| 1 | #define TORCH_ASSERT_ONLY_METHOD_OPERATORS |
|---|---|
| 2 | // @generated by torchgen/gen.py from RegisterFunctionalization.cpp |
| 3 | |
| 4 | #include <ATen/core/LegacyTypeDispatch.h> |
| 5 | #include <ATen/EmptyTensor.h> |
| 6 | #include <ATen/FunctionalTensorWrapper.h> |
| 7 | #include <ATen/FunctionalInverses.h> |
| 8 | #include <torch/library.h> |
| 9 | |
| 10 | #ifndef AT_PER_OPERATOR_HEADERS |
| 11 | #include <ATen/Operators.h> |
| 12 | #include <ATen/NativeFunctions.h> |
| 13 | #else |
| 14 | // needed for the meta tensor calls to get stride info in functionalization |
| 15 | #include <ATen/ops/empty_strided_native.h> |
| 16 | // needed for special handling of copy_(). |
| 17 | // See Note [functionalizating copy_() and not preserving strides] |
| 18 | #include <ATen/ops/to_ops.h> |
| 19 | #include <ATen/ops/expand_copy_ops.h> |
| 20 | |
| 21 | #include <ATen/ops/_new_zeros_with_same_feature_meta_native.h> |
| 22 | #include <ATen/ops/_new_zeros_with_same_feature_meta_ops.h> |
| 23 | #include <ATen/ops/_new_zeros_with_same_feature_meta_native.h> |
| 24 | #include <ATen/ops/_new_zeros_with_same_feature_meta_ops.h> |
| 25 | #include <ATen/ops/_cudnn_init_dropout_state_native.h> |
| 26 | #include <ATen/ops/_cudnn_init_dropout_state_ops.h> |
| 27 | #include <ATen/ops/_cudnn_init_dropout_state_native.h> |
| 28 | #include <ATen/ops/_cudnn_init_dropout_state_ops.h> |
| 29 | #include <ATen/ops/angle_native.h> |
| 30 | #include <ATen/ops/angle_ops.h> |
| 31 | #include <ATen/ops/angle_native.h> |
| 32 | #include <ATen/ops/angle_ops.h> |
| 33 | #include <ATen/ops/sgn_native.h> |
| 34 | #include <ATen/ops/sgn_ops.h> |
| 35 | #include <ATen/ops/sgn_native.h> |
| 36 | #include <ATen/ops/sgn_ops.h> |
| 37 | #include <ATen/ops/sgn_native.h> |
| 38 | #include <ATen/ops/sgn_ops.h> |
| 39 | #include <ATen/ops/_add_relu_native.h> |
| 40 | #include <ATen/ops/_add_relu_ops.h> |
| 41 | #include <ATen/ops/_add_relu_native.h> |
| 42 | #include <ATen/ops/_add_relu_ops.h> |
| 43 | #include <ATen/ops/_add_relu_native.h> |
| 44 | #include <ATen/ops/_add_relu_ops.h> |
| 45 | #include <ATen/ops/_add_relu_native.h> |
| 46 | #include <ATen/ops/_add_relu_ops.h> |
| 47 | #include <ATen/ops/_add_relu_native.h> |
| 48 | #include <ATen/ops/_add_relu_ops.h> |
| 49 | #include <ATen/ops/_add_relu_native.h> |
| 50 | #include <ATen/ops/_add_relu_ops.h> |
| 51 | #include <ATen/ops/any_native.h> |
| 52 | #include <ATen/ops/any_ops.h> |
| 53 | #include <ATen/ops/any_native.h> |
| 54 | #include <ATen/ops/any_ops.h> |
| 55 | #include <ATen/ops/any_native.h> |
| 56 | #include <ATen/ops/any_ops.h> |
| 57 | #include <ATen/ops/any_native.h> |
| 58 | #include <ATen/ops/any_ops.h> |
| 59 | #include <ATen/ops/argmin_native.h> |
| 60 | #include <ATen/ops/argmin_ops.h> |
| 61 | #include <ATen/ops/argmin_native.h> |
| 62 | #include <ATen/ops/argmin_ops.h> |
| 63 | #include <ATen/ops/acosh_native.h> |
| 64 | #include <ATen/ops/acosh_ops.h> |
| 65 | #include <ATen/ops/acosh_native.h> |
| 66 | #include <ATen/ops/acosh_ops.h> |
| 67 | #include <ATen/ops/acosh_native.h> |
| 68 | #include <ATen/ops/acosh_ops.h> |
| 69 | #include <ATen/ops/arcsin_native.h> |
| 70 | #include <ATen/ops/arcsin_ops.h> |
| 71 | #include <ATen/ops/arcsin_native.h> |
| 72 | #include <ATen/ops/arcsin_ops.h> |
| 73 | #include <ATen/ops/arcsin_native.h> |
| 74 | #include <ATen/ops/arcsin_ops.h> |
| 75 | #include <ATen/ops/arctan_native.h> |
| 76 | #include <ATen/ops/arctan_ops.h> |
| 77 | #include <ATen/ops/arctan_native.h> |
| 78 | #include <ATen/ops/arctan_ops.h> |
| 79 | #include <ATen/ops/arctan_native.h> |
| 80 | #include <ATen/ops/arctan_ops.h> |
| 81 | #include <ATen/ops/bincount_native.h> |
| 82 | #include <ATen/ops/bincount_ops.h> |
| 83 | #include <ATen/ops/bincount_native.h> |
| 84 | #include <ATen/ops/bincount_ops.h> |
| 85 | #include <ATen/ops/copysign_native.h> |
| 86 | #include <ATen/ops/copysign_ops.h> |
| 87 | #include <ATen/ops/copysign_native.h> |
| 88 | #include <ATen/ops/copysign_ops.h> |
| 89 | #include <ATen/ops/copysign_native.h> |
| 90 | #include <ATen/ops/copysign_ops.h> |
| 91 | #include <ATen/ops/copysign_native.h> |
| 92 | #include <ATen/ops/copysign_ops.h> |
| 93 | #include <ATen/ops/copysign_native.h> |
| 94 | #include <ATen/ops/copysign_ops.h> |
| 95 | #include <ATen/ops/copysign_native.h> |
| 96 | #include <ATen/ops/copysign_ops.h> |
| 97 | #include <ATen/ops/logical_or_native.h> |
| 98 | #include <ATen/ops/logical_or_ops.h> |
| 99 | #include <ATen/ops/logical_or_native.h> |
| 100 | #include <ATen/ops/logical_or_ops.h> |
| 101 | #include <ATen/ops/logical_or_native.h> |
| 102 | #include <ATen/ops/logical_or_ops.h> |
| 103 | #include <ATen/ops/cat_native.h> |
| 104 | #include <ATen/ops/cat_ops.h> |
| 105 | #include <ATen/ops/cat_native.h> |
| 106 | #include <ATen/ops/cat_ops.h> |
| 107 | #include <ATen/ops/cat_native.h> |
| 108 | #include <ATen/ops/cat_ops.h> |
| 109 | #include <ATen/ops/cat_native.h> |
| 110 | #include <ATen/ops/cat_ops.h> |
| 111 | #include <ATen/ops/concat_native.h> |
| 112 | #include <ATen/ops/concat_ops.h> |
| 113 | #include <ATen/ops/concat_native.h> |
| 114 | #include <ATen/ops/concat_ops.h> |
| 115 | #include <ATen/ops/concat_native.h> |
| 116 | #include <ATen/ops/concat_ops.h> |
| 117 | #include <ATen/ops/concat_native.h> |
| 118 | #include <ATen/ops/concat_ops.h> |
| 119 | #include <ATen/ops/ceil_native.h> |
| 120 | #include <ATen/ops/ceil_ops.h> |
| 121 | #include <ATen/ops/ceil_native.h> |
| 122 | #include <ATen/ops/ceil_ops.h> |
| 123 | #include <ATen/ops/ceil_native.h> |
| 124 | #include <ATen/ops/ceil_ops.h> |
| 125 | #include <ATen/ops/polar_native.h> |
| 126 | #include <ATen/ops/polar_ops.h> |
| 127 | #include <ATen/ops/polar_native.h> |
| 128 | #include <ATen/ops/polar_ops.h> |
| 129 | #include <ATen/ops/convolution_native.h> |
| 130 | #include <ATen/ops/convolution_ops.h> |
| 131 | #include <ATen/ops/convolution_native.h> |
| 132 | #include <ATen/ops/convolution_ops.h> |
| 133 | #include <ATen/ops/convolution_overrideable_native.h> |
| 134 | #include <ATen/ops/convolution_overrideable_ops.h> |
| 135 | #include <ATen/ops/convolution_overrideable_native.h> |
| 136 | #include <ATen/ops/convolution_overrideable_ops.h> |
| 137 | #include <ATen/ops/convolution_backward_overrideable_native.h> |
| 138 | #include <ATen/ops/convolution_backward_overrideable_ops.h> |
| 139 | #include <ATen/ops/convolution_backward_overrideable_native.h> |
| 140 | #include <ATen/ops/convolution_backward_overrideable_ops.h> |
| 141 | #include <ATen/ops/cudnn_affine_grid_generator_native.h> |
| 142 | #include <ATen/ops/cudnn_affine_grid_generator_ops.h> |
| 143 | #include <ATen/ops/cudnn_affine_grid_generator_native.h> |
| 144 | #include <ATen/ops/cudnn_affine_grid_generator_ops.h> |
| 145 | #include <ATen/ops/cudnn_batch_norm_backward_native.h> |
| 146 | #include <ATen/ops/cudnn_batch_norm_backward_ops.h> |
| 147 | #include <ATen/ops/cudnn_batch_norm_backward_native.h> |
| 148 | #include <ATen/ops/cudnn_batch_norm_backward_ops.h> |
| 149 | #include <ATen/ops/cudnn_convolution_relu_native.h> |
| 150 | #include <ATen/ops/cudnn_convolution_relu_ops.h> |
| 151 | #include <ATen/ops/cudnn_convolution_relu_native.h> |
| 152 | #include <ATen/ops/cudnn_convolution_relu_ops.h> |
| 153 | #include <ATen/ops/cudnn_convolution_add_relu_native.h> |
| 154 | #include <ATen/ops/cudnn_convolution_add_relu_ops.h> |
| 155 | #include <ATen/ops/cudnn_convolution_add_relu_native.h> |
| 156 | #include <ATen/ops/cudnn_convolution_add_relu_ops.h> |
| 157 | #include <ATen/ops/cummax_native.h> |
| 158 | #include <ATen/ops/cummax_ops.h> |
| 159 | #include <ATen/ops/cummax_native.h> |
| 160 | #include <ATen/ops/cummax_ops.h> |
| 161 | #include <ATen/ops/cummax_native.h> |
| 162 | #include <ATen/ops/cummax_ops.h> |
| 163 | #include <ATen/ops/cummax_native.h> |
| 164 | #include <ATen/ops/cummax_ops.h> |
| 165 | #include <ATen/ops/diff_native.h> |
| 166 | #include <ATen/ops/diff_ops.h> |
| 167 | #include <ATen/ops/diff_native.h> |
| 168 | #include <ATen/ops/diff_ops.h> |
| 169 | #include <ATen/ops/embedding_renorm_native.h> |
| 170 | #include <ATen/ops/embedding_renorm_ops.h> |
| 171 | #include <ATen/ops/embedding_renorm_native.h> |
| 172 | #include <ATen/ops/embedding_renorm_ops.h> |
| 173 | #include <ATen/ops/embedding_renorm_native.h> |
| 174 | #include <ATen/ops/embedding_renorm_ops.h> |
| 175 | #include <ATen/ops/_embedding_bag_forward_only_native.h> |
| 176 | #include <ATen/ops/_embedding_bag_forward_only_ops.h> |
| 177 | #include <ATen/ops/_embedding_bag_forward_only_native.h> |
| 178 | #include <ATen/ops/_embedding_bag_forward_only_ops.h> |
| 179 | #include <ATen/ops/_embedding_bag_dense_backward_native.h> |
| 180 | #include <ATen/ops/_embedding_bag_dense_backward_ops.h> |
| 181 | #include <ATen/ops/_embedding_bag_dense_backward_native.h> |
| 182 | #include <ATen/ops/_embedding_bag_dense_backward_ops.h> |
| 183 | #include <ATen/ops/empty_native.h> |
| 184 | #include <ATen/ops/empty_ops.h> |
| 185 | #include <ATen/ops/empty_native.h> |
| 186 | #include <ATen/ops/empty_ops.h> |
| 187 | #include <ATen/ops/empty_native.h> |
| 188 | #include <ATen/ops/empty_ops.h> |
| 189 | #include <ATen/ops/empty_native.h> |
| 190 | #include <ATen/ops/empty_ops.h> |
| 191 | #include <ATen/ops/empty_like_native.h> |
| 192 | #include <ATen/ops/empty_like_ops.h> |
| 193 | #include <ATen/ops/empty_like_native.h> |
| 194 | #include <ATen/ops/empty_like_ops.h> |
| 195 | #include <ATen/ops/erf_native.h> |
| 196 | #include <ATen/ops/erf_ops.h> |
| 197 | #include <ATen/ops/erf_native.h> |
| 198 | #include <ATen/ops/erf_ops.h> |
| 199 | #include <ATen/ops/erf_native.h> |
| 200 | #include <ATen/ops/erf_ops.h> |
| 201 | #include <ATen/ops/erfc_native.h> |
| 202 | #include <ATen/ops/erfc_ops.h> |
| 203 | #include <ATen/ops/erfc_native.h> |
| 204 | #include <ATen/ops/erfc_ops.h> |
| 205 | #include <ATen/ops/erfc_native.h> |
| 206 | #include <ATen/ops/erfc_ops.h> |
| 207 | #include <ATen/ops/eye_native.h> |
| 208 | #include <ATen/ops/eye_ops.h> |
| 209 | #include <ATen/ops/eye_native.h> |
| 210 | #include <ATen/ops/eye_ops.h> |
| 211 | #include <ATen/ops/eye_native.h> |
| 212 | #include <ATen/ops/eye_ops.h> |
| 213 | #include <ATen/ops/eye_native.h> |
| 214 | #include <ATen/ops/eye_ops.h> |
| 215 | #include <ATen/ops/gcd_native.h> |
| 216 | #include <ATen/ops/gcd_ops.h> |
| 217 | #include <ATen/ops/gcd_native.h> |
| 218 | #include <ATen/ops/gcd_ops.h> |
| 219 | #include <ATen/ops/gcd_native.h> |
| 220 | #include <ATen/ops/gcd_ops.h> |
| 221 | #include <ATen/ops/grid_sampler_2d_backward_native.h> |
| 222 | #include <ATen/ops/grid_sampler_2d_backward_ops.h> |
| 223 | #include <ATen/ops/grid_sampler_2d_backward_native.h> |
| 224 | #include <ATen/ops/grid_sampler_2d_backward_ops.h> |
| 225 | #include <ATen/ops/native_group_norm_backward_native.h> |
| 226 | #include <ATen/ops/native_group_norm_backward_ops.h> |
| 227 | #include <ATen/ops/native_group_norm_backward_native.h> |
| 228 | #include <ATen/ops/native_group_norm_backward_ops.h> |
| 229 | #include <ATen/ops/_fft_r2c_native.h> |
| 230 | #include <ATen/ops/_fft_r2c_ops.h> |
| 231 | #include <ATen/ops/_fft_r2c_native.h> |
| 232 | #include <ATen/ops/_fft_r2c_ops.h> |
| 233 | #include <ATen/ops/index_native.h> |
| 234 | #include <ATen/ops/index_ops.h> |
| 235 | #include <ATen/ops/index_native.h> |
| 236 | #include <ATen/ops/index_ops.h> |
| 237 | #include <ATen/ops/index_put_native.h> |
| 238 | #include <ATen/ops/index_put_ops.h> |
| 239 | #include <ATen/ops/index_put_native.h> |
| 240 | #include <ATen/ops/index_put_ops.h> |
| 241 | #include <ATen/ops/index_put_native.h> |
| 242 | #include <ATen/ops/index_put_ops.h> |
| 243 | #include <ATen/ops/linear_backward_native.h> |
| 244 | #include <ATen/ops/linear_backward_ops.h> |
| 245 | #include <ATen/ops/linear_backward_native.h> |
| 246 | #include <ATen/ops/linear_backward_ops.h> |
| 247 | #include <ATen/ops/mkldnn_linear_backward_weights_native.h> |
| 248 | #include <ATen/ops/mkldnn_linear_backward_weights_ops.h> |
| 249 | #include <ATen/ops/mkldnn_linear_backward_weights_native.h> |
| 250 | #include <ATen/ops/mkldnn_linear_backward_weights_ops.h> |
| 251 | #include <ATen/ops/logaddexp_native.h> |
| 252 | #include <ATen/ops/logaddexp_ops.h> |
| 253 | #include <ATen/ops/logaddexp_native.h> |
| 254 | #include <ATen/ops/logaddexp_ops.h> |
| 255 | #include <ATen/ops/_logcumsumexp_native.h> |
| 256 | #include <ATen/ops/_logcumsumexp_ops.h> |
| 257 | #include <ATen/ops/_logcumsumexp_native.h> |
| 258 | #include <ATen/ops/_logcumsumexp_ops.h> |
| 259 | #include <ATen/ops/logcumsumexp_native.h> |
| 260 | #include <ATen/ops/logcumsumexp_ops.h> |
| 261 | #include <ATen/ops/logcumsumexp_native.h> |
| 262 | #include <ATen/ops/logcumsumexp_ops.h> |
| 263 | #include <ATen/ops/logcumsumexp_native.h> |
| 264 | #include <ATen/ops/logcumsumexp_ops.h> |
| 265 | #include <ATen/ops/logcumsumexp_native.h> |
| 266 | #include <ATen/ops/logcumsumexp_ops.h> |
| 267 | #include <ATen/ops/matmul_backward_native.h> |
| 268 | #include <ATen/ops/matmul_backward_ops.h> |
| 269 | #include <ATen/ops/matmul_backward_native.h> |
| 270 | #include <ATen/ops/matmul_backward_ops.h> |
| 271 | #include <ATen/ops/mps_max_pool2d_backward_native.h> |
| 272 | #include <ATen/ops/mps_max_pool2d_backward_ops.h> |
| 273 | #include <ATen/ops/mps_max_pool2d_backward_native.h> |
| 274 | #include <ATen/ops/mps_max_pool2d_backward_ops.h> |
| 275 | #include <ATen/ops/median_native.h> |
| 276 | #include <ATen/ops/median_ops.h> |
| 277 | #include <ATen/ops/median_native.h> |
| 278 | #include <ATen/ops/median_ops.h> |
| 279 | #include <ATen/ops/median_native.h> |
| 280 | #include <ATen/ops/median_ops.h> |
| 281 | #include <ATen/ops/median_native.h> |
| 282 | #include <ATen/ops/median_ops.h> |
| 283 | #include <ATen/ops/median_native.h> |
| 284 | #include <ATen/ops/median_ops.h> |
| 285 | #include <ATen/ops/median_native.h> |
| 286 | #include <ATen/ops/median_ops.h> |
| 287 | #include <ATen/ops/amin_native.h> |
| 288 | #include <ATen/ops/amin_ops.h> |
| 289 | #include <ATen/ops/amin_native.h> |
| 290 | #include <ATen/ops/amin_ops.h> |
| 291 | #include <ATen/ops/mkldnn_rnn_layer_backward_native.h> |
| 292 | #include <ATen/ops/mkldnn_rnn_layer_backward_ops.h> |
| 293 | #include <ATen/ops/mkldnn_rnn_layer_backward_native.h> |
| 294 | #include <ATen/ops/mkldnn_rnn_layer_backward_ops.h> |
| 295 | #include <ATen/ops/miopen_convolution_native.h> |
| 296 | #include <ATen/ops/miopen_convolution_ops.h> |
| 297 | #include <ATen/ops/miopen_convolution_native.h> |
| 298 | #include <ATen/ops/miopen_convolution_ops.h> |
| 299 | #include <ATen/ops/miopen_depthwise_convolution_native.h> |
| 300 | #include <ATen/ops/miopen_depthwise_convolution_ops.h> |
| 301 | #include <ATen/ops/miopen_depthwise_convolution_native.h> |
| 302 | #include <ATen/ops/miopen_depthwise_convolution_ops.h> |
| 303 | #include <ATen/ops/miopen_rnn_backward_native.h> |
| 304 | #include <ATen/ops/miopen_rnn_backward_ops.h> |
| 305 | #include <ATen/ops/miopen_rnn_backward_native.h> |
| 306 | #include <ATen/ops/miopen_rnn_backward_ops.h> |
| 307 | #include <ATen/ops/native_batch_norm_native.h> |
| 308 | #include <ATen/ops/native_batch_norm_ops.h> |
| 309 | #include <ATen/ops/native_batch_norm_native.h> |
| 310 | #include <ATen/ops/native_batch_norm_ops.h> |
| 311 | #include <ATen/ops/batch_norm_elemt_native.h> |
| 312 | #include <ATen/ops/batch_norm_elemt_ops.h> |
| 313 | #include <ATen/ops/batch_norm_elemt_native.h> |
| 314 | #include <ATen/ops/batch_norm_elemt_ops.h> |
| 315 | #include <ATen/ops/batch_norm_update_stats_native.h> |
| 316 | #include <ATen/ops/batch_norm_update_stats_ops.h> |
| 317 | #include <ATen/ops/batch_norm_update_stats_native.h> |
| 318 | #include <ATen/ops/batch_norm_update_stats_ops.h> |
| 319 | #include <ATen/ops/_nnpack_spatial_convolution_native.h> |
| 320 | #include <ATen/ops/_nnpack_spatial_convolution_ops.h> |
| 321 | #include <ATen/ops/_nnpack_spatial_convolution_native.h> |
| 322 | #include <ATen/ops/_nnpack_spatial_convolution_ops.h> |
| 323 | #include <ATen/ops/ones_like_native.h> |
| 324 | #include <ATen/ops/ones_like_ops.h> |
| 325 | #include <ATen/ops/ones_like_native.h> |
| 326 | #include <ATen/ops/ones_like_ops.h> |
| 327 | #include <ATen/ops/channel_shuffle_native.h> |
| 328 | #include <ATen/ops/channel_shuffle_ops.h> |
| 329 | #include <ATen/ops/channel_shuffle_native.h> |
| 330 | #include <ATen/ops/channel_shuffle_ops.h> |
| 331 | #include <ATen/ops/randint_native.h> |
| 332 | #include <ATen/ops/randint_ops.h> |
| 333 | #include <ATen/ops/randint_native.h> |
| 334 | #include <ATen/ops/randint_ops.h> |
| 335 | #include <ATen/ops/randint_native.h> |
| 336 | #include <ATen/ops/randint_ops.h> |
| 337 | #include <ATen/ops/randint_native.h> |
| 338 | #include <ATen/ops/randint_ops.h> |
| 339 | #include <ATen/ops/randint_native.h> |
| 340 | #include <ATen/ops/randint_ops.h> |
| 341 | #include <ATen/ops/randint_native.h> |
| 342 | #include <ATen/ops/randint_ops.h> |
| 343 | #include <ATen/ops/randint_native.h> |
| 344 | #include <ATen/ops/randint_ops.h> |
| 345 | #include <ATen/ops/randint_native.h> |
| 346 | #include <ATen/ops/randint_ops.h> |
| 347 | #include <ATen/ops/randperm_native.h> |
| 348 | #include <ATen/ops/randperm_ops.h> |
| 349 | #include <ATen/ops/randperm_native.h> |
| 350 | #include <ATen/ops/randperm_ops.h> |
| 351 | #include <ATen/ops/randperm_native.h> |
| 352 | #include <ATen/ops/randperm_ops.h> |
| 353 | #include <ATen/ops/randperm_native.h> |
| 354 | #include <ATen/ops/randperm_ops.h> |
| 355 | #include <ATen/ops/range_native.h> |
| 356 | #include <ATen/ops/range_ops.h> |
| 357 | #include <ATen/ops/range_native.h> |
| 358 | #include <ATen/ops/range_ops.h> |
| 359 | #include <ATen/ops/range_native.h> |
| 360 | #include <ATen/ops/range_ops.h> |
| 361 | #include <ATen/ops/range_native.h> |
| 362 | #include <ATen/ops/range_ops.h> |
| 363 | #include <ATen/ops/reciprocal_native.h> |
| 364 | #include <ATen/ops/reciprocal_ops.h> |
| 365 | #include <ATen/ops/reciprocal_native.h> |
| 366 | #include <ATen/ops/reciprocal_ops.h> |
| 367 | #include <ATen/ops/reciprocal_native.h> |
| 368 | #include <ATen/ops/reciprocal_ops.h> |
| 369 | #include <ATen/ops/gelu_backward_native.h> |
| 370 | #include <ATen/ops/gelu_backward_ops.h> |
| 371 | #include <ATen/ops/gelu_backward_native.h> |
| 372 | #include <ATen/ops/gelu_backward_ops.h> |
| 373 | #include <ATen/ops/hardshrink_native.h> |
| 374 | #include <ATen/ops/hardshrink_ops.h> |
| 375 | #include <ATen/ops/hardshrink_native.h> |
| 376 | #include <ATen/ops/hardshrink_ops.h> |
| 377 | #include <ATen/ops/hardshrink_backward_native.h> |
| 378 | #include <ATen/ops/hardshrink_backward_ops.h> |
| 379 | #include <ATen/ops/hardshrink_backward_native.h> |
| 380 | #include <ATen/ops/hardshrink_backward_ops.h> |
| 381 | #include <ATen/ops/silu_native.h> |
| 382 | #include <ATen/ops/silu_ops.h> |
| 383 | #include <ATen/ops/silu_native.h> |
| 384 | #include <ATen/ops/silu_ops.h> |
| 385 | #include <ATen/ops/silu_native.h> |
| 386 | #include <ATen/ops/silu_ops.h> |
| 387 | #include <ATen/ops/silu_backward_native.h> |
| 388 | #include <ATen/ops/silu_backward_ops.h> |
| 389 | #include <ATen/ops/silu_backward_native.h> |
| 390 | #include <ATen/ops/silu_backward_ops.h> |
| 391 | #include <ATen/ops/sin_native.h> |
| 392 | #include <ATen/ops/sin_ops.h> |
| 393 | #include <ATen/ops/sin_native.h> |
| 394 | #include <ATen/ops/sin_ops.h> |
| 395 | #include <ATen/ops/sin_native.h> |
| 396 | #include <ATen/ops/sin_ops.h> |
| 397 | #include <ATen/ops/_softmax_native.h> |
| 398 | #include <ATen/ops/_softmax_ops.h> |
| 399 | #include <ATen/ops/_softmax_native.h> |
| 400 | #include <ATen/ops/_softmax_ops.h> |
| 401 | #include <ATen/ops/sspaddmm_native.h> |
| 402 | #include <ATen/ops/sspaddmm_ops.h> |
| 403 | #include <ATen/ops/sspaddmm_native.h> |
| 404 | #include <ATen/ops/sspaddmm_ops.h> |
| 405 | #include <ATen/ops/_stack_native.h> |
| 406 | #include <ATen/ops/_stack_ops.h> |
| 407 | #include <ATen/ops/_stack_native.h> |
| 408 | #include <ATen/ops/_stack_ops.h> |
| 409 | #include <ATen/ops/hstack_native.h> |
| 410 | #include <ATen/ops/hstack_ops.h> |
| 411 | #include <ATen/ops/hstack_native.h> |
| 412 | #include <ATen/ops/hstack_ops.h> |
| 413 | #include <ATen/ops/dstack_native.h> |
| 414 | #include <ATen/ops/dstack_ops.h> |
| 415 | #include <ATen/ops/dstack_native.h> |
| 416 | #include <ATen/ops/dstack_ops.h> |
| 417 | #include <ATen/ops/sum_native.h> |
| 418 | #include <ATen/ops/sum_ops.h> |
| 419 | #include <ATen/ops/sum_native.h> |
| 420 | #include <ATen/ops/sum_ops.h> |
| 421 | #include <ATen/ops/sum_native.h> |
| 422 | #include <ATen/ops/sum_ops.h> |
| 423 | #include <ATen/ops/sum_native.h> |
| 424 | #include <ATen/ops/sum_ops.h> |
| 425 | #include <ATen/ops/sum_native.h> |
| 426 | #include <ATen/ops/sum_ops.h> |
| 427 | #include <ATen/ops/sum_native.h> |
| 428 | #include <ATen/ops/sum_ops.h> |
| 429 | #include <ATen/ops/std_native.h> |
| 430 | #include <ATen/ops/std_ops.h> |
| 431 | #include <ATen/ops/std_native.h> |
| 432 | #include <ATen/ops/std_ops.h> |
| 433 | #include <ATen/ops/std_native.h> |
| 434 | #include <ATen/ops/std_ops.h> |
| 435 | #include <ATen/ops/std_native.h> |
| 436 | #include <ATen/ops/std_ops.h> |
| 437 | #include <ATen/ops/std_native.h> |
| 438 | #include <ATen/ops/std_ops.h> |
| 439 | #include <ATen/ops/std_native.h> |
| 440 | #include <ATen/ops/std_ops.h> |
| 441 | #include <ATen/ops/std_native.h> |
| 442 | #include <ATen/ops/std_ops.h> |
| 443 | #include <ATen/ops/std_native.h> |
| 444 | #include <ATen/ops/std_ops.h> |
| 445 | #include <ATen/ops/tan_native.h> |
| 446 | #include <ATen/ops/tan_ops.h> |
| 447 | #include <ATen/ops/tan_native.h> |
| 448 | #include <ATen/ops/tan_ops.h> |
| 449 | #include <ATen/ops/tan_native.h> |
| 450 | #include <ATen/ops/tan_ops.h> |
| 451 | #include <ATen/ops/tensordot_native.h> |
| 452 | #include <ATen/ops/tensordot_ops.h> |
| 453 | #include <ATen/ops/tensordot_native.h> |
| 454 | #include <ATen/ops/tensordot_ops.h> |
| 455 | #include <ATen/ops/threshold_native.h> |
| 456 | #include <ATen/ops/threshold_ops.h> |
| 457 | #include <ATen/ops/threshold_native.h> |
| 458 | #include <ATen/ops/threshold_ops.h> |
| 459 | #include <ATen/ops/threshold_native.h> |
| 460 | #include <ATen/ops/threshold_ops.h> |
| 461 | #include <ATen/ops/_nested_tensor_strides_native.h> |
| 462 | #include <ATen/ops/_nested_tensor_strides_ops.h> |
| 463 | #include <ATen/ops/_nested_tensor_strides_native.h> |
| 464 | #include <ATen/ops/_nested_tensor_strides_ops.h> |
| 465 | #include <ATen/ops/fix_native.h> |
| 466 | #include <ATen/ops/fix_ops.h> |
| 467 | #include <ATen/ops/fix_native.h> |
| 468 | #include <ATen/ops/fix_ops.h> |
| 469 | #include <ATen/ops/fix_native.h> |
| 470 | #include <ATen/ops/fix_ops.h> |
| 471 | #include <ATen/ops/unique_consecutive_native.h> |
| 472 | #include <ATen/ops/unique_consecutive_ops.h> |
| 473 | #include <ATen/ops/unique_consecutive_native.h> |
| 474 | #include <ATen/ops/unique_consecutive_ops.h> |
| 475 | #include <ATen/ops/var_native.h> |
| 476 | #include <ATen/ops/var_ops.h> |
| 477 | #include <ATen/ops/var_native.h> |
| 478 | #include <ATen/ops/var_ops.h> |
| 479 | #include <ATen/ops/var_native.h> |
| 480 | #include <ATen/ops/var_ops.h> |
| 481 | #include <ATen/ops/var_native.h> |
| 482 | #include <ATen/ops/var_ops.h> |
| 483 | #include <ATen/ops/var_native.h> |
| 484 | #include <ATen/ops/var_ops.h> |
| 485 | #include <ATen/ops/var_native.h> |
| 486 | #include <ATen/ops/var_ops.h> |
| 487 | #include <ATen/ops/var_native.h> |
| 488 | #include <ATen/ops/var_ops.h> |
| 489 | #include <ATen/ops/var_native.h> |
| 490 | #include <ATen/ops/var_ops.h> |
| 491 | #include <ATen/ops/_standard_gamma_grad_native.h> |
| 492 | #include <ATen/ops/_standard_gamma_grad_ops.h> |
| 493 | #include <ATen/ops/_standard_gamma_grad_native.h> |
| 494 | #include <ATen/ops/_standard_gamma_grad_ops.h> |
| 495 | #include <ATen/ops/poisson_native.h> |
| 496 | #include <ATen/ops/poisson_ops.h> |
| 497 | #include <ATen/ops/poisson_native.h> |
| 498 | #include <ATen/ops/poisson_ops.h> |
| 499 | #include <ATen/ops/_sparse_csr_sum_native.h> |
| 500 | #include <ATen/ops/_sparse_csr_sum_ops.h> |
| 501 | #include <ATen/ops/_sparse_csr_sum_native.h> |
| 502 | #include <ATen/ops/_sparse_csr_sum_ops.h> |
| 503 | #include <ATen/ops/_sparse_softmax_backward_data_native.h> |
| 504 | #include <ATen/ops/_sparse_softmax_backward_data_ops.h> |
| 505 | #include <ATen/ops/_sparse_softmax_backward_data_native.h> |
| 506 | #include <ATen/ops/_sparse_softmax_backward_data_ops.h> |
| 507 | #include <ATen/ops/_sparse_log_softmax_native.h> |
| 508 | #include <ATen/ops/_sparse_log_softmax_ops.h> |
| 509 | #include <ATen/ops/_sparse_log_softmax_native.h> |
| 510 | #include <ATen/ops/_sparse_log_softmax_ops.h> |
| 511 | #include <ATen/ops/_sparse_log_softmax_backward_data_native.h> |
| 512 | #include <ATen/ops/_sparse_log_softmax_backward_data_ops.h> |
| 513 | #include <ATen/ops/_sparse_log_softmax_backward_data_native.h> |
| 514 | #include <ATen/ops/_sparse_log_softmax_backward_data_ops.h> |
| 515 | #include <ATen/ops/resize_as_sparse_native.h> |
| 516 | #include <ATen/ops/resize_as_sparse_ops.h> |
| 517 | #include <ATen/ops/resize_as_sparse_native.h> |
| 518 | #include <ATen/ops/resize_as_sparse_ops.h> |
| 519 | #include <ATen/ops/resize_as_sparse_native.h> |
| 520 | #include <ATen/ops/resize_as_sparse_ops.h> |
| 521 | #include <ATen/ops/sub_native.h> |
| 522 | #include <ATen/ops/sub_ops.h> |
| 523 | #include <ATen/ops/sub_native.h> |
| 524 | #include <ATen/ops/sub_ops.h> |
| 525 | #include <ATen/ops/sub_native.h> |
| 526 | #include <ATen/ops/sub_ops.h> |
| 527 | #include <ATen/ops/sub_native.h> |
| 528 | #include <ATen/ops/sub_ops.h> |
| 529 | #include <ATen/ops/sub_native.h> |
| 530 | #include <ATen/ops/sub_ops.h> |
| 531 | #include <ATen/ops/sub_native.h> |
| 532 | #include <ATen/ops/sub_ops.h> |
| 533 | #include <ATen/ops/copy_sparse_to_sparse_native.h> |
| 534 | #include <ATen/ops/copy_sparse_to_sparse_ops.h> |
| 535 | #include <ATen/ops/copy_sparse_to_sparse_native.h> |
| 536 | #include <ATen/ops/copy_sparse_to_sparse_ops.h> |
| 537 | #include <ATen/ops/copy_sparse_to_sparse_native.h> |
| 538 | #include <ATen/ops/copy_sparse_to_sparse_ops.h> |
| 539 | #include <ATen/ops/mkldnn_reorder_conv2d_weight_native.h> |
| 540 | #include <ATen/ops/mkldnn_reorder_conv2d_weight_ops.h> |
| 541 | #include <ATen/ops/mkldnn_reorder_conv2d_weight_native.h> |
| 542 | #include <ATen/ops/mkldnn_reorder_conv2d_weight_ops.h> |
| 543 | #include <ATen/ops/_lstm_mps_native.h> |
| 544 | #include <ATen/ops/_lstm_mps_ops.h> |
| 545 | #include <ATen/ops/_lstm_mps_native.h> |
| 546 | #include <ATen/ops/_lstm_mps_ops.h> |
| 547 | #include <ATen/ops/_thnn_fused_gru_cell_backward_native.h> |
| 548 | #include <ATen/ops/_thnn_fused_gru_cell_backward_ops.h> |
| 549 | #include <ATen/ops/_thnn_fused_gru_cell_backward_native.h> |
| 550 | #include <ATen/ops/_thnn_fused_gru_cell_backward_ops.h> |
| 551 | #include <ATen/ops/set_native.h> |
| 552 | #include <ATen/ops/set_ops.h> |
| 553 | #include <ATen/ops/set_native.h> |
| 554 | #include <ATen/ops/set_ops.h> |
| 555 | #include <ATen/ops/set_native.h> |
| 556 | #include <ATen/ops/set_ops.h> |
| 557 | #include <ATen/ops/set_native.h> |
| 558 | #include <ATen/ops/set_ops.h> |
| 559 | #include <ATen/ops/set_native.h> |
| 560 | #include <ATen/ops/set_ops.h> |
| 561 | #include <ATen/ops/set_native.h> |
| 562 | #include <ATen/ops/set_ops.h> |
| 563 | #include <ATen/ops/set_native.h> |
| 564 | #include <ATen/ops/set_ops.h> |
| 565 | #include <ATen/ops/set_native.h> |
| 566 | #include <ATen/ops/set_ops.h> |
| 567 | #include <ATen/ops/set_native.h> |
| 568 | #include <ATen/ops/set_ops.h> |
| 569 | #include <ATen/ops/set_native.h> |
| 570 | #include <ATen/ops/set_ops.h> |
| 571 | #include <ATen/ops/set_native.h> |
| 572 | #include <ATen/ops/set_ops.h> |
| 573 | #include <ATen/ops/set_native.h> |
| 574 | #include <ATen/ops/set_ops.h> |
| 575 | #include <ATen/ops/lift_native.h> |
| 576 | #include <ATen/ops/lift_ops.h> |
| 577 | #include <ATen/ops/lift_native.h> |
| 578 | #include <ATen/ops/lift_ops.h> |
| 579 | #include <ATen/ops/masked_scatter_native.h> |
| 580 | #include <ATen/ops/masked_scatter_ops.h> |
| 581 | #include <ATen/ops/masked_scatter_native.h> |
| 582 | #include <ATen/ops/masked_scatter_ops.h> |
| 583 | #include <ATen/ops/masked_scatter_native.h> |
| 584 | #include <ATen/ops/masked_scatter_ops.h> |
| 585 | #include <ATen/ops/index_reduce_native.h> |
| 586 | #include <ATen/ops/index_reduce_ops.h> |
| 587 | #include <ATen/ops/index_reduce_native.h> |
| 588 | #include <ATen/ops/index_reduce_ops.h> |
| 589 | #include <ATen/ops/index_reduce_native.h> |
| 590 | #include <ATen/ops/index_reduce_ops.h> |
| 591 | #include <ATen/ops/eq_native.h> |
| 592 | #include <ATen/ops/eq_ops.h> |
| 593 | #include <ATen/ops/eq_native.h> |
| 594 | #include <ATen/ops/eq_ops.h> |
| 595 | #include <ATen/ops/eq_native.h> |
| 596 | #include <ATen/ops/eq_ops.h> |
| 597 | #include <ATen/ops/eq_native.h> |
| 598 | #include <ATen/ops/eq_ops.h> |
| 599 | #include <ATen/ops/eq_native.h> |
| 600 | #include <ATen/ops/eq_ops.h> |
| 601 | #include <ATen/ops/eq_native.h> |
| 602 | #include <ATen/ops/eq_ops.h> |
| 603 | #include <ATen/ops/bitwise_and_native.h> |
| 604 | #include <ATen/ops/bitwise_and_ops.h> |
| 605 | #include <ATen/ops/bitwise_and_native.h> |
| 606 | #include <ATen/ops/bitwise_and_ops.h> |
| 607 | #include <ATen/ops/bitwise_and_native.h> |
| 608 | #include <ATen/ops/bitwise_and_ops.h> |
| 609 | #include <ATen/ops/bitwise_and_native.h> |
| 610 | #include <ATen/ops/bitwise_and_ops.h> |
| 611 | #include <ATen/ops/bitwise_and_native.h> |
| 612 | #include <ATen/ops/bitwise_and_ops.h> |
| 613 | #include <ATen/ops/bitwise_and_native.h> |
| 614 | #include <ATen/ops/bitwise_and_ops.h> |
| 615 | #include <ATen/ops/bitwise_and_native.h> |
| 616 | #include <ATen/ops/bitwise_and_ops.h> |
| 617 | #include <ATen/ops/bitwise_and_native.h> |
| 618 | #include <ATen/ops/bitwise_and_ops.h> |
| 619 | #include <ATen/ops/bitwise_or_native.h> |
| 620 | #include <ATen/ops/bitwise_or_ops.h> |
| 621 | #include <ATen/ops/bitwise_or_native.h> |
| 622 | #include <ATen/ops/bitwise_or_ops.h> |
| 623 | #include <ATen/ops/bitwise_or_native.h> |
| 624 | #include <ATen/ops/bitwise_or_ops.h> |
| 625 | #include <ATen/ops/bitwise_or_native.h> |
| 626 | #include <ATen/ops/bitwise_or_ops.h> |
| 627 | #include <ATen/ops/bitwise_or_native.h> |
| 628 | #include <ATen/ops/bitwise_or_ops.h> |
| 629 | #include <ATen/ops/bitwise_or_native.h> |
| 630 | #include <ATen/ops/bitwise_or_ops.h> |
| 631 | #include <ATen/ops/bitwise_or_native.h> |
| 632 | #include <ATen/ops/bitwise_or_ops.h> |
| 633 | #include <ATen/ops/bitwise_or_native.h> |
| 634 | #include <ATen/ops/bitwise_or_ops.h> |
| 635 | #include <ATen/ops/bitwise_left_shift_native.h> |
| 636 | #include <ATen/ops/bitwise_left_shift_ops.h> |
| 637 | #include <ATen/ops/bitwise_left_shift_native.h> |
| 638 | #include <ATen/ops/bitwise_left_shift_ops.h> |
| 639 | #include <ATen/ops/bitwise_left_shift_native.h> |
| 640 | #include <ATen/ops/bitwise_left_shift_ops.h> |
| 641 | #include <ATen/ops/bitwise_left_shift_native.h> |
| 642 | #include <ATen/ops/bitwise_left_shift_ops.h> |
| 643 | #include <ATen/ops/bitwise_left_shift_native.h> |
| 644 | #include <ATen/ops/bitwise_left_shift_ops.h> |
| 645 | #include <ATen/ops/bitwise_left_shift_native.h> |
| 646 | #include <ATen/ops/bitwise_left_shift_ops.h> |
| 647 | #include <ATen/ops/bitwise_left_shift_native.h> |
| 648 | #include <ATen/ops/bitwise_left_shift_ops.h> |
| 649 | #include <ATen/ops/bitwise_left_shift_native.h> |
| 650 | #include <ATen/ops/bitwise_left_shift_ops.h> |
| 651 | #include <ATen/ops/rshift_native.h> |
| 652 | #include <ATen/ops/rshift_ops.h> |
| 653 | #include <ATen/ops/rshift_native.h> |
| 654 | #include <ATen/ops/rshift_ops.h> |
| 655 | #include <ATen/ops/rshift_native.h> |
| 656 | #include <ATen/ops/rshift_ops.h> |
| 657 | #include <ATen/ops/rshift_native.h> |
| 658 | #include <ATen/ops/rshift_ops.h> |
| 659 | #include <ATen/ops/rshift_native.h> |
| 660 | #include <ATen/ops/rshift_ops.h> |
| 661 | #include <ATen/ops/rshift_native.h> |
| 662 | #include <ATen/ops/rshift_ops.h> |
| 663 | #include <ATen/ops/tril_native.h> |
| 664 | #include <ATen/ops/tril_ops.h> |
| 665 | #include <ATen/ops/tril_native.h> |
| 666 | #include <ATen/ops/tril_ops.h> |
| 667 | #include <ATen/ops/tril_native.h> |
| 668 | #include <ATen/ops/tril_ops.h> |
| 669 | #include <ATen/ops/triu_native.h> |
| 670 | #include <ATen/ops/triu_ops.h> |
| 671 | #include <ATen/ops/triu_native.h> |
| 672 | #include <ATen/ops/triu_ops.h> |
| 673 | #include <ATen/ops/triu_native.h> |
| 674 | #include <ATen/ops/triu_ops.h> |
| 675 | #include <ATen/ops/digamma_native.h> |
| 676 | #include <ATen/ops/digamma_ops.h> |
| 677 | #include <ATen/ops/digamma_native.h> |
| 678 | #include <ATen/ops/digamma_ops.h> |
| 679 | #include <ATen/ops/digamma_native.h> |
| 680 | #include <ATen/ops/digamma_ops.h> |
| 681 | #include <ATen/ops/lerp_native.h> |
| 682 | #include <ATen/ops/lerp_ops.h> |
| 683 | #include <ATen/ops/lerp_native.h> |
| 684 | #include <ATen/ops/lerp_ops.h> |
| 685 | #include <ATen/ops/lerp_native.h> |
| 686 | #include <ATen/ops/lerp_ops.h> |
| 687 | #include <ATen/ops/lerp_native.h> |
| 688 | #include <ATen/ops/lerp_ops.h> |
| 689 | #include <ATen/ops/lerp_native.h> |
| 690 | #include <ATen/ops/lerp_ops.h> |
| 691 | #include <ATen/ops/lerp_native.h> |
| 692 | #include <ATen/ops/lerp_ops.h> |
| 693 | #include <ATen/ops/uniform_native.h> |
| 694 | #include <ATen/ops/uniform_ops.h> |
| 695 | #include <ATen/ops/uniform_native.h> |
| 696 | #include <ATen/ops/uniform_ops.h> |
| 697 | #include <ATen/ops/uniform_native.h> |
| 698 | #include <ATen/ops/uniform_ops.h> |
| 699 | #include <ATen/ops/tril_indices_native.h> |
| 700 | #include <ATen/ops/tril_indices_ops.h> |
| 701 | #include <ATen/ops/tril_indices_native.h> |
| 702 | #include <ATen/ops/tril_indices_ops.h> |
| 703 | #include <ATen/ops/triu_indices_native.h> |
| 704 | #include <ATen/ops/triu_indices_ops.h> |
| 705 | #include <ATen/ops/triu_indices_native.h> |
| 706 | #include <ATen/ops/triu_indices_ops.h> |
| 707 | #include <ATen/ops/ge_native.h> |
| 708 | #include <ATen/ops/ge_ops.h> |
| 709 | #include <ATen/ops/ge_native.h> |
| 710 | #include <ATen/ops/ge_ops.h> |
| 711 | #include <ATen/ops/ge_native.h> |
| 712 | #include <ATen/ops/ge_ops.h> |
| 713 | #include <ATen/ops/ge_native.h> |
| 714 | #include <ATen/ops/ge_ops.h> |
| 715 | #include <ATen/ops/ge_native.h> |
| 716 | #include <ATen/ops/ge_ops.h> |
| 717 | #include <ATen/ops/ge_native.h> |
| 718 | #include <ATen/ops/ge_ops.h> |
| 719 | #include <ATen/ops/le_native.h> |
| 720 | #include <ATen/ops/le_ops.h> |
| 721 | #include <ATen/ops/le_native.h> |
| 722 | #include <ATen/ops/le_ops.h> |
| 723 | #include <ATen/ops/le_native.h> |
| 724 | #include <ATen/ops/le_ops.h> |
| 725 | #include <ATen/ops/le_native.h> |
| 726 | #include <ATen/ops/le_ops.h> |
| 727 | #include <ATen/ops/le_native.h> |
| 728 | #include <ATen/ops/le_ops.h> |
| 729 | #include <ATen/ops/le_native.h> |
| 730 | #include <ATen/ops/le_ops.h> |
| 731 | #include <ATen/ops/gt_native.h> |
| 732 | #include <ATen/ops/gt_ops.h> |
| 733 | #include <ATen/ops/gt_native.h> |
| 734 | #include <ATen/ops/gt_ops.h> |
| 735 | #include <ATen/ops/gt_native.h> |
| 736 | #include <ATen/ops/gt_ops.h> |
| 737 | #include <ATen/ops/gt_native.h> |
| 738 | #include <ATen/ops/gt_ops.h> |
| 739 | #include <ATen/ops/gt_native.h> |
| 740 | #include <ATen/ops/gt_ops.h> |
| 741 | #include <ATen/ops/gt_native.h> |
| 742 | #include <ATen/ops/gt_ops.h> |
| 743 | #include <ATen/ops/nonzero_native.h> |
| 744 | #include <ATen/ops/nonzero_ops.h> |
| 745 | #include <ATen/ops/nonzero_native.h> |
| 746 | #include <ATen/ops/nonzero_ops.h> |
| 747 | #include <ATen/ops/addcdiv_native.h> |
| 748 | #include <ATen/ops/addcdiv_ops.h> |
| 749 | #include <ATen/ops/addcdiv_native.h> |
| 750 | #include <ATen/ops/addcdiv_ops.h> |
| 751 | #include <ATen/ops/addcdiv_native.h> |
| 752 | #include <ATen/ops/addcdiv_ops.h> |
| 753 | #include <ATen/ops/triangular_solve_native.h> |
| 754 | #include <ATen/ops/triangular_solve_ops.h> |
| 755 | #include <ATen/ops/triangular_solve_native.h> |
| 756 | #include <ATen/ops/triangular_solve_ops.h> |
| 757 | #include <ATen/ops/cholesky_solve_native.h> |
| 758 | #include <ATen/ops/cholesky_solve_ops.h> |
| 759 | #include <ATen/ops/cholesky_solve_native.h> |
| 760 | #include <ATen/ops/cholesky_solve_ops.h> |
| 761 | #include <ATen/ops/cholesky_inverse_native.h> |
| 762 | #include <ATen/ops/cholesky_inverse_ops.h> |
| 763 | #include <ATen/ops/cholesky_inverse_native.h> |
| 764 | #include <ATen/ops/cholesky_inverse_ops.h> |
| 765 | #include <ATen/ops/qr_native.h> |
| 766 | #include <ATen/ops/qr_ops.h> |
| 767 | #include <ATen/ops/qr_native.h> |
| 768 | #include <ATen/ops/qr_ops.h> |
| 769 | #include <ATen/ops/geqrf_native.h> |
| 770 | #include <ATen/ops/geqrf_ops.h> |
| 771 | #include <ATen/ops/geqrf_native.h> |
| 772 | #include <ATen/ops/geqrf_ops.h> |
| 773 | #include <ATen/ops/orgqr_native.h> |
| 774 | #include <ATen/ops/orgqr_ops.h> |
| 775 | #include <ATen/ops/orgqr_native.h> |
| 776 | #include <ATen/ops/orgqr_ops.h> |
| 777 | #include <ATen/ops/lu_solve_native.h> |
| 778 | #include <ATen/ops/lu_solve_ops.h> |
| 779 | #include <ATen/ops/lu_solve_native.h> |
| 780 | #include <ATen/ops/lu_solve_ops.h> |
| 781 | #include <ATen/ops/lgamma_native.h> |
| 782 | #include <ATen/ops/lgamma_ops.h> |
| 783 | #include <ATen/ops/lgamma_native.h> |
| 784 | #include <ATen/ops/lgamma_ops.h> |
| 785 | #include <ATen/ops/lgamma_native.h> |
| 786 | #include <ATen/ops/lgamma_ops.h> |
| 787 | #include <ATen/ops/erfinv_native.h> |
| 788 | #include <ATen/ops/erfinv_ops.h> |
| 789 | #include <ATen/ops/erfinv_native.h> |
| 790 | #include <ATen/ops/erfinv_ops.h> |
| 791 | #include <ATen/ops/erfinv_native.h> |
| 792 | #include <ATen/ops/erfinv_ops.h> |
| 793 | #include <ATen/ops/i0_native.h> |
| 794 | #include <ATen/ops/i0_ops.h> |
| 795 | #include <ATen/ops/i0_native.h> |
| 796 | #include <ATen/ops/i0_ops.h> |
| 797 | #include <ATen/ops/i0_native.h> |
| 798 | #include <ATen/ops/i0_ops.h> |
| 799 | #include <ATen/ops/sign_native.h> |
| 800 | #include <ATen/ops/sign_ops.h> |
| 801 | #include <ATen/ops/sign_native.h> |
| 802 | #include <ATen/ops/sign_ops.h> |
| 803 | #include <ATen/ops/sign_native.h> |
| 804 | #include <ATen/ops/sign_ops.h> |
| 805 | #include <ATen/ops/signbit_native.h> |
| 806 | #include <ATen/ops/signbit_ops.h> |
| 807 | #include <ATen/ops/signbit_native.h> |
| 808 | #include <ATen/ops/signbit_ops.h> |
| 809 | #include <ATen/ops/atan2_native.h> |
| 810 | #include <ATen/ops/atan2_ops.h> |
| 811 | #include <ATen/ops/atan2_native.h> |
| 812 | #include <ATen/ops/atan2_ops.h> |
| 813 | #include <ATen/ops/atan2_native.h> |
| 814 | #include <ATen/ops/atan2_ops.h> |
| 815 | #include <ATen/ops/_histogramdd_bin_edges_native.h> |
| 816 | #include <ATen/ops/_histogramdd_bin_edges_ops.h> |
| 817 | #include <ATen/ops/_histogramdd_bin_edges_native.h> |
| 818 | #include <ATen/ops/_histogramdd_bin_edges_ops.h> |
| 819 | #include <ATen/ops/_histogramdd_from_bin_tensors_native.h> |
| 820 | #include <ATen/ops/_histogramdd_from_bin_tensors_ops.h> |
| 821 | #include <ATen/ops/_histogramdd_from_bin_tensors_native.h> |
| 822 | #include <ATen/ops/_histogramdd_from_bin_tensors_ops.h> |
| 823 | #include <ATen/ops/fmod_native.h> |
| 824 | #include <ATen/ops/fmod_ops.h> |
| 825 | #include <ATen/ops/fmod_native.h> |
| 826 | #include <ATen/ops/fmod_ops.h> |
| 827 | #include <ATen/ops/fmod_native.h> |
| 828 | #include <ATen/ops/fmod_ops.h> |
| 829 | #include <ATen/ops/fmod_native.h> |
| 830 | #include <ATen/ops/fmod_ops.h> |
| 831 | #include <ATen/ops/fmod_native.h> |
| 832 | #include <ATen/ops/fmod_ops.h> |
| 833 | #include <ATen/ops/fmod_native.h> |
| 834 | #include <ATen/ops/fmod_ops.h> |
| 835 | #include <ATen/ops/nextafter_native.h> |
| 836 | #include <ATen/ops/nextafter_ops.h> |
| 837 | #include <ATen/ops/nextafter_native.h> |
| 838 | #include <ATen/ops/nextafter_ops.h> |
| 839 | #include <ATen/ops/nextafter_native.h> |
| 840 | #include <ATen/ops/nextafter_ops.h> |
| 841 | #include <ATen/ops/minimum_native.h> |
| 842 | #include <ATen/ops/minimum_ops.h> |
| 843 | #include <ATen/ops/minimum_native.h> |
| 844 | #include <ATen/ops/minimum_ops.h> |
| 845 | #include <ATen/ops/topk_native.h> |
| 846 | #include <ATen/ops/topk_ops.h> |
| 847 | #include <ATen/ops/topk_native.h> |
| 848 | #include <ATen/ops/topk_ops.h> |
| 849 | #include <ATen/ops/any_native.h> |
| 850 | #include <ATen/ops/any_ops.h> |
| 851 | #include <ATen/ops/any_native.h> |
| 852 | #include <ATen/ops/any_ops.h> |
| 853 | #include <ATen/ops/_foreach_mul_native.h> |
| 854 | #include <ATen/ops/_foreach_mul_ops.h> |
| 855 | #include <ATen/ops/_foreach_mul_native.h> |
| 856 | #include <ATen/ops/_foreach_mul_ops.h> |
| 857 | #include <ATen/ops/_foreach_mul_native.h> |
| 858 | #include <ATen/ops/_foreach_mul_ops.h> |
| 859 | #include <ATen/ops/_foreach_div_native.h> |
| 860 | #include <ATen/ops/_foreach_div_ops.h> |
| 861 | #include <ATen/ops/_foreach_div_native.h> |
| 862 | #include <ATen/ops/_foreach_div_ops.h> |
| 863 | #include <ATen/ops/_foreach_div_native.h> |
| 864 | #include <ATen/ops/_foreach_div_ops.h> |
| 865 | #include <ATen/ops/_foreach_clamp_max_native.h> |
| 866 | #include <ATen/ops/_foreach_clamp_max_ops.h> |
| 867 | #include <ATen/ops/_foreach_clamp_max_native.h> |
| 868 | #include <ATen/ops/_foreach_clamp_max_ops.h> |
| 869 | #include <ATen/ops/_foreach_clamp_max_native.h> |
| 870 | #include <ATen/ops/_foreach_clamp_max_ops.h> |
| 871 | #include <ATen/ops/_foreach_minimum_native.h> |
| 872 | #include <ATen/ops/_foreach_minimum_ops.h> |
| 873 | #include <ATen/ops/_foreach_minimum_native.h> |
| 874 | #include <ATen/ops/_foreach_minimum_ops.h> |
| 875 | #include <ATen/ops/_foreach_minimum_native.h> |
| 876 | #include <ATen/ops/_foreach_minimum_ops.h> |
| 877 | #include <ATen/ops/_foreach_mul_native.h> |
| 878 | #include <ATen/ops/_foreach_mul_ops.h> |
| 879 | #include <ATen/ops/_foreach_mul_native.h> |
| 880 | #include <ATen/ops/_foreach_mul_ops.h> |
| 881 | #include <ATen/ops/_foreach_mul_native.h> |
| 882 | #include <ATen/ops/_foreach_mul_ops.h> |
| 883 | #include <ATen/ops/_foreach_div_native.h> |
| 884 | #include <ATen/ops/_foreach_div_ops.h> |
| 885 | #include <ATen/ops/_foreach_div_native.h> |
| 886 | #include <ATen/ops/_foreach_div_ops.h> |
| 887 | #include <ATen/ops/_foreach_div_native.h> |
| 888 | #include <ATen/ops/_foreach_div_ops.h> |
| 889 | #include <ATen/ops/_foreach_clamp_max_native.h> |
| 890 | #include <ATen/ops/_foreach_clamp_max_ops.h> |
| 891 | #include <ATen/ops/_foreach_clamp_max_native.h> |
| 892 | #include <ATen/ops/_foreach_clamp_max_ops.h> |
| 893 | #include <ATen/ops/_foreach_clamp_max_native.h> |
| 894 | #include <ATen/ops/_foreach_clamp_max_ops.h> |
| 895 | #include <ATen/ops/_foreach_minimum_native.h> |
| 896 | #include <ATen/ops/_foreach_minimum_ops.h> |
| 897 | #include <ATen/ops/_foreach_minimum_native.h> |
| 898 | #include <ATen/ops/_foreach_minimum_ops.h> |
| 899 | #include <ATen/ops/_foreach_minimum_native.h> |
| 900 | #include <ATen/ops/_foreach_minimum_ops.h> |
| 901 | #include <ATen/ops/_foreach_div_native.h> |
| 902 | #include <ATen/ops/_foreach_div_ops.h> |
| 903 | #include <ATen/ops/_foreach_div_native.h> |
| 904 | #include <ATen/ops/_foreach_div_ops.h> |
| 905 | #include <ATen/ops/_foreach_div_native.h> |
| 906 | #include <ATen/ops/_foreach_div_ops.h> |
| 907 | #include <ATen/ops/_foreach_mul_native.h> |
| 908 | #include <ATen/ops/_foreach_mul_ops.h> |
| 909 | #include <ATen/ops/_foreach_mul_native.h> |
| 910 | #include <ATen/ops/_foreach_mul_ops.h> |
| 911 | #include <ATen/ops/_foreach_mul_native.h> |
| 912 | #include <ATen/ops/_foreach_mul_ops.h> |
| 913 | #include <ATen/ops/_foreach_clamp_max_native.h> |
| 914 | #include <ATen/ops/_foreach_clamp_max_ops.h> |
| 915 | #include <ATen/ops/_foreach_clamp_max_native.h> |
| 916 | #include <ATen/ops/_foreach_clamp_max_ops.h> |
| 917 | #include <ATen/ops/_foreach_clamp_max_native.h> |
| 918 | #include <ATen/ops/_foreach_clamp_max_ops.h> |
| 919 | #include <ATen/ops/_foreach_minimum_native.h> |
| 920 | #include <ATen/ops/_foreach_minimum_ops.h> |
| 921 | #include <ATen/ops/_foreach_minimum_native.h> |
| 922 | #include <ATen/ops/_foreach_minimum_ops.h> |
| 923 | #include <ATen/ops/_foreach_minimum_native.h> |
| 924 | #include <ATen/ops/_foreach_minimum_ops.h> |
| 925 | #include <ATen/ops/_foreach_expm1_native.h> |
| 926 | #include <ATen/ops/_foreach_expm1_ops.h> |
| 927 | #include <ATen/ops/_foreach_expm1_native.h> |
| 928 | #include <ATen/ops/_foreach_expm1_ops.h> |
| 929 | #include <ATen/ops/_foreach_expm1_native.h> |
| 930 | #include <ATen/ops/_foreach_expm1_ops.h> |
| 931 | #include <ATen/ops/_foreach_tanh_native.h> |
| 932 | #include <ATen/ops/_foreach_tanh_ops.h> |
| 933 | #include <ATen/ops/_foreach_tanh_native.h> |
| 934 | #include <ATen/ops/_foreach_tanh_ops.h> |
| 935 | #include <ATen/ops/_foreach_tanh_native.h> |
| 936 | #include <ATen/ops/_foreach_tanh_ops.h> |
| 937 | #include <ATen/ops/_foreach_sin_native.h> |
| 938 | #include <ATen/ops/_foreach_sin_ops.h> |
| 939 | #include <ATen/ops/_foreach_sin_native.h> |
| 940 | #include <ATen/ops/_foreach_sin_ops.h> |
| 941 | #include <ATen/ops/_foreach_sin_native.h> |
| 942 | #include <ATen/ops/_foreach_sin_ops.h> |
| 943 | #include <ATen/ops/_foreach_frac_native.h> |
| 944 | #include <ATen/ops/_foreach_frac_ops.h> |
| 945 | #include <ATen/ops/_foreach_frac_native.h> |
| 946 | #include <ATen/ops/_foreach_frac_ops.h> |
| 947 | #include <ATen/ops/_foreach_frac_native.h> |
| 948 | #include <ATen/ops/_foreach_frac_ops.h> |
| 949 | #include <ATen/ops/_foreach_reciprocal_native.h> |
| 950 | #include <ATen/ops/_foreach_reciprocal_ops.h> |
| 951 | #include <ATen/ops/_foreach_reciprocal_native.h> |
| 952 | #include <ATen/ops/_foreach_reciprocal_ops.h> |
| 953 | #include <ATen/ops/_foreach_reciprocal_native.h> |
| 954 | #include <ATen/ops/_foreach_reciprocal_ops.h> |
| 955 | #include <ATen/ops/_foreach_trunc_native.h> |
| 956 | #include <ATen/ops/_foreach_trunc_ops.h> |
| 957 | #include <ATen/ops/_foreach_trunc_native.h> |
| 958 | #include <ATen/ops/_foreach_trunc_ops.h> |
| 959 | #include <ATen/ops/_foreach_trunc_native.h> |
| 960 | #include <ATen/ops/_foreach_trunc_ops.h> |
| 961 | #include <ATen/ops/_convert_indices_from_csr_to_coo_native.h> |
| 962 | #include <ATen/ops/_convert_indices_from_csr_to_coo_ops.h> |
| 963 | #include <ATen/ops/_convert_indices_from_csr_to_coo_native.h> |
| 964 | #include <ATen/ops/_convert_indices_from_csr_to_coo_ops.h> |
| 965 | #include <ATen/ops/multilabel_margin_loss_backward_native.h> |
| 966 | #include <ATen/ops/multilabel_margin_loss_backward_ops.h> |
| 967 | #include <ATen/ops/multilabel_margin_loss_backward_native.h> |
| 968 | #include <ATen/ops/multilabel_margin_loss_backward_ops.h> |
| 969 | #include <ATen/ops/nll_loss_native.h> |
| 970 | #include <ATen/ops/nll_loss_ops.h> |
| 971 | #include <ATen/ops/nll_loss_native.h> |
| 972 | #include <ATen/ops/nll_loss_ops.h> |
| 973 | #include <ATen/ops/nll_loss_backward_native.h> |
| 974 | #include <ATen/ops/nll_loss_backward_ops.h> |
| 975 | #include <ATen/ops/nll_loss_backward_native.h> |
| 976 | #include <ATen/ops/nll_loss_backward_ops.h> |
| 977 | #include <ATen/ops/nll_loss2d_native.h> |
| 978 | #include <ATen/ops/nll_loss2d_ops.h> |
| 979 | #include <ATen/ops/nll_loss2d_native.h> |
| 980 | #include <ATen/ops/nll_loss2d_ops.h> |
| 981 | #include <ATen/ops/nll_loss2d_backward_native.h> |
| 982 | #include <ATen/ops/nll_loss2d_backward_ops.h> |
| 983 | #include <ATen/ops/nll_loss2d_backward_native.h> |
| 984 | #include <ATen/ops/nll_loss2d_backward_ops.h> |
| 985 | #include <ATen/ops/smooth_l1_loss_native.h> |
| 986 | #include <ATen/ops/smooth_l1_loss_ops.h> |
| 987 | #include <ATen/ops/smooth_l1_loss_native.h> |
| 988 | #include <ATen/ops/smooth_l1_loss_ops.h> |
| 989 | #include <ATen/ops/huber_loss_native.h> |
| 990 | #include <ATen/ops/huber_loss_ops.h> |
| 991 | #include <ATen/ops/huber_loss_native.h> |
| 992 | #include <ATen/ops/huber_loss_ops.h> |
| 993 | #include <ATen/ops/soft_margin_loss_backward_native.h> |
| 994 | #include <ATen/ops/soft_margin_loss_backward_ops.h> |
| 995 | #include <ATen/ops/soft_margin_loss_backward_native.h> |
| 996 | #include <ATen/ops/soft_margin_loss_backward_ops.h> |
| 997 | #include <ATen/ops/elu_native.h> |
| 998 | #include <ATen/ops/elu_ops.h> |
| 999 | #include <ATen/ops/elu_native.h> |
| 1000 | #include <ATen/ops/elu_ops.h> |
| 1001 | #include <ATen/ops/elu_native.h> |
| 1002 | #include <ATen/ops/elu_ops.h> |
| 1003 | #include <ATen/ops/glu_native.h> |
| 1004 | #include <ATen/ops/glu_ops.h> |
| 1005 | #include <ATen/ops/glu_native.h> |
| 1006 | #include <ATen/ops/glu_ops.h> |
| 1007 | #include <ATen/ops/hardsigmoid_native.h> |
| 1008 | #include <ATen/ops/hardsigmoid_ops.h> |
| 1009 | #include <ATen/ops/hardsigmoid_native.h> |
| 1010 | #include <ATen/ops/hardsigmoid_ops.h> |
| 1011 | #include <ATen/ops/hardsigmoid_native.h> |
| 1012 | #include <ATen/ops/hardsigmoid_ops.h> |
| 1013 | #include <ATen/ops/leaky_relu_backward_native.h> |
| 1014 | #include <ATen/ops/leaky_relu_backward_ops.h> |
| 1015 | #include <ATen/ops/leaky_relu_backward_native.h> |
| 1016 | #include <ATen/ops/leaky_relu_backward_ops.h> |
| 1017 | #include <ATen/ops/softshrink_native.h> |
| 1018 | #include <ATen/ops/softshrink_ops.h> |
| 1019 | #include <ATen/ops/softshrink_native.h> |
| 1020 | #include <ATen/ops/softshrink_ops.h> |
| 1021 | #include <ATen/ops/_adaptive_avg_pool2d_native.h> |
| 1022 | #include <ATen/ops/_adaptive_avg_pool2d_ops.h> |
| 1023 | #include <ATen/ops/_adaptive_avg_pool2d_native.h> |
| 1024 | #include <ATen/ops/_adaptive_avg_pool2d_ops.h> |
| 1025 | #include <ATen/ops/_adaptive_avg_pool3d_native.h> |
| 1026 | #include <ATen/ops/_adaptive_avg_pool3d_ops.h> |
| 1027 | #include <ATen/ops/_adaptive_avg_pool3d_native.h> |
| 1028 | #include <ATen/ops/_adaptive_avg_pool3d_ops.h> |
| 1029 | #include <ATen/ops/avg_pool3d_native.h> |
| 1030 | #include <ATen/ops/avg_pool3d_ops.h> |
| 1031 | #include <ATen/ops/avg_pool3d_native.h> |
| 1032 | #include <ATen/ops/avg_pool3d_ops.h> |
| 1033 | #include <ATen/ops/fractional_max_pool3d_native.h> |
| 1034 | #include <ATen/ops/fractional_max_pool3d_ops.h> |
| 1035 | #include <ATen/ops/fractional_max_pool3d_native.h> |
| 1036 | #include <ATen/ops/fractional_max_pool3d_ops.h> |
| 1037 | #include <ATen/ops/max_pool2d_with_indices_backward_native.h> |
| 1038 | #include <ATen/ops/max_pool2d_with_indices_backward_ops.h> |
| 1039 | #include <ATen/ops/max_pool2d_with_indices_backward_native.h> |
| 1040 | #include <ATen/ops/max_pool2d_with_indices_backward_ops.h> |
| 1041 | #include <ATen/ops/max_unpool3d_native.h> |
| 1042 | #include <ATen/ops/max_unpool3d_ops.h> |
| 1043 | #include <ATen/ops/max_unpool3d_native.h> |
| 1044 | #include <ATen/ops/max_unpool3d_ops.h> |
| 1045 | #include <ATen/ops/reflection_pad1d_native.h> |
| 1046 | #include <ATen/ops/reflection_pad1d_ops.h> |
| 1047 | #include <ATen/ops/reflection_pad1d_native.h> |
| 1048 | #include <ATen/ops/reflection_pad1d_ops.h> |
| 1049 | #include <ATen/ops/reflection_pad1d_backward_native.h> |
| 1050 | #include <ATen/ops/reflection_pad1d_backward_ops.h> |
| 1051 | #include <ATen/ops/reflection_pad1d_backward_native.h> |
| 1052 | #include <ATen/ops/reflection_pad1d_backward_ops.h> |
| 1053 | #include <ATen/ops/reflection_pad2d_native.h> |
| 1054 | #include <ATen/ops/reflection_pad2d_ops.h> |
| 1055 | #include <ATen/ops/reflection_pad2d_native.h> |
| 1056 | #include <ATen/ops/reflection_pad2d_ops.h> |
| 1057 | #include <ATen/ops/reflection_pad3d_native.h> |
| 1058 | #include <ATen/ops/reflection_pad3d_ops.h> |
| 1059 | #include <ATen/ops/reflection_pad3d_native.h> |
| 1060 | #include <ATen/ops/reflection_pad3d_ops.h> |
| 1061 | #include <ATen/ops/replication_pad1d_native.h> |
| 1062 | #include <ATen/ops/replication_pad1d_ops.h> |
| 1063 | #include <ATen/ops/replication_pad1d_native.h> |
| 1064 | #include <ATen/ops/replication_pad1d_ops.h> |
| 1065 | #include <ATen/ops/replication_pad1d_backward_native.h> |
| 1066 | #include <ATen/ops/replication_pad1d_backward_ops.h> |
| 1067 | #include <ATen/ops/replication_pad1d_backward_native.h> |
| 1068 | #include <ATen/ops/replication_pad1d_backward_ops.h> |
| 1069 | #include <ATen/ops/replication_pad2d_backward_native.h> |
| 1070 | #include <ATen/ops/replication_pad2d_backward_ops.h> |
| 1071 | #include <ATen/ops/replication_pad2d_backward_native.h> |
| 1072 | #include <ATen/ops/replication_pad2d_backward_ops.h> |
| 1073 | #include <ATen/ops/replication_pad3d_native.h> |
| 1074 | #include <ATen/ops/replication_pad3d_ops.h> |
| 1075 | #include <ATen/ops/replication_pad3d_native.h> |
| 1076 | #include <ATen/ops/replication_pad3d_ops.h> |
| 1077 | #include <ATen/ops/replication_pad3d_backward_native.h> |
| 1078 | #include <ATen/ops/replication_pad3d_backward_ops.h> |
| 1079 | #include <ATen/ops/replication_pad3d_backward_native.h> |
| 1080 | #include <ATen/ops/replication_pad3d_backward_ops.h> |
| 1081 | #include <ATen/ops/upsample_nearest2d_native.h> |
| 1082 | #include <ATen/ops/upsample_nearest2d_ops.h> |
| 1083 | #include <ATen/ops/upsample_nearest2d_native.h> |
| 1084 | #include <ATen/ops/upsample_nearest2d_ops.h> |
| 1085 | #include <ATen/ops/_upsample_nearest_exact2d_native.h> |
| 1086 | #include <ATen/ops/_upsample_nearest_exact2d_ops.h> |
| 1087 | #include <ATen/ops/_upsample_nearest_exact2d_native.h> |
| 1088 | #include <ATen/ops/_upsample_nearest_exact2d_ops.h> |
| 1089 | #include <ATen/ops/_upsample_nearest_exact2d_backward_native.h> |
| 1090 | #include <ATen/ops/_upsample_nearest_exact2d_backward_ops.h> |
| 1091 | #include <ATen/ops/_upsample_nearest_exact2d_backward_native.h> |
| 1092 | #include <ATen/ops/_upsample_nearest_exact2d_backward_ops.h> |
| 1093 | #include <ATen/ops/_upsample_nearest_exact3d_backward_native.h> |
| 1094 | #include <ATen/ops/_upsample_nearest_exact3d_backward_ops.h> |
| 1095 | #include <ATen/ops/_upsample_nearest_exact3d_backward_native.h> |
| 1096 | #include <ATen/ops/_upsample_nearest_exact3d_backward_ops.h> |
| 1097 | #include <ATen/ops/sigmoid_backward_native.h> |
| 1098 | #include <ATen/ops/sigmoid_backward_ops.h> |
| 1099 | #include <ATen/ops/sigmoid_backward_native.h> |
| 1100 | #include <ATen/ops/sigmoid_backward_ops.h> |
| 1101 | #include <ATen/ops/slow_conv_transpose2d_native.h> |
| 1102 | #include <ATen/ops/slow_conv_transpose2d_ops.h> |
| 1103 | #include <ATen/ops/slow_conv_transpose2d_native.h> |
| 1104 | #include <ATen/ops/slow_conv_transpose2d_ops.h> |
| 1105 | #include <ATen/ops/_slow_conv2d_forward_native.h> |
| 1106 | #include <ATen/ops/_slow_conv2d_forward_ops.h> |
| 1107 | #include <ATen/ops/_slow_conv2d_forward_native.h> |
| 1108 | #include <ATen/ops/_slow_conv2d_forward_ops.h> |
| 1109 | #include <ATen/ops/conv_depthwise3d_native.h> |
| 1110 | #include <ATen/ops/conv_depthwise3d_ops.h> |
| 1111 | #include <ATen/ops/conv_depthwise3d_native.h> |
| 1112 | #include <ATen/ops/conv_depthwise3d_ops.h> |
| 1113 | #include <ATen/ops/slow_conv_dilated2d_native.h> |
| 1114 | #include <ATen/ops/slow_conv_dilated2d_ops.h> |
| 1115 | #include <ATen/ops/slow_conv_dilated2d_native.h> |
| 1116 | #include <ATen/ops/slow_conv_dilated2d_ops.h> |
| 1117 | #include <ATen/ops/special_ndtri_native.h> |
| 1118 | #include <ATen/ops/special_ndtri_ops.h> |
| 1119 | #include <ATen/ops/special_ndtri_native.h> |
| 1120 | #include <ATen/ops/special_ndtri_ops.h> |
| 1121 | #include <ATen/ops/special_erfc_native.h> |
| 1122 | #include <ATen/ops/special_erfc_ops.h> |
| 1123 | #include <ATen/ops/special_erfc_native.h> |
| 1124 | #include <ATen/ops/special_erfc_ops.h> |
| 1125 | #include <ATen/ops/special_logit_native.h> |
| 1126 | #include <ATen/ops/special_logit_ops.h> |
| 1127 | #include <ATen/ops/special_logit_native.h> |
| 1128 | #include <ATen/ops/special_logit_ops.h> |
| 1129 | #include <ATen/ops/special_polygamma_native.h> |
| 1130 | #include <ATen/ops/special_polygamma_ops.h> |
| 1131 | #include <ATen/ops/special_polygamma_native.h> |
| 1132 | #include <ATen/ops/special_polygamma_ops.h> |
| 1133 | #include <ATen/ops/special_sinc_native.h> |
| 1134 | #include <ATen/ops/special_sinc_ops.h> |
| 1135 | #include <ATen/ops/special_sinc_native.h> |
| 1136 | #include <ATen/ops/special_sinc_ops.h> |
| 1137 | #include <ATen/ops/fft_ifft_native.h> |
| 1138 | #include <ATen/ops/fft_ifft_ops.h> |
| 1139 | #include <ATen/ops/fft_ifft_native.h> |
| 1140 | #include <ATen/ops/fft_ifft_ops.h> |
| 1141 | #include <ATen/ops/fft_ihfft_native.h> |
| 1142 | #include <ATen/ops/fft_ihfft_ops.h> |
| 1143 | #include <ATen/ops/fft_ihfft_native.h> |
| 1144 | #include <ATen/ops/fft_ihfft_ops.h> |
| 1145 | #include <ATen/ops/fft_ifft2_native.h> |
| 1146 | #include <ATen/ops/fft_ifft2_ops.h> |
| 1147 | #include <ATen/ops/fft_ifft2_native.h> |
| 1148 | #include <ATen/ops/fft_ifft2_ops.h> |
| 1149 | #include <ATen/ops/fft_ihfftn_native.h> |
| 1150 | #include <ATen/ops/fft_ihfftn_ops.h> |
| 1151 | #include <ATen/ops/fft_ihfftn_native.h> |
| 1152 | #include <ATen/ops/fft_ihfftn_ops.h> |
| 1153 | #include <ATen/ops/linalg_cholesky_ex_native.h> |
| 1154 | #include <ATen/ops/linalg_cholesky_ex_ops.h> |
| 1155 | #include <ATen/ops/linalg_cholesky_ex_native.h> |
| 1156 | #include <ATen/ops/linalg_cholesky_ex_ops.h> |
| 1157 | #include <ATen/ops/linalg_cross_native.h> |
| 1158 | #include <ATen/ops/linalg_cross_ops.h> |
| 1159 | #include <ATen/ops/linalg_cross_native.h> |
| 1160 | #include <ATen/ops/linalg_cross_ops.h> |
| 1161 | #include <ATen/ops/linalg_lu_solve_native.h> |
| 1162 | #include <ATen/ops/linalg_lu_solve_ops.h> |
| 1163 | #include <ATen/ops/linalg_lu_solve_native.h> |
| 1164 | #include <ATen/ops/linalg_lu_solve_ops.h> |
| 1165 | #include <ATen/ops/linalg_matmul_native.h> |
| 1166 | #include <ATen/ops/linalg_matmul_ops.h> |
| 1167 | #include <ATen/ops/linalg_matmul_native.h> |
| 1168 | #include <ATen/ops/linalg_matmul_ops.h> |
| 1169 | #include <ATen/ops/linalg_vecdot_native.h> |
| 1170 | #include <ATen/ops/linalg_vecdot_ops.h> |
| 1171 | #include <ATen/ops/linalg_vecdot_native.h> |
| 1172 | #include <ATen/ops/linalg_vecdot_ops.h> |
| 1173 | #include <ATen/ops/linalg_eigvalsh_native.h> |
| 1174 | #include <ATen/ops/linalg_eigvalsh_ops.h> |
| 1175 | #include <ATen/ops/linalg_eigvalsh_native.h> |
| 1176 | #include <ATen/ops/linalg_eigvalsh_ops.h> |
| 1177 | #include <ATen/ops/linalg_inv_ex_native.h> |
| 1178 | #include <ATen/ops/linalg_inv_ex_ops.h> |
| 1179 | #include <ATen/ops/linalg_inv_ex_native.h> |
| 1180 | #include <ATen/ops/linalg_inv_ex_ops.h> |
| 1181 | #include <ATen/ops/inverse_native.h> |
| 1182 | #include <ATen/ops/inverse_ops.h> |
| 1183 | #include <ATen/ops/inverse_native.h> |
| 1184 | #include <ATen/ops/inverse_ops.h> |
| 1185 | #include <ATen/ops/outer_native.h> |
| 1186 | #include <ATen/ops/outer_ops.h> |
| 1187 | #include <ATen/ops/outer_native.h> |
| 1188 | #include <ATen/ops/outer_ops.h> |
| 1189 | #include <ATen/ops/linalg_svd_native.h> |
| 1190 | #include <ATen/ops/linalg_svd_ops.h> |
| 1191 | #include <ATen/ops/linalg_svd_native.h> |
| 1192 | #include <ATen/ops/linalg_svd_ops.h> |
| 1193 | #include <ATen/ops/linalg_cond_native.h> |
| 1194 | #include <ATen/ops/linalg_cond_ops.h> |
| 1195 | #include <ATen/ops/linalg_cond_native.h> |
| 1196 | #include <ATen/ops/linalg_cond_ops.h> |
| 1197 | #include <ATen/ops/linalg_cond_native.h> |
| 1198 | #include <ATen/ops/linalg_cond_ops.h> |
| 1199 | #include <ATen/ops/linalg_cond_native.h> |
| 1200 | #include <ATen/ops/linalg_cond_ops.h> |
| 1201 | #include <ATen/ops/linalg_solve_ex_native.h> |
| 1202 | #include <ATen/ops/linalg_solve_ex_ops.h> |
| 1203 | #include <ATen/ops/linalg_solve_ex_native.h> |
| 1204 | #include <ATen/ops/linalg_solve_ex_ops.h> |
| 1205 | #include <ATen/ops/linalg_tensorsolve_native.h> |
| 1206 | #include <ATen/ops/linalg_tensorsolve_ops.h> |
| 1207 | #include <ATen/ops/linalg_tensorsolve_native.h> |
| 1208 | #include <ATen/ops/linalg_tensorsolve_ops.h> |
| 1209 | #include <ATen/ops/linalg_matrix_rank_native.h> |
| 1210 | #include <ATen/ops/linalg_matrix_rank_ops.h> |
| 1211 | #include <ATen/ops/linalg_matrix_rank_native.h> |
| 1212 | #include <ATen/ops/linalg_matrix_rank_ops.h> |
| 1213 | #include <ATen/ops/linalg_matrix_rank_native.h> |
| 1214 | #include <ATen/ops/linalg_matrix_rank_ops.h> |
| 1215 | #include <ATen/ops/linalg_matrix_rank_native.h> |
| 1216 | #include <ATen/ops/linalg_matrix_rank_ops.h> |
| 1217 | #include <ATen/ops/linalg_matrix_rank_native.h> |
| 1218 | #include <ATen/ops/linalg_matrix_rank_ops.h> |
| 1219 | #include <ATen/ops/linalg_matrix_rank_native.h> |
| 1220 | #include <ATen/ops/linalg_matrix_rank_ops.h> |
| 1221 | #include <ATen/ops/linalg_matrix_rank_native.h> |
| 1222 | #include <ATen/ops/linalg_matrix_rank_ops.h> |
| 1223 | #include <ATen/ops/linalg_matrix_rank_native.h> |
| 1224 | #include <ATen/ops/linalg_matrix_rank_ops.h> |
| 1225 | #include <ATen/ops/_test_optional_floatlist_native.h> |
| 1226 | #include <ATen/ops/_test_optional_floatlist_ops.h> |
| 1227 | #include <ATen/ops/_test_optional_floatlist_native.h> |
| 1228 | #include <ATen/ops/_test_optional_floatlist_ops.h> |
| 1229 | #include <ATen/ops/_test_warn_in_autograd_native.h> |
| 1230 | #include <ATen/ops/_test_warn_in_autograd_ops.h> |
| 1231 | #include <ATen/ops/_test_warn_in_autograd_native.h> |
| 1232 | #include <ATen/ops/_test_warn_in_autograd_ops.h> |
| 1233 | #include <ATen/ops/_segment_reduce_backward_native.h> |
| 1234 | #include <ATen/ops/_segment_reduce_backward_ops.h> |
| 1235 | #include <ATen/ops/_segment_reduce_backward_native.h> |
| 1236 | #include <ATen/ops/_segment_reduce_backward_ops.h> |
| 1237 | #include <ATen/ops/_sparse_broadcast_to_copy_native.h> |
| 1238 | #include <ATen/ops/_sparse_broadcast_to_copy_ops.h> |
| 1239 | #include <ATen/ops/_sparse_broadcast_to_copy_native.h> |
| 1240 | #include <ATen/ops/_sparse_broadcast_to_copy_ops.h> |
| 1241 | #include <ATen/ops/unsqueeze_copy_native.h> |
| 1242 | #include <ATen/ops/unsqueeze_copy_ops.h> |
| 1243 | #include <ATen/ops/unsqueeze_copy_native.h> |
| 1244 | #include <ATen/ops/unsqueeze_copy_ops.h> |
| 1245 | #include <ATen/ops/values_copy_native.h> |
| 1246 | #include <ATen/ops/values_copy_ops.h> |
| 1247 | #include <ATen/ops/values_copy_native.h> |
| 1248 | #include <ATen/ops/values_copy_ops.h> |
| 1249 | #include <ATen/ops/to_padded_tensor_native.h> |
| 1250 | #include <ATen/ops/to_padded_tensor_ops.h> |
| 1251 | #include <ATen/ops/to_padded_tensor_native.h> |
| 1252 | #include <ATen/ops/to_padded_tensor_ops.h> |
| 1253 | #include <ATen/ops/_triton_scaled_dot_attention_native.h> |
| 1254 | #include <ATen/ops/_triton_scaled_dot_attention_ops.h> |
| 1255 | #include <ATen/ops/_triton_scaled_dot_attention_native.h> |
| 1256 | #include <ATen/ops/_triton_scaled_dot_attention_ops.h> |
| 1257 | #include <ATen/ops/special_bessel_y0_native.h> |
| 1258 | #include <ATen/ops/special_bessel_y0_ops.h> |
| 1259 | #include <ATen/ops/special_bessel_y0_native.h> |
| 1260 | #include <ATen/ops/special_bessel_y0_ops.h> |
| 1261 | #include <ATen/ops/special_chebyshev_polynomial_t_native.h> |
| 1262 | #include <ATen/ops/special_chebyshev_polynomial_t_ops.h> |
| 1263 | #include <ATen/ops/special_chebyshev_polynomial_t_native.h> |
| 1264 | #include <ATen/ops/special_chebyshev_polynomial_t_ops.h> |
| 1265 | #include <ATen/ops/special_chebyshev_polynomial_t_native.h> |
| 1266 | #include <ATen/ops/special_chebyshev_polynomial_t_ops.h> |
| 1267 | #include <ATen/ops/special_chebyshev_polynomial_t_native.h> |
| 1268 | #include <ATen/ops/special_chebyshev_polynomial_t_ops.h> |
| 1269 | #include <ATen/ops/special_chebyshev_polynomial_t_native.h> |
| 1270 | #include <ATen/ops/special_chebyshev_polynomial_t_ops.h> |
| 1271 | #include <ATen/ops/special_chebyshev_polynomial_t_native.h> |
| 1272 | #include <ATen/ops/special_chebyshev_polynomial_t_ops.h> |
| 1273 | #include <ATen/ops/special_chebyshev_polynomial_u_native.h> |
| 1274 | #include <ATen/ops/special_chebyshev_polynomial_u_ops.h> |
| 1275 | #include <ATen/ops/special_chebyshev_polynomial_u_native.h> |
| 1276 | #include <ATen/ops/special_chebyshev_polynomial_u_ops.h> |
| 1277 | #include <ATen/ops/special_chebyshev_polynomial_u_native.h> |
| 1278 | #include <ATen/ops/special_chebyshev_polynomial_u_ops.h> |
| 1279 | #include <ATen/ops/special_chebyshev_polynomial_u_native.h> |
| 1280 | #include <ATen/ops/special_chebyshev_polynomial_u_ops.h> |
| 1281 | #include <ATen/ops/special_chebyshev_polynomial_u_native.h> |
| 1282 | #include <ATen/ops/special_chebyshev_polynomial_u_ops.h> |
| 1283 | #include <ATen/ops/special_chebyshev_polynomial_u_native.h> |
| 1284 | #include <ATen/ops/special_chebyshev_polynomial_u_ops.h> |
| 1285 | #include <ATen/ops/special_hermite_polynomial_h_native.h> |
| 1286 | #include <ATen/ops/special_hermite_polynomial_h_ops.h> |
| 1287 | #include <ATen/ops/special_hermite_polynomial_h_native.h> |
| 1288 | #include <ATen/ops/special_hermite_polynomial_h_ops.h> |
| 1289 | #include <ATen/ops/special_hermite_polynomial_h_native.h> |
| 1290 | #include <ATen/ops/special_hermite_polynomial_h_ops.h> |
| 1291 | #include <ATen/ops/special_hermite_polynomial_h_native.h> |
| 1292 | #include <ATen/ops/special_hermite_polynomial_h_ops.h> |
| 1293 | #include <ATen/ops/special_hermite_polynomial_h_native.h> |
| 1294 | #include <ATen/ops/special_hermite_polynomial_h_ops.h> |
| 1295 | #include <ATen/ops/special_hermite_polynomial_h_native.h> |
| 1296 | #include <ATen/ops/special_hermite_polynomial_h_ops.h> |
| 1297 | #include <ATen/ops/special_modified_bessel_k1_native.h> |
| 1298 | #include <ATen/ops/special_modified_bessel_k1_ops.h> |
| 1299 | #include <ATen/ops/special_modified_bessel_k1_native.h> |
| 1300 | #include <ATen/ops/special_modified_bessel_k1_ops.h> |
| 1301 | #include <ATen/ops/special_scaled_modified_bessel_k0_native.h> |
| 1302 | #include <ATen/ops/special_scaled_modified_bessel_k0_ops.h> |
| 1303 | #include <ATen/ops/special_scaled_modified_bessel_k0_native.h> |
| 1304 | #include <ATen/ops/special_scaled_modified_bessel_k0_ops.h> |
| 1305 | #include <ATen/ops/special_scaled_modified_bessel_k1_native.h> |
| 1306 | #include <ATen/ops/special_scaled_modified_bessel_k1_ops.h> |
| 1307 | #include <ATen/ops/special_scaled_modified_bessel_k1_native.h> |
| 1308 | #include <ATen/ops/special_scaled_modified_bessel_k1_ops.h> |
| 1309 | #include <ATen/ops/special_spherical_bessel_j0_native.h> |
| 1310 | #include <ATen/ops/special_spherical_bessel_j0_ops.h> |
| 1311 | #include <ATen/ops/special_spherical_bessel_j0_native.h> |
| 1312 | #include <ATen/ops/special_spherical_bessel_j0_ops.h> |
| 1313 | #include <ATen/ops/refine_names_native.h> |
| 1314 | #include <ATen/ops/refine_names_ops.h> |
| 1315 | #include <ATen/ops/real_native.h> |
| 1316 | #include <ATen/ops/real_ops.h> |
| 1317 | #include <ATen/ops/_neg_view_native.h> |
| 1318 | #include <ATen/ops/_neg_view_ops.h> |
| 1319 | #include <ATen/ops/_neg_view_copy_native.h> |
| 1320 | #include <ATen/ops/_neg_view_copy_ops.h> |
| 1321 | #include <ATen/ops/diagonal_native.h> |
| 1322 | #include <ATen/ops/diagonal_ops.h> |
| 1323 | #include <ATen/ops/diagonal_copy_native.h> |
| 1324 | #include <ATen/ops/diagonal_copy_ops.h> |
| 1325 | #include <ATen/ops/diagonal_native.h> |
| 1326 | #include <ATen/ops/diagonal_ops.h> |
| 1327 | #include <ATen/ops/narrow_native.h> |
| 1328 | #include <ATen/ops/narrow_ops.h> |
| 1329 | #include <ATen/ops/narrow_copy_native.h> |
| 1330 | #include <ATen/ops/narrow_copy_ops.h> |
| 1331 | #include <ATen/ops/narrow_native.h> |
| 1332 | #include <ATen/ops/narrow_ops.h> |
| 1333 | #include <ATen/ops/numpy_T_native.h> |
| 1334 | #include <ATen/ops/numpy_T_ops.h> |
| 1335 | #include <ATen/ops/select_native.h> |
| 1336 | #include <ATen/ops/select_ops.h> |
| 1337 | #include <ATen/ops/select_native.h> |
| 1338 | #include <ATen/ops/select_ops.h> |
| 1339 | #include <ATen/ops/select_copy_native.h> |
| 1340 | #include <ATen/ops/select_copy_ops.h> |
| 1341 | #include <ATen/ops/split_with_sizes_native.h> |
| 1342 | #include <ATen/ops/split_with_sizes_ops.h> |
| 1343 | #include <ATen/ops/split_with_sizes_copy_native.h> |
| 1344 | #include <ATen/ops/split_with_sizes_copy_ops.h> |
| 1345 | #include <ATen/ops/vsplit_native.h> |
| 1346 | #include <ATen/ops/vsplit_ops.h> |
| 1347 | #include <ATen/ops/vsplit_native.h> |
| 1348 | #include <ATen/ops/vsplit_ops.h> |
| 1349 | #include <ATen/ops/squeeze_native.h> |
| 1350 | #include <ATen/ops/squeeze_ops.h> |
| 1351 | #include <ATen/ops/squeeze_copy_native.h> |
| 1352 | #include <ATen/ops/squeeze_copy_ops.h> |
| 1353 | #include <ATen/ops/squeeze_native.h> |
| 1354 | #include <ATen/ops/squeeze_ops.h> |
| 1355 | #include <ATen/ops/squeeze_copy_native.h> |
| 1356 | #include <ATen/ops/squeeze_copy_ops.h> |
| 1357 | #include <ATen/ops/squeeze_native.h> |
| 1358 | #include <ATen/ops/squeeze_ops.h> |
| 1359 | #include <ATen/ops/squeeze_native.h> |
| 1360 | #include <ATen/ops/squeeze_ops.h> |
| 1361 | #include <ATen/ops/squeeze_copy_native.h> |
| 1362 | #include <ATen/ops/squeeze_copy_ops.h> |
| 1363 | #include <ATen/ops/view_as_native.h> |
| 1364 | #include <ATen/ops/view_as_ops.h> |
| 1365 | #include <ATen/ops/unbind_native.h> |
| 1366 | #include <ATen/ops/unbind_ops.h> |
| 1367 | #include <ATen/ops/unbind_copy_native.h> |
| 1368 | #include <ATen/ops/unbind_copy_ops.h> |
| 1369 | #include <ATen/ops/unbind_native.h> |
| 1370 | #include <ATen/ops/unbind_ops.h> |
| 1371 | #include <ATen/ops/alias_native.h> |
| 1372 | #include <ATen/ops/alias_ops.h> |
| 1373 | #include <ATen/ops/alias_copy_native.h> |
| 1374 | #include <ATen/ops/alias_copy_ops.h> |
| 1375 | #include <ATen/ops/_cast_Char_native.h> |
| 1376 | #include <ATen/ops/_cast_Char_ops.h> |
| 1377 | #include <ATen/ops/_cast_Half_native.h> |
| 1378 | #include <ATen/ops/_cast_Half_ops.h> |
| 1379 | #include <ATen/ops/_backward_native.h> |
| 1380 | #include <ATen/ops/_backward_ops.h> |
| 1381 | #include <ATen/ops/set_data_native.h> |
| 1382 | #include <ATen/ops/set_data_ops.h> |
| 1383 | #include <ATen/ops/data_native.h> |
| 1384 | #include <ATen/ops/data_ops.h> |
| 1385 | #include <ATen/ops/requires_grad_native.h> |
| 1386 | #include <ATen/ops/requires_grad_ops.h> |
| 1387 | #include <ATen/ops/align_as_native.h> |
| 1388 | #include <ATen/ops/align_as_ops.h> |
| 1389 | #include <ATen/ops/_sobol_engine_ff_native.h> |
| 1390 | #include <ATen/ops/_sobol_engine_ff_ops.h> |
| 1391 | #include <ATen/ops/feature_alpha_dropout_native.h> |
| 1392 | #include <ATen/ops/feature_alpha_dropout_ops.h> |
| 1393 | #include <ATen/ops/feature_alpha_dropout_native.h> |
| 1394 | #include <ATen/ops/feature_alpha_dropout_ops.h> |
| 1395 | #include <ATen/ops/_is_all_true_native.h> |
| 1396 | #include <ATen/ops/_is_all_true_ops.h> |
| 1397 | #include <ATen/ops/allclose_native.h> |
| 1398 | #include <ATen/ops/allclose_ops.h> |
| 1399 | #include <ATen/ops/atleast_1d_native.h> |
| 1400 | #include <ATen/ops/atleast_1d_ops.h> |
| 1401 | #include <ATen/ops/atleast_1d_native.h> |
| 1402 | #include <ATen/ops/atleast_1d_ops.h> |
| 1403 | #include <ATen/ops/_batch_norm_impl_index_native.h> |
| 1404 | #include <ATen/ops/_batch_norm_impl_index_ops.h> |
| 1405 | #include <ATen/ops/bilinear_native.h> |
| 1406 | #include <ATen/ops/bilinear_ops.h> |
| 1407 | #include <ATen/ops/unsafe_chunk_native.h> |
| 1408 | #include <ATen/ops/unsafe_chunk_ops.h> |
| 1409 | #include <ATen/ops/conv_transpose1d_native.h> |
| 1410 | #include <ATen/ops/conv_transpose1d_ops.h> |
| 1411 | #include <ATen/ops/conv_transpose2d_native.h> |
| 1412 | #include <ATen/ops/conv_transpose2d_ops.h> |
| 1413 | #include <ATen/ops/conv_transpose3d_native.h> |
| 1414 | #include <ATen/ops/conv_transpose3d_ops.h> |
| 1415 | #include <ATen/ops/cosine_embedding_loss_native.h> |
| 1416 | #include <ATen/ops/cosine_embedding_loss_ops.h> |
| 1417 | #include <ATen/ops/cumulative_trapezoid_native.h> |
| 1418 | #include <ATen/ops/cumulative_trapezoid_ops.h> |
| 1419 | #include <ATen/ops/cumulative_trapezoid_native.h> |
| 1420 | #include <ATen/ops/cumulative_trapezoid_ops.h> |
| 1421 | #include <ATen/ops/ctc_loss_native.h> |
| 1422 | #include <ATen/ops/ctc_loss_ops.h> |
| 1423 | #include <ATen/ops/ctc_loss_native.h> |
| 1424 | #include <ATen/ops/ctc_loss_ops.h> |
| 1425 | #include <ATen/ops/gradient_native.h> |
| 1426 | #include <ATen/ops/gradient_ops.h> |
| 1427 | #include <ATen/ops/gradient_native.h> |
| 1428 | #include <ATen/ops/gradient_ops.h> |
| 1429 | #include <ATen/ops/gradient_native.h> |
| 1430 | #include <ATen/ops/gradient_ops.h> |
| 1431 | #include <ATen/ops/gradient_native.h> |
| 1432 | #include <ATen/ops/gradient_ops.h> |
| 1433 | #include <ATen/ops/gradient_native.h> |
| 1434 | #include <ATen/ops/gradient_ops.h> |
| 1435 | #include <ATen/ops/gradient_native.h> |
| 1436 | #include <ATen/ops/gradient_ops.h> |
| 1437 | #include <ATen/ops/gradient_native.h> |
| 1438 | #include <ATen/ops/gradient_ops.h> |
| 1439 | #include <ATen/ops/einsum_native.h> |
| 1440 | #include <ATen/ops/einsum_ops.h> |
| 1441 | #include <ATen/ops/grid_sampler_native.h> |
| 1442 | #include <ATen/ops/grid_sampler_ops.h> |
| 1443 | #include <ATen/ops/_grid_sampler_2d_cpu_fallback_backward_native.h> |
| 1444 | #include <ATen/ops/_grid_sampler_2d_cpu_fallback_backward_ops.h> |
| 1445 | #include <ATen/ops/hinge_embedding_loss_native.h> |
| 1446 | #include <ATen/ops/hinge_embedding_loss_ops.h> |
| 1447 | #include <ATen/ops/_cufft_set_plan_cache_max_size_native.h> |
| 1448 | #include <ATen/ops/_cufft_set_plan_cache_max_size_ops.h> |
| 1449 | #include <ATen/ops/_cufft_clear_plan_cache_native.h> |
| 1450 | #include <ATen/ops/_cufft_clear_plan_cache_ops.h> |
| 1451 | #include <ATen/ops/isclose_native.h> |
| 1452 | #include <ATen/ops/isclose_ops.h> |
| 1453 | #include <ATen/ops/is_floating_point_native.h> |
| 1454 | #include <ATen/ops/is_floating_point_ops.h> |
| 1455 | #include <ATen/ops/is_conj_native.h> |
| 1456 | #include <ATen/ops/is_conj_ops.h> |
| 1457 | #include <ATen/ops/isreal_native.h> |
| 1458 | #include <ATen/ops/isreal_ops.h> |
| 1459 | #include <ATen/ops/is_nonzero_native.h> |
| 1460 | #include <ATen/ops/is_nonzero_ops.h> |
| 1461 | #include <ATen/ops/layer_norm_native.h> |
| 1462 | #include <ATen/ops/layer_norm_ops.h> |
| 1463 | #include <ATen/ops/fbgemm_pack_gemm_matrix_fp16_native.h> |
| 1464 | #include <ATen/ops/fbgemm_pack_gemm_matrix_fp16_ops.h> |
| 1465 | #include <ATen/ops/fbgemm_pack_quantized_matrix_native.h> |
| 1466 | #include <ATen/ops/fbgemm_pack_quantized_matrix_ops.h> |
| 1467 | #include <ATen/ops/fbgemm_pack_quantized_matrix_native.h> |
| 1468 | #include <ATen/ops/fbgemm_pack_quantized_matrix_ops.h> |
| 1469 | #include <ATen/ops/value_selecting_reduction_backward_native.h> |
| 1470 | #include <ATen/ops/value_selecting_reduction_backward_ops.h> |
| 1471 | #include <ATen/ops/max_pool1d_native.h> |
| 1472 | #include <ATen/ops/max_pool1d_ops.h> |
| 1473 | #include <ATen/ops/pairwise_distance_native.h> |
| 1474 | #include <ATen/ops/pairwise_distance_ops.h> |
| 1475 | #include <ATen/ops/pdist_native.h> |
| 1476 | #include <ATen/ops/pdist_ops.h> |
| 1477 | #include <ATen/ops/_nested_select_backward_native.h> |
| 1478 | #include <ATen/ops/_nested_select_backward_ops.h> |
| 1479 | #include <ATen/ops/size_native.h> |
| 1480 | #include <ATen/ops/size_ops.h> |
| 1481 | #include <ATen/ops/size_native.h> |
| 1482 | #include <ATen/ops/size_ops.h> |
| 1483 | #include <ATen/ops/smm_native.h> |
| 1484 | #include <ATen/ops/smm_ops.h> |
| 1485 | #include <ATen/ops/stride_native.h> |
| 1486 | #include <ATen/ops/stride_ops.h> |
| 1487 | #include <ATen/ops/stride_native.h> |
| 1488 | #include <ATen/ops/stride_ops.h> |
| 1489 | #include <ATen/ops/sum_to_size_native.h> |
| 1490 | #include <ATen/ops/sum_to_size_ops.h> |
| 1491 | #include <ATen/ops/std_native.h> |
| 1492 | #include <ATen/ops/std_ops.h> |
| 1493 | #include <ATen/ops/one_hot_native.h> |
| 1494 | #include <ATen/ops/one_hot_ops.h> |
| 1495 | #include <ATen/ops/fliplr_native.h> |
| 1496 | #include <ATen/ops/fliplr_ops.h> |
| 1497 | #include <ATen/ops/triplet_margin_loss_native.h> |
| 1498 | #include <ATen/ops/triplet_margin_loss_ops.h> |
| 1499 | #include <ATen/ops/type_as_native.h> |
| 1500 | #include <ATen/ops/type_as_ops.h> |
| 1501 | #include <ATen/ops/var_native.h> |
| 1502 | #include <ATen/ops/var_ops.h> |
| 1503 | #include <ATen/ops/_sparse_log_softmax_native.h> |
| 1504 | #include <ATen/ops/_sparse_log_softmax_ops.h> |
| 1505 | #include <ATen/ops/_sparse_log_softmax_native.h> |
| 1506 | #include <ATen/ops/_sparse_log_softmax_ops.h> |
| 1507 | #include <ATen/ops/sparse_csc_tensor_native.h> |
| 1508 | #include <ATen/ops/sparse_csc_tensor_ops.h> |
| 1509 | #include <ATen/ops/sparse_csc_tensor_native.h> |
| 1510 | #include <ATen/ops/sparse_csc_tensor_ops.h> |
| 1511 | #include <ATen/ops/_sparse_bsr_tensor_unsafe_native.h> |
| 1512 | #include <ATen/ops/_sparse_bsr_tensor_unsafe_ops.h> |
| 1513 | #include <ATen/ops/_validate_sparse_compressed_tensor_args_native.h> |
| 1514 | #include <ATen/ops/_validate_sparse_compressed_tensor_args_ops.h> |
| 1515 | #include <ATen/ops/_validate_sparse_csr_tensor_args_native.h> |
| 1516 | #include <ATen/ops/_validate_sparse_csr_tensor_args_ops.h> |
| 1517 | #include <ATen/ops/_validate_sparse_csc_tensor_args_native.h> |
| 1518 | #include <ATen/ops/_validate_sparse_csc_tensor_args_ops.h> |
| 1519 | #include <ATen/ops/_to_cpu_native.h> |
| 1520 | #include <ATen/ops/_to_cpu_ops.h> |
| 1521 | #include <ATen/ops/_dimI_native.h> |
| 1522 | #include <ATen/ops/_dimI_ops.h> |
| 1523 | #include <ATen/ops/q_per_channel_axis_native.h> |
| 1524 | #include <ATen/ops/q_per_channel_axis_ops.h> |
| 1525 | #include <ATen/ops/fake_quantize_per_tensor_affine_cachemask_backward_native.h> |
| 1526 | #include <ATen/ops/fake_quantize_per_tensor_affine_cachemask_backward_ops.h> |
| 1527 | #include <ATen/ops/fake_quantize_per_channel_affine_cachemask_backward_native.h> |
| 1528 | #include <ATen/ops/fake_quantize_per_channel_affine_cachemask_backward_ops.h> |
| 1529 | #include <ATen/ops/rnn_tanh_cell_native.h> |
| 1530 | #include <ATen/ops/rnn_tanh_cell_ops.h> |
| 1531 | #include <ATen/ops/set_native.h> |
| 1532 | #include <ATen/ops/set_ops.h> |
| 1533 | #include <ATen/ops/xor_native.h> |
| 1534 | #include <ATen/ops/xor_ops.h> |
| 1535 | #include <ATen/ops/xor_native.h> |
| 1536 | #include <ATen/ops/xor_ops.h> |
| 1537 | #include <ATen/ops/xor_native.h> |
| 1538 | #include <ATen/ops/xor_ops.h> |
| 1539 | #include <ATen/ops/xor_native.h> |
| 1540 | #include <ATen/ops/xor_ops.h> |
| 1541 | #include <ATen/ops/trace_backward_native.h> |
| 1542 | #include <ATen/ops/trace_backward_ops.h> |
| 1543 | #include <ATen/ops/masked_select_backward_native.h> |
| 1544 | #include <ATen/ops/masked_select_backward_ops.h> |
| 1545 | #include <ATen/ops/argwhere_native.h> |
| 1546 | #include <ATen/ops/argwhere_ops.h> |
| 1547 | #include <ATen/ops/cross_entropy_loss_native.h> |
| 1548 | #include <ATen/ops/cross_entropy_loss_ops.h> |
| 1549 | #include <ATen/ops/equal_native.h> |
| 1550 | #include <ATen/ops/equal_ops.h> |
| 1551 | #include <ATen/ops/adaptive_avg_pool3d_backward_native.h> |
| 1552 | #include <ATen/ops/adaptive_avg_pool3d_backward_ops.h> |
| 1553 | #include <ATen/ops/_pad_enum_native.h> |
| 1554 | #include <ATen/ops/_pad_enum_ops.h> |
| 1555 | #include <ATen/ops/pad_native.h> |
| 1556 | #include <ATen/ops/pad_ops.h> |
| 1557 | #include <ATen/ops/upsample_nearest2d_native.h> |
| 1558 | #include <ATen/ops/upsample_nearest2d_ops.h> |
| 1559 | #include <ATen/ops/_upsample_nearest_exact2d_native.h> |
| 1560 | #include <ATen/ops/_upsample_nearest_exact2d_ops.h> |
| 1561 | #include <ATen/ops/_add_batch_dim_native.h> |
| 1562 | #include <ATen/ops/_add_batch_dim_ops.h> |
| 1563 | #include <ATen/ops/_remove_batch_dim_native.h> |
| 1564 | #include <ATen/ops/_remove_batch_dim_ops.h> |
| 1565 | #include <ATen/ops/fft_ifftshift_native.h> |
| 1566 | #include <ATen/ops/fft_ifftshift_ops.h> |
| 1567 | #include <ATen/ops/_test_serialization_subcmul_native.h> |
| 1568 | #include <ATen/ops/_test_serialization_subcmul_ops.h> |
| 1569 | #include <ATen/ops/unflatten_dense_tensors_native.h> |
| 1570 | #include <ATen/ops/unflatten_dense_tensors_ops.h> |
| 1571 | #include <ATen/ops/_scaled_dot_product_flash_attention_native.h> |
| 1572 | #include <ATen/ops/_scaled_dot_product_flash_attention_ops.h> |
| 1573 | #include <ATen/ops/_scaled_dot_product_efficient_attention_native.h> |
| 1574 | #include <ATen/ops/_scaled_dot_product_efficient_attention_ops.h> |
| 1575 | #include <ATen/ops/_flash_attention_backward_native.h> |
| 1576 | #include <ATen/ops/_flash_attention_backward_ops.h> |
| 1577 | #include <ATen/ops/_efficient_attention_forward_native.h> |
| 1578 | #include <ATen/ops/_efficient_attention_forward_ops.h> |
| 1579 | #endif |
| 1580 | |
| 1581 | namespace at { |
| 1582 | namespace functionalization { |
| 1583 | |
| 1584 | // This keyset is used by functionalization when it calls into meta kernels |
| 1585 | // to accurately propagate stride metadata. |
| 1586 | // Exclude any modes: the purpose of calling into meta kernels is only as an implementation |
| 1587 | // detail to perform shape inference, and we don't want any modal keys to run. |
| 1588 | // Specifically, we want to prevent functionalization and Python modes from running. |
| 1589 | constexpr auto exclude_keys_for_meta_dispatch = |
| 1590 | c10::functorch_transforms_ks | |
| 1591 | c10::DispatchKeySet({ |
| 1592 | c10::DispatchKey::FuncTorchDynamicLayerBackMode, |
| 1593 | c10::DispatchKey::FuncTorchDynamicLayerFrontMode, |
| 1594 | c10::DispatchKey::Python |
| 1595 | }); |
| 1596 | |
| 1597 | |
| 1598 | inline Tensor to_meta(const Tensor& t) { |
| 1599 | if (!t.defined()) return t; |
| 1600 | return at::native::empty_strided_meta_symint(t.sym_sizes(), t.sym_strides(), |
| 1601 | /*dtype=*/c10::make_optional(t.scalar_type()), /*layout=*/c10::make_optional(t.layout()), |
| 1602 | /*device=*/c10::make_optional(c10::Device(kMeta)), /*pin_memory=*/c10::nullopt); |
| 1603 | } |
| 1604 | |
| 1605 | inline c10::optional<Tensor> to_meta(const c10::optional<Tensor>& t) { |
| 1606 | if (t.has_value()) { |
| 1607 | return c10::make_optional<Tensor>(to_meta(*t)); |
| 1608 | } |
| 1609 | return c10::nullopt; |
| 1610 | } |
| 1611 | |
| 1612 | inline std::vector<Tensor> to_meta(at::ITensorListRef t_list) { |
| 1613 | std::vector<Tensor> outputs; |
| 1614 | outputs.reserve(t_list.size()); |
| 1615 | for (const auto& tensor : t_list) { |
| 1616 | outputs.push_back(to_meta(tensor)); |
| 1617 | } |
| 1618 | return outputs; |
| 1619 | } |
| 1620 | |
| 1621 | inline c10::List<Tensor> to_meta(const c10::List<Tensor>& t_list) { |
| 1622 | c10::List<Tensor> outputs; |
| 1623 | outputs.reserve(t_list.size()); |
| 1624 | for (const auto i : c10::irange(t_list.size())) { |
| 1625 | outputs.push_back(to_meta(t_list[i])); |
| 1626 | } |
| 1627 | return outputs; |
| 1628 | } |
| 1629 | |
| 1630 | inline c10::List<c10::optional<Tensor>> to_meta(const c10::List<c10::optional<Tensor>>& t_list) { |
| 1631 | c10::List<c10::optional<Tensor>> outputs; |
| 1632 | outputs.reserve(t_list.size()); |
| 1633 | for (const auto i : c10::irange(t_list.size())) { |
| 1634 | outputs.push_back(to_meta(t_list[i])); |
| 1635 | } |
| 1636 | return outputs; |
| 1637 | } |
| 1638 | |
| 1639 | |
| 1640 | |
| 1641 | at::Tensor & _new_zeros_with_same_feature_meta_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, int64_t self_num_batch_dims, at::Tensor & out) { |
| 1642 | if (false) { |
| 1643 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 1644 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 1645 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 1646 | auto self_meta = to_meta(self); |
| 1647 | auto other_meta = to_meta(other); |
| 1648 | auto out_meta = to_meta(out); |
| 1649 | at::AutoDispatchSkipFunctionalize func_guard; |
| 1650 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 1651 | at::_ops::_new_zeros_with_same_feature_meta_out::call(self_meta, other_meta, self_num_batch_dims, out_meta); |
| 1652 | } |
| 1653 | |
| 1654 | at::Tensor self_; |
| 1655 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 1656 | at::functionalization::impl::sync(self); |
| 1657 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 1658 | } else { |
| 1659 | self_ = self; |
| 1660 | } |
| 1661 | |
| 1662 | at::Tensor other_; |
| 1663 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 1664 | at::functionalization::impl::sync(other); |
| 1665 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 1666 | } else { |
| 1667 | other_ = other; |
| 1668 | } |
| 1669 | |
| 1670 | at::Tensor out_; |
| 1671 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 1672 | at::functionalization::impl::sync(out); |
| 1673 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 1674 | } else { |
| 1675 | out_ = out; |
| 1676 | } |
| 1677 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 1678 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 1679 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 1680 | TORCH_INTERNAL_ASSERT(false, |
| 1681 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 1682 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 1683 | } else { |
| 1684 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 1685 | at::AutoDispatchSkipFunctionalize guard; |
| 1686 | at::Tensor tmp_output = at::_ops::_new_zeros_with_same_feature_meta_out::call(self_, other_, self_num_batch_dims, out_); |
| 1687 | return out;; |
| 1688 | } |
| 1689 | } else { |
| 1690 | at::Tensor tmp_output; |
| 1691 | { |
| 1692 | at::AutoDispatchSkipFunctionalize guard; |
| 1693 | tmp_output = at::_ops::_new_zeros_with_same_feature_meta::call(self_, other_, self_num_batch_dims); |
| 1694 | } |
| 1695 | at::functionalization::impl::replace_(out, tmp_output); |
| 1696 | at::functionalization::impl::commit_update(out); |
| 1697 | at::functionalization::impl::sync(out); |
| 1698 | return out; |
| 1699 | } |
| 1700 | } |
| 1701 | |
| 1702 | at::Tensor & _cudnn_init_dropout_state_out_out(c10::DispatchKeySet dispatchKeySet, double dropout, bool train, int64_t dropout_seed, at::Tensor & out) { |
| 1703 | if (false) { |
| 1704 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 1705 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 1706 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 1707 | auto out_meta = to_meta(out); |
| 1708 | at::AutoDispatchSkipFunctionalize func_guard; |
| 1709 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 1710 | at::_ops::_cudnn_init_dropout_state_out::call(dropout, train, dropout_seed, out_meta); |
| 1711 | } |
| 1712 | |
| 1713 | at::Tensor out_; |
| 1714 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 1715 | at::functionalization::impl::sync(out); |
| 1716 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 1717 | } else { |
| 1718 | out_ = out; |
| 1719 | } |
| 1720 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 1721 | if ((false)) { |
| 1722 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 1723 | TORCH_INTERNAL_ASSERT(false, |
| 1724 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 1725 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 1726 | } else { |
| 1727 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 1728 | at::AutoDispatchSkipFunctionalize guard; |
| 1729 | at::Tensor tmp_output = at::_ops::_cudnn_init_dropout_state_out::call(dropout, train, dropout_seed, out_); |
| 1730 | return out;; |
| 1731 | } |
| 1732 | } else { |
| 1733 | at::Tensor tmp_output; |
| 1734 | { |
| 1735 | at::AutoDispatchSkipFunctionalize guard; |
| 1736 | tmp_output = at::_ops::_cudnn_init_dropout_state::call(dropout, train, dropout_seed, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 1737 | } |
| 1738 | at::functionalization::impl::replace_(out, tmp_output); |
| 1739 | at::functionalization::impl::commit_update(out); |
| 1740 | at::functionalization::impl::sync(out); |
| 1741 | return out; |
| 1742 | } |
| 1743 | } |
| 1744 | |
| 1745 | at::Tensor & angle_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 1746 | if (false) { |
| 1747 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 1748 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 1749 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 1750 | auto self_meta = to_meta(self); |
| 1751 | auto out_meta = to_meta(out); |
| 1752 | at::AutoDispatchSkipFunctionalize func_guard; |
| 1753 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 1754 | at::_ops::angle_out::call(self_meta, out_meta); |
| 1755 | } |
| 1756 | |
| 1757 | at::Tensor self_; |
| 1758 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 1759 | at::functionalization::impl::sync(self); |
| 1760 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 1761 | } else { |
| 1762 | self_ = self; |
| 1763 | } |
| 1764 | |
| 1765 | at::Tensor out_; |
| 1766 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 1767 | at::functionalization::impl::sync(out); |
| 1768 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 1769 | } else { |
| 1770 | out_ = out; |
| 1771 | } |
| 1772 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 1773 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 1774 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 1775 | TORCH_INTERNAL_ASSERT(false, |
| 1776 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 1777 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 1778 | } else { |
| 1779 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 1780 | at::AutoDispatchSkipFunctionalize guard; |
| 1781 | at::Tensor tmp_output = at::_ops::angle_out::call(self_, out_); |
| 1782 | return out;; |
| 1783 | } |
| 1784 | } else { |
| 1785 | at::Tensor tmp_output; |
| 1786 | { |
| 1787 | at::AutoDispatchSkipFunctionalize guard; |
| 1788 | tmp_output = at::_ops::angle::call(self_); |
| 1789 | } |
| 1790 | at::functionalization::impl::replace_(out, tmp_output); |
| 1791 | at::functionalization::impl::commit_update(out); |
| 1792 | at::functionalization::impl::sync(out); |
| 1793 | return out; |
| 1794 | } |
| 1795 | } |
| 1796 | |
| 1797 | at::Tensor & sgn_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 1798 | if (false) { |
| 1799 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 1800 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 1801 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 1802 | auto self_meta = to_meta(self); |
| 1803 | auto out_meta = to_meta(out); |
| 1804 | at::AutoDispatchSkipFunctionalize func_guard; |
| 1805 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 1806 | at::_ops::sgn_out::call(self_meta, out_meta); |
| 1807 | } |
| 1808 | |
| 1809 | at::Tensor self_; |
| 1810 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 1811 | at::functionalization::impl::sync(self); |
| 1812 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 1813 | } else { |
| 1814 | self_ = self; |
| 1815 | } |
| 1816 | |
| 1817 | at::Tensor out_; |
| 1818 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 1819 | at::functionalization::impl::sync(out); |
| 1820 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 1821 | } else { |
| 1822 | out_ = out; |
| 1823 | } |
| 1824 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 1825 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 1826 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 1827 | TORCH_INTERNAL_ASSERT(false, |
| 1828 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 1829 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 1830 | } else { |
| 1831 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 1832 | at::AutoDispatchSkipFunctionalize guard; |
| 1833 | at::Tensor tmp_output = at::_ops::sgn_out::call(self_, out_); |
| 1834 | return out;; |
| 1835 | } |
| 1836 | } else { |
| 1837 | at::Tensor tmp_output; |
| 1838 | { |
| 1839 | at::AutoDispatchSkipFunctionalize guard; |
| 1840 | tmp_output = at::_ops::sgn::call(self_); |
| 1841 | } |
| 1842 | at::functionalization::impl::replace_(out, tmp_output); |
| 1843 | at::functionalization::impl::commit_update(out); |
| 1844 | at::functionalization::impl::sync(out); |
| 1845 | return out; |
| 1846 | } |
| 1847 | } |
| 1848 | |
| 1849 | at::Tensor & sgn_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 1850 | if (true) { |
| 1851 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 1852 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 1853 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 1854 | auto self_meta = to_meta(self); |
| 1855 | at::AutoDispatchSkipFunctionalize func_guard; |
| 1856 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 1857 | at::_ops::sgn_::call(self_meta); |
| 1858 | } |
| 1859 | |
| 1860 | at::Tensor self_; |
| 1861 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 1862 | at::functionalization::impl::sync(self); |
| 1863 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 1864 | } else { |
| 1865 | self_ = self; |
| 1866 | } |
| 1867 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 1868 | if ((false)) { |
| 1869 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 1870 | TORCH_INTERNAL_ASSERT(false, |
| 1871 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 1872 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 1873 | } else { |
| 1874 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 1875 | at::AutoDispatchSkipFunctionalize guard; |
| 1876 | at::Tensor tmp_output = at::_ops::sgn_::call(self_); |
| 1877 | return self;; |
| 1878 | } |
| 1879 | } else { |
| 1880 | at::Tensor tmp_output; |
| 1881 | { |
| 1882 | at::AutoDispatchSkipFunctionalize guard; |
| 1883 | tmp_output = at::_ops::sgn::call(self_); |
| 1884 | } |
| 1885 | at::functionalization::impl::replace_(self, tmp_output); |
| 1886 | at::functionalization::impl::commit_update(self); |
| 1887 | at::functionalization::impl::sync(self); |
| 1888 | return self; |
| 1889 | } |
| 1890 | } |
| 1891 | |
| 1892 | at::Tensor & _add_relu_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) { |
| 1893 | if (false) { |
| 1894 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 1895 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 1896 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 1897 | auto self_meta = to_meta(self); |
| 1898 | auto other_meta = to_meta(other); |
| 1899 | auto out_meta = to_meta(out); |
| 1900 | at::AutoDispatchSkipFunctionalize func_guard; |
| 1901 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 1902 | at::_ops::_add_relu_out::call(self_meta, other_meta, alpha, out_meta); |
| 1903 | } |
| 1904 | |
| 1905 | at::Tensor self_; |
| 1906 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 1907 | at::functionalization::impl::sync(self); |
| 1908 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 1909 | } else { |
| 1910 | self_ = self; |
| 1911 | } |
| 1912 | |
| 1913 | at::Tensor other_; |
| 1914 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 1915 | at::functionalization::impl::sync(other); |
| 1916 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 1917 | } else { |
| 1918 | other_ = other; |
| 1919 | } |
| 1920 | |
| 1921 | at::Tensor out_; |
| 1922 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 1923 | at::functionalization::impl::sync(out); |
| 1924 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 1925 | } else { |
| 1926 | out_ = out; |
| 1927 | } |
| 1928 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 1929 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 1930 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 1931 | TORCH_INTERNAL_ASSERT(false, |
| 1932 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 1933 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 1934 | } else { |
| 1935 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 1936 | at::AutoDispatchSkipFunctionalize guard; |
| 1937 | at::Tensor tmp_output = at::_ops::_add_relu_out::call(self_, other_, alpha, out_); |
| 1938 | return out;; |
| 1939 | } |
| 1940 | } else { |
| 1941 | at::Tensor tmp_output; |
| 1942 | { |
| 1943 | at::AutoDispatchSkipFunctionalize guard; |
| 1944 | tmp_output = at::_ops::_add_relu_Tensor::call(self_, other_, alpha); |
| 1945 | } |
| 1946 | at::functionalization::impl::replace_(out, tmp_output); |
| 1947 | at::functionalization::impl::commit_update(out); |
| 1948 | at::functionalization::impl::sync(out); |
| 1949 | return out; |
| 1950 | } |
| 1951 | } |
| 1952 | |
| 1953 | at::Tensor & _add_relu__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 1954 | if (true) { |
| 1955 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 1956 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 1957 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 1958 | auto self_meta = to_meta(self); |
| 1959 | auto other_meta = to_meta(other); |
| 1960 | at::AutoDispatchSkipFunctionalize func_guard; |
| 1961 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 1962 | at::_ops::_add_relu__Tensor::call(self_meta, other_meta, alpha); |
| 1963 | } |
| 1964 | |
| 1965 | at::Tensor self_; |
| 1966 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 1967 | at::functionalization::impl::sync(self); |
| 1968 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 1969 | } else { |
| 1970 | self_ = self; |
| 1971 | } |
| 1972 | |
| 1973 | at::Tensor other_; |
| 1974 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 1975 | at::functionalization::impl::sync(other); |
| 1976 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 1977 | } else { |
| 1978 | other_ = other; |
| 1979 | } |
| 1980 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 1981 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 1982 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 1983 | TORCH_INTERNAL_ASSERT(false, |
| 1984 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 1985 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 1986 | } else { |
| 1987 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 1988 | at::AutoDispatchSkipFunctionalize guard; |
| 1989 | at::Tensor tmp_output = at::_ops::_add_relu__Tensor::call(self_, other_, alpha); |
| 1990 | return self;; |
| 1991 | } |
| 1992 | } else { |
| 1993 | at::Tensor tmp_output; |
| 1994 | { |
| 1995 | at::AutoDispatchSkipFunctionalize guard; |
| 1996 | tmp_output = at::_ops::_add_relu_Tensor::call(self_, other_, alpha); |
| 1997 | } |
| 1998 | at::functionalization::impl::replace_(self, tmp_output); |
| 1999 | at::functionalization::impl::commit_update(self); |
| 2000 | at::functionalization::impl::sync(self); |
| 2001 | return self; |
| 2002 | } |
| 2003 | } |
| 2004 | |
| 2005 | at::Tensor & _add_relu_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha, at::Tensor & out) { |
| 2006 | if (false) { |
| 2007 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2008 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2009 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2010 | auto self_meta = to_meta(self); |
| 2011 | auto out_meta = to_meta(out); |
| 2012 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2013 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2014 | at::_ops::_add_relu_Scalar_out::call(self_meta, other, alpha, out_meta); |
| 2015 | } |
| 2016 | |
| 2017 | at::Tensor self_; |
| 2018 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2019 | at::functionalization::impl::sync(self); |
| 2020 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2021 | } else { |
| 2022 | self_ = self; |
| 2023 | } |
| 2024 | |
| 2025 | at::Tensor out_; |
| 2026 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2027 | at::functionalization::impl::sync(out); |
| 2028 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2029 | } else { |
| 2030 | out_ = out; |
| 2031 | } |
| 2032 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2033 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 2034 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2035 | TORCH_INTERNAL_ASSERT(false, |
| 2036 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2037 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2038 | } else { |
| 2039 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2040 | at::AutoDispatchSkipFunctionalize guard; |
| 2041 | at::Tensor tmp_output = at::_ops::_add_relu_Scalar_out::call(self_, other, alpha, out_); |
| 2042 | return out;; |
| 2043 | } |
| 2044 | } else { |
| 2045 | at::Tensor tmp_output; |
| 2046 | { |
| 2047 | at::AutoDispatchSkipFunctionalize guard; |
| 2048 | tmp_output = at::_ops::_add_relu_Scalar::call(self_, other, alpha); |
| 2049 | } |
| 2050 | at::functionalization::impl::replace_(out, tmp_output); |
| 2051 | at::functionalization::impl::commit_update(out); |
| 2052 | at::functionalization::impl::sync(out); |
| 2053 | return out; |
| 2054 | } |
| 2055 | } |
| 2056 | |
| 2057 | at::Tensor & _add_relu__Scalar(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha) { |
| 2058 | if (true) { |
| 2059 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2060 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2061 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2062 | auto self_meta = to_meta(self); |
| 2063 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2064 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2065 | at::_ops::_add_relu__Scalar::call(self_meta, other, alpha); |
| 2066 | } |
| 2067 | |
| 2068 | at::Tensor self_; |
| 2069 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2070 | at::functionalization::impl::sync(self); |
| 2071 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2072 | } else { |
| 2073 | self_ = self; |
| 2074 | } |
| 2075 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 2076 | if ((false)) { |
| 2077 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2078 | TORCH_INTERNAL_ASSERT(false, |
| 2079 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2080 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2081 | } else { |
| 2082 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2083 | at::AutoDispatchSkipFunctionalize guard; |
| 2084 | at::Tensor tmp_output = at::_ops::_add_relu__Scalar::call(self_, other, alpha); |
| 2085 | return self;; |
| 2086 | } |
| 2087 | } else { |
| 2088 | at::Tensor tmp_output; |
| 2089 | { |
| 2090 | at::AutoDispatchSkipFunctionalize guard; |
| 2091 | tmp_output = at::_ops::_add_relu_Scalar::call(self_, other, alpha); |
| 2092 | } |
| 2093 | at::functionalization::impl::replace_(self, tmp_output); |
| 2094 | at::functionalization::impl::commit_update(self); |
| 2095 | at::functionalization::impl::sync(self); |
| 2096 | return self; |
| 2097 | } |
| 2098 | } |
| 2099 | |
| 2100 | at::Tensor & any_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & out) { |
| 2101 | if (false) { |
| 2102 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2103 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2104 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2105 | auto self_meta = to_meta(self); |
| 2106 | auto out_meta = to_meta(out); |
| 2107 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2108 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2109 | at::_ops::any_out::call(self_meta, dim, keepdim, out_meta); |
| 2110 | } |
| 2111 | |
| 2112 | at::Tensor self_; |
| 2113 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2114 | at::functionalization::impl::sync(self); |
| 2115 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2116 | } else { |
| 2117 | self_ = self; |
| 2118 | } |
| 2119 | |
| 2120 | at::Tensor out_; |
| 2121 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2122 | at::functionalization::impl::sync(out); |
| 2123 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2124 | } else { |
| 2125 | out_ = out; |
| 2126 | } |
| 2127 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2128 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 2129 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2130 | TORCH_INTERNAL_ASSERT(false, |
| 2131 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2132 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2133 | } else { |
| 2134 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2135 | at::AutoDispatchSkipFunctionalize guard; |
| 2136 | at::Tensor tmp_output = at::_ops::any_out::call(self_, dim, keepdim, out_); |
| 2137 | return out;; |
| 2138 | } |
| 2139 | } else { |
| 2140 | at::Tensor tmp_output; |
| 2141 | { |
| 2142 | at::AutoDispatchSkipFunctionalize guard; |
| 2143 | tmp_output = at::_ops::any_dim::call(self_, dim, keepdim); |
| 2144 | } |
| 2145 | at::functionalization::impl::replace_(out, tmp_output); |
| 2146 | at::functionalization::impl::commit_update(out); |
| 2147 | at::functionalization::impl::sync(out); |
| 2148 | return out; |
| 2149 | } |
| 2150 | } |
| 2151 | |
| 2152 | at::Tensor & any_out_dimname_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & out) { |
| 2153 | if (false) { |
| 2154 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2155 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2156 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2157 | auto self_meta = to_meta(self); |
| 2158 | auto out_meta = to_meta(out); |
| 2159 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2160 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2161 | at::_ops::any_dimname_out::call(self_meta, dim, keepdim, out_meta); |
| 2162 | } |
| 2163 | |
| 2164 | at::Tensor self_; |
| 2165 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2166 | at::functionalization::impl::sync(self); |
| 2167 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2168 | } else { |
| 2169 | self_ = self; |
| 2170 | } |
| 2171 | |
| 2172 | at::Tensor out_; |
| 2173 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2174 | at::functionalization::impl::sync(out); |
| 2175 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2176 | } else { |
| 2177 | out_ = out; |
| 2178 | } |
| 2179 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2180 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 2181 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2182 | TORCH_INTERNAL_ASSERT(false, |
| 2183 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2184 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2185 | } else { |
| 2186 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2187 | at::AutoDispatchSkipFunctionalize guard; |
| 2188 | at::Tensor tmp_output = at::_ops::any_dimname_out::call(self_, dim, keepdim, out_); |
| 2189 | return out;; |
| 2190 | } |
| 2191 | } else { |
| 2192 | at::Tensor tmp_output; |
| 2193 | { |
| 2194 | at::AutoDispatchSkipFunctionalize guard; |
| 2195 | tmp_output = at::_ops::any_dimname::call(self_, dim, keepdim); |
| 2196 | } |
| 2197 | at::functionalization::impl::replace_(out, tmp_output); |
| 2198 | at::functionalization::impl::commit_update(out); |
| 2199 | at::functionalization::impl::sync(out); |
| 2200 | return out; |
| 2201 | } |
| 2202 | } |
| 2203 | |
| 2204 | at::Tensor & argmin_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim, at::Tensor & out) { |
| 2205 | if (false) { |
| 2206 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2207 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2208 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2209 | auto self_meta = to_meta(self); |
| 2210 | auto out_meta = to_meta(out); |
| 2211 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2212 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2213 | at::_ops::argmin_out::call(self_meta, dim, keepdim, out_meta); |
| 2214 | } |
| 2215 | |
| 2216 | at::Tensor self_; |
| 2217 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2218 | at::functionalization::impl::sync(self); |
| 2219 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2220 | } else { |
| 2221 | self_ = self; |
| 2222 | } |
| 2223 | |
| 2224 | at::Tensor out_; |
| 2225 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2226 | at::functionalization::impl::sync(out); |
| 2227 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2228 | } else { |
| 2229 | out_ = out; |
| 2230 | } |
| 2231 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2232 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 2233 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2234 | TORCH_INTERNAL_ASSERT(false, |
| 2235 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2236 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2237 | } else { |
| 2238 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2239 | at::AutoDispatchSkipFunctionalize guard; |
| 2240 | at::Tensor tmp_output = at::_ops::argmin_out::call(self_, dim, keepdim, out_); |
| 2241 | return out;; |
| 2242 | } |
| 2243 | } else { |
| 2244 | at::Tensor tmp_output; |
| 2245 | { |
| 2246 | at::AutoDispatchSkipFunctionalize guard; |
| 2247 | tmp_output = at::_ops::argmin::call(self_, dim, keepdim); |
| 2248 | } |
| 2249 | at::functionalization::impl::replace_(out, tmp_output); |
| 2250 | at::functionalization::impl::commit_update(out); |
| 2251 | at::functionalization::impl::sync(out); |
| 2252 | return out; |
| 2253 | } |
| 2254 | } |
| 2255 | |
| 2256 | at::Tensor & acosh_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 2257 | if (false) { |
| 2258 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2259 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2260 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2261 | auto self_meta = to_meta(self); |
| 2262 | auto out_meta = to_meta(out); |
| 2263 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2264 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2265 | at::_ops::acosh_out::call(self_meta, out_meta); |
| 2266 | } |
| 2267 | |
| 2268 | at::Tensor self_; |
| 2269 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2270 | at::functionalization::impl::sync(self); |
| 2271 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2272 | } else { |
| 2273 | self_ = self; |
| 2274 | } |
| 2275 | |
| 2276 | at::Tensor out_; |
| 2277 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2278 | at::functionalization::impl::sync(out); |
| 2279 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2280 | } else { |
| 2281 | out_ = out; |
| 2282 | } |
| 2283 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2284 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 2285 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2286 | TORCH_INTERNAL_ASSERT(false, |
| 2287 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2288 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2289 | } else { |
| 2290 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2291 | at::AutoDispatchSkipFunctionalize guard; |
| 2292 | at::Tensor tmp_output = at::_ops::acosh_out::call(self_, out_); |
| 2293 | return out;; |
| 2294 | } |
| 2295 | } else { |
| 2296 | at::Tensor tmp_output; |
| 2297 | { |
| 2298 | at::AutoDispatchSkipFunctionalize guard; |
| 2299 | tmp_output = at::_ops::acosh::call(self_); |
| 2300 | } |
| 2301 | at::functionalization::impl::replace_(out, tmp_output); |
| 2302 | at::functionalization::impl::commit_update(out); |
| 2303 | at::functionalization::impl::sync(out); |
| 2304 | return out; |
| 2305 | } |
| 2306 | } |
| 2307 | |
| 2308 | at::Tensor & acosh_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 2309 | if (true) { |
| 2310 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2311 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2312 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2313 | auto self_meta = to_meta(self); |
| 2314 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2315 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2316 | at::_ops::acosh_::call(self_meta); |
| 2317 | } |
| 2318 | |
| 2319 | at::Tensor self_; |
| 2320 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2321 | at::functionalization::impl::sync(self); |
| 2322 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2323 | } else { |
| 2324 | self_ = self; |
| 2325 | } |
| 2326 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 2327 | if ((false)) { |
| 2328 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2329 | TORCH_INTERNAL_ASSERT(false, |
| 2330 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2331 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2332 | } else { |
| 2333 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2334 | at::AutoDispatchSkipFunctionalize guard; |
| 2335 | at::Tensor tmp_output = at::_ops::acosh_::call(self_); |
| 2336 | return self;; |
| 2337 | } |
| 2338 | } else { |
| 2339 | at::Tensor tmp_output; |
| 2340 | { |
| 2341 | at::AutoDispatchSkipFunctionalize guard; |
| 2342 | tmp_output = at::_ops::acosh::call(self_); |
| 2343 | } |
| 2344 | at::functionalization::impl::replace_(self, tmp_output); |
| 2345 | at::functionalization::impl::commit_update(self); |
| 2346 | at::functionalization::impl::sync(self); |
| 2347 | return self; |
| 2348 | } |
| 2349 | } |
| 2350 | |
| 2351 | at::Tensor & arcsin_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 2352 | if (false) { |
| 2353 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2354 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2355 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2356 | auto self_meta = to_meta(self); |
| 2357 | auto out_meta = to_meta(out); |
| 2358 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2359 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2360 | at::_ops::arcsin_out::call(self_meta, out_meta); |
| 2361 | } |
| 2362 | |
| 2363 | at::Tensor self_; |
| 2364 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2365 | at::functionalization::impl::sync(self); |
| 2366 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2367 | } else { |
| 2368 | self_ = self; |
| 2369 | } |
| 2370 | |
| 2371 | at::Tensor out_; |
| 2372 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2373 | at::functionalization::impl::sync(out); |
| 2374 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2375 | } else { |
| 2376 | out_ = out; |
| 2377 | } |
| 2378 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2379 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 2380 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2381 | TORCH_INTERNAL_ASSERT(false, |
| 2382 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2383 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2384 | } else { |
| 2385 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2386 | at::AutoDispatchSkipFunctionalize guard; |
| 2387 | at::Tensor tmp_output = at::_ops::arcsin_out::call(self_, out_); |
| 2388 | return out;; |
| 2389 | } |
| 2390 | } else { |
| 2391 | at::Tensor tmp_output; |
| 2392 | { |
| 2393 | at::AutoDispatchSkipFunctionalize guard; |
| 2394 | tmp_output = at::_ops::arcsin::call(self_); |
| 2395 | } |
| 2396 | at::functionalization::impl::replace_(out, tmp_output); |
| 2397 | at::functionalization::impl::commit_update(out); |
| 2398 | at::functionalization::impl::sync(out); |
| 2399 | return out; |
| 2400 | } |
| 2401 | } |
| 2402 | |
| 2403 | at::Tensor & arcsin_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 2404 | if (true) { |
| 2405 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2406 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2407 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2408 | auto self_meta = to_meta(self); |
| 2409 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2410 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2411 | at::_ops::arcsin_::call(self_meta); |
| 2412 | } |
| 2413 | |
| 2414 | at::Tensor self_; |
| 2415 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2416 | at::functionalization::impl::sync(self); |
| 2417 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2418 | } else { |
| 2419 | self_ = self; |
| 2420 | } |
| 2421 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 2422 | if ((false)) { |
| 2423 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2424 | TORCH_INTERNAL_ASSERT(false, |
| 2425 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2426 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2427 | } else { |
| 2428 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2429 | at::AutoDispatchSkipFunctionalize guard; |
| 2430 | at::Tensor tmp_output = at::_ops::arcsin_::call(self_); |
| 2431 | return self;; |
| 2432 | } |
| 2433 | } else { |
| 2434 | at::Tensor tmp_output; |
| 2435 | { |
| 2436 | at::AutoDispatchSkipFunctionalize guard; |
| 2437 | tmp_output = at::_ops::arcsin::call(self_); |
| 2438 | } |
| 2439 | at::functionalization::impl::replace_(self, tmp_output); |
| 2440 | at::functionalization::impl::commit_update(self); |
| 2441 | at::functionalization::impl::sync(self); |
| 2442 | return self; |
| 2443 | } |
| 2444 | } |
| 2445 | |
| 2446 | at::Tensor & arctan_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 2447 | if (false) { |
| 2448 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2449 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2450 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2451 | auto self_meta = to_meta(self); |
| 2452 | auto out_meta = to_meta(out); |
| 2453 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2454 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2455 | at::_ops::arctan_out::call(self_meta, out_meta); |
| 2456 | } |
| 2457 | |
| 2458 | at::Tensor self_; |
| 2459 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2460 | at::functionalization::impl::sync(self); |
| 2461 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2462 | } else { |
| 2463 | self_ = self; |
| 2464 | } |
| 2465 | |
| 2466 | at::Tensor out_; |
| 2467 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2468 | at::functionalization::impl::sync(out); |
| 2469 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2470 | } else { |
| 2471 | out_ = out; |
| 2472 | } |
| 2473 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2474 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 2475 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2476 | TORCH_INTERNAL_ASSERT(false, |
| 2477 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2478 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2479 | } else { |
| 2480 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2481 | at::AutoDispatchSkipFunctionalize guard; |
| 2482 | at::Tensor tmp_output = at::_ops::arctan_out::call(self_, out_); |
| 2483 | return out;; |
| 2484 | } |
| 2485 | } else { |
| 2486 | at::Tensor tmp_output; |
| 2487 | { |
| 2488 | at::AutoDispatchSkipFunctionalize guard; |
| 2489 | tmp_output = at::_ops::arctan::call(self_); |
| 2490 | } |
| 2491 | at::functionalization::impl::replace_(out, tmp_output); |
| 2492 | at::functionalization::impl::commit_update(out); |
| 2493 | at::functionalization::impl::sync(out); |
| 2494 | return out; |
| 2495 | } |
| 2496 | } |
| 2497 | |
| 2498 | at::Tensor & arctan_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 2499 | if (true) { |
| 2500 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2501 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2502 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2503 | auto self_meta = to_meta(self); |
| 2504 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2505 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2506 | at::_ops::arctan_::call(self_meta); |
| 2507 | } |
| 2508 | |
| 2509 | at::Tensor self_; |
| 2510 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2511 | at::functionalization::impl::sync(self); |
| 2512 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2513 | } else { |
| 2514 | self_ = self; |
| 2515 | } |
| 2516 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 2517 | if ((false)) { |
| 2518 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2519 | TORCH_INTERNAL_ASSERT(false, |
| 2520 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2521 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2522 | } else { |
| 2523 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2524 | at::AutoDispatchSkipFunctionalize guard; |
| 2525 | at::Tensor tmp_output = at::_ops::arctan_::call(self_); |
| 2526 | return self;; |
| 2527 | } |
| 2528 | } else { |
| 2529 | at::Tensor tmp_output; |
| 2530 | { |
| 2531 | at::AutoDispatchSkipFunctionalize guard; |
| 2532 | tmp_output = at::_ops::arctan::call(self_); |
| 2533 | } |
| 2534 | at::functionalization::impl::replace_(self, tmp_output); |
| 2535 | at::functionalization::impl::commit_update(self); |
| 2536 | at::functionalization::impl::sync(self); |
| 2537 | return self; |
| 2538 | } |
| 2539 | } |
| 2540 | |
| 2541 | at::Tensor & bincount_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional<at::Tensor> & weights, int64_t minlength, at::Tensor & out) { |
| 2542 | if (false) { |
| 2543 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2544 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2545 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2546 | auto self_meta = to_meta(self); |
| 2547 | auto weights_meta = to_meta(weights); |
| 2548 | auto out_meta = to_meta(out); |
| 2549 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2550 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2551 | at::_ops::bincount_out::call(self_meta, weights_meta, minlength, out_meta); |
| 2552 | } |
| 2553 | |
| 2554 | at::Tensor self_; |
| 2555 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2556 | at::functionalization::impl::sync(self); |
| 2557 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2558 | } else { |
| 2559 | self_ = self; |
| 2560 | } |
| 2561 | |
| 2562 | c10::optional<at::Tensor> weights_; |
| 2563 | if (at::functionalization::impl::isFunctionalTensor(weights)) { |
| 2564 | at::functionalization::impl::sync(weights); |
| 2565 | weights_ = at::functionalization::impl::from_functional_tensor(weights); |
| 2566 | } else { |
| 2567 | weights_ = weights; |
| 2568 | } |
| 2569 | |
| 2570 | at::Tensor out_; |
| 2571 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2572 | at::functionalization::impl::sync(out); |
| 2573 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2574 | } else { |
| 2575 | out_ = out; |
| 2576 | } |
| 2577 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2578 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(weights))) { |
| 2579 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2580 | TORCH_INTERNAL_ASSERT(false, |
| 2581 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2582 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2583 | } else { |
| 2584 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2585 | at::AutoDispatchSkipFunctionalize guard; |
| 2586 | at::Tensor tmp_output = at::_ops::bincount_out::call(self_, weights_, minlength, out_); |
| 2587 | return out;; |
| 2588 | } |
| 2589 | } else { |
| 2590 | at::Tensor tmp_output; |
| 2591 | { |
| 2592 | at::AutoDispatchSkipFunctionalize guard; |
| 2593 | tmp_output = at::_ops::bincount::call(self_, weights_, minlength); |
| 2594 | } |
| 2595 | at::functionalization::impl::replace_(out, tmp_output); |
| 2596 | at::functionalization::impl::commit_update(out); |
| 2597 | at::functionalization::impl::sync(out); |
| 2598 | return out; |
| 2599 | } |
| 2600 | } |
| 2601 | |
| 2602 | at::Tensor & copysign_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 2603 | if (false) { |
| 2604 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2605 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2606 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2607 | auto self_meta = to_meta(self); |
| 2608 | auto other_meta = to_meta(other); |
| 2609 | auto out_meta = to_meta(out); |
| 2610 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2611 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2612 | at::_ops::copysign_out::call(self_meta, other_meta, out_meta); |
| 2613 | } |
| 2614 | |
| 2615 | at::Tensor self_; |
| 2616 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2617 | at::functionalization::impl::sync(self); |
| 2618 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2619 | } else { |
| 2620 | self_ = self; |
| 2621 | } |
| 2622 | |
| 2623 | at::Tensor other_; |
| 2624 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 2625 | at::functionalization::impl::sync(other); |
| 2626 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 2627 | } else { |
| 2628 | other_ = other; |
| 2629 | } |
| 2630 | |
| 2631 | at::Tensor out_; |
| 2632 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2633 | at::functionalization::impl::sync(out); |
| 2634 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2635 | } else { |
| 2636 | out_ = out; |
| 2637 | } |
| 2638 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2639 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 2640 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2641 | TORCH_INTERNAL_ASSERT(false, |
| 2642 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2643 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2644 | } else { |
| 2645 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2646 | at::AutoDispatchSkipFunctionalize guard; |
| 2647 | at::Tensor tmp_output = at::_ops::copysign_out::call(self_, other_, out_); |
| 2648 | return out;; |
| 2649 | } |
| 2650 | } else { |
| 2651 | at::Tensor tmp_output; |
| 2652 | { |
| 2653 | at::AutoDispatchSkipFunctionalize guard; |
| 2654 | tmp_output = at::_ops::copysign_Tensor::call(self_, other_); |
| 2655 | } |
| 2656 | at::functionalization::impl::replace_(out, tmp_output); |
| 2657 | at::functionalization::impl::commit_update(out); |
| 2658 | at::functionalization::impl::sync(out); |
| 2659 | return out; |
| 2660 | } |
| 2661 | } |
| 2662 | |
| 2663 | at::Tensor & copysign__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 2664 | if (true) { |
| 2665 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2666 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2667 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2668 | auto self_meta = to_meta(self); |
| 2669 | auto other_meta = to_meta(other); |
| 2670 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2671 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2672 | at::_ops::copysign__Tensor::call(self_meta, other_meta); |
| 2673 | } |
| 2674 | |
| 2675 | at::Tensor self_; |
| 2676 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2677 | at::functionalization::impl::sync(self); |
| 2678 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2679 | } else { |
| 2680 | self_ = self; |
| 2681 | } |
| 2682 | |
| 2683 | at::Tensor other_; |
| 2684 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 2685 | at::functionalization::impl::sync(other); |
| 2686 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 2687 | } else { |
| 2688 | other_ = other; |
| 2689 | } |
| 2690 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 2691 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 2692 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2693 | TORCH_INTERNAL_ASSERT(false, |
| 2694 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2695 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2696 | } else { |
| 2697 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2698 | at::AutoDispatchSkipFunctionalize guard; |
| 2699 | at::Tensor tmp_output = at::_ops::copysign__Tensor::call(self_, other_); |
| 2700 | return self;; |
| 2701 | } |
| 2702 | } else { |
| 2703 | at::Tensor tmp_output; |
| 2704 | { |
| 2705 | at::AutoDispatchSkipFunctionalize guard; |
| 2706 | tmp_output = at::_ops::copysign_Tensor::call(self_, other_); |
| 2707 | } |
| 2708 | at::functionalization::impl::replace_(self, tmp_output); |
| 2709 | at::functionalization::impl::commit_update(self); |
| 2710 | at::functionalization::impl::sync(self); |
| 2711 | return self; |
| 2712 | } |
| 2713 | } |
| 2714 | |
| 2715 | at::Tensor & copysign_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 2716 | if (false) { |
| 2717 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2718 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2719 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2720 | auto self_meta = to_meta(self); |
| 2721 | auto out_meta = to_meta(out); |
| 2722 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2723 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2724 | at::_ops::copysign_Scalar_out::call(self_meta, other, out_meta); |
| 2725 | } |
| 2726 | |
| 2727 | at::Tensor self_; |
| 2728 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2729 | at::functionalization::impl::sync(self); |
| 2730 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2731 | } else { |
| 2732 | self_ = self; |
| 2733 | } |
| 2734 | |
| 2735 | at::Tensor out_; |
| 2736 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2737 | at::functionalization::impl::sync(out); |
| 2738 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2739 | } else { |
| 2740 | out_ = out; |
| 2741 | } |
| 2742 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2743 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 2744 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2745 | TORCH_INTERNAL_ASSERT(false, |
| 2746 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2747 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2748 | } else { |
| 2749 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2750 | at::AutoDispatchSkipFunctionalize guard; |
| 2751 | at::Tensor tmp_output = at::_ops::copysign_Scalar_out::call(self_, other, out_); |
| 2752 | return out;; |
| 2753 | } |
| 2754 | } else { |
| 2755 | at::Tensor tmp_output; |
| 2756 | { |
| 2757 | at::AutoDispatchSkipFunctionalize guard; |
| 2758 | tmp_output = at::_ops::copysign_Scalar::call(self_, other); |
| 2759 | } |
| 2760 | at::functionalization::impl::replace_(out, tmp_output); |
| 2761 | at::functionalization::impl::commit_update(out); |
| 2762 | at::functionalization::impl::sync(out); |
| 2763 | return out; |
| 2764 | } |
| 2765 | } |
| 2766 | |
| 2767 | at::Tensor & copysign__Scalar(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { |
| 2768 | if (true) { |
| 2769 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2770 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2771 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2772 | auto self_meta = to_meta(self); |
| 2773 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2774 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2775 | at::_ops::copysign__Scalar::call(self_meta, other); |
| 2776 | } |
| 2777 | |
| 2778 | at::Tensor self_; |
| 2779 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2780 | at::functionalization::impl::sync(self); |
| 2781 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2782 | } else { |
| 2783 | self_ = self; |
| 2784 | } |
| 2785 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 2786 | if ((false)) { |
| 2787 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2788 | TORCH_INTERNAL_ASSERT(false, |
| 2789 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2790 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2791 | } else { |
| 2792 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2793 | at::AutoDispatchSkipFunctionalize guard; |
| 2794 | at::Tensor tmp_output = at::_ops::copysign__Scalar::call(self_, other); |
| 2795 | return self;; |
| 2796 | } |
| 2797 | } else { |
| 2798 | at::Tensor tmp_output; |
| 2799 | { |
| 2800 | at::AutoDispatchSkipFunctionalize guard; |
| 2801 | tmp_output = at::_ops::copysign_Scalar::call(self_, other); |
| 2802 | } |
| 2803 | at::functionalization::impl::replace_(self, tmp_output); |
| 2804 | at::functionalization::impl::commit_update(self); |
| 2805 | at::functionalization::impl::sync(self); |
| 2806 | return self; |
| 2807 | } |
| 2808 | } |
| 2809 | |
| 2810 | at::Tensor & logical_or_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 2811 | if (false) { |
| 2812 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2813 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2814 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2815 | auto self_meta = to_meta(self); |
| 2816 | auto other_meta = to_meta(other); |
| 2817 | auto out_meta = to_meta(out); |
| 2818 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2819 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2820 | at::_ops::logical_or_out::call(self_meta, other_meta, out_meta); |
| 2821 | } |
| 2822 | |
| 2823 | at::Tensor self_; |
| 2824 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2825 | at::functionalization::impl::sync(self); |
| 2826 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2827 | } else { |
| 2828 | self_ = self; |
| 2829 | } |
| 2830 | |
| 2831 | at::Tensor other_; |
| 2832 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 2833 | at::functionalization::impl::sync(other); |
| 2834 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 2835 | } else { |
| 2836 | other_ = other; |
| 2837 | } |
| 2838 | |
| 2839 | at::Tensor out_; |
| 2840 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2841 | at::functionalization::impl::sync(out); |
| 2842 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2843 | } else { |
| 2844 | out_ = out; |
| 2845 | } |
| 2846 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2847 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 2848 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2849 | TORCH_INTERNAL_ASSERT(false, |
| 2850 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2851 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2852 | } else { |
| 2853 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2854 | at::AutoDispatchSkipFunctionalize guard; |
| 2855 | at::Tensor tmp_output = at::_ops::logical_or_out::call(self_, other_, out_); |
| 2856 | return out;; |
| 2857 | } |
| 2858 | } else { |
| 2859 | at::Tensor tmp_output; |
| 2860 | { |
| 2861 | at::AutoDispatchSkipFunctionalize guard; |
| 2862 | tmp_output = at::_ops::logical_or::call(self_, other_); |
| 2863 | } |
| 2864 | at::functionalization::impl::replace_(out, tmp_output); |
| 2865 | at::functionalization::impl::commit_update(out); |
| 2866 | at::functionalization::impl::sync(out); |
| 2867 | return out; |
| 2868 | } |
| 2869 | } |
| 2870 | |
| 2871 | at::Tensor & logical_or_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 2872 | if (true) { |
| 2873 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2874 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2875 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2876 | auto self_meta = to_meta(self); |
| 2877 | auto other_meta = to_meta(other); |
| 2878 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2879 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2880 | at::_ops::logical_or_::call(self_meta, other_meta); |
| 2881 | } |
| 2882 | |
| 2883 | at::Tensor self_; |
| 2884 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2885 | at::functionalization::impl::sync(self); |
| 2886 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2887 | } else { |
| 2888 | self_ = self; |
| 2889 | } |
| 2890 | |
| 2891 | at::Tensor other_; |
| 2892 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 2893 | at::functionalization::impl::sync(other); |
| 2894 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 2895 | } else { |
| 2896 | other_ = other; |
| 2897 | } |
| 2898 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 2899 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 2900 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2901 | TORCH_INTERNAL_ASSERT(false, |
| 2902 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2903 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2904 | } else { |
| 2905 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2906 | at::AutoDispatchSkipFunctionalize guard; |
| 2907 | at::Tensor tmp_output = at::_ops::logical_or_::call(self_, other_); |
| 2908 | return self;; |
| 2909 | } |
| 2910 | } else { |
| 2911 | at::Tensor tmp_output; |
| 2912 | { |
| 2913 | at::AutoDispatchSkipFunctionalize guard; |
| 2914 | tmp_output = at::_ops::logical_or::call(self_, other_); |
| 2915 | } |
| 2916 | at::functionalization::impl::replace_(self, tmp_output); |
| 2917 | at::functionalization::impl::commit_update(self); |
| 2918 | at::functionalization::impl::sync(self); |
| 2919 | return self; |
| 2920 | } |
| 2921 | } |
| 2922 | |
| 2923 | at::Tensor & cat_out_out(c10::DispatchKeySet dispatchKeySet, const at::ITensorListRef & tensors, int64_t dim, at::Tensor & out) { |
| 2924 | if (false) { |
| 2925 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2926 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2927 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2928 | auto tensors_meta = to_meta(tensors); |
| 2929 | auto out_meta = to_meta(out); |
| 2930 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2931 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2932 | at::_ops::cat_out::call(tensors_meta, dim, out_meta); |
| 2933 | } |
| 2934 | |
| 2935 | ::std::vector<at::Tensor> tensors_; |
| 2936 | if (at::functionalization::impl::isFunctionalTensor(tensors)) { |
| 2937 | at::functionalization::impl::sync(tensors); |
| 2938 | tensors_ = at::functionalization::impl::from_functional_tensor(tensors); |
| 2939 | } else { |
| 2940 | tensors_ = {tensors.begin(), tensors.end()}; |
| 2941 | } |
| 2942 | |
| 2943 | at::Tensor out_; |
| 2944 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2945 | at::functionalization::impl::sync(out); |
| 2946 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2947 | } else { |
| 2948 | out_ = out; |
| 2949 | } |
| 2950 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2951 | if ((false || at::functionalization::impl::isFunctionalTensor(tensors))) { |
| 2952 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2953 | TORCH_INTERNAL_ASSERT(false, |
| 2954 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2955 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2956 | } else { |
| 2957 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2958 | at::AutoDispatchSkipFunctionalize guard; |
| 2959 | at::Tensor tmp_output = at::_ops::cat_out::call(tensors_, dim, out_); |
| 2960 | return out;; |
| 2961 | } |
| 2962 | } else { |
| 2963 | at::Tensor tmp_output; |
| 2964 | { |
| 2965 | at::AutoDispatchSkipFunctionalize guard; |
| 2966 | tmp_output = at::_ops::cat::call(tensors_, dim); |
| 2967 | } |
| 2968 | at::functionalization::impl::replace_(out, tmp_output); |
| 2969 | at::functionalization::impl::commit_update(out); |
| 2970 | at::functionalization::impl::sync(out); |
| 2971 | return out; |
| 2972 | } |
| 2973 | } |
| 2974 | |
| 2975 | at::Tensor & cat_out_names_out(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Dimname dim, at::Tensor & out) { |
| 2976 | if (false) { |
| 2977 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2978 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2979 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2980 | auto tensors_meta = to_meta(tensors); |
| 2981 | auto out_meta = to_meta(out); |
| 2982 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2983 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2984 | at::_ops::cat_names_out::call(tensors_meta, dim, out_meta); |
| 2985 | } |
| 2986 | |
| 2987 | ::std::vector<at::Tensor> tensors_; |
| 2988 | if (at::functionalization::impl::isFunctionalTensor(tensors)) { |
| 2989 | at::functionalization::impl::sync(tensors); |
| 2990 | tensors_ = at::functionalization::impl::from_functional_tensor(tensors); |
| 2991 | } else { |
| 2992 | tensors_ = tensors.vec(); |
| 2993 | } |
| 2994 | |
| 2995 | at::Tensor out_; |
| 2996 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2997 | at::functionalization::impl::sync(out); |
| 2998 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2999 | } else { |
| 3000 | out_ = out; |
| 3001 | } |
| 3002 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3003 | if ((false || at::functionalization::impl::isFunctionalTensor(tensors))) { |
| 3004 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3005 | TORCH_INTERNAL_ASSERT(false, |
| 3006 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3007 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3008 | } else { |
| 3009 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3010 | at::AutoDispatchSkipFunctionalize guard; |
| 3011 | at::Tensor tmp_output = at::_ops::cat_names_out::call(tensors_, dim, out_); |
| 3012 | return out;; |
| 3013 | } |
| 3014 | } else { |
| 3015 | at::Tensor tmp_output; |
| 3016 | { |
| 3017 | at::AutoDispatchSkipFunctionalize guard; |
| 3018 | tmp_output = at::_ops::cat_names::call(tensors_, dim); |
| 3019 | } |
| 3020 | at::functionalization::impl::replace_(out, tmp_output); |
| 3021 | at::functionalization::impl::commit_update(out); |
| 3022 | at::functionalization::impl::sync(out); |
| 3023 | return out; |
| 3024 | } |
| 3025 | } |
| 3026 | |
| 3027 | at::Tensor & concat_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, int64_t dim, at::Tensor & out) { |
| 3028 | if (false) { |
| 3029 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3030 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3031 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3032 | auto tensors_meta = to_meta(tensors); |
| 3033 | auto out_meta = to_meta(out); |
| 3034 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3035 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3036 | at::_ops::concat_out::call(tensors_meta, dim, out_meta); |
| 3037 | } |
| 3038 | |
| 3039 | ::std::vector<at::Tensor> tensors_; |
| 3040 | if (at::functionalization::impl::isFunctionalTensor(tensors)) { |
| 3041 | at::functionalization::impl::sync(tensors); |
| 3042 | tensors_ = at::functionalization::impl::from_functional_tensor(tensors); |
| 3043 | } else { |
| 3044 | tensors_ = tensors.vec(); |
| 3045 | } |
| 3046 | |
| 3047 | at::Tensor out_; |
| 3048 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3049 | at::functionalization::impl::sync(out); |
| 3050 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3051 | } else { |
| 3052 | out_ = out; |
| 3053 | } |
| 3054 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3055 | if ((false || at::functionalization::impl::isFunctionalTensor(tensors))) { |
| 3056 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3057 | TORCH_INTERNAL_ASSERT(false, |
| 3058 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3059 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3060 | } else { |
| 3061 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3062 | at::AutoDispatchSkipFunctionalize guard; |
| 3063 | at::Tensor tmp_output = at::_ops::concat_out::call(tensors_, dim, out_); |
| 3064 | return out;; |
| 3065 | } |
| 3066 | } else { |
| 3067 | at::Tensor tmp_output; |
| 3068 | { |
| 3069 | at::AutoDispatchSkipFunctionalize guard; |
| 3070 | tmp_output = at::_ops::concat::call(tensors_, dim); |
| 3071 | } |
| 3072 | at::functionalization::impl::replace_(out, tmp_output); |
| 3073 | at::functionalization::impl::commit_update(out); |
| 3074 | at::functionalization::impl::sync(out); |
| 3075 | return out; |
| 3076 | } |
| 3077 | } |
| 3078 | |
| 3079 | at::Tensor & concat_out_names_out(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Dimname dim, at::Tensor & out) { |
| 3080 | if (false) { |
| 3081 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3082 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3083 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3084 | auto tensors_meta = to_meta(tensors); |
| 3085 | auto out_meta = to_meta(out); |
| 3086 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3087 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3088 | at::_ops::concat_names_out::call(tensors_meta, dim, out_meta); |
| 3089 | } |
| 3090 | |
| 3091 | ::std::vector<at::Tensor> tensors_; |
| 3092 | if (at::functionalization::impl::isFunctionalTensor(tensors)) { |
| 3093 | at::functionalization::impl::sync(tensors); |
| 3094 | tensors_ = at::functionalization::impl::from_functional_tensor(tensors); |
| 3095 | } else { |
| 3096 | tensors_ = tensors.vec(); |
| 3097 | } |
| 3098 | |
| 3099 | at::Tensor out_; |
| 3100 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3101 | at::functionalization::impl::sync(out); |
| 3102 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3103 | } else { |
| 3104 | out_ = out; |
| 3105 | } |
| 3106 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3107 | if ((false || at::functionalization::impl::isFunctionalTensor(tensors))) { |
| 3108 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3109 | TORCH_INTERNAL_ASSERT(false, |
| 3110 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3111 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3112 | } else { |
| 3113 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3114 | at::AutoDispatchSkipFunctionalize guard; |
| 3115 | at::Tensor tmp_output = at::_ops::concat_names_out::call(tensors_, dim, out_); |
| 3116 | return out;; |
| 3117 | } |
| 3118 | } else { |
| 3119 | at::Tensor tmp_output; |
| 3120 | { |
| 3121 | at::AutoDispatchSkipFunctionalize guard; |
| 3122 | tmp_output = at::_ops::concat_names::call(tensors_, dim); |
| 3123 | } |
| 3124 | at::functionalization::impl::replace_(out, tmp_output); |
| 3125 | at::functionalization::impl::commit_update(out); |
| 3126 | at::functionalization::impl::sync(out); |
| 3127 | return out; |
| 3128 | } |
| 3129 | } |
| 3130 | |
| 3131 | at::Tensor & ceil_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 3132 | if (false) { |
| 3133 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3134 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3135 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3136 | auto self_meta = to_meta(self); |
| 3137 | auto out_meta = to_meta(out); |
| 3138 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3139 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3140 | at::_ops::ceil_out::call(self_meta, out_meta); |
| 3141 | } |
| 3142 | |
| 3143 | at::Tensor self_; |
| 3144 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3145 | at::functionalization::impl::sync(self); |
| 3146 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3147 | } else { |
| 3148 | self_ = self; |
| 3149 | } |
| 3150 | |
| 3151 | at::Tensor out_; |
| 3152 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3153 | at::functionalization::impl::sync(out); |
| 3154 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3155 | } else { |
| 3156 | out_ = out; |
| 3157 | } |
| 3158 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3159 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 3160 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3161 | TORCH_INTERNAL_ASSERT(false, |
| 3162 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3163 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3164 | } else { |
| 3165 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3166 | at::AutoDispatchSkipFunctionalize guard; |
| 3167 | at::Tensor tmp_output = at::_ops::ceil_out::call(self_, out_); |
| 3168 | return out;; |
| 3169 | } |
| 3170 | } else { |
| 3171 | at::Tensor tmp_output; |
| 3172 | { |
| 3173 | at::AutoDispatchSkipFunctionalize guard; |
| 3174 | tmp_output = at::_ops::ceil::call(self_); |
| 3175 | } |
| 3176 | at::functionalization::impl::replace_(out, tmp_output); |
| 3177 | at::functionalization::impl::commit_update(out); |
| 3178 | at::functionalization::impl::sync(out); |
| 3179 | return out; |
| 3180 | } |
| 3181 | } |
| 3182 | |
| 3183 | at::Tensor & ceil_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 3184 | if (true) { |
| 3185 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3186 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3187 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3188 | auto self_meta = to_meta(self); |
| 3189 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3190 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3191 | at::_ops::ceil_::call(self_meta); |
| 3192 | } |
| 3193 | |
| 3194 | at::Tensor self_; |
| 3195 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3196 | at::functionalization::impl::sync(self); |
| 3197 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3198 | } else { |
| 3199 | self_ = self; |
| 3200 | } |
| 3201 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 3202 | if ((false)) { |
| 3203 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3204 | TORCH_INTERNAL_ASSERT(false, |
| 3205 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3206 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3207 | } else { |
| 3208 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3209 | at::AutoDispatchSkipFunctionalize guard; |
| 3210 | at::Tensor tmp_output = at::_ops::ceil_::call(self_); |
| 3211 | return self;; |
| 3212 | } |
| 3213 | } else { |
| 3214 | at::Tensor tmp_output; |
| 3215 | { |
| 3216 | at::AutoDispatchSkipFunctionalize guard; |
| 3217 | tmp_output = at::_ops::ceil::call(self_); |
| 3218 | } |
| 3219 | at::functionalization::impl::replace_(self, tmp_output); |
| 3220 | at::functionalization::impl::commit_update(self); |
| 3221 | at::functionalization::impl::sync(self); |
| 3222 | return self; |
| 3223 | } |
| 3224 | } |
| 3225 | |
| 3226 | at::Tensor & polar_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & abs, const at::Tensor & angle, at::Tensor & out) { |
| 3227 | if (false) { |
| 3228 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3229 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3230 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3231 | auto abs_meta = to_meta(abs); |
| 3232 | auto angle_meta = to_meta(angle); |
| 3233 | auto out_meta = to_meta(out); |
| 3234 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3235 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3236 | at::_ops::polar_out::call(abs_meta, angle_meta, out_meta); |
| 3237 | } |
| 3238 | |
| 3239 | at::Tensor abs_; |
| 3240 | if (at::functionalization::impl::isFunctionalTensor(abs)) { |
| 3241 | at::functionalization::impl::sync(abs); |
| 3242 | abs_ = at::functionalization::impl::from_functional_tensor(abs); |
| 3243 | } else { |
| 3244 | abs_ = abs; |
| 3245 | } |
| 3246 | |
| 3247 | at::Tensor angle_; |
| 3248 | if (at::functionalization::impl::isFunctionalTensor(angle)) { |
| 3249 | at::functionalization::impl::sync(angle); |
| 3250 | angle_ = at::functionalization::impl::from_functional_tensor(angle); |
| 3251 | } else { |
| 3252 | angle_ = angle; |
| 3253 | } |
| 3254 | |
| 3255 | at::Tensor out_; |
| 3256 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3257 | at::functionalization::impl::sync(out); |
| 3258 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3259 | } else { |
| 3260 | out_ = out; |
| 3261 | } |
| 3262 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3263 | if ((false || at::functionalization::impl::isFunctionalTensor(abs) || at::functionalization::impl::isFunctionalTensor(angle))) { |
| 3264 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3265 | TORCH_INTERNAL_ASSERT(false, |
| 3266 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3267 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3268 | } else { |
| 3269 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3270 | at::AutoDispatchSkipFunctionalize guard; |
| 3271 | at::Tensor tmp_output = at::_ops::polar_out::call(abs_, angle_, out_); |
| 3272 | return out;; |
| 3273 | } |
| 3274 | } else { |
| 3275 | at::Tensor tmp_output; |
| 3276 | { |
| 3277 | at::AutoDispatchSkipFunctionalize guard; |
| 3278 | tmp_output = at::_ops::polar::call(abs_, angle_); |
| 3279 | } |
| 3280 | at::functionalization::impl::replace_(out, tmp_output); |
| 3281 | at::functionalization::impl::commit_update(out); |
| 3282 | at::functionalization::impl::sync(out); |
| 3283 | return out; |
| 3284 | } |
| 3285 | } |
| 3286 | |
| 3287 | at::Tensor & convolution_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::SymIntArrayRef padding, at::IntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, int64_t groups, at::Tensor & out) { |
| 3288 | if (false) { |
| 3289 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3290 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3291 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3292 | auto input_meta = to_meta(input); |
| 3293 | auto weight_meta = to_meta(weight); |
| 3294 | auto bias_meta = to_meta(bias); |
| 3295 | auto out_meta = to_meta(out); |
| 3296 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3297 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3298 | at::_ops::convolution_out::call(input_meta, weight_meta, bias_meta, stride, padding, dilation, transposed, output_padding, groups, out_meta); |
| 3299 | } |
| 3300 | |
| 3301 | at::Tensor input_; |
| 3302 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 3303 | at::functionalization::impl::sync(input); |
| 3304 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 3305 | } else { |
| 3306 | input_ = input; |
| 3307 | } |
| 3308 | |
| 3309 | at::Tensor weight_; |
| 3310 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 3311 | at::functionalization::impl::sync(weight); |
| 3312 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 3313 | } else { |
| 3314 | weight_ = weight; |
| 3315 | } |
| 3316 | |
| 3317 | c10::optional<at::Tensor> bias_; |
| 3318 | if (at::functionalization::impl::isFunctionalTensor(bias)) { |
| 3319 | at::functionalization::impl::sync(bias); |
| 3320 | bias_ = at::functionalization::impl::from_functional_tensor(bias); |
| 3321 | } else { |
| 3322 | bias_ = bias; |
| 3323 | } |
| 3324 | |
| 3325 | at::Tensor out_; |
| 3326 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3327 | at::functionalization::impl::sync(out); |
| 3328 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3329 | } else { |
| 3330 | out_ = out; |
| 3331 | } |
| 3332 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3333 | if ((false || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(bias))) { |
| 3334 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3335 | TORCH_INTERNAL_ASSERT(false, |
| 3336 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3337 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3338 | } else { |
| 3339 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3340 | at::AutoDispatchSkipFunctionalize guard; |
| 3341 | at::Tensor tmp_output = at::_ops::convolution_out::call(input_, weight_, bias_, stride, padding, dilation, transposed, output_padding, groups, out_); |
| 3342 | return out;; |
| 3343 | } |
| 3344 | } else { |
| 3345 | at::Tensor tmp_output; |
| 3346 | { |
| 3347 | at::AutoDispatchSkipFunctionalize guard; |
| 3348 | tmp_output = at::_ops::convolution::call(input_, weight_, bias_, stride, padding, dilation, transposed, output_padding, groups); |
| 3349 | } |
| 3350 | at::functionalization::impl::replace_(out, tmp_output); |
| 3351 | at::functionalization::impl::commit_update(out); |
| 3352 | at::functionalization::impl::sync(out); |
| 3353 | return out; |
| 3354 | } |
| 3355 | } |
| 3356 | |
| 3357 | at::Tensor & convolution_overrideable_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, at::Tensor & out) { |
| 3358 | if (false) { |
| 3359 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3360 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3361 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3362 | auto input_meta = to_meta(input); |
| 3363 | auto weight_meta = to_meta(weight); |
| 3364 | auto bias_meta = to_meta(bias); |
| 3365 | auto out_meta = to_meta(out); |
| 3366 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3367 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3368 | at::_ops::convolution_overrideable_out::call(input_meta, weight_meta, bias_meta, stride, padding, dilation, transposed, output_padding, groups, out_meta); |
| 3369 | } |
| 3370 | |
| 3371 | at::Tensor input_; |
| 3372 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 3373 | at::functionalization::impl::sync(input); |
| 3374 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 3375 | } else { |
| 3376 | input_ = input; |
| 3377 | } |
| 3378 | |
| 3379 | at::Tensor weight_; |
| 3380 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 3381 | at::functionalization::impl::sync(weight); |
| 3382 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 3383 | } else { |
| 3384 | weight_ = weight; |
| 3385 | } |
| 3386 | |
| 3387 | c10::optional<at::Tensor> bias_; |
| 3388 | if (at::functionalization::impl::isFunctionalTensor(bias)) { |
| 3389 | at::functionalization::impl::sync(bias); |
| 3390 | bias_ = at::functionalization::impl::from_functional_tensor(bias); |
| 3391 | } else { |
| 3392 | bias_ = bias; |
| 3393 | } |
| 3394 | |
| 3395 | at::Tensor out_; |
| 3396 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3397 | at::functionalization::impl::sync(out); |
| 3398 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3399 | } else { |
| 3400 | out_ = out; |
| 3401 | } |
| 3402 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3403 | if ((false || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(bias))) { |
| 3404 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3405 | TORCH_INTERNAL_ASSERT(false, |
| 3406 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3407 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3408 | } else { |
| 3409 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3410 | at::AutoDispatchSkipFunctionalize guard; |
| 3411 | at::Tensor tmp_output = at::_ops::convolution_overrideable_out::call(input_, weight_, bias_, stride, padding, dilation, transposed, output_padding, groups, out_); |
| 3412 | return out;; |
| 3413 | } |
| 3414 | } else { |
| 3415 | at::Tensor tmp_output; |
| 3416 | { |
| 3417 | at::AutoDispatchSkipFunctionalize guard; |
| 3418 | tmp_output = at::_ops::convolution_overrideable::call(input_, weight_, bias_, stride, padding, dilation, transposed, output_padding, groups); |
| 3419 | } |
| 3420 | at::functionalization::impl::replace_(out, tmp_output); |
| 3421 | at::functionalization::impl::commit_update(out); |
| 3422 | at::functionalization::impl::sync(out); |
| 3423 | return out; |
| 3424 | } |
| 3425 | } |
| 3426 | |
| 3427 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> convolution_backward_overrideable_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 3428 | if (false) { |
| 3429 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3430 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3431 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3432 | auto grad_output_meta = to_meta(grad_output); |
| 3433 | auto input_meta = to_meta(input); |
| 3434 | auto weight_meta = to_meta(weight); |
| 3435 | auto out0_meta = to_meta(out0); |
| 3436 | auto out1_meta = to_meta(out1); |
| 3437 | auto out2_meta = to_meta(out2); |
| 3438 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3439 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3440 | at::_ops::convolution_backward_overrideable_out::call(grad_output_meta, input_meta, weight_meta, stride, padding, dilation, transposed, output_padding, groups, output_mask, out0_meta, out1_meta, out2_meta); |
| 3441 | } |
| 3442 | |
| 3443 | at::Tensor grad_output_; |
| 3444 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 3445 | at::functionalization::impl::sync(grad_output); |
| 3446 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 3447 | } else { |
| 3448 | grad_output_ = grad_output; |
| 3449 | } |
| 3450 | |
| 3451 | at::Tensor input_; |
| 3452 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 3453 | at::functionalization::impl::sync(input); |
| 3454 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 3455 | } else { |
| 3456 | input_ = input; |
| 3457 | } |
| 3458 | |
| 3459 | at::Tensor weight_; |
| 3460 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 3461 | at::functionalization::impl::sync(weight); |
| 3462 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 3463 | } else { |
| 3464 | weight_ = weight; |
| 3465 | } |
| 3466 | |
| 3467 | at::Tensor out0_; |
| 3468 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 3469 | at::functionalization::impl::sync(out0); |
| 3470 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 3471 | } else { |
| 3472 | out0_ = out0; |
| 3473 | } |
| 3474 | |
| 3475 | at::Tensor out1_; |
| 3476 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 3477 | at::functionalization::impl::sync(out1); |
| 3478 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 3479 | } else { |
| 3480 | out1_ = out1; |
| 3481 | } |
| 3482 | |
| 3483 | at::Tensor out2_; |
| 3484 | if (at::functionalization::impl::isFunctionalTensor(out2)) { |
| 3485 | at::functionalization::impl::sync(out2); |
| 3486 | out2_ = at::functionalization::impl::from_functional_tensor(out2); |
| 3487 | } else { |
| 3488 | out2_ = out2; |
| 3489 | } |
| 3490 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1) && at::functionalization::impl::isFunctionalTensor(out2))) { |
| 3491 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(weight))) { |
| 3492 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3493 | TORCH_INTERNAL_ASSERT(false, |
| 3494 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3495 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3496 | } else { |
| 3497 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3498 | at::AutoDispatchSkipFunctionalize guard; |
| 3499 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::convolution_backward_overrideable_out::call(grad_output_, input_, weight_, stride, padding, dilation, transposed, output_padding, groups, output_mask, out0_, out1_, out2_); |
| 3500 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2);; |
| 3501 | } |
| 3502 | } else { |
| 3503 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 3504 | { |
| 3505 | at::AutoDispatchSkipFunctionalize guard; |
| 3506 | tmp_output = at::_ops::convolution_backward_overrideable::call(grad_output_, input_, weight_, stride, padding, dilation, transposed, output_padding, groups, output_mask); |
| 3507 | } |
| 3508 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 3509 | at::functionalization::impl::commit_update(out0); |
| 3510 | at::functionalization::impl::sync(out0); |
| 3511 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 3512 | at::functionalization::impl::commit_update(out1); |
| 3513 | at::functionalization::impl::sync(out1); |
| 3514 | at::functionalization::impl::replace_(out2, std::get<2>(tmp_output)); |
| 3515 | at::functionalization::impl::commit_update(out2); |
| 3516 | at::functionalization::impl::sync(out2); |
| 3517 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2); |
| 3518 | } |
| 3519 | } |
| 3520 | |
| 3521 | at::Tensor & cudnn_affine_grid_generator_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & theta, int64_t N, int64_t C, int64_t H, int64_t W, at::Tensor & out) { |
| 3522 | if (false) { |
| 3523 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3524 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3525 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3526 | auto theta_meta = to_meta(theta); |
| 3527 | auto out_meta = to_meta(out); |
| 3528 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3529 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3530 | at::_ops::cudnn_affine_grid_generator_out::call(theta_meta, N, C, H, W, out_meta); |
| 3531 | } |
| 3532 | |
| 3533 | at::Tensor theta_; |
| 3534 | if (at::functionalization::impl::isFunctionalTensor(theta)) { |
| 3535 | at::functionalization::impl::sync(theta); |
| 3536 | theta_ = at::functionalization::impl::from_functional_tensor(theta); |
| 3537 | } else { |
| 3538 | theta_ = theta; |
| 3539 | } |
| 3540 | |
| 3541 | at::Tensor out_; |
| 3542 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3543 | at::functionalization::impl::sync(out); |
| 3544 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3545 | } else { |
| 3546 | out_ = out; |
| 3547 | } |
| 3548 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3549 | if ((false || at::functionalization::impl::isFunctionalTensor(theta))) { |
| 3550 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3551 | TORCH_INTERNAL_ASSERT(false, |
| 3552 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3553 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3554 | } else { |
| 3555 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3556 | at::AutoDispatchSkipFunctionalize guard; |
| 3557 | at::Tensor tmp_output = at::_ops::cudnn_affine_grid_generator_out::call(theta_, N, C, H, W, out_); |
| 3558 | return out;; |
| 3559 | } |
| 3560 | } else { |
| 3561 | at::Tensor tmp_output; |
| 3562 | { |
| 3563 | at::AutoDispatchSkipFunctionalize guard; |
| 3564 | tmp_output = at::_ops::cudnn_affine_grid_generator::call(theta_, N, C, H, W); |
| 3565 | } |
| 3566 | at::functionalization::impl::replace_(out, tmp_output); |
| 3567 | at::functionalization::impl::commit_update(out); |
| 3568 | at::functionalization::impl::sync(out); |
| 3569 | return out; |
| 3570 | } |
| 3571 | } |
| 3572 | |
| 3573 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> cudnn_batch_norm_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & grad_output, const at::Tensor & weight, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, const c10::optional<at::Tensor> & save_mean, const c10::optional<at::Tensor> & save_var, double epsilon, const at::Tensor & reserveSpace, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 3574 | if (false) { |
| 3575 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3576 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3577 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3578 | auto input_meta = to_meta(input); |
| 3579 | auto grad_output_meta = to_meta(grad_output); |
| 3580 | auto weight_meta = to_meta(weight); |
| 3581 | auto running_mean_meta = to_meta(running_mean); |
| 3582 | auto running_var_meta = to_meta(running_var); |
| 3583 | auto save_mean_meta = to_meta(save_mean); |
| 3584 | auto save_var_meta = to_meta(save_var); |
| 3585 | auto reserveSpace_meta = to_meta(reserveSpace); |
| 3586 | auto out0_meta = to_meta(out0); |
| 3587 | auto out1_meta = to_meta(out1); |
| 3588 | auto out2_meta = to_meta(out2); |
| 3589 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3590 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3591 | at::_ops::cudnn_batch_norm_backward_out::call(input_meta, grad_output_meta, weight_meta, running_mean_meta, running_var_meta, save_mean_meta, save_var_meta, epsilon, reserveSpace_meta, out0_meta, out1_meta, out2_meta); |
| 3592 | } |
| 3593 | |
| 3594 | at::Tensor input_; |
| 3595 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 3596 | at::functionalization::impl::sync(input); |
| 3597 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 3598 | } else { |
| 3599 | input_ = input; |
| 3600 | } |
| 3601 | |
| 3602 | at::Tensor grad_output_; |
| 3603 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 3604 | at::functionalization::impl::sync(grad_output); |
| 3605 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 3606 | } else { |
| 3607 | grad_output_ = grad_output; |
| 3608 | } |
| 3609 | |
| 3610 | at::Tensor weight_; |
| 3611 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 3612 | at::functionalization::impl::sync(weight); |
| 3613 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 3614 | } else { |
| 3615 | weight_ = weight; |
| 3616 | } |
| 3617 | |
| 3618 | c10::optional<at::Tensor> running_mean_; |
| 3619 | if (at::functionalization::impl::isFunctionalTensor(running_mean)) { |
| 3620 | at::functionalization::impl::sync(running_mean); |
| 3621 | running_mean_ = at::functionalization::impl::from_functional_tensor(running_mean); |
| 3622 | } else { |
| 3623 | running_mean_ = running_mean; |
| 3624 | } |
| 3625 | |
| 3626 | c10::optional<at::Tensor> running_var_; |
| 3627 | if (at::functionalization::impl::isFunctionalTensor(running_var)) { |
| 3628 | at::functionalization::impl::sync(running_var); |
| 3629 | running_var_ = at::functionalization::impl::from_functional_tensor(running_var); |
| 3630 | } else { |
| 3631 | running_var_ = running_var; |
| 3632 | } |
| 3633 | |
| 3634 | c10::optional<at::Tensor> save_mean_; |
| 3635 | if (at::functionalization::impl::isFunctionalTensor(save_mean)) { |
| 3636 | at::functionalization::impl::sync(save_mean); |
| 3637 | save_mean_ = at::functionalization::impl::from_functional_tensor(save_mean); |
| 3638 | } else { |
| 3639 | save_mean_ = save_mean; |
| 3640 | } |
| 3641 | |
| 3642 | c10::optional<at::Tensor> save_var_; |
| 3643 | if (at::functionalization::impl::isFunctionalTensor(save_var)) { |
| 3644 | at::functionalization::impl::sync(save_var); |
| 3645 | save_var_ = at::functionalization::impl::from_functional_tensor(save_var); |
| 3646 | } else { |
| 3647 | save_var_ = save_var; |
| 3648 | } |
| 3649 | |
| 3650 | at::Tensor reserveSpace_; |
| 3651 | if (at::functionalization::impl::isFunctionalTensor(reserveSpace)) { |
| 3652 | at::functionalization::impl::sync(reserveSpace); |
| 3653 | reserveSpace_ = at::functionalization::impl::from_functional_tensor(reserveSpace); |
| 3654 | } else { |
| 3655 | reserveSpace_ = reserveSpace; |
| 3656 | } |
| 3657 | |
| 3658 | at::Tensor out0_; |
| 3659 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 3660 | at::functionalization::impl::sync(out0); |
| 3661 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 3662 | } else { |
| 3663 | out0_ = out0; |
| 3664 | } |
| 3665 | |
| 3666 | at::Tensor out1_; |
| 3667 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 3668 | at::functionalization::impl::sync(out1); |
| 3669 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 3670 | } else { |
| 3671 | out1_ = out1; |
| 3672 | } |
| 3673 | |
| 3674 | at::Tensor out2_; |
| 3675 | if (at::functionalization::impl::isFunctionalTensor(out2)) { |
| 3676 | at::functionalization::impl::sync(out2); |
| 3677 | out2_ = at::functionalization::impl::from_functional_tensor(out2); |
| 3678 | } else { |
| 3679 | out2_ = out2; |
| 3680 | } |
| 3681 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1) && at::functionalization::impl::isFunctionalTensor(out2))) { |
| 3682 | if ((false || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(running_mean) || at::functionalization::impl::isFunctionalTensor(running_var) || at::functionalization::impl::isFunctionalTensor(save_mean) || at::functionalization::impl::isFunctionalTensor(save_var) || at::functionalization::impl::isFunctionalTensor(reserveSpace))) { |
| 3683 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3684 | TORCH_INTERNAL_ASSERT(false, |
| 3685 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3686 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3687 | } else { |
| 3688 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3689 | at::AutoDispatchSkipFunctionalize guard; |
| 3690 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::cudnn_batch_norm_backward_out::call(input_, grad_output_, weight_, running_mean_, running_var_, save_mean_, save_var_, epsilon, reserveSpace_, out0_, out1_, out2_); |
| 3691 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2);; |
| 3692 | } |
| 3693 | } else { |
| 3694 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 3695 | { |
| 3696 | at::AutoDispatchSkipFunctionalize guard; |
| 3697 | tmp_output = at::_ops::cudnn_batch_norm_backward::call(input_, grad_output_, weight_, running_mean_, running_var_, save_mean_, save_var_, epsilon, reserveSpace_); |
| 3698 | } |
| 3699 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 3700 | at::functionalization::impl::commit_update(out0); |
| 3701 | at::functionalization::impl::sync(out0); |
| 3702 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 3703 | at::functionalization::impl::commit_update(out1); |
| 3704 | at::functionalization::impl::sync(out1); |
| 3705 | at::functionalization::impl::replace_(out2, std::get<2>(tmp_output)); |
| 3706 | at::functionalization::impl::commit_update(out2); |
| 3707 | at::functionalization::impl::sync(out2); |
| 3708 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2); |
| 3709 | } |
| 3710 | } |
| 3711 | |
| 3712 | at::Tensor & cudnn_convolution_relu_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups, at::Tensor & out) { |
| 3713 | if (false) { |
| 3714 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3715 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3716 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3717 | auto self_meta = to_meta(self); |
| 3718 | auto weight_meta = to_meta(weight); |
| 3719 | auto bias_meta = to_meta(bias); |
| 3720 | auto out_meta = to_meta(out); |
| 3721 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3722 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3723 | at::_ops::cudnn_convolution_relu_out::call(self_meta, weight_meta, bias_meta, stride, padding, dilation, groups, out_meta); |
| 3724 | } |
| 3725 | |
| 3726 | at::Tensor self_; |
| 3727 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3728 | at::functionalization::impl::sync(self); |
| 3729 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3730 | } else { |
| 3731 | self_ = self; |
| 3732 | } |
| 3733 | |
| 3734 | at::Tensor weight_; |
| 3735 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 3736 | at::functionalization::impl::sync(weight); |
| 3737 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 3738 | } else { |
| 3739 | weight_ = weight; |
| 3740 | } |
| 3741 | |
| 3742 | c10::optional<at::Tensor> bias_; |
| 3743 | if (at::functionalization::impl::isFunctionalTensor(bias)) { |
| 3744 | at::functionalization::impl::sync(bias); |
| 3745 | bias_ = at::functionalization::impl::from_functional_tensor(bias); |
| 3746 | } else { |
| 3747 | bias_ = bias; |
| 3748 | } |
| 3749 | |
| 3750 | at::Tensor out_; |
| 3751 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3752 | at::functionalization::impl::sync(out); |
| 3753 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3754 | } else { |
| 3755 | out_ = out; |
| 3756 | } |
| 3757 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3758 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(bias))) { |
| 3759 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3760 | TORCH_INTERNAL_ASSERT(false, |
| 3761 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3762 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3763 | } else { |
| 3764 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3765 | at::AutoDispatchSkipFunctionalize guard; |
| 3766 | at::Tensor tmp_output = at::_ops::cudnn_convolution_relu_out::call(self_, weight_, bias_, stride, padding, dilation, groups, out_); |
| 3767 | return out;; |
| 3768 | } |
| 3769 | } else { |
| 3770 | at::Tensor tmp_output; |
| 3771 | { |
| 3772 | at::AutoDispatchSkipFunctionalize guard; |
| 3773 | tmp_output = at::_ops::cudnn_convolution_relu::call(self_, weight_, bias_, stride, padding, dilation, groups); |
| 3774 | } |
| 3775 | at::functionalization::impl::replace_(out, tmp_output); |
| 3776 | at::functionalization::impl::commit_update(out); |
| 3777 | at::functionalization::impl::sync(out); |
| 3778 | return out; |
| 3779 | } |
| 3780 | } |
| 3781 | |
| 3782 | at::Tensor & cudnn_convolution_add_relu_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const c10::optional<at::Scalar> & alpha, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups, at::Tensor & out) { |
| 3783 | if (false) { |
| 3784 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3785 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3786 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3787 | auto self_meta = to_meta(self); |
| 3788 | auto weight_meta = to_meta(weight); |
| 3789 | auto z_meta = to_meta(z); |
| 3790 | auto bias_meta = to_meta(bias); |
| 3791 | auto out_meta = to_meta(out); |
| 3792 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3793 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3794 | at::_ops::cudnn_convolution_add_relu_out::call(self_meta, weight_meta, z_meta, alpha, bias_meta, stride, padding, dilation, groups, out_meta); |
| 3795 | } |
| 3796 | |
| 3797 | at::Tensor self_; |
| 3798 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3799 | at::functionalization::impl::sync(self); |
| 3800 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3801 | } else { |
| 3802 | self_ = self; |
| 3803 | } |
| 3804 | |
| 3805 | at::Tensor weight_; |
| 3806 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 3807 | at::functionalization::impl::sync(weight); |
| 3808 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 3809 | } else { |
| 3810 | weight_ = weight; |
| 3811 | } |
| 3812 | |
| 3813 | at::Tensor z_; |
| 3814 | if (at::functionalization::impl::isFunctionalTensor(z)) { |
| 3815 | at::functionalization::impl::sync(z); |
| 3816 | z_ = at::functionalization::impl::from_functional_tensor(z); |
| 3817 | } else { |
| 3818 | z_ = z; |
| 3819 | } |
| 3820 | |
| 3821 | c10::optional<at::Tensor> bias_; |
| 3822 | if (at::functionalization::impl::isFunctionalTensor(bias)) { |
| 3823 | at::functionalization::impl::sync(bias); |
| 3824 | bias_ = at::functionalization::impl::from_functional_tensor(bias); |
| 3825 | } else { |
| 3826 | bias_ = bias; |
| 3827 | } |
| 3828 | |
| 3829 | at::Tensor out_; |
| 3830 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3831 | at::functionalization::impl::sync(out); |
| 3832 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3833 | } else { |
| 3834 | out_ = out; |
| 3835 | } |
| 3836 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3837 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(z) || at::functionalization::impl::isFunctionalTensor(bias))) { |
| 3838 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3839 | TORCH_INTERNAL_ASSERT(false, |
| 3840 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3841 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3842 | } else { |
| 3843 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3844 | at::AutoDispatchSkipFunctionalize guard; |
| 3845 | at::Tensor tmp_output = at::_ops::cudnn_convolution_add_relu_out::call(self_, weight_, z_, alpha, bias_, stride, padding, dilation, groups, out_); |
| 3846 | return out;; |
| 3847 | } |
| 3848 | } else { |
| 3849 | at::Tensor tmp_output; |
| 3850 | { |
| 3851 | at::AutoDispatchSkipFunctionalize guard; |
| 3852 | tmp_output = at::_ops::cudnn_convolution_add_relu::call(self_, weight_, z_, alpha, bias_, stride, padding, dilation, groups); |
| 3853 | } |
| 3854 | at::functionalization::impl::replace_(out, tmp_output); |
| 3855 | at::functionalization::impl::commit_update(out); |
| 3856 | at::functionalization::impl::sync(out); |
| 3857 | return out; |
| 3858 | } |
| 3859 | } |
| 3860 | |
| 3861 | ::std::tuple<at::Tensor &,at::Tensor &> cummax_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, at::Tensor & values, at::Tensor & indices) { |
| 3862 | if (false) { |
| 3863 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3864 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3865 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3866 | auto self_meta = to_meta(self); |
| 3867 | auto values_meta = to_meta(values); |
| 3868 | auto indices_meta = to_meta(indices); |
| 3869 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3870 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3871 | at::_ops::cummax_out::call(self_meta, dim, values_meta, indices_meta); |
| 3872 | } |
| 3873 | |
| 3874 | at::Tensor self_; |
| 3875 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3876 | at::functionalization::impl::sync(self); |
| 3877 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3878 | } else { |
| 3879 | self_ = self; |
| 3880 | } |
| 3881 | |
| 3882 | at::Tensor values_; |
| 3883 | if (at::functionalization::impl::isFunctionalTensor(values)) { |
| 3884 | at::functionalization::impl::sync(values); |
| 3885 | values_ = at::functionalization::impl::from_functional_tensor(values); |
| 3886 | } else { |
| 3887 | values_ = values; |
| 3888 | } |
| 3889 | |
| 3890 | at::Tensor indices_; |
| 3891 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 3892 | at::functionalization::impl::sync(indices); |
| 3893 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 3894 | } else { |
| 3895 | indices_ = indices; |
| 3896 | } |
| 3897 | if (!(true && at::functionalization::impl::isFunctionalTensor(values) && at::functionalization::impl::isFunctionalTensor(indices))) { |
| 3898 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 3899 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3900 | TORCH_INTERNAL_ASSERT(false, |
| 3901 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3902 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3903 | } else { |
| 3904 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3905 | at::AutoDispatchSkipFunctionalize guard; |
| 3906 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::cummax_out::call(self_, dim, values_, indices_); |
| 3907 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices);; |
| 3908 | } |
| 3909 | } else { |
| 3910 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 3911 | { |
| 3912 | at::AutoDispatchSkipFunctionalize guard; |
| 3913 | tmp_output = at::_ops::cummax::call(self_, dim); |
| 3914 | } |
| 3915 | at::functionalization::impl::replace_(values, std::get<0>(tmp_output)); |
| 3916 | at::functionalization::impl::commit_update(values); |
| 3917 | at::functionalization::impl::sync(values); |
| 3918 | at::functionalization::impl::replace_(indices, std::get<1>(tmp_output)); |
| 3919 | at::functionalization::impl::commit_update(indices); |
| 3920 | at::functionalization::impl::sync(indices); |
| 3921 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices); |
| 3922 | } |
| 3923 | } |
| 3924 | |
| 3925 | ::std::tuple<at::Tensor &,at::Tensor &> cummax_out_dimname_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, at::Tensor & values, at::Tensor & indices) { |
| 3926 | if (false) { |
| 3927 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3928 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3929 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3930 | auto self_meta = to_meta(self); |
| 3931 | auto values_meta = to_meta(values); |
| 3932 | auto indices_meta = to_meta(indices); |
| 3933 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3934 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3935 | at::_ops::cummax_dimname_out::call(self_meta, dim, values_meta, indices_meta); |
| 3936 | } |
| 3937 | |
| 3938 | at::Tensor self_; |
| 3939 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3940 | at::functionalization::impl::sync(self); |
| 3941 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3942 | } else { |
| 3943 | self_ = self; |
| 3944 | } |
| 3945 | |
| 3946 | at::Tensor values_; |
| 3947 | if (at::functionalization::impl::isFunctionalTensor(values)) { |
| 3948 | at::functionalization::impl::sync(values); |
| 3949 | values_ = at::functionalization::impl::from_functional_tensor(values); |
| 3950 | } else { |
| 3951 | values_ = values; |
| 3952 | } |
| 3953 | |
| 3954 | at::Tensor indices_; |
| 3955 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 3956 | at::functionalization::impl::sync(indices); |
| 3957 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 3958 | } else { |
| 3959 | indices_ = indices; |
| 3960 | } |
| 3961 | if (!(true && at::functionalization::impl::isFunctionalTensor(values) && at::functionalization::impl::isFunctionalTensor(indices))) { |
| 3962 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 3963 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3964 | TORCH_INTERNAL_ASSERT(false, |
| 3965 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3966 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3967 | } else { |
| 3968 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3969 | at::AutoDispatchSkipFunctionalize guard; |
| 3970 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::cummax_dimname_out::call(self_, dim, values_, indices_); |
| 3971 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices);; |
| 3972 | } |
| 3973 | } else { |
| 3974 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 3975 | { |
| 3976 | at::AutoDispatchSkipFunctionalize guard; |
| 3977 | tmp_output = at::_ops::cummax_dimname::call(self_, dim); |
| 3978 | } |
| 3979 | at::functionalization::impl::replace_(values, std::get<0>(tmp_output)); |
| 3980 | at::functionalization::impl::commit_update(values); |
| 3981 | at::functionalization::impl::sync(values); |
| 3982 | at::functionalization::impl::replace_(indices, std::get<1>(tmp_output)); |
| 3983 | at::functionalization::impl::commit_update(indices); |
| 3984 | at::functionalization::impl::sync(indices); |
| 3985 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices); |
| 3986 | } |
| 3987 | } |
| 3988 | |
| 3989 | at::Tensor & diff_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t n, int64_t dim, const c10::optional<at::Tensor> & prepend, const c10::optional<at::Tensor> & append, at::Tensor & out) { |
| 3990 | if (false) { |
| 3991 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3992 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3993 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3994 | auto self_meta = to_meta(self); |
| 3995 | auto prepend_meta = to_meta(prepend); |
| 3996 | auto append_meta = to_meta(append); |
| 3997 | auto out_meta = to_meta(out); |
| 3998 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3999 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4000 | at::_ops::diff_out::call(self_meta, n, dim, prepend_meta, append_meta, out_meta); |
| 4001 | } |
| 4002 | |
| 4003 | at::Tensor self_; |
| 4004 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4005 | at::functionalization::impl::sync(self); |
| 4006 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4007 | } else { |
| 4008 | self_ = self; |
| 4009 | } |
| 4010 | |
| 4011 | c10::optional<at::Tensor> prepend_; |
| 4012 | if (at::functionalization::impl::isFunctionalTensor(prepend)) { |
| 4013 | at::functionalization::impl::sync(prepend); |
| 4014 | prepend_ = at::functionalization::impl::from_functional_tensor(prepend); |
| 4015 | } else { |
| 4016 | prepend_ = prepend; |
| 4017 | } |
| 4018 | |
| 4019 | c10::optional<at::Tensor> append_; |
| 4020 | if (at::functionalization::impl::isFunctionalTensor(append)) { |
| 4021 | at::functionalization::impl::sync(append); |
| 4022 | append_ = at::functionalization::impl::from_functional_tensor(append); |
| 4023 | } else { |
| 4024 | append_ = append; |
| 4025 | } |
| 4026 | |
| 4027 | at::Tensor out_; |
| 4028 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4029 | at::functionalization::impl::sync(out); |
| 4030 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4031 | } else { |
| 4032 | out_ = out; |
| 4033 | } |
| 4034 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4035 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(prepend) || at::functionalization::impl::isFunctionalTensor(append))) { |
| 4036 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4037 | TORCH_INTERNAL_ASSERT(false, |
| 4038 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4039 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4040 | } else { |
| 4041 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4042 | at::AutoDispatchSkipFunctionalize guard; |
| 4043 | at::Tensor tmp_output = at::_ops::diff_out::call(self_, n, dim, prepend_, append_, out_); |
| 4044 | return out;; |
| 4045 | } |
| 4046 | } else { |
| 4047 | at::Tensor tmp_output; |
| 4048 | { |
| 4049 | at::AutoDispatchSkipFunctionalize guard; |
| 4050 | tmp_output = at::_ops::diff::call(self_, n, dim, prepend_, append_); |
| 4051 | } |
| 4052 | at::functionalization::impl::replace_(out, tmp_output); |
| 4053 | at::functionalization::impl::commit_update(out); |
| 4054 | at::functionalization::impl::sync(out); |
| 4055 | return out; |
| 4056 | } |
| 4057 | } |
| 4058 | |
| 4059 | at::Tensor & embedding_renorm_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & indices, double max_norm, double norm_type, at::Tensor & out) { |
| 4060 | if (false) { |
| 4061 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4062 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4063 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4064 | auto self_meta = to_meta(self); |
| 4065 | auto indices_meta = to_meta(indices); |
| 4066 | auto out_meta = to_meta(out); |
| 4067 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4068 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4069 | at::_ops::embedding_renorm_out::call(self_meta, indices_meta, max_norm, norm_type, out_meta); |
| 4070 | } |
| 4071 | |
| 4072 | at::Tensor self_; |
| 4073 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4074 | at::functionalization::impl::sync(self); |
| 4075 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4076 | } else { |
| 4077 | self_ = self; |
| 4078 | } |
| 4079 | |
| 4080 | at::Tensor indices_; |
| 4081 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 4082 | at::functionalization::impl::sync(indices); |
| 4083 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 4084 | } else { |
| 4085 | indices_ = indices; |
| 4086 | } |
| 4087 | |
| 4088 | at::Tensor out_; |
| 4089 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4090 | at::functionalization::impl::sync(out); |
| 4091 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4092 | } else { |
| 4093 | out_ = out; |
| 4094 | } |
| 4095 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4096 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(indices))) { |
| 4097 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4098 | TORCH_INTERNAL_ASSERT(false, |
| 4099 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4100 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4101 | } else { |
| 4102 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4103 | at::AutoDispatchSkipFunctionalize guard; |
| 4104 | at::Tensor tmp_output = at::_ops::embedding_renorm_out::call(self_, indices_, max_norm, norm_type, out_); |
| 4105 | return out;; |
| 4106 | } |
| 4107 | } else { |
| 4108 | at::Tensor tmp_output; |
| 4109 | { |
| 4110 | at::AutoDispatchSkipFunctionalize guard; |
| 4111 | tmp_output = at::_ops::embedding_renorm::call(self_, indices_, max_norm, norm_type); |
| 4112 | } |
| 4113 | at::functionalization::impl::replace_(out, tmp_output); |
| 4114 | at::functionalization::impl::commit_update(out); |
| 4115 | at::functionalization::impl::sync(out); |
| 4116 | return out; |
| 4117 | } |
| 4118 | } |
| 4119 | |
| 4120 | at::Tensor & embedding_renorm_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & indices, double max_norm, double norm_type) { |
| 4121 | if (true) { |
| 4122 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4123 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4124 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4125 | auto self_meta = to_meta(self); |
| 4126 | auto indices_meta = to_meta(indices); |
| 4127 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4128 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4129 | at::_ops::embedding_renorm_::call(self_meta, indices_meta, max_norm, norm_type); |
| 4130 | } |
| 4131 | |
| 4132 | at::Tensor self_; |
| 4133 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4134 | at::functionalization::impl::sync(self); |
| 4135 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4136 | } else { |
| 4137 | self_ = self; |
| 4138 | } |
| 4139 | |
| 4140 | at::Tensor indices_; |
| 4141 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 4142 | at::functionalization::impl::sync(indices); |
| 4143 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 4144 | } else { |
| 4145 | indices_ = indices; |
| 4146 | } |
| 4147 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 4148 | if ((false || at::functionalization::impl::isFunctionalTensor(indices))) { |
| 4149 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4150 | TORCH_INTERNAL_ASSERT(false, |
| 4151 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4152 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4153 | } else { |
| 4154 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4155 | at::AutoDispatchSkipFunctionalize guard; |
| 4156 | at::Tensor tmp_output = at::_ops::embedding_renorm_::call(self_, indices_, max_norm, norm_type); |
| 4157 | return self;; |
| 4158 | } |
| 4159 | } else { |
| 4160 | at::Tensor tmp_output; |
| 4161 | { |
| 4162 | at::AutoDispatchSkipFunctionalize guard; |
| 4163 | tmp_output = at::_ops::embedding_renorm::call(self_, indices_, max_norm, norm_type); |
| 4164 | } |
| 4165 | at::functionalization::impl::replace_(self, tmp_output); |
| 4166 | at::functionalization::impl::commit_update(self); |
| 4167 | at::functionalization::impl::sync(self); |
| 4168 | return self; |
| 4169 | } |
| 4170 | } |
| 4171 | |
| 4172 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _embedding_bag_forward_only_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional<at::Tensor> & per_sample_weights, bool include_last_offset, int64_t padding_idx, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3) { |
| 4173 | if (false) { |
| 4174 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4175 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4176 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4177 | auto weight_meta = to_meta(weight); |
| 4178 | auto indices_meta = to_meta(indices); |
| 4179 | auto offsets_meta = to_meta(offsets); |
| 4180 | auto per_sample_weights_meta = to_meta(per_sample_weights); |
| 4181 | auto out0_meta = to_meta(out0); |
| 4182 | auto out1_meta = to_meta(out1); |
| 4183 | auto out2_meta = to_meta(out2); |
| 4184 | auto out3_meta = to_meta(out3); |
| 4185 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4186 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4187 | at::_ops::_embedding_bag_forward_only_out::call(weight_meta, indices_meta, offsets_meta, scale_grad_by_freq, mode, sparse, per_sample_weights_meta, include_last_offset, padding_idx, out0_meta, out1_meta, out2_meta, out3_meta); |
| 4188 | } |
| 4189 | |
| 4190 | at::Tensor weight_; |
| 4191 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 4192 | at::functionalization::impl::sync(weight); |
| 4193 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 4194 | } else { |
| 4195 | weight_ = weight; |
| 4196 | } |
| 4197 | |
| 4198 | at::Tensor indices_; |
| 4199 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 4200 | at::functionalization::impl::sync(indices); |
| 4201 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 4202 | } else { |
| 4203 | indices_ = indices; |
| 4204 | } |
| 4205 | |
| 4206 | at::Tensor offsets_; |
| 4207 | if (at::functionalization::impl::isFunctionalTensor(offsets)) { |
| 4208 | at::functionalization::impl::sync(offsets); |
| 4209 | offsets_ = at::functionalization::impl::from_functional_tensor(offsets); |
| 4210 | } else { |
| 4211 | offsets_ = offsets; |
| 4212 | } |
| 4213 | |
| 4214 | c10::optional<at::Tensor> per_sample_weights_; |
| 4215 | if (at::functionalization::impl::isFunctionalTensor(per_sample_weights)) { |
| 4216 | at::functionalization::impl::sync(per_sample_weights); |
| 4217 | per_sample_weights_ = at::functionalization::impl::from_functional_tensor(per_sample_weights); |
| 4218 | } else { |
| 4219 | per_sample_weights_ = per_sample_weights; |
| 4220 | } |
| 4221 | |
| 4222 | at::Tensor out0_; |
| 4223 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 4224 | at::functionalization::impl::sync(out0); |
| 4225 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 4226 | } else { |
| 4227 | out0_ = out0; |
| 4228 | } |
| 4229 | |
| 4230 | at::Tensor out1_; |
| 4231 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 4232 | at::functionalization::impl::sync(out1); |
| 4233 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 4234 | } else { |
| 4235 | out1_ = out1; |
| 4236 | } |
| 4237 | |
| 4238 | at::Tensor out2_; |
| 4239 | if (at::functionalization::impl::isFunctionalTensor(out2)) { |
| 4240 | at::functionalization::impl::sync(out2); |
| 4241 | out2_ = at::functionalization::impl::from_functional_tensor(out2); |
| 4242 | } else { |
| 4243 | out2_ = out2; |
| 4244 | } |
| 4245 | |
| 4246 | at::Tensor out3_; |
| 4247 | if (at::functionalization::impl::isFunctionalTensor(out3)) { |
| 4248 | at::functionalization::impl::sync(out3); |
| 4249 | out3_ = at::functionalization::impl::from_functional_tensor(out3); |
| 4250 | } else { |
| 4251 | out3_ = out3; |
| 4252 | } |
| 4253 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1) && at::functionalization::impl::isFunctionalTensor(out2) && at::functionalization::impl::isFunctionalTensor(out3))) { |
| 4254 | if ((false || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(indices) || at::functionalization::impl::isFunctionalTensor(offsets) || at::functionalization::impl::isFunctionalTensor(per_sample_weights))) { |
| 4255 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4256 | TORCH_INTERNAL_ASSERT(false, |
| 4257 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4258 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4259 | } else { |
| 4260 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4261 | at::AutoDispatchSkipFunctionalize guard; |
| 4262 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::_embedding_bag_forward_only_out::call(weight_, indices_, offsets_, scale_grad_by_freq, mode, sparse, per_sample_weights_, include_last_offset, padding_idx, out0_, out1_, out2_, out3_); |
| 4263 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2, out3);; |
| 4264 | } |
| 4265 | } else { |
| 4266 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 4267 | { |
| 4268 | at::AutoDispatchSkipFunctionalize guard; |
| 4269 | tmp_output = at::_ops::_embedding_bag_forward_only::call(weight_, indices_, offsets_, scale_grad_by_freq, mode, sparse, per_sample_weights_, include_last_offset, padding_idx); |
| 4270 | } |
| 4271 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 4272 | at::functionalization::impl::commit_update(out0); |
| 4273 | at::functionalization::impl::sync(out0); |
| 4274 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 4275 | at::functionalization::impl::commit_update(out1); |
| 4276 | at::functionalization::impl::sync(out1); |
| 4277 | at::functionalization::impl::replace_(out2, std::get<2>(tmp_output)); |
| 4278 | at::functionalization::impl::commit_update(out2); |
| 4279 | at::functionalization::impl::sync(out2); |
| 4280 | at::functionalization::impl::replace_(out3, std::get<3>(tmp_output)); |
| 4281 | at::functionalization::impl::commit_update(out3); |
| 4282 | at::functionalization::impl::sync(out3); |
| 4283 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2, out3); |
| 4284 | } |
| 4285 | } |
| 4286 | |
| 4287 | at::Tensor & _embedding_bag_dense_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, const c10::optional<at::Tensor> & per_sample_weights, int64_t padding_idx, at::Tensor & out) { |
| 4288 | if (false) { |
| 4289 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4290 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4291 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4292 | auto grad_meta = to_meta(grad); |
| 4293 | auto indices_meta = to_meta(indices); |
| 4294 | auto offset2bag_meta = to_meta(offset2bag); |
| 4295 | auto bag_size_meta = to_meta(bag_size); |
| 4296 | auto maximum_indices_meta = to_meta(maximum_indices); |
| 4297 | auto per_sample_weights_meta = to_meta(per_sample_weights); |
| 4298 | auto out_meta = to_meta(out); |
| 4299 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4300 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4301 | at::_ops::_embedding_bag_dense_backward_out::call(grad_meta, indices_meta, offset2bag_meta, bag_size_meta, maximum_indices_meta, num_weights, scale_grad_by_freq, mode, per_sample_weights_meta, padding_idx, out_meta); |
| 4302 | } |
| 4303 | |
| 4304 | at::Tensor grad_; |
| 4305 | if (at::functionalization::impl::isFunctionalTensor(grad)) { |
| 4306 | at::functionalization::impl::sync(grad); |
| 4307 | grad_ = at::functionalization::impl::from_functional_tensor(grad); |
| 4308 | } else { |
| 4309 | grad_ = grad; |
| 4310 | } |
| 4311 | |
| 4312 | at::Tensor indices_; |
| 4313 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 4314 | at::functionalization::impl::sync(indices); |
| 4315 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 4316 | } else { |
| 4317 | indices_ = indices; |
| 4318 | } |
| 4319 | |
| 4320 | at::Tensor offset2bag_; |
| 4321 | if (at::functionalization::impl::isFunctionalTensor(offset2bag)) { |
| 4322 | at::functionalization::impl::sync(offset2bag); |
| 4323 | offset2bag_ = at::functionalization::impl::from_functional_tensor(offset2bag); |
| 4324 | } else { |
| 4325 | offset2bag_ = offset2bag; |
| 4326 | } |
| 4327 | |
| 4328 | at::Tensor bag_size_; |
| 4329 | if (at::functionalization::impl::isFunctionalTensor(bag_size)) { |
| 4330 | at::functionalization::impl::sync(bag_size); |
| 4331 | bag_size_ = at::functionalization::impl::from_functional_tensor(bag_size); |
| 4332 | } else { |
| 4333 | bag_size_ = bag_size; |
| 4334 | } |
| 4335 | |
| 4336 | at::Tensor maximum_indices_; |
| 4337 | if (at::functionalization::impl::isFunctionalTensor(maximum_indices)) { |
| 4338 | at::functionalization::impl::sync(maximum_indices); |
| 4339 | maximum_indices_ = at::functionalization::impl::from_functional_tensor(maximum_indices); |
| 4340 | } else { |
| 4341 | maximum_indices_ = maximum_indices; |
| 4342 | } |
| 4343 | |
| 4344 | c10::optional<at::Tensor> per_sample_weights_; |
| 4345 | if (at::functionalization::impl::isFunctionalTensor(per_sample_weights)) { |
| 4346 | at::functionalization::impl::sync(per_sample_weights); |
| 4347 | per_sample_weights_ = at::functionalization::impl::from_functional_tensor(per_sample_weights); |
| 4348 | } else { |
| 4349 | per_sample_weights_ = per_sample_weights; |
| 4350 | } |
| 4351 | |
| 4352 | at::Tensor out_; |
| 4353 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4354 | at::functionalization::impl::sync(out); |
| 4355 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4356 | } else { |
| 4357 | out_ = out; |
| 4358 | } |
| 4359 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4360 | if ((false || at::functionalization::impl::isFunctionalTensor(grad) || at::functionalization::impl::isFunctionalTensor(indices) || at::functionalization::impl::isFunctionalTensor(offset2bag) || at::functionalization::impl::isFunctionalTensor(bag_size) || at::functionalization::impl::isFunctionalTensor(maximum_indices) || at::functionalization::impl::isFunctionalTensor(per_sample_weights))) { |
| 4361 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4362 | TORCH_INTERNAL_ASSERT(false, |
| 4363 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4364 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4365 | } else { |
| 4366 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4367 | at::AutoDispatchSkipFunctionalize guard; |
| 4368 | at::Tensor tmp_output = at::_ops::_embedding_bag_dense_backward_out::call(grad_, indices_, offset2bag_, bag_size_, maximum_indices_, num_weights, scale_grad_by_freq, mode, per_sample_weights_, padding_idx, out_); |
| 4369 | return out;; |
| 4370 | } |
| 4371 | } else { |
| 4372 | at::Tensor tmp_output; |
| 4373 | { |
| 4374 | at::AutoDispatchSkipFunctionalize guard; |
| 4375 | tmp_output = at::_ops::_embedding_bag_dense_backward::call(grad_, indices_, offset2bag_, bag_size_, maximum_indices_, num_weights, scale_grad_by_freq, mode, per_sample_weights_, padding_idx); |
| 4376 | } |
| 4377 | at::functionalization::impl::replace_(out, tmp_output); |
| 4378 | at::functionalization::impl::commit_update(out); |
| 4379 | at::functionalization::impl::sync(out); |
| 4380 | return out; |
| 4381 | } |
| 4382 | } |
| 4383 | |
| 4384 | at::Tensor & empty_out_names_out(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional<at::DimnameList> names, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out) { |
| 4385 | if (false) { |
| 4386 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4387 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4388 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4389 | auto out_meta = to_meta(out); |
| 4390 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4391 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4392 | at::_ops::empty_names_out::call(size, names, memory_format, out_meta); |
| 4393 | } |
| 4394 | |
| 4395 | at::Tensor out_; |
| 4396 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4397 | at::functionalization::impl::sync(out); |
| 4398 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4399 | } else { |
| 4400 | out_ = out; |
| 4401 | } |
| 4402 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4403 | if ((false)) { |
| 4404 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4405 | TORCH_INTERNAL_ASSERT(false, |
| 4406 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4407 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4408 | } else { |
| 4409 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4410 | at::AutoDispatchSkipFunctionalize guard; |
| 4411 | at::Tensor tmp_output = at::_ops::empty_names_out::call(size, names, memory_format, out_); |
| 4412 | return out;; |
| 4413 | } |
| 4414 | } else { |
| 4415 | at::Tensor tmp_output; |
| 4416 | { |
| 4417 | at::AutoDispatchSkipFunctionalize guard; |
| 4418 | tmp_output = at::_ops::empty_names::call(size, names, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt, memory_format); |
| 4419 | } |
| 4420 | at::functionalization::impl::replace_(out, tmp_output); |
| 4421 | at::functionalization::impl::commit_update(out); |
| 4422 | at::functionalization::impl::sync(out); |
| 4423 | return out; |
| 4424 | } |
| 4425 | } |
| 4426 | |
| 4427 | at::Tensor & empty_out_out(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out) { |
| 4428 | if (false) { |
| 4429 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4430 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4431 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4432 | auto out_meta = to_meta(out); |
| 4433 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4434 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4435 | at::_ops::empty_out::call(size, memory_format, out_meta); |
| 4436 | } |
| 4437 | |
| 4438 | at::Tensor out_; |
| 4439 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4440 | at::functionalization::impl::sync(out); |
| 4441 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4442 | } else { |
| 4443 | out_ = out; |
| 4444 | } |
| 4445 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4446 | if ((false)) { |
| 4447 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4448 | TORCH_INTERNAL_ASSERT(false, |
| 4449 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4450 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4451 | } else { |
| 4452 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4453 | at::AutoDispatchSkipFunctionalize guard; |
| 4454 | at::Tensor tmp_output = at::_ops::empty_out::call(size, memory_format, out_); |
| 4455 | return out;; |
| 4456 | } |
| 4457 | } else { |
| 4458 | at::Tensor tmp_output; |
| 4459 | { |
| 4460 | at::AutoDispatchSkipFunctionalize guard; |
| 4461 | tmp_output = at::_ops::empty_memory_format::call(size, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt, memory_format); |
| 4462 | } |
| 4463 | at::functionalization::impl::replace_(out, tmp_output); |
| 4464 | at::functionalization::impl::commit_update(out); |
| 4465 | at::functionalization::impl::sync(out); |
| 4466 | return out; |
| 4467 | } |
| 4468 | } |
| 4469 | |
| 4470 | at::Tensor & empty_like_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out) { |
| 4471 | if (false) { |
| 4472 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4473 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4474 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4475 | auto self_meta = to_meta(self); |
| 4476 | auto out_meta = to_meta(out); |
| 4477 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4478 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4479 | at::_ops::empty_like_out::call(self_meta, memory_format, out_meta); |
| 4480 | } |
| 4481 | |
| 4482 | at::Tensor self_; |
| 4483 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4484 | at::functionalization::impl::sync(self); |
| 4485 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4486 | } else { |
| 4487 | self_ = self; |
| 4488 | } |
| 4489 | |
| 4490 | at::Tensor out_; |
| 4491 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4492 | at::functionalization::impl::sync(out); |
| 4493 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4494 | } else { |
| 4495 | out_ = out; |
| 4496 | } |
| 4497 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4498 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 4499 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4500 | TORCH_INTERNAL_ASSERT(false, |
| 4501 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4502 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4503 | } else { |
| 4504 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4505 | at::AutoDispatchSkipFunctionalize guard; |
| 4506 | at::Tensor tmp_output = at::_ops::empty_like_out::call(self_, memory_format, out_); |
| 4507 | return out;; |
| 4508 | } |
| 4509 | } else { |
| 4510 | at::Tensor tmp_output; |
| 4511 | { |
| 4512 | at::AutoDispatchSkipFunctionalize guard; |
| 4513 | tmp_output = at::_ops::empty_like::call(self_, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt, memory_format); |
| 4514 | } |
| 4515 | at::functionalization::impl::replace_(out, tmp_output); |
| 4516 | at::functionalization::impl::commit_update(out); |
| 4517 | at::functionalization::impl::sync(out); |
| 4518 | return out; |
| 4519 | } |
| 4520 | } |
| 4521 | |
| 4522 | at::Tensor & erf_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 4523 | if (false) { |
| 4524 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4525 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4526 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4527 | auto self_meta = to_meta(self); |
| 4528 | auto out_meta = to_meta(out); |
| 4529 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4530 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4531 | at::_ops::erf_out::call(self_meta, out_meta); |
| 4532 | } |
| 4533 | |
| 4534 | at::Tensor self_; |
| 4535 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4536 | at::functionalization::impl::sync(self); |
| 4537 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4538 | } else { |
| 4539 | self_ = self; |
| 4540 | } |
| 4541 | |
| 4542 | at::Tensor out_; |
| 4543 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4544 | at::functionalization::impl::sync(out); |
| 4545 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4546 | } else { |
| 4547 | out_ = out; |
| 4548 | } |
| 4549 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4550 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 4551 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4552 | TORCH_INTERNAL_ASSERT(false, |
| 4553 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4554 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4555 | } else { |
| 4556 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4557 | at::AutoDispatchSkipFunctionalize guard; |
| 4558 | at::Tensor tmp_output = at::_ops::erf_out::call(self_, out_); |
| 4559 | return out;; |
| 4560 | } |
| 4561 | } else { |
| 4562 | at::Tensor tmp_output; |
| 4563 | { |
| 4564 | at::AutoDispatchSkipFunctionalize guard; |
| 4565 | tmp_output = at::_ops::erf::call(self_); |
| 4566 | } |
| 4567 | at::functionalization::impl::replace_(out, tmp_output); |
| 4568 | at::functionalization::impl::commit_update(out); |
| 4569 | at::functionalization::impl::sync(out); |
| 4570 | return out; |
| 4571 | } |
| 4572 | } |
| 4573 | |
| 4574 | at::Tensor & erf_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 4575 | if (true) { |
| 4576 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4577 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4578 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4579 | auto self_meta = to_meta(self); |
| 4580 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4581 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4582 | at::_ops::erf_::call(self_meta); |
| 4583 | } |
| 4584 | |
| 4585 | at::Tensor self_; |
| 4586 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4587 | at::functionalization::impl::sync(self); |
| 4588 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4589 | } else { |
| 4590 | self_ = self; |
| 4591 | } |
| 4592 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 4593 | if ((false)) { |
| 4594 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4595 | TORCH_INTERNAL_ASSERT(false, |
| 4596 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4597 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4598 | } else { |
| 4599 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4600 | at::AutoDispatchSkipFunctionalize guard; |
| 4601 | at::Tensor tmp_output = at::_ops::erf_::call(self_); |
| 4602 | return self;; |
| 4603 | } |
| 4604 | } else { |
| 4605 | at::Tensor tmp_output; |
| 4606 | { |
| 4607 | at::AutoDispatchSkipFunctionalize guard; |
| 4608 | tmp_output = at::_ops::erf::call(self_); |
| 4609 | } |
| 4610 | at::functionalization::impl::replace_(self, tmp_output); |
| 4611 | at::functionalization::impl::commit_update(self); |
| 4612 | at::functionalization::impl::sync(self); |
| 4613 | return self; |
| 4614 | } |
| 4615 | } |
| 4616 | |
| 4617 | at::Tensor & erfc_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 4618 | if (false) { |
| 4619 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4620 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4621 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4622 | auto self_meta = to_meta(self); |
| 4623 | auto out_meta = to_meta(out); |
| 4624 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4625 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4626 | at::_ops::erfc_out::call(self_meta, out_meta); |
| 4627 | } |
| 4628 | |
| 4629 | at::Tensor self_; |
| 4630 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4631 | at::functionalization::impl::sync(self); |
| 4632 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4633 | } else { |
| 4634 | self_ = self; |
| 4635 | } |
| 4636 | |
| 4637 | at::Tensor out_; |
| 4638 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4639 | at::functionalization::impl::sync(out); |
| 4640 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4641 | } else { |
| 4642 | out_ = out; |
| 4643 | } |
| 4644 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4645 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 4646 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4647 | TORCH_INTERNAL_ASSERT(false, |
| 4648 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4649 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4650 | } else { |
| 4651 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4652 | at::AutoDispatchSkipFunctionalize guard; |
| 4653 | at::Tensor tmp_output = at::_ops::erfc_out::call(self_, out_); |
| 4654 | return out;; |
| 4655 | } |
| 4656 | } else { |
| 4657 | at::Tensor tmp_output; |
| 4658 | { |
| 4659 | at::AutoDispatchSkipFunctionalize guard; |
| 4660 | tmp_output = at::_ops::erfc::call(self_); |
| 4661 | } |
| 4662 | at::functionalization::impl::replace_(out, tmp_output); |
| 4663 | at::functionalization::impl::commit_update(out); |
| 4664 | at::functionalization::impl::sync(out); |
| 4665 | return out; |
| 4666 | } |
| 4667 | } |
| 4668 | |
| 4669 | at::Tensor & erfc_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 4670 | if (true) { |
| 4671 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4672 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4673 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4674 | auto self_meta = to_meta(self); |
| 4675 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4676 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4677 | at::_ops::erfc_::call(self_meta); |
| 4678 | } |
| 4679 | |
| 4680 | at::Tensor self_; |
| 4681 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4682 | at::functionalization::impl::sync(self); |
| 4683 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4684 | } else { |
| 4685 | self_ = self; |
| 4686 | } |
| 4687 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 4688 | if ((false)) { |
| 4689 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4690 | TORCH_INTERNAL_ASSERT(false, |
| 4691 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4692 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4693 | } else { |
| 4694 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4695 | at::AutoDispatchSkipFunctionalize guard; |
| 4696 | at::Tensor tmp_output = at::_ops::erfc_::call(self_); |
| 4697 | return self;; |
| 4698 | } |
| 4699 | } else { |
| 4700 | at::Tensor tmp_output; |
| 4701 | { |
| 4702 | at::AutoDispatchSkipFunctionalize guard; |
| 4703 | tmp_output = at::_ops::erfc::call(self_); |
| 4704 | } |
| 4705 | at::functionalization::impl::replace_(self, tmp_output); |
| 4706 | at::functionalization::impl::commit_update(self); |
| 4707 | at::functionalization::impl::sync(self); |
| 4708 | return self; |
| 4709 | } |
| 4710 | } |
| 4711 | |
| 4712 | at::Tensor & eye_out_out(c10::DispatchKeySet dispatchKeySet, int64_t n, at::Tensor & out) { |
| 4713 | if (false) { |
| 4714 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4715 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4716 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4717 | auto out_meta = to_meta(out); |
| 4718 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4719 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4720 | at::_ops::eye_out::call(n, out_meta); |
| 4721 | } |
| 4722 | |
| 4723 | at::Tensor out_; |
| 4724 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4725 | at::functionalization::impl::sync(out); |
| 4726 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4727 | } else { |
| 4728 | out_ = out; |
| 4729 | } |
| 4730 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4731 | if ((false)) { |
| 4732 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4733 | TORCH_INTERNAL_ASSERT(false, |
| 4734 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4735 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4736 | } else { |
| 4737 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4738 | at::AutoDispatchSkipFunctionalize guard; |
| 4739 | at::Tensor tmp_output = at::_ops::eye_out::call(n, out_); |
| 4740 | return out;; |
| 4741 | } |
| 4742 | } else { |
| 4743 | at::Tensor tmp_output; |
| 4744 | { |
| 4745 | at::AutoDispatchSkipFunctionalize guard; |
| 4746 | tmp_output = at::_ops::eye::call(n, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 4747 | } |
| 4748 | at::functionalization::impl::replace_(out, tmp_output); |
| 4749 | at::functionalization::impl::commit_update(out); |
| 4750 | at::functionalization::impl::sync(out); |
| 4751 | return out; |
| 4752 | } |
| 4753 | } |
| 4754 | |
| 4755 | at::Tensor & eye_out_m_out(c10::DispatchKeySet dispatchKeySet, int64_t n, int64_t m, at::Tensor & out) { |
| 4756 | if (false) { |
| 4757 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4758 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4759 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4760 | auto out_meta = to_meta(out); |
| 4761 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4762 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4763 | at::_ops::eye_m_out::call(n, m, out_meta); |
| 4764 | } |
| 4765 | |
| 4766 | at::Tensor out_; |
| 4767 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4768 | at::functionalization::impl::sync(out); |
| 4769 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4770 | } else { |
| 4771 | out_ = out; |
| 4772 | } |
| 4773 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4774 | if ((false)) { |
| 4775 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4776 | TORCH_INTERNAL_ASSERT(false, |
| 4777 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4778 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4779 | } else { |
| 4780 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4781 | at::AutoDispatchSkipFunctionalize guard; |
| 4782 | at::Tensor tmp_output = at::_ops::eye_m_out::call(n, m, out_); |
| 4783 | return out;; |
| 4784 | } |
| 4785 | } else { |
| 4786 | at::Tensor tmp_output; |
| 4787 | { |
| 4788 | at::AutoDispatchSkipFunctionalize guard; |
| 4789 | tmp_output = at::_ops::eye_m::call(n, m, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 4790 | } |
| 4791 | at::functionalization::impl::replace_(out, tmp_output); |
| 4792 | at::functionalization::impl::commit_update(out); |
| 4793 | at::functionalization::impl::sync(out); |
| 4794 | return out; |
| 4795 | } |
| 4796 | } |
| 4797 | |
| 4798 | at::Tensor & gcd_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 4799 | if (false) { |
| 4800 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4801 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4802 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4803 | auto self_meta = to_meta(self); |
| 4804 | auto other_meta = to_meta(other); |
| 4805 | auto out_meta = to_meta(out); |
| 4806 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4807 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4808 | at::_ops::gcd_out::call(self_meta, other_meta, out_meta); |
| 4809 | } |
| 4810 | |
| 4811 | at::Tensor self_; |
| 4812 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4813 | at::functionalization::impl::sync(self); |
| 4814 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4815 | } else { |
| 4816 | self_ = self; |
| 4817 | } |
| 4818 | |
| 4819 | at::Tensor other_; |
| 4820 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 4821 | at::functionalization::impl::sync(other); |
| 4822 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 4823 | } else { |
| 4824 | other_ = other; |
| 4825 | } |
| 4826 | |
| 4827 | at::Tensor out_; |
| 4828 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4829 | at::functionalization::impl::sync(out); |
| 4830 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4831 | } else { |
| 4832 | out_ = out; |
| 4833 | } |
| 4834 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4835 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 4836 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4837 | TORCH_INTERNAL_ASSERT(false, |
| 4838 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4839 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4840 | } else { |
| 4841 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4842 | at::AutoDispatchSkipFunctionalize guard; |
| 4843 | at::Tensor tmp_output = at::_ops::gcd_out::call(self_, other_, out_); |
| 4844 | return out;; |
| 4845 | } |
| 4846 | } else { |
| 4847 | at::Tensor tmp_output; |
| 4848 | { |
| 4849 | at::AutoDispatchSkipFunctionalize guard; |
| 4850 | tmp_output = at::_ops::gcd::call(self_, other_); |
| 4851 | } |
| 4852 | at::functionalization::impl::replace_(out, tmp_output); |
| 4853 | at::functionalization::impl::commit_update(out); |
| 4854 | at::functionalization::impl::sync(out); |
| 4855 | return out; |
| 4856 | } |
| 4857 | } |
| 4858 | |
| 4859 | at::Tensor & gcd_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 4860 | if (true) { |
| 4861 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4862 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4863 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4864 | auto self_meta = to_meta(self); |
| 4865 | auto other_meta = to_meta(other); |
| 4866 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4867 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4868 | at::_ops::gcd_::call(self_meta, other_meta); |
| 4869 | } |
| 4870 | |
| 4871 | at::Tensor self_; |
| 4872 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4873 | at::functionalization::impl::sync(self); |
| 4874 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4875 | } else { |
| 4876 | self_ = self; |
| 4877 | } |
| 4878 | |
| 4879 | at::Tensor other_; |
| 4880 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 4881 | at::functionalization::impl::sync(other); |
| 4882 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 4883 | } else { |
| 4884 | other_ = other; |
| 4885 | } |
| 4886 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 4887 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 4888 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4889 | TORCH_INTERNAL_ASSERT(false, |
| 4890 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4891 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4892 | } else { |
| 4893 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4894 | at::AutoDispatchSkipFunctionalize guard; |
| 4895 | at::Tensor tmp_output = at::_ops::gcd_::call(self_, other_); |
| 4896 | return self;; |
| 4897 | } |
| 4898 | } else { |
| 4899 | at::Tensor tmp_output; |
| 4900 | { |
| 4901 | at::AutoDispatchSkipFunctionalize guard; |
| 4902 | tmp_output = at::_ops::gcd::call(self_, other_); |
| 4903 | } |
| 4904 | at::functionalization::impl::replace_(self, tmp_output); |
| 4905 | at::functionalization::impl::commit_update(self); |
| 4906 | at::functionalization::impl::sync(self); |
| 4907 | return self; |
| 4908 | } |
| 4909 | } |
| 4910 | |
| 4911 | ::std::tuple<at::Tensor &,at::Tensor &> grid_sampler_2d_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array<bool,2> output_mask, at::Tensor & out0, at::Tensor & out1) { |
| 4912 | if (false) { |
| 4913 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4914 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4915 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4916 | auto grad_output_meta = to_meta(grad_output); |
| 4917 | auto input_meta = to_meta(input); |
| 4918 | auto grid_meta = to_meta(grid); |
| 4919 | auto out0_meta = to_meta(out0); |
| 4920 | auto out1_meta = to_meta(out1); |
| 4921 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4922 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4923 | at::_ops::grid_sampler_2d_backward_out::call(grad_output_meta, input_meta, grid_meta, interpolation_mode, padding_mode, align_corners, output_mask, out0_meta, out1_meta); |
| 4924 | } |
| 4925 | |
| 4926 | at::Tensor grad_output_; |
| 4927 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 4928 | at::functionalization::impl::sync(grad_output); |
| 4929 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 4930 | } else { |
| 4931 | grad_output_ = grad_output; |
| 4932 | } |
| 4933 | |
| 4934 | at::Tensor input_; |
| 4935 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 4936 | at::functionalization::impl::sync(input); |
| 4937 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 4938 | } else { |
| 4939 | input_ = input; |
| 4940 | } |
| 4941 | |
| 4942 | at::Tensor grid_; |
| 4943 | if (at::functionalization::impl::isFunctionalTensor(grid)) { |
| 4944 | at::functionalization::impl::sync(grid); |
| 4945 | grid_ = at::functionalization::impl::from_functional_tensor(grid); |
| 4946 | } else { |
| 4947 | grid_ = grid; |
| 4948 | } |
| 4949 | |
| 4950 | at::Tensor out0_; |
| 4951 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 4952 | at::functionalization::impl::sync(out0); |
| 4953 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 4954 | } else { |
| 4955 | out0_ = out0; |
| 4956 | } |
| 4957 | |
| 4958 | at::Tensor out1_; |
| 4959 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 4960 | at::functionalization::impl::sync(out1); |
| 4961 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 4962 | } else { |
| 4963 | out1_ = out1; |
| 4964 | } |
| 4965 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1))) { |
| 4966 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(grid))) { |
| 4967 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4968 | TORCH_INTERNAL_ASSERT(false, |
| 4969 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4970 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4971 | } else { |
| 4972 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4973 | at::AutoDispatchSkipFunctionalize guard; |
| 4974 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::grid_sampler_2d_backward_out::call(grad_output_, input_, grid_, interpolation_mode, padding_mode, align_corners, output_mask, out0_, out1_); |
| 4975 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1);; |
| 4976 | } |
| 4977 | } else { |
| 4978 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 4979 | { |
| 4980 | at::AutoDispatchSkipFunctionalize guard; |
| 4981 | tmp_output = at::_ops::grid_sampler_2d_backward::call(grad_output_, input_, grid_, interpolation_mode, padding_mode, align_corners, output_mask); |
| 4982 | } |
| 4983 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 4984 | at::functionalization::impl::commit_update(out0); |
| 4985 | at::functionalization::impl::sync(out0); |
| 4986 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 4987 | at::functionalization::impl::commit_update(out1); |
| 4988 | at::functionalization::impl::sync(out1); |
| 4989 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1); |
| 4990 | } |
| 4991 | } |
| 4992 | |
| 4993 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_group_norm_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const c10::optional<at::Tensor> & weight, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 4994 | if (false) { |
| 4995 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4996 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4997 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4998 | auto grad_out_meta = to_meta(grad_out); |
| 4999 | auto input_meta = to_meta(input); |
| 5000 | auto mean_meta = to_meta(mean); |
| 5001 | auto rstd_meta = to_meta(rstd); |
| 5002 | auto weight_meta = to_meta(weight); |
| 5003 | auto out0_meta = to_meta(out0); |
| 5004 | auto out1_meta = to_meta(out1); |
| 5005 | auto out2_meta = to_meta(out2); |
| 5006 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5007 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5008 | at::_ops::native_group_norm_backward_out::call(grad_out_meta, input_meta, mean_meta, rstd_meta, weight_meta, N, C, HxW, group, output_mask, out0_meta, out1_meta, out2_meta); |
| 5009 | } |
| 5010 | |
| 5011 | at::Tensor grad_out_; |
| 5012 | if (at::functionalization::impl::isFunctionalTensor(grad_out)) { |
| 5013 | at::functionalization::impl::sync(grad_out); |
| 5014 | grad_out_ = at::functionalization::impl::from_functional_tensor(grad_out); |
| 5015 | } else { |
| 5016 | grad_out_ = grad_out; |
| 5017 | } |
| 5018 | |
| 5019 | at::Tensor input_; |
| 5020 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 5021 | at::functionalization::impl::sync(input); |
| 5022 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 5023 | } else { |
| 5024 | input_ = input; |
| 5025 | } |
| 5026 | |
| 5027 | at::Tensor mean_; |
| 5028 | if (at::functionalization::impl::isFunctionalTensor(mean)) { |
| 5029 | at::functionalization::impl::sync(mean); |
| 5030 | mean_ = at::functionalization::impl::from_functional_tensor(mean); |
| 5031 | } else { |
| 5032 | mean_ = mean; |
| 5033 | } |
| 5034 | |
| 5035 | at::Tensor rstd_; |
| 5036 | if (at::functionalization::impl::isFunctionalTensor(rstd)) { |
| 5037 | at::functionalization::impl::sync(rstd); |
| 5038 | rstd_ = at::functionalization::impl::from_functional_tensor(rstd); |
| 5039 | } else { |
| 5040 | rstd_ = rstd; |
| 5041 | } |
| 5042 | |
| 5043 | c10::optional<at::Tensor> weight_; |
| 5044 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 5045 | at::functionalization::impl::sync(weight); |
| 5046 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 5047 | } else { |
| 5048 | weight_ = weight; |
| 5049 | } |
| 5050 | |
| 5051 | at::Tensor out0_; |
| 5052 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 5053 | at::functionalization::impl::sync(out0); |
| 5054 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 5055 | } else { |
| 5056 | out0_ = out0; |
| 5057 | } |
| 5058 | |
| 5059 | at::Tensor out1_; |
| 5060 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 5061 | at::functionalization::impl::sync(out1); |
| 5062 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 5063 | } else { |
| 5064 | out1_ = out1; |
| 5065 | } |
| 5066 | |
| 5067 | at::Tensor out2_; |
| 5068 | if (at::functionalization::impl::isFunctionalTensor(out2)) { |
| 5069 | at::functionalization::impl::sync(out2); |
| 5070 | out2_ = at::functionalization::impl::from_functional_tensor(out2); |
| 5071 | } else { |
| 5072 | out2_ = out2; |
| 5073 | } |
| 5074 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1) && at::functionalization::impl::isFunctionalTensor(out2))) { |
| 5075 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_out) || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(mean) || at::functionalization::impl::isFunctionalTensor(rstd) || at::functionalization::impl::isFunctionalTensor(weight))) { |
| 5076 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5077 | TORCH_INTERNAL_ASSERT(false, |
| 5078 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5079 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5080 | } else { |
| 5081 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5082 | at::AutoDispatchSkipFunctionalize guard; |
| 5083 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::native_group_norm_backward_out::call(grad_out_, input_, mean_, rstd_, weight_, N, C, HxW, group, output_mask, out0_, out1_, out2_); |
| 5084 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2);; |
| 5085 | } |
| 5086 | } else { |
| 5087 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 5088 | { |
| 5089 | at::AutoDispatchSkipFunctionalize guard; |
| 5090 | tmp_output = at::_ops::native_group_norm_backward::call(grad_out_, input_, mean_, rstd_, weight_, N, C, HxW, group, output_mask); |
| 5091 | } |
| 5092 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 5093 | at::functionalization::impl::commit_update(out0); |
| 5094 | at::functionalization::impl::sync(out0); |
| 5095 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 5096 | at::functionalization::impl::commit_update(out1); |
| 5097 | at::functionalization::impl::sync(out1); |
| 5098 | at::functionalization::impl::replace_(out2, std::get<2>(tmp_output)); |
| 5099 | at::functionalization::impl::commit_update(out2); |
| 5100 | at::functionalization::impl::sync(out2); |
| 5101 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2); |
| 5102 | } |
| 5103 | } |
| 5104 | |
| 5105 | at::Tensor & _fft_r2c_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool onesided, at::Tensor & out) { |
| 5106 | if (false) { |
| 5107 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5108 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5109 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5110 | auto self_meta = to_meta(self); |
| 5111 | auto out_meta = to_meta(out); |
| 5112 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5113 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5114 | at::_ops::_fft_r2c_out::call(self_meta, dim, normalization, onesided, out_meta); |
| 5115 | } |
| 5116 | |
| 5117 | at::Tensor self_; |
| 5118 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5119 | at::functionalization::impl::sync(self); |
| 5120 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5121 | } else { |
| 5122 | self_ = self; |
| 5123 | } |
| 5124 | |
| 5125 | at::Tensor out_; |
| 5126 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5127 | at::functionalization::impl::sync(out); |
| 5128 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5129 | } else { |
| 5130 | out_ = out; |
| 5131 | } |
| 5132 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5133 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 5134 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5135 | TORCH_INTERNAL_ASSERT(false, |
| 5136 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5137 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5138 | } else { |
| 5139 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5140 | at::AutoDispatchSkipFunctionalize guard; |
| 5141 | at::Tensor tmp_output = at::_ops::_fft_r2c_out::call(self_, dim, normalization, onesided, out_); |
| 5142 | return out;; |
| 5143 | } |
| 5144 | } else { |
| 5145 | at::Tensor tmp_output; |
| 5146 | { |
| 5147 | at::AutoDispatchSkipFunctionalize guard; |
| 5148 | tmp_output = at::_ops::_fft_r2c::call(self_, dim, normalization, onesided); |
| 5149 | } |
| 5150 | at::functionalization::impl::replace_(out, tmp_output); |
| 5151 | at::functionalization::impl::commit_update(out); |
| 5152 | at::functionalization::impl::sync(out); |
| 5153 | return out; |
| 5154 | } |
| 5155 | } |
| 5156 | |
| 5157 | at::Tensor & index_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::List<c10::optional<at::Tensor>> & indices, at::Tensor & out) { |
| 5158 | if (false) { |
| 5159 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5160 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5161 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5162 | auto self_meta = to_meta(self); |
| 5163 | auto indices_meta = to_meta(indices); |
| 5164 | auto out_meta = to_meta(out); |
| 5165 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5166 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5167 | at::_ops::index_Tensor_out::call(self_meta, indices_meta, out_meta); |
| 5168 | } |
| 5169 | |
| 5170 | at::Tensor self_; |
| 5171 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5172 | at::functionalization::impl::sync(self); |
| 5173 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5174 | } else { |
| 5175 | self_ = self; |
| 5176 | } |
| 5177 | |
| 5178 | c10::List<c10::optional<at::Tensor>> indices_; |
| 5179 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 5180 | at::functionalization::impl::sync(indices); |
| 5181 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 5182 | } else { |
| 5183 | indices_ = indices; |
| 5184 | } |
| 5185 | |
| 5186 | at::Tensor out_; |
| 5187 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5188 | at::functionalization::impl::sync(out); |
| 5189 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5190 | } else { |
| 5191 | out_ = out; |
| 5192 | } |
| 5193 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5194 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(indices))) { |
| 5195 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5196 | TORCH_INTERNAL_ASSERT(false, |
| 5197 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5198 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5199 | } else { |
| 5200 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5201 | at::AutoDispatchSkipFunctionalize guard; |
| 5202 | at::Tensor tmp_output = at::_ops::index_Tensor_out::call(self_, indices_, out_); |
| 5203 | return out;; |
| 5204 | } |
| 5205 | } else { |
| 5206 | at::Tensor tmp_output; |
| 5207 | { |
| 5208 | at::AutoDispatchSkipFunctionalize guard; |
| 5209 | tmp_output = at::_ops::index_Tensor::call(self_, indices_); |
| 5210 | } |
| 5211 | at::functionalization::impl::replace_(out, tmp_output); |
| 5212 | at::functionalization::impl::commit_update(out); |
| 5213 | at::functionalization::impl::sync(out); |
| 5214 | return out; |
| 5215 | } |
| 5216 | } |
| 5217 | |
| 5218 | at::Tensor & index_put_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::List<c10::optional<at::Tensor>> & indices, const at::Tensor & values, bool accumulate, at::Tensor & out) { |
| 5219 | if (false) { |
| 5220 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5221 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5222 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5223 | auto self_meta = to_meta(self); |
| 5224 | auto indices_meta = to_meta(indices); |
| 5225 | auto values_meta = to_meta(values); |
| 5226 | auto out_meta = to_meta(out); |
| 5227 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5228 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5229 | at::_ops::index_put_out::call(self_meta, indices_meta, values_meta, accumulate, out_meta); |
| 5230 | } |
| 5231 | |
| 5232 | at::Tensor self_; |
| 5233 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5234 | at::functionalization::impl::sync(self); |
| 5235 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5236 | } else { |
| 5237 | self_ = self; |
| 5238 | } |
| 5239 | |
| 5240 | c10::List<c10::optional<at::Tensor>> indices_; |
| 5241 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 5242 | at::functionalization::impl::sync(indices); |
| 5243 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 5244 | } else { |
| 5245 | indices_ = indices; |
| 5246 | } |
| 5247 | |
| 5248 | at::Tensor values_; |
| 5249 | if (at::functionalization::impl::isFunctionalTensor(values)) { |
| 5250 | at::functionalization::impl::sync(values); |
| 5251 | values_ = at::functionalization::impl::from_functional_tensor(values); |
| 5252 | } else { |
| 5253 | values_ = values; |
| 5254 | } |
| 5255 | |
| 5256 | at::Tensor out_; |
| 5257 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5258 | at::functionalization::impl::sync(out); |
| 5259 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5260 | } else { |
| 5261 | out_ = out; |
| 5262 | } |
| 5263 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5264 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(indices) || at::functionalization::impl::isFunctionalTensor(values))) { |
| 5265 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5266 | TORCH_INTERNAL_ASSERT(false, |
| 5267 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5268 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5269 | } else { |
| 5270 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5271 | at::AutoDispatchSkipFunctionalize guard; |
| 5272 | at::Tensor tmp_output = at::_ops::index_put_out::call(self_, indices_, values_, accumulate, out_); |
| 5273 | return out;; |
| 5274 | } |
| 5275 | } else { |
| 5276 | at::Tensor tmp_output; |
| 5277 | { |
| 5278 | at::AutoDispatchSkipFunctionalize guard; |
| 5279 | tmp_output = at::_ops::index_put::call(self_, indices_, values_, accumulate); |
| 5280 | } |
| 5281 | at::functionalization::impl::replace_(out, tmp_output); |
| 5282 | at::functionalization::impl::commit_update(out); |
| 5283 | at::functionalization::impl::sync(out); |
| 5284 | return out; |
| 5285 | } |
| 5286 | } |
| 5287 | |
| 5288 | at::Tensor & index_put_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const c10::List<c10::optional<at::Tensor>> & indices, const at::Tensor & values, bool accumulate) { |
| 5289 | if (true) { |
| 5290 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5291 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5292 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5293 | auto self_meta = to_meta(self); |
| 5294 | auto indices_meta = to_meta(indices); |
| 5295 | auto values_meta = to_meta(values); |
| 5296 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5297 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5298 | at::_ops::index_put_::call(self_meta, indices_meta, values_meta, accumulate); |
| 5299 | } |
| 5300 | |
| 5301 | at::Tensor self_; |
| 5302 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5303 | at::functionalization::impl::sync(self); |
| 5304 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5305 | } else { |
| 5306 | self_ = self; |
| 5307 | } |
| 5308 | |
| 5309 | c10::List<c10::optional<at::Tensor>> indices_; |
| 5310 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 5311 | at::functionalization::impl::sync(indices); |
| 5312 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 5313 | } else { |
| 5314 | indices_ = indices; |
| 5315 | } |
| 5316 | |
| 5317 | at::Tensor values_; |
| 5318 | if (at::functionalization::impl::isFunctionalTensor(values)) { |
| 5319 | at::functionalization::impl::sync(values); |
| 5320 | values_ = at::functionalization::impl::from_functional_tensor(values); |
| 5321 | } else { |
| 5322 | values_ = values; |
| 5323 | } |
| 5324 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 5325 | if ((false || at::functionalization::impl::isFunctionalTensor(indices) || at::functionalization::impl::isFunctionalTensor(values))) { |
| 5326 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5327 | TORCH_INTERNAL_ASSERT(false, |
| 5328 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5329 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5330 | } else { |
| 5331 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5332 | at::AutoDispatchSkipFunctionalize guard; |
| 5333 | at::Tensor tmp_output = at::_ops::index_put_::call(self_, indices_, values_, accumulate); |
| 5334 | return self;; |
| 5335 | } |
| 5336 | } else { |
| 5337 | at::Tensor tmp_output; |
| 5338 | { |
| 5339 | at::AutoDispatchSkipFunctionalize guard; |
| 5340 | tmp_output = at::_ops::index_put::call(self_, indices_, values_, accumulate); |
| 5341 | } |
| 5342 | at::functionalization::impl::replace_(self, tmp_output); |
| 5343 | at::functionalization::impl::commit_update(self); |
| 5344 | at::functionalization::impl::sync(self); |
| 5345 | return self; |
| 5346 | } |
| 5347 | } |
| 5348 | |
| 5349 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linear_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 5350 | if (false) { |
| 5351 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5352 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5353 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5354 | auto self_meta = to_meta(self); |
| 5355 | auto grad_output_meta = to_meta(grad_output); |
| 5356 | auto weight_meta = to_meta(weight); |
| 5357 | auto out0_meta = to_meta(out0); |
| 5358 | auto out1_meta = to_meta(out1); |
| 5359 | auto out2_meta = to_meta(out2); |
| 5360 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5361 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5362 | at::_ops::linear_backward_out::call(self_meta, grad_output_meta, weight_meta, output_mask, out0_meta, out1_meta, out2_meta); |
| 5363 | } |
| 5364 | |
| 5365 | at::Tensor self_; |
| 5366 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5367 | at::functionalization::impl::sync(self); |
| 5368 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5369 | } else { |
| 5370 | self_ = self; |
| 5371 | } |
| 5372 | |
| 5373 | at::Tensor grad_output_; |
| 5374 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 5375 | at::functionalization::impl::sync(grad_output); |
| 5376 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 5377 | } else { |
| 5378 | grad_output_ = grad_output; |
| 5379 | } |
| 5380 | |
| 5381 | at::Tensor weight_; |
| 5382 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 5383 | at::functionalization::impl::sync(weight); |
| 5384 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 5385 | } else { |
| 5386 | weight_ = weight; |
| 5387 | } |
| 5388 | |
| 5389 | at::Tensor out0_; |
| 5390 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 5391 | at::functionalization::impl::sync(out0); |
| 5392 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 5393 | } else { |
| 5394 | out0_ = out0; |
| 5395 | } |
| 5396 | |
| 5397 | at::Tensor out1_; |
| 5398 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 5399 | at::functionalization::impl::sync(out1); |
| 5400 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 5401 | } else { |
| 5402 | out1_ = out1; |
| 5403 | } |
| 5404 | |
| 5405 | at::Tensor out2_; |
| 5406 | if (at::functionalization::impl::isFunctionalTensor(out2)) { |
| 5407 | at::functionalization::impl::sync(out2); |
| 5408 | out2_ = at::functionalization::impl::from_functional_tensor(out2); |
| 5409 | } else { |
| 5410 | out2_ = out2; |
| 5411 | } |
| 5412 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1) && at::functionalization::impl::isFunctionalTensor(out2))) { |
| 5413 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(weight))) { |
| 5414 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5415 | TORCH_INTERNAL_ASSERT(false, |
| 5416 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5417 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5418 | } else { |
| 5419 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5420 | at::AutoDispatchSkipFunctionalize guard; |
| 5421 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::linear_backward_out::call(self_, grad_output_, weight_, output_mask, out0_, out1_, out2_); |
| 5422 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2);; |
| 5423 | } |
| 5424 | } else { |
| 5425 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 5426 | { |
| 5427 | at::AutoDispatchSkipFunctionalize guard; |
| 5428 | tmp_output = at::_ops::linear_backward::call(self_, grad_output_, weight_, output_mask); |
| 5429 | } |
| 5430 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 5431 | at::functionalization::impl::commit_update(out0); |
| 5432 | at::functionalization::impl::sync(out0); |
| 5433 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 5434 | at::functionalization::impl::commit_update(out1); |
| 5435 | at::functionalization::impl::sync(out1); |
| 5436 | at::functionalization::impl::replace_(out2, std::get<2>(tmp_output)); |
| 5437 | at::functionalization::impl::commit_update(out2); |
| 5438 | at::functionalization::impl::sync(out2); |
| 5439 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2); |
| 5440 | } |
| 5441 | } |
| 5442 | |
| 5443 | ::std::tuple<at::Tensor &,at::Tensor &> mkldnn_linear_backward_weights_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, bool bias_defined, at::Tensor & out0, at::Tensor & out1) { |
| 5444 | if (false) { |
| 5445 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5446 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5447 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5448 | auto grad_output_meta = to_meta(grad_output); |
| 5449 | auto input_meta = to_meta(input); |
| 5450 | auto weight_meta = to_meta(weight); |
| 5451 | auto out0_meta = to_meta(out0); |
| 5452 | auto out1_meta = to_meta(out1); |
| 5453 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5454 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5455 | at::_ops::mkldnn_linear_backward_weights_out::call(grad_output_meta, input_meta, weight_meta, bias_defined, out0_meta, out1_meta); |
| 5456 | } |
| 5457 | |
| 5458 | at::Tensor grad_output_; |
| 5459 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 5460 | at::functionalization::impl::sync(grad_output); |
| 5461 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 5462 | } else { |
| 5463 | grad_output_ = grad_output; |
| 5464 | } |
| 5465 | |
| 5466 | at::Tensor input_; |
| 5467 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 5468 | at::functionalization::impl::sync(input); |
| 5469 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 5470 | } else { |
| 5471 | input_ = input; |
| 5472 | } |
| 5473 | |
| 5474 | at::Tensor weight_; |
| 5475 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 5476 | at::functionalization::impl::sync(weight); |
| 5477 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 5478 | } else { |
| 5479 | weight_ = weight; |
| 5480 | } |
| 5481 | |
| 5482 | at::Tensor out0_; |
| 5483 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 5484 | at::functionalization::impl::sync(out0); |
| 5485 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 5486 | } else { |
| 5487 | out0_ = out0; |
| 5488 | } |
| 5489 | |
| 5490 | at::Tensor out1_; |
| 5491 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 5492 | at::functionalization::impl::sync(out1); |
| 5493 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 5494 | } else { |
| 5495 | out1_ = out1; |
| 5496 | } |
| 5497 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1))) { |
| 5498 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(weight))) { |
| 5499 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5500 | TORCH_INTERNAL_ASSERT(false, |
| 5501 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5502 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5503 | } else { |
| 5504 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5505 | at::AutoDispatchSkipFunctionalize guard; |
| 5506 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::mkldnn_linear_backward_weights_out::call(grad_output_, input_, weight_, bias_defined, out0_, out1_); |
| 5507 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1);; |
| 5508 | } |
| 5509 | } else { |
| 5510 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 5511 | { |
| 5512 | at::AutoDispatchSkipFunctionalize guard; |
| 5513 | tmp_output = at::_ops::mkldnn_linear_backward_weights::call(grad_output_, input_, weight_, bias_defined); |
| 5514 | } |
| 5515 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 5516 | at::functionalization::impl::commit_update(out0); |
| 5517 | at::functionalization::impl::sync(out0); |
| 5518 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 5519 | at::functionalization::impl::commit_update(out1); |
| 5520 | at::functionalization::impl::sync(out1); |
| 5521 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1); |
| 5522 | } |
| 5523 | } |
| 5524 | |
| 5525 | at::Tensor & logaddexp_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 5526 | if (false) { |
| 5527 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5528 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5529 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5530 | auto self_meta = to_meta(self); |
| 5531 | auto other_meta = to_meta(other); |
| 5532 | auto out_meta = to_meta(out); |
| 5533 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5534 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5535 | at::_ops::logaddexp_out::call(self_meta, other_meta, out_meta); |
| 5536 | } |
| 5537 | |
| 5538 | at::Tensor self_; |
| 5539 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5540 | at::functionalization::impl::sync(self); |
| 5541 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5542 | } else { |
| 5543 | self_ = self; |
| 5544 | } |
| 5545 | |
| 5546 | at::Tensor other_; |
| 5547 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 5548 | at::functionalization::impl::sync(other); |
| 5549 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 5550 | } else { |
| 5551 | other_ = other; |
| 5552 | } |
| 5553 | |
| 5554 | at::Tensor out_; |
| 5555 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5556 | at::functionalization::impl::sync(out); |
| 5557 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5558 | } else { |
| 5559 | out_ = out; |
| 5560 | } |
| 5561 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5562 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 5563 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5564 | TORCH_INTERNAL_ASSERT(false, |
| 5565 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5566 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5567 | } else { |
| 5568 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5569 | at::AutoDispatchSkipFunctionalize guard; |
| 5570 | at::Tensor tmp_output = at::_ops::logaddexp_out::call(self_, other_, out_); |
| 5571 | return out;; |
| 5572 | } |
| 5573 | } else { |
| 5574 | at::Tensor tmp_output; |
| 5575 | { |
| 5576 | at::AutoDispatchSkipFunctionalize guard; |
| 5577 | tmp_output = at::_ops::logaddexp::call(self_, other_); |
| 5578 | } |
| 5579 | at::functionalization::impl::replace_(out, tmp_output); |
| 5580 | at::functionalization::impl::commit_update(out); |
| 5581 | at::functionalization::impl::sync(out); |
| 5582 | return out; |
| 5583 | } |
| 5584 | } |
| 5585 | |
| 5586 | at::Tensor & _logcumsumexp_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, at::Tensor & out) { |
| 5587 | if (false) { |
| 5588 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5589 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5590 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5591 | auto self_meta = to_meta(self); |
| 5592 | auto out_meta = to_meta(out); |
| 5593 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5594 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5595 | at::_ops::_logcumsumexp_out::call(self_meta, dim, out_meta); |
| 5596 | } |
| 5597 | |
| 5598 | at::Tensor self_; |
| 5599 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5600 | at::functionalization::impl::sync(self); |
| 5601 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5602 | } else { |
| 5603 | self_ = self; |
| 5604 | } |
| 5605 | |
| 5606 | at::Tensor out_; |
| 5607 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5608 | at::functionalization::impl::sync(out); |
| 5609 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5610 | } else { |
| 5611 | out_ = out; |
| 5612 | } |
| 5613 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5614 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 5615 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5616 | TORCH_INTERNAL_ASSERT(false, |
| 5617 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5618 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5619 | } else { |
| 5620 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5621 | at::AutoDispatchSkipFunctionalize guard; |
| 5622 | at::Tensor tmp_output = at::_ops::_logcumsumexp_out::call(self_, dim, out_); |
| 5623 | return out;; |
| 5624 | } |
| 5625 | } else { |
| 5626 | at::Tensor tmp_output; |
| 5627 | { |
| 5628 | at::AutoDispatchSkipFunctionalize guard; |
| 5629 | tmp_output = at::_ops::_logcumsumexp::call(self_, dim); |
| 5630 | } |
| 5631 | at::functionalization::impl::replace_(out, tmp_output); |
| 5632 | at::functionalization::impl::commit_update(out); |
| 5633 | at::functionalization::impl::sync(out); |
| 5634 | return out; |
| 5635 | } |
| 5636 | } |
| 5637 | |
| 5638 | at::Tensor & logcumsumexp_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, at::Tensor & out) { |
| 5639 | if (false) { |
| 5640 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5641 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5642 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5643 | auto self_meta = to_meta(self); |
| 5644 | auto out_meta = to_meta(out); |
| 5645 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5646 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5647 | at::_ops::logcumsumexp_out::call(self_meta, dim, out_meta); |
| 5648 | } |
| 5649 | |
| 5650 | at::Tensor self_; |
| 5651 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5652 | at::functionalization::impl::sync(self); |
| 5653 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5654 | } else { |
| 5655 | self_ = self; |
| 5656 | } |
| 5657 | |
| 5658 | at::Tensor out_; |
| 5659 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5660 | at::functionalization::impl::sync(out); |
| 5661 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5662 | } else { |
| 5663 | out_ = out; |
| 5664 | } |
| 5665 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5666 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 5667 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5668 | TORCH_INTERNAL_ASSERT(false, |
| 5669 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5670 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5671 | } else { |
| 5672 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5673 | at::AutoDispatchSkipFunctionalize guard; |
| 5674 | at::Tensor tmp_output = at::_ops::logcumsumexp_out::call(self_, dim, out_); |
| 5675 | return out;; |
| 5676 | } |
| 5677 | } else { |
| 5678 | at::Tensor tmp_output; |
| 5679 | { |
| 5680 | at::AutoDispatchSkipFunctionalize guard; |
| 5681 | tmp_output = at::_ops::logcumsumexp::call(self_, dim); |
| 5682 | } |
| 5683 | at::functionalization::impl::replace_(out, tmp_output); |
| 5684 | at::functionalization::impl::commit_update(out); |
| 5685 | at::functionalization::impl::sync(out); |
| 5686 | return out; |
| 5687 | } |
| 5688 | } |
| 5689 | |
| 5690 | at::Tensor & logcumsumexp_out_dimname_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, at::Tensor & out) { |
| 5691 | if (false) { |
| 5692 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5693 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5694 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5695 | auto self_meta = to_meta(self); |
| 5696 | auto out_meta = to_meta(out); |
| 5697 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5698 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5699 | at::_ops::logcumsumexp_dimname_out::call(self_meta, dim, out_meta); |
| 5700 | } |
| 5701 | |
| 5702 | at::Tensor self_; |
| 5703 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5704 | at::functionalization::impl::sync(self); |
| 5705 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5706 | } else { |
| 5707 | self_ = self; |
| 5708 | } |
| 5709 | |
| 5710 | at::Tensor out_; |
| 5711 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5712 | at::functionalization::impl::sync(out); |
| 5713 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5714 | } else { |
| 5715 | out_ = out; |
| 5716 | } |
| 5717 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5718 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 5719 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5720 | TORCH_INTERNAL_ASSERT(false, |
| 5721 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5722 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5723 | } else { |
| 5724 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5725 | at::AutoDispatchSkipFunctionalize guard; |
| 5726 | at::Tensor tmp_output = at::_ops::logcumsumexp_dimname_out::call(self_, dim, out_); |
| 5727 | return out;; |
| 5728 | } |
| 5729 | } else { |
| 5730 | at::Tensor tmp_output; |
| 5731 | { |
| 5732 | at::AutoDispatchSkipFunctionalize guard; |
| 5733 | tmp_output = at::_ops::logcumsumexp_dimname::call(self_, dim); |
| 5734 | } |
| 5735 | at::functionalization::impl::replace_(out, tmp_output); |
| 5736 | at::functionalization::impl::commit_update(out); |
| 5737 | at::functionalization::impl::sync(out); |
| 5738 | return out; |
| 5739 | } |
| 5740 | } |
| 5741 | |
| 5742 | ::std::tuple<at::Tensor &,at::Tensor &> matmul_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & self, const at::Tensor & other, ::std::array<bool,2> mask, at::Tensor & out0, at::Tensor & out1) { |
| 5743 | if (false) { |
| 5744 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5745 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5746 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5747 | auto grad_meta = to_meta(grad); |
| 5748 | auto self_meta = to_meta(self); |
| 5749 | auto other_meta = to_meta(other); |
| 5750 | auto out0_meta = to_meta(out0); |
| 5751 | auto out1_meta = to_meta(out1); |
| 5752 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5753 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5754 | at::_ops::matmul_backward_out::call(grad_meta, self_meta, other_meta, mask, out0_meta, out1_meta); |
| 5755 | } |
| 5756 | |
| 5757 | at::Tensor grad_; |
| 5758 | if (at::functionalization::impl::isFunctionalTensor(grad)) { |
| 5759 | at::functionalization::impl::sync(grad); |
| 5760 | grad_ = at::functionalization::impl::from_functional_tensor(grad); |
| 5761 | } else { |
| 5762 | grad_ = grad; |
| 5763 | } |
| 5764 | |
| 5765 | at::Tensor self_; |
| 5766 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5767 | at::functionalization::impl::sync(self); |
| 5768 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5769 | } else { |
| 5770 | self_ = self; |
| 5771 | } |
| 5772 | |
| 5773 | at::Tensor other_; |
| 5774 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 5775 | at::functionalization::impl::sync(other); |
| 5776 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 5777 | } else { |
| 5778 | other_ = other; |
| 5779 | } |
| 5780 | |
| 5781 | at::Tensor out0_; |
| 5782 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 5783 | at::functionalization::impl::sync(out0); |
| 5784 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 5785 | } else { |
| 5786 | out0_ = out0; |
| 5787 | } |
| 5788 | |
| 5789 | at::Tensor out1_; |
| 5790 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 5791 | at::functionalization::impl::sync(out1); |
| 5792 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 5793 | } else { |
| 5794 | out1_ = out1; |
| 5795 | } |
| 5796 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1))) { |
| 5797 | if ((false || at::functionalization::impl::isFunctionalTensor(grad) || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 5798 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5799 | TORCH_INTERNAL_ASSERT(false, |
| 5800 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5801 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5802 | } else { |
| 5803 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5804 | at::AutoDispatchSkipFunctionalize guard; |
| 5805 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::matmul_backward_out::call(grad_, self_, other_, mask, out0_, out1_); |
| 5806 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1);; |
| 5807 | } |
| 5808 | } else { |
| 5809 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 5810 | { |
| 5811 | at::AutoDispatchSkipFunctionalize guard; |
| 5812 | tmp_output = at::_ops::matmul_backward::call(grad_, self_, other_, mask); |
| 5813 | } |
| 5814 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 5815 | at::functionalization::impl::commit_update(out0); |
| 5816 | at::functionalization::impl::sync(out0); |
| 5817 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 5818 | at::functionalization::impl::commit_update(out1); |
| 5819 | at::functionalization::impl::sync(out1); |
| 5820 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1); |
| 5821 | } |
| 5822 | } |
| 5823 | |
| 5824 | at::Tensor & mps_max_pool2d_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out) { |
| 5825 | if (false) { |
| 5826 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5827 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5828 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5829 | auto grad_output_meta = to_meta(grad_output); |
| 5830 | auto self_meta = to_meta(self); |
| 5831 | auto out_meta = to_meta(out); |
| 5832 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5833 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5834 | at::_ops::mps_max_pool2d_backward_out::call(grad_output_meta, self_meta, kernel_size, stride, padding, dilation, ceil_mode, out_meta); |
| 5835 | } |
| 5836 | |
| 5837 | at::Tensor grad_output_; |
| 5838 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 5839 | at::functionalization::impl::sync(grad_output); |
| 5840 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 5841 | } else { |
| 5842 | grad_output_ = grad_output; |
| 5843 | } |
| 5844 | |
| 5845 | at::Tensor self_; |
| 5846 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5847 | at::functionalization::impl::sync(self); |
| 5848 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5849 | } else { |
| 5850 | self_ = self; |
| 5851 | } |
| 5852 | |
| 5853 | at::Tensor out_; |
| 5854 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5855 | at::functionalization::impl::sync(out); |
| 5856 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5857 | } else { |
| 5858 | out_ = out; |
| 5859 | } |
| 5860 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5861 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self))) { |
| 5862 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5863 | TORCH_INTERNAL_ASSERT(false, |
| 5864 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5865 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5866 | } else { |
| 5867 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5868 | at::AutoDispatchSkipFunctionalize guard; |
| 5869 | at::Tensor tmp_output = at::_ops::mps_max_pool2d_backward_out::call(grad_output_, self_, kernel_size, stride, padding, dilation, ceil_mode, out_); |
| 5870 | return out;; |
| 5871 | } |
| 5872 | } else { |
| 5873 | at::Tensor tmp_output; |
| 5874 | { |
| 5875 | at::AutoDispatchSkipFunctionalize guard; |
| 5876 | tmp_output = at::_ops::mps_max_pool2d_backward::call(grad_output_, self_, kernel_size, stride, padding, dilation, ceil_mode); |
| 5877 | } |
| 5878 | at::functionalization::impl::replace_(out, tmp_output); |
| 5879 | at::functionalization::impl::commit_update(out); |
| 5880 | at::functionalization::impl::sync(out); |
| 5881 | return out; |
| 5882 | } |
| 5883 | } |
| 5884 | |
| 5885 | at::Tensor & median_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 5886 | if (false) { |
| 5887 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5888 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5889 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5890 | auto self_meta = to_meta(self); |
| 5891 | auto out_meta = to_meta(out); |
| 5892 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5893 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5894 | at::_ops::median_out::call(self_meta, out_meta); |
| 5895 | } |
| 5896 | |
| 5897 | at::Tensor self_; |
| 5898 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5899 | at::functionalization::impl::sync(self); |
| 5900 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5901 | } else { |
| 5902 | self_ = self; |
| 5903 | } |
| 5904 | |
| 5905 | at::Tensor out_; |
| 5906 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5907 | at::functionalization::impl::sync(out); |
| 5908 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5909 | } else { |
| 5910 | out_ = out; |
| 5911 | } |
| 5912 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5913 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 5914 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5915 | TORCH_INTERNAL_ASSERT(false, |
| 5916 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5917 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5918 | } else { |
| 5919 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5920 | at::AutoDispatchSkipFunctionalize guard; |
| 5921 | at::Tensor tmp_output = at::_ops::median_out::call(self_, out_); |
| 5922 | return out;; |
| 5923 | } |
| 5924 | } else { |
| 5925 | at::Tensor tmp_output; |
| 5926 | { |
| 5927 | at::AutoDispatchSkipFunctionalize guard; |
| 5928 | tmp_output = at::_ops::median::call(self_); |
| 5929 | } |
| 5930 | at::functionalization::impl::replace_(out, tmp_output); |
| 5931 | at::functionalization::impl::commit_update(out); |
| 5932 | at::functionalization::impl::sync(out); |
| 5933 | return out; |
| 5934 | } |
| 5935 | } |
| 5936 | |
| 5937 | ::std::tuple<at::Tensor &,at::Tensor &> median_out_dim_values(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & values, at::Tensor & indices) { |
| 5938 | if (false) { |
| 5939 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5940 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5941 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5942 | auto self_meta = to_meta(self); |
| 5943 | auto values_meta = to_meta(values); |
| 5944 | auto indices_meta = to_meta(indices); |
| 5945 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5946 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5947 | at::_ops::median_dim_values::call(self_meta, dim, keepdim, values_meta, indices_meta); |
| 5948 | } |
| 5949 | |
| 5950 | at::Tensor self_; |
| 5951 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5952 | at::functionalization::impl::sync(self); |
| 5953 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5954 | } else { |
| 5955 | self_ = self; |
| 5956 | } |
| 5957 | |
| 5958 | at::Tensor values_; |
| 5959 | if (at::functionalization::impl::isFunctionalTensor(values)) { |
| 5960 | at::functionalization::impl::sync(values); |
| 5961 | values_ = at::functionalization::impl::from_functional_tensor(values); |
| 5962 | } else { |
| 5963 | values_ = values; |
| 5964 | } |
| 5965 | |
| 5966 | at::Tensor indices_; |
| 5967 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 5968 | at::functionalization::impl::sync(indices); |
| 5969 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 5970 | } else { |
| 5971 | indices_ = indices; |
| 5972 | } |
| 5973 | if (!(true && at::functionalization::impl::isFunctionalTensor(values) && at::functionalization::impl::isFunctionalTensor(indices))) { |
| 5974 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 5975 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5976 | TORCH_INTERNAL_ASSERT(false, |
| 5977 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5978 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5979 | } else { |
| 5980 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5981 | at::AutoDispatchSkipFunctionalize guard; |
| 5982 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::median_dim_values::call(self_, dim, keepdim, values_, indices_); |
| 5983 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices);; |
| 5984 | } |
| 5985 | } else { |
| 5986 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 5987 | { |
| 5988 | at::AutoDispatchSkipFunctionalize guard; |
| 5989 | tmp_output = at::_ops::median_dim::call(self_, dim, keepdim); |
| 5990 | } |
| 5991 | at::functionalization::impl::replace_(values, std::get<0>(tmp_output)); |
| 5992 | at::functionalization::impl::commit_update(values); |
| 5993 | at::functionalization::impl::sync(values); |
| 5994 | at::functionalization::impl::replace_(indices, std::get<1>(tmp_output)); |
| 5995 | at::functionalization::impl::commit_update(indices); |
| 5996 | at::functionalization::impl::sync(indices); |
| 5997 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices); |
| 5998 | } |
| 5999 | } |
| 6000 | |
| 6001 | ::std::tuple<at::Tensor &,at::Tensor &> median_out_names_dim_values(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & values, at::Tensor & indices) { |
| 6002 | if (false) { |
| 6003 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6004 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6005 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6006 | auto self_meta = to_meta(self); |
| 6007 | auto values_meta = to_meta(values); |
| 6008 | auto indices_meta = to_meta(indices); |
| 6009 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6010 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6011 | at::_ops::median_names_dim_values::call(self_meta, dim, keepdim, values_meta, indices_meta); |
| 6012 | } |
| 6013 | |
| 6014 | at::Tensor self_; |
| 6015 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6016 | at::functionalization::impl::sync(self); |
| 6017 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6018 | } else { |
| 6019 | self_ = self; |
| 6020 | } |
| 6021 | |
| 6022 | at::Tensor values_; |
| 6023 | if (at::functionalization::impl::isFunctionalTensor(values)) { |
| 6024 | at::functionalization::impl::sync(values); |
| 6025 | values_ = at::functionalization::impl::from_functional_tensor(values); |
| 6026 | } else { |
| 6027 | values_ = values; |
| 6028 | } |
| 6029 | |
| 6030 | at::Tensor indices_; |
| 6031 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 6032 | at::functionalization::impl::sync(indices); |
| 6033 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 6034 | } else { |
| 6035 | indices_ = indices; |
| 6036 | } |
| 6037 | if (!(true && at::functionalization::impl::isFunctionalTensor(values) && at::functionalization::impl::isFunctionalTensor(indices))) { |
| 6038 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 6039 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6040 | TORCH_INTERNAL_ASSERT(false, |
| 6041 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6042 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6043 | } else { |
| 6044 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6045 | at::AutoDispatchSkipFunctionalize guard; |
| 6046 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::median_names_dim_values::call(self_, dim, keepdim, values_, indices_); |
| 6047 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices);; |
| 6048 | } |
| 6049 | } else { |
| 6050 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 6051 | { |
| 6052 | at::AutoDispatchSkipFunctionalize guard; |
| 6053 | tmp_output = at::_ops::median_names_dim::call(self_, dim, keepdim); |
| 6054 | } |
| 6055 | at::functionalization::impl::replace_(values, std::get<0>(tmp_output)); |
| 6056 | at::functionalization::impl::commit_update(values); |
| 6057 | at::functionalization::impl::sync(values); |
| 6058 | at::functionalization::impl::replace_(indices, std::get<1>(tmp_output)); |
| 6059 | at::functionalization::impl::commit_update(indices); |
| 6060 | at::functionalization::impl::sync(indices); |
| 6061 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices); |
| 6062 | } |
| 6063 | } |
| 6064 | |
| 6065 | at::Tensor & amin_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { |
| 6066 | if (false) { |
| 6067 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6068 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6069 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6070 | auto self_meta = to_meta(self); |
| 6071 | auto out_meta = to_meta(out); |
| 6072 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6073 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6074 | at::_ops::amin_out::call(self_meta, dim, keepdim, out_meta); |
| 6075 | } |
| 6076 | |
| 6077 | at::Tensor self_; |
| 6078 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6079 | at::functionalization::impl::sync(self); |
| 6080 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6081 | } else { |
| 6082 | self_ = self; |
| 6083 | } |
| 6084 | |
| 6085 | at::Tensor out_; |
| 6086 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 6087 | at::functionalization::impl::sync(out); |
| 6088 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 6089 | } else { |
| 6090 | out_ = out; |
| 6091 | } |
| 6092 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 6093 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 6094 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6095 | TORCH_INTERNAL_ASSERT(false, |
| 6096 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6097 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6098 | } else { |
| 6099 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6100 | at::AutoDispatchSkipFunctionalize guard; |
| 6101 | at::Tensor tmp_output = at::_ops::amin_out::call(self_, dim, keepdim, out_); |
| 6102 | return out;; |
| 6103 | } |
| 6104 | } else { |
| 6105 | at::Tensor tmp_output; |
| 6106 | { |
| 6107 | at::AutoDispatchSkipFunctionalize guard; |
| 6108 | tmp_output = at::_ops::amin::call(self_, dim, keepdim); |
| 6109 | } |
| 6110 | at::functionalization::impl::replace_(out, tmp_output); |
| 6111 | at::functionalization::impl::commit_update(out); |
| 6112 | at::functionalization::impl::sync(out); |
| 6113 | return out; |
| 6114 | } |
| 6115 | } |
| 6116 | |
| 6117 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> mkldnn_rnn_layer_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & weight4, const at::Tensor & hx_, const at::Tensor & cx_tmp, const at::Tensor & output, const at::Tensor & hy_, const at::Tensor & cy_, const c10::optional<at::Tensor> & grad_output, const c10::optional<at::Tensor> & grad_hy, const c10::optional<at::Tensor> & grad_cy, bool reverse, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool train, bool bidirectional, at::IntArrayRef batch_sizes, bool batch_first, const at::Tensor & workspace, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, at::Tensor & out5, at::Tensor & out6) { |
| 6118 | if (false) { |
| 6119 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6120 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6121 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6122 | auto input_meta = to_meta(input); |
| 6123 | auto weight1_meta = to_meta(weight1); |
| 6124 | auto weight2_meta = to_meta(weight2); |
| 6125 | auto weight3_meta = to_meta(weight3); |
| 6126 | auto weight4_meta = to_meta(weight4); |
| 6127 | auto hx__meta = to_meta(hx_); |
| 6128 | auto cx_tmp_meta = to_meta(cx_tmp); |
| 6129 | auto output_meta = to_meta(output); |
| 6130 | auto hy__meta = to_meta(hy_); |
| 6131 | auto cy__meta = to_meta(cy_); |
| 6132 | auto grad_output_meta = to_meta(grad_output); |
| 6133 | auto grad_hy_meta = to_meta(grad_hy); |
| 6134 | auto grad_cy_meta = to_meta(grad_cy); |
| 6135 | auto workspace_meta = to_meta(workspace); |
| 6136 | auto out0_meta = to_meta(out0); |
| 6137 | auto out1_meta = to_meta(out1); |
| 6138 | auto out2_meta = to_meta(out2); |
| 6139 | auto out3_meta = to_meta(out3); |
| 6140 | auto out4_meta = to_meta(out4); |
| 6141 | auto out5_meta = to_meta(out5); |
| 6142 | auto out6_meta = to_meta(out6); |
| 6143 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6144 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6145 | at::_ops::mkldnn_rnn_layer_backward_out::call(input_meta, weight1_meta, weight2_meta, weight3_meta, weight4_meta, hx__meta, cx_tmp_meta, output_meta, hy__meta, cy__meta, grad_output_meta, grad_hy_meta, grad_cy_meta, reverse, mode, hidden_size, num_layers, has_biases, train, bidirectional, batch_sizes, batch_first, workspace_meta, out0_meta, out1_meta, out2_meta, out3_meta, out4_meta, out5_meta, out6_meta); |
| 6146 | } |
| 6147 | |
| 6148 | at::Tensor input_; |
| 6149 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 6150 | at::functionalization::impl::sync(input); |
| 6151 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 6152 | } else { |
| 6153 | input_ = input; |
| 6154 | } |
| 6155 | |
| 6156 | at::Tensor weight1_; |
| 6157 | if (at::functionalization::impl::isFunctionalTensor(weight1)) { |
| 6158 | at::functionalization::impl::sync(weight1); |
| 6159 | weight1_ = at::functionalization::impl::from_functional_tensor(weight1); |
| 6160 | } else { |
| 6161 | weight1_ = weight1; |
| 6162 | } |
| 6163 | |
| 6164 | at::Tensor weight2_; |
| 6165 | if (at::functionalization::impl::isFunctionalTensor(weight2)) { |
| 6166 | at::functionalization::impl::sync(weight2); |
| 6167 | weight2_ = at::functionalization::impl::from_functional_tensor(weight2); |
| 6168 | } else { |
| 6169 | weight2_ = weight2; |
| 6170 | } |
| 6171 | |
| 6172 | at::Tensor weight3_; |
| 6173 | if (at::functionalization::impl::isFunctionalTensor(weight3)) { |
| 6174 | at::functionalization::impl::sync(weight3); |
| 6175 | weight3_ = at::functionalization::impl::from_functional_tensor(weight3); |
| 6176 | } else { |
| 6177 | weight3_ = weight3; |
| 6178 | } |
| 6179 | |
| 6180 | at::Tensor weight4_; |
| 6181 | if (at::functionalization::impl::isFunctionalTensor(weight4)) { |
| 6182 | at::functionalization::impl::sync(weight4); |
| 6183 | weight4_ = at::functionalization::impl::from_functional_tensor(weight4); |
| 6184 | } else { |
| 6185 | weight4_ = weight4; |
| 6186 | } |
| 6187 | |
| 6188 | at::Tensor hx__; |
| 6189 | if (at::functionalization::impl::isFunctionalTensor(hx_)) { |
| 6190 | at::functionalization::impl::sync(hx_); |
| 6191 | hx__ = at::functionalization::impl::from_functional_tensor(hx_); |
| 6192 | } else { |
| 6193 | hx__ = hx_; |
| 6194 | } |
| 6195 | |
| 6196 | at::Tensor cx_tmp_; |
| 6197 | if (at::functionalization::impl::isFunctionalTensor(cx_tmp)) { |
| 6198 | at::functionalization::impl::sync(cx_tmp); |
| 6199 | cx_tmp_ = at::functionalization::impl::from_functional_tensor(cx_tmp); |
| 6200 | } else { |
| 6201 | cx_tmp_ = cx_tmp; |
| 6202 | } |
| 6203 | |
| 6204 | at::Tensor output_; |
| 6205 | if (at::functionalization::impl::isFunctionalTensor(output)) { |
| 6206 | at::functionalization::impl::sync(output); |
| 6207 | output_ = at::functionalization::impl::from_functional_tensor(output); |
| 6208 | } else { |
| 6209 | output_ = output; |
| 6210 | } |
| 6211 | |
| 6212 | at::Tensor hy__; |
| 6213 | if (at::functionalization::impl::isFunctionalTensor(hy_)) { |
| 6214 | at::functionalization::impl::sync(hy_); |
| 6215 | hy__ = at::functionalization::impl::from_functional_tensor(hy_); |
| 6216 | } else { |
| 6217 | hy__ = hy_; |
| 6218 | } |
| 6219 | |
| 6220 | at::Tensor cy__; |
| 6221 | if (at::functionalization::impl::isFunctionalTensor(cy_)) { |
| 6222 | at::functionalization::impl::sync(cy_); |
| 6223 | cy__ = at::functionalization::impl::from_functional_tensor(cy_); |
| 6224 | } else { |
| 6225 | cy__ = cy_; |
| 6226 | } |
| 6227 | |
| 6228 | c10::optional<at::Tensor> grad_output_; |
| 6229 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 6230 | at::functionalization::impl::sync(grad_output); |
| 6231 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 6232 | } else { |
| 6233 | grad_output_ = grad_output; |
| 6234 | } |
| 6235 | |
| 6236 | c10::optional<at::Tensor> grad_hy_; |
| 6237 | if (at::functionalization::impl::isFunctionalTensor(grad_hy)) { |
| 6238 | at::functionalization::impl::sync(grad_hy); |
| 6239 | grad_hy_ = at::functionalization::impl::from_functional_tensor(grad_hy); |
| 6240 | } else { |
| 6241 | grad_hy_ = grad_hy; |
| 6242 | } |
| 6243 | |
| 6244 | c10::optional<at::Tensor> grad_cy_; |
| 6245 | if (at::functionalization::impl::isFunctionalTensor(grad_cy)) { |
| 6246 | at::functionalization::impl::sync(grad_cy); |
| 6247 | grad_cy_ = at::functionalization::impl::from_functional_tensor(grad_cy); |
| 6248 | } else { |
| 6249 | grad_cy_ = grad_cy; |
| 6250 | } |
| 6251 | |
| 6252 | at::Tensor workspace_; |
| 6253 | if (at::functionalization::impl::isFunctionalTensor(workspace)) { |
| 6254 | at::functionalization::impl::sync(workspace); |
| 6255 | workspace_ = at::functionalization::impl::from_functional_tensor(workspace); |
| 6256 | } else { |
| 6257 | workspace_ = workspace; |
| 6258 | } |
| 6259 | |
| 6260 | at::Tensor out0_; |
| 6261 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 6262 | at::functionalization::impl::sync(out0); |
| 6263 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 6264 | } else { |
| 6265 | out0_ = out0; |
| 6266 | } |
| 6267 | |
| 6268 | at::Tensor out1_; |
| 6269 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 6270 | at::functionalization::impl::sync(out1); |
| 6271 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 6272 | } else { |
| 6273 | out1_ = out1; |
| 6274 | } |
| 6275 | |
| 6276 | at::Tensor out2_; |
| 6277 | if (at::functionalization::impl::isFunctionalTensor(out2)) { |
| 6278 | at::functionalization::impl::sync(out2); |
| 6279 | out2_ = at::functionalization::impl::from_functional_tensor(out2); |
| 6280 | } else { |
| 6281 | out2_ = out2; |
| 6282 | } |
| 6283 | |
| 6284 | at::Tensor out3_; |
| 6285 | if (at::functionalization::impl::isFunctionalTensor(out3)) { |
| 6286 | at::functionalization::impl::sync(out3); |
| 6287 | out3_ = at::functionalization::impl::from_functional_tensor(out3); |
| 6288 | } else { |
| 6289 | out3_ = out3; |
| 6290 | } |
| 6291 | |
| 6292 | at::Tensor out4_; |
| 6293 | if (at::functionalization::impl::isFunctionalTensor(out4)) { |
| 6294 | at::functionalization::impl::sync(out4); |
| 6295 | out4_ = at::functionalization::impl::from_functional_tensor(out4); |
| 6296 | } else { |
| 6297 | out4_ = out4; |
| 6298 | } |
| 6299 | |
| 6300 | at::Tensor out5_; |
| 6301 | if (at::functionalization::impl::isFunctionalTensor(out5)) { |
| 6302 | at::functionalization::impl::sync(out5); |
| 6303 | out5_ = at::functionalization::impl::from_functional_tensor(out5); |
| 6304 | } else { |
| 6305 | out5_ = out5; |
| 6306 | } |
| 6307 | |
| 6308 | at::Tensor out6_; |
| 6309 | if (at::functionalization::impl::isFunctionalTensor(out6)) { |
| 6310 | at::functionalization::impl::sync(out6); |
| 6311 | out6_ = at::functionalization::impl::from_functional_tensor(out6); |
| 6312 | } else { |
| 6313 | out6_ = out6; |
| 6314 | } |
| 6315 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1) && at::functionalization::impl::isFunctionalTensor(out2) && at::functionalization::impl::isFunctionalTensor(out3) && at::functionalization::impl::isFunctionalTensor(out4) && at::functionalization::impl::isFunctionalTensor(out5) && at::functionalization::impl::isFunctionalTensor(out6))) { |
| 6316 | if ((false || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(weight1) || at::functionalization::impl::isFunctionalTensor(weight2) || at::functionalization::impl::isFunctionalTensor(weight3) || at::functionalization::impl::isFunctionalTensor(weight4) || at::functionalization::impl::isFunctionalTensor(hx_) || at::functionalization::impl::isFunctionalTensor(cx_tmp) || at::functionalization::impl::isFunctionalTensor(output) || at::functionalization::impl::isFunctionalTensor(hy_) || at::functionalization::impl::isFunctionalTensor(cy_) || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(grad_hy) || at::functionalization::impl::isFunctionalTensor(grad_cy) || at::functionalization::impl::isFunctionalTensor(workspace))) { |
| 6317 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6318 | TORCH_INTERNAL_ASSERT(false, |
| 6319 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6320 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6321 | } else { |
| 6322 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6323 | at::AutoDispatchSkipFunctionalize guard; |
| 6324 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::mkldnn_rnn_layer_backward_out::call(input_, weight1_, weight2_, weight3_, weight4_, hx__, cx_tmp_, output_, hy__, cy__, grad_output_, grad_hy_, grad_cy_, reverse, mode, hidden_size, num_layers, has_biases, train, bidirectional, batch_sizes, batch_first, workspace_, out0_, out1_, out2_, out3_, out4_, out5_, out6_); |
| 6325 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2, out3, out4, out5, out6);; |
| 6326 | } |
| 6327 | } else { |
| 6328 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 6329 | { |
| 6330 | at::AutoDispatchSkipFunctionalize guard; |
| 6331 | tmp_output = at::_ops::mkldnn_rnn_layer_backward::call(input_, weight1_, weight2_, weight3_, weight4_, hx__, cx_tmp_, output_, hy__, cy__, grad_output_, grad_hy_, grad_cy_, reverse, mode, hidden_size, num_layers, has_biases, train, bidirectional, batch_sizes, batch_first, workspace_); |
| 6332 | } |
| 6333 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 6334 | at::functionalization::impl::commit_update(out0); |
| 6335 | at::functionalization::impl::sync(out0); |
| 6336 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 6337 | at::functionalization::impl::commit_update(out1); |
| 6338 | at::functionalization::impl::sync(out1); |
| 6339 | at::functionalization::impl::replace_(out2, std::get<2>(tmp_output)); |
| 6340 | at::functionalization::impl::commit_update(out2); |
| 6341 | at::functionalization::impl::sync(out2); |
| 6342 | at::functionalization::impl::replace_(out3, std::get<3>(tmp_output)); |
| 6343 | at::functionalization::impl::commit_update(out3); |
| 6344 | at::functionalization::impl::sync(out3); |
| 6345 | at::functionalization::impl::replace_(out4, std::get<4>(tmp_output)); |
| 6346 | at::functionalization::impl::commit_update(out4); |
| 6347 | at::functionalization::impl::sync(out4); |
| 6348 | at::functionalization::impl::replace_(out5, std::get<5>(tmp_output)); |
| 6349 | at::functionalization::impl::commit_update(out5); |
| 6350 | at::functionalization::impl::sync(out5); |
| 6351 | at::functionalization::impl::replace_(out6, std::get<6>(tmp_output)); |
| 6352 | at::functionalization::impl::commit_update(out6); |
| 6353 | at::functionalization::impl::sync(out6); |
| 6354 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2, out3, out4, out5, out6); |
| 6355 | } |
| 6356 | } |
| 6357 | |
| 6358 | at::Tensor & miopen_convolution_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, at::Tensor & out) { |
| 6359 | if (false) { |
| 6360 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6361 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6362 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6363 | auto self_meta = to_meta(self); |
| 6364 | auto weight_meta = to_meta(weight); |
| 6365 | auto bias_meta = to_meta(bias); |
| 6366 | auto out_meta = to_meta(out); |
| 6367 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6368 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6369 | at::_ops::miopen_convolution_out::call(self_meta, weight_meta, bias_meta, padding, stride, dilation, groups, benchmark, deterministic, out_meta); |
| 6370 | } |
| 6371 | |
| 6372 | at::Tensor self_; |
| 6373 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6374 | at::functionalization::impl::sync(self); |
| 6375 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6376 | } else { |
| 6377 | self_ = self; |
| 6378 | } |
| 6379 | |
| 6380 | at::Tensor weight_; |
| 6381 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 6382 | at::functionalization::impl::sync(weight); |
| 6383 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 6384 | } else { |
| 6385 | weight_ = weight; |
| 6386 | } |
| 6387 | |
| 6388 | c10::optional<at::Tensor> bias_; |
| 6389 | if (at::functionalization::impl::isFunctionalTensor(bias)) { |
| 6390 | at::functionalization::impl::sync(bias); |
| 6391 | bias_ = at::functionalization::impl::from_functional_tensor(bias); |
| 6392 | } else { |
| 6393 | bias_ = bias; |
| 6394 | } |
| 6395 | |
| 6396 | at::Tensor out_; |
| 6397 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 6398 | at::functionalization::impl::sync(out); |
| 6399 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 6400 | } else { |
| 6401 | out_ = out; |
| 6402 | } |
| 6403 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 6404 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(bias))) { |
| 6405 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6406 | TORCH_INTERNAL_ASSERT(false, |
| 6407 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6408 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6409 | } else { |
| 6410 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6411 | at::AutoDispatchSkipFunctionalize guard; |
| 6412 | at::Tensor tmp_output = at::_ops::miopen_convolution_out::call(self_, weight_, bias_, padding, stride, dilation, groups, benchmark, deterministic, out_); |
| 6413 | return out;; |
| 6414 | } |
| 6415 | } else { |
| 6416 | at::Tensor tmp_output; |
| 6417 | { |
| 6418 | at::AutoDispatchSkipFunctionalize guard; |
| 6419 | tmp_output = at::_ops::miopen_convolution::call(self_, weight_, bias_, padding, stride, dilation, groups, benchmark, deterministic); |
| 6420 | } |
| 6421 | at::functionalization::impl::replace_(out, tmp_output); |
| 6422 | at::functionalization::impl::commit_update(out); |
| 6423 | at::functionalization::impl::sync(out); |
| 6424 | return out; |
| 6425 | } |
| 6426 | } |
| 6427 | |
| 6428 | at::Tensor & miopen_depthwise_convolution_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, at::Tensor & out) { |
| 6429 | if (false) { |
| 6430 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6431 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6432 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6433 | auto self_meta = to_meta(self); |
| 6434 | auto weight_meta = to_meta(weight); |
| 6435 | auto bias_meta = to_meta(bias); |
| 6436 | auto out_meta = to_meta(out); |
| 6437 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6438 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6439 | at::_ops::miopen_depthwise_convolution_out::call(self_meta, weight_meta, bias_meta, padding, stride, dilation, groups, benchmark, deterministic, out_meta); |
| 6440 | } |
| 6441 | |
| 6442 | at::Tensor self_; |
| 6443 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6444 | at::functionalization::impl::sync(self); |
| 6445 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6446 | } else { |
| 6447 | self_ = self; |
| 6448 | } |
| 6449 | |
| 6450 | at::Tensor weight_; |
| 6451 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 6452 | at::functionalization::impl::sync(weight); |
| 6453 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 6454 | } else { |
| 6455 | weight_ = weight; |
| 6456 | } |
| 6457 | |
| 6458 | c10::optional<at::Tensor> bias_; |
| 6459 | if (at::functionalization::impl::isFunctionalTensor(bias)) { |
| 6460 | at::functionalization::impl::sync(bias); |
| 6461 | bias_ = at::functionalization::impl::from_functional_tensor(bias); |
| 6462 | } else { |
| 6463 | bias_ = bias; |
| 6464 | } |
| 6465 | |
| 6466 | at::Tensor out_; |
| 6467 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 6468 | at::functionalization::impl::sync(out); |
| 6469 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 6470 | } else { |
| 6471 | out_ = out; |
| 6472 | } |
| 6473 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 6474 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(bias))) { |
| 6475 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6476 | TORCH_INTERNAL_ASSERT(false, |
| 6477 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6478 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6479 | } else { |
| 6480 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6481 | at::AutoDispatchSkipFunctionalize guard; |
| 6482 | at::Tensor tmp_output = at::_ops::miopen_depthwise_convolution_out::call(self_, weight_, bias_, padding, stride, dilation, groups, benchmark, deterministic, out_); |
| 6483 | return out;; |
| 6484 | } |
| 6485 | } else { |
| 6486 | at::Tensor tmp_output; |
| 6487 | { |
| 6488 | at::AutoDispatchSkipFunctionalize guard; |
| 6489 | tmp_output = at::_ops::miopen_depthwise_convolution::call(self_, weight_, bias_, padding, stride, dilation, groups, benchmark, deterministic); |
| 6490 | } |
| 6491 | at::functionalization::impl::replace_(out, tmp_output); |
| 6492 | at::functionalization::impl::commit_update(out); |
| 6493 | at::functionalization::impl::sync(out); |
| 6494 | return out; |
| 6495 | } |
| 6496 | } |
| 6497 | |
| 6498 | void miopen_rnn_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const c10::optional<at::Tensor> & cx, const at::Tensor & output, const c10::optional<at::Tensor> & grad_output, const c10::optional<at::Tensor> & grad_hy, const c10::optional<at::Tensor> & grad_cy, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const c10::optional<at::Tensor> & dropout_state, const at::Tensor & reserve, ::std::array<bool,4> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::TensorList out3) { |
| 6499 | if (false) { |
| 6500 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6501 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6502 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6503 | auto input_meta = to_meta(input); |
| 6504 | auto weight_meta = to_meta(weight); |
| 6505 | auto weight_buf_meta = to_meta(weight_buf); |
| 6506 | auto hx_meta = to_meta(hx); |
| 6507 | auto cx_meta = to_meta(cx); |
| 6508 | auto output_meta = to_meta(output); |
| 6509 | auto grad_output_meta = to_meta(grad_output); |
| 6510 | auto grad_hy_meta = to_meta(grad_hy); |
| 6511 | auto grad_cy_meta = to_meta(grad_cy); |
| 6512 | auto dropout_state_meta = to_meta(dropout_state); |
| 6513 | auto reserve_meta = to_meta(reserve); |
| 6514 | auto out0_meta = to_meta(out0); |
| 6515 | auto out1_meta = to_meta(out1); |
| 6516 | auto out2_meta = to_meta(out2); |
| 6517 | auto out3_meta = to_meta(out3); |
| 6518 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6519 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6520 | at::_ops::miopen_rnn_backward_out::call(input_meta, weight_meta, weight_stride0, weight_buf_meta, hx_meta, cx_meta, output_meta, grad_output_meta, grad_hy_meta, grad_cy_meta, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state_meta, reserve_meta, output_mask, out0_meta, out1_meta, out2_meta, out3_meta); |
| 6521 | } |
| 6522 | |
| 6523 | at::Tensor input_; |
| 6524 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 6525 | at::functionalization::impl::sync(input); |
| 6526 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 6527 | } else { |
| 6528 | input_ = input; |
| 6529 | } |
| 6530 | |
| 6531 | ::std::vector<at::Tensor> weight_; |
| 6532 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 6533 | at::functionalization::impl::sync(weight); |
| 6534 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 6535 | } else { |
| 6536 | weight_ = weight.vec(); |
| 6537 | } |
| 6538 | |
| 6539 | at::Tensor weight_buf_; |
| 6540 | if (at::functionalization::impl::isFunctionalTensor(weight_buf)) { |
| 6541 | at::functionalization::impl::sync(weight_buf); |
| 6542 | weight_buf_ = at::functionalization::impl::from_functional_tensor(weight_buf); |
| 6543 | } else { |
| 6544 | weight_buf_ = weight_buf; |
| 6545 | } |
| 6546 | |
| 6547 | at::Tensor hx_; |
| 6548 | if (at::functionalization::impl::isFunctionalTensor(hx)) { |
| 6549 | at::functionalization::impl::sync(hx); |
| 6550 | hx_ = at::functionalization::impl::from_functional_tensor(hx); |
| 6551 | } else { |
| 6552 | hx_ = hx; |
| 6553 | } |
| 6554 | |
| 6555 | c10::optional<at::Tensor> cx_; |
| 6556 | if (at::functionalization::impl::isFunctionalTensor(cx)) { |
| 6557 | at::functionalization::impl::sync(cx); |
| 6558 | cx_ = at::functionalization::impl::from_functional_tensor(cx); |
| 6559 | } else { |
| 6560 | cx_ = cx; |
| 6561 | } |
| 6562 | |
| 6563 | at::Tensor output_; |
| 6564 | if (at::functionalization::impl::isFunctionalTensor(output)) { |
| 6565 | at::functionalization::impl::sync(output); |
| 6566 | output_ = at::functionalization::impl::from_functional_tensor(output); |
| 6567 | } else { |
| 6568 | output_ = output; |
| 6569 | } |
| 6570 | |
| 6571 | c10::optional<at::Tensor> grad_output_; |
| 6572 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 6573 | at::functionalization::impl::sync(grad_output); |
| 6574 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 6575 | } else { |
| 6576 | grad_output_ = grad_output; |
| 6577 | } |
| 6578 | |
| 6579 | c10::optional<at::Tensor> grad_hy_; |
| 6580 | if (at::functionalization::impl::isFunctionalTensor(grad_hy)) { |
| 6581 | at::functionalization::impl::sync(grad_hy); |
| 6582 | grad_hy_ = at::functionalization::impl::from_functional_tensor(grad_hy); |
| 6583 | } else { |
| 6584 | grad_hy_ = grad_hy; |
| 6585 | } |
| 6586 | |
| 6587 | c10::optional<at::Tensor> grad_cy_; |
| 6588 | if (at::functionalization::impl::isFunctionalTensor(grad_cy)) { |
| 6589 | at::functionalization::impl::sync(grad_cy); |
| 6590 | grad_cy_ = at::functionalization::impl::from_functional_tensor(grad_cy); |
| 6591 | } else { |
| 6592 | grad_cy_ = grad_cy; |
| 6593 | } |
| 6594 | |
| 6595 | c10::optional<at::Tensor> dropout_state_; |
| 6596 | if (at::functionalization::impl::isFunctionalTensor(dropout_state)) { |
| 6597 | at::functionalization::impl::sync(dropout_state); |
| 6598 | dropout_state_ = at::functionalization::impl::from_functional_tensor(dropout_state); |
| 6599 | } else { |
| 6600 | dropout_state_ = dropout_state; |
| 6601 | } |
| 6602 | |
| 6603 | at::Tensor reserve_; |
| 6604 | if (at::functionalization::impl::isFunctionalTensor(reserve)) { |
| 6605 | at::functionalization::impl::sync(reserve); |
| 6606 | reserve_ = at::functionalization::impl::from_functional_tensor(reserve); |
| 6607 | } else { |
| 6608 | reserve_ = reserve; |
| 6609 | } |
| 6610 | |
| 6611 | at::Tensor out0_; |
| 6612 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 6613 | at::functionalization::impl::sync(out0); |
| 6614 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 6615 | } else { |
| 6616 | out0_ = out0; |
| 6617 | } |
| 6618 | |
| 6619 | at::Tensor out1_; |
| 6620 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 6621 | at::functionalization::impl::sync(out1); |
| 6622 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 6623 | } else { |
| 6624 | out1_ = out1; |
| 6625 | } |
| 6626 | |
| 6627 | at::Tensor out2_; |
| 6628 | if (at::functionalization::impl::isFunctionalTensor(out2)) { |
| 6629 | at::functionalization::impl::sync(out2); |
| 6630 | out2_ = at::functionalization::impl::from_functional_tensor(out2); |
| 6631 | } else { |
| 6632 | out2_ = out2; |
| 6633 | } |
| 6634 | |
| 6635 | ::std::vector<at::Tensor> out3_; |
| 6636 | if (at::functionalization::impl::isFunctionalTensor(out3)) { |
| 6637 | at::functionalization::impl::sync(out3); |
| 6638 | out3_ = at::functionalization::impl::from_functional_tensor(out3); |
| 6639 | } else { |
| 6640 | out3_ = out3.vec(); |
| 6641 | } |
| 6642 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1) && at::functionalization::impl::isFunctionalTensor(out2) && at::functionalization::impl::isFunctionalTensor(out3))) { |
| 6643 | if ((false || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(weight_buf) || at::functionalization::impl::isFunctionalTensor(hx) || at::functionalization::impl::isFunctionalTensor(cx) || at::functionalization::impl::isFunctionalTensor(output) || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(grad_hy) || at::functionalization::impl::isFunctionalTensor(grad_cy) || at::functionalization::impl::isFunctionalTensor(dropout_state) || at::functionalization::impl::isFunctionalTensor(reserve))) { |
| 6644 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6645 | TORCH_INTERNAL_ASSERT(false, |
| 6646 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6647 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6648 | } else { |
| 6649 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6650 | at::AutoDispatchSkipFunctionalize guard; |
| 6651 | at::_ops::miopen_rnn_backward_out::call(input_, weight_, weight_stride0, weight_buf_, hx_, cx_, output_, grad_output_, grad_hy_, grad_cy_, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state_, reserve_, output_mask, out0_, out1_, out2_, out3_); |
| 6652 | ; |
| 6653 | } |
| 6654 | } else { |
| 6655 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,::std::vector<at::Tensor>> tmp_output; |
| 6656 | { |
| 6657 | at::AutoDispatchSkipFunctionalize guard; |
| 6658 | tmp_output = at::_ops::miopen_rnn_backward::call(input_, weight_, weight_stride0, weight_buf_, hx_, cx_, output_, grad_output_, grad_hy_, grad_cy_, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state_, reserve_, output_mask); |
| 6659 | } |
| 6660 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 6661 | at::functionalization::impl::commit_update(out0); |
| 6662 | at::functionalization::impl::sync(out0); |
| 6663 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 6664 | at::functionalization::impl::commit_update(out1); |
| 6665 | at::functionalization::impl::sync(out1); |
| 6666 | at::functionalization::impl::replace_(out2, std::get<2>(tmp_output)); |
| 6667 | at::functionalization::impl::commit_update(out2); |
| 6668 | at::functionalization::impl::sync(out2); |
| 6669 | at::functionalization::impl::replace_(out3, std::get<3>(tmp_output)); |
| 6670 | at::functionalization::impl::commit_update(out3); |
| 6671 | at::functionalization::impl::sync(out3); |
| 6672 | |
| 6673 | } |
| 6674 | } |
| 6675 | |
| 6676 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_batch_norm_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, bool training, double momentum, double eps, at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd) { |
| 6677 | if (false) { |
| 6678 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6679 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6680 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6681 | auto input_meta = to_meta(input); |
| 6682 | auto weight_meta = to_meta(weight); |
| 6683 | auto bias_meta = to_meta(bias); |
| 6684 | auto running_mean_meta = to_meta(running_mean); |
| 6685 | auto running_var_meta = to_meta(running_var); |
| 6686 | auto out_meta = to_meta(out); |
| 6687 | auto save_mean_meta = to_meta(save_mean); |
| 6688 | auto save_invstd_meta = to_meta(save_invstd); |
| 6689 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6690 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6691 | at::_ops::native_batch_norm_out::call(input_meta, weight_meta, bias_meta, running_mean_meta, running_var_meta, training, momentum, eps, out_meta, save_mean_meta, save_invstd_meta); |
| 6692 | } |
| 6693 | |
| 6694 | at::Tensor input_; |
| 6695 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 6696 | at::functionalization::impl::sync(input); |
| 6697 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 6698 | } else { |
| 6699 | input_ = input; |
| 6700 | } |
| 6701 | |
| 6702 | c10::optional<at::Tensor> weight_; |
| 6703 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 6704 | at::functionalization::impl::sync(weight); |
| 6705 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 6706 | } else { |
| 6707 | weight_ = weight; |
| 6708 | } |
| 6709 | |
| 6710 | c10::optional<at::Tensor> bias_; |
| 6711 | if (at::functionalization::impl::isFunctionalTensor(bias)) { |
| 6712 | at::functionalization::impl::sync(bias); |
| 6713 | bias_ = at::functionalization::impl::from_functional_tensor(bias); |
| 6714 | } else { |
| 6715 | bias_ = bias; |
| 6716 | } |
| 6717 | |
| 6718 | c10::optional<at::Tensor> running_mean_; |
| 6719 | if (at::functionalization::impl::isFunctionalTensor(running_mean)) { |
| 6720 | at::functionalization::impl::sync(running_mean); |
| 6721 | running_mean_ = at::functionalization::impl::from_functional_tensor(running_mean); |
| 6722 | } else { |
| 6723 | running_mean_ = running_mean; |
| 6724 | } |
| 6725 | |
| 6726 | c10::optional<at::Tensor> running_var_; |
| 6727 | if (at::functionalization::impl::isFunctionalTensor(running_var)) { |
| 6728 | at::functionalization::impl::sync(running_var); |
| 6729 | running_var_ = at::functionalization::impl::from_functional_tensor(running_var); |
| 6730 | } else { |
| 6731 | running_var_ = running_var; |
| 6732 | } |
| 6733 | |
| 6734 | at::Tensor out_; |
| 6735 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 6736 | at::functionalization::impl::sync(out); |
| 6737 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 6738 | } else { |
| 6739 | out_ = out; |
| 6740 | } |
| 6741 | |
| 6742 | at::Tensor save_mean_; |
| 6743 | if (at::functionalization::impl::isFunctionalTensor(save_mean)) { |
| 6744 | at::functionalization::impl::sync(save_mean); |
| 6745 | save_mean_ = at::functionalization::impl::from_functional_tensor(save_mean); |
| 6746 | } else { |
| 6747 | save_mean_ = save_mean; |
| 6748 | } |
| 6749 | |
| 6750 | at::Tensor save_invstd_; |
| 6751 | if (at::functionalization::impl::isFunctionalTensor(save_invstd)) { |
| 6752 | at::functionalization::impl::sync(save_invstd); |
| 6753 | save_invstd_ = at::functionalization::impl::from_functional_tensor(save_invstd); |
| 6754 | } else { |
| 6755 | save_invstd_ = save_invstd; |
| 6756 | } |
| 6757 | if (!(true && at::functionalization::impl::isFunctionalTensor(out) && at::functionalization::impl::isFunctionalTensor(save_mean) && at::functionalization::impl::isFunctionalTensor(save_invstd))) { |
| 6758 | if ((false || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(bias) || at::functionalization::impl::isFunctionalTensor(running_mean) || at::functionalization::impl::isFunctionalTensor(running_var))) { |
| 6759 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6760 | TORCH_INTERNAL_ASSERT(false, |
| 6761 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6762 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6763 | } else { |
| 6764 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6765 | at::AutoDispatchSkipFunctionalize guard; |
| 6766 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::native_batch_norm_out::call(input_, weight_, bias_, running_mean_, running_var_, training, momentum, eps, out_, save_mean_, save_invstd_); |
| 6767 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out, save_mean, save_invstd);; |
| 6768 | } |
| 6769 | } else { |
| 6770 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 6771 | { |
| 6772 | at::AutoDispatchSkipFunctionalize guard; |
| 6773 | tmp_output = at::_ops::native_batch_norm::call(input_, weight_, bias_, running_mean_, running_var_, training, momentum, eps); |
| 6774 | } |
| 6775 | at::functionalization::impl::replace_(out, std::get<0>(tmp_output)); |
| 6776 | at::functionalization::impl::commit_update(out); |
| 6777 | at::functionalization::impl::sync(out); |
| 6778 | at::functionalization::impl::replace_(save_mean, std::get<1>(tmp_output)); |
| 6779 | at::functionalization::impl::commit_update(save_mean); |
| 6780 | at::functionalization::impl::sync(save_mean); |
| 6781 | at::functionalization::impl::replace_(save_invstd, std::get<2>(tmp_output)); |
| 6782 | at::functionalization::impl::commit_update(save_invstd); |
| 6783 | at::functionalization::impl::sync(save_invstd); |
| 6784 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out, save_mean, save_invstd); |
| 6785 | } |
| 6786 | } |
| 6787 | |
| 6788 | at::Tensor & batch_norm_elemt_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, const at::Tensor & mean, const at::Tensor & invstd, double eps, at::Tensor & out) { |
| 6789 | if (false) { |
| 6790 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6791 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6792 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6793 | auto input_meta = to_meta(input); |
| 6794 | auto weight_meta = to_meta(weight); |
| 6795 | auto bias_meta = to_meta(bias); |
| 6796 | auto mean_meta = to_meta(mean); |
| 6797 | auto invstd_meta = to_meta(invstd); |
| 6798 | auto out_meta = to_meta(out); |
| 6799 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6800 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6801 | at::_ops::batch_norm_elemt_out::call(input_meta, weight_meta, bias_meta, mean_meta, invstd_meta, eps, out_meta); |
| 6802 | } |
| 6803 | |
| 6804 | at::Tensor input_; |
| 6805 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 6806 | at::functionalization::impl::sync(input); |
| 6807 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 6808 | } else { |
| 6809 | input_ = input; |
| 6810 | } |
| 6811 | |
| 6812 | c10::optional<at::Tensor> weight_; |
| 6813 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 6814 | at::functionalization::impl::sync(weight); |
| 6815 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 6816 | } else { |
| 6817 | weight_ = weight; |
| 6818 | } |
| 6819 | |
| 6820 | c10::optional<at::Tensor> bias_; |
| 6821 | if (at::functionalization::impl::isFunctionalTensor(bias)) { |
| 6822 | at::functionalization::impl::sync(bias); |
| 6823 | bias_ = at::functionalization::impl::from_functional_tensor(bias); |
| 6824 | } else { |
| 6825 | bias_ = bias; |
| 6826 | } |
| 6827 | |
| 6828 | at::Tensor mean_; |
| 6829 | if (at::functionalization::impl::isFunctionalTensor(mean)) { |
| 6830 | at::functionalization::impl::sync(mean); |
| 6831 | mean_ = at::functionalization::impl::from_functional_tensor(mean); |
| 6832 | } else { |
| 6833 | mean_ = mean; |
| 6834 | } |
| 6835 | |
| 6836 | at::Tensor invstd_; |
| 6837 | if (at::functionalization::impl::isFunctionalTensor(invstd)) { |
| 6838 | at::functionalization::impl::sync(invstd); |
| 6839 | invstd_ = at::functionalization::impl::from_functional_tensor(invstd); |
| 6840 | } else { |
| 6841 | invstd_ = invstd; |
| 6842 | } |
| 6843 | |
| 6844 | at::Tensor out_; |
| 6845 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 6846 | at::functionalization::impl::sync(out); |
| 6847 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 6848 | } else { |
| 6849 | out_ = out; |
| 6850 | } |
| 6851 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 6852 | if ((false || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(bias) || at::functionalization::impl::isFunctionalTensor(mean) || at::functionalization::impl::isFunctionalTensor(invstd))) { |
| 6853 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6854 | TORCH_INTERNAL_ASSERT(false, |
| 6855 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6856 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6857 | } else { |
| 6858 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6859 | at::AutoDispatchSkipFunctionalize guard; |
| 6860 | at::Tensor tmp_output = at::_ops::batch_norm_elemt_out::call(input_, weight_, bias_, mean_, invstd_, eps, out_); |
| 6861 | return out;; |
| 6862 | } |
| 6863 | } else { |
| 6864 | at::Tensor tmp_output; |
| 6865 | { |
| 6866 | at::AutoDispatchSkipFunctionalize guard; |
| 6867 | tmp_output = at::_ops::batch_norm_elemt::call(input_, weight_, bias_, mean_, invstd_, eps); |
| 6868 | } |
| 6869 | at::functionalization::impl::replace_(out, tmp_output); |
| 6870 | at::functionalization::impl::commit_update(out); |
| 6871 | at::functionalization::impl::sync(out); |
| 6872 | return out; |
| 6873 | } |
| 6874 | } |
| 6875 | |
| 6876 | ::std::tuple<at::Tensor &,at::Tensor &> batch_norm_update_stats_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, double momentum, at::Tensor & out0, at::Tensor & out1) { |
| 6877 | if (false) { |
| 6878 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6879 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6880 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6881 | auto input_meta = to_meta(input); |
| 6882 | auto running_mean_meta = to_meta(running_mean); |
| 6883 | auto running_var_meta = to_meta(running_var); |
| 6884 | auto out0_meta = to_meta(out0); |
| 6885 | auto out1_meta = to_meta(out1); |
| 6886 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6887 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6888 | at::_ops::batch_norm_update_stats_out::call(input_meta, running_mean_meta, running_var_meta, momentum, out0_meta, out1_meta); |
| 6889 | } |
| 6890 | |
| 6891 | at::Tensor input_; |
| 6892 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 6893 | at::functionalization::impl::sync(input); |
| 6894 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 6895 | } else { |
| 6896 | input_ = input; |
| 6897 | } |
| 6898 | |
| 6899 | c10::optional<at::Tensor> running_mean_; |
| 6900 | if (at::functionalization::impl::isFunctionalTensor(running_mean)) { |
| 6901 | at::functionalization::impl::sync(running_mean); |
| 6902 | running_mean_ = at::functionalization::impl::from_functional_tensor(running_mean); |
| 6903 | } else { |
| 6904 | running_mean_ = running_mean; |
| 6905 | } |
| 6906 | |
| 6907 | c10::optional<at::Tensor> running_var_; |
| 6908 | if (at::functionalization::impl::isFunctionalTensor(running_var)) { |
| 6909 | at::functionalization::impl::sync(running_var); |
| 6910 | running_var_ = at::functionalization::impl::from_functional_tensor(running_var); |
| 6911 | } else { |
| 6912 | running_var_ = running_var; |
| 6913 | } |
| 6914 | |
| 6915 | at::Tensor out0_; |
| 6916 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 6917 | at::functionalization::impl::sync(out0); |
| 6918 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 6919 | } else { |
| 6920 | out0_ = out0; |
| 6921 | } |
| 6922 | |
| 6923 | at::Tensor out1_; |
| 6924 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 6925 | at::functionalization::impl::sync(out1); |
| 6926 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 6927 | } else { |
| 6928 | out1_ = out1; |
| 6929 | } |
| 6930 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1))) { |
| 6931 | if ((false || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(running_mean) || at::functionalization::impl::isFunctionalTensor(running_var))) { |
| 6932 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6933 | TORCH_INTERNAL_ASSERT(false, |
| 6934 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6935 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6936 | } else { |
| 6937 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6938 | at::AutoDispatchSkipFunctionalize guard; |
| 6939 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::batch_norm_update_stats_out::call(input_, running_mean_, running_var_, momentum, out0_, out1_); |
| 6940 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1);; |
| 6941 | } |
| 6942 | } else { |
| 6943 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 6944 | { |
| 6945 | at::AutoDispatchSkipFunctionalize guard; |
| 6946 | tmp_output = at::_ops::batch_norm_update_stats::call(input_, running_mean_, running_var_, momentum); |
| 6947 | } |
| 6948 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 6949 | at::functionalization::impl::commit_update(out0); |
| 6950 | at::functionalization::impl::sync(out0); |
| 6951 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 6952 | at::functionalization::impl::commit_update(out1); |
| 6953 | at::functionalization::impl::sync(out1); |
| 6954 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1); |
| 6955 | } |
| 6956 | } |
| 6957 | |
| 6958 | at::Tensor & _nnpack_spatial_convolution_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, at::IntArrayRef stride, at::Tensor & out) { |
| 6959 | if (false) { |
| 6960 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6961 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6962 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6963 | auto input_meta = to_meta(input); |
| 6964 | auto weight_meta = to_meta(weight); |
| 6965 | auto bias_meta = to_meta(bias); |
| 6966 | auto out_meta = to_meta(out); |
| 6967 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6968 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6969 | at::_ops::_nnpack_spatial_convolution_out::call(input_meta, weight_meta, bias_meta, padding, stride, out_meta); |
| 6970 | } |
| 6971 | |
| 6972 | at::Tensor input_; |
| 6973 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 6974 | at::functionalization::impl::sync(input); |
| 6975 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 6976 | } else { |
| 6977 | input_ = input; |
| 6978 | } |
| 6979 | |
| 6980 | at::Tensor weight_; |
| 6981 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 6982 | at::functionalization::impl::sync(weight); |
| 6983 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 6984 | } else { |
| 6985 | weight_ = weight; |
| 6986 | } |
| 6987 | |
| 6988 | c10::optional<at::Tensor> bias_; |
| 6989 | if (at::functionalization::impl::isFunctionalTensor(bias)) { |
| 6990 | at::functionalization::impl::sync(bias); |
| 6991 | bias_ = at::functionalization::impl::from_functional_tensor(bias); |
| 6992 | } else { |
| 6993 | bias_ = bias; |
| 6994 | } |
| 6995 | |
| 6996 | at::Tensor out_; |
| 6997 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 6998 | at::functionalization::impl::sync(out); |
| 6999 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7000 | } else { |
| 7001 | out_ = out; |
| 7002 | } |
| 7003 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7004 | if ((false || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(bias))) { |
| 7005 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7006 | TORCH_INTERNAL_ASSERT(false, |
| 7007 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7008 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7009 | } else { |
| 7010 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7011 | at::AutoDispatchSkipFunctionalize guard; |
| 7012 | at::Tensor tmp_output = at::_ops::_nnpack_spatial_convolution_out::call(input_, weight_, bias_, padding, stride, out_); |
| 7013 | return out;; |
| 7014 | } |
| 7015 | } else { |
| 7016 | at::Tensor tmp_output; |
| 7017 | { |
| 7018 | at::AutoDispatchSkipFunctionalize guard; |
| 7019 | tmp_output = at::_ops::_nnpack_spatial_convolution::call(input_, weight_, bias_, padding, stride); |
| 7020 | } |
| 7021 | at::functionalization::impl::replace_(out, tmp_output); |
| 7022 | at::functionalization::impl::commit_update(out); |
| 7023 | at::functionalization::impl::sync(out); |
| 7024 | return out; |
| 7025 | } |
| 7026 | } |
| 7027 | |
| 7028 | at::Tensor & ones_like_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out) { |
| 7029 | if (false) { |
| 7030 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7031 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7032 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7033 | auto self_meta = to_meta(self); |
| 7034 | auto out_meta = to_meta(out); |
| 7035 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7036 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7037 | at::_ops::ones_like_out::call(self_meta, memory_format, out_meta); |
| 7038 | } |
| 7039 | |
| 7040 | at::Tensor self_; |
| 7041 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7042 | at::functionalization::impl::sync(self); |
| 7043 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7044 | } else { |
| 7045 | self_ = self; |
| 7046 | } |
| 7047 | |
| 7048 | at::Tensor out_; |
| 7049 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7050 | at::functionalization::impl::sync(out); |
| 7051 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7052 | } else { |
| 7053 | out_ = out; |
| 7054 | } |
| 7055 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7056 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 7057 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7058 | TORCH_INTERNAL_ASSERT(false, |
| 7059 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7060 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7061 | } else { |
| 7062 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7063 | at::AutoDispatchSkipFunctionalize guard; |
| 7064 | at::Tensor tmp_output = at::_ops::ones_like_out::call(self_, memory_format, out_); |
| 7065 | return out;; |
| 7066 | } |
| 7067 | } else { |
| 7068 | at::Tensor tmp_output; |
| 7069 | { |
| 7070 | at::AutoDispatchSkipFunctionalize guard; |
| 7071 | tmp_output = at::_ops::ones_like::call(self_, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt, memory_format); |
| 7072 | } |
| 7073 | at::functionalization::impl::replace_(out, tmp_output); |
| 7074 | at::functionalization::impl::commit_update(out); |
| 7075 | at::functionalization::impl::sync(out); |
| 7076 | return out; |
| 7077 | } |
| 7078 | } |
| 7079 | |
| 7080 | at::Tensor & channel_shuffle_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t groups, at::Tensor & out) { |
| 7081 | if (false) { |
| 7082 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7083 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7084 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7085 | auto self_meta = to_meta(self); |
| 7086 | auto out_meta = to_meta(out); |
| 7087 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7088 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7089 | at::_ops::channel_shuffle_out::call(self_meta, groups, out_meta); |
| 7090 | } |
| 7091 | |
| 7092 | at::Tensor self_; |
| 7093 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7094 | at::functionalization::impl::sync(self); |
| 7095 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7096 | } else { |
| 7097 | self_ = self; |
| 7098 | } |
| 7099 | |
| 7100 | at::Tensor out_; |
| 7101 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7102 | at::functionalization::impl::sync(out); |
| 7103 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7104 | } else { |
| 7105 | out_ = out; |
| 7106 | } |
| 7107 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7108 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 7109 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7110 | TORCH_INTERNAL_ASSERT(false, |
| 7111 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7112 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7113 | } else { |
| 7114 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7115 | at::AutoDispatchSkipFunctionalize guard; |
| 7116 | at::Tensor tmp_output = at::_ops::channel_shuffle_out::call(self_, groups, out_); |
| 7117 | return out;; |
| 7118 | } |
| 7119 | } else { |
| 7120 | at::Tensor tmp_output; |
| 7121 | { |
| 7122 | at::AutoDispatchSkipFunctionalize guard; |
| 7123 | tmp_output = at::_ops::channel_shuffle::call(self_, groups); |
| 7124 | } |
| 7125 | at::functionalization::impl::replace_(out, tmp_output); |
| 7126 | at::functionalization::impl::commit_update(out); |
| 7127 | at::functionalization::impl::sync(out); |
| 7128 | return out; |
| 7129 | } |
| 7130 | } |
| 7131 | |
| 7132 | at::Tensor & randint_out_out(c10::DispatchKeySet dispatchKeySet, int64_t high, c10::SymIntArrayRef size, at::Tensor & out) { |
| 7133 | if (false) { |
| 7134 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7135 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7136 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7137 | auto out_meta = to_meta(out); |
| 7138 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7139 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7140 | at::_ops::randint_out::call(high, size, out_meta); |
| 7141 | } |
| 7142 | |
| 7143 | at::Tensor out_; |
| 7144 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7145 | at::functionalization::impl::sync(out); |
| 7146 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7147 | } else { |
| 7148 | out_ = out; |
| 7149 | } |
| 7150 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7151 | if ((false)) { |
| 7152 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7153 | TORCH_INTERNAL_ASSERT(false, |
| 7154 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7155 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7156 | } else { |
| 7157 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7158 | at::AutoDispatchSkipFunctionalize guard; |
| 7159 | at::Tensor tmp_output = at::_ops::randint_out::call(high, size, out_); |
| 7160 | return out;; |
| 7161 | } |
| 7162 | } else { |
| 7163 | at::Tensor tmp_output; |
| 7164 | { |
| 7165 | at::AutoDispatchSkipFunctionalize guard; |
| 7166 | tmp_output = at::_ops::randint::call(high, size, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 7167 | } |
| 7168 | at::functionalization::impl::replace_(out, tmp_output); |
| 7169 | at::functionalization::impl::commit_update(out); |
| 7170 | at::functionalization::impl::sync(out); |
| 7171 | return out; |
| 7172 | } |
| 7173 | } |
| 7174 | |
| 7175 | at::Tensor & randint_out_generator_out(c10::DispatchKeySet dispatchKeySet, int64_t high, c10::SymIntArrayRef size, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 7176 | if (false) { |
| 7177 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7178 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7179 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7180 | auto out_meta = to_meta(out); |
| 7181 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7182 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7183 | at::_ops::randint_generator_out::call(high, size, generator, out_meta); |
| 7184 | } |
| 7185 | |
| 7186 | at::Tensor out_; |
| 7187 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7188 | at::functionalization::impl::sync(out); |
| 7189 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7190 | } else { |
| 7191 | out_ = out; |
| 7192 | } |
| 7193 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7194 | if ((false)) { |
| 7195 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7196 | TORCH_INTERNAL_ASSERT(false, |
| 7197 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7198 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7199 | } else { |
| 7200 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7201 | at::AutoDispatchSkipFunctionalize guard; |
| 7202 | at::Tensor tmp_output = at::_ops::randint_generator_out::call(high, size, generator, out_); |
| 7203 | return out;; |
| 7204 | } |
| 7205 | } else { |
| 7206 | at::Tensor tmp_output; |
| 7207 | { |
| 7208 | at::AutoDispatchSkipFunctionalize guard; |
| 7209 | tmp_output = at::_ops::randint_generator::call(high, size, generator, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 7210 | } |
| 7211 | at::functionalization::impl::replace_(out, tmp_output); |
| 7212 | at::functionalization::impl::commit_update(out); |
| 7213 | at::functionalization::impl::sync(out); |
| 7214 | return out; |
| 7215 | } |
| 7216 | } |
| 7217 | |
| 7218 | at::Tensor & randint_out_low_out(c10::DispatchKeySet dispatchKeySet, int64_t low, int64_t high, c10::SymIntArrayRef size, at::Tensor & out) { |
| 7219 | if (false) { |
| 7220 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7221 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7222 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7223 | auto out_meta = to_meta(out); |
| 7224 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7225 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7226 | at::_ops::randint_low_out::call(low, high, size, out_meta); |
| 7227 | } |
| 7228 | |
| 7229 | at::Tensor out_; |
| 7230 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7231 | at::functionalization::impl::sync(out); |
| 7232 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7233 | } else { |
| 7234 | out_ = out; |
| 7235 | } |
| 7236 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7237 | if ((false)) { |
| 7238 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7239 | TORCH_INTERNAL_ASSERT(false, |
| 7240 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7241 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7242 | } else { |
| 7243 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7244 | at::AutoDispatchSkipFunctionalize guard; |
| 7245 | at::Tensor tmp_output = at::_ops::randint_low_out::call(low, high, size, out_); |
| 7246 | return out;; |
| 7247 | } |
| 7248 | } else { |
| 7249 | at::Tensor tmp_output; |
| 7250 | { |
| 7251 | at::AutoDispatchSkipFunctionalize guard; |
| 7252 | tmp_output = at::_ops::randint_low::call(low, high, size, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 7253 | } |
| 7254 | at::functionalization::impl::replace_(out, tmp_output); |
| 7255 | at::functionalization::impl::commit_update(out); |
| 7256 | at::functionalization::impl::sync(out); |
| 7257 | return out; |
| 7258 | } |
| 7259 | } |
| 7260 | |
| 7261 | at::Tensor & randint_out_low_generator_out(c10::DispatchKeySet dispatchKeySet, int64_t low, int64_t high, c10::SymIntArrayRef size, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 7262 | if (false) { |
| 7263 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7264 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7265 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7266 | auto out_meta = to_meta(out); |
| 7267 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7268 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7269 | at::_ops::randint_low_generator_out::call(low, high, size, generator, out_meta); |
| 7270 | } |
| 7271 | |
| 7272 | at::Tensor out_; |
| 7273 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7274 | at::functionalization::impl::sync(out); |
| 7275 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7276 | } else { |
| 7277 | out_ = out; |
| 7278 | } |
| 7279 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7280 | if ((false)) { |
| 7281 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7282 | TORCH_INTERNAL_ASSERT(false, |
| 7283 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7284 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7285 | } else { |
| 7286 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7287 | at::AutoDispatchSkipFunctionalize guard; |
| 7288 | at::Tensor tmp_output = at::_ops::randint_low_generator_out::call(low, high, size, generator, out_); |
| 7289 | return out;; |
| 7290 | } |
| 7291 | } else { |
| 7292 | at::Tensor tmp_output; |
| 7293 | { |
| 7294 | at::AutoDispatchSkipFunctionalize guard; |
| 7295 | tmp_output = at::_ops::randint_low_generator::call(low, high, size, generator, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 7296 | } |
| 7297 | at::functionalization::impl::replace_(out, tmp_output); |
| 7298 | at::functionalization::impl::commit_update(out); |
| 7299 | at::functionalization::impl::sync(out); |
| 7300 | return out; |
| 7301 | } |
| 7302 | } |
| 7303 | |
| 7304 | at::Tensor & randperm_out_out(c10::DispatchKeySet dispatchKeySet, int64_t n, at::Tensor & out) { |
| 7305 | if (false) { |
| 7306 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7307 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7308 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7309 | auto out_meta = to_meta(out); |
| 7310 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7311 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7312 | at::_ops::randperm_out::call(n, out_meta); |
| 7313 | } |
| 7314 | |
| 7315 | at::Tensor out_; |
| 7316 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7317 | at::functionalization::impl::sync(out); |
| 7318 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7319 | } else { |
| 7320 | out_ = out; |
| 7321 | } |
| 7322 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7323 | if ((false)) { |
| 7324 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7325 | TORCH_INTERNAL_ASSERT(false, |
| 7326 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7327 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7328 | } else { |
| 7329 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7330 | at::AutoDispatchSkipFunctionalize guard; |
| 7331 | at::Tensor tmp_output = at::_ops::randperm_out::call(n, out_); |
| 7332 | return out;; |
| 7333 | } |
| 7334 | } else { |
| 7335 | at::Tensor tmp_output; |
| 7336 | { |
| 7337 | at::AutoDispatchSkipFunctionalize guard; |
| 7338 | tmp_output = at::_ops::randperm::call(n, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 7339 | } |
| 7340 | at::functionalization::impl::replace_(out, tmp_output); |
| 7341 | at::functionalization::impl::commit_update(out); |
| 7342 | at::functionalization::impl::sync(out); |
| 7343 | return out; |
| 7344 | } |
| 7345 | } |
| 7346 | |
| 7347 | at::Tensor & randperm_out_generator_out(c10::DispatchKeySet dispatchKeySet, int64_t n, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 7348 | if (false) { |
| 7349 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7350 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7351 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7352 | auto out_meta = to_meta(out); |
| 7353 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7354 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7355 | at::_ops::randperm_generator_out::call(n, generator, out_meta); |
| 7356 | } |
| 7357 | |
| 7358 | at::Tensor out_; |
| 7359 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7360 | at::functionalization::impl::sync(out); |
| 7361 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7362 | } else { |
| 7363 | out_ = out; |
| 7364 | } |
| 7365 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7366 | if ((false)) { |
| 7367 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7368 | TORCH_INTERNAL_ASSERT(false, |
| 7369 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7370 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7371 | } else { |
| 7372 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7373 | at::AutoDispatchSkipFunctionalize guard; |
| 7374 | at::Tensor tmp_output = at::_ops::randperm_generator_out::call(n, generator, out_); |
| 7375 | return out;; |
| 7376 | } |
| 7377 | } else { |
| 7378 | at::Tensor tmp_output; |
| 7379 | { |
| 7380 | at::AutoDispatchSkipFunctionalize guard; |
| 7381 | tmp_output = at::_ops::randperm_generator::call(n, generator, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 7382 | } |
| 7383 | at::functionalization::impl::replace_(out, tmp_output); |
| 7384 | at::functionalization::impl::commit_update(out); |
| 7385 | at::functionalization::impl::sync(out); |
| 7386 | return out; |
| 7387 | } |
| 7388 | } |
| 7389 | |
| 7390 | at::Tensor & range_out_out(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Scalar & end, const at::Scalar & step, at::Tensor & out) { |
| 7391 | if (false) { |
| 7392 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7393 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7394 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7395 | auto out_meta = to_meta(out); |
| 7396 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7397 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7398 | at::_ops::range_out::call(start, end, step, out_meta); |
| 7399 | } |
| 7400 | |
| 7401 | at::Tensor out_; |
| 7402 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7403 | at::functionalization::impl::sync(out); |
| 7404 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7405 | } else { |
| 7406 | out_ = out; |
| 7407 | } |
| 7408 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7409 | if ((false)) { |
| 7410 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7411 | TORCH_INTERNAL_ASSERT(false, |
| 7412 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7413 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7414 | } else { |
| 7415 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7416 | at::AutoDispatchSkipFunctionalize guard; |
| 7417 | at::Tensor tmp_output = at::_ops::range_out::call(start, end, step, out_); |
| 7418 | return out;; |
| 7419 | } |
| 7420 | } else { |
| 7421 | at::Tensor tmp_output; |
| 7422 | { |
| 7423 | at::AutoDispatchSkipFunctionalize guard; |
| 7424 | tmp_output = at::_ops::range_step::call(start, end, step, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 7425 | } |
| 7426 | at::functionalization::impl::replace_(out, tmp_output); |
| 7427 | at::functionalization::impl::commit_update(out); |
| 7428 | at::functionalization::impl::sync(out); |
| 7429 | return out; |
| 7430 | } |
| 7431 | } |
| 7432 | |
| 7433 | at::Tensor & range_out_out_(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Scalar & end, at::Tensor & out) { |
| 7434 | if (false) { |
| 7435 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7436 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7437 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7438 | auto out_meta = to_meta(out); |
| 7439 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7440 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7441 | at::_ops::range_out_::call(start, end, out_meta); |
| 7442 | } |
| 7443 | |
| 7444 | at::Tensor out_; |
| 7445 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7446 | at::functionalization::impl::sync(out); |
| 7447 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7448 | } else { |
| 7449 | out_ = out; |
| 7450 | } |
| 7451 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7452 | if ((false)) { |
| 7453 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7454 | TORCH_INTERNAL_ASSERT(false, |
| 7455 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7456 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7457 | } else { |
| 7458 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7459 | at::AutoDispatchSkipFunctionalize guard; |
| 7460 | at::Tensor tmp_output = at::_ops::range_out_::call(start, end, out_); |
| 7461 | return out;; |
| 7462 | } |
| 7463 | } else { |
| 7464 | at::Tensor tmp_output; |
| 7465 | { |
| 7466 | at::AutoDispatchSkipFunctionalize guard; |
| 7467 | tmp_output = at::_ops::range::call(start, end, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 7468 | } |
| 7469 | at::functionalization::impl::replace_(out, tmp_output); |
| 7470 | at::functionalization::impl::commit_update(out); |
| 7471 | at::functionalization::impl::sync(out); |
| 7472 | return out; |
| 7473 | } |
| 7474 | } |
| 7475 | |
| 7476 | at::Tensor & reciprocal_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 7477 | if (false) { |
| 7478 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7479 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7480 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7481 | auto self_meta = to_meta(self); |
| 7482 | auto out_meta = to_meta(out); |
| 7483 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7484 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7485 | at::_ops::reciprocal_out::call(self_meta, out_meta); |
| 7486 | } |
| 7487 | |
| 7488 | at::Tensor self_; |
| 7489 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7490 | at::functionalization::impl::sync(self); |
| 7491 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7492 | } else { |
| 7493 | self_ = self; |
| 7494 | } |
| 7495 | |
| 7496 | at::Tensor out_; |
| 7497 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7498 | at::functionalization::impl::sync(out); |
| 7499 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7500 | } else { |
| 7501 | out_ = out; |
| 7502 | } |
| 7503 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7504 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 7505 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7506 | TORCH_INTERNAL_ASSERT(false, |
| 7507 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7508 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7509 | } else { |
| 7510 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7511 | at::AutoDispatchSkipFunctionalize guard; |
| 7512 | at::Tensor tmp_output = at::_ops::reciprocal_out::call(self_, out_); |
| 7513 | return out;; |
| 7514 | } |
| 7515 | } else { |
| 7516 | at::Tensor tmp_output; |
| 7517 | { |
| 7518 | at::AutoDispatchSkipFunctionalize guard; |
| 7519 | tmp_output = at::_ops::reciprocal::call(self_); |
| 7520 | } |
| 7521 | at::functionalization::impl::replace_(out, tmp_output); |
| 7522 | at::functionalization::impl::commit_update(out); |
| 7523 | at::functionalization::impl::sync(out); |
| 7524 | return out; |
| 7525 | } |
| 7526 | } |
| 7527 | |
| 7528 | at::Tensor & reciprocal_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 7529 | if (true) { |
| 7530 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7531 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7532 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7533 | auto self_meta = to_meta(self); |
| 7534 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7535 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7536 | at::_ops::reciprocal_::call(self_meta); |
| 7537 | } |
| 7538 | |
| 7539 | at::Tensor self_; |
| 7540 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7541 | at::functionalization::impl::sync(self); |
| 7542 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7543 | } else { |
| 7544 | self_ = self; |
| 7545 | } |
| 7546 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 7547 | if ((false)) { |
| 7548 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7549 | TORCH_INTERNAL_ASSERT(false, |
| 7550 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7551 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7552 | } else { |
| 7553 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7554 | at::AutoDispatchSkipFunctionalize guard; |
| 7555 | at::Tensor tmp_output = at::_ops::reciprocal_::call(self_); |
| 7556 | return self;; |
| 7557 | } |
| 7558 | } else { |
| 7559 | at::Tensor tmp_output; |
| 7560 | { |
| 7561 | at::AutoDispatchSkipFunctionalize guard; |
| 7562 | tmp_output = at::_ops::reciprocal::call(self_); |
| 7563 | } |
| 7564 | at::functionalization::impl::replace_(self, tmp_output); |
| 7565 | at::functionalization::impl::commit_update(self); |
| 7566 | at::functionalization::impl::sync(self); |
| 7567 | return self; |
| 7568 | } |
| 7569 | } |
| 7570 | |
| 7571 | at::Tensor & gelu_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::string_view approximate, at::Tensor & grad_input) { |
| 7572 | if (false) { |
| 7573 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7574 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7575 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7576 | auto grad_output_meta = to_meta(grad_output); |
| 7577 | auto self_meta = to_meta(self); |
| 7578 | auto grad_input_meta = to_meta(grad_input); |
| 7579 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7580 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7581 | at::_ops::gelu_backward_grad_input::call(grad_output_meta, self_meta, approximate, grad_input_meta); |
| 7582 | } |
| 7583 | |
| 7584 | at::Tensor grad_output_; |
| 7585 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 7586 | at::functionalization::impl::sync(grad_output); |
| 7587 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 7588 | } else { |
| 7589 | grad_output_ = grad_output; |
| 7590 | } |
| 7591 | |
| 7592 | at::Tensor self_; |
| 7593 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7594 | at::functionalization::impl::sync(self); |
| 7595 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7596 | } else { |
| 7597 | self_ = self; |
| 7598 | } |
| 7599 | |
| 7600 | at::Tensor grad_input_; |
| 7601 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 7602 | at::functionalization::impl::sync(grad_input); |
| 7603 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 7604 | } else { |
| 7605 | grad_input_ = grad_input; |
| 7606 | } |
| 7607 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 7608 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self))) { |
| 7609 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7610 | TORCH_INTERNAL_ASSERT(false, |
| 7611 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7612 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7613 | } else { |
| 7614 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7615 | at::AutoDispatchSkipFunctionalize guard; |
| 7616 | at::Tensor tmp_output = at::_ops::gelu_backward_grad_input::call(grad_output_, self_, approximate, grad_input_); |
| 7617 | return grad_input;; |
| 7618 | } |
| 7619 | } else { |
| 7620 | at::Tensor tmp_output; |
| 7621 | { |
| 7622 | at::AutoDispatchSkipFunctionalize guard; |
| 7623 | tmp_output = at::_ops::gelu_backward::call(grad_output_, self_, approximate); |
| 7624 | } |
| 7625 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 7626 | at::functionalization::impl::commit_update(grad_input); |
| 7627 | at::functionalization::impl::sync(grad_input); |
| 7628 | return grad_input; |
| 7629 | } |
| 7630 | } |
| 7631 | |
| 7632 | at::Tensor & hardshrink_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & lambd, at::Tensor & out) { |
| 7633 | if (false) { |
| 7634 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7635 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7636 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7637 | auto self_meta = to_meta(self); |
| 7638 | auto out_meta = to_meta(out); |
| 7639 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7640 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7641 | at::_ops::hardshrink_out::call(self_meta, lambd, out_meta); |
| 7642 | } |
| 7643 | |
| 7644 | at::Tensor self_; |
| 7645 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7646 | at::functionalization::impl::sync(self); |
| 7647 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7648 | } else { |
| 7649 | self_ = self; |
| 7650 | } |
| 7651 | |
| 7652 | at::Tensor out_; |
| 7653 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7654 | at::functionalization::impl::sync(out); |
| 7655 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7656 | } else { |
| 7657 | out_ = out; |
| 7658 | } |
| 7659 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7660 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 7661 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7662 | TORCH_INTERNAL_ASSERT(false, |
| 7663 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7664 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7665 | } else { |
| 7666 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7667 | at::AutoDispatchSkipFunctionalize guard; |
| 7668 | at::Tensor tmp_output = at::_ops::hardshrink_out::call(self_, lambd, out_); |
| 7669 | return out;; |
| 7670 | } |
| 7671 | } else { |
| 7672 | at::Tensor tmp_output; |
| 7673 | { |
| 7674 | at::AutoDispatchSkipFunctionalize guard; |
| 7675 | tmp_output = at::_ops::hardshrink::call(self_, lambd); |
| 7676 | } |
| 7677 | at::functionalization::impl::replace_(out, tmp_output); |
| 7678 | at::functionalization::impl::commit_update(out); |
| 7679 | at::functionalization::impl::sync(out); |
| 7680 | return out; |
| 7681 | } |
| 7682 | } |
| 7683 | |
| 7684 | at::Tensor & hardshrink_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & self, const at::Scalar & lambd, at::Tensor & grad_input) { |
| 7685 | if (false) { |
| 7686 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7687 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7688 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7689 | auto grad_out_meta = to_meta(grad_out); |
| 7690 | auto self_meta = to_meta(self); |
| 7691 | auto grad_input_meta = to_meta(grad_input); |
| 7692 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7693 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7694 | at::_ops::hardshrink_backward_grad_input::call(grad_out_meta, self_meta, lambd, grad_input_meta); |
| 7695 | } |
| 7696 | |
| 7697 | at::Tensor grad_out_; |
| 7698 | if (at::functionalization::impl::isFunctionalTensor(grad_out)) { |
| 7699 | at::functionalization::impl::sync(grad_out); |
| 7700 | grad_out_ = at::functionalization::impl::from_functional_tensor(grad_out); |
| 7701 | } else { |
| 7702 | grad_out_ = grad_out; |
| 7703 | } |
| 7704 | |
| 7705 | at::Tensor self_; |
| 7706 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7707 | at::functionalization::impl::sync(self); |
| 7708 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7709 | } else { |
| 7710 | self_ = self; |
| 7711 | } |
| 7712 | |
| 7713 | at::Tensor grad_input_; |
| 7714 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 7715 | at::functionalization::impl::sync(grad_input); |
| 7716 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 7717 | } else { |
| 7718 | grad_input_ = grad_input; |
| 7719 | } |
| 7720 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 7721 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_out) || at::functionalization::impl::isFunctionalTensor(self))) { |
| 7722 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7723 | TORCH_INTERNAL_ASSERT(false, |
| 7724 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7725 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7726 | } else { |
| 7727 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7728 | at::AutoDispatchSkipFunctionalize guard; |
| 7729 | at::Tensor tmp_output = at::_ops::hardshrink_backward_grad_input::call(grad_out_, self_, lambd, grad_input_); |
| 7730 | return grad_input;; |
| 7731 | } |
| 7732 | } else { |
| 7733 | at::Tensor tmp_output; |
| 7734 | { |
| 7735 | at::AutoDispatchSkipFunctionalize guard; |
| 7736 | tmp_output = at::_ops::hardshrink_backward::call(grad_out_, self_, lambd); |
| 7737 | } |
| 7738 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 7739 | at::functionalization::impl::commit_update(grad_input); |
| 7740 | at::functionalization::impl::sync(grad_input); |
| 7741 | return grad_input; |
| 7742 | } |
| 7743 | } |
| 7744 | |
| 7745 | at::Tensor & silu_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 7746 | if (false) { |
| 7747 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7748 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7749 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7750 | auto self_meta = to_meta(self); |
| 7751 | auto out_meta = to_meta(out); |
| 7752 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7753 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7754 | at::_ops::silu_out::call(self_meta, out_meta); |
| 7755 | } |
| 7756 | |
| 7757 | at::Tensor self_; |
| 7758 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7759 | at::functionalization::impl::sync(self); |
| 7760 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7761 | } else { |
| 7762 | self_ = self; |
| 7763 | } |
| 7764 | |
| 7765 | at::Tensor out_; |
| 7766 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7767 | at::functionalization::impl::sync(out); |
| 7768 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7769 | } else { |
| 7770 | out_ = out; |
| 7771 | } |
| 7772 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7773 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 7774 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7775 | TORCH_INTERNAL_ASSERT(false, |
| 7776 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7777 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7778 | } else { |
| 7779 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7780 | at::AutoDispatchSkipFunctionalize guard; |
| 7781 | at::Tensor tmp_output = at::_ops::silu_out::call(self_, out_); |
| 7782 | return out;; |
| 7783 | } |
| 7784 | } else { |
| 7785 | at::Tensor tmp_output; |
| 7786 | { |
| 7787 | at::AutoDispatchSkipFunctionalize guard; |
| 7788 | tmp_output = at::_ops::silu::call(self_); |
| 7789 | } |
| 7790 | at::functionalization::impl::replace_(out, tmp_output); |
| 7791 | at::functionalization::impl::commit_update(out); |
| 7792 | at::functionalization::impl::sync(out); |
| 7793 | return out; |
| 7794 | } |
| 7795 | } |
| 7796 | |
| 7797 | at::Tensor & silu_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 7798 | if (true) { |
| 7799 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7800 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7801 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7802 | auto self_meta = to_meta(self); |
| 7803 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7804 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7805 | at::_ops::silu_::call(self_meta); |
| 7806 | } |
| 7807 | |
| 7808 | at::Tensor self_; |
| 7809 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7810 | at::functionalization::impl::sync(self); |
| 7811 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7812 | } else { |
| 7813 | self_ = self; |
| 7814 | } |
| 7815 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 7816 | if ((false)) { |
| 7817 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7818 | TORCH_INTERNAL_ASSERT(false, |
| 7819 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7820 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7821 | } else { |
| 7822 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7823 | at::AutoDispatchSkipFunctionalize guard; |
| 7824 | at::Tensor tmp_output = at::_ops::silu_::call(self_); |
| 7825 | return self;; |
| 7826 | } |
| 7827 | } else { |
| 7828 | at::Tensor tmp_output; |
| 7829 | { |
| 7830 | at::AutoDispatchSkipFunctionalize guard; |
| 7831 | tmp_output = at::_ops::silu::call(self_); |
| 7832 | } |
| 7833 | at::functionalization::impl::replace_(self, tmp_output); |
| 7834 | at::functionalization::impl::commit_update(self); |
| 7835 | at::functionalization::impl::sync(self); |
| 7836 | return self; |
| 7837 | } |
| 7838 | } |
| 7839 | |
| 7840 | at::Tensor & silu_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & grad_input) { |
| 7841 | if (false) { |
| 7842 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7843 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7844 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7845 | auto grad_output_meta = to_meta(grad_output); |
| 7846 | auto self_meta = to_meta(self); |
| 7847 | auto grad_input_meta = to_meta(grad_input); |
| 7848 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7849 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7850 | at::_ops::silu_backward_grad_input::call(grad_output_meta, self_meta, grad_input_meta); |
| 7851 | } |
| 7852 | |
| 7853 | at::Tensor grad_output_; |
| 7854 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 7855 | at::functionalization::impl::sync(grad_output); |
| 7856 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 7857 | } else { |
| 7858 | grad_output_ = grad_output; |
| 7859 | } |
| 7860 | |
| 7861 | at::Tensor self_; |
| 7862 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7863 | at::functionalization::impl::sync(self); |
| 7864 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7865 | } else { |
| 7866 | self_ = self; |
| 7867 | } |
| 7868 | |
| 7869 | at::Tensor grad_input_; |
| 7870 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 7871 | at::functionalization::impl::sync(grad_input); |
| 7872 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 7873 | } else { |
| 7874 | grad_input_ = grad_input; |
| 7875 | } |
| 7876 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 7877 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self))) { |
| 7878 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7879 | TORCH_INTERNAL_ASSERT(false, |
| 7880 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7881 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7882 | } else { |
| 7883 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7884 | at::AutoDispatchSkipFunctionalize guard; |
| 7885 | at::Tensor tmp_output = at::_ops::silu_backward_grad_input::call(grad_output_, self_, grad_input_); |
| 7886 | return grad_input;; |
| 7887 | } |
| 7888 | } else { |
| 7889 | at::Tensor tmp_output; |
| 7890 | { |
| 7891 | at::AutoDispatchSkipFunctionalize guard; |
| 7892 | tmp_output = at::_ops::silu_backward::call(grad_output_, self_); |
| 7893 | } |
| 7894 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 7895 | at::functionalization::impl::commit_update(grad_input); |
| 7896 | at::functionalization::impl::sync(grad_input); |
| 7897 | return grad_input; |
| 7898 | } |
| 7899 | } |
| 7900 | |
| 7901 | at::Tensor & sin_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 7902 | if (false) { |
| 7903 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7904 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7905 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7906 | auto self_meta = to_meta(self); |
| 7907 | auto out_meta = to_meta(out); |
| 7908 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7909 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7910 | at::_ops::sin_out::call(self_meta, out_meta); |
| 7911 | } |
| 7912 | |
| 7913 | at::Tensor self_; |
| 7914 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7915 | at::functionalization::impl::sync(self); |
| 7916 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7917 | } else { |
| 7918 | self_ = self; |
| 7919 | } |
| 7920 | |
| 7921 | at::Tensor out_; |
| 7922 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7923 | at::functionalization::impl::sync(out); |
| 7924 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7925 | } else { |
| 7926 | out_ = out; |
| 7927 | } |
| 7928 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7929 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 7930 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7931 | TORCH_INTERNAL_ASSERT(false, |
| 7932 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7933 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7934 | } else { |
| 7935 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7936 | at::AutoDispatchSkipFunctionalize guard; |
| 7937 | at::Tensor tmp_output = at::_ops::sin_out::call(self_, out_); |
| 7938 | return out;; |
| 7939 | } |
| 7940 | } else { |
| 7941 | at::Tensor tmp_output; |
| 7942 | { |
| 7943 | at::AutoDispatchSkipFunctionalize guard; |
| 7944 | tmp_output = at::_ops::sin::call(self_); |
| 7945 | } |
| 7946 | at::functionalization::impl::replace_(out, tmp_output); |
| 7947 | at::functionalization::impl::commit_update(out); |
| 7948 | at::functionalization::impl::sync(out); |
| 7949 | return out; |
| 7950 | } |
| 7951 | } |
| 7952 | |
| 7953 | at::Tensor & sin_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 7954 | if (true) { |
| 7955 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7956 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7957 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7958 | auto self_meta = to_meta(self); |
| 7959 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7960 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7961 | at::_ops::sin_::call(self_meta); |
| 7962 | } |
| 7963 | |
| 7964 | at::Tensor self_; |
| 7965 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7966 | at::functionalization::impl::sync(self); |
| 7967 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7968 | } else { |
| 7969 | self_ = self; |
| 7970 | } |
| 7971 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 7972 | if ((false)) { |
| 7973 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7974 | TORCH_INTERNAL_ASSERT(false, |
| 7975 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7976 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7977 | } else { |
| 7978 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7979 | at::AutoDispatchSkipFunctionalize guard; |
| 7980 | at::Tensor tmp_output = at::_ops::sin_::call(self_); |
| 7981 | return self;; |
| 7982 | } |
| 7983 | } else { |
| 7984 | at::Tensor tmp_output; |
| 7985 | { |
| 7986 | at::AutoDispatchSkipFunctionalize guard; |
| 7987 | tmp_output = at::_ops::sin::call(self_); |
| 7988 | } |
| 7989 | at::functionalization::impl::replace_(self, tmp_output); |
| 7990 | at::functionalization::impl::commit_update(self); |
| 7991 | at::functionalization::impl::sync(self); |
| 7992 | return self; |
| 7993 | } |
| 7994 | } |
| 7995 | |
| 7996 | at::Tensor & _softmax_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool half_to_float, at::Tensor & out) { |
| 7997 | if (false) { |
| 7998 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7999 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8000 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8001 | auto self_meta = to_meta(self); |
| 8002 | auto out_meta = to_meta(out); |
| 8003 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8004 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8005 | at::_ops::_softmax_out::call(self_meta, dim, half_to_float, out_meta); |
| 8006 | } |
| 8007 | |
| 8008 | at::Tensor self_; |
| 8009 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8010 | at::functionalization::impl::sync(self); |
| 8011 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8012 | } else { |
| 8013 | self_ = self; |
| 8014 | } |
| 8015 | |
| 8016 | at::Tensor out_; |
| 8017 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8018 | at::functionalization::impl::sync(out); |
| 8019 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8020 | } else { |
| 8021 | out_ = out; |
| 8022 | } |
| 8023 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8024 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8025 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8026 | TORCH_INTERNAL_ASSERT(false, |
| 8027 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8028 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8029 | } else { |
| 8030 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8031 | at::AutoDispatchSkipFunctionalize guard; |
| 8032 | at::Tensor tmp_output = at::_ops::_softmax_out::call(self_, dim, half_to_float, out_); |
| 8033 | return out;; |
| 8034 | } |
| 8035 | } else { |
| 8036 | at::Tensor tmp_output; |
| 8037 | { |
| 8038 | at::AutoDispatchSkipFunctionalize guard; |
| 8039 | tmp_output = at::_ops::_softmax::call(self_, dim, half_to_float); |
| 8040 | } |
| 8041 | at::functionalization::impl::replace_(out, tmp_output); |
| 8042 | at::functionalization::impl::commit_update(out); |
| 8043 | at::functionalization::impl::sync(out); |
| 8044 | return out; |
| 8045 | } |
| 8046 | } |
| 8047 | |
| 8048 | at::Tensor & sspaddmm_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { |
| 8049 | if (false) { |
| 8050 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8051 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8052 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8053 | auto self_meta = to_meta(self); |
| 8054 | auto mat1_meta = to_meta(mat1); |
| 8055 | auto mat2_meta = to_meta(mat2); |
| 8056 | auto out_meta = to_meta(out); |
| 8057 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8058 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8059 | at::_ops::sspaddmm_out::call(self_meta, mat1_meta, mat2_meta, beta, alpha, out_meta); |
| 8060 | } |
| 8061 | |
| 8062 | at::Tensor self_; |
| 8063 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8064 | at::functionalization::impl::sync(self); |
| 8065 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8066 | } else { |
| 8067 | self_ = self; |
| 8068 | } |
| 8069 | |
| 8070 | at::Tensor mat1_; |
| 8071 | if (at::functionalization::impl::isFunctionalTensor(mat1)) { |
| 8072 | at::functionalization::impl::sync(mat1); |
| 8073 | mat1_ = at::functionalization::impl::from_functional_tensor(mat1); |
| 8074 | } else { |
| 8075 | mat1_ = mat1; |
| 8076 | } |
| 8077 | |
| 8078 | at::Tensor mat2_; |
| 8079 | if (at::functionalization::impl::isFunctionalTensor(mat2)) { |
| 8080 | at::functionalization::impl::sync(mat2); |
| 8081 | mat2_ = at::functionalization::impl::from_functional_tensor(mat2); |
| 8082 | } else { |
| 8083 | mat2_ = mat2; |
| 8084 | } |
| 8085 | |
| 8086 | at::Tensor out_; |
| 8087 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8088 | at::functionalization::impl::sync(out); |
| 8089 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8090 | } else { |
| 8091 | out_ = out; |
| 8092 | } |
| 8093 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8094 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(mat1) || at::functionalization::impl::isFunctionalTensor(mat2))) { |
| 8095 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8096 | TORCH_INTERNAL_ASSERT(false, |
| 8097 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8098 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8099 | } else { |
| 8100 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8101 | at::AutoDispatchSkipFunctionalize guard; |
| 8102 | at::Tensor tmp_output = at::_ops::sspaddmm_out::call(self_, mat1_, mat2_, beta, alpha, out_); |
| 8103 | return out;; |
| 8104 | } |
| 8105 | } else { |
| 8106 | at::Tensor tmp_output; |
| 8107 | { |
| 8108 | at::AutoDispatchSkipFunctionalize guard; |
| 8109 | tmp_output = at::_ops::sspaddmm::call(self_, mat1_, mat2_, beta, alpha); |
| 8110 | } |
| 8111 | at::functionalization::impl::replace_(out, tmp_output); |
| 8112 | at::functionalization::impl::commit_update(out); |
| 8113 | at::functionalization::impl::sync(out); |
| 8114 | return out; |
| 8115 | } |
| 8116 | } |
| 8117 | |
| 8118 | at::Tensor & _stack_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, int64_t dim, at::Tensor & out) { |
| 8119 | if (false) { |
| 8120 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8121 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8122 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8123 | auto tensors_meta = to_meta(tensors); |
| 8124 | auto out_meta = to_meta(out); |
| 8125 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8126 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8127 | at::_ops::_stack_out::call(tensors_meta, dim, out_meta); |
| 8128 | } |
| 8129 | |
| 8130 | ::std::vector<at::Tensor> tensors_; |
| 8131 | if (at::functionalization::impl::isFunctionalTensor(tensors)) { |
| 8132 | at::functionalization::impl::sync(tensors); |
| 8133 | tensors_ = at::functionalization::impl::from_functional_tensor(tensors); |
| 8134 | } else { |
| 8135 | tensors_ = tensors.vec(); |
| 8136 | } |
| 8137 | |
| 8138 | at::Tensor out_; |
| 8139 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8140 | at::functionalization::impl::sync(out); |
| 8141 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8142 | } else { |
| 8143 | out_ = out; |
| 8144 | } |
| 8145 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8146 | if ((false || at::functionalization::impl::isFunctionalTensor(tensors))) { |
| 8147 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8148 | TORCH_INTERNAL_ASSERT(false, |
| 8149 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8150 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8151 | } else { |
| 8152 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8153 | at::AutoDispatchSkipFunctionalize guard; |
| 8154 | at::Tensor tmp_output = at::_ops::_stack_out::call(tensors_, dim, out_); |
| 8155 | return out;; |
| 8156 | } |
| 8157 | } else { |
| 8158 | at::Tensor tmp_output; |
| 8159 | { |
| 8160 | at::AutoDispatchSkipFunctionalize guard; |
| 8161 | tmp_output = at::_ops::_stack::call(tensors_, dim); |
| 8162 | } |
| 8163 | at::functionalization::impl::replace_(out, tmp_output); |
| 8164 | at::functionalization::impl::commit_update(out); |
| 8165 | at::functionalization::impl::sync(out); |
| 8166 | return out; |
| 8167 | } |
| 8168 | } |
| 8169 | |
| 8170 | at::Tensor & hstack_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Tensor & out) { |
| 8171 | if (false) { |
| 8172 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8173 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8174 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8175 | auto tensors_meta = to_meta(tensors); |
| 8176 | auto out_meta = to_meta(out); |
| 8177 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8178 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8179 | at::_ops::hstack_out::call(tensors_meta, out_meta); |
| 8180 | } |
| 8181 | |
| 8182 | ::std::vector<at::Tensor> tensors_; |
| 8183 | if (at::functionalization::impl::isFunctionalTensor(tensors)) { |
| 8184 | at::functionalization::impl::sync(tensors); |
| 8185 | tensors_ = at::functionalization::impl::from_functional_tensor(tensors); |
| 8186 | } else { |
| 8187 | tensors_ = tensors.vec(); |
| 8188 | } |
| 8189 | |
| 8190 | at::Tensor out_; |
| 8191 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8192 | at::functionalization::impl::sync(out); |
| 8193 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8194 | } else { |
| 8195 | out_ = out; |
| 8196 | } |
| 8197 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8198 | if ((false || at::functionalization::impl::isFunctionalTensor(tensors))) { |
| 8199 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8200 | TORCH_INTERNAL_ASSERT(false, |
| 8201 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8202 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8203 | } else { |
| 8204 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8205 | at::AutoDispatchSkipFunctionalize guard; |
| 8206 | at::Tensor tmp_output = at::_ops::hstack_out::call(tensors_, out_); |
| 8207 | return out;; |
| 8208 | } |
| 8209 | } else { |
| 8210 | at::Tensor tmp_output; |
| 8211 | { |
| 8212 | at::AutoDispatchSkipFunctionalize guard; |
| 8213 | tmp_output = at::_ops::hstack::call(tensors_); |
| 8214 | } |
| 8215 | at::functionalization::impl::replace_(out, tmp_output); |
| 8216 | at::functionalization::impl::commit_update(out); |
| 8217 | at::functionalization::impl::sync(out); |
| 8218 | return out; |
| 8219 | } |
| 8220 | } |
| 8221 | |
| 8222 | at::Tensor & dstack_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Tensor & out) { |
| 8223 | if (false) { |
| 8224 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8225 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8226 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8227 | auto tensors_meta = to_meta(tensors); |
| 8228 | auto out_meta = to_meta(out); |
| 8229 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8230 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8231 | at::_ops::dstack_out::call(tensors_meta, out_meta); |
| 8232 | } |
| 8233 | |
| 8234 | ::std::vector<at::Tensor> tensors_; |
| 8235 | if (at::functionalization::impl::isFunctionalTensor(tensors)) { |
| 8236 | at::functionalization::impl::sync(tensors); |
| 8237 | tensors_ = at::functionalization::impl::from_functional_tensor(tensors); |
| 8238 | } else { |
| 8239 | tensors_ = tensors.vec(); |
| 8240 | } |
| 8241 | |
| 8242 | at::Tensor out_; |
| 8243 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8244 | at::functionalization::impl::sync(out); |
| 8245 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8246 | } else { |
| 8247 | out_ = out; |
| 8248 | } |
| 8249 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8250 | if ((false || at::functionalization::impl::isFunctionalTensor(tensors))) { |
| 8251 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8252 | TORCH_INTERNAL_ASSERT(false, |
| 8253 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8254 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8255 | } else { |
| 8256 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8257 | at::AutoDispatchSkipFunctionalize guard; |
| 8258 | at::Tensor tmp_output = at::_ops::dstack_out::call(tensors_, out_); |
| 8259 | return out;; |
| 8260 | } |
| 8261 | } else { |
| 8262 | at::Tensor tmp_output; |
| 8263 | { |
| 8264 | at::AutoDispatchSkipFunctionalize guard; |
| 8265 | tmp_output = at::_ops::dstack::call(tensors_); |
| 8266 | } |
| 8267 | at::functionalization::impl::replace_(out, tmp_output); |
| 8268 | at::functionalization::impl::commit_update(out); |
| 8269 | at::functionalization::impl::sync(out); |
| 8270 | return out; |
| 8271 | } |
| 8272 | } |
| 8273 | |
| 8274 | at::Tensor & sum_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 8275 | if (false) { |
| 8276 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8277 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8278 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8279 | auto self_meta = to_meta(self); |
| 8280 | auto out_meta = to_meta(out); |
| 8281 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8282 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8283 | at::_ops::sum_out::call(self_meta, dtype, out_meta); |
| 8284 | } |
| 8285 | |
| 8286 | at::Tensor self_; |
| 8287 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8288 | at::functionalization::impl::sync(self); |
| 8289 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8290 | } else { |
| 8291 | self_ = self; |
| 8292 | } |
| 8293 | |
| 8294 | at::Tensor out_; |
| 8295 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8296 | at::functionalization::impl::sync(out); |
| 8297 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8298 | } else { |
| 8299 | out_ = out; |
| 8300 | } |
| 8301 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8302 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8303 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8304 | TORCH_INTERNAL_ASSERT(false, |
| 8305 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8306 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8307 | } else { |
| 8308 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8309 | at::AutoDispatchSkipFunctionalize guard; |
| 8310 | at::Tensor tmp_output = at::_ops::sum_out::call(self_, dtype, out_); |
| 8311 | return out;; |
| 8312 | } |
| 8313 | } else { |
| 8314 | at::Tensor tmp_output; |
| 8315 | { |
| 8316 | at::AutoDispatchSkipFunctionalize guard; |
| 8317 | tmp_output = at::_ops::sum::call(self_, dtype); |
| 8318 | } |
| 8319 | at::functionalization::impl::replace_(out, tmp_output); |
| 8320 | at::functionalization::impl::commit_update(out); |
| 8321 | at::functionalization::impl::sync(out); |
| 8322 | return out; |
| 8323 | } |
| 8324 | } |
| 8325 | |
| 8326 | at::Tensor & sum_out_IntList_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 8327 | if (false) { |
| 8328 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8329 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8330 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8331 | auto self_meta = to_meta(self); |
| 8332 | auto out_meta = to_meta(out); |
| 8333 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8334 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8335 | at::_ops::sum_IntList_out::call(self_meta, dim, keepdim, dtype, out_meta); |
| 8336 | } |
| 8337 | |
| 8338 | at::Tensor self_; |
| 8339 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8340 | at::functionalization::impl::sync(self); |
| 8341 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8342 | } else { |
| 8343 | self_ = self; |
| 8344 | } |
| 8345 | |
| 8346 | at::Tensor out_; |
| 8347 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8348 | at::functionalization::impl::sync(out); |
| 8349 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8350 | } else { |
| 8351 | out_ = out; |
| 8352 | } |
| 8353 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8354 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8355 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8356 | TORCH_INTERNAL_ASSERT(false, |
| 8357 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8358 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8359 | } else { |
| 8360 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8361 | at::AutoDispatchSkipFunctionalize guard; |
| 8362 | at::Tensor tmp_output = at::_ops::sum_IntList_out::call(self_, dim, keepdim, dtype, out_); |
| 8363 | return out;; |
| 8364 | } |
| 8365 | } else { |
| 8366 | at::Tensor tmp_output; |
| 8367 | { |
| 8368 | at::AutoDispatchSkipFunctionalize guard; |
| 8369 | tmp_output = at::_ops::sum_dim_IntList::call(self_, dim, keepdim, dtype); |
| 8370 | } |
| 8371 | at::functionalization::impl::replace_(out, tmp_output); |
| 8372 | at::functionalization::impl::commit_update(out); |
| 8373 | at::functionalization::impl::sync(out); |
| 8374 | return out; |
| 8375 | } |
| 8376 | } |
| 8377 | |
| 8378 | at::Tensor & sum_out_DimnameList_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 8379 | if (false) { |
| 8380 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8381 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8382 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8383 | auto self_meta = to_meta(self); |
| 8384 | auto out_meta = to_meta(out); |
| 8385 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8386 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8387 | at::_ops::sum_DimnameList_out::call(self_meta, dim, keepdim, dtype, out_meta); |
| 8388 | } |
| 8389 | |
| 8390 | at::Tensor self_; |
| 8391 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8392 | at::functionalization::impl::sync(self); |
| 8393 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8394 | } else { |
| 8395 | self_ = self; |
| 8396 | } |
| 8397 | |
| 8398 | at::Tensor out_; |
| 8399 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8400 | at::functionalization::impl::sync(out); |
| 8401 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8402 | } else { |
| 8403 | out_ = out; |
| 8404 | } |
| 8405 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8406 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8407 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8408 | TORCH_INTERNAL_ASSERT(false, |
| 8409 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8410 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8411 | } else { |
| 8412 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8413 | at::AutoDispatchSkipFunctionalize guard; |
| 8414 | at::Tensor tmp_output = at::_ops::sum_DimnameList_out::call(self_, dim, keepdim, dtype, out_); |
| 8415 | return out;; |
| 8416 | } |
| 8417 | } else { |
| 8418 | at::Tensor tmp_output; |
| 8419 | { |
| 8420 | at::AutoDispatchSkipFunctionalize guard; |
| 8421 | tmp_output = at::_ops::sum_dim_DimnameList::call(self_, dim, keepdim, dtype); |
| 8422 | } |
| 8423 | at::functionalization::impl::replace_(out, tmp_output); |
| 8424 | at::functionalization::impl::commit_update(out); |
| 8425 | at::functionalization::impl::sync(out); |
| 8426 | return out; |
| 8427 | } |
| 8428 | } |
| 8429 | |
| 8430 | at::Tensor & std_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim, at::Tensor & out) { |
| 8431 | if (false) { |
| 8432 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8433 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8434 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8435 | auto self_meta = to_meta(self); |
| 8436 | auto out_meta = to_meta(out); |
| 8437 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8438 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8439 | at::_ops::std_out::call(self_meta, dim, unbiased, keepdim, out_meta); |
| 8440 | } |
| 8441 | |
| 8442 | at::Tensor self_; |
| 8443 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8444 | at::functionalization::impl::sync(self); |
| 8445 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8446 | } else { |
| 8447 | self_ = self; |
| 8448 | } |
| 8449 | |
| 8450 | at::Tensor out_; |
| 8451 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8452 | at::functionalization::impl::sync(out); |
| 8453 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8454 | } else { |
| 8455 | out_ = out; |
| 8456 | } |
| 8457 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8458 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8459 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8460 | TORCH_INTERNAL_ASSERT(false, |
| 8461 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8462 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8463 | } else { |
| 8464 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8465 | at::AutoDispatchSkipFunctionalize guard; |
| 8466 | at::Tensor tmp_output = at::_ops::std_out::call(self_, dim, unbiased, keepdim, out_); |
| 8467 | return out;; |
| 8468 | } |
| 8469 | } else { |
| 8470 | at::Tensor tmp_output; |
| 8471 | { |
| 8472 | at::AutoDispatchSkipFunctionalize guard; |
| 8473 | tmp_output = at::_ops::std_dim::call(self_, dim, unbiased, keepdim); |
| 8474 | } |
| 8475 | at::functionalization::impl::replace_(out, tmp_output); |
| 8476 | at::functionalization::impl::commit_update(out); |
| 8477 | at::functionalization::impl::sync(out); |
| 8478 | return out; |
| 8479 | } |
| 8480 | } |
| 8481 | |
| 8482 | at::Tensor & std_out_correction_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, c10::optional<int64_t> correction, bool keepdim, at::Tensor & out) { |
| 8483 | if (false) { |
| 8484 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8485 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8486 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8487 | auto self_meta = to_meta(self); |
| 8488 | auto out_meta = to_meta(out); |
| 8489 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8490 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8491 | at::_ops::std_correction_out::call(self_meta, dim, correction, keepdim, out_meta); |
| 8492 | } |
| 8493 | |
| 8494 | at::Tensor self_; |
| 8495 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8496 | at::functionalization::impl::sync(self); |
| 8497 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8498 | } else { |
| 8499 | self_ = self; |
| 8500 | } |
| 8501 | |
| 8502 | at::Tensor out_; |
| 8503 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8504 | at::functionalization::impl::sync(out); |
| 8505 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8506 | } else { |
| 8507 | out_ = out; |
| 8508 | } |
| 8509 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8510 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8511 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8512 | TORCH_INTERNAL_ASSERT(false, |
| 8513 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8514 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8515 | } else { |
| 8516 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8517 | at::AutoDispatchSkipFunctionalize guard; |
| 8518 | at::Tensor tmp_output = at::_ops::std_correction_out::call(self_, dim, correction, keepdim, out_); |
| 8519 | return out;; |
| 8520 | } |
| 8521 | } else { |
| 8522 | at::Tensor tmp_output; |
| 8523 | { |
| 8524 | at::AutoDispatchSkipFunctionalize guard; |
| 8525 | tmp_output = at::_ops::std_correction::call(self_, dim, correction, keepdim); |
| 8526 | } |
| 8527 | at::functionalization::impl::replace_(out, tmp_output); |
| 8528 | at::functionalization::impl::commit_update(out); |
| 8529 | at::functionalization::impl::sync(out); |
| 8530 | return out; |
| 8531 | } |
| 8532 | } |
| 8533 | |
| 8534 | at::Tensor & std_out_names_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim, at::Tensor & out) { |
| 8535 | if (false) { |
| 8536 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8537 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8538 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8539 | auto self_meta = to_meta(self); |
| 8540 | auto out_meta = to_meta(out); |
| 8541 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8542 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8543 | at::_ops::std_names_out::call(self_meta, dim, unbiased, keepdim, out_meta); |
| 8544 | } |
| 8545 | |
| 8546 | at::Tensor self_; |
| 8547 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8548 | at::functionalization::impl::sync(self); |
| 8549 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8550 | } else { |
| 8551 | self_ = self; |
| 8552 | } |
| 8553 | |
| 8554 | at::Tensor out_; |
| 8555 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8556 | at::functionalization::impl::sync(out); |
| 8557 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8558 | } else { |
| 8559 | out_ = out; |
| 8560 | } |
| 8561 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8562 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8563 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8564 | TORCH_INTERNAL_ASSERT(false, |
| 8565 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8566 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8567 | } else { |
| 8568 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8569 | at::AutoDispatchSkipFunctionalize guard; |
| 8570 | at::Tensor tmp_output = at::_ops::std_names_out::call(self_, dim, unbiased, keepdim, out_); |
| 8571 | return out;; |
| 8572 | } |
| 8573 | } else { |
| 8574 | at::Tensor tmp_output; |
| 8575 | { |
| 8576 | at::AutoDispatchSkipFunctionalize guard; |
| 8577 | tmp_output = at::_ops::std_names_dim::call(self_, dim, unbiased, keepdim); |
| 8578 | } |
| 8579 | at::functionalization::impl::replace_(out, tmp_output); |
| 8580 | at::functionalization::impl::commit_update(out); |
| 8581 | at::functionalization::impl::sync(out); |
| 8582 | return out; |
| 8583 | } |
| 8584 | } |
| 8585 | |
| 8586 | at::Tensor & std_out_correction_names_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, c10::optional<int64_t> correction, bool keepdim, at::Tensor & out) { |
| 8587 | if (false) { |
| 8588 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8589 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8590 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8591 | auto self_meta = to_meta(self); |
| 8592 | auto out_meta = to_meta(out); |
| 8593 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8594 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8595 | at::_ops::std_correction_names_out::call(self_meta, dim, correction, keepdim, out_meta); |
| 8596 | } |
| 8597 | |
| 8598 | at::Tensor self_; |
| 8599 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8600 | at::functionalization::impl::sync(self); |
| 8601 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8602 | } else { |
| 8603 | self_ = self; |
| 8604 | } |
| 8605 | |
| 8606 | at::Tensor out_; |
| 8607 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8608 | at::functionalization::impl::sync(out); |
| 8609 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8610 | } else { |
| 8611 | out_ = out; |
| 8612 | } |
| 8613 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8614 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8615 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8616 | TORCH_INTERNAL_ASSERT(false, |
| 8617 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8618 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8619 | } else { |
| 8620 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8621 | at::AutoDispatchSkipFunctionalize guard; |
| 8622 | at::Tensor tmp_output = at::_ops::std_correction_names_out::call(self_, dim, correction, keepdim, out_); |
| 8623 | return out;; |
| 8624 | } |
| 8625 | } else { |
| 8626 | at::Tensor tmp_output; |
| 8627 | { |
| 8628 | at::AutoDispatchSkipFunctionalize guard; |
| 8629 | tmp_output = at::_ops::std_correction_names::call(self_, dim, correction, keepdim); |
| 8630 | } |
| 8631 | at::functionalization::impl::replace_(out, tmp_output); |
| 8632 | at::functionalization::impl::commit_update(out); |
| 8633 | at::functionalization::impl::sync(out); |
| 8634 | return out; |
| 8635 | } |
| 8636 | } |
| 8637 | |
| 8638 | at::Tensor & tan_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 8639 | if (false) { |
| 8640 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8641 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8642 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8643 | auto self_meta = to_meta(self); |
| 8644 | auto out_meta = to_meta(out); |
| 8645 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8646 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8647 | at::_ops::tan_out::call(self_meta, out_meta); |
| 8648 | } |
| 8649 | |
| 8650 | at::Tensor self_; |
| 8651 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8652 | at::functionalization::impl::sync(self); |
| 8653 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8654 | } else { |
| 8655 | self_ = self; |
| 8656 | } |
| 8657 | |
| 8658 | at::Tensor out_; |
| 8659 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8660 | at::functionalization::impl::sync(out); |
| 8661 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8662 | } else { |
| 8663 | out_ = out; |
| 8664 | } |
| 8665 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8666 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8667 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8668 | TORCH_INTERNAL_ASSERT(false, |
| 8669 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8670 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8671 | } else { |
| 8672 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8673 | at::AutoDispatchSkipFunctionalize guard; |
| 8674 | at::Tensor tmp_output = at::_ops::tan_out::call(self_, out_); |
| 8675 | return out;; |
| 8676 | } |
| 8677 | } else { |
| 8678 | at::Tensor tmp_output; |
| 8679 | { |
| 8680 | at::AutoDispatchSkipFunctionalize guard; |
| 8681 | tmp_output = at::_ops::tan::call(self_); |
| 8682 | } |
| 8683 | at::functionalization::impl::replace_(out, tmp_output); |
| 8684 | at::functionalization::impl::commit_update(out); |
| 8685 | at::functionalization::impl::sync(out); |
| 8686 | return out; |
| 8687 | } |
| 8688 | } |
| 8689 | |
| 8690 | at::Tensor & tan_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 8691 | if (true) { |
| 8692 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8693 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8694 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8695 | auto self_meta = to_meta(self); |
| 8696 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8697 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8698 | at::_ops::tan_::call(self_meta); |
| 8699 | } |
| 8700 | |
| 8701 | at::Tensor self_; |
| 8702 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8703 | at::functionalization::impl::sync(self); |
| 8704 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8705 | } else { |
| 8706 | self_ = self; |
| 8707 | } |
| 8708 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 8709 | if ((false)) { |
| 8710 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8711 | TORCH_INTERNAL_ASSERT(false, |
| 8712 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8713 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8714 | } else { |
| 8715 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8716 | at::AutoDispatchSkipFunctionalize guard; |
| 8717 | at::Tensor tmp_output = at::_ops::tan_::call(self_); |
| 8718 | return self;; |
| 8719 | } |
| 8720 | } else { |
| 8721 | at::Tensor tmp_output; |
| 8722 | { |
| 8723 | at::AutoDispatchSkipFunctionalize guard; |
| 8724 | tmp_output = at::_ops::tan::call(self_); |
| 8725 | } |
| 8726 | at::functionalization::impl::replace_(self, tmp_output); |
| 8727 | at::functionalization::impl::commit_update(self); |
| 8728 | at::functionalization::impl::sync(self); |
| 8729 | return self; |
| 8730 | } |
| 8731 | } |
| 8732 | |
| 8733 | at::Tensor & tensordot_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other, at::Tensor & out) { |
| 8734 | if (false) { |
| 8735 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8736 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8737 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8738 | auto self_meta = to_meta(self); |
| 8739 | auto other_meta = to_meta(other); |
| 8740 | auto out_meta = to_meta(out); |
| 8741 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8742 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8743 | at::_ops::tensordot_out::call(self_meta, other_meta, dims_self, dims_other, out_meta); |
| 8744 | } |
| 8745 | |
| 8746 | at::Tensor self_; |
| 8747 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8748 | at::functionalization::impl::sync(self); |
| 8749 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8750 | } else { |
| 8751 | self_ = self; |
| 8752 | } |
| 8753 | |
| 8754 | at::Tensor other_; |
| 8755 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 8756 | at::functionalization::impl::sync(other); |
| 8757 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 8758 | } else { |
| 8759 | other_ = other; |
| 8760 | } |
| 8761 | |
| 8762 | at::Tensor out_; |
| 8763 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8764 | at::functionalization::impl::sync(out); |
| 8765 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8766 | } else { |
| 8767 | out_ = out; |
| 8768 | } |
| 8769 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8770 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 8771 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8772 | TORCH_INTERNAL_ASSERT(false, |
| 8773 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8774 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8775 | } else { |
| 8776 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8777 | at::AutoDispatchSkipFunctionalize guard; |
| 8778 | at::Tensor tmp_output = at::_ops::tensordot_out::call(self_, other_, dims_self, dims_other, out_); |
| 8779 | return out;; |
| 8780 | } |
| 8781 | } else { |
| 8782 | at::Tensor tmp_output; |
| 8783 | { |
| 8784 | at::AutoDispatchSkipFunctionalize guard; |
| 8785 | tmp_output = at::_ops::tensordot::call(self_, other_, dims_self, dims_other); |
| 8786 | } |
| 8787 | at::functionalization::impl::replace_(out, tmp_output); |
| 8788 | at::functionalization::impl::commit_update(out); |
| 8789 | at::functionalization::impl::sync(out); |
| 8790 | return out; |
| 8791 | } |
| 8792 | } |
| 8793 | |
| 8794 | at::Tensor & threshold_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value, at::Tensor & out) { |
| 8795 | if (false) { |
| 8796 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8797 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8798 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8799 | auto self_meta = to_meta(self); |
| 8800 | auto out_meta = to_meta(out); |
| 8801 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8802 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8803 | at::_ops::threshold_out::call(self_meta, threshold, value, out_meta); |
| 8804 | } |
| 8805 | |
| 8806 | at::Tensor self_; |
| 8807 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8808 | at::functionalization::impl::sync(self); |
| 8809 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8810 | } else { |
| 8811 | self_ = self; |
| 8812 | } |
| 8813 | |
| 8814 | at::Tensor out_; |
| 8815 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8816 | at::functionalization::impl::sync(out); |
| 8817 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8818 | } else { |
| 8819 | out_ = out; |
| 8820 | } |
| 8821 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8822 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8823 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8824 | TORCH_INTERNAL_ASSERT(false, |
| 8825 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8826 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8827 | } else { |
| 8828 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8829 | at::AutoDispatchSkipFunctionalize guard; |
| 8830 | at::Tensor tmp_output = at::_ops::threshold_out::call(self_, threshold, value, out_); |
| 8831 | return out;; |
| 8832 | } |
| 8833 | } else { |
| 8834 | at::Tensor tmp_output; |
| 8835 | { |
| 8836 | at::AutoDispatchSkipFunctionalize guard; |
| 8837 | tmp_output = at::_ops::threshold::call(self_, threshold, value); |
| 8838 | } |
| 8839 | at::functionalization::impl::replace_(out, tmp_output); |
| 8840 | at::functionalization::impl::commit_update(out); |
| 8841 | at::functionalization::impl::sync(out); |
| 8842 | return out; |
| 8843 | } |
| 8844 | } |
| 8845 | |
| 8846 | at::Tensor & threshold_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value) { |
| 8847 | if (true) { |
| 8848 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8849 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8850 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8851 | auto self_meta = to_meta(self); |
| 8852 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8853 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8854 | at::_ops::threshold_::call(self_meta, threshold, value); |
| 8855 | } |
| 8856 | |
| 8857 | at::Tensor self_; |
| 8858 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8859 | at::functionalization::impl::sync(self); |
| 8860 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8861 | } else { |
| 8862 | self_ = self; |
| 8863 | } |
| 8864 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 8865 | if ((false)) { |
| 8866 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8867 | TORCH_INTERNAL_ASSERT(false, |
| 8868 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8869 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8870 | } else { |
| 8871 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8872 | at::AutoDispatchSkipFunctionalize guard; |
| 8873 | at::Tensor tmp_output = at::_ops::threshold_::call(self_, threshold, value); |
| 8874 | return self;; |
| 8875 | } |
| 8876 | } else { |
| 8877 | at::Tensor tmp_output; |
| 8878 | { |
| 8879 | at::AutoDispatchSkipFunctionalize guard; |
| 8880 | tmp_output = at::_ops::threshold::call(self_, threshold, value); |
| 8881 | } |
| 8882 | at::functionalization::impl::replace_(self, tmp_output); |
| 8883 | at::functionalization::impl::commit_update(self); |
| 8884 | at::functionalization::impl::sync(self); |
| 8885 | return self; |
| 8886 | } |
| 8887 | } |
| 8888 | |
| 8889 | at::Tensor & _nested_tensor_strides_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 8890 | if (false) { |
| 8891 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8892 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8893 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8894 | auto self_meta = to_meta(self); |
| 8895 | auto out_meta = to_meta(out); |
| 8896 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8897 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8898 | at::_ops::_nested_tensor_strides_out::call(self_meta, out_meta); |
| 8899 | } |
| 8900 | |
| 8901 | at::Tensor self_; |
| 8902 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8903 | at::functionalization::impl::sync(self); |
| 8904 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8905 | } else { |
| 8906 | self_ = self; |
| 8907 | } |
| 8908 | |
| 8909 | at::Tensor out_; |
| 8910 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8911 | at::functionalization::impl::sync(out); |
| 8912 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8913 | } else { |
| 8914 | out_ = out; |
| 8915 | } |
| 8916 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8917 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8918 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8919 | TORCH_INTERNAL_ASSERT(false, |
| 8920 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8921 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8922 | } else { |
| 8923 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8924 | at::AutoDispatchSkipFunctionalize guard; |
| 8925 | at::Tensor tmp_output = at::_ops::_nested_tensor_strides_out::call(self_, out_); |
| 8926 | return out;; |
| 8927 | } |
| 8928 | } else { |
| 8929 | at::Tensor tmp_output; |
| 8930 | { |
| 8931 | at::AutoDispatchSkipFunctionalize guard; |
| 8932 | tmp_output = at::_ops::_nested_tensor_strides::call(self_); |
| 8933 | } |
| 8934 | at::functionalization::impl::replace_(out, tmp_output); |
| 8935 | at::functionalization::impl::commit_update(out); |
| 8936 | at::functionalization::impl::sync(out); |
| 8937 | return out; |
| 8938 | } |
| 8939 | } |
| 8940 | |
| 8941 | at::Tensor & fix_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 8942 | if (false) { |
| 8943 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8944 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8945 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8946 | auto self_meta = to_meta(self); |
| 8947 | auto out_meta = to_meta(out); |
| 8948 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8949 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8950 | at::_ops::fix_out::call(self_meta, out_meta); |
| 8951 | } |
| 8952 | |
| 8953 | at::Tensor self_; |
| 8954 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8955 | at::functionalization::impl::sync(self); |
| 8956 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8957 | } else { |
| 8958 | self_ = self; |
| 8959 | } |
| 8960 | |
| 8961 | at::Tensor out_; |
| 8962 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8963 | at::functionalization::impl::sync(out); |
| 8964 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8965 | } else { |
| 8966 | out_ = out; |
| 8967 | } |
| 8968 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8969 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8970 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8971 | TORCH_INTERNAL_ASSERT(false, |
| 8972 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8973 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8974 | } else { |
| 8975 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8976 | at::AutoDispatchSkipFunctionalize guard; |
| 8977 | at::Tensor tmp_output = at::_ops::fix_out::call(self_, out_); |
| 8978 | return out;; |
| 8979 | } |
| 8980 | } else { |
| 8981 | at::Tensor tmp_output; |
| 8982 | { |
| 8983 | at::AutoDispatchSkipFunctionalize guard; |
| 8984 | tmp_output = at::_ops::fix::call(self_); |
| 8985 | } |
| 8986 | at::functionalization::impl::replace_(out, tmp_output); |
| 8987 | at::functionalization::impl::commit_update(out); |
| 8988 | at::functionalization::impl::sync(out); |
| 8989 | return out; |
| 8990 | } |
| 8991 | } |
| 8992 | |
| 8993 | at::Tensor & fix_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 8994 | if (true) { |
| 8995 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8996 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8997 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8998 | auto self_meta = to_meta(self); |
| 8999 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9000 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9001 | at::_ops::fix_::call(self_meta); |
| 9002 | } |
| 9003 | |
| 9004 | at::Tensor self_; |
| 9005 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9006 | at::functionalization::impl::sync(self); |
| 9007 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9008 | } else { |
| 9009 | self_ = self; |
| 9010 | } |
| 9011 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 9012 | if ((false)) { |
| 9013 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9014 | TORCH_INTERNAL_ASSERT(false, |
| 9015 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9016 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9017 | } else { |
| 9018 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9019 | at::AutoDispatchSkipFunctionalize guard; |
| 9020 | at::Tensor tmp_output = at::_ops::fix_::call(self_); |
| 9021 | return self;; |
| 9022 | } |
| 9023 | } else { |
| 9024 | at::Tensor tmp_output; |
| 9025 | { |
| 9026 | at::AutoDispatchSkipFunctionalize guard; |
| 9027 | tmp_output = at::_ops::fix::call(self_); |
| 9028 | } |
| 9029 | at::functionalization::impl::replace_(self, tmp_output); |
| 9030 | at::functionalization::impl::commit_update(self); |
| 9031 | at::functionalization::impl::sync(self); |
| 9032 | return self; |
| 9033 | } |
| 9034 | } |
| 9035 | |
| 9036 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_consecutive_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool return_inverse, bool return_counts, c10::optional<int64_t> dim, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 9037 | if (false) { |
| 9038 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9039 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9040 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9041 | auto self_meta = to_meta(self); |
| 9042 | auto out0_meta = to_meta(out0); |
| 9043 | auto out1_meta = to_meta(out1); |
| 9044 | auto out2_meta = to_meta(out2); |
| 9045 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9046 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9047 | at::_ops::unique_consecutive_out::call(self_meta, return_inverse, return_counts, dim, out0_meta, out1_meta, out2_meta); |
| 9048 | } |
| 9049 | |
| 9050 | at::Tensor self_; |
| 9051 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9052 | at::functionalization::impl::sync(self); |
| 9053 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9054 | } else { |
| 9055 | self_ = self; |
| 9056 | } |
| 9057 | |
| 9058 | at::Tensor out0_; |
| 9059 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 9060 | at::functionalization::impl::sync(out0); |
| 9061 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 9062 | } else { |
| 9063 | out0_ = out0; |
| 9064 | } |
| 9065 | |
| 9066 | at::Tensor out1_; |
| 9067 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 9068 | at::functionalization::impl::sync(out1); |
| 9069 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 9070 | } else { |
| 9071 | out1_ = out1; |
| 9072 | } |
| 9073 | |
| 9074 | at::Tensor out2_; |
| 9075 | if (at::functionalization::impl::isFunctionalTensor(out2)) { |
| 9076 | at::functionalization::impl::sync(out2); |
| 9077 | out2_ = at::functionalization::impl::from_functional_tensor(out2); |
| 9078 | } else { |
| 9079 | out2_ = out2; |
| 9080 | } |
| 9081 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1) && at::functionalization::impl::isFunctionalTensor(out2))) { |
| 9082 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9083 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9084 | TORCH_INTERNAL_ASSERT(false, |
| 9085 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9086 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9087 | } else { |
| 9088 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9089 | at::AutoDispatchSkipFunctionalize guard; |
| 9090 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::unique_consecutive_out::call(self_, return_inverse, return_counts, dim, out0_, out1_, out2_); |
| 9091 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2);; |
| 9092 | } |
| 9093 | } else { |
| 9094 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 9095 | { |
| 9096 | at::AutoDispatchSkipFunctionalize guard; |
| 9097 | tmp_output = at::_ops::unique_consecutive::call(self_, return_inverse, return_counts, dim); |
| 9098 | } |
| 9099 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 9100 | at::functionalization::impl::commit_update(out0); |
| 9101 | at::functionalization::impl::sync(out0); |
| 9102 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 9103 | at::functionalization::impl::commit_update(out1); |
| 9104 | at::functionalization::impl::sync(out1); |
| 9105 | at::functionalization::impl::replace_(out2, std::get<2>(tmp_output)); |
| 9106 | at::functionalization::impl::commit_update(out2); |
| 9107 | at::functionalization::impl::sync(out2); |
| 9108 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2); |
| 9109 | } |
| 9110 | } |
| 9111 | |
| 9112 | at::Tensor & var_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim, at::Tensor & out) { |
| 9113 | if (false) { |
| 9114 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9115 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9116 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9117 | auto self_meta = to_meta(self); |
| 9118 | auto out_meta = to_meta(out); |
| 9119 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9120 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9121 | at::_ops::var_out::call(self_meta, dim, unbiased, keepdim, out_meta); |
| 9122 | } |
| 9123 | |
| 9124 | at::Tensor self_; |
| 9125 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9126 | at::functionalization::impl::sync(self); |
| 9127 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9128 | } else { |
| 9129 | self_ = self; |
| 9130 | } |
| 9131 | |
| 9132 | at::Tensor out_; |
| 9133 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9134 | at::functionalization::impl::sync(out); |
| 9135 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9136 | } else { |
| 9137 | out_ = out; |
| 9138 | } |
| 9139 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9140 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9141 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9142 | TORCH_INTERNAL_ASSERT(false, |
| 9143 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9144 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9145 | } else { |
| 9146 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9147 | at::AutoDispatchSkipFunctionalize guard; |
| 9148 | at::Tensor tmp_output = at::_ops::var_out::call(self_, dim, unbiased, keepdim, out_); |
| 9149 | return out;; |
| 9150 | } |
| 9151 | } else { |
| 9152 | at::Tensor tmp_output; |
| 9153 | { |
| 9154 | at::AutoDispatchSkipFunctionalize guard; |
| 9155 | tmp_output = at::_ops::var_dim::call(self_, dim, unbiased, keepdim); |
| 9156 | } |
| 9157 | at::functionalization::impl::replace_(out, tmp_output); |
| 9158 | at::functionalization::impl::commit_update(out); |
| 9159 | at::functionalization::impl::sync(out); |
| 9160 | return out; |
| 9161 | } |
| 9162 | } |
| 9163 | |
| 9164 | at::Tensor & var_out_correction_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, c10::optional<int64_t> correction, bool keepdim, at::Tensor & out) { |
| 9165 | if (false) { |
| 9166 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9167 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9168 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9169 | auto self_meta = to_meta(self); |
| 9170 | auto out_meta = to_meta(out); |
| 9171 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9172 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9173 | at::_ops::var_correction_out::call(self_meta, dim, correction, keepdim, out_meta); |
| 9174 | } |
| 9175 | |
| 9176 | at::Tensor self_; |
| 9177 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9178 | at::functionalization::impl::sync(self); |
| 9179 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9180 | } else { |
| 9181 | self_ = self; |
| 9182 | } |
| 9183 | |
| 9184 | at::Tensor out_; |
| 9185 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9186 | at::functionalization::impl::sync(out); |
| 9187 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9188 | } else { |
| 9189 | out_ = out; |
| 9190 | } |
| 9191 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9192 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9193 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9194 | TORCH_INTERNAL_ASSERT(false, |
| 9195 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9196 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9197 | } else { |
| 9198 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9199 | at::AutoDispatchSkipFunctionalize guard; |
| 9200 | at::Tensor tmp_output = at::_ops::var_correction_out::call(self_, dim, correction, keepdim, out_); |
| 9201 | return out;; |
| 9202 | } |
| 9203 | } else { |
| 9204 | at::Tensor tmp_output; |
| 9205 | { |
| 9206 | at::AutoDispatchSkipFunctionalize guard; |
| 9207 | tmp_output = at::_ops::var_correction::call(self_, dim, correction, keepdim); |
| 9208 | } |
| 9209 | at::functionalization::impl::replace_(out, tmp_output); |
| 9210 | at::functionalization::impl::commit_update(out); |
| 9211 | at::functionalization::impl::sync(out); |
| 9212 | return out; |
| 9213 | } |
| 9214 | } |
| 9215 | |
| 9216 | at::Tensor & var_out_names_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim, at::Tensor & out) { |
| 9217 | if (false) { |
| 9218 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9219 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9220 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9221 | auto self_meta = to_meta(self); |
| 9222 | auto out_meta = to_meta(out); |
| 9223 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9224 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9225 | at::_ops::var_names_out::call(self_meta, dim, unbiased, keepdim, out_meta); |
| 9226 | } |
| 9227 | |
| 9228 | at::Tensor self_; |
| 9229 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9230 | at::functionalization::impl::sync(self); |
| 9231 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9232 | } else { |
| 9233 | self_ = self; |
| 9234 | } |
| 9235 | |
| 9236 | at::Tensor out_; |
| 9237 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9238 | at::functionalization::impl::sync(out); |
| 9239 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9240 | } else { |
| 9241 | out_ = out; |
| 9242 | } |
| 9243 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9244 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9245 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9246 | TORCH_INTERNAL_ASSERT(false, |
| 9247 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9248 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9249 | } else { |
| 9250 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9251 | at::AutoDispatchSkipFunctionalize guard; |
| 9252 | at::Tensor tmp_output = at::_ops::var_names_out::call(self_, dim, unbiased, keepdim, out_); |
| 9253 | return out;; |
| 9254 | } |
| 9255 | } else { |
| 9256 | at::Tensor tmp_output; |
| 9257 | { |
| 9258 | at::AutoDispatchSkipFunctionalize guard; |
| 9259 | tmp_output = at::_ops::var_names_dim::call(self_, dim, unbiased, keepdim); |
| 9260 | } |
| 9261 | at::functionalization::impl::replace_(out, tmp_output); |
| 9262 | at::functionalization::impl::commit_update(out); |
| 9263 | at::functionalization::impl::sync(out); |
| 9264 | return out; |
| 9265 | } |
| 9266 | } |
| 9267 | |
| 9268 | at::Tensor & var_out_correction_names_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, c10::optional<int64_t> correction, bool keepdim, at::Tensor & out) { |
| 9269 | if (false) { |
| 9270 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9271 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9272 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9273 | auto self_meta = to_meta(self); |
| 9274 | auto out_meta = to_meta(out); |
| 9275 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9276 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9277 | at::_ops::var_correction_names_out::call(self_meta, dim, correction, keepdim, out_meta); |
| 9278 | } |
| 9279 | |
| 9280 | at::Tensor self_; |
| 9281 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9282 | at::functionalization::impl::sync(self); |
| 9283 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9284 | } else { |
| 9285 | self_ = self; |
| 9286 | } |
| 9287 | |
| 9288 | at::Tensor out_; |
| 9289 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9290 | at::functionalization::impl::sync(out); |
| 9291 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9292 | } else { |
| 9293 | out_ = out; |
| 9294 | } |
| 9295 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9296 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9297 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9298 | TORCH_INTERNAL_ASSERT(false, |
| 9299 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9300 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9301 | } else { |
| 9302 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9303 | at::AutoDispatchSkipFunctionalize guard; |
| 9304 | at::Tensor tmp_output = at::_ops::var_correction_names_out::call(self_, dim, correction, keepdim, out_); |
| 9305 | return out;; |
| 9306 | } |
| 9307 | } else { |
| 9308 | at::Tensor tmp_output; |
| 9309 | { |
| 9310 | at::AutoDispatchSkipFunctionalize guard; |
| 9311 | tmp_output = at::_ops::var_correction_names::call(self_, dim, correction, keepdim); |
| 9312 | } |
| 9313 | at::functionalization::impl::replace_(out, tmp_output); |
| 9314 | at::functionalization::impl::commit_update(out); |
| 9315 | at::functionalization::impl::sync(out); |
| 9316 | return out; |
| 9317 | } |
| 9318 | } |
| 9319 | |
| 9320 | at::Tensor & _standard_gamma_grad_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & output, at::Tensor & out) { |
| 9321 | if (false) { |
| 9322 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9323 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9324 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9325 | auto self_meta = to_meta(self); |
| 9326 | auto output_meta = to_meta(output); |
| 9327 | auto out_meta = to_meta(out); |
| 9328 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9329 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9330 | at::_ops::_standard_gamma_grad_out::call(self_meta, output_meta, out_meta); |
| 9331 | } |
| 9332 | |
| 9333 | at::Tensor self_; |
| 9334 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9335 | at::functionalization::impl::sync(self); |
| 9336 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9337 | } else { |
| 9338 | self_ = self; |
| 9339 | } |
| 9340 | |
| 9341 | at::Tensor output_; |
| 9342 | if (at::functionalization::impl::isFunctionalTensor(output)) { |
| 9343 | at::functionalization::impl::sync(output); |
| 9344 | output_ = at::functionalization::impl::from_functional_tensor(output); |
| 9345 | } else { |
| 9346 | output_ = output; |
| 9347 | } |
| 9348 | |
| 9349 | at::Tensor out_; |
| 9350 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9351 | at::functionalization::impl::sync(out); |
| 9352 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9353 | } else { |
| 9354 | out_ = out; |
| 9355 | } |
| 9356 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9357 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(output))) { |
| 9358 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9359 | TORCH_INTERNAL_ASSERT(false, |
| 9360 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9361 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9362 | } else { |
| 9363 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9364 | at::AutoDispatchSkipFunctionalize guard; |
| 9365 | at::Tensor tmp_output = at::_ops::_standard_gamma_grad_out::call(self_, output_, out_); |
| 9366 | return out;; |
| 9367 | } |
| 9368 | } else { |
| 9369 | at::Tensor tmp_output; |
| 9370 | { |
| 9371 | at::AutoDispatchSkipFunctionalize guard; |
| 9372 | tmp_output = at::_ops::_standard_gamma_grad::call(self_, output_); |
| 9373 | } |
| 9374 | at::functionalization::impl::replace_(out, tmp_output); |
| 9375 | at::functionalization::impl::commit_update(out); |
| 9376 | at::functionalization::impl::sync(out); |
| 9377 | return out; |
| 9378 | } |
| 9379 | } |
| 9380 | |
| 9381 | at::Tensor & poisson_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 9382 | if (false) { |
| 9383 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9384 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9385 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9386 | auto self_meta = to_meta(self); |
| 9387 | auto out_meta = to_meta(out); |
| 9388 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9389 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9390 | at::_ops::poisson_out::call(self_meta, generator, out_meta); |
| 9391 | } |
| 9392 | |
| 9393 | at::Tensor self_; |
| 9394 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9395 | at::functionalization::impl::sync(self); |
| 9396 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9397 | } else { |
| 9398 | self_ = self; |
| 9399 | } |
| 9400 | |
| 9401 | at::Tensor out_; |
| 9402 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9403 | at::functionalization::impl::sync(out); |
| 9404 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9405 | } else { |
| 9406 | out_ = out; |
| 9407 | } |
| 9408 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9409 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9410 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9411 | TORCH_INTERNAL_ASSERT(false, |
| 9412 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9413 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9414 | } else { |
| 9415 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9416 | at::AutoDispatchSkipFunctionalize guard; |
| 9417 | at::Tensor tmp_output = at::_ops::poisson_out::call(self_, generator, out_); |
| 9418 | return out;; |
| 9419 | } |
| 9420 | } else { |
| 9421 | at::Tensor tmp_output; |
| 9422 | { |
| 9423 | at::AutoDispatchSkipFunctionalize guard; |
| 9424 | tmp_output = at::_ops::poisson::call(self_, generator); |
| 9425 | } |
| 9426 | at::functionalization::impl::replace_(out, tmp_output); |
| 9427 | at::functionalization::impl::commit_update(out); |
| 9428 | at::functionalization::impl::sync(out); |
| 9429 | return out; |
| 9430 | } |
| 9431 | } |
| 9432 | |
| 9433 | at::Tensor & _sparse_csr_sum_out_dim_dtype_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 9434 | if (false) { |
| 9435 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9436 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9437 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9438 | auto self_meta = to_meta(self); |
| 9439 | auto out_meta = to_meta(out); |
| 9440 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9441 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9442 | at::_ops::_sparse_csr_sum_dim_dtype_out::call(self_meta, dim, keepdim, dtype, out_meta); |
| 9443 | } |
| 9444 | |
| 9445 | at::Tensor self_; |
| 9446 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9447 | at::functionalization::impl::sync(self); |
| 9448 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9449 | } else { |
| 9450 | self_ = self; |
| 9451 | } |
| 9452 | |
| 9453 | at::Tensor out_; |
| 9454 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9455 | at::functionalization::impl::sync(out); |
| 9456 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9457 | } else { |
| 9458 | out_ = out; |
| 9459 | } |
| 9460 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9461 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9462 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9463 | TORCH_INTERNAL_ASSERT(false, |
| 9464 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9465 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9466 | } else { |
| 9467 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9468 | at::AutoDispatchSkipFunctionalize guard; |
| 9469 | at::Tensor tmp_output = at::_ops::_sparse_csr_sum_dim_dtype_out::call(self_, dim, keepdim, dtype, out_); |
| 9470 | return out;; |
| 9471 | } |
| 9472 | } else { |
| 9473 | at::Tensor tmp_output; |
| 9474 | { |
| 9475 | at::AutoDispatchSkipFunctionalize guard; |
| 9476 | tmp_output = at::_ops::_sparse_csr_sum_dim_dtype::call(self_, dim, keepdim, dtype); |
| 9477 | } |
| 9478 | at::functionalization::impl::replace_(out, tmp_output); |
| 9479 | at::functionalization::impl::commit_update(out); |
| 9480 | at::functionalization::impl::sync(out); |
| 9481 | return out; |
| 9482 | } |
| 9483 | } |
| 9484 | |
| 9485 | at::Tensor & _sparse_softmax_backward_data_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self, at::Tensor & out) { |
| 9486 | if (false) { |
| 9487 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9488 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9489 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9490 | auto grad_output_meta = to_meta(grad_output); |
| 9491 | auto output_meta = to_meta(output); |
| 9492 | auto self_meta = to_meta(self); |
| 9493 | auto out_meta = to_meta(out); |
| 9494 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9495 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9496 | at::_ops::_sparse_softmax_backward_data_out::call(grad_output_meta, output_meta, dim, self_meta, out_meta); |
| 9497 | } |
| 9498 | |
| 9499 | at::Tensor grad_output_; |
| 9500 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 9501 | at::functionalization::impl::sync(grad_output); |
| 9502 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 9503 | } else { |
| 9504 | grad_output_ = grad_output; |
| 9505 | } |
| 9506 | |
| 9507 | at::Tensor output_; |
| 9508 | if (at::functionalization::impl::isFunctionalTensor(output)) { |
| 9509 | at::functionalization::impl::sync(output); |
| 9510 | output_ = at::functionalization::impl::from_functional_tensor(output); |
| 9511 | } else { |
| 9512 | output_ = output; |
| 9513 | } |
| 9514 | |
| 9515 | at::Tensor self_; |
| 9516 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9517 | at::functionalization::impl::sync(self); |
| 9518 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9519 | } else { |
| 9520 | self_ = self; |
| 9521 | } |
| 9522 | |
| 9523 | at::Tensor out_; |
| 9524 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9525 | at::functionalization::impl::sync(out); |
| 9526 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9527 | } else { |
| 9528 | out_ = out; |
| 9529 | } |
| 9530 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9531 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(output) || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9532 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9533 | TORCH_INTERNAL_ASSERT(false, |
| 9534 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9535 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9536 | } else { |
| 9537 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9538 | at::AutoDispatchSkipFunctionalize guard; |
| 9539 | at::Tensor tmp_output = at::_ops::_sparse_softmax_backward_data_out::call(grad_output_, output_, dim, self_, out_); |
| 9540 | return out;; |
| 9541 | } |
| 9542 | } else { |
| 9543 | at::Tensor tmp_output; |
| 9544 | { |
| 9545 | at::AutoDispatchSkipFunctionalize guard; |
| 9546 | tmp_output = at::_ops::_sparse_softmax_backward_data::call(grad_output_, output_, dim, self_); |
| 9547 | } |
| 9548 | at::functionalization::impl::replace_(out, tmp_output); |
| 9549 | at::functionalization::impl::commit_update(out); |
| 9550 | at::functionalization::impl::sync(out); |
| 9551 | return out; |
| 9552 | } |
| 9553 | } |
| 9554 | |
| 9555 | at::Tensor & _sparse_log_softmax_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool half_to_float, at::Tensor & out) { |
| 9556 | if (false) { |
| 9557 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9558 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9559 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9560 | auto self_meta = to_meta(self); |
| 9561 | auto out_meta = to_meta(out); |
| 9562 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9563 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9564 | at::_ops::_sparse_log_softmax_out::call(self_meta, dim, half_to_float, out_meta); |
| 9565 | } |
| 9566 | |
| 9567 | at::Tensor self_; |
| 9568 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9569 | at::functionalization::impl::sync(self); |
| 9570 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9571 | } else { |
| 9572 | self_ = self; |
| 9573 | } |
| 9574 | |
| 9575 | at::Tensor out_; |
| 9576 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9577 | at::functionalization::impl::sync(out); |
| 9578 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9579 | } else { |
| 9580 | out_ = out; |
| 9581 | } |
| 9582 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9583 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9584 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9585 | TORCH_INTERNAL_ASSERT(false, |
| 9586 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9587 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9588 | } else { |
| 9589 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9590 | at::AutoDispatchSkipFunctionalize guard; |
| 9591 | at::Tensor tmp_output = at::_ops::_sparse_log_softmax_out::call(self_, dim, half_to_float, out_); |
| 9592 | return out;; |
| 9593 | } |
| 9594 | } else { |
| 9595 | at::Tensor tmp_output; |
| 9596 | { |
| 9597 | at::AutoDispatchSkipFunctionalize guard; |
| 9598 | tmp_output = at::_ops::_sparse_log_softmax::call(self_, dim, half_to_float); |
| 9599 | } |
| 9600 | at::functionalization::impl::replace_(out, tmp_output); |
| 9601 | at::functionalization::impl::commit_update(out); |
| 9602 | at::functionalization::impl::sync(out); |
| 9603 | return out; |
| 9604 | } |
| 9605 | } |
| 9606 | |
| 9607 | at::Tensor & _sparse_log_softmax_backward_data_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self, at::Tensor & out) { |
| 9608 | if (false) { |
| 9609 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9610 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9611 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9612 | auto grad_output_meta = to_meta(grad_output); |
| 9613 | auto output_meta = to_meta(output); |
| 9614 | auto self_meta = to_meta(self); |
| 9615 | auto out_meta = to_meta(out); |
| 9616 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9617 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9618 | at::_ops::_sparse_log_softmax_backward_data_out::call(grad_output_meta, output_meta, dim, self_meta, out_meta); |
| 9619 | } |
| 9620 | |
| 9621 | at::Tensor grad_output_; |
| 9622 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 9623 | at::functionalization::impl::sync(grad_output); |
| 9624 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 9625 | } else { |
| 9626 | grad_output_ = grad_output; |
| 9627 | } |
| 9628 | |
| 9629 | at::Tensor output_; |
| 9630 | if (at::functionalization::impl::isFunctionalTensor(output)) { |
| 9631 | at::functionalization::impl::sync(output); |
| 9632 | output_ = at::functionalization::impl::from_functional_tensor(output); |
| 9633 | } else { |
| 9634 | output_ = output; |
| 9635 | } |
| 9636 | |
| 9637 | at::Tensor self_; |
| 9638 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9639 | at::functionalization::impl::sync(self); |
| 9640 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9641 | } else { |
| 9642 | self_ = self; |
| 9643 | } |
| 9644 | |
| 9645 | at::Tensor out_; |
| 9646 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9647 | at::functionalization::impl::sync(out); |
| 9648 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9649 | } else { |
| 9650 | out_ = out; |
| 9651 | } |
| 9652 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9653 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(output) || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9654 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9655 | TORCH_INTERNAL_ASSERT(false, |
| 9656 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9657 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9658 | } else { |
| 9659 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9660 | at::AutoDispatchSkipFunctionalize guard; |
| 9661 | at::Tensor tmp_output = at::_ops::_sparse_log_softmax_backward_data_out::call(grad_output_, output_, dim, self_, out_); |
| 9662 | return out;; |
| 9663 | } |
| 9664 | } else { |
| 9665 | at::Tensor tmp_output; |
| 9666 | { |
| 9667 | at::AutoDispatchSkipFunctionalize guard; |
| 9668 | tmp_output = at::_ops::_sparse_log_softmax_backward_data::call(grad_output_, output_, dim, self_); |
| 9669 | } |
| 9670 | at::functionalization::impl::replace_(out, tmp_output); |
| 9671 | at::functionalization::impl::commit_update(out); |
| 9672 | at::functionalization::impl::sync(out); |
| 9673 | return out; |
| 9674 | } |
| 9675 | } |
| 9676 | |
| 9677 | const at::Tensor & resize_as_sparse_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & the_template, const at::Tensor & out) { |
| 9678 | if (false) { |
| 9679 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9680 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9681 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9682 | auto self_meta = to_meta(self); |
| 9683 | auto the_template_meta = to_meta(the_template); |
| 9684 | auto out_meta = to_meta(out); |
| 9685 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9686 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9687 | at::_ops::resize_as_sparse_out::call(self_meta, the_template_meta, out_meta); |
| 9688 | } |
| 9689 | |
| 9690 | at::Tensor self_; |
| 9691 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9692 | at::functionalization::impl::sync(self); |
| 9693 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9694 | } else { |
| 9695 | self_ = self; |
| 9696 | } |
| 9697 | |
| 9698 | at::Tensor the_template_; |
| 9699 | if (at::functionalization::impl::isFunctionalTensor(the_template)) { |
| 9700 | at::functionalization::impl::sync(the_template); |
| 9701 | the_template_ = at::functionalization::impl::from_functional_tensor(the_template); |
| 9702 | } else { |
| 9703 | the_template_ = the_template; |
| 9704 | } |
| 9705 | |
| 9706 | at::Tensor out_; |
| 9707 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9708 | at::functionalization::impl::sync(out); |
| 9709 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9710 | } else { |
| 9711 | out_ = out; |
| 9712 | } |
| 9713 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9714 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(the_template))) { |
| 9715 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9716 | TORCH_INTERNAL_ASSERT(false, |
| 9717 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9718 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9719 | } else { |
| 9720 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9721 | at::AutoDispatchSkipFunctionalize guard; |
| 9722 | at::Tensor tmp_output = at::_ops::resize_as_sparse_out::call(self_, the_template_, out_); |
| 9723 | return out;; |
| 9724 | } |
| 9725 | } else { |
| 9726 | at::Tensor tmp_output; |
| 9727 | { |
| 9728 | at::AutoDispatchSkipFunctionalize guard; |
| 9729 | tmp_output = at::_ops::resize_as_sparse::call(self_, the_template_); |
| 9730 | } |
| 9731 | at::functionalization::impl::replace_(out, tmp_output); |
| 9732 | at::functionalization::impl::commit_update(out); |
| 9733 | at::functionalization::impl::sync(out); |
| 9734 | return out; |
| 9735 | } |
| 9736 | } |
| 9737 | |
| 9738 | const at::Tensor & resize_as_sparse_(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & the_template) { |
| 9739 | if (true) { |
| 9740 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9741 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9742 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9743 | auto self_meta = to_meta(self); |
| 9744 | auto the_template_meta = to_meta(the_template); |
| 9745 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9746 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9747 | at::_ops::resize_as_sparse_::call(self_meta, the_template_meta); |
| 9748 | } |
| 9749 | |
| 9750 | at::Tensor self_; |
| 9751 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9752 | at::functionalization::impl::sync(self); |
| 9753 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9754 | } else { |
| 9755 | self_ = self; |
| 9756 | } |
| 9757 | |
| 9758 | at::Tensor the_template_; |
| 9759 | if (at::functionalization::impl::isFunctionalTensor(the_template)) { |
| 9760 | at::functionalization::impl::sync(the_template); |
| 9761 | the_template_ = at::functionalization::impl::from_functional_tensor(the_template); |
| 9762 | } else { |
| 9763 | the_template_ = the_template; |
| 9764 | } |
| 9765 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 9766 | if ((false || at::functionalization::impl::isFunctionalTensor(the_template))) { |
| 9767 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9768 | TORCH_INTERNAL_ASSERT(false, |
| 9769 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9770 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9771 | } else { |
| 9772 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9773 | at::AutoDispatchSkipFunctionalize guard; |
| 9774 | at::Tensor tmp_output = at::_ops::resize_as_sparse_::call(self_, the_template_); |
| 9775 | return self;; |
| 9776 | } |
| 9777 | } else { |
| 9778 | at::Tensor tmp_output; |
| 9779 | { |
| 9780 | at::AutoDispatchSkipFunctionalize guard; |
| 9781 | tmp_output = at::_ops::resize_as_sparse::call(self_, the_template_); |
| 9782 | } |
| 9783 | at::functionalization::impl::replace_(self, tmp_output); |
| 9784 | at::functionalization::impl::commit_update(self); |
| 9785 | at::functionalization::impl::sync(self); |
| 9786 | return self; |
| 9787 | } |
| 9788 | } |
| 9789 | |
| 9790 | at::Tensor & sub_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) { |
| 9791 | if (false) { |
| 9792 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9793 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9794 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9795 | auto self_meta = to_meta(self); |
| 9796 | auto other_meta = to_meta(other); |
| 9797 | auto out_meta = to_meta(out); |
| 9798 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9799 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9800 | at::_ops::sub_out::call(self_meta, other_meta, alpha, out_meta); |
| 9801 | } |
| 9802 | |
| 9803 | at::Tensor self_; |
| 9804 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9805 | at::functionalization::impl::sync(self); |
| 9806 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9807 | } else { |
| 9808 | self_ = self; |
| 9809 | } |
| 9810 | |
| 9811 | at::Tensor other_; |
| 9812 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 9813 | at::functionalization::impl::sync(other); |
| 9814 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 9815 | } else { |
| 9816 | other_ = other; |
| 9817 | } |
| 9818 | |
| 9819 | at::Tensor out_; |
| 9820 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9821 | at::functionalization::impl::sync(out); |
| 9822 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9823 | } else { |
| 9824 | out_ = out; |
| 9825 | } |
| 9826 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9827 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 9828 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9829 | TORCH_INTERNAL_ASSERT(false, |
| 9830 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9831 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9832 | } else { |
| 9833 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9834 | at::AutoDispatchSkipFunctionalize guard; |
| 9835 | at::Tensor tmp_output = at::_ops::sub_out::call(self_, other_, alpha, out_); |
| 9836 | return out;; |
| 9837 | } |
| 9838 | } else { |
| 9839 | at::Tensor tmp_output; |
| 9840 | { |
| 9841 | at::AutoDispatchSkipFunctionalize guard; |
| 9842 | tmp_output = at::_ops::sub_Tensor::call(self_, other_, alpha); |
| 9843 | } |
| 9844 | at::functionalization::impl::replace_(out, tmp_output); |
| 9845 | at::functionalization::impl::commit_update(out); |
| 9846 | at::functionalization::impl::sync(out); |
| 9847 | return out; |
| 9848 | } |
| 9849 | } |
| 9850 | |
| 9851 | at::Tensor & sub__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 9852 | if (true) { |
| 9853 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9854 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9855 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9856 | auto self_meta = to_meta(self); |
| 9857 | auto other_meta = to_meta(other); |
| 9858 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9859 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9860 | at::_ops::sub__Tensor::call(self_meta, other_meta, alpha); |
| 9861 | } |
| 9862 | |
| 9863 | at::Tensor self_; |
| 9864 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9865 | at::functionalization::impl::sync(self); |
| 9866 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9867 | } else { |
| 9868 | self_ = self; |
| 9869 | } |
| 9870 | |
| 9871 | at::Tensor other_; |
| 9872 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 9873 | at::functionalization::impl::sync(other); |
| 9874 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 9875 | } else { |
| 9876 | other_ = other; |
| 9877 | } |
| 9878 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 9879 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 9880 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9881 | TORCH_INTERNAL_ASSERT(false, |
| 9882 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9883 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9884 | } else { |
| 9885 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9886 | at::AutoDispatchSkipFunctionalize guard; |
| 9887 | at::Tensor tmp_output = at::_ops::sub__Tensor::call(self_, other_, alpha); |
| 9888 | return self;; |
| 9889 | } |
| 9890 | } else { |
| 9891 | at::Tensor tmp_output; |
| 9892 | { |
| 9893 | at::AutoDispatchSkipFunctionalize guard; |
| 9894 | tmp_output = at::_ops::sub_Tensor::call(self_, other_, alpha); |
| 9895 | } |
| 9896 | at::functionalization::impl::replace_(self, tmp_output); |
| 9897 | at::functionalization::impl::commit_update(self); |
| 9898 | at::functionalization::impl::sync(self); |
| 9899 | return self; |
| 9900 | } |
| 9901 | } |
| 9902 | |
| 9903 | at::Tensor & sub_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha, at::Tensor & out) { |
| 9904 | if (false) { |
| 9905 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9906 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9907 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9908 | auto self_meta = to_meta(self); |
| 9909 | auto out_meta = to_meta(out); |
| 9910 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9911 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9912 | at::_ops::sub_Scalar_out::call(self_meta, other, alpha, out_meta); |
| 9913 | } |
| 9914 | |
| 9915 | at::Tensor self_; |
| 9916 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9917 | at::functionalization::impl::sync(self); |
| 9918 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9919 | } else { |
| 9920 | self_ = self; |
| 9921 | } |
| 9922 | |
| 9923 | at::Tensor out_; |
| 9924 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9925 | at::functionalization::impl::sync(out); |
| 9926 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9927 | } else { |
| 9928 | out_ = out; |
| 9929 | } |
| 9930 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9931 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9932 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9933 | TORCH_INTERNAL_ASSERT(false, |
| 9934 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9935 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9936 | } else { |
| 9937 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9938 | at::AutoDispatchSkipFunctionalize guard; |
| 9939 | at::Tensor tmp_output = at::_ops::sub_Scalar_out::call(self_, other, alpha, out_); |
| 9940 | return out;; |
| 9941 | } |
| 9942 | } else { |
| 9943 | at::Tensor tmp_output; |
| 9944 | { |
| 9945 | at::AutoDispatchSkipFunctionalize guard; |
| 9946 | tmp_output = at::_ops::sub_Scalar::call(self_, other, alpha); |
| 9947 | } |
| 9948 | at::functionalization::impl::replace_(out, tmp_output); |
| 9949 | at::functionalization::impl::commit_update(out); |
| 9950 | at::functionalization::impl::sync(out); |
| 9951 | return out; |
| 9952 | } |
| 9953 | } |
| 9954 | |
| 9955 | at::Tensor & sub__Scalar(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha) { |
| 9956 | if (true) { |
| 9957 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9958 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9959 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9960 | auto self_meta = to_meta(self); |
| 9961 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9962 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9963 | at::_ops::sub__Scalar::call(self_meta, other, alpha); |
| 9964 | } |
| 9965 | |
| 9966 | at::Tensor self_; |
| 9967 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9968 | at::functionalization::impl::sync(self); |
| 9969 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9970 | } else { |
| 9971 | self_ = self; |
| 9972 | } |
| 9973 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 9974 | if ((false)) { |
| 9975 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9976 | TORCH_INTERNAL_ASSERT(false, |
| 9977 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9978 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9979 | } else { |
| 9980 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9981 | at::AutoDispatchSkipFunctionalize guard; |
| 9982 | at::Tensor tmp_output = at::_ops::sub__Scalar::call(self_, other, alpha); |
| 9983 | return self;; |
| 9984 | } |
| 9985 | } else { |
| 9986 | at::Tensor tmp_output; |
| 9987 | { |
| 9988 | at::AutoDispatchSkipFunctionalize guard; |
| 9989 | tmp_output = at::_ops::sub_Scalar::call(self_, other, alpha); |
| 9990 | } |
| 9991 | at::functionalization::impl::replace_(self, tmp_output); |
| 9992 | at::functionalization::impl::commit_update(self); |
| 9993 | at::functionalization::impl::sync(self); |
| 9994 | return self; |
| 9995 | } |
| 9996 | } |
| 9997 | |
| 9998 | at::Tensor & copy_sparse_to_sparse_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, bool non_blocking, at::Tensor & out) { |
| 9999 | if (false) { |
| 10000 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10001 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10002 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10003 | auto self_meta = to_meta(self); |
| 10004 | auto src_meta = to_meta(src); |
| 10005 | auto out_meta = to_meta(out); |
| 10006 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10007 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10008 | at::_ops::copy_sparse_to_sparse_out::call(self_meta, src_meta, non_blocking, out_meta); |
| 10009 | } |
| 10010 | |
| 10011 | at::Tensor self_; |
| 10012 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10013 | at::functionalization::impl::sync(self); |
| 10014 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10015 | } else { |
| 10016 | self_ = self; |
| 10017 | } |
| 10018 | |
| 10019 | at::Tensor src_; |
| 10020 | if (at::functionalization::impl::isFunctionalTensor(src)) { |
| 10021 | at::functionalization::impl::sync(src); |
| 10022 | src_ = at::functionalization::impl::from_functional_tensor(src); |
| 10023 | } else { |
| 10024 | src_ = src; |
| 10025 | } |
| 10026 | |
| 10027 | at::Tensor out_; |
| 10028 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10029 | at::functionalization::impl::sync(out); |
| 10030 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10031 | } else { |
| 10032 | out_ = out; |
| 10033 | } |
| 10034 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10035 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(src))) { |
| 10036 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10037 | TORCH_INTERNAL_ASSERT(false, |
| 10038 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10039 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10040 | } else { |
| 10041 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10042 | at::AutoDispatchSkipFunctionalize guard; |
| 10043 | at::Tensor tmp_output = at::_ops::copy_sparse_to_sparse_out::call(self_, src_, non_blocking, out_); |
| 10044 | return out;; |
| 10045 | } |
| 10046 | } else { |
| 10047 | at::Tensor tmp_output; |
| 10048 | { |
| 10049 | at::AutoDispatchSkipFunctionalize guard; |
| 10050 | tmp_output = at::_ops::copy_sparse_to_sparse::call(self_, src_, non_blocking); |
| 10051 | } |
| 10052 | at::functionalization::impl::replace_(out, tmp_output); |
| 10053 | at::functionalization::impl::commit_update(out); |
| 10054 | at::functionalization::impl::sync(out); |
| 10055 | return out; |
| 10056 | } |
| 10057 | } |
| 10058 | |
| 10059 | at::Tensor & copy_sparse_to_sparse_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & src, bool non_blocking) { |
| 10060 | if (true) { |
| 10061 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10062 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10063 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10064 | auto self_meta = to_meta(self); |
| 10065 | auto src_meta = to_meta(src); |
| 10066 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10067 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10068 | at::_ops::copy_sparse_to_sparse_::call(self_meta, src_meta, non_blocking); |
| 10069 | } |
| 10070 | |
| 10071 | at::Tensor self_; |
| 10072 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10073 | at::functionalization::impl::sync(self); |
| 10074 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10075 | } else { |
| 10076 | self_ = self; |
| 10077 | } |
| 10078 | |
| 10079 | at::Tensor src_; |
| 10080 | if (at::functionalization::impl::isFunctionalTensor(src)) { |
| 10081 | at::functionalization::impl::sync(src); |
| 10082 | src_ = at::functionalization::impl::from_functional_tensor(src); |
| 10083 | } else { |
| 10084 | src_ = src; |
| 10085 | } |
| 10086 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 10087 | if ((false || at::functionalization::impl::isFunctionalTensor(src))) { |
| 10088 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10089 | TORCH_INTERNAL_ASSERT(false, |
| 10090 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10091 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10092 | } else { |
| 10093 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10094 | at::AutoDispatchSkipFunctionalize guard; |
| 10095 | at::Tensor tmp_output = at::_ops::copy_sparse_to_sparse_::call(self_, src_, non_blocking); |
| 10096 | return self;; |
| 10097 | } |
| 10098 | } else { |
| 10099 | at::Tensor tmp_output; |
| 10100 | { |
| 10101 | at::AutoDispatchSkipFunctionalize guard; |
| 10102 | tmp_output = at::_ops::copy_sparse_to_sparse::call(self_, src_, non_blocking); |
| 10103 | } |
| 10104 | at::functionalization::impl::replace_(self, tmp_output); |
| 10105 | at::functionalization::impl::commit_update(self); |
| 10106 | at::functionalization::impl::sync(self); |
| 10107 | return self; |
| 10108 | } |
| 10109 | } |
| 10110 | |
| 10111 | at::Tensor & mkldnn_reorder_conv2d_weight_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, at::OptionalIntArrayRef input_size, at::Tensor & out) { |
| 10112 | if (false) { |
| 10113 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10114 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10115 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10116 | auto self_meta = to_meta(self); |
| 10117 | auto out_meta = to_meta(out); |
| 10118 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10119 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10120 | at::_ops::mkldnn_reorder_conv2d_weight_out::call(self_meta, padding, stride, dilation, groups, input_size, out_meta); |
| 10121 | } |
| 10122 | |
| 10123 | at::Tensor self_; |
| 10124 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10125 | at::functionalization::impl::sync(self); |
| 10126 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10127 | } else { |
| 10128 | self_ = self; |
| 10129 | } |
| 10130 | |
| 10131 | at::Tensor out_; |
| 10132 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10133 | at::functionalization::impl::sync(out); |
| 10134 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10135 | } else { |
| 10136 | out_ = out; |
| 10137 | } |
| 10138 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10139 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 10140 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10141 | TORCH_INTERNAL_ASSERT(false, |
| 10142 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10143 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10144 | } else { |
| 10145 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10146 | at::AutoDispatchSkipFunctionalize guard; |
| 10147 | at::Tensor tmp_output = at::_ops::mkldnn_reorder_conv2d_weight_out::call(self_, padding, stride, dilation, groups, input_size, out_); |
| 10148 | return out;; |
| 10149 | } |
| 10150 | } else { |
| 10151 | at::Tensor tmp_output; |
| 10152 | { |
| 10153 | at::AutoDispatchSkipFunctionalize guard; |
| 10154 | tmp_output = at::_ops::mkldnn_reorder_conv2d_weight::call(self_, padding, stride, dilation, groups, input_size); |
| 10155 | } |
| 10156 | at::functionalization::impl::replace_(out, tmp_output); |
| 10157 | at::functionalization::impl::commit_update(out); |
| 10158 | at::functionalization::impl::sync(out); |
| 10159 | return out; |
| 10160 | } |
| 10161 | } |
| 10162 | |
| 10163 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _lstm_mps_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4) { |
| 10164 | if (false) { |
| 10165 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10166 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10167 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10168 | auto input_meta = to_meta(input); |
| 10169 | auto hx_meta = to_meta(hx); |
| 10170 | auto params_meta = to_meta(params); |
| 10171 | auto out0_meta = to_meta(out0); |
| 10172 | auto out1_meta = to_meta(out1); |
| 10173 | auto out2_meta = to_meta(out2); |
| 10174 | auto out3_meta = to_meta(out3); |
| 10175 | auto out4_meta = to_meta(out4); |
| 10176 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10177 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10178 | at::_ops::_lstm_mps_out::call(input_meta, hx_meta, params_meta, has_biases, num_layers, dropout, train, bidirectional, batch_first, out0_meta, out1_meta, out2_meta, out3_meta, out4_meta); |
| 10179 | } |
| 10180 | |
| 10181 | at::Tensor input_; |
| 10182 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 10183 | at::functionalization::impl::sync(input); |
| 10184 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 10185 | } else { |
| 10186 | input_ = input; |
| 10187 | } |
| 10188 | |
| 10189 | ::std::vector<at::Tensor> hx_; |
| 10190 | if (at::functionalization::impl::isFunctionalTensor(hx)) { |
| 10191 | at::functionalization::impl::sync(hx); |
| 10192 | hx_ = at::functionalization::impl::from_functional_tensor(hx); |
| 10193 | } else { |
| 10194 | hx_ = hx.vec(); |
| 10195 | } |
| 10196 | |
| 10197 | ::std::vector<at::Tensor> params_; |
| 10198 | if (at::functionalization::impl::isFunctionalTensor(params)) { |
| 10199 | at::functionalization::impl::sync(params); |
| 10200 | params_ = at::functionalization::impl::from_functional_tensor(params); |
| 10201 | } else { |
| 10202 | params_ = params.vec(); |
| 10203 | } |
| 10204 | |
| 10205 | at::Tensor out0_; |
| 10206 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 10207 | at::functionalization::impl::sync(out0); |
| 10208 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 10209 | } else { |
| 10210 | out0_ = out0; |
| 10211 | } |
| 10212 | |
| 10213 | at::Tensor out1_; |
| 10214 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 10215 | at::functionalization::impl::sync(out1); |
| 10216 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 10217 | } else { |
| 10218 | out1_ = out1; |
| 10219 | } |
| 10220 | |
| 10221 | at::Tensor out2_; |
| 10222 | if (at::functionalization::impl::isFunctionalTensor(out2)) { |
| 10223 | at::functionalization::impl::sync(out2); |
| 10224 | out2_ = at::functionalization::impl::from_functional_tensor(out2); |
| 10225 | } else { |
| 10226 | out2_ = out2; |
| 10227 | } |
| 10228 | |
| 10229 | at::Tensor out3_; |
| 10230 | if (at::functionalization::impl::isFunctionalTensor(out3)) { |
| 10231 | at::functionalization::impl::sync(out3); |
| 10232 | out3_ = at::functionalization::impl::from_functional_tensor(out3); |
| 10233 | } else { |
| 10234 | out3_ = out3; |
| 10235 | } |
| 10236 | |
| 10237 | at::Tensor out4_; |
| 10238 | if (at::functionalization::impl::isFunctionalTensor(out4)) { |
| 10239 | at::functionalization::impl::sync(out4); |
| 10240 | out4_ = at::functionalization::impl::from_functional_tensor(out4); |
| 10241 | } else { |
| 10242 | out4_ = out4; |
| 10243 | } |
| 10244 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1) && at::functionalization::impl::isFunctionalTensor(out2) && at::functionalization::impl::isFunctionalTensor(out3) && at::functionalization::impl::isFunctionalTensor(out4))) { |
| 10245 | if ((false || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(hx) || at::functionalization::impl::isFunctionalTensor(params))) { |
| 10246 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10247 | TORCH_INTERNAL_ASSERT(false, |
| 10248 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10249 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10250 | } else { |
| 10251 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10252 | at::AutoDispatchSkipFunctionalize guard; |
| 10253 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::_lstm_mps_out::call(input_, hx_, params_, has_biases, num_layers, dropout, train, bidirectional, batch_first, out0_, out1_, out2_, out3_, out4_); |
| 10254 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2, out3, out4);; |
| 10255 | } |
| 10256 | } else { |
| 10257 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 10258 | { |
| 10259 | at::AutoDispatchSkipFunctionalize guard; |
| 10260 | tmp_output = at::_ops::_lstm_mps::call(input_, hx_, params_, has_biases, num_layers, dropout, train, bidirectional, batch_first); |
| 10261 | } |
| 10262 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 10263 | at::functionalization::impl::commit_update(out0); |
| 10264 | at::functionalization::impl::sync(out0); |
| 10265 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 10266 | at::functionalization::impl::commit_update(out1); |
| 10267 | at::functionalization::impl::sync(out1); |
| 10268 | at::functionalization::impl::replace_(out2, std::get<2>(tmp_output)); |
| 10269 | at::functionalization::impl::commit_update(out2); |
| 10270 | at::functionalization::impl::sync(out2); |
| 10271 | at::functionalization::impl::replace_(out3, std::get<3>(tmp_output)); |
| 10272 | at::functionalization::impl::commit_update(out3); |
| 10273 | at::functionalization::impl::sync(out3); |
| 10274 | at::functionalization::impl::replace_(out4, std::get<4>(tmp_output)); |
| 10275 | at::functionalization::impl::commit_update(out4); |
| 10276 | at::functionalization::impl::sync(out4); |
| 10277 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2, out3, out4); |
| 10278 | } |
| 10279 | } |
| 10280 | |
| 10281 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _thnn_fused_gru_cell_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_hy, const at::Tensor & workspace, bool has_bias, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4) { |
| 10282 | if (false) { |
| 10283 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10284 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10285 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10286 | auto grad_hy_meta = to_meta(grad_hy); |
| 10287 | auto workspace_meta = to_meta(workspace); |
| 10288 | auto out0_meta = to_meta(out0); |
| 10289 | auto out1_meta = to_meta(out1); |
| 10290 | auto out2_meta = to_meta(out2); |
| 10291 | auto out3_meta = to_meta(out3); |
| 10292 | auto out4_meta = to_meta(out4); |
| 10293 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10294 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10295 | at::_ops::_thnn_fused_gru_cell_backward_out::call(grad_hy_meta, workspace_meta, has_bias, out0_meta, out1_meta, out2_meta, out3_meta, out4_meta); |
| 10296 | } |
| 10297 | |
| 10298 | at::Tensor grad_hy_; |
| 10299 | if (at::functionalization::impl::isFunctionalTensor(grad_hy)) { |
| 10300 | at::functionalization::impl::sync(grad_hy); |
| 10301 | grad_hy_ = at::functionalization::impl::from_functional_tensor(grad_hy); |
| 10302 | } else { |
| 10303 | grad_hy_ = grad_hy; |
| 10304 | } |
| 10305 | |
| 10306 | at::Tensor workspace_; |
| 10307 | if (at::functionalization::impl::isFunctionalTensor(workspace)) { |
| 10308 | at::functionalization::impl::sync(workspace); |
| 10309 | workspace_ = at::functionalization::impl::from_functional_tensor(workspace); |
| 10310 | } else { |
| 10311 | workspace_ = workspace; |
| 10312 | } |
| 10313 | |
| 10314 | at::Tensor out0_; |
| 10315 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 10316 | at::functionalization::impl::sync(out0); |
| 10317 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 10318 | } else { |
| 10319 | out0_ = out0; |
| 10320 | } |
| 10321 | |
| 10322 | at::Tensor out1_; |
| 10323 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 10324 | at::functionalization::impl::sync(out1); |
| 10325 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 10326 | } else { |
| 10327 | out1_ = out1; |
| 10328 | } |
| 10329 | |
| 10330 | at::Tensor out2_; |
| 10331 | if (at::functionalization::impl::isFunctionalTensor(out2)) { |
| 10332 | at::functionalization::impl::sync(out2); |
| 10333 | out2_ = at::functionalization::impl::from_functional_tensor(out2); |
| 10334 | } else { |
| 10335 | out2_ = out2; |
| 10336 | } |
| 10337 | |
| 10338 | at::Tensor out3_; |
| 10339 | if (at::functionalization::impl::isFunctionalTensor(out3)) { |
| 10340 | at::functionalization::impl::sync(out3); |
| 10341 | out3_ = at::functionalization::impl::from_functional_tensor(out3); |
| 10342 | } else { |
| 10343 | out3_ = out3; |
| 10344 | } |
| 10345 | |
| 10346 | at::Tensor out4_; |
| 10347 | if (at::functionalization::impl::isFunctionalTensor(out4)) { |
| 10348 | at::functionalization::impl::sync(out4); |
| 10349 | out4_ = at::functionalization::impl::from_functional_tensor(out4); |
| 10350 | } else { |
| 10351 | out4_ = out4; |
| 10352 | } |
| 10353 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1) && at::functionalization::impl::isFunctionalTensor(out2) && at::functionalization::impl::isFunctionalTensor(out3) && at::functionalization::impl::isFunctionalTensor(out4))) { |
| 10354 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_hy) || at::functionalization::impl::isFunctionalTensor(workspace))) { |
| 10355 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10356 | TORCH_INTERNAL_ASSERT(false, |
| 10357 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10358 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10359 | } else { |
| 10360 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10361 | at::AutoDispatchSkipFunctionalize guard; |
| 10362 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::_thnn_fused_gru_cell_backward_out::call(grad_hy_, workspace_, has_bias, out0_, out1_, out2_, out3_, out4_); |
| 10363 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2, out3, out4);; |
| 10364 | } |
| 10365 | } else { |
| 10366 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 10367 | { |
| 10368 | at::AutoDispatchSkipFunctionalize guard; |
| 10369 | tmp_output = at::_ops::_thnn_fused_gru_cell_backward::call(grad_hy_, workspace_, has_bias); |
| 10370 | } |
| 10371 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 10372 | at::functionalization::impl::commit_update(out0); |
| 10373 | at::functionalization::impl::sync(out0); |
| 10374 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 10375 | at::functionalization::impl::commit_update(out1); |
| 10376 | at::functionalization::impl::sync(out1); |
| 10377 | at::functionalization::impl::replace_(out2, std::get<2>(tmp_output)); |
| 10378 | at::functionalization::impl::commit_update(out2); |
| 10379 | at::functionalization::impl::sync(out2); |
| 10380 | at::functionalization::impl::replace_(out3, std::get<3>(tmp_output)); |
| 10381 | at::functionalization::impl::commit_update(out3); |
| 10382 | at::functionalization::impl::sync(out3); |
| 10383 | at::functionalization::impl::replace_(out4, std::get<4>(tmp_output)); |
| 10384 | at::functionalization::impl::commit_update(out4); |
| 10385 | at::functionalization::impl::sync(out4); |
| 10386 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2, out3, out4); |
| 10387 | } |
| 10388 | } |
| 10389 | |
| 10390 | at::Tensor & set_out_source_Storage_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Storage source, at::Tensor & out) { |
| 10391 | if (false) { |
| 10392 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10393 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10394 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10395 | auto self_meta = to_meta(self); |
| 10396 | auto out_meta = to_meta(out); |
| 10397 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10398 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10399 | at::_ops::set_source_Storage_out::call(self_meta, source, out_meta); |
| 10400 | } |
| 10401 | |
| 10402 | at::Tensor self_; |
| 10403 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10404 | at::functionalization::impl::sync(self); |
| 10405 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10406 | } else { |
| 10407 | self_ = self; |
| 10408 | } |
| 10409 | |
| 10410 | at::Tensor out_; |
| 10411 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10412 | at::functionalization::impl::sync(out); |
| 10413 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10414 | } else { |
| 10415 | out_ = out; |
| 10416 | } |
| 10417 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10418 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 10419 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10420 | TORCH_INTERNAL_ASSERT(false, |
| 10421 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10422 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10423 | } else { |
| 10424 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10425 | at::AutoDispatchSkipFunctionalize guard; |
| 10426 | at::Tensor tmp_output = at::_ops::set_source_Storage_out::call(self_, source, out_); |
| 10427 | return out;; |
| 10428 | } |
| 10429 | } else { |
| 10430 | at::Tensor tmp_output; |
| 10431 | { |
| 10432 | at::AutoDispatchSkipFunctionalize guard; |
| 10433 | tmp_output = at::_ops::set_source_Storage::call(self_, source); |
| 10434 | } |
| 10435 | at::functionalization::impl::replace_(out, tmp_output); |
| 10436 | at::functionalization::impl::commit_update(out); |
| 10437 | at::functionalization::impl::sync(out); |
| 10438 | return out; |
| 10439 | } |
| 10440 | } |
| 10441 | |
| 10442 | at::Tensor & set__source_Storage(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, at::Storage source) { |
| 10443 | if (false) { |
| 10444 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10445 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10446 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10447 | auto self_meta = to_meta(self); |
| 10448 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10449 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10450 | at::_ops::set__source_Storage::call(self_meta, source); |
| 10451 | } |
| 10452 | |
| 10453 | at::Tensor self_; |
| 10454 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10455 | at::functionalization::impl::sync(self); |
| 10456 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10457 | } else { |
| 10458 | self_ = self; |
| 10459 | } |
| 10460 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 10461 | if ((false)) { |
| 10462 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10463 | TORCH_INTERNAL_ASSERT(false, |
| 10464 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10465 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10466 | } else { |
| 10467 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10468 | at::AutoDispatchSkipFunctionalize guard; |
| 10469 | at::Tensor tmp_output = at::_ops::set__source_Storage::call(self_, source); |
| 10470 | return self;; |
| 10471 | } |
| 10472 | } else { |
| 10473 | at::Tensor tmp_output; |
| 10474 | { |
| 10475 | at::AutoDispatchSkipFunctionalize guard; |
| 10476 | tmp_output = at::_ops::set_source_Storage::call(self_, source); |
| 10477 | } |
| 10478 | at::functionalization::impl::replace_(self, tmp_output); |
| 10479 | at::functionalization::impl::commit_update(self); |
| 10480 | at::functionalization::impl::sync(self); |
| 10481 | return self; |
| 10482 | } |
| 10483 | } |
| 10484 | |
| 10485 | at::Tensor & set_out_source_Storage_storage_offset_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Storage source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::Tensor & out) { |
| 10486 | if (false) { |
| 10487 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10488 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10489 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10490 | auto self_meta = to_meta(self); |
| 10491 | auto out_meta = to_meta(out); |
| 10492 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10493 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10494 | at::_ops::set_source_Storage_storage_offset_out::call(self_meta, source, storage_offset, size, stride, out_meta); |
| 10495 | } |
| 10496 | |
| 10497 | at::Tensor self_; |
| 10498 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10499 | at::functionalization::impl::sync(self); |
| 10500 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10501 | } else { |
| 10502 | self_ = self; |
| 10503 | } |
| 10504 | |
| 10505 | at::Tensor out_; |
| 10506 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10507 | at::functionalization::impl::sync(out); |
| 10508 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10509 | } else { |
| 10510 | out_ = out; |
| 10511 | } |
| 10512 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10513 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 10514 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10515 | TORCH_INTERNAL_ASSERT(false, |
| 10516 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10517 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10518 | } else { |
| 10519 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10520 | at::AutoDispatchSkipFunctionalize guard; |
| 10521 | at::Tensor tmp_output = at::_ops::set_source_Storage_storage_offset_out::call(self_, source, storage_offset, size, stride, out_); |
| 10522 | return out;; |
| 10523 | } |
| 10524 | } else { |
| 10525 | at::Tensor tmp_output; |
| 10526 | { |
| 10527 | at::AutoDispatchSkipFunctionalize guard; |
| 10528 | tmp_output = at::_ops::set_source_Storage_storage_offset::call(self_, source, storage_offset, size, stride); |
| 10529 | } |
| 10530 | at::functionalization::impl::replace_(out, tmp_output); |
| 10531 | at::functionalization::impl::commit_update(out); |
| 10532 | at::functionalization::impl::sync(out); |
| 10533 | return out; |
| 10534 | } |
| 10535 | } |
| 10536 | |
| 10537 | at::Tensor & set__source_Storage_storage_offset(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, at::Storage source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) { |
| 10538 | if (false) { |
| 10539 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10540 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10541 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10542 | auto self_meta = to_meta(self); |
| 10543 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10544 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10545 | at::_ops::set__source_Storage_storage_offset::call(self_meta, source, storage_offset, size, stride); |
| 10546 | } |
| 10547 | |
| 10548 | at::Tensor self_; |
| 10549 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10550 | at::functionalization::impl::sync(self); |
| 10551 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10552 | } else { |
| 10553 | self_ = self; |
| 10554 | } |
| 10555 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 10556 | if ((false)) { |
| 10557 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10558 | TORCH_INTERNAL_ASSERT(false, |
| 10559 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10560 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10561 | } else { |
| 10562 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10563 | at::AutoDispatchSkipFunctionalize guard; |
| 10564 | at::Tensor tmp_output = at::_ops::set__source_Storage_storage_offset::call(self_, source, storage_offset, size, stride); |
| 10565 | return self;; |
| 10566 | } |
| 10567 | } else { |
| 10568 | at::Tensor tmp_output; |
| 10569 | { |
| 10570 | at::AutoDispatchSkipFunctionalize guard; |
| 10571 | tmp_output = at::_ops::set_source_Storage_storage_offset::call(self_, source, storage_offset, size, stride); |
| 10572 | } |
| 10573 | at::functionalization::impl::replace_(self, tmp_output); |
| 10574 | at::functionalization::impl::commit_update(self); |
| 10575 | at::functionalization::impl::sync(self); |
| 10576 | return self; |
| 10577 | } |
| 10578 | } |
| 10579 | |
| 10580 | at::Tensor & set_out_source_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & source, at::Tensor & out) { |
| 10581 | if (false) { |
| 10582 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10583 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10584 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10585 | auto self_meta = to_meta(self); |
| 10586 | auto source_meta = to_meta(source); |
| 10587 | auto out_meta = to_meta(out); |
| 10588 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10589 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10590 | at::_ops::set_source_Tensor_out::call(self_meta, source_meta, out_meta); |
| 10591 | } |
| 10592 | |
| 10593 | at::Tensor self_; |
| 10594 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10595 | at::functionalization::impl::sync(self); |
| 10596 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10597 | } else { |
| 10598 | self_ = self; |
| 10599 | } |
| 10600 | |
| 10601 | at::Tensor source_; |
| 10602 | if (at::functionalization::impl::isFunctionalTensor(source)) { |
| 10603 | at::functionalization::impl::sync(source); |
| 10604 | source_ = at::functionalization::impl::from_functional_tensor(source); |
| 10605 | } else { |
| 10606 | source_ = source; |
| 10607 | } |
| 10608 | |
| 10609 | at::Tensor out_; |
| 10610 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10611 | at::functionalization::impl::sync(out); |
| 10612 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10613 | } else { |
| 10614 | out_ = out; |
| 10615 | } |
| 10616 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10617 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(source))) { |
| 10618 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10619 | TORCH_INTERNAL_ASSERT(false, |
| 10620 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10621 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10622 | } else { |
| 10623 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10624 | at::AutoDispatchSkipFunctionalize guard; |
| 10625 | at::Tensor tmp_output = at::_ops::set_source_Tensor_out::call(self_, source_, out_); |
| 10626 | return out;; |
| 10627 | } |
| 10628 | } else { |
| 10629 | at::Tensor tmp_output; |
| 10630 | { |
| 10631 | at::AutoDispatchSkipFunctionalize guard; |
| 10632 | tmp_output = at::_ops::set_source_Tensor::call(self_, source_); |
| 10633 | } |
| 10634 | at::functionalization::impl::replace_(out, tmp_output); |
| 10635 | at::functionalization::impl::commit_update(out); |
| 10636 | at::functionalization::impl::sync(out); |
| 10637 | return out; |
| 10638 | } |
| 10639 | } |
| 10640 | |
| 10641 | at::Tensor & set__source_Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & source) { |
| 10642 | if (true) { |
| 10643 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10644 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10645 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10646 | auto self_meta = to_meta(self); |
| 10647 | auto source_meta = to_meta(source); |
| 10648 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10649 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10650 | at::_ops::set__source_Tensor::call(self_meta, source_meta); |
| 10651 | } |
| 10652 | |
| 10653 | at::Tensor self_; |
| 10654 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10655 | at::functionalization::impl::sync(self); |
| 10656 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10657 | } else { |
| 10658 | self_ = self; |
| 10659 | } |
| 10660 | |
| 10661 | at::Tensor source_; |
| 10662 | if (at::functionalization::impl::isFunctionalTensor(source)) { |
| 10663 | at::functionalization::impl::sync(source); |
| 10664 | source_ = at::functionalization::impl::from_functional_tensor(source); |
| 10665 | } else { |
| 10666 | source_ = source; |
| 10667 | } |
| 10668 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 10669 | if ((false || at::functionalization::impl::isFunctionalTensor(source))) { |
| 10670 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10671 | TORCH_INTERNAL_ASSERT(false, |
| 10672 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10673 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10674 | } else { |
| 10675 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10676 | at::AutoDispatchSkipFunctionalize guard; |
| 10677 | at::Tensor tmp_output = at::_ops::set__source_Tensor::call(self_, source_); |
| 10678 | return self;; |
| 10679 | } |
| 10680 | } else { |
| 10681 | at::Tensor tmp_output; |
| 10682 | { |
| 10683 | at::AutoDispatchSkipFunctionalize guard; |
| 10684 | tmp_output = at::_ops::set_source_Tensor::call(self_, source_); |
| 10685 | } |
| 10686 | at::functionalization::impl::replace_(self, tmp_output); |
| 10687 | at::functionalization::impl::commit_update(self); |
| 10688 | at::functionalization::impl::sync(self); |
| 10689 | return self; |
| 10690 | } |
| 10691 | } |
| 10692 | |
| 10693 | at::Tensor & set_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 10694 | if (false) { |
| 10695 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10696 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10697 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10698 | auto self_meta = to_meta(self); |
| 10699 | auto out_meta = to_meta(out); |
| 10700 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10701 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10702 | at::_ops::set_out::call(self_meta, out_meta); |
| 10703 | } |
| 10704 | |
| 10705 | at::Tensor self_; |
| 10706 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10707 | at::functionalization::impl::sync(self); |
| 10708 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10709 | } else { |
| 10710 | self_ = self; |
| 10711 | } |
| 10712 | |
| 10713 | at::Tensor out_; |
| 10714 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10715 | at::functionalization::impl::sync(out); |
| 10716 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10717 | } else { |
| 10718 | out_ = out; |
| 10719 | } |
| 10720 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10721 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 10722 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10723 | TORCH_INTERNAL_ASSERT(false, |
| 10724 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10725 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10726 | } else { |
| 10727 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10728 | at::AutoDispatchSkipFunctionalize guard; |
| 10729 | at::Tensor tmp_output = at::_ops::set_out::call(self_, out_); |
| 10730 | return out;; |
| 10731 | } |
| 10732 | } else { |
| 10733 | at::Tensor tmp_output; |
| 10734 | { |
| 10735 | at::AutoDispatchSkipFunctionalize guard; |
| 10736 | tmp_output = at::_ops::set::call(self_); |
| 10737 | } |
| 10738 | at::functionalization::impl::replace_(out, tmp_output); |
| 10739 | at::functionalization::impl::commit_update(out); |
| 10740 | at::functionalization::impl::sync(out); |
| 10741 | return out; |
| 10742 | } |
| 10743 | } |
| 10744 | |
| 10745 | at::Tensor & set_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 10746 | if (true) { |
| 10747 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10748 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10749 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10750 | auto self_meta = to_meta(self); |
| 10751 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10752 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10753 | at::_ops::set_::call(self_meta); |
| 10754 | } |
| 10755 | |
| 10756 | at::Tensor self_; |
| 10757 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10758 | at::functionalization::impl::sync(self); |
| 10759 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10760 | } else { |
| 10761 | self_ = self; |
| 10762 | } |
| 10763 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 10764 | if ((false)) { |
| 10765 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10766 | TORCH_INTERNAL_ASSERT(false, |
| 10767 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10768 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10769 | } else { |
| 10770 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10771 | at::AutoDispatchSkipFunctionalize guard; |
| 10772 | at::Tensor tmp_output = at::_ops::set_::call(self_); |
| 10773 | return self;; |
| 10774 | } |
| 10775 | } else { |
| 10776 | at::Tensor tmp_output; |
| 10777 | { |
| 10778 | at::AutoDispatchSkipFunctionalize guard; |
| 10779 | tmp_output = at::_ops::set::call(self_); |
| 10780 | } |
| 10781 | at::functionalization::impl::replace_(self, tmp_output); |
| 10782 | at::functionalization::impl::commit_update(self); |
| 10783 | at::functionalization::impl::sync(self); |
| 10784 | return self; |
| 10785 | } |
| 10786 | } |
| 10787 | |
| 10788 | at::Tensor & lift_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 10789 | if (false) { |
| 10790 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10791 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10792 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10793 | auto self_meta = to_meta(self); |
| 10794 | auto out_meta = to_meta(out); |
| 10795 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10796 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10797 | at::_ops::lift_out::call(self_meta, out_meta); |
| 10798 | } |
| 10799 | |
| 10800 | at::Tensor self_; |
| 10801 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10802 | at::functionalization::impl::sync(self); |
| 10803 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10804 | } else { |
| 10805 | self_ = self; |
| 10806 | } |
| 10807 | |
| 10808 | at::Tensor out_; |
| 10809 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10810 | at::functionalization::impl::sync(out); |
| 10811 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10812 | } else { |
| 10813 | out_ = out; |
| 10814 | } |
| 10815 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10816 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 10817 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10818 | TORCH_INTERNAL_ASSERT(false, |
| 10819 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10820 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10821 | } else { |
| 10822 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10823 | at::AutoDispatchSkipFunctionalize guard; |
| 10824 | at::Tensor tmp_output = at::_ops::lift_out::call(self_, out_); |
| 10825 | return out;; |
| 10826 | } |
| 10827 | } else { |
| 10828 | at::Tensor tmp_output; |
| 10829 | { |
| 10830 | at::AutoDispatchSkipFunctionalize guard; |
| 10831 | tmp_output = at::_ops::lift::call(self_); |
| 10832 | } |
| 10833 | at::functionalization::impl::replace_(out, tmp_output); |
| 10834 | at::functionalization::impl::commit_update(out); |
| 10835 | at::functionalization::impl::sync(out); |
| 10836 | return out; |
| 10837 | } |
| 10838 | } |
| 10839 | |
| 10840 | at::Tensor & masked_scatter_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mask, const at::Tensor & source, at::Tensor & out) { |
| 10841 | if (false) { |
| 10842 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10843 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10844 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10845 | auto self_meta = to_meta(self); |
| 10846 | auto mask_meta = to_meta(mask); |
| 10847 | auto source_meta = to_meta(source); |
| 10848 | auto out_meta = to_meta(out); |
| 10849 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10850 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10851 | at::_ops::masked_scatter_out::call(self_meta, mask_meta, source_meta, out_meta); |
| 10852 | } |
| 10853 | |
| 10854 | at::Tensor self_; |
| 10855 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10856 | at::functionalization::impl::sync(self); |
| 10857 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10858 | } else { |
| 10859 | self_ = self; |
| 10860 | } |
| 10861 | |
| 10862 | at::Tensor mask_; |
| 10863 | if (at::functionalization::impl::isFunctionalTensor(mask)) { |
| 10864 | at::functionalization::impl::sync(mask); |
| 10865 | mask_ = at::functionalization::impl::from_functional_tensor(mask); |
| 10866 | } else { |
| 10867 | mask_ = mask; |
| 10868 | } |
| 10869 | |
| 10870 | at::Tensor source_; |
| 10871 | if (at::functionalization::impl::isFunctionalTensor(source)) { |
| 10872 | at::functionalization::impl::sync(source); |
| 10873 | source_ = at::functionalization::impl::from_functional_tensor(source); |
| 10874 | } else { |
| 10875 | source_ = source; |
| 10876 | } |
| 10877 | |
| 10878 | at::Tensor out_; |
| 10879 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10880 | at::functionalization::impl::sync(out); |
| 10881 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10882 | } else { |
| 10883 | out_ = out; |
| 10884 | } |
| 10885 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10886 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(mask) || at::functionalization::impl::isFunctionalTensor(source))) { |
| 10887 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10888 | TORCH_INTERNAL_ASSERT(false, |
| 10889 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10890 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10891 | } else { |
| 10892 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10893 | at::AutoDispatchSkipFunctionalize guard; |
| 10894 | at::Tensor tmp_output = at::_ops::masked_scatter_out::call(self_, mask_, source_, out_); |
| 10895 | return out;; |
| 10896 | } |
| 10897 | } else { |
| 10898 | at::Tensor tmp_output; |
| 10899 | { |
| 10900 | at::AutoDispatchSkipFunctionalize guard; |
| 10901 | tmp_output = at::_ops::masked_scatter::call(self_, mask_, source_); |
| 10902 | } |
| 10903 | at::functionalization::impl::replace_(out, tmp_output); |
| 10904 | at::functionalization::impl::commit_update(out); |
| 10905 | at::functionalization::impl::sync(out); |
| 10906 | return out; |
| 10907 | } |
| 10908 | } |
| 10909 | |
| 10910 | at::Tensor & masked_scatter_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & mask, const at::Tensor & source) { |
| 10911 | if (true) { |
| 10912 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10913 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10914 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10915 | auto self_meta = to_meta(self); |
| 10916 | auto mask_meta = to_meta(mask); |
| 10917 | auto source_meta = to_meta(source); |
| 10918 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10919 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10920 | at::_ops::masked_scatter_::call(self_meta, mask_meta, source_meta); |
| 10921 | } |
| 10922 | |
| 10923 | at::Tensor self_; |
| 10924 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10925 | at::functionalization::impl::sync(self); |
| 10926 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10927 | } else { |
| 10928 | self_ = self; |
| 10929 | } |
| 10930 | |
| 10931 | at::Tensor mask_; |
| 10932 | if (at::functionalization::impl::isFunctionalTensor(mask)) { |
| 10933 | at::functionalization::impl::sync(mask); |
| 10934 | mask_ = at::functionalization::impl::from_functional_tensor(mask); |
| 10935 | } else { |
| 10936 | mask_ = mask; |
| 10937 | } |
| 10938 | |
| 10939 | at::Tensor source_; |
| 10940 | if (at::functionalization::impl::isFunctionalTensor(source)) { |
| 10941 | at::functionalization::impl::sync(source); |
| 10942 | source_ = at::functionalization::impl::from_functional_tensor(source); |
| 10943 | } else { |
| 10944 | source_ = source; |
| 10945 | } |
| 10946 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 10947 | if ((false || at::functionalization::impl::isFunctionalTensor(mask) || at::functionalization::impl::isFunctionalTensor(source))) { |
| 10948 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10949 | TORCH_INTERNAL_ASSERT(false, |
| 10950 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10951 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10952 | } else { |
| 10953 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10954 | at::AutoDispatchSkipFunctionalize guard; |
| 10955 | at::Tensor tmp_output = at::_ops::masked_scatter_::call(self_, mask_, source_); |
| 10956 | return self;; |
| 10957 | } |
| 10958 | } else { |
| 10959 | at::Tensor tmp_output; |
| 10960 | { |
| 10961 | at::AutoDispatchSkipFunctionalize guard; |
| 10962 | tmp_output = at::_ops::masked_scatter::call(self_, mask_, source_); |
| 10963 | } |
| 10964 | at::functionalization::impl::replace_(self, tmp_output); |
| 10965 | at::functionalization::impl::commit_update(self); |
| 10966 | at::functionalization::impl::sync(self); |
| 10967 | return self; |
| 10968 | } |
| 10969 | } |
| 10970 | |
| 10971 | at::Tensor & index_reduce_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self, at::Tensor & out) { |
| 10972 | if (false) { |
| 10973 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10974 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10975 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10976 | auto self_meta = to_meta(self); |
| 10977 | auto index_meta = to_meta(index); |
| 10978 | auto source_meta = to_meta(source); |
| 10979 | auto out_meta = to_meta(out); |
| 10980 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10981 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10982 | at::_ops::index_reduce_out::call(self_meta, dim, index_meta, source_meta, reduce, include_self, out_meta); |
| 10983 | } |
| 10984 | |
| 10985 | at::Tensor self_; |
| 10986 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10987 | at::functionalization::impl::sync(self); |
| 10988 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10989 | } else { |
| 10990 | self_ = self; |
| 10991 | } |
| 10992 | |
| 10993 | at::Tensor index_; |
| 10994 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 10995 | at::functionalization::impl::sync(index); |
| 10996 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 10997 | } else { |
| 10998 | index_ = index; |
| 10999 | } |
| 11000 | |
| 11001 | at::Tensor source_; |
| 11002 | if (at::functionalization::impl::isFunctionalTensor(source)) { |
| 11003 | at::functionalization::impl::sync(source); |
| 11004 | source_ = at::functionalization::impl::from_functional_tensor(source); |
| 11005 | } else { |
| 11006 | source_ = source; |
| 11007 | } |
| 11008 | |
| 11009 | at::Tensor out_; |
| 11010 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11011 | at::functionalization::impl::sync(out); |
| 11012 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11013 | } else { |
| 11014 | out_ = out; |
| 11015 | } |
| 11016 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11017 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(index) || at::functionalization::impl::isFunctionalTensor(source))) { |
| 11018 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11019 | TORCH_INTERNAL_ASSERT(false, |
| 11020 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11021 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11022 | } else { |
| 11023 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11024 | at::AutoDispatchSkipFunctionalize guard; |
| 11025 | at::Tensor tmp_output = at::_ops::index_reduce_out::call(self_, dim, index_, source_, reduce, include_self, out_); |
| 11026 | return out;; |
| 11027 | } |
| 11028 | } else { |
| 11029 | at::Tensor tmp_output; |
| 11030 | { |
| 11031 | at::AutoDispatchSkipFunctionalize guard; |
| 11032 | tmp_output = at::_ops::index_reduce::call(self_, dim, index_, source_, reduce, include_self); |
| 11033 | } |
| 11034 | at::functionalization::impl::replace_(out, tmp_output); |
| 11035 | at::functionalization::impl::commit_update(out); |
| 11036 | at::functionalization::impl::sync(out); |
| 11037 | return out; |
| 11038 | } |
| 11039 | } |
| 11040 | |
| 11041 | at::Tensor & index_reduce_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self) { |
| 11042 | if (true) { |
| 11043 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11044 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11045 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11046 | auto self_meta = to_meta(self); |
| 11047 | auto index_meta = to_meta(index); |
| 11048 | auto source_meta = to_meta(source); |
| 11049 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11050 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11051 | at::_ops::index_reduce_::call(self_meta, dim, index_meta, source_meta, reduce, include_self); |
| 11052 | } |
| 11053 | |
| 11054 | at::Tensor self_; |
| 11055 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11056 | at::functionalization::impl::sync(self); |
| 11057 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11058 | } else { |
| 11059 | self_ = self; |
| 11060 | } |
| 11061 | |
| 11062 | at::Tensor index_; |
| 11063 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 11064 | at::functionalization::impl::sync(index); |
| 11065 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 11066 | } else { |
| 11067 | index_ = index; |
| 11068 | } |
| 11069 | |
| 11070 | at::Tensor source_; |
| 11071 | if (at::functionalization::impl::isFunctionalTensor(source)) { |
| 11072 | at::functionalization::impl::sync(source); |
| 11073 | source_ = at::functionalization::impl::from_functional_tensor(source); |
| 11074 | } else { |
| 11075 | source_ = source; |
| 11076 | } |
| 11077 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 11078 | if ((false || at::functionalization::impl::isFunctionalTensor(index) || at::functionalization::impl::isFunctionalTensor(source))) { |
| 11079 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11080 | TORCH_INTERNAL_ASSERT(false, |
| 11081 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11082 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11083 | } else { |
| 11084 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11085 | at::AutoDispatchSkipFunctionalize guard; |
| 11086 | at::Tensor tmp_output = at::_ops::index_reduce_::call(self_, dim, index_, source_, reduce, include_self); |
| 11087 | return self;; |
| 11088 | } |
| 11089 | } else { |
| 11090 | at::Tensor tmp_output; |
| 11091 | { |
| 11092 | at::AutoDispatchSkipFunctionalize guard; |
| 11093 | tmp_output = at::_ops::index_reduce::call(self_, dim, index_, source_, reduce, include_self); |
| 11094 | } |
| 11095 | at::functionalization::impl::replace_(self, tmp_output); |
| 11096 | at::functionalization::impl::commit_update(self); |
| 11097 | at::functionalization::impl::sync(self); |
| 11098 | return self; |
| 11099 | } |
| 11100 | } |
| 11101 | |
| 11102 | at::Tensor & eq_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 11103 | if (false) { |
| 11104 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11105 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11106 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11107 | auto self_meta = to_meta(self); |
| 11108 | auto out_meta = to_meta(out); |
| 11109 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11110 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11111 | at::_ops::eq_Scalar_out::call(self_meta, other, out_meta); |
| 11112 | } |
| 11113 | |
| 11114 | at::Tensor self_; |
| 11115 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11116 | at::functionalization::impl::sync(self); |
| 11117 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11118 | } else { |
| 11119 | self_ = self; |
| 11120 | } |
| 11121 | |
| 11122 | at::Tensor out_; |
| 11123 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11124 | at::functionalization::impl::sync(out); |
| 11125 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11126 | } else { |
| 11127 | out_ = out; |
| 11128 | } |
| 11129 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11130 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 11131 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11132 | TORCH_INTERNAL_ASSERT(false, |
| 11133 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11134 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11135 | } else { |
| 11136 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11137 | at::AutoDispatchSkipFunctionalize guard; |
| 11138 | at::Tensor tmp_output = at::_ops::eq_Scalar_out::call(self_, other, out_); |
| 11139 | return out;; |
| 11140 | } |
| 11141 | } else { |
| 11142 | at::Tensor tmp_output; |
| 11143 | { |
| 11144 | at::AutoDispatchSkipFunctionalize guard; |
| 11145 | tmp_output = at::_ops::eq_Scalar::call(self_, other); |
| 11146 | } |
| 11147 | at::functionalization::impl::replace_(out, tmp_output); |
| 11148 | at::functionalization::impl::commit_update(out); |
| 11149 | at::functionalization::impl::sync(out); |
| 11150 | return out; |
| 11151 | } |
| 11152 | } |
| 11153 | |
| 11154 | at::Tensor & eq__Scalar(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { |
| 11155 | if (true) { |
| 11156 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11157 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11158 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11159 | auto self_meta = to_meta(self); |
| 11160 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11161 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11162 | at::_ops::eq__Scalar::call(self_meta, other); |
| 11163 | } |
| 11164 | |
| 11165 | at::Tensor self_; |
| 11166 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11167 | at::functionalization::impl::sync(self); |
| 11168 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11169 | } else { |
| 11170 | self_ = self; |
| 11171 | } |
| 11172 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 11173 | if ((false)) { |
| 11174 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11175 | TORCH_INTERNAL_ASSERT(false, |
| 11176 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11177 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11178 | } else { |
| 11179 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11180 | at::AutoDispatchSkipFunctionalize guard; |
| 11181 | at::Tensor tmp_output = at::_ops::eq__Scalar::call(self_, other); |
| 11182 | return self;; |
| 11183 | } |
| 11184 | } else { |
| 11185 | at::Tensor tmp_output; |
| 11186 | { |
| 11187 | at::AutoDispatchSkipFunctionalize guard; |
| 11188 | tmp_output = at::_ops::eq_Scalar::call(self_, other); |
| 11189 | } |
| 11190 | at::functionalization::impl::replace_(self, tmp_output); |
| 11191 | at::functionalization::impl::commit_update(self); |
| 11192 | at::functionalization::impl::sync(self); |
| 11193 | return self; |
| 11194 | } |
| 11195 | } |
| 11196 | |
| 11197 | at::Tensor & eq_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 11198 | if (false) { |
| 11199 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11200 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11201 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11202 | auto self_meta = to_meta(self); |
| 11203 | auto other_meta = to_meta(other); |
| 11204 | auto out_meta = to_meta(out); |
| 11205 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11206 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11207 | at::_ops::eq_Tensor_out::call(self_meta, other_meta, out_meta); |
| 11208 | } |
| 11209 | |
| 11210 | at::Tensor self_; |
| 11211 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11212 | at::functionalization::impl::sync(self); |
| 11213 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11214 | } else { |
| 11215 | self_ = self; |
| 11216 | } |
| 11217 | |
| 11218 | at::Tensor other_; |
| 11219 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 11220 | at::functionalization::impl::sync(other); |
| 11221 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 11222 | } else { |
| 11223 | other_ = other; |
| 11224 | } |
| 11225 | |
| 11226 | at::Tensor out_; |
| 11227 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11228 | at::functionalization::impl::sync(out); |
| 11229 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11230 | } else { |
| 11231 | out_ = out; |
| 11232 | } |
| 11233 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11234 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 11235 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11236 | TORCH_INTERNAL_ASSERT(false, |
| 11237 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11238 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11239 | } else { |
| 11240 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11241 | at::AutoDispatchSkipFunctionalize guard; |
| 11242 | at::Tensor tmp_output = at::_ops::eq_Tensor_out::call(self_, other_, out_); |
| 11243 | return out;; |
| 11244 | } |
| 11245 | } else { |
| 11246 | at::Tensor tmp_output; |
| 11247 | { |
| 11248 | at::AutoDispatchSkipFunctionalize guard; |
| 11249 | tmp_output = at::_ops::eq_Tensor::call(self_, other_); |
| 11250 | } |
| 11251 | at::functionalization::impl::replace_(out, tmp_output); |
| 11252 | at::functionalization::impl::commit_update(out); |
| 11253 | at::functionalization::impl::sync(out); |
| 11254 | return out; |
| 11255 | } |
| 11256 | } |
| 11257 | |
| 11258 | at::Tensor & eq__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 11259 | if (true) { |
| 11260 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11261 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11262 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11263 | auto self_meta = to_meta(self); |
| 11264 | auto other_meta = to_meta(other); |
| 11265 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11266 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11267 | at::_ops::eq__Tensor::call(self_meta, other_meta); |
| 11268 | } |
| 11269 | |
| 11270 | at::Tensor self_; |
| 11271 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11272 | at::functionalization::impl::sync(self); |
| 11273 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11274 | } else { |
| 11275 | self_ = self; |
| 11276 | } |
| 11277 | |
| 11278 | at::Tensor other_; |
| 11279 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 11280 | at::functionalization::impl::sync(other); |
| 11281 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 11282 | } else { |
| 11283 | other_ = other; |
| 11284 | } |
| 11285 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 11286 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 11287 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11288 | TORCH_INTERNAL_ASSERT(false, |
| 11289 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11290 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11291 | } else { |
| 11292 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11293 | at::AutoDispatchSkipFunctionalize guard; |
| 11294 | at::Tensor tmp_output = at::_ops::eq__Tensor::call(self_, other_); |
| 11295 | return self;; |
| 11296 | } |
| 11297 | } else { |
| 11298 | at::Tensor tmp_output; |
| 11299 | { |
| 11300 | at::AutoDispatchSkipFunctionalize guard; |
| 11301 | tmp_output = at::_ops::eq_Tensor::call(self_, other_); |
| 11302 | } |
| 11303 | at::functionalization::impl::replace_(self, tmp_output); |
| 11304 | at::functionalization::impl::commit_update(self); |
| 11305 | at::functionalization::impl::sync(self); |
| 11306 | return self; |
| 11307 | } |
| 11308 | } |
| 11309 | |
| 11310 | at::Tensor & bitwise_and_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 11311 | if (false) { |
| 11312 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11313 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11314 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11315 | auto self_meta = to_meta(self); |
| 11316 | auto other_meta = to_meta(other); |
| 11317 | auto out_meta = to_meta(out); |
| 11318 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11319 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11320 | at::_ops::bitwise_and_Tensor_out::call(self_meta, other_meta, out_meta); |
| 11321 | } |
| 11322 | |
| 11323 | at::Tensor self_; |
| 11324 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11325 | at::functionalization::impl::sync(self); |
| 11326 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11327 | } else { |
| 11328 | self_ = self; |
| 11329 | } |
| 11330 | |
| 11331 | at::Tensor other_; |
| 11332 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 11333 | at::functionalization::impl::sync(other); |
| 11334 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 11335 | } else { |
| 11336 | other_ = other; |
| 11337 | } |
| 11338 | |
| 11339 | at::Tensor out_; |
| 11340 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11341 | at::functionalization::impl::sync(out); |
| 11342 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11343 | } else { |
| 11344 | out_ = out; |
| 11345 | } |
| 11346 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11347 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 11348 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11349 | TORCH_INTERNAL_ASSERT(false, |
| 11350 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11351 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11352 | } else { |
| 11353 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11354 | at::AutoDispatchSkipFunctionalize guard; |
| 11355 | at::Tensor tmp_output = at::_ops::bitwise_and_Tensor_out::call(self_, other_, out_); |
| 11356 | return out;; |
| 11357 | } |
| 11358 | } else { |
| 11359 | at::Tensor tmp_output; |
| 11360 | { |
| 11361 | at::AutoDispatchSkipFunctionalize guard; |
| 11362 | tmp_output = at::_ops::bitwise_and_Tensor::call(self_, other_); |
| 11363 | } |
| 11364 | at::functionalization::impl::replace_(out, tmp_output); |
| 11365 | at::functionalization::impl::commit_update(out); |
| 11366 | at::functionalization::impl::sync(out); |
| 11367 | return out; |
| 11368 | } |
| 11369 | } |
| 11370 | |
| 11371 | at::Tensor & bitwise_and__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 11372 | if (true) { |
| 11373 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11374 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11375 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11376 | auto self_meta = to_meta(self); |
| 11377 | auto other_meta = to_meta(other); |
| 11378 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11379 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11380 | at::_ops::bitwise_and__Tensor::call(self_meta, other_meta); |
| 11381 | } |
| 11382 | |
| 11383 | at::Tensor self_; |
| 11384 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11385 | at::functionalization::impl::sync(self); |
| 11386 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11387 | } else { |
| 11388 | self_ = self; |
| 11389 | } |
| 11390 | |
| 11391 | at::Tensor other_; |
| 11392 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 11393 | at::functionalization::impl::sync(other); |
| 11394 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 11395 | } else { |
| 11396 | other_ = other; |
| 11397 | } |
| 11398 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 11399 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 11400 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11401 | TORCH_INTERNAL_ASSERT(false, |
| 11402 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11403 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11404 | } else { |
| 11405 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11406 | at::AutoDispatchSkipFunctionalize guard; |
| 11407 | at::Tensor tmp_output = at::_ops::bitwise_and__Tensor::call(self_, other_); |
| 11408 | return self;; |
| 11409 | } |
| 11410 | } else { |
| 11411 | at::Tensor tmp_output; |
| 11412 | { |
| 11413 | at::AutoDispatchSkipFunctionalize guard; |
| 11414 | tmp_output = at::_ops::bitwise_and_Tensor::call(self_, other_); |
| 11415 | } |
| 11416 | at::functionalization::impl::replace_(self, tmp_output); |
| 11417 | at::functionalization::impl::commit_update(self); |
| 11418 | at::functionalization::impl::sync(self); |
| 11419 | return self; |
| 11420 | } |
| 11421 | } |
| 11422 | |
| 11423 | at::Tensor & bitwise_and_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 11424 | if (false) { |
| 11425 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11426 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11427 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11428 | auto self_meta = to_meta(self); |
| 11429 | auto out_meta = to_meta(out); |
| 11430 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11431 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11432 | at::_ops::bitwise_and_Scalar_out::call(self_meta, other, out_meta); |
| 11433 | } |
| 11434 | |
| 11435 | at::Tensor self_; |
| 11436 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11437 | at::functionalization::impl::sync(self); |
| 11438 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11439 | } else { |
| 11440 | self_ = self; |
| 11441 | } |
| 11442 | |
| 11443 | at::Tensor out_; |
| 11444 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11445 | at::functionalization::impl::sync(out); |
| 11446 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11447 | } else { |
| 11448 | out_ = out; |
| 11449 | } |
| 11450 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11451 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 11452 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11453 | TORCH_INTERNAL_ASSERT(false, |
| 11454 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11455 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11456 | } else { |
| 11457 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11458 | at::AutoDispatchSkipFunctionalize guard; |
| 11459 | at::Tensor tmp_output = at::_ops::bitwise_and_Scalar_out::call(self_, other, out_); |
| 11460 | return out;; |
| 11461 | } |
| 11462 | } else { |
| 11463 | at::Tensor tmp_output; |
| 11464 | { |
| 11465 | at::AutoDispatchSkipFunctionalize guard; |
| 11466 | tmp_output = at::_ops::bitwise_and_Scalar::call(self_, other); |
| 11467 | } |
| 11468 | at::functionalization::impl::replace_(out, tmp_output); |
| 11469 | at::functionalization::impl::commit_update(out); |
| 11470 | at::functionalization::impl::sync(out); |
| 11471 | return out; |
| 11472 | } |
| 11473 | } |
| 11474 | |
| 11475 | at::Tensor & bitwise_and__Scalar(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { |
| 11476 | if (true) { |
| 11477 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11478 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11479 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11480 | auto self_meta = to_meta(self); |
| 11481 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11482 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11483 | at::_ops::bitwise_and__Scalar::call(self_meta, other); |
| 11484 | } |
| 11485 | |
| 11486 | at::Tensor self_; |
| 11487 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11488 | at::functionalization::impl::sync(self); |
| 11489 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11490 | } else { |
| 11491 | self_ = self; |
| 11492 | } |
| 11493 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 11494 | if ((false)) { |
| 11495 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11496 | TORCH_INTERNAL_ASSERT(false, |
| 11497 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11498 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11499 | } else { |
| 11500 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11501 | at::AutoDispatchSkipFunctionalize guard; |
| 11502 | at::Tensor tmp_output = at::_ops::bitwise_and__Scalar::call(self_, other); |
| 11503 | return self;; |
| 11504 | } |
| 11505 | } else { |
| 11506 | at::Tensor tmp_output; |
| 11507 | { |
| 11508 | at::AutoDispatchSkipFunctionalize guard; |
| 11509 | tmp_output = at::_ops::bitwise_and_Scalar::call(self_, other); |
| 11510 | } |
| 11511 | at::functionalization::impl::replace_(self, tmp_output); |
| 11512 | at::functionalization::impl::commit_update(self); |
| 11513 | at::functionalization::impl::sync(self); |
| 11514 | return self; |
| 11515 | } |
| 11516 | } |
| 11517 | |
| 11518 | at::Tensor & bitwise_and_out_Scalar_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other, at::Tensor & out) { |
| 11519 | if (false) { |
| 11520 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11521 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11522 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11523 | auto other_meta = to_meta(other); |
| 11524 | auto out_meta = to_meta(out); |
| 11525 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11526 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11527 | at::_ops::bitwise_and_Scalar_Tensor_out::call(self, other_meta, out_meta); |
| 11528 | } |
| 11529 | |
| 11530 | at::Tensor other_; |
| 11531 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 11532 | at::functionalization::impl::sync(other); |
| 11533 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 11534 | } else { |
| 11535 | other_ = other; |
| 11536 | } |
| 11537 | |
| 11538 | at::Tensor out_; |
| 11539 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11540 | at::functionalization::impl::sync(out); |
| 11541 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11542 | } else { |
| 11543 | out_ = out; |
| 11544 | } |
| 11545 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11546 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 11547 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11548 | TORCH_INTERNAL_ASSERT(false, |
| 11549 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11550 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11551 | } else { |
| 11552 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11553 | at::AutoDispatchSkipFunctionalize guard; |
| 11554 | at::Tensor tmp_output = at::_ops::bitwise_and_Scalar_Tensor_out::call(self, other_, out_); |
| 11555 | return out;; |
| 11556 | } |
| 11557 | } else { |
| 11558 | at::Tensor tmp_output; |
| 11559 | { |
| 11560 | at::AutoDispatchSkipFunctionalize guard; |
| 11561 | tmp_output = at::_ops::bitwise_and_Scalar_Tensor::call(self, other_); |
| 11562 | } |
| 11563 | at::functionalization::impl::replace_(out, tmp_output); |
| 11564 | at::functionalization::impl::commit_update(out); |
| 11565 | at::functionalization::impl::sync(out); |
| 11566 | return out; |
| 11567 | } |
| 11568 | } |
| 11569 | |
| 11570 | at::Tensor & bitwise_or_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 11571 | if (false) { |
| 11572 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11573 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11574 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11575 | auto self_meta = to_meta(self); |
| 11576 | auto other_meta = to_meta(other); |
| 11577 | auto out_meta = to_meta(out); |
| 11578 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11579 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11580 | at::_ops::bitwise_or_Tensor_out::call(self_meta, other_meta, out_meta); |
| 11581 | } |
| 11582 | |
| 11583 | at::Tensor self_; |
| 11584 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11585 | at::functionalization::impl::sync(self); |
| 11586 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11587 | } else { |
| 11588 | self_ = self; |
| 11589 | } |
| 11590 | |
| 11591 | at::Tensor other_; |
| 11592 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 11593 | at::functionalization::impl::sync(other); |
| 11594 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 11595 | } else { |
| 11596 | other_ = other; |
| 11597 | } |
| 11598 | |
| 11599 | at::Tensor out_; |
| 11600 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11601 | at::functionalization::impl::sync(out); |
| 11602 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11603 | } else { |
| 11604 | out_ = out; |
| 11605 | } |
| 11606 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11607 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 11608 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11609 | TORCH_INTERNAL_ASSERT(false, |
| 11610 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11611 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11612 | } else { |
| 11613 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11614 | at::AutoDispatchSkipFunctionalize guard; |
| 11615 | at::Tensor tmp_output = at::_ops::bitwise_or_Tensor_out::call(self_, other_, out_); |
| 11616 | return out;; |
| 11617 | } |
| 11618 | } else { |
| 11619 | at::Tensor tmp_output; |
| 11620 | { |
| 11621 | at::AutoDispatchSkipFunctionalize guard; |
| 11622 | tmp_output = at::_ops::bitwise_or_Tensor::call(self_, other_); |
| 11623 | } |
| 11624 | at::functionalization::impl::replace_(out, tmp_output); |
| 11625 | at::functionalization::impl::commit_update(out); |
| 11626 | at::functionalization::impl::sync(out); |
| 11627 | return out; |
| 11628 | } |
| 11629 | } |
| 11630 | |
| 11631 | at::Tensor & bitwise_or__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 11632 | if (true) { |
| 11633 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11634 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11635 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11636 | auto self_meta = to_meta(self); |
| 11637 | auto other_meta = to_meta(other); |
| 11638 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11639 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11640 | at::_ops::bitwise_or__Tensor::call(self_meta, other_meta); |
| 11641 | } |
| 11642 | |
| 11643 | at::Tensor self_; |
| 11644 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11645 | at::functionalization::impl::sync(self); |
| 11646 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11647 | } else { |
| 11648 | self_ = self; |
| 11649 | } |
| 11650 | |
| 11651 | at::Tensor other_; |
| 11652 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 11653 | at::functionalization::impl::sync(other); |
| 11654 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 11655 | } else { |
| 11656 | other_ = other; |
| 11657 | } |
| 11658 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 11659 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 11660 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11661 | TORCH_INTERNAL_ASSERT(false, |
| 11662 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11663 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11664 | } else { |
| 11665 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11666 | at::AutoDispatchSkipFunctionalize guard; |
| 11667 | at::Tensor tmp_output = at::_ops::bitwise_or__Tensor::call(self_, other_); |
| 11668 | return self;; |
| 11669 | } |
| 11670 | } else { |
| 11671 | at::Tensor tmp_output; |
| 11672 | { |
| 11673 | at::AutoDispatchSkipFunctionalize guard; |
| 11674 | tmp_output = at::_ops::bitwise_or_Tensor::call(self_, other_); |
| 11675 | } |
| 11676 | at::functionalization::impl::replace_(self, tmp_output); |
| 11677 | at::functionalization::impl::commit_update(self); |
| 11678 | at::functionalization::impl::sync(self); |
| 11679 | return self; |
| 11680 | } |
| 11681 | } |
| 11682 | |
| 11683 | at::Tensor & bitwise_or_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 11684 | if (false) { |
| 11685 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11686 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11687 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11688 | auto self_meta = to_meta(self); |
| 11689 | auto out_meta = to_meta(out); |
| 11690 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11691 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11692 | at::_ops::bitwise_or_Scalar_out::call(self_meta, other, out_meta); |
| 11693 | } |
| 11694 | |
| 11695 | at::Tensor self_; |
| 11696 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11697 | at::functionalization::impl::sync(self); |
| 11698 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11699 | } else { |
| 11700 | self_ = self; |
| 11701 | } |
| 11702 | |
| 11703 | at::Tensor out_; |
| 11704 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11705 | at::functionalization::impl::sync(out); |
| 11706 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11707 | } else { |
| 11708 | out_ = out; |
| 11709 | } |
| 11710 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11711 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 11712 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11713 | TORCH_INTERNAL_ASSERT(false, |
| 11714 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11715 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11716 | } else { |
| 11717 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11718 | at::AutoDispatchSkipFunctionalize guard; |
| 11719 | at::Tensor tmp_output = at::_ops::bitwise_or_Scalar_out::call(self_, other, out_); |
| 11720 | return out;; |
| 11721 | } |
| 11722 | } else { |
| 11723 | at::Tensor tmp_output; |
| 11724 | { |
| 11725 | at::AutoDispatchSkipFunctionalize guard; |
| 11726 | tmp_output = at::_ops::bitwise_or_Scalar::call(self_, other); |
| 11727 | } |
| 11728 | at::functionalization::impl::replace_(out, tmp_output); |
| 11729 | at::functionalization::impl::commit_update(out); |
| 11730 | at::functionalization::impl::sync(out); |
| 11731 | return out; |
| 11732 | } |
| 11733 | } |
| 11734 | |
| 11735 | at::Tensor & bitwise_or__Scalar(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { |
| 11736 | if (true) { |
| 11737 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11738 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11739 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11740 | auto self_meta = to_meta(self); |
| 11741 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11742 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11743 | at::_ops::bitwise_or__Scalar::call(self_meta, other); |
| 11744 | } |
| 11745 | |
| 11746 | at::Tensor self_; |
| 11747 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11748 | at::functionalization::impl::sync(self); |
| 11749 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11750 | } else { |
| 11751 | self_ = self; |
| 11752 | } |
| 11753 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 11754 | if ((false)) { |
| 11755 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11756 | TORCH_INTERNAL_ASSERT(false, |
| 11757 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11758 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11759 | } else { |
| 11760 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11761 | at::AutoDispatchSkipFunctionalize guard; |
| 11762 | at::Tensor tmp_output = at::_ops::bitwise_or__Scalar::call(self_, other); |
| 11763 | return self;; |
| 11764 | } |
| 11765 | } else { |
| 11766 | at::Tensor tmp_output; |
| 11767 | { |
| 11768 | at::AutoDispatchSkipFunctionalize guard; |
| 11769 | tmp_output = at::_ops::bitwise_or_Scalar::call(self_, other); |
| 11770 | } |
| 11771 | at::functionalization::impl::replace_(self, tmp_output); |
| 11772 | at::functionalization::impl::commit_update(self); |
| 11773 | at::functionalization::impl::sync(self); |
| 11774 | return self; |
| 11775 | } |
| 11776 | } |
| 11777 | |
| 11778 | at::Tensor & bitwise_or_out_Scalar_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other, at::Tensor & out) { |
| 11779 | if (false) { |
| 11780 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11781 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11782 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11783 | auto other_meta = to_meta(other); |
| 11784 | auto out_meta = to_meta(out); |
| 11785 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11786 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11787 | at::_ops::bitwise_or_Scalar_Tensor_out::call(self, other_meta, out_meta); |
| 11788 | } |
| 11789 | |
| 11790 | at::Tensor other_; |
| 11791 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 11792 | at::functionalization::impl::sync(other); |
| 11793 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 11794 | } else { |
| 11795 | other_ = other; |
| 11796 | } |
| 11797 | |
| 11798 | at::Tensor out_; |
| 11799 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11800 | at::functionalization::impl::sync(out); |
| 11801 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11802 | } else { |
| 11803 | out_ = out; |
| 11804 | } |
| 11805 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11806 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 11807 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11808 | TORCH_INTERNAL_ASSERT(false, |
| 11809 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11810 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11811 | } else { |
| 11812 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11813 | at::AutoDispatchSkipFunctionalize guard; |
| 11814 | at::Tensor tmp_output = at::_ops::bitwise_or_Scalar_Tensor_out::call(self, other_, out_); |
| 11815 | return out;; |
| 11816 | } |
| 11817 | } else { |
| 11818 | at::Tensor tmp_output; |
| 11819 | { |
| 11820 | at::AutoDispatchSkipFunctionalize guard; |
| 11821 | tmp_output = at::_ops::bitwise_or_Scalar_Tensor::call(self, other_); |
| 11822 | } |
| 11823 | at::functionalization::impl::replace_(out, tmp_output); |
| 11824 | at::functionalization::impl::commit_update(out); |
| 11825 | at::functionalization::impl::sync(out); |
| 11826 | return out; |
| 11827 | } |
| 11828 | } |
| 11829 | |
| 11830 | at::Tensor & bitwise_left_shift_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 11831 | if (false) { |
| 11832 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11833 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11834 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11835 | auto self_meta = to_meta(self); |
| 11836 | auto other_meta = to_meta(other); |
| 11837 | auto out_meta = to_meta(out); |
| 11838 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11839 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11840 | at::_ops::bitwise_left_shift_Tensor_out::call(self_meta, other_meta, out_meta); |
| 11841 | } |
| 11842 | |
| 11843 | at::Tensor self_; |
| 11844 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11845 | at::functionalization::impl::sync(self); |
| 11846 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11847 | } else { |
| 11848 | self_ = self; |
| 11849 | } |
| 11850 | |
| 11851 | at::Tensor other_; |
| 11852 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 11853 | at::functionalization::impl::sync(other); |
| 11854 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 11855 | } else { |
| 11856 | other_ = other; |
| 11857 | } |
| 11858 | |
| 11859 | at::Tensor out_; |
| 11860 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11861 | at::functionalization::impl::sync(out); |
| 11862 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11863 | } else { |
| 11864 | out_ = out; |
| 11865 | } |
| 11866 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11867 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 11868 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11869 | TORCH_INTERNAL_ASSERT(false, |
| 11870 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11871 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11872 | } else { |
| 11873 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11874 | at::AutoDispatchSkipFunctionalize guard; |
| 11875 | at::Tensor tmp_output = at::_ops::bitwise_left_shift_Tensor_out::call(self_, other_, out_); |
| 11876 | return out;; |
| 11877 | } |
| 11878 | } else { |
| 11879 | at::Tensor tmp_output; |
| 11880 | { |
| 11881 | at::AutoDispatchSkipFunctionalize guard; |
| 11882 | tmp_output = at::_ops::bitwise_left_shift_Tensor::call(self_, other_); |
| 11883 | } |
| 11884 | at::functionalization::impl::replace_(out, tmp_output); |
| 11885 | at::functionalization::impl::commit_update(out); |
| 11886 | at::functionalization::impl::sync(out); |
| 11887 | return out; |
| 11888 | } |
| 11889 | } |
| 11890 | |
| 11891 | at::Tensor & bitwise_left_shift__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 11892 | if (true) { |
| 11893 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11894 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11895 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11896 | auto self_meta = to_meta(self); |
| 11897 | auto other_meta = to_meta(other); |
| 11898 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11899 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11900 | at::_ops::bitwise_left_shift__Tensor::call(self_meta, other_meta); |
| 11901 | } |
| 11902 | |
| 11903 | at::Tensor self_; |
| 11904 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11905 | at::functionalization::impl::sync(self); |
| 11906 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11907 | } else { |
| 11908 | self_ = self; |
| 11909 | } |
| 11910 | |
| 11911 | at::Tensor other_; |
| 11912 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 11913 | at::functionalization::impl::sync(other); |
| 11914 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 11915 | } else { |
| 11916 | other_ = other; |
| 11917 | } |
| 11918 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 11919 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 11920 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11921 | TORCH_INTERNAL_ASSERT(false, |
| 11922 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11923 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11924 | } else { |
| 11925 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11926 | at::AutoDispatchSkipFunctionalize guard; |
| 11927 | at::Tensor tmp_output = at::_ops::bitwise_left_shift__Tensor::call(self_, other_); |
| 11928 | return self;; |
| 11929 | } |
| 11930 | } else { |
| 11931 | at::Tensor tmp_output; |
| 11932 | { |
| 11933 | at::AutoDispatchSkipFunctionalize guard; |
| 11934 | tmp_output = at::_ops::bitwise_left_shift_Tensor::call(self_, other_); |
| 11935 | } |
| 11936 | at::functionalization::impl::replace_(self, tmp_output); |
| 11937 | at::functionalization::impl::commit_update(self); |
| 11938 | at::functionalization::impl::sync(self); |
| 11939 | return self; |
| 11940 | } |
| 11941 | } |
| 11942 | |
| 11943 | at::Tensor & bitwise_left_shift_out_Tensor_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 11944 | if (false) { |
| 11945 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11946 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11947 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11948 | auto self_meta = to_meta(self); |
| 11949 | auto out_meta = to_meta(out); |
| 11950 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11951 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11952 | at::_ops::bitwise_left_shift_Tensor_Scalar_out::call(self_meta, other, out_meta); |
| 11953 | } |
| 11954 | |
| 11955 | at::Tensor self_; |
| 11956 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11957 | at::functionalization::impl::sync(self); |
| 11958 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11959 | } else { |
| 11960 | self_ = self; |
| 11961 | } |
| 11962 | |
| 11963 | at::Tensor out_; |
| 11964 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11965 | at::functionalization::impl::sync(out); |
| 11966 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11967 | } else { |
| 11968 | out_ = out; |
| 11969 | } |
| 11970 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11971 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 11972 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11973 | TORCH_INTERNAL_ASSERT(false, |
| 11974 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11975 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11976 | } else { |
| 11977 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11978 | at::AutoDispatchSkipFunctionalize guard; |
| 11979 | at::Tensor tmp_output = at::_ops::bitwise_left_shift_Tensor_Scalar_out::call(self_, other, out_); |
| 11980 | return out;; |
| 11981 | } |
| 11982 | } else { |
| 11983 | at::Tensor tmp_output; |
| 11984 | { |
| 11985 | at::AutoDispatchSkipFunctionalize guard; |
| 11986 | tmp_output = at::_ops::bitwise_left_shift_Tensor_Scalar::call(self_, other); |
| 11987 | } |
| 11988 | at::functionalization::impl::replace_(out, tmp_output); |
| 11989 | at::functionalization::impl::commit_update(out); |
| 11990 | at::functionalization::impl::sync(out); |
| 11991 | return out; |
| 11992 | } |
| 11993 | } |
| 11994 | |
| 11995 | at::Tensor & bitwise_left_shift__Tensor_Scalar(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { |
| 11996 | if (true) { |
| 11997 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11998 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11999 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12000 | auto self_meta = to_meta(self); |
| 12001 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12002 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12003 | at::_ops::bitwise_left_shift__Tensor_Scalar::call(self_meta, other); |
| 12004 | } |
| 12005 | |
| 12006 | at::Tensor self_; |
| 12007 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12008 | at::functionalization::impl::sync(self); |
| 12009 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12010 | } else { |
| 12011 | self_ = self; |
| 12012 | } |
| 12013 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 12014 | if ((false)) { |
| 12015 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12016 | TORCH_INTERNAL_ASSERT(false, |
| 12017 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12018 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12019 | } else { |
| 12020 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12021 | at::AutoDispatchSkipFunctionalize guard; |
| 12022 | at::Tensor tmp_output = at::_ops::bitwise_left_shift__Tensor_Scalar::call(self_, other); |
| 12023 | return self;; |
| 12024 | } |
| 12025 | } else { |
| 12026 | at::Tensor tmp_output; |
| 12027 | { |
| 12028 | at::AutoDispatchSkipFunctionalize guard; |
| 12029 | tmp_output = at::_ops::bitwise_left_shift_Tensor_Scalar::call(self_, other); |
| 12030 | } |
| 12031 | at::functionalization::impl::replace_(self, tmp_output); |
| 12032 | at::functionalization::impl::commit_update(self); |
| 12033 | at::functionalization::impl::sync(self); |
| 12034 | return self; |
| 12035 | } |
| 12036 | } |
| 12037 | |
| 12038 | at::Tensor & bitwise_left_shift_out_Scalar_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other, at::Tensor & out) { |
| 12039 | if (false) { |
| 12040 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12041 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12042 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12043 | auto other_meta = to_meta(other); |
| 12044 | auto out_meta = to_meta(out); |
| 12045 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12046 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12047 | at::_ops::bitwise_left_shift_Scalar_Tensor_out::call(self, other_meta, out_meta); |
| 12048 | } |
| 12049 | |
| 12050 | at::Tensor other_; |
| 12051 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 12052 | at::functionalization::impl::sync(other); |
| 12053 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 12054 | } else { |
| 12055 | other_ = other; |
| 12056 | } |
| 12057 | |
| 12058 | at::Tensor out_; |
| 12059 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12060 | at::functionalization::impl::sync(out); |
| 12061 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12062 | } else { |
| 12063 | out_ = out; |
| 12064 | } |
| 12065 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12066 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 12067 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12068 | TORCH_INTERNAL_ASSERT(false, |
| 12069 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12070 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12071 | } else { |
| 12072 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12073 | at::AutoDispatchSkipFunctionalize guard; |
| 12074 | at::Tensor tmp_output = at::_ops::bitwise_left_shift_Scalar_Tensor_out::call(self, other_, out_); |
| 12075 | return out;; |
| 12076 | } |
| 12077 | } else { |
| 12078 | at::Tensor tmp_output; |
| 12079 | { |
| 12080 | at::AutoDispatchSkipFunctionalize guard; |
| 12081 | tmp_output = at::_ops::bitwise_left_shift_Scalar_Tensor::call(self, other_); |
| 12082 | } |
| 12083 | at::functionalization::impl::replace_(out, tmp_output); |
| 12084 | at::functionalization::impl::commit_update(out); |
| 12085 | at::functionalization::impl::sync(out); |
| 12086 | return out; |
| 12087 | } |
| 12088 | } |
| 12089 | |
| 12090 | at::Tensor & __rshift___out_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 12091 | if (false) { |
| 12092 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12093 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12094 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12095 | auto self_meta = to_meta(self); |
| 12096 | auto out_meta = to_meta(out); |
| 12097 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12098 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12099 | at::_ops::__rshift___Scalar_out::call(self_meta, other, out_meta); |
| 12100 | } |
| 12101 | |
| 12102 | at::Tensor self_; |
| 12103 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12104 | at::functionalization::impl::sync(self); |
| 12105 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12106 | } else { |
| 12107 | self_ = self; |
| 12108 | } |
| 12109 | |
| 12110 | at::Tensor out_; |
| 12111 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12112 | at::functionalization::impl::sync(out); |
| 12113 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12114 | } else { |
| 12115 | out_ = out; |
| 12116 | } |
| 12117 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12118 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 12119 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12120 | TORCH_INTERNAL_ASSERT(false, |
| 12121 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12122 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12123 | } else { |
| 12124 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12125 | at::AutoDispatchSkipFunctionalize guard; |
| 12126 | at::Tensor tmp_output = at::_ops::__rshift___Scalar_out::call(self_, other, out_); |
| 12127 | return out;; |
| 12128 | } |
| 12129 | } else { |
| 12130 | at::Tensor tmp_output; |
| 12131 | { |
| 12132 | at::AutoDispatchSkipFunctionalize guard; |
| 12133 | tmp_output = at::_ops::__rshift___Scalar::call(self_, other); |
| 12134 | } |
| 12135 | at::functionalization::impl::replace_(out, tmp_output); |
| 12136 | at::functionalization::impl::commit_update(out); |
| 12137 | at::functionalization::impl::sync(out); |
| 12138 | return out; |
| 12139 | } |
| 12140 | } |
| 12141 | |
| 12142 | at::Tensor & __irshift___Scalar(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { |
| 12143 | if (true) { |
| 12144 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12145 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12146 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12147 | auto self_meta = to_meta(self); |
| 12148 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12149 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12150 | at::_ops::__irshift___Scalar::call(self_meta, other); |
| 12151 | } |
| 12152 | |
| 12153 | at::Tensor self_; |
| 12154 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12155 | at::functionalization::impl::sync(self); |
| 12156 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12157 | } else { |
| 12158 | self_ = self; |
| 12159 | } |
| 12160 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 12161 | if ((false)) { |
| 12162 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12163 | TORCH_INTERNAL_ASSERT(false, |
| 12164 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12165 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12166 | } else { |
| 12167 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12168 | at::AutoDispatchSkipFunctionalize guard; |
| 12169 | at::Tensor tmp_output = at::_ops::__irshift___Scalar::call(self_, other); |
| 12170 | return self;; |
| 12171 | } |
| 12172 | } else { |
| 12173 | at::Tensor tmp_output; |
| 12174 | { |
| 12175 | at::AutoDispatchSkipFunctionalize guard; |
| 12176 | tmp_output = at::_ops::__rshift___Scalar::call(self_, other); |
| 12177 | } |
| 12178 | at::functionalization::impl::replace_(self, tmp_output); |
| 12179 | at::functionalization::impl::commit_update(self); |
| 12180 | at::functionalization::impl::sync(self); |
| 12181 | return self; |
| 12182 | } |
| 12183 | } |
| 12184 | |
| 12185 | at::Tensor & __rshift___out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 12186 | if (false) { |
| 12187 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12188 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12189 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12190 | auto self_meta = to_meta(self); |
| 12191 | auto other_meta = to_meta(other); |
| 12192 | auto out_meta = to_meta(out); |
| 12193 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12194 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12195 | at::_ops::__rshift___Tensor_out::call(self_meta, other_meta, out_meta); |
| 12196 | } |
| 12197 | |
| 12198 | at::Tensor self_; |
| 12199 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12200 | at::functionalization::impl::sync(self); |
| 12201 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12202 | } else { |
| 12203 | self_ = self; |
| 12204 | } |
| 12205 | |
| 12206 | at::Tensor other_; |
| 12207 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 12208 | at::functionalization::impl::sync(other); |
| 12209 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 12210 | } else { |
| 12211 | other_ = other; |
| 12212 | } |
| 12213 | |
| 12214 | at::Tensor out_; |
| 12215 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12216 | at::functionalization::impl::sync(out); |
| 12217 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12218 | } else { |
| 12219 | out_ = out; |
| 12220 | } |
| 12221 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12222 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 12223 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12224 | TORCH_INTERNAL_ASSERT(false, |
| 12225 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12226 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12227 | } else { |
| 12228 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12229 | at::AutoDispatchSkipFunctionalize guard; |
| 12230 | at::Tensor tmp_output = at::_ops::__rshift___Tensor_out::call(self_, other_, out_); |
| 12231 | return out;; |
| 12232 | } |
| 12233 | } else { |
| 12234 | at::Tensor tmp_output; |
| 12235 | { |
| 12236 | at::AutoDispatchSkipFunctionalize guard; |
| 12237 | tmp_output = at::_ops::__rshift___Tensor::call(self_, other_); |
| 12238 | } |
| 12239 | at::functionalization::impl::replace_(out, tmp_output); |
| 12240 | at::functionalization::impl::commit_update(out); |
| 12241 | at::functionalization::impl::sync(out); |
| 12242 | return out; |
| 12243 | } |
| 12244 | } |
| 12245 | |
| 12246 | at::Tensor & __irshift___Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 12247 | if (true) { |
| 12248 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12249 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12250 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12251 | auto self_meta = to_meta(self); |
| 12252 | auto other_meta = to_meta(other); |
| 12253 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12254 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12255 | at::_ops::__irshift___Tensor::call(self_meta, other_meta); |
| 12256 | } |
| 12257 | |
| 12258 | at::Tensor self_; |
| 12259 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12260 | at::functionalization::impl::sync(self); |
| 12261 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12262 | } else { |
| 12263 | self_ = self; |
| 12264 | } |
| 12265 | |
| 12266 | at::Tensor other_; |
| 12267 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 12268 | at::functionalization::impl::sync(other); |
| 12269 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 12270 | } else { |
| 12271 | other_ = other; |
| 12272 | } |
| 12273 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 12274 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 12275 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12276 | TORCH_INTERNAL_ASSERT(false, |
| 12277 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12278 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12279 | } else { |
| 12280 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12281 | at::AutoDispatchSkipFunctionalize guard; |
| 12282 | at::Tensor tmp_output = at::_ops::__irshift___Tensor::call(self_, other_); |
| 12283 | return self;; |
| 12284 | } |
| 12285 | } else { |
| 12286 | at::Tensor tmp_output; |
| 12287 | { |
| 12288 | at::AutoDispatchSkipFunctionalize guard; |
| 12289 | tmp_output = at::_ops::__rshift___Tensor::call(self_, other_); |
| 12290 | } |
| 12291 | at::functionalization::impl::replace_(self, tmp_output); |
| 12292 | at::functionalization::impl::commit_update(self); |
| 12293 | at::functionalization::impl::sync(self); |
| 12294 | return self; |
| 12295 | } |
| 12296 | } |
| 12297 | |
| 12298 | at::Tensor & tril_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t diagonal, at::Tensor & out) { |
| 12299 | if (false) { |
| 12300 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12301 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12302 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12303 | auto self_meta = to_meta(self); |
| 12304 | auto out_meta = to_meta(out); |
| 12305 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12306 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12307 | at::_ops::tril_out::call(self_meta, diagonal, out_meta); |
| 12308 | } |
| 12309 | |
| 12310 | at::Tensor self_; |
| 12311 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12312 | at::functionalization::impl::sync(self); |
| 12313 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12314 | } else { |
| 12315 | self_ = self; |
| 12316 | } |
| 12317 | |
| 12318 | at::Tensor out_; |
| 12319 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12320 | at::functionalization::impl::sync(out); |
| 12321 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12322 | } else { |
| 12323 | out_ = out; |
| 12324 | } |
| 12325 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12326 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 12327 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12328 | TORCH_INTERNAL_ASSERT(false, |
| 12329 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12330 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12331 | } else { |
| 12332 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12333 | at::AutoDispatchSkipFunctionalize guard; |
| 12334 | at::Tensor tmp_output = at::_ops::tril_out::call(self_, diagonal, out_); |
| 12335 | return out;; |
| 12336 | } |
| 12337 | } else { |
| 12338 | at::Tensor tmp_output; |
| 12339 | { |
| 12340 | at::AutoDispatchSkipFunctionalize guard; |
| 12341 | tmp_output = at::_ops::tril::call(self_, diagonal); |
| 12342 | } |
| 12343 | at::functionalization::impl::replace_(out, tmp_output); |
| 12344 | at::functionalization::impl::commit_update(out); |
| 12345 | at::functionalization::impl::sync(out); |
| 12346 | return out; |
| 12347 | } |
| 12348 | } |
| 12349 | |
| 12350 | at::Tensor & tril_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t diagonal) { |
| 12351 | if (true) { |
| 12352 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12353 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12354 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12355 | auto self_meta = to_meta(self); |
| 12356 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12357 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12358 | at::_ops::tril_::call(self_meta, diagonal); |
| 12359 | } |
| 12360 | |
| 12361 | at::Tensor self_; |
| 12362 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12363 | at::functionalization::impl::sync(self); |
| 12364 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12365 | } else { |
| 12366 | self_ = self; |
| 12367 | } |
| 12368 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 12369 | if ((false)) { |
| 12370 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12371 | TORCH_INTERNAL_ASSERT(false, |
| 12372 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12373 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12374 | } else { |
| 12375 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12376 | at::AutoDispatchSkipFunctionalize guard; |
| 12377 | at::Tensor tmp_output = at::_ops::tril_::call(self_, diagonal); |
| 12378 | return self;; |
| 12379 | } |
| 12380 | } else { |
| 12381 | at::Tensor tmp_output; |
| 12382 | { |
| 12383 | at::AutoDispatchSkipFunctionalize guard; |
| 12384 | tmp_output = at::_ops::tril::call(self_, diagonal); |
| 12385 | } |
| 12386 | at::functionalization::impl::replace_(self, tmp_output); |
| 12387 | at::functionalization::impl::commit_update(self); |
| 12388 | at::functionalization::impl::sync(self); |
| 12389 | return self; |
| 12390 | } |
| 12391 | } |
| 12392 | |
| 12393 | at::Tensor & triu_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t diagonal, at::Tensor & out) { |
| 12394 | if (false) { |
| 12395 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12396 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12397 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12398 | auto self_meta = to_meta(self); |
| 12399 | auto out_meta = to_meta(out); |
| 12400 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12401 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12402 | at::_ops::triu_out::call(self_meta, diagonal, out_meta); |
| 12403 | } |
| 12404 | |
| 12405 | at::Tensor self_; |
| 12406 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12407 | at::functionalization::impl::sync(self); |
| 12408 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12409 | } else { |
| 12410 | self_ = self; |
| 12411 | } |
| 12412 | |
| 12413 | at::Tensor out_; |
| 12414 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12415 | at::functionalization::impl::sync(out); |
| 12416 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12417 | } else { |
| 12418 | out_ = out; |
| 12419 | } |
| 12420 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12421 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 12422 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12423 | TORCH_INTERNAL_ASSERT(false, |
| 12424 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12425 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12426 | } else { |
| 12427 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12428 | at::AutoDispatchSkipFunctionalize guard; |
| 12429 | at::Tensor tmp_output = at::_ops::triu_out::call(self_, diagonal, out_); |
| 12430 | return out;; |
| 12431 | } |
| 12432 | } else { |
| 12433 | at::Tensor tmp_output; |
| 12434 | { |
| 12435 | at::AutoDispatchSkipFunctionalize guard; |
| 12436 | tmp_output = at::_ops::triu::call(self_, diagonal); |
| 12437 | } |
| 12438 | at::functionalization::impl::replace_(out, tmp_output); |
| 12439 | at::functionalization::impl::commit_update(out); |
| 12440 | at::functionalization::impl::sync(out); |
| 12441 | return out; |
| 12442 | } |
| 12443 | } |
| 12444 | |
| 12445 | at::Tensor & triu_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t diagonal) { |
| 12446 | if (true) { |
| 12447 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12448 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12449 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12450 | auto self_meta = to_meta(self); |
| 12451 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12452 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12453 | at::_ops::triu_::call(self_meta, diagonal); |
| 12454 | } |
| 12455 | |
| 12456 | at::Tensor self_; |
| 12457 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12458 | at::functionalization::impl::sync(self); |
| 12459 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12460 | } else { |
| 12461 | self_ = self; |
| 12462 | } |
| 12463 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 12464 | if ((false)) { |
| 12465 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12466 | TORCH_INTERNAL_ASSERT(false, |
| 12467 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12468 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12469 | } else { |
| 12470 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12471 | at::AutoDispatchSkipFunctionalize guard; |
| 12472 | at::Tensor tmp_output = at::_ops::triu_::call(self_, diagonal); |
| 12473 | return self;; |
| 12474 | } |
| 12475 | } else { |
| 12476 | at::Tensor tmp_output; |
| 12477 | { |
| 12478 | at::AutoDispatchSkipFunctionalize guard; |
| 12479 | tmp_output = at::_ops::triu::call(self_, diagonal); |
| 12480 | } |
| 12481 | at::functionalization::impl::replace_(self, tmp_output); |
| 12482 | at::functionalization::impl::commit_update(self); |
| 12483 | at::functionalization::impl::sync(self); |
| 12484 | return self; |
| 12485 | } |
| 12486 | } |
| 12487 | |
| 12488 | at::Tensor & digamma_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 12489 | if (false) { |
| 12490 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12491 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12492 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12493 | auto self_meta = to_meta(self); |
| 12494 | auto out_meta = to_meta(out); |
| 12495 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12496 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12497 | at::_ops::digamma_out::call(self_meta, out_meta); |
| 12498 | } |
| 12499 | |
| 12500 | at::Tensor self_; |
| 12501 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12502 | at::functionalization::impl::sync(self); |
| 12503 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12504 | } else { |
| 12505 | self_ = self; |
| 12506 | } |
| 12507 | |
| 12508 | at::Tensor out_; |
| 12509 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12510 | at::functionalization::impl::sync(out); |
| 12511 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12512 | } else { |
| 12513 | out_ = out; |
| 12514 | } |
| 12515 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12516 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 12517 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12518 | TORCH_INTERNAL_ASSERT(false, |
| 12519 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12520 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12521 | } else { |
| 12522 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12523 | at::AutoDispatchSkipFunctionalize guard; |
| 12524 | at::Tensor tmp_output = at::_ops::digamma_out::call(self_, out_); |
| 12525 | return out;; |
| 12526 | } |
| 12527 | } else { |
| 12528 | at::Tensor tmp_output; |
| 12529 | { |
| 12530 | at::AutoDispatchSkipFunctionalize guard; |
| 12531 | tmp_output = at::_ops::digamma::call(self_); |
| 12532 | } |
| 12533 | at::functionalization::impl::replace_(out, tmp_output); |
| 12534 | at::functionalization::impl::commit_update(out); |
| 12535 | at::functionalization::impl::sync(out); |
| 12536 | return out; |
| 12537 | } |
| 12538 | } |
| 12539 | |
| 12540 | at::Tensor & digamma_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 12541 | if (true) { |
| 12542 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12543 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12544 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12545 | auto self_meta = to_meta(self); |
| 12546 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12547 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12548 | at::_ops::digamma_::call(self_meta); |
| 12549 | } |
| 12550 | |
| 12551 | at::Tensor self_; |
| 12552 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12553 | at::functionalization::impl::sync(self); |
| 12554 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12555 | } else { |
| 12556 | self_ = self; |
| 12557 | } |
| 12558 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 12559 | if ((false)) { |
| 12560 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12561 | TORCH_INTERNAL_ASSERT(false, |
| 12562 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12563 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12564 | } else { |
| 12565 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12566 | at::AutoDispatchSkipFunctionalize guard; |
| 12567 | at::Tensor tmp_output = at::_ops::digamma_::call(self_); |
| 12568 | return self;; |
| 12569 | } |
| 12570 | } else { |
| 12571 | at::Tensor tmp_output; |
| 12572 | { |
| 12573 | at::AutoDispatchSkipFunctionalize guard; |
| 12574 | tmp_output = at::_ops::digamma::call(self_); |
| 12575 | } |
| 12576 | at::functionalization::impl::replace_(self, tmp_output); |
| 12577 | at::functionalization::impl::commit_update(self); |
| 12578 | at::functionalization::impl::sync(self); |
| 12579 | return self; |
| 12580 | } |
| 12581 | } |
| 12582 | |
| 12583 | at::Tensor & lerp_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & end, const at::Scalar & weight, at::Tensor & out) { |
| 12584 | if (false) { |
| 12585 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12586 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12587 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12588 | auto self_meta = to_meta(self); |
| 12589 | auto end_meta = to_meta(end); |
| 12590 | auto out_meta = to_meta(out); |
| 12591 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12592 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12593 | at::_ops::lerp_Scalar_out::call(self_meta, end_meta, weight, out_meta); |
| 12594 | } |
| 12595 | |
| 12596 | at::Tensor self_; |
| 12597 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12598 | at::functionalization::impl::sync(self); |
| 12599 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12600 | } else { |
| 12601 | self_ = self; |
| 12602 | } |
| 12603 | |
| 12604 | at::Tensor end_; |
| 12605 | if (at::functionalization::impl::isFunctionalTensor(end)) { |
| 12606 | at::functionalization::impl::sync(end); |
| 12607 | end_ = at::functionalization::impl::from_functional_tensor(end); |
| 12608 | } else { |
| 12609 | end_ = end; |
| 12610 | } |
| 12611 | |
| 12612 | at::Tensor out_; |
| 12613 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12614 | at::functionalization::impl::sync(out); |
| 12615 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12616 | } else { |
| 12617 | out_ = out; |
| 12618 | } |
| 12619 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12620 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(end))) { |
| 12621 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12622 | TORCH_INTERNAL_ASSERT(false, |
| 12623 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12624 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12625 | } else { |
| 12626 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12627 | at::AutoDispatchSkipFunctionalize guard; |
| 12628 | at::Tensor tmp_output = at::_ops::lerp_Scalar_out::call(self_, end_, weight, out_); |
| 12629 | return out;; |
| 12630 | } |
| 12631 | } else { |
| 12632 | at::Tensor tmp_output; |
| 12633 | { |
| 12634 | at::AutoDispatchSkipFunctionalize guard; |
| 12635 | tmp_output = at::_ops::lerp_Scalar::call(self_, end_, weight); |
| 12636 | } |
| 12637 | at::functionalization::impl::replace_(out, tmp_output); |
| 12638 | at::functionalization::impl::commit_update(out); |
| 12639 | at::functionalization::impl::sync(out); |
| 12640 | return out; |
| 12641 | } |
| 12642 | } |
| 12643 | |
| 12644 | at::Tensor & lerp__Scalar(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & end, const at::Scalar & weight) { |
| 12645 | if (true) { |
| 12646 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12647 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12648 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12649 | auto self_meta = to_meta(self); |
| 12650 | auto end_meta = to_meta(end); |
| 12651 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12652 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12653 | at::_ops::lerp__Scalar::call(self_meta, end_meta, weight); |
| 12654 | } |
| 12655 | |
| 12656 | at::Tensor self_; |
| 12657 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12658 | at::functionalization::impl::sync(self); |
| 12659 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12660 | } else { |
| 12661 | self_ = self; |
| 12662 | } |
| 12663 | |
| 12664 | at::Tensor end_; |
| 12665 | if (at::functionalization::impl::isFunctionalTensor(end)) { |
| 12666 | at::functionalization::impl::sync(end); |
| 12667 | end_ = at::functionalization::impl::from_functional_tensor(end); |
| 12668 | } else { |
| 12669 | end_ = end; |
| 12670 | } |
| 12671 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 12672 | if ((false || at::functionalization::impl::isFunctionalTensor(end))) { |
| 12673 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12674 | TORCH_INTERNAL_ASSERT(false, |
| 12675 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12676 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12677 | } else { |
| 12678 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12679 | at::AutoDispatchSkipFunctionalize guard; |
| 12680 | at::Tensor tmp_output = at::_ops::lerp__Scalar::call(self_, end_, weight); |
| 12681 | return self;; |
| 12682 | } |
| 12683 | } else { |
| 12684 | at::Tensor tmp_output; |
| 12685 | { |
| 12686 | at::AutoDispatchSkipFunctionalize guard; |
| 12687 | tmp_output = at::_ops::lerp_Scalar::call(self_, end_, weight); |
| 12688 | } |
| 12689 | at::functionalization::impl::replace_(self, tmp_output); |
| 12690 | at::functionalization::impl::commit_update(self); |
| 12691 | at::functionalization::impl::sync(self); |
| 12692 | return self; |
| 12693 | } |
| 12694 | } |
| 12695 | |
| 12696 | at::Tensor & lerp_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & end, const at::Tensor & weight, at::Tensor & out) { |
| 12697 | if (false) { |
| 12698 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12699 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12700 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12701 | auto self_meta = to_meta(self); |
| 12702 | auto end_meta = to_meta(end); |
| 12703 | auto weight_meta = to_meta(weight); |
| 12704 | auto out_meta = to_meta(out); |
| 12705 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12706 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12707 | at::_ops::lerp_Tensor_out::call(self_meta, end_meta, weight_meta, out_meta); |
| 12708 | } |
| 12709 | |
| 12710 | at::Tensor self_; |
| 12711 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12712 | at::functionalization::impl::sync(self); |
| 12713 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12714 | } else { |
| 12715 | self_ = self; |
| 12716 | } |
| 12717 | |
| 12718 | at::Tensor end_; |
| 12719 | if (at::functionalization::impl::isFunctionalTensor(end)) { |
| 12720 | at::functionalization::impl::sync(end); |
| 12721 | end_ = at::functionalization::impl::from_functional_tensor(end); |
| 12722 | } else { |
| 12723 | end_ = end; |
| 12724 | } |
| 12725 | |
| 12726 | at::Tensor weight_; |
| 12727 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 12728 | at::functionalization::impl::sync(weight); |
| 12729 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 12730 | } else { |
| 12731 | weight_ = weight; |
| 12732 | } |
| 12733 | |
| 12734 | at::Tensor out_; |
| 12735 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12736 | at::functionalization::impl::sync(out); |
| 12737 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12738 | } else { |
| 12739 | out_ = out; |
| 12740 | } |
| 12741 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12742 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(end) || at::functionalization::impl::isFunctionalTensor(weight))) { |
| 12743 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12744 | TORCH_INTERNAL_ASSERT(false, |
| 12745 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12746 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12747 | } else { |
| 12748 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12749 | at::AutoDispatchSkipFunctionalize guard; |
| 12750 | at::Tensor tmp_output = at::_ops::lerp_Tensor_out::call(self_, end_, weight_, out_); |
| 12751 | return out;; |
| 12752 | } |
| 12753 | } else { |
| 12754 | at::Tensor tmp_output; |
| 12755 | { |
| 12756 | at::AutoDispatchSkipFunctionalize guard; |
| 12757 | tmp_output = at::_ops::lerp_Tensor::call(self_, end_, weight_); |
| 12758 | } |
| 12759 | at::functionalization::impl::replace_(out, tmp_output); |
| 12760 | at::functionalization::impl::commit_update(out); |
| 12761 | at::functionalization::impl::sync(out); |
| 12762 | return out; |
| 12763 | } |
| 12764 | } |
| 12765 | |
| 12766 | at::Tensor & lerp__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & end, const at::Tensor & weight) { |
| 12767 | if (true) { |
| 12768 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12769 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12770 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12771 | auto self_meta = to_meta(self); |
| 12772 | auto end_meta = to_meta(end); |
| 12773 | auto weight_meta = to_meta(weight); |
| 12774 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12775 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12776 | at::_ops::lerp__Tensor::call(self_meta, end_meta, weight_meta); |
| 12777 | } |
| 12778 | |
| 12779 | at::Tensor self_; |
| 12780 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12781 | at::functionalization::impl::sync(self); |
| 12782 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12783 | } else { |
| 12784 | self_ = self; |
| 12785 | } |
| 12786 | |
| 12787 | at::Tensor end_; |
| 12788 | if (at::functionalization::impl::isFunctionalTensor(end)) { |
| 12789 | at::functionalization::impl::sync(end); |
| 12790 | end_ = at::functionalization::impl::from_functional_tensor(end); |
| 12791 | } else { |
| 12792 | end_ = end; |
| 12793 | } |
| 12794 | |
| 12795 | at::Tensor weight_; |
| 12796 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 12797 | at::functionalization::impl::sync(weight); |
| 12798 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 12799 | } else { |
| 12800 | weight_ = weight; |
| 12801 | } |
| 12802 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 12803 | if ((false || at::functionalization::impl::isFunctionalTensor(end) || at::functionalization::impl::isFunctionalTensor(weight))) { |
| 12804 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12805 | TORCH_INTERNAL_ASSERT(false, |
| 12806 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12807 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12808 | } else { |
| 12809 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12810 | at::AutoDispatchSkipFunctionalize guard; |
| 12811 | at::Tensor tmp_output = at::_ops::lerp__Tensor::call(self_, end_, weight_); |
| 12812 | return self;; |
| 12813 | } |
| 12814 | } else { |
| 12815 | at::Tensor tmp_output; |
| 12816 | { |
| 12817 | at::AutoDispatchSkipFunctionalize guard; |
| 12818 | tmp_output = at::_ops::lerp_Tensor::call(self_, end_, weight_); |
| 12819 | } |
| 12820 | at::functionalization::impl::replace_(self, tmp_output); |
| 12821 | at::functionalization::impl::commit_update(self); |
| 12822 | at::functionalization::impl::sync(self); |
| 12823 | return self; |
| 12824 | } |
| 12825 | } |
| 12826 | |
| 12827 | at::Tensor & uniform_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double from, double to, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 12828 | if (false) { |
| 12829 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12830 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12831 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12832 | auto self_meta = to_meta(self); |
| 12833 | auto out_meta = to_meta(out); |
| 12834 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12835 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12836 | at::_ops::uniform_out::call(self_meta, from, to, generator, out_meta); |
| 12837 | } |
| 12838 | |
| 12839 | at::Tensor self_; |
| 12840 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12841 | at::functionalization::impl::sync(self); |
| 12842 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12843 | } else { |
| 12844 | self_ = self; |
| 12845 | } |
| 12846 | |
| 12847 | at::Tensor out_; |
| 12848 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12849 | at::functionalization::impl::sync(out); |
| 12850 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12851 | } else { |
| 12852 | out_ = out; |
| 12853 | } |
| 12854 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12855 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 12856 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12857 | TORCH_INTERNAL_ASSERT(false, |
| 12858 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12859 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12860 | } else { |
| 12861 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12862 | at::AutoDispatchSkipFunctionalize guard; |
| 12863 | at::Tensor tmp_output = at::_ops::uniform_out::call(self_, from, to, generator, out_); |
| 12864 | return out;; |
| 12865 | } |
| 12866 | } else { |
| 12867 | at::Tensor tmp_output; |
| 12868 | { |
| 12869 | at::AutoDispatchSkipFunctionalize guard; |
| 12870 | tmp_output = at::_ops::uniform::call(self_, from, to, generator); |
| 12871 | } |
| 12872 | at::functionalization::impl::replace_(out, tmp_output); |
| 12873 | at::functionalization::impl::commit_update(out); |
| 12874 | at::functionalization::impl::sync(out); |
| 12875 | return out; |
| 12876 | } |
| 12877 | } |
| 12878 | |
| 12879 | at::Tensor & uniform_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, double from, double to, c10::optional<at::Generator> generator) { |
| 12880 | if (true) { |
| 12881 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12882 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12883 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12884 | auto self_meta = to_meta(self); |
| 12885 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12886 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12887 | at::_ops::uniform_::call(self_meta, from, to, generator); |
| 12888 | } |
| 12889 | |
| 12890 | at::Tensor self_; |
| 12891 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12892 | at::functionalization::impl::sync(self); |
| 12893 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12894 | } else { |
| 12895 | self_ = self; |
| 12896 | } |
| 12897 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 12898 | if ((false)) { |
| 12899 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12900 | TORCH_INTERNAL_ASSERT(false, |
| 12901 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12902 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12903 | } else { |
| 12904 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12905 | at::AutoDispatchSkipFunctionalize guard; |
| 12906 | at::Tensor tmp_output = at::_ops::uniform_::call(self_, from, to, generator); |
| 12907 | return self;; |
| 12908 | } |
| 12909 | } else { |
| 12910 | at::Tensor tmp_output; |
| 12911 | { |
| 12912 | at::AutoDispatchSkipFunctionalize guard; |
| 12913 | tmp_output = at::_ops::uniform::call(self_, from, to, generator); |
| 12914 | } |
| 12915 | at::functionalization::impl::replace_(self, tmp_output); |
| 12916 | at::functionalization::impl::commit_update(self); |
| 12917 | at::functionalization::impl::sync(self); |
| 12918 | return self; |
| 12919 | } |
| 12920 | } |
| 12921 | |
| 12922 | at::Tensor & tril_indices_out_out(c10::DispatchKeySet dispatchKeySet, int64_t row, int64_t col, int64_t offset, at::Tensor & out) { |
| 12923 | if (false) { |
| 12924 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12925 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12926 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12927 | auto out_meta = to_meta(out); |
| 12928 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12929 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12930 | at::_ops::tril_indices_out::call(row, col, offset, out_meta); |
| 12931 | } |
| 12932 | |
| 12933 | at::Tensor out_; |
| 12934 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12935 | at::functionalization::impl::sync(out); |
| 12936 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12937 | } else { |
| 12938 | out_ = out; |
| 12939 | } |
| 12940 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12941 | if ((false)) { |
| 12942 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12943 | TORCH_INTERNAL_ASSERT(false, |
| 12944 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12945 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12946 | } else { |
| 12947 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12948 | at::AutoDispatchSkipFunctionalize guard; |
| 12949 | at::Tensor tmp_output = at::_ops::tril_indices_out::call(row, col, offset, out_); |
| 12950 | return out;; |
| 12951 | } |
| 12952 | } else { |
| 12953 | at::Tensor tmp_output; |
| 12954 | { |
| 12955 | at::AutoDispatchSkipFunctionalize guard; |
| 12956 | tmp_output = at::_ops::tril_indices::call(row, col, offset, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 12957 | } |
| 12958 | at::functionalization::impl::replace_(out, tmp_output); |
| 12959 | at::functionalization::impl::commit_update(out); |
| 12960 | at::functionalization::impl::sync(out); |
| 12961 | return out; |
| 12962 | } |
| 12963 | } |
| 12964 | |
| 12965 | at::Tensor & triu_indices_out_out(c10::DispatchKeySet dispatchKeySet, int64_t row, int64_t col, int64_t offset, at::Tensor & out) { |
| 12966 | if (false) { |
| 12967 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12968 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12969 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12970 | auto out_meta = to_meta(out); |
| 12971 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12972 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12973 | at::_ops::triu_indices_out::call(row, col, offset, out_meta); |
| 12974 | } |
| 12975 | |
| 12976 | at::Tensor out_; |
| 12977 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12978 | at::functionalization::impl::sync(out); |
| 12979 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12980 | } else { |
| 12981 | out_ = out; |
| 12982 | } |
| 12983 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12984 | if ((false)) { |
| 12985 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12986 | TORCH_INTERNAL_ASSERT(false, |
| 12987 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12988 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12989 | } else { |
| 12990 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12991 | at::AutoDispatchSkipFunctionalize guard; |
| 12992 | at::Tensor tmp_output = at::_ops::triu_indices_out::call(row, col, offset, out_); |
| 12993 | return out;; |
| 12994 | } |
| 12995 | } else { |
| 12996 | at::Tensor tmp_output; |
| 12997 | { |
| 12998 | at::AutoDispatchSkipFunctionalize guard; |
| 12999 | tmp_output = at::_ops::triu_indices::call(row, col, offset, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 13000 | } |
| 13001 | at::functionalization::impl::replace_(out, tmp_output); |
| 13002 | at::functionalization::impl::commit_update(out); |
| 13003 | at::functionalization::impl::sync(out); |
| 13004 | return out; |
| 13005 | } |
| 13006 | } |
| 13007 | |
| 13008 | at::Tensor & ge_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 13009 | if (false) { |
| 13010 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13011 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13012 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13013 | auto self_meta = to_meta(self); |
| 13014 | auto out_meta = to_meta(out); |
| 13015 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13016 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13017 | at::_ops::ge_Scalar_out::call(self_meta, other, out_meta); |
| 13018 | } |
| 13019 | |
| 13020 | at::Tensor self_; |
| 13021 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13022 | at::functionalization::impl::sync(self); |
| 13023 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13024 | } else { |
| 13025 | self_ = self; |
| 13026 | } |
| 13027 | |
| 13028 | at::Tensor out_; |
| 13029 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13030 | at::functionalization::impl::sync(out); |
| 13031 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13032 | } else { |
| 13033 | out_ = out; |
| 13034 | } |
| 13035 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13036 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 13037 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13038 | TORCH_INTERNAL_ASSERT(false, |
| 13039 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13040 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13041 | } else { |
| 13042 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13043 | at::AutoDispatchSkipFunctionalize guard; |
| 13044 | at::Tensor tmp_output = at::_ops::ge_Scalar_out::call(self_, other, out_); |
| 13045 | return out;; |
| 13046 | } |
| 13047 | } else { |
| 13048 | at::Tensor tmp_output; |
| 13049 | { |
| 13050 | at::AutoDispatchSkipFunctionalize guard; |
| 13051 | tmp_output = at::_ops::ge_Scalar::call(self_, other); |
| 13052 | } |
| 13053 | at::functionalization::impl::replace_(out, tmp_output); |
| 13054 | at::functionalization::impl::commit_update(out); |
| 13055 | at::functionalization::impl::sync(out); |
| 13056 | return out; |
| 13057 | } |
| 13058 | } |
| 13059 | |
| 13060 | at::Tensor & ge__Scalar(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { |
| 13061 | if (true) { |
| 13062 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13063 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13064 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13065 | auto self_meta = to_meta(self); |
| 13066 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13067 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13068 | at::_ops::ge__Scalar::call(self_meta, other); |
| 13069 | } |
| 13070 | |
| 13071 | at::Tensor self_; |
| 13072 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13073 | at::functionalization::impl::sync(self); |
| 13074 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13075 | } else { |
| 13076 | self_ = self; |
| 13077 | } |
| 13078 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 13079 | if ((false)) { |
| 13080 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13081 | TORCH_INTERNAL_ASSERT(false, |
| 13082 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13083 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13084 | } else { |
| 13085 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13086 | at::AutoDispatchSkipFunctionalize guard; |
| 13087 | at::Tensor tmp_output = at::_ops::ge__Scalar::call(self_, other); |
| 13088 | return self;; |
| 13089 | } |
| 13090 | } else { |
| 13091 | at::Tensor tmp_output; |
| 13092 | { |
| 13093 | at::AutoDispatchSkipFunctionalize guard; |
| 13094 | tmp_output = at::_ops::ge_Scalar::call(self_, other); |
| 13095 | } |
| 13096 | at::functionalization::impl::replace_(self, tmp_output); |
| 13097 | at::functionalization::impl::commit_update(self); |
| 13098 | at::functionalization::impl::sync(self); |
| 13099 | return self; |
| 13100 | } |
| 13101 | } |
| 13102 | |
| 13103 | at::Tensor & ge_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 13104 | if (false) { |
| 13105 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13106 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13107 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13108 | auto self_meta = to_meta(self); |
| 13109 | auto other_meta = to_meta(other); |
| 13110 | auto out_meta = to_meta(out); |
| 13111 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13112 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13113 | at::_ops::ge_Tensor_out::call(self_meta, other_meta, out_meta); |
| 13114 | } |
| 13115 | |
| 13116 | at::Tensor self_; |
| 13117 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13118 | at::functionalization::impl::sync(self); |
| 13119 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13120 | } else { |
| 13121 | self_ = self; |
| 13122 | } |
| 13123 | |
| 13124 | at::Tensor other_; |
| 13125 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 13126 | at::functionalization::impl::sync(other); |
| 13127 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 13128 | } else { |
| 13129 | other_ = other; |
| 13130 | } |
| 13131 | |
| 13132 | at::Tensor out_; |
| 13133 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13134 | at::functionalization::impl::sync(out); |
| 13135 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13136 | } else { |
| 13137 | out_ = out; |
| 13138 | } |
| 13139 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13140 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 13141 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13142 | TORCH_INTERNAL_ASSERT(false, |
| 13143 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13144 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13145 | } else { |
| 13146 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13147 | at::AutoDispatchSkipFunctionalize guard; |
| 13148 | at::Tensor tmp_output = at::_ops::ge_Tensor_out::call(self_, other_, out_); |
| 13149 | return out;; |
| 13150 | } |
| 13151 | } else { |
| 13152 | at::Tensor tmp_output; |
| 13153 | { |
| 13154 | at::AutoDispatchSkipFunctionalize guard; |
| 13155 | tmp_output = at::_ops::ge_Tensor::call(self_, other_); |
| 13156 | } |
| 13157 | at::functionalization::impl::replace_(out, tmp_output); |
| 13158 | at::functionalization::impl::commit_update(out); |
| 13159 | at::functionalization::impl::sync(out); |
| 13160 | return out; |
| 13161 | } |
| 13162 | } |
| 13163 | |
| 13164 | at::Tensor & ge__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 13165 | if (true) { |
| 13166 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13167 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13168 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13169 | auto self_meta = to_meta(self); |
| 13170 | auto other_meta = to_meta(other); |
| 13171 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13172 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13173 | at::_ops::ge__Tensor::call(self_meta, other_meta); |
| 13174 | } |
| 13175 | |
| 13176 | at::Tensor self_; |
| 13177 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13178 | at::functionalization::impl::sync(self); |
| 13179 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13180 | } else { |
| 13181 | self_ = self; |
| 13182 | } |
| 13183 | |
| 13184 | at::Tensor other_; |
| 13185 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 13186 | at::functionalization::impl::sync(other); |
| 13187 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 13188 | } else { |
| 13189 | other_ = other; |
| 13190 | } |
| 13191 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 13192 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 13193 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13194 | TORCH_INTERNAL_ASSERT(false, |
| 13195 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13196 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13197 | } else { |
| 13198 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13199 | at::AutoDispatchSkipFunctionalize guard; |
| 13200 | at::Tensor tmp_output = at::_ops::ge__Tensor::call(self_, other_); |
| 13201 | return self;; |
| 13202 | } |
| 13203 | } else { |
| 13204 | at::Tensor tmp_output; |
| 13205 | { |
| 13206 | at::AutoDispatchSkipFunctionalize guard; |
| 13207 | tmp_output = at::_ops::ge_Tensor::call(self_, other_); |
| 13208 | } |
| 13209 | at::functionalization::impl::replace_(self, tmp_output); |
| 13210 | at::functionalization::impl::commit_update(self); |
| 13211 | at::functionalization::impl::sync(self); |
| 13212 | return self; |
| 13213 | } |
| 13214 | } |
| 13215 | |
| 13216 | at::Tensor & le_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 13217 | if (false) { |
| 13218 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13219 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13220 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13221 | auto self_meta = to_meta(self); |
| 13222 | auto out_meta = to_meta(out); |
| 13223 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13224 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13225 | at::_ops::le_Scalar_out::call(self_meta, other, out_meta); |
| 13226 | } |
| 13227 | |
| 13228 | at::Tensor self_; |
| 13229 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13230 | at::functionalization::impl::sync(self); |
| 13231 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13232 | } else { |
| 13233 | self_ = self; |
| 13234 | } |
| 13235 | |
| 13236 | at::Tensor out_; |
| 13237 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13238 | at::functionalization::impl::sync(out); |
| 13239 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13240 | } else { |
| 13241 | out_ = out; |
| 13242 | } |
| 13243 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13244 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 13245 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13246 | TORCH_INTERNAL_ASSERT(false, |
| 13247 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13248 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13249 | } else { |
| 13250 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13251 | at::AutoDispatchSkipFunctionalize guard; |
| 13252 | at::Tensor tmp_output = at::_ops::le_Scalar_out::call(self_, other, out_); |
| 13253 | return out;; |
| 13254 | } |
| 13255 | } else { |
| 13256 | at::Tensor tmp_output; |
| 13257 | { |
| 13258 | at::AutoDispatchSkipFunctionalize guard; |
| 13259 | tmp_output = at::_ops::le_Scalar::call(self_, other); |
| 13260 | } |
| 13261 | at::functionalization::impl::replace_(out, tmp_output); |
| 13262 | at::functionalization::impl::commit_update(out); |
| 13263 | at::functionalization::impl::sync(out); |
| 13264 | return out; |
| 13265 | } |
| 13266 | } |
| 13267 | |
| 13268 | at::Tensor & le__Scalar(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { |
| 13269 | if (true) { |
| 13270 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13271 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13272 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13273 | auto self_meta = to_meta(self); |
| 13274 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13275 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13276 | at::_ops::le__Scalar::call(self_meta, other); |
| 13277 | } |
| 13278 | |
| 13279 | at::Tensor self_; |
| 13280 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13281 | at::functionalization::impl::sync(self); |
| 13282 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13283 | } else { |
| 13284 | self_ = self; |
| 13285 | } |
| 13286 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 13287 | if ((false)) { |
| 13288 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13289 | TORCH_INTERNAL_ASSERT(false, |
| 13290 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13291 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13292 | } else { |
| 13293 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13294 | at::AutoDispatchSkipFunctionalize guard; |
| 13295 | at::Tensor tmp_output = at::_ops::le__Scalar::call(self_, other); |
| 13296 | return self;; |
| 13297 | } |
| 13298 | } else { |
| 13299 | at::Tensor tmp_output; |
| 13300 | { |
| 13301 | at::AutoDispatchSkipFunctionalize guard; |
| 13302 | tmp_output = at::_ops::le_Scalar::call(self_, other); |
| 13303 | } |
| 13304 | at::functionalization::impl::replace_(self, tmp_output); |
| 13305 | at::functionalization::impl::commit_update(self); |
| 13306 | at::functionalization::impl::sync(self); |
| 13307 | return self; |
| 13308 | } |
| 13309 | } |
| 13310 | |
| 13311 | at::Tensor & le_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 13312 | if (false) { |
| 13313 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13314 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13315 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13316 | auto self_meta = to_meta(self); |
| 13317 | auto other_meta = to_meta(other); |
| 13318 | auto out_meta = to_meta(out); |
| 13319 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13320 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13321 | at::_ops::le_Tensor_out::call(self_meta, other_meta, out_meta); |
| 13322 | } |
| 13323 | |
| 13324 | at::Tensor self_; |
| 13325 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13326 | at::functionalization::impl::sync(self); |
| 13327 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13328 | } else { |
| 13329 | self_ = self; |
| 13330 | } |
| 13331 | |
| 13332 | at::Tensor other_; |
| 13333 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 13334 | at::functionalization::impl::sync(other); |
| 13335 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 13336 | } else { |
| 13337 | other_ = other; |
| 13338 | } |
| 13339 | |
| 13340 | at::Tensor out_; |
| 13341 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13342 | at::functionalization::impl::sync(out); |
| 13343 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13344 | } else { |
| 13345 | out_ = out; |
| 13346 | } |
| 13347 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13348 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 13349 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13350 | TORCH_INTERNAL_ASSERT(false, |
| 13351 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13352 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13353 | } else { |
| 13354 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13355 | at::AutoDispatchSkipFunctionalize guard; |
| 13356 | at::Tensor tmp_output = at::_ops::le_Tensor_out::call(self_, other_, out_); |
| 13357 | return out;; |
| 13358 | } |
| 13359 | } else { |
| 13360 | at::Tensor tmp_output; |
| 13361 | { |
| 13362 | at::AutoDispatchSkipFunctionalize guard; |
| 13363 | tmp_output = at::_ops::le_Tensor::call(self_, other_); |
| 13364 | } |
| 13365 | at::functionalization::impl::replace_(out, tmp_output); |
| 13366 | at::functionalization::impl::commit_update(out); |
| 13367 | at::functionalization::impl::sync(out); |
| 13368 | return out; |
| 13369 | } |
| 13370 | } |
| 13371 | |
| 13372 | at::Tensor & le__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 13373 | if (true) { |
| 13374 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13375 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13376 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13377 | auto self_meta = to_meta(self); |
| 13378 | auto other_meta = to_meta(other); |
| 13379 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13380 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13381 | at::_ops::le__Tensor::call(self_meta, other_meta); |
| 13382 | } |
| 13383 | |
| 13384 | at::Tensor self_; |
| 13385 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13386 | at::functionalization::impl::sync(self); |
| 13387 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13388 | } else { |
| 13389 | self_ = self; |
| 13390 | } |
| 13391 | |
| 13392 | at::Tensor other_; |
| 13393 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 13394 | at::functionalization::impl::sync(other); |
| 13395 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 13396 | } else { |
| 13397 | other_ = other; |
| 13398 | } |
| 13399 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 13400 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 13401 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13402 | TORCH_INTERNAL_ASSERT(false, |
| 13403 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13404 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13405 | } else { |
| 13406 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13407 | at::AutoDispatchSkipFunctionalize guard; |
| 13408 | at::Tensor tmp_output = at::_ops::le__Tensor::call(self_, other_); |
| 13409 | return self;; |
| 13410 | } |
| 13411 | } else { |
| 13412 | at::Tensor tmp_output; |
| 13413 | { |
| 13414 | at::AutoDispatchSkipFunctionalize guard; |
| 13415 | tmp_output = at::_ops::le_Tensor::call(self_, other_); |
| 13416 | } |
| 13417 | at::functionalization::impl::replace_(self, tmp_output); |
| 13418 | at::functionalization::impl::commit_update(self); |
| 13419 | at::functionalization::impl::sync(self); |
| 13420 | return self; |
| 13421 | } |
| 13422 | } |
| 13423 | |
| 13424 | at::Tensor & gt_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 13425 | if (false) { |
| 13426 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13427 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13428 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13429 | auto self_meta = to_meta(self); |
| 13430 | auto out_meta = to_meta(out); |
| 13431 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13432 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13433 | at::_ops::gt_Scalar_out::call(self_meta, other, out_meta); |
| 13434 | } |
| 13435 | |
| 13436 | at::Tensor self_; |
| 13437 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13438 | at::functionalization::impl::sync(self); |
| 13439 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13440 | } else { |
| 13441 | self_ = self; |
| 13442 | } |
| 13443 | |
| 13444 | at::Tensor out_; |
| 13445 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13446 | at::functionalization::impl::sync(out); |
| 13447 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13448 | } else { |
| 13449 | out_ = out; |
| 13450 | } |
| 13451 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13452 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 13453 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13454 | TORCH_INTERNAL_ASSERT(false, |
| 13455 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13456 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13457 | } else { |
| 13458 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13459 | at::AutoDispatchSkipFunctionalize guard; |
| 13460 | at::Tensor tmp_output = at::_ops::gt_Scalar_out::call(self_, other, out_); |
| 13461 | return out;; |
| 13462 | } |
| 13463 | } else { |
| 13464 | at::Tensor tmp_output; |
| 13465 | { |
| 13466 | at::AutoDispatchSkipFunctionalize guard; |
| 13467 | tmp_output = at::_ops::gt_Scalar::call(self_, other); |
| 13468 | } |
| 13469 | at::functionalization::impl::replace_(out, tmp_output); |
| 13470 | at::functionalization::impl::commit_update(out); |
| 13471 | at::functionalization::impl::sync(out); |
| 13472 | return out; |
| 13473 | } |
| 13474 | } |
| 13475 | |
| 13476 | at::Tensor & gt__Scalar(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { |
| 13477 | if (true) { |
| 13478 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13479 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13480 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13481 | auto self_meta = to_meta(self); |
| 13482 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13483 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13484 | at::_ops::gt__Scalar::call(self_meta, other); |
| 13485 | } |
| 13486 | |
| 13487 | at::Tensor self_; |
| 13488 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13489 | at::functionalization::impl::sync(self); |
| 13490 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13491 | } else { |
| 13492 | self_ = self; |
| 13493 | } |
| 13494 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 13495 | if ((false)) { |
| 13496 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13497 | TORCH_INTERNAL_ASSERT(false, |
| 13498 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13499 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13500 | } else { |
| 13501 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13502 | at::AutoDispatchSkipFunctionalize guard; |
| 13503 | at::Tensor tmp_output = at::_ops::gt__Scalar::call(self_, other); |
| 13504 | return self;; |
| 13505 | } |
| 13506 | } else { |
| 13507 | at::Tensor tmp_output; |
| 13508 | { |
| 13509 | at::AutoDispatchSkipFunctionalize guard; |
| 13510 | tmp_output = at::_ops::gt_Scalar::call(self_, other); |
| 13511 | } |
| 13512 | at::functionalization::impl::replace_(self, tmp_output); |
| 13513 | at::functionalization::impl::commit_update(self); |
| 13514 | at::functionalization::impl::sync(self); |
| 13515 | return self; |
| 13516 | } |
| 13517 | } |
| 13518 | |
| 13519 | at::Tensor & gt_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 13520 | if (false) { |
| 13521 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13522 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13523 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13524 | auto self_meta = to_meta(self); |
| 13525 | auto other_meta = to_meta(other); |
| 13526 | auto out_meta = to_meta(out); |
| 13527 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13528 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13529 | at::_ops::gt_Tensor_out::call(self_meta, other_meta, out_meta); |
| 13530 | } |
| 13531 | |
| 13532 | at::Tensor self_; |
| 13533 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13534 | at::functionalization::impl::sync(self); |
| 13535 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13536 | } else { |
| 13537 | self_ = self; |
| 13538 | } |
| 13539 | |
| 13540 | at::Tensor other_; |
| 13541 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 13542 | at::functionalization::impl::sync(other); |
| 13543 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 13544 | } else { |
| 13545 | other_ = other; |
| 13546 | } |
| 13547 | |
| 13548 | at::Tensor out_; |
| 13549 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13550 | at::functionalization::impl::sync(out); |
| 13551 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13552 | } else { |
| 13553 | out_ = out; |
| 13554 | } |
| 13555 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13556 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 13557 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13558 | TORCH_INTERNAL_ASSERT(false, |
| 13559 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13560 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13561 | } else { |
| 13562 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13563 | at::AutoDispatchSkipFunctionalize guard; |
| 13564 | at::Tensor tmp_output = at::_ops::gt_Tensor_out::call(self_, other_, out_); |
| 13565 | return out;; |
| 13566 | } |
| 13567 | } else { |
| 13568 | at::Tensor tmp_output; |
| 13569 | { |
| 13570 | at::AutoDispatchSkipFunctionalize guard; |
| 13571 | tmp_output = at::_ops::gt_Tensor::call(self_, other_); |
| 13572 | } |
| 13573 | at::functionalization::impl::replace_(out, tmp_output); |
| 13574 | at::functionalization::impl::commit_update(out); |
| 13575 | at::functionalization::impl::sync(out); |
| 13576 | return out; |
| 13577 | } |
| 13578 | } |
| 13579 | |
| 13580 | at::Tensor & gt__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 13581 | if (true) { |
| 13582 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13583 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13584 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13585 | auto self_meta = to_meta(self); |
| 13586 | auto other_meta = to_meta(other); |
| 13587 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13588 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13589 | at::_ops::gt__Tensor::call(self_meta, other_meta); |
| 13590 | } |
| 13591 | |
| 13592 | at::Tensor self_; |
| 13593 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13594 | at::functionalization::impl::sync(self); |
| 13595 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13596 | } else { |
| 13597 | self_ = self; |
| 13598 | } |
| 13599 | |
| 13600 | at::Tensor other_; |
| 13601 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 13602 | at::functionalization::impl::sync(other); |
| 13603 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 13604 | } else { |
| 13605 | other_ = other; |
| 13606 | } |
| 13607 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 13608 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 13609 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13610 | TORCH_INTERNAL_ASSERT(false, |
| 13611 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13612 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13613 | } else { |
| 13614 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13615 | at::AutoDispatchSkipFunctionalize guard; |
| 13616 | at::Tensor tmp_output = at::_ops::gt__Tensor::call(self_, other_); |
| 13617 | return self;; |
| 13618 | } |
| 13619 | } else { |
| 13620 | at::Tensor tmp_output; |
| 13621 | { |
| 13622 | at::AutoDispatchSkipFunctionalize guard; |
| 13623 | tmp_output = at::_ops::gt_Tensor::call(self_, other_); |
| 13624 | } |
| 13625 | at::functionalization::impl::replace_(self, tmp_output); |
| 13626 | at::functionalization::impl::commit_update(self); |
| 13627 | at::functionalization::impl::sync(self); |
| 13628 | return self; |
| 13629 | } |
| 13630 | } |
| 13631 | |
| 13632 | at::Tensor & nonzero_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 13633 | if (false) { |
| 13634 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13635 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13636 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13637 | auto self_meta = to_meta(self); |
| 13638 | auto out_meta = to_meta(out); |
| 13639 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13640 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13641 | at::_ops::nonzero_out::call(self_meta, out_meta); |
| 13642 | } |
| 13643 | |
| 13644 | at::Tensor self_; |
| 13645 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13646 | at::functionalization::impl::sync(self); |
| 13647 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13648 | } else { |
| 13649 | self_ = self; |
| 13650 | } |
| 13651 | |
| 13652 | at::Tensor out_; |
| 13653 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13654 | at::functionalization::impl::sync(out); |
| 13655 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13656 | } else { |
| 13657 | out_ = out; |
| 13658 | } |
| 13659 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13660 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 13661 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13662 | TORCH_INTERNAL_ASSERT(false, |
| 13663 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13664 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13665 | } else { |
| 13666 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13667 | at::AutoDispatchSkipFunctionalize guard; |
| 13668 | at::Tensor tmp_output = at::_ops::nonzero_out::call(self_, out_); |
| 13669 | return out;; |
| 13670 | } |
| 13671 | } else { |
| 13672 | at::Tensor tmp_output; |
| 13673 | { |
| 13674 | at::AutoDispatchSkipFunctionalize guard; |
| 13675 | tmp_output = at::_ops::nonzero::call(self_); |
| 13676 | } |
| 13677 | at::functionalization::impl::replace_(out, tmp_output); |
| 13678 | at::functionalization::impl::commit_update(out); |
| 13679 | at::functionalization::impl::sync(out); |
| 13680 | return out; |
| 13681 | } |
| 13682 | } |
| 13683 | |
| 13684 | at::Tensor & addcdiv_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value, at::Tensor & out) { |
| 13685 | if (false) { |
| 13686 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13687 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13688 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13689 | auto self_meta = to_meta(self); |
| 13690 | auto tensor1_meta = to_meta(tensor1); |
| 13691 | auto tensor2_meta = to_meta(tensor2); |
| 13692 | auto out_meta = to_meta(out); |
| 13693 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13694 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13695 | at::_ops::addcdiv_out::call(self_meta, tensor1_meta, tensor2_meta, value, out_meta); |
| 13696 | } |
| 13697 | |
| 13698 | at::Tensor self_; |
| 13699 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13700 | at::functionalization::impl::sync(self); |
| 13701 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13702 | } else { |
| 13703 | self_ = self; |
| 13704 | } |
| 13705 | |
| 13706 | at::Tensor tensor1_; |
| 13707 | if (at::functionalization::impl::isFunctionalTensor(tensor1)) { |
| 13708 | at::functionalization::impl::sync(tensor1); |
| 13709 | tensor1_ = at::functionalization::impl::from_functional_tensor(tensor1); |
| 13710 | } else { |
| 13711 | tensor1_ = tensor1; |
| 13712 | } |
| 13713 | |
| 13714 | at::Tensor tensor2_; |
| 13715 | if (at::functionalization::impl::isFunctionalTensor(tensor2)) { |
| 13716 | at::functionalization::impl::sync(tensor2); |
| 13717 | tensor2_ = at::functionalization::impl::from_functional_tensor(tensor2); |
| 13718 | } else { |
| 13719 | tensor2_ = tensor2; |
| 13720 | } |
| 13721 | |
| 13722 | at::Tensor out_; |
| 13723 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13724 | at::functionalization::impl::sync(out); |
| 13725 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13726 | } else { |
| 13727 | out_ = out; |
| 13728 | } |
| 13729 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13730 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(tensor1) || at::functionalization::impl::isFunctionalTensor(tensor2))) { |
| 13731 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13732 | TORCH_INTERNAL_ASSERT(false, |
| 13733 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13734 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13735 | } else { |
| 13736 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13737 | at::AutoDispatchSkipFunctionalize guard; |
| 13738 | at::Tensor tmp_output = at::_ops::addcdiv_out::call(self_, tensor1_, tensor2_, value, out_); |
| 13739 | return out;; |
| 13740 | } |
| 13741 | } else { |
| 13742 | at::Tensor tmp_output; |
| 13743 | { |
| 13744 | at::AutoDispatchSkipFunctionalize guard; |
| 13745 | tmp_output = at::_ops::addcdiv::call(self_, tensor1_, tensor2_, value); |
| 13746 | } |
| 13747 | at::functionalization::impl::replace_(out, tmp_output); |
| 13748 | at::functionalization::impl::commit_update(out); |
| 13749 | at::functionalization::impl::sync(out); |
| 13750 | return out; |
| 13751 | } |
| 13752 | } |
| 13753 | |
| 13754 | at::Tensor & addcdiv_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value) { |
| 13755 | if (true) { |
| 13756 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13757 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13758 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13759 | auto self_meta = to_meta(self); |
| 13760 | auto tensor1_meta = to_meta(tensor1); |
| 13761 | auto tensor2_meta = to_meta(tensor2); |
| 13762 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13763 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13764 | at::_ops::addcdiv_::call(self_meta, tensor1_meta, tensor2_meta, value); |
| 13765 | } |
| 13766 | |
| 13767 | at::Tensor self_; |
| 13768 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13769 | at::functionalization::impl::sync(self); |
| 13770 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13771 | } else { |
| 13772 | self_ = self; |
| 13773 | } |
| 13774 | |
| 13775 | at::Tensor tensor1_; |
| 13776 | if (at::functionalization::impl::isFunctionalTensor(tensor1)) { |
| 13777 | at::functionalization::impl::sync(tensor1); |
| 13778 | tensor1_ = at::functionalization::impl::from_functional_tensor(tensor1); |
| 13779 | } else { |
| 13780 | tensor1_ = tensor1; |
| 13781 | } |
| 13782 | |
| 13783 | at::Tensor tensor2_; |
| 13784 | if (at::functionalization::impl::isFunctionalTensor(tensor2)) { |
| 13785 | at::functionalization::impl::sync(tensor2); |
| 13786 | tensor2_ = at::functionalization::impl::from_functional_tensor(tensor2); |
| 13787 | } else { |
| 13788 | tensor2_ = tensor2; |
| 13789 | } |
| 13790 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 13791 | if ((false || at::functionalization::impl::isFunctionalTensor(tensor1) || at::functionalization::impl::isFunctionalTensor(tensor2))) { |
| 13792 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13793 | TORCH_INTERNAL_ASSERT(false, |
| 13794 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13795 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13796 | } else { |
| 13797 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13798 | at::AutoDispatchSkipFunctionalize guard; |
| 13799 | at::Tensor tmp_output = at::_ops::addcdiv_::call(self_, tensor1_, tensor2_, value); |
| 13800 | return self;; |
| 13801 | } |
| 13802 | } else { |
| 13803 | at::Tensor tmp_output; |
| 13804 | { |
| 13805 | at::AutoDispatchSkipFunctionalize guard; |
| 13806 | tmp_output = at::_ops::addcdiv::call(self_, tensor1_, tensor2_, value); |
| 13807 | } |
| 13808 | at::functionalization::impl::replace_(self, tmp_output); |
| 13809 | at::functionalization::impl::commit_update(self); |
| 13810 | at::functionalization::impl::sync(self); |
| 13811 | return self; |
| 13812 | } |
| 13813 | } |
| 13814 | |
| 13815 | ::std::tuple<at::Tensor &,at::Tensor &> triangular_solve_out_X(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular, at::Tensor & X, at::Tensor & M) { |
| 13816 | if (false) { |
| 13817 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13818 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13819 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13820 | auto self_meta = to_meta(self); |
| 13821 | auto A_meta = to_meta(A); |
| 13822 | auto X_meta = to_meta(X); |
| 13823 | auto M_meta = to_meta(M); |
| 13824 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13825 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13826 | at::_ops::triangular_solve_X::call(self_meta, A_meta, upper, transpose, unitriangular, X_meta, M_meta); |
| 13827 | } |
| 13828 | |
| 13829 | at::Tensor self_; |
| 13830 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13831 | at::functionalization::impl::sync(self); |
| 13832 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13833 | } else { |
| 13834 | self_ = self; |
| 13835 | } |
| 13836 | |
| 13837 | at::Tensor A_; |
| 13838 | if (at::functionalization::impl::isFunctionalTensor(A)) { |
| 13839 | at::functionalization::impl::sync(A); |
| 13840 | A_ = at::functionalization::impl::from_functional_tensor(A); |
| 13841 | } else { |
| 13842 | A_ = A; |
| 13843 | } |
| 13844 | |
| 13845 | at::Tensor X_; |
| 13846 | if (at::functionalization::impl::isFunctionalTensor(X)) { |
| 13847 | at::functionalization::impl::sync(X); |
| 13848 | X_ = at::functionalization::impl::from_functional_tensor(X); |
| 13849 | } else { |
| 13850 | X_ = X; |
| 13851 | } |
| 13852 | |
| 13853 | at::Tensor M_; |
| 13854 | if (at::functionalization::impl::isFunctionalTensor(M)) { |
| 13855 | at::functionalization::impl::sync(M); |
| 13856 | M_ = at::functionalization::impl::from_functional_tensor(M); |
| 13857 | } else { |
| 13858 | M_ = M; |
| 13859 | } |
| 13860 | if (!(true && at::functionalization::impl::isFunctionalTensor(X) && at::functionalization::impl::isFunctionalTensor(M))) { |
| 13861 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(A))) { |
| 13862 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13863 | TORCH_INTERNAL_ASSERT(false, |
| 13864 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13865 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13866 | } else { |
| 13867 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13868 | at::AutoDispatchSkipFunctionalize guard; |
| 13869 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::triangular_solve_X::call(self_, A_, upper, transpose, unitriangular, X_, M_); |
| 13870 | return ::std::tuple<at::Tensor &,at::Tensor &>(X, M);; |
| 13871 | } |
| 13872 | } else { |
| 13873 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 13874 | { |
| 13875 | at::AutoDispatchSkipFunctionalize guard; |
| 13876 | tmp_output = at::_ops::triangular_solve::call(self_, A_, upper, transpose, unitriangular); |
| 13877 | } |
| 13878 | at::functionalization::impl::replace_(X, std::get<0>(tmp_output)); |
| 13879 | at::functionalization::impl::commit_update(X); |
| 13880 | at::functionalization::impl::sync(X); |
| 13881 | at::functionalization::impl::replace_(M, std::get<1>(tmp_output)); |
| 13882 | at::functionalization::impl::commit_update(M); |
| 13883 | at::functionalization::impl::sync(M); |
| 13884 | return ::std::tuple<at::Tensor &,at::Tensor &>(X, M); |
| 13885 | } |
| 13886 | } |
| 13887 | |
| 13888 | at::Tensor & cholesky_solve_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & input2, bool upper, at::Tensor & out) { |
| 13889 | if (false) { |
| 13890 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13891 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13892 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13893 | auto self_meta = to_meta(self); |
| 13894 | auto input2_meta = to_meta(input2); |
| 13895 | auto out_meta = to_meta(out); |
| 13896 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13897 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13898 | at::_ops::cholesky_solve_out::call(self_meta, input2_meta, upper, out_meta); |
| 13899 | } |
| 13900 | |
| 13901 | at::Tensor self_; |
| 13902 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13903 | at::functionalization::impl::sync(self); |
| 13904 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13905 | } else { |
| 13906 | self_ = self; |
| 13907 | } |
| 13908 | |
| 13909 | at::Tensor input2_; |
| 13910 | if (at::functionalization::impl::isFunctionalTensor(input2)) { |
| 13911 | at::functionalization::impl::sync(input2); |
| 13912 | input2_ = at::functionalization::impl::from_functional_tensor(input2); |
| 13913 | } else { |
| 13914 | input2_ = input2; |
| 13915 | } |
| 13916 | |
| 13917 | at::Tensor out_; |
| 13918 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13919 | at::functionalization::impl::sync(out); |
| 13920 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13921 | } else { |
| 13922 | out_ = out; |
| 13923 | } |
| 13924 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13925 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(input2))) { |
| 13926 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13927 | TORCH_INTERNAL_ASSERT(false, |
| 13928 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13929 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13930 | } else { |
| 13931 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13932 | at::AutoDispatchSkipFunctionalize guard; |
| 13933 | at::Tensor tmp_output = at::_ops::cholesky_solve_out::call(self_, input2_, upper, out_); |
| 13934 | return out;; |
| 13935 | } |
| 13936 | } else { |
| 13937 | at::Tensor tmp_output; |
| 13938 | { |
| 13939 | at::AutoDispatchSkipFunctionalize guard; |
| 13940 | tmp_output = at::_ops::cholesky_solve::call(self_, input2_, upper); |
| 13941 | } |
| 13942 | at::functionalization::impl::replace_(out, tmp_output); |
| 13943 | at::functionalization::impl::commit_update(out); |
| 13944 | at::functionalization::impl::sync(out); |
| 13945 | return out; |
| 13946 | } |
| 13947 | } |
| 13948 | |
| 13949 | at::Tensor & cholesky_inverse_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool upper, at::Tensor & out) { |
| 13950 | if (false) { |
| 13951 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13952 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13953 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13954 | auto self_meta = to_meta(self); |
| 13955 | auto out_meta = to_meta(out); |
| 13956 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13957 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13958 | at::_ops::cholesky_inverse_out::call(self_meta, upper, out_meta); |
| 13959 | } |
| 13960 | |
| 13961 | at::Tensor self_; |
| 13962 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13963 | at::functionalization::impl::sync(self); |
| 13964 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13965 | } else { |
| 13966 | self_ = self; |
| 13967 | } |
| 13968 | |
| 13969 | at::Tensor out_; |
| 13970 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13971 | at::functionalization::impl::sync(out); |
| 13972 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13973 | } else { |
| 13974 | out_ = out; |
| 13975 | } |
| 13976 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13977 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 13978 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13979 | TORCH_INTERNAL_ASSERT(false, |
| 13980 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13981 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13982 | } else { |
| 13983 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13984 | at::AutoDispatchSkipFunctionalize guard; |
| 13985 | at::Tensor tmp_output = at::_ops::cholesky_inverse_out::call(self_, upper, out_); |
| 13986 | return out;; |
| 13987 | } |
| 13988 | } else { |
| 13989 | at::Tensor tmp_output; |
| 13990 | { |
| 13991 | at::AutoDispatchSkipFunctionalize guard; |
| 13992 | tmp_output = at::_ops::cholesky_inverse::call(self_, upper); |
| 13993 | } |
| 13994 | at::functionalization::impl::replace_(out, tmp_output); |
| 13995 | at::functionalization::impl::commit_update(out); |
| 13996 | at::functionalization::impl::sync(out); |
| 13997 | return out; |
| 13998 | } |
| 13999 | } |
| 14000 | |
| 14001 | ::std::tuple<at::Tensor &,at::Tensor &> qr_out_Q(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool some, at::Tensor & Q, at::Tensor & R) { |
| 14002 | if (false) { |
| 14003 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14004 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14005 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14006 | auto self_meta = to_meta(self); |
| 14007 | auto Q_meta = to_meta(Q); |
| 14008 | auto R_meta = to_meta(R); |
| 14009 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14010 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14011 | at::_ops::qr_Q::call(self_meta, some, Q_meta, R_meta); |
| 14012 | } |
| 14013 | |
| 14014 | at::Tensor self_; |
| 14015 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14016 | at::functionalization::impl::sync(self); |
| 14017 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14018 | } else { |
| 14019 | self_ = self; |
| 14020 | } |
| 14021 | |
| 14022 | at::Tensor Q_; |
| 14023 | if (at::functionalization::impl::isFunctionalTensor(Q)) { |
| 14024 | at::functionalization::impl::sync(Q); |
| 14025 | Q_ = at::functionalization::impl::from_functional_tensor(Q); |
| 14026 | } else { |
| 14027 | Q_ = Q; |
| 14028 | } |
| 14029 | |
| 14030 | at::Tensor R_; |
| 14031 | if (at::functionalization::impl::isFunctionalTensor(R)) { |
| 14032 | at::functionalization::impl::sync(R); |
| 14033 | R_ = at::functionalization::impl::from_functional_tensor(R); |
| 14034 | } else { |
| 14035 | R_ = R; |
| 14036 | } |
| 14037 | if (!(true && at::functionalization::impl::isFunctionalTensor(Q) && at::functionalization::impl::isFunctionalTensor(R))) { |
| 14038 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 14039 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14040 | TORCH_INTERNAL_ASSERT(false, |
| 14041 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14042 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14043 | } else { |
| 14044 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14045 | at::AutoDispatchSkipFunctionalize guard; |
| 14046 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::qr_Q::call(self_, some, Q_, R_); |
| 14047 | return ::std::tuple<at::Tensor &,at::Tensor &>(Q, R);; |
| 14048 | } |
| 14049 | } else { |
| 14050 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 14051 | { |
| 14052 | at::AutoDispatchSkipFunctionalize guard; |
| 14053 | tmp_output = at::_ops::qr::call(self_, some); |
| 14054 | } |
| 14055 | at::functionalization::impl::replace_(Q, std::get<0>(tmp_output)); |
| 14056 | at::functionalization::impl::commit_update(Q); |
| 14057 | at::functionalization::impl::sync(Q); |
| 14058 | at::functionalization::impl::replace_(R, std::get<1>(tmp_output)); |
| 14059 | at::functionalization::impl::commit_update(R); |
| 14060 | at::functionalization::impl::sync(R); |
| 14061 | return ::std::tuple<at::Tensor &,at::Tensor &>(Q, R); |
| 14062 | } |
| 14063 | } |
| 14064 | |
| 14065 | ::std::tuple<at::Tensor &,at::Tensor &> geqrf_out_a(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & a, at::Tensor & tau) { |
| 14066 | if (false) { |
| 14067 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14068 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14069 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14070 | auto self_meta = to_meta(self); |
| 14071 | auto a_meta = to_meta(a); |
| 14072 | auto tau_meta = to_meta(tau); |
| 14073 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14074 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14075 | at::_ops::geqrf_a::call(self_meta, a_meta, tau_meta); |
| 14076 | } |
| 14077 | |
| 14078 | at::Tensor self_; |
| 14079 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14080 | at::functionalization::impl::sync(self); |
| 14081 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14082 | } else { |
| 14083 | self_ = self; |
| 14084 | } |
| 14085 | |
| 14086 | at::Tensor a_; |
| 14087 | if (at::functionalization::impl::isFunctionalTensor(a)) { |
| 14088 | at::functionalization::impl::sync(a); |
| 14089 | a_ = at::functionalization::impl::from_functional_tensor(a); |
| 14090 | } else { |
| 14091 | a_ = a; |
| 14092 | } |
| 14093 | |
| 14094 | at::Tensor tau_; |
| 14095 | if (at::functionalization::impl::isFunctionalTensor(tau)) { |
| 14096 | at::functionalization::impl::sync(tau); |
| 14097 | tau_ = at::functionalization::impl::from_functional_tensor(tau); |
| 14098 | } else { |
| 14099 | tau_ = tau; |
| 14100 | } |
| 14101 | if (!(true && at::functionalization::impl::isFunctionalTensor(a) && at::functionalization::impl::isFunctionalTensor(tau))) { |
| 14102 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 14103 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14104 | TORCH_INTERNAL_ASSERT(false, |
| 14105 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14106 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14107 | } else { |
| 14108 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14109 | at::AutoDispatchSkipFunctionalize guard; |
| 14110 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::geqrf_a::call(self_, a_, tau_); |
| 14111 | return ::std::tuple<at::Tensor &,at::Tensor &>(a, tau);; |
| 14112 | } |
| 14113 | } else { |
| 14114 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 14115 | { |
| 14116 | at::AutoDispatchSkipFunctionalize guard; |
| 14117 | tmp_output = at::_ops::geqrf::call(self_); |
| 14118 | } |
| 14119 | at::functionalization::impl::replace_(a, std::get<0>(tmp_output)); |
| 14120 | at::functionalization::impl::commit_update(a); |
| 14121 | at::functionalization::impl::sync(a); |
| 14122 | at::functionalization::impl::replace_(tau, std::get<1>(tmp_output)); |
| 14123 | at::functionalization::impl::commit_update(tau); |
| 14124 | at::functionalization::impl::sync(tau); |
| 14125 | return ::std::tuple<at::Tensor &,at::Tensor &>(a, tau); |
| 14126 | } |
| 14127 | } |
| 14128 | |
| 14129 | at::Tensor & orgqr_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & input2, at::Tensor & out) { |
| 14130 | if (false) { |
| 14131 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14132 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14133 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14134 | auto self_meta = to_meta(self); |
| 14135 | auto input2_meta = to_meta(input2); |
| 14136 | auto out_meta = to_meta(out); |
| 14137 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14138 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14139 | at::_ops::orgqr_out::call(self_meta, input2_meta, out_meta); |
| 14140 | } |
| 14141 | |
| 14142 | at::Tensor self_; |
| 14143 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14144 | at::functionalization::impl::sync(self); |
| 14145 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14146 | } else { |
| 14147 | self_ = self; |
| 14148 | } |
| 14149 | |
| 14150 | at::Tensor input2_; |
| 14151 | if (at::functionalization::impl::isFunctionalTensor(input2)) { |
| 14152 | at::functionalization::impl::sync(input2); |
| 14153 | input2_ = at::functionalization::impl::from_functional_tensor(input2); |
| 14154 | } else { |
| 14155 | input2_ = input2; |
| 14156 | } |
| 14157 | |
| 14158 | at::Tensor out_; |
| 14159 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14160 | at::functionalization::impl::sync(out); |
| 14161 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14162 | } else { |
| 14163 | out_ = out; |
| 14164 | } |
| 14165 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14166 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(input2))) { |
| 14167 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14168 | TORCH_INTERNAL_ASSERT(false, |
| 14169 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14170 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14171 | } else { |
| 14172 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14173 | at::AutoDispatchSkipFunctionalize guard; |
| 14174 | at::Tensor tmp_output = at::_ops::orgqr_out::call(self_, input2_, out_); |
| 14175 | return out;; |
| 14176 | } |
| 14177 | } else { |
| 14178 | at::Tensor tmp_output; |
| 14179 | { |
| 14180 | at::AutoDispatchSkipFunctionalize guard; |
| 14181 | tmp_output = at::_ops::orgqr::call(self_, input2_); |
| 14182 | } |
| 14183 | at::functionalization::impl::replace_(out, tmp_output); |
| 14184 | at::functionalization::impl::commit_update(out); |
| 14185 | at::functionalization::impl::sync(out); |
| 14186 | return out; |
| 14187 | } |
| 14188 | } |
| 14189 | |
| 14190 | at::Tensor & lu_solve_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & LU_data, const at::Tensor & LU_pivots, at::Tensor & out) { |
| 14191 | if (false) { |
| 14192 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14193 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14194 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14195 | auto self_meta = to_meta(self); |
| 14196 | auto LU_data_meta = to_meta(LU_data); |
| 14197 | auto LU_pivots_meta = to_meta(LU_pivots); |
| 14198 | auto out_meta = to_meta(out); |
| 14199 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14200 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14201 | at::_ops::lu_solve_out::call(self_meta, LU_data_meta, LU_pivots_meta, out_meta); |
| 14202 | } |
| 14203 | |
| 14204 | at::Tensor self_; |
| 14205 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14206 | at::functionalization::impl::sync(self); |
| 14207 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14208 | } else { |
| 14209 | self_ = self; |
| 14210 | } |
| 14211 | |
| 14212 | at::Tensor LU_data_; |
| 14213 | if (at::functionalization::impl::isFunctionalTensor(LU_data)) { |
| 14214 | at::functionalization::impl::sync(LU_data); |
| 14215 | LU_data_ = at::functionalization::impl::from_functional_tensor(LU_data); |
| 14216 | } else { |
| 14217 | LU_data_ = LU_data; |
| 14218 | } |
| 14219 | |
| 14220 | at::Tensor LU_pivots_; |
| 14221 | if (at::functionalization::impl::isFunctionalTensor(LU_pivots)) { |
| 14222 | at::functionalization::impl::sync(LU_pivots); |
| 14223 | LU_pivots_ = at::functionalization::impl::from_functional_tensor(LU_pivots); |
| 14224 | } else { |
| 14225 | LU_pivots_ = LU_pivots; |
| 14226 | } |
| 14227 | |
| 14228 | at::Tensor out_; |
| 14229 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14230 | at::functionalization::impl::sync(out); |
| 14231 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14232 | } else { |
| 14233 | out_ = out; |
| 14234 | } |
| 14235 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14236 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(LU_data) || at::functionalization::impl::isFunctionalTensor(LU_pivots))) { |
| 14237 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14238 | TORCH_INTERNAL_ASSERT(false, |
| 14239 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14240 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14241 | } else { |
| 14242 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14243 | at::AutoDispatchSkipFunctionalize guard; |
| 14244 | at::Tensor tmp_output = at::_ops::lu_solve_out::call(self_, LU_data_, LU_pivots_, out_); |
| 14245 | return out;; |
| 14246 | } |
| 14247 | } else { |
| 14248 | at::Tensor tmp_output; |
| 14249 | { |
| 14250 | at::AutoDispatchSkipFunctionalize guard; |
| 14251 | tmp_output = at::_ops::lu_solve::call(self_, LU_data_, LU_pivots_); |
| 14252 | } |
| 14253 | at::functionalization::impl::replace_(out, tmp_output); |
| 14254 | at::functionalization::impl::commit_update(out); |
| 14255 | at::functionalization::impl::sync(out); |
| 14256 | return out; |
| 14257 | } |
| 14258 | } |
| 14259 | |
| 14260 | at::Tensor & lgamma_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 14261 | if (false) { |
| 14262 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14263 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14264 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14265 | auto self_meta = to_meta(self); |
| 14266 | auto out_meta = to_meta(out); |
| 14267 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14268 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14269 | at::_ops::lgamma_out::call(self_meta, out_meta); |
| 14270 | } |
| 14271 | |
| 14272 | at::Tensor self_; |
| 14273 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14274 | at::functionalization::impl::sync(self); |
| 14275 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14276 | } else { |
| 14277 | self_ = self; |
| 14278 | } |
| 14279 | |
| 14280 | at::Tensor out_; |
| 14281 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14282 | at::functionalization::impl::sync(out); |
| 14283 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14284 | } else { |
| 14285 | out_ = out; |
| 14286 | } |
| 14287 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14288 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 14289 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14290 | TORCH_INTERNAL_ASSERT(false, |
| 14291 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14292 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14293 | } else { |
| 14294 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14295 | at::AutoDispatchSkipFunctionalize guard; |
| 14296 | at::Tensor tmp_output = at::_ops::lgamma_out::call(self_, out_); |
| 14297 | return out;; |
| 14298 | } |
| 14299 | } else { |
| 14300 | at::Tensor tmp_output; |
| 14301 | { |
| 14302 | at::AutoDispatchSkipFunctionalize guard; |
| 14303 | tmp_output = at::_ops::lgamma::call(self_); |
| 14304 | } |
| 14305 | at::functionalization::impl::replace_(out, tmp_output); |
| 14306 | at::functionalization::impl::commit_update(out); |
| 14307 | at::functionalization::impl::sync(out); |
| 14308 | return out; |
| 14309 | } |
| 14310 | } |
| 14311 | |
| 14312 | at::Tensor & lgamma_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 14313 | if (true) { |
| 14314 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14315 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14316 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14317 | auto self_meta = to_meta(self); |
| 14318 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14319 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14320 | at::_ops::lgamma_::call(self_meta); |
| 14321 | } |
| 14322 | |
| 14323 | at::Tensor self_; |
| 14324 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14325 | at::functionalization::impl::sync(self); |
| 14326 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14327 | } else { |
| 14328 | self_ = self; |
| 14329 | } |
| 14330 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 14331 | if ((false)) { |
| 14332 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14333 | TORCH_INTERNAL_ASSERT(false, |
| 14334 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14335 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14336 | } else { |
| 14337 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14338 | at::AutoDispatchSkipFunctionalize guard; |
| 14339 | at::Tensor tmp_output = at::_ops::lgamma_::call(self_); |
| 14340 | return self;; |
| 14341 | } |
| 14342 | } else { |
| 14343 | at::Tensor tmp_output; |
| 14344 | { |
| 14345 | at::AutoDispatchSkipFunctionalize guard; |
| 14346 | tmp_output = at::_ops::lgamma::call(self_); |
| 14347 | } |
| 14348 | at::functionalization::impl::replace_(self, tmp_output); |
| 14349 | at::functionalization::impl::commit_update(self); |
| 14350 | at::functionalization::impl::sync(self); |
| 14351 | return self; |
| 14352 | } |
| 14353 | } |
| 14354 | |
| 14355 | at::Tensor & erfinv_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 14356 | if (false) { |
| 14357 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14358 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14359 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14360 | auto self_meta = to_meta(self); |
| 14361 | auto out_meta = to_meta(out); |
| 14362 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14363 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14364 | at::_ops::erfinv_out::call(self_meta, out_meta); |
| 14365 | } |
| 14366 | |
| 14367 | at::Tensor self_; |
| 14368 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14369 | at::functionalization::impl::sync(self); |
| 14370 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14371 | } else { |
| 14372 | self_ = self; |
| 14373 | } |
| 14374 | |
| 14375 | at::Tensor out_; |
| 14376 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14377 | at::functionalization::impl::sync(out); |
| 14378 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14379 | } else { |
| 14380 | out_ = out; |
| 14381 | } |
| 14382 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14383 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 14384 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14385 | TORCH_INTERNAL_ASSERT(false, |
| 14386 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14387 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14388 | } else { |
| 14389 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14390 | at::AutoDispatchSkipFunctionalize guard; |
| 14391 | at::Tensor tmp_output = at::_ops::erfinv_out::call(self_, out_); |
| 14392 | return out;; |
| 14393 | } |
| 14394 | } else { |
| 14395 | at::Tensor tmp_output; |
| 14396 | { |
| 14397 | at::AutoDispatchSkipFunctionalize guard; |
| 14398 | tmp_output = at::_ops::erfinv::call(self_); |
| 14399 | } |
| 14400 | at::functionalization::impl::replace_(out, tmp_output); |
| 14401 | at::functionalization::impl::commit_update(out); |
| 14402 | at::functionalization::impl::sync(out); |
| 14403 | return out; |
| 14404 | } |
| 14405 | } |
| 14406 | |
| 14407 | at::Tensor & erfinv_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 14408 | if (true) { |
| 14409 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14410 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14411 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14412 | auto self_meta = to_meta(self); |
| 14413 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14414 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14415 | at::_ops::erfinv_::call(self_meta); |
| 14416 | } |
| 14417 | |
| 14418 | at::Tensor self_; |
| 14419 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14420 | at::functionalization::impl::sync(self); |
| 14421 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14422 | } else { |
| 14423 | self_ = self; |
| 14424 | } |
| 14425 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 14426 | if ((false)) { |
| 14427 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14428 | TORCH_INTERNAL_ASSERT(false, |
| 14429 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14430 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14431 | } else { |
| 14432 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14433 | at::AutoDispatchSkipFunctionalize guard; |
| 14434 | at::Tensor tmp_output = at::_ops::erfinv_::call(self_); |
| 14435 | return self;; |
| 14436 | } |
| 14437 | } else { |
| 14438 | at::Tensor tmp_output; |
| 14439 | { |
| 14440 | at::AutoDispatchSkipFunctionalize guard; |
| 14441 | tmp_output = at::_ops::erfinv::call(self_); |
| 14442 | } |
| 14443 | at::functionalization::impl::replace_(self, tmp_output); |
| 14444 | at::functionalization::impl::commit_update(self); |
| 14445 | at::functionalization::impl::sync(self); |
| 14446 | return self; |
| 14447 | } |
| 14448 | } |
| 14449 | |
| 14450 | at::Tensor & i0_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 14451 | if (false) { |
| 14452 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14453 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14454 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14455 | auto self_meta = to_meta(self); |
| 14456 | auto out_meta = to_meta(out); |
| 14457 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14458 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14459 | at::_ops::i0_out::call(self_meta, out_meta); |
| 14460 | } |
| 14461 | |
| 14462 | at::Tensor self_; |
| 14463 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14464 | at::functionalization::impl::sync(self); |
| 14465 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14466 | } else { |
| 14467 | self_ = self; |
| 14468 | } |
| 14469 | |
| 14470 | at::Tensor out_; |
| 14471 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14472 | at::functionalization::impl::sync(out); |
| 14473 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14474 | } else { |
| 14475 | out_ = out; |
| 14476 | } |
| 14477 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14478 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 14479 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14480 | TORCH_INTERNAL_ASSERT(false, |
| 14481 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14482 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14483 | } else { |
| 14484 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14485 | at::AutoDispatchSkipFunctionalize guard; |
| 14486 | at::Tensor tmp_output = at::_ops::i0_out::call(self_, out_); |
| 14487 | return out;; |
| 14488 | } |
| 14489 | } else { |
| 14490 | at::Tensor tmp_output; |
| 14491 | { |
| 14492 | at::AutoDispatchSkipFunctionalize guard; |
| 14493 | tmp_output = at::_ops::i0::call(self_); |
| 14494 | } |
| 14495 | at::functionalization::impl::replace_(out, tmp_output); |
| 14496 | at::functionalization::impl::commit_update(out); |
| 14497 | at::functionalization::impl::sync(out); |
| 14498 | return out; |
| 14499 | } |
| 14500 | } |
| 14501 | |
| 14502 | at::Tensor & i0_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 14503 | if (true) { |
| 14504 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14505 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14506 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14507 | auto self_meta = to_meta(self); |
| 14508 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14509 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14510 | at::_ops::i0_::call(self_meta); |
| 14511 | } |
| 14512 | |
| 14513 | at::Tensor self_; |
| 14514 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14515 | at::functionalization::impl::sync(self); |
| 14516 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14517 | } else { |
| 14518 | self_ = self; |
| 14519 | } |
| 14520 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 14521 | if ((false)) { |
| 14522 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14523 | TORCH_INTERNAL_ASSERT(false, |
| 14524 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14525 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14526 | } else { |
| 14527 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14528 | at::AutoDispatchSkipFunctionalize guard; |
| 14529 | at::Tensor tmp_output = at::_ops::i0_::call(self_); |
| 14530 | return self;; |
| 14531 | } |
| 14532 | } else { |
| 14533 | at::Tensor tmp_output; |
| 14534 | { |
| 14535 | at::AutoDispatchSkipFunctionalize guard; |
| 14536 | tmp_output = at::_ops::i0::call(self_); |
| 14537 | } |
| 14538 | at::functionalization::impl::replace_(self, tmp_output); |
| 14539 | at::functionalization::impl::commit_update(self); |
| 14540 | at::functionalization::impl::sync(self); |
| 14541 | return self; |
| 14542 | } |
| 14543 | } |
| 14544 | |
| 14545 | at::Tensor & sign_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 14546 | if (false) { |
| 14547 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14548 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14549 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14550 | auto self_meta = to_meta(self); |
| 14551 | auto out_meta = to_meta(out); |
| 14552 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14553 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14554 | at::_ops::sign_out::call(self_meta, out_meta); |
| 14555 | } |
| 14556 | |
| 14557 | at::Tensor self_; |
| 14558 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14559 | at::functionalization::impl::sync(self); |
| 14560 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14561 | } else { |
| 14562 | self_ = self; |
| 14563 | } |
| 14564 | |
| 14565 | at::Tensor out_; |
| 14566 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14567 | at::functionalization::impl::sync(out); |
| 14568 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14569 | } else { |
| 14570 | out_ = out; |
| 14571 | } |
| 14572 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14573 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 14574 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14575 | TORCH_INTERNAL_ASSERT(false, |
| 14576 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14577 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14578 | } else { |
| 14579 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14580 | at::AutoDispatchSkipFunctionalize guard; |
| 14581 | at::Tensor tmp_output = at::_ops::sign_out::call(self_, out_); |
| 14582 | return out;; |
| 14583 | } |
| 14584 | } else { |
| 14585 | at::Tensor tmp_output; |
| 14586 | { |
| 14587 | at::AutoDispatchSkipFunctionalize guard; |
| 14588 | tmp_output = at::_ops::sign::call(self_); |
| 14589 | } |
| 14590 | at::functionalization::impl::replace_(out, tmp_output); |
| 14591 | at::functionalization::impl::commit_update(out); |
| 14592 | at::functionalization::impl::sync(out); |
| 14593 | return out; |
| 14594 | } |
| 14595 | } |
| 14596 | |
| 14597 | at::Tensor & sign_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 14598 | if (true) { |
| 14599 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14600 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14601 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14602 | auto self_meta = to_meta(self); |
| 14603 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14604 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14605 | at::_ops::sign_::call(self_meta); |
| 14606 | } |
| 14607 | |
| 14608 | at::Tensor self_; |
| 14609 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14610 | at::functionalization::impl::sync(self); |
| 14611 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14612 | } else { |
| 14613 | self_ = self; |
| 14614 | } |
| 14615 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 14616 | if ((false)) { |
| 14617 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14618 | TORCH_INTERNAL_ASSERT(false, |
| 14619 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14620 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14621 | } else { |
| 14622 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14623 | at::AutoDispatchSkipFunctionalize guard; |
| 14624 | at::Tensor tmp_output = at::_ops::sign_::call(self_); |
| 14625 | return self;; |
| 14626 | } |
| 14627 | } else { |
| 14628 | at::Tensor tmp_output; |
| 14629 | { |
| 14630 | at::AutoDispatchSkipFunctionalize guard; |
| 14631 | tmp_output = at::_ops::sign::call(self_); |
| 14632 | } |
| 14633 | at::functionalization::impl::replace_(self, tmp_output); |
| 14634 | at::functionalization::impl::commit_update(self); |
| 14635 | at::functionalization::impl::sync(self); |
| 14636 | return self; |
| 14637 | } |
| 14638 | } |
| 14639 | |
| 14640 | at::Tensor & signbit_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 14641 | if (false) { |
| 14642 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14643 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14644 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14645 | auto self_meta = to_meta(self); |
| 14646 | auto out_meta = to_meta(out); |
| 14647 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14648 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14649 | at::_ops::signbit_out::call(self_meta, out_meta); |
| 14650 | } |
| 14651 | |
| 14652 | at::Tensor self_; |
| 14653 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14654 | at::functionalization::impl::sync(self); |
| 14655 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14656 | } else { |
| 14657 | self_ = self; |
| 14658 | } |
| 14659 | |
| 14660 | at::Tensor out_; |
| 14661 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14662 | at::functionalization::impl::sync(out); |
| 14663 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14664 | } else { |
| 14665 | out_ = out; |
| 14666 | } |
| 14667 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14668 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 14669 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14670 | TORCH_INTERNAL_ASSERT(false, |
| 14671 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14672 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14673 | } else { |
| 14674 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14675 | at::AutoDispatchSkipFunctionalize guard; |
| 14676 | at::Tensor tmp_output = at::_ops::signbit_out::call(self_, out_); |
| 14677 | return out;; |
| 14678 | } |
| 14679 | } else { |
| 14680 | at::Tensor tmp_output; |
| 14681 | { |
| 14682 | at::AutoDispatchSkipFunctionalize guard; |
| 14683 | tmp_output = at::_ops::signbit::call(self_); |
| 14684 | } |
| 14685 | at::functionalization::impl::replace_(out, tmp_output); |
| 14686 | at::functionalization::impl::commit_update(out); |
| 14687 | at::functionalization::impl::sync(out); |
| 14688 | return out; |
| 14689 | } |
| 14690 | } |
| 14691 | |
| 14692 | at::Tensor & atan2_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 14693 | if (false) { |
| 14694 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14695 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14696 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14697 | auto self_meta = to_meta(self); |
| 14698 | auto other_meta = to_meta(other); |
| 14699 | auto out_meta = to_meta(out); |
| 14700 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14701 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14702 | at::_ops::atan2_out::call(self_meta, other_meta, out_meta); |
| 14703 | } |
| 14704 | |
| 14705 | at::Tensor self_; |
| 14706 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14707 | at::functionalization::impl::sync(self); |
| 14708 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14709 | } else { |
| 14710 | self_ = self; |
| 14711 | } |
| 14712 | |
| 14713 | at::Tensor other_; |
| 14714 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 14715 | at::functionalization::impl::sync(other); |
| 14716 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 14717 | } else { |
| 14718 | other_ = other; |
| 14719 | } |
| 14720 | |
| 14721 | at::Tensor out_; |
| 14722 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14723 | at::functionalization::impl::sync(out); |
| 14724 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14725 | } else { |
| 14726 | out_ = out; |
| 14727 | } |
| 14728 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14729 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 14730 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14731 | TORCH_INTERNAL_ASSERT(false, |
| 14732 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14733 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14734 | } else { |
| 14735 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14736 | at::AutoDispatchSkipFunctionalize guard; |
| 14737 | at::Tensor tmp_output = at::_ops::atan2_out::call(self_, other_, out_); |
| 14738 | return out;; |
| 14739 | } |
| 14740 | } else { |
| 14741 | at::Tensor tmp_output; |
| 14742 | { |
| 14743 | at::AutoDispatchSkipFunctionalize guard; |
| 14744 | tmp_output = at::_ops::atan2::call(self_, other_); |
| 14745 | } |
| 14746 | at::functionalization::impl::replace_(out, tmp_output); |
| 14747 | at::functionalization::impl::commit_update(out); |
| 14748 | at::functionalization::impl::sync(out); |
| 14749 | return out; |
| 14750 | } |
| 14751 | } |
| 14752 | |
| 14753 | at::Tensor & atan2_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 14754 | if (true) { |
| 14755 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14756 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14757 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14758 | auto self_meta = to_meta(self); |
| 14759 | auto other_meta = to_meta(other); |
| 14760 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14761 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14762 | at::_ops::atan2_::call(self_meta, other_meta); |
| 14763 | } |
| 14764 | |
| 14765 | at::Tensor self_; |
| 14766 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14767 | at::functionalization::impl::sync(self); |
| 14768 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14769 | } else { |
| 14770 | self_ = self; |
| 14771 | } |
| 14772 | |
| 14773 | at::Tensor other_; |
| 14774 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 14775 | at::functionalization::impl::sync(other); |
| 14776 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 14777 | } else { |
| 14778 | other_ = other; |
| 14779 | } |
| 14780 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 14781 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 14782 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14783 | TORCH_INTERNAL_ASSERT(false, |
| 14784 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14785 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14786 | } else { |
| 14787 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14788 | at::AutoDispatchSkipFunctionalize guard; |
| 14789 | at::Tensor tmp_output = at::_ops::atan2_::call(self_, other_); |
| 14790 | return self;; |
| 14791 | } |
| 14792 | } else { |
| 14793 | at::Tensor tmp_output; |
| 14794 | { |
| 14795 | at::AutoDispatchSkipFunctionalize guard; |
| 14796 | tmp_output = at::_ops::atan2::call(self_, other_); |
| 14797 | } |
| 14798 | at::functionalization::impl::replace_(self, tmp_output); |
| 14799 | at::functionalization::impl::commit_update(self); |
| 14800 | at::functionalization::impl::sync(self); |
| 14801 | return self; |
| 14802 | } |
| 14803 | } |
| 14804 | |
| 14805 | void _histogramdd_bin_edges_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef bins, c10::optional<at::ArrayRef<double>> range, const c10::optional<at::Tensor> & weight, bool density, at::TensorList out) { |
| 14806 | if (false) { |
| 14807 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14808 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14809 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14810 | auto self_meta = to_meta(self); |
| 14811 | auto weight_meta = to_meta(weight); |
| 14812 | auto out_meta = to_meta(out); |
| 14813 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14814 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14815 | at::_ops::_histogramdd_bin_edges_out::call(self_meta, bins, range, weight_meta, density, out_meta); |
| 14816 | } |
| 14817 | |
| 14818 | at::Tensor self_; |
| 14819 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14820 | at::functionalization::impl::sync(self); |
| 14821 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14822 | } else { |
| 14823 | self_ = self; |
| 14824 | } |
| 14825 | |
| 14826 | c10::optional<at::Tensor> weight_; |
| 14827 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 14828 | at::functionalization::impl::sync(weight); |
| 14829 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 14830 | } else { |
| 14831 | weight_ = weight; |
| 14832 | } |
| 14833 | |
| 14834 | ::std::vector<at::Tensor> out_; |
| 14835 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14836 | at::functionalization::impl::sync(out); |
| 14837 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14838 | } else { |
| 14839 | out_ = out.vec(); |
| 14840 | } |
| 14841 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14842 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(weight))) { |
| 14843 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14844 | TORCH_INTERNAL_ASSERT(false, |
| 14845 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14846 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14847 | } else { |
| 14848 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14849 | at::AutoDispatchSkipFunctionalize guard; |
| 14850 | at::_ops::_histogramdd_bin_edges_out::call(self_, bins, range, weight_, density, out_); |
| 14851 | ; |
| 14852 | } |
| 14853 | } else { |
| 14854 | ::std::vector<at::Tensor> tmp_output; |
| 14855 | { |
| 14856 | at::AutoDispatchSkipFunctionalize guard; |
| 14857 | tmp_output = at::_ops::_histogramdd_bin_edges::call(self_, bins, range, weight_, density); |
| 14858 | } |
| 14859 | at::functionalization::impl::replace_(out, tmp_output); |
| 14860 | at::functionalization::impl::commit_update(out); |
| 14861 | at::functionalization::impl::sync(out); |
| 14862 | |
| 14863 | } |
| 14864 | } |
| 14865 | |
| 14866 | at::Tensor & _histogramdd_from_bin_tensors_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::TensorList bins, const c10::optional<at::Tensor> & weight, bool density, at::Tensor & out) { |
| 14867 | if (false) { |
| 14868 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14869 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14870 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14871 | auto self_meta = to_meta(self); |
| 14872 | auto bins_meta = to_meta(bins); |
| 14873 | auto weight_meta = to_meta(weight); |
| 14874 | auto out_meta = to_meta(out); |
| 14875 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14876 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14877 | at::_ops::_histogramdd_from_bin_tensors_out::call(self_meta, bins_meta, weight_meta, density, out_meta); |
| 14878 | } |
| 14879 | |
| 14880 | at::Tensor self_; |
| 14881 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14882 | at::functionalization::impl::sync(self); |
| 14883 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14884 | } else { |
| 14885 | self_ = self; |
| 14886 | } |
| 14887 | |
| 14888 | ::std::vector<at::Tensor> bins_; |
| 14889 | if (at::functionalization::impl::isFunctionalTensor(bins)) { |
| 14890 | at::functionalization::impl::sync(bins); |
| 14891 | bins_ = at::functionalization::impl::from_functional_tensor(bins); |
| 14892 | } else { |
| 14893 | bins_ = bins.vec(); |
| 14894 | } |
| 14895 | |
| 14896 | c10::optional<at::Tensor> weight_; |
| 14897 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 14898 | at::functionalization::impl::sync(weight); |
| 14899 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 14900 | } else { |
| 14901 | weight_ = weight; |
| 14902 | } |
| 14903 | |
| 14904 | at::Tensor out_; |
| 14905 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14906 | at::functionalization::impl::sync(out); |
| 14907 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14908 | } else { |
| 14909 | out_ = out; |
| 14910 | } |
| 14911 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14912 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(bins) || at::functionalization::impl::isFunctionalTensor(weight))) { |
| 14913 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14914 | TORCH_INTERNAL_ASSERT(false, |
| 14915 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14916 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14917 | } else { |
| 14918 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14919 | at::AutoDispatchSkipFunctionalize guard; |
| 14920 | at::Tensor tmp_output = at::_ops::_histogramdd_from_bin_tensors_out::call(self_, bins_, weight_, density, out_); |
| 14921 | return out;; |
| 14922 | } |
| 14923 | } else { |
| 14924 | at::Tensor tmp_output; |
| 14925 | { |
| 14926 | at::AutoDispatchSkipFunctionalize guard; |
| 14927 | tmp_output = at::_ops::_histogramdd_from_bin_tensors::call(self_, bins_, weight_, density); |
| 14928 | } |
| 14929 | at::functionalization::impl::replace_(out, tmp_output); |
| 14930 | at::functionalization::impl::commit_update(out); |
| 14931 | at::functionalization::impl::sync(out); |
| 14932 | return out; |
| 14933 | } |
| 14934 | } |
| 14935 | |
| 14936 | at::Tensor & fmod_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 14937 | if (false) { |
| 14938 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14939 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14940 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14941 | auto self_meta = to_meta(self); |
| 14942 | auto out_meta = to_meta(out); |
| 14943 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14944 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14945 | at::_ops::fmod_Scalar_out::call(self_meta, other, out_meta); |
| 14946 | } |
| 14947 | |
| 14948 | at::Tensor self_; |
| 14949 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14950 | at::functionalization::impl::sync(self); |
| 14951 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14952 | } else { |
| 14953 | self_ = self; |
| 14954 | } |
| 14955 | |
| 14956 | at::Tensor out_; |
| 14957 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14958 | at::functionalization::impl::sync(out); |
| 14959 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14960 | } else { |
| 14961 | out_ = out; |
| 14962 | } |
| 14963 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14964 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 14965 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14966 | TORCH_INTERNAL_ASSERT(false, |
| 14967 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14968 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14969 | } else { |
| 14970 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14971 | at::AutoDispatchSkipFunctionalize guard; |
| 14972 | at::Tensor tmp_output = at::_ops::fmod_Scalar_out::call(self_, other, out_); |
| 14973 | return out;; |
| 14974 | } |
| 14975 | } else { |
| 14976 | at::Tensor tmp_output; |
| 14977 | { |
| 14978 | at::AutoDispatchSkipFunctionalize guard; |
| 14979 | tmp_output = at::_ops::fmod_Scalar::call(self_, other); |
| 14980 | } |
| 14981 | at::functionalization::impl::replace_(out, tmp_output); |
| 14982 | at::functionalization::impl::commit_update(out); |
| 14983 | at::functionalization::impl::sync(out); |
| 14984 | return out; |
| 14985 | } |
| 14986 | } |
| 14987 | |
| 14988 | at::Tensor & fmod__Scalar(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { |
| 14989 | if (true) { |
| 14990 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14991 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14992 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14993 | auto self_meta = to_meta(self); |
| 14994 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14995 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14996 | at::_ops::fmod__Scalar::call(self_meta, other); |
| 14997 | } |
| 14998 | |
| 14999 | at::Tensor self_; |
| 15000 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15001 | at::functionalization::impl::sync(self); |
| 15002 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15003 | } else { |
| 15004 | self_ = self; |
| 15005 | } |
| 15006 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 15007 | if ((false)) { |
| 15008 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15009 | TORCH_INTERNAL_ASSERT(false, |
| 15010 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15011 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15012 | } else { |
| 15013 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15014 | at::AutoDispatchSkipFunctionalize guard; |
| 15015 | at::Tensor tmp_output = at::_ops::fmod__Scalar::call(self_, other); |
| 15016 | return self;; |
| 15017 | } |
| 15018 | } else { |
| 15019 | at::Tensor tmp_output; |
| 15020 | { |
| 15021 | at::AutoDispatchSkipFunctionalize guard; |
| 15022 | tmp_output = at::_ops::fmod_Scalar::call(self_, other); |
| 15023 | } |
| 15024 | at::functionalization::impl::replace_(self, tmp_output); |
| 15025 | at::functionalization::impl::commit_update(self); |
| 15026 | at::functionalization::impl::sync(self); |
| 15027 | return self; |
| 15028 | } |
| 15029 | } |
| 15030 | |
| 15031 | at::Tensor & fmod_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 15032 | if (false) { |
| 15033 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15034 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15035 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15036 | auto self_meta = to_meta(self); |
| 15037 | auto other_meta = to_meta(other); |
| 15038 | auto out_meta = to_meta(out); |
| 15039 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15040 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15041 | at::_ops::fmod_Tensor_out::call(self_meta, other_meta, out_meta); |
| 15042 | } |
| 15043 | |
| 15044 | at::Tensor self_; |
| 15045 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15046 | at::functionalization::impl::sync(self); |
| 15047 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15048 | } else { |
| 15049 | self_ = self; |
| 15050 | } |
| 15051 | |
| 15052 | at::Tensor other_; |
| 15053 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 15054 | at::functionalization::impl::sync(other); |
| 15055 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 15056 | } else { |
| 15057 | other_ = other; |
| 15058 | } |
| 15059 | |
| 15060 | at::Tensor out_; |
| 15061 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15062 | at::functionalization::impl::sync(out); |
| 15063 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15064 | } else { |
| 15065 | out_ = out; |
| 15066 | } |
| 15067 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15068 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 15069 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15070 | TORCH_INTERNAL_ASSERT(false, |
| 15071 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15072 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15073 | } else { |
| 15074 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15075 | at::AutoDispatchSkipFunctionalize guard; |
| 15076 | at::Tensor tmp_output = at::_ops::fmod_Tensor_out::call(self_, other_, out_); |
| 15077 | return out;; |
| 15078 | } |
| 15079 | } else { |
| 15080 | at::Tensor tmp_output; |
| 15081 | { |
| 15082 | at::AutoDispatchSkipFunctionalize guard; |
| 15083 | tmp_output = at::_ops::fmod_Tensor::call(self_, other_); |
| 15084 | } |
| 15085 | at::functionalization::impl::replace_(out, tmp_output); |
| 15086 | at::functionalization::impl::commit_update(out); |
| 15087 | at::functionalization::impl::sync(out); |
| 15088 | return out; |
| 15089 | } |
| 15090 | } |
| 15091 | |
| 15092 | at::Tensor & fmod__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 15093 | if (true) { |
| 15094 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15095 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15096 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15097 | auto self_meta = to_meta(self); |
| 15098 | auto other_meta = to_meta(other); |
| 15099 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15100 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15101 | at::_ops::fmod__Tensor::call(self_meta, other_meta); |
| 15102 | } |
| 15103 | |
| 15104 | at::Tensor self_; |
| 15105 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15106 | at::functionalization::impl::sync(self); |
| 15107 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15108 | } else { |
| 15109 | self_ = self; |
| 15110 | } |
| 15111 | |
| 15112 | at::Tensor other_; |
| 15113 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 15114 | at::functionalization::impl::sync(other); |
| 15115 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 15116 | } else { |
| 15117 | other_ = other; |
| 15118 | } |
| 15119 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 15120 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 15121 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15122 | TORCH_INTERNAL_ASSERT(false, |
| 15123 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15124 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15125 | } else { |
| 15126 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15127 | at::AutoDispatchSkipFunctionalize guard; |
| 15128 | at::Tensor tmp_output = at::_ops::fmod__Tensor::call(self_, other_); |
| 15129 | return self;; |
| 15130 | } |
| 15131 | } else { |
| 15132 | at::Tensor tmp_output; |
| 15133 | { |
| 15134 | at::AutoDispatchSkipFunctionalize guard; |
| 15135 | tmp_output = at::_ops::fmod_Tensor::call(self_, other_); |
| 15136 | } |
| 15137 | at::functionalization::impl::replace_(self, tmp_output); |
| 15138 | at::functionalization::impl::commit_update(self); |
| 15139 | at::functionalization::impl::sync(self); |
| 15140 | return self; |
| 15141 | } |
| 15142 | } |
| 15143 | |
| 15144 | at::Tensor & nextafter_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 15145 | if (false) { |
| 15146 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15147 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15148 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15149 | auto self_meta = to_meta(self); |
| 15150 | auto other_meta = to_meta(other); |
| 15151 | auto out_meta = to_meta(out); |
| 15152 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15153 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15154 | at::_ops::nextafter_out::call(self_meta, other_meta, out_meta); |
| 15155 | } |
| 15156 | |
| 15157 | at::Tensor self_; |
| 15158 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15159 | at::functionalization::impl::sync(self); |
| 15160 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15161 | } else { |
| 15162 | self_ = self; |
| 15163 | } |
| 15164 | |
| 15165 | at::Tensor other_; |
| 15166 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 15167 | at::functionalization::impl::sync(other); |
| 15168 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 15169 | } else { |
| 15170 | other_ = other; |
| 15171 | } |
| 15172 | |
| 15173 | at::Tensor out_; |
| 15174 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15175 | at::functionalization::impl::sync(out); |
| 15176 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15177 | } else { |
| 15178 | out_ = out; |
| 15179 | } |
| 15180 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15181 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 15182 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15183 | TORCH_INTERNAL_ASSERT(false, |
| 15184 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15185 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15186 | } else { |
| 15187 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15188 | at::AutoDispatchSkipFunctionalize guard; |
| 15189 | at::Tensor tmp_output = at::_ops::nextafter_out::call(self_, other_, out_); |
| 15190 | return out;; |
| 15191 | } |
| 15192 | } else { |
| 15193 | at::Tensor tmp_output; |
| 15194 | { |
| 15195 | at::AutoDispatchSkipFunctionalize guard; |
| 15196 | tmp_output = at::_ops::nextafter::call(self_, other_); |
| 15197 | } |
| 15198 | at::functionalization::impl::replace_(out, tmp_output); |
| 15199 | at::functionalization::impl::commit_update(out); |
| 15200 | at::functionalization::impl::sync(out); |
| 15201 | return out; |
| 15202 | } |
| 15203 | } |
| 15204 | |
| 15205 | at::Tensor & nextafter_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 15206 | if (true) { |
| 15207 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15208 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15209 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15210 | auto self_meta = to_meta(self); |
| 15211 | auto other_meta = to_meta(other); |
| 15212 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15213 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15214 | at::_ops::nextafter_::call(self_meta, other_meta); |
| 15215 | } |
| 15216 | |
| 15217 | at::Tensor self_; |
| 15218 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15219 | at::functionalization::impl::sync(self); |
| 15220 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15221 | } else { |
| 15222 | self_ = self; |
| 15223 | } |
| 15224 | |
| 15225 | at::Tensor other_; |
| 15226 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 15227 | at::functionalization::impl::sync(other); |
| 15228 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 15229 | } else { |
| 15230 | other_ = other; |
| 15231 | } |
| 15232 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 15233 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 15234 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15235 | TORCH_INTERNAL_ASSERT(false, |
| 15236 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15237 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15238 | } else { |
| 15239 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15240 | at::AutoDispatchSkipFunctionalize guard; |
| 15241 | at::Tensor tmp_output = at::_ops::nextafter_::call(self_, other_); |
| 15242 | return self;; |
| 15243 | } |
| 15244 | } else { |
| 15245 | at::Tensor tmp_output; |
| 15246 | { |
| 15247 | at::AutoDispatchSkipFunctionalize guard; |
| 15248 | tmp_output = at::_ops::nextafter::call(self_, other_); |
| 15249 | } |
| 15250 | at::functionalization::impl::replace_(self, tmp_output); |
| 15251 | at::functionalization::impl::commit_update(self); |
| 15252 | at::functionalization::impl::sync(self); |
| 15253 | return self; |
| 15254 | } |
| 15255 | } |
| 15256 | |
| 15257 | at::Tensor & minimum_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 15258 | if (false) { |
| 15259 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15260 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15261 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15262 | auto self_meta = to_meta(self); |
| 15263 | auto other_meta = to_meta(other); |
| 15264 | auto out_meta = to_meta(out); |
| 15265 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15266 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15267 | at::_ops::minimum_out::call(self_meta, other_meta, out_meta); |
| 15268 | } |
| 15269 | |
| 15270 | at::Tensor self_; |
| 15271 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15272 | at::functionalization::impl::sync(self); |
| 15273 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15274 | } else { |
| 15275 | self_ = self; |
| 15276 | } |
| 15277 | |
| 15278 | at::Tensor other_; |
| 15279 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 15280 | at::functionalization::impl::sync(other); |
| 15281 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 15282 | } else { |
| 15283 | other_ = other; |
| 15284 | } |
| 15285 | |
| 15286 | at::Tensor out_; |
| 15287 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15288 | at::functionalization::impl::sync(out); |
| 15289 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15290 | } else { |
| 15291 | out_ = out; |
| 15292 | } |
| 15293 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15294 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 15295 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15296 | TORCH_INTERNAL_ASSERT(false, |
| 15297 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15298 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15299 | } else { |
| 15300 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15301 | at::AutoDispatchSkipFunctionalize guard; |
| 15302 | at::Tensor tmp_output = at::_ops::minimum_out::call(self_, other_, out_); |
| 15303 | return out;; |
| 15304 | } |
| 15305 | } else { |
| 15306 | at::Tensor tmp_output; |
| 15307 | { |
| 15308 | at::AutoDispatchSkipFunctionalize guard; |
| 15309 | tmp_output = at::_ops::minimum::call(self_, other_); |
| 15310 | } |
| 15311 | at::functionalization::impl::replace_(out, tmp_output); |
| 15312 | at::functionalization::impl::commit_update(out); |
| 15313 | at::functionalization::impl::sync(out); |
| 15314 | return out; |
| 15315 | } |
| 15316 | } |
| 15317 | |
| 15318 | ::std::tuple<at::Tensor &,at::Tensor &> topk_out_values(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted, at::Tensor & values, at::Tensor & indices) { |
| 15319 | if (false) { |
| 15320 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15321 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15322 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15323 | auto self_meta = to_meta(self); |
| 15324 | auto values_meta = to_meta(values); |
| 15325 | auto indices_meta = to_meta(indices); |
| 15326 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15327 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15328 | at::_ops::topk_values::call(self_meta, k, dim, largest, sorted, values_meta, indices_meta); |
| 15329 | } |
| 15330 | |
| 15331 | at::Tensor self_; |
| 15332 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15333 | at::functionalization::impl::sync(self); |
| 15334 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15335 | } else { |
| 15336 | self_ = self; |
| 15337 | } |
| 15338 | |
| 15339 | at::Tensor values_; |
| 15340 | if (at::functionalization::impl::isFunctionalTensor(values)) { |
| 15341 | at::functionalization::impl::sync(values); |
| 15342 | values_ = at::functionalization::impl::from_functional_tensor(values); |
| 15343 | } else { |
| 15344 | values_ = values; |
| 15345 | } |
| 15346 | |
| 15347 | at::Tensor indices_; |
| 15348 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 15349 | at::functionalization::impl::sync(indices); |
| 15350 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 15351 | } else { |
| 15352 | indices_ = indices; |
| 15353 | } |
| 15354 | if (!(true && at::functionalization::impl::isFunctionalTensor(values) && at::functionalization::impl::isFunctionalTensor(indices))) { |
| 15355 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 15356 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15357 | TORCH_INTERNAL_ASSERT(false, |
| 15358 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15359 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15360 | } else { |
| 15361 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15362 | at::AutoDispatchSkipFunctionalize guard; |
| 15363 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::topk_values::call(self_, k, dim, largest, sorted, values_, indices_); |
| 15364 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices);; |
| 15365 | } |
| 15366 | } else { |
| 15367 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 15368 | { |
| 15369 | at::AutoDispatchSkipFunctionalize guard; |
| 15370 | tmp_output = at::_ops::topk::call(self_, k, dim, largest, sorted); |
| 15371 | } |
| 15372 | at::functionalization::impl::replace_(values, std::get<0>(tmp_output)); |
| 15373 | at::functionalization::impl::commit_update(values); |
| 15374 | at::functionalization::impl::sync(values); |
| 15375 | at::functionalization::impl::replace_(indices, std::get<1>(tmp_output)); |
| 15376 | at::functionalization::impl::commit_update(indices); |
| 15377 | at::functionalization::impl::sync(indices); |
| 15378 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices); |
| 15379 | } |
| 15380 | } |
| 15381 | |
| 15382 | at::Tensor & any_out_all_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 15383 | if (false) { |
| 15384 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15385 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15386 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15387 | auto self_meta = to_meta(self); |
| 15388 | auto out_meta = to_meta(out); |
| 15389 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15390 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15391 | at::_ops::any_all_out::call(self_meta, out_meta); |
| 15392 | } |
| 15393 | |
| 15394 | at::Tensor self_; |
| 15395 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15396 | at::functionalization::impl::sync(self); |
| 15397 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15398 | } else { |
| 15399 | self_ = self; |
| 15400 | } |
| 15401 | |
| 15402 | at::Tensor out_; |
| 15403 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15404 | at::functionalization::impl::sync(out); |
| 15405 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15406 | } else { |
| 15407 | out_ = out; |
| 15408 | } |
| 15409 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15410 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 15411 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15412 | TORCH_INTERNAL_ASSERT(false, |
| 15413 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15414 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15415 | } else { |
| 15416 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15417 | at::AutoDispatchSkipFunctionalize guard; |
| 15418 | at::Tensor tmp_output = at::_ops::any_all_out::call(self_, out_); |
| 15419 | return out;; |
| 15420 | } |
| 15421 | } else { |
| 15422 | at::Tensor tmp_output; |
| 15423 | { |
| 15424 | at::AutoDispatchSkipFunctionalize guard; |
| 15425 | tmp_output = at::_ops::any::call(self_); |
| 15426 | } |
| 15427 | at::functionalization::impl::replace_(out, tmp_output); |
| 15428 | at::functionalization::impl::commit_update(out); |
| 15429 | at::functionalization::impl::sync(out); |
| 15430 | return out; |
| 15431 | } |
| 15432 | } |
| 15433 | |
| 15434 | void _foreach_mul_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar, at::TensorList out) { |
| 15435 | if (false) { |
| 15436 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15437 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15438 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15439 | auto self_meta = to_meta(self); |
| 15440 | auto out_meta = to_meta(out); |
| 15441 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15442 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15443 | at::_ops::_foreach_mul_Scalar_out::call(self_meta, scalar, out_meta); |
| 15444 | } |
| 15445 | |
| 15446 | ::std::vector<at::Tensor> self_; |
| 15447 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15448 | at::functionalization::impl::sync(self); |
| 15449 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15450 | } else { |
| 15451 | self_ = self.vec(); |
| 15452 | } |
| 15453 | |
| 15454 | ::std::vector<at::Tensor> out_; |
| 15455 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15456 | at::functionalization::impl::sync(out); |
| 15457 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15458 | } else { |
| 15459 | out_ = out.vec(); |
| 15460 | } |
| 15461 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15462 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 15463 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15464 | TORCH_INTERNAL_ASSERT(false, |
| 15465 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15466 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15467 | } else { |
| 15468 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15469 | at::AutoDispatchSkipFunctionalize guard; |
| 15470 | at::_ops::_foreach_mul_Scalar_out::call(self_, scalar, out_); |
| 15471 | ; |
| 15472 | } |
| 15473 | } else { |
| 15474 | ::std::vector<at::Tensor> tmp_output; |
| 15475 | { |
| 15476 | at::AutoDispatchSkipFunctionalize guard; |
| 15477 | tmp_output = at::_ops::_foreach_mul_Scalar::call(self_, scalar); |
| 15478 | } |
| 15479 | at::functionalization::impl::replace_(out, tmp_output); |
| 15480 | at::functionalization::impl::commit_update(out); |
| 15481 | at::functionalization::impl::sync(out); |
| 15482 | |
| 15483 | } |
| 15484 | } |
| 15485 | |
| 15486 | void _foreach_mul__Scalar(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { |
| 15487 | if (true) { |
| 15488 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15489 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15490 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15491 | auto self_meta = to_meta(self); |
| 15492 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15493 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15494 | at::_ops::_foreach_mul__Scalar::call(self_meta, scalar); |
| 15495 | } |
| 15496 | |
| 15497 | ::std::vector<at::Tensor> self_; |
| 15498 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15499 | at::functionalization::impl::sync(self); |
| 15500 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15501 | } else { |
| 15502 | self_ = self.vec(); |
| 15503 | } |
| 15504 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 15505 | if ((false)) { |
| 15506 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15507 | TORCH_INTERNAL_ASSERT(false, |
| 15508 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15509 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15510 | } else { |
| 15511 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15512 | at::AutoDispatchSkipFunctionalize guard; |
| 15513 | at::_ops::_foreach_mul__Scalar::call(self_, scalar); |
| 15514 | ; |
| 15515 | } |
| 15516 | } else { |
| 15517 | ::std::vector<at::Tensor> tmp_output; |
| 15518 | { |
| 15519 | at::AutoDispatchSkipFunctionalize guard; |
| 15520 | tmp_output = at::_ops::_foreach_mul_Scalar::call(self_, scalar); |
| 15521 | } |
| 15522 | at::functionalization::impl::replace_(self, tmp_output); |
| 15523 | at::functionalization::impl::commit_update(self); |
| 15524 | at::functionalization::impl::sync(self); |
| 15525 | |
| 15526 | } |
| 15527 | } |
| 15528 | |
| 15529 | void _foreach_div_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar, at::TensorList out) { |
| 15530 | if (false) { |
| 15531 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15532 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15533 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15534 | auto self_meta = to_meta(self); |
| 15535 | auto out_meta = to_meta(out); |
| 15536 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15537 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15538 | at::_ops::_foreach_div_Scalar_out::call(self_meta, scalar, out_meta); |
| 15539 | } |
| 15540 | |
| 15541 | ::std::vector<at::Tensor> self_; |
| 15542 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15543 | at::functionalization::impl::sync(self); |
| 15544 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15545 | } else { |
| 15546 | self_ = self.vec(); |
| 15547 | } |
| 15548 | |
| 15549 | ::std::vector<at::Tensor> out_; |
| 15550 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15551 | at::functionalization::impl::sync(out); |
| 15552 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15553 | } else { |
| 15554 | out_ = out.vec(); |
| 15555 | } |
| 15556 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15557 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 15558 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15559 | TORCH_INTERNAL_ASSERT(false, |
| 15560 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15561 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15562 | } else { |
| 15563 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15564 | at::AutoDispatchSkipFunctionalize guard; |
| 15565 | at::_ops::_foreach_div_Scalar_out::call(self_, scalar, out_); |
| 15566 | ; |
| 15567 | } |
| 15568 | } else { |
| 15569 | ::std::vector<at::Tensor> tmp_output; |
| 15570 | { |
| 15571 | at::AutoDispatchSkipFunctionalize guard; |
| 15572 | tmp_output = at::_ops::_foreach_div_Scalar::call(self_, scalar); |
| 15573 | } |
| 15574 | at::functionalization::impl::replace_(out, tmp_output); |
| 15575 | at::functionalization::impl::commit_update(out); |
| 15576 | at::functionalization::impl::sync(out); |
| 15577 | |
| 15578 | } |
| 15579 | } |
| 15580 | |
| 15581 | void _foreach_div__Scalar(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { |
| 15582 | if (true) { |
| 15583 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15584 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15585 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15586 | auto self_meta = to_meta(self); |
| 15587 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15588 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15589 | at::_ops::_foreach_div__Scalar::call(self_meta, scalar); |
| 15590 | } |
| 15591 | |
| 15592 | ::std::vector<at::Tensor> self_; |
| 15593 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15594 | at::functionalization::impl::sync(self); |
| 15595 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15596 | } else { |
| 15597 | self_ = self.vec(); |
| 15598 | } |
| 15599 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 15600 | if ((false)) { |
| 15601 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15602 | TORCH_INTERNAL_ASSERT(false, |
| 15603 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15604 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15605 | } else { |
| 15606 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15607 | at::AutoDispatchSkipFunctionalize guard; |
| 15608 | at::_ops::_foreach_div__Scalar::call(self_, scalar); |
| 15609 | ; |
| 15610 | } |
| 15611 | } else { |
| 15612 | ::std::vector<at::Tensor> tmp_output; |
| 15613 | { |
| 15614 | at::AutoDispatchSkipFunctionalize guard; |
| 15615 | tmp_output = at::_ops::_foreach_div_Scalar::call(self_, scalar); |
| 15616 | } |
| 15617 | at::functionalization::impl::replace_(self, tmp_output); |
| 15618 | at::functionalization::impl::commit_update(self); |
| 15619 | at::functionalization::impl::sync(self); |
| 15620 | |
| 15621 | } |
| 15622 | } |
| 15623 | |
| 15624 | void _foreach_clamp_max_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar, at::TensorList out) { |
| 15625 | if (false) { |
| 15626 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15627 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15628 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15629 | auto self_meta = to_meta(self); |
| 15630 | auto out_meta = to_meta(out); |
| 15631 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15632 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15633 | at::_ops::_foreach_clamp_max_Scalar_out::call(self_meta, scalar, out_meta); |
| 15634 | } |
| 15635 | |
| 15636 | ::std::vector<at::Tensor> self_; |
| 15637 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15638 | at::functionalization::impl::sync(self); |
| 15639 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15640 | } else { |
| 15641 | self_ = self.vec(); |
| 15642 | } |
| 15643 | |
| 15644 | ::std::vector<at::Tensor> out_; |
| 15645 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15646 | at::functionalization::impl::sync(out); |
| 15647 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15648 | } else { |
| 15649 | out_ = out.vec(); |
| 15650 | } |
| 15651 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15652 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 15653 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15654 | TORCH_INTERNAL_ASSERT(false, |
| 15655 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15656 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15657 | } else { |
| 15658 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15659 | at::AutoDispatchSkipFunctionalize guard; |
| 15660 | at::_ops::_foreach_clamp_max_Scalar_out::call(self_, scalar, out_); |
| 15661 | ; |
| 15662 | } |
| 15663 | } else { |
| 15664 | ::std::vector<at::Tensor> tmp_output; |
| 15665 | { |
| 15666 | at::AutoDispatchSkipFunctionalize guard; |
| 15667 | tmp_output = at::_ops::_foreach_clamp_max_Scalar::call(self_, scalar); |
| 15668 | } |
| 15669 | at::functionalization::impl::replace_(out, tmp_output); |
| 15670 | at::functionalization::impl::commit_update(out); |
| 15671 | at::functionalization::impl::sync(out); |
| 15672 | |
| 15673 | } |
| 15674 | } |
| 15675 | |
| 15676 | void _foreach_clamp_max__Scalar(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { |
| 15677 | if (true) { |
| 15678 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15679 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15680 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15681 | auto self_meta = to_meta(self); |
| 15682 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15683 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15684 | at::_ops::_foreach_clamp_max__Scalar::call(self_meta, scalar); |
| 15685 | } |
| 15686 | |
| 15687 | ::std::vector<at::Tensor> self_; |
| 15688 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15689 | at::functionalization::impl::sync(self); |
| 15690 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15691 | } else { |
| 15692 | self_ = self.vec(); |
| 15693 | } |
| 15694 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 15695 | if ((false)) { |
| 15696 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15697 | TORCH_INTERNAL_ASSERT(false, |
| 15698 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15699 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15700 | } else { |
| 15701 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15702 | at::AutoDispatchSkipFunctionalize guard; |
| 15703 | at::_ops::_foreach_clamp_max__Scalar::call(self_, scalar); |
| 15704 | ; |
| 15705 | } |
| 15706 | } else { |
| 15707 | ::std::vector<at::Tensor> tmp_output; |
| 15708 | { |
| 15709 | at::AutoDispatchSkipFunctionalize guard; |
| 15710 | tmp_output = at::_ops::_foreach_clamp_max_Scalar::call(self_, scalar); |
| 15711 | } |
| 15712 | at::functionalization::impl::replace_(self, tmp_output); |
| 15713 | at::functionalization::impl::commit_update(self); |
| 15714 | at::functionalization::impl::sync(self); |
| 15715 | |
| 15716 | } |
| 15717 | } |
| 15718 | |
| 15719 | void _foreach_minimum_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar, at::TensorList out) { |
| 15720 | if (false) { |
| 15721 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15722 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15723 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15724 | auto self_meta = to_meta(self); |
| 15725 | auto out_meta = to_meta(out); |
| 15726 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15727 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15728 | at::_ops::_foreach_minimum_Scalar_out::call(self_meta, scalar, out_meta); |
| 15729 | } |
| 15730 | |
| 15731 | ::std::vector<at::Tensor> self_; |
| 15732 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15733 | at::functionalization::impl::sync(self); |
| 15734 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15735 | } else { |
| 15736 | self_ = self.vec(); |
| 15737 | } |
| 15738 | |
| 15739 | ::std::vector<at::Tensor> out_; |
| 15740 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15741 | at::functionalization::impl::sync(out); |
| 15742 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15743 | } else { |
| 15744 | out_ = out.vec(); |
| 15745 | } |
| 15746 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15747 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 15748 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15749 | TORCH_INTERNAL_ASSERT(false, |
| 15750 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15751 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15752 | } else { |
| 15753 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15754 | at::AutoDispatchSkipFunctionalize guard; |
| 15755 | at::_ops::_foreach_minimum_Scalar_out::call(self_, scalar, out_); |
| 15756 | ; |
| 15757 | } |
| 15758 | } else { |
| 15759 | ::std::vector<at::Tensor> tmp_output; |
| 15760 | { |
| 15761 | at::AutoDispatchSkipFunctionalize guard; |
| 15762 | tmp_output = at::_ops::_foreach_minimum_Scalar::call(self_, scalar); |
| 15763 | } |
| 15764 | at::functionalization::impl::replace_(out, tmp_output); |
| 15765 | at::functionalization::impl::commit_update(out); |
| 15766 | at::functionalization::impl::sync(out); |
| 15767 | |
| 15768 | } |
| 15769 | } |
| 15770 | |
| 15771 | void _foreach_minimum__Scalar(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { |
| 15772 | if (true) { |
| 15773 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15774 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15775 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15776 | auto self_meta = to_meta(self); |
| 15777 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15778 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15779 | at::_ops::_foreach_minimum__Scalar::call(self_meta, scalar); |
| 15780 | } |
| 15781 | |
| 15782 | ::std::vector<at::Tensor> self_; |
| 15783 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15784 | at::functionalization::impl::sync(self); |
| 15785 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15786 | } else { |
| 15787 | self_ = self.vec(); |
| 15788 | } |
| 15789 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 15790 | if ((false)) { |
| 15791 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15792 | TORCH_INTERNAL_ASSERT(false, |
| 15793 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15794 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15795 | } else { |
| 15796 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15797 | at::AutoDispatchSkipFunctionalize guard; |
| 15798 | at::_ops::_foreach_minimum__Scalar::call(self_, scalar); |
| 15799 | ; |
| 15800 | } |
| 15801 | } else { |
| 15802 | ::std::vector<at::Tensor> tmp_output; |
| 15803 | { |
| 15804 | at::AutoDispatchSkipFunctionalize guard; |
| 15805 | tmp_output = at::_ops::_foreach_minimum_Scalar::call(self_, scalar); |
| 15806 | } |
| 15807 | at::functionalization::impl::replace_(self, tmp_output); |
| 15808 | at::functionalization::impl::commit_update(self); |
| 15809 | at::functionalization::impl::sync(self); |
| 15810 | |
| 15811 | } |
| 15812 | } |
| 15813 | |
| 15814 | void _foreach_mul_out_List_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other, at::TensorList out) { |
| 15815 | if (false) { |
| 15816 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15817 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15818 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15819 | auto self_meta = to_meta(self); |
| 15820 | auto other_meta = to_meta(other); |
| 15821 | auto out_meta = to_meta(out); |
| 15822 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15823 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15824 | at::_ops::_foreach_mul_List_out::call(self_meta, other_meta, out_meta); |
| 15825 | } |
| 15826 | |
| 15827 | ::std::vector<at::Tensor> self_; |
| 15828 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15829 | at::functionalization::impl::sync(self); |
| 15830 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15831 | } else { |
| 15832 | self_ = self.vec(); |
| 15833 | } |
| 15834 | |
| 15835 | ::std::vector<at::Tensor> other_; |
| 15836 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 15837 | at::functionalization::impl::sync(other); |
| 15838 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 15839 | } else { |
| 15840 | other_ = other.vec(); |
| 15841 | } |
| 15842 | |
| 15843 | ::std::vector<at::Tensor> out_; |
| 15844 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15845 | at::functionalization::impl::sync(out); |
| 15846 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15847 | } else { |
| 15848 | out_ = out.vec(); |
| 15849 | } |
| 15850 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15851 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 15852 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15853 | TORCH_INTERNAL_ASSERT(false, |
| 15854 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15855 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15856 | } else { |
| 15857 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15858 | at::AutoDispatchSkipFunctionalize guard; |
| 15859 | at::_ops::_foreach_mul_List_out::call(self_, other_, out_); |
| 15860 | ; |
| 15861 | } |
| 15862 | } else { |
| 15863 | ::std::vector<at::Tensor> tmp_output; |
| 15864 | { |
| 15865 | at::AutoDispatchSkipFunctionalize guard; |
| 15866 | tmp_output = at::_ops::_foreach_mul_List::call(self_, other_); |
| 15867 | } |
| 15868 | at::functionalization::impl::replace_(out, tmp_output); |
| 15869 | at::functionalization::impl::commit_update(out); |
| 15870 | at::functionalization::impl::sync(out); |
| 15871 | |
| 15872 | } |
| 15873 | } |
| 15874 | |
| 15875 | void _foreach_mul__List(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other) { |
| 15876 | if (true) { |
| 15877 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15878 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15879 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15880 | auto self_meta = to_meta(self); |
| 15881 | auto other_meta = to_meta(other); |
| 15882 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15883 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15884 | at::_ops::_foreach_mul__List::call(self_meta, other_meta); |
| 15885 | } |
| 15886 | |
| 15887 | ::std::vector<at::Tensor> self_; |
| 15888 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15889 | at::functionalization::impl::sync(self); |
| 15890 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15891 | } else { |
| 15892 | self_ = self.vec(); |
| 15893 | } |
| 15894 | |
| 15895 | ::std::vector<at::Tensor> other_; |
| 15896 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 15897 | at::functionalization::impl::sync(other); |
| 15898 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 15899 | } else { |
| 15900 | other_ = other.vec(); |
| 15901 | } |
| 15902 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 15903 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 15904 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15905 | TORCH_INTERNAL_ASSERT(false, |
| 15906 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15907 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15908 | } else { |
| 15909 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15910 | at::AutoDispatchSkipFunctionalize guard; |
| 15911 | at::_ops::_foreach_mul__List::call(self_, other_); |
| 15912 | ; |
| 15913 | } |
| 15914 | } else { |
| 15915 | ::std::vector<at::Tensor> tmp_output; |
| 15916 | { |
| 15917 | at::AutoDispatchSkipFunctionalize guard; |
| 15918 | tmp_output = at::_ops::_foreach_mul_List::call(self_, other_); |
| 15919 | } |
| 15920 | at::functionalization::impl::replace_(self, tmp_output); |
| 15921 | at::functionalization::impl::commit_update(self); |
| 15922 | at::functionalization::impl::sync(self); |
| 15923 | |
| 15924 | } |
| 15925 | } |
| 15926 | |
| 15927 | void _foreach_div_out_List_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other, at::TensorList out) { |
| 15928 | if (false) { |
| 15929 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15930 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15931 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15932 | auto self_meta = to_meta(self); |
| 15933 | auto other_meta = to_meta(other); |
| 15934 | auto out_meta = to_meta(out); |
| 15935 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15936 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15937 | at::_ops::_foreach_div_List_out::call(self_meta, other_meta, out_meta); |
| 15938 | } |
| 15939 | |
| 15940 | ::std::vector<at::Tensor> self_; |
| 15941 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15942 | at::functionalization::impl::sync(self); |
| 15943 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15944 | } else { |
| 15945 | self_ = self.vec(); |
| 15946 | } |
| 15947 | |
| 15948 | ::std::vector<at::Tensor> other_; |
| 15949 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 15950 | at::functionalization::impl::sync(other); |
| 15951 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 15952 | } else { |
| 15953 | other_ = other.vec(); |
| 15954 | } |
| 15955 | |
| 15956 | ::std::vector<at::Tensor> out_; |
| 15957 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15958 | at::functionalization::impl::sync(out); |
| 15959 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15960 | } else { |
| 15961 | out_ = out.vec(); |
| 15962 | } |
| 15963 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15964 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 15965 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15966 | TORCH_INTERNAL_ASSERT(false, |
| 15967 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15968 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15969 | } else { |
| 15970 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15971 | at::AutoDispatchSkipFunctionalize guard; |
| 15972 | at::_ops::_foreach_div_List_out::call(self_, other_, out_); |
| 15973 | ; |
| 15974 | } |
| 15975 | } else { |
| 15976 | ::std::vector<at::Tensor> tmp_output; |
| 15977 | { |
| 15978 | at::AutoDispatchSkipFunctionalize guard; |
| 15979 | tmp_output = at::_ops::_foreach_div_List::call(self_, other_); |
| 15980 | } |
| 15981 | at::functionalization::impl::replace_(out, tmp_output); |
| 15982 | at::functionalization::impl::commit_update(out); |
| 15983 | at::functionalization::impl::sync(out); |
| 15984 | |
| 15985 | } |
| 15986 | } |
| 15987 | |
| 15988 | void _foreach_div__List(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other) { |
| 15989 | if (true) { |
| 15990 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15991 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15992 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15993 | auto self_meta = to_meta(self); |
| 15994 | auto other_meta = to_meta(other); |
| 15995 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15996 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15997 | at::_ops::_foreach_div__List::call(self_meta, other_meta); |
| 15998 | } |
| 15999 | |
| 16000 | ::std::vector<at::Tensor> self_; |
| 16001 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16002 | at::functionalization::impl::sync(self); |
| 16003 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16004 | } else { |
| 16005 | self_ = self.vec(); |
| 16006 | } |
| 16007 | |
| 16008 | ::std::vector<at::Tensor> other_; |
| 16009 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 16010 | at::functionalization::impl::sync(other); |
| 16011 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 16012 | } else { |
| 16013 | other_ = other.vec(); |
| 16014 | } |
| 16015 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 16016 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 16017 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16018 | TORCH_INTERNAL_ASSERT(false, |
| 16019 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16020 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16021 | } else { |
| 16022 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16023 | at::AutoDispatchSkipFunctionalize guard; |
| 16024 | at::_ops::_foreach_div__List::call(self_, other_); |
| 16025 | ; |
| 16026 | } |
| 16027 | } else { |
| 16028 | ::std::vector<at::Tensor> tmp_output; |
| 16029 | { |
| 16030 | at::AutoDispatchSkipFunctionalize guard; |
| 16031 | tmp_output = at::_ops::_foreach_div_List::call(self_, other_); |
| 16032 | } |
| 16033 | at::functionalization::impl::replace_(self, tmp_output); |
| 16034 | at::functionalization::impl::commit_update(self); |
| 16035 | at::functionalization::impl::sync(self); |
| 16036 | |
| 16037 | } |
| 16038 | } |
| 16039 | |
| 16040 | void _foreach_clamp_max_out_List_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other, at::TensorList out) { |
| 16041 | if (false) { |
| 16042 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16043 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16044 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16045 | auto self_meta = to_meta(self); |
| 16046 | auto other_meta = to_meta(other); |
| 16047 | auto out_meta = to_meta(out); |
| 16048 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16049 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16050 | at::_ops::_foreach_clamp_max_List_out::call(self_meta, other_meta, out_meta); |
| 16051 | } |
| 16052 | |
| 16053 | ::std::vector<at::Tensor> self_; |
| 16054 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16055 | at::functionalization::impl::sync(self); |
| 16056 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16057 | } else { |
| 16058 | self_ = self.vec(); |
| 16059 | } |
| 16060 | |
| 16061 | ::std::vector<at::Tensor> other_; |
| 16062 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 16063 | at::functionalization::impl::sync(other); |
| 16064 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 16065 | } else { |
| 16066 | other_ = other.vec(); |
| 16067 | } |
| 16068 | |
| 16069 | ::std::vector<at::Tensor> out_; |
| 16070 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16071 | at::functionalization::impl::sync(out); |
| 16072 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16073 | } else { |
| 16074 | out_ = out.vec(); |
| 16075 | } |
| 16076 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16077 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 16078 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16079 | TORCH_INTERNAL_ASSERT(false, |
| 16080 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16081 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16082 | } else { |
| 16083 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16084 | at::AutoDispatchSkipFunctionalize guard; |
| 16085 | at::_ops::_foreach_clamp_max_List_out::call(self_, other_, out_); |
| 16086 | ; |
| 16087 | } |
| 16088 | } else { |
| 16089 | ::std::vector<at::Tensor> tmp_output; |
| 16090 | { |
| 16091 | at::AutoDispatchSkipFunctionalize guard; |
| 16092 | tmp_output = at::_ops::_foreach_clamp_max_List::call(self_, other_); |
| 16093 | } |
| 16094 | at::functionalization::impl::replace_(out, tmp_output); |
| 16095 | at::functionalization::impl::commit_update(out); |
| 16096 | at::functionalization::impl::sync(out); |
| 16097 | |
| 16098 | } |
| 16099 | } |
| 16100 | |
| 16101 | void _foreach_clamp_max__List(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other) { |
| 16102 | if (true) { |
| 16103 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16104 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16105 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16106 | auto self_meta = to_meta(self); |
| 16107 | auto other_meta = to_meta(other); |
| 16108 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16109 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16110 | at::_ops::_foreach_clamp_max__List::call(self_meta, other_meta); |
| 16111 | } |
| 16112 | |
| 16113 | ::std::vector<at::Tensor> self_; |
| 16114 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16115 | at::functionalization::impl::sync(self); |
| 16116 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16117 | } else { |
| 16118 | self_ = self.vec(); |
| 16119 | } |
| 16120 | |
| 16121 | ::std::vector<at::Tensor> other_; |
| 16122 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 16123 | at::functionalization::impl::sync(other); |
| 16124 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 16125 | } else { |
| 16126 | other_ = other.vec(); |
| 16127 | } |
| 16128 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 16129 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 16130 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16131 | TORCH_INTERNAL_ASSERT(false, |
| 16132 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16133 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16134 | } else { |
| 16135 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16136 | at::AutoDispatchSkipFunctionalize guard; |
| 16137 | at::_ops::_foreach_clamp_max__List::call(self_, other_); |
| 16138 | ; |
| 16139 | } |
| 16140 | } else { |
| 16141 | ::std::vector<at::Tensor> tmp_output; |
| 16142 | { |
| 16143 | at::AutoDispatchSkipFunctionalize guard; |
| 16144 | tmp_output = at::_ops::_foreach_clamp_max_List::call(self_, other_); |
| 16145 | } |
| 16146 | at::functionalization::impl::replace_(self, tmp_output); |
| 16147 | at::functionalization::impl::commit_update(self); |
| 16148 | at::functionalization::impl::sync(self); |
| 16149 | |
| 16150 | } |
| 16151 | } |
| 16152 | |
| 16153 | void _foreach_minimum_out_List_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other, at::TensorList out) { |
| 16154 | if (false) { |
| 16155 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16156 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16157 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16158 | auto self_meta = to_meta(self); |
| 16159 | auto other_meta = to_meta(other); |
| 16160 | auto out_meta = to_meta(out); |
| 16161 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16162 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16163 | at::_ops::_foreach_minimum_List_out::call(self_meta, other_meta, out_meta); |
| 16164 | } |
| 16165 | |
| 16166 | ::std::vector<at::Tensor> self_; |
| 16167 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16168 | at::functionalization::impl::sync(self); |
| 16169 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16170 | } else { |
| 16171 | self_ = self.vec(); |
| 16172 | } |
| 16173 | |
| 16174 | ::std::vector<at::Tensor> other_; |
| 16175 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 16176 | at::functionalization::impl::sync(other); |
| 16177 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 16178 | } else { |
| 16179 | other_ = other.vec(); |
| 16180 | } |
| 16181 | |
| 16182 | ::std::vector<at::Tensor> out_; |
| 16183 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16184 | at::functionalization::impl::sync(out); |
| 16185 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16186 | } else { |
| 16187 | out_ = out.vec(); |
| 16188 | } |
| 16189 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16190 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 16191 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16192 | TORCH_INTERNAL_ASSERT(false, |
| 16193 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16194 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16195 | } else { |
| 16196 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16197 | at::AutoDispatchSkipFunctionalize guard; |
| 16198 | at::_ops::_foreach_minimum_List_out::call(self_, other_, out_); |
| 16199 | ; |
| 16200 | } |
| 16201 | } else { |
| 16202 | ::std::vector<at::Tensor> tmp_output; |
| 16203 | { |
| 16204 | at::AutoDispatchSkipFunctionalize guard; |
| 16205 | tmp_output = at::_ops::_foreach_minimum_List::call(self_, other_); |
| 16206 | } |
| 16207 | at::functionalization::impl::replace_(out, tmp_output); |
| 16208 | at::functionalization::impl::commit_update(out); |
| 16209 | at::functionalization::impl::sync(out); |
| 16210 | |
| 16211 | } |
| 16212 | } |
| 16213 | |
| 16214 | void _foreach_minimum__List(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other) { |
| 16215 | if (true) { |
| 16216 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16217 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16218 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16219 | auto self_meta = to_meta(self); |
| 16220 | auto other_meta = to_meta(other); |
| 16221 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16222 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16223 | at::_ops::_foreach_minimum__List::call(self_meta, other_meta); |
| 16224 | } |
| 16225 | |
| 16226 | ::std::vector<at::Tensor> self_; |
| 16227 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16228 | at::functionalization::impl::sync(self); |
| 16229 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16230 | } else { |
| 16231 | self_ = self.vec(); |
| 16232 | } |
| 16233 | |
| 16234 | ::std::vector<at::Tensor> other_; |
| 16235 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 16236 | at::functionalization::impl::sync(other); |
| 16237 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 16238 | } else { |
| 16239 | other_ = other.vec(); |
| 16240 | } |
| 16241 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 16242 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 16243 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16244 | TORCH_INTERNAL_ASSERT(false, |
| 16245 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16246 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16247 | } else { |
| 16248 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16249 | at::AutoDispatchSkipFunctionalize guard; |
| 16250 | at::_ops::_foreach_minimum__List::call(self_, other_); |
| 16251 | ; |
| 16252 | } |
| 16253 | } else { |
| 16254 | ::std::vector<at::Tensor> tmp_output; |
| 16255 | { |
| 16256 | at::AutoDispatchSkipFunctionalize guard; |
| 16257 | tmp_output = at::_ops::_foreach_minimum_List::call(self_, other_); |
| 16258 | } |
| 16259 | at::functionalization::impl::replace_(self, tmp_output); |
| 16260 | at::functionalization::impl::commit_update(self); |
| 16261 | at::functionalization::impl::sync(self); |
| 16262 | |
| 16263 | } |
| 16264 | } |
| 16265 | |
| 16266 | void _foreach_div_out_ScalarList_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out) { |
| 16267 | if (false) { |
| 16268 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16269 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16270 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16271 | auto self_meta = to_meta(self); |
| 16272 | auto out_meta = to_meta(out); |
| 16273 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16274 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16275 | at::_ops::_foreach_div_ScalarList_out::call(self_meta, scalars, out_meta); |
| 16276 | } |
| 16277 | |
| 16278 | ::std::vector<at::Tensor> self_; |
| 16279 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16280 | at::functionalization::impl::sync(self); |
| 16281 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16282 | } else { |
| 16283 | self_ = self.vec(); |
| 16284 | } |
| 16285 | |
| 16286 | ::std::vector<at::Tensor> out_; |
| 16287 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16288 | at::functionalization::impl::sync(out); |
| 16289 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16290 | } else { |
| 16291 | out_ = out.vec(); |
| 16292 | } |
| 16293 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16294 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16295 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16296 | TORCH_INTERNAL_ASSERT(false, |
| 16297 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16298 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16299 | } else { |
| 16300 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16301 | at::AutoDispatchSkipFunctionalize guard; |
| 16302 | at::_ops::_foreach_div_ScalarList_out::call(self_, scalars, out_); |
| 16303 | ; |
| 16304 | } |
| 16305 | } else { |
| 16306 | ::std::vector<at::Tensor> tmp_output; |
| 16307 | { |
| 16308 | at::AutoDispatchSkipFunctionalize guard; |
| 16309 | tmp_output = at::_ops::_foreach_div_ScalarList::call(self_, scalars); |
| 16310 | } |
| 16311 | at::functionalization::impl::replace_(out, tmp_output); |
| 16312 | at::functionalization::impl::commit_update(out); |
| 16313 | at::functionalization::impl::sync(out); |
| 16314 | |
| 16315 | } |
| 16316 | } |
| 16317 | |
| 16318 | void _foreach_div__ScalarList(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 16319 | if (true) { |
| 16320 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16321 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16322 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16323 | auto self_meta = to_meta(self); |
| 16324 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16325 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16326 | at::_ops::_foreach_div__ScalarList::call(self_meta, scalars); |
| 16327 | } |
| 16328 | |
| 16329 | ::std::vector<at::Tensor> self_; |
| 16330 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16331 | at::functionalization::impl::sync(self); |
| 16332 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16333 | } else { |
| 16334 | self_ = self.vec(); |
| 16335 | } |
| 16336 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 16337 | if ((false)) { |
| 16338 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16339 | TORCH_INTERNAL_ASSERT(false, |
| 16340 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16341 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16342 | } else { |
| 16343 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16344 | at::AutoDispatchSkipFunctionalize guard; |
| 16345 | at::_ops::_foreach_div__ScalarList::call(self_, scalars); |
| 16346 | ; |
| 16347 | } |
| 16348 | } else { |
| 16349 | ::std::vector<at::Tensor> tmp_output; |
| 16350 | { |
| 16351 | at::AutoDispatchSkipFunctionalize guard; |
| 16352 | tmp_output = at::_ops::_foreach_div_ScalarList::call(self_, scalars); |
| 16353 | } |
| 16354 | at::functionalization::impl::replace_(self, tmp_output); |
| 16355 | at::functionalization::impl::commit_update(self); |
| 16356 | at::functionalization::impl::sync(self); |
| 16357 | |
| 16358 | } |
| 16359 | } |
| 16360 | |
| 16361 | void _foreach_mul_out_ScalarList_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out) { |
| 16362 | if (false) { |
| 16363 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16364 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16365 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16366 | auto self_meta = to_meta(self); |
| 16367 | auto out_meta = to_meta(out); |
| 16368 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16369 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16370 | at::_ops::_foreach_mul_ScalarList_out::call(self_meta, scalars, out_meta); |
| 16371 | } |
| 16372 | |
| 16373 | ::std::vector<at::Tensor> self_; |
| 16374 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16375 | at::functionalization::impl::sync(self); |
| 16376 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16377 | } else { |
| 16378 | self_ = self.vec(); |
| 16379 | } |
| 16380 | |
| 16381 | ::std::vector<at::Tensor> out_; |
| 16382 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16383 | at::functionalization::impl::sync(out); |
| 16384 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16385 | } else { |
| 16386 | out_ = out.vec(); |
| 16387 | } |
| 16388 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16389 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16390 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16391 | TORCH_INTERNAL_ASSERT(false, |
| 16392 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16393 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16394 | } else { |
| 16395 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16396 | at::AutoDispatchSkipFunctionalize guard; |
| 16397 | at::_ops::_foreach_mul_ScalarList_out::call(self_, scalars, out_); |
| 16398 | ; |
| 16399 | } |
| 16400 | } else { |
| 16401 | ::std::vector<at::Tensor> tmp_output; |
| 16402 | { |
| 16403 | at::AutoDispatchSkipFunctionalize guard; |
| 16404 | tmp_output = at::_ops::_foreach_mul_ScalarList::call(self_, scalars); |
| 16405 | } |
| 16406 | at::functionalization::impl::replace_(out, tmp_output); |
| 16407 | at::functionalization::impl::commit_update(out); |
| 16408 | at::functionalization::impl::sync(out); |
| 16409 | |
| 16410 | } |
| 16411 | } |
| 16412 | |
| 16413 | void _foreach_mul__ScalarList(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 16414 | if (true) { |
| 16415 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16416 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16417 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16418 | auto self_meta = to_meta(self); |
| 16419 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16420 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16421 | at::_ops::_foreach_mul__ScalarList::call(self_meta, scalars); |
| 16422 | } |
| 16423 | |
| 16424 | ::std::vector<at::Tensor> self_; |
| 16425 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16426 | at::functionalization::impl::sync(self); |
| 16427 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16428 | } else { |
| 16429 | self_ = self.vec(); |
| 16430 | } |
| 16431 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 16432 | if ((false)) { |
| 16433 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16434 | TORCH_INTERNAL_ASSERT(false, |
| 16435 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16436 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16437 | } else { |
| 16438 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16439 | at::AutoDispatchSkipFunctionalize guard; |
| 16440 | at::_ops::_foreach_mul__ScalarList::call(self_, scalars); |
| 16441 | ; |
| 16442 | } |
| 16443 | } else { |
| 16444 | ::std::vector<at::Tensor> tmp_output; |
| 16445 | { |
| 16446 | at::AutoDispatchSkipFunctionalize guard; |
| 16447 | tmp_output = at::_ops::_foreach_mul_ScalarList::call(self_, scalars); |
| 16448 | } |
| 16449 | at::functionalization::impl::replace_(self, tmp_output); |
| 16450 | at::functionalization::impl::commit_update(self); |
| 16451 | at::functionalization::impl::sync(self); |
| 16452 | |
| 16453 | } |
| 16454 | } |
| 16455 | |
| 16456 | void _foreach_clamp_max_out_ScalarList_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out) { |
| 16457 | if (false) { |
| 16458 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16459 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16460 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16461 | auto self_meta = to_meta(self); |
| 16462 | auto out_meta = to_meta(out); |
| 16463 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16464 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16465 | at::_ops::_foreach_clamp_max_ScalarList_out::call(self_meta, scalars, out_meta); |
| 16466 | } |
| 16467 | |
| 16468 | ::std::vector<at::Tensor> self_; |
| 16469 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16470 | at::functionalization::impl::sync(self); |
| 16471 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16472 | } else { |
| 16473 | self_ = self.vec(); |
| 16474 | } |
| 16475 | |
| 16476 | ::std::vector<at::Tensor> out_; |
| 16477 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16478 | at::functionalization::impl::sync(out); |
| 16479 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16480 | } else { |
| 16481 | out_ = out.vec(); |
| 16482 | } |
| 16483 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16484 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16485 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16486 | TORCH_INTERNAL_ASSERT(false, |
| 16487 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16488 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16489 | } else { |
| 16490 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16491 | at::AutoDispatchSkipFunctionalize guard; |
| 16492 | at::_ops::_foreach_clamp_max_ScalarList_out::call(self_, scalars, out_); |
| 16493 | ; |
| 16494 | } |
| 16495 | } else { |
| 16496 | ::std::vector<at::Tensor> tmp_output; |
| 16497 | { |
| 16498 | at::AutoDispatchSkipFunctionalize guard; |
| 16499 | tmp_output = at::_ops::_foreach_clamp_max_ScalarList::call(self_, scalars); |
| 16500 | } |
| 16501 | at::functionalization::impl::replace_(out, tmp_output); |
| 16502 | at::functionalization::impl::commit_update(out); |
| 16503 | at::functionalization::impl::sync(out); |
| 16504 | |
| 16505 | } |
| 16506 | } |
| 16507 | |
| 16508 | void _foreach_clamp_max__ScalarList(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 16509 | if (true) { |
| 16510 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16511 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16512 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16513 | auto self_meta = to_meta(self); |
| 16514 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16515 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16516 | at::_ops::_foreach_clamp_max__ScalarList::call(self_meta, scalars); |
| 16517 | } |
| 16518 | |
| 16519 | ::std::vector<at::Tensor> self_; |
| 16520 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16521 | at::functionalization::impl::sync(self); |
| 16522 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16523 | } else { |
| 16524 | self_ = self.vec(); |
| 16525 | } |
| 16526 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 16527 | if ((false)) { |
| 16528 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16529 | TORCH_INTERNAL_ASSERT(false, |
| 16530 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16531 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16532 | } else { |
| 16533 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16534 | at::AutoDispatchSkipFunctionalize guard; |
| 16535 | at::_ops::_foreach_clamp_max__ScalarList::call(self_, scalars); |
| 16536 | ; |
| 16537 | } |
| 16538 | } else { |
| 16539 | ::std::vector<at::Tensor> tmp_output; |
| 16540 | { |
| 16541 | at::AutoDispatchSkipFunctionalize guard; |
| 16542 | tmp_output = at::_ops::_foreach_clamp_max_ScalarList::call(self_, scalars); |
| 16543 | } |
| 16544 | at::functionalization::impl::replace_(self, tmp_output); |
| 16545 | at::functionalization::impl::commit_update(self); |
| 16546 | at::functionalization::impl::sync(self); |
| 16547 | |
| 16548 | } |
| 16549 | } |
| 16550 | |
| 16551 | void _foreach_minimum_out_ScalarList_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out) { |
| 16552 | if (false) { |
| 16553 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16554 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16555 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16556 | auto self_meta = to_meta(self); |
| 16557 | auto out_meta = to_meta(out); |
| 16558 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16559 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16560 | at::_ops::_foreach_minimum_ScalarList_out::call(self_meta, scalars, out_meta); |
| 16561 | } |
| 16562 | |
| 16563 | ::std::vector<at::Tensor> self_; |
| 16564 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16565 | at::functionalization::impl::sync(self); |
| 16566 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16567 | } else { |
| 16568 | self_ = self.vec(); |
| 16569 | } |
| 16570 | |
| 16571 | ::std::vector<at::Tensor> out_; |
| 16572 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16573 | at::functionalization::impl::sync(out); |
| 16574 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16575 | } else { |
| 16576 | out_ = out.vec(); |
| 16577 | } |
| 16578 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16579 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16580 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16581 | TORCH_INTERNAL_ASSERT(false, |
| 16582 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16583 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16584 | } else { |
| 16585 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16586 | at::AutoDispatchSkipFunctionalize guard; |
| 16587 | at::_ops::_foreach_minimum_ScalarList_out::call(self_, scalars, out_); |
| 16588 | ; |
| 16589 | } |
| 16590 | } else { |
| 16591 | ::std::vector<at::Tensor> tmp_output; |
| 16592 | { |
| 16593 | at::AutoDispatchSkipFunctionalize guard; |
| 16594 | tmp_output = at::_ops::_foreach_minimum_ScalarList::call(self_, scalars); |
| 16595 | } |
| 16596 | at::functionalization::impl::replace_(out, tmp_output); |
| 16597 | at::functionalization::impl::commit_update(out); |
| 16598 | at::functionalization::impl::sync(out); |
| 16599 | |
| 16600 | } |
| 16601 | } |
| 16602 | |
| 16603 | void _foreach_minimum__ScalarList(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 16604 | if (true) { |
| 16605 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16606 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16607 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16608 | auto self_meta = to_meta(self); |
| 16609 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16610 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16611 | at::_ops::_foreach_minimum__ScalarList::call(self_meta, scalars); |
| 16612 | } |
| 16613 | |
| 16614 | ::std::vector<at::Tensor> self_; |
| 16615 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16616 | at::functionalization::impl::sync(self); |
| 16617 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16618 | } else { |
| 16619 | self_ = self.vec(); |
| 16620 | } |
| 16621 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 16622 | if ((false)) { |
| 16623 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16624 | TORCH_INTERNAL_ASSERT(false, |
| 16625 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16626 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16627 | } else { |
| 16628 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16629 | at::AutoDispatchSkipFunctionalize guard; |
| 16630 | at::_ops::_foreach_minimum__ScalarList::call(self_, scalars); |
| 16631 | ; |
| 16632 | } |
| 16633 | } else { |
| 16634 | ::std::vector<at::Tensor> tmp_output; |
| 16635 | { |
| 16636 | at::AutoDispatchSkipFunctionalize guard; |
| 16637 | tmp_output = at::_ops::_foreach_minimum_ScalarList::call(self_, scalars); |
| 16638 | } |
| 16639 | at::functionalization::impl::replace_(self, tmp_output); |
| 16640 | at::functionalization::impl::commit_update(self); |
| 16641 | at::functionalization::impl::sync(self); |
| 16642 | |
| 16643 | } |
| 16644 | } |
| 16645 | |
| 16646 | void _foreach_expm1_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 16647 | if (false) { |
| 16648 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16649 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16650 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16651 | auto self_meta = to_meta(self); |
| 16652 | auto out_meta = to_meta(out); |
| 16653 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16654 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16655 | at::_ops::_foreach_expm1_out::call(self_meta, out_meta); |
| 16656 | } |
| 16657 | |
| 16658 | ::std::vector<at::Tensor> self_; |
| 16659 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16660 | at::functionalization::impl::sync(self); |
| 16661 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16662 | } else { |
| 16663 | self_ = self.vec(); |
| 16664 | } |
| 16665 | |
| 16666 | ::std::vector<at::Tensor> out_; |
| 16667 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16668 | at::functionalization::impl::sync(out); |
| 16669 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16670 | } else { |
| 16671 | out_ = out.vec(); |
| 16672 | } |
| 16673 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16674 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16675 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16676 | TORCH_INTERNAL_ASSERT(false, |
| 16677 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16678 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16679 | } else { |
| 16680 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16681 | at::AutoDispatchSkipFunctionalize guard; |
| 16682 | at::_ops::_foreach_expm1_out::call(self_, out_); |
| 16683 | ; |
| 16684 | } |
| 16685 | } else { |
| 16686 | ::std::vector<at::Tensor> tmp_output; |
| 16687 | { |
| 16688 | at::AutoDispatchSkipFunctionalize guard; |
| 16689 | tmp_output = at::_ops::_foreach_expm1::call(self_); |
| 16690 | } |
| 16691 | at::functionalization::impl::replace_(out, tmp_output); |
| 16692 | at::functionalization::impl::commit_update(out); |
| 16693 | at::functionalization::impl::sync(out); |
| 16694 | |
| 16695 | } |
| 16696 | } |
| 16697 | |
| 16698 | void _foreach_expm1_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 16699 | if (true) { |
| 16700 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16701 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16702 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16703 | auto self_meta = to_meta(self); |
| 16704 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16705 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16706 | at::_ops::_foreach_expm1_::call(self_meta); |
| 16707 | } |
| 16708 | |
| 16709 | ::std::vector<at::Tensor> self_; |
| 16710 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16711 | at::functionalization::impl::sync(self); |
| 16712 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16713 | } else { |
| 16714 | self_ = self.vec(); |
| 16715 | } |
| 16716 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 16717 | if ((false)) { |
| 16718 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16719 | TORCH_INTERNAL_ASSERT(false, |
| 16720 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16721 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16722 | } else { |
| 16723 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16724 | at::AutoDispatchSkipFunctionalize guard; |
| 16725 | at::_ops::_foreach_expm1_::call(self_); |
| 16726 | ; |
| 16727 | } |
| 16728 | } else { |
| 16729 | ::std::vector<at::Tensor> tmp_output; |
| 16730 | { |
| 16731 | at::AutoDispatchSkipFunctionalize guard; |
| 16732 | tmp_output = at::_ops::_foreach_expm1::call(self_); |
| 16733 | } |
| 16734 | at::functionalization::impl::replace_(self, tmp_output); |
| 16735 | at::functionalization::impl::commit_update(self); |
| 16736 | at::functionalization::impl::sync(self); |
| 16737 | |
| 16738 | } |
| 16739 | } |
| 16740 | |
| 16741 | void _foreach_tanh_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 16742 | if (false) { |
| 16743 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16744 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16745 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16746 | auto self_meta = to_meta(self); |
| 16747 | auto out_meta = to_meta(out); |
| 16748 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16749 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16750 | at::_ops::_foreach_tanh_out::call(self_meta, out_meta); |
| 16751 | } |
| 16752 | |
| 16753 | ::std::vector<at::Tensor> self_; |
| 16754 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16755 | at::functionalization::impl::sync(self); |
| 16756 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16757 | } else { |
| 16758 | self_ = self.vec(); |
| 16759 | } |
| 16760 | |
| 16761 | ::std::vector<at::Tensor> out_; |
| 16762 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16763 | at::functionalization::impl::sync(out); |
| 16764 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16765 | } else { |
| 16766 | out_ = out.vec(); |
| 16767 | } |
| 16768 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16769 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16770 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16771 | TORCH_INTERNAL_ASSERT(false, |
| 16772 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16773 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16774 | } else { |
| 16775 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16776 | at::AutoDispatchSkipFunctionalize guard; |
| 16777 | at::_ops::_foreach_tanh_out::call(self_, out_); |
| 16778 | ; |
| 16779 | } |
| 16780 | } else { |
| 16781 | ::std::vector<at::Tensor> tmp_output; |
| 16782 | { |
| 16783 | at::AutoDispatchSkipFunctionalize guard; |
| 16784 | tmp_output = at::_ops::_foreach_tanh::call(self_); |
| 16785 | } |
| 16786 | at::functionalization::impl::replace_(out, tmp_output); |
| 16787 | at::functionalization::impl::commit_update(out); |
| 16788 | at::functionalization::impl::sync(out); |
| 16789 | |
| 16790 | } |
| 16791 | } |
| 16792 | |
| 16793 | void _foreach_tanh_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 16794 | if (true) { |
| 16795 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16796 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16797 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16798 | auto self_meta = to_meta(self); |
| 16799 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16800 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16801 | at::_ops::_foreach_tanh_::call(self_meta); |
| 16802 | } |
| 16803 | |
| 16804 | ::std::vector<at::Tensor> self_; |
| 16805 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16806 | at::functionalization::impl::sync(self); |
| 16807 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16808 | } else { |
| 16809 | self_ = self.vec(); |
| 16810 | } |
| 16811 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 16812 | if ((false)) { |
| 16813 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16814 | TORCH_INTERNAL_ASSERT(false, |
| 16815 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16816 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16817 | } else { |
| 16818 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16819 | at::AutoDispatchSkipFunctionalize guard; |
| 16820 | at::_ops::_foreach_tanh_::call(self_); |
| 16821 | ; |
| 16822 | } |
| 16823 | } else { |
| 16824 | ::std::vector<at::Tensor> tmp_output; |
| 16825 | { |
| 16826 | at::AutoDispatchSkipFunctionalize guard; |
| 16827 | tmp_output = at::_ops::_foreach_tanh::call(self_); |
| 16828 | } |
| 16829 | at::functionalization::impl::replace_(self, tmp_output); |
| 16830 | at::functionalization::impl::commit_update(self); |
| 16831 | at::functionalization::impl::sync(self); |
| 16832 | |
| 16833 | } |
| 16834 | } |
| 16835 | |
| 16836 | void _foreach_sin_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 16837 | if (false) { |
| 16838 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16839 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16840 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16841 | auto self_meta = to_meta(self); |
| 16842 | auto out_meta = to_meta(out); |
| 16843 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16844 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16845 | at::_ops::_foreach_sin_out::call(self_meta, out_meta); |
| 16846 | } |
| 16847 | |
| 16848 | ::std::vector<at::Tensor> self_; |
| 16849 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16850 | at::functionalization::impl::sync(self); |
| 16851 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16852 | } else { |
| 16853 | self_ = self.vec(); |
| 16854 | } |
| 16855 | |
| 16856 | ::std::vector<at::Tensor> out_; |
| 16857 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16858 | at::functionalization::impl::sync(out); |
| 16859 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16860 | } else { |
| 16861 | out_ = out.vec(); |
| 16862 | } |
| 16863 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16864 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16865 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16866 | TORCH_INTERNAL_ASSERT(false, |
| 16867 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16868 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16869 | } else { |
| 16870 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16871 | at::AutoDispatchSkipFunctionalize guard; |
| 16872 | at::_ops::_foreach_sin_out::call(self_, out_); |
| 16873 | ; |
| 16874 | } |
| 16875 | } else { |
| 16876 | ::std::vector<at::Tensor> tmp_output; |
| 16877 | { |
| 16878 | at::AutoDispatchSkipFunctionalize guard; |
| 16879 | tmp_output = at::_ops::_foreach_sin::call(self_); |
| 16880 | } |
| 16881 | at::functionalization::impl::replace_(out, tmp_output); |
| 16882 | at::functionalization::impl::commit_update(out); |
| 16883 | at::functionalization::impl::sync(out); |
| 16884 | |
| 16885 | } |
| 16886 | } |
| 16887 | |
| 16888 | void _foreach_sin_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 16889 | if (true) { |
| 16890 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16891 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16892 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16893 | auto self_meta = to_meta(self); |
| 16894 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16895 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16896 | at::_ops::_foreach_sin_::call(self_meta); |
| 16897 | } |
| 16898 | |
| 16899 | ::std::vector<at::Tensor> self_; |
| 16900 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16901 | at::functionalization::impl::sync(self); |
| 16902 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16903 | } else { |
| 16904 | self_ = self.vec(); |
| 16905 | } |
| 16906 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 16907 | if ((false)) { |
| 16908 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16909 | TORCH_INTERNAL_ASSERT(false, |
| 16910 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16911 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16912 | } else { |
| 16913 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16914 | at::AutoDispatchSkipFunctionalize guard; |
| 16915 | at::_ops::_foreach_sin_::call(self_); |
| 16916 | ; |
| 16917 | } |
| 16918 | } else { |
| 16919 | ::std::vector<at::Tensor> tmp_output; |
| 16920 | { |
| 16921 | at::AutoDispatchSkipFunctionalize guard; |
| 16922 | tmp_output = at::_ops::_foreach_sin::call(self_); |
| 16923 | } |
| 16924 | at::functionalization::impl::replace_(self, tmp_output); |
| 16925 | at::functionalization::impl::commit_update(self); |
| 16926 | at::functionalization::impl::sync(self); |
| 16927 | |
| 16928 | } |
| 16929 | } |
| 16930 | |
| 16931 | void _foreach_frac_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 16932 | if (false) { |
| 16933 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16934 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16935 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16936 | auto self_meta = to_meta(self); |
| 16937 | auto out_meta = to_meta(out); |
| 16938 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16939 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16940 | at::_ops::_foreach_frac_out::call(self_meta, out_meta); |
| 16941 | } |
| 16942 | |
| 16943 | ::std::vector<at::Tensor> self_; |
| 16944 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16945 | at::functionalization::impl::sync(self); |
| 16946 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16947 | } else { |
| 16948 | self_ = self.vec(); |
| 16949 | } |
| 16950 | |
| 16951 | ::std::vector<at::Tensor> out_; |
| 16952 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16953 | at::functionalization::impl::sync(out); |
| 16954 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16955 | } else { |
| 16956 | out_ = out.vec(); |
| 16957 | } |
| 16958 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16959 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16960 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16961 | TORCH_INTERNAL_ASSERT(false, |
| 16962 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16963 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16964 | } else { |
| 16965 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16966 | at::AutoDispatchSkipFunctionalize guard; |
| 16967 | at::_ops::_foreach_frac_out::call(self_, out_); |
| 16968 | ; |
| 16969 | } |
| 16970 | } else { |
| 16971 | ::std::vector<at::Tensor> tmp_output; |
| 16972 | { |
| 16973 | at::AutoDispatchSkipFunctionalize guard; |
| 16974 | tmp_output = at::_ops::_foreach_frac::call(self_); |
| 16975 | } |
| 16976 | at::functionalization::impl::replace_(out, tmp_output); |
| 16977 | at::functionalization::impl::commit_update(out); |
| 16978 | at::functionalization::impl::sync(out); |
| 16979 | |
| 16980 | } |
| 16981 | } |
| 16982 | |
| 16983 | void _foreach_frac_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 16984 | if (true) { |
| 16985 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16986 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16987 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16988 | auto self_meta = to_meta(self); |
| 16989 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16990 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16991 | at::_ops::_foreach_frac_::call(self_meta); |
| 16992 | } |
| 16993 | |
| 16994 | ::std::vector<at::Tensor> self_; |
| 16995 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16996 | at::functionalization::impl::sync(self); |
| 16997 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16998 | } else { |
| 16999 | self_ = self.vec(); |
| 17000 | } |
| 17001 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 17002 | if ((false)) { |
| 17003 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17004 | TORCH_INTERNAL_ASSERT(false, |
| 17005 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17006 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17007 | } else { |
| 17008 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17009 | at::AutoDispatchSkipFunctionalize guard; |
| 17010 | at::_ops::_foreach_frac_::call(self_); |
| 17011 | ; |
| 17012 | } |
| 17013 | } else { |
| 17014 | ::std::vector<at::Tensor> tmp_output; |
| 17015 | { |
| 17016 | at::AutoDispatchSkipFunctionalize guard; |
| 17017 | tmp_output = at::_ops::_foreach_frac::call(self_); |
| 17018 | } |
| 17019 | at::functionalization::impl::replace_(self, tmp_output); |
| 17020 | at::functionalization::impl::commit_update(self); |
| 17021 | at::functionalization::impl::sync(self); |
| 17022 | |
| 17023 | } |
| 17024 | } |
| 17025 | |
| 17026 | void _foreach_reciprocal_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 17027 | if (false) { |
| 17028 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17029 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17030 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17031 | auto self_meta = to_meta(self); |
| 17032 | auto out_meta = to_meta(out); |
| 17033 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17034 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17035 | at::_ops::_foreach_reciprocal_out::call(self_meta, out_meta); |
| 17036 | } |
| 17037 | |
| 17038 | ::std::vector<at::Tensor> self_; |
| 17039 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17040 | at::functionalization::impl::sync(self); |
| 17041 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17042 | } else { |
| 17043 | self_ = self.vec(); |
| 17044 | } |
| 17045 | |
| 17046 | ::std::vector<at::Tensor> out_; |
| 17047 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17048 | at::functionalization::impl::sync(out); |
| 17049 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17050 | } else { |
| 17051 | out_ = out.vec(); |
| 17052 | } |
| 17053 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 17054 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 17055 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17056 | TORCH_INTERNAL_ASSERT(false, |
| 17057 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17058 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17059 | } else { |
| 17060 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17061 | at::AutoDispatchSkipFunctionalize guard; |
| 17062 | at::_ops::_foreach_reciprocal_out::call(self_, out_); |
| 17063 | ; |
| 17064 | } |
| 17065 | } else { |
| 17066 | ::std::vector<at::Tensor> tmp_output; |
| 17067 | { |
| 17068 | at::AutoDispatchSkipFunctionalize guard; |
| 17069 | tmp_output = at::_ops::_foreach_reciprocal::call(self_); |
| 17070 | } |
| 17071 | at::functionalization::impl::replace_(out, tmp_output); |
| 17072 | at::functionalization::impl::commit_update(out); |
| 17073 | at::functionalization::impl::sync(out); |
| 17074 | |
| 17075 | } |
| 17076 | } |
| 17077 | |
| 17078 | void _foreach_reciprocal_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 17079 | if (true) { |
| 17080 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17081 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17082 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17083 | auto self_meta = to_meta(self); |
| 17084 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17085 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17086 | at::_ops::_foreach_reciprocal_::call(self_meta); |
| 17087 | } |
| 17088 | |
| 17089 | ::std::vector<at::Tensor> self_; |
| 17090 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17091 | at::functionalization::impl::sync(self); |
| 17092 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17093 | } else { |
| 17094 | self_ = self.vec(); |
| 17095 | } |
| 17096 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 17097 | if ((false)) { |
| 17098 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17099 | TORCH_INTERNAL_ASSERT(false, |
| 17100 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17101 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17102 | } else { |
| 17103 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17104 | at::AutoDispatchSkipFunctionalize guard; |
| 17105 | at::_ops::_foreach_reciprocal_::call(self_); |
| 17106 | ; |
| 17107 | } |
| 17108 | } else { |
| 17109 | ::std::vector<at::Tensor> tmp_output; |
| 17110 | { |
| 17111 | at::AutoDispatchSkipFunctionalize guard; |
| 17112 | tmp_output = at::_ops::_foreach_reciprocal::call(self_); |
| 17113 | } |
| 17114 | at::functionalization::impl::replace_(self, tmp_output); |
| 17115 | at::functionalization::impl::commit_update(self); |
| 17116 | at::functionalization::impl::sync(self); |
| 17117 | |
| 17118 | } |
| 17119 | } |
| 17120 | |
| 17121 | void _foreach_trunc_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 17122 | if (false) { |
| 17123 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17124 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17125 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17126 | auto self_meta = to_meta(self); |
| 17127 | auto out_meta = to_meta(out); |
| 17128 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17129 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17130 | at::_ops::_foreach_trunc_out::call(self_meta, out_meta); |
| 17131 | } |
| 17132 | |
| 17133 | ::std::vector<at::Tensor> self_; |
| 17134 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17135 | at::functionalization::impl::sync(self); |
| 17136 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17137 | } else { |
| 17138 | self_ = self.vec(); |
| 17139 | } |
| 17140 | |
| 17141 | ::std::vector<at::Tensor> out_; |
| 17142 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17143 | at::functionalization::impl::sync(out); |
| 17144 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17145 | } else { |
| 17146 | out_ = out.vec(); |
| 17147 | } |
| 17148 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 17149 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 17150 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17151 | TORCH_INTERNAL_ASSERT(false, |
| 17152 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17153 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17154 | } else { |
| 17155 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17156 | at::AutoDispatchSkipFunctionalize guard; |
| 17157 | at::_ops::_foreach_trunc_out::call(self_, out_); |
| 17158 | ; |
| 17159 | } |
| 17160 | } else { |
| 17161 | ::std::vector<at::Tensor> tmp_output; |
| 17162 | { |
| 17163 | at::AutoDispatchSkipFunctionalize guard; |
| 17164 | tmp_output = at::_ops::_foreach_trunc::call(self_); |
| 17165 | } |
| 17166 | at::functionalization::impl::replace_(out, tmp_output); |
| 17167 | at::functionalization::impl::commit_update(out); |
| 17168 | at::functionalization::impl::sync(out); |
| 17169 | |
| 17170 | } |
| 17171 | } |
| 17172 | |
| 17173 | void _foreach_trunc_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 17174 | if (true) { |
| 17175 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17176 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17177 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17178 | auto self_meta = to_meta(self); |
| 17179 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17180 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17181 | at::_ops::_foreach_trunc_::call(self_meta); |
| 17182 | } |
| 17183 | |
| 17184 | ::std::vector<at::Tensor> self_; |
| 17185 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17186 | at::functionalization::impl::sync(self); |
| 17187 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17188 | } else { |
| 17189 | self_ = self.vec(); |
| 17190 | } |
| 17191 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 17192 | if ((false)) { |
| 17193 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17194 | TORCH_INTERNAL_ASSERT(false, |
| 17195 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17196 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17197 | } else { |
| 17198 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17199 | at::AutoDispatchSkipFunctionalize guard; |
| 17200 | at::_ops::_foreach_trunc_::call(self_); |
| 17201 | ; |
| 17202 | } |
| 17203 | } else { |
| 17204 | ::std::vector<at::Tensor> tmp_output; |
| 17205 | { |
| 17206 | at::AutoDispatchSkipFunctionalize guard; |
| 17207 | tmp_output = at::_ops::_foreach_trunc::call(self_); |
| 17208 | } |
| 17209 | at::functionalization::impl::replace_(self, tmp_output); |
| 17210 | at::functionalization::impl::commit_update(self); |
| 17211 | at::functionalization::impl::sync(self); |
| 17212 | |
| 17213 | } |
| 17214 | } |
| 17215 | |
| 17216 | at::Tensor & _convert_indices_from_csr_to_coo_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & crow_indices, const at::Tensor & col_indices, bool out_int32, bool transpose, at::Tensor & out) { |
| 17217 | if (false) { |
| 17218 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17219 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17220 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17221 | auto crow_indices_meta = to_meta(crow_indices); |
| 17222 | auto col_indices_meta = to_meta(col_indices); |
| 17223 | auto out_meta = to_meta(out); |
| 17224 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17225 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17226 | at::_ops::_convert_indices_from_csr_to_coo_out::call(crow_indices_meta, col_indices_meta, out_int32, transpose, out_meta); |
| 17227 | } |
| 17228 | |
| 17229 | at::Tensor crow_indices_; |
| 17230 | if (at::functionalization::impl::isFunctionalTensor(crow_indices)) { |
| 17231 | at::functionalization::impl::sync(crow_indices); |
| 17232 | crow_indices_ = at::functionalization::impl::from_functional_tensor(crow_indices); |
| 17233 | } else { |
| 17234 | crow_indices_ = crow_indices; |
| 17235 | } |
| 17236 | |
| 17237 | at::Tensor col_indices_; |
| 17238 | if (at::functionalization::impl::isFunctionalTensor(col_indices)) { |
| 17239 | at::functionalization::impl::sync(col_indices); |
| 17240 | col_indices_ = at::functionalization::impl::from_functional_tensor(col_indices); |
| 17241 | } else { |
| 17242 | col_indices_ = col_indices; |
| 17243 | } |
| 17244 | |
| 17245 | at::Tensor out_; |
| 17246 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17247 | at::functionalization::impl::sync(out); |
| 17248 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17249 | } else { |
| 17250 | out_ = out; |
| 17251 | } |
| 17252 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 17253 | if ((false || at::functionalization::impl::isFunctionalTensor(crow_indices) || at::functionalization::impl::isFunctionalTensor(col_indices))) { |
| 17254 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17255 | TORCH_INTERNAL_ASSERT(false, |
| 17256 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17257 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17258 | } else { |
| 17259 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17260 | at::AutoDispatchSkipFunctionalize guard; |
| 17261 | at::Tensor tmp_output = at::_ops::_convert_indices_from_csr_to_coo_out::call(crow_indices_, col_indices_, out_int32, transpose, out_); |
| 17262 | return out;; |
| 17263 | } |
| 17264 | } else { |
| 17265 | at::Tensor tmp_output; |
| 17266 | { |
| 17267 | at::AutoDispatchSkipFunctionalize guard; |
| 17268 | tmp_output = at::_ops::_convert_indices_from_csr_to_coo::call(crow_indices_, col_indices_, out_int32, transpose); |
| 17269 | } |
| 17270 | at::functionalization::impl::replace_(out, tmp_output); |
| 17271 | at::functionalization::impl::commit_update(out); |
| 17272 | at::functionalization::impl::sync(out); |
| 17273 | return out; |
| 17274 | } |
| 17275 | } |
| 17276 | |
| 17277 | at::Tensor & multilabel_margin_loss_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, const at::Tensor & is_target, at::Tensor & grad_input) { |
| 17278 | if (false) { |
| 17279 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17280 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17281 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17282 | auto grad_output_meta = to_meta(grad_output); |
| 17283 | auto self_meta = to_meta(self); |
| 17284 | auto target_meta = to_meta(target); |
| 17285 | auto is_target_meta = to_meta(is_target); |
| 17286 | auto grad_input_meta = to_meta(grad_input); |
| 17287 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17288 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17289 | at::_ops::multilabel_margin_loss_backward_grad_input::call(grad_output_meta, self_meta, target_meta, reduction, is_target_meta, grad_input_meta); |
| 17290 | } |
| 17291 | |
| 17292 | at::Tensor grad_output_; |
| 17293 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 17294 | at::functionalization::impl::sync(grad_output); |
| 17295 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 17296 | } else { |
| 17297 | grad_output_ = grad_output; |
| 17298 | } |
| 17299 | |
| 17300 | at::Tensor self_; |
| 17301 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17302 | at::functionalization::impl::sync(self); |
| 17303 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17304 | } else { |
| 17305 | self_ = self; |
| 17306 | } |
| 17307 | |
| 17308 | at::Tensor target_; |
| 17309 | if (at::functionalization::impl::isFunctionalTensor(target)) { |
| 17310 | at::functionalization::impl::sync(target); |
| 17311 | target_ = at::functionalization::impl::from_functional_tensor(target); |
| 17312 | } else { |
| 17313 | target_ = target; |
| 17314 | } |
| 17315 | |
| 17316 | at::Tensor is_target_; |
| 17317 | if (at::functionalization::impl::isFunctionalTensor(is_target)) { |
| 17318 | at::functionalization::impl::sync(is_target); |
| 17319 | is_target_ = at::functionalization::impl::from_functional_tensor(is_target); |
| 17320 | } else { |
| 17321 | is_target_ = is_target; |
| 17322 | } |
| 17323 | |
| 17324 | at::Tensor grad_input_; |
| 17325 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 17326 | at::functionalization::impl::sync(grad_input); |
| 17327 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 17328 | } else { |
| 17329 | grad_input_ = grad_input; |
| 17330 | } |
| 17331 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 17332 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(target) || at::functionalization::impl::isFunctionalTensor(is_target))) { |
| 17333 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17334 | TORCH_INTERNAL_ASSERT(false, |
| 17335 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17336 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17337 | } else { |
| 17338 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17339 | at::AutoDispatchSkipFunctionalize guard; |
| 17340 | at::Tensor tmp_output = at::_ops::multilabel_margin_loss_backward_grad_input::call(grad_output_, self_, target_, reduction, is_target_, grad_input_); |
| 17341 | return grad_input;; |
| 17342 | } |
| 17343 | } else { |
| 17344 | at::Tensor tmp_output; |
| 17345 | { |
| 17346 | at::AutoDispatchSkipFunctionalize guard; |
| 17347 | tmp_output = at::_ops::multilabel_margin_loss_backward::call(grad_output_, self_, target_, reduction, is_target_); |
| 17348 | } |
| 17349 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 17350 | at::functionalization::impl::commit_update(grad_input); |
| 17351 | at::functionalization::impl::sync(grad_input); |
| 17352 | return grad_input; |
| 17353 | } |
| 17354 | } |
| 17355 | |
| 17356 | at::Tensor & nll_loss_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, at::Tensor & out) { |
| 17357 | if (false) { |
| 17358 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17359 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17360 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17361 | auto self_meta = to_meta(self); |
| 17362 | auto target_meta = to_meta(target); |
| 17363 | auto weight_meta = to_meta(weight); |
| 17364 | auto out_meta = to_meta(out); |
| 17365 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17366 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17367 | at::_ops::nll_loss_out::call(self_meta, target_meta, weight_meta, reduction, ignore_index, out_meta); |
| 17368 | } |
| 17369 | |
| 17370 | at::Tensor self_; |
| 17371 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17372 | at::functionalization::impl::sync(self); |
| 17373 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17374 | } else { |
| 17375 | self_ = self; |
| 17376 | } |
| 17377 | |
| 17378 | at::Tensor target_; |
| 17379 | if (at::functionalization::impl::isFunctionalTensor(target)) { |
| 17380 | at::functionalization::impl::sync(target); |
| 17381 | target_ = at::functionalization::impl::from_functional_tensor(target); |
| 17382 | } else { |
| 17383 | target_ = target; |
| 17384 | } |
| 17385 | |
| 17386 | c10::optional<at::Tensor> weight_; |
| 17387 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 17388 | at::functionalization::impl::sync(weight); |
| 17389 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 17390 | } else { |
| 17391 | weight_ = weight; |
| 17392 | } |
| 17393 | |
| 17394 | at::Tensor out_; |
| 17395 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17396 | at::functionalization::impl::sync(out); |
| 17397 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17398 | } else { |
| 17399 | out_ = out; |
| 17400 | } |
| 17401 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 17402 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(target) || at::functionalization::impl::isFunctionalTensor(weight))) { |
| 17403 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17404 | TORCH_INTERNAL_ASSERT(false, |
| 17405 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17406 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17407 | } else { |
| 17408 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17409 | at::AutoDispatchSkipFunctionalize guard; |
| 17410 | at::Tensor tmp_output = at::_ops::nll_loss_out::call(self_, target_, weight_, reduction, ignore_index, out_); |
| 17411 | return out;; |
| 17412 | } |
| 17413 | } else { |
| 17414 | at::Tensor tmp_output; |
| 17415 | { |
| 17416 | at::AutoDispatchSkipFunctionalize guard; |
| 17417 | tmp_output = at::_ops::nll_loss::call(self_, target_, weight_, reduction, ignore_index); |
| 17418 | } |
| 17419 | at::functionalization::impl::replace_(out, tmp_output); |
| 17420 | at::functionalization::impl::commit_update(out); |
| 17421 | at::functionalization::impl::sync(out); |
| 17422 | return out; |
| 17423 | } |
| 17424 | } |
| 17425 | |
| 17426 | at::Tensor & nll_loss_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight, at::Tensor & grad_input) { |
| 17427 | if (false) { |
| 17428 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17429 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17430 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17431 | auto grad_output_meta = to_meta(grad_output); |
| 17432 | auto self_meta = to_meta(self); |
| 17433 | auto target_meta = to_meta(target); |
| 17434 | auto weight_meta = to_meta(weight); |
| 17435 | auto total_weight_meta = to_meta(total_weight); |
| 17436 | auto grad_input_meta = to_meta(grad_input); |
| 17437 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17438 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17439 | at::_ops::nll_loss_backward_grad_input::call(grad_output_meta, self_meta, target_meta, weight_meta, reduction, ignore_index, total_weight_meta, grad_input_meta); |
| 17440 | } |
| 17441 | |
| 17442 | at::Tensor grad_output_; |
| 17443 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 17444 | at::functionalization::impl::sync(grad_output); |
| 17445 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 17446 | } else { |
| 17447 | grad_output_ = grad_output; |
| 17448 | } |
| 17449 | |
| 17450 | at::Tensor self_; |
| 17451 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17452 | at::functionalization::impl::sync(self); |
| 17453 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17454 | } else { |
| 17455 | self_ = self; |
| 17456 | } |
| 17457 | |
| 17458 | at::Tensor target_; |
| 17459 | if (at::functionalization::impl::isFunctionalTensor(target)) { |
| 17460 | at::functionalization::impl::sync(target); |
| 17461 | target_ = at::functionalization::impl::from_functional_tensor(target); |
| 17462 | } else { |
| 17463 | target_ = target; |
| 17464 | } |
| 17465 | |
| 17466 | c10::optional<at::Tensor> weight_; |
| 17467 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 17468 | at::functionalization::impl::sync(weight); |
| 17469 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 17470 | } else { |
| 17471 | weight_ = weight; |
| 17472 | } |
| 17473 | |
| 17474 | at::Tensor total_weight_; |
| 17475 | if (at::functionalization::impl::isFunctionalTensor(total_weight)) { |
| 17476 | at::functionalization::impl::sync(total_weight); |
| 17477 | total_weight_ = at::functionalization::impl::from_functional_tensor(total_weight); |
| 17478 | } else { |
| 17479 | total_weight_ = total_weight; |
| 17480 | } |
| 17481 | |
| 17482 | at::Tensor grad_input_; |
| 17483 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 17484 | at::functionalization::impl::sync(grad_input); |
| 17485 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 17486 | } else { |
| 17487 | grad_input_ = grad_input; |
| 17488 | } |
| 17489 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 17490 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(target) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(total_weight))) { |
| 17491 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17492 | TORCH_INTERNAL_ASSERT(false, |
| 17493 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17494 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17495 | } else { |
| 17496 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17497 | at::AutoDispatchSkipFunctionalize guard; |
| 17498 | at::Tensor tmp_output = at::_ops::nll_loss_backward_grad_input::call(grad_output_, self_, target_, weight_, reduction, ignore_index, total_weight_, grad_input_); |
| 17499 | return grad_input;; |
| 17500 | } |
| 17501 | } else { |
| 17502 | at::Tensor tmp_output; |
| 17503 | { |
| 17504 | at::AutoDispatchSkipFunctionalize guard; |
| 17505 | tmp_output = at::_ops::nll_loss_backward::call(grad_output_, self_, target_, weight_, reduction, ignore_index, total_weight_); |
| 17506 | } |
| 17507 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 17508 | at::functionalization::impl::commit_update(grad_input); |
| 17509 | at::functionalization::impl::sync(grad_input); |
| 17510 | return grad_input; |
| 17511 | } |
| 17512 | } |
| 17513 | |
| 17514 | at::Tensor & nll_loss2d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, at::Tensor & out) { |
| 17515 | if (false) { |
| 17516 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17517 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17518 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17519 | auto self_meta = to_meta(self); |
| 17520 | auto target_meta = to_meta(target); |
| 17521 | auto weight_meta = to_meta(weight); |
| 17522 | auto out_meta = to_meta(out); |
| 17523 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17524 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17525 | at::_ops::nll_loss2d_out::call(self_meta, target_meta, weight_meta, reduction, ignore_index, out_meta); |
| 17526 | } |
| 17527 | |
| 17528 | at::Tensor self_; |
| 17529 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17530 | at::functionalization::impl::sync(self); |
| 17531 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17532 | } else { |
| 17533 | self_ = self; |
| 17534 | } |
| 17535 | |
| 17536 | at::Tensor target_; |
| 17537 | if (at::functionalization::impl::isFunctionalTensor(target)) { |
| 17538 | at::functionalization::impl::sync(target); |
| 17539 | target_ = at::functionalization::impl::from_functional_tensor(target); |
| 17540 | } else { |
| 17541 | target_ = target; |
| 17542 | } |
| 17543 | |
| 17544 | c10::optional<at::Tensor> weight_; |
| 17545 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 17546 | at::functionalization::impl::sync(weight); |
| 17547 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 17548 | } else { |
| 17549 | weight_ = weight; |
| 17550 | } |
| 17551 | |
| 17552 | at::Tensor out_; |
| 17553 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17554 | at::functionalization::impl::sync(out); |
| 17555 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17556 | } else { |
| 17557 | out_ = out; |
| 17558 | } |
| 17559 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 17560 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(target) || at::functionalization::impl::isFunctionalTensor(weight))) { |
| 17561 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17562 | TORCH_INTERNAL_ASSERT(false, |
| 17563 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17564 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17565 | } else { |
| 17566 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17567 | at::AutoDispatchSkipFunctionalize guard; |
| 17568 | at::Tensor tmp_output = at::_ops::nll_loss2d_out::call(self_, target_, weight_, reduction, ignore_index, out_); |
| 17569 | return out;; |
| 17570 | } |
| 17571 | } else { |
| 17572 | at::Tensor tmp_output; |
| 17573 | { |
| 17574 | at::AutoDispatchSkipFunctionalize guard; |
| 17575 | tmp_output = at::_ops::nll_loss2d::call(self_, target_, weight_, reduction, ignore_index); |
| 17576 | } |
| 17577 | at::functionalization::impl::replace_(out, tmp_output); |
| 17578 | at::functionalization::impl::commit_update(out); |
| 17579 | at::functionalization::impl::sync(out); |
| 17580 | return out; |
| 17581 | } |
| 17582 | } |
| 17583 | |
| 17584 | at::Tensor & nll_loss2d_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight, at::Tensor & grad_input) { |
| 17585 | if (false) { |
| 17586 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17587 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17588 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17589 | auto grad_output_meta = to_meta(grad_output); |
| 17590 | auto self_meta = to_meta(self); |
| 17591 | auto target_meta = to_meta(target); |
| 17592 | auto weight_meta = to_meta(weight); |
| 17593 | auto total_weight_meta = to_meta(total_weight); |
| 17594 | auto grad_input_meta = to_meta(grad_input); |
| 17595 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17596 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17597 | at::_ops::nll_loss2d_backward_grad_input::call(grad_output_meta, self_meta, target_meta, weight_meta, reduction, ignore_index, total_weight_meta, grad_input_meta); |
| 17598 | } |
| 17599 | |
| 17600 | at::Tensor grad_output_; |
| 17601 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 17602 | at::functionalization::impl::sync(grad_output); |
| 17603 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 17604 | } else { |
| 17605 | grad_output_ = grad_output; |
| 17606 | } |
| 17607 | |
| 17608 | at::Tensor self_; |
| 17609 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17610 | at::functionalization::impl::sync(self); |
| 17611 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17612 | } else { |
| 17613 | self_ = self; |
| 17614 | } |
| 17615 | |
| 17616 | at::Tensor target_; |
| 17617 | if (at::functionalization::impl::isFunctionalTensor(target)) { |
| 17618 | at::functionalization::impl::sync(target); |
| 17619 | target_ = at::functionalization::impl::from_functional_tensor(target); |
| 17620 | } else { |
| 17621 | target_ = target; |
| 17622 | } |
| 17623 | |
| 17624 | c10::optional<at::Tensor> weight_; |
| 17625 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 17626 | at::functionalization::impl::sync(weight); |
| 17627 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 17628 | } else { |
| 17629 | weight_ = weight; |
| 17630 | } |
| 17631 | |
| 17632 | at::Tensor total_weight_; |
| 17633 | if (at::functionalization::impl::isFunctionalTensor(total_weight)) { |
| 17634 | at::functionalization::impl::sync(total_weight); |
| 17635 | total_weight_ = at::functionalization::impl::from_functional_tensor(total_weight); |
| 17636 | } else { |
| 17637 | total_weight_ = total_weight; |
| 17638 | } |
| 17639 | |
| 17640 | at::Tensor grad_input_; |
| 17641 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 17642 | at::functionalization::impl::sync(grad_input); |
| 17643 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 17644 | } else { |
| 17645 | grad_input_ = grad_input; |
| 17646 | } |
| 17647 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 17648 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(target) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(total_weight))) { |
| 17649 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17650 | TORCH_INTERNAL_ASSERT(false, |
| 17651 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17652 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17653 | } else { |
| 17654 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17655 | at::AutoDispatchSkipFunctionalize guard; |
| 17656 | at::Tensor tmp_output = at::_ops::nll_loss2d_backward_grad_input::call(grad_output_, self_, target_, weight_, reduction, ignore_index, total_weight_, grad_input_); |
| 17657 | return grad_input;; |
| 17658 | } |
| 17659 | } else { |
| 17660 | at::Tensor tmp_output; |
| 17661 | { |
| 17662 | at::AutoDispatchSkipFunctionalize guard; |
| 17663 | tmp_output = at::_ops::nll_loss2d_backward::call(grad_output_, self_, target_, weight_, reduction, ignore_index, total_weight_); |
| 17664 | } |
| 17665 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 17666 | at::functionalization::impl::commit_update(grad_input); |
| 17667 | at::functionalization::impl::sync(grad_input); |
| 17668 | return grad_input; |
| 17669 | } |
| 17670 | } |
| 17671 | |
| 17672 | at::Tensor & smooth_l1_loss_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta, at::Tensor & out) { |
| 17673 | if (false) { |
| 17674 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17675 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17676 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17677 | auto self_meta = to_meta(self); |
| 17678 | auto target_meta = to_meta(target); |
| 17679 | auto out_meta = to_meta(out); |
| 17680 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17681 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17682 | at::_ops::smooth_l1_loss_out::call(self_meta, target_meta, reduction, beta, out_meta); |
| 17683 | } |
| 17684 | |
| 17685 | at::Tensor self_; |
| 17686 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17687 | at::functionalization::impl::sync(self); |
| 17688 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17689 | } else { |
| 17690 | self_ = self; |
| 17691 | } |
| 17692 | |
| 17693 | at::Tensor target_; |
| 17694 | if (at::functionalization::impl::isFunctionalTensor(target)) { |
| 17695 | at::functionalization::impl::sync(target); |
| 17696 | target_ = at::functionalization::impl::from_functional_tensor(target); |
| 17697 | } else { |
| 17698 | target_ = target; |
| 17699 | } |
| 17700 | |
| 17701 | at::Tensor out_; |
| 17702 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17703 | at::functionalization::impl::sync(out); |
| 17704 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17705 | } else { |
| 17706 | out_ = out; |
| 17707 | } |
| 17708 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 17709 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(target))) { |
| 17710 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17711 | TORCH_INTERNAL_ASSERT(false, |
| 17712 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17713 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17714 | } else { |
| 17715 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17716 | at::AutoDispatchSkipFunctionalize guard; |
| 17717 | at::Tensor tmp_output = at::_ops::smooth_l1_loss_out::call(self_, target_, reduction, beta, out_); |
| 17718 | return out;; |
| 17719 | } |
| 17720 | } else { |
| 17721 | at::Tensor tmp_output; |
| 17722 | { |
| 17723 | at::AutoDispatchSkipFunctionalize guard; |
| 17724 | tmp_output = at::_ops::smooth_l1_loss::call(self_, target_, reduction, beta); |
| 17725 | } |
| 17726 | at::functionalization::impl::replace_(out, tmp_output); |
| 17727 | at::functionalization::impl::commit_update(out); |
| 17728 | at::functionalization::impl::sync(out); |
| 17729 | return out; |
| 17730 | } |
| 17731 | } |
| 17732 | |
| 17733 | at::Tensor & huber_loss_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta, at::Tensor & out) { |
| 17734 | if (false) { |
| 17735 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17736 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17737 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17738 | auto self_meta = to_meta(self); |
| 17739 | auto target_meta = to_meta(target); |
| 17740 | auto out_meta = to_meta(out); |
| 17741 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17742 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17743 | at::_ops::huber_loss_out::call(self_meta, target_meta, reduction, delta, out_meta); |
| 17744 | } |
| 17745 | |
| 17746 | at::Tensor self_; |
| 17747 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17748 | at::functionalization::impl::sync(self); |
| 17749 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17750 | } else { |
| 17751 | self_ = self; |
| 17752 | } |
| 17753 | |
| 17754 | at::Tensor target_; |
| 17755 | if (at::functionalization::impl::isFunctionalTensor(target)) { |
| 17756 | at::functionalization::impl::sync(target); |
| 17757 | target_ = at::functionalization::impl::from_functional_tensor(target); |
| 17758 | } else { |
| 17759 | target_ = target; |
| 17760 | } |
| 17761 | |
| 17762 | at::Tensor out_; |
| 17763 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17764 | at::functionalization::impl::sync(out); |
| 17765 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17766 | } else { |
| 17767 | out_ = out; |
| 17768 | } |
| 17769 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 17770 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(target))) { |
| 17771 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17772 | TORCH_INTERNAL_ASSERT(false, |
| 17773 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17774 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17775 | } else { |
| 17776 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17777 | at::AutoDispatchSkipFunctionalize guard; |
| 17778 | at::Tensor tmp_output = at::_ops::huber_loss_out::call(self_, target_, reduction, delta, out_); |
| 17779 | return out;; |
| 17780 | } |
| 17781 | } else { |
| 17782 | at::Tensor tmp_output; |
| 17783 | { |
| 17784 | at::AutoDispatchSkipFunctionalize guard; |
| 17785 | tmp_output = at::_ops::huber_loss::call(self_, target_, reduction, delta); |
| 17786 | } |
| 17787 | at::functionalization::impl::replace_(out, tmp_output); |
| 17788 | at::functionalization::impl::commit_update(out); |
| 17789 | at::functionalization::impl::sync(out); |
| 17790 | return out; |
| 17791 | } |
| 17792 | } |
| 17793 | |
| 17794 | at::Tensor & soft_margin_loss_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & grad_input) { |
| 17795 | if (false) { |
| 17796 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17797 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17798 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17799 | auto grad_output_meta = to_meta(grad_output); |
| 17800 | auto self_meta = to_meta(self); |
| 17801 | auto target_meta = to_meta(target); |
| 17802 | auto grad_input_meta = to_meta(grad_input); |
| 17803 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17804 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17805 | at::_ops::soft_margin_loss_backward_grad_input::call(grad_output_meta, self_meta, target_meta, reduction, grad_input_meta); |
| 17806 | } |
| 17807 | |
| 17808 | at::Tensor grad_output_; |
| 17809 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 17810 | at::functionalization::impl::sync(grad_output); |
| 17811 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 17812 | } else { |
| 17813 | grad_output_ = grad_output; |
| 17814 | } |
| 17815 | |
| 17816 | at::Tensor self_; |
| 17817 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17818 | at::functionalization::impl::sync(self); |
| 17819 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17820 | } else { |
| 17821 | self_ = self; |
| 17822 | } |
| 17823 | |
| 17824 | at::Tensor target_; |
| 17825 | if (at::functionalization::impl::isFunctionalTensor(target)) { |
| 17826 | at::functionalization::impl::sync(target); |
| 17827 | target_ = at::functionalization::impl::from_functional_tensor(target); |
| 17828 | } else { |
| 17829 | target_ = target; |
| 17830 | } |
| 17831 | |
| 17832 | at::Tensor grad_input_; |
| 17833 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 17834 | at::functionalization::impl::sync(grad_input); |
| 17835 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 17836 | } else { |
| 17837 | grad_input_ = grad_input; |
| 17838 | } |
| 17839 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 17840 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(target))) { |
| 17841 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17842 | TORCH_INTERNAL_ASSERT(false, |
| 17843 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17844 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17845 | } else { |
| 17846 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17847 | at::AutoDispatchSkipFunctionalize guard; |
| 17848 | at::Tensor tmp_output = at::_ops::soft_margin_loss_backward_grad_input::call(grad_output_, self_, target_, reduction, grad_input_); |
| 17849 | return grad_input;; |
| 17850 | } |
| 17851 | } else { |
| 17852 | at::Tensor tmp_output; |
| 17853 | { |
| 17854 | at::AutoDispatchSkipFunctionalize guard; |
| 17855 | tmp_output = at::_ops::soft_margin_loss_backward::call(grad_output_, self_, target_, reduction); |
| 17856 | } |
| 17857 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 17858 | at::functionalization::impl::commit_update(grad_input); |
| 17859 | at::functionalization::impl::sync(grad_input); |
| 17860 | return grad_input; |
| 17861 | } |
| 17862 | } |
| 17863 | |
| 17864 | at::Tensor & elu_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale, at::Tensor & out) { |
| 17865 | if (false) { |
| 17866 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17867 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17868 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17869 | auto self_meta = to_meta(self); |
| 17870 | auto out_meta = to_meta(out); |
| 17871 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17872 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17873 | at::_ops::elu_out::call(self_meta, alpha, scale, input_scale, out_meta); |
| 17874 | } |
| 17875 | |
| 17876 | at::Tensor self_; |
| 17877 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17878 | at::functionalization::impl::sync(self); |
| 17879 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17880 | } else { |
| 17881 | self_ = self; |
| 17882 | } |
| 17883 | |
| 17884 | at::Tensor out_; |
| 17885 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17886 | at::functionalization::impl::sync(out); |
| 17887 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17888 | } else { |
| 17889 | out_ = out; |
| 17890 | } |
| 17891 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 17892 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 17893 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17894 | TORCH_INTERNAL_ASSERT(false, |
| 17895 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17896 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17897 | } else { |
| 17898 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17899 | at::AutoDispatchSkipFunctionalize guard; |
| 17900 | at::Tensor tmp_output = at::_ops::elu_out::call(self_, alpha, scale, input_scale, out_); |
| 17901 | return out;; |
| 17902 | } |
| 17903 | } else { |
| 17904 | at::Tensor tmp_output; |
| 17905 | { |
| 17906 | at::AutoDispatchSkipFunctionalize guard; |
| 17907 | tmp_output = at::_ops::elu::call(self_, alpha, scale, input_scale); |
| 17908 | } |
| 17909 | at::functionalization::impl::replace_(out, tmp_output); |
| 17910 | at::functionalization::impl::commit_update(out); |
| 17911 | at::functionalization::impl::sync(out); |
| 17912 | return out; |
| 17913 | } |
| 17914 | } |
| 17915 | |
| 17916 | at::Tensor & elu_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale) { |
| 17917 | if (true) { |
| 17918 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17919 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17920 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17921 | auto self_meta = to_meta(self); |
| 17922 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17923 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17924 | at::_ops::elu_::call(self_meta, alpha, scale, input_scale); |
| 17925 | } |
| 17926 | |
| 17927 | at::Tensor self_; |
| 17928 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17929 | at::functionalization::impl::sync(self); |
| 17930 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17931 | } else { |
| 17932 | self_ = self; |
| 17933 | } |
| 17934 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 17935 | if ((false)) { |
| 17936 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17937 | TORCH_INTERNAL_ASSERT(false, |
| 17938 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17939 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17940 | } else { |
| 17941 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17942 | at::AutoDispatchSkipFunctionalize guard; |
| 17943 | at::Tensor tmp_output = at::_ops::elu_::call(self_, alpha, scale, input_scale); |
| 17944 | return self;; |
| 17945 | } |
| 17946 | } else { |
| 17947 | at::Tensor tmp_output; |
| 17948 | { |
| 17949 | at::AutoDispatchSkipFunctionalize guard; |
| 17950 | tmp_output = at::_ops::elu::call(self_, alpha, scale, input_scale); |
| 17951 | } |
| 17952 | at::functionalization::impl::replace_(self, tmp_output); |
| 17953 | at::functionalization::impl::commit_update(self); |
| 17954 | at::functionalization::impl::sync(self); |
| 17955 | return self; |
| 17956 | } |
| 17957 | } |
| 17958 | |
| 17959 | at::Tensor & glu_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, at::Tensor & out) { |
| 17960 | if (false) { |
| 17961 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17962 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17963 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17964 | auto self_meta = to_meta(self); |
| 17965 | auto out_meta = to_meta(out); |
| 17966 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17967 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17968 | at::_ops::glu_out::call(self_meta, dim, out_meta); |
| 17969 | } |
| 17970 | |
| 17971 | at::Tensor self_; |
| 17972 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17973 | at::functionalization::impl::sync(self); |
| 17974 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17975 | } else { |
| 17976 | self_ = self; |
| 17977 | } |
| 17978 | |
| 17979 | at::Tensor out_; |
| 17980 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17981 | at::functionalization::impl::sync(out); |
| 17982 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17983 | } else { |
| 17984 | out_ = out; |
| 17985 | } |
| 17986 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 17987 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 17988 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17989 | TORCH_INTERNAL_ASSERT(false, |
| 17990 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17991 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17992 | } else { |
| 17993 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17994 | at::AutoDispatchSkipFunctionalize guard; |
| 17995 | at::Tensor tmp_output = at::_ops::glu_out::call(self_, dim, out_); |
| 17996 | return out;; |
| 17997 | } |
| 17998 | } else { |
| 17999 | at::Tensor tmp_output; |
| 18000 | { |
| 18001 | at::AutoDispatchSkipFunctionalize guard; |
| 18002 | tmp_output = at::_ops::glu::call(self_, dim); |
| 18003 | } |
| 18004 | at::functionalization::impl::replace_(out, tmp_output); |
| 18005 | at::functionalization::impl::commit_update(out); |
| 18006 | at::functionalization::impl::sync(out); |
| 18007 | return out; |
| 18008 | } |
| 18009 | } |
| 18010 | |
| 18011 | at::Tensor & hardsigmoid_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 18012 | if (false) { |
| 18013 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18014 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18015 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18016 | auto self_meta = to_meta(self); |
| 18017 | auto out_meta = to_meta(out); |
| 18018 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18019 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18020 | at::_ops::hardsigmoid_out::call(self_meta, out_meta); |
| 18021 | } |
| 18022 | |
| 18023 | at::Tensor self_; |
| 18024 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18025 | at::functionalization::impl::sync(self); |
| 18026 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18027 | } else { |
| 18028 | self_ = self; |
| 18029 | } |
| 18030 | |
| 18031 | at::Tensor out_; |
| 18032 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18033 | at::functionalization::impl::sync(out); |
| 18034 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18035 | } else { |
| 18036 | out_ = out; |
| 18037 | } |
| 18038 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18039 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18040 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18041 | TORCH_INTERNAL_ASSERT(false, |
| 18042 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18043 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18044 | } else { |
| 18045 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18046 | at::AutoDispatchSkipFunctionalize guard; |
| 18047 | at::Tensor tmp_output = at::_ops::hardsigmoid_out::call(self_, out_); |
| 18048 | return out;; |
| 18049 | } |
| 18050 | } else { |
| 18051 | at::Tensor tmp_output; |
| 18052 | { |
| 18053 | at::AutoDispatchSkipFunctionalize guard; |
| 18054 | tmp_output = at::_ops::hardsigmoid::call(self_); |
| 18055 | } |
| 18056 | at::functionalization::impl::replace_(out, tmp_output); |
| 18057 | at::functionalization::impl::commit_update(out); |
| 18058 | at::functionalization::impl::sync(out); |
| 18059 | return out; |
| 18060 | } |
| 18061 | } |
| 18062 | |
| 18063 | at::Tensor & hardsigmoid_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 18064 | if (true) { |
| 18065 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18066 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18067 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18068 | auto self_meta = to_meta(self); |
| 18069 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18070 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18071 | at::_ops::hardsigmoid_::call(self_meta); |
| 18072 | } |
| 18073 | |
| 18074 | at::Tensor self_; |
| 18075 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18076 | at::functionalization::impl::sync(self); |
| 18077 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18078 | } else { |
| 18079 | self_ = self; |
| 18080 | } |
| 18081 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 18082 | if ((false)) { |
| 18083 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18084 | TORCH_INTERNAL_ASSERT(false, |
| 18085 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18086 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18087 | } else { |
| 18088 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18089 | at::AutoDispatchSkipFunctionalize guard; |
| 18090 | at::Tensor tmp_output = at::_ops::hardsigmoid_::call(self_); |
| 18091 | return self;; |
| 18092 | } |
| 18093 | } else { |
| 18094 | at::Tensor tmp_output; |
| 18095 | { |
| 18096 | at::AutoDispatchSkipFunctionalize guard; |
| 18097 | tmp_output = at::_ops::hardsigmoid::call(self_); |
| 18098 | } |
| 18099 | at::functionalization::impl::replace_(self, tmp_output); |
| 18100 | at::functionalization::impl::commit_update(self); |
| 18101 | at::functionalization::impl::sync(self); |
| 18102 | return self; |
| 18103 | } |
| 18104 | } |
| 18105 | |
| 18106 | at::Tensor & leaky_relu_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & negative_slope, bool self_is_result, at::Tensor & grad_input) { |
| 18107 | if (false) { |
| 18108 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18109 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18110 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18111 | auto grad_output_meta = to_meta(grad_output); |
| 18112 | auto self_meta = to_meta(self); |
| 18113 | auto grad_input_meta = to_meta(grad_input); |
| 18114 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18115 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18116 | at::_ops::leaky_relu_backward_grad_input::call(grad_output_meta, self_meta, negative_slope, self_is_result, grad_input_meta); |
| 18117 | } |
| 18118 | |
| 18119 | at::Tensor grad_output_; |
| 18120 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 18121 | at::functionalization::impl::sync(grad_output); |
| 18122 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 18123 | } else { |
| 18124 | grad_output_ = grad_output; |
| 18125 | } |
| 18126 | |
| 18127 | at::Tensor self_; |
| 18128 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18129 | at::functionalization::impl::sync(self); |
| 18130 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18131 | } else { |
| 18132 | self_ = self; |
| 18133 | } |
| 18134 | |
| 18135 | at::Tensor grad_input_; |
| 18136 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 18137 | at::functionalization::impl::sync(grad_input); |
| 18138 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 18139 | } else { |
| 18140 | grad_input_ = grad_input; |
| 18141 | } |
| 18142 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 18143 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18144 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18145 | TORCH_INTERNAL_ASSERT(false, |
| 18146 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18147 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18148 | } else { |
| 18149 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18150 | at::AutoDispatchSkipFunctionalize guard; |
| 18151 | at::Tensor tmp_output = at::_ops::leaky_relu_backward_grad_input::call(grad_output_, self_, negative_slope, self_is_result, grad_input_); |
| 18152 | return grad_input;; |
| 18153 | } |
| 18154 | } else { |
| 18155 | at::Tensor tmp_output; |
| 18156 | { |
| 18157 | at::AutoDispatchSkipFunctionalize guard; |
| 18158 | tmp_output = at::_ops::leaky_relu_backward::call(grad_output_, self_, negative_slope, self_is_result); |
| 18159 | } |
| 18160 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 18161 | at::functionalization::impl::commit_update(grad_input); |
| 18162 | at::functionalization::impl::sync(grad_input); |
| 18163 | return grad_input; |
| 18164 | } |
| 18165 | } |
| 18166 | |
| 18167 | at::Tensor & softshrink_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & lambd, at::Tensor & out) { |
| 18168 | if (false) { |
| 18169 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18170 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18171 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18172 | auto self_meta = to_meta(self); |
| 18173 | auto out_meta = to_meta(out); |
| 18174 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18175 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18176 | at::_ops::softshrink_out::call(self_meta, lambd, out_meta); |
| 18177 | } |
| 18178 | |
| 18179 | at::Tensor self_; |
| 18180 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18181 | at::functionalization::impl::sync(self); |
| 18182 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18183 | } else { |
| 18184 | self_ = self; |
| 18185 | } |
| 18186 | |
| 18187 | at::Tensor out_; |
| 18188 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18189 | at::functionalization::impl::sync(out); |
| 18190 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18191 | } else { |
| 18192 | out_ = out; |
| 18193 | } |
| 18194 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18195 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18196 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18197 | TORCH_INTERNAL_ASSERT(false, |
| 18198 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18199 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18200 | } else { |
| 18201 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18202 | at::AutoDispatchSkipFunctionalize guard; |
| 18203 | at::Tensor tmp_output = at::_ops::softshrink_out::call(self_, lambd, out_); |
| 18204 | return out;; |
| 18205 | } |
| 18206 | } else { |
| 18207 | at::Tensor tmp_output; |
| 18208 | { |
| 18209 | at::AutoDispatchSkipFunctionalize guard; |
| 18210 | tmp_output = at::_ops::softshrink::call(self_, lambd); |
| 18211 | } |
| 18212 | at::functionalization::impl::replace_(out, tmp_output); |
| 18213 | at::functionalization::impl::commit_update(out); |
| 18214 | at::functionalization::impl::sync(out); |
| 18215 | return out; |
| 18216 | } |
| 18217 | } |
| 18218 | |
| 18219 | at::Tensor & _adaptive_avg_pool2d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out) { |
| 18220 | if (false) { |
| 18221 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18222 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18223 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18224 | auto self_meta = to_meta(self); |
| 18225 | auto out_meta = to_meta(out); |
| 18226 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18227 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18228 | at::_ops::_adaptive_avg_pool2d_out::call(self_meta, output_size, out_meta); |
| 18229 | } |
| 18230 | |
| 18231 | at::Tensor self_; |
| 18232 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18233 | at::functionalization::impl::sync(self); |
| 18234 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18235 | } else { |
| 18236 | self_ = self; |
| 18237 | } |
| 18238 | |
| 18239 | at::Tensor out_; |
| 18240 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18241 | at::functionalization::impl::sync(out); |
| 18242 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18243 | } else { |
| 18244 | out_ = out; |
| 18245 | } |
| 18246 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18247 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18248 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18249 | TORCH_INTERNAL_ASSERT(false, |
| 18250 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18251 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18252 | } else { |
| 18253 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18254 | at::AutoDispatchSkipFunctionalize guard; |
| 18255 | at::Tensor tmp_output = at::_ops::_adaptive_avg_pool2d_out::call(self_, output_size, out_); |
| 18256 | return out;; |
| 18257 | } |
| 18258 | } else { |
| 18259 | at::Tensor tmp_output; |
| 18260 | { |
| 18261 | at::AutoDispatchSkipFunctionalize guard; |
| 18262 | tmp_output = at::_ops::_adaptive_avg_pool2d::call(self_, output_size); |
| 18263 | } |
| 18264 | at::functionalization::impl::replace_(out, tmp_output); |
| 18265 | at::functionalization::impl::commit_update(out); |
| 18266 | at::functionalization::impl::sync(out); |
| 18267 | return out; |
| 18268 | } |
| 18269 | } |
| 18270 | |
| 18271 | at::Tensor & _adaptive_avg_pool3d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out) { |
| 18272 | if (false) { |
| 18273 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18274 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18275 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18276 | auto self_meta = to_meta(self); |
| 18277 | auto out_meta = to_meta(out); |
| 18278 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18279 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18280 | at::_ops::_adaptive_avg_pool3d_out::call(self_meta, output_size, out_meta); |
| 18281 | } |
| 18282 | |
| 18283 | at::Tensor self_; |
| 18284 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18285 | at::functionalization::impl::sync(self); |
| 18286 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18287 | } else { |
| 18288 | self_ = self; |
| 18289 | } |
| 18290 | |
| 18291 | at::Tensor out_; |
| 18292 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18293 | at::functionalization::impl::sync(out); |
| 18294 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18295 | } else { |
| 18296 | out_ = out; |
| 18297 | } |
| 18298 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18299 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18300 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18301 | TORCH_INTERNAL_ASSERT(false, |
| 18302 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18303 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18304 | } else { |
| 18305 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18306 | at::AutoDispatchSkipFunctionalize guard; |
| 18307 | at::Tensor tmp_output = at::_ops::_adaptive_avg_pool3d_out::call(self_, output_size, out_); |
| 18308 | return out;; |
| 18309 | } |
| 18310 | } else { |
| 18311 | at::Tensor tmp_output; |
| 18312 | { |
| 18313 | at::AutoDispatchSkipFunctionalize guard; |
| 18314 | tmp_output = at::_ops::_adaptive_avg_pool3d::call(self_, output_size); |
| 18315 | } |
| 18316 | at::functionalization::impl::replace_(out, tmp_output); |
| 18317 | at::functionalization::impl::commit_update(out); |
| 18318 | at::functionalization::impl::sync(out); |
| 18319 | return out; |
| 18320 | } |
| 18321 | } |
| 18322 | |
| 18323 | at::Tensor & avg_pool3d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional<int64_t> divisor_override, at::Tensor & out) { |
| 18324 | if (false) { |
| 18325 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18326 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18327 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18328 | auto self_meta = to_meta(self); |
| 18329 | auto out_meta = to_meta(out); |
| 18330 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18331 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18332 | at::_ops::avg_pool3d_out::call(self_meta, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, out_meta); |
| 18333 | } |
| 18334 | |
| 18335 | at::Tensor self_; |
| 18336 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18337 | at::functionalization::impl::sync(self); |
| 18338 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18339 | } else { |
| 18340 | self_ = self; |
| 18341 | } |
| 18342 | |
| 18343 | at::Tensor out_; |
| 18344 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18345 | at::functionalization::impl::sync(out); |
| 18346 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18347 | } else { |
| 18348 | out_ = out; |
| 18349 | } |
| 18350 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18351 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18352 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18353 | TORCH_INTERNAL_ASSERT(false, |
| 18354 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18355 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18356 | } else { |
| 18357 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18358 | at::AutoDispatchSkipFunctionalize guard; |
| 18359 | at::Tensor tmp_output = at::_ops::avg_pool3d_out::call(self_, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, out_); |
| 18360 | return out;; |
| 18361 | } |
| 18362 | } else { |
| 18363 | at::Tensor tmp_output; |
| 18364 | { |
| 18365 | at::AutoDispatchSkipFunctionalize guard; |
| 18366 | tmp_output = at::_ops::avg_pool3d::call(self_, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); |
| 18367 | } |
| 18368 | at::functionalization::impl::replace_(out, tmp_output); |
| 18369 | at::functionalization::impl::commit_update(out); |
| 18370 | at::functionalization::impl::sync(out); |
| 18371 | return out; |
| 18372 | } |
| 18373 | } |
| 18374 | |
| 18375 | ::std::tuple<at::Tensor &,at::Tensor &> fractional_max_pool3d_out_output(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples, at::Tensor & output, at::Tensor & indices) { |
| 18376 | if (false) { |
| 18377 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18378 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18379 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18380 | auto self_meta = to_meta(self); |
| 18381 | auto random_samples_meta = to_meta(random_samples); |
| 18382 | auto output_meta = to_meta(output); |
| 18383 | auto indices_meta = to_meta(indices); |
| 18384 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18385 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18386 | at::_ops::fractional_max_pool3d_output::call(self_meta, kernel_size, output_size, random_samples_meta, output_meta, indices_meta); |
| 18387 | } |
| 18388 | |
| 18389 | at::Tensor self_; |
| 18390 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18391 | at::functionalization::impl::sync(self); |
| 18392 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18393 | } else { |
| 18394 | self_ = self; |
| 18395 | } |
| 18396 | |
| 18397 | at::Tensor random_samples_; |
| 18398 | if (at::functionalization::impl::isFunctionalTensor(random_samples)) { |
| 18399 | at::functionalization::impl::sync(random_samples); |
| 18400 | random_samples_ = at::functionalization::impl::from_functional_tensor(random_samples); |
| 18401 | } else { |
| 18402 | random_samples_ = random_samples; |
| 18403 | } |
| 18404 | |
| 18405 | at::Tensor output_; |
| 18406 | if (at::functionalization::impl::isFunctionalTensor(output)) { |
| 18407 | at::functionalization::impl::sync(output); |
| 18408 | output_ = at::functionalization::impl::from_functional_tensor(output); |
| 18409 | } else { |
| 18410 | output_ = output; |
| 18411 | } |
| 18412 | |
| 18413 | at::Tensor indices_; |
| 18414 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 18415 | at::functionalization::impl::sync(indices); |
| 18416 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 18417 | } else { |
| 18418 | indices_ = indices; |
| 18419 | } |
| 18420 | if (!(true && at::functionalization::impl::isFunctionalTensor(output) && at::functionalization::impl::isFunctionalTensor(indices))) { |
| 18421 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(random_samples))) { |
| 18422 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18423 | TORCH_INTERNAL_ASSERT(false, |
| 18424 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18425 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18426 | } else { |
| 18427 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18428 | at::AutoDispatchSkipFunctionalize guard; |
| 18429 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::fractional_max_pool3d_output::call(self_, kernel_size, output_size, random_samples_, output_, indices_); |
| 18430 | return ::std::tuple<at::Tensor &,at::Tensor &>(output, indices);; |
| 18431 | } |
| 18432 | } else { |
| 18433 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 18434 | { |
| 18435 | at::AutoDispatchSkipFunctionalize guard; |
| 18436 | tmp_output = at::_ops::fractional_max_pool3d::call(self_, kernel_size, output_size, random_samples_); |
| 18437 | } |
| 18438 | at::functionalization::impl::replace_(output, std::get<0>(tmp_output)); |
| 18439 | at::functionalization::impl::commit_update(output); |
| 18440 | at::functionalization::impl::sync(output); |
| 18441 | at::functionalization::impl::replace_(indices, std::get<1>(tmp_output)); |
| 18442 | at::functionalization::impl::commit_update(indices); |
| 18443 | at::functionalization::impl::sync(indices); |
| 18444 | return ::std::tuple<at::Tensor &,at::Tensor &>(output, indices); |
| 18445 | } |
| 18446 | } |
| 18447 | |
| 18448 | at::Tensor & max_pool2d_with_indices_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, const at::Tensor & indices, at::Tensor & grad_input) { |
| 18449 | if (false) { |
| 18450 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18451 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18452 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18453 | auto grad_output_meta = to_meta(grad_output); |
| 18454 | auto self_meta = to_meta(self); |
| 18455 | auto indices_meta = to_meta(indices); |
| 18456 | auto grad_input_meta = to_meta(grad_input); |
| 18457 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18458 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18459 | at::_ops::max_pool2d_with_indices_backward_grad_input::call(grad_output_meta, self_meta, kernel_size, stride, padding, dilation, ceil_mode, indices_meta, grad_input_meta); |
| 18460 | } |
| 18461 | |
| 18462 | at::Tensor grad_output_; |
| 18463 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 18464 | at::functionalization::impl::sync(grad_output); |
| 18465 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 18466 | } else { |
| 18467 | grad_output_ = grad_output; |
| 18468 | } |
| 18469 | |
| 18470 | at::Tensor self_; |
| 18471 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18472 | at::functionalization::impl::sync(self); |
| 18473 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18474 | } else { |
| 18475 | self_ = self; |
| 18476 | } |
| 18477 | |
| 18478 | at::Tensor indices_; |
| 18479 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 18480 | at::functionalization::impl::sync(indices); |
| 18481 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 18482 | } else { |
| 18483 | indices_ = indices; |
| 18484 | } |
| 18485 | |
| 18486 | at::Tensor grad_input_; |
| 18487 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 18488 | at::functionalization::impl::sync(grad_input); |
| 18489 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 18490 | } else { |
| 18491 | grad_input_ = grad_input; |
| 18492 | } |
| 18493 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 18494 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(indices))) { |
| 18495 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18496 | TORCH_INTERNAL_ASSERT(false, |
| 18497 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18498 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18499 | } else { |
| 18500 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18501 | at::AutoDispatchSkipFunctionalize guard; |
| 18502 | at::Tensor tmp_output = at::_ops::max_pool2d_with_indices_backward_grad_input::call(grad_output_, self_, kernel_size, stride, padding, dilation, ceil_mode, indices_, grad_input_); |
| 18503 | return grad_input;; |
| 18504 | } |
| 18505 | } else { |
| 18506 | at::Tensor tmp_output; |
| 18507 | { |
| 18508 | at::AutoDispatchSkipFunctionalize guard; |
| 18509 | tmp_output = at::_ops::max_pool2d_with_indices_backward::call(grad_output_, self_, kernel_size, stride, padding, dilation, ceil_mode, indices_); |
| 18510 | } |
| 18511 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 18512 | at::functionalization::impl::commit_update(grad_input); |
| 18513 | at::functionalization::impl::sync(grad_input); |
| 18514 | return grad_input; |
| 18515 | } |
| 18516 | } |
| 18517 | |
| 18518 | at::Tensor & max_unpool3d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & out) { |
| 18519 | if (false) { |
| 18520 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18521 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18522 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18523 | auto self_meta = to_meta(self); |
| 18524 | auto indices_meta = to_meta(indices); |
| 18525 | auto out_meta = to_meta(out); |
| 18526 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18527 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18528 | at::_ops::max_unpool3d_out::call(self_meta, indices_meta, output_size, stride, padding, out_meta); |
| 18529 | } |
| 18530 | |
| 18531 | at::Tensor self_; |
| 18532 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18533 | at::functionalization::impl::sync(self); |
| 18534 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18535 | } else { |
| 18536 | self_ = self; |
| 18537 | } |
| 18538 | |
| 18539 | at::Tensor indices_; |
| 18540 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 18541 | at::functionalization::impl::sync(indices); |
| 18542 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 18543 | } else { |
| 18544 | indices_ = indices; |
| 18545 | } |
| 18546 | |
| 18547 | at::Tensor out_; |
| 18548 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18549 | at::functionalization::impl::sync(out); |
| 18550 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18551 | } else { |
| 18552 | out_ = out; |
| 18553 | } |
| 18554 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18555 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(indices))) { |
| 18556 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18557 | TORCH_INTERNAL_ASSERT(false, |
| 18558 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18559 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18560 | } else { |
| 18561 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18562 | at::AutoDispatchSkipFunctionalize guard; |
| 18563 | at::Tensor tmp_output = at::_ops::max_unpool3d_out::call(self_, indices_, output_size, stride, padding, out_); |
| 18564 | return out;; |
| 18565 | } |
| 18566 | } else { |
| 18567 | at::Tensor tmp_output; |
| 18568 | { |
| 18569 | at::AutoDispatchSkipFunctionalize guard; |
| 18570 | tmp_output = at::_ops::max_unpool3d::call(self_, indices_, output_size, stride, padding); |
| 18571 | } |
| 18572 | at::functionalization::impl::replace_(out, tmp_output); |
| 18573 | at::functionalization::impl::commit_update(out); |
| 18574 | at::functionalization::impl::sync(out); |
| 18575 | return out; |
| 18576 | } |
| 18577 | } |
| 18578 | |
| 18579 | at::Tensor & reflection_pad1d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) { |
| 18580 | if (false) { |
| 18581 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18582 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18583 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18584 | auto self_meta = to_meta(self); |
| 18585 | auto out_meta = to_meta(out); |
| 18586 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18587 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18588 | at::_ops::reflection_pad1d_out::call(self_meta, padding, out_meta); |
| 18589 | } |
| 18590 | |
| 18591 | at::Tensor self_; |
| 18592 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18593 | at::functionalization::impl::sync(self); |
| 18594 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18595 | } else { |
| 18596 | self_ = self; |
| 18597 | } |
| 18598 | |
| 18599 | at::Tensor out_; |
| 18600 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18601 | at::functionalization::impl::sync(out); |
| 18602 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18603 | } else { |
| 18604 | out_ = out; |
| 18605 | } |
| 18606 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18607 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18608 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18609 | TORCH_INTERNAL_ASSERT(false, |
| 18610 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18611 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18612 | } else { |
| 18613 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18614 | at::AutoDispatchSkipFunctionalize guard; |
| 18615 | at::Tensor tmp_output = at::_ops::reflection_pad1d_out::call(self_, padding, out_); |
| 18616 | return out;; |
| 18617 | } |
| 18618 | } else { |
| 18619 | at::Tensor tmp_output; |
| 18620 | { |
| 18621 | at::AutoDispatchSkipFunctionalize guard; |
| 18622 | tmp_output = at::_ops::reflection_pad1d::call(self_, padding); |
| 18623 | } |
| 18624 | at::functionalization::impl::replace_(out, tmp_output); |
| 18625 | at::functionalization::impl::commit_update(out); |
| 18626 | at::functionalization::impl::sync(out); |
| 18627 | return out; |
| 18628 | } |
| 18629 | } |
| 18630 | |
| 18631 | at::Tensor & reflection_pad1d_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) { |
| 18632 | if (false) { |
| 18633 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18634 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18635 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18636 | auto grad_output_meta = to_meta(grad_output); |
| 18637 | auto self_meta = to_meta(self); |
| 18638 | auto grad_input_meta = to_meta(grad_input); |
| 18639 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18640 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18641 | at::_ops::reflection_pad1d_backward_grad_input::call(grad_output_meta, self_meta, padding, grad_input_meta); |
| 18642 | } |
| 18643 | |
| 18644 | at::Tensor grad_output_; |
| 18645 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 18646 | at::functionalization::impl::sync(grad_output); |
| 18647 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 18648 | } else { |
| 18649 | grad_output_ = grad_output; |
| 18650 | } |
| 18651 | |
| 18652 | at::Tensor self_; |
| 18653 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18654 | at::functionalization::impl::sync(self); |
| 18655 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18656 | } else { |
| 18657 | self_ = self; |
| 18658 | } |
| 18659 | |
| 18660 | at::Tensor grad_input_; |
| 18661 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 18662 | at::functionalization::impl::sync(grad_input); |
| 18663 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 18664 | } else { |
| 18665 | grad_input_ = grad_input; |
| 18666 | } |
| 18667 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 18668 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18669 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18670 | TORCH_INTERNAL_ASSERT(false, |
| 18671 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18672 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18673 | } else { |
| 18674 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18675 | at::AutoDispatchSkipFunctionalize guard; |
| 18676 | at::Tensor tmp_output = at::_ops::reflection_pad1d_backward_grad_input::call(grad_output_, self_, padding, grad_input_); |
| 18677 | return grad_input;; |
| 18678 | } |
| 18679 | } else { |
| 18680 | at::Tensor tmp_output; |
| 18681 | { |
| 18682 | at::AutoDispatchSkipFunctionalize guard; |
| 18683 | tmp_output = at::_ops::reflection_pad1d_backward::call(grad_output_, self_, padding); |
| 18684 | } |
| 18685 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 18686 | at::functionalization::impl::commit_update(grad_input); |
| 18687 | at::functionalization::impl::sync(grad_input); |
| 18688 | return grad_input; |
| 18689 | } |
| 18690 | } |
| 18691 | |
| 18692 | at::Tensor & reflection_pad2d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) { |
| 18693 | if (false) { |
| 18694 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18695 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18696 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18697 | auto self_meta = to_meta(self); |
| 18698 | auto out_meta = to_meta(out); |
| 18699 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18700 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18701 | at::_ops::reflection_pad2d_out::call(self_meta, padding, out_meta); |
| 18702 | } |
| 18703 | |
| 18704 | at::Tensor self_; |
| 18705 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18706 | at::functionalization::impl::sync(self); |
| 18707 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18708 | } else { |
| 18709 | self_ = self; |
| 18710 | } |
| 18711 | |
| 18712 | at::Tensor out_; |
| 18713 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18714 | at::functionalization::impl::sync(out); |
| 18715 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18716 | } else { |
| 18717 | out_ = out; |
| 18718 | } |
| 18719 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18720 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18721 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18722 | TORCH_INTERNAL_ASSERT(false, |
| 18723 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18724 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18725 | } else { |
| 18726 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18727 | at::AutoDispatchSkipFunctionalize guard; |
| 18728 | at::Tensor tmp_output = at::_ops::reflection_pad2d_out::call(self_, padding, out_); |
| 18729 | return out;; |
| 18730 | } |
| 18731 | } else { |
| 18732 | at::Tensor tmp_output; |
| 18733 | { |
| 18734 | at::AutoDispatchSkipFunctionalize guard; |
| 18735 | tmp_output = at::_ops::reflection_pad2d::call(self_, padding); |
| 18736 | } |
| 18737 | at::functionalization::impl::replace_(out, tmp_output); |
| 18738 | at::functionalization::impl::commit_update(out); |
| 18739 | at::functionalization::impl::sync(out); |
| 18740 | return out; |
| 18741 | } |
| 18742 | } |
| 18743 | |
| 18744 | at::Tensor & reflection_pad3d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) { |
| 18745 | if (false) { |
| 18746 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18747 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18748 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18749 | auto self_meta = to_meta(self); |
| 18750 | auto out_meta = to_meta(out); |
| 18751 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18752 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18753 | at::_ops::reflection_pad3d_out::call(self_meta, padding, out_meta); |
| 18754 | } |
| 18755 | |
| 18756 | at::Tensor self_; |
| 18757 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18758 | at::functionalization::impl::sync(self); |
| 18759 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18760 | } else { |
| 18761 | self_ = self; |
| 18762 | } |
| 18763 | |
| 18764 | at::Tensor out_; |
| 18765 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18766 | at::functionalization::impl::sync(out); |
| 18767 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18768 | } else { |
| 18769 | out_ = out; |
| 18770 | } |
| 18771 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18772 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18773 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18774 | TORCH_INTERNAL_ASSERT(false, |
| 18775 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18776 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18777 | } else { |
| 18778 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18779 | at::AutoDispatchSkipFunctionalize guard; |
| 18780 | at::Tensor tmp_output = at::_ops::reflection_pad3d_out::call(self_, padding, out_); |
| 18781 | return out;; |
| 18782 | } |
| 18783 | } else { |
| 18784 | at::Tensor tmp_output; |
| 18785 | { |
| 18786 | at::AutoDispatchSkipFunctionalize guard; |
| 18787 | tmp_output = at::_ops::reflection_pad3d::call(self_, padding); |
| 18788 | } |
| 18789 | at::functionalization::impl::replace_(out, tmp_output); |
| 18790 | at::functionalization::impl::commit_update(out); |
| 18791 | at::functionalization::impl::sync(out); |
| 18792 | return out; |
| 18793 | } |
| 18794 | } |
| 18795 | |
| 18796 | at::Tensor & replication_pad1d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) { |
| 18797 | if (false) { |
| 18798 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18799 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18800 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18801 | auto self_meta = to_meta(self); |
| 18802 | auto out_meta = to_meta(out); |
| 18803 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18804 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18805 | at::_ops::replication_pad1d_out::call(self_meta, padding, out_meta); |
| 18806 | } |
| 18807 | |
| 18808 | at::Tensor self_; |
| 18809 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18810 | at::functionalization::impl::sync(self); |
| 18811 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18812 | } else { |
| 18813 | self_ = self; |
| 18814 | } |
| 18815 | |
| 18816 | at::Tensor out_; |
| 18817 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18818 | at::functionalization::impl::sync(out); |
| 18819 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18820 | } else { |
| 18821 | out_ = out; |
| 18822 | } |
| 18823 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18824 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18825 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18826 | TORCH_INTERNAL_ASSERT(false, |
| 18827 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18828 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18829 | } else { |
| 18830 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18831 | at::AutoDispatchSkipFunctionalize guard; |
| 18832 | at::Tensor tmp_output = at::_ops::replication_pad1d_out::call(self_, padding, out_); |
| 18833 | return out;; |
| 18834 | } |
| 18835 | } else { |
| 18836 | at::Tensor tmp_output; |
| 18837 | { |
| 18838 | at::AutoDispatchSkipFunctionalize guard; |
| 18839 | tmp_output = at::_ops::replication_pad1d::call(self_, padding); |
| 18840 | } |
| 18841 | at::functionalization::impl::replace_(out, tmp_output); |
| 18842 | at::functionalization::impl::commit_update(out); |
| 18843 | at::functionalization::impl::sync(out); |
| 18844 | return out; |
| 18845 | } |
| 18846 | } |
| 18847 | |
| 18848 | at::Tensor & replication_pad1d_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) { |
| 18849 | if (false) { |
| 18850 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18851 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18852 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18853 | auto grad_output_meta = to_meta(grad_output); |
| 18854 | auto self_meta = to_meta(self); |
| 18855 | auto grad_input_meta = to_meta(grad_input); |
| 18856 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18857 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18858 | at::_ops::replication_pad1d_backward_grad_input::call(grad_output_meta, self_meta, padding, grad_input_meta); |
| 18859 | } |
| 18860 | |
| 18861 | at::Tensor grad_output_; |
| 18862 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 18863 | at::functionalization::impl::sync(grad_output); |
| 18864 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 18865 | } else { |
| 18866 | grad_output_ = grad_output; |
| 18867 | } |
| 18868 | |
| 18869 | at::Tensor self_; |
| 18870 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18871 | at::functionalization::impl::sync(self); |
| 18872 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18873 | } else { |
| 18874 | self_ = self; |
| 18875 | } |
| 18876 | |
| 18877 | at::Tensor grad_input_; |
| 18878 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 18879 | at::functionalization::impl::sync(grad_input); |
| 18880 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 18881 | } else { |
| 18882 | grad_input_ = grad_input; |
| 18883 | } |
| 18884 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 18885 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18886 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18887 | TORCH_INTERNAL_ASSERT(false, |
| 18888 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18889 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18890 | } else { |
| 18891 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18892 | at::AutoDispatchSkipFunctionalize guard; |
| 18893 | at::Tensor tmp_output = at::_ops::replication_pad1d_backward_grad_input::call(grad_output_, self_, padding, grad_input_); |
| 18894 | return grad_input;; |
| 18895 | } |
| 18896 | } else { |
| 18897 | at::Tensor tmp_output; |
| 18898 | { |
| 18899 | at::AutoDispatchSkipFunctionalize guard; |
| 18900 | tmp_output = at::_ops::replication_pad1d_backward::call(grad_output_, self_, padding); |
| 18901 | } |
| 18902 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 18903 | at::functionalization::impl::commit_update(grad_input); |
| 18904 | at::functionalization::impl::sync(grad_input); |
| 18905 | return grad_input; |
| 18906 | } |
| 18907 | } |
| 18908 | |
| 18909 | at::Tensor & replication_pad2d_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) { |
| 18910 | if (false) { |
| 18911 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18912 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18913 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18914 | auto grad_output_meta = to_meta(grad_output); |
| 18915 | auto self_meta = to_meta(self); |
| 18916 | auto grad_input_meta = to_meta(grad_input); |
| 18917 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18918 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18919 | at::_ops::replication_pad2d_backward_grad_input::call(grad_output_meta, self_meta, padding, grad_input_meta); |
| 18920 | } |
| 18921 | |
| 18922 | at::Tensor grad_output_; |
| 18923 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 18924 | at::functionalization::impl::sync(grad_output); |
| 18925 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 18926 | } else { |
| 18927 | grad_output_ = grad_output; |
| 18928 | } |
| 18929 | |
| 18930 | at::Tensor self_; |
| 18931 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18932 | at::functionalization::impl::sync(self); |
| 18933 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18934 | } else { |
| 18935 | self_ = self; |
| 18936 | } |
| 18937 | |
| 18938 | at::Tensor grad_input_; |
| 18939 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 18940 | at::functionalization::impl::sync(grad_input); |
| 18941 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 18942 | } else { |
| 18943 | grad_input_ = grad_input; |
| 18944 | } |
| 18945 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 18946 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18947 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18948 | TORCH_INTERNAL_ASSERT(false, |
| 18949 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18950 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18951 | } else { |
| 18952 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18953 | at::AutoDispatchSkipFunctionalize guard; |
| 18954 | at::Tensor tmp_output = at::_ops::replication_pad2d_backward_grad_input::call(grad_output_, self_, padding, grad_input_); |
| 18955 | return grad_input;; |
| 18956 | } |
| 18957 | } else { |
| 18958 | at::Tensor tmp_output; |
| 18959 | { |
| 18960 | at::AutoDispatchSkipFunctionalize guard; |
| 18961 | tmp_output = at::_ops::replication_pad2d_backward::call(grad_output_, self_, padding); |
| 18962 | } |
| 18963 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 18964 | at::functionalization::impl::commit_update(grad_input); |
| 18965 | at::functionalization::impl::sync(grad_input); |
| 18966 | return grad_input; |
| 18967 | } |
| 18968 | } |
| 18969 | |
| 18970 | at::Tensor & replication_pad3d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) { |
| 18971 | if (false) { |
| 18972 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18973 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18974 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18975 | auto self_meta = to_meta(self); |
| 18976 | auto out_meta = to_meta(out); |
| 18977 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18978 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18979 | at::_ops::replication_pad3d_out::call(self_meta, padding, out_meta); |
| 18980 | } |
| 18981 | |
| 18982 | at::Tensor self_; |
| 18983 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18984 | at::functionalization::impl::sync(self); |
| 18985 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18986 | } else { |
| 18987 | self_ = self; |
| 18988 | } |
| 18989 | |
| 18990 | at::Tensor out_; |
| 18991 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18992 | at::functionalization::impl::sync(out); |
| 18993 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18994 | } else { |
| 18995 | out_ = out; |
| 18996 | } |
| 18997 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18998 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18999 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19000 | TORCH_INTERNAL_ASSERT(false, |
| 19001 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19002 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19003 | } else { |
| 19004 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19005 | at::AutoDispatchSkipFunctionalize guard; |
| 19006 | at::Tensor tmp_output = at::_ops::replication_pad3d_out::call(self_, padding, out_); |
| 19007 | return out;; |
| 19008 | } |
| 19009 | } else { |
| 19010 | at::Tensor tmp_output; |
| 19011 | { |
| 19012 | at::AutoDispatchSkipFunctionalize guard; |
| 19013 | tmp_output = at::_ops::replication_pad3d::call(self_, padding); |
| 19014 | } |
| 19015 | at::functionalization::impl::replace_(out, tmp_output); |
| 19016 | at::functionalization::impl::commit_update(out); |
| 19017 | at::functionalization::impl::sync(out); |
| 19018 | return out; |
| 19019 | } |
| 19020 | } |
| 19021 | |
| 19022 | at::Tensor & replication_pad3d_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) { |
| 19023 | if (false) { |
| 19024 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19025 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19026 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19027 | auto grad_output_meta = to_meta(grad_output); |
| 19028 | auto self_meta = to_meta(self); |
| 19029 | auto grad_input_meta = to_meta(grad_input); |
| 19030 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19031 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19032 | at::_ops::replication_pad3d_backward_grad_input::call(grad_output_meta, self_meta, padding, grad_input_meta); |
| 19033 | } |
| 19034 | |
| 19035 | at::Tensor grad_output_; |
| 19036 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 19037 | at::functionalization::impl::sync(grad_output); |
| 19038 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 19039 | } else { |
| 19040 | grad_output_ = grad_output; |
| 19041 | } |
| 19042 | |
| 19043 | at::Tensor self_; |
| 19044 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19045 | at::functionalization::impl::sync(self); |
| 19046 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19047 | } else { |
| 19048 | self_ = self; |
| 19049 | } |
| 19050 | |
| 19051 | at::Tensor grad_input_; |
| 19052 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 19053 | at::functionalization::impl::sync(grad_input); |
| 19054 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 19055 | } else { |
| 19056 | grad_input_ = grad_input; |
| 19057 | } |
| 19058 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 19059 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self))) { |
| 19060 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19061 | TORCH_INTERNAL_ASSERT(false, |
| 19062 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19063 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19064 | } else { |
| 19065 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19066 | at::AutoDispatchSkipFunctionalize guard; |
| 19067 | at::Tensor tmp_output = at::_ops::replication_pad3d_backward_grad_input::call(grad_output_, self_, padding, grad_input_); |
| 19068 | return grad_input;; |
| 19069 | } |
| 19070 | } else { |
| 19071 | at::Tensor tmp_output; |
| 19072 | { |
| 19073 | at::AutoDispatchSkipFunctionalize guard; |
| 19074 | tmp_output = at::_ops::replication_pad3d_backward::call(grad_output_, self_, padding); |
| 19075 | } |
| 19076 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 19077 | at::functionalization::impl::commit_update(grad_input); |
| 19078 | at::functionalization::impl::sync(grad_input); |
| 19079 | return grad_input; |
| 19080 | } |
| 19081 | } |
| 19082 | |
| 19083 | at::Tensor & upsample_nearest2d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 19084 | if (false) { |
| 19085 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19086 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19087 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19088 | auto self_meta = to_meta(self); |
| 19089 | auto out_meta = to_meta(out); |
| 19090 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19091 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19092 | at::_ops::upsample_nearest2d_out::call(self_meta, output_size, scales_h, scales_w, out_meta); |
| 19093 | } |
| 19094 | |
| 19095 | at::Tensor self_; |
| 19096 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19097 | at::functionalization::impl::sync(self); |
| 19098 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19099 | } else { |
| 19100 | self_ = self; |
| 19101 | } |
| 19102 | |
| 19103 | at::Tensor out_; |
| 19104 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19105 | at::functionalization::impl::sync(out); |
| 19106 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19107 | } else { |
| 19108 | out_ = out; |
| 19109 | } |
| 19110 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19111 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 19112 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19113 | TORCH_INTERNAL_ASSERT(false, |
| 19114 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19115 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19116 | } else { |
| 19117 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19118 | at::AutoDispatchSkipFunctionalize guard; |
| 19119 | at::Tensor tmp_output = at::_ops::upsample_nearest2d_out::call(self_, output_size, scales_h, scales_w, out_); |
| 19120 | return out;; |
| 19121 | } |
| 19122 | } else { |
| 19123 | at::Tensor tmp_output; |
| 19124 | { |
| 19125 | at::AutoDispatchSkipFunctionalize guard; |
| 19126 | tmp_output = at::_ops::upsample_nearest2d::call(self_, output_size, scales_h, scales_w); |
| 19127 | } |
| 19128 | at::functionalization::impl::replace_(out, tmp_output); |
| 19129 | at::functionalization::impl::commit_update(out); |
| 19130 | at::functionalization::impl::sync(out); |
| 19131 | return out; |
| 19132 | } |
| 19133 | } |
| 19134 | |
| 19135 | at::Tensor & _upsample_nearest_exact2d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 19136 | if (false) { |
| 19137 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19138 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19139 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19140 | auto self_meta = to_meta(self); |
| 19141 | auto out_meta = to_meta(out); |
| 19142 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19143 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19144 | at::_ops::_upsample_nearest_exact2d_out::call(self_meta, output_size, scales_h, scales_w, out_meta); |
| 19145 | } |
| 19146 | |
| 19147 | at::Tensor self_; |
| 19148 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19149 | at::functionalization::impl::sync(self); |
| 19150 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19151 | } else { |
| 19152 | self_ = self; |
| 19153 | } |
| 19154 | |
| 19155 | at::Tensor out_; |
| 19156 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19157 | at::functionalization::impl::sync(out); |
| 19158 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19159 | } else { |
| 19160 | out_ = out; |
| 19161 | } |
| 19162 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19163 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 19164 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19165 | TORCH_INTERNAL_ASSERT(false, |
| 19166 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19167 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19168 | } else { |
| 19169 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19170 | at::AutoDispatchSkipFunctionalize guard; |
| 19171 | at::Tensor tmp_output = at::_ops::_upsample_nearest_exact2d_out::call(self_, output_size, scales_h, scales_w, out_); |
| 19172 | return out;; |
| 19173 | } |
| 19174 | } else { |
| 19175 | at::Tensor tmp_output; |
| 19176 | { |
| 19177 | at::AutoDispatchSkipFunctionalize guard; |
| 19178 | tmp_output = at::_ops::_upsample_nearest_exact2d::call(self_, output_size, scales_h, scales_w); |
| 19179 | } |
| 19180 | at::functionalization::impl::replace_(out, tmp_output); |
| 19181 | at::functionalization::impl::commit_update(out); |
| 19182 | at::functionalization::impl::sync(out); |
| 19183 | return out; |
| 19184 | } |
| 19185 | } |
| 19186 | |
| 19187 | at::Tensor & _upsample_nearest_exact2d_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) { |
| 19188 | if (false) { |
| 19189 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19190 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19191 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19192 | auto grad_output_meta = to_meta(grad_output); |
| 19193 | auto grad_input_meta = to_meta(grad_input); |
| 19194 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19195 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19196 | at::_ops::_upsample_nearest_exact2d_backward_grad_input::call(grad_output_meta, output_size, input_size, scales_h, scales_w, grad_input_meta); |
| 19197 | } |
| 19198 | |
| 19199 | at::Tensor grad_output_; |
| 19200 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 19201 | at::functionalization::impl::sync(grad_output); |
| 19202 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 19203 | } else { |
| 19204 | grad_output_ = grad_output; |
| 19205 | } |
| 19206 | |
| 19207 | at::Tensor grad_input_; |
| 19208 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 19209 | at::functionalization::impl::sync(grad_input); |
| 19210 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 19211 | } else { |
| 19212 | grad_input_ = grad_input; |
| 19213 | } |
| 19214 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 19215 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output))) { |
| 19216 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19217 | TORCH_INTERNAL_ASSERT(false, |
| 19218 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19219 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19220 | } else { |
| 19221 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19222 | at::AutoDispatchSkipFunctionalize guard; |
| 19223 | at::Tensor tmp_output = at::_ops::_upsample_nearest_exact2d_backward_grad_input::call(grad_output_, output_size, input_size, scales_h, scales_w, grad_input_); |
| 19224 | return grad_input;; |
| 19225 | } |
| 19226 | } else { |
| 19227 | at::Tensor tmp_output; |
| 19228 | { |
| 19229 | at::AutoDispatchSkipFunctionalize guard; |
| 19230 | tmp_output = at::_ops::_upsample_nearest_exact2d_backward::call(grad_output_, output_size, input_size, scales_h, scales_w); |
| 19231 | } |
| 19232 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 19233 | at::functionalization::impl::commit_update(grad_input); |
| 19234 | at::functionalization::impl::sync(grad_input); |
| 19235 | return grad_input; |
| 19236 | } |
| 19237 | } |
| 19238 | |
| 19239 | at::Tensor & _upsample_nearest_exact3d_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) { |
| 19240 | if (false) { |
| 19241 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19242 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19243 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19244 | auto grad_output_meta = to_meta(grad_output); |
| 19245 | auto grad_input_meta = to_meta(grad_input); |
| 19246 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19247 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19248 | at::_ops::_upsample_nearest_exact3d_backward_grad_input::call(grad_output_meta, output_size, input_size, scales_d, scales_h, scales_w, grad_input_meta); |
| 19249 | } |
| 19250 | |
| 19251 | at::Tensor grad_output_; |
| 19252 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 19253 | at::functionalization::impl::sync(grad_output); |
| 19254 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 19255 | } else { |
| 19256 | grad_output_ = grad_output; |
| 19257 | } |
| 19258 | |
| 19259 | at::Tensor grad_input_; |
| 19260 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 19261 | at::functionalization::impl::sync(grad_input); |
| 19262 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 19263 | } else { |
| 19264 | grad_input_ = grad_input; |
| 19265 | } |
| 19266 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 19267 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output))) { |
| 19268 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19269 | TORCH_INTERNAL_ASSERT(false, |
| 19270 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19271 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19272 | } else { |
| 19273 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19274 | at::AutoDispatchSkipFunctionalize guard; |
| 19275 | at::Tensor tmp_output = at::_ops::_upsample_nearest_exact3d_backward_grad_input::call(grad_output_, output_size, input_size, scales_d, scales_h, scales_w, grad_input_); |
| 19276 | return grad_input;; |
| 19277 | } |
| 19278 | } else { |
| 19279 | at::Tensor tmp_output; |
| 19280 | { |
| 19281 | at::AutoDispatchSkipFunctionalize guard; |
| 19282 | tmp_output = at::_ops::_upsample_nearest_exact3d_backward::call(grad_output_, output_size, input_size, scales_d, scales_h, scales_w); |
| 19283 | } |
| 19284 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 19285 | at::functionalization::impl::commit_update(grad_input); |
| 19286 | at::functionalization::impl::sync(grad_input); |
| 19287 | return grad_input; |
| 19288 | } |
| 19289 | } |
| 19290 | |
| 19291 | at::Tensor & sigmoid_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, at::Tensor & grad_input) { |
| 19292 | if (false) { |
| 19293 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19294 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19295 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19296 | auto grad_output_meta = to_meta(grad_output); |
| 19297 | auto output_meta = to_meta(output); |
| 19298 | auto grad_input_meta = to_meta(grad_input); |
| 19299 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19300 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19301 | at::_ops::sigmoid_backward_grad_input::call(grad_output_meta, output_meta, grad_input_meta); |
| 19302 | } |
| 19303 | |
| 19304 | at::Tensor grad_output_; |
| 19305 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 19306 | at::functionalization::impl::sync(grad_output); |
| 19307 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 19308 | } else { |
| 19309 | grad_output_ = grad_output; |
| 19310 | } |
| 19311 | |
| 19312 | at::Tensor output_; |
| 19313 | if (at::functionalization::impl::isFunctionalTensor(output)) { |
| 19314 | at::functionalization::impl::sync(output); |
| 19315 | output_ = at::functionalization::impl::from_functional_tensor(output); |
| 19316 | } else { |
| 19317 | output_ = output; |
| 19318 | } |
| 19319 | |
| 19320 | at::Tensor grad_input_; |
| 19321 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 19322 | at::functionalization::impl::sync(grad_input); |
| 19323 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 19324 | } else { |
| 19325 | grad_input_ = grad_input; |
| 19326 | } |
| 19327 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 19328 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(output))) { |
| 19329 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19330 | TORCH_INTERNAL_ASSERT(false, |
| 19331 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19332 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19333 | } else { |
| 19334 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19335 | at::AutoDispatchSkipFunctionalize guard; |
| 19336 | at::Tensor tmp_output = at::_ops::sigmoid_backward_grad_input::call(grad_output_, output_, grad_input_); |
| 19337 | return grad_input;; |
| 19338 | } |
| 19339 | } else { |
| 19340 | at::Tensor tmp_output; |
| 19341 | { |
| 19342 | at::AutoDispatchSkipFunctionalize guard; |
| 19343 | tmp_output = at::_ops::sigmoid_backward::call(grad_output_, output_); |
| 19344 | } |
| 19345 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 19346 | at::functionalization::impl::commit_update(grad_input); |
| 19347 | at::functionalization::impl::sync(grad_input); |
| 19348 | return grad_input; |
| 19349 | } |
| 19350 | } |
| 19351 | |
| 19352 | at::Tensor & slow_conv_transpose2d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, at::IntArrayRef dilation, at::Tensor & out) { |
| 19353 | if (false) { |
| 19354 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19355 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19356 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19357 | auto self_meta = to_meta(self); |
| 19358 | auto weight_meta = to_meta(weight); |
| 19359 | auto bias_meta = to_meta(bias); |
| 19360 | auto out_meta = to_meta(out); |
| 19361 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19362 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19363 | at::_ops::slow_conv_transpose2d_out::call(self_meta, weight_meta, kernel_size, bias_meta, stride, padding, output_padding, dilation, out_meta); |
| 19364 | } |
| 19365 | |
| 19366 | at::Tensor self_; |
| 19367 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19368 | at::functionalization::impl::sync(self); |
| 19369 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19370 | } else { |
| 19371 | self_ = self; |
| 19372 | } |
| 19373 | |
| 19374 | at::Tensor weight_; |
| 19375 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 19376 | at::functionalization::impl::sync(weight); |
| 19377 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 19378 | } else { |
| 19379 | weight_ = weight; |
| 19380 | } |
| 19381 | |
| 19382 | c10::optional<at::Tensor> bias_; |
| 19383 | if (at::functionalization::impl::isFunctionalTensor(bias)) { |
| 19384 | at::functionalization::impl::sync(bias); |
| 19385 | bias_ = at::functionalization::impl::from_functional_tensor(bias); |
| 19386 | } else { |
| 19387 | bias_ = bias; |
| 19388 | } |
| 19389 | |
| 19390 | at::Tensor out_; |
| 19391 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19392 | at::functionalization::impl::sync(out); |
| 19393 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19394 | } else { |
| 19395 | out_ = out; |
| 19396 | } |
| 19397 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19398 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(bias))) { |
| 19399 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19400 | TORCH_INTERNAL_ASSERT(false, |
| 19401 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19402 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19403 | } else { |
| 19404 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19405 | at::AutoDispatchSkipFunctionalize guard; |
| 19406 | at::Tensor tmp_output = at::_ops::slow_conv_transpose2d_out::call(self_, weight_, kernel_size, bias_, stride, padding, output_padding, dilation, out_); |
| 19407 | return out;; |
| 19408 | } |
| 19409 | } else { |
| 19410 | at::Tensor tmp_output; |
| 19411 | { |
| 19412 | at::AutoDispatchSkipFunctionalize guard; |
| 19413 | tmp_output = at::_ops::slow_conv_transpose2d::call(self_, weight_, kernel_size, bias_, stride, padding, output_padding, dilation); |
| 19414 | } |
| 19415 | at::functionalization::impl::replace_(out, tmp_output); |
| 19416 | at::functionalization::impl::commit_update(out); |
| 19417 | at::functionalization::impl::sync(out); |
| 19418 | return out; |
| 19419 | } |
| 19420 | } |
| 19421 | |
| 19422 | at::Tensor & _slow_conv2d_forward_out_output(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & output) { |
| 19423 | if (false) { |
| 19424 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19425 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19426 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19427 | auto self_meta = to_meta(self); |
| 19428 | auto weight_meta = to_meta(weight); |
| 19429 | auto bias_meta = to_meta(bias); |
| 19430 | auto output_meta = to_meta(output); |
| 19431 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19432 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19433 | at::_ops::_slow_conv2d_forward_output::call(self_meta, weight_meta, kernel_size, bias_meta, stride, padding, output_meta); |
| 19434 | } |
| 19435 | |
| 19436 | at::Tensor self_; |
| 19437 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19438 | at::functionalization::impl::sync(self); |
| 19439 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19440 | } else { |
| 19441 | self_ = self; |
| 19442 | } |
| 19443 | |
| 19444 | at::Tensor weight_; |
| 19445 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 19446 | at::functionalization::impl::sync(weight); |
| 19447 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 19448 | } else { |
| 19449 | weight_ = weight; |
| 19450 | } |
| 19451 | |
| 19452 | c10::optional<at::Tensor> bias_; |
| 19453 | if (at::functionalization::impl::isFunctionalTensor(bias)) { |
| 19454 | at::functionalization::impl::sync(bias); |
| 19455 | bias_ = at::functionalization::impl::from_functional_tensor(bias); |
| 19456 | } else { |
| 19457 | bias_ = bias; |
| 19458 | } |
| 19459 | |
| 19460 | at::Tensor output_; |
| 19461 | if (at::functionalization::impl::isFunctionalTensor(output)) { |
| 19462 | at::functionalization::impl::sync(output); |
| 19463 | output_ = at::functionalization::impl::from_functional_tensor(output); |
| 19464 | } else { |
| 19465 | output_ = output; |
| 19466 | } |
| 19467 | if (!(true && at::functionalization::impl::isFunctionalTensor(output))) { |
| 19468 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(bias))) { |
| 19469 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19470 | TORCH_INTERNAL_ASSERT(false, |
| 19471 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19472 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19473 | } else { |
| 19474 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19475 | at::AutoDispatchSkipFunctionalize guard; |
| 19476 | at::Tensor tmp_output = at::_ops::_slow_conv2d_forward_output::call(self_, weight_, kernel_size, bias_, stride, padding, output_); |
| 19477 | return output;; |
| 19478 | } |
| 19479 | } else { |
| 19480 | at::Tensor tmp_output; |
| 19481 | { |
| 19482 | at::AutoDispatchSkipFunctionalize guard; |
| 19483 | tmp_output = at::_ops::_slow_conv2d_forward::call(self_, weight_, kernel_size, bias_, stride, padding); |
| 19484 | } |
| 19485 | at::functionalization::impl::replace_(output, tmp_output); |
| 19486 | at::functionalization::impl::commit_update(output); |
| 19487 | at::functionalization::impl::sync(output); |
| 19488 | return output; |
| 19489 | } |
| 19490 | } |
| 19491 | |
| 19492 | at::Tensor & conv_depthwise3d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::SymIntArrayRef padding, at::IntArrayRef dilation, at::Tensor & out) { |
| 19493 | if (false) { |
| 19494 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19495 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19496 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19497 | auto self_meta = to_meta(self); |
| 19498 | auto weight_meta = to_meta(weight); |
| 19499 | auto bias_meta = to_meta(bias); |
| 19500 | auto out_meta = to_meta(out); |
| 19501 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19502 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19503 | at::_ops::conv_depthwise3d_out::call(self_meta, weight_meta, kernel_size, bias_meta, stride, padding, dilation, out_meta); |
| 19504 | } |
| 19505 | |
| 19506 | at::Tensor self_; |
| 19507 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19508 | at::functionalization::impl::sync(self); |
| 19509 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19510 | } else { |
| 19511 | self_ = self; |
| 19512 | } |
| 19513 | |
| 19514 | at::Tensor weight_; |
| 19515 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 19516 | at::functionalization::impl::sync(weight); |
| 19517 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 19518 | } else { |
| 19519 | weight_ = weight; |
| 19520 | } |
| 19521 | |
| 19522 | c10::optional<at::Tensor> bias_; |
| 19523 | if (at::functionalization::impl::isFunctionalTensor(bias)) { |
| 19524 | at::functionalization::impl::sync(bias); |
| 19525 | bias_ = at::functionalization::impl::from_functional_tensor(bias); |
| 19526 | } else { |
| 19527 | bias_ = bias; |
| 19528 | } |
| 19529 | |
| 19530 | at::Tensor out_; |
| 19531 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19532 | at::functionalization::impl::sync(out); |
| 19533 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19534 | } else { |
| 19535 | out_ = out; |
| 19536 | } |
| 19537 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19538 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(bias))) { |
| 19539 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19540 | TORCH_INTERNAL_ASSERT(false, |
| 19541 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19542 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19543 | } else { |
| 19544 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19545 | at::AutoDispatchSkipFunctionalize guard; |
| 19546 | at::Tensor tmp_output = at::_ops::conv_depthwise3d_out::call(self_, weight_, kernel_size, bias_, stride, padding, dilation, out_); |
| 19547 | return out;; |
| 19548 | } |
| 19549 | } else { |
| 19550 | at::Tensor tmp_output; |
| 19551 | { |
| 19552 | at::AutoDispatchSkipFunctionalize guard; |
| 19553 | tmp_output = at::_ops::conv_depthwise3d::call(self_, weight_, kernel_size, bias_, stride, padding, dilation); |
| 19554 | } |
| 19555 | at::functionalization::impl::replace_(out, tmp_output); |
| 19556 | at::functionalization::impl::commit_update(out); |
| 19557 | at::functionalization::impl::sync(out); |
| 19558 | return out; |
| 19559 | } |
| 19560 | } |
| 19561 | |
| 19562 | at::Tensor & slow_conv_dilated2d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::SymIntArrayRef padding, at::IntArrayRef dilation, at::Tensor & out) { |
| 19563 | if (false) { |
| 19564 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19565 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19566 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19567 | auto self_meta = to_meta(self); |
| 19568 | auto weight_meta = to_meta(weight); |
| 19569 | auto bias_meta = to_meta(bias); |
| 19570 | auto out_meta = to_meta(out); |
| 19571 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19572 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19573 | at::_ops::slow_conv_dilated2d_out::call(self_meta, weight_meta, kernel_size, bias_meta, stride, padding, dilation, out_meta); |
| 19574 | } |
| 19575 | |
| 19576 | at::Tensor self_; |
| 19577 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19578 | at::functionalization::impl::sync(self); |
| 19579 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19580 | } else { |
| 19581 | self_ = self; |
| 19582 | } |
| 19583 | |
| 19584 | at::Tensor weight_; |
| 19585 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 19586 | at::functionalization::impl::sync(weight); |
| 19587 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 19588 | } else { |
| 19589 | weight_ = weight; |
| 19590 | } |
| 19591 | |
| 19592 | c10::optional<at::Tensor> bias_; |
| 19593 | if (at::functionalization::impl::isFunctionalTensor(bias)) { |
| 19594 | at::functionalization::impl::sync(bias); |
| 19595 | bias_ = at::functionalization::impl::from_functional_tensor(bias); |
| 19596 | } else { |
| 19597 | bias_ = bias; |
| 19598 | } |
| 19599 | |
| 19600 | at::Tensor out_; |
| 19601 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19602 | at::functionalization::impl::sync(out); |
| 19603 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19604 | } else { |
| 19605 | out_ = out; |
| 19606 | } |
| 19607 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19608 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(bias))) { |
| 19609 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19610 | TORCH_INTERNAL_ASSERT(false, |
| 19611 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19612 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19613 | } else { |
| 19614 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19615 | at::AutoDispatchSkipFunctionalize guard; |
| 19616 | at::Tensor tmp_output = at::_ops::slow_conv_dilated2d_out::call(self_, weight_, kernel_size, bias_, stride, padding, dilation, out_); |
| 19617 | return out;; |
| 19618 | } |
| 19619 | } else { |
| 19620 | at::Tensor tmp_output; |
| 19621 | { |
| 19622 | at::AutoDispatchSkipFunctionalize guard; |
| 19623 | tmp_output = at::_ops::slow_conv_dilated2d::call(self_, weight_, kernel_size, bias_, stride, padding, dilation); |
| 19624 | } |
| 19625 | at::functionalization::impl::replace_(out, tmp_output); |
| 19626 | at::functionalization::impl::commit_update(out); |
| 19627 | at::functionalization::impl::sync(out); |
| 19628 | return out; |
| 19629 | } |
| 19630 | } |
| 19631 | |
| 19632 | at::Tensor & special_ndtri_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 19633 | if (false) { |
| 19634 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19635 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19636 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19637 | auto self_meta = to_meta(self); |
| 19638 | auto out_meta = to_meta(out); |
| 19639 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19640 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19641 | at::_ops::special_ndtri_out::call(self_meta, out_meta); |
| 19642 | } |
| 19643 | |
| 19644 | at::Tensor self_; |
| 19645 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19646 | at::functionalization::impl::sync(self); |
| 19647 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19648 | } else { |
| 19649 | self_ = self; |
| 19650 | } |
| 19651 | |
| 19652 | at::Tensor out_; |
| 19653 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19654 | at::functionalization::impl::sync(out); |
| 19655 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19656 | } else { |
| 19657 | out_ = out; |
| 19658 | } |
| 19659 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19660 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 19661 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19662 | TORCH_INTERNAL_ASSERT(false, |
| 19663 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19664 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19665 | } else { |
| 19666 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19667 | at::AutoDispatchSkipFunctionalize guard; |
| 19668 | at::Tensor tmp_output = at::_ops::special_ndtri_out::call(self_, out_); |
| 19669 | return out;; |
| 19670 | } |
| 19671 | } else { |
| 19672 | at::Tensor tmp_output; |
| 19673 | { |
| 19674 | at::AutoDispatchSkipFunctionalize guard; |
| 19675 | tmp_output = at::_ops::special_ndtri::call(self_); |
| 19676 | } |
| 19677 | at::functionalization::impl::replace_(out, tmp_output); |
| 19678 | at::functionalization::impl::commit_update(out); |
| 19679 | at::functionalization::impl::sync(out); |
| 19680 | return out; |
| 19681 | } |
| 19682 | } |
| 19683 | |
| 19684 | at::Tensor & special_erfc_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 19685 | if (false) { |
| 19686 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19687 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19688 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19689 | auto self_meta = to_meta(self); |
| 19690 | auto out_meta = to_meta(out); |
| 19691 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19692 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19693 | at::_ops::special_erfc_out::call(self_meta, out_meta); |
| 19694 | } |
| 19695 | |
| 19696 | at::Tensor self_; |
| 19697 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19698 | at::functionalization::impl::sync(self); |
| 19699 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19700 | } else { |
| 19701 | self_ = self; |
| 19702 | } |
| 19703 | |
| 19704 | at::Tensor out_; |
| 19705 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19706 | at::functionalization::impl::sync(out); |
| 19707 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19708 | } else { |
| 19709 | out_ = out; |
| 19710 | } |
| 19711 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19712 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 19713 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19714 | TORCH_INTERNAL_ASSERT(false, |
| 19715 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19716 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19717 | } else { |
| 19718 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19719 | at::AutoDispatchSkipFunctionalize guard; |
| 19720 | at::Tensor tmp_output = at::_ops::special_erfc_out::call(self_, out_); |
| 19721 | return out;; |
| 19722 | } |
| 19723 | } else { |
| 19724 | at::Tensor tmp_output; |
| 19725 | { |
| 19726 | at::AutoDispatchSkipFunctionalize guard; |
| 19727 | tmp_output = at::_ops::special_erfc::call(self_); |
| 19728 | } |
| 19729 | at::functionalization::impl::replace_(out, tmp_output); |
| 19730 | at::functionalization::impl::commit_update(out); |
| 19731 | at::functionalization::impl::sync(out); |
| 19732 | return out; |
| 19733 | } |
| 19734 | } |
| 19735 | |
| 19736 | at::Tensor & special_logit_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<double> eps, at::Tensor & out) { |
| 19737 | if (false) { |
| 19738 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19739 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19740 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19741 | auto self_meta = to_meta(self); |
| 19742 | auto out_meta = to_meta(out); |
| 19743 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19744 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19745 | at::_ops::special_logit_out::call(self_meta, eps, out_meta); |
| 19746 | } |
| 19747 | |
| 19748 | at::Tensor self_; |
| 19749 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19750 | at::functionalization::impl::sync(self); |
| 19751 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19752 | } else { |
| 19753 | self_ = self; |
| 19754 | } |
| 19755 | |
| 19756 | at::Tensor out_; |
| 19757 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19758 | at::functionalization::impl::sync(out); |
| 19759 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19760 | } else { |
| 19761 | out_ = out; |
| 19762 | } |
| 19763 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19764 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 19765 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19766 | TORCH_INTERNAL_ASSERT(false, |
| 19767 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19768 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19769 | } else { |
| 19770 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19771 | at::AutoDispatchSkipFunctionalize guard; |
| 19772 | at::Tensor tmp_output = at::_ops::special_logit_out::call(self_, eps, out_); |
| 19773 | return out;; |
| 19774 | } |
| 19775 | } else { |
| 19776 | at::Tensor tmp_output; |
| 19777 | { |
| 19778 | at::AutoDispatchSkipFunctionalize guard; |
| 19779 | tmp_output = at::_ops::special_logit::call(self_, eps); |
| 19780 | } |
| 19781 | at::functionalization::impl::replace_(out, tmp_output); |
| 19782 | at::functionalization::impl::commit_update(out); |
| 19783 | at::functionalization::impl::sync(out); |
| 19784 | return out; |
| 19785 | } |
| 19786 | } |
| 19787 | |
| 19788 | at::Tensor & special_polygamma_out_out(c10::DispatchKeySet dispatchKeySet, int64_t n, const at::Tensor & self, at::Tensor & out) { |
| 19789 | if (false) { |
| 19790 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19791 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19792 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19793 | auto self_meta = to_meta(self); |
| 19794 | auto out_meta = to_meta(out); |
| 19795 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19796 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19797 | at::_ops::special_polygamma_out::call(n, self_meta, out_meta); |
| 19798 | } |
| 19799 | |
| 19800 | at::Tensor self_; |
| 19801 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19802 | at::functionalization::impl::sync(self); |
| 19803 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19804 | } else { |
| 19805 | self_ = self; |
| 19806 | } |
| 19807 | |
| 19808 | at::Tensor out_; |
| 19809 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19810 | at::functionalization::impl::sync(out); |
| 19811 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19812 | } else { |
| 19813 | out_ = out; |
| 19814 | } |
| 19815 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19816 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 19817 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19818 | TORCH_INTERNAL_ASSERT(false, |
| 19819 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19820 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19821 | } else { |
| 19822 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19823 | at::AutoDispatchSkipFunctionalize guard; |
| 19824 | at::Tensor tmp_output = at::_ops::special_polygamma_out::call(n, self_, out_); |
| 19825 | return out;; |
| 19826 | } |
| 19827 | } else { |
| 19828 | at::Tensor tmp_output; |
| 19829 | { |
| 19830 | at::AutoDispatchSkipFunctionalize guard; |
| 19831 | tmp_output = at::_ops::special_polygamma::call(n, self_); |
| 19832 | } |
| 19833 | at::functionalization::impl::replace_(out, tmp_output); |
| 19834 | at::functionalization::impl::commit_update(out); |
| 19835 | at::functionalization::impl::sync(out); |
| 19836 | return out; |
| 19837 | } |
| 19838 | } |
| 19839 | |
| 19840 | at::Tensor & special_sinc_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 19841 | if (false) { |
| 19842 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19843 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19844 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19845 | auto self_meta = to_meta(self); |
| 19846 | auto out_meta = to_meta(out); |
| 19847 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19848 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19849 | at::_ops::special_sinc_out::call(self_meta, out_meta); |
| 19850 | } |
| 19851 | |
| 19852 | at::Tensor self_; |
| 19853 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19854 | at::functionalization::impl::sync(self); |
| 19855 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19856 | } else { |
| 19857 | self_ = self; |
| 19858 | } |
| 19859 | |
| 19860 | at::Tensor out_; |
| 19861 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19862 | at::functionalization::impl::sync(out); |
| 19863 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19864 | } else { |
| 19865 | out_ = out; |
| 19866 | } |
| 19867 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19868 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 19869 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19870 | TORCH_INTERNAL_ASSERT(false, |
| 19871 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19872 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19873 | } else { |
| 19874 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19875 | at::AutoDispatchSkipFunctionalize guard; |
| 19876 | at::Tensor tmp_output = at::_ops::special_sinc_out::call(self_, out_); |
| 19877 | return out;; |
| 19878 | } |
| 19879 | } else { |
| 19880 | at::Tensor tmp_output; |
| 19881 | { |
| 19882 | at::AutoDispatchSkipFunctionalize guard; |
| 19883 | tmp_output = at::_ops::special_sinc::call(self_); |
| 19884 | } |
| 19885 | at::functionalization::impl::replace_(out, tmp_output); |
| 19886 | at::functionalization::impl::commit_update(out); |
| 19887 | at::functionalization::impl::sync(out); |
| 19888 | return out; |
| 19889 | } |
| 19890 | } |
| 19891 | |
| 19892 | at::Tensor & fft_ifft_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<int64_t> n, int64_t dim, c10::optional<c10::string_view> norm, at::Tensor & out) { |
| 19893 | if (false) { |
| 19894 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19895 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19896 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19897 | auto self_meta = to_meta(self); |
| 19898 | auto out_meta = to_meta(out); |
| 19899 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19900 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19901 | at::_ops::fft_ifft_out::call(self_meta, n, dim, norm, out_meta); |
| 19902 | } |
| 19903 | |
| 19904 | at::Tensor self_; |
| 19905 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19906 | at::functionalization::impl::sync(self); |
| 19907 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19908 | } else { |
| 19909 | self_ = self; |
| 19910 | } |
| 19911 | |
| 19912 | at::Tensor out_; |
| 19913 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19914 | at::functionalization::impl::sync(out); |
| 19915 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19916 | } else { |
| 19917 | out_ = out; |
| 19918 | } |
| 19919 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19920 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 19921 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19922 | TORCH_INTERNAL_ASSERT(false, |
| 19923 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19924 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19925 | } else { |
| 19926 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19927 | at::AutoDispatchSkipFunctionalize guard; |
| 19928 | at::Tensor tmp_output = at::_ops::fft_ifft_out::call(self_, n, dim, norm, out_); |
| 19929 | return out;; |
| 19930 | } |
| 19931 | } else { |
| 19932 | at::Tensor tmp_output; |
| 19933 | { |
| 19934 | at::AutoDispatchSkipFunctionalize guard; |
| 19935 | tmp_output = at::_ops::fft_ifft::call(self_, n, dim, norm); |
| 19936 | } |
| 19937 | at::functionalization::impl::replace_(out, tmp_output); |
| 19938 | at::functionalization::impl::commit_update(out); |
| 19939 | at::functionalization::impl::sync(out); |
| 19940 | return out; |
| 19941 | } |
| 19942 | } |
| 19943 | |
| 19944 | at::Tensor & fft_ihfft_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<int64_t> n, int64_t dim, c10::optional<c10::string_view> norm, at::Tensor & out) { |
| 19945 | if (false) { |
| 19946 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19947 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19948 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19949 | auto self_meta = to_meta(self); |
| 19950 | auto out_meta = to_meta(out); |
| 19951 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19952 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19953 | at::_ops::fft_ihfft_out::call(self_meta, n, dim, norm, out_meta); |
| 19954 | } |
| 19955 | |
| 19956 | at::Tensor self_; |
| 19957 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19958 | at::functionalization::impl::sync(self); |
| 19959 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19960 | } else { |
| 19961 | self_ = self; |
| 19962 | } |
| 19963 | |
| 19964 | at::Tensor out_; |
| 19965 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19966 | at::functionalization::impl::sync(out); |
| 19967 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19968 | } else { |
| 19969 | out_ = out; |
| 19970 | } |
| 19971 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19972 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 19973 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19974 | TORCH_INTERNAL_ASSERT(false, |
| 19975 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19976 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19977 | } else { |
| 19978 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19979 | at::AutoDispatchSkipFunctionalize guard; |
| 19980 | at::Tensor tmp_output = at::_ops::fft_ihfft_out::call(self_, n, dim, norm, out_); |
| 19981 | return out;; |
| 19982 | } |
| 19983 | } else { |
| 19984 | at::Tensor tmp_output; |
| 19985 | { |
| 19986 | at::AutoDispatchSkipFunctionalize guard; |
| 19987 | tmp_output = at::_ops::fft_ihfft::call(self_, n, dim, norm); |
| 19988 | } |
| 19989 | at::functionalization::impl::replace_(out, tmp_output); |
| 19990 | at::functionalization::impl::commit_update(out); |
| 19991 | at::functionalization::impl::sync(out); |
| 19992 | return out; |
| 19993 | } |
| 19994 | } |
| 19995 | |
| 19996 | at::Tensor & fft_ifft2_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional<c10::string_view> norm, at::Tensor & out) { |
| 19997 | if (false) { |
| 19998 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19999 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20000 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20001 | auto self_meta = to_meta(self); |
| 20002 | auto out_meta = to_meta(out); |
| 20003 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20004 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20005 | at::_ops::fft_ifft2_out::call(self_meta, s, dim, norm, out_meta); |
| 20006 | } |
| 20007 | |
| 20008 | at::Tensor self_; |
| 20009 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20010 | at::functionalization::impl::sync(self); |
| 20011 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20012 | } else { |
| 20013 | self_ = self; |
| 20014 | } |
| 20015 | |
| 20016 | at::Tensor out_; |
| 20017 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20018 | at::functionalization::impl::sync(out); |
| 20019 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20020 | } else { |
| 20021 | out_ = out; |
| 20022 | } |
| 20023 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20024 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 20025 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20026 | TORCH_INTERNAL_ASSERT(false, |
| 20027 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20028 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20029 | } else { |
| 20030 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20031 | at::AutoDispatchSkipFunctionalize guard; |
| 20032 | at::Tensor tmp_output = at::_ops::fft_ifft2_out::call(self_, s, dim, norm, out_); |
| 20033 | return out;; |
| 20034 | } |
| 20035 | } else { |
| 20036 | at::Tensor tmp_output; |
| 20037 | { |
| 20038 | at::AutoDispatchSkipFunctionalize guard; |
| 20039 | tmp_output = at::_ops::fft_ifft2::call(self_, s, dim, norm); |
| 20040 | } |
| 20041 | at::functionalization::impl::replace_(out, tmp_output); |
| 20042 | at::functionalization::impl::commit_update(out); |
| 20043 | at::functionalization::impl::sync(out); |
| 20044 | return out; |
| 20045 | } |
| 20046 | } |
| 20047 | |
| 20048 | const at::Tensor & fft_ihfftn_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional<c10::string_view> norm, const at::Tensor & out) { |
| 20049 | if (false) { |
| 20050 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20051 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20052 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20053 | auto self_meta = to_meta(self); |
| 20054 | auto out_meta = to_meta(out); |
| 20055 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20056 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20057 | at::_ops::fft_ihfftn_out::call(self_meta, s, dim, norm, out_meta); |
| 20058 | } |
| 20059 | |
| 20060 | at::Tensor self_; |
| 20061 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20062 | at::functionalization::impl::sync(self); |
| 20063 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20064 | } else { |
| 20065 | self_ = self; |
| 20066 | } |
| 20067 | |
| 20068 | at::Tensor out_; |
| 20069 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20070 | at::functionalization::impl::sync(out); |
| 20071 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20072 | } else { |
| 20073 | out_ = out; |
| 20074 | } |
| 20075 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20076 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 20077 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20078 | TORCH_INTERNAL_ASSERT(false, |
| 20079 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20080 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20081 | } else { |
| 20082 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20083 | at::AutoDispatchSkipFunctionalize guard; |
| 20084 | at::Tensor tmp_output = at::_ops::fft_ihfftn_out::call(self_, s, dim, norm, out_); |
| 20085 | return out;; |
| 20086 | } |
| 20087 | } else { |
| 20088 | at::Tensor tmp_output; |
| 20089 | { |
| 20090 | at::AutoDispatchSkipFunctionalize guard; |
| 20091 | tmp_output = at::_ops::fft_ihfftn::call(self_, s, dim, norm); |
| 20092 | } |
| 20093 | at::functionalization::impl::replace_(out, tmp_output); |
| 20094 | at::functionalization::impl::commit_update(out); |
| 20095 | at::functionalization::impl::sync(out); |
| 20096 | return out; |
| 20097 | } |
| 20098 | } |
| 20099 | |
| 20100 | ::std::tuple<at::Tensor &,at::Tensor &> linalg_cholesky_ex_out_L(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool upper, bool check_errors, at::Tensor & L, at::Tensor & info) { |
| 20101 | if (false) { |
| 20102 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20103 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20104 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20105 | auto self_meta = to_meta(self); |
| 20106 | auto L_meta = to_meta(L); |
| 20107 | auto info_meta = to_meta(info); |
| 20108 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20109 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20110 | at::_ops::linalg_cholesky_ex_L::call(self_meta, upper, check_errors, L_meta, info_meta); |
| 20111 | } |
| 20112 | |
| 20113 | at::Tensor self_; |
| 20114 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20115 | at::functionalization::impl::sync(self); |
| 20116 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20117 | } else { |
| 20118 | self_ = self; |
| 20119 | } |
| 20120 | |
| 20121 | at::Tensor L_; |
| 20122 | if (at::functionalization::impl::isFunctionalTensor(L)) { |
| 20123 | at::functionalization::impl::sync(L); |
| 20124 | L_ = at::functionalization::impl::from_functional_tensor(L); |
| 20125 | } else { |
| 20126 | L_ = L; |
| 20127 | } |
| 20128 | |
| 20129 | at::Tensor info_; |
| 20130 | if (at::functionalization::impl::isFunctionalTensor(info)) { |
| 20131 | at::functionalization::impl::sync(info); |
| 20132 | info_ = at::functionalization::impl::from_functional_tensor(info); |
| 20133 | } else { |
| 20134 | info_ = info; |
| 20135 | } |
| 20136 | if (!(true && at::functionalization::impl::isFunctionalTensor(L) && at::functionalization::impl::isFunctionalTensor(info))) { |
| 20137 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 20138 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20139 | TORCH_INTERNAL_ASSERT(false, |
| 20140 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20141 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20142 | } else { |
| 20143 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20144 | at::AutoDispatchSkipFunctionalize guard; |
| 20145 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::linalg_cholesky_ex_L::call(self_, upper, check_errors, L_, info_); |
| 20146 | return ::std::tuple<at::Tensor &,at::Tensor &>(L, info);; |
| 20147 | } |
| 20148 | } else { |
| 20149 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 20150 | { |
| 20151 | at::AutoDispatchSkipFunctionalize guard; |
| 20152 | tmp_output = at::_ops::linalg_cholesky_ex::call(self_, upper, check_errors); |
| 20153 | } |
| 20154 | at::functionalization::impl::replace_(L, std::get<0>(tmp_output)); |
| 20155 | at::functionalization::impl::commit_update(L); |
| 20156 | at::functionalization::impl::sync(L); |
| 20157 | at::functionalization::impl::replace_(info, std::get<1>(tmp_output)); |
| 20158 | at::functionalization::impl::commit_update(info); |
| 20159 | at::functionalization::impl::sync(info); |
| 20160 | return ::std::tuple<at::Tensor &,at::Tensor &>(L, info); |
| 20161 | } |
| 20162 | } |
| 20163 | |
| 20164 | at::Tensor & linalg_cross_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, int64_t dim, at::Tensor & out) { |
| 20165 | if (false) { |
| 20166 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20167 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20168 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20169 | auto self_meta = to_meta(self); |
| 20170 | auto other_meta = to_meta(other); |
| 20171 | auto out_meta = to_meta(out); |
| 20172 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20173 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20174 | at::_ops::linalg_cross_out::call(self_meta, other_meta, dim, out_meta); |
| 20175 | } |
| 20176 | |
| 20177 | at::Tensor self_; |
| 20178 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20179 | at::functionalization::impl::sync(self); |
| 20180 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20181 | } else { |
| 20182 | self_ = self; |
| 20183 | } |
| 20184 | |
| 20185 | at::Tensor other_; |
| 20186 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 20187 | at::functionalization::impl::sync(other); |
| 20188 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 20189 | } else { |
| 20190 | other_ = other; |
| 20191 | } |
| 20192 | |
| 20193 | at::Tensor out_; |
| 20194 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20195 | at::functionalization::impl::sync(out); |
| 20196 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20197 | } else { |
| 20198 | out_ = out; |
| 20199 | } |
| 20200 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20201 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 20202 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20203 | TORCH_INTERNAL_ASSERT(false, |
| 20204 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20205 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20206 | } else { |
| 20207 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20208 | at::AutoDispatchSkipFunctionalize guard; |
| 20209 | at::Tensor tmp_output = at::_ops::linalg_cross_out::call(self_, other_, dim, out_); |
| 20210 | return out;; |
| 20211 | } |
| 20212 | } else { |
| 20213 | at::Tensor tmp_output; |
| 20214 | { |
| 20215 | at::AutoDispatchSkipFunctionalize guard; |
| 20216 | tmp_output = at::_ops::linalg_cross::call(self_, other_, dim); |
| 20217 | } |
| 20218 | at::functionalization::impl::replace_(out, tmp_output); |
| 20219 | at::functionalization::impl::commit_update(out); |
| 20220 | at::functionalization::impl::sync(out); |
| 20221 | return out; |
| 20222 | } |
| 20223 | } |
| 20224 | |
| 20225 | at::Tensor & linalg_lu_solve_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & LU, const at::Tensor & pivots, const at::Tensor & B, bool left, bool adjoint, at::Tensor & out) { |
| 20226 | if (false) { |
| 20227 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20228 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20229 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20230 | auto LU_meta = to_meta(LU); |
| 20231 | auto pivots_meta = to_meta(pivots); |
| 20232 | auto B_meta = to_meta(B); |
| 20233 | auto out_meta = to_meta(out); |
| 20234 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20235 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20236 | at::_ops::linalg_lu_solve_out::call(LU_meta, pivots_meta, B_meta, left, adjoint, out_meta); |
| 20237 | } |
| 20238 | |
| 20239 | at::Tensor LU_; |
| 20240 | if (at::functionalization::impl::isFunctionalTensor(LU)) { |
| 20241 | at::functionalization::impl::sync(LU); |
| 20242 | LU_ = at::functionalization::impl::from_functional_tensor(LU); |
| 20243 | } else { |
| 20244 | LU_ = LU; |
| 20245 | } |
| 20246 | |
| 20247 | at::Tensor pivots_; |
| 20248 | if (at::functionalization::impl::isFunctionalTensor(pivots)) { |
| 20249 | at::functionalization::impl::sync(pivots); |
| 20250 | pivots_ = at::functionalization::impl::from_functional_tensor(pivots); |
| 20251 | } else { |
| 20252 | pivots_ = pivots; |
| 20253 | } |
| 20254 | |
| 20255 | at::Tensor B_; |
| 20256 | if (at::functionalization::impl::isFunctionalTensor(B)) { |
| 20257 | at::functionalization::impl::sync(B); |
| 20258 | B_ = at::functionalization::impl::from_functional_tensor(B); |
| 20259 | } else { |
| 20260 | B_ = B; |
| 20261 | } |
| 20262 | |
| 20263 | at::Tensor out_; |
| 20264 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20265 | at::functionalization::impl::sync(out); |
| 20266 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20267 | } else { |
| 20268 | out_ = out; |
| 20269 | } |
| 20270 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20271 | if ((false || at::functionalization::impl::isFunctionalTensor(LU) || at::functionalization::impl::isFunctionalTensor(pivots) || at::functionalization::impl::isFunctionalTensor(B))) { |
| 20272 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20273 | TORCH_INTERNAL_ASSERT(false, |
| 20274 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20275 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20276 | } else { |
| 20277 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20278 | at::AutoDispatchSkipFunctionalize guard; |
| 20279 | at::Tensor tmp_output = at::_ops::linalg_lu_solve_out::call(LU_, pivots_, B_, left, adjoint, out_); |
| 20280 | return out;; |
| 20281 | } |
| 20282 | } else { |
| 20283 | at::Tensor tmp_output; |
| 20284 | { |
| 20285 | at::AutoDispatchSkipFunctionalize guard; |
| 20286 | tmp_output = at::_ops::linalg_lu_solve::call(LU_, pivots_, B_, left, adjoint); |
| 20287 | } |
| 20288 | at::functionalization::impl::replace_(out, tmp_output); |
| 20289 | at::functionalization::impl::commit_update(out); |
| 20290 | at::functionalization::impl::sync(out); |
| 20291 | return out; |
| 20292 | } |
| 20293 | } |
| 20294 | |
| 20295 | at::Tensor & linalg_matmul_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 20296 | if (false) { |
| 20297 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20298 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20299 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20300 | auto self_meta = to_meta(self); |
| 20301 | auto other_meta = to_meta(other); |
| 20302 | auto out_meta = to_meta(out); |
| 20303 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20304 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20305 | at::_ops::linalg_matmul_out::call(self_meta, other_meta, out_meta); |
| 20306 | } |
| 20307 | |
| 20308 | at::Tensor self_; |
| 20309 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20310 | at::functionalization::impl::sync(self); |
| 20311 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20312 | } else { |
| 20313 | self_ = self; |
| 20314 | } |
| 20315 | |
| 20316 | at::Tensor other_; |
| 20317 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 20318 | at::functionalization::impl::sync(other); |
| 20319 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 20320 | } else { |
| 20321 | other_ = other; |
| 20322 | } |
| 20323 | |
| 20324 | at::Tensor out_; |
| 20325 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20326 | at::functionalization::impl::sync(out); |
| 20327 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20328 | } else { |
| 20329 | out_ = out; |
| 20330 | } |
| 20331 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20332 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 20333 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20334 | TORCH_INTERNAL_ASSERT(false, |
| 20335 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20336 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20337 | } else { |
| 20338 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20339 | at::AutoDispatchSkipFunctionalize guard; |
| 20340 | at::Tensor tmp_output = at::_ops::linalg_matmul_out::call(self_, other_, out_); |
| 20341 | return out;; |
| 20342 | } |
| 20343 | } else { |
| 20344 | at::Tensor tmp_output; |
| 20345 | { |
| 20346 | at::AutoDispatchSkipFunctionalize guard; |
| 20347 | tmp_output = at::_ops::linalg_matmul::call(self_, other_); |
| 20348 | } |
| 20349 | at::functionalization::impl::replace_(out, tmp_output); |
| 20350 | at::functionalization::impl::commit_update(out); |
| 20351 | at::functionalization::impl::sync(out); |
| 20352 | return out; |
| 20353 | } |
| 20354 | } |
| 20355 | |
| 20356 | at::Tensor & linalg_vecdot_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & y, int64_t dim, at::Tensor & out) { |
| 20357 | if (false) { |
| 20358 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20359 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20360 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20361 | auto x_meta = to_meta(x); |
| 20362 | auto y_meta = to_meta(y); |
| 20363 | auto out_meta = to_meta(out); |
| 20364 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20365 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20366 | at::_ops::linalg_vecdot_out::call(x_meta, y_meta, dim, out_meta); |
| 20367 | } |
| 20368 | |
| 20369 | at::Tensor x_; |
| 20370 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 20371 | at::functionalization::impl::sync(x); |
| 20372 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 20373 | } else { |
| 20374 | x_ = x; |
| 20375 | } |
| 20376 | |
| 20377 | at::Tensor y_; |
| 20378 | if (at::functionalization::impl::isFunctionalTensor(y)) { |
| 20379 | at::functionalization::impl::sync(y); |
| 20380 | y_ = at::functionalization::impl::from_functional_tensor(y); |
| 20381 | } else { |
| 20382 | y_ = y; |
| 20383 | } |
| 20384 | |
| 20385 | at::Tensor out_; |
| 20386 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20387 | at::functionalization::impl::sync(out); |
| 20388 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20389 | } else { |
| 20390 | out_ = out; |
| 20391 | } |
| 20392 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20393 | if ((false || at::functionalization::impl::isFunctionalTensor(x) || at::functionalization::impl::isFunctionalTensor(y))) { |
| 20394 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20395 | TORCH_INTERNAL_ASSERT(false, |
| 20396 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20397 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20398 | } else { |
| 20399 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20400 | at::AutoDispatchSkipFunctionalize guard; |
| 20401 | at::Tensor tmp_output = at::_ops::linalg_vecdot_out::call(x_, y_, dim, out_); |
| 20402 | return out;; |
| 20403 | } |
| 20404 | } else { |
| 20405 | at::Tensor tmp_output; |
| 20406 | { |
| 20407 | at::AutoDispatchSkipFunctionalize guard; |
| 20408 | tmp_output = at::_ops::linalg_vecdot::call(x_, y_, dim); |
| 20409 | } |
| 20410 | at::functionalization::impl::replace_(out, tmp_output); |
| 20411 | at::functionalization::impl::commit_update(out); |
| 20412 | at::functionalization::impl::sync(out); |
| 20413 | return out; |
| 20414 | } |
| 20415 | } |
| 20416 | |
| 20417 | at::Tensor & linalg_eigvalsh_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::string_view UPLO, at::Tensor & out) { |
| 20418 | if (false) { |
| 20419 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20420 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20421 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20422 | auto self_meta = to_meta(self); |
| 20423 | auto out_meta = to_meta(out); |
| 20424 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20425 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20426 | at::_ops::linalg_eigvalsh_out::call(self_meta, UPLO, out_meta); |
| 20427 | } |
| 20428 | |
| 20429 | at::Tensor self_; |
| 20430 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20431 | at::functionalization::impl::sync(self); |
| 20432 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20433 | } else { |
| 20434 | self_ = self; |
| 20435 | } |
| 20436 | |
| 20437 | at::Tensor out_; |
| 20438 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20439 | at::functionalization::impl::sync(out); |
| 20440 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20441 | } else { |
| 20442 | out_ = out; |
| 20443 | } |
| 20444 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20445 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 20446 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20447 | TORCH_INTERNAL_ASSERT(false, |
| 20448 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20449 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20450 | } else { |
| 20451 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20452 | at::AutoDispatchSkipFunctionalize guard; |
| 20453 | at::Tensor tmp_output = at::_ops::linalg_eigvalsh_out::call(self_, UPLO, out_); |
| 20454 | return out;; |
| 20455 | } |
| 20456 | } else { |
| 20457 | at::Tensor tmp_output; |
| 20458 | { |
| 20459 | at::AutoDispatchSkipFunctionalize guard; |
| 20460 | tmp_output = at::_ops::linalg_eigvalsh::call(self_, UPLO); |
| 20461 | } |
| 20462 | at::functionalization::impl::replace_(out, tmp_output); |
| 20463 | at::functionalization::impl::commit_update(out); |
| 20464 | at::functionalization::impl::sync(out); |
| 20465 | return out; |
| 20466 | } |
| 20467 | } |
| 20468 | |
| 20469 | ::std::tuple<at::Tensor &,at::Tensor &> linalg_inv_ex_out_inverse(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, bool check_errors, at::Tensor & inverse, at::Tensor & info) { |
| 20470 | if (false) { |
| 20471 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20472 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20473 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20474 | auto A_meta = to_meta(A); |
| 20475 | auto inverse_meta = to_meta(inverse); |
| 20476 | auto info_meta = to_meta(info); |
| 20477 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20478 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20479 | at::_ops::linalg_inv_ex_inverse::call(A_meta, check_errors, inverse_meta, info_meta); |
| 20480 | } |
| 20481 | |
| 20482 | at::Tensor A_; |
| 20483 | if (at::functionalization::impl::isFunctionalTensor(A)) { |
| 20484 | at::functionalization::impl::sync(A); |
| 20485 | A_ = at::functionalization::impl::from_functional_tensor(A); |
| 20486 | } else { |
| 20487 | A_ = A; |
| 20488 | } |
| 20489 | |
| 20490 | at::Tensor inverse_; |
| 20491 | if (at::functionalization::impl::isFunctionalTensor(inverse)) { |
| 20492 | at::functionalization::impl::sync(inverse); |
| 20493 | inverse_ = at::functionalization::impl::from_functional_tensor(inverse); |
| 20494 | } else { |
| 20495 | inverse_ = inverse; |
| 20496 | } |
| 20497 | |
| 20498 | at::Tensor info_; |
| 20499 | if (at::functionalization::impl::isFunctionalTensor(info)) { |
| 20500 | at::functionalization::impl::sync(info); |
| 20501 | info_ = at::functionalization::impl::from_functional_tensor(info); |
| 20502 | } else { |
| 20503 | info_ = info; |
| 20504 | } |
| 20505 | if (!(true && at::functionalization::impl::isFunctionalTensor(inverse) && at::functionalization::impl::isFunctionalTensor(info))) { |
| 20506 | if ((false || at::functionalization::impl::isFunctionalTensor(A))) { |
| 20507 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20508 | TORCH_INTERNAL_ASSERT(false, |
| 20509 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20510 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20511 | } else { |
| 20512 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20513 | at::AutoDispatchSkipFunctionalize guard; |
| 20514 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::linalg_inv_ex_inverse::call(A_, check_errors, inverse_, info_); |
| 20515 | return ::std::tuple<at::Tensor &,at::Tensor &>(inverse, info);; |
| 20516 | } |
| 20517 | } else { |
| 20518 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 20519 | { |
| 20520 | at::AutoDispatchSkipFunctionalize guard; |
| 20521 | tmp_output = at::_ops::linalg_inv_ex::call(A_, check_errors); |
| 20522 | } |
| 20523 | at::functionalization::impl::replace_(inverse, std::get<0>(tmp_output)); |
| 20524 | at::functionalization::impl::commit_update(inverse); |
| 20525 | at::functionalization::impl::sync(inverse); |
| 20526 | at::functionalization::impl::replace_(info, std::get<1>(tmp_output)); |
| 20527 | at::functionalization::impl::commit_update(info); |
| 20528 | at::functionalization::impl::sync(info); |
| 20529 | return ::std::tuple<at::Tensor &,at::Tensor &>(inverse, info); |
| 20530 | } |
| 20531 | } |
| 20532 | |
| 20533 | at::Tensor & inverse_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 20534 | if (false) { |
| 20535 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20536 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20537 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20538 | auto self_meta = to_meta(self); |
| 20539 | auto out_meta = to_meta(out); |
| 20540 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20541 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20542 | at::_ops::inverse_out::call(self_meta, out_meta); |
| 20543 | } |
| 20544 | |
| 20545 | at::Tensor self_; |
| 20546 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20547 | at::functionalization::impl::sync(self); |
| 20548 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20549 | } else { |
| 20550 | self_ = self; |
| 20551 | } |
| 20552 | |
| 20553 | at::Tensor out_; |
| 20554 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20555 | at::functionalization::impl::sync(out); |
| 20556 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20557 | } else { |
| 20558 | out_ = out; |
| 20559 | } |
| 20560 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20561 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 20562 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20563 | TORCH_INTERNAL_ASSERT(false, |
| 20564 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20565 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20566 | } else { |
| 20567 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20568 | at::AutoDispatchSkipFunctionalize guard; |
| 20569 | at::Tensor tmp_output = at::_ops::inverse_out::call(self_, out_); |
| 20570 | return out;; |
| 20571 | } |
| 20572 | } else { |
| 20573 | at::Tensor tmp_output; |
| 20574 | { |
| 20575 | at::AutoDispatchSkipFunctionalize guard; |
| 20576 | tmp_output = at::_ops::inverse::call(self_); |
| 20577 | } |
| 20578 | at::functionalization::impl::replace_(out, tmp_output); |
| 20579 | at::functionalization::impl::commit_update(out); |
| 20580 | at::functionalization::impl::sync(out); |
| 20581 | return out; |
| 20582 | } |
| 20583 | } |
| 20584 | |
| 20585 | at::Tensor & outer_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & vec2, at::Tensor & out) { |
| 20586 | if (false) { |
| 20587 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20588 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20589 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20590 | auto self_meta = to_meta(self); |
| 20591 | auto vec2_meta = to_meta(vec2); |
| 20592 | auto out_meta = to_meta(out); |
| 20593 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20594 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20595 | at::_ops::outer_out::call(self_meta, vec2_meta, out_meta); |
| 20596 | } |
| 20597 | |
| 20598 | at::Tensor self_; |
| 20599 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20600 | at::functionalization::impl::sync(self); |
| 20601 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20602 | } else { |
| 20603 | self_ = self; |
| 20604 | } |
| 20605 | |
| 20606 | at::Tensor vec2_; |
| 20607 | if (at::functionalization::impl::isFunctionalTensor(vec2)) { |
| 20608 | at::functionalization::impl::sync(vec2); |
| 20609 | vec2_ = at::functionalization::impl::from_functional_tensor(vec2); |
| 20610 | } else { |
| 20611 | vec2_ = vec2; |
| 20612 | } |
| 20613 | |
| 20614 | at::Tensor out_; |
| 20615 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20616 | at::functionalization::impl::sync(out); |
| 20617 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20618 | } else { |
| 20619 | out_ = out; |
| 20620 | } |
| 20621 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20622 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(vec2))) { |
| 20623 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20624 | TORCH_INTERNAL_ASSERT(false, |
| 20625 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20626 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20627 | } else { |
| 20628 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20629 | at::AutoDispatchSkipFunctionalize guard; |
| 20630 | at::Tensor tmp_output = at::_ops::outer_out::call(self_, vec2_, out_); |
| 20631 | return out;; |
| 20632 | } |
| 20633 | } else { |
| 20634 | at::Tensor tmp_output; |
| 20635 | { |
| 20636 | at::AutoDispatchSkipFunctionalize guard; |
| 20637 | tmp_output = at::_ops::outer::call(self_, vec2_); |
| 20638 | } |
| 20639 | at::functionalization::impl::replace_(out, tmp_output); |
| 20640 | at::functionalization::impl::commit_update(out); |
| 20641 | at::functionalization::impl::sync(out); |
| 20642 | return out; |
| 20643 | } |
| 20644 | } |
| 20645 | |
| 20646 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linalg_svd_out_U(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, bool full_matrices, c10::optional<c10::string_view> driver, at::Tensor & U, at::Tensor & S, at::Tensor & Vh) { |
| 20647 | if (false) { |
| 20648 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20649 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20650 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20651 | auto A_meta = to_meta(A); |
| 20652 | auto U_meta = to_meta(U); |
| 20653 | auto S_meta = to_meta(S); |
| 20654 | auto Vh_meta = to_meta(Vh); |
| 20655 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20656 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20657 | at::_ops::linalg_svd_U::call(A_meta, full_matrices, driver, U_meta, S_meta, Vh_meta); |
| 20658 | } |
| 20659 | |
| 20660 | at::Tensor A_; |
| 20661 | if (at::functionalization::impl::isFunctionalTensor(A)) { |
| 20662 | at::functionalization::impl::sync(A); |
| 20663 | A_ = at::functionalization::impl::from_functional_tensor(A); |
| 20664 | } else { |
| 20665 | A_ = A; |
| 20666 | } |
| 20667 | |
| 20668 | at::Tensor U_; |
| 20669 | if (at::functionalization::impl::isFunctionalTensor(U)) { |
| 20670 | at::functionalization::impl::sync(U); |
| 20671 | U_ = at::functionalization::impl::from_functional_tensor(U); |
| 20672 | } else { |
| 20673 | U_ = U; |
| 20674 | } |
| 20675 | |
| 20676 | at::Tensor S_; |
| 20677 | if (at::functionalization::impl::isFunctionalTensor(S)) { |
| 20678 | at::functionalization::impl::sync(S); |
| 20679 | S_ = at::functionalization::impl::from_functional_tensor(S); |
| 20680 | } else { |
| 20681 | S_ = S; |
| 20682 | } |
| 20683 | |
| 20684 | at::Tensor Vh_; |
| 20685 | if (at::functionalization::impl::isFunctionalTensor(Vh)) { |
| 20686 | at::functionalization::impl::sync(Vh); |
| 20687 | Vh_ = at::functionalization::impl::from_functional_tensor(Vh); |
| 20688 | } else { |
| 20689 | Vh_ = Vh; |
| 20690 | } |
| 20691 | if (!(true && at::functionalization::impl::isFunctionalTensor(U) && at::functionalization::impl::isFunctionalTensor(S) && at::functionalization::impl::isFunctionalTensor(Vh))) { |
| 20692 | if ((false || at::functionalization::impl::isFunctionalTensor(A))) { |
| 20693 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20694 | TORCH_INTERNAL_ASSERT(false, |
| 20695 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20696 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20697 | } else { |
| 20698 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20699 | at::AutoDispatchSkipFunctionalize guard; |
| 20700 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::linalg_svd_U::call(A_, full_matrices, driver, U_, S_, Vh_); |
| 20701 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(U, S, Vh);; |
| 20702 | } |
| 20703 | } else { |
| 20704 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 20705 | { |
| 20706 | at::AutoDispatchSkipFunctionalize guard; |
| 20707 | tmp_output = at::_ops::linalg_svd::call(A_, full_matrices, driver); |
| 20708 | } |
| 20709 | at::functionalization::impl::replace_(U, std::get<0>(tmp_output)); |
| 20710 | at::functionalization::impl::commit_update(U); |
| 20711 | at::functionalization::impl::sync(U); |
| 20712 | at::functionalization::impl::replace_(S, std::get<1>(tmp_output)); |
| 20713 | at::functionalization::impl::commit_update(S); |
| 20714 | at::functionalization::impl::sync(S); |
| 20715 | at::functionalization::impl::replace_(Vh, std::get<2>(tmp_output)); |
| 20716 | at::functionalization::impl::commit_update(Vh); |
| 20717 | at::functionalization::impl::sync(Vh); |
| 20718 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(U, S, Vh); |
| 20719 | } |
| 20720 | } |
| 20721 | |
| 20722 | at::Tensor & linalg_cond_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional<at::Scalar> & p, at::Tensor & out) { |
| 20723 | if (false) { |
| 20724 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20725 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20726 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20727 | auto self_meta = to_meta(self); |
| 20728 | auto out_meta = to_meta(out); |
| 20729 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20730 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20731 | at::_ops::linalg_cond_out::call(self_meta, p, out_meta); |
| 20732 | } |
| 20733 | |
| 20734 | at::Tensor self_; |
| 20735 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20736 | at::functionalization::impl::sync(self); |
| 20737 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20738 | } else { |
| 20739 | self_ = self; |
| 20740 | } |
| 20741 | |
| 20742 | at::Tensor out_; |
| 20743 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20744 | at::functionalization::impl::sync(out); |
| 20745 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20746 | } else { |
| 20747 | out_ = out; |
| 20748 | } |
| 20749 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20750 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 20751 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20752 | TORCH_INTERNAL_ASSERT(false, |
| 20753 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20754 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20755 | } else { |
| 20756 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20757 | at::AutoDispatchSkipFunctionalize guard; |
| 20758 | at::Tensor tmp_output = at::_ops::linalg_cond_out::call(self_, p, out_); |
| 20759 | return out;; |
| 20760 | } |
| 20761 | } else { |
| 20762 | at::Tensor tmp_output; |
| 20763 | { |
| 20764 | at::AutoDispatchSkipFunctionalize guard; |
| 20765 | tmp_output = at::_ops::linalg_cond::call(self_, p); |
| 20766 | } |
| 20767 | at::functionalization::impl::replace_(out, tmp_output); |
| 20768 | at::functionalization::impl::commit_update(out); |
| 20769 | at::functionalization::impl::sync(out); |
| 20770 | return out; |
| 20771 | } |
| 20772 | } |
| 20773 | |
| 20774 | at::Tensor & linalg_cond_out_p_str_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::string_view p, at::Tensor & out) { |
| 20775 | if (false) { |
| 20776 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20777 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20778 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20779 | auto self_meta = to_meta(self); |
| 20780 | auto out_meta = to_meta(out); |
| 20781 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20782 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20783 | at::_ops::linalg_cond_p_str_out::call(self_meta, p, out_meta); |
| 20784 | } |
| 20785 | |
| 20786 | at::Tensor self_; |
| 20787 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20788 | at::functionalization::impl::sync(self); |
| 20789 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20790 | } else { |
| 20791 | self_ = self; |
| 20792 | } |
| 20793 | |
| 20794 | at::Tensor out_; |
| 20795 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20796 | at::functionalization::impl::sync(out); |
| 20797 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20798 | } else { |
| 20799 | out_ = out; |
| 20800 | } |
| 20801 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20802 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 20803 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20804 | TORCH_INTERNAL_ASSERT(false, |
| 20805 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20806 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20807 | } else { |
| 20808 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20809 | at::AutoDispatchSkipFunctionalize guard; |
| 20810 | at::Tensor tmp_output = at::_ops::linalg_cond_p_str_out::call(self_, p, out_); |
| 20811 | return out;; |
| 20812 | } |
| 20813 | } else { |
| 20814 | at::Tensor tmp_output; |
| 20815 | { |
| 20816 | at::AutoDispatchSkipFunctionalize guard; |
| 20817 | tmp_output = at::_ops::linalg_cond_p_str::call(self_, p); |
| 20818 | } |
| 20819 | at::functionalization::impl::replace_(out, tmp_output); |
| 20820 | at::functionalization::impl::commit_update(out); |
| 20821 | at::functionalization::impl::sync(out); |
| 20822 | return out; |
| 20823 | } |
| 20824 | } |
| 20825 | |
| 20826 | ::std::tuple<at::Tensor &,at::Tensor &> linalg_solve_ex_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, const at::Tensor & B, bool left, bool check_errors, at::Tensor & result, at::Tensor & info) { |
| 20827 | if (false) { |
| 20828 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20829 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20830 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20831 | auto A_meta = to_meta(A); |
| 20832 | auto B_meta = to_meta(B); |
| 20833 | auto result_meta = to_meta(result); |
| 20834 | auto info_meta = to_meta(info); |
| 20835 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20836 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20837 | at::_ops::linalg_solve_ex_out::call(A_meta, B_meta, left, check_errors, result_meta, info_meta); |
| 20838 | } |
| 20839 | |
| 20840 | at::Tensor A_; |
| 20841 | if (at::functionalization::impl::isFunctionalTensor(A)) { |
| 20842 | at::functionalization::impl::sync(A); |
| 20843 | A_ = at::functionalization::impl::from_functional_tensor(A); |
| 20844 | } else { |
| 20845 | A_ = A; |
| 20846 | } |
| 20847 | |
| 20848 | at::Tensor B_; |
| 20849 | if (at::functionalization::impl::isFunctionalTensor(B)) { |
| 20850 | at::functionalization::impl::sync(B); |
| 20851 | B_ = at::functionalization::impl::from_functional_tensor(B); |
| 20852 | } else { |
| 20853 | B_ = B; |
| 20854 | } |
| 20855 | |
| 20856 | at::Tensor result_; |
| 20857 | if (at::functionalization::impl::isFunctionalTensor(result)) { |
| 20858 | at::functionalization::impl::sync(result); |
| 20859 | result_ = at::functionalization::impl::from_functional_tensor(result); |
| 20860 | } else { |
| 20861 | result_ = result; |
| 20862 | } |
| 20863 | |
| 20864 | at::Tensor info_; |
| 20865 | if (at::functionalization::impl::isFunctionalTensor(info)) { |
| 20866 | at::functionalization::impl::sync(info); |
| 20867 | info_ = at::functionalization::impl::from_functional_tensor(info); |
| 20868 | } else { |
| 20869 | info_ = info; |
| 20870 | } |
| 20871 | if (!(true && at::functionalization::impl::isFunctionalTensor(result) && at::functionalization::impl::isFunctionalTensor(info))) { |
| 20872 | if ((false || at::functionalization::impl::isFunctionalTensor(A) || at::functionalization::impl::isFunctionalTensor(B))) { |
| 20873 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20874 | TORCH_INTERNAL_ASSERT(false, |
| 20875 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20876 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20877 | } else { |
| 20878 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20879 | at::AutoDispatchSkipFunctionalize guard; |
| 20880 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::linalg_solve_ex_out::call(A_, B_, left, check_errors, result_, info_); |
| 20881 | return ::std::tuple<at::Tensor &,at::Tensor &>(result, info);; |
| 20882 | } |
| 20883 | } else { |
| 20884 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 20885 | { |
| 20886 | at::AutoDispatchSkipFunctionalize guard; |
| 20887 | tmp_output = at::_ops::linalg_solve_ex::call(A_, B_, left, check_errors); |
| 20888 | } |
| 20889 | at::functionalization::impl::replace_(result, std::get<0>(tmp_output)); |
| 20890 | at::functionalization::impl::commit_update(result); |
| 20891 | at::functionalization::impl::sync(result); |
| 20892 | at::functionalization::impl::replace_(info, std::get<1>(tmp_output)); |
| 20893 | at::functionalization::impl::commit_update(info); |
| 20894 | at::functionalization::impl::sync(info); |
| 20895 | return ::std::tuple<at::Tensor &,at::Tensor &>(result, info); |
| 20896 | } |
| 20897 | } |
| 20898 | |
| 20899 | at::Tensor & linalg_tensorsolve_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::OptionalIntArrayRef dims, at::Tensor & out) { |
| 20900 | if (false) { |
| 20901 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20902 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20903 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20904 | auto self_meta = to_meta(self); |
| 20905 | auto other_meta = to_meta(other); |
| 20906 | auto out_meta = to_meta(out); |
| 20907 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20908 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20909 | at::_ops::linalg_tensorsolve_out::call(self_meta, other_meta, dims, out_meta); |
| 20910 | } |
| 20911 | |
| 20912 | at::Tensor self_; |
| 20913 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20914 | at::functionalization::impl::sync(self); |
| 20915 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20916 | } else { |
| 20917 | self_ = self; |
| 20918 | } |
| 20919 | |
| 20920 | at::Tensor other_; |
| 20921 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 20922 | at::functionalization::impl::sync(other); |
| 20923 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 20924 | } else { |
| 20925 | other_ = other; |
| 20926 | } |
| 20927 | |
| 20928 | at::Tensor out_; |
| 20929 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20930 | at::functionalization::impl::sync(out); |
| 20931 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20932 | } else { |
| 20933 | out_ = out; |
| 20934 | } |
| 20935 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20936 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 20937 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20938 | TORCH_INTERNAL_ASSERT(false, |
| 20939 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20940 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20941 | } else { |
| 20942 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20943 | at::AutoDispatchSkipFunctionalize guard; |
| 20944 | at::Tensor tmp_output = at::_ops::linalg_tensorsolve_out::call(self_, other_, dims, out_); |
| 20945 | return out;; |
| 20946 | } |
| 20947 | } else { |
| 20948 | at::Tensor tmp_output; |
| 20949 | { |
| 20950 | at::AutoDispatchSkipFunctionalize guard; |
| 20951 | tmp_output = at::_ops::linalg_tensorsolve::call(self_, other_, dims); |
| 20952 | } |
| 20953 | at::functionalization::impl::replace_(out, tmp_output); |
| 20954 | at::functionalization::impl::commit_update(out); |
| 20955 | at::functionalization::impl::sync(out); |
| 20956 | return out; |
| 20957 | } |
| 20958 | } |
| 20959 | |
| 20960 | at::Tensor & linalg_matrix_rank_out_atol_rtol_tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional<at::Tensor> & atol, const c10::optional<at::Tensor> & rtol, bool hermitian, at::Tensor & out) { |
| 20961 | if (false) { |
| 20962 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20963 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20964 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20965 | auto input_meta = to_meta(input); |
| 20966 | auto atol_meta = to_meta(atol); |
| 20967 | auto rtol_meta = to_meta(rtol); |
| 20968 | auto out_meta = to_meta(out); |
| 20969 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20970 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20971 | at::_ops::linalg_matrix_rank_atol_rtol_tensor_out::call(input_meta, atol_meta, rtol_meta, hermitian, out_meta); |
| 20972 | } |
| 20973 | |
| 20974 | at::Tensor input_; |
| 20975 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 20976 | at::functionalization::impl::sync(input); |
| 20977 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 20978 | } else { |
| 20979 | input_ = input; |
| 20980 | } |
| 20981 | |
| 20982 | c10::optional<at::Tensor> atol_; |
| 20983 | if (at::functionalization::impl::isFunctionalTensor(atol)) { |
| 20984 | at::functionalization::impl::sync(atol); |
| 20985 | atol_ = at::functionalization::impl::from_functional_tensor(atol); |
| 20986 | } else { |
| 20987 | atol_ = atol; |
| 20988 | } |
| 20989 | |
| 20990 | c10::optional<at::Tensor> rtol_; |
| 20991 | if (at::functionalization::impl::isFunctionalTensor(rtol)) { |
| 20992 | at::functionalization::impl::sync(rtol); |
| 20993 | rtol_ = at::functionalization::impl::from_functional_tensor(rtol); |
| 20994 | } else { |
| 20995 | rtol_ = rtol; |
| 20996 | } |
| 20997 | |
| 20998 | at::Tensor out_; |
| 20999 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21000 | at::functionalization::impl::sync(out); |
| 21001 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21002 | } else { |
| 21003 | out_ = out; |
| 21004 | } |
| 21005 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21006 | if ((false || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(atol) || at::functionalization::impl::isFunctionalTensor(rtol))) { |
| 21007 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21008 | TORCH_INTERNAL_ASSERT(false, |
| 21009 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21010 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21011 | } else { |
| 21012 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21013 | at::AutoDispatchSkipFunctionalize guard; |
| 21014 | at::Tensor tmp_output = at::_ops::linalg_matrix_rank_atol_rtol_tensor_out::call(input_, atol_, rtol_, hermitian, out_); |
| 21015 | return out;; |
| 21016 | } |
| 21017 | } else { |
| 21018 | at::Tensor tmp_output; |
| 21019 | { |
| 21020 | at::AutoDispatchSkipFunctionalize guard; |
| 21021 | tmp_output = at::_ops::linalg_matrix_rank_atol_rtol_tensor::call(input_, atol_, rtol_, hermitian); |
| 21022 | } |
| 21023 | at::functionalization::impl::replace_(out, tmp_output); |
| 21024 | at::functionalization::impl::commit_update(out); |
| 21025 | at::functionalization::impl::sync(out); |
| 21026 | return out; |
| 21027 | } |
| 21028 | } |
| 21029 | |
| 21030 | at::Tensor & linalg_matrix_rank_out_atol_rtol_float_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<double> atol, c10::optional<double> rtol, bool hermitian, at::Tensor & out) { |
| 21031 | if (false) { |
| 21032 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21033 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21034 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21035 | auto self_meta = to_meta(self); |
| 21036 | auto out_meta = to_meta(out); |
| 21037 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21038 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21039 | at::_ops::linalg_matrix_rank_atol_rtol_float_out::call(self_meta, atol, rtol, hermitian, out_meta); |
| 21040 | } |
| 21041 | |
| 21042 | at::Tensor self_; |
| 21043 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 21044 | at::functionalization::impl::sync(self); |
| 21045 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 21046 | } else { |
| 21047 | self_ = self; |
| 21048 | } |
| 21049 | |
| 21050 | at::Tensor out_; |
| 21051 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21052 | at::functionalization::impl::sync(out); |
| 21053 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21054 | } else { |
| 21055 | out_ = out; |
| 21056 | } |
| 21057 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21058 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 21059 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21060 | TORCH_INTERNAL_ASSERT(false, |
| 21061 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21062 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21063 | } else { |
| 21064 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21065 | at::AutoDispatchSkipFunctionalize guard; |
| 21066 | at::Tensor tmp_output = at::_ops::linalg_matrix_rank_atol_rtol_float_out::call(self_, atol, rtol, hermitian, out_); |
| 21067 | return out;; |
| 21068 | } |
| 21069 | } else { |
| 21070 | at::Tensor tmp_output; |
| 21071 | { |
| 21072 | at::AutoDispatchSkipFunctionalize guard; |
| 21073 | tmp_output = at::_ops::linalg_matrix_rank_atol_rtol_float::call(self_, atol, rtol, hermitian); |
| 21074 | } |
| 21075 | at::functionalization::impl::replace_(out, tmp_output); |
| 21076 | at::functionalization::impl::commit_update(out); |
| 21077 | at::functionalization::impl::sync(out); |
| 21078 | return out; |
| 21079 | } |
| 21080 | } |
| 21081 | |
| 21082 | at::Tensor & linalg_matrix_rank_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double tol, bool hermitian, at::Tensor & out) { |
| 21083 | if (false) { |
| 21084 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21085 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21086 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21087 | auto self_meta = to_meta(self); |
| 21088 | auto out_meta = to_meta(out); |
| 21089 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21090 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21091 | at::_ops::linalg_matrix_rank_out::call(self_meta, tol, hermitian, out_meta); |
| 21092 | } |
| 21093 | |
| 21094 | at::Tensor self_; |
| 21095 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 21096 | at::functionalization::impl::sync(self); |
| 21097 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 21098 | } else { |
| 21099 | self_ = self; |
| 21100 | } |
| 21101 | |
| 21102 | at::Tensor out_; |
| 21103 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21104 | at::functionalization::impl::sync(out); |
| 21105 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21106 | } else { |
| 21107 | out_ = out; |
| 21108 | } |
| 21109 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21110 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 21111 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21112 | TORCH_INTERNAL_ASSERT(false, |
| 21113 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21114 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21115 | } else { |
| 21116 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21117 | at::AutoDispatchSkipFunctionalize guard; |
| 21118 | at::Tensor tmp_output = at::_ops::linalg_matrix_rank_out::call(self_, tol, hermitian, out_); |
| 21119 | return out;; |
| 21120 | } |
| 21121 | } else { |
| 21122 | at::Tensor tmp_output; |
| 21123 | { |
| 21124 | at::AutoDispatchSkipFunctionalize guard; |
| 21125 | tmp_output = at::_ops::linalg_matrix_rank::call(self_, tol, hermitian); |
| 21126 | } |
| 21127 | at::functionalization::impl::replace_(out, tmp_output); |
| 21128 | at::functionalization::impl::commit_update(out); |
| 21129 | at::functionalization::impl::sync(out); |
| 21130 | return out; |
| 21131 | } |
| 21132 | } |
| 21133 | |
| 21134 | at::Tensor & linalg_matrix_rank_out_out_tol_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & tol, bool hermitian, at::Tensor & out) { |
| 21135 | if (false) { |
| 21136 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21137 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21138 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21139 | auto input_meta = to_meta(input); |
| 21140 | auto tol_meta = to_meta(tol); |
| 21141 | auto out_meta = to_meta(out); |
| 21142 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21143 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21144 | at::_ops::linalg_matrix_rank_out_tol_tensor::call(input_meta, tol_meta, hermitian, out_meta); |
| 21145 | } |
| 21146 | |
| 21147 | at::Tensor input_; |
| 21148 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 21149 | at::functionalization::impl::sync(input); |
| 21150 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 21151 | } else { |
| 21152 | input_ = input; |
| 21153 | } |
| 21154 | |
| 21155 | at::Tensor tol_; |
| 21156 | if (at::functionalization::impl::isFunctionalTensor(tol)) { |
| 21157 | at::functionalization::impl::sync(tol); |
| 21158 | tol_ = at::functionalization::impl::from_functional_tensor(tol); |
| 21159 | } else { |
| 21160 | tol_ = tol; |
| 21161 | } |
| 21162 | |
| 21163 | at::Tensor out_; |
| 21164 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21165 | at::functionalization::impl::sync(out); |
| 21166 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21167 | } else { |
| 21168 | out_ = out; |
| 21169 | } |
| 21170 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21171 | if ((false || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(tol))) { |
| 21172 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21173 | TORCH_INTERNAL_ASSERT(false, |
| 21174 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21175 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21176 | } else { |
| 21177 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21178 | at::AutoDispatchSkipFunctionalize guard; |
| 21179 | at::Tensor tmp_output = at::_ops::linalg_matrix_rank_out_tol_tensor::call(input_, tol_, hermitian, out_); |
| 21180 | return out;; |
| 21181 | } |
| 21182 | } else { |
| 21183 | at::Tensor tmp_output; |
| 21184 | { |
| 21185 | at::AutoDispatchSkipFunctionalize guard; |
| 21186 | tmp_output = at::_ops::linalg_matrix_rank_tol_tensor::call(input_, tol_, hermitian); |
| 21187 | } |
| 21188 | at::functionalization::impl::replace_(out, tmp_output); |
| 21189 | at::functionalization::impl::commit_update(out); |
| 21190 | at::functionalization::impl::sync(out); |
| 21191 | return out; |
| 21192 | } |
| 21193 | } |
| 21194 | |
| 21195 | at::Tensor & _test_optional_floatlist_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & values, c10::optional<at::ArrayRef<double>> addends, at::Tensor & out) { |
| 21196 | if (false) { |
| 21197 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21198 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21199 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21200 | auto values_meta = to_meta(values); |
| 21201 | auto out_meta = to_meta(out); |
| 21202 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21203 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21204 | at::_ops::_test_optional_floatlist_out::call(values_meta, addends, out_meta); |
| 21205 | } |
| 21206 | |
| 21207 | at::Tensor values_; |
| 21208 | if (at::functionalization::impl::isFunctionalTensor(values)) { |
| 21209 | at::functionalization::impl::sync(values); |
| 21210 | values_ = at::functionalization::impl::from_functional_tensor(values); |
| 21211 | } else { |
| 21212 | values_ = values; |
| 21213 | } |
| 21214 | |
| 21215 | at::Tensor out_; |
| 21216 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21217 | at::functionalization::impl::sync(out); |
| 21218 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21219 | } else { |
| 21220 | out_ = out; |
| 21221 | } |
| 21222 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21223 | if ((false || at::functionalization::impl::isFunctionalTensor(values))) { |
| 21224 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21225 | TORCH_INTERNAL_ASSERT(false, |
| 21226 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21227 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21228 | } else { |
| 21229 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21230 | at::AutoDispatchSkipFunctionalize guard; |
| 21231 | at::Tensor tmp_output = at::_ops::_test_optional_floatlist_out::call(values_, addends, out_); |
| 21232 | return out;; |
| 21233 | } |
| 21234 | } else { |
| 21235 | at::Tensor tmp_output; |
| 21236 | { |
| 21237 | at::AutoDispatchSkipFunctionalize guard; |
| 21238 | tmp_output = at::_ops::_test_optional_floatlist::call(values_, addends); |
| 21239 | } |
| 21240 | at::functionalization::impl::replace_(out, tmp_output); |
| 21241 | at::functionalization::impl::commit_update(out); |
| 21242 | at::functionalization::impl::sync(out); |
| 21243 | return out; |
| 21244 | } |
| 21245 | } |
| 21246 | |
| 21247 | at::Tensor & _test_warn_in_autograd_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 21248 | if (false) { |
| 21249 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21250 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21251 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21252 | auto self_meta = to_meta(self); |
| 21253 | auto out_meta = to_meta(out); |
| 21254 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21255 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21256 | at::_ops::_test_warn_in_autograd_out::call(self_meta, out_meta); |
| 21257 | } |
| 21258 | |
| 21259 | at::Tensor self_; |
| 21260 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 21261 | at::functionalization::impl::sync(self); |
| 21262 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 21263 | } else { |
| 21264 | self_ = self; |
| 21265 | } |
| 21266 | |
| 21267 | at::Tensor out_; |
| 21268 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21269 | at::functionalization::impl::sync(out); |
| 21270 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21271 | } else { |
| 21272 | out_ = out; |
| 21273 | } |
| 21274 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21275 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 21276 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21277 | TORCH_INTERNAL_ASSERT(false, |
| 21278 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21279 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21280 | } else { |
| 21281 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21282 | at::AutoDispatchSkipFunctionalize guard; |
| 21283 | at::Tensor tmp_output = at::_ops::_test_warn_in_autograd_out::call(self_, out_); |
| 21284 | return out;; |
| 21285 | } |
| 21286 | } else { |
| 21287 | at::Tensor tmp_output; |
| 21288 | { |
| 21289 | at::AutoDispatchSkipFunctionalize guard; |
| 21290 | tmp_output = at::_ops::_test_warn_in_autograd::call(self_); |
| 21291 | } |
| 21292 | at::functionalization::impl::replace_(out, tmp_output); |
| 21293 | at::functionalization::impl::commit_update(out); |
| 21294 | at::functionalization::impl::sync(out); |
| 21295 | return out; |
| 21296 | } |
| 21297 | } |
| 21298 | |
| 21299 | at::Tensor & _segment_reduce_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & output, const at::Tensor & data, c10::string_view reduce, const c10::optional<at::Tensor> & lengths, const c10::optional<at::Tensor> & offsets, int64_t axis, const c10::optional<at::Scalar> & initial, at::Tensor & out) { |
| 21300 | if (false) { |
| 21301 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21302 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21303 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21304 | auto grad_meta = to_meta(grad); |
| 21305 | auto output_meta = to_meta(output); |
| 21306 | auto data_meta = to_meta(data); |
| 21307 | auto lengths_meta = to_meta(lengths); |
| 21308 | auto offsets_meta = to_meta(offsets); |
| 21309 | auto out_meta = to_meta(out); |
| 21310 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21311 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21312 | at::_ops::_segment_reduce_backward_out::call(grad_meta, output_meta, data_meta, reduce, lengths_meta, offsets_meta, axis, initial, out_meta); |
| 21313 | } |
| 21314 | |
| 21315 | at::Tensor grad_; |
| 21316 | if (at::functionalization::impl::isFunctionalTensor(grad)) { |
| 21317 | at::functionalization::impl::sync(grad); |
| 21318 | grad_ = at::functionalization::impl::from_functional_tensor(grad); |
| 21319 | } else { |
| 21320 | grad_ = grad; |
| 21321 | } |
| 21322 | |
| 21323 | at::Tensor output_; |
| 21324 | if (at::functionalization::impl::isFunctionalTensor(output)) { |
| 21325 | at::functionalization::impl::sync(output); |
| 21326 | output_ = at::functionalization::impl::from_functional_tensor(output); |
| 21327 | } else { |
| 21328 | output_ = output; |
| 21329 | } |
| 21330 | |
| 21331 | at::Tensor data_; |
| 21332 | if (at::functionalization::impl::isFunctionalTensor(data)) { |
| 21333 | at::functionalization::impl::sync(data); |
| 21334 | data_ = at::functionalization::impl::from_functional_tensor(data); |
| 21335 | } else { |
| 21336 | data_ = data; |
| 21337 | } |
| 21338 | |
| 21339 | c10::optional<at::Tensor> lengths_; |
| 21340 | if (at::functionalization::impl::isFunctionalTensor(lengths)) { |
| 21341 | at::functionalization::impl::sync(lengths); |
| 21342 | lengths_ = at::functionalization::impl::from_functional_tensor(lengths); |
| 21343 | } else { |
| 21344 | lengths_ = lengths; |
| 21345 | } |
| 21346 | |
| 21347 | c10::optional<at::Tensor> offsets_; |
| 21348 | if (at::functionalization::impl::isFunctionalTensor(offsets)) { |
| 21349 | at::functionalization::impl::sync(offsets); |
| 21350 | offsets_ = at::functionalization::impl::from_functional_tensor(offsets); |
| 21351 | } else { |
| 21352 | offsets_ = offsets; |
| 21353 | } |
| 21354 | |
| 21355 | at::Tensor out_; |
| 21356 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21357 | at::functionalization::impl::sync(out); |
| 21358 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21359 | } else { |
| 21360 | out_ = out; |
| 21361 | } |
| 21362 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21363 | if ((false || at::functionalization::impl::isFunctionalTensor(grad) || at::functionalization::impl::isFunctionalTensor(output) || at::functionalization::impl::isFunctionalTensor(data) || at::functionalization::impl::isFunctionalTensor(lengths) || at::functionalization::impl::isFunctionalTensor(offsets))) { |
| 21364 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21365 | TORCH_INTERNAL_ASSERT(false, |
| 21366 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21367 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21368 | } else { |
| 21369 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21370 | at::AutoDispatchSkipFunctionalize guard; |
| 21371 | at::Tensor tmp_output = at::_ops::_segment_reduce_backward_out::call(grad_, output_, data_, reduce, lengths_, offsets_, axis, initial, out_); |
| 21372 | return out;; |
| 21373 | } |
| 21374 | } else { |
| 21375 | at::Tensor tmp_output; |
| 21376 | { |
| 21377 | at::AutoDispatchSkipFunctionalize guard; |
| 21378 | tmp_output = at::_ops::_segment_reduce_backward::call(grad_, output_, data_, reduce, lengths_, offsets_, axis, initial); |
| 21379 | } |
| 21380 | at::functionalization::impl::replace_(out, tmp_output); |
| 21381 | at::functionalization::impl::commit_update(out); |
| 21382 | at::functionalization::impl::sync(out); |
| 21383 | return out; |
| 21384 | } |
| 21385 | } |
| 21386 | |
| 21387 | at::Tensor & _sparse_broadcast_to_copy_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::Tensor & out) { |
| 21388 | if (false) { |
| 21389 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21390 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21391 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21392 | auto self_meta = to_meta(self); |
| 21393 | auto out_meta = to_meta(out); |
| 21394 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21395 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21396 | at::_ops::_sparse_broadcast_to_copy_out::call(self_meta, size, out_meta); |
| 21397 | } |
| 21398 | |
| 21399 | at::Tensor self_; |
| 21400 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 21401 | at::functionalization::impl::sync(self); |
| 21402 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 21403 | } else { |
| 21404 | self_ = self; |
| 21405 | } |
| 21406 | |
| 21407 | at::Tensor out_; |
| 21408 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21409 | at::functionalization::impl::sync(out); |
| 21410 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21411 | } else { |
| 21412 | out_ = out; |
| 21413 | } |
| 21414 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21415 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 21416 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21417 | TORCH_INTERNAL_ASSERT(false, |
| 21418 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21419 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21420 | } else { |
| 21421 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21422 | at::AutoDispatchSkipFunctionalize guard; |
| 21423 | at::Tensor tmp_output = at::_ops::_sparse_broadcast_to_copy_out::call(self_, size, out_); |
| 21424 | return out;; |
| 21425 | } |
| 21426 | } else { |
| 21427 | at::Tensor tmp_output; |
| 21428 | { |
| 21429 | at::AutoDispatchSkipFunctionalize guard; |
| 21430 | tmp_output = at::_ops::_sparse_broadcast_to_copy::call(self_, size); |
| 21431 | } |
| 21432 | at::functionalization::impl::replace_(out, tmp_output); |
| 21433 | at::functionalization::impl::commit_update(out); |
| 21434 | at::functionalization::impl::sync(out); |
| 21435 | return out; |
| 21436 | } |
| 21437 | } |
| 21438 | |
| 21439 | at::Tensor & unsqueeze_copy_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, at::Tensor & out) { |
| 21440 | if (false) { |
| 21441 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21442 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21443 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21444 | auto self_meta = to_meta(self); |
| 21445 | auto out_meta = to_meta(out); |
| 21446 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21447 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21448 | at::_ops::unsqueeze_copy_out::call(self_meta, dim, out_meta); |
| 21449 | } |
| 21450 | |
| 21451 | at::Tensor self_; |
| 21452 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 21453 | at::functionalization::impl::sync(self); |
| 21454 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 21455 | } else { |
| 21456 | self_ = self; |
| 21457 | } |
| 21458 | |
| 21459 | at::Tensor out_; |
| 21460 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21461 | at::functionalization::impl::sync(out); |
| 21462 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21463 | } else { |
| 21464 | out_ = out; |
| 21465 | } |
| 21466 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21467 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 21468 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21469 | TORCH_INTERNAL_ASSERT(false, |
| 21470 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21471 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21472 | } else { |
| 21473 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21474 | at::AutoDispatchSkipFunctionalize guard; |
| 21475 | at::Tensor tmp_output = at::_ops::unsqueeze_copy_out::call(self_, dim, out_); |
| 21476 | return out;; |
| 21477 | } |
| 21478 | } else { |
| 21479 | at::Tensor tmp_output; |
| 21480 | { |
| 21481 | at::AutoDispatchSkipFunctionalize guard; |
| 21482 | tmp_output = at::_ops::unsqueeze_copy::call(self_, dim); |
| 21483 | } |
| 21484 | at::functionalization::impl::replace_(out, tmp_output); |
| 21485 | at::functionalization::impl::commit_update(out); |
| 21486 | at::functionalization::impl::sync(out); |
| 21487 | return out; |
| 21488 | } |
| 21489 | } |
| 21490 | |
| 21491 | at::Tensor & values_copy_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 21492 | if (false) { |
| 21493 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21494 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21495 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21496 | auto self_meta = to_meta(self); |
| 21497 | auto out_meta = to_meta(out); |
| 21498 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21499 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21500 | at::_ops::values_copy_out::call(self_meta, out_meta); |
| 21501 | } |
| 21502 | |
| 21503 | at::Tensor self_; |
| 21504 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 21505 | at::functionalization::impl::sync(self); |
| 21506 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 21507 | } else { |
| 21508 | self_ = self; |
| 21509 | } |
| 21510 | |
| 21511 | at::Tensor out_; |
| 21512 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21513 | at::functionalization::impl::sync(out); |
| 21514 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21515 | } else { |
| 21516 | out_ = out; |
| 21517 | } |
| 21518 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21519 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 21520 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21521 | TORCH_INTERNAL_ASSERT(false, |
| 21522 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21523 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21524 | } else { |
| 21525 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21526 | at::AutoDispatchSkipFunctionalize guard; |
| 21527 | at::Tensor tmp_output = at::_ops::values_copy_out::call(self_, out_); |
| 21528 | return out;; |
| 21529 | } |
| 21530 | } else { |
| 21531 | at::Tensor tmp_output; |
| 21532 | { |
| 21533 | at::AutoDispatchSkipFunctionalize guard; |
| 21534 | tmp_output = at::_ops::values_copy::call(self_); |
| 21535 | } |
| 21536 | at::functionalization::impl::replace_(out, tmp_output); |
| 21537 | at::functionalization::impl::commit_update(out); |
| 21538 | at::functionalization::impl::sync(out); |
| 21539 | return out; |
| 21540 | } |
| 21541 | } |
| 21542 | |
| 21543 | at::Tensor & to_padded_tensor_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double padding, at::OptionalSymIntArrayRef output_size, at::Tensor & out) { |
| 21544 | if (false) { |
| 21545 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21546 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21547 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21548 | auto self_meta = to_meta(self); |
| 21549 | auto out_meta = to_meta(out); |
| 21550 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21551 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21552 | at::_ops::to_padded_tensor_out::call(self_meta, padding, output_size, out_meta); |
| 21553 | } |
| 21554 | |
| 21555 | at::Tensor self_; |
| 21556 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 21557 | at::functionalization::impl::sync(self); |
| 21558 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 21559 | } else { |
| 21560 | self_ = self; |
| 21561 | } |
| 21562 | |
| 21563 | at::Tensor out_; |
| 21564 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21565 | at::functionalization::impl::sync(out); |
| 21566 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21567 | } else { |
| 21568 | out_ = out; |
| 21569 | } |
| 21570 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21571 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 21572 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21573 | TORCH_INTERNAL_ASSERT(false, |
| 21574 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21575 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21576 | } else { |
| 21577 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21578 | at::AutoDispatchSkipFunctionalize guard; |
| 21579 | at::Tensor tmp_output = at::_ops::to_padded_tensor_out::call(self_, padding, output_size, out_); |
| 21580 | return out;; |
| 21581 | } |
| 21582 | } else { |
| 21583 | at::Tensor tmp_output; |
| 21584 | { |
| 21585 | at::AutoDispatchSkipFunctionalize guard; |
| 21586 | tmp_output = at::_ops::to_padded_tensor::call(self_, padding, output_size); |
| 21587 | } |
| 21588 | at::functionalization::impl::replace_(out, tmp_output); |
| 21589 | at::functionalization::impl::commit_update(out); |
| 21590 | at::functionalization::impl::sync(out); |
| 21591 | return out; |
| 21592 | } |
| 21593 | } |
| 21594 | |
| 21595 | at::Tensor & _triton_scaled_dot_attention_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & q, const at::Tensor & k, const at::Tensor & v, double dropout_p, at::Tensor & out) { |
| 21596 | if (false) { |
| 21597 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21598 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21599 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21600 | auto q_meta = to_meta(q); |
| 21601 | auto k_meta = to_meta(k); |
| 21602 | auto v_meta = to_meta(v); |
| 21603 | auto out_meta = to_meta(out); |
| 21604 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21605 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21606 | at::_ops::_triton_scaled_dot_attention_out::call(q_meta, k_meta, v_meta, dropout_p, out_meta); |
| 21607 | } |
| 21608 | |
| 21609 | at::Tensor q_; |
| 21610 | if (at::functionalization::impl::isFunctionalTensor(q)) { |
| 21611 | at::functionalization::impl::sync(q); |
| 21612 | q_ = at::functionalization::impl::from_functional_tensor(q); |
| 21613 | } else { |
| 21614 | q_ = q; |
| 21615 | } |
| 21616 | |
| 21617 | at::Tensor k_; |
| 21618 | if (at::functionalization::impl::isFunctionalTensor(k)) { |
| 21619 | at::functionalization::impl::sync(k); |
| 21620 | k_ = at::functionalization::impl::from_functional_tensor(k); |
| 21621 | } else { |
| 21622 | k_ = k; |
| 21623 | } |
| 21624 | |
| 21625 | at::Tensor v_; |
| 21626 | if (at::functionalization::impl::isFunctionalTensor(v)) { |
| 21627 | at::functionalization::impl::sync(v); |
| 21628 | v_ = at::functionalization::impl::from_functional_tensor(v); |
| 21629 | } else { |
| 21630 | v_ = v; |
| 21631 | } |
| 21632 | |
| 21633 | at::Tensor out_; |
| 21634 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21635 | at::functionalization::impl::sync(out); |
| 21636 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21637 | } else { |
| 21638 | out_ = out; |
| 21639 | } |
| 21640 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21641 | if ((false || at::functionalization::impl::isFunctionalTensor(q) || at::functionalization::impl::isFunctionalTensor(k) || at::functionalization::impl::isFunctionalTensor(v))) { |
| 21642 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21643 | TORCH_INTERNAL_ASSERT(false, |
| 21644 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21645 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21646 | } else { |
| 21647 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21648 | at::AutoDispatchSkipFunctionalize guard; |
| 21649 | at::Tensor tmp_output = at::_ops::_triton_scaled_dot_attention_out::call(q_, k_, v_, dropout_p, out_); |
| 21650 | return out;; |
| 21651 | } |
| 21652 | } else { |
| 21653 | at::Tensor tmp_output; |
| 21654 | { |
| 21655 | at::AutoDispatchSkipFunctionalize guard; |
| 21656 | tmp_output = at::_ops::_triton_scaled_dot_attention::call(q_, k_, v_, dropout_p); |
| 21657 | } |
| 21658 | at::functionalization::impl::replace_(out, tmp_output); |
| 21659 | at::functionalization::impl::commit_update(out); |
| 21660 | at::functionalization::impl::sync(out); |
| 21661 | return out; |
| 21662 | } |
| 21663 | } |
| 21664 | |
| 21665 | at::Tensor & special_bessel_y0_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 21666 | if (false) { |
| 21667 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21668 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21669 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21670 | auto self_meta = to_meta(self); |
| 21671 | auto out_meta = to_meta(out); |
| 21672 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21673 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21674 | at::_ops::special_bessel_y0_out::call(self_meta, out_meta); |
| 21675 | } |
| 21676 | |
| 21677 | at::Tensor self_; |
| 21678 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 21679 | at::functionalization::impl::sync(self); |
| 21680 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 21681 | } else { |
| 21682 | self_ = self; |
| 21683 | } |
| 21684 | |
| 21685 | at::Tensor out_; |
| 21686 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21687 | at::functionalization::impl::sync(out); |
| 21688 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21689 | } else { |
| 21690 | out_ = out; |
| 21691 | } |
| 21692 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21693 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 21694 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21695 | TORCH_INTERNAL_ASSERT(false, |
| 21696 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21697 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21698 | } else { |
| 21699 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21700 | at::AutoDispatchSkipFunctionalize guard; |
| 21701 | at::Tensor tmp_output = at::_ops::special_bessel_y0_out::call(self_, out_); |
| 21702 | return out;; |
| 21703 | } |
| 21704 | } else { |
| 21705 | at::Tensor tmp_output; |
| 21706 | { |
| 21707 | at::AutoDispatchSkipFunctionalize guard; |
| 21708 | tmp_output = at::_ops::special_bessel_y0::call(self_); |
| 21709 | } |
| 21710 | at::functionalization::impl::replace_(out, tmp_output); |
| 21711 | at::functionalization::impl::commit_update(out); |
| 21712 | at::functionalization::impl::sync(out); |
| 21713 | return out; |
| 21714 | } |
| 21715 | } |
| 21716 | |
| 21717 | at::Tensor & special_chebyshev_polynomial_t_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 21718 | if (false) { |
| 21719 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21720 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21721 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21722 | auto x_meta = to_meta(x); |
| 21723 | auto n_meta = to_meta(n); |
| 21724 | auto out_meta = to_meta(out); |
| 21725 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21726 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21727 | at::_ops::special_chebyshev_polynomial_t_out::call(x_meta, n_meta, out_meta); |
| 21728 | } |
| 21729 | |
| 21730 | at::Tensor x_; |
| 21731 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 21732 | at::functionalization::impl::sync(x); |
| 21733 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 21734 | } else { |
| 21735 | x_ = x; |
| 21736 | } |
| 21737 | |
| 21738 | at::Tensor n_; |
| 21739 | if (at::functionalization::impl::isFunctionalTensor(n)) { |
| 21740 | at::functionalization::impl::sync(n); |
| 21741 | n_ = at::functionalization::impl::from_functional_tensor(n); |
| 21742 | } else { |
| 21743 | n_ = n; |
| 21744 | } |
| 21745 | |
| 21746 | at::Tensor out_; |
| 21747 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21748 | at::functionalization::impl::sync(out); |
| 21749 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21750 | } else { |
| 21751 | out_ = out; |
| 21752 | } |
| 21753 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21754 | if ((false || at::functionalization::impl::isFunctionalTensor(x) || at::functionalization::impl::isFunctionalTensor(n))) { |
| 21755 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21756 | TORCH_INTERNAL_ASSERT(false, |
| 21757 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21758 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21759 | } else { |
| 21760 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21761 | at::AutoDispatchSkipFunctionalize guard; |
| 21762 | at::Tensor tmp_output = at::_ops::special_chebyshev_polynomial_t_out::call(x_, n_, out_); |
| 21763 | return out;; |
| 21764 | } |
| 21765 | } else { |
| 21766 | at::Tensor tmp_output; |
| 21767 | { |
| 21768 | at::AutoDispatchSkipFunctionalize guard; |
| 21769 | tmp_output = at::_ops::special_chebyshev_polynomial_t::call(x_, n_); |
| 21770 | } |
| 21771 | at::functionalization::impl::replace_(out, tmp_output); |
| 21772 | at::functionalization::impl::commit_update(out); |
| 21773 | at::functionalization::impl::sync(out); |
| 21774 | return out; |
| 21775 | } |
| 21776 | } |
| 21777 | |
| 21778 | at::Tensor & special_chebyshev_polynomial_t_out_x_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { |
| 21779 | if (false) { |
| 21780 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21781 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21782 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21783 | auto n_meta = to_meta(n); |
| 21784 | auto out_meta = to_meta(out); |
| 21785 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21786 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21787 | at::_ops::special_chebyshev_polynomial_t_x_scalar_out::call(x, n_meta, out_meta); |
| 21788 | } |
| 21789 | |
| 21790 | at::Tensor n_; |
| 21791 | if (at::functionalization::impl::isFunctionalTensor(n)) { |
| 21792 | at::functionalization::impl::sync(n); |
| 21793 | n_ = at::functionalization::impl::from_functional_tensor(n); |
| 21794 | } else { |
| 21795 | n_ = n; |
| 21796 | } |
| 21797 | |
| 21798 | at::Tensor out_; |
| 21799 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21800 | at::functionalization::impl::sync(out); |
| 21801 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21802 | } else { |
| 21803 | out_ = out; |
| 21804 | } |
| 21805 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21806 | if ((false || at::functionalization::impl::isFunctionalTensor(n))) { |
| 21807 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21808 | TORCH_INTERNAL_ASSERT(false, |
| 21809 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21810 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21811 | } else { |
| 21812 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21813 | at::AutoDispatchSkipFunctionalize guard; |
| 21814 | at::Tensor tmp_output = at::_ops::special_chebyshev_polynomial_t_x_scalar_out::call(x, n_, out_); |
| 21815 | return out;; |
| 21816 | } |
| 21817 | } else { |
| 21818 | at::Tensor tmp_output; |
| 21819 | { |
| 21820 | at::AutoDispatchSkipFunctionalize guard; |
| 21821 | tmp_output = at::_ops::special_chebyshev_polynomial_t_x_scalar::call(x, n_); |
| 21822 | } |
| 21823 | at::functionalization::impl::replace_(out, tmp_output); |
| 21824 | at::functionalization::impl::commit_update(out); |
| 21825 | at::functionalization::impl::sync(out); |
| 21826 | return out; |
| 21827 | } |
| 21828 | } |
| 21829 | |
| 21830 | at::Tensor & special_chebyshev_polynomial_t_out_n_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { |
| 21831 | if (false) { |
| 21832 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21833 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21834 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21835 | auto x_meta = to_meta(x); |
| 21836 | auto out_meta = to_meta(out); |
| 21837 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21838 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21839 | at::_ops::special_chebyshev_polynomial_t_n_scalar_out::call(x_meta, n, out_meta); |
| 21840 | } |
| 21841 | |
| 21842 | at::Tensor x_; |
| 21843 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 21844 | at::functionalization::impl::sync(x); |
| 21845 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 21846 | } else { |
| 21847 | x_ = x; |
| 21848 | } |
| 21849 | |
| 21850 | at::Tensor out_; |
| 21851 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21852 | at::functionalization::impl::sync(out); |
| 21853 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21854 | } else { |
| 21855 | out_ = out; |
| 21856 | } |
| 21857 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21858 | if ((false || at::functionalization::impl::isFunctionalTensor(x))) { |
| 21859 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21860 | TORCH_INTERNAL_ASSERT(false, |
| 21861 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21862 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21863 | } else { |
| 21864 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21865 | at::AutoDispatchSkipFunctionalize guard; |
| 21866 | at::Tensor tmp_output = at::_ops::special_chebyshev_polynomial_t_n_scalar_out::call(x_, n, out_); |
| 21867 | return out;; |
| 21868 | } |
| 21869 | } else { |
| 21870 | at::Tensor tmp_output; |
| 21871 | { |
| 21872 | at::AutoDispatchSkipFunctionalize guard; |
| 21873 | tmp_output = at::_ops::special_chebyshev_polynomial_t_n_scalar::call(x_, n); |
| 21874 | } |
| 21875 | at::functionalization::impl::replace_(out, tmp_output); |
| 21876 | at::functionalization::impl::commit_update(out); |
| 21877 | at::functionalization::impl::sync(out); |
| 21878 | return out; |
| 21879 | } |
| 21880 | } |
| 21881 | |
| 21882 | at::Tensor & special_chebyshev_polynomial_u_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 21883 | if (false) { |
| 21884 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21885 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21886 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21887 | auto x_meta = to_meta(x); |
| 21888 | auto n_meta = to_meta(n); |
| 21889 | auto out_meta = to_meta(out); |
| 21890 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21891 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21892 | at::_ops::special_chebyshev_polynomial_u_out::call(x_meta, n_meta, out_meta); |
| 21893 | } |
| 21894 | |
| 21895 | at::Tensor x_; |
| 21896 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 21897 | at::functionalization::impl::sync(x); |
| 21898 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 21899 | } else { |
| 21900 | x_ = x; |
| 21901 | } |
| 21902 | |
| 21903 | at::Tensor n_; |
| 21904 | if (at::functionalization::impl::isFunctionalTensor(n)) { |
| 21905 | at::functionalization::impl::sync(n); |
| 21906 | n_ = at::functionalization::impl::from_functional_tensor(n); |
| 21907 | } else { |
| 21908 | n_ = n; |
| 21909 | } |
| 21910 | |
| 21911 | at::Tensor out_; |
| 21912 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21913 | at::functionalization::impl::sync(out); |
| 21914 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21915 | } else { |
| 21916 | out_ = out; |
| 21917 | } |
| 21918 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21919 | if ((false || at::functionalization::impl::isFunctionalTensor(x) || at::functionalization::impl::isFunctionalTensor(n))) { |
| 21920 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21921 | TORCH_INTERNAL_ASSERT(false, |
| 21922 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21923 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21924 | } else { |
| 21925 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21926 | at::AutoDispatchSkipFunctionalize guard; |
| 21927 | at::Tensor tmp_output = at::_ops::special_chebyshev_polynomial_u_out::call(x_, n_, out_); |
| 21928 | return out;; |
| 21929 | } |
| 21930 | } else { |
| 21931 | at::Tensor tmp_output; |
| 21932 | { |
| 21933 | at::AutoDispatchSkipFunctionalize guard; |
| 21934 | tmp_output = at::_ops::special_chebyshev_polynomial_u::call(x_, n_); |
| 21935 | } |
| 21936 | at::functionalization::impl::replace_(out, tmp_output); |
| 21937 | at::functionalization::impl::commit_update(out); |
| 21938 | at::functionalization::impl::sync(out); |
| 21939 | return out; |
| 21940 | } |
| 21941 | } |
| 21942 | |
| 21943 | at::Tensor & special_chebyshev_polynomial_u_out_x_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { |
| 21944 | if (false) { |
| 21945 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21946 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21947 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21948 | auto n_meta = to_meta(n); |
| 21949 | auto out_meta = to_meta(out); |
| 21950 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21951 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21952 | at::_ops::special_chebyshev_polynomial_u_x_scalar_out::call(x, n_meta, out_meta); |
| 21953 | } |
| 21954 | |
| 21955 | at::Tensor n_; |
| 21956 | if (at::functionalization::impl::isFunctionalTensor(n)) { |
| 21957 | at::functionalization::impl::sync(n); |
| 21958 | n_ = at::functionalization::impl::from_functional_tensor(n); |
| 21959 | } else { |
| 21960 | n_ = n; |
| 21961 | } |
| 21962 | |
| 21963 | at::Tensor out_; |
| 21964 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21965 | at::functionalization::impl::sync(out); |
| 21966 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21967 | } else { |
| 21968 | out_ = out; |
| 21969 | } |
| 21970 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21971 | if ((false || at::functionalization::impl::isFunctionalTensor(n))) { |
| 21972 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21973 | TORCH_INTERNAL_ASSERT(false, |
| 21974 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21975 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21976 | } else { |
| 21977 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21978 | at::AutoDispatchSkipFunctionalize guard; |
| 21979 | at::Tensor tmp_output = at::_ops::special_chebyshev_polynomial_u_x_scalar_out::call(x, n_, out_); |
| 21980 | return out;; |
| 21981 | } |
| 21982 | } else { |
| 21983 | at::Tensor tmp_output; |
| 21984 | { |
| 21985 | at::AutoDispatchSkipFunctionalize guard; |
| 21986 | tmp_output = at::_ops::special_chebyshev_polynomial_u_x_scalar::call(x, n_); |
| 21987 | } |
| 21988 | at::functionalization::impl::replace_(out, tmp_output); |
| 21989 | at::functionalization::impl::commit_update(out); |
| 21990 | at::functionalization::impl::sync(out); |
| 21991 | return out; |
| 21992 | } |
| 21993 | } |
| 21994 | |
| 21995 | at::Tensor & special_chebyshev_polynomial_u_out_n_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { |
| 21996 | if (false) { |
| 21997 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21998 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21999 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 22000 | auto x_meta = to_meta(x); |
| 22001 | auto out_meta = to_meta(out); |
| 22002 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22003 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22004 | at::_ops::special_chebyshev_polynomial_u_n_scalar_out::call(x_meta, n, out_meta); |
| 22005 | } |
| 22006 | |
| 22007 | at::Tensor x_; |
| 22008 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 22009 | at::functionalization::impl::sync(x); |
| 22010 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 22011 | } else { |
| 22012 | x_ = x; |
| 22013 | } |
| 22014 | |
| 22015 | at::Tensor out_; |
| 22016 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 22017 | at::functionalization::impl::sync(out); |
| 22018 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 22019 | } else { |
| 22020 | out_ = out; |
| 22021 | } |
| 22022 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 22023 | if ((false || at::functionalization::impl::isFunctionalTensor(x))) { |
| 22024 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 22025 | TORCH_INTERNAL_ASSERT(false, |
| 22026 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 22027 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 22028 | } else { |
| 22029 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 22030 | at::AutoDispatchSkipFunctionalize guard; |
| 22031 | at::Tensor tmp_output = at::_ops::special_chebyshev_polynomial_u_n_scalar_out::call(x_, n, out_); |
| 22032 | return out;; |
| 22033 | } |
| 22034 | } else { |
| 22035 | at::Tensor tmp_output; |
| 22036 | { |
| 22037 | at::AutoDispatchSkipFunctionalize guard; |
| 22038 | tmp_output = at::_ops::special_chebyshev_polynomial_u_n_scalar::call(x_, n); |
| 22039 | } |
| 22040 | at::functionalization::impl::replace_(out, tmp_output); |
| 22041 | at::functionalization::impl::commit_update(out); |
| 22042 | at::functionalization::impl::sync(out); |
| 22043 | return out; |
| 22044 | } |
| 22045 | } |
| 22046 | |
| 22047 | at::Tensor & special_hermite_polynomial_h_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 22048 | if (false) { |
| 22049 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 22050 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 22051 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 22052 | auto x_meta = to_meta(x); |
| 22053 | auto n_meta = to_meta(n); |
| 22054 | auto out_meta = to_meta(out); |
| 22055 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22056 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22057 | at::_ops::special_hermite_polynomial_h_out::call(x_meta, n_meta, out_meta); |
| 22058 | } |
| 22059 | |
| 22060 | at::Tensor x_; |
| 22061 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 22062 | at::functionalization::impl::sync(x); |
| 22063 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 22064 | } else { |
| 22065 | x_ = x; |
| 22066 | } |
| 22067 | |
| 22068 | at::Tensor n_; |
| 22069 | if (at::functionalization::impl::isFunctionalTensor(n)) { |
| 22070 | at::functionalization::impl::sync(n); |
| 22071 | n_ = at::functionalization::impl::from_functional_tensor(n); |
| 22072 | } else { |
| 22073 | n_ = n; |
| 22074 | } |
| 22075 | |
| 22076 | at::Tensor out_; |
| 22077 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 22078 | at::functionalization::impl::sync(out); |
| 22079 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 22080 | } else { |
| 22081 | out_ = out; |
| 22082 | } |
| 22083 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 22084 | if ((false || at::functionalization::impl::isFunctionalTensor(x) || at::functionalization::impl::isFunctionalTensor(n))) { |
| 22085 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 22086 | TORCH_INTERNAL_ASSERT(false, |
| 22087 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 22088 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 22089 | } else { |
| 22090 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 22091 | at::AutoDispatchSkipFunctionalize guard; |
| 22092 | at::Tensor tmp_output = at::_ops::special_hermite_polynomial_h_out::call(x_, n_, out_); |
| 22093 | return out;; |
| 22094 | } |
| 22095 | } else { |
| 22096 | at::Tensor tmp_output; |
| 22097 | { |
| 22098 | at::AutoDispatchSkipFunctionalize guard; |
| 22099 | tmp_output = at::_ops::special_hermite_polynomial_h::call(x_, n_); |
| 22100 | } |
| 22101 | at::functionalization::impl::replace_(out, tmp_output); |
| 22102 | at::functionalization::impl::commit_update(out); |
| 22103 | at::functionalization::impl::sync(out); |
| 22104 | return out; |
| 22105 | } |
| 22106 | } |
| 22107 | |
| 22108 | at::Tensor & special_hermite_polynomial_h_out_x_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { |
| 22109 | if (false) { |
| 22110 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 22111 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 22112 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 22113 | auto n_meta = to_meta(n); |
| 22114 | auto out_meta = to_meta(out); |
| 22115 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22116 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22117 | at::_ops::special_hermite_polynomial_h_x_scalar_out::call(x, n_meta, out_meta); |
| 22118 | } |
| 22119 | |
| 22120 | at::Tensor n_; |
| 22121 | if (at::functionalization::impl::isFunctionalTensor(n)) { |
| 22122 | at::functionalization::impl::sync(n); |
| 22123 | n_ = at::functionalization::impl::from_functional_tensor(n); |
| 22124 | } else { |
| 22125 | n_ = n; |
| 22126 | } |
| 22127 | |
| 22128 | at::Tensor out_; |
| 22129 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 22130 | at::functionalization::impl::sync(out); |
| 22131 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 22132 | } else { |
| 22133 | out_ = out; |
| 22134 | } |
| 22135 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 22136 | if ((false || at::functionalization::impl::isFunctionalTensor(n))) { |
| 22137 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 22138 | TORCH_INTERNAL_ASSERT(false, |
| 22139 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 22140 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 22141 | } else { |
| 22142 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 22143 | at::AutoDispatchSkipFunctionalize guard; |
| 22144 | at::Tensor tmp_output = at::_ops::special_hermite_polynomial_h_x_scalar_out::call(x, n_, out_); |
| 22145 | return out;; |
| 22146 | } |
| 22147 | } else { |
| 22148 | at::Tensor tmp_output; |
| 22149 | { |
| 22150 | at::AutoDispatchSkipFunctionalize guard; |
| 22151 | tmp_output = at::_ops::special_hermite_polynomial_h_x_scalar::call(x, n_); |
| 22152 | } |
| 22153 | at::functionalization::impl::replace_(out, tmp_output); |
| 22154 | at::functionalization::impl::commit_update(out); |
| 22155 | at::functionalization::impl::sync(out); |
| 22156 | return out; |
| 22157 | } |
| 22158 | } |
| 22159 | |
| 22160 | at::Tensor & special_hermite_polynomial_h_out_n_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { |
| 22161 | if (false) { |
| 22162 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 22163 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 22164 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 22165 | auto x_meta = to_meta(x); |
| 22166 | auto out_meta = to_meta(out); |
| 22167 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22168 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22169 | at::_ops::special_hermite_polynomial_h_n_scalar_out::call(x_meta, n, out_meta); |
| 22170 | } |
| 22171 | |
| 22172 | at::Tensor x_; |
| 22173 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 22174 | at::functionalization::impl::sync(x); |
| 22175 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 22176 | } else { |
| 22177 | x_ = x; |
| 22178 | } |
| 22179 | |
| 22180 | at::Tensor out_; |
| 22181 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 22182 | at::functionalization::impl::sync(out); |
| 22183 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 22184 | } else { |
| 22185 | out_ = out; |
| 22186 | } |
| 22187 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 22188 | if ((false || at::functionalization::impl::isFunctionalTensor(x))) { |
| 22189 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 22190 | TORCH_INTERNAL_ASSERT(false, |
| 22191 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 22192 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 22193 | } else { |
| 22194 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 22195 | at::AutoDispatchSkipFunctionalize guard; |
| 22196 | at::Tensor tmp_output = at::_ops::special_hermite_polynomial_h_n_scalar_out::call(x_, n, out_); |
| 22197 | return out;; |
| 22198 | } |
| 22199 | } else { |
| 22200 | at::Tensor tmp_output; |
| 22201 | { |
| 22202 | at::AutoDispatchSkipFunctionalize guard; |
| 22203 | tmp_output = at::_ops::special_hermite_polynomial_h_n_scalar::call(x_, n); |
| 22204 | } |
| 22205 | at::functionalization::impl::replace_(out, tmp_output); |
| 22206 | at::functionalization::impl::commit_update(out); |
| 22207 | at::functionalization::impl::sync(out); |
| 22208 | return out; |
| 22209 | } |
| 22210 | } |
| 22211 | |
| 22212 | at::Tensor & special_modified_bessel_k1_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 22213 | if (false) { |
| 22214 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 22215 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 22216 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 22217 | auto self_meta = to_meta(self); |
| 22218 | auto out_meta = to_meta(out); |
| 22219 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22220 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22221 | at::_ops::special_modified_bessel_k1_out::call(self_meta, out_meta); |
| 22222 | } |
| 22223 | |
| 22224 | at::Tensor self_; |
| 22225 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 22226 | at::functionalization::impl::sync(self); |
| 22227 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 22228 | } else { |
| 22229 | self_ = self; |
| 22230 | } |
| 22231 | |
| 22232 | at::Tensor out_; |
| 22233 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 22234 | at::functionalization::impl::sync(out); |
| 22235 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 22236 | } else { |
| 22237 | out_ = out; |
| 22238 | } |
| 22239 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 22240 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 22241 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 22242 | TORCH_INTERNAL_ASSERT(false, |
| 22243 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 22244 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 22245 | } else { |
| 22246 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 22247 | at::AutoDispatchSkipFunctionalize guard; |
| 22248 | at::Tensor tmp_output = at::_ops::special_modified_bessel_k1_out::call(self_, out_); |
| 22249 | return out;; |
| 22250 | } |
| 22251 | } else { |
| 22252 | at::Tensor tmp_output; |
| 22253 | { |
| 22254 | at::AutoDispatchSkipFunctionalize guard; |
| 22255 | tmp_output = at::_ops::special_modified_bessel_k1::call(self_); |
| 22256 | } |
| 22257 | at::functionalization::impl::replace_(out, tmp_output); |
| 22258 | at::functionalization::impl::commit_update(out); |
| 22259 | at::functionalization::impl::sync(out); |
| 22260 | return out; |
| 22261 | } |
| 22262 | } |
| 22263 | |
| 22264 | at::Tensor & special_scaled_modified_bessel_k0_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, at::Tensor & out) { |
| 22265 | if (false) { |
| 22266 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 22267 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 22268 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 22269 | auto x_meta = to_meta(x); |
| 22270 | auto out_meta = to_meta(out); |
| 22271 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22272 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22273 | at::_ops::special_scaled_modified_bessel_k0_out::call(x_meta, out_meta); |
| 22274 | } |
| 22275 | |
| 22276 | at::Tensor x_; |
| 22277 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 22278 | at::functionalization::impl::sync(x); |
| 22279 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 22280 | } else { |
| 22281 | x_ = x; |
| 22282 | } |
| 22283 | |
| 22284 | at::Tensor out_; |
| 22285 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 22286 | at::functionalization::impl::sync(out); |
| 22287 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 22288 | } else { |
| 22289 | out_ = out; |
| 22290 | } |
| 22291 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 22292 | if ((false || at::functionalization::impl::isFunctionalTensor(x))) { |
| 22293 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 22294 | TORCH_INTERNAL_ASSERT(false, |
| 22295 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 22296 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 22297 | } else { |
| 22298 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 22299 | at::AutoDispatchSkipFunctionalize guard; |
| 22300 | at::Tensor tmp_output = at::_ops::special_scaled_modified_bessel_k0_out::call(x_, out_); |
| 22301 | return out;; |
| 22302 | } |
| 22303 | } else { |
| 22304 | at::Tensor tmp_output; |
| 22305 | { |
| 22306 | at::AutoDispatchSkipFunctionalize guard; |
| 22307 | tmp_output = at::_ops::special_scaled_modified_bessel_k0::call(x_); |
| 22308 | } |
| 22309 | at::functionalization::impl::replace_(out, tmp_output); |
| 22310 | at::functionalization::impl::commit_update(out); |
| 22311 | at::functionalization::impl::sync(out); |
| 22312 | return out; |
| 22313 | } |
| 22314 | } |
| 22315 | |
| 22316 | at::Tensor & special_scaled_modified_bessel_k1_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, at::Tensor & out) { |
| 22317 | if (false) { |
| 22318 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 22319 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 22320 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 22321 | auto x_meta = to_meta(x); |
| 22322 | auto out_meta = to_meta(out); |
| 22323 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22324 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22325 | at::_ops::special_scaled_modified_bessel_k1_out::call(x_meta, out_meta); |
| 22326 | } |
| 22327 | |
| 22328 | at::Tensor x_; |
| 22329 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 22330 | at::functionalization::impl::sync(x); |
| 22331 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 22332 | } else { |
| 22333 | x_ = x; |
| 22334 | } |
| 22335 | |
| 22336 | at::Tensor out_; |
| 22337 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 22338 | at::functionalization::impl::sync(out); |
| 22339 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 22340 | } else { |
| 22341 | out_ = out; |
| 22342 | } |
| 22343 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 22344 | if ((false || at::functionalization::impl::isFunctionalTensor(x))) { |
| 22345 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 22346 | TORCH_INTERNAL_ASSERT(false, |
| 22347 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 22348 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 22349 | } else { |
| 22350 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 22351 | at::AutoDispatchSkipFunctionalize guard; |
| 22352 | at::Tensor tmp_output = at::_ops::special_scaled_modified_bessel_k1_out::call(x_, out_); |
| 22353 | return out;; |
| 22354 | } |
| 22355 | } else { |
| 22356 | at::Tensor tmp_output; |
| 22357 | { |
| 22358 | at::AutoDispatchSkipFunctionalize guard; |
| 22359 | tmp_output = at::_ops::special_scaled_modified_bessel_k1::call(x_); |
| 22360 | } |
| 22361 | at::functionalization::impl::replace_(out, tmp_output); |
| 22362 | at::functionalization::impl::commit_update(out); |
| 22363 | at::functionalization::impl::sync(out); |
| 22364 | return out; |
| 22365 | } |
| 22366 | } |
| 22367 | |
| 22368 | at::Tensor & special_spherical_bessel_j0_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, at::Tensor & out) { |
| 22369 | if (false) { |
| 22370 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 22371 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 22372 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 22373 | auto x_meta = to_meta(x); |
| 22374 | auto out_meta = to_meta(out); |
| 22375 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22376 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22377 | at::_ops::special_spherical_bessel_j0_out::call(x_meta, out_meta); |
| 22378 | } |
| 22379 | |
| 22380 | at::Tensor x_; |
| 22381 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 22382 | at::functionalization::impl::sync(x); |
| 22383 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 22384 | } else { |
| 22385 | x_ = x; |
| 22386 | } |
| 22387 | |
| 22388 | at::Tensor out_; |
| 22389 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 22390 | at::functionalization::impl::sync(out); |
| 22391 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 22392 | } else { |
| 22393 | out_ = out; |
| 22394 | } |
| 22395 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 22396 | if ((false || at::functionalization::impl::isFunctionalTensor(x))) { |
| 22397 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 22398 | TORCH_INTERNAL_ASSERT(false, |
| 22399 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 22400 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 22401 | } else { |
| 22402 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 22403 | at::AutoDispatchSkipFunctionalize guard; |
| 22404 | at::Tensor tmp_output = at::_ops::special_spherical_bessel_j0_out::call(x_, out_); |
| 22405 | return out;; |
| 22406 | } |
| 22407 | } else { |
| 22408 | at::Tensor tmp_output; |
| 22409 | { |
| 22410 | at::AutoDispatchSkipFunctionalize guard; |
| 22411 | tmp_output = at::_ops::special_spherical_bessel_j0::call(x_); |
| 22412 | } |
| 22413 | at::functionalization::impl::replace_(out, tmp_output); |
| 22414 | at::functionalization::impl::commit_update(out); |
| 22415 | at::functionalization::impl::sync(out); |
| 22416 | return out; |
| 22417 | } |
| 22418 | } |
| 22419 | |
| 22420 | at::Tensor _neg_view(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 22421 | |
| 22422 | at::Tensor self_; |
| 22423 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 22424 | |
| 22425 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 22426 | } else { |
| 22427 | self_ = self; |
| 22428 | } |
| 22429 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 22430 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 22431 | at::AutoDispatchSkipFunctionalize guard; |
| 22432 | return at::_ops::_neg_view::call(self_); |
| 22433 | } |
| 22434 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 22435 | auto compute_reference_meta = |
| 22436 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 22437 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 22438 | at::Tensor reference_tensor_output; |
| 22439 | if (compute_reference_meta) { |
| 22440 | auto self_meta = to_meta(self); |
| 22441 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22442 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22443 | reference_tensor_output = at::_ops::_neg_view::call(self_meta); |
| 22444 | } |
| 22445 | at::Tensor tmp_output; |
| 22446 | { |
| 22447 | at::AutoDispatchSkipFunctionalize guard; |
| 22448 | if (reapply_views) { |
| 22449 | tmp_output = at::_ops::_neg_view::call(self_); |
| 22450 | } else { |
| 22451 | tmp_output = at::_ops::_neg_view_copy::call(self_); |
| 22452 | } |
| 22453 | } |
| 22454 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 22455 | [reapply_views = reapply_views](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 22456 | if (reapply_views) { |
| 22457 | return at::_ops::_neg_view::call(base); |
| 22458 | } else { |
| 22459 | return at::_ops::_neg_view_copy::call(base); |
| 22460 | } |
| 22461 | }, |
| 22462 | [reapply_views = reapply_views](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 22463 | return at::functionalization::FunctionalInverses::_neg_view_copy_inverse(base, mutated_view, reapply_views); |
| 22464 | } |
| 22465 | ); |
| 22466 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 22467 | // See Note [Propagating strides in the functionalization pass] |
| 22468 | if (compute_reference_meta) { |
| 22469 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 22470 | } |
| 22471 | return out; |
| 22472 | } |
| 22473 | |
| 22474 | at::Tensor diagonal(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2) { |
| 22475 | |
| 22476 | at::Tensor self_; |
| 22477 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 22478 | |
| 22479 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 22480 | } else { |
| 22481 | self_ = self; |
| 22482 | } |
| 22483 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 22484 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 22485 | at::AutoDispatchSkipFunctionalize guard; |
| 22486 | return at::_ops::diagonal::call(self_, offset, dim1, dim2); |
| 22487 | } |
| 22488 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 22489 | auto compute_reference_meta = |
| 22490 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 22491 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 22492 | at::Tensor reference_tensor_output; |
| 22493 | if (compute_reference_meta) { |
| 22494 | auto self_meta = to_meta(self); |
| 22495 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22496 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22497 | reference_tensor_output = at::_ops::diagonal::call(self_meta, offset, dim1, dim2); |
| 22498 | } |
| 22499 | at::Tensor tmp_output; |
| 22500 | { |
| 22501 | at::AutoDispatchSkipFunctionalize guard; |
| 22502 | if (reapply_views) { |
| 22503 | tmp_output = at::_ops::diagonal::call(self_, offset, dim1, dim2); |
| 22504 | } else { |
| 22505 | tmp_output = at::_ops::diagonal_copy::call(self_, offset, dim1, dim2); |
| 22506 | } |
| 22507 | } |
| 22508 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 22509 | [reapply_views = reapply_views, offset = offset, dim1 = dim1, dim2 = dim2](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 22510 | if (reapply_views) { |
| 22511 | return at::_ops::diagonal::call(base, offset, dim1, dim2); |
| 22512 | } else { |
| 22513 | return at::_ops::diagonal_copy::call(base, offset, dim1, dim2); |
| 22514 | } |
| 22515 | }, |
| 22516 | [reapply_views = reapply_views, offset = offset, dim1 = dim1, dim2 = dim2](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 22517 | return at::functionalization::FunctionalInverses::diagonal_copy_inverse(base, mutated_view, reapply_views, offset, dim1, dim2); |
| 22518 | } |
| 22519 | ); |
| 22520 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 22521 | // See Note [Propagating strides in the functionalization pass] |
| 22522 | if (compute_reference_meta) { |
| 22523 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 22524 | } |
| 22525 | return out; |
| 22526 | } |
| 22527 | |
| 22528 | at::Tensor select_int(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::SymInt index) { |
| 22529 | |
| 22530 | at::Tensor self_; |
| 22531 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 22532 | |
| 22533 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 22534 | } else { |
| 22535 | self_ = self; |
| 22536 | } |
| 22537 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 22538 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 22539 | at::AutoDispatchSkipFunctionalize guard; |
| 22540 | return at::_ops::select_int::call(self_, dim, index); |
| 22541 | } |
| 22542 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 22543 | auto compute_reference_meta = |
| 22544 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 22545 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 22546 | at::Tensor reference_tensor_output; |
| 22547 | if (compute_reference_meta) { |
| 22548 | auto self_meta = to_meta(self); |
| 22549 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22550 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22551 | reference_tensor_output = at::_ops::select_int::call(self_meta, dim, index); |
| 22552 | } |
| 22553 | at::Tensor tmp_output; |
| 22554 | { |
| 22555 | at::AutoDispatchSkipFunctionalize guard; |
| 22556 | if (reapply_views) { |
| 22557 | tmp_output = at::_ops::select_int::call(self_, dim, index); |
| 22558 | } else { |
| 22559 | tmp_output = at::_ops::select_copy_int::call(self_, dim, index); |
| 22560 | } |
| 22561 | } |
| 22562 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 22563 | [reapply_views = reapply_views, dim = dim, index = index](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 22564 | if (reapply_views) { |
| 22565 | return at::_ops::select_int::call(base, dim, index); |
| 22566 | } else { |
| 22567 | return at::_ops::select_copy_int::call(base, dim, index); |
| 22568 | } |
| 22569 | }, |
| 22570 | [reapply_views = reapply_views, dim = dim, index = index](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 22571 | return at::functionalization::FunctionalInverses::select_copy_int_inverse(base, mutated_view, reapply_views, dim, index); |
| 22572 | } |
| 22573 | ); |
| 22574 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 22575 | // See Note [Propagating strides in the functionalization pass] |
| 22576 | if (compute_reference_meta) { |
| 22577 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 22578 | } |
| 22579 | return out; |
| 22580 | } |
| 22581 | |
| 22582 | ::std::vector<at::Tensor> split_with_sizes(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim) { |
| 22583 | |
| 22584 | at::Tensor self_; |
| 22585 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 22586 | |
| 22587 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 22588 | } else { |
| 22589 | self_ = self; |
| 22590 | } |
| 22591 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 22592 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 22593 | at::AutoDispatchSkipFunctionalize guard; |
| 22594 | return at::_ops::split_with_sizes::call(self_, split_sizes, dim); |
| 22595 | } |
| 22596 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 22597 | auto compute_reference_meta = |
| 22598 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 22599 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 22600 | ::std::vector<at::Tensor> reference_tensor_output; |
| 22601 | if (compute_reference_meta) { |
| 22602 | auto self_meta = to_meta(self); |
| 22603 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22604 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22605 | reference_tensor_output = at::_ops::split_with_sizes::call(self_meta, split_sizes, dim); |
| 22606 | } |
| 22607 | ::std::vector<at::Tensor> tmp_output; |
| 22608 | { |
| 22609 | at::AutoDispatchSkipFunctionalize guard; |
| 22610 | if (reapply_views) { |
| 22611 | tmp_output = at::_ops::split_with_sizes::call(self_, split_sizes, dim); |
| 22612 | } else { |
| 22613 | tmp_output = at::_ops::split_with_sizes_copy::call(self_, split_sizes, dim); |
| 22614 | } |
| 22615 | } |
| 22616 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 22617 | [reapply_views = reapply_views, split_sizes = split_sizes.vec(), dim = dim](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 22618 | if (reapply_views) { |
| 22619 | return at::_ops::split_with_sizes::call(base, split_sizes, dim)[mutated_view_idx]; |
| 22620 | } else { |
| 22621 | return at::_ops::split_with_sizes_copy::call(base, split_sizes, dim)[mutated_view_idx]; |
| 22622 | } |
| 22623 | }, |
| 22624 | [reapply_views = reapply_views, split_sizes = split_sizes.vec(), dim = dim](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 22625 | return at::functionalization::FunctionalInverses::split_with_sizes_copy_inverse(base, mutated_view, reapply_views, mutated_view_idx, split_sizes, dim); |
| 22626 | } |
| 22627 | ); |
| 22628 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 22629 | // See Note [Propagating strides in the functionalization pass] |
| 22630 | if (compute_reference_meta) { |
| 22631 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 22632 | } |
| 22633 | return out; |
| 22634 | } |
| 22635 | |
| 22636 | at::Tensor squeeze(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 22637 | |
| 22638 | at::Tensor self_; |
| 22639 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 22640 | |
| 22641 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 22642 | } else { |
| 22643 | self_ = self; |
| 22644 | } |
| 22645 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 22646 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 22647 | at::AutoDispatchSkipFunctionalize guard; |
| 22648 | return at::_ops::squeeze::call(self_); |
| 22649 | } |
| 22650 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 22651 | auto compute_reference_meta = |
| 22652 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 22653 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 22654 | at::Tensor reference_tensor_output; |
| 22655 | if (compute_reference_meta) { |
| 22656 | auto self_meta = to_meta(self); |
| 22657 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22658 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22659 | reference_tensor_output = at::_ops::squeeze::call(self_meta); |
| 22660 | } |
| 22661 | at::Tensor tmp_output; |
| 22662 | { |
| 22663 | at::AutoDispatchSkipFunctionalize guard; |
| 22664 | if (reapply_views) { |
| 22665 | tmp_output = at::_ops::squeeze::call(self_); |
| 22666 | } else { |
| 22667 | tmp_output = at::_ops::squeeze_copy::call(self_); |
| 22668 | } |
| 22669 | } |
| 22670 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 22671 | [reapply_views = reapply_views](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 22672 | if (reapply_views) { |
| 22673 | return at::_ops::squeeze::call(base); |
| 22674 | } else { |
| 22675 | return at::_ops::squeeze_copy::call(base); |
| 22676 | } |
| 22677 | }, |
| 22678 | [reapply_views = reapply_views](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 22679 | return at::functionalization::FunctionalInverses::squeeze_copy_inverse(base, mutated_view, reapply_views); |
| 22680 | } |
| 22681 | ); |
| 22682 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 22683 | // See Note [Propagating strides in the functionalization pass] |
| 22684 | if (compute_reference_meta) { |
| 22685 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 22686 | } |
| 22687 | return out; |
| 22688 | } |
| 22689 | |
| 22690 | at::Tensor & squeeze_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 22691 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 22692 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 22693 | |
| 22694 | at::Tensor self_; |
| 22695 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 22696 | |
| 22697 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 22698 | } else { |
| 22699 | self_ = self; |
| 22700 | } |
| 22701 | at::AutoDispatchSkipFunctionalize guard; |
| 22702 | return at::_ops::squeeze_::call(self_); |
| 22703 | } |
| 22704 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 22705 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 22706 | [reapply_views = reapply_views](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 22707 | if (reapply_views) { |
| 22708 | return at::_ops::squeeze::call(base); |
| 22709 | } else { |
| 22710 | return at::_ops::squeeze_copy::call(base); |
| 22711 | } |
| 22712 | }, |
| 22713 | [reapply_views = reapply_views](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 22714 | return at::functionalization::FunctionalInverses::squeeze_copy_inverse(base, mutated_view, reapply_views); |
| 22715 | } |
| 22716 | ); |
| 22717 | auto compute_reference_meta = |
| 22718 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 22719 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 22720 | at::Tensor reference_tensor_output; |
| 22721 | if (compute_reference_meta) { |
| 22722 | auto self_meta = to_meta(self); |
| 22723 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22724 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22725 | reference_tensor_output = at::_ops::squeeze_::call(self_meta); |
| 22726 | } |
| 22727 | // This function adds the above view meta to the current tensor and replays them off the base, |
| 22728 | // mutating the size/stride info of the current FunctionalTensorWrapper. |
| 22729 | // Because of this, we need to make sure to run the reference shape function above, |
| 22730 | // BEFORE doing this (otherwise we'll end up runnin the reference function using the wrong sizes/strides) |
| 22731 | at::functionalization::impl::mutate_view_meta(self, view_meta); |
| 22732 | // See Note [Propagating strides in the functionalization pass] |
| 22733 | // XLA/LTC don't implement the logic to propagate strides correctly, so we need to rely |
| 22734 | // on a reference implementation here (instead of relying on the output from the forward lambda |
| 22735 | // having the correct stride info) |
| 22736 | if (compute_reference_meta) { |
| 22737 | at::functionalization::impl::set_sizes_strides_offset(self, reference_tensor_output); |
| 22738 | } |
| 22739 | return self; |
| 22740 | } |
| 22741 | |
| 22742 | at::Tensor squeeze_dim(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim) { |
| 22743 | |
| 22744 | at::Tensor self_; |
| 22745 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 22746 | |
| 22747 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 22748 | } else { |
| 22749 | self_ = self; |
| 22750 | } |
| 22751 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 22752 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 22753 | at::AutoDispatchSkipFunctionalize guard; |
| 22754 | return at::_ops::squeeze_dim::call(self_, dim); |
| 22755 | } |
| 22756 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 22757 | auto compute_reference_meta = |
| 22758 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 22759 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 22760 | at::Tensor reference_tensor_output; |
| 22761 | if (compute_reference_meta) { |
| 22762 | auto self_meta = to_meta(self); |
| 22763 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22764 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22765 | reference_tensor_output = at::_ops::squeeze_dim::call(self_meta, dim); |
| 22766 | } |
| 22767 | at::Tensor tmp_output; |
| 22768 | { |
| 22769 | at::AutoDispatchSkipFunctionalize guard; |
| 22770 | if (reapply_views) { |
| 22771 | tmp_output = at::_ops::squeeze_dim::call(self_, dim); |
| 22772 | } else { |
| 22773 | tmp_output = at::_ops::squeeze_copy_dim::call(self_, dim); |
| 22774 | } |
| 22775 | } |
| 22776 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 22777 | [reapply_views = reapply_views, dim = dim](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 22778 | if (reapply_views) { |
| 22779 | return at::_ops::squeeze_dim::call(base, dim); |
| 22780 | } else { |
| 22781 | return at::_ops::squeeze_copy_dim::call(base, dim); |
| 22782 | } |
| 22783 | }, |
| 22784 | [reapply_views = reapply_views, dim = dim](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 22785 | return at::functionalization::FunctionalInverses::squeeze_copy_dim_inverse(base, mutated_view, reapply_views, dim); |
| 22786 | } |
| 22787 | ); |
| 22788 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 22789 | // See Note [Propagating strides in the functionalization pass] |
| 22790 | if (compute_reference_meta) { |
| 22791 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 22792 | } |
| 22793 | return out; |
| 22794 | } |
| 22795 | |
| 22796 | at::Tensor & squeeze__dim(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim) { |
| 22797 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 22798 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 22799 | |
| 22800 | at::Tensor self_; |
| 22801 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 22802 | |
| 22803 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 22804 | } else { |
| 22805 | self_ = self; |
| 22806 | } |
| 22807 | at::AutoDispatchSkipFunctionalize guard; |
| 22808 | return at::_ops::squeeze__dim::call(self_, dim); |
| 22809 | } |
| 22810 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 22811 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 22812 | [reapply_views = reapply_views, dim = dim](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 22813 | if (reapply_views) { |
| 22814 | return at::_ops::squeeze_dim::call(base, dim); |
| 22815 | } else { |
| 22816 | return at::_ops::squeeze_copy_dim::call(base, dim); |
| 22817 | } |
| 22818 | }, |
| 22819 | [reapply_views = reapply_views, dim = dim](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 22820 | return at::functionalization::FunctionalInverses::squeeze_copy_dim_inverse(base, mutated_view, reapply_views, dim); |
| 22821 | } |
| 22822 | ); |
| 22823 | auto compute_reference_meta = |
| 22824 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 22825 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 22826 | at::Tensor reference_tensor_output; |
| 22827 | if (compute_reference_meta) { |
| 22828 | auto self_meta = to_meta(self); |
| 22829 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22830 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22831 | reference_tensor_output = at::_ops::squeeze__dim::call(self_meta, dim); |
| 22832 | } |
| 22833 | // This function adds the above view meta to the current tensor and replays them off the base, |
| 22834 | // mutating the size/stride info of the current FunctionalTensorWrapper. |
| 22835 | // Because of this, we need to make sure to run the reference shape function above, |
| 22836 | // BEFORE doing this (otherwise we'll end up runnin the reference function using the wrong sizes/strides) |
| 22837 | at::functionalization::impl::mutate_view_meta(self, view_meta); |
| 22838 | // See Note [Propagating strides in the functionalization pass] |
| 22839 | // XLA/LTC don't implement the logic to propagate strides correctly, so we need to rely |
| 22840 | // on a reference implementation here (instead of relying on the output from the forward lambda |
| 22841 | // having the correct stride info) |
| 22842 | if (compute_reference_meta) { |
| 22843 | at::functionalization::impl::set_sizes_strides_offset(self, reference_tensor_output); |
| 22844 | } |
| 22845 | return self; |
| 22846 | } |
| 22847 | |
| 22848 | at::Tensor squeeze_dims(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim) { |
| 22849 | |
| 22850 | at::Tensor self_; |
| 22851 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 22852 | |
| 22853 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 22854 | } else { |
| 22855 | self_ = self; |
| 22856 | } |
| 22857 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 22858 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 22859 | at::AutoDispatchSkipFunctionalize guard; |
| 22860 | return at::_ops::squeeze_dims::call(self_, dim); |
| 22861 | } |
| 22862 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 22863 | auto compute_reference_meta = |
| 22864 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 22865 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 22866 | at::Tensor reference_tensor_output; |
| 22867 | if (compute_reference_meta) { |
| 22868 | auto self_meta = to_meta(self); |
| 22869 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22870 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22871 | reference_tensor_output = at::_ops::squeeze_dims::call(self_meta, dim); |
| 22872 | } |
| 22873 | at::Tensor tmp_output; |
| 22874 | { |
| 22875 | at::AutoDispatchSkipFunctionalize guard; |
| 22876 | if (reapply_views) { |
| 22877 | tmp_output = at::_ops::squeeze_dims::call(self_, dim); |
| 22878 | } else { |
| 22879 | tmp_output = at::_ops::squeeze_copy_dims::call(self_, dim); |
| 22880 | } |
| 22881 | } |
| 22882 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 22883 | [reapply_views = reapply_views, dim = dim.vec()](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 22884 | if (reapply_views) { |
| 22885 | return at::_ops::squeeze_dims::call(base, dim); |
| 22886 | } else { |
| 22887 | return at::_ops::squeeze_copy_dims::call(base, dim); |
| 22888 | } |
| 22889 | }, |
| 22890 | [reapply_views = reapply_views, dim = dim.vec()](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 22891 | return at::functionalization::FunctionalInverses::squeeze_copy_dims_inverse(base, mutated_view, reapply_views, dim); |
| 22892 | } |
| 22893 | ); |
| 22894 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 22895 | // See Note [Propagating strides in the functionalization pass] |
| 22896 | if (compute_reference_meta) { |
| 22897 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 22898 | } |
| 22899 | return out; |
| 22900 | } |
| 22901 | |
| 22902 | at::Tensor & squeeze__dims(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, at::IntArrayRef dim) { |
| 22903 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 22904 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 22905 | |
| 22906 | at::Tensor self_; |
| 22907 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 22908 | |
| 22909 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 22910 | } else { |
| 22911 | self_ = self; |
| 22912 | } |
| 22913 | at::AutoDispatchSkipFunctionalize guard; |
| 22914 | return at::_ops::squeeze__dims::call(self_, dim); |
| 22915 | } |
| 22916 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 22917 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 22918 | [reapply_views = reapply_views, dim = dim.vec()](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 22919 | if (reapply_views) { |
| 22920 | return at::_ops::squeeze_dims::call(base, dim); |
| 22921 | } else { |
| 22922 | return at::_ops::squeeze_copy_dims::call(base, dim); |
| 22923 | } |
| 22924 | }, |
| 22925 | [reapply_views = reapply_views, dim = dim.vec()](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 22926 | return at::functionalization::FunctionalInverses::squeeze_copy_dims_inverse(base, mutated_view, reapply_views, dim); |
| 22927 | } |
| 22928 | ); |
| 22929 | auto compute_reference_meta = |
| 22930 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 22931 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 22932 | at::Tensor reference_tensor_output; |
| 22933 | if (compute_reference_meta) { |
| 22934 | auto self_meta = to_meta(self); |
| 22935 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22936 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22937 | reference_tensor_output = at::_ops::squeeze__dims::call(self_meta, dim); |
| 22938 | } |
| 22939 | // This function adds the above view meta to the current tensor and replays them off the base, |
| 22940 | // mutating the size/stride info of the current FunctionalTensorWrapper. |
| 22941 | // Because of this, we need to make sure to run the reference shape function above, |
| 22942 | // BEFORE doing this (otherwise we'll end up runnin the reference function using the wrong sizes/strides) |
| 22943 | at::functionalization::impl::mutate_view_meta(self, view_meta); |
| 22944 | // See Note [Propagating strides in the functionalization pass] |
| 22945 | // XLA/LTC don't implement the logic to propagate strides correctly, so we need to rely |
| 22946 | // on a reference implementation here (instead of relying on the output from the forward lambda |
| 22947 | // having the correct stride info) |
| 22948 | if (compute_reference_meta) { |
| 22949 | at::functionalization::impl::set_sizes_strides_offset(self, reference_tensor_output); |
| 22950 | } |
| 22951 | return self; |
| 22952 | } |
| 22953 | |
| 22954 | ::std::vector<at::Tensor> unbind_int(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim) { |
| 22955 | |
| 22956 | at::Tensor self_; |
| 22957 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 22958 | |
| 22959 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 22960 | } else { |
| 22961 | self_ = self; |
| 22962 | } |
| 22963 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 22964 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 22965 | at::AutoDispatchSkipFunctionalize guard; |
| 22966 | return at::_ops::unbind_int::call(self_, dim); |
| 22967 | } |
| 22968 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 22969 | auto compute_reference_meta = |
| 22970 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 22971 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 22972 | ::std::vector<at::Tensor> reference_tensor_output; |
| 22973 | if (compute_reference_meta) { |
| 22974 | auto self_meta = to_meta(self); |
| 22975 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22976 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22977 | reference_tensor_output = at::_ops::unbind_int::call(self_meta, dim); |
| 22978 | } |
| 22979 | ::std::vector<at::Tensor> tmp_output; |
| 22980 | { |
| 22981 | at::AutoDispatchSkipFunctionalize guard; |
| 22982 | if (reapply_views) { |
| 22983 | tmp_output = at::_ops::unbind_int::call(self_, dim); |
| 22984 | } else { |
| 22985 | tmp_output = at::_ops::unbind_copy_int::call(self_, dim); |
| 22986 | } |
| 22987 | } |
| 22988 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 22989 | [reapply_views = reapply_views, dim = dim](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 22990 | if (reapply_views) { |
| 22991 | return at::_ops::unbind_int::call(base, dim)[mutated_view_idx]; |
| 22992 | } else { |
| 22993 | return at::_ops::unbind_copy_int::call(base, dim)[mutated_view_idx]; |
| 22994 | } |
| 22995 | }, |
| 22996 | [reapply_views = reapply_views, dim = dim](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 22997 | return at::functionalization::FunctionalInverses::unbind_copy_int_inverse(base, mutated_view, reapply_views, mutated_view_idx, dim); |
| 22998 | } |
| 22999 | ); |
| 23000 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 23001 | // See Note [Propagating strides in the functionalization pass] |
| 23002 | if (compute_reference_meta) { |
| 23003 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 23004 | } |
| 23005 | return out; |
| 23006 | } |
| 23007 | |
| 23008 | at::Tensor alias(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 23009 | |
| 23010 | at::Tensor self_; |
| 23011 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 23012 | |
| 23013 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 23014 | } else { |
| 23015 | self_ = self; |
| 23016 | } |
| 23017 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 23018 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 23019 | at::AutoDispatchSkipFunctionalize guard; |
| 23020 | return at::_ops::alias::call(self_); |
| 23021 | } |
| 23022 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 23023 | auto compute_reference_meta = |
| 23024 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 23025 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 23026 | at::Tensor reference_tensor_output; |
| 23027 | if (compute_reference_meta) { |
| 23028 | auto self_meta = to_meta(self); |
| 23029 | at::AutoDispatchSkipFunctionalize func_guard; |
| 23030 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 23031 | reference_tensor_output = at::_ops::alias::call(self_meta); |
| 23032 | } |
| 23033 | at::Tensor tmp_output; |
| 23034 | { |
| 23035 | at::AutoDispatchSkipFunctionalize guard; |
| 23036 | if (reapply_views) { |
| 23037 | tmp_output = at::_ops::alias::call(self_); |
| 23038 | } else { |
| 23039 | tmp_output = at::_ops::alias_copy::call(self_); |
| 23040 | } |
| 23041 | } |
| 23042 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 23043 | [reapply_views = reapply_views](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 23044 | if (reapply_views) { |
| 23045 | return at::_ops::alias::call(base); |
| 23046 | } else { |
| 23047 | return at::_ops::alias_copy::call(base); |
| 23048 | } |
| 23049 | }, |
| 23050 | [reapply_views = reapply_views](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 23051 | return at::functionalization::FunctionalInverses::alias_copy_inverse(base, mutated_view, reapply_views); |
| 23052 | } |
| 23053 | ); |
| 23054 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 23055 | // See Note [Propagating strides in the functionalization pass] |
| 23056 | if (compute_reference_meta) { |
| 23057 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 23058 | } |
| 23059 | return out; |
| 23060 | } |
| 23061 | |
| 23062 | } // namespace functionalization |
| 23063 | |
| 23064 | namespace { |
| 23065 | |
| 23066 | TORCH_LIBRARY_IMPL(aten, Functionalize, m) { |
| 23067 | m.impl("_new_zeros_with_same_feature_meta.out", TORCH_FN(functionalization::_new_zeros_with_same_feature_meta_out_out)); |
| 23068 | m.impl("_cudnn_init_dropout_state.out", TORCH_FN(functionalization::_cudnn_init_dropout_state_out_out)); |
| 23069 | m.impl("angle.out", TORCH_FN(functionalization::angle_out_out)); |
| 23070 | m.impl("sgn.out", TORCH_FN(functionalization::sgn_out_out)); |
| 23071 | m.impl("sgn_", TORCH_FN(functionalization::sgn_)); |
| 23072 | m.impl("_add_relu.out", TORCH_FN(functionalization::_add_relu_out_out)); |
| 23073 | m.impl("_add_relu_.Tensor", TORCH_FN(functionalization::_add_relu__Tensor)); |
| 23074 | m.impl("_add_relu.Scalar_out", TORCH_FN(functionalization::_add_relu_out_Scalar_out)); |
| 23075 | m.impl("_add_relu_.Scalar", TORCH_FN(functionalization::_add_relu__Scalar)); |
| 23076 | m.impl("any.out", TORCH_FN(functionalization::any_out_out)); |
| 23077 | m.impl("any.dimname", static_cast<at::Tensor (*)(const at::Tensor & self, at::Dimname dim, bool keepdim)>(at::native::any)); |
| 23078 | m.impl("any.dimname_out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & out)>(at::native::any_out)); |
| 23079 | m.impl("argmin.out", TORCH_FN(functionalization::argmin_out_out)); |
| 23080 | m.impl("acosh.out", TORCH_FN(functionalization::acosh_out_out)); |
| 23081 | m.impl("acosh_", TORCH_FN(functionalization::acosh_)); |
| 23082 | m.impl("arcsin", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::arcsin)); |
| 23083 | m.impl("arcsin.out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::Tensor & out)>(at::native::arcsin_out)); |
| 23084 | m.impl("arcsin_", static_cast<at::Tensor & (*)(at::Tensor & self)>(at::native::arcsin_)); |
| 23085 | m.impl("arctan", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::arctan)); |
| 23086 | m.impl("arctan.out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::Tensor & out)>(at::native::arctan_out)); |
| 23087 | m.impl("arctan_", static_cast<at::Tensor & (*)(at::Tensor & self)>(at::native::arctan_)); |
| 23088 | m.impl("bincount.out", TORCH_FN(functionalization::bincount_out_out)); |
| 23089 | m.impl("copysign.out", TORCH_FN(functionalization::copysign_out_out)); |
| 23090 | m.impl("copysign_.Tensor", TORCH_FN(functionalization::copysign__Tensor)); |
| 23091 | m.impl("copysign.Scalar_out", TORCH_FN(functionalization::copysign_out_Scalar_out)); |
| 23092 | m.impl("copysign_.Scalar", TORCH_FN(functionalization::copysign__Scalar)); |
| 23093 | m.impl("logical_or.out", TORCH_FN(functionalization::logical_or_out_out)); |
| 23094 | m.impl("logical_or_", TORCH_FN(functionalization::logical_or_)); |
| 23095 | m.impl("cat.out", TORCH_FN(functionalization::cat_out_out)); |
| 23096 | m.impl("cat.names", static_cast<at::Tensor (*)(at::TensorList tensors, at::Dimname dim)>(at::native::cat)); |
| 23097 | m.impl("cat.names_out", static_cast<at::Tensor & (*)(at::TensorList tensors, at::Dimname dim, at::Tensor & out)>(at::native::cat_out)); |
| 23098 | m.impl("concat", static_cast<at::Tensor (*)(at::TensorList tensors, int64_t dim)>(at::native::concat)); |
| 23099 | m.impl("concat.out", static_cast<at::Tensor & (*)(at::TensorList tensors, int64_t dim, at::Tensor & out)>(at::native::concat_out)); |
| 23100 | m.impl("concat.names", static_cast<at::Tensor (*)(at::TensorList tensors, at::Dimname dim)>(at::native::concat)); |
| 23101 | m.impl("concat.names_out", static_cast<at::Tensor & (*)(at::TensorList tensors, at::Dimname dim, at::Tensor & out)>(at::native::concat_out)); |
| 23102 | m.impl("ceil.out", TORCH_FN(functionalization::ceil_out_out)); |
| 23103 | m.impl("ceil_", TORCH_FN(functionalization::ceil_)); |
| 23104 | m.impl("polar.out", TORCH_FN(functionalization::polar_out_out)); |
| 23105 | m.impl("convolution.out", TORCH_FN(functionalization::convolution_out_out)); |
| 23106 | m.impl("convolution_overrideable.out", TORCH_FN(functionalization::convolution_overrideable_out_out)); |
| 23107 | m.impl("convolution_backward_overrideable.out", TORCH_FN(functionalization::convolution_backward_overrideable_out_out)); |
| 23108 | m.impl("cudnn_affine_grid_generator.out", TORCH_FN(functionalization::cudnn_affine_grid_generator_out_out)); |
| 23109 | m.impl("cudnn_batch_norm_backward.out", TORCH_FN(functionalization::cudnn_batch_norm_backward_out_out)); |
| 23110 | m.impl("cudnn_convolution_relu.out", TORCH_FN(functionalization::cudnn_convolution_relu_out_out)); |
| 23111 | m.impl("cudnn_convolution_add_relu.out", TORCH_FN(functionalization::cudnn_convolution_add_relu_out_out)); |
| 23112 | m.impl("cummax.out", TORCH_FN(functionalization::cummax_out_out)); |
| 23113 | m.impl("cummax.dimname", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & self, at::Dimname dim)>(at::native::cummax)); |
| 23114 | m.impl("cummax.dimname_out", static_cast<::std::tuple<at::Tensor &,at::Tensor &> (*)(const at::Tensor & self, at::Dimname dim, at::Tensor & values, at::Tensor & indices)>(at::native::cummax_out)); |
| 23115 | m.impl("diff", static_cast<at::Tensor (*)(const at::Tensor & self, int64_t n, int64_t dim, const c10::optional<at::Tensor> & prepend, const c10::optional<at::Tensor> & append)>(at::native::diff)); |
| 23116 | m.impl("diff.out", static_cast<at::Tensor & (*)(const at::Tensor & self, int64_t n, int64_t dim, const c10::optional<at::Tensor> & prepend, const c10::optional<at::Tensor> & append, at::Tensor & out)>(at::native::diff_out)); |
| 23117 | m.impl("embedding_renorm.out", TORCH_FN(functionalization::embedding_renorm_out_out)); |
| 23118 | m.impl("embedding_renorm_", TORCH_FN(functionalization::embedding_renorm_)); |
| 23119 | m.impl("_embedding_bag_forward_only.out", TORCH_FN(functionalization::_embedding_bag_forward_only_out_out)); |
| 23120 | m.impl("_embedding_bag_dense_backward.out", TORCH_FN(functionalization::_embedding_bag_dense_backward_out_out)); |
| 23121 | m.impl("empty.names_out", TORCH_FN(functionalization::empty_out_names_out)); |
| 23122 | m.impl("empty.out", static_cast<at::Tensor & (*)(at::IntArrayRef size, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out)>(at::native::empty_out)); |
| 23123 | m.impl("empty_like.out", TORCH_FN(functionalization::empty_like_out_out)); |
| 23124 | m.impl("erf.out", TORCH_FN(functionalization::erf_out_out)); |
| 23125 | m.impl("erf_", TORCH_FN(functionalization::erf_)); |
| 23126 | m.impl("erfc.out", TORCH_FN(functionalization::erfc_out_out)); |
| 23127 | m.impl("erfc_", TORCH_FN(functionalization::erfc_)); |
| 23128 | m.impl("eye.out", TORCH_FN(functionalization::eye_out_out)); |
| 23129 | m.impl("eye.m_out", TORCH_FN(functionalization::eye_out_m_out)); |
| 23130 | m.impl("gcd.out", TORCH_FN(functionalization::gcd_out_out)); |
| 23131 | m.impl("gcd_", TORCH_FN(functionalization::gcd_)); |
| 23132 | m.impl("grid_sampler_2d_backward.out", TORCH_FN(functionalization::grid_sampler_2d_backward_out_out)); |
| 23133 | m.impl("native_group_norm_backward.out", TORCH_FN(functionalization::native_group_norm_backward_out_out)); |
| 23134 | m.impl("_fft_r2c.out", TORCH_FN(functionalization::_fft_r2c_out_out)); |
| 23135 | m.impl("index.Tensor_out", TORCH_FN(functionalization::index_out_Tensor_out)); |
| 23136 | m.impl("index_put.out", TORCH_FN(functionalization::index_put_out_out)); |
| 23137 | m.impl("index_put_", TORCH_FN(functionalization::index_put_)); |
| 23138 | m.impl("linear_backward.out", TORCH_FN(functionalization::linear_backward_out_out)); |
| 23139 | m.impl("mkldnn_linear_backward_weights.out", TORCH_FN(functionalization::mkldnn_linear_backward_weights_out_out)); |
| 23140 | m.impl("logaddexp.out", TORCH_FN(functionalization::logaddexp_out_out)); |
| 23141 | m.impl("_logcumsumexp.out", TORCH_FN(functionalization::_logcumsumexp_out_out)); |
| 23142 | m.impl("logcumsumexp.out", TORCH_FN(functionalization::logcumsumexp_out_out)); |
| 23143 | m.impl("logcumsumexp.dimname", static_cast<at::Tensor (*)(const at::Tensor & self, at::Dimname dim)>(at::native::logcumsumexp)); |
| 23144 | m.impl("logcumsumexp.dimname_out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::Dimname dim, at::Tensor & out)>(at::native::logcumsumexp_out)); |
| 23145 | m.impl("matmul_backward.out", TORCH_FN(functionalization::matmul_backward_out_out)); |
| 23146 | m.impl("mps_max_pool2d_backward.out", TORCH_FN(functionalization::mps_max_pool2d_backward_out_out)); |
| 23147 | m.impl("median.out", TORCH_FN(functionalization::median_out_out)); |
| 23148 | m.impl("median.dim_values", TORCH_FN(functionalization::median_out_dim_values)); |
| 23149 | m.impl("median.names_dim", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & self, at::Dimname dim, bool keepdim)>(at::native::median)); |
| 23150 | m.impl("median.names_dim_values", static_cast<::std::tuple<at::Tensor &,at::Tensor &> (*)(const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & values, at::Tensor & indices)>(at::native::median_out)); |
| 23151 | m.impl("amin.out", TORCH_FN(functionalization::amin_out_out)); |
| 23152 | m.impl("mkldnn_rnn_layer_backward.out", TORCH_FN(functionalization::mkldnn_rnn_layer_backward_out_out)); |
| 23153 | m.impl("miopen_convolution.out", TORCH_FN(functionalization::miopen_convolution_out_out)); |
| 23154 | m.impl("miopen_depthwise_convolution.out", TORCH_FN(functionalization::miopen_depthwise_convolution_out_out)); |
| 23155 | m.impl("miopen_rnn_backward.out", TORCH_FN(functionalization::miopen_rnn_backward_out_out)); |
| 23156 | m.impl("native_batch_norm.out", TORCH_FN(functionalization::native_batch_norm_out_out)); |
| 23157 | m.impl("batch_norm_elemt.out", TORCH_FN(functionalization::batch_norm_elemt_out_out)); |
| 23158 | m.impl("batch_norm_update_stats.out", TORCH_FN(functionalization::batch_norm_update_stats_out_out)); |
| 23159 | m.impl("_nnpack_spatial_convolution.out", TORCH_FN(functionalization::_nnpack_spatial_convolution_out_out)); |
| 23160 | m.impl("ones_like.out", TORCH_FN(functionalization::ones_like_out_out)); |
| 23161 | m.impl("channel_shuffle.out", TORCH_FN(functionalization::channel_shuffle_out_out)); |
| 23162 | m.impl("randint.out", TORCH_FN(functionalization::randint_out_out)); |
| 23163 | m.impl("randint.generator_out", TORCH_FN(functionalization::randint_out_generator_out)); |
| 23164 | m.impl("randint.low_out", TORCH_FN(functionalization::randint_out_low_out)); |
| 23165 | m.impl("randint.low_generator_out", TORCH_FN(functionalization::randint_out_low_generator_out)); |
| 23166 | m.impl("randperm.out", TORCH_FN(functionalization::randperm_out_out)); |
| 23167 | m.impl("randperm.generator_out", TORCH_FN(functionalization::randperm_out_generator_out)); |
| 23168 | m.impl("range.out", TORCH_FN(functionalization::range_out_out)); |
| 23169 | m.impl("range.out_", TORCH_FN(functionalization::range_out_out_)); |
| 23170 | m.impl("reciprocal.out", TORCH_FN(functionalization::reciprocal_out_out)); |
| 23171 | m.impl("reciprocal_", TORCH_FN(functionalization::reciprocal_)); |
| 23172 | m.impl("gelu_backward.grad_input", TORCH_FN(functionalization::gelu_backward_out_grad_input)); |
| 23173 | m.impl("hardshrink.out", TORCH_FN(functionalization::hardshrink_out_out)); |
| 23174 | m.impl("hardshrink_backward.grad_input", TORCH_FN(functionalization::hardshrink_backward_out_grad_input)); |
| 23175 | m.impl("silu.out", TORCH_FN(functionalization::silu_out_out)); |
| 23176 | m.impl("silu_", TORCH_FN(functionalization::silu_)); |
| 23177 | m.impl("silu_backward", static_cast<at::Tensor (*)(const at::Tensor & grad_output, const at::Tensor & self)>(at::native::math_silu_backward)); |
| 23178 | m.impl("silu_backward.grad_input", TORCH_FN(functionalization::silu_backward_out_grad_input)); |
| 23179 | m.impl("sin.out", TORCH_FN(functionalization::sin_out_out)); |
| 23180 | m.impl("sin_", TORCH_FN(functionalization::sin_)); |
| 23181 | m.impl("_softmax.out", TORCH_FN(functionalization::_softmax_out_out)); |
| 23182 | m.impl("sspaddmm", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha)>(at::native::sspaddmm)); |
| 23183 | m.impl("sspaddmm.out", TORCH_FN(functionalization::sspaddmm_out_out)); |
| 23184 | m.impl("_stack.out", TORCH_FN(functionalization::_stack_out_out)); |
| 23185 | m.impl("hstack", static_cast<at::Tensor (*)(at::TensorList tensors)>(at::native::hstack)); |
| 23186 | m.impl("hstack.out", static_cast<at::Tensor & (*)(at::TensorList tensors, at::Tensor & out)>(at::native::hstack_out)); |
| 23187 | m.impl("dstack", static_cast<at::Tensor (*)(at::TensorList tensors)>(at::native::dstack)); |
| 23188 | m.impl("dstack.out", static_cast<at::Tensor & (*)(at::TensorList tensors, at::Tensor & out)>(at::native::dstack_out)); |
| 23189 | m.impl("sum.out", TORCH_FN(functionalization::sum_out_out)); |
| 23190 | m.impl("sum.IntList_out", TORCH_FN(functionalization::sum_out_IntList_out)); |
| 23191 | m.impl("sum.dim_DimnameList", static_cast<at::Tensor (*)(const at::Tensor & self, at::DimnameList dim, bool keepdim, c10::optional<at::ScalarType> dtype)>(at::native::sum)); |
| 23192 | m.impl("sum.DimnameList_out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::DimnameList dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out)>(at::native::sum_out)); |
| 23193 | m.impl("std.dim", static_cast<at::Tensor (*)(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim)>(at::native::std)); |
| 23194 | m.impl("std.out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim, at::Tensor & out)>(at::native::std_out)); |
| 23195 | m.impl("std.correction_out", TORCH_FN(functionalization::std_out_correction_out)); |
| 23196 | m.impl("std.names_dim", static_cast<at::Tensor (*)(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim)>(at::native::std)); |
| 23197 | m.impl("std.names_out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim, at::Tensor & out)>(at::native::std_out)); |
| 23198 | m.impl("std.correction_names", static_cast<at::Tensor (*)(const at::Tensor & self, at::DimnameList dim, c10::optional<int64_t> correction, bool keepdim)>(at::native::std)); |
| 23199 | m.impl("std.correction_names_out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::DimnameList dim, c10::optional<int64_t> correction, bool keepdim, at::Tensor & out)>(at::native::std_out)); |
| 23200 | m.impl("tan.out", TORCH_FN(functionalization::tan_out_out)); |
| 23201 | m.impl("tan_", TORCH_FN(functionalization::tan_)); |
| 23202 | m.impl("tensordot", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other)>(at::native::tensordot)); |
| 23203 | m.impl("tensordot.out", TORCH_FN(functionalization::tensordot_out_out)); |
| 23204 | m.impl("threshold.out", TORCH_FN(functionalization::threshold_out_out)); |
| 23205 | m.impl("threshold_", TORCH_FN(functionalization::threshold_)); |
| 23206 | m.impl("_nested_tensor_strides.out", TORCH_FN(functionalization::_nested_tensor_strides_out_out)); |
| 23207 | m.impl("fix", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::fix)); |
| 23208 | m.impl("fix.out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::Tensor & out)>(at::native::fix_out)); |
| 23209 | m.impl("fix_", static_cast<at::Tensor & (*)(at::Tensor & self)>(at::native::fix_)); |
| 23210 | m.impl("unique_consecutive.out", TORCH_FN(functionalization::unique_consecutive_out_out)); |
| 23211 | m.impl("var.dim", static_cast<at::Tensor (*)(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim)>(at::native::var)); |
| 23212 | m.impl("var.out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim, at::Tensor & out)>(at::native::var_out)); |
| 23213 | m.impl("var.correction_out", TORCH_FN(functionalization::var_out_correction_out)); |
| 23214 | m.impl("var.names_dim", static_cast<at::Tensor (*)(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim)>(at::native::var)); |
| 23215 | m.impl("var.names_out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim, at::Tensor & out)>(at::native::var_out)); |
| 23216 | m.impl("var.correction_names", static_cast<at::Tensor (*)(const at::Tensor & self, at::DimnameList dim, c10::optional<int64_t> correction, bool keepdim)>(at::native::var)); |
| 23217 | m.impl("var.correction_names_out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::DimnameList dim, c10::optional<int64_t> correction, bool keepdim, at::Tensor & out)>(at::native::var_out)); |
| 23218 | m.impl("_standard_gamma_grad.out", TORCH_FN(functionalization::_standard_gamma_grad_out_out)); |
| 23219 | m.impl("poisson.out", TORCH_FN(functionalization::poisson_out_out)); |
| 23220 | m.impl("_sparse_csr_sum.dim_dtype_out", TORCH_FN(functionalization::_sparse_csr_sum_out_dim_dtype_out)); |
| 23221 | m.impl("_sparse_softmax_backward_data.out", TORCH_FN(functionalization::_sparse_softmax_backward_data_out_out)); |
| 23222 | m.impl("_sparse_log_softmax.out", TORCH_FN(functionalization::_sparse_log_softmax_out_out)); |
| 23223 | m.impl("_sparse_log_softmax_backward_data.out", TORCH_FN(functionalization::_sparse_log_softmax_backward_data_out_out)); |
| 23224 | m.impl("resize_as_sparse.out", TORCH_FN(functionalization::resize_as_sparse_out_out)); |
| 23225 | m.impl("resize_as_sparse_", TORCH_FN(functionalization::resize_as_sparse_)); |
| 23226 | m.impl("sub.out", TORCH_FN(functionalization::sub_out_out)); |
| 23227 | m.impl("sub_.Tensor", TORCH_FN(functionalization::sub__Tensor)); |
| 23228 | m.impl("sub.Scalar_out", TORCH_FN(functionalization::sub_out_Scalar_out)); |
| 23229 | m.impl("sub_.Scalar", TORCH_FN(functionalization::sub__Scalar)); |
| 23230 | m.impl("copy_sparse_to_sparse.out", TORCH_FN(functionalization::copy_sparse_to_sparse_out_out)); |
| 23231 | m.impl("copy_sparse_to_sparse_", TORCH_FN(functionalization::copy_sparse_to_sparse_)); |
| 23232 | m.impl("mkldnn_reorder_conv2d_weight.out", TORCH_FN(functionalization::mkldnn_reorder_conv2d_weight_out_out)); |
| 23233 | m.impl("_lstm_mps.out", TORCH_FN(functionalization::_lstm_mps_out_out)); |
| 23234 | m.impl("_thnn_fused_gru_cell_backward.out", TORCH_FN(functionalization::_thnn_fused_gru_cell_backward_out_out)); |
| 23235 | m.impl("set.source_Storage_out", TORCH_FN(functionalization::set_out_source_Storage_out)); |
| 23236 | m.impl("set_.source_Storage", TORCH_FN(functionalization::set__source_Storage)); |
| 23237 | m.impl("set.source_Storage_storage_offset_out", TORCH_FN(functionalization::set_out_source_Storage_storage_offset_out)); |
| 23238 | m.impl("set_.source_Storage_storage_offset", TORCH_FN(functionalization::set__source_Storage_storage_offset)); |
| 23239 | m.impl("set.source_Tensor_out", TORCH_FN(functionalization::set_out_source_Tensor_out)); |
| 23240 | m.impl("set_.source_Tensor", TORCH_FN(functionalization::set__source_Tensor)); |
| 23241 | m.impl("set.out", TORCH_FN(functionalization::set_out_out)); |
| 23242 | m.impl("set_", TORCH_FN(functionalization::set_)); |
| 23243 | m.impl("masked_scatter.out", TORCH_FN(functionalization::masked_scatter_out_out)); |
| 23244 | m.impl("masked_scatter_", TORCH_FN(functionalization::masked_scatter_)); |
| 23245 | m.impl("index_reduce.out", TORCH_FN(functionalization::index_reduce_out_out)); |
| 23246 | m.impl("index_reduce_", TORCH_FN(functionalization::index_reduce_)); |
| 23247 | m.impl("eq.Scalar_out", TORCH_FN(functionalization::eq_out_Scalar_out)); |
| 23248 | m.impl("eq_.Scalar", TORCH_FN(functionalization::eq__Scalar)); |
| 23249 | m.impl("eq.Tensor_out", TORCH_FN(functionalization::eq_out_Tensor_out)); |
| 23250 | m.impl("eq_.Tensor", TORCH_FN(functionalization::eq__Tensor)); |
| 23251 | m.impl("bitwise_and.Tensor_out", TORCH_FN(functionalization::bitwise_and_out_Tensor_out)); |
| 23252 | m.impl("bitwise_and_.Tensor", TORCH_FN(functionalization::bitwise_and__Tensor)); |
| 23253 | m.impl("bitwise_and.Scalar_out", TORCH_FN(functionalization::bitwise_and_out_Scalar_out)); |
| 23254 | m.impl("bitwise_and_.Scalar", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Scalar & other)>(at::native::bitwise_and_)); |
| 23255 | m.impl("bitwise_and.Scalar_Tensor_out", TORCH_FN(functionalization::bitwise_and_out_Scalar_Tensor_out)); |
| 23256 | m.impl("bitwise_or.Tensor_out", TORCH_FN(functionalization::bitwise_or_out_Tensor_out)); |
| 23257 | m.impl("bitwise_or_.Tensor", TORCH_FN(functionalization::bitwise_or__Tensor)); |
| 23258 | m.impl("bitwise_or.Scalar", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Scalar & other)>(at::native::bitwise_or)); |
| 23259 | m.impl("bitwise_or.Scalar_out", TORCH_FN(functionalization::bitwise_or_out_Scalar_out)); |
| 23260 | m.impl("bitwise_or_.Scalar", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Scalar & other)>(at::native::bitwise_or_)); |
| 23261 | m.impl("bitwise_or.Scalar_Tensor_out", TORCH_FN(functionalization::bitwise_or_out_Scalar_Tensor_out)); |
| 23262 | m.impl("bitwise_left_shift.Tensor_out", TORCH_FN(functionalization::bitwise_left_shift_out_Tensor_out)); |
| 23263 | m.impl("bitwise_left_shift_.Tensor", TORCH_FN(functionalization::bitwise_left_shift__Tensor)); |
| 23264 | m.impl("bitwise_left_shift.Tensor_Scalar_out", TORCH_FN(functionalization::bitwise_left_shift_out_Tensor_Scalar_out)); |
| 23265 | m.impl("bitwise_left_shift_.Tensor_Scalar", TORCH_FN(functionalization::bitwise_left_shift__Tensor_Scalar)); |
| 23266 | m.impl("bitwise_left_shift.Scalar_Tensor_out", TORCH_FN(functionalization::bitwise_left_shift_out_Scalar_Tensor_out)); |
| 23267 | m.impl("__rshift__.Scalar_out", TORCH_FN(functionalization::__rshift___out_Scalar_out)); |
| 23268 | m.impl("__irshift__.Scalar", TORCH_FN(functionalization::__irshift___Scalar)); |
| 23269 | m.impl("__rshift__.Tensor_out", TORCH_FN(functionalization::__rshift___out_Tensor_out)); |
| 23270 | m.impl("__irshift__.Tensor", TORCH_FN(functionalization::__irshift___Tensor)); |
| 23271 | m.impl("tril.out", TORCH_FN(functionalization::tril_out_out)); |
| 23272 | m.impl("tril_", TORCH_FN(functionalization::tril_)); |
| 23273 | m.impl("triu.out", TORCH_FN(functionalization::triu_out_out)); |
| 23274 | m.impl("triu_", TORCH_FN(functionalization::triu_)); |
| 23275 | m.impl("digamma.out", TORCH_FN(functionalization::digamma_out_out)); |
| 23276 | m.impl("digamma_", TORCH_FN(functionalization::digamma_)); |
| 23277 | m.impl("lerp.Scalar_out", TORCH_FN(functionalization::lerp_out_Scalar_out)); |
| 23278 | m.impl("lerp_.Scalar", TORCH_FN(functionalization::lerp__Scalar)); |
| 23279 | m.impl("lerp.Tensor_out", TORCH_FN(functionalization::lerp_out_Tensor_out)); |
| 23280 | m.impl("lerp_.Tensor", TORCH_FN(functionalization::lerp__Tensor)); |
| 23281 | m.impl("uniform.out", TORCH_FN(functionalization::uniform_out_out)); |
| 23282 | m.impl("uniform_", TORCH_FN(functionalization::uniform_)); |
| 23283 | m.impl("tril_indices.out", TORCH_FN(functionalization::tril_indices_out_out)); |
| 23284 | m.impl("triu_indices.out", TORCH_FN(functionalization::triu_indices_out_out)); |
| 23285 | m.impl("ge.Scalar_out", TORCH_FN(functionalization::ge_out_Scalar_out)); |
| 23286 | m.impl("ge_.Scalar", TORCH_FN(functionalization::ge__Scalar)); |
| 23287 | m.impl("ge.Tensor_out", TORCH_FN(functionalization::ge_out_Tensor_out)); |
| 23288 | m.impl("ge_.Tensor", TORCH_FN(functionalization::ge__Tensor)); |
| 23289 | m.impl("le.Scalar_out", TORCH_FN(functionalization::le_out_Scalar_out)); |
| 23290 | m.impl("le_.Scalar", TORCH_FN(functionalization::le__Scalar)); |
| 23291 | m.impl("le.Tensor_out", TORCH_FN(functionalization::le_out_Tensor_out)); |
| 23292 | m.impl("le_.Tensor", TORCH_FN(functionalization::le__Tensor)); |
| 23293 | m.impl("gt.Scalar_out", TORCH_FN(functionalization::gt_out_Scalar_out)); |
| 23294 | m.impl("gt_.Scalar", TORCH_FN(functionalization::gt__Scalar)); |
| 23295 | m.impl("gt.Tensor_out", TORCH_FN(functionalization::gt_out_Tensor_out)); |
| 23296 | m.impl("gt_.Tensor", TORCH_FN(functionalization::gt__Tensor)); |
| 23297 | m.impl("nonzero.out", TORCH_FN(functionalization::nonzero_out_out)); |
| 23298 | m.impl("addcdiv.out", TORCH_FN(functionalization::addcdiv_out_out)); |
| 23299 | m.impl("addcdiv_", TORCH_FN(functionalization::addcdiv_)); |
| 23300 | m.impl("triangular_solve.X", TORCH_FN(functionalization::triangular_solve_out_X)); |
| 23301 | m.impl("cholesky_solve.out", TORCH_FN(functionalization::cholesky_solve_out_out)); |
| 23302 | m.impl("cholesky_inverse.out", TORCH_FN(functionalization::cholesky_inverse_out_out)); |
| 23303 | m.impl("qr", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & self, bool some)>(at::native::qr)); |
| 23304 | m.impl("qr.Q", static_cast<::std::tuple<at::Tensor &,at::Tensor &> (*)(const at::Tensor & self, bool some, at::Tensor & Q, at::Tensor & R)>(at::native::qr_out)); |
| 23305 | m.impl("geqrf.a", TORCH_FN(functionalization::geqrf_out_a)); |
| 23306 | m.impl("orgqr", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & input2)>(at::native::orgqr)); |
| 23307 | m.impl("orgqr.out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & input2, at::Tensor & out)>(at::native::orgqr_out)); |
| 23308 | m.impl("lu_solve", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & LU_data, const at::Tensor & LU_pivots)>(at::native::lu_solve)); |
| 23309 | m.impl("lu_solve.out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & LU_data, const at::Tensor & LU_pivots, at::Tensor & out)>(at::native::lu_solve_out)); |
| 23310 | m.impl("lgamma.out", TORCH_FN(functionalization::lgamma_out_out)); |
| 23311 | m.impl("lgamma_", TORCH_FN(functionalization::lgamma_)); |
| 23312 | m.impl("erfinv.out", TORCH_FN(functionalization::erfinv_out_out)); |
| 23313 | m.impl("erfinv_", TORCH_FN(functionalization::erfinv_)); |
| 23314 | m.impl("i0.out", TORCH_FN(functionalization::i0_out_out)); |
| 23315 | m.impl("i0_", TORCH_FN(functionalization::i0_)); |
| 23316 | m.impl("sign.out", TORCH_FN(functionalization::sign_out_out)); |
| 23317 | m.impl("sign_", TORCH_FN(functionalization::sign_)); |
| 23318 | m.impl("signbit.out", TORCH_FN(functionalization::signbit_out_out)); |
| 23319 | m.impl("atan2.out", TORCH_FN(functionalization::atan2_out_out)); |
| 23320 | m.impl("atan2_", TORCH_FN(functionalization::atan2_)); |
| 23321 | m.impl("_histogramdd_bin_edges.out", TORCH_FN(functionalization::_histogramdd_bin_edges_out_out)); |
| 23322 | m.impl("_histogramdd_from_bin_tensors.out", TORCH_FN(functionalization::_histogramdd_from_bin_tensors_out_out)); |
| 23323 | m.impl("fmod.Scalar_out", TORCH_FN(functionalization::fmod_out_Scalar_out)); |
| 23324 | m.impl("fmod_.Scalar", TORCH_FN(functionalization::fmod__Scalar)); |
| 23325 | m.impl("fmod.Tensor_out", TORCH_FN(functionalization::fmod_out_Tensor_out)); |
| 23326 | m.impl("fmod_.Tensor", TORCH_FN(functionalization::fmod__Tensor)); |
| 23327 | m.impl("nextafter.out", TORCH_FN(functionalization::nextafter_out_out)); |
| 23328 | m.impl("nextafter_", TORCH_FN(functionalization::nextafter_)); |
| 23329 | m.impl("minimum.out", TORCH_FN(functionalization::minimum_out_out)); |
| 23330 | m.impl("topk.values", TORCH_FN(functionalization::topk_out_values)); |
| 23331 | m.impl("any.all_out", TORCH_FN(functionalization::any_out_all_out)); |
| 23332 | m.impl("_foreach_mul.Scalar_out", TORCH_FN(functionalization::_foreach_mul_out_Scalar_out)); |
| 23333 | m.impl("_foreach_mul_.Scalar", TORCH_FN(functionalization::_foreach_mul__Scalar)); |
| 23334 | m.impl("_foreach_div.Scalar_out", TORCH_FN(functionalization::_foreach_div_out_Scalar_out)); |
| 23335 | m.impl("_foreach_div_.Scalar", TORCH_FN(functionalization::_foreach_div__Scalar)); |
| 23336 | m.impl("_foreach_clamp_max.Scalar_out", TORCH_FN(functionalization::_foreach_clamp_max_out_Scalar_out)); |
| 23337 | m.impl("_foreach_clamp_max_.Scalar", TORCH_FN(functionalization::_foreach_clamp_max__Scalar)); |
| 23338 | m.impl("_foreach_minimum.Scalar_out", TORCH_FN(functionalization::_foreach_minimum_out_Scalar_out)); |
| 23339 | m.impl("_foreach_minimum_.Scalar", TORCH_FN(functionalization::_foreach_minimum__Scalar)); |
| 23340 | m.impl("_foreach_mul.List_out", TORCH_FN(functionalization::_foreach_mul_out_List_out)); |
| 23341 | m.impl("_foreach_mul_.List", TORCH_FN(functionalization::_foreach_mul__List)); |
| 23342 | m.impl("_foreach_div.List_out", TORCH_FN(functionalization::_foreach_div_out_List_out)); |
| 23343 | m.impl("_foreach_div_.List", TORCH_FN(functionalization::_foreach_div__List)); |
| 23344 | m.impl("_foreach_clamp_max.List_out", TORCH_FN(functionalization::_foreach_clamp_max_out_List_out)); |
| 23345 | m.impl("_foreach_clamp_max_.List", TORCH_FN(functionalization::_foreach_clamp_max__List)); |
| 23346 | m.impl("_foreach_minimum.List_out", TORCH_FN(functionalization::_foreach_minimum_out_List_out)); |
| 23347 | m.impl("_foreach_minimum_.List", TORCH_FN(functionalization::_foreach_minimum__List)); |
| 23348 | m.impl("_foreach_div.ScalarList_out", TORCH_FN(functionalization::_foreach_div_out_ScalarList_out)); |
| 23349 | m.impl("_foreach_div_.ScalarList", TORCH_FN(functionalization::_foreach_div__ScalarList)); |
| 23350 | m.impl("_foreach_mul.ScalarList_out", TORCH_FN(functionalization::_foreach_mul_out_ScalarList_out)); |
| 23351 | m.impl("_foreach_mul_.ScalarList", TORCH_FN(functionalization::_foreach_mul__ScalarList)); |
| 23352 | m.impl("_foreach_clamp_max.ScalarList_out", TORCH_FN(functionalization::_foreach_clamp_max_out_ScalarList_out)); |
| 23353 | m.impl("_foreach_clamp_max_.ScalarList", TORCH_FN(functionalization::_foreach_clamp_max__ScalarList)); |
| 23354 | m.impl("_foreach_minimum.ScalarList_out", TORCH_FN(functionalization::_foreach_minimum_out_ScalarList_out)); |
| 23355 | m.impl("_foreach_minimum_.ScalarList", TORCH_FN(functionalization::_foreach_minimum__ScalarList)); |
| 23356 | m.impl("_foreach_expm1.out", TORCH_FN(functionalization::_foreach_expm1_out_out)); |
| 23357 | m.impl("_foreach_expm1_", TORCH_FN(functionalization::_foreach_expm1_)); |
| 23358 | m.impl("_foreach_tanh.out", TORCH_FN(functionalization::_foreach_tanh_out_out)); |
| 23359 | m.impl("_foreach_tanh_", TORCH_FN(functionalization::_foreach_tanh_)); |
| 23360 | m.impl("_foreach_sin.out", TORCH_FN(functionalization::_foreach_sin_out_out)); |
| 23361 | m.impl("_foreach_sin_", TORCH_FN(functionalization::_foreach_sin_)); |
| 23362 | m.impl("_foreach_frac.out", TORCH_FN(functionalization::_foreach_frac_out_out)); |
| 23363 | m.impl("_foreach_frac_", TORCH_FN(functionalization::_foreach_frac_)); |
| 23364 | m.impl("_foreach_reciprocal.out", TORCH_FN(functionalization::_foreach_reciprocal_out_out)); |
| 23365 | m.impl("_foreach_reciprocal_", TORCH_FN(functionalization::_foreach_reciprocal_)); |
| 23366 | m.impl("_foreach_trunc.out", TORCH_FN(functionalization::_foreach_trunc_out_out)); |
| 23367 | m.impl("_foreach_trunc_", TORCH_FN(functionalization::_foreach_trunc_)); |
| 23368 | m.impl("_convert_indices_from_csr_to_coo.out", TORCH_FN(functionalization::_convert_indices_from_csr_to_coo_out_out)); |
| 23369 | m.impl("multilabel_margin_loss_backward.grad_input", TORCH_FN(functionalization::multilabel_margin_loss_backward_out_grad_input)); |
| 23370 | m.impl("nll_loss", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index)>(at::native::nll_loss_symint)); |
| 23371 | m.impl("nll_loss.out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index, at::Tensor & out)>(at::native::nll_loss_out)); |
| 23372 | m.impl("nll_loss_backward.grad_input", TORCH_FN(functionalization::nll_loss_backward_out_grad_input)); |
| 23373 | m.impl("nll_loss2d", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index)>(at::native::nll_loss2d_symint)); |
| 23374 | m.impl("nll_loss2d.out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index, at::Tensor & out)>(at::native::nll_loss2d_out)); |
| 23375 | m.impl("nll_loss2d_backward.grad_input", TORCH_FN(functionalization::nll_loss2d_backward_out_grad_input)); |
| 23376 | m.impl("smooth_l1_loss.out", TORCH_FN(functionalization::smooth_l1_loss_out_out)); |
| 23377 | m.impl("huber_loss.out", TORCH_FN(functionalization::huber_loss_out_out)); |
| 23378 | m.impl("soft_margin_loss_backward.grad_input", TORCH_FN(functionalization::soft_margin_loss_backward_out_grad_input)); |
| 23379 | m.impl("elu.out", TORCH_FN(functionalization::elu_out_out)); |
| 23380 | m.impl("elu_", TORCH_FN(functionalization::elu_)); |
| 23381 | m.impl("glu.out", TORCH_FN(functionalization::glu_out_out)); |
| 23382 | m.impl("hardsigmoid.out", TORCH_FN(functionalization::hardsigmoid_out_out)); |
| 23383 | m.impl("hardsigmoid_", TORCH_FN(functionalization::hardsigmoid_)); |
| 23384 | m.impl("leaky_relu_backward.grad_input", TORCH_FN(functionalization::leaky_relu_backward_out_grad_input)); |
| 23385 | m.impl("softshrink.out", TORCH_FN(functionalization::softshrink_out_out)); |
| 23386 | m.impl("_adaptive_avg_pool2d.out", TORCH_FN(functionalization::_adaptive_avg_pool2d_out_out)); |
| 23387 | m.impl("_adaptive_avg_pool3d.out", TORCH_FN(functionalization::_adaptive_avg_pool3d_out_out)); |
| 23388 | m.impl("avg_pool3d.out", TORCH_FN(functionalization::avg_pool3d_out_out)); |
| 23389 | m.impl("fractional_max_pool3d.output", TORCH_FN(functionalization::fractional_max_pool3d_out_output)); |
| 23390 | m.impl("max_pool2d_with_indices_backward.grad_input", TORCH_FN(functionalization::max_pool2d_with_indices_backward_out_grad_input)); |
| 23391 | m.impl("max_unpool3d.out", TORCH_FN(functionalization::max_unpool3d_out_out)); |
| 23392 | m.impl("reflection_pad1d.out", TORCH_FN(functionalization::reflection_pad1d_out_out)); |
| 23393 | m.impl("reflection_pad1d_backward.grad_input", TORCH_FN(functionalization::reflection_pad1d_backward_out_grad_input)); |
| 23394 | m.impl("reflection_pad2d.out", TORCH_FN(functionalization::reflection_pad2d_out_out)); |
| 23395 | m.impl("reflection_pad3d.out", TORCH_FN(functionalization::reflection_pad3d_out_out)); |
| 23396 | m.impl("replication_pad1d.out", TORCH_FN(functionalization::replication_pad1d_out_out)); |
| 23397 | m.impl("replication_pad1d_backward.grad_input", TORCH_FN(functionalization::replication_pad1d_backward_out_grad_input)); |
| 23398 | m.impl("replication_pad2d_backward.grad_input", TORCH_FN(functionalization::replication_pad2d_backward_out_grad_input)); |
| 23399 | m.impl("replication_pad3d.out", TORCH_FN(functionalization::replication_pad3d_out_out)); |
| 23400 | m.impl("replication_pad3d_backward.grad_input", TORCH_FN(functionalization::replication_pad3d_backward_out_grad_input)); |
| 23401 | m.impl("upsample_nearest2d.out", TORCH_FN(functionalization::upsample_nearest2d_out_out)); |
| 23402 | m.impl("_upsample_nearest_exact2d.out", TORCH_FN(functionalization::_upsample_nearest_exact2d_out_out)); |
| 23403 | m.impl("_upsample_nearest_exact2d_backward.grad_input", TORCH_FN(functionalization::_upsample_nearest_exact2d_backward_out_grad_input)); |
| 23404 | m.impl("_upsample_nearest_exact3d_backward.grad_input", TORCH_FN(functionalization::_upsample_nearest_exact3d_backward_out_grad_input)); |
| 23405 | m.impl("sigmoid_backward.grad_input", TORCH_FN(functionalization::sigmoid_backward_out_grad_input)); |
| 23406 | m.impl("slow_conv_transpose2d.out", TORCH_FN(functionalization::slow_conv_transpose2d_out_out)); |
| 23407 | m.impl("_slow_conv2d_forward.output", TORCH_FN(functionalization::_slow_conv2d_forward_out_output)); |
| 23408 | m.impl("conv_depthwise3d.out", TORCH_FN(functionalization::conv_depthwise3d_out_out)); |
| 23409 | m.impl("slow_conv_dilated2d.out", TORCH_FN(functionalization::slow_conv_dilated2d_out_out)); |
| 23410 | m.impl("special_ndtri.out", TORCH_FN(functionalization::special_ndtri_out_out)); |
| 23411 | m.impl("special_erfc", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::special_erfc)); |
| 23412 | m.impl("special_erfc.out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::Tensor & out)>(at::native::special_erfc_out)); |
| 23413 | m.impl("special_logit", static_cast<at::Tensor (*)(const at::Tensor & self, c10::optional<double> eps)>(at::native::special_logit)); |
| 23414 | m.impl("special_logit.out", static_cast<at::Tensor & (*)(const at::Tensor & self, c10::optional<double> eps, at::Tensor & out)>(at::native::special_logit_out)); |
| 23415 | m.impl("special_polygamma", static_cast<at::Tensor (*)(int64_t n, const at::Tensor & self)>(at::native::special_polygamma)); |
| 23416 | m.impl("special_polygamma.out", static_cast<at::Tensor & (*)(int64_t n, const at::Tensor & self, at::Tensor & out)>(at::native::special_polygamma_out)); |
| 23417 | m.impl("special_sinc", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::special_sinc)); |
| 23418 | m.impl("special_sinc.out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::Tensor & out)>(at::native::special_sinc_out)); |
| 23419 | m.impl("fft_ifft", static_cast<at::Tensor (*)(const at::Tensor & self, c10::optional<int64_t> n, int64_t dim, c10::optional<c10::string_view> norm)>(at::native::fft_ifft)); |
| 23420 | m.impl("fft_ifft.out", static_cast<at::Tensor & (*)(const at::Tensor & self, c10::optional<int64_t> n, int64_t dim, c10::optional<c10::string_view> norm, at::Tensor & out)>(at::native::fft_ifft_out)); |
| 23421 | m.impl("fft_ihfft", static_cast<at::Tensor (*)(const at::Tensor & self, c10::optional<int64_t> n, int64_t dim, c10::optional<c10::string_view> norm)>(at::native::fft_ihfft)); |
| 23422 | m.impl("fft_ihfft.out", static_cast<at::Tensor & (*)(const at::Tensor & self, c10::optional<int64_t> n, int64_t dim, c10::optional<c10::string_view> norm, at::Tensor & out)>(at::native::fft_ihfft_out)); |
| 23423 | m.impl("fft_ifft2", static_cast<at::Tensor (*)(const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional<c10::string_view> norm)>(at::native::fft_ifft2)); |
| 23424 | m.impl("fft_ifft2.out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional<c10::string_view> norm, at::Tensor & out)>(at::native::fft_ifft2_out)); |
| 23425 | m.impl("fft_ihfftn", static_cast<at::Tensor (*)(const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional<c10::string_view> norm)>(at::native::fft_ihfftn)); |
| 23426 | m.impl("fft_ihfftn.out", static_cast<const at::Tensor & (*)(const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional<c10::string_view> norm, const at::Tensor & out)>(at::native::fft_ihfftn_out)); |
| 23427 | m.impl("linalg_cholesky_ex.L", TORCH_FN(functionalization::linalg_cholesky_ex_out_L)); |
| 23428 | m.impl("linalg_cross.out", TORCH_FN(functionalization::linalg_cross_out_out)); |
| 23429 | m.impl("linalg_lu_solve.out", TORCH_FN(functionalization::linalg_lu_solve_out_out)); |
| 23430 | m.impl("linalg_matmul", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other)>(at::native::linalg_matmul)); |
| 23431 | m.impl("linalg_matmul.out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & other, at::Tensor & out)>(at::native::linalg_matmul_out)); |
| 23432 | m.impl("linalg_vecdot", static_cast<at::Tensor (*)(const at::Tensor & x, const at::Tensor & y, int64_t dim)>(at::native::linalg_vecdot)); |
| 23433 | m.impl("linalg_vecdot.out", static_cast<at::Tensor & (*)(const at::Tensor & x, const at::Tensor & y, int64_t dim, at::Tensor & out)>(at::native::linalg_vecdot_out)); |
| 23434 | m.impl("linalg_eigvalsh", static_cast<at::Tensor (*)(const at::Tensor & self, c10::string_view UPLO)>(at::native::linalg_eigvalsh)); |
| 23435 | m.impl("linalg_eigvalsh.out", static_cast<at::Tensor & (*)(const at::Tensor & self, c10::string_view UPLO, at::Tensor & out)>(at::native::linalg_eigvalsh_out)); |
| 23436 | m.impl("linalg_inv_ex.inverse", TORCH_FN(functionalization::linalg_inv_ex_out_inverse)); |
| 23437 | m.impl("inverse", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::inverse)); |
| 23438 | m.impl("inverse.out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::Tensor & out)>(at::native::inverse_out)); |
| 23439 | m.impl("outer", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & vec2)>(at::native::outer)); |
| 23440 | m.impl("outer.out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & vec2, at::Tensor & out)>(at::native::outer_out)); |
| 23441 | m.impl("linalg_svd", static_cast<::std::tuple<at::Tensor,at::Tensor,at::Tensor> (*)(const at::Tensor & A, bool full_matrices, c10::optional<c10::string_view> driver)>(at::native::linalg_svd)); |
| 23442 | m.impl("linalg_svd.U", static_cast<::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> (*)(const at::Tensor & A, bool full_matrices, c10::optional<c10::string_view> driver, at::Tensor & U, at::Tensor & S, at::Tensor & Vh)>(at::native::linalg_svd_out)); |
| 23443 | m.impl("linalg_cond", static_cast<at::Tensor (*)(const at::Tensor & self, const c10::optional<at::Scalar> & p)>(at::native::linalg_cond)); |
| 23444 | m.impl("linalg_cond.out", static_cast<at::Tensor & (*)(const at::Tensor & self, const c10::optional<at::Scalar> & p, at::Tensor & out)>(at::native::linalg_cond_out)); |
| 23445 | m.impl("linalg_cond.p_str", static_cast<at::Tensor (*)(const at::Tensor & self, c10::string_view p)>(at::native::linalg_cond)); |
| 23446 | m.impl("linalg_cond.p_str_out", static_cast<at::Tensor & (*)(const at::Tensor & self, c10::string_view p, at::Tensor & out)>(at::native::linalg_cond_out)); |
| 23447 | m.impl("linalg_solve_ex", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & A, const at::Tensor & B, bool left, bool check_errors)>(at::native::linalg_solve_ex)); |
| 23448 | m.impl("linalg_solve_ex.out", static_cast<::std::tuple<at::Tensor &,at::Tensor &> (*)(const at::Tensor & A, const at::Tensor & B, bool left, bool check_errors, at::Tensor & result, at::Tensor & info)>(at::native::linalg_solve_ex_out)); |
| 23449 | m.impl("linalg_tensorsolve", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other, at::OptionalIntArrayRef dims)>(at::native::linalg_tensorsolve)); |
| 23450 | m.impl("linalg_tensorsolve.out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & other, at::OptionalIntArrayRef dims, at::Tensor & out)>(at::native::linalg_tensorsolve_out)); |
| 23451 | m.impl("linalg_matrix_rank.atol_rtol_tensor", static_cast<at::Tensor (*)(const at::Tensor & input, const c10::optional<at::Tensor> & atol, const c10::optional<at::Tensor> & rtol, bool hermitian)>(at::native::linalg_matrix_rank)); |
| 23452 | m.impl("linalg_matrix_rank.atol_rtol_tensor_out", static_cast<at::Tensor & (*)(const at::Tensor & input, const c10::optional<at::Tensor> & atol, const c10::optional<at::Tensor> & rtol, bool hermitian, at::Tensor & out)>(at::native::linalg_matrix_rank_out)); |
| 23453 | m.impl("linalg_matrix_rank.atol_rtol_float", static_cast<at::Tensor (*)(const at::Tensor & self, c10::optional<double> atol, c10::optional<double> rtol, bool hermitian)>(at::native::linalg_matrix_rank)); |
| 23454 | m.impl("linalg_matrix_rank.atol_rtol_float_out", static_cast<at::Tensor & (*)(const at::Tensor & self, c10::optional<double> atol, c10::optional<double> rtol, bool hermitian, at::Tensor & out)>(at::native::linalg_matrix_rank_out)); |
| 23455 | m.impl("linalg_matrix_rank", static_cast<at::Tensor (*)(const at::Tensor & self, double tol, bool hermitian)>(at::native::linalg_matrix_rank)); |
| 23456 | m.impl("linalg_matrix_rank.out", static_cast<at::Tensor & (*)(const at::Tensor & self, double tol, bool hermitian, at::Tensor & out)>(at::native::linalg_matrix_rank_out)); |
| 23457 | m.impl("linalg_matrix_rank.tol_tensor", static_cast<at::Tensor (*)(const at::Tensor & input, const at::Tensor & tol, bool hermitian)>(at::native::linalg_matrix_rank)); |
| 23458 | m.impl("linalg_matrix_rank.out_tol_tensor", static_cast<at::Tensor & (*)(const at::Tensor & input, const at::Tensor & tol, bool hermitian, at::Tensor & out)>(at::native::linalg_matrix_rank_out)); |
| 23459 | m.impl("_test_optional_floatlist.out", TORCH_FN(functionalization::_test_optional_floatlist_out_out)); |
| 23460 | m.impl("_test_warn_in_autograd.out", TORCH_FN(functionalization::_test_warn_in_autograd_out_out)); |
| 23461 | m.impl("_segment_reduce_backward.out", TORCH_FN(functionalization::_segment_reduce_backward_out_out)); |
| 23462 | m.impl("_sparse_broadcast_to_copy.out", TORCH_FN(functionalization::_sparse_broadcast_to_copy_out_out)); |
| 23463 | m.impl("unsqueeze_copy.out", TORCH_FN(functionalization::unsqueeze_copy_out_out)); |
| 23464 | m.impl("values_copy.out", TORCH_FN(functionalization::values_copy_out_out)); |
| 23465 | m.impl("to_padded_tensor.out", TORCH_FN(functionalization::to_padded_tensor_out_out)); |
| 23466 | m.impl("_triton_scaled_dot_attention.out", TORCH_FN(functionalization::_triton_scaled_dot_attention_out_out)); |
| 23467 | m.impl("special_bessel_y0.out", TORCH_FN(functionalization::special_bessel_y0_out_out)); |
| 23468 | m.impl("special_chebyshev_polynomial_t.out", TORCH_FN(functionalization::special_chebyshev_polynomial_t_out_out)); |
| 23469 | m.impl("special_chebyshev_polynomial_t.x_scalar", static_cast<at::Tensor (*)(const at::Scalar & x, const at::Tensor & n)>(at::native::special_chebyshev_polynomial_t)); |
| 23470 | m.impl("special_chebyshev_polynomial_t.x_scalar_out", static_cast<at::Tensor & (*)(const at::Scalar & x, const at::Tensor & n, at::Tensor & out)>(at::native::special_chebyshev_polynomial_t_out)); |
| 23471 | m.impl("special_chebyshev_polynomial_t.n_scalar", static_cast<at::Tensor (*)(const at::Tensor & x, const at::Scalar & n)>(at::native::special_chebyshev_polynomial_t)); |
| 23472 | m.impl("special_chebyshev_polynomial_t.n_scalar_out", TORCH_FN(functionalization::special_chebyshev_polynomial_t_out_n_scalar_out)); |
| 23473 | m.impl("special_chebyshev_polynomial_u.out", TORCH_FN(functionalization::special_chebyshev_polynomial_u_out_out)); |
| 23474 | m.impl("special_chebyshev_polynomial_u.x_scalar", static_cast<at::Tensor (*)(const at::Scalar & x, const at::Tensor & n)>(at::native::special_chebyshev_polynomial_u)); |
| 23475 | m.impl("special_chebyshev_polynomial_u.x_scalar_out", static_cast<at::Tensor & (*)(const at::Scalar & x, const at::Tensor & n, at::Tensor & out)>(at::native::special_chebyshev_polynomial_u_out)); |
| 23476 | m.impl("special_chebyshev_polynomial_u.n_scalar", static_cast<at::Tensor (*)(const at::Tensor & x, const at::Scalar & n)>(at::native::special_chebyshev_polynomial_u)); |
| 23477 | m.impl("special_chebyshev_polynomial_u.n_scalar_out", TORCH_FN(functionalization::special_chebyshev_polynomial_u_out_n_scalar_out)); |
| 23478 | m.impl("special_hermite_polynomial_h.out", TORCH_FN(functionalization::special_hermite_polynomial_h_out_out)); |
| 23479 | m.impl("special_hermite_polynomial_h.x_scalar", static_cast<at::Tensor (*)(const at::Scalar & x, const at::Tensor & n)>(at::native::special_hermite_polynomial_h)); |
| 23480 | m.impl("special_hermite_polynomial_h.x_scalar_out", static_cast<at::Tensor & (*)(const at::Scalar & x, const at::Tensor & n, at::Tensor & out)>(at::native::special_hermite_polynomial_h_out)); |
| 23481 | m.impl("special_hermite_polynomial_h.n_scalar", static_cast<at::Tensor (*)(const at::Tensor & x, const at::Scalar & n)>(at::native::special_hermite_polynomial_h)); |
| 23482 | m.impl("special_hermite_polynomial_h.n_scalar_out", TORCH_FN(functionalization::special_hermite_polynomial_h_out_n_scalar_out)); |
| 23483 | m.impl("special_modified_bessel_k1.out", TORCH_FN(functionalization::special_modified_bessel_k1_out_out)); |
| 23484 | m.impl("special_scaled_modified_bessel_k0.out", TORCH_FN(functionalization::special_scaled_modified_bessel_k0_out_out)); |
| 23485 | m.impl("special_scaled_modified_bessel_k1.out", TORCH_FN(functionalization::special_scaled_modified_bessel_k1_out_out)); |
| 23486 | m.impl("special_spherical_bessel_j0.out", TORCH_FN(functionalization::special_spherical_bessel_j0_out_out)); |
| 23487 | m.impl("refine_names", static_cast<at::Tensor (*)(const at::Tensor & self, at::DimnameList names)>(at::native::refine_names)); |
| 23488 | m.impl("real", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::real)); |
| 23489 | m.impl("_neg_view", TORCH_FN(functionalization::_neg_view)); |
| 23490 | m.impl("diagonal", TORCH_FN(functionalization::diagonal)); |
| 23491 | m.impl("diagonal.Dimname", static_cast<at::Tensor (*)(const at::Tensor & self, at::Dimname outdim, at::Dimname dim1, at::Dimname dim2, int64_t offset)>(at::native::diagonal)); |
| 23492 | m.impl("narrow", static_cast<at::Tensor (*)(const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length)>(at::native::narrow_symint)); |
| 23493 | m.impl("narrow.Tensor", static_cast<at::Tensor (*)(const at::Tensor & self, int64_t dim, const at::Tensor & start, c10::SymInt length)>(at::native::narrow_tensor_symint)); |
| 23494 | m.impl("numpy_T", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::numpy_T)); |
| 23495 | m.impl("select.Dimname", static_cast<at::Tensor (*)(const at::Tensor & self, at::Dimname dim, int64_t index)>(at::native::select)); |
| 23496 | m.impl("select.int", TORCH_FN(functionalization::select_int)); |
| 23497 | m.impl("split_with_sizes", TORCH_FN(functionalization::split_with_sizes)); |
| 23498 | m.impl("vsplit.int", static_cast<::std::vector<at::Tensor> (*)(const at::Tensor & self, int64_t sections)>(at::native::vsplit)); |
| 23499 | m.impl("vsplit.array", static_cast<::std::vector<at::Tensor> (*)(const at::Tensor & self, at::IntArrayRef indices)>(at::native::vsplit)); |
| 23500 | m.impl("squeeze", TORCH_FN(functionalization::squeeze)); |
| 23501 | m.impl("squeeze_", TORCH_FN(functionalization::squeeze_)); |
| 23502 | m.impl("squeeze.dim", TORCH_FN(functionalization::squeeze_dim)); |
| 23503 | m.impl("squeeze_.dim", TORCH_FN(functionalization::squeeze__dim)); |
| 23504 | m.impl("squeeze.dimname", static_cast<at::Tensor (*)(const at::Tensor & self, at::Dimname dim)>(at::native::squeeze)); |
| 23505 | m.impl("squeeze_.dimname", static_cast<at::Tensor & (*)(at::Tensor & self, at::Dimname dim)>(at::native::squeeze_)); |
| 23506 | m.impl("squeeze.dims", TORCH_FN(functionalization::squeeze_dims)); |
| 23507 | m.impl("squeeze_.dims", TORCH_FN(functionalization::squeeze__dims)); |
| 23508 | m.impl("view_as", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other)>(at::native::view_as)); |
| 23509 | m.impl("unbind.int", TORCH_FN(functionalization::unbind_int)); |
| 23510 | m.impl("unbind.Dimname", static_cast<::std::vector<at::Tensor> (*)(const at::Tensor & self, at::Dimname dim)>(at::native::unbind)); |
| 23511 | m.impl("alias", TORCH_FN(functionalization::alias)); |
| 23512 | m.impl("_cast_Char", static_cast<at::Tensor (*)(const at::Tensor & self, bool non_blocking)>(at::native::_cast_Char)); |
| 23513 | m.impl("_cast_Half", static_cast<at::Tensor (*)(const at::Tensor & self, bool non_blocking)>(at::native::_cast_Half)); |
| 23514 | m.impl("_backward", static_cast<void (*)(const at::Tensor & self, at::TensorList inputs, const c10::optional<at::Tensor> & gradient, c10::optional<bool> retain_graph, bool create_graph)>(at::native::_backward)); |
| 23515 | m.impl("set_data", static_cast<void (*)(at::Tensor & self, const at::Tensor & new_data)>(at::native::set_data)); |
| 23516 | m.impl("data", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::data)); |
| 23517 | m.impl("requires_grad_", static_cast<at::Tensor & (*)(at::Tensor & self, bool requires_grad)>(at::native::requires_grad_)); |
| 23518 | m.impl("align_as", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other)>(at::native::align_as)); |
| 23519 | m.impl("_sobol_engine_ff_", static_cast<at::Tensor & (*)(at::Tensor & self, int64_t n, const at::Tensor & sobolstate, int64_t dimension, int64_t num_generated)>(at::native::_sobol_engine_ff_)); |
| 23520 | m.impl("feature_alpha_dropout", static_cast<at::Tensor (*)(const at::Tensor & input, double p, bool train)>(at::native::feature_alpha_dropout)); |
| 23521 | m.impl("feature_alpha_dropout_", static_cast<at::Tensor & (*)(at::Tensor & self, double p, bool train)>(at::native::feature_alpha_dropout_)); |
| 23522 | m.impl("atleast_1d", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::atleast_1d)); |
| 23523 | m.impl("atleast_1d.Sequence", static_cast<::std::vector<at::Tensor> (*)(at::TensorList tensors)>(at::native::atleast_1d)); |
| 23524 | m.impl("_batch_norm_impl_index", static_cast<::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,int64_t> (*)(const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, bool training, double momentum, double eps, bool cudnn_enabled)>(at::native::_batch_norm_impl_index)); |
| 23525 | m.impl("bilinear", static_cast<at::Tensor (*)(const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & weight, const c10::optional<at::Tensor> & bias)>(at::native::bilinear)); |
| 23526 | m.impl("unsafe_chunk", static_cast<::std::vector<at::Tensor> (*)(const at::Tensor & self, int64_t chunks, int64_t dim)>(at::native::unsafe_chunk)); |
| 23527 | m.impl("conv_transpose1d", static_cast<at::Tensor (*)(const at::Tensor & input, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef output_padding, int64_t groups, at::IntArrayRef dilation)>(at::native::conv_transpose1d)); |
| 23528 | m.impl("conv_transpose2d.input", static_cast<at::Tensor (*)(const at::Tensor & input, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef output_padding, int64_t groups, at::IntArrayRef dilation)>(at::native::conv_transpose2d)); |
| 23529 | m.impl("conv_transpose3d.input", static_cast<at::Tensor (*)(const at::Tensor & input, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef output_padding, int64_t groups, at::IntArrayRef dilation)>(at::native::conv_transpose3d)); |
| 23530 | m.impl("cosine_embedding_loss", static_cast<at::Tensor (*)(const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & target, double margin, int64_t reduction)>(at::native::cosine_embedding_loss)); |
| 23531 | m.impl("cumulative_trapezoid.x", static_cast<at::Tensor (*)(const at::Tensor & y, const at::Tensor & x, int64_t dim)>(at::native::cumulative_trapezoid)); |
| 23532 | m.impl("cumulative_trapezoid.dx", static_cast<at::Tensor (*)(const at::Tensor & y, const at::Scalar & dx, int64_t dim)>(at::native::cumulative_trapezoid)); |
| 23533 | m.impl("ctc_loss.IntList", static_cast<at::Tensor (*)(const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank, int64_t reduction, bool zero_infinity)>(at::native::ctc_loss)); |
| 23534 | m.impl("ctc_loss.Tensor", static_cast<at::Tensor (*)(const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank, int64_t reduction, bool zero_infinity)>(at::native::ctc_loss)); |
| 23535 | m.impl("gradient.scalarint", static_cast<::std::vector<at::Tensor> (*)(const at::Tensor & self, const c10::optional<at::Scalar> & spacing, c10::optional<int64_t> dim, int64_t edge_order)>(at::native::gradient)); |
| 23536 | m.impl("gradient.scalararray", static_cast<::std::vector<at::Tensor> (*)(const at::Tensor & self, const at::Scalar & spacing, at::IntArrayRef dim, int64_t edge_order)>(at::native::gradient)); |
| 23537 | m.impl("gradient.array", static_cast<::std::vector<at::Tensor> (*)(const at::Tensor & self, at::IntArrayRef dim, int64_t edge_order)>(at::native::gradient)); |
| 23538 | m.impl("gradient.scalarrayint", static_cast<::std::vector<at::Tensor> (*)(const at::Tensor & self, at::ArrayRef<at::Scalar> spacing, c10::optional<int64_t> dim, int64_t edge_order)>(at::native::gradient)); |
| 23539 | m.impl("gradient.scalarrayarray", static_cast<::std::vector<at::Tensor> (*)(const at::Tensor & self, at::ArrayRef<at::Scalar> spacing, at::IntArrayRef dim, int64_t edge_order)>(at::native::gradient)); |
| 23540 | m.impl("gradient.tensorarrayint", static_cast<::std::vector<at::Tensor> (*)(const at::Tensor & self, at::TensorList spacing, c10::optional<int64_t> dim, int64_t edge_order)>(at::native::gradient)); |
| 23541 | m.impl("gradient.tensorarray", static_cast<::std::vector<at::Tensor> (*)(const at::Tensor & self, at::TensorList spacing, at::IntArrayRef dim, int64_t edge_order)>(at::native::gradient)); |
| 23542 | m.impl("einsum", static_cast<at::Tensor (*)(c10::string_view equation, at::TensorList tensors, at::OptionalIntArrayRef path)>(at::native::einsum)); |
| 23543 | m.impl("grid_sampler", static_cast<at::Tensor (*)(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners)>(at::native::grid_sampler)); |
| 23544 | m.impl("_grid_sampler_2d_cpu_fallback_backward", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners)>(at::native::_grid_sampler_2d_cpu_fallback_backward)); |
| 23545 | m.impl("hinge_embedding_loss", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & target, double margin, int64_t reduction)>(at::native::hinge_embedding_loss)); |
| 23546 | m.impl("_cufft_set_plan_cache_max_size", static_cast<void (*)(int64_t device_index, int64_t max_size)>(at::native::_cufft_set_plan_cache_max_size)); |
| 23547 | m.impl("_cufft_clear_plan_cache", static_cast<void (*)(int64_t device_index)>(at::native::_cufft_clear_plan_cache)); |
| 23548 | m.impl("isclose", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other, double rtol, double atol, bool equal_nan)>(at::native::isclose)); |
| 23549 | m.impl("is_floating_point", static_cast<bool (*)(const at::Tensor & self)>(at::native::is_floating_point)); |
| 23550 | m.impl("is_conj", static_cast<bool (*)(const at::Tensor & self)>(at::native::is_conj)); |
| 23551 | m.impl("isreal", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::isreal)); |
| 23552 | m.impl("is_nonzero", static_cast<bool (*)(const at::Tensor & self)>(at::native::is_nonzero)); |
| 23553 | m.impl("layer_norm", static_cast<at::Tensor (*)(const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, double eps, bool cudnn_enable)>(at::native::layer_norm_symint)); |
| 23554 | m.impl("fbgemm_pack_gemm_matrix_fp16", static_cast<at::Tensor (*)(const at::Tensor & input)>(at::native::fbgemm_pack_gemm_matrix_fp16)); |
| 23555 | m.impl("fbgemm_pack_quantized_matrix", static_cast<at::Tensor (*)(const at::Tensor & input)>(at::native::fbgemm_pack_quantized_matrix)); |
| 23556 | m.impl("fbgemm_pack_quantized_matrix.KN", static_cast<at::Tensor (*)(const at::Tensor & input, int64_t K, int64_t N)>(at::native::fbgemm_pack_quantized_matrix)); |
| 23557 | m.impl("value_selecting_reduction_backward", static_cast<at::Tensor (*)(const at::Tensor & grad, int64_t dim, const at::Tensor & indices, c10::SymIntArrayRef sizes, bool keepdim)>(at::native::value_selecting_reduction_backward_symint)); |
| 23558 | m.impl("max_pool1d", static_cast<at::Tensor (*)(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode)>(at::native::max_pool1d)); |
| 23559 | m.impl("pairwise_distance", static_cast<at::Tensor (*)(const at::Tensor & x1, const at::Tensor & x2, double p, double eps, bool keepdim)>(at::native::pairwise_distance)); |
| 23560 | m.impl("pdist", static_cast<at::Tensor (*)(const at::Tensor & self, double p)>(at::native::pdist)); |
| 23561 | m.impl("size.int", static_cast<int64_t (*)(const at::Tensor & self, int64_t dim)>(at::native::size)); |
| 23562 | m.impl("size.Dimname", static_cast<int64_t (*)(const at::Tensor & self, at::Dimname dim)>(at::native::size)); |
| 23563 | m.impl("smm", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & mat2)>(at::native::smm)); |
| 23564 | m.impl("stride.int", static_cast<int64_t (*)(const at::Tensor & self, int64_t dim)>(at::native::stride)); |
| 23565 | m.impl("stride.Dimname", static_cast<int64_t (*)(const at::Tensor & self, at::Dimname dim)>(at::native::stride)); |
| 23566 | m.impl("sum_to_size", static_cast<at::Tensor (*)(const at::Tensor & self, at::IntArrayRef size)>(at::native::sum_to_size)); |
| 23567 | m.impl("std", static_cast<at::Tensor (*)(const at::Tensor & self, bool unbiased)>(at::native::std)); |
| 23568 | m.impl("one_hot", static_cast<at::Tensor (*)(const at::Tensor & self, int64_t num_classes)>(at::native::one_hot)); |
| 23569 | m.impl("fliplr", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::fliplr)); |
| 23570 | m.impl("triplet_margin_loss", static_cast<at::Tensor (*)(const at::Tensor & anchor, const at::Tensor & positive, const at::Tensor & negative, double margin, double p, double eps, bool swap, int64_t reduction)>(at::native::triplet_margin_loss)); |
| 23571 | m.impl("type_as", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other)>(at::native::type_as)); |
| 23572 | m.impl("var", static_cast<at::Tensor (*)(const at::Tensor & self, bool unbiased)>(at::native::var)); |
| 23573 | m.impl("_sparse_log_softmax.int", static_cast<at::Tensor (*)(const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype)>(at::native::_sparse_log_softmax)); |
| 23574 | m.impl("_sparse_log_softmax.Dimname", static_cast<at::Tensor (*)(const at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype)>(at::native::_sparse_log_softmax)); |
| 23575 | m.impl("sparse_csc_tensor.ccol_row_value_size", static_cast<at::Tensor (*)(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory)>(at::native::sparse_csc_tensor)); |
| 23576 | m.impl("sparse_csc_tensor.ccol_row_value", static_cast<at::Tensor (*)(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory)>(at::native::sparse_csc_tensor)); |
| 23577 | m.impl("_sparse_bsr_tensor_unsafe", static_cast<at::Tensor (*)(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory)>(at::native::_sparse_bsr_tensor_unsafe)); |
| 23578 | m.impl("_validate_sparse_compressed_tensor_args", static_cast<void (*)(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, at::IntArrayRef size, at::Layout layout)>(at::native::_validate_sparse_compressed_tensor_args)); |
| 23579 | m.impl("_validate_sparse_csr_tensor_args", static_cast<void (*)(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size)>(at::native::_validate_sparse_csr_tensor_args)); |
| 23580 | m.impl("_validate_sparse_csc_tensor_args", static_cast<void (*)(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size)>(at::native::_validate_sparse_csc_tensor_args)); |
| 23581 | m.impl("_to_cpu", static_cast<::std::vector<at::Tensor> (*)(at::TensorList tensors)>(at::native::_to_cpu)); |
| 23582 | m.impl("fake_quantize_per_tensor_affine_cachemask_backward", static_cast<at::Tensor (*)(const at::Tensor & grad, const at::Tensor & mask)>(at::native::fake_quantize_per_tensor_affine_cachemask_backward)); |
| 23583 | m.impl("fake_quantize_per_channel_affine_cachemask_backward", static_cast<at::Tensor (*)(const at::Tensor & grad, const at::Tensor & mask)>(at::native::fake_quantize_per_channel_affine_cachemask_backward)); |
| 23584 | m.impl("rnn_tanh_cell", static_cast<at::Tensor (*)(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const c10::optional<at::Tensor> & b_ih, const c10::optional<at::Tensor> & b_hh)>(at::native::rnn_tanh_cell)); |
| 23585 | m.impl("set_.source_Tensor_storage_offset", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Tensor & source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride)>(at::native::set__symint)); |
| 23586 | m.impl("__xor__.Scalar", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Scalar & other)>(at::native::__xor__)); |
| 23587 | m.impl("__xor__.Tensor", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other)>(at::native::__xor__)); |
| 23588 | m.impl("__ixor__.Scalar", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Scalar & other)>(at::native::__ixor__)); |
| 23589 | m.impl("__ixor__.Tensor", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Tensor & other)>(at::native::__ixor__)); |
| 23590 | m.impl("trace_backward", static_cast<at::Tensor (*)(const at::Tensor & grad, c10::SymIntArrayRef sizes)>(at::native::trace_backward_symint)); |
| 23591 | m.impl("masked_select_backward", static_cast<at::Tensor (*)(const at::Tensor & grad, const at::Tensor & input, const at::Tensor & mask)>(at::native::masked_select_backward)); |
| 23592 | m.impl("argwhere", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::argwhere)); |
| 23593 | m.impl("cross_entropy_loss", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, double label_smoothing)>(at::native::cross_entropy_loss_symint)); |
| 23594 | m.impl("_pad_enum", static_cast<at::Tensor (*)(const at::Tensor & self, c10::SymIntArrayRef pad, int64_t mode, c10::optional<double> value)>(at::native::_pad_enum_symint)); |
| 23595 | m.impl("pad", static_cast<at::Tensor (*)(const at::Tensor & self, c10::SymIntArrayRef pad, c10::string_view mode, c10::optional<double> value)>(at::native::pad_symint)); |
| 23596 | m.impl("upsample_nearest2d.vec", static_cast<at::Tensor (*)(const at::Tensor & input, at::OptionalIntArrayRef output_size, c10::optional<at::ArrayRef<double>> scale_factors)>(at::native::upsample_nearest2d)); |
| 23597 | m.impl("_upsample_nearest_exact2d.vec", static_cast<at::Tensor (*)(const at::Tensor & input, at::OptionalIntArrayRef output_size, c10::optional<at::ArrayRef<double>> scale_factors)>(at::native::_upsample_nearest_exact2d)); |
| 23598 | m.impl("_add_batch_dim", static_cast<at::Tensor (*)(const at::Tensor & self, int64_t batch_dim, int64_t level)>(at::native::_add_batch_dim)); |
| 23599 | m.impl("_remove_batch_dim", static_cast<at::Tensor (*)(const at::Tensor & self, int64_t level, int64_t batch_size, int64_t out_dim)>(at::native::_remove_batch_dim)); |
| 23600 | m.impl("fft_ifftshift", static_cast<at::Tensor (*)(const at::Tensor & self, at::OptionalIntArrayRef dim)>(at::native::fft_ifftshift)); |
| 23601 | m.impl("_test_serialization_subcmul", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha)>(at::native::_test_serialization_subcmul)); |
| 23602 | m.impl("unflatten_dense_tensors", static_cast<::std::vector<at::Tensor> (*)(const at::Tensor & flat, at::TensorList tensors)>(at::native::unflatten_dense_tensors));; |
| 23603 | } |
| 23604 | |
| 23605 | } // namespace |
| 23606 | |
| 23607 | } // namespace at |
| 23608 |
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