem); 76 Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_conv( 77 d_conv, num_elem); 78 79 gpu_float.device(gpu_device) = gpu_float.random(); 80 gpu_half.device(gpu_device) = gpu_float.cast<Eigen::half>(); 81 gpu_conv.device(gpu_device) = gpu_half.cast<float>(); 82 83 Tensor<float, 1> initial(num_elem); 84 Tensor<float, 1> final(num_elem); 85 gpu_device.memcpyDeviceToHost(initial.data(), d_float, num_elem*sizeof(float)); 86 gpu_device.memcpyDeviceToHost(final.data(), d_conv, num_elem*sizeof(float)); 87 88 for (int i = 0; i < num_elem; ++i) { 89 VERIFY_IS_APPROX(initial(i), final(i)); 90 } 91 92 gpu_device.deallocate(d_float); 93 gpu_device.deallocate(d_half); 94 gpu_device.deallocate(d_conv); 95 } 96 97 template<typename> 98 void test_gpu_unary() { 99 Eigen::GpuStreamDevice stream; 100 Eigen::GpuDevice gpu_device(&stream); 101 int num_elem = 101; 102 103 float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); 104 float* d_res_half = (float*)gpu_device.allocate(num_elem * sizeof(float)); 105 float* d_res_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); 106 107 Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float( 108 d_float, num_elem); 109 Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_half( 110 d_res_half, num_elem); 111 Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_float( 112 d_res_float, num_elem); 113 114 gpu_float.device(gpu_device) = gpu_float.random() - gpu_float.constant(0.5f); 115 gpu_res_float.device(gpu_device) = gpu_float.abs(); 116 gpu_res_half.device(gpu_device) = gpu_float.cast<Eigen::half>().abs().cast<float>(); 117 118 Tensor<float, 1> half_prec(num_elem); 119 Tensor<float, 1> full_prec(num_elem); 120 gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem*sizeof(float)); 121 gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem*sizeof(float)); 122 gpu_device.synchronize(); 123 124 for (int i = 0; i < num_elem; ++i) { 125 std::cout << "Checking unary " << i << std::endl; 126 VERIFY_IS_APPROX(full_prec(i), half_prec(i)); 127 } 128 129 gpu_device.deallocate(d_float); 130 gpu_device.deallocate(d_res_half); 131 gpu_device.deallocate(d_res_float); 132 } 133 134 template<typename> 135 void test_gpu_elementwise() { 136 Eigen::GpuStreamDevice stream; 137 Eigen::GpuDevice gpu_device(&stream); 138 int num_elem = 101; 139 140 float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float)); 141 float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float)); 142 float* d_res_half = (float*)gpu_device.allocate(num_elem * sizeof(float)); 143 float* d_res_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); 144 145 Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float1( 146 d_float1, num_elem); 147 Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float2( 148 d_float2, num_elem); 149 Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_half( 150 d_res_half, num_elem); 151 Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_float( 152 d_res_float, num_elem); 153 154 gpu_float1.device(gpu_device) = gpu_float1.random(); 155 gpu_float2.device(gpu_device) = gpu_float2.random(); 156 gpu_res_float.device(gpu_device) = (gpu_float1 + gpu_float2) * gpu_float1; 157 gpu_res_half.device(gpu_device) = ((gpu_float1.cast<Eigen::half>() + gpu_float2.cast<Eigen::half>()) * gpu_float1.cast<Eigen::half>()).cast<float>(); 158 159 Tensor<float, 1> half_prec(num_elem); 160 Tensor<float, 1> full_prec(num_elem); 161 gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem*sizeof(float)); 162 gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem*sizeof(float)); 163 gpu_device.synchronize(); 164 165 for (int i = 0; i < num_elem; ++i) { 166 std::cout << "Checking elemwise " << i << ": full prec = " << full_prec(i) << " vs half prec = " << half_prec(i) << std::endl; 167 VERIFY_IS_APPROX(static_cast<Eigen::half>(full_prec(i)), static_cast<Eigen::half>(half_prec(i))); 168 } 169 170 gpu_device.deallocate(d_float1); 171 gpu_device.deallocate(d_float2); 172 gpu_device.deallocate(d_res_half); 173 gpu_device.deallocate(d_res_float); 174 } 175 176 template<typename> 177 void test_gpu_trancendental() { 178 Eigen::GpuStreamDevice stream; 179 Eigen::GpuDevice gpu_device(&stream); 180 int num_elem = 101; 181 182 float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float)); 183 float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float)); 184 float* d_float3 = (float*)gpu_device.allocate(num_elem * sizeof(float)); 185 Eigen::half* d_res1_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); 186 Eigen::half* d_res1_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); 187 Eigen::half* d_res2_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); 188 Eigen::half* d_res2_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); 189 Eigen::half* d_res3_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); 190 Eigen::half* d_res3_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); 191 192 Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float1(d_float1, num_elem); 193 Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float2(d_float2, num_elem); 194 Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float3(d_float3, num_elem); 195 Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res1_half(d_res1_half, num_elem); 196 Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res1_float(d_res1_float, num_elem); 197 Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res2_half(d_res2_half, num_elem); 198 Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res2_float(d_res2_float, num_elem); 199 Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res3_half(d_res3_half, num_elem); 200 Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res3_float(d_res3_float, num_elem); 201 Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res4_half(d_res3_half, num_elem); 202 Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res4_float(d_res3_float, num_elem); 203 204 gpu_float1.device(gpu_device) = gpu_float1.random() - gpu_float1.constant(0.5f); 205 gpu_float2.device(gpu_device) = gpu_float2.random() + gpu_float1.constant(0.5f); 206 gpu_float3.device(gpu_device) = gpu_float3.random(); 207 gpu_res1_float.device(gpu_device) = gpu_float1.exp().cast<Eigen::half>(); 208 gpu_res2_float.device(gpu_device) = gpu_float2.log().cast<Eigen::half>(); 209 gpu_res3_float.device(gpu_device) = gpu_float3.log1p().cast<Eigen::half>(); 210 gpu_res4_float.device(gpu_device) = gpu_float3.expm1().cast<Eigen::half>(); 211 212 gpu_res1_half.device(gpu_device) = gpu_float1.cast<Eigen::half>(); 213 gpu_res1_half.device(gpu_device) = gpu_res1_half.exp(); 214 215 gpu_res2_half.device(gpu_device) = gpu_float2.cast<Eigen::half>(); 216 gpu_res2_half.device(gpu_device) = gpu_res2_half.log(); 217 218 gpu_res3_half.device(gpu_device) = gpu_float3.cast<Eigen::half>(); 219 gpu_res3_half.device(gpu_device) = gpu_res3_half.log1p(); 220 221 gpu_res3_half.device(gpu_device) = gpu_float3.cast<Eigen::half>(); 222 gpu_res3_half.device(gpu_device) = gpu_res3_half.expm1(); 223 224 Tensor<float, 1> input1(num_elem); 225 Tensor<Eigen::half, 1> half_prec1(num_elem); 226 Tensor<Eigen::half, 1> full_prec1(num_elem); 227 Tensor<float, 1> input2(num_elem); 228 Tensor<Eigen::half, 1> half_prec2(num_elem); 229 Tensor<Eigen::half, 1> full_prec2(num_elem); 230 Tensor<float, 1> input3(num_elem); 231 Tensor<Eigen::half, 1> half_prec3(num_elem); 232 Tensor<Eigen::half, 1> full_prec3(num_elem); 233 gpu_device.memcpyDeviceToHost(input1.data(), d_float1, num_elem*sizeof(float)); 234 gpu_device.memcpyDeviceToHost(input2.data(), d_float2, num_elem*sizeof(float)); 235 gpu_device.memcpyDeviceToHost(input3.data(), d_float3, num_elem*sizeof(float)); 236 gpu_device.memcpyDeviceToHost(half_prec1.data(), d_res1_half, num_elem*sizeof(Eigen::half)); 237 gpu_device.memcpyDeviceToHost(full_prec1.data(), d_res1_float, num_elem*sizeof(Eigen::half)); 238 gpu_device.memcpyDeviceToHost(half_prec2.data(), d_res2_half, num_elem*sizeof(Eigen::half)); 239 gpu_device.memcpyDeviceToHost(full_prec2.data(), d_res2_float, num_elem*sizeof(Eigen::half)); 240 gpu_device.memcpyDeviceToHost(half_prec3.data(), d_res3_half, num_elem*sizeof(Eigen::half)); 241 gpu_device.memcpyDeviceToHost(full_prec3.data(), d_res3_float, num_elem*sizeof(Eigen::half)); 242 gpu_device.synchronize(); 243 244 for (int i = 0; i < num_elem; ++i) { 245 std::cout << "Checking elemwise exp " << i << " input = " << input1(i) << " full = " << full_prec1(i) << " half = " << half_prec1(i) << std::endl; 246 VERIFY_IS_APPROX(full_prec1(i), half_prec1(i)); 247 } 248 for (int i = 0; i < num_elem; ++i) { 249 std::cout << "Checking elemwise log " << i << " input = " << input2(i) << " full = " << full_prec2(i) << " half = " << half_prec2(i) << std::endl; 250 if(std::abs(input2(i)-1.f)<0.05f) // log lacks accuracy nearby 1 251 VERIFY_IS_APPROX(full_prec2(i)+Eigen::half(0.1f), half_prec2(i)+Eigen::half(0.1f)); 252 else 253 VERIFY_IS_APPROX(full_prec2(i), half_prec2(i)); 254 } 255 for (int i = 0; i < num_elem; ++i) { 256 std::cout << "Checking elemwise plog1 " << i << " input = " << input3(i) << " full = " << full_prec3(i) << " half = " << half_prec3(i) << std::endl; 257 VERIFY_IS_APPROX(full_prec3(i), half_prec3(i)); 258 } 259 gpu_device.deallocate(d_float1); 260 gpu_device.deallocate(d_float2); 261 gpu_device.deallocate(d_float3); 262 gpu_device.deallocate(d_res1_half); 263 gpu_device.deallocate(d_res1_float); 264 gpu_device.deallocate(d_res2_half); 265 gpu_device.deallocate(d_res2_float); 266 gpu_device.deallocate(d_res3_float); 267 gpu_device.deallocate(d_res3_half); 268 } 269 270 template<typename> 271 void test_gpu_contractions() { 272 Eigen::GpuStreamDevice stream; 273 Eigen::GpuDevice gpu_device(&stream); 274 int rows = 23; 275 int cols = 23; 276 int num_elem = rows*cols; 277 278 float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float)); 279 float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float)); 280 Eigen::half* d_res_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); 281 Eigen::half* d_res_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half)); 282 283 Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float1( 284 d_float1, rows, cols); 285 Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float2( 286 d_float2, rows, cols); 287 Eigen::TensorMap<Eigen::Tensor<Eigen::half, 2>, Eigen::Aligned> gpu_res_half( 288 d_res_half, rows, cols); 289 Eigen::TensorMap<Eigen::Tensor<Eigen::half, 2>, Eigen::Aligned> gpu_res_float( 290 d_res_float, rows, cols); 291 292 gpu_float1.device(gpu_device) = gpu_float1.random() - gpu_float1.constant(0.5f); 293 gpu_float2.device(gpu_device) = gpu_float2.random() - gpu_float2.constant(0.5f); 294 295 typedef Tensor<float, 2>::DimensionPair DimPair; 296 Eigen::array<DimPair, 1> dims(DimPair(1, 0)); 297 gpu_res_float.device(gpu_device) = gpu_float1.contract(gpu_float2, dims).cast<Eigen::half>(); 298 gpu_res_half.device(gpu_device) = gpu_float1.cast<Eigen::half>().contract(gpu_float2.cast<Eigen::half>(), dims); 299 300 Tensor<Eigen::half, 2> half_prec(rows, cols); 301 Tensor<Eigen::half, 2> full_prec(rows, cols); 302 gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem*sizeof(Eigen::half)); 303 gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem*sizeof(Eigen::half)); 304 gpu_device.synchronize(); 305 306 for (int i = 0; i < rows; ++i) { 307 for (int j = 0; j < cols; ++j) { 308 std::cout << "Checking contract " << i << " " << j << full_prec(i, j) << " " << half_prec(i, j) << std::endl; 309 if (numext::abs(full_prec(i, j) - half_prec(i, j)) > Eigen::half(1e-2f)) { 310 VERIFY_IS_APPROX(full_prec(i, j), half_prec(i, j)); 311 } 312 } 313 } 314 315 gpu_device.deallocate(d_float1); 316 gpu_device.deallocate(d_float2); 317 gpu_device.deallocate(d_res_half); 318 gpu_device.deallocate(d_res_float); 319 } 320 321 template<typename> 322 void test_gpu_reductions(int size1, int size2, int redux) { 323 324 std::cout << "Reducing " << size1 << " by " << size2 325 << " tensor along dim " << redux << std::endl; 326 327 Eigen::GpuStreamDevice stream; 328 Eigen::GpuDevice gpu_device(&stream); 329 int num_elem = size1*size2; 330 int result_size = (redux == 1 ? size1 : size2); 331 332 float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); 333 Eigen::half* d_res_half = (Eigen::half*)gpu_device.allocate(result_size * sizeof(Eigen::half)); 334 Eigen::half* d_res_float = (Eigen::half*)gpu_device.allocate(result_size * sizeof(Eigen::half)); 335 336 Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float( 337 d_float, size1, size2); 338 Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res_half( 339 d_res_half, result_size); 340 Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res_float( 341 d_res_float, result_size); 342 343 gpu_float.device(gpu_device) = gpu_float.random() * 2.0f; 344 345 Eigen::array<int, 1> redux_dim = {redux}; 346 gpu_res_float.device(gpu_device) = gpu_float.sum(redux_dim).cast<Eigen::half>(); 347 gpu_res_half.device(gpu_device) = gpu_float.cast<Eigen::half>().sum(redux_dim); 348 349 Tensor<Eigen::half, 1> half_prec(result_size); 350 Tensor<Eigen::half, 1> full_prec(result_size); 351 gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, result_size*sizeof(Eigen::half)); 352 gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, result_size*sizeof(Eigen::half)); 353 gpu_device.synchronize(); 354 355 for (int i = 0; i < result_size; ++i) { 356 std::cout << "EXPECTED " << full_prec(i) << " GOT " << half_prec(i) << std::endl; 357 VERIFY_IS_APPROX(full_prec(i), half_prec(i)); 358 } 359 360 gpu_device.deallocate(d_float); 361 gpu_device.deallocate(d_res_half); 362 gpu_device.deallocate(d_res_float); 363 } 364 365 template<typename> 366 void test_gpu_reductions() { 367 test_gpu_reductions<void>(13, 13, 0); 368 test_gpu_reductions<void>(13, 13, 1); 369 370 test_gpu_reductions<void>(35, 36, 0); 371 test_gpu_reductions<void>(35, 36, 1); 372 373 test_gpu_reductions<void>(36, 35, 0); 374 test_gpu_reductions<void>(36, 35, 1); 375 } 376 377 template<typename> 378 void test_gpu_full_reductions() { 379 Eigen::GpuStreamDevice stream; 380 Eigen::GpuDevice gpu_device(&stream); 381 int size = 13; 382 int num_elem = size*size; 383 384 float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); 385 Eigen::half* d_res_half = (Eigen::half*)gpu_device.allocate(1 * sizeof(Eigen::half)); 386 Eigen::half* d_res_float = (Eigen::half*)gpu_device.allocate(1 * sizeof(Eigen::half)); 387 388 Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float( 389 d_float, size, size); 390 Eigen::TensorMap<Eigen::Tensor<Eigen::half, 0>, Eigen::Aligned> gpu_res_half( 391 d_res_half); 392 Eigen::TensorMap<Eigen::Tensor<Eigen::half, 0>, Eigen::Aligned> gpu_res_float( 393 d_res_float); 394 395 gpu_float.device(gpu_device) = gpu_float.random(); 396 397 gpu_res_float.device(gpu_device) = gpu_float.sum().cast<Eigen::half>(); 398 gpu_res_half.device(gpu_device) = gpu_float.cast<Eigen::half>().sum(); 399 400 Tensor<Eigen::half, 0> half_prec; 401 Tensor<Eigen::half, 0> full_prec; 402 gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, sizeof(Eigen::half)); 403 gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, sizeof(Eigen::half)); 404 gpu_device.synchronize(); 405 406 VERIFY_IS_APPROX(full_prec(), half_prec()); 407 408 gpu_res_float.device(gpu_device) = gpu_float.maximum().cast<Eigen::half>(); 409 gpu_res_half.device(gpu_device) = gpu_float.cast<Eigen::half>().maximum(); 410 gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, sizeof(Eigen::half)); 411 gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, sizeof(Eigen::half)); 412 gpu_device.synchronize(); 413 414 VERIFY_IS_APPROX(full_prec(), half_prec()); 415 416 gpu_device.deallocate(d_float); 417 gpu_device.deallocate(d_res_half); 418 gpu_device.deallocate(d_res_float); 419 } 420 421 template<typename> 422 void test_gpu_forced_evals() { 423 424 Eigen::GpuStreamDevice stream; 425 Eigen::GpuDevice gpu_device(&stream); 426 int num_elem = 101; 427 428 float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); 429 float* d_res_half1 = (float*)gpu_device.allocate(num_elem * sizeof(float)); 430 float* d_res_half2 = (float*)gpu_device.allocate(num_elem * sizeof(float)); 431 float* d_res_float = (float*)gpu_device.allocate(num_elem * sizeof(float)); 432 433 Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float( 434 d_float, num_elem); 435 Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_half1( 436 d_res_half1, num_elem); 437 Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Unaligned> gpu_res_half2( 438 d_res_half2, num_elem); 439 Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_float( 440 d_res_float, num_elem); 441 442 Eigen::array<int, 1> no_bcast; 443 no_bcast[0] = 1; 444 445 gpu_float.device(gpu_device) = gpu_float.random() - gpu_float.constant(0.5f); 446 gpu_res_float.device(gpu_device) = gpu_float.abs(); 447 gpu_res_half1.device(gpu_device) = gpu_float.cast<Eigen::half>().abs().eval().cast<float>(); 448 gpu_res_half2.device(gpu_device) = gpu_float.cast<Eigen::half>().abs().broadcast(no_bcast).eval().cast<float>(); 449 450 Tensor<float, 1> half_prec1(num_elem); 451 Tensor<float, 1> half_prec2(num_elem); 452 Tensor<float, 1> full_prec(num_elem); 453 gpu_device.memcpyDeviceToHost(half_prec1.data(), d_res_half1, num_elem*sizeof(float)); 454 gpu_device.memcpyDeviceToHost(half_prec2.data(), d_res_half2, num_elem*sizeof(float)); 455 gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem*sizeof(float)); 456 gpu_device.synchronize(); 457 458 for (int i = 0; i < num_elem; ++i) { 459 std::cout << "Checking forced eval " << i << full_prec(i) << " vs " << half_prec1(i) << " vs " << half_prec2(i) << std::endl; 460 VERIFY_IS_APPROX(full_prec(i), half_prec1(i)); 461 VERIFY_IS_APPROX(full_prec(i), half_prec2(i)); 462 } 463 464 gpu_device.deallocate(d_float); 465 gpu_device.deallocate(d_res_half1); 466 gpu_device.deallocate(d_res_half2); 467 gpu_device.deallocate(d_res_float); 468 } 469 #endif 470 471 472 EIGEN_DECLARE_TEST(cxx11_tensor_of_float16_gpu) 473 { 474 CALL_SUBTEST_1(test_gpu_numext<void>()); 475 476 #ifdef EIGEN_HAS_GPU_FP16 477 CALL_SUBTEST_1(test_gpu_conversion<void>()); 478 CALL_SUBTEST_1(test_gpu_unary<void>()); 479 CALL_SUBTEST_1(test_gpu_elementwise<void>()); 480 CALL_SUBTEST_1(test_gpu_trancendental<void>()); 481 CALL_SUBTEST_2(test_gpu_contractions<void>()); 482 CALL_SUBTEST_3(test_gpu_reductions<void>()); 483 CALL_SUBTEST_4(test_gpu_full_reductions<void>()); 484 CALL_SUBTEST_5(test_gpu_forced_evals<void>()); 485 #else 486 std::cout << "Half floats are not supported by this version of gpu: skipping the test" << std::endl; 487 #endif 488 }