| 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/_masked_scale_native.h> |
| 22 | #include <ATen/ops/_masked_scale_ops.h> |
| 23 | #include <ATen/ops/_masked_scale_native.h> |
| 24 | #include <ATen/ops/_masked_scale_ops.h> |
| 25 | #include <ATen/ops/native_dropout_native.h> |
| 26 | #include <ATen/ops/native_dropout_ops.h> |
| 27 | #include <ATen/ops/native_dropout_native.h> |
| 28 | #include <ATen/ops/native_dropout_ops.h> |
| 29 | #include <ATen/ops/native_dropout_backward_native.h> |
| 30 | #include <ATen/ops/native_dropout_backward_ops.h> |
| 31 | #include <ATen/ops/native_dropout_backward_native.h> |
| 32 | #include <ATen/ops/native_dropout_backward_ops.h> |
| 33 | #include <ATen/ops/asinh_native.h> |
| 34 | #include <ATen/ops/asinh_ops.h> |
| 35 | #include <ATen/ops/asinh_native.h> |
| 36 | #include <ATen/ops/asinh_ops.h> |
| 37 | #include <ATen/ops/asinh_native.h> |
| 38 | #include <ATen/ops/asinh_ops.h> |
| 39 | #include <ATen/ops/arctanh_native.h> |
| 40 | #include <ATen/ops/arctanh_ops.h> |
| 41 | #include <ATen/ops/arctanh_native.h> |
| 42 | #include <ATen/ops/arctanh_ops.h> |
| 43 | #include <ATen/ops/arctanh_native.h> |
| 44 | #include <ATen/ops/arctanh_ops.h> |
| 45 | #include <ATen/ops/baddbmm_native.h> |
| 46 | #include <ATen/ops/baddbmm_ops.h> |
| 47 | #include <ATen/ops/baddbmm_native.h> |
| 48 | #include <ATen/ops/baddbmm_ops.h> |
| 49 | #include <ATen/ops/baddbmm_native.h> |
| 50 | #include <ATen/ops/baddbmm_ops.h> |
| 51 | #include <ATen/ops/quantized_batch_norm_native.h> |
| 52 | #include <ATen/ops/quantized_batch_norm_ops.h> |
| 53 | #include <ATen/ops/quantized_batch_norm_native.h> |
| 54 | #include <ATen/ops/quantized_batch_norm_ops.h> |
| 55 | #include <ATen/ops/bernoulli_native.h> |
| 56 | #include <ATen/ops/bernoulli_ops.h> |
| 57 | #include <ATen/ops/bernoulli_native.h> |
| 58 | #include <ATen/ops/bernoulli_ops.h> |
| 59 | #include <ATen/ops/bernoulli_native.h> |
| 60 | #include <ATen/ops/bernoulli_ops.h> |
| 61 | #include <ATen/ops/bernoulli_native.h> |
| 62 | #include <ATen/ops/bernoulli_ops.h> |
| 63 | #include <ATen/ops/bernoulli_native.h> |
| 64 | #include <ATen/ops/bernoulli_ops.h> |
| 65 | #include <ATen/ops/bernoulli_native.h> |
| 66 | #include <ATen/ops/bernoulli_ops.h> |
| 67 | #include <ATen/ops/bernoulli_native.h> |
| 68 | #include <ATen/ops/bernoulli_ops.h> |
| 69 | #include <ATen/ops/bernoulli_native.h> |
| 70 | #include <ATen/ops/bernoulli_ops.h> |
| 71 | #include <ATen/ops/bmm_native.h> |
| 72 | #include <ATen/ops/bmm_ops.h> |
| 73 | #include <ATen/ops/bmm_native.h> |
| 74 | #include <ATen/ops/bmm_ops.h> |
| 75 | #include <ATen/ops/clamp_max_native.h> |
| 76 | #include <ATen/ops/clamp_max_ops.h> |
| 77 | #include <ATen/ops/clamp_max_native.h> |
| 78 | #include <ATen/ops/clamp_max_ops.h> |
| 79 | #include <ATen/ops/clamp_max_native.h> |
| 80 | #include <ATen/ops/clamp_max_ops.h> |
| 81 | #include <ATen/ops/clamp_max_native.h> |
| 82 | #include <ATen/ops/clamp_max_ops.h> |
| 83 | #include <ATen/ops/clamp_max_native.h> |
| 84 | #include <ATen/ops/clamp_max_ops.h> |
| 85 | #include <ATen/ops/clamp_max_native.h> |
| 86 | #include <ATen/ops/clamp_max_ops.h> |
| 87 | #include <ATen/ops/clamp_min_native.h> |
| 88 | #include <ATen/ops/clamp_min_ops.h> |
| 89 | #include <ATen/ops/clamp_min_native.h> |
| 90 | #include <ATen/ops/clamp_min_ops.h> |
| 91 | #include <ATen/ops/clamp_min_native.h> |
| 92 | #include <ATen/ops/clamp_min_ops.h> |
| 93 | #include <ATen/ops/clamp_min_native.h> |
| 94 | #include <ATen/ops/clamp_min_ops.h> |
| 95 | #include <ATen/ops/clamp_min_native.h> |
| 96 | #include <ATen/ops/clamp_min_ops.h> |
| 97 | #include <ATen/ops/clamp_min_native.h> |
| 98 | #include <ATen/ops/clamp_min_ops.h> |
| 99 | #include <ATen/ops/clip_native.h> |
| 100 | #include <ATen/ops/clip_ops.h> |
| 101 | #include <ATen/ops/clip_native.h> |
| 102 | #include <ATen/ops/clip_ops.h> |
| 103 | #include <ATen/ops/clip_native.h> |
| 104 | #include <ATen/ops/clip_ops.h> |
| 105 | #include <ATen/ops/clip_native.h> |
| 106 | #include <ATen/ops/clip_ops.h> |
| 107 | #include <ATen/ops/clip_native.h> |
| 108 | #include <ATen/ops/clip_ops.h> |
| 109 | #include <ATen/ops/clip_native.h> |
| 110 | #include <ATen/ops/clip_ops.h> |
| 111 | #include <ATen/ops/complex_native.h> |
| 112 | #include <ATen/ops/complex_ops.h> |
| 113 | #include <ATen/ops/complex_native.h> |
| 114 | #include <ATen/ops/complex_ops.h> |
| 115 | #include <ATen/ops/constant_pad_nd_native.h> |
| 116 | #include <ATen/ops/constant_pad_nd_ops.h> |
| 117 | #include <ATen/ops/constant_pad_nd_native.h> |
| 118 | #include <ATen/ops/constant_pad_nd_ops.h> |
| 119 | #include <ATen/ops/conv_tbc_native.h> |
| 120 | #include <ATen/ops/conv_tbc_ops.h> |
| 121 | #include <ATen/ops/conv_tbc_native.h> |
| 122 | #include <ATen/ops/conv_tbc_ops.h> |
| 123 | #include <ATen/ops/_copy_from_and_resize_native.h> |
| 124 | #include <ATen/ops/_copy_from_and_resize_ops.h> |
| 125 | #include <ATen/ops/_copy_from_and_resize_native.h> |
| 126 | #include <ATen/ops/_copy_from_and_resize_ops.h> |
| 127 | #include <ATen/ops/cos_native.h> |
| 128 | #include <ATen/ops/cos_ops.h> |
| 129 | #include <ATen/ops/cos_native.h> |
| 130 | #include <ATen/ops/cos_ops.h> |
| 131 | #include <ATen/ops/cos_native.h> |
| 132 | #include <ATen/ops/cos_ops.h> |
| 133 | #include <ATen/ops/count_nonzero_native.h> |
| 134 | #include <ATen/ops/count_nonzero_ops.h> |
| 135 | #include <ATen/ops/count_nonzero_native.h> |
| 136 | #include <ATen/ops/count_nonzero_ops.h> |
| 137 | #include <ATen/ops/count_nonzero_native.h> |
| 138 | #include <ATen/ops/count_nonzero_ops.h> |
| 139 | #include <ATen/ops/count_nonzero_native.h> |
| 140 | #include <ATen/ops/count_nonzero_ops.h> |
| 141 | #include <ATen/ops/cudnn_affine_grid_generator_backward_native.h> |
| 142 | #include <ATen/ops/cudnn_affine_grid_generator_backward_ops.h> |
| 143 | #include <ATen/ops/cudnn_affine_grid_generator_backward_native.h> |
| 144 | #include <ATen/ops/cudnn_affine_grid_generator_backward_ops.h> |
| 145 | #include <ATen/ops/cudnn_batch_norm_native.h> |
| 146 | #include <ATen/ops/cudnn_batch_norm_ops.h> |
| 147 | #include <ATen/ops/cudnn_batch_norm_native.h> |
| 148 | #include <ATen/ops/cudnn_batch_norm_ops.h> |
| 149 | #include <ATen/ops/mps_convolution_transpose_backward_native.h> |
| 150 | #include <ATen/ops/mps_convolution_transpose_backward_ops.h> |
| 151 | #include <ATen/ops/mps_convolution_transpose_backward_native.h> |
| 152 | #include <ATen/ops/mps_convolution_transpose_backward_ops.h> |
| 153 | #include <ATen/ops/cudnn_grid_sampler_backward_native.h> |
| 154 | #include <ATen/ops/cudnn_grid_sampler_backward_ops.h> |
| 155 | #include <ATen/ops/cudnn_grid_sampler_backward_native.h> |
| 156 | #include <ATen/ops/cudnn_grid_sampler_backward_ops.h> |
| 157 | #include <ATen/ops/cummin_native.h> |
| 158 | #include <ATen/ops/cummin_ops.h> |
| 159 | #include <ATen/ops/cummin_native.h> |
| 160 | #include <ATen/ops/cummin_ops.h> |
| 161 | #include <ATen/ops/cummin_native.h> |
| 162 | #include <ATen/ops/cummin_ops.h> |
| 163 | #include <ATen/ops/cummin_native.h> |
| 164 | #include <ATen/ops/cummin_ops.h> |
| 165 | #include <ATen/ops/cumsum_native.h> |
| 166 | #include <ATen/ops/cumsum_ops.h> |
| 167 | #include <ATen/ops/cumsum_native.h> |
| 168 | #include <ATen/ops/cumsum_ops.h> |
| 169 | #include <ATen/ops/cumsum_native.h> |
| 170 | #include <ATen/ops/cumsum_ops.h> |
| 171 | #include <ATen/ops/cumsum_native.h> |
| 172 | #include <ATen/ops/cumsum_ops.h> |
| 173 | #include <ATen/ops/cumsum_native.h> |
| 174 | #include <ATen/ops/cumsum_ops.h> |
| 175 | #include <ATen/ops/cumsum_native.h> |
| 176 | #include <ATen/ops/cumsum_ops.h> |
| 177 | #include <ATen/ops/_ctc_loss_native.h> |
| 178 | #include <ATen/ops/_ctc_loss_ops.h> |
| 179 | #include <ATen/ops/_ctc_loss_native.h> |
| 180 | #include <ATen/ops/_ctc_loss_ops.h> |
| 181 | #include <ATen/ops/_ctc_loss_native.h> |
| 182 | #include <ATen/ops/_ctc_loss_ops.h> |
| 183 | #include <ATen/ops/_ctc_loss_native.h> |
| 184 | #include <ATen/ops/_ctc_loss_ops.h> |
| 185 | #include <ATen/ops/_ctc_loss_backward_native.h> |
| 186 | #include <ATen/ops/_ctc_loss_backward_ops.h> |
| 187 | #include <ATen/ops/_ctc_loss_backward_native.h> |
| 188 | #include <ATen/ops/_ctc_loss_backward_ops.h> |
| 189 | #include <ATen/ops/embedding_native.h> |
| 190 | #include <ATen/ops/embedding_ops.h> |
| 191 | #include <ATen/ops/embedding_native.h> |
| 192 | #include <ATen/ops/embedding_ops.h> |
| 193 | #include <ATen/ops/embedding_dense_backward_native.h> |
| 194 | #include <ATen/ops/embedding_dense_backward_ops.h> |
| 195 | #include <ATen/ops/embedding_dense_backward_native.h> |
| 196 | #include <ATen/ops/embedding_dense_backward_ops.h> |
| 197 | #include <ATen/ops/new_zeros_native.h> |
| 198 | #include <ATen/ops/new_zeros_ops.h> |
| 199 | #include <ATen/ops/new_zeros_native.h> |
| 200 | #include <ATen/ops/new_zeros_ops.h> |
| 201 | #include <ATen/ops/new_ones_native.h> |
| 202 | #include <ATen/ops/new_ones_ops.h> |
| 203 | #include <ATen/ops/new_ones_native.h> |
| 204 | #include <ATen/ops/new_ones_ops.h> |
| 205 | #include <ATen/ops/_empty_per_channel_affine_quantized_native.h> |
| 206 | #include <ATen/ops/_empty_per_channel_affine_quantized_ops.h> |
| 207 | #include <ATen/ops/_empty_per_channel_affine_quantized_native.h> |
| 208 | #include <ATen/ops/_empty_per_channel_affine_quantized_ops.h> |
| 209 | #include <ATen/ops/resize_native.h> |
| 210 | #include <ATen/ops/resize_ops.h> |
| 211 | #include <ATen/ops/resize_native.h> |
| 212 | #include <ATen/ops/resize_ops.h> |
| 213 | #include <ATen/ops/resize_native.h> |
| 214 | #include <ATen/ops/resize_ops.h> |
| 215 | #include <ATen/ops/exp2_native.h> |
| 216 | #include <ATen/ops/exp2_ops.h> |
| 217 | #include <ATen/ops/exp2_native.h> |
| 218 | #include <ATen/ops/exp2_ops.h> |
| 219 | #include <ATen/ops/exp2_native.h> |
| 220 | #include <ATen/ops/exp2_ops.h> |
| 221 | #include <ATen/ops/_grid_sampler_2d_cpu_fallback_native.h> |
| 222 | #include <ATen/ops/_grid_sampler_2d_cpu_fallback_ops.h> |
| 223 | #include <ATen/ops/_grid_sampler_2d_cpu_fallback_native.h> |
| 224 | #include <ATen/ops/_grid_sampler_2d_cpu_fallback_ops.h> |
| 225 | #include <ATen/ops/grid_sampler_3d_backward_native.h> |
| 226 | #include <ATen/ops/grid_sampler_3d_backward_ops.h> |
| 227 | #include <ATen/ops/grid_sampler_3d_backward_native.h> |
| 228 | #include <ATen/ops/grid_sampler_3d_backward_ops.h> |
| 229 | #include <ATen/ops/_fft_c2c_native.h> |
| 230 | #include <ATen/ops/_fft_c2c_ops.h> |
| 231 | #include <ATen/ops/_fft_c2c_native.h> |
| 232 | #include <ATen/ops/_fft_c2c_ops.h> |
| 233 | #include <ATen/ops/index_copy_native.h> |
| 234 | #include <ATen/ops/index_copy_ops.h> |
| 235 | #include <ATen/ops/index_copy_native.h> |
| 236 | #include <ATen/ops/index_copy_ops.h> |
| 237 | #include <ATen/ops/index_copy_native.h> |
| 238 | #include <ATen/ops/index_copy_ops.h> |
| 239 | #include <ATen/ops/isin_native.h> |
| 240 | #include <ATen/ops/isin_ops.h> |
| 241 | #include <ATen/ops/isin_native.h> |
| 242 | #include <ATen/ops/isin_ops.h> |
| 243 | #include <ATen/ops/isin_native.h> |
| 244 | #include <ATen/ops/isin_ops.h> |
| 245 | #include <ATen/ops/isin_native.h> |
| 246 | #include <ATen/ops/isin_ops.h> |
| 247 | #include <ATen/ops/isin_native.h> |
| 248 | #include <ATen/ops/isin_ops.h> |
| 249 | #include <ATen/ops/isin_native.h> |
| 250 | #include <ATen/ops/isin_ops.h> |
| 251 | #include <ATen/ops/kron_native.h> |
| 252 | #include <ATen/ops/kron_ops.h> |
| 253 | #include <ATen/ops/kron_native.h> |
| 254 | #include <ATen/ops/kron_ops.h> |
| 255 | #include <ATen/ops/nan_to_num_native.h> |
| 256 | #include <ATen/ops/nan_to_num_ops.h> |
| 257 | #include <ATen/ops/nan_to_num_native.h> |
| 258 | #include <ATen/ops/nan_to_num_ops.h> |
| 259 | #include <ATen/ops/nan_to_num_native.h> |
| 260 | #include <ATen/ops/nan_to_num_ops.h> |
| 261 | #include <ATen/ops/linear_native.h> |
| 262 | #include <ATen/ops/linear_ops.h> |
| 263 | #include <ATen/ops/linear_native.h> |
| 264 | #include <ATen/ops/linear_ops.h> |
| 265 | #include <ATen/ops/mkldnn_linear_native.h> |
| 266 | #include <ATen/ops/mkldnn_linear_ops.h> |
| 267 | #include <ATen/ops/mkldnn_linear_native.h> |
| 268 | #include <ATen/ops/mkldnn_linear_ops.h> |
| 269 | #include <ATen/ops/linspace_native.h> |
| 270 | #include <ATen/ops/linspace_ops.h> |
| 271 | #include <ATen/ops/linspace_native.h> |
| 272 | #include <ATen/ops/linspace_ops.h> |
| 273 | #include <ATen/ops/log_native.h> |
| 274 | #include <ATen/ops/log_ops.h> |
| 275 | #include <ATen/ops/log_native.h> |
| 276 | #include <ATen/ops/log_ops.h> |
| 277 | #include <ATen/ops/log_native.h> |
| 278 | #include <ATen/ops/log_ops.h> |
| 279 | #include <ATen/ops/log_softmax_native.h> |
| 280 | #include <ATen/ops/log_softmax_ops.h> |
| 281 | #include <ATen/ops/log_softmax_native.h> |
| 282 | #include <ATen/ops/log_softmax_ops.h> |
| 283 | #include <ATen/ops/_log_softmax_native.h> |
| 284 | #include <ATen/ops/_log_softmax_ops.h> |
| 285 | #include <ATen/ops/_log_softmax_native.h> |
| 286 | #include <ATen/ops/_log_softmax_ops.h> |
| 287 | #include <ATen/ops/_log_softmax_backward_data_native.h> |
| 288 | #include <ATen/ops/_log_softmax_backward_data_ops.h> |
| 289 | #include <ATen/ops/_log_softmax_backward_data_native.h> |
| 290 | #include <ATen/ops/_log_softmax_backward_data_ops.h> |
| 291 | #include <ATen/ops/max_native.h> |
| 292 | #include <ATen/ops/max_ops.h> |
| 293 | #include <ATen/ops/max_native.h> |
| 294 | #include <ATen/ops/max_ops.h> |
| 295 | #include <ATen/ops/max_native.h> |
| 296 | #include <ATen/ops/max_ops.h> |
| 297 | #include <ATen/ops/max_native.h> |
| 298 | #include <ATen/ops/max_ops.h> |
| 299 | #include <ATen/ops/amax_native.h> |
| 300 | #include <ATen/ops/amax_ops.h> |
| 301 | #include <ATen/ops/amax_native.h> |
| 302 | #include <ATen/ops/amax_ops.h> |
| 303 | #include <ATen/ops/mkldnn_max_pool2d_backward_native.h> |
| 304 | #include <ATen/ops/mkldnn_max_pool2d_backward_ops.h> |
| 305 | #include <ATen/ops/mkldnn_max_pool2d_backward_native.h> |
| 306 | #include <ATen/ops/mkldnn_max_pool2d_backward_ops.h> |
| 307 | #include <ATen/ops/mkldnn_max_pool3d_native.h> |
| 308 | #include <ATen/ops/mkldnn_max_pool3d_ops.h> |
| 309 | #include <ATen/ops/mkldnn_max_pool3d_native.h> |
| 310 | #include <ATen/ops/mkldnn_max_pool3d_ops.h> |
| 311 | #include <ATen/ops/quantized_max_pool2d_native.h> |
| 312 | #include <ATen/ops/quantized_max_pool2d_ops.h> |
| 313 | #include <ATen/ops/quantized_max_pool2d_native.h> |
| 314 | #include <ATen/ops/quantized_max_pool2d_ops.h> |
| 315 | #include <ATen/ops/mean_native.h> |
| 316 | #include <ATen/ops/mean_ops.h> |
| 317 | #include <ATen/ops/mean_native.h> |
| 318 | #include <ATen/ops/mean_ops.h> |
| 319 | #include <ATen/ops/mean_native.h> |
| 320 | #include <ATen/ops/mean_ops.h> |
| 321 | #include <ATen/ops/mean_native.h> |
| 322 | #include <ATen/ops/mean_ops.h> |
| 323 | #include <ATen/ops/nanmedian_native.h> |
| 324 | #include <ATen/ops/nanmedian_ops.h> |
| 325 | #include <ATen/ops/nanmedian_native.h> |
| 326 | #include <ATen/ops/nanmedian_ops.h> |
| 327 | #include <ATen/ops/nanmedian_native.h> |
| 328 | #include <ATen/ops/nanmedian_ops.h> |
| 329 | #include <ATen/ops/nanmedian_native.h> |
| 330 | #include <ATen/ops/nanmedian_ops.h> |
| 331 | #include <ATen/ops/nanmedian_native.h> |
| 332 | #include <ATen/ops/nanmedian_ops.h> |
| 333 | #include <ATen/ops/nanmedian_native.h> |
| 334 | #include <ATen/ops/nanmedian_ops.h> |
| 335 | #include <ATen/ops/mode_native.h> |
| 336 | #include <ATen/ops/mode_ops.h> |
| 337 | #include <ATen/ops/mode_native.h> |
| 338 | #include <ATen/ops/mode_ops.h> |
| 339 | #include <ATen/ops/mode_native.h> |
| 340 | #include <ATen/ops/mode_ops.h> |
| 341 | #include <ATen/ops/mode_native.h> |
| 342 | #include <ATen/ops/mode_ops.h> |
| 343 | #include <ATen/ops/multiply_native.h> |
| 344 | #include <ATen/ops/multiply_ops.h> |
| 345 | #include <ATen/ops/multiply_native.h> |
| 346 | #include <ATen/ops/multiply_ops.h> |
| 347 | #include <ATen/ops/multiply_native.h> |
| 348 | #include <ATen/ops/multiply_ops.h> |
| 349 | #include <ATen/ops/narrow_copy_native.h> |
| 350 | #include <ATen/ops/narrow_copy_ops.h> |
| 351 | #include <ATen/ops/narrow_copy_native.h> |
| 352 | #include <ATen/ops/narrow_copy_ops.h> |
| 353 | #include <ATen/ops/batch_norm_gather_stats_native.h> |
| 354 | #include <ATen/ops/batch_norm_gather_stats_ops.h> |
| 355 | #include <ATen/ops/batch_norm_gather_stats_native.h> |
| 356 | #include <ATen/ops/batch_norm_gather_stats_ops.h> |
| 357 | #include <ATen/ops/batch_norm_gather_stats_with_counts_native.h> |
| 358 | #include <ATen/ops/batch_norm_gather_stats_with_counts_ops.h> |
| 359 | #include <ATen/ops/batch_norm_gather_stats_with_counts_native.h> |
| 360 | #include <ATen/ops/batch_norm_gather_stats_with_counts_ops.h> |
| 361 | #include <ATen/ops/native_batch_norm_backward_native.h> |
| 362 | #include <ATen/ops/native_batch_norm_backward_ops.h> |
| 363 | #include <ATen/ops/native_batch_norm_backward_native.h> |
| 364 | #include <ATen/ops/native_batch_norm_backward_ops.h> |
| 365 | #include <ATen/ops/ones_native.h> |
| 366 | #include <ATen/ops/ones_ops.h> |
| 367 | #include <ATen/ops/ones_native.h> |
| 368 | #include <ATen/ops/ones_ops.h> |
| 369 | #include <ATen/ops/ones_native.h> |
| 370 | #include <ATen/ops/ones_ops.h> |
| 371 | #include <ATen/ops/ones_native.h> |
| 372 | #include <ATen/ops/ones_ops.h> |
| 373 | #include <ATen/ops/_pdist_forward_native.h> |
| 374 | #include <ATen/ops/_pdist_forward_ops.h> |
| 375 | #include <ATen/ops/_pdist_forward_native.h> |
| 376 | #include <ATen/ops/_pdist_forward_ops.h> |
| 377 | #include <ATen/ops/_pdist_backward_native.h> |
| 378 | #include <ATen/ops/_pdist_backward_ops.h> |
| 379 | #include <ATen/ops/_pdist_backward_native.h> |
| 380 | #include <ATen/ops/_pdist_backward_ops.h> |
| 381 | #include <ATen/ops/pixel_shuffle_native.h> |
| 382 | #include <ATen/ops/pixel_shuffle_ops.h> |
| 383 | #include <ATen/ops/pixel_shuffle_native.h> |
| 384 | #include <ATen/ops/pixel_shuffle_ops.h> |
| 385 | #include <ATen/ops/_pin_memory_native.h> |
| 386 | #include <ATen/ops/_pin_memory_ops.h> |
| 387 | #include <ATen/ops/_pin_memory_native.h> |
| 388 | #include <ATen/ops/_pin_memory_ops.h> |
| 389 | #include <ATen/ops/randn_native.h> |
| 390 | #include <ATen/ops/randn_ops.h> |
| 391 | #include <ATen/ops/randn_native.h> |
| 392 | #include <ATen/ops/randn_ops.h> |
| 393 | #include <ATen/ops/randn_native.h> |
| 394 | #include <ATen/ops/randn_ops.h> |
| 395 | #include <ATen/ops/randn_native.h> |
| 396 | #include <ATen/ops/randn_ops.h> |
| 397 | #include <ATen/ops/randn_native.h> |
| 398 | #include <ATen/ops/randn_ops.h> |
| 399 | #include <ATen/ops/randn_native.h> |
| 400 | #include <ATen/ops/randn_ops.h> |
| 401 | #include <ATen/ops/randn_native.h> |
| 402 | #include <ATen/ops/randn_ops.h> |
| 403 | #include <ATen/ops/randn_native.h> |
| 404 | #include <ATen/ops/randn_ops.h> |
| 405 | #include <ATen/ops/randn_like_native.h> |
| 406 | #include <ATen/ops/randn_like_ops.h> |
| 407 | #include <ATen/ops/randn_like_native.h> |
| 408 | #include <ATen/ops/randn_like_ops.h> |
| 409 | #include <ATen/ops/neg_native.h> |
| 410 | #include <ATen/ops/neg_ops.h> |
| 411 | #include <ATen/ops/neg_native.h> |
| 412 | #include <ATen/ops/neg_ops.h> |
| 413 | #include <ATen/ops/neg_native.h> |
| 414 | #include <ATen/ops/neg_ops.h> |
| 415 | #include <ATen/ops/negative_native.h> |
| 416 | #include <ATen/ops/negative_ops.h> |
| 417 | #include <ATen/ops/negative_native.h> |
| 418 | #include <ATen/ops/negative_ops.h> |
| 419 | #include <ATen/ops/negative_native.h> |
| 420 | #include <ATen/ops/negative_ops.h> |
| 421 | #include <ATen/ops/repeat_interleave_native.h> |
| 422 | #include <ATen/ops/repeat_interleave_ops.h> |
| 423 | #include <ATen/ops/repeat_interleave_native.h> |
| 424 | #include <ATen/ops/repeat_interleave_ops.h> |
| 425 | #include <ATen/ops/gelu_native.h> |
| 426 | #include <ATen/ops/gelu_ops.h> |
| 427 | #include <ATen/ops/gelu_native.h> |
| 428 | #include <ATen/ops/gelu_ops.h> |
| 429 | #include <ATen/ops/gelu_native.h> |
| 430 | #include <ATen/ops/gelu_ops.h> |
| 431 | #include <ATen/ops/select_backward_native.h> |
| 432 | #include <ATen/ops/select_backward_ops.h> |
| 433 | #include <ATen/ops/select_backward_native.h> |
| 434 | #include <ATen/ops/select_backward_ops.h> |
| 435 | #include <ATen/ops/mish_native.h> |
| 436 | #include <ATen/ops/mish_ops.h> |
| 437 | #include <ATen/ops/mish_native.h> |
| 438 | #include <ATen/ops/mish_ops.h> |
| 439 | #include <ATen/ops/mish_native.h> |
| 440 | #include <ATen/ops/mish_ops.h> |
| 441 | #include <ATen/ops/slice_scatter_native.h> |
| 442 | #include <ATen/ops/slice_scatter_ops.h> |
| 443 | #include <ATen/ops/slice_scatter_native.h> |
| 444 | #include <ATen/ops/slice_scatter_ops.h> |
| 445 | #include <ATen/ops/diagonal_scatter_native.h> |
| 446 | #include <ATen/ops/diagonal_scatter_ops.h> |
| 447 | #include <ATen/ops/diagonal_scatter_native.h> |
| 448 | #include <ATen/ops/diagonal_scatter_ops.h> |
| 449 | #include <ATen/ops/_softmax_backward_data_native.h> |
| 450 | #include <ATen/ops/_softmax_backward_data_ops.h> |
| 451 | #include <ATen/ops/_softmax_backward_data_native.h> |
| 452 | #include <ATen/ops/_softmax_backward_data_ops.h> |
| 453 | #include <ATen/ops/unsafe_split_native.h> |
| 454 | #include <ATen/ops/unsafe_split_ops.h> |
| 455 | #include <ATen/ops/unsafe_split_native.h> |
| 456 | #include <ATen/ops/unsafe_split_ops.h> |
| 457 | #include <ATen/ops/unsafe_split_with_sizes_native.h> |
| 458 | #include <ATen/ops/unsafe_split_with_sizes_ops.h> |
| 459 | #include <ATen/ops/unsafe_split_with_sizes_native.h> |
| 460 | #include <ATen/ops/unsafe_split_with_sizes_ops.h> |
| 461 | #include <ATen/ops/square_native.h> |
| 462 | #include <ATen/ops/square_ops.h> |
| 463 | #include <ATen/ops/square_native.h> |
| 464 | #include <ATen/ops/square_ops.h> |
| 465 | #include <ATen/ops/square_native.h> |
| 466 | #include <ATen/ops/square_ops.h> |
| 467 | #include <ATen/ops/tanh_native.h> |
| 468 | #include <ATen/ops/tanh_ops.h> |
| 469 | #include <ATen/ops/tanh_native.h> |
| 470 | #include <ATen/ops/tanh_ops.h> |
| 471 | #include <ATen/ops/tanh_native.h> |
| 472 | #include <ATen/ops/tanh_ops.h> |
| 473 | #include <ATen/ops/roll_native.h> |
| 474 | #include <ATen/ops/roll_ops.h> |
| 475 | #include <ATen/ops/roll_native.h> |
| 476 | #include <ATen/ops/roll_ops.h> |
| 477 | #include <ATen/ops/rot90_native.h> |
| 478 | #include <ATen/ops/rot90_ops.h> |
| 479 | #include <ATen/ops/rot90_native.h> |
| 480 | #include <ATen/ops/rot90_ops.h> |
| 481 | #include <ATen/ops/_trilinear_native.h> |
| 482 | #include <ATen/ops/_trilinear_ops.h> |
| 483 | #include <ATen/ops/_trilinear_native.h> |
| 484 | #include <ATen/ops/_trilinear_ops.h> |
| 485 | #include <ATen/ops/_unique_native.h> |
| 486 | #include <ATen/ops/_unique_ops.h> |
| 487 | #include <ATen/ops/_unique_native.h> |
| 488 | #include <ATen/ops/_unique_ops.h> |
| 489 | #include <ATen/ops/_unique2_native.h> |
| 490 | #include <ATen/ops/_unique2_ops.h> |
| 491 | #include <ATen/ops/_unique2_native.h> |
| 492 | #include <ATen/ops/_unique2_ops.h> |
| 493 | #include <ATen/ops/_weight_norm_interface_native.h> |
| 494 | #include <ATen/ops/_weight_norm_interface_ops.h> |
| 495 | #include <ATen/ops/_weight_norm_interface_native.h> |
| 496 | #include <ATen/ops/_weight_norm_interface_ops.h> |
| 497 | #include <ATen/ops/_efficientzerotensor_native.h> |
| 498 | #include <ATen/ops/_efficientzerotensor_ops.h> |
| 499 | #include <ATen/ops/_efficientzerotensor_native.h> |
| 500 | #include <ATen/ops/_efficientzerotensor_ops.h> |
| 501 | #include <ATen/ops/_standard_gamma_native.h> |
| 502 | #include <ATen/ops/_standard_gamma_ops.h> |
| 503 | #include <ATen/ops/_standard_gamma_native.h> |
| 504 | #include <ATen/ops/_standard_gamma_ops.h> |
| 505 | #include <ATen/ops/_dirichlet_grad_native.h> |
| 506 | #include <ATen/ops/_dirichlet_grad_ops.h> |
| 507 | #include <ATen/ops/_dirichlet_grad_native.h> |
| 508 | #include <ATen/ops/_dirichlet_grad_ops.h> |
| 509 | #include <ATen/ops/norm_native.h> |
| 510 | #include <ATen/ops/norm_ops.h> |
| 511 | #include <ATen/ops/norm_native.h> |
| 512 | #include <ATen/ops/norm_ops.h> |
| 513 | #include <ATen/ops/norm_native.h> |
| 514 | #include <ATen/ops/norm_ops.h> |
| 515 | #include <ATen/ops/norm_native.h> |
| 516 | #include <ATen/ops/norm_ops.h> |
| 517 | #include <ATen/ops/norm_native.h> |
| 518 | #include <ATen/ops/norm_ops.h> |
| 519 | #include <ATen/ops/norm_native.h> |
| 520 | #include <ATen/ops/norm_ops.h> |
| 521 | #include <ATen/ops/norm_native.h> |
| 522 | #include <ATen/ops/norm_ops.h> |
| 523 | #include <ATen/ops/norm_native.h> |
| 524 | #include <ATen/ops/norm_ops.h> |
| 525 | #include <ATen/ops/norm_native.h> |
| 526 | #include <ATen/ops/norm_ops.h> |
| 527 | #include <ATen/ops/norm_native.h> |
| 528 | #include <ATen/ops/norm_ops.h> |
| 529 | #include <ATen/ops/norm_native.h> |
| 530 | #include <ATen/ops/norm_ops.h> |
| 531 | #include <ATen/ops/norm_native.h> |
| 532 | #include <ATen/ops/norm_ops.h> |
| 533 | #include <ATen/ops/frexp_native.h> |
| 534 | #include <ATen/ops/frexp_ops.h> |
| 535 | #include <ATen/ops/frexp_native.h> |
| 536 | #include <ATen/ops/frexp_ops.h> |
| 537 | #include <ATen/ops/frobenius_norm_native.h> |
| 538 | #include <ATen/ops/frobenius_norm_ops.h> |
| 539 | #include <ATen/ops/frobenius_norm_native.h> |
| 540 | #include <ATen/ops/frobenius_norm_ops.h> |
| 541 | #include <ATen/ops/nuclear_norm_native.h> |
| 542 | #include <ATen/ops/nuclear_norm_ops.h> |
| 543 | #include <ATen/ops/nuclear_norm_native.h> |
| 544 | #include <ATen/ops/nuclear_norm_ops.h> |
| 545 | #include <ATen/ops/nuclear_norm_native.h> |
| 546 | #include <ATen/ops/nuclear_norm_ops.h> |
| 547 | #include <ATen/ops/nuclear_norm_native.h> |
| 548 | #include <ATen/ops/nuclear_norm_ops.h> |
| 549 | #include <ATen/ops/subtract_native.h> |
| 550 | #include <ATen/ops/subtract_ops.h> |
| 551 | #include <ATen/ops/subtract_native.h> |
| 552 | #include <ATen/ops/subtract_ops.h> |
| 553 | #include <ATen/ops/subtract_native.h> |
| 554 | #include <ATen/ops/subtract_ops.h> |
| 555 | #include <ATen/ops/sparse_sampled_addmm_native.h> |
| 556 | #include <ATen/ops/sparse_sampled_addmm_ops.h> |
| 557 | #include <ATen/ops/sparse_sampled_addmm_native.h> |
| 558 | #include <ATen/ops/sparse_sampled_addmm_ops.h> |
| 559 | #include <ATen/ops/_addmm_activation_native.h> |
| 560 | #include <ATen/ops/_addmm_activation_ops.h> |
| 561 | #include <ATen/ops/_addmm_activation_native.h> |
| 562 | #include <ATen/ops/_addmm_activation_ops.h> |
| 563 | #include <ATen/ops/_to_dense_native.h> |
| 564 | #include <ATen/ops/_to_dense_ops.h> |
| 565 | #include <ATen/ops/_to_dense_native.h> |
| 566 | #include <ATen/ops/_to_dense_ops.h> |
| 567 | #include <ATen/ops/_coalesced_native.h> |
| 568 | #include <ATen/ops/_coalesced_ops.h> |
| 569 | #include <ATen/ops/_coalesced_native.h> |
| 570 | #include <ATen/ops/_coalesced_ops.h> |
| 571 | #include <ATen/ops/_coalesced_native.h> |
| 572 | #include <ATen/ops/_coalesced_ops.h> |
| 573 | #include <ATen/ops/to_sparse_csr_native.h> |
| 574 | #include <ATen/ops/to_sparse_csr_ops.h> |
| 575 | #include <ATen/ops/to_sparse_csr_native.h> |
| 576 | #include <ATen/ops/to_sparse_csr_ops.h> |
| 577 | #include <ATen/ops/to_sparse_csc_native.h> |
| 578 | #include <ATen/ops/to_sparse_csc_ops.h> |
| 579 | #include <ATen/ops/to_sparse_csc_native.h> |
| 580 | #include <ATen/ops/to_sparse_csc_ops.h> |
| 581 | #include <ATen/ops/to_sparse_bsc_native.h> |
| 582 | #include <ATen/ops/to_sparse_bsc_ops.h> |
| 583 | #include <ATen/ops/to_sparse_bsc_native.h> |
| 584 | #include <ATen/ops/to_sparse_bsc_ops.h> |
| 585 | #include <ATen/ops/quantize_per_tensor_dynamic_native.h> |
| 586 | #include <ATen/ops/quantize_per_tensor_dynamic_ops.h> |
| 587 | #include <ATen/ops/quantize_per_tensor_dynamic_native.h> |
| 588 | #include <ATen/ops/quantize_per_tensor_dynamic_ops.h> |
| 589 | #include <ATen/ops/quantize_per_tensor_native.h> |
| 590 | #include <ATen/ops/quantize_per_tensor_ops.h> |
| 591 | #include <ATen/ops/quantize_per_tensor_native.h> |
| 592 | #include <ATen/ops/quantize_per_tensor_ops.h> |
| 593 | #include <ATen/ops/quantize_per_tensor_native.h> |
| 594 | #include <ATen/ops/quantize_per_tensor_ops.h> |
| 595 | #include <ATen/ops/quantize_per_tensor_native.h> |
| 596 | #include <ATen/ops/quantize_per_tensor_ops.h> |
| 597 | #include <ATen/ops/quantize_per_tensor_native.h> |
| 598 | #include <ATen/ops/quantize_per_tensor_ops.h> |
| 599 | #include <ATen/ops/quantize_per_tensor_native.h> |
| 600 | #include <ATen/ops/quantize_per_tensor_ops.h> |
| 601 | #include <ATen/ops/quantize_per_channel_native.h> |
| 602 | #include <ATen/ops/quantize_per_channel_ops.h> |
| 603 | #include <ATen/ops/quantize_per_channel_native.h> |
| 604 | #include <ATen/ops/quantize_per_channel_ops.h> |
| 605 | #include <ATen/ops/_make_per_channel_quantized_tensor_native.h> |
| 606 | #include <ATen/ops/_make_per_channel_quantized_tensor_ops.h> |
| 607 | #include <ATen/ops/_make_per_channel_quantized_tensor_native.h> |
| 608 | #include <ATen/ops/_make_per_channel_quantized_tensor_ops.h> |
| 609 | #include <ATen/ops/fake_quantize_per_tensor_affine_cachemask_native.h> |
| 610 | #include <ATen/ops/fake_quantize_per_tensor_affine_cachemask_ops.h> |
| 611 | #include <ATen/ops/fake_quantize_per_tensor_affine_cachemask_native.h> |
| 612 | #include <ATen/ops/fake_quantize_per_tensor_affine_cachemask_ops.h> |
| 613 | #include <ATen/ops/_fake_quantize_per_tensor_affine_cachemask_tensor_qparams_native.h> |
| 614 | #include <ATen/ops/_fake_quantize_per_tensor_affine_cachemask_tensor_qparams_ops.h> |
| 615 | #include <ATen/ops/_fake_quantize_per_tensor_affine_cachemask_tensor_qparams_native.h> |
| 616 | #include <ATen/ops/_fake_quantize_per_tensor_affine_cachemask_tensor_qparams_ops.h> |
| 617 | #include <ATen/ops/_fake_quantize_learnable_per_tensor_affine_native.h> |
| 618 | #include <ATen/ops/_fake_quantize_learnable_per_tensor_affine_ops.h> |
| 619 | #include <ATen/ops/_fake_quantize_learnable_per_tensor_affine_native.h> |
| 620 | #include <ATen/ops/_fake_quantize_learnable_per_tensor_affine_ops.h> |
| 621 | #include <ATen/ops/fake_quantize_per_channel_affine_cachemask_native.h> |
| 622 | #include <ATen/ops/fake_quantize_per_channel_affine_cachemask_ops.h> |
| 623 | #include <ATen/ops/fake_quantize_per_channel_affine_cachemask_native.h> |
| 624 | #include <ATen/ops/fake_quantize_per_channel_affine_cachemask_ops.h> |
| 625 | #include <ATen/ops/lstm_mps_backward_native.h> |
| 626 | #include <ATen/ops/lstm_mps_backward_ops.h> |
| 627 | #include <ATen/ops/lstm_mps_backward_native.h> |
| 628 | #include <ATen/ops/lstm_mps_backward_ops.h> |
| 629 | #include <ATen/ops/lift_fresh_copy_native.h> |
| 630 | #include <ATen/ops/lift_fresh_copy_ops.h> |
| 631 | #include <ATen/ops/lift_fresh_copy_native.h> |
| 632 | #include <ATen/ops/lift_fresh_copy_ops.h> |
| 633 | #include <ATen/ops/_masked_softmax_backward_native.h> |
| 634 | #include <ATen/ops/_masked_softmax_backward_ops.h> |
| 635 | #include <ATen/ops/_masked_softmax_backward_native.h> |
| 636 | #include <ATen/ops/_masked_softmax_backward_ops.h> |
| 637 | #include <ATen/ops/put_native.h> |
| 638 | #include <ATen/ops/put_ops.h> |
| 639 | #include <ATen/ops/put_native.h> |
| 640 | #include <ATen/ops/put_ops.h> |
| 641 | #include <ATen/ops/put_native.h> |
| 642 | #include <ATen/ops/put_ops.h> |
| 643 | #include <ATen/ops/index_add_native.h> |
| 644 | #include <ATen/ops/index_add_ops.h> |
| 645 | #include <ATen/ops/index_add_native.h> |
| 646 | #include <ATen/ops/index_add_ops.h> |
| 647 | #include <ATen/ops/index_add_native.h> |
| 648 | #include <ATen/ops/index_add_ops.h> |
| 649 | #include <ATen/ops/index_fill_native.h> |
| 650 | #include <ATen/ops/index_fill_ops.h> |
| 651 | #include <ATen/ops/index_fill_native.h> |
| 652 | #include <ATen/ops/index_fill_ops.h> |
| 653 | #include <ATen/ops/index_fill_native.h> |
| 654 | #include <ATen/ops/index_fill_ops.h> |
| 655 | #include <ATen/ops/index_fill_native.h> |
| 656 | #include <ATen/ops/index_fill_ops.h> |
| 657 | #include <ATen/ops/index_fill_native.h> |
| 658 | #include <ATen/ops/index_fill_ops.h> |
| 659 | #include <ATen/ops/index_fill_native.h> |
| 660 | #include <ATen/ops/index_fill_ops.h> |
| 661 | #include <ATen/ops/scatter_native.h> |
| 662 | #include <ATen/ops/scatter_ops.h> |
| 663 | #include <ATen/ops/scatter_native.h> |
| 664 | #include <ATen/ops/scatter_ops.h> |
| 665 | #include <ATen/ops/scatter_native.h> |
| 666 | #include <ATen/ops/scatter_ops.h> |
| 667 | #include <ATen/ops/scatter_native.h> |
| 668 | #include <ATen/ops/scatter_ops.h> |
| 669 | #include <ATen/ops/scatter_native.h> |
| 670 | #include <ATen/ops/scatter_ops.h> |
| 671 | #include <ATen/ops/scatter_native.h> |
| 672 | #include <ATen/ops/scatter_ops.h> |
| 673 | #include <ATen/ops/scatter_native.h> |
| 674 | #include <ATen/ops/scatter_ops.h> |
| 675 | #include <ATen/ops/scatter_native.h> |
| 676 | #include <ATen/ops/scatter_ops.h> |
| 677 | #include <ATen/ops/scatter_native.h> |
| 678 | #include <ATen/ops/scatter_ops.h> |
| 679 | #include <ATen/ops/scatter_native.h> |
| 680 | #include <ATen/ops/scatter_ops.h> |
| 681 | #include <ATen/ops/scatter_native.h> |
| 682 | #include <ATen/ops/scatter_ops.h> |
| 683 | #include <ATen/ops/scatter_native.h> |
| 684 | #include <ATen/ops/scatter_ops.h> |
| 685 | #include <ATen/ops/scatter_add_native.h> |
| 686 | #include <ATen/ops/scatter_add_ops.h> |
| 687 | #include <ATen/ops/scatter_add_native.h> |
| 688 | #include <ATen/ops/scatter_add_ops.h> |
| 689 | #include <ATen/ops/scatter_add_native.h> |
| 690 | #include <ATen/ops/scatter_add_ops.h> |
| 691 | #include <ATen/ops/lshift_native.h> |
| 692 | #include <ATen/ops/lshift_ops.h> |
| 693 | #include <ATen/ops/lshift_native.h> |
| 694 | #include <ATen/ops/lshift_ops.h> |
| 695 | #include <ATen/ops/lshift_native.h> |
| 696 | #include <ATen/ops/lshift_ops.h> |
| 697 | #include <ATen/ops/lshift_native.h> |
| 698 | #include <ATen/ops/lshift_ops.h> |
| 699 | #include <ATen/ops/lshift_native.h> |
| 700 | #include <ATen/ops/lshift_ops.h> |
| 701 | #include <ATen/ops/lshift_native.h> |
| 702 | #include <ATen/ops/lshift_ops.h> |
| 703 | #include <ATen/ops/greater_equal_native.h> |
| 704 | #include <ATen/ops/greater_equal_ops.h> |
| 705 | #include <ATen/ops/greater_equal_native.h> |
| 706 | #include <ATen/ops/greater_equal_ops.h> |
| 707 | #include <ATen/ops/greater_equal_native.h> |
| 708 | #include <ATen/ops/greater_equal_ops.h> |
| 709 | #include <ATen/ops/greater_equal_native.h> |
| 710 | #include <ATen/ops/greater_equal_ops.h> |
| 711 | #include <ATen/ops/greater_equal_native.h> |
| 712 | #include <ATen/ops/greater_equal_ops.h> |
| 713 | #include <ATen/ops/greater_equal_native.h> |
| 714 | #include <ATen/ops/greater_equal_ops.h> |
| 715 | #include <ATen/ops/less_equal_native.h> |
| 716 | #include <ATen/ops/less_equal_ops.h> |
| 717 | #include <ATen/ops/less_equal_native.h> |
| 718 | #include <ATen/ops/less_equal_ops.h> |
| 719 | #include <ATen/ops/less_equal_native.h> |
| 720 | #include <ATen/ops/less_equal_ops.h> |
| 721 | #include <ATen/ops/less_equal_native.h> |
| 722 | #include <ATen/ops/less_equal_ops.h> |
| 723 | #include <ATen/ops/less_equal_native.h> |
| 724 | #include <ATen/ops/less_equal_ops.h> |
| 725 | #include <ATen/ops/less_equal_native.h> |
| 726 | #include <ATen/ops/less_equal_ops.h> |
| 727 | #include <ATen/ops/greater_native.h> |
| 728 | #include <ATen/ops/greater_ops.h> |
| 729 | #include <ATen/ops/greater_native.h> |
| 730 | #include <ATen/ops/greater_ops.h> |
| 731 | #include <ATen/ops/greater_native.h> |
| 732 | #include <ATen/ops/greater_ops.h> |
| 733 | #include <ATen/ops/greater_native.h> |
| 734 | #include <ATen/ops/greater_ops.h> |
| 735 | #include <ATen/ops/greater_native.h> |
| 736 | #include <ATen/ops/greater_ops.h> |
| 737 | #include <ATen/ops/greater_native.h> |
| 738 | #include <ATen/ops/greater_ops.h> |
| 739 | #include <ATen/ops/lt_native.h> |
| 740 | #include <ATen/ops/lt_ops.h> |
| 741 | #include <ATen/ops/lt_native.h> |
| 742 | #include <ATen/ops/lt_ops.h> |
| 743 | #include <ATen/ops/lt_native.h> |
| 744 | #include <ATen/ops/lt_ops.h> |
| 745 | #include <ATen/ops/lt_native.h> |
| 746 | #include <ATen/ops/lt_ops.h> |
| 747 | #include <ATen/ops/lt_native.h> |
| 748 | #include <ATen/ops/lt_ops.h> |
| 749 | #include <ATen/ops/lt_native.h> |
| 750 | #include <ATen/ops/lt_ops.h> |
| 751 | #include <ATen/ops/less_native.h> |
| 752 | #include <ATen/ops/less_ops.h> |
| 753 | #include <ATen/ops/less_native.h> |
| 754 | #include <ATen/ops/less_ops.h> |
| 755 | #include <ATen/ops/less_native.h> |
| 756 | #include <ATen/ops/less_ops.h> |
| 757 | #include <ATen/ops/less_native.h> |
| 758 | #include <ATen/ops/less_ops.h> |
| 759 | #include <ATen/ops/less_native.h> |
| 760 | #include <ATen/ops/less_ops.h> |
| 761 | #include <ATen/ops/less_native.h> |
| 762 | #include <ATen/ops/less_ops.h> |
| 763 | #include <ATen/ops/take_native.h> |
| 764 | #include <ATen/ops/take_ops.h> |
| 765 | #include <ATen/ops/take_native.h> |
| 766 | #include <ATen/ops/take_ops.h> |
| 767 | #include <ATen/ops/gather_native.h> |
| 768 | #include <ATen/ops/gather_ops.h> |
| 769 | #include <ATen/ops/gather_native.h> |
| 770 | #include <ATen/ops/gather_ops.h> |
| 771 | #include <ATen/ops/gather_native.h> |
| 772 | #include <ATen/ops/gather_ops.h> |
| 773 | #include <ATen/ops/gather_native.h> |
| 774 | #include <ATen/ops/gather_ops.h> |
| 775 | #include <ATen/ops/cholesky_native.h> |
| 776 | #include <ATen/ops/cholesky_ops.h> |
| 777 | #include <ATen/ops/cholesky_native.h> |
| 778 | #include <ATen/ops/cholesky_ops.h> |
| 779 | #include <ATen/ops/_cholesky_solve_helper_native.h> |
| 780 | #include <ATen/ops/_cholesky_solve_helper_ops.h> |
| 781 | #include <ATen/ops/_cholesky_solve_helper_native.h> |
| 782 | #include <ATen/ops/_cholesky_solve_helper_ops.h> |
| 783 | #include <ATen/ops/polygamma_native.h> |
| 784 | #include <ATen/ops/polygamma_ops.h> |
| 785 | #include <ATen/ops/polygamma_native.h> |
| 786 | #include <ATen/ops/polygamma_ops.h> |
| 787 | #include <ATen/ops/igamma_native.h> |
| 788 | #include <ATen/ops/igamma_ops.h> |
| 789 | #include <ATen/ops/igamma_native.h> |
| 790 | #include <ATen/ops/igamma_ops.h> |
| 791 | #include <ATen/ops/igamma_native.h> |
| 792 | #include <ATen/ops/igamma_ops.h> |
| 793 | #include <ATen/ops/fmin_native.h> |
| 794 | #include <ATen/ops/fmin_ops.h> |
| 795 | #include <ATen/ops/fmin_native.h> |
| 796 | #include <ATen/ops/fmin_ops.h> |
| 797 | #include <ATen/ops/max_native.h> |
| 798 | #include <ATen/ops/max_ops.h> |
| 799 | #include <ATen/ops/max_native.h> |
| 800 | #include <ATen/ops/max_ops.h> |
| 801 | #include <ATen/ops/fmax_native.h> |
| 802 | #include <ATen/ops/fmax_ops.h> |
| 803 | #include <ATen/ops/fmax_native.h> |
| 804 | #include <ATen/ops/fmax_ops.h> |
| 805 | #include <ATen/ops/maximum_native.h> |
| 806 | #include <ATen/ops/maximum_ops.h> |
| 807 | #include <ATen/ops/maximum_native.h> |
| 808 | #include <ATen/ops/maximum_ops.h> |
| 809 | #include <ATen/ops/max_native.h> |
| 810 | #include <ATen/ops/max_ops.h> |
| 811 | #include <ATen/ops/max_native.h> |
| 812 | #include <ATen/ops/max_ops.h> |
| 813 | #include <ATen/ops/_amp_foreach_non_finite_check_and_unscale_native.h> |
| 814 | #include <ATen/ops/_amp_foreach_non_finite_check_and_unscale_ops.h> |
| 815 | #include <ATen/ops/_amp_foreach_non_finite_check_and_unscale_native.h> |
| 816 | #include <ATen/ops/_amp_foreach_non_finite_check_and_unscale_ops.h> |
| 817 | #include <ATen/ops/_amp_foreach_non_finite_check_and_unscale_native.h> |
| 818 | #include <ATen/ops/_amp_foreach_non_finite_check_and_unscale_ops.h> |
| 819 | #include <ATen/ops/_foreach_sub_native.h> |
| 820 | #include <ATen/ops/_foreach_sub_ops.h> |
| 821 | #include <ATen/ops/_foreach_sub_native.h> |
| 822 | #include <ATen/ops/_foreach_sub_ops.h> |
| 823 | #include <ATen/ops/_foreach_sub_native.h> |
| 824 | #include <ATen/ops/_foreach_sub_ops.h> |
| 825 | #include <ATen/ops/_foreach_maximum_native.h> |
| 826 | #include <ATen/ops/_foreach_maximum_ops.h> |
| 827 | #include <ATen/ops/_foreach_maximum_native.h> |
| 828 | #include <ATen/ops/_foreach_maximum_ops.h> |
| 829 | #include <ATen/ops/_foreach_maximum_native.h> |
| 830 | #include <ATen/ops/_foreach_maximum_ops.h> |
| 831 | #include <ATen/ops/_foreach_sub_native.h> |
| 832 | #include <ATen/ops/_foreach_sub_ops.h> |
| 833 | #include <ATen/ops/_foreach_sub_native.h> |
| 834 | #include <ATen/ops/_foreach_sub_ops.h> |
| 835 | #include <ATen/ops/_foreach_sub_native.h> |
| 836 | #include <ATen/ops/_foreach_sub_ops.h> |
| 837 | #include <ATen/ops/_foreach_maximum_native.h> |
| 838 | #include <ATen/ops/_foreach_maximum_ops.h> |
| 839 | #include <ATen/ops/_foreach_maximum_native.h> |
| 840 | #include <ATen/ops/_foreach_maximum_ops.h> |
| 841 | #include <ATen/ops/_foreach_maximum_native.h> |
| 842 | #include <ATen/ops/_foreach_maximum_ops.h> |
| 843 | #include <ATen/ops/_foreach_sub_native.h> |
| 844 | #include <ATen/ops/_foreach_sub_ops.h> |
| 845 | #include <ATen/ops/_foreach_sub_native.h> |
| 846 | #include <ATen/ops/_foreach_sub_ops.h> |
| 847 | #include <ATen/ops/_foreach_sub_native.h> |
| 848 | #include <ATen/ops/_foreach_sub_ops.h> |
| 849 | #include <ATen/ops/_foreach_maximum_native.h> |
| 850 | #include <ATen/ops/_foreach_maximum_ops.h> |
| 851 | #include <ATen/ops/_foreach_maximum_native.h> |
| 852 | #include <ATen/ops/_foreach_maximum_ops.h> |
| 853 | #include <ATen/ops/_foreach_maximum_native.h> |
| 854 | #include <ATen/ops/_foreach_maximum_ops.h> |
| 855 | #include <ATen/ops/_foreach_exp_native.h> |
| 856 | #include <ATen/ops/_foreach_exp_ops.h> |
| 857 | #include <ATen/ops/_foreach_exp_native.h> |
| 858 | #include <ATen/ops/_foreach_exp_ops.h> |
| 859 | #include <ATen/ops/_foreach_exp_native.h> |
| 860 | #include <ATen/ops/_foreach_exp_ops.h> |
| 861 | #include <ATen/ops/_foreach_sqrt_native.h> |
| 862 | #include <ATen/ops/_foreach_sqrt_ops.h> |
| 863 | #include <ATen/ops/_foreach_sqrt_native.h> |
| 864 | #include <ATen/ops/_foreach_sqrt_ops.h> |
| 865 | #include <ATen/ops/_foreach_sqrt_native.h> |
| 866 | #include <ATen/ops/_foreach_sqrt_ops.h> |
| 867 | #include <ATen/ops/_foreach_abs_native.h> |
| 868 | #include <ATen/ops/_foreach_abs_ops.h> |
| 869 | #include <ATen/ops/_foreach_abs_native.h> |
| 870 | #include <ATen/ops/_foreach_abs_ops.h> |
| 871 | #include <ATen/ops/_foreach_abs_native.h> |
| 872 | #include <ATen/ops/_foreach_abs_ops.h> |
| 873 | #include <ATen/ops/_foreach_acos_native.h> |
| 874 | #include <ATen/ops/_foreach_acos_ops.h> |
| 875 | #include <ATen/ops/_foreach_acos_native.h> |
| 876 | #include <ATen/ops/_foreach_acos_ops.h> |
| 877 | #include <ATen/ops/_foreach_acos_native.h> |
| 878 | #include <ATen/ops/_foreach_acos_ops.h> |
| 879 | #include <ATen/ops/_foreach_cos_native.h> |
| 880 | #include <ATen/ops/_foreach_cos_ops.h> |
| 881 | #include <ATen/ops/_foreach_cos_native.h> |
| 882 | #include <ATen/ops/_foreach_cos_ops.h> |
| 883 | #include <ATen/ops/_foreach_cos_native.h> |
| 884 | #include <ATen/ops/_foreach_cos_ops.h> |
| 885 | #include <ATen/ops/_foreach_floor_native.h> |
| 886 | #include <ATen/ops/_foreach_floor_ops.h> |
| 887 | #include <ATen/ops/_foreach_floor_native.h> |
| 888 | #include <ATen/ops/_foreach_floor_ops.h> |
| 889 | #include <ATen/ops/_foreach_floor_native.h> |
| 890 | #include <ATen/ops/_foreach_floor_ops.h> |
| 891 | #include <ATen/ops/_foreach_log10_native.h> |
| 892 | #include <ATen/ops/_foreach_log10_ops.h> |
| 893 | #include <ATen/ops/_foreach_log10_native.h> |
| 894 | #include <ATen/ops/_foreach_log10_ops.h> |
| 895 | #include <ATen/ops/_foreach_log10_native.h> |
| 896 | #include <ATen/ops/_foreach_log10_ops.h> |
| 897 | #include <ATen/ops/_foreach_neg_native.h> |
| 898 | #include <ATen/ops/_foreach_neg_ops.h> |
| 899 | #include <ATen/ops/_foreach_neg_native.h> |
| 900 | #include <ATen/ops/_foreach_neg_ops.h> |
| 901 | #include <ATen/ops/_foreach_neg_native.h> |
| 902 | #include <ATen/ops/_foreach_neg_ops.h> |
| 903 | #include <ATen/ops/_foreach_tan_native.h> |
| 904 | #include <ATen/ops/_foreach_tan_ops.h> |
| 905 | #include <ATen/ops/_foreach_tan_native.h> |
| 906 | #include <ATen/ops/_foreach_tan_ops.h> |
| 907 | #include <ATen/ops/_foreach_tan_native.h> |
| 908 | #include <ATen/ops/_foreach_tan_ops.h> |
| 909 | #include <ATen/ops/_foreach_sigmoid_native.h> |
| 910 | #include <ATen/ops/_foreach_sigmoid_ops.h> |
| 911 | #include <ATen/ops/_foreach_sigmoid_native.h> |
| 912 | #include <ATen/ops/_foreach_sigmoid_ops.h> |
| 913 | #include <ATen/ops/_foreach_sigmoid_native.h> |
| 914 | #include <ATen/ops/_foreach_sigmoid_ops.h> |
| 915 | #include <ATen/ops/_foreach_norm_native.h> |
| 916 | #include <ATen/ops/_foreach_norm_ops.h> |
| 917 | #include <ATen/ops/_foreach_norm_native.h> |
| 918 | #include <ATen/ops/_foreach_norm_ops.h> |
| 919 | #include <ATen/ops/searchsorted_native.h> |
| 920 | #include <ATen/ops/searchsorted_ops.h> |
| 921 | #include <ATen/ops/searchsorted_native.h> |
| 922 | #include <ATen/ops/searchsorted_ops.h> |
| 923 | #include <ATen/ops/searchsorted_native.h> |
| 924 | #include <ATen/ops/searchsorted_ops.h> |
| 925 | #include <ATen/ops/searchsorted_native.h> |
| 926 | #include <ATen/ops/searchsorted_ops.h> |
| 927 | #include <ATen/ops/mse_loss_backward_native.h> |
| 928 | #include <ATen/ops/mse_loss_backward_ops.h> |
| 929 | #include <ATen/ops/mse_loss_backward_native.h> |
| 930 | #include <ATen/ops/mse_loss_backward_ops.h> |
| 931 | #include <ATen/ops/smooth_l1_loss_backward_native.h> |
| 932 | #include <ATen/ops/smooth_l1_loss_backward_ops.h> |
| 933 | #include <ATen/ops/smooth_l1_loss_backward_native.h> |
| 934 | #include <ATen/ops/smooth_l1_loss_backward_ops.h> |
| 935 | #include <ATen/ops/huber_loss_backward_native.h> |
| 936 | #include <ATen/ops/huber_loss_backward_ops.h> |
| 937 | #include <ATen/ops/huber_loss_backward_native.h> |
| 938 | #include <ATen/ops/huber_loss_backward_ops.h> |
| 939 | #include <ATen/ops/elu_backward_native.h> |
| 940 | #include <ATen/ops/elu_backward_ops.h> |
| 941 | #include <ATen/ops/elu_backward_native.h> |
| 942 | #include <ATen/ops/elu_backward_ops.h> |
| 943 | #include <ATen/ops/glu_jvp_native.h> |
| 944 | #include <ATen/ops/glu_jvp_ops.h> |
| 945 | #include <ATen/ops/glu_jvp_native.h> |
| 946 | #include <ATen/ops/glu_jvp_ops.h> |
| 947 | #include <ATen/ops/hardsigmoid_backward_native.h> |
| 948 | #include <ATen/ops/hardsigmoid_backward_ops.h> |
| 949 | #include <ATen/ops/hardsigmoid_backward_native.h> |
| 950 | #include <ATen/ops/hardsigmoid_backward_ops.h> |
| 951 | #include <ATen/ops/log_sigmoid_native.h> |
| 952 | #include <ATen/ops/log_sigmoid_ops.h> |
| 953 | #include <ATen/ops/log_sigmoid_native.h> |
| 954 | #include <ATen/ops/log_sigmoid_ops.h> |
| 955 | #include <ATen/ops/log_sigmoid_forward_native.h> |
| 956 | #include <ATen/ops/log_sigmoid_forward_ops.h> |
| 957 | #include <ATen/ops/log_sigmoid_forward_native.h> |
| 958 | #include <ATen/ops/log_sigmoid_forward_ops.h> |
| 959 | #include <ATen/ops/rrelu_with_noise_native.h> |
| 960 | #include <ATen/ops/rrelu_with_noise_ops.h> |
| 961 | #include <ATen/ops/rrelu_with_noise_native.h> |
| 962 | #include <ATen/ops/rrelu_with_noise_ops.h> |
| 963 | #include <ATen/ops/rrelu_with_noise_native.h> |
| 964 | #include <ATen/ops/rrelu_with_noise_ops.h> |
| 965 | #include <ATen/ops/rrelu_with_noise_backward_native.h> |
| 966 | #include <ATen/ops/rrelu_with_noise_backward_ops.h> |
| 967 | #include <ATen/ops/rrelu_with_noise_backward_native.h> |
| 968 | #include <ATen/ops/rrelu_with_noise_backward_ops.h> |
| 969 | #include <ATen/ops/softplus_backward_native.h> |
| 970 | #include <ATen/ops/softplus_backward_ops.h> |
| 971 | #include <ATen/ops/softplus_backward_native.h> |
| 972 | #include <ATen/ops/softplus_backward_ops.h> |
| 973 | #include <ATen/ops/mkldnn_adaptive_avg_pool2d_native.h> |
| 974 | #include <ATen/ops/mkldnn_adaptive_avg_pool2d_ops.h> |
| 975 | #include <ATen/ops/mkldnn_adaptive_avg_pool2d_native.h> |
| 976 | #include <ATen/ops/mkldnn_adaptive_avg_pool2d_ops.h> |
| 977 | #include <ATen/ops/adaptive_max_pool2d_native.h> |
| 978 | #include <ATen/ops/adaptive_max_pool2d_ops.h> |
| 979 | #include <ATen/ops/adaptive_max_pool2d_native.h> |
| 980 | #include <ATen/ops/adaptive_max_pool2d_ops.h> |
| 981 | #include <ATen/ops/adaptive_max_pool3d_native.h> |
| 982 | #include <ATen/ops/adaptive_max_pool3d_ops.h> |
| 983 | #include <ATen/ops/adaptive_max_pool3d_native.h> |
| 984 | #include <ATen/ops/adaptive_max_pool3d_ops.h> |
| 985 | #include <ATen/ops/avg_pool2d_backward_native.h> |
| 986 | #include <ATen/ops/avg_pool2d_backward_ops.h> |
| 987 | #include <ATen/ops/avg_pool2d_backward_native.h> |
| 988 | #include <ATen/ops/avg_pool2d_backward_ops.h> |
| 989 | #include <ATen/ops/max_pool2d_with_indices_native.h> |
| 990 | #include <ATen/ops/max_pool2d_with_indices_ops.h> |
| 991 | #include <ATen/ops/max_pool2d_with_indices_native.h> |
| 992 | #include <ATen/ops/max_pool2d_with_indices_ops.h> |
| 993 | #include <ATen/ops/upsample_linear1d_native.h> |
| 994 | #include <ATen/ops/upsample_linear1d_ops.h> |
| 995 | #include <ATen/ops/upsample_linear1d_native.h> |
| 996 | #include <ATen/ops/upsample_linear1d_ops.h> |
| 997 | #include <ATen/ops/upsample_linear1d_backward_native.h> |
| 998 | #include <ATen/ops/upsample_linear1d_backward_ops.h> |
| 999 | #include <ATen/ops/upsample_linear1d_backward_native.h> |
| 1000 | #include <ATen/ops/upsample_linear1d_backward_ops.h> |
| 1001 | #include <ATen/ops/upsample_bicubic2d_native.h> |
| 1002 | #include <ATen/ops/upsample_bicubic2d_ops.h> |
| 1003 | #include <ATen/ops/upsample_bicubic2d_native.h> |
| 1004 | #include <ATen/ops/upsample_bicubic2d_ops.h> |
| 1005 | #include <ATen/ops/upsample_bicubic2d_backward_native.h> |
| 1006 | #include <ATen/ops/upsample_bicubic2d_backward_ops.h> |
| 1007 | #include <ATen/ops/upsample_bicubic2d_backward_native.h> |
| 1008 | #include <ATen/ops/upsample_bicubic2d_backward_ops.h> |
| 1009 | #include <ATen/ops/_upsample_bicubic2d_aa_backward_native.h> |
| 1010 | #include <ATen/ops/_upsample_bicubic2d_aa_backward_ops.h> |
| 1011 | #include <ATen/ops/_upsample_bicubic2d_aa_backward_native.h> |
| 1012 | #include <ATen/ops/_upsample_bicubic2d_aa_backward_ops.h> |
| 1013 | #include <ATen/ops/upsample_nearest1d_native.h> |
| 1014 | #include <ATen/ops/upsample_nearest1d_ops.h> |
| 1015 | #include <ATen/ops/upsample_nearest1d_native.h> |
| 1016 | #include <ATen/ops/upsample_nearest1d_ops.h> |
| 1017 | #include <ATen/ops/_upsample_nearest_exact1d_native.h> |
| 1018 | #include <ATen/ops/_upsample_nearest_exact1d_ops.h> |
| 1019 | #include <ATen/ops/_upsample_nearest_exact1d_native.h> |
| 1020 | #include <ATen/ops/_upsample_nearest_exact1d_ops.h> |
| 1021 | #include <ATen/ops/upsample_nearest1d_backward_native.h> |
| 1022 | #include <ATen/ops/upsample_nearest1d_backward_ops.h> |
| 1023 | #include <ATen/ops/upsample_nearest1d_backward_native.h> |
| 1024 | #include <ATen/ops/upsample_nearest1d_backward_ops.h> |
| 1025 | #include <ATen/ops/upsample_nearest3d_native.h> |
| 1026 | #include <ATen/ops/upsample_nearest3d_ops.h> |
| 1027 | #include <ATen/ops/upsample_nearest3d_native.h> |
| 1028 | #include <ATen/ops/upsample_nearest3d_ops.h> |
| 1029 | #include <ATen/ops/slow_conv_transpose3d_native.h> |
| 1030 | #include <ATen/ops/slow_conv_transpose3d_ops.h> |
| 1031 | #include <ATen/ops/slow_conv_transpose3d_native.h> |
| 1032 | #include <ATen/ops/slow_conv_transpose3d_ops.h> |
| 1033 | #include <ATen/ops/isposinf_native.h> |
| 1034 | #include <ATen/ops/isposinf_ops.h> |
| 1035 | #include <ATen/ops/isposinf_native.h> |
| 1036 | #include <ATen/ops/isposinf_ops.h> |
| 1037 | #include <ATen/ops/special_entr_native.h> |
| 1038 | #include <ATen/ops/special_entr_ops.h> |
| 1039 | #include <ATen/ops/special_entr_native.h> |
| 1040 | #include <ATen/ops/special_entr_ops.h> |
| 1041 | #include <ATen/ops/special_psi_native.h> |
| 1042 | #include <ATen/ops/special_psi_ops.h> |
| 1043 | #include <ATen/ops/special_psi_native.h> |
| 1044 | #include <ATen/ops/special_psi_ops.h> |
| 1045 | #include <ATen/ops/special_erfinv_native.h> |
| 1046 | #include <ATen/ops/special_erfinv_ops.h> |
| 1047 | #include <ATen/ops/special_erfinv_native.h> |
| 1048 | #include <ATen/ops/special_erfinv_ops.h> |
| 1049 | #include <ATen/ops/special_ndtr_native.h> |
| 1050 | #include <ATen/ops/special_ndtr_ops.h> |
| 1051 | #include <ATen/ops/special_ndtr_native.h> |
| 1052 | #include <ATen/ops/special_ndtr_ops.h> |
| 1053 | #include <ATen/ops/special_xlogy_native.h> |
| 1054 | #include <ATen/ops/special_xlogy_ops.h> |
| 1055 | #include <ATen/ops/special_xlogy_native.h> |
| 1056 | #include <ATen/ops/special_xlogy_ops.h> |
| 1057 | #include <ATen/ops/special_xlogy_native.h> |
| 1058 | #include <ATen/ops/special_xlogy_ops.h> |
| 1059 | #include <ATen/ops/special_xlogy_native.h> |
| 1060 | #include <ATen/ops/special_xlogy_ops.h> |
| 1061 | #include <ATen/ops/special_xlogy_native.h> |
| 1062 | #include <ATen/ops/special_xlogy_ops.h> |
| 1063 | #include <ATen/ops/special_xlogy_native.h> |
| 1064 | #include <ATen/ops/special_xlogy_ops.h> |
| 1065 | #include <ATen/ops/special_zeta_native.h> |
| 1066 | #include <ATen/ops/special_zeta_ops.h> |
| 1067 | #include <ATen/ops/special_zeta_native.h> |
| 1068 | #include <ATen/ops/special_zeta_ops.h> |
| 1069 | #include <ATen/ops/special_zeta_native.h> |
| 1070 | #include <ATen/ops/special_zeta_ops.h> |
| 1071 | #include <ATen/ops/special_zeta_native.h> |
| 1072 | #include <ATen/ops/special_zeta_ops.h> |
| 1073 | #include <ATen/ops/special_zeta_native.h> |
| 1074 | #include <ATen/ops/special_zeta_ops.h> |
| 1075 | #include <ATen/ops/special_zeta_native.h> |
| 1076 | #include <ATen/ops/special_zeta_ops.h> |
| 1077 | #include <ATen/ops/special_i0_native.h> |
| 1078 | #include <ATen/ops/special_i0_ops.h> |
| 1079 | #include <ATen/ops/special_i0_native.h> |
| 1080 | #include <ATen/ops/special_i0_ops.h> |
| 1081 | #include <ATen/ops/special_i0e_native.h> |
| 1082 | #include <ATen/ops/special_i0e_ops.h> |
| 1083 | #include <ATen/ops/special_i0e_native.h> |
| 1084 | #include <ATen/ops/special_i0e_ops.h> |
| 1085 | #include <ATen/ops/special_expit_native.h> |
| 1086 | #include <ATen/ops/special_expit_ops.h> |
| 1087 | #include <ATen/ops/special_expit_native.h> |
| 1088 | #include <ATen/ops/special_expit_ops.h> |
| 1089 | #include <ATen/ops/special_round_native.h> |
| 1090 | #include <ATen/ops/special_round_ops.h> |
| 1091 | #include <ATen/ops/special_round_native.h> |
| 1092 | #include <ATen/ops/special_round_ops.h> |
| 1093 | #include <ATen/ops/special_gammainc_native.h> |
| 1094 | #include <ATen/ops/special_gammainc_ops.h> |
| 1095 | #include <ATen/ops/special_gammainc_native.h> |
| 1096 | #include <ATen/ops/special_gammainc_ops.h> |
| 1097 | #include <ATen/ops/fft_irfft_native.h> |
| 1098 | #include <ATen/ops/fft_irfft_ops.h> |
| 1099 | #include <ATen/ops/fft_irfft_native.h> |
| 1100 | #include <ATen/ops/fft_irfft_ops.h> |
| 1101 | #include <ATen/ops/fft_fft2_native.h> |
| 1102 | #include <ATen/ops/fft_fft2_ops.h> |
| 1103 | #include <ATen/ops/fft_fft2_native.h> |
| 1104 | #include <ATen/ops/fft_fft2_ops.h> |
| 1105 | #include <ATen/ops/fft_rfftfreq_native.h> |
| 1106 | #include <ATen/ops/fft_rfftfreq_ops.h> |
| 1107 | #include <ATen/ops/fft_rfftfreq_native.h> |
| 1108 | #include <ATen/ops/fft_rfftfreq_ops.h> |
| 1109 | #include <ATen/ops/linalg_cholesky_native.h> |
| 1110 | #include <ATen/ops/linalg_cholesky_ops.h> |
| 1111 | #include <ATen/ops/linalg_cholesky_native.h> |
| 1112 | #include <ATen/ops/linalg_cholesky_ops.h> |
| 1113 | #include <ATen/ops/linalg_lu_native.h> |
| 1114 | #include <ATen/ops/linalg_lu_ops.h> |
| 1115 | #include <ATen/ops/linalg_lu_native.h> |
| 1116 | #include <ATen/ops/linalg_lu_ops.h> |
| 1117 | #include <ATen/ops/_linalg_det_native.h> |
| 1118 | #include <ATen/ops/_linalg_det_ops.h> |
| 1119 | #include <ATen/ops/_linalg_det_native.h> |
| 1120 | #include <ATen/ops/_linalg_det_ops.h> |
| 1121 | #include <ATen/ops/slogdet_native.h> |
| 1122 | #include <ATen/ops/slogdet_ops.h> |
| 1123 | #include <ATen/ops/slogdet_native.h> |
| 1124 | #include <ATen/ops/slogdet_ops.h> |
| 1125 | #include <ATen/ops/linalg_eig_native.h> |
| 1126 | #include <ATen/ops/linalg_eig_ops.h> |
| 1127 | #include <ATen/ops/linalg_eig_native.h> |
| 1128 | #include <ATen/ops/linalg_eig_ops.h> |
| 1129 | #include <ATen/ops/linalg_inv_native.h> |
| 1130 | #include <ATen/ops/linalg_inv_ops.h> |
| 1131 | #include <ATen/ops/linalg_inv_native.h> |
| 1132 | #include <ATen/ops/linalg_inv_ops.h> |
| 1133 | #include <ATen/ops/_linalg_svd_native.h> |
| 1134 | #include <ATen/ops/_linalg_svd_ops.h> |
| 1135 | #include <ATen/ops/_linalg_svd_native.h> |
| 1136 | #include <ATen/ops/_linalg_svd_ops.h> |
| 1137 | #include <ATen/ops/linalg_svdvals_native.h> |
| 1138 | #include <ATen/ops/linalg_svdvals_ops.h> |
| 1139 | #include <ATen/ops/linalg_svdvals_native.h> |
| 1140 | #include <ATen/ops/linalg_svdvals_ops.h> |
| 1141 | #include <ATen/ops/linalg_pinv_native.h> |
| 1142 | #include <ATen/ops/linalg_pinv_ops.h> |
| 1143 | #include <ATen/ops/linalg_pinv_native.h> |
| 1144 | #include <ATen/ops/linalg_pinv_ops.h> |
| 1145 | #include <ATen/ops/linalg_pinv_native.h> |
| 1146 | #include <ATen/ops/linalg_pinv_ops.h> |
| 1147 | #include <ATen/ops/linalg_pinv_native.h> |
| 1148 | #include <ATen/ops/linalg_pinv_ops.h> |
| 1149 | #include <ATen/ops/linalg_pinv_native.h> |
| 1150 | #include <ATen/ops/linalg_pinv_ops.h> |
| 1151 | #include <ATen/ops/linalg_pinv_native.h> |
| 1152 | #include <ATen/ops/linalg_pinv_ops.h> |
| 1153 | #include <ATen/ops/linalg_pinv_native.h> |
| 1154 | #include <ATen/ops/linalg_pinv_ops.h> |
| 1155 | #include <ATen/ops/linalg_pinv_native.h> |
| 1156 | #include <ATen/ops/linalg_pinv_ops.h> |
| 1157 | #include <ATen/ops/linalg_qr_native.h> |
| 1158 | #include <ATen/ops/linalg_qr_ops.h> |
| 1159 | #include <ATen/ops/linalg_qr_native.h> |
| 1160 | #include <ATen/ops/linalg_qr_ops.h> |
| 1161 | #include <ATen/ops/_test_optional_intlist_native.h> |
| 1162 | #include <ATen/ops/_test_optional_intlist_ops.h> |
| 1163 | #include <ATen/ops/_test_optional_intlist_native.h> |
| 1164 | #include <ATen/ops/_test_optional_intlist_ops.h> |
| 1165 | #include <ATen/ops/_fw_primal_copy_native.h> |
| 1166 | #include <ATen/ops/_fw_primal_copy_ops.h> |
| 1167 | #include <ATen/ops/_fw_primal_copy_native.h> |
| 1168 | #include <ATen/ops/_fw_primal_copy_ops.h> |
| 1169 | #include <ATen/ops/as_strided_copy_native.h> |
| 1170 | #include <ATen/ops/as_strided_copy_ops.h> |
| 1171 | #include <ATen/ops/as_strided_copy_native.h> |
| 1172 | #include <ATen/ops/as_strided_copy_ops.h> |
| 1173 | #include <ATen/ops/expand_copy_native.h> |
| 1174 | #include <ATen/ops/expand_copy_ops.h> |
| 1175 | #include <ATen/ops/expand_copy_native.h> |
| 1176 | #include <ATen/ops/expand_copy_ops.h> |
| 1177 | #include <ATen/ops/_reshape_alias_copy_native.h> |
| 1178 | #include <ATen/ops/_reshape_alias_copy_ops.h> |
| 1179 | #include <ATen/ops/_reshape_alias_copy_native.h> |
| 1180 | #include <ATen/ops/_reshape_alias_copy_ops.h> |
| 1181 | #include <ATen/ops/select_copy_native.h> |
| 1182 | #include <ATen/ops/select_copy_ops.h> |
| 1183 | #include <ATen/ops/select_copy_native.h> |
| 1184 | #include <ATen/ops/select_copy_ops.h> |
| 1185 | #include <ATen/ops/split_with_sizes_copy_native.h> |
| 1186 | #include <ATen/ops/split_with_sizes_copy_ops.h> |
| 1187 | #include <ATen/ops/split_with_sizes_copy_native.h> |
| 1188 | #include <ATen/ops/split_with_sizes_copy_ops.h> |
| 1189 | #include <ATen/ops/squeeze_copy_native.h> |
| 1190 | #include <ATen/ops/squeeze_copy_ops.h> |
| 1191 | #include <ATen/ops/squeeze_copy_native.h> |
| 1192 | #include <ATen/ops/squeeze_copy_ops.h> |
| 1193 | #include <ATen/ops/squeeze_copy_native.h> |
| 1194 | #include <ATen/ops/squeeze_copy_ops.h> |
| 1195 | #include <ATen/ops/squeeze_copy_native.h> |
| 1196 | #include <ATen/ops/squeeze_copy_ops.h> |
| 1197 | #include <ATen/ops/squeeze_copy_native.h> |
| 1198 | #include <ATen/ops/squeeze_copy_ops.h> |
| 1199 | #include <ATen/ops/squeeze_copy_native.h> |
| 1200 | #include <ATen/ops/squeeze_copy_ops.h> |
| 1201 | #include <ATen/ops/_indices_copy_native.h> |
| 1202 | #include <ATen/ops/_indices_copy_ops.h> |
| 1203 | #include <ATen/ops/_indices_copy_native.h> |
| 1204 | #include <ATen/ops/_indices_copy_ops.h> |
| 1205 | #include <ATen/ops/_values_copy_native.h> |
| 1206 | #include <ATen/ops/_values_copy_ops.h> |
| 1207 | #include <ATen/ops/_values_copy_native.h> |
| 1208 | #include <ATen/ops/_values_copy_ops.h> |
| 1209 | #include <ATen/ops/crow_indices_copy_native.h> |
| 1210 | #include <ATen/ops/crow_indices_copy_ops.h> |
| 1211 | #include <ATen/ops/crow_indices_copy_native.h> |
| 1212 | #include <ATen/ops/crow_indices_copy_ops.h> |
| 1213 | #include <ATen/ops/col_indices_copy_native.h> |
| 1214 | #include <ATen/ops/col_indices_copy_ops.h> |
| 1215 | #include <ATen/ops/col_indices_copy_native.h> |
| 1216 | #include <ATen/ops/col_indices_copy_ops.h> |
| 1217 | #include <ATen/ops/unbind_copy_native.h> |
| 1218 | #include <ATen/ops/unbind_copy_ops.h> |
| 1219 | #include <ATen/ops/unbind_copy_native.h> |
| 1220 | #include <ATen/ops/unbind_copy_ops.h> |
| 1221 | #include <ATen/ops/view_copy_native.h> |
| 1222 | #include <ATen/ops/view_copy_ops.h> |
| 1223 | #include <ATen/ops/view_copy_native.h> |
| 1224 | #include <ATen/ops/view_copy_ops.h> |
| 1225 | #include <ATen/ops/view_copy_native.h> |
| 1226 | #include <ATen/ops/view_copy_ops.h> |
| 1227 | #include <ATen/ops/view_copy_native.h> |
| 1228 | #include <ATen/ops/view_copy_ops.h> |
| 1229 | #include <ATen/ops/alias_copy_native.h> |
| 1230 | #include <ATen/ops/alias_copy_ops.h> |
| 1231 | #include <ATen/ops/alias_copy_native.h> |
| 1232 | #include <ATen/ops/alias_copy_ops.h> |
| 1233 | #include <ATen/ops/special_airy_ai_native.h> |
| 1234 | #include <ATen/ops/special_airy_ai_ops.h> |
| 1235 | #include <ATen/ops/special_airy_ai_native.h> |
| 1236 | #include <ATen/ops/special_airy_ai_ops.h> |
| 1237 | #include <ATen/ops/special_bessel_j0_native.h> |
| 1238 | #include <ATen/ops/special_bessel_j0_ops.h> |
| 1239 | #include <ATen/ops/special_bessel_j0_native.h> |
| 1240 | #include <ATen/ops/special_bessel_j0_ops.h> |
| 1241 | #include <ATen/ops/special_chebyshev_polynomial_v_native.h> |
| 1242 | #include <ATen/ops/special_chebyshev_polynomial_v_ops.h> |
| 1243 | #include <ATen/ops/special_chebyshev_polynomial_v_native.h> |
| 1244 | #include <ATen/ops/special_chebyshev_polynomial_v_ops.h> |
| 1245 | #include <ATen/ops/special_chebyshev_polynomial_v_native.h> |
| 1246 | #include <ATen/ops/special_chebyshev_polynomial_v_ops.h> |
| 1247 | #include <ATen/ops/special_chebyshev_polynomial_v_native.h> |
| 1248 | #include <ATen/ops/special_chebyshev_polynomial_v_ops.h> |
| 1249 | #include <ATen/ops/special_chebyshev_polynomial_v_native.h> |
| 1250 | #include <ATen/ops/special_chebyshev_polynomial_v_ops.h> |
| 1251 | #include <ATen/ops/special_chebyshev_polynomial_v_native.h> |
| 1252 | #include <ATen/ops/special_chebyshev_polynomial_v_ops.h> |
| 1253 | #include <ATen/ops/special_chebyshev_polynomial_w_native.h> |
| 1254 | #include <ATen/ops/special_chebyshev_polynomial_w_ops.h> |
| 1255 | #include <ATen/ops/special_chebyshev_polynomial_w_native.h> |
| 1256 | #include <ATen/ops/special_chebyshev_polynomial_w_ops.h> |
| 1257 | #include <ATen/ops/special_chebyshev_polynomial_w_native.h> |
| 1258 | #include <ATen/ops/special_chebyshev_polynomial_w_ops.h> |
| 1259 | #include <ATen/ops/special_chebyshev_polynomial_w_native.h> |
| 1260 | #include <ATen/ops/special_chebyshev_polynomial_w_ops.h> |
| 1261 | #include <ATen/ops/special_chebyshev_polynomial_w_native.h> |
| 1262 | #include <ATen/ops/special_chebyshev_polynomial_w_ops.h> |
| 1263 | #include <ATen/ops/special_chebyshev_polynomial_w_native.h> |
| 1264 | #include <ATen/ops/special_chebyshev_polynomial_w_ops.h> |
| 1265 | #include <ATen/ops/special_hermite_polynomial_he_native.h> |
| 1266 | #include <ATen/ops/special_hermite_polynomial_he_ops.h> |
| 1267 | #include <ATen/ops/special_hermite_polynomial_he_native.h> |
| 1268 | #include <ATen/ops/special_hermite_polynomial_he_ops.h> |
| 1269 | #include <ATen/ops/special_hermite_polynomial_he_native.h> |
| 1270 | #include <ATen/ops/special_hermite_polynomial_he_ops.h> |
| 1271 | #include <ATen/ops/special_hermite_polynomial_he_native.h> |
| 1272 | #include <ATen/ops/special_hermite_polynomial_he_ops.h> |
| 1273 | #include <ATen/ops/special_hermite_polynomial_he_native.h> |
| 1274 | #include <ATen/ops/special_hermite_polynomial_he_ops.h> |
| 1275 | #include <ATen/ops/special_hermite_polynomial_he_native.h> |
| 1276 | #include <ATen/ops/special_hermite_polynomial_he_ops.h> |
| 1277 | #include <ATen/ops/special_laguerre_polynomial_l_native.h> |
| 1278 | #include <ATen/ops/special_laguerre_polynomial_l_ops.h> |
| 1279 | #include <ATen/ops/special_laguerre_polynomial_l_native.h> |
| 1280 | #include <ATen/ops/special_laguerre_polynomial_l_ops.h> |
| 1281 | #include <ATen/ops/special_laguerre_polynomial_l_native.h> |
| 1282 | #include <ATen/ops/special_laguerre_polynomial_l_ops.h> |
| 1283 | #include <ATen/ops/special_laguerre_polynomial_l_native.h> |
| 1284 | #include <ATen/ops/special_laguerre_polynomial_l_ops.h> |
| 1285 | #include <ATen/ops/special_laguerre_polynomial_l_native.h> |
| 1286 | #include <ATen/ops/special_laguerre_polynomial_l_ops.h> |
| 1287 | #include <ATen/ops/special_laguerre_polynomial_l_native.h> |
| 1288 | #include <ATen/ops/special_laguerre_polynomial_l_ops.h> |
| 1289 | #include <ATen/ops/special_shifted_chebyshev_polynomial_v_native.h> |
| 1290 | #include <ATen/ops/special_shifted_chebyshev_polynomial_v_ops.h> |
| 1291 | #include <ATen/ops/special_shifted_chebyshev_polynomial_v_native.h> |
| 1292 | #include <ATen/ops/special_shifted_chebyshev_polynomial_v_ops.h> |
| 1293 | #include <ATen/ops/special_shifted_chebyshev_polynomial_v_native.h> |
| 1294 | #include <ATen/ops/special_shifted_chebyshev_polynomial_v_ops.h> |
| 1295 | #include <ATen/ops/special_shifted_chebyshev_polynomial_v_native.h> |
| 1296 | #include <ATen/ops/special_shifted_chebyshev_polynomial_v_ops.h> |
| 1297 | #include <ATen/ops/special_shifted_chebyshev_polynomial_v_native.h> |
| 1298 | #include <ATen/ops/special_shifted_chebyshev_polynomial_v_ops.h> |
| 1299 | #include <ATen/ops/special_shifted_chebyshev_polynomial_v_native.h> |
| 1300 | #include <ATen/ops/special_shifted_chebyshev_polynomial_v_ops.h> |
| 1301 | #include <ATen/ops/_fused_adamw_native.h> |
| 1302 | #include <ATen/ops/_fused_adamw_ops.h> |
| 1303 | #include <ATen/ops/_fused_adamw_native.h> |
| 1304 | #include <ATen/ops/_fused_adamw_ops.h> |
| 1305 | #include <ATen/ops/_fused_adamw_native.h> |
| 1306 | #include <ATen/ops/_fused_adamw_ops.h> |
| 1307 | #include <ATen/ops/_fw_primal_native.h> |
| 1308 | #include <ATen/ops/_fw_primal_ops.h> |
| 1309 | #include <ATen/ops/_fw_primal_copy_native.h> |
| 1310 | #include <ATen/ops/_fw_primal_copy_ops.h> |
| 1311 | #include <ATen/ops/_make_dual_native.h> |
| 1312 | #include <ATen/ops/_make_dual_ops.h> |
| 1313 | #include <ATen/ops/_make_dual_copy_native.h> |
| 1314 | #include <ATen/ops/_make_dual_copy_ops.h> |
| 1315 | #include <ATen/ops/_unpack_dual_native.h> |
| 1316 | #include <ATen/ops/_unpack_dual_ops.h> |
| 1317 | #include <ATen/ops/align_to_native.h> |
| 1318 | #include <ATen/ops/align_to_ops.h> |
| 1319 | #include <ATen/ops/align_to_native.h> |
| 1320 | #include <ATen/ops/align_to_ops.h> |
| 1321 | #include <ATen/ops/view_as_complex_native.h> |
| 1322 | #include <ATen/ops/view_as_complex_ops.h> |
| 1323 | #include <ATen/ops/view_as_complex_copy_native.h> |
| 1324 | #include <ATen/ops/view_as_complex_copy_ops.h> |
| 1325 | #include <ATen/ops/resolve_neg_native.h> |
| 1326 | #include <ATen/ops/resolve_neg_ops.h> |
| 1327 | #include <ATen/ops/linalg_diagonal_native.h> |
| 1328 | #include <ATen/ops/linalg_diagonal_ops.h> |
| 1329 | #include <ATen/ops/expand_native.h> |
| 1330 | #include <ATen/ops/expand_ops.h> |
| 1331 | #include <ATen/ops/expand_copy_native.h> |
| 1332 | #include <ATen/ops/expand_copy_ops.h> |
| 1333 | #include <ATen/ops/matrix_H_native.h> |
| 1334 | #include <ATen/ops/matrix_H_ops.h> |
| 1335 | #include <ATen/ops/adjoint_native.h> |
| 1336 | #include <ATen/ops/adjoint_ops.h> |
| 1337 | #include <ATen/ops/reshape_as_native.h> |
| 1338 | #include <ATen/ops/reshape_as_ops.h> |
| 1339 | #include <ATen/ops/transpose_native.h> |
| 1340 | #include <ATen/ops/transpose_ops.h> |
| 1341 | #include <ATen/ops/transpose_copy_native.h> |
| 1342 | #include <ATen/ops/transpose_copy_ops.h> |
| 1343 | #include <ATen/ops/transpose_native.h> |
| 1344 | #include <ATen/ops/transpose_ops.h> |
| 1345 | #include <ATen/ops/_nested_view_from_buffer_native.h> |
| 1346 | #include <ATen/ops/_nested_view_from_buffer_ops.h> |
| 1347 | #include <ATen/ops/_nested_view_from_buffer_copy_native.h> |
| 1348 | #include <ATen/ops/_nested_view_from_buffer_copy_ops.h> |
| 1349 | #include <ATen/ops/unsqueeze_native.h> |
| 1350 | #include <ATen/ops/unsqueeze_ops.h> |
| 1351 | #include <ATen/ops/unsqueeze_copy_native.h> |
| 1352 | #include <ATen/ops/unsqueeze_copy_ops.h> |
| 1353 | #include <ATen/ops/_values_native.h> |
| 1354 | #include <ATen/ops/_values_ops.h> |
| 1355 | #include <ATen/ops/_values_copy_native.h> |
| 1356 | #include <ATen/ops/_values_copy_ops.h> |
| 1357 | #include <ATen/ops/ccol_indices_native.h> |
| 1358 | #include <ATen/ops/ccol_indices_ops.h> |
| 1359 | #include <ATen/ops/ccol_indices_copy_native.h> |
| 1360 | #include <ATen/ops/ccol_indices_copy_ops.h> |
| 1361 | #include <ATen/ops/_autocast_to_full_precision_native.h> |
| 1362 | #include <ATen/ops/_autocast_to_full_precision_ops.h> |
| 1363 | #include <ATen/ops/to_native.h> |
| 1364 | #include <ATen/ops/to_ops.h> |
| 1365 | #include <ATen/ops/to_native.h> |
| 1366 | #include <ATen/ops/to_ops.h> |
| 1367 | #include <ATen/ops/to_native.h> |
| 1368 | #include <ATen/ops/to_ops.h> |
| 1369 | #include <ATen/ops/to_native.h> |
| 1370 | #include <ATen/ops/to_ops.h> |
| 1371 | #include <ATen/ops/view_native.h> |
| 1372 | #include <ATen/ops/view_ops.h> |
| 1373 | #include <ATen/ops/view_copy_native.h> |
| 1374 | #include <ATen/ops/view_copy_ops.h> |
| 1375 | #include <ATen/ops/view_native.h> |
| 1376 | #include <ATen/ops/view_ops.h> |
| 1377 | #include <ATen/ops/view_copy_native.h> |
| 1378 | #include <ATen/ops/view_copy_ops.h> |
| 1379 | #include <ATen/ops/_cast_Long_native.h> |
| 1380 | #include <ATen/ops/_cast_Long_ops.h> |
| 1381 | #include <ATen/ops/_version_native.h> |
| 1382 | #include <ATen/ops/_version_ops.h> |
| 1383 | #include <ATen/ops/retain_grad_native.h> |
| 1384 | #include <ATen/ops/retain_grad_ops.h> |
| 1385 | #include <ATen/ops/retains_grad_native.h> |
| 1386 | #include <ATen/ops/retains_grad_ops.h> |
| 1387 | #include <ATen/ops/_has_same_storage_numel_native.h> |
| 1388 | #include <ATen/ops/_has_same_storage_numel_ops.h> |
| 1389 | #include <ATen/ops/align_tensors_native.h> |
| 1390 | #include <ATen/ops/align_tensors_ops.h> |
| 1391 | #include <ATen/ops/_assert_tensor_metadata_native.h> |
| 1392 | #include <ATen/ops/_assert_tensor_metadata_ops.h> |
| 1393 | #include <ATen/ops/_debug_has_internal_overlap_native.h> |
| 1394 | #include <ATen/ops/_debug_has_internal_overlap_ops.h> |
| 1395 | #include <ATen/ops/_sobol_engine_draw_native.h> |
| 1396 | #include <ATen/ops/_sobol_engine_draw_ops.h> |
| 1397 | #include <ATen/ops/_sobol_engine_scramble_native.h> |
| 1398 | #include <ATen/ops/_sobol_engine_scramble_ops.h> |
| 1399 | #include <ATen/ops/feature_dropout_native.h> |
| 1400 | #include <ATen/ops/feature_dropout_ops.h> |
| 1401 | #include <ATen/ops/feature_dropout_native.h> |
| 1402 | #include <ATen/ops/feature_dropout_ops.h> |
| 1403 | #include <ATen/ops/alpha_dropout_native.h> |
| 1404 | #include <ATen/ops/alpha_dropout_ops.h> |
| 1405 | #include <ATen/ops/alpha_dropout_native.h> |
| 1406 | #include <ATen/ops/alpha_dropout_ops.h> |
| 1407 | #include <ATen/ops/chalf_native.h> |
| 1408 | #include <ATen/ops/chalf_ops.h> |
| 1409 | #include <ATen/ops/avg_pool1d_native.h> |
| 1410 | #include <ATen/ops/avg_pool1d_ops.h> |
| 1411 | #include <ATen/ops/adaptive_avg_pool1d_native.h> |
| 1412 | #include <ATen/ops/adaptive_avg_pool1d_ops.h> |
| 1413 | #include <ATen/ops/affine_grid_generator_backward_native.h> |
| 1414 | #include <ATen/ops/affine_grid_generator_backward_ops.h> |
| 1415 | #include <ATen/ops/_test_check_tensor_native.h> |
| 1416 | #include <ATen/ops/_test_check_tensor_ops.h> |
| 1417 | #include <ATen/ops/atleast_3d_native.h> |
| 1418 | #include <ATen/ops/atleast_3d_ops.h> |
| 1419 | #include <ATen/ops/atleast_3d_native.h> |
| 1420 | #include <ATen/ops/atleast_3d_ops.h> |
| 1421 | #include <ATen/ops/batch_norm_native.h> |
| 1422 | #include <ATen/ops/batch_norm_ops.h> |
| 1423 | #include <ATen/ops/broadcast_tensors_native.h> |
| 1424 | #include <ATen/ops/broadcast_tensors_ops.h> |
| 1425 | #include <ATen/ops/_convolution_mode_native.h> |
| 1426 | #include <ATen/ops/_convolution_mode_ops.h> |
| 1427 | #include <ATen/ops/conv3d_native.h> |
| 1428 | #include <ATen/ops/conv3d_ops.h> |
| 1429 | #include <ATen/ops/conv3d_native.h> |
| 1430 | #include <ATen/ops/conv3d_ops.h> |
| 1431 | #include <ATen/ops/_cummax_helper_native.h> |
| 1432 | #include <ATen/ops/_cummax_helper_ops.h> |
| 1433 | #include <ATen/ops/_ctc_loss_backward_native.h> |
| 1434 | #include <ATen/ops/_ctc_loss_backward_ops.h> |
| 1435 | #include <ATen/ops/diagflat_native.h> |
| 1436 | #include <ATen/ops/diagflat_ops.h> |
| 1437 | #include <ATen/ops/fill_diagonal_native.h> |
| 1438 | #include <ATen/ops/fill_diagonal_ops.h> |
| 1439 | #include <ATen/ops/index_copy_native.h> |
| 1440 | #include <ATen/ops/index_copy_ops.h> |
| 1441 | #include <ATen/ops/index_copy_native.h> |
| 1442 | #include <ATen/ops/index_copy_ops.h> |
| 1443 | #include <ATen/ops/instance_norm_native.h> |
| 1444 | #include <ATen/ops/instance_norm_ops.h> |
| 1445 | #include <ATen/ops/is_complex_native.h> |
| 1446 | #include <ATen/ops/is_complex_ops.h> |
| 1447 | #include <ATen/ops/fbgemm_linear_int8_weight_fp32_activation_native.h> |
| 1448 | #include <ATen/ops/fbgemm_linear_int8_weight_fp32_activation_ops.h> |
| 1449 | #include <ATen/ops/fbgemm_linear_int8_weight_native.h> |
| 1450 | #include <ATen/ops/fbgemm_linear_int8_weight_ops.h> |
| 1451 | #include <ATen/ops/fbgemm_linear_quantize_weight_native.h> |
| 1452 | #include <ATen/ops/fbgemm_linear_quantize_weight_ops.h> |
| 1453 | #include <ATen/ops/fbgemm_linear_fp16_weight_native.h> |
| 1454 | #include <ATen/ops/fbgemm_linear_fp16_weight_ops.h> |
| 1455 | #include <ATen/ops/log_softmax_native.h> |
| 1456 | #include <ATen/ops/log_softmax_ops.h> |
| 1457 | #include <ATen/ops/max_pool1d_with_indices_native.h> |
| 1458 | #include <ATen/ops/max_pool1d_with_indices_ops.h> |
| 1459 | #include <ATen/ops/max_pool3d_native.h> |
| 1460 | #include <ATen/ops/max_pool3d_ops.h> |
| 1461 | #include <ATen/ops/mean_native.h> |
| 1462 | #include <ATen/ops/mean_ops.h> |
| 1463 | #include <ATen/ops/multiply_native.h> |
| 1464 | #include <ATen/ops/multiply_ops.h> |
| 1465 | #include <ATen/ops/multiply_native.h> |
| 1466 | #include <ATen/ops/multiply_ops.h> |
| 1467 | #include <ATen/ops/is_vulkan_available_native.h> |
| 1468 | #include <ATen/ops/is_vulkan_available_ops.h> |
| 1469 | #include <ATen/ops/cdist_native.h> |
| 1470 | #include <ATen/ops/cdist_ops.h> |
| 1471 | #include <ATen/ops/is_pinned_native.h> |
| 1472 | #include <ATen/ops/is_pinned_ops.h> |
| 1473 | #include <ATen/ops/poisson_nll_loss_native.h> |
| 1474 | #include <ATen/ops/poisson_nll_loss_ops.h> |
| 1475 | #include <ATen/ops/repeat_interleave_native.h> |
| 1476 | #include <ATen/ops/repeat_interleave_ops.h> |
| 1477 | #include <ATen/ops/repeat_interleave_native.h> |
| 1478 | #include <ATen/ops/repeat_interleave_ops.h> |
| 1479 | #include <ATen/ops/_prelu_kernel_native.h> |
| 1480 | #include <ATen/ops/_prelu_kernel_ops.h> |
| 1481 | #include <ATen/ops/infinitely_differentiable_gelu_backward_native.h> |
| 1482 | #include <ATen/ops/infinitely_differentiable_gelu_backward_ops.h> |
| 1483 | #include <ATen/ops/mish_backward_native.h> |
| 1484 | #include <ATen/ops/mish_backward_ops.h> |
| 1485 | #include <ATen/ops/_nested_tensor_offsets_native.h> |
| 1486 | #include <ATen/ops/_nested_tensor_offsets_ops.h> |
| 1487 | #include <ATen/ops/subtract_native.h> |
| 1488 | #include <ATen/ops/subtract_ops.h> |
| 1489 | #include <ATen/ops/subtract_native.h> |
| 1490 | #include <ATen/ops/subtract_ops.h> |
| 1491 | #include <ATen/ops/_sparse_mm_reduce_impl_backward_native.h> |
| 1492 | #include <ATen/ops/_sparse_mm_reduce_impl_backward_ops.h> |
| 1493 | #include <ATen/ops/sparse_compressed_tensor_native.h> |
| 1494 | #include <ATen/ops/sparse_compressed_tensor_ops.h> |
| 1495 | #include <ATen/ops/sparse_bsr_tensor_native.h> |
| 1496 | #include <ATen/ops/sparse_bsr_tensor_ops.h> |
| 1497 | #include <ATen/ops/sparse_compressed_tensor_native.h> |
| 1498 | #include <ATen/ops/sparse_compressed_tensor_ops.h> |
| 1499 | #include <ATen/ops/sparse_bsr_tensor_native.h> |
| 1500 | #include <ATen/ops/sparse_bsr_tensor_ops.h> |
| 1501 | #include <ATen/ops/_sparse_csc_tensor_unsafe_native.h> |
| 1502 | #include <ATen/ops/_sparse_csc_tensor_unsafe_ops.h> |
| 1503 | #include <ATen/ops/_sparse_coo_tensor_unsafe_native.h> |
| 1504 | #include <ATen/ops/_sparse_coo_tensor_unsafe_ops.h> |
| 1505 | #include <ATen/ops/_validate_sparse_coo_tensor_args_native.h> |
| 1506 | #include <ATen/ops/_validate_sparse_coo_tensor_args_ops.h> |
| 1507 | #include <ATen/ops/to_dense_native.h> |
| 1508 | #include <ATen/ops/to_dense_ops.h> |
| 1509 | #include <ATen/ops/to_dense_backward_native.h> |
| 1510 | #include <ATen/ops/to_dense_backward_ops.h> |
| 1511 | #include <ATen/ops/_nnz_native.h> |
| 1512 | #include <ATen/ops/_nnz_ops.h> |
| 1513 | #include <ATen/ops/_fake_quantize_learnable_per_tensor_affine_backward_native.h> |
| 1514 | #include <ATen/ops/_fake_quantize_learnable_per_tensor_affine_backward_ops.h> |
| 1515 | #include <ATen/ops/fake_quantize_per_channel_affine_native.h> |
| 1516 | #include <ATen/ops/fake_quantize_per_channel_affine_ops.h> |
| 1517 | #include <ATen/ops/_fake_quantize_learnable_per_channel_affine_backward_native.h> |
| 1518 | #include <ATen/ops/_fake_quantize_learnable_per_channel_affine_backward_ops.h> |
| 1519 | #include <ATen/ops/choose_qparams_optimized_native.h> |
| 1520 | #include <ATen/ops/choose_qparams_optimized_ops.h> |
| 1521 | #include <ATen/ops/combinations_native.h> |
| 1522 | #include <ATen/ops/combinations_ops.h> |
| 1523 | #include <ATen/ops/result_type_native.h> |
| 1524 | #include <ATen/ops/result_type_ops.h> |
| 1525 | #include <ATen/ops/result_type_native.h> |
| 1526 | #include <ATen/ops/result_type_ops.h> |
| 1527 | #include <ATen/ops/result_type_native.h> |
| 1528 | #include <ATen/ops/result_type_ops.h> |
| 1529 | #include <ATen/ops/result_type_native.h> |
| 1530 | #include <ATen/ops/result_type_ops.h> |
| 1531 | #include <ATen/ops/_thnn_differentiable_gru_cell_backward_native.h> |
| 1532 | #include <ATen/ops/_thnn_differentiable_gru_cell_backward_ops.h> |
| 1533 | #include <ATen/ops/lstm_native.h> |
| 1534 | #include <ATen/ops/lstm_ops.h> |
| 1535 | #include <ATen/ops/lstm_native.h> |
| 1536 | #include <ATen/ops/lstm_ops.h> |
| 1537 | #include <ATen/ops/gru_native.h> |
| 1538 | #include <ATen/ops/gru_ops.h> |
| 1539 | #include <ATen/ops/gru_native.h> |
| 1540 | #include <ATen/ops/gru_ops.h> |
| 1541 | #include <ATen/ops/lstm_cell_native.h> |
| 1542 | #include <ATen/ops/lstm_cell_ops.h> |
| 1543 | #include <ATen/ops/quantized_gru_cell_native.h> |
| 1544 | #include <ATen/ops/quantized_gru_cell_ops.h> |
| 1545 | #include <ATen/ops/quantized_rnn_tanh_cell_native.h> |
| 1546 | #include <ATen/ops/quantized_rnn_tanh_cell_ops.h> |
| 1547 | #include <ATen/ops/index_add_native.h> |
| 1548 | #include <ATen/ops/index_add_ops.h> |
| 1549 | #include <ATen/ops/index_fill_native.h> |
| 1550 | #include <ATen/ops/index_fill_ops.h> |
| 1551 | #include <ATen/ops/index_fill_native.h> |
| 1552 | #include <ATen/ops/index_fill_ops.h> |
| 1553 | #include <ATen/ops/index_fill_native.h> |
| 1554 | #include <ATen/ops/index_fill_ops.h> |
| 1555 | #include <ATen/ops/index_fill_native.h> |
| 1556 | #include <ATen/ops/index_fill_ops.h> |
| 1557 | #include <ATen/ops/scatter_native.h> |
| 1558 | #include <ATen/ops/scatter_ops.h> |
| 1559 | #include <ATen/ops/scatter_native.h> |
| 1560 | #include <ATen/ops/scatter_ops.h> |
| 1561 | #include <ATen/ops/scatter_add_native.h> |
| 1562 | #include <ATen/ops/scatter_add_ops.h> |
| 1563 | #include <ATen/ops/and_native.h> |
| 1564 | #include <ATen/ops/and_ops.h> |
| 1565 | #include <ATen/ops/and_native.h> |
| 1566 | #include <ATen/ops/and_ops.h> |
| 1567 | #include <ATen/ops/and_native.h> |
| 1568 | #include <ATen/ops/and_ops.h> |
| 1569 | #include <ATen/ops/and_native.h> |
| 1570 | #include <ATen/ops/and_ops.h> |
| 1571 | #include <ATen/ops/or_native.h> |
| 1572 | #include <ATen/ops/or_ops.h> |
| 1573 | #include <ATen/ops/or_native.h> |
| 1574 | #include <ATen/ops/or_ops.h> |
| 1575 | #include <ATen/ops/or_native.h> |
| 1576 | #include <ATen/ops/or_ops.h> |
| 1577 | #include <ATen/ops/or_native.h> |
| 1578 | #include <ATen/ops/or_ops.h> |
| 1579 | #include <ATen/ops/index_select_backward_native.h> |
| 1580 | #include <ATen/ops/index_select_backward_ops.h> |
| 1581 | #include <ATen/ops/nonzero_numpy_native.h> |
| 1582 | #include <ATen/ops/nonzero_numpy_ops.h> |
| 1583 | #include <ATen/ops/_linalg_check_errors_native.h> |
| 1584 | #include <ATen/ops/_linalg_check_errors_ops.h> |
| 1585 | #include <ATen/ops/_lu_with_info_native.h> |
| 1586 | #include <ATen/ops/_lu_with_info_ops.h> |
| 1587 | #include <ATen/ops/polygamma_native.h> |
| 1588 | #include <ATen/ops/polygamma_ops.h> |
| 1589 | #include <ATen/ops/nll_loss_nd_native.h> |
| 1590 | #include <ATen/ops/nll_loss_nd_ops.h> |
| 1591 | #include <ATen/ops/upsample_linear1d_native.h> |
| 1592 | #include <ATen/ops/upsample_linear1d_ops.h> |
| 1593 | #include <ATen/ops/upsample_bicubic2d_native.h> |
| 1594 | #include <ATen/ops/upsample_bicubic2d_ops.h> |
| 1595 | #include <ATen/ops/upsample_nearest1d_native.h> |
| 1596 | #include <ATen/ops/upsample_nearest1d_ops.h> |
| 1597 | #include <ATen/ops/_upsample_nearest_exact1d_native.h> |
| 1598 | #include <ATen/ops/_upsample_nearest_exact1d_ops.h> |
| 1599 | #include <ATen/ops/upsample_nearest3d_native.h> |
| 1600 | #include <ATen/ops/upsample_nearest3d_ops.h> |
| 1601 | #include <ATen/ops/det_native.h> |
| 1602 | #include <ATen/ops/det_ops.h> |
| 1603 | #include <ATen/ops/logdet_native.h> |
| 1604 | #include <ATen/ops/logdet_ops.h> |
| 1605 | #include <ATen/ops/_test_string_default_native.h> |
| 1606 | #include <ATen/ops/_test_string_default_ops.h> |
| 1607 | #include <ATen/ops/_scaled_dot_product_flash_attention_backward_native.h> |
| 1608 | #include <ATen/ops/_scaled_dot_product_flash_attention_backward_ops.h> |
| 1609 | #include <ATen/ops/_flash_attention_forward_native.h> |
| 1610 | #include <ATen/ops/_flash_attention_forward_ops.h> |
| 1611 | #include <ATen/ops/_efficient_attention_backward_native.h> |
| 1612 | #include <ATen/ops/_efficient_attention_backward_ops.h> |
| 1613 | #endif |
| 1614 | |
| 1615 | namespace at { |
| 1616 | namespace functionalization { |
| 1617 | |
| 1618 | // This keyset is used by functionalization when it calls into meta kernels |
| 1619 | // to accurately propagate stride metadata. |
| 1620 | // Exclude any modes: the purpose of calling into meta kernels is only as an implementation |
| 1621 | // detail to perform shape inference, and we don't want any modal keys to run. |
| 1622 | // Specifically, we want to prevent functionalization and Python modes from running. |
| 1623 | constexpr auto exclude_keys_for_meta_dispatch = |
| 1624 | c10::functorch_transforms_ks | |
| 1625 | c10::DispatchKeySet({ |
| 1626 | c10::DispatchKey::FuncTorchDynamicLayerBackMode, |
| 1627 | c10::DispatchKey::FuncTorchDynamicLayerFrontMode, |
| 1628 | c10::DispatchKey::Python |
| 1629 | }); |
| 1630 | |
| 1631 | |
| 1632 | inline Tensor to_meta(const Tensor& t) { |
| 1633 | if (!t.defined()) return t; |
| 1634 | return at::native::empty_strided_meta_symint(t.sym_sizes(), t.sym_strides(), |
| 1635 | /*dtype=*/c10::make_optional(t.scalar_type()), /*layout=*/c10::make_optional(t.layout()), |
| 1636 | /*device=*/c10::make_optional(c10::Device(kMeta)), /*pin_memory=*/c10::nullopt); |
| 1637 | } |
| 1638 | |
| 1639 | inline c10::optional<Tensor> to_meta(const c10::optional<Tensor>& t) { |
| 1640 | if (t.has_value()) { |
| 1641 | return c10::make_optional<Tensor>(to_meta(*t)); |
| 1642 | } |
| 1643 | return c10::nullopt; |
| 1644 | } |
| 1645 | |
| 1646 | inline std::vector<Tensor> to_meta(at::ITensorListRef t_list) { |
| 1647 | std::vector<Tensor> outputs; |
| 1648 | outputs.reserve(t_list.size()); |
| 1649 | for (const auto& tensor : t_list) { |
| 1650 | outputs.push_back(to_meta(tensor)); |
| 1651 | } |
| 1652 | return outputs; |
| 1653 | } |
| 1654 | |
| 1655 | inline c10::List<Tensor> to_meta(const c10::List<Tensor>& t_list) { |
| 1656 | c10::List<Tensor> outputs; |
| 1657 | outputs.reserve(t_list.size()); |
| 1658 | for (const auto i : c10::irange(t_list.size())) { |
| 1659 | outputs.push_back(to_meta(t_list[i])); |
| 1660 | } |
| 1661 | return outputs; |
| 1662 | } |
| 1663 | |
| 1664 | inline c10::List<c10::optional<Tensor>> to_meta(const c10::List<c10::optional<Tensor>>& t_list) { |
| 1665 | c10::List<c10::optional<Tensor>> outputs; |
| 1666 | outputs.reserve(t_list.size()); |
| 1667 | for (const auto i : c10::irange(t_list.size())) { |
| 1668 | outputs.push_back(to_meta(t_list[i])); |
| 1669 | } |
| 1670 | return outputs; |
| 1671 | } |
| 1672 | |
| 1673 | |
| 1674 | |
| 1675 | at::Tensor & _masked_scale_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mask, double scale, at::Tensor & out) { |
| 1676 | if (false) { |
| 1677 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 1678 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 1679 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 1680 | auto self_meta = to_meta(self); |
| 1681 | auto mask_meta = to_meta(mask); |
| 1682 | auto out_meta = to_meta(out); |
| 1683 | at::AutoDispatchSkipFunctionalize func_guard; |
| 1684 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 1685 | at::_ops::_masked_scale_out::call(self_meta, mask_meta, scale, out_meta); |
| 1686 | } |
| 1687 | |
| 1688 | at::Tensor self_; |
| 1689 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 1690 | at::functionalization::impl::sync(self); |
| 1691 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 1692 | } else { |
| 1693 | self_ = self; |
| 1694 | } |
| 1695 | |
| 1696 | at::Tensor mask_; |
| 1697 | if (at::functionalization::impl::isFunctionalTensor(mask)) { |
| 1698 | at::functionalization::impl::sync(mask); |
| 1699 | mask_ = at::functionalization::impl::from_functional_tensor(mask); |
| 1700 | } else { |
| 1701 | mask_ = mask; |
| 1702 | } |
| 1703 | |
| 1704 | at::Tensor out_; |
| 1705 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 1706 | at::functionalization::impl::sync(out); |
| 1707 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 1708 | } else { |
| 1709 | out_ = out; |
| 1710 | } |
| 1711 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 1712 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(mask))) { |
| 1713 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 1714 | TORCH_INTERNAL_ASSERT(false, |
| 1715 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 1716 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 1717 | } else { |
| 1718 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 1719 | at::AutoDispatchSkipFunctionalize guard; |
| 1720 | at::Tensor tmp_output = at::_ops::_masked_scale_out::call(self_, mask_, scale, out_); |
| 1721 | return out;; |
| 1722 | } |
| 1723 | } else { |
| 1724 | at::Tensor tmp_output; |
| 1725 | { |
| 1726 | at::AutoDispatchSkipFunctionalize guard; |
| 1727 | tmp_output = at::_ops::_masked_scale::call(self_, mask_, scale); |
| 1728 | } |
| 1729 | at::functionalization::impl::replace_(out, tmp_output); |
| 1730 | at::functionalization::impl::commit_update(out); |
| 1731 | at::functionalization::impl::sync(out); |
| 1732 | return out; |
| 1733 | } |
| 1734 | } |
| 1735 | |
| 1736 | ::std::tuple<at::Tensor &,at::Tensor &> native_dropout_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, double p, c10::optional<bool> train, at::Tensor & out0, at::Tensor & out1) { |
| 1737 | if (false) { |
| 1738 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 1739 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 1740 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 1741 | auto input_meta = to_meta(input); |
| 1742 | auto out0_meta = to_meta(out0); |
| 1743 | auto out1_meta = to_meta(out1); |
| 1744 | at::AutoDispatchSkipFunctionalize func_guard; |
| 1745 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 1746 | at::_ops::native_dropout_out::call(input_meta, p, train, out0_meta, out1_meta); |
| 1747 | } |
| 1748 | |
| 1749 | at::Tensor input_; |
| 1750 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 1751 | at::functionalization::impl::sync(input); |
| 1752 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 1753 | } else { |
| 1754 | input_ = input; |
| 1755 | } |
| 1756 | |
| 1757 | at::Tensor out0_; |
| 1758 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 1759 | at::functionalization::impl::sync(out0); |
| 1760 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 1761 | } else { |
| 1762 | out0_ = out0; |
| 1763 | } |
| 1764 | |
| 1765 | at::Tensor out1_; |
| 1766 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 1767 | at::functionalization::impl::sync(out1); |
| 1768 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 1769 | } else { |
| 1770 | out1_ = out1; |
| 1771 | } |
| 1772 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1))) { |
| 1773 | if ((false || at::functionalization::impl::isFunctionalTensor(input))) { |
| 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 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::native_dropout_out::call(input_, p, train, out0_, out1_); |
| 1782 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1);; |
| 1783 | } |
| 1784 | } else { |
| 1785 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 1786 | { |
| 1787 | at::AutoDispatchSkipFunctionalize guard; |
| 1788 | tmp_output = at::_ops::native_dropout::call(input_, p, train); |
| 1789 | } |
| 1790 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 1791 | at::functionalization::impl::commit_update(out0); |
| 1792 | at::functionalization::impl::sync(out0); |
| 1793 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 1794 | at::functionalization::impl::commit_update(out1); |
| 1795 | at::functionalization::impl::sync(out1); |
| 1796 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1); |
| 1797 | } |
| 1798 | } |
| 1799 | |
| 1800 | at::Tensor & native_dropout_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & mask, double scale, at::Tensor & out) { |
| 1801 | if (false) { |
| 1802 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 1803 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 1804 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 1805 | auto grad_output_meta = to_meta(grad_output); |
| 1806 | auto mask_meta = to_meta(mask); |
| 1807 | auto out_meta = to_meta(out); |
| 1808 | at::AutoDispatchSkipFunctionalize func_guard; |
| 1809 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 1810 | at::_ops::native_dropout_backward_out::call(grad_output_meta, mask_meta, scale, out_meta); |
| 1811 | } |
| 1812 | |
| 1813 | at::Tensor grad_output_; |
| 1814 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 1815 | at::functionalization::impl::sync(grad_output); |
| 1816 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 1817 | } else { |
| 1818 | grad_output_ = grad_output; |
| 1819 | } |
| 1820 | |
| 1821 | at::Tensor mask_; |
| 1822 | if (at::functionalization::impl::isFunctionalTensor(mask)) { |
| 1823 | at::functionalization::impl::sync(mask); |
| 1824 | mask_ = at::functionalization::impl::from_functional_tensor(mask); |
| 1825 | } else { |
| 1826 | mask_ = mask; |
| 1827 | } |
| 1828 | |
| 1829 | at::Tensor out_; |
| 1830 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 1831 | at::functionalization::impl::sync(out); |
| 1832 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 1833 | } else { |
| 1834 | out_ = out; |
| 1835 | } |
| 1836 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 1837 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(mask))) { |
| 1838 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 1839 | TORCH_INTERNAL_ASSERT(false, |
| 1840 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 1841 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 1842 | } else { |
| 1843 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 1844 | at::AutoDispatchSkipFunctionalize guard; |
| 1845 | at::Tensor tmp_output = at::_ops::native_dropout_backward_out::call(grad_output_, mask_, scale, out_); |
| 1846 | return out;; |
| 1847 | } |
| 1848 | } else { |
| 1849 | at::Tensor tmp_output; |
| 1850 | { |
| 1851 | at::AutoDispatchSkipFunctionalize guard; |
| 1852 | tmp_output = at::_ops::native_dropout_backward::call(grad_output_, mask_, scale); |
| 1853 | } |
| 1854 | at::functionalization::impl::replace_(out, tmp_output); |
| 1855 | at::functionalization::impl::commit_update(out); |
| 1856 | at::functionalization::impl::sync(out); |
| 1857 | return out; |
| 1858 | } |
| 1859 | } |
| 1860 | |
| 1861 | at::Tensor & asinh_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 1862 | if (false) { |
| 1863 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 1864 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 1865 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 1866 | auto self_meta = to_meta(self); |
| 1867 | auto out_meta = to_meta(out); |
| 1868 | at::AutoDispatchSkipFunctionalize func_guard; |
| 1869 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 1870 | at::_ops::asinh_out::call(self_meta, out_meta); |
| 1871 | } |
| 1872 | |
| 1873 | at::Tensor self_; |
| 1874 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 1875 | at::functionalization::impl::sync(self); |
| 1876 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 1877 | } else { |
| 1878 | self_ = self; |
| 1879 | } |
| 1880 | |
| 1881 | at::Tensor out_; |
| 1882 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 1883 | at::functionalization::impl::sync(out); |
| 1884 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 1885 | } else { |
| 1886 | out_ = out; |
| 1887 | } |
| 1888 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 1889 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 1890 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 1891 | TORCH_INTERNAL_ASSERT(false, |
| 1892 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 1893 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 1894 | } else { |
| 1895 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 1896 | at::AutoDispatchSkipFunctionalize guard; |
| 1897 | at::Tensor tmp_output = at::_ops::asinh_out::call(self_, out_); |
| 1898 | return out;; |
| 1899 | } |
| 1900 | } else { |
| 1901 | at::Tensor tmp_output; |
| 1902 | { |
| 1903 | at::AutoDispatchSkipFunctionalize guard; |
| 1904 | tmp_output = at::_ops::asinh::call(self_); |
| 1905 | } |
| 1906 | at::functionalization::impl::replace_(out, tmp_output); |
| 1907 | at::functionalization::impl::commit_update(out); |
| 1908 | at::functionalization::impl::sync(out); |
| 1909 | return out; |
| 1910 | } |
| 1911 | } |
| 1912 | |
| 1913 | at::Tensor & asinh_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 1914 | if (true) { |
| 1915 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 1916 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 1917 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 1918 | auto self_meta = to_meta(self); |
| 1919 | at::AutoDispatchSkipFunctionalize func_guard; |
| 1920 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 1921 | at::_ops::asinh_::call(self_meta); |
| 1922 | } |
| 1923 | |
| 1924 | at::Tensor self_; |
| 1925 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 1926 | at::functionalization::impl::sync(self); |
| 1927 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 1928 | } else { |
| 1929 | self_ = self; |
| 1930 | } |
| 1931 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 1932 | if ((false)) { |
| 1933 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 1934 | TORCH_INTERNAL_ASSERT(false, |
| 1935 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 1936 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 1937 | } else { |
| 1938 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 1939 | at::AutoDispatchSkipFunctionalize guard; |
| 1940 | at::Tensor tmp_output = at::_ops::asinh_::call(self_); |
| 1941 | return self;; |
| 1942 | } |
| 1943 | } else { |
| 1944 | at::Tensor tmp_output; |
| 1945 | { |
| 1946 | at::AutoDispatchSkipFunctionalize guard; |
| 1947 | tmp_output = at::_ops::asinh::call(self_); |
| 1948 | } |
| 1949 | at::functionalization::impl::replace_(self, tmp_output); |
| 1950 | at::functionalization::impl::commit_update(self); |
| 1951 | at::functionalization::impl::sync(self); |
| 1952 | return self; |
| 1953 | } |
| 1954 | } |
| 1955 | |
| 1956 | at::Tensor & arctanh_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 1957 | if (false) { |
| 1958 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 1959 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 1960 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 1961 | auto self_meta = to_meta(self); |
| 1962 | auto out_meta = to_meta(out); |
| 1963 | at::AutoDispatchSkipFunctionalize func_guard; |
| 1964 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 1965 | at::_ops::arctanh_out::call(self_meta, out_meta); |
| 1966 | } |
| 1967 | |
| 1968 | at::Tensor self_; |
| 1969 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 1970 | at::functionalization::impl::sync(self); |
| 1971 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 1972 | } else { |
| 1973 | self_ = self; |
| 1974 | } |
| 1975 | |
| 1976 | at::Tensor out_; |
| 1977 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 1978 | at::functionalization::impl::sync(out); |
| 1979 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 1980 | } else { |
| 1981 | out_ = out; |
| 1982 | } |
| 1983 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 1984 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 1985 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 1986 | TORCH_INTERNAL_ASSERT(false, |
| 1987 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 1988 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 1989 | } else { |
| 1990 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 1991 | at::AutoDispatchSkipFunctionalize guard; |
| 1992 | at::Tensor tmp_output = at::_ops::arctanh_out::call(self_, out_); |
| 1993 | return out;; |
| 1994 | } |
| 1995 | } else { |
| 1996 | at::Tensor tmp_output; |
| 1997 | { |
| 1998 | at::AutoDispatchSkipFunctionalize guard; |
| 1999 | tmp_output = at::_ops::arctanh::call(self_); |
| 2000 | } |
| 2001 | at::functionalization::impl::replace_(out, tmp_output); |
| 2002 | at::functionalization::impl::commit_update(out); |
| 2003 | at::functionalization::impl::sync(out); |
| 2004 | return out; |
| 2005 | } |
| 2006 | } |
| 2007 | |
| 2008 | at::Tensor & arctanh_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 2009 | if (true) { |
| 2010 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2011 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2012 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2013 | auto self_meta = to_meta(self); |
| 2014 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2015 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2016 | at::_ops::arctanh_::call(self_meta); |
| 2017 | } |
| 2018 | |
| 2019 | at::Tensor self_; |
| 2020 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2021 | at::functionalization::impl::sync(self); |
| 2022 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2023 | } else { |
| 2024 | self_ = self; |
| 2025 | } |
| 2026 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 2027 | if ((false)) { |
| 2028 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2029 | TORCH_INTERNAL_ASSERT(false, |
| 2030 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2031 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2032 | } else { |
| 2033 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2034 | at::AutoDispatchSkipFunctionalize guard; |
| 2035 | at::Tensor tmp_output = at::_ops::arctanh_::call(self_); |
| 2036 | return self;; |
| 2037 | } |
| 2038 | } else { |
| 2039 | at::Tensor tmp_output; |
| 2040 | { |
| 2041 | at::AutoDispatchSkipFunctionalize guard; |
| 2042 | tmp_output = at::_ops::arctanh::call(self_); |
| 2043 | } |
| 2044 | at::functionalization::impl::replace_(self, tmp_output); |
| 2045 | at::functionalization::impl::commit_update(self); |
| 2046 | at::functionalization::impl::sync(self); |
| 2047 | return self; |
| 2048 | } |
| 2049 | } |
| 2050 | |
| 2051 | at::Tensor & baddbmm_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { |
| 2052 | if (false) { |
| 2053 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2054 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2055 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2056 | auto self_meta = to_meta(self); |
| 2057 | auto batch1_meta = to_meta(batch1); |
| 2058 | auto batch2_meta = to_meta(batch2); |
| 2059 | auto out_meta = to_meta(out); |
| 2060 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2061 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2062 | at::_ops::baddbmm_out::call(self_meta, batch1_meta, batch2_meta, beta, alpha, out_meta); |
| 2063 | } |
| 2064 | |
| 2065 | at::Tensor self_; |
| 2066 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2067 | at::functionalization::impl::sync(self); |
| 2068 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2069 | } else { |
| 2070 | self_ = self; |
| 2071 | } |
| 2072 | |
| 2073 | at::Tensor batch1_; |
| 2074 | if (at::functionalization::impl::isFunctionalTensor(batch1)) { |
| 2075 | at::functionalization::impl::sync(batch1); |
| 2076 | batch1_ = at::functionalization::impl::from_functional_tensor(batch1); |
| 2077 | } else { |
| 2078 | batch1_ = batch1; |
| 2079 | } |
| 2080 | |
| 2081 | at::Tensor batch2_; |
| 2082 | if (at::functionalization::impl::isFunctionalTensor(batch2)) { |
| 2083 | at::functionalization::impl::sync(batch2); |
| 2084 | batch2_ = at::functionalization::impl::from_functional_tensor(batch2); |
| 2085 | } else { |
| 2086 | batch2_ = batch2; |
| 2087 | } |
| 2088 | |
| 2089 | at::Tensor out_; |
| 2090 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2091 | at::functionalization::impl::sync(out); |
| 2092 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2093 | } else { |
| 2094 | out_ = out; |
| 2095 | } |
| 2096 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2097 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(batch1) || at::functionalization::impl::isFunctionalTensor(batch2))) { |
| 2098 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2099 | TORCH_INTERNAL_ASSERT(false, |
| 2100 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2101 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2102 | } else { |
| 2103 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2104 | at::AutoDispatchSkipFunctionalize guard; |
| 2105 | at::Tensor tmp_output = at::_ops::baddbmm_out::call(self_, batch1_, batch2_, beta, alpha, out_); |
| 2106 | return out;; |
| 2107 | } |
| 2108 | } else { |
| 2109 | at::Tensor tmp_output; |
| 2110 | { |
| 2111 | at::AutoDispatchSkipFunctionalize guard; |
| 2112 | tmp_output = at::_ops::baddbmm::call(self_, batch1_, batch2_, beta, alpha); |
| 2113 | } |
| 2114 | at::functionalization::impl::replace_(out, tmp_output); |
| 2115 | at::functionalization::impl::commit_update(out); |
| 2116 | at::functionalization::impl::sync(out); |
| 2117 | return out; |
| 2118 | } |
| 2119 | } |
| 2120 | |
| 2121 | at::Tensor & baddbmm_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 2122 | if (true) { |
| 2123 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2124 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2125 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2126 | auto self_meta = to_meta(self); |
| 2127 | auto batch1_meta = to_meta(batch1); |
| 2128 | auto batch2_meta = to_meta(batch2); |
| 2129 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2130 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2131 | at::_ops::baddbmm_::call(self_meta, batch1_meta, batch2_meta, beta, alpha); |
| 2132 | } |
| 2133 | |
| 2134 | at::Tensor self_; |
| 2135 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2136 | at::functionalization::impl::sync(self); |
| 2137 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2138 | } else { |
| 2139 | self_ = self; |
| 2140 | } |
| 2141 | |
| 2142 | at::Tensor batch1_; |
| 2143 | if (at::functionalization::impl::isFunctionalTensor(batch1)) { |
| 2144 | at::functionalization::impl::sync(batch1); |
| 2145 | batch1_ = at::functionalization::impl::from_functional_tensor(batch1); |
| 2146 | } else { |
| 2147 | batch1_ = batch1; |
| 2148 | } |
| 2149 | |
| 2150 | at::Tensor batch2_; |
| 2151 | if (at::functionalization::impl::isFunctionalTensor(batch2)) { |
| 2152 | at::functionalization::impl::sync(batch2); |
| 2153 | batch2_ = at::functionalization::impl::from_functional_tensor(batch2); |
| 2154 | } else { |
| 2155 | batch2_ = batch2; |
| 2156 | } |
| 2157 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 2158 | if ((false || at::functionalization::impl::isFunctionalTensor(batch1) || at::functionalization::impl::isFunctionalTensor(batch2))) { |
| 2159 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2160 | TORCH_INTERNAL_ASSERT(false, |
| 2161 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2162 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2163 | } else { |
| 2164 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2165 | at::AutoDispatchSkipFunctionalize guard; |
| 2166 | at::Tensor tmp_output = at::_ops::baddbmm_::call(self_, batch1_, batch2_, beta, alpha); |
| 2167 | return self;; |
| 2168 | } |
| 2169 | } else { |
| 2170 | at::Tensor tmp_output; |
| 2171 | { |
| 2172 | at::AutoDispatchSkipFunctionalize guard; |
| 2173 | tmp_output = at::_ops::baddbmm::call(self_, batch1_, batch2_, beta, alpha); |
| 2174 | } |
| 2175 | at::functionalization::impl::replace_(self, tmp_output); |
| 2176 | at::functionalization::impl::commit_update(self); |
| 2177 | at::functionalization::impl::sync(self); |
| 2178 | return self; |
| 2179 | } |
| 2180 | } |
| 2181 | |
| 2182 | at::Tensor & quantized_batch_norm_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 & var, double eps, double output_scale, int64_t output_zero_point, at::Tensor & out) { |
| 2183 | if (false) { |
| 2184 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2185 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2186 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2187 | auto input_meta = to_meta(input); |
| 2188 | auto weight_meta = to_meta(weight); |
| 2189 | auto bias_meta = to_meta(bias); |
| 2190 | auto mean_meta = to_meta(mean); |
| 2191 | auto var_meta = to_meta(var); |
| 2192 | auto out_meta = to_meta(out); |
| 2193 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2194 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2195 | at::_ops::quantized_batch_norm_out::call(input_meta, weight_meta, bias_meta, mean_meta, var_meta, eps, output_scale, output_zero_point, out_meta); |
| 2196 | } |
| 2197 | |
| 2198 | at::Tensor input_; |
| 2199 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 2200 | at::functionalization::impl::sync(input); |
| 2201 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 2202 | } else { |
| 2203 | input_ = input; |
| 2204 | } |
| 2205 | |
| 2206 | c10::optional<at::Tensor> weight_; |
| 2207 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 2208 | at::functionalization::impl::sync(weight); |
| 2209 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 2210 | } else { |
| 2211 | weight_ = weight; |
| 2212 | } |
| 2213 | |
| 2214 | c10::optional<at::Tensor> bias_; |
| 2215 | if (at::functionalization::impl::isFunctionalTensor(bias)) { |
| 2216 | at::functionalization::impl::sync(bias); |
| 2217 | bias_ = at::functionalization::impl::from_functional_tensor(bias); |
| 2218 | } else { |
| 2219 | bias_ = bias; |
| 2220 | } |
| 2221 | |
| 2222 | at::Tensor mean_; |
| 2223 | if (at::functionalization::impl::isFunctionalTensor(mean)) { |
| 2224 | at::functionalization::impl::sync(mean); |
| 2225 | mean_ = at::functionalization::impl::from_functional_tensor(mean); |
| 2226 | } else { |
| 2227 | mean_ = mean; |
| 2228 | } |
| 2229 | |
| 2230 | at::Tensor var_; |
| 2231 | if (at::functionalization::impl::isFunctionalTensor(var)) { |
| 2232 | at::functionalization::impl::sync(var); |
| 2233 | var_ = at::functionalization::impl::from_functional_tensor(var); |
| 2234 | } else { |
| 2235 | var_ = var; |
| 2236 | } |
| 2237 | |
| 2238 | at::Tensor out_; |
| 2239 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2240 | at::functionalization::impl::sync(out); |
| 2241 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2242 | } else { |
| 2243 | out_ = out; |
| 2244 | } |
| 2245 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2246 | 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(var))) { |
| 2247 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2248 | TORCH_INTERNAL_ASSERT(false, |
| 2249 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2250 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2251 | } else { |
| 2252 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2253 | at::AutoDispatchSkipFunctionalize guard; |
| 2254 | at::Tensor tmp_output = at::_ops::quantized_batch_norm_out::call(input_, weight_, bias_, mean_, var_, eps, output_scale, output_zero_point, out_); |
| 2255 | return out;; |
| 2256 | } |
| 2257 | } else { |
| 2258 | at::Tensor tmp_output; |
| 2259 | { |
| 2260 | at::AutoDispatchSkipFunctionalize guard; |
| 2261 | tmp_output = at::_ops::quantized_batch_norm::call(input_, weight_, bias_, mean_, var_, eps, output_scale, output_zero_point); |
| 2262 | } |
| 2263 | at::functionalization::impl::replace_(out, tmp_output); |
| 2264 | at::functionalization::impl::commit_update(out); |
| 2265 | at::functionalization::impl::sync(out); |
| 2266 | return out; |
| 2267 | } |
| 2268 | } |
| 2269 | |
| 2270 | at::Tensor & bernoulli_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 2271 | if (false) { |
| 2272 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2273 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2274 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2275 | auto self_meta = to_meta(self); |
| 2276 | auto out_meta = to_meta(out); |
| 2277 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2278 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2279 | at::_ops::bernoulli_out::call(self_meta, generator, out_meta); |
| 2280 | } |
| 2281 | |
| 2282 | at::Tensor self_; |
| 2283 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2284 | at::functionalization::impl::sync(self); |
| 2285 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2286 | } else { |
| 2287 | self_ = self; |
| 2288 | } |
| 2289 | |
| 2290 | at::Tensor out_; |
| 2291 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2292 | at::functionalization::impl::sync(out); |
| 2293 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2294 | } else { |
| 2295 | out_ = out; |
| 2296 | } |
| 2297 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2298 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 2299 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2300 | TORCH_INTERNAL_ASSERT(false, |
| 2301 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2302 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2303 | } else { |
| 2304 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2305 | at::AutoDispatchSkipFunctionalize guard; |
| 2306 | at::Tensor tmp_output = at::_ops::bernoulli_out::call(self_, generator, out_); |
| 2307 | return out;; |
| 2308 | } |
| 2309 | } else { |
| 2310 | at::Tensor tmp_output; |
| 2311 | { |
| 2312 | at::AutoDispatchSkipFunctionalize guard; |
| 2313 | tmp_output = at::_ops::bernoulli::call(self_, generator); |
| 2314 | } |
| 2315 | at::functionalization::impl::replace_(out, tmp_output); |
| 2316 | at::functionalization::impl::commit_update(out); |
| 2317 | at::functionalization::impl::sync(out); |
| 2318 | return out; |
| 2319 | } |
| 2320 | } |
| 2321 | |
| 2322 | at::Tensor & bernoulli_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & p, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 2323 | if (false) { |
| 2324 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2325 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2326 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2327 | auto self_meta = to_meta(self); |
| 2328 | auto p_meta = to_meta(p); |
| 2329 | auto out_meta = to_meta(out); |
| 2330 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2331 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2332 | at::_ops::bernoulli_Tensor_out::call(self_meta, p_meta, generator, out_meta); |
| 2333 | } |
| 2334 | |
| 2335 | at::Tensor self_; |
| 2336 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2337 | at::functionalization::impl::sync(self); |
| 2338 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2339 | } else { |
| 2340 | self_ = self; |
| 2341 | } |
| 2342 | |
| 2343 | at::Tensor p_; |
| 2344 | if (at::functionalization::impl::isFunctionalTensor(p)) { |
| 2345 | at::functionalization::impl::sync(p); |
| 2346 | p_ = at::functionalization::impl::from_functional_tensor(p); |
| 2347 | } else { |
| 2348 | p_ = p; |
| 2349 | } |
| 2350 | |
| 2351 | at::Tensor out_; |
| 2352 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2353 | at::functionalization::impl::sync(out); |
| 2354 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2355 | } else { |
| 2356 | out_ = out; |
| 2357 | } |
| 2358 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2359 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(p))) { |
| 2360 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2361 | TORCH_INTERNAL_ASSERT(false, |
| 2362 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2363 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2364 | } else { |
| 2365 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2366 | at::AutoDispatchSkipFunctionalize guard; |
| 2367 | at::Tensor tmp_output = at::_ops::bernoulli_Tensor_out::call(self_, p_, generator, out_); |
| 2368 | return out;; |
| 2369 | } |
| 2370 | } else { |
| 2371 | at::Tensor tmp_output; |
| 2372 | { |
| 2373 | at::AutoDispatchSkipFunctionalize guard; |
| 2374 | tmp_output = at::_ops::bernoulli_Tensor::call(self_, p_, generator); |
| 2375 | } |
| 2376 | at::functionalization::impl::replace_(out, tmp_output); |
| 2377 | at::functionalization::impl::commit_update(out); |
| 2378 | at::functionalization::impl::sync(out); |
| 2379 | return out; |
| 2380 | } |
| 2381 | } |
| 2382 | |
| 2383 | at::Tensor & bernoulli__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & p, c10::optional<at::Generator> generator) { |
| 2384 | if (true) { |
| 2385 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2386 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2387 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2388 | auto self_meta = to_meta(self); |
| 2389 | auto p_meta = to_meta(p); |
| 2390 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2391 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2392 | at::_ops::bernoulli__Tensor::call(self_meta, p_meta, generator); |
| 2393 | } |
| 2394 | |
| 2395 | at::Tensor self_; |
| 2396 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2397 | at::functionalization::impl::sync(self); |
| 2398 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2399 | } else { |
| 2400 | self_ = self; |
| 2401 | } |
| 2402 | |
| 2403 | at::Tensor p_; |
| 2404 | if (at::functionalization::impl::isFunctionalTensor(p)) { |
| 2405 | at::functionalization::impl::sync(p); |
| 2406 | p_ = at::functionalization::impl::from_functional_tensor(p); |
| 2407 | } else { |
| 2408 | p_ = p; |
| 2409 | } |
| 2410 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 2411 | if ((false || at::functionalization::impl::isFunctionalTensor(p))) { |
| 2412 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2413 | TORCH_INTERNAL_ASSERT(false, |
| 2414 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2415 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2416 | } else { |
| 2417 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2418 | at::AutoDispatchSkipFunctionalize guard; |
| 2419 | at::Tensor tmp_output = at::_ops::bernoulli__Tensor::call(self_, p_, generator); |
| 2420 | return self;; |
| 2421 | } |
| 2422 | } else { |
| 2423 | at::Tensor tmp_output; |
| 2424 | { |
| 2425 | at::AutoDispatchSkipFunctionalize guard; |
| 2426 | tmp_output = at::_ops::bernoulli_Tensor::call(self_, p_, generator); |
| 2427 | } |
| 2428 | at::functionalization::impl::replace_(self, tmp_output); |
| 2429 | at::functionalization::impl::commit_update(self); |
| 2430 | at::functionalization::impl::sync(self); |
| 2431 | return self; |
| 2432 | } |
| 2433 | } |
| 2434 | |
| 2435 | at::Tensor & bernoulli_out_float_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double p, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 2436 | if (false) { |
| 2437 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2438 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2439 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2440 | auto self_meta = to_meta(self); |
| 2441 | auto out_meta = to_meta(out); |
| 2442 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2443 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2444 | at::_ops::bernoulli_float_out::call(self_meta, p, generator, out_meta); |
| 2445 | } |
| 2446 | |
| 2447 | at::Tensor self_; |
| 2448 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2449 | at::functionalization::impl::sync(self); |
| 2450 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2451 | } else { |
| 2452 | self_ = self; |
| 2453 | } |
| 2454 | |
| 2455 | at::Tensor out_; |
| 2456 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2457 | at::functionalization::impl::sync(out); |
| 2458 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2459 | } else { |
| 2460 | out_ = out; |
| 2461 | } |
| 2462 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2463 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 2464 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2465 | TORCH_INTERNAL_ASSERT(false, |
| 2466 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2467 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2468 | } else { |
| 2469 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2470 | at::AutoDispatchSkipFunctionalize guard; |
| 2471 | at::Tensor tmp_output = at::_ops::bernoulli_float_out::call(self_, p, generator, out_); |
| 2472 | return out;; |
| 2473 | } |
| 2474 | } else { |
| 2475 | at::Tensor tmp_output; |
| 2476 | { |
| 2477 | at::AutoDispatchSkipFunctionalize guard; |
| 2478 | tmp_output = at::_ops::bernoulli_p::call(self_, p, generator); |
| 2479 | } |
| 2480 | at::functionalization::impl::replace_(out, tmp_output); |
| 2481 | at::functionalization::impl::commit_update(out); |
| 2482 | at::functionalization::impl::sync(out); |
| 2483 | return out; |
| 2484 | } |
| 2485 | } |
| 2486 | |
| 2487 | at::Tensor & bernoulli__float(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, double p, c10::optional<at::Generator> generator) { |
| 2488 | if (true) { |
| 2489 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2490 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2491 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2492 | auto self_meta = to_meta(self); |
| 2493 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2494 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2495 | at::_ops::bernoulli__float::call(self_meta, p, generator); |
| 2496 | } |
| 2497 | |
| 2498 | at::Tensor self_; |
| 2499 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2500 | at::functionalization::impl::sync(self); |
| 2501 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2502 | } else { |
| 2503 | self_ = self; |
| 2504 | } |
| 2505 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 2506 | if ((false)) { |
| 2507 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2508 | TORCH_INTERNAL_ASSERT(false, |
| 2509 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2510 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2511 | } else { |
| 2512 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2513 | at::AutoDispatchSkipFunctionalize guard; |
| 2514 | at::Tensor tmp_output = at::_ops::bernoulli__float::call(self_, p, generator); |
| 2515 | return self;; |
| 2516 | } |
| 2517 | } else { |
| 2518 | at::Tensor tmp_output; |
| 2519 | { |
| 2520 | at::AutoDispatchSkipFunctionalize guard; |
| 2521 | tmp_output = at::_ops::bernoulli_p::call(self_, p, generator); |
| 2522 | } |
| 2523 | at::functionalization::impl::replace_(self, tmp_output); |
| 2524 | at::functionalization::impl::commit_update(self); |
| 2525 | at::functionalization::impl::sync(self); |
| 2526 | return self; |
| 2527 | } |
| 2528 | } |
| 2529 | |
| 2530 | at::Tensor & bmm_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat2, at::Tensor & out) { |
| 2531 | if (false) { |
| 2532 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2533 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2534 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2535 | auto self_meta = to_meta(self); |
| 2536 | auto mat2_meta = to_meta(mat2); |
| 2537 | auto out_meta = to_meta(out); |
| 2538 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2539 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2540 | at::_ops::bmm_out::call(self_meta, mat2_meta, out_meta); |
| 2541 | } |
| 2542 | |
| 2543 | at::Tensor self_; |
| 2544 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2545 | at::functionalization::impl::sync(self); |
| 2546 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2547 | } else { |
| 2548 | self_ = self; |
| 2549 | } |
| 2550 | |
| 2551 | at::Tensor mat2_; |
| 2552 | if (at::functionalization::impl::isFunctionalTensor(mat2)) { |
| 2553 | at::functionalization::impl::sync(mat2); |
| 2554 | mat2_ = at::functionalization::impl::from_functional_tensor(mat2); |
| 2555 | } else { |
| 2556 | mat2_ = mat2; |
| 2557 | } |
| 2558 | |
| 2559 | at::Tensor out_; |
| 2560 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2561 | at::functionalization::impl::sync(out); |
| 2562 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2563 | } else { |
| 2564 | out_ = out; |
| 2565 | } |
| 2566 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2567 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(mat2))) { |
| 2568 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2569 | TORCH_INTERNAL_ASSERT(false, |
| 2570 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2571 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2572 | } else { |
| 2573 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2574 | at::AutoDispatchSkipFunctionalize guard; |
| 2575 | at::Tensor tmp_output = at::_ops::bmm_out::call(self_, mat2_, out_); |
| 2576 | return out;; |
| 2577 | } |
| 2578 | } else { |
| 2579 | at::Tensor tmp_output; |
| 2580 | { |
| 2581 | at::AutoDispatchSkipFunctionalize guard; |
| 2582 | tmp_output = at::_ops::bmm::call(self_, mat2_); |
| 2583 | } |
| 2584 | at::functionalization::impl::replace_(out, tmp_output); |
| 2585 | at::functionalization::impl::commit_update(out); |
| 2586 | at::functionalization::impl::sync(out); |
| 2587 | return out; |
| 2588 | } |
| 2589 | } |
| 2590 | |
| 2591 | at::Tensor & clamp_max_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & max, at::Tensor & out) { |
| 2592 | if (false) { |
| 2593 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2594 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2595 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2596 | auto self_meta = to_meta(self); |
| 2597 | auto out_meta = to_meta(out); |
| 2598 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2599 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2600 | at::_ops::clamp_max_out::call(self_meta, max, out_meta); |
| 2601 | } |
| 2602 | |
| 2603 | at::Tensor self_; |
| 2604 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2605 | at::functionalization::impl::sync(self); |
| 2606 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2607 | } else { |
| 2608 | self_ = self; |
| 2609 | } |
| 2610 | |
| 2611 | at::Tensor out_; |
| 2612 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2613 | at::functionalization::impl::sync(out); |
| 2614 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2615 | } else { |
| 2616 | out_ = out; |
| 2617 | } |
| 2618 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2619 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 2620 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2621 | TORCH_INTERNAL_ASSERT(false, |
| 2622 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2623 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2624 | } else { |
| 2625 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2626 | at::AutoDispatchSkipFunctionalize guard; |
| 2627 | at::Tensor tmp_output = at::_ops::clamp_max_out::call(self_, max, out_); |
| 2628 | return out;; |
| 2629 | } |
| 2630 | } else { |
| 2631 | at::Tensor tmp_output; |
| 2632 | { |
| 2633 | at::AutoDispatchSkipFunctionalize guard; |
| 2634 | tmp_output = at::_ops::clamp_max::call(self_, max); |
| 2635 | } |
| 2636 | at::functionalization::impl::replace_(out, tmp_output); |
| 2637 | at::functionalization::impl::commit_update(out); |
| 2638 | at::functionalization::impl::sync(out); |
| 2639 | return out; |
| 2640 | } |
| 2641 | } |
| 2642 | |
| 2643 | at::Tensor & clamp_max_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & max) { |
| 2644 | if (true) { |
| 2645 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2646 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2647 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2648 | auto self_meta = to_meta(self); |
| 2649 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2650 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2651 | at::_ops::clamp_max_::call(self_meta, max); |
| 2652 | } |
| 2653 | |
| 2654 | at::Tensor self_; |
| 2655 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2656 | at::functionalization::impl::sync(self); |
| 2657 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2658 | } else { |
| 2659 | self_ = self; |
| 2660 | } |
| 2661 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 2662 | if ((false)) { |
| 2663 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2664 | TORCH_INTERNAL_ASSERT(false, |
| 2665 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2666 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2667 | } else { |
| 2668 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2669 | at::AutoDispatchSkipFunctionalize guard; |
| 2670 | at::Tensor tmp_output = at::_ops::clamp_max_::call(self_, max); |
| 2671 | return self;; |
| 2672 | } |
| 2673 | } else { |
| 2674 | at::Tensor tmp_output; |
| 2675 | { |
| 2676 | at::AutoDispatchSkipFunctionalize guard; |
| 2677 | tmp_output = at::_ops::clamp_max::call(self_, max); |
| 2678 | } |
| 2679 | at::functionalization::impl::replace_(self, tmp_output); |
| 2680 | at::functionalization::impl::commit_update(self); |
| 2681 | at::functionalization::impl::sync(self); |
| 2682 | return self; |
| 2683 | } |
| 2684 | } |
| 2685 | |
| 2686 | at::Tensor & clamp_max_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & max, at::Tensor & out) { |
| 2687 | if (false) { |
| 2688 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2689 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2690 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2691 | auto self_meta = to_meta(self); |
| 2692 | auto max_meta = to_meta(max); |
| 2693 | auto out_meta = to_meta(out); |
| 2694 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2695 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2696 | at::_ops::clamp_max_Tensor_out::call(self_meta, max_meta, out_meta); |
| 2697 | } |
| 2698 | |
| 2699 | at::Tensor self_; |
| 2700 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2701 | at::functionalization::impl::sync(self); |
| 2702 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2703 | } else { |
| 2704 | self_ = self; |
| 2705 | } |
| 2706 | |
| 2707 | at::Tensor max_; |
| 2708 | if (at::functionalization::impl::isFunctionalTensor(max)) { |
| 2709 | at::functionalization::impl::sync(max); |
| 2710 | max_ = at::functionalization::impl::from_functional_tensor(max); |
| 2711 | } else { |
| 2712 | max_ = max; |
| 2713 | } |
| 2714 | |
| 2715 | at::Tensor out_; |
| 2716 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2717 | at::functionalization::impl::sync(out); |
| 2718 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2719 | } else { |
| 2720 | out_ = out; |
| 2721 | } |
| 2722 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2723 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(max))) { |
| 2724 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2725 | TORCH_INTERNAL_ASSERT(false, |
| 2726 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2727 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2728 | } else { |
| 2729 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2730 | at::AutoDispatchSkipFunctionalize guard; |
| 2731 | at::Tensor tmp_output = at::_ops::clamp_max_Tensor_out::call(self_, max_, out_); |
| 2732 | return out;; |
| 2733 | } |
| 2734 | } else { |
| 2735 | at::Tensor tmp_output; |
| 2736 | { |
| 2737 | at::AutoDispatchSkipFunctionalize guard; |
| 2738 | tmp_output = at::_ops::clamp_max_Tensor::call(self_, max_); |
| 2739 | } |
| 2740 | at::functionalization::impl::replace_(out, tmp_output); |
| 2741 | at::functionalization::impl::commit_update(out); |
| 2742 | at::functionalization::impl::sync(out); |
| 2743 | return out; |
| 2744 | } |
| 2745 | } |
| 2746 | |
| 2747 | at::Tensor & clamp_max__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & max) { |
| 2748 | if (true) { |
| 2749 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2750 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2751 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2752 | auto self_meta = to_meta(self); |
| 2753 | auto max_meta = to_meta(max); |
| 2754 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2755 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2756 | at::_ops::clamp_max__Tensor::call(self_meta, max_meta); |
| 2757 | } |
| 2758 | |
| 2759 | at::Tensor self_; |
| 2760 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2761 | at::functionalization::impl::sync(self); |
| 2762 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2763 | } else { |
| 2764 | self_ = self; |
| 2765 | } |
| 2766 | |
| 2767 | at::Tensor max_; |
| 2768 | if (at::functionalization::impl::isFunctionalTensor(max)) { |
| 2769 | at::functionalization::impl::sync(max); |
| 2770 | max_ = at::functionalization::impl::from_functional_tensor(max); |
| 2771 | } else { |
| 2772 | max_ = max; |
| 2773 | } |
| 2774 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 2775 | if ((false || at::functionalization::impl::isFunctionalTensor(max))) { |
| 2776 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2777 | TORCH_INTERNAL_ASSERT(false, |
| 2778 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2779 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2780 | } else { |
| 2781 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2782 | at::AutoDispatchSkipFunctionalize guard; |
| 2783 | at::Tensor tmp_output = at::_ops::clamp_max__Tensor::call(self_, max_); |
| 2784 | return self;; |
| 2785 | } |
| 2786 | } else { |
| 2787 | at::Tensor tmp_output; |
| 2788 | { |
| 2789 | at::AutoDispatchSkipFunctionalize guard; |
| 2790 | tmp_output = at::_ops::clamp_max_Tensor::call(self_, max_); |
| 2791 | } |
| 2792 | at::functionalization::impl::replace_(self, tmp_output); |
| 2793 | at::functionalization::impl::commit_update(self); |
| 2794 | at::functionalization::impl::sync(self); |
| 2795 | return self; |
| 2796 | } |
| 2797 | } |
| 2798 | |
| 2799 | at::Tensor & clamp_min_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & min, at::Tensor & out) { |
| 2800 | if (false) { |
| 2801 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2802 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2803 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2804 | auto self_meta = to_meta(self); |
| 2805 | auto out_meta = to_meta(out); |
| 2806 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2807 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2808 | at::_ops::clamp_min_out::call(self_meta, min, out_meta); |
| 2809 | } |
| 2810 | |
| 2811 | at::Tensor self_; |
| 2812 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2813 | at::functionalization::impl::sync(self); |
| 2814 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2815 | } else { |
| 2816 | self_ = self; |
| 2817 | } |
| 2818 | |
| 2819 | at::Tensor out_; |
| 2820 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2821 | at::functionalization::impl::sync(out); |
| 2822 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2823 | } else { |
| 2824 | out_ = out; |
| 2825 | } |
| 2826 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2827 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 2828 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2829 | TORCH_INTERNAL_ASSERT(false, |
| 2830 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2831 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2832 | } else { |
| 2833 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2834 | at::AutoDispatchSkipFunctionalize guard; |
| 2835 | at::Tensor tmp_output = at::_ops::clamp_min_out::call(self_, min, out_); |
| 2836 | return out;; |
| 2837 | } |
| 2838 | } else { |
| 2839 | at::Tensor tmp_output; |
| 2840 | { |
| 2841 | at::AutoDispatchSkipFunctionalize guard; |
| 2842 | tmp_output = at::_ops::clamp_min::call(self_, min); |
| 2843 | } |
| 2844 | at::functionalization::impl::replace_(out, tmp_output); |
| 2845 | at::functionalization::impl::commit_update(out); |
| 2846 | at::functionalization::impl::sync(out); |
| 2847 | return out; |
| 2848 | } |
| 2849 | } |
| 2850 | |
| 2851 | at::Tensor & clamp_min_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & min) { |
| 2852 | if (true) { |
| 2853 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2854 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2855 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2856 | auto self_meta = to_meta(self); |
| 2857 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2858 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2859 | at::_ops::clamp_min_::call(self_meta, min); |
| 2860 | } |
| 2861 | |
| 2862 | at::Tensor self_; |
| 2863 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2864 | at::functionalization::impl::sync(self); |
| 2865 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2866 | } else { |
| 2867 | self_ = self; |
| 2868 | } |
| 2869 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 2870 | if ((false)) { |
| 2871 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2872 | TORCH_INTERNAL_ASSERT(false, |
| 2873 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2874 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2875 | } else { |
| 2876 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2877 | at::AutoDispatchSkipFunctionalize guard; |
| 2878 | at::Tensor tmp_output = at::_ops::clamp_min_::call(self_, min); |
| 2879 | return self;; |
| 2880 | } |
| 2881 | } else { |
| 2882 | at::Tensor tmp_output; |
| 2883 | { |
| 2884 | at::AutoDispatchSkipFunctionalize guard; |
| 2885 | tmp_output = at::_ops::clamp_min::call(self_, min); |
| 2886 | } |
| 2887 | at::functionalization::impl::replace_(self, tmp_output); |
| 2888 | at::functionalization::impl::commit_update(self); |
| 2889 | at::functionalization::impl::sync(self); |
| 2890 | return self; |
| 2891 | } |
| 2892 | } |
| 2893 | |
| 2894 | at::Tensor & clamp_min_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & min, at::Tensor & out) { |
| 2895 | if (false) { |
| 2896 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2897 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2898 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2899 | auto self_meta = to_meta(self); |
| 2900 | auto min_meta = to_meta(min); |
| 2901 | auto out_meta = to_meta(out); |
| 2902 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2903 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2904 | at::_ops::clamp_min_Tensor_out::call(self_meta, min_meta, out_meta); |
| 2905 | } |
| 2906 | |
| 2907 | at::Tensor self_; |
| 2908 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2909 | at::functionalization::impl::sync(self); |
| 2910 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2911 | } else { |
| 2912 | self_ = self; |
| 2913 | } |
| 2914 | |
| 2915 | at::Tensor min_; |
| 2916 | if (at::functionalization::impl::isFunctionalTensor(min)) { |
| 2917 | at::functionalization::impl::sync(min); |
| 2918 | min_ = at::functionalization::impl::from_functional_tensor(min); |
| 2919 | } else { |
| 2920 | min_ = min; |
| 2921 | } |
| 2922 | |
| 2923 | at::Tensor out_; |
| 2924 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2925 | at::functionalization::impl::sync(out); |
| 2926 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2927 | } else { |
| 2928 | out_ = out; |
| 2929 | } |
| 2930 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2931 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(min))) { |
| 2932 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2933 | TORCH_INTERNAL_ASSERT(false, |
| 2934 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2935 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2936 | } else { |
| 2937 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2938 | at::AutoDispatchSkipFunctionalize guard; |
| 2939 | at::Tensor tmp_output = at::_ops::clamp_min_Tensor_out::call(self_, min_, out_); |
| 2940 | return out;; |
| 2941 | } |
| 2942 | } else { |
| 2943 | at::Tensor tmp_output; |
| 2944 | { |
| 2945 | at::AutoDispatchSkipFunctionalize guard; |
| 2946 | tmp_output = at::_ops::clamp_min_Tensor::call(self_, min_); |
| 2947 | } |
| 2948 | at::functionalization::impl::replace_(out, tmp_output); |
| 2949 | at::functionalization::impl::commit_update(out); |
| 2950 | at::functionalization::impl::sync(out); |
| 2951 | return out; |
| 2952 | } |
| 2953 | } |
| 2954 | |
| 2955 | at::Tensor & clamp_min__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & min) { |
| 2956 | if (true) { |
| 2957 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2958 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2959 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2960 | auto self_meta = to_meta(self); |
| 2961 | auto min_meta = to_meta(min); |
| 2962 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2963 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2964 | at::_ops::clamp_min__Tensor::call(self_meta, min_meta); |
| 2965 | } |
| 2966 | |
| 2967 | at::Tensor self_; |
| 2968 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2969 | at::functionalization::impl::sync(self); |
| 2970 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2971 | } else { |
| 2972 | self_ = self; |
| 2973 | } |
| 2974 | |
| 2975 | at::Tensor min_; |
| 2976 | if (at::functionalization::impl::isFunctionalTensor(min)) { |
| 2977 | at::functionalization::impl::sync(min); |
| 2978 | min_ = at::functionalization::impl::from_functional_tensor(min); |
| 2979 | } else { |
| 2980 | min_ = min; |
| 2981 | } |
| 2982 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 2983 | if ((false || at::functionalization::impl::isFunctionalTensor(min))) { |
| 2984 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2985 | TORCH_INTERNAL_ASSERT(false, |
| 2986 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2987 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2988 | } else { |
| 2989 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2990 | at::AutoDispatchSkipFunctionalize guard; |
| 2991 | at::Tensor tmp_output = at::_ops::clamp_min__Tensor::call(self_, min_); |
| 2992 | return self;; |
| 2993 | } |
| 2994 | } else { |
| 2995 | at::Tensor tmp_output; |
| 2996 | { |
| 2997 | at::AutoDispatchSkipFunctionalize guard; |
| 2998 | tmp_output = at::_ops::clamp_min_Tensor::call(self_, min_); |
| 2999 | } |
| 3000 | at::functionalization::impl::replace_(self, tmp_output); |
| 3001 | at::functionalization::impl::commit_update(self); |
| 3002 | at::functionalization::impl::sync(self); |
| 3003 | return self; |
| 3004 | } |
| 3005 | } |
| 3006 | |
| 3007 | at::Tensor & clip_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional<at::Scalar> & min, const c10::optional<at::Scalar> & max, at::Tensor & out) { |
| 3008 | if (false) { |
| 3009 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3010 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3011 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3012 | auto self_meta = to_meta(self); |
| 3013 | auto out_meta = to_meta(out); |
| 3014 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3015 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3016 | at::_ops::clip_out::call(self_meta, min, max, out_meta); |
| 3017 | } |
| 3018 | |
| 3019 | at::Tensor self_; |
| 3020 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3021 | at::functionalization::impl::sync(self); |
| 3022 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3023 | } else { |
| 3024 | self_ = self; |
| 3025 | } |
| 3026 | |
| 3027 | at::Tensor out_; |
| 3028 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3029 | at::functionalization::impl::sync(out); |
| 3030 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3031 | } else { |
| 3032 | out_ = out; |
| 3033 | } |
| 3034 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3035 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 3036 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3037 | TORCH_INTERNAL_ASSERT(false, |
| 3038 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3039 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3040 | } else { |
| 3041 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3042 | at::AutoDispatchSkipFunctionalize guard; |
| 3043 | at::Tensor tmp_output = at::_ops::clip_out::call(self_, min, max, out_); |
| 3044 | return out;; |
| 3045 | } |
| 3046 | } else { |
| 3047 | at::Tensor tmp_output; |
| 3048 | { |
| 3049 | at::AutoDispatchSkipFunctionalize guard; |
| 3050 | tmp_output = at::_ops::clip::call(self_, min, max); |
| 3051 | } |
| 3052 | at::functionalization::impl::replace_(out, tmp_output); |
| 3053 | at::functionalization::impl::commit_update(out); |
| 3054 | at::functionalization::impl::sync(out); |
| 3055 | return out; |
| 3056 | } |
| 3057 | } |
| 3058 | |
| 3059 | at::Tensor & clip_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const c10::optional<at::Scalar> & min, const c10::optional<at::Scalar> & max) { |
| 3060 | if (true) { |
| 3061 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3062 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3063 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3064 | auto self_meta = to_meta(self); |
| 3065 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3066 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3067 | at::_ops::clip_::call(self_meta, min, max); |
| 3068 | } |
| 3069 | |
| 3070 | at::Tensor self_; |
| 3071 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3072 | at::functionalization::impl::sync(self); |
| 3073 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3074 | } else { |
| 3075 | self_ = self; |
| 3076 | } |
| 3077 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 3078 | if ((false)) { |
| 3079 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3080 | TORCH_INTERNAL_ASSERT(false, |
| 3081 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3082 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3083 | } else { |
| 3084 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3085 | at::AutoDispatchSkipFunctionalize guard; |
| 3086 | at::Tensor tmp_output = at::_ops::clip_::call(self_, min, max); |
| 3087 | return self;; |
| 3088 | } |
| 3089 | } else { |
| 3090 | at::Tensor tmp_output; |
| 3091 | { |
| 3092 | at::AutoDispatchSkipFunctionalize guard; |
| 3093 | tmp_output = at::_ops::clip::call(self_, min, max); |
| 3094 | } |
| 3095 | at::functionalization::impl::replace_(self, tmp_output); |
| 3096 | at::functionalization::impl::commit_update(self); |
| 3097 | at::functionalization::impl::sync(self); |
| 3098 | return self; |
| 3099 | } |
| 3100 | } |
| 3101 | |
| 3102 | at::Tensor & clip_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional<at::Tensor> & min, const c10::optional<at::Tensor> & max, at::Tensor & out) { |
| 3103 | if (false) { |
| 3104 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3105 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3106 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3107 | auto self_meta = to_meta(self); |
| 3108 | auto min_meta = to_meta(min); |
| 3109 | auto max_meta = to_meta(max); |
| 3110 | auto out_meta = to_meta(out); |
| 3111 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3112 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3113 | at::_ops::clip_Tensor_out::call(self_meta, min_meta, max_meta, out_meta); |
| 3114 | } |
| 3115 | |
| 3116 | at::Tensor self_; |
| 3117 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3118 | at::functionalization::impl::sync(self); |
| 3119 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3120 | } else { |
| 3121 | self_ = self; |
| 3122 | } |
| 3123 | |
| 3124 | c10::optional<at::Tensor> min_; |
| 3125 | if (at::functionalization::impl::isFunctionalTensor(min)) { |
| 3126 | at::functionalization::impl::sync(min); |
| 3127 | min_ = at::functionalization::impl::from_functional_tensor(min); |
| 3128 | } else { |
| 3129 | min_ = min; |
| 3130 | } |
| 3131 | |
| 3132 | c10::optional<at::Tensor> max_; |
| 3133 | if (at::functionalization::impl::isFunctionalTensor(max)) { |
| 3134 | at::functionalization::impl::sync(max); |
| 3135 | max_ = at::functionalization::impl::from_functional_tensor(max); |
| 3136 | } else { |
| 3137 | max_ = max; |
| 3138 | } |
| 3139 | |
| 3140 | at::Tensor out_; |
| 3141 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3142 | at::functionalization::impl::sync(out); |
| 3143 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3144 | } else { |
| 3145 | out_ = out; |
| 3146 | } |
| 3147 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3148 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(min) || at::functionalization::impl::isFunctionalTensor(max))) { |
| 3149 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3150 | TORCH_INTERNAL_ASSERT(false, |
| 3151 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3152 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3153 | } else { |
| 3154 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3155 | at::AutoDispatchSkipFunctionalize guard; |
| 3156 | at::Tensor tmp_output = at::_ops::clip_Tensor_out::call(self_, min_, max_, out_); |
| 3157 | return out;; |
| 3158 | } |
| 3159 | } else { |
| 3160 | at::Tensor tmp_output; |
| 3161 | { |
| 3162 | at::AutoDispatchSkipFunctionalize guard; |
| 3163 | tmp_output = at::_ops::clip_Tensor::call(self_, min_, max_); |
| 3164 | } |
| 3165 | at::functionalization::impl::replace_(out, tmp_output); |
| 3166 | at::functionalization::impl::commit_update(out); |
| 3167 | at::functionalization::impl::sync(out); |
| 3168 | return out; |
| 3169 | } |
| 3170 | } |
| 3171 | |
| 3172 | at::Tensor & clip__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const c10::optional<at::Tensor> & min, const c10::optional<at::Tensor> & max) { |
| 3173 | if (true) { |
| 3174 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3175 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3176 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3177 | auto self_meta = to_meta(self); |
| 3178 | auto min_meta = to_meta(min); |
| 3179 | auto max_meta = to_meta(max); |
| 3180 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3181 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3182 | at::_ops::clip__Tensor::call(self_meta, min_meta, max_meta); |
| 3183 | } |
| 3184 | |
| 3185 | at::Tensor self_; |
| 3186 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3187 | at::functionalization::impl::sync(self); |
| 3188 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3189 | } else { |
| 3190 | self_ = self; |
| 3191 | } |
| 3192 | |
| 3193 | c10::optional<at::Tensor> min_; |
| 3194 | if (at::functionalization::impl::isFunctionalTensor(min)) { |
| 3195 | at::functionalization::impl::sync(min); |
| 3196 | min_ = at::functionalization::impl::from_functional_tensor(min); |
| 3197 | } else { |
| 3198 | min_ = min; |
| 3199 | } |
| 3200 | |
| 3201 | c10::optional<at::Tensor> max_; |
| 3202 | if (at::functionalization::impl::isFunctionalTensor(max)) { |
| 3203 | at::functionalization::impl::sync(max); |
| 3204 | max_ = at::functionalization::impl::from_functional_tensor(max); |
| 3205 | } else { |
| 3206 | max_ = max; |
| 3207 | } |
| 3208 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 3209 | if ((false || at::functionalization::impl::isFunctionalTensor(min) || at::functionalization::impl::isFunctionalTensor(max))) { |
| 3210 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3211 | TORCH_INTERNAL_ASSERT(false, |
| 3212 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3213 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3214 | } else { |
| 3215 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3216 | at::AutoDispatchSkipFunctionalize guard; |
| 3217 | at::Tensor tmp_output = at::_ops::clip__Tensor::call(self_, min_, max_); |
| 3218 | return self;; |
| 3219 | } |
| 3220 | } else { |
| 3221 | at::Tensor tmp_output; |
| 3222 | { |
| 3223 | at::AutoDispatchSkipFunctionalize guard; |
| 3224 | tmp_output = at::_ops::clip_Tensor::call(self_, min_, max_); |
| 3225 | } |
| 3226 | at::functionalization::impl::replace_(self, tmp_output); |
| 3227 | at::functionalization::impl::commit_update(self); |
| 3228 | at::functionalization::impl::sync(self); |
| 3229 | return self; |
| 3230 | } |
| 3231 | } |
| 3232 | |
| 3233 | at::Tensor & complex_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & real, const at::Tensor & imag, at::Tensor & out) { |
| 3234 | if (false) { |
| 3235 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3236 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3237 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3238 | auto real_meta = to_meta(real); |
| 3239 | auto imag_meta = to_meta(imag); |
| 3240 | auto out_meta = to_meta(out); |
| 3241 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3242 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3243 | at::_ops::complex_out::call(real_meta, imag_meta, out_meta); |
| 3244 | } |
| 3245 | |
| 3246 | at::Tensor real_; |
| 3247 | if (at::functionalization::impl::isFunctionalTensor(real)) { |
| 3248 | at::functionalization::impl::sync(real); |
| 3249 | real_ = at::functionalization::impl::from_functional_tensor(real); |
| 3250 | } else { |
| 3251 | real_ = real; |
| 3252 | } |
| 3253 | |
| 3254 | at::Tensor imag_; |
| 3255 | if (at::functionalization::impl::isFunctionalTensor(imag)) { |
| 3256 | at::functionalization::impl::sync(imag); |
| 3257 | imag_ = at::functionalization::impl::from_functional_tensor(imag); |
| 3258 | } else { |
| 3259 | imag_ = imag; |
| 3260 | } |
| 3261 | |
| 3262 | at::Tensor out_; |
| 3263 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3264 | at::functionalization::impl::sync(out); |
| 3265 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3266 | } else { |
| 3267 | out_ = out; |
| 3268 | } |
| 3269 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3270 | if ((false || at::functionalization::impl::isFunctionalTensor(real) || at::functionalization::impl::isFunctionalTensor(imag))) { |
| 3271 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3272 | TORCH_INTERNAL_ASSERT(false, |
| 3273 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3274 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3275 | } else { |
| 3276 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3277 | at::AutoDispatchSkipFunctionalize guard; |
| 3278 | at::Tensor tmp_output = at::_ops::complex_out::call(real_, imag_, out_); |
| 3279 | return out;; |
| 3280 | } |
| 3281 | } else { |
| 3282 | at::Tensor tmp_output; |
| 3283 | { |
| 3284 | at::AutoDispatchSkipFunctionalize guard; |
| 3285 | tmp_output = at::_ops::complex::call(real_, imag_); |
| 3286 | } |
| 3287 | at::functionalization::impl::replace_(out, tmp_output); |
| 3288 | at::functionalization::impl::commit_update(out); |
| 3289 | at::functionalization::impl::sync(out); |
| 3290 | return out; |
| 3291 | } |
| 3292 | } |
| 3293 | |
| 3294 | at::Tensor & constant_pad_nd_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef pad, const at::Scalar & value, at::Tensor & out) { |
| 3295 | if (false) { |
| 3296 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3297 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3298 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3299 | auto self_meta = to_meta(self); |
| 3300 | auto out_meta = to_meta(out); |
| 3301 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3302 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3303 | at::_ops::constant_pad_nd_out::call(self_meta, pad, value, out_meta); |
| 3304 | } |
| 3305 | |
| 3306 | at::Tensor self_; |
| 3307 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3308 | at::functionalization::impl::sync(self); |
| 3309 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3310 | } else { |
| 3311 | self_ = self; |
| 3312 | } |
| 3313 | |
| 3314 | at::Tensor out_; |
| 3315 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3316 | at::functionalization::impl::sync(out); |
| 3317 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3318 | } else { |
| 3319 | out_ = out; |
| 3320 | } |
| 3321 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3322 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 3323 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3324 | TORCH_INTERNAL_ASSERT(false, |
| 3325 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3326 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3327 | } else { |
| 3328 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3329 | at::AutoDispatchSkipFunctionalize guard; |
| 3330 | at::Tensor tmp_output = at::_ops::constant_pad_nd_out::call(self_, pad, value, out_); |
| 3331 | return out;; |
| 3332 | } |
| 3333 | } else { |
| 3334 | at::Tensor tmp_output; |
| 3335 | { |
| 3336 | at::AutoDispatchSkipFunctionalize guard; |
| 3337 | tmp_output = at::_ops::constant_pad_nd::call(self_, pad, value); |
| 3338 | } |
| 3339 | at::functionalization::impl::replace_(out, tmp_output); |
| 3340 | at::functionalization::impl::commit_update(out); |
| 3341 | at::functionalization::impl::sync(out); |
| 3342 | return out; |
| 3343 | } |
| 3344 | } |
| 3345 | |
| 3346 | at::Tensor & conv_tbc_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const at::Tensor & bias, int64_t pad, at::Tensor & out) { |
| 3347 | if (false) { |
| 3348 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3349 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3350 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3351 | auto self_meta = to_meta(self); |
| 3352 | auto weight_meta = to_meta(weight); |
| 3353 | auto bias_meta = to_meta(bias); |
| 3354 | auto out_meta = to_meta(out); |
| 3355 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3356 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3357 | at::_ops::conv_tbc_out::call(self_meta, weight_meta, bias_meta, pad, out_meta); |
| 3358 | } |
| 3359 | |
| 3360 | at::Tensor self_; |
| 3361 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3362 | at::functionalization::impl::sync(self); |
| 3363 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3364 | } else { |
| 3365 | self_ = self; |
| 3366 | } |
| 3367 | |
| 3368 | at::Tensor weight_; |
| 3369 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 3370 | at::functionalization::impl::sync(weight); |
| 3371 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 3372 | } else { |
| 3373 | weight_ = weight; |
| 3374 | } |
| 3375 | |
| 3376 | at::Tensor bias_; |
| 3377 | if (at::functionalization::impl::isFunctionalTensor(bias)) { |
| 3378 | at::functionalization::impl::sync(bias); |
| 3379 | bias_ = at::functionalization::impl::from_functional_tensor(bias); |
| 3380 | } else { |
| 3381 | bias_ = bias; |
| 3382 | } |
| 3383 | |
| 3384 | at::Tensor out_; |
| 3385 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3386 | at::functionalization::impl::sync(out); |
| 3387 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3388 | } else { |
| 3389 | out_ = out; |
| 3390 | } |
| 3391 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3392 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(bias))) { |
| 3393 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3394 | TORCH_INTERNAL_ASSERT(false, |
| 3395 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3396 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3397 | } else { |
| 3398 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3399 | at::AutoDispatchSkipFunctionalize guard; |
| 3400 | at::Tensor tmp_output = at::_ops::conv_tbc_out::call(self_, weight_, bias_, pad, out_); |
| 3401 | return out;; |
| 3402 | } |
| 3403 | } else { |
| 3404 | at::Tensor tmp_output; |
| 3405 | { |
| 3406 | at::AutoDispatchSkipFunctionalize guard; |
| 3407 | tmp_output = at::_ops::conv_tbc::call(self_, weight_, bias_, pad); |
| 3408 | } |
| 3409 | at::functionalization::impl::replace_(out, tmp_output); |
| 3410 | at::functionalization::impl::commit_update(out); |
| 3411 | at::functionalization::impl::sync(out); |
| 3412 | return out; |
| 3413 | } |
| 3414 | } |
| 3415 | |
| 3416 | at::Tensor & _copy_from_and_resize_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & dst, at::Tensor & out) { |
| 3417 | if (false) { |
| 3418 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3419 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3420 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3421 | auto self_meta = to_meta(self); |
| 3422 | auto dst_meta = to_meta(dst); |
| 3423 | auto out_meta = to_meta(out); |
| 3424 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3425 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3426 | at::_ops::_copy_from_and_resize_out::call(self_meta, dst_meta, out_meta); |
| 3427 | } |
| 3428 | |
| 3429 | at::Tensor self_; |
| 3430 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3431 | at::functionalization::impl::sync(self); |
| 3432 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3433 | } else { |
| 3434 | self_ = self; |
| 3435 | } |
| 3436 | |
| 3437 | at::Tensor dst_; |
| 3438 | if (at::functionalization::impl::isFunctionalTensor(dst)) { |
| 3439 | at::functionalization::impl::sync(dst); |
| 3440 | dst_ = at::functionalization::impl::from_functional_tensor(dst); |
| 3441 | } else { |
| 3442 | dst_ = dst; |
| 3443 | } |
| 3444 | |
| 3445 | at::Tensor out_; |
| 3446 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3447 | at::functionalization::impl::sync(out); |
| 3448 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3449 | } else { |
| 3450 | out_ = out; |
| 3451 | } |
| 3452 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3453 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(dst))) { |
| 3454 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3455 | TORCH_INTERNAL_ASSERT(false, |
| 3456 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3457 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3458 | } else { |
| 3459 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3460 | at::AutoDispatchSkipFunctionalize guard; |
| 3461 | at::Tensor tmp_output = at::_ops::_copy_from_and_resize_out::call(self_, dst_, out_); |
| 3462 | return out;; |
| 3463 | } |
| 3464 | } else { |
| 3465 | at::Tensor tmp_output; |
| 3466 | { |
| 3467 | at::AutoDispatchSkipFunctionalize guard; |
| 3468 | tmp_output = at::_ops::_copy_from_and_resize::call(self_, dst_); |
| 3469 | } |
| 3470 | at::functionalization::impl::replace_(out, tmp_output); |
| 3471 | at::functionalization::impl::commit_update(out); |
| 3472 | at::functionalization::impl::sync(out); |
| 3473 | return out; |
| 3474 | } |
| 3475 | } |
| 3476 | |
| 3477 | at::Tensor & cos_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 3478 | if (false) { |
| 3479 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3480 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3481 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3482 | auto self_meta = to_meta(self); |
| 3483 | auto out_meta = to_meta(out); |
| 3484 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3485 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3486 | at::_ops::cos_out::call(self_meta, out_meta); |
| 3487 | } |
| 3488 | |
| 3489 | at::Tensor self_; |
| 3490 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3491 | at::functionalization::impl::sync(self); |
| 3492 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3493 | } else { |
| 3494 | self_ = self; |
| 3495 | } |
| 3496 | |
| 3497 | at::Tensor out_; |
| 3498 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3499 | at::functionalization::impl::sync(out); |
| 3500 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3501 | } else { |
| 3502 | out_ = out; |
| 3503 | } |
| 3504 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3505 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 3506 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3507 | TORCH_INTERNAL_ASSERT(false, |
| 3508 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3509 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3510 | } else { |
| 3511 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3512 | at::AutoDispatchSkipFunctionalize guard; |
| 3513 | at::Tensor tmp_output = at::_ops::cos_out::call(self_, out_); |
| 3514 | return out;; |
| 3515 | } |
| 3516 | } else { |
| 3517 | at::Tensor tmp_output; |
| 3518 | { |
| 3519 | at::AutoDispatchSkipFunctionalize guard; |
| 3520 | tmp_output = at::_ops::cos::call(self_); |
| 3521 | } |
| 3522 | at::functionalization::impl::replace_(out, tmp_output); |
| 3523 | at::functionalization::impl::commit_update(out); |
| 3524 | at::functionalization::impl::sync(out); |
| 3525 | return out; |
| 3526 | } |
| 3527 | } |
| 3528 | |
| 3529 | at::Tensor & cos_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 3530 | if (true) { |
| 3531 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3532 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3533 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3534 | auto self_meta = to_meta(self); |
| 3535 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3536 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3537 | at::_ops::cos_::call(self_meta); |
| 3538 | } |
| 3539 | |
| 3540 | at::Tensor self_; |
| 3541 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3542 | at::functionalization::impl::sync(self); |
| 3543 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3544 | } else { |
| 3545 | self_ = self; |
| 3546 | } |
| 3547 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 3548 | if ((false)) { |
| 3549 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3550 | TORCH_INTERNAL_ASSERT(false, |
| 3551 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3552 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3553 | } else { |
| 3554 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3555 | at::AutoDispatchSkipFunctionalize guard; |
| 3556 | at::Tensor tmp_output = at::_ops::cos_::call(self_); |
| 3557 | return self;; |
| 3558 | } |
| 3559 | } else { |
| 3560 | at::Tensor tmp_output; |
| 3561 | { |
| 3562 | at::AutoDispatchSkipFunctionalize guard; |
| 3563 | tmp_output = at::_ops::cos::call(self_); |
| 3564 | } |
| 3565 | at::functionalization::impl::replace_(self, tmp_output); |
| 3566 | at::functionalization::impl::commit_update(self); |
| 3567 | at::functionalization::impl::sync(self); |
| 3568 | return self; |
| 3569 | } |
| 3570 | } |
| 3571 | |
| 3572 | at::Tensor & count_nonzero_out_dim_IntList_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, at::Tensor & out) { |
| 3573 | if (false) { |
| 3574 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3575 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3576 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3577 | auto self_meta = to_meta(self); |
| 3578 | auto out_meta = to_meta(out); |
| 3579 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3580 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3581 | at::_ops::count_nonzero_dim_IntList_out::call(self_meta, dim, out_meta); |
| 3582 | } |
| 3583 | |
| 3584 | at::Tensor self_; |
| 3585 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3586 | at::functionalization::impl::sync(self); |
| 3587 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3588 | } else { |
| 3589 | self_ = self; |
| 3590 | } |
| 3591 | |
| 3592 | at::Tensor out_; |
| 3593 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3594 | at::functionalization::impl::sync(out); |
| 3595 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3596 | } else { |
| 3597 | out_ = out; |
| 3598 | } |
| 3599 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3600 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 3601 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3602 | TORCH_INTERNAL_ASSERT(false, |
| 3603 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3604 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3605 | } else { |
| 3606 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3607 | at::AutoDispatchSkipFunctionalize guard; |
| 3608 | at::Tensor tmp_output = at::_ops::count_nonzero_dim_IntList_out::call(self_, dim, out_); |
| 3609 | return out;; |
| 3610 | } |
| 3611 | } else { |
| 3612 | at::Tensor tmp_output; |
| 3613 | { |
| 3614 | at::AutoDispatchSkipFunctionalize guard; |
| 3615 | tmp_output = at::_ops::count_nonzero_dim_IntList::call(self_, dim); |
| 3616 | } |
| 3617 | at::functionalization::impl::replace_(out, tmp_output); |
| 3618 | at::functionalization::impl::commit_update(out); |
| 3619 | at::functionalization::impl::sync(out); |
| 3620 | return out; |
| 3621 | } |
| 3622 | } |
| 3623 | |
| 3624 | at::Tensor & count_nonzero_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<int64_t> dim, at::Tensor & out) { |
| 3625 | if (false) { |
| 3626 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3627 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3628 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3629 | auto self_meta = to_meta(self); |
| 3630 | auto out_meta = to_meta(out); |
| 3631 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3632 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3633 | at::_ops::count_nonzero_out::call(self_meta, dim, out_meta); |
| 3634 | } |
| 3635 | |
| 3636 | at::Tensor self_; |
| 3637 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3638 | at::functionalization::impl::sync(self); |
| 3639 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3640 | } else { |
| 3641 | self_ = self; |
| 3642 | } |
| 3643 | |
| 3644 | at::Tensor out_; |
| 3645 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3646 | at::functionalization::impl::sync(out); |
| 3647 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3648 | } else { |
| 3649 | out_ = out; |
| 3650 | } |
| 3651 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3652 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 3653 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3654 | TORCH_INTERNAL_ASSERT(false, |
| 3655 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3656 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3657 | } else { |
| 3658 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3659 | at::AutoDispatchSkipFunctionalize guard; |
| 3660 | at::Tensor tmp_output = at::_ops::count_nonzero_out::call(self_, dim, out_); |
| 3661 | return out;; |
| 3662 | } |
| 3663 | } else { |
| 3664 | at::Tensor tmp_output; |
| 3665 | { |
| 3666 | at::AutoDispatchSkipFunctionalize guard; |
| 3667 | tmp_output = at::_ops::count_nonzero::call(self_, dim); |
| 3668 | } |
| 3669 | at::functionalization::impl::replace_(out, tmp_output); |
| 3670 | at::functionalization::impl::commit_update(out); |
| 3671 | at::functionalization::impl::sync(out); |
| 3672 | return out; |
| 3673 | } |
| 3674 | } |
| 3675 | |
| 3676 | at::Tensor & cudnn_affine_grid_generator_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, int64_t N, int64_t C, int64_t H, int64_t W, at::Tensor & out) { |
| 3677 | if (false) { |
| 3678 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3679 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3680 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3681 | auto grad_meta = to_meta(grad); |
| 3682 | auto out_meta = to_meta(out); |
| 3683 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3684 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3685 | at::_ops::cudnn_affine_grid_generator_backward_out::call(grad_meta, N, C, H, W, out_meta); |
| 3686 | } |
| 3687 | |
| 3688 | at::Tensor grad_; |
| 3689 | if (at::functionalization::impl::isFunctionalTensor(grad)) { |
| 3690 | at::functionalization::impl::sync(grad); |
| 3691 | grad_ = at::functionalization::impl::from_functional_tensor(grad); |
| 3692 | } else { |
| 3693 | grad_ = grad; |
| 3694 | } |
| 3695 | |
| 3696 | at::Tensor out_; |
| 3697 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3698 | at::functionalization::impl::sync(out); |
| 3699 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3700 | } else { |
| 3701 | out_ = out; |
| 3702 | } |
| 3703 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3704 | if ((false || at::functionalization::impl::isFunctionalTensor(grad))) { |
| 3705 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3706 | TORCH_INTERNAL_ASSERT(false, |
| 3707 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3708 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3709 | } else { |
| 3710 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3711 | at::AutoDispatchSkipFunctionalize guard; |
| 3712 | at::Tensor tmp_output = at::_ops::cudnn_affine_grid_generator_backward_out::call(grad_, N, C, H, W, out_); |
| 3713 | return out;; |
| 3714 | } |
| 3715 | } else { |
| 3716 | at::Tensor tmp_output; |
| 3717 | { |
| 3718 | at::AutoDispatchSkipFunctionalize guard; |
| 3719 | tmp_output = at::_ops::cudnn_affine_grid_generator_backward::call(grad_, N, C, H, W); |
| 3720 | } |
| 3721 | at::functionalization::impl::replace_(out, tmp_output); |
| 3722 | at::functionalization::impl::commit_update(out); |
| 3723 | at::functionalization::impl::sync(out); |
| 3724 | return out; |
| 3725 | } |
| 3726 | } |
| 3727 | |
| 3728 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> cudnn_batch_norm_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const 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 exponential_average_factor, double epsilon, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3) { |
| 3729 | if (false) { |
| 3730 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3731 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3732 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3733 | auto input_meta = to_meta(input); |
| 3734 | auto weight_meta = to_meta(weight); |
| 3735 | auto bias_meta = to_meta(bias); |
| 3736 | auto running_mean_meta = to_meta(running_mean); |
| 3737 | auto running_var_meta = to_meta(running_var); |
| 3738 | auto out0_meta = to_meta(out0); |
| 3739 | auto out1_meta = to_meta(out1); |
| 3740 | auto out2_meta = to_meta(out2); |
| 3741 | auto out3_meta = to_meta(out3); |
| 3742 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3743 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3744 | at::_ops::cudnn_batch_norm_out::call(input_meta, weight_meta, bias_meta, running_mean_meta, running_var_meta, training, exponential_average_factor, epsilon, out0_meta, out1_meta, out2_meta, out3_meta); |
| 3745 | } |
| 3746 | |
| 3747 | at::Tensor input_; |
| 3748 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 3749 | at::functionalization::impl::sync(input); |
| 3750 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 3751 | } else { |
| 3752 | input_ = input; |
| 3753 | } |
| 3754 | |
| 3755 | at::Tensor weight_; |
| 3756 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 3757 | at::functionalization::impl::sync(weight); |
| 3758 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 3759 | } else { |
| 3760 | weight_ = weight; |
| 3761 | } |
| 3762 | |
| 3763 | c10::optional<at::Tensor> bias_; |
| 3764 | if (at::functionalization::impl::isFunctionalTensor(bias)) { |
| 3765 | at::functionalization::impl::sync(bias); |
| 3766 | bias_ = at::functionalization::impl::from_functional_tensor(bias); |
| 3767 | } else { |
| 3768 | bias_ = bias; |
| 3769 | } |
| 3770 | |
| 3771 | c10::optional<at::Tensor> running_mean_; |
| 3772 | if (at::functionalization::impl::isFunctionalTensor(running_mean)) { |
| 3773 | at::functionalization::impl::sync(running_mean); |
| 3774 | running_mean_ = at::functionalization::impl::from_functional_tensor(running_mean); |
| 3775 | } else { |
| 3776 | running_mean_ = running_mean; |
| 3777 | } |
| 3778 | |
| 3779 | c10::optional<at::Tensor> running_var_; |
| 3780 | if (at::functionalization::impl::isFunctionalTensor(running_var)) { |
| 3781 | at::functionalization::impl::sync(running_var); |
| 3782 | running_var_ = at::functionalization::impl::from_functional_tensor(running_var); |
| 3783 | } else { |
| 3784 | running_var_ = running_var; |
| 3785 | } |
| 3786 | |
| 3787 | at::Tensor out0_; |
| 3788 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 3789 | at::functionalization::impl::sync(out0); |
| 3790 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 3791 | } else { |
| 3792 | out0_ = out0; |
| 3793 | } |
| 3794 | |
| 3795 | at::Tensor out1_; |
| 3796 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 3797 | at::functionalization::impl::sync(out1); |
| 3798 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 3799 | } else { |
| 3800 | out1_ = out1; |
| 3801 | } |
| 3802 | |
| 3803 | at::Tensor out2_; |
| 3804 | if (at::functionalization::impl::isFunctionalTensor(out2)) { |
| 3805 | at::functionalization::impl::sync(out2); |
| 3806 | out2_ = at::functionalization::impl::from_functional_tensor(out2); |
| 3807 | } else { |
| 3808 | out2_ = out2; |
| 3809 | } |
| 3810 | |
| 3811 | at::Tensor out3_; |
| 3812 | if (at::functionalization::impl::isFunctionalTensor(out3)) { |
| 3813 | at::functionalization::impl::sync(out3); |
| 3814 | out3_ = at::functionalization::impl::from_functional_tensor(out3); |
| 3815 | } else { |
| 3816 | out3_ = out3; |
| 3817 | } |
| 3818 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1) && at::functionalization::impl::isFunctionalTensor(out2) && at::functionalization::impl::isFunctionalTensor(out3))) { |
| 3819 | 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))) { |
| 3820 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3821 | TORCH_INTERNAL_ASSERT(false, |
| 3822 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3823 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3824 | } else { |
| 3825 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3826 | at::AutoDispatchSkipFunctionalize guard; |
| 3827 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::cudnn_batch_norm_out::call(input_, weight_, bias_, running_mean_, running_var_, training, exponential_average_factor, epsilon, out0_, out1_, out2_, out3_); |
| 3828 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2, out3);; |
| 3829 | } |
| 3830 | } else { |
| 3831 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 3832 | { |
| 3833 | at::AutoDispatchSkipFunctionalize guard; |
| 3834 | tmp_output = at::_ops::cudnn_batch_norm::call(input_, weight_, bias_, running_mean_, running_var_, training, exponential_average_factor, epsilon); |
| 3835 | } |
| 3836 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 3837 | at::functionalization::impl::commit_update(out0); |
| 3838 | at::functionalization::impl::sync(out0); |
| 3839 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 3840 | at::functionalization::impl::commit_update(out1); |
| 3841 | at::functionalization::impl::sync(out1); |
| 3842 | at::functionalization::impl::replace_(out2, std::get<2>(tmp_output)); |
| 3843 | at::functionalization::impl::commit_update(out2); |
| 3844 | at::functionalization::impl::sync(out2); |
| 3845 | at::functionalization::impl::replace_(out3, std::get<3>(tmp_output)); |
| 3846 | at::functionalization::impl::commit_update(out3); |
| 3847 | at::functionalization::impl::sync(out3); |
| 3848 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2, out3); |
| 3849 | } |
| 3850 | } |
| 3851 | |
| 3852 | ::std::tuple<at::Tensor &,at::Tensor &> mps_convolution_transpose_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, ::std::array<bool,2> output_mask, at::Tensor & out0, at::Tensor & out1) { |
| 3853 | if (false) { |
| 3854 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3855 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3856 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3857 | auto self_meta = to_meta(self); |
| 3858 | auto grad_output_meta = to_meta(grad_output); |
| 3859 | auto weight_meta = to_meta(weight); |
| 3860 | auto out0_meta = to_meta(out0); |
| 3861 | auto out1_meta = to_meta(out1); |
| 3862 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3863 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3864 | at::_ops::mps_convolution_transpose_backward_out::call(self_meta, grad_output_meta, weight_meta, padding, output_padding, stride, dilation, groups, output_mask, out0_meta, out1_meta); |
| 3865 | } |
| 3866 | |
| 3867 | at::Tensor self_; |
| 3868 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3869 | at::functionalization::impl::sync(self); |
| 3870 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3871 | } else { |
| 3872 | self_ = self; |
| 3873 | } |
| 3874 | |
| 3875 | at::Tensor grad_output_; |
| 3876 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 3877 | at::functionalization::impl::sync(grad_output); |
| 3878 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 3879 | } else { |
| 3880 | grad_output_ = grad_output; |
| 3881 | } |
| 3882 | |
| 3883 | at::Tensor weight_; |
| 3884 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 3885 | at::functionalization::impl::sync(weight); |
| 3886 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 3887 | } else { |
| 3888 | weight_ = weight; |
| 3889 | } |
| 3890 | |
| 3891 | at::Tensor out0_; |
| 3892 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 3893 | at::functionalization::impl::sync(out0); |
| 3894 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 3895 | } else { |
| 3896 | out0_ = out0; |
| 3897 | } |
| 3898 | |
| 3899 | at::Tensor out1_; |
| 3900 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 3901 | at::functionalization::impl::sync(out1); |
| 3902 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 3903 | } else { |
| 3904 | out1_ = out1; |
| 3905 | } |
| 3906 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1))) { |
| 3907 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(weight))) { |
| 3908 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3909 | TORCH_INTERNAL_ASSERT(false, |
| 3910 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3911 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3912 | } else { |
| 3913 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3914 | at::AutoDispatchSkipFunctionalize guard; |
| 3915 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::mps_convolution_transpose_backward_out::call(self_, grad_output_, weight_, padding, output_padding, stride, dilation, groups, output_mask, out0_, out1_); |
| 3916 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1);; |
| 3917 | } |
| 3918 | } else { |
| 3919 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 3920 | { |
| 3921 | at::AutoDispatchSkipFunctionalize guard; |
| 3922 | tmp_output = at::_ops::mps_convolution_transpose_backward::call(self_, grad_output_, weight_, padding, output_padding, stride, dilation, groups, output_mask); |
| 3923 | } |
| 3924 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 3925 | at::functionalization::impl::commit_update(out0); |
| 3926 | at::functionalization::impl::sync(out0); |
| 3927 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 3928 | at::functionalization::impl::commit_update(out1); |
| 3929 | at::functionalization::impl::sync(out1); |
| 3930 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1); |
| 3931 | } |
| 3932 | } |
| 3933 | |
| 3934 | ::std::tuple<at::Tensor &,at::Tensor &> cudnn_grid_sampler_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & grid, const at::Tensor & grad_output, at::Tensor & out0, at::Tensor & out1) { |
| 3935 | if (false) { |
| 3936 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3937 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3938 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3939 | auto self_meta = to_meta(self); |
| 3940 | auto grid_meta = to_meta(grid); |
| 3941 | auto grad_output_meta = to_meta(grad_output); |
| 3942 | auto out0_meta = to_meta(out0); |
| 3943 | auto out1_meta = to_meta(out1); |
| 3944 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3945 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3946 | at::_ops::cudnn_grid_sampler_backward_out::call(self_meta, grid_meta, grad_output_meta, out0_meta, out1_meta); |
| 3947 | } |
| 3948 | |
| 3949 | at::Tensor self_; |
| 3950 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3951 | at::functionalization::impl::sync(self); |
| 3952 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3953 | } else { |
| 3954 | self_ = self; |
| 3955 | } |
| 3956 | |
| 3957 | at::Tensor grid_; |
| 3958 | if (at::functionalization::impl::isFunctionalTensor(grid)) { |
| 3959 | at::functionalization::impl::sync(grid); |
| 3960 | grid_ = at::functionalization::impl::from_functional_tensor(grid); |
| 3961 | } else { |
| 3962 | grid_ = grid; |
| 3963 | } |
| 3964 | |
| 3965 | at::Tensor grad_output_; |
| 3966 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 3967 | at::functionalization::impl::sync(grad_output); |
| 3968 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 3969 | } else { |
| 3970 | grad_output_ = grad_output; |
| 3971 | } |
| 3972 | |
| 3973 | at::Tensor out0_; |
| 3974 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 3975 | at::functionalization::impl::sync(out0); |
| 3976 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 3977 | } else { |
| 3978 | out0_ = out0; |
| 3979 | } |
| 3980 | |
| 3981 | at::Tensor out1_; |
| 3982 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 3983 | at::functionalization::impl::sync(out1); |
| 3984 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 3985 | } else { |
| 3986 | out1_ = out1; |
| 3987 | } |
| 3988 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1))) { |
| 3989 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(grid) || at::functionalization::impl::isFunctionalTensor(grad_output))) { |
| 3990 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3991 | TORCH_INTERNAL_ASSERT(false, |
| 3992 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3993 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3994 | } else { |
| 3995 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3996 | at::AutoDispatchSkipFunctionalize guard; |
| 3997 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::cudnn_grid_sampler_backward_out::call(self_, grid_, grad_output_, out0_, out1_); |
| 3998 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1);; |
| 3999 | } |
| 4000 | } else { |
| 4001 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 4002 | { |
| 4003 | at::AutoDispatchSkipFunctionalize guard; |
| 4004 | tmp_output = at::_ops::cudnn_grid_sampler_backward::call(self_, grid_, grad_output_); |
| 4005 | } |
| 4006 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 4007 | at::functionalization::impl::commit_update(out0); |
| 4008 | at::functionalization::impl::sync(out0); |
| 4009 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 4010 | at::functionalization::impl::commit_update(out1); |
| 4011 | at::functionalization::impl::sync(out1); |
| 4012 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1); |
| 4013 | } |
| 4014 | } |
| 4015 | |
| 4016 | ::std::tuple<at::Tensor &,at::Tensor &> cummin_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, at::Tensor & values, at::Tensor & indices) { |
| 4017 | if (false) { |
| 4018 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4019 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4020 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4021 | auto self_meta = to_meta(self); |
| 4022 | auto values_meta = to_meta(values); |
| 4023 | auto indices_meta = to_meta(indices); |
| 4024 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4025 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4026 | at::_ops::cummin_out::call(self_meta, dim, values_meta, indices_meta); |
| 4027 | } |
| 4028 | |
| 4029 | at::Tensor self_; |
| 4030 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4031 | at::functionalization::impl::sync(self); |
| 4032 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4033 | } else { |
| 4034 | self_ = self; |
| 4035 | } |
| 4036 | |
| 4037 | at::Tensor values_; |
| 4038 | if (at::functionalization::impl::isFunctionalTensor(values)) { |
| 4039 | at::functionalization::impl::sync(values); |
| 4040 | values_ = at::functionalization::impl::from_functional_tensor(values); |
| 4041 | } else { |
| 4042 | values_ = values; |
| 4043 | } |
| 4044 | |
| 4045 | at::Tensor indices_; |
| 4046 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 4047 | at::functionalization::impl::sync(indices); |
| 4048 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 4049 | } else { |
| 4050 | indices_ = indices; |
| 4051 | } |
| 4052 | if (!(true && at::functionalization::impl::isFunctionalTensor(values) && at::functionalization::impl::isFunctionalTensor(indices))) { |
| 4053 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 4054 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4055 | TORCH_INTERNAL_ASSERT(false, |
| 4056 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4057 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4058 | } else { |
| 4059 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4060 | at::AutoDispatchSkipFunctionalize guard; |
| 4061 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::cummin_out::call(self_, dim, values_, indices_); |
| 4062 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices);; |
| 4063 | } |
| 4064 | } else { |
| 4065 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 4066 | { |
| 4067 | at::AutoDispatchSkipFunctionalize guard; |
| 4068 | tmp_output = at::_ops::cummin::call(self_, dim); |
| 4069 | } |
| 4070 | at::functionalization::impl::replace_(values, std::get<0>(tmp_output)); |
| 4071 | at::functionalization::impl::commit_update(values); |
| 4072 | at::functionalization::impl::sync(values); |
| 4073 | at::functionalization::impl::replace_(indices, std::get<1>(tmp_output)); |
| 4074 | at::functionalization::impl::commit_update(indices); |
| 4075 | at::functionalization::impl::sync(indices); |
| 4076 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices); |
| 4077 | } |
| 4078 | } |
| 4079 | |
| 4080 | ::std::tuple<at::Tensor &,at::Tensor &> cummin_out_dimname_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, at::Tensor & values, at::Tensor & indices) { |
| 4081 | if (false) { |
| 4082 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4083 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4084 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4085 | auto self_meta = to_meta(self); |
| 4086 | auto values_meta = to_meta(values); |
| 4087 | auto indices_meta = to_meta(indices); |
| 4088 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4089 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4090 | at::_ops::cummin_dimname_out::call(self_meta, dim, values_meta, indices_meta); |
| 4091 | } |
| 4092 | |
| 4093 | at::Tensor self_; |
| 4094 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4095 | at::functionalization::impl::sync(self); |
| 4096 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4097 | } else { |
| 4098 | self_ = self; |
| 4099 | } |
| 4100 | |
| 4101 | at::Tensor values_; |
| 4102 | if (at::functionalization::impl::isFunctionalTensor(values)) { |
| 4103 | at::functionalization::impl::sync(values); |
| 4104 | values_ = at::functionalization::impl::from_functional_tensor(values); |
| 4105 | } else { |
| 4106 | values_ = values; |
| 4107 | } |
| 4108 | |
| 4109 | at::Tensor indices_; |
| 4110 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 4111 | at::functionalization::impl::sync(indices); |
| 4112 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 4113 | } else { |
| 4114 | indices_ = indices; |
| 4115 | } |
| 4116 | if (!(true && at::functionalization::impl::isFunctionalTensor(values) && at::functionalization::impl::isFunctionalTensor(indices))) { |
| 4117 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 4118 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4119 | TORCH_INTERNAL_ASSERT(false, |
| 4120 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4121 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4122 | } else { |
| 4123 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4124 | at::AutoDispatchSkipFunctionalize guard; |
| 4125 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::cummin_dimname_out::call(self_, dim, values_, indices_); |
| 4126 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices);; |
| 4127 | } |
| 4128 | } else { |
| 4129 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 4130 | { |
| 4131 | at::AutoDispatchSkipFunctionalize guard; |
| 4132 | tmp_output = at::_ops::cummin_dimname::call(self_, dim); |
| 4133 | } |
| 4134 | at::functionalization::impl::replace_(values, std::get<0>(tmp_output)); |
| 4135 | at::functionalization::impl::commit_update(values); |
| 4136 | at::functionalization::impl::sync(values); |
| 4137 | at::functionalization::impl::replace_(indices, std::get<1>(tmp_output)); |
| 4138 | at::functionalization::impl::commit_update(indices); |
| 4139 | at::functionalization::impl::sync(indices); |
| 4140 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices); |
| 4141 | } |
| 4142 | } |
| 4143 | |
| 4144 | at::Tensor & cumsum_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 4145 | if (false) { |
| 4146 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4147 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4148 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4149 | auto self_meta = to_meta(self); |
| 4150 | auto out_meta = to_meta(out); |
| 4151 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4152 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4153 | at::_ops::cumsum_out::call(self_meta, dim, dtype, out_meta); |
| 4154 | } |
| 4155 | |
| 4156 | at::Tensor self_; |
| 4157 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4158 | at::functionalization::impl::sync(self); |
| 4159 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4160 | } else { |
| 4161 | self_ = self; |
| 4162 | } |
| 4163 | |
| 4164 | at::Tensor out_; |
| 4165 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4166 | at::functionalization::impl::sync(out); |
| 4167 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4168 | } else { |
| 4169 | out_ = out; |
| 4170 | } |
| 4171 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4172 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 4173 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4174 | TORCH_INTERNAL_ASSERT(false, |
| 4175 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4176 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4177 | } else { |
| 4178 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4179 | at::AutoDispatchSkipFunctionalize guard; |
| 4180 | at::Tensor tmp_output = at::_ops::cumsum_out::call(self_, dim, dtype, out_); |
| 4181 | return out;; |
| 4182 | } |
| 4183 | } else { |
| 4184 | at::Tensor tmp_output; |
| 4185 | { |
| 4186 | at::AutoDispatchSkipFunctionalize guard; |
| 4187 | tmp_output = at::_ops::cumsum::call(self_, dim, dtype); |
| 4188 | } |
| 4189 | at::functionalization::impl::replace_(out, tmp_output); |
| 4190 | at::functionalization::impl::commit_update(out); |
| 4191 | at::functionalization::impl::sync(out); |
| 4192 | return out; |
| 4193 | } |
| 4194 | } |
| 4195 | |
| 4196 | at::Tensor & cumsum_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 4197 | if (true) { |
| 4198 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4199 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4200 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4201 | auto self_meta = to_meta(self); |
| 4202 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4203 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4204 | at::_ops::cumsum_::call(self_meta, dim, dtype); |
| 4205 | } |
| 4206 | |
| 4207 | at::Tensor self_; |
| 4208 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4209 | at::functionalization::impl::sync(self); |
| 4210 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4211 | } else { |
| 4212 | self_ = self; |
| 4213 | } |
| 4214 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 4215 | if ((false)) { |
| 4216 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4217 | TORCH_INTERNAL_ASSERT(false, |
| 4218 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4219 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4220 | } else { |
| 4221 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4222 | at::AutoDispatchSkipFunctionalize guard; |
| 4223 | at::Tensor tmp_output = at::_ops::cumsum_::call(self_, dim, dtype); |
| 4224 | return self;; |
| 4225 | } |
| 4226 | } else { |
| 4227 | at::Tensor tmp_output; |
| 4228 | { |
| 4229 | at::AutoDispatchSkipFunctionalize guard; |
| 4230 | tmp_output = at::_ops::cumsum::call(self_, dim, dtype); |
| 4231 | } |
| 4232 | at::functionalization::impl::replace_(self, tmp_output); |
| 4233 | at::functionalization::impl::commit_update(self); |
| 4234 | at::functionalization::impl::sync(self); |
| 4235 | return self; |
| 4236 | } |
| 4237 | } |
| 4238 | |
| 4239 | at::Tensor & cumsum_out_dimname_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 4240 | if (false) { |
| 4241 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4242 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4243 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4244 | auto self_meta = to_meta(self); |
| 4245 | auto out_meta = to_meta(out); |
| 4246 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4247 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4248 | at::_ops::cumsum_dimname_out::call(self_meta, dim, dtype, out_meta); |
| 4249 | } |
| 4250 | |
| 4251 | at::Tensor self_; |
| 4252 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4253 | at::functionalization::impl::sync(self); |
| 4254 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4255 | } else { |
| 4256 | self_ = self; |
| 4257 | } |
| 4258 | |
| 4259 | at::Tensor out_; |
| 4260 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4261 | at::functionalization::impl::sync(out); |
| 4262 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4263 | } else { |
| 4264 | out_ = out; |
| 4265 | } |
| 4266 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4267 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 4268 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4269 | TORCH_INTERNAL_ASSERT(false, |
| 4270 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4271 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4272 | } else { |
| 4273 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4274 | at::AutoDispatchSkipFunctionalize guard; |
| 4275 | at::Tensor tmp_output = at::_ops::cumsum_dimname_out::call(self_, dim, dtype, out_); |
| 4276 | return out;; |
| 4277 | } |
| 4278 | } else { |
| 4279 | at::Tensor tmp_output; |
| 4280 | { |
| 4281 | at::AutoDispatchSkipFunctionalize guard; |
| 4282 | tmp_output = at::_ops::cumsum_dimname::call(self_, dim, dtype); |
| 4283 | } |
| 4284 | at::functionalization::impl::replace_(out, tmp_output); |
| 4285 | at::functionalization::impl::commit_update(out); |
| 4286 | at::functionalization::impl::sync(out); |
| 4287 | return out; |
| 4288 | } |
| 4289 | } |
| 4290 | |
| 4291 | at::Tensor & cumsum__dimname(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype) { |
| 4292 | if (true) { |
| 4293 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4294 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4295 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4296 | auto self_meta = to_meta(self); |
| 4297 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4298 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4299 | at::_ops::cumsum__dimname::call(self_meta, dim, dtype); |
| 4300 | } |
| 4301 | |
| 4302 | at::Tensor self_; |
| 4303 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4304 | at::functionalization::impl::sync(self); |
| 4305 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4306 | } else { |
| 4307 | self_ = self; |
| 4308 | } |
| 4309 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 4310 | if ((false)) { |
| 4311 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4312 | TORCH_INTERNAL_ASSERT(false, |
| 4313 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4314 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4315 | } else { |
| 4316 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4317 | at::AutoDispatchSkipFunctionalize guard; |
| 4318 | at::Tensor tmp_output = at::_ops::cumsum__dimname::call(self_, dim, dtype); |
| 4319 | return self;; |
| 4320 | } |
| 4321 | } else { |
| 4322 | at::Tensor tmp_output; |
| 4323 | { |
| 4324 | at::AutoDispatchSkipFunctionalize guard; |
| 4325 | tmp_output = at::_ops::cumsum_dimname::call(self_, dim, dtype); |
| 4326 | } |
| 4327 | at::functionalization::impl::replace_(self, tmp_output); |
| 4328 | at::functionalization::impl::commit_update(self); |
| 4329 | at::functionalization::impl::sync(self); |
| 4330 | return self; |
| 4331 | } |
| 4332 | } |
| 4333 | |
| 4334 | ::std::tuple<at::Tensor &,at::Tensor &> _ctc_loss_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank, bool zero_infinity, at::Tensor & out0, at::Tensor & out1) { |
| 4335 | if (false) { |
| 4336 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4337 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4338 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4339 | auto log_probs_meta = to_meta(log_probs); |
| 4340 | auto targets_meta = to_meta(targets); |
| 4341 | auto out0_meta = to_meta(out0); |
| 4342 | auto out1_meta = to_meta(out1); |
| 4343 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4344 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4345 | at::_ops::_ctc_loss_out::call(log_probs_meta, targets_meta, input_lengths, target_lengths, blank, zero_infinity, out0_meta, out1_meta); |
| 4346 | } |
| 4347 | |
| 4348 | at::Tensor log_probs_; |
| 4349 | if (at::functionalization::impl::isFunctionalTensor(log_probs)) { |
| 4350 | at::functionalization::impl::sync(log_probs); |
| 4351 | log_probs_ = at::functionalization::impl::from_functional_tensor(log_probs); |
| 4352 | } else { |
| 4353 | log_probs_ = log_probs; |
| 4354 | } |
| 4355 | |
| 4356 | at::Tensor targets_; |
| 4357 | if (at::functionalization::impl::isFunctionalTensor(targets)) { |
| 4358 | at::functionalization::impl::sync(targets); |
| 4359 | targets_ = at::functionalization::impl::from_functional_tensor(targets); |
| 4360 | } else { |
| 4361 | targets_ = targets; |
| 4362 | } |
| 4363 | |
| 4364 | at::Tensor out0_; |
| 4365 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 4366 | at::functionalization::impl::sync(out0); |
| 4367 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 4368 | } else { |
| 4369 | out0_ = out0; |
| 4370 | } |
| 4371 | |
| 4372 | at::Tensor out1_; |
| 4373 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 4374 | at::functionalization::impl::sync(out1); |
| 4375 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 4376 | } else { |
| 4377 | out1_ = out1; |
| 4378 | } |
| 4379 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1))) { |
| 4380 | if ((false || at::functionalization::impl::isFunctionalTensor(log_probs) || at::functionalization::impl::isFunctionalTensor(targets))) { |
| 4381 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4382 | TORCH_INTERNAL_ASSERT(false, |
| 4383 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4384 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4385 | } else { |
| 4386 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4387 | at::AutoDispatchSkipFunctionalize guard; |
| 4388 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::_ctc_loss_out::call(log_probs_, targets_, input_lengths, target_lengths, blank, zero_infinity, out0_, out1_); |
| 4389 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1);; |
| 4390 | } |
| 4391 | } else { |
| 4392 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 4393 | { |
| 4394 | at::AutoDispatchSkipFunctionalize guard; |
| 4395 | tmp_output = at::_ops::_ctc_loss::call(log_probs_, targets_, input_lengths, target_lengths, blank, zero_infinity); |
| 4396 | } |
| 4397 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 4398 | at::functionalization::impl::commit_update(out0); |
| 4399 | at::functionalization::impl::sync(out0); |
| 4400 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 4401 | at::functionalization::impl::commit_update(out1); |
| 4402 | at::functionalization::impl::sync(out1); |
| 4403 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1); |
| 4404 | } |
| 4405 | } |
| 4406 | |
| 4407 | ::std::tuple<at::Tensor &,at::Tensor &> _ctc_loss_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank, bool zero_infinity, at::Tensor & out0, at::Tensor & out1) { |
| 4408 | if (false) { |
| 4409 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4410 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4411 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4412 | auto log_probs_meta = to_meta(log_probs); |
| 4413 | auto targets_meta = to_meta(targets); |
| 4414 | auto input_lengths_meta = to_meta(input_lengths); |
| 4415 | auto target_lengths_meta = to_meta(target_lengths); |
| 4416 | auto out0_meta = to_meta(out0); |
| 4417 | auto out1_meta = to_meta(out1); |
| 4418 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4419 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4420 | at::_ops::_ctc_loss_Tensor_out::call(log_probs_meta, targets_meta, input_lengths_meta, target_lengths_meta, blank, zero_infinity, out0_meta, out1_meta); |
| 4421 | } |
| 4422 | |
| 4423 | at::Tensor log_probs_; |
| 4424 | if (at::functionalization::impl::isFunctionalTensor(log_probs)) { |
| 4425 | at::functionalization::impl::sync(log_probs); |
| 4426 | log_probs_ = at::functionalization::impl::from_functional_tensor(log_probs); |
| 4427 | } else { |
| 4428 | log_probs_ = log_probs; |
| 4429 | } |
| 4430 | |
| 4431 | at::Tensor targets_; |
| 4432 | if (at::functionalization::impl::isFunctionalTensor(targets)) { |
| 4433 | at::functionalization::impl::sync(targets); |
| 4434 | targets_ = at::functionalization::impl::from_functional_tensor(targets); |
| 4435 | } else { |
| 4436 | targets_ = targets; |
| 4437 | } |
| 4438 | |
| 4439 | at::Tensor input_lengths_; |
| 4440 | if (at::functionalization::impl::isFunctionalTensor(input_lengths)) { |
| 4441 | at::functionalization::impl::sync(input_lengths); |
| 4442 | input_lengths_ = at::functionalization::impl::from_functional_tensor(input_lengths); |
| 4443 | } else { |
| 4444 | input_lengths_ = input_lengths; |
| 4445 | } |
| 4446 | |
| 4447 | at::Tensor target_lengths_; |
| 4448 | if (at::functionalization::impl::isFunctionalTensor(target_lengths)) { |
| 4449 | at::functionalization::impl::sync(target_lengths); |
| 4450 | target_lengths_ = at::functionalization::impl::from_functional_tensor(target_lengths); |
| 4451 | } else { |
| 4452 | target_lengths_ = target_lengths; |
| 4453 | } |
| 4454 | |
| 4455 | at::Tensor out0_; |
| 4456 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 4457 | at::functionalization::impl::sync(out0); |
| 4458 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 4459 | } else { |
| 4460 | out0_ = out0; |
| 4461 | } |
| 4462 | |
| 4463 | at::Tensor out1_; |
| 4464 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 4465 | at::functionalization::impl::sync(out1); |
| 4466 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 4467 | } else { |
| 4468 | out1_ = out1; |
| 4469 | } |
| 4470 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1))) { |
| 4471 | if ((false || at::functionalization::impl::isFunctionalTensor(log_probs) || at::functionalization::impl::isFunctionalTensor(targets) || at::functionalization::impl::isFunctionalTensor(input_lengths) || at::functionalization::impl::isFunctionalTensor(target_lengths))) { |
| 4472 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4473 | TORCH_INTERNAL_ASSERT(false, |
| 4474 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4475 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4476 | } else { |
| 4477 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4478 | at::AutoDispatchSkipFunctionalize guard; |
| 4479 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::_ctc_loss_Tensor_out::call(log_probs_, targets_, input_lengths_, target_lengths_, blank, zero_infinity, out0_, out1_); |
| 4480 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1);; |
| 4481 | } |
| 4482 | } else { |
| 4483 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 4484 | { |
| 4485 | at::AutoDispatchSkipFunctionalize guard; |
| 4486 | tmp_output = at::_ops::_ctc_loss_Tensor::call(log_probs_, targets_, input_lengths_, target_lengths_, blank, zero_infinity); |
| 4487 | } |
| 4488 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 4489 | at::functionalization::impl::commit_update(out0); |
| 4490 | at::functionalization::impl::sync(out0); |
| 4491 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 4492 | at::functionalization::impl::commit_update(out1); |
| 4493 | at::functionalization::impl::sync(out1); |
| 4494 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1); |
| 4495 | } |
| 4496 | } |
| 4497 | |
| 4498 | at::Tensor & _ctc_loss_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, const at::Tensor & neg_log_likelihood, const at::Tensor & log_alpha, int64_t blank, bool zero_infinity, at::Tensor & out) { |
| 4499 | if (false) { |
| 4500 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4501 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4502 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4503 | auto grad_meta = to_meta(grad); |
| 4504 | auto log_probs_meta = to_meta(log_probs); |
| 4505 | auto targets_meta = to_meta(targets); |
| 4506 | auto neg_log_likelihood_meta = to_meta(neg_log_likelihood); |
| 4507 | auto log_alpha_meta = to_meta(log_alpha); |
| 4508 | auto out_meta = to_meta(out); |
| 4509 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4510 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4511 | at::_ops::_ctc_loss_backward_out::call(grad_meta, log_probs_meta, targets_meta, input_lengths, target_lengths, neg_log_likelihood_meta, log_alpha_meta, blank, zero_infinity, out_meta); |
| 4512 | } |
| 4513 | |
| 4514 | at::Tensor grad_; |
| 4515 | if (at::functionalization::impl::isFunctionalTensor(grad)) { |
| 4516 | at::functionalization::impl::sync(grad); |
| 4517 | grad_ = at::functionalization::impl::from_functional_tensor(grad); |
| 4518 | } else { |
| 4519 | grad_ = grad; |
| 4520 | } |
| 4521 | |
| 4522 | at::Tensor log_probs_; |
| 4523 | if (at::functionalization::impl::isFunctionalTensor(log_probs)) { |
| 4524 | at::functionalization::impl::sync(log_probs); |
| 4525 | log_probs_ = at::functionalization::impl::from_functional_tensor(log_probs); |
| 4526 | } else { |
| 4527 | log_probs_ = log_probs; |
| 4528 | } |
| 4529 | |
| 4530 | at::Tensor targets_; |
| 4531 | if (at::functionalization::impl::isFunctionalTensor(targets)) { |
| 4532 | at::functionalization::impl::sync(targets); |
| 4533 | targets_ = at::functionalization::impl::from_functional_tensor(targets); |
| 4534 | } else { |
| 4535 | targets_ = targets; |
| 4536 | } |
| 4537 | |
| 4538 | at::Tensor neg_log_likelihood_; |
| 4539 | if (at::functionalization::impl::isFunctionalTensor(neg_log_likelihood)) { |
| 4540 | at::functionalization::impl::sync(neg_log_likelihood); |
| 4541 | neg_log_likelihood_ = at::functionalization::impl::from_functional_tensor(neg_log_likelihood); |
| 4542 | } else { |
| 4543 | neg_log_likelihood_ = neg_log_likelihood; |
| 4544 | } |
| 4545 | |
| 4546 | at::Tensor log_alpha_; |
| 4547 | if (at::functionalization::impl::isFunctionalTensor(log_alpha)) { |
| 4548 | at::functionalization::impl::sync(log_alpha); |
| 4549 | log_alpha_ = at::functionalization::impl::from_functional_tensor(log_alpha); |
| 4550 | } else { |
| 4551 | log_alpha_ = log_alpha; |
| 4552 | } |
| 4553 | |
| 4554 | at::Tensor out_; |
| 4555 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4556 | at::functionalization::impl::sync(out); |
| 4557 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4558 | } else { |
| 4559 | out_ = out; |
| 4560 | } |
| 4561 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4562 | if ((false || at::functionalization::impl::isFunctionalTensor(grad) || at::functionalization::impl::isFunctionalTensor(log_probs) || at::functionalization::impl::isFunctionalTensor(targets) || at::functionalization::impl::isFunctionalTensor(neg_log_likelihood) || at::functionalization::impl::isFunctionalTensor(log_alpha))) { |
| 4563 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4564 | TORCH_INTERNAL_ASSERT(false, |
| 4565 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4566 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4567 | } else { |
| 4568 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4569 | at::AutoDispatchSkipFunctionalize guard; |
| 4570 | at::Tensor tmp_output = at::_ops::_ctc_loss_backward_out::call(grad_, log_probs_, targets_, input_lengths, target_lengths, neg_log_likelihood_, log_alpha_, blank, zero_infinity, out_); |
| 4571 | return out;; |
| 4572 | } |
| 4573 | } else { |
| 4574 | at::Tensor tmp_output; |
| 4575 | { |
| 4576 | at::AutoDispatchSkipFunctionalize guard; |
| 4577 | tmp_output = at::_ops::_ctc_loss_backward::call(grad_, log_probs_, targets_, input_lengths, target_lengths, neg_log_likelihood_, log_alpha_, blank, zero_infinity); |
| 4578 | } |
| 4579 | at::functionalization::impl::replace_(out, tmp_output); |
| 4580 | at::functionalization::impl::commit_update(out); |
| 4581 | at::functionalization::impl::sync(out); |
| 4582 | return out; |
| 4583 | } |
| 4584 | } |
| 4585 | |
| 4586 | at::Tensor & embedding_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & weight, const at::Tensor & indices, c10::SymInt padding_idx, bool scale_grad_by_freq, bool sparse, at::Tensor & out) { |
| 4587 | if (false) { |
| 4588 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4589 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4590 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4591 | auto weight_meta = to_meta(weight); |
| 4592 | auto indices_meta = to_meta(indices); |
| 4593 | auto out_meta = to_meta(out); |
| 4594 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4595 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4596 | at::_ops::embedding_out::call(weight_meta, indices_meta, padding_idx, scale_grad_by_freq, sparse, out_meta); |
| 4597 | } |
| 4598 | |
| 4599 | at::Tensor weight_; |
| 4600 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 4601 | at::functionalization::impl::sync(weight); |
| 4602 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 4603 | } else { |
| 4604 | weight_ = weight; |
| 4605 | } |
| 4606 | |
| 4607 | at::Tensor indices_; |
| 4608 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 4609 | at::functionalization::impl::sync(indices); |
| 4610 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 4611 | } else { |
| 4612 | indices_ = indices; |
| 4613 | } |
| 4614 | |
| 4615 | at::Tensor out_; |
| 4616 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4617 | at::functionalization::impl::sync(out); |
| 4618 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4619 | } else { |
| 4620 | out_ = out; |
| 4621 | } |
| 4622 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4623 | if ((false || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(indices))) { |
| 4624 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4625 | TORCH_INTERNAL_ASSERT(false, |
| 4626 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4627 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4628 | } else { |
| 4629 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4630 | at::AutoDispatchSkipFunctionalize guard; |
| 4631 | at::Tensor tmp_output = at::_ops::embedding_out::call(weight_, indices_, padding_idx, scale_grad_by_freq, sparse, out_); |
| 4632 | return out;; |
| 4633 | } |
| 4634 | } else { |
| 4635 | at::Tensor tmp_output; |
| 4636 | { |
| 4637 | at::AutoDispatchSkipFunctionalize guard; |
| 4638 | tmp_output = at::_ops::embedding::call(weight_, indices_, padding_idx, scale_grad_by_freq, sparse); |
| 4639 | } |
| 4640 | at::functionalization::impl::replace_(out, tmp_output); |
| 4641 | at::functionalization::impl::commit_update(out); |
| 4642 | at::functionalization::impl::sync(out); |
| 4643 | return out; |
| 4644 | } |
| 4645 | } |
| 4646 | |
| 4647 | at::Tensor & embedding_dense_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & indices, c10::SymInt num_weights, c10::SymInt padding_idx, bool scale_grad_by_freq, at::Tensor & out) { |
| 4648 | if (false) { |
| 4649 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4650 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4651 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4652 | auto grad_output_meta = to_meta(grad_output); |
| 4653 | auto indices_meta = to_meta(indices); |
| 4654 | auto out_meta = to_meta(out); |
| 4655 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4656 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4657 | at::_ops::embedding_dense_backward_out::call(grad_output_meta, indices_meta, num_weights, padding_idx, scale_grad_by_freq, out_meta); |
| 4658 | } |
| 4659 | |
| 4660 | at::Tensor grad_output_; |
| 4661 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 4662 | at::functionalization::impl::sync(grad_output); |
| 4663 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 4664 | } else { |
| 4665 | grad_output_ = grad_output; |
| 4666 | } |
| 4667 | |
| 4668 | at::Tensor indices_; |
| 4669 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 4670 | at::functionalization::impl::sync(indices); |
| 4671 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 4672 | } else { |
| 4673 | indices_ = indices; |
| 4674 | } |
| 4675 | |
| 4676 | at::Tensor out_; |
| 4677 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4678 | at::functionalization::impl::sync(out); |
| 4679 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4680 | } else { |
| 4681 | out_ = out; |
| 4682 | } |
| 4683 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4684 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(indices))) { |
| 4685 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4686 | TORCH_INTERNAL_ASSERT(false, |
| 4687 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4688 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4689 | } else { |
| 4690 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4691 | at::AutoDispatchSkipFunctionalize guard; |
| 4692 | at::Tensor tmp_output = at::_ops::embedding_dense_backward_out::call(grad_output_, indices_, num_weights, padding_idx, scale_grad_by_freq, out_); |
| 4693 | return out;; |
| 4694 | } |
| 4695 | } else { |
| 4696 | at::Tensor tmp_output; |
| 4697 | { |
| 4698 | at::AutoDispatchSkipFunctionalize guard; |
| 4699 | tmp_output = at::_ops::embedding_dense_backward::call(grad_output_, indices_, num_weights, padding_idx, scale_grad_by_freq); |
| 4700 | } |
| 4701 | at::functionalization::impl::replace_(out, tmp_output); |
| 4702 | at::functionalization::impl::commit_update(out); |
| 4703 | at::functionalization::impl::sync(out); |
| 4704 | return out; |
| 4705 | } |
| 4706 | } |
| 4707 | |
| 4708 | at::Tensor & new_zeros_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out) { |
| 4709 | if (false) { |
| 4710 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4711 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4712 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4713 | auto self_meta = to_meta(self); |
| 4714 | auto out_meta = to_meta(out); |
| 4715 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4716 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4717 | at::_ops::new_zeros_out::call(self_meta, size, out_meta); |
| 4718 | } |
| 4719 | |
| 4720 | at::Tensor self_; |
| 4721 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4722 | at::functionalization::impl::sync(self); |
| 4723 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4724 | } else { |
| 4725 | self_ = self; |
| 4726 | } |
| 4727 | |
| 4728 | at::Tensor out_; |
| 4729 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4730 | at::functionalization::impl::sync(out); |
| 4731 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4732 | } else { |
| 4733 | out_ = out; |
| 4734 | } |
| 4735 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4736 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 4737 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4738 | TORCH_INTERNAL_ASSERT(false, |
| 4739 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4740 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4741 | } else { |
| 4742 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4743 | at::AutoDispatchSkipFunctionalize guard; |
| 4744 | at::Tensor tmp_output = at::_ops::new_zeros_out::call(self_, size, out_); |
| 4745 | return out;; |
| 4746 | } |
| 4747 | } else { |
| 4748 | at::Tensor tmp_output; |
| 4749 | { |
| 4750 | at::AutoDispatchSkipFunctionalize guard; |
| 4751 | tmp_output = at::_ops::new_zeros::call(self_, size, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 4752 | } |
| 4753 | at::functionalization::impl::replace_(out, tmp_output); |
| 4754 | at::functionalization::impl::commit_update(out); |
| 4755 | at::functionalization::impl::sync(out); |
| 4756 | return out; |
| 4757 | } |
| 4758 | } |
| 4759 | |
| 4760 | at::Tensor & new_ones_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out) { |
| 4761 | if (false) { |
| 4762 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4763 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4764 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4765 | auto self_meta = to_meta(self); |
| 4766 | auto out_meta = to_meta(out); |
| 4767 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4768 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4769 | at::_ops::new_ones_out::call(self_meta, size, out_meta); |
| 4770 | } |
| 4771 | |
| 4772 | at::Tensor self_; |
| 4773 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4774 | at::functionalization::impl::sync(self); |
| 4775 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4776 | } else { |
| 4777 | self_ = self; |
| 4778 | } |
| 4779 | |
| 4780 | at::Tensor out_; |
| 4781 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4782 | at::functionalization::impl::sync(out); |
| 4783 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4784 | } else { |
| 4785 | out_ = out; |
| 4786 | } |
| 4787 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4788 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 4789 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4790 | TORCH_INTERNAL_ASSERT(false, |
| 4791 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4792 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4793 | } else { |
| 4794 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4795 | at::AutoDispatchSkipFunctionalize guard; |
| 4796 | at::Tensor tmp_output = at::_ops::new_ones_out::call(self_, size, out_); |
| 4797 | return out;; |
| 4798 | } |
| 4799 | } else { |
| 4800 | at::Tensor tmp_output; |
| 4801 | { |
| 4802 | at::AutoDispatchSkipFunctionalize guard; |
| 4803 | tmp_output = at::_ops::new_ones::call(self_, size, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 4804 | } |
| 4805 | at::functionalization::impl::replace_(out, tmp_output); |
| 4806 | at::functionalization::impl::commit_update(out); |
| 4807 | at::functionalization::impl::sync(out); |
| 4808 | return out; |
| 4809 | } |
| 4810 | } |
| 4811 | |
| 4812 | at::Tensor & _empty_per_channel_affine_quantized_out_out(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out) { |
| 4813 | if (false) { |
| 4814 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4815 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4816 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4817 | auto scales_meta = to_meta(scales); |
| 4818 | auto zero_points_meta = to_meta(zero_points); |
| 4819 | auto out_meta = to_meta(out); |
| 4820 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4821 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4822 | at::_ops::_empty_per_channel_affine_quantized_out::call(size, scales_meta, zero_points_meta, axis, memory_format, out_meta); |
| 4823 | } |
| 4824 | |
| 4825 | at::Tensor scales_; |
| 4826 | if (at::functionalization::impl::isFunctionalTensor(scales)) { |
| 4827 | at::functionalization::impl::sync(scales); |
| 4828 | scales_ = at::functionalization::impl::from_functional_tensor(scales); |
| 4829 | } else { |
| 4830 | scales_ = scales; |
| 4831 | } |
| 4832 | |
| 4833 | at::Tensor zero_points_; |
| 4834 | if (at::functionalization::impl::isFunctionalTensor(zero_points)) { |
| 4835 | at::functionalization::impl::sync(zero_points); |
| 4836 | zero_points_ = at::functionalization::impl::from_functional_tensor(zero_points); |
| 4837 | } else { |
| 4838 | zero_points_ = zero_points; |
| 4839 | } |
| 4840 | |
| 4841 | at::Tensor out_; |
| 4842 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4843 | at::functionalization::impl::sync(out); |
| 4844 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4845 | } else { |
| 4846 | out_ = out; |
| 4847 | } |
| 4848 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4849 | if ((false || at::functionalization::impl::isFunctionalTensor(scales) || at::functionalization::impl::isFunctionalTensor(zero_points))) { |
| 4850 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4851 | TORCH_INTERNAL_ASSERT(false, |
| 4852 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4853 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4854 | } else { |
| 4855 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4856 | at::AutoDispatchSkipFunctionalize guard; |
| 4857 | at::Tensor tmp_output = at::_ops::_empty_per_channel_affine_quantized_out::call(size, scales_, zero_points_, axis, memory_format, out_); |
| 4858 | return out;; |
| 4859 | } |
| 4860 | } else { |
| 4861 | at::Tensor tmp_output; |
| 4862 | { |
| 4863 | at::AutoDispatchSkipFunctionalize guard; |
| 4864 | tmp_output = at::_ops::_empty_per_channel_affine_quantized::call(size, scales_, zero_points_, axis, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt, memory_format); |
| 4865 | } |
| 4866 | at::functionalization::impl::replace_(out, tmp_output); |
| 4867 | at::functionalization::impl::commit_update(out); |
| 4868 | at::functionalization::impl::sync(out); |
| 4869 | return out; |
| 4870 | } |
| 4871 | } |
| 4872 | |
| 4873 | const at::Tensor & resize_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::optional<at::MemoryFormat> memory_format, const at::Tensor & out) { |
| 4874 | if (false) { |
| 4875 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4876 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4877 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4878 | auto self_meta = to_meta(self); |
| 4879 | auto out_meta = to_meta(out); |
| 4880 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4881 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4882 | at::_ops::resize_out::call(self_meta, size, memory_format, out_meta); |
| 4883 | } |
| 4884 | |
| 4885 | at::Tensor self_; |
| 4886 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4887 | at::functionalization::impl::sync(self); |
| 4888 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4889 | } else { |
| 4890 | self_ = self; |
| 4891 | } |
| 4892 | |
| 4893 | at::Tensor out_; |
| 4894 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4895 | at::functionalization::impl::sync(out); |
| 4896 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4897 | } else { |
| 4898 | out_ = out; |
| 4899 | } |
| 4900 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4901 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 4902 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4903 | TORCH_INTERNAL_ASSERT(false, |
| 4904 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4905 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4906 | } else { |
| 4907 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4908 | at::AutoDispatchSkipFunctionalize guard; |
| 4909 | at::Tensor tmp_output = at::_ops::resize_out::call(self_, size, memory_format, out_); |
| 4910 | return out;; |
| 4911 | } |
| 4912 | } else { |
| 4913 | at::Tensor tmp_output; |
| 4914 | { |
| 4915 | at::AutoDispatchSkipFunctionalize guard; |
| 4916 | tmp_output = at::_ops::resize::call(self_, size, memory_format); |
| 4917 | } |
| 4918 | at::functionalization::impl::replace_(out, tmp_output); |
| 4919 | at::functionalization::impl::commit_update(out); |
| 4920 | at::functionalization::impl::sync(out); |
| 4921 | return out; |
| 4922 | } |
| 4923 | } |
| 4924 | |
| 4925 | const at::Tensor & resize_(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::optional<at::MemoryFormat> memory_format) { |
| 4926 | if (true) { |
| 4927 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4928 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4929 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4930 | auto self_meta = to_meta(self); |
| 4931 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4932 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4933 | at::_ops::resize_::call(self_meta, size, memory_format); |
| 4934 | } |
| 4935 | |
| 4936 | at::Tensor self_; |
| 4937 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4938 | at::functionalization::impl::sync(self); |
| 4939 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4940 | } else { |
| 4941 | self_ = self; |
| 4942 | } |
| 4943 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 4944 | if ((false)) { |
| 4945 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4946 | TORCH_INTERNAL_ASSERT(false, |
| 4947 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4948 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4949 | } else { |
| 4950 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4951 | at::AutoDispatchSkipFunctionalize guard; |
| 4952 | at::Tensor tmp_output = at::_ops::resize_::call(self_, size, memory_format); |
| 4953 | return self;; |
| 4954 | } |
| 4955 | } else { |
| 4956 | at::Tensor tmp_output; |
| 4957 | { |
| 4958 | at::AutoDispatchSkipFunctionalize guard; |
| 4959 | tmp_output = at::_ops::resize::call(self_, size, memory_format); |
| 4960 | } |
| 4961 | at::functionalization::impl::replace_(self, tmp_output); |
| 4962 | at::functionalization::impl::commit_update(self); |
| 4963 | at::functionalization::impl::sync(self); |
| 4964 | return self; |
| 4965 | } |
| 4966 | } |
| 4967 | |
| 4968 | at::Tensor & exp2_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 4969 | if (false) { |
| 4970 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4971 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4972 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4973 | auto self_meta = to_meta(self); |
| 4974 | auto out_meta = to_meta(out); |
| 4975 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4976 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4977 | at::_ops::exp2_out::call(self_meta, out_meta); |
| 4978 | } |
| 4979 | |
| 4980 | at::Tensor self_; |
| 4981 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4982 | at::functionalization::impl::sync(self); |
| 4983 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4984 | } else { |
| 4985 | self_ = self; |
| 4986 | } |
| 4987 | |
| 4988 | at::Tensor out_; |
| 4989 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4990 | at::functionalization::impl::sync(out); |
| 4991 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4992 | } else { |
| 4993 | out_ = out; |
| 4994 | } |
| 4995 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4996 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 4997 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4998 | TORCH_INTERNAL_ASSERT(false, |
| 4999 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5000 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5001 | } else { |
| 5002 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5003 | at::AutoDispatchSkipFunctionalize guard; |
| 5004 | at::Tensor tmp_output = at::_ops::exp2_out::call(self_, out_); |
| 5005 | return out;; |
| 5006 | } |
| 5007 | } else { |
| 5008 | at::Tensor tmp_output; |
| 5009 | { |
| 5010 | at::AutoDispatchSkipFunctionalize guard; |
| 5011 | tmp_output = at::_ops::exp2::call(self_); |
| 5012 | } |
| 5013 | at::functionalization::impl::replace_(out, tmp_output); |
| 5014 | at::functionalization::impl::commit_update(out); |
| 5015 | at::functionalization::impl::sync(out); |
| 5016 | return out; |
| 5017 | } |
| 5018 | } |
| 5019 | |
| 5020 | at::Tensor & exp2_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 5021 | if (true) { |
| 5022 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5023 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5024 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5025 | auto self_meta = to_meta(self); |
| 5026 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5027 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5028 | at::_ops::exp2_::call(self_meta); |
| 5029 | } |
| 5030 | |
| 5031 | at::Tensor self_; |
| 5032 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5033 | at::functionalization::impl::sync(self); |
| 5034 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5035 | } else { |
| 5036 | self_ = self; |
| 5037 | } |
| 5038 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 5039 | if ((false)) { |
| 5040 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5041 | TORCH_INTERNAL_ASSERT(false, |
| 5042 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5043 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5044 | } else { |
| 5045 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5046 | at::AutoDispatchSkipFunctionalize guard; |
| 5047 | at::Tensor tmp_output = at::_ops::exp2_::call(self_); |
| 5048 | return self;; |
| 5049 | } |
| 5050 | } else { |
| 5051 | at::Tensor tmp_output; |
| 5052 | { |
| 5053 | at::AutoDispatchSkipFunctionalize guard; |
| 5054 | tmp_output = at::_ops::exp2::call(self_); |
| 5055 | } |
| 5056 | at::functionalization::impl::replace_(self, tmp_output); |
| 5057 | at::functionalization::impl::commit_update(self); |
| 5058 | at::functionalization::impl::sync(self); |
| 5059 | return self; |
| 5060 | } |
| 5061 | } |
| 5062 | |
| 5063 | at::Tensor & _grid_sampler_2d_cpu_fallback_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, at::Tensor & out) { |
| 5064 | if (false) { |
| 5065 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5066 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5067 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5068 | auto input_meta = to_meta(input); |
| 5069 | auto grid_meta = to_meta(grid); |
| 5070 | auto out_meta = to_meta(out); |
| 5071 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5072 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5073 | at::_ops::_grid_sampler_2d_cpu_fallback_out::call(input_meta, grid_meta, interpolation_mode, padding_mode, align_corners, out_meta); |
| 5074 | } |
| 5075 | |
| 5076 | at::Tensor input_; |
| 5077 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 5078 | at::functionalization::impl::sync(input); |
| 5079 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 5080 | } else { |
| 5081 | input_ = input; |
| 5082 | } |
| 5083 | |
| 5084 | at::Tensor grid_; |
| 5085 | if (at::functionalization::impl::isFunctionalTensor(grid)) { |
| 5086 | at::functionalization::impl::sync(grid); |
| 5087 | grid_ = at::functionalization::impl::from_functional_tensor(grid); |
| 5088 | } else { |
| 5089 | grid_ = grid; |
| 5090 | } |
| 5091 | |
| 5092 | at::Tensor out_; |
| 5093 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5094 | at::functionalization::impl::sync(out); |
| 5095 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5096 | } else { |
| 5097 | out_ = out; |
| 5098 | } |
| 5099 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5100 | if ((false || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(grid))) { |
| 5101 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5102 | TORCH_INTERNAL_ASSERT(false, |
| 5103 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5104 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5105 | } else { |
| 5106 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5107 | at::AutoDispatchSkipFunctionalize guard; |
| 5108 | at::Tensor tmp_output = at::_ops::_grid_sampler_2d_cpu_fallback_out::call(input_, grid_, interpolation_mode, padding_mode, align_corners, out_); |
| 5109 | return out;; |
| 5110 | } |
| 5111 | } else { |
| 5112 | at::Tensor tmp_output; |
| 5113 | { |
| 5114 | at::AutoDispatchSkipFunctionalize guard; |
| 5115 | tmp_output = at::_ops::_grid_sampler_2d_cpu_fallback::call(input_, grid_, interpolation_mode, padding_mode, align_corners); |
| 5116 | } |
| 5117 | at::functionalization::impl::replace_(out, tmp_output); |
| 5118 | at::functionalization::impl::commit_update(out); |
| 5119 | at::functionalization::impl::sync(out); |
| 5120 | return out; |
| 5121 | } |
| 5122 | } |
| 5123 | |
| 5124 | ::std::tuple<at::Tensor &,at::Tensor &> grid_sampler_3d_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) { |
| 5125 | if (false) { |
| 5126 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5127 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5128 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5129 | auto grad_output_meta = to_meta(grad_output); |
| 5130 | auto input_meta = to_meta(input); |
| 5131 | auto grid_meta = to_meta(grid); |
| 5132 | auto out0_meta = to_meta(out0); |
| 5133 | auto out1_meta = to_meta(out1); |
| 5134 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5135 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5136 | at::_ops::grid_sampler_3d_backward_out::call(grad_output_meta, input_meta, grid_meta, interpolation_mode, padding_mode, align_corners, output_mask, out0_meta, out1_meta); |
| 5137 | } |
| 5138 | |
| 5139 | at::Tensor grad_output_; |
| 5140 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 5141 | at::functionalization::impl::sync(grad_output); |
| 5142 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 5143 | } else { |
| 5144 | grad_output_ = grad_output; |
| 5145 | } |
| 5146 | |
| 5147 | at::Tensor input_; |
| 5148 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 5149 | at::functionalization::impl::sync(input); |
| 5150 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 5151 | } else { |
| 5152 | input_ = input; |
| 5153 | } |
| 5154 | |
| 5155 | at::Tensor grid_; |
| 5156 | if (at::functionalization::impl::isFunctionalTensor(grid)) { |
| 5157 | at::functionalization::impl::sync(grid); |
| 5158 | grid_ = at::functionalization::impl::from_functional_tensor(grid); |
| 5159 | } else { |
| 5160 | grid_ = grid; |
| 5161 | } |
| 5162 | |
| 5163 | at::Tensor out0_; |
| 5164 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 5165 | at::functionalization::impl::sync(out0); |
| 5166 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 5167 | } else { |
| 5168 | out0_ = out0; |
| 5169 | } |
| 5170 | |
| 5171 | at::Tensor out1_; |
| 5172 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 5173 | at::functionalization::impl::sync(out1); |
| 5174 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 5175 | } else { |
| 5176 | out1_ = out1; |
| 5177 | } |
| 5178 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1))) { |
| 5179 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(grid))) { |
| 5180 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5181 | TORCH_INTERNAL_ASSERT(false, |
| 5182 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5183 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5184 | } else { |
| 5185 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5186 | at::AutoDispatchSkipFunctionalize guard; |
| 5187 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::grid_sampler_3d_backward_out::call(grad_output_, input_, grid_, interpolation_mode, padding_mode, align_corners, output_mask, out0_, out1_); |
| 5188 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1);; |
| 5189 | } |
| 5190 | } else { |
| 5191 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 5192 | { |
| 5193 | at::AutoDispatchSkipFunctionalize guard; |
| 5194 | tmp_output = at::_ops::grid_sampler_3d_backward::call(grad_output_, input_, grid_, interpolation_mode, padding_mode, align_corners, output_mask); |
| 5195 | } |
| 5196 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 5197 | at::functionalization::impl::commit_update(out0); |
| 5198 | at::functionalization::impl::sync(out0); |
| 5199 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 5200 | at::functionalization::impl::commit_update(out1); |
| 5201 | at::functionalization::impl::sync(out1); |
| 5202 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1); |
| 5203 | } |
| 5204 | } |
| 5205 | |
| 5206 | at::Tensor & _fft_c2c_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef dim, int64_t normalization, bool forward, at::Tensor & out) { |
| 5207 | if (false) { |
| 5208 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5209 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5210 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5211 | auto self_meta = to_meta(self); |
| 5212 | auto out_meta = to_meta(out); |
| 5213 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5214 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5215 | at::_ops::_fft_c2c_out::call(self_meta, dim, normalization, forward, out_meta); |
| 5216 | } |
| 5217 | |
| 5218 | at::Tensor self_; |
| 5219 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5220 | at::functionalization::impl::sync(self); |
| 5221 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5222 | } else { |
| 5223 | self_ = self; |
| 5224 | } |
| 5225 | |
| 5226 | at::Tensor out_; |
| 5227 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5228 | at::functionalization::impl::sync(out); |
| 5229 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5230 | } else { |
| 5231 | out_ = out; |
| 5232 | } |
| 5233 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5234 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 5235 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5236 | TORCH_INTERNAL_ASSERT(false, |
| 5237 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5238 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5239 | } else { |
| 5240 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5241 | at::AutoDispatchSkipFunctionalize guard; |
| 5242 | at::Tensor tmp_output = at::_ops::_fft_c2c_out::call(self_, dim, normalization, forward, out_); |
| 5243 | return out;; |
| 5244 | } |
| 5245 | } else { |
| 5246 | at::Tensor tmp_output; |
| 5247 | { |
| 5248 | at::AutoDispatchSkipFunctionalize guard; |
| 5249 | tmp_output = at::_ops::_fft_c2c::call(self_, dim, normalization, forward); |
| 5250 | } |
| 5251 | at::functionalization::impl::replace_(out, tmp_output); |
| 5252 | at::functionalization::impl::commit_update(out); |
| 5253 | at::functionalization::impl::sync(out); |
| 5254 | return out; |
| 5255 | } |
| 5256 | } |
| 5257 | |
| 5258 | at::Tensor & index_copy_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, at::Tensor & out) { |
| 5259 | if (false) { |
| 5260 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5261 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5262 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5263 | auto self_meta = to_meta(self); |
| 5264 | auto index_meta = to_meta(index); |
| 5265 | auto source_meta = to_meta(source); |
| 5266 | auto out_meta = to_meta(out); |
| 5267 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5268 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5269 | at::_ops::index_copy_out::call(self_meta, dim, index_meta, source_meta, out_meta); |
| 5270 | } |
| 5271 | |
| 5272 | at::Tensor self_; |
| 5273 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5274 | at::functionalization::impl::sync(self); |
| 5275 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5276 | } else { |
| 5277 | self_ = self; |
| 5278 | } |
| 5279 | |
| 5280 | at::Tensor index_; |
| 5281 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 5282 | at::functionalization::impl::sync(index); |
| 5283 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 5284 | } else { |
| 5285 | index_ = index; |
| 5286 | } |
| 5287 | |
| 5288 | at::Tensor source_; |
| 5289 | if (at::functionalization::impl::isFunctionalTensor(source)) { |
| 5290 | at::functionalization::impl::sync(source); |
| 5291 | source_ = at::functionalization::impl::from_functional_tensor(source); |
| 5292 | } else { |
| 5293 | source_ = source; |
| 5294 | } |
| 5295 | |
| 5296 | at::Tensor out_; |
| 5297 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5298 | at::functionalization::impl::sync(out); |
| 5299 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5300 | } else { |
| 5301 | out_ = out; |
| 5302 | } |
| 5303 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5304 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(index) || at::functionalization::impl::isFunctionalTensor(source))) { |
| 5305 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5306 | TORCH_INTERNAL_ASSERT(false, |
| 5307 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5308 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5309 | } else { |
| 5310 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5311 | at::AutoDispatchSkipFunctionalize guard; |
| 5312 | at::Tensor tmp_output = at::_ops::index_copy_out::call(self_, dim, index_, source_, out_); |
| 5313 | return out;; |
| 5314 | } |
| 5315 | } else { |
| 5316 | at::Tensor tmp_output; |
| 5317 | { |
| 5318 | at::AutoDispatchSkipFunctionalize guard; |
| 5319 | tmp_output = at::_ops::index_copy::call(self_, dim, index_, source_); |
| 5320 | } |
| 5321 | at::functionalization::impl::replace_(out, tmp_output); |
| 5322 | at::functionalization::impl::commit_update(out); |
| 5323 | at::functionalization::impl::sync(out); |
| 5324 | return out; |
| 5325 | } |
| 5326 | } |
| 5327 | |
| 5328 | at::Tensor & index_copy_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source) { |
| 5329 | if (true) { |
| 5330 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5331 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5332 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5333 | auto self_meta = to_meta(self); |
| 5334 | auto index_meta = to_meta(index); |
| 5335 | auto source_meta = to_meta(source); |
| 5336 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5337 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5338 | at::_ops::index_copy_::call(self_meta, dim, index_meta, source_meta); |
| 5339 | } |
| 5340 | |
| 5341 | at::Tensor self_; |
| 5342 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5343 | at::functionalization::impl::sync(self); |
| 5344 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5345 | } else { |
| 5346 | self_ = self; |
| 5347 | } |
| 5348 | |
| 5349 | at::Tensor index_; |
| 5350 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 5351 | at::functionalization::impl::sync(index); |
| 5352 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 5353 | } else { |
| 5354 | index_ = index; |
| 5355 | } |
| 5356 | |
| 5357 | at::Tensor source_; |
| 5358 | if (at::functionalization::impl::isFunctionalTensor(source)) { |
| 5359 | at::functionalization::impl::sync(source); |
| 5360 | source_ = at::functionalization::impl::from_functional_tensor(source); |
| 5361 | } else { |
| 5362 | source_ = source; |
| 5363 | } |
| 5364 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 5365 | if ((false || at::functionalization::impl::isFunctionalTensor(index) || at::functionalization::impl::isFunctionalTensor(source))) { |
| 5366 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5367 | TORCH_INTERNAL_ASSERT(false, |
| 5368 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5369 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5370 | } else { |
| 5371 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5372 | at::AutoDispatchSkipFunctionalize guard; |
| 5373 | at::Tensor tmp_output = at::_ops::index_copy_::call(self_, dim, index_, source_); |
| 5374 | return self;; |
| 5375 | } |
| 5376 | } else { |
| 5377 | at::Tensor tmp_output; |
| 5378 | { |
| 5379 | at::AutoDispatchSkipFunctionalize guard; |
| 5380 | tmp_output = at::_ops::index_copy::call(self_, dim, index_, source_); |
| 5381 | } |
| 5382 | at::functionalization::impl::replace_(self, tmp_output); |
| 5383 | at::functionalization::impl::commit_update(self); |
| 5384 | at::functionalization::impl::sync(self); |
| 5385 | return self; |
| 5386 | } |
| 5387 | } |
| 5388 | |
| 5389 | at::Tensor & isin_out_Tensor_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & elements, const at::Tensor & test_elements, bool assume_unique, bool invert, at::Tensor & out) { |
| 5390 | if (false) { |
| 5391 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5392 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5393 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5394 | auto elements_meta = to_meta(elements); |
| 5395 | auto test_elements_meta = to_meta(test_elements); |
| 5396 | auto out_meta = to_meta(out); |
| 5397 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5398 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5399 | at::_ops::isin_Tensor_Tensor_out::call(elements_meta, test_elements_meta, assume_unique, invert, out_meta); |
| 5400 | } |
| 5401 | |
| 5402 | at::Tensor elements_; |
| 5403 | if (at::functionalization::impl::isFunctionalTensor(elements)) { |
| 5404 | at::functionalization::impl::sync(elements); |
| 5405 | elements_ = at::functionalization::impl::from_functional_tensor(elements); |
| 5406 | } else { |
| 5407 | elements_ = elements; |
| 5408 | } |
| 5409 | |
| 5410 | at::Tensor test_elements_; |
| 5411 | if (at::functionalization::impl::isFunctionalTensor(test_elements)) { |
| 5412 | at::functionalization::impl::sync(test_elements); |
| 5413 | test_elements_ = at::functionalization::impl::from_functional_tensor(test_elements); |
| 5414 | } else { |
| 5415 | test_elements_ = test_elements; |
| 5416 | } |
| 5417 | |
| 5418 | at::Tensor out_; |
| 5419 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5420 | at::functionalization::impl::sync(out); |
| 5421 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5422 | } else { |
| 5423 | out_ = out; |
| 5424 | } |
| 5425 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5426 | if ((false || at::functionalization::impl::isFunctionalTensor(elements) || at::functionalization::impl::isFunctionalTensor(test_elements))) { |
| 5427 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5428 | TORCH_INTERNAL_ASSERT(false, |
| 5429 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5430 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5431 | } else { |
| 5432 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5433 | at::AutoDispatchSkipFunctionalize guard; |
| 5434 | at::Tensor tmp_output = at::_ops::isin_Tensor_Tensor_out::call(elements_, test_elements_, assume_unique, invert, out_); |
| 5435 | return out;; |
| 5436 | } |
| 5437 | } else { |
| 5438 | at::Tensor tmp_output; |
| 5439 | { |
| 5440 | at::AutoDispatchSkipFunctionalize guard; |
| 5441 | tmp_output = at::_ops::isin_Tensor_Tensor::call(elements_, test_elements_, assume_unique, invert); |
| 5442 | } |
| 5443 | at::functionalization::impl::replace_(out, tmp_output); |
| 5444 | at::functionalization::impl::commit_update(out); |
| 5445 | at::functionalization::impl::sync(out); |
| 5446 | return out; |
| 5447 | } |
| 5448 | } |
| 5449 | |
| 5450 | at::Tensor & isin_out_Tensor_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & elements, const at::Scalar & test_element, bool assume_unique, bool invert, at::Tensor & out) { |
| 5451 | if (false) { |
| 5452 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5453 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5454 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5455 | auto elements_meta = to_meta(elements); |
| 5456 | auto out_meta = to_meta(out); |
| 5457 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5458 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5459 | at::_ops::isin_Tensor_Scalar_out::call(elements_meta, test_element, assume_unique, invert, out_meta); |
| 5460 | } |
| 5461 | |
| 5462 | at::Tensor elements_; |
| 5463 | if (at::functionalization::impl::isFunctionalTensor(elements)) { |
| 5464 | at::functionalization::impl::sync(elements); |
| 5465 | elements_ = at::functionalization::impl::from_functional_tensor(elements); |
| 5466 | } else { |
| 5467 | elements_ = elements; |
| 5468 | } |
| 5469 | |
| 5470 | at::Tensor out_; |
| 5471 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5472 | at::functionalization::impl::sync(out); |
| 5473 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5474 | } else { |
| 5475 | out_ = out; |
| 5476 | } |
| 5477 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5478 | if ((false || at::functionalization::impl::isFunctionalTensor(elements))) { |
| 5479 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5480 | TORCH_INTERNAL_ASSERT(false, |
| 5481 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5482 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5483 | } else { |
| 5484 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5485 | at::AutoDispatchSkipFunctionalize guard; |
| 5486 | at::Tensor tmp_output = at::_ops::isin_Tensor_Scalar_out::call(elements_, test_element, assume_unique, invert, out_); |
| 5487 | return out;; |
| 5488 | } |
| 5489 | } else { |
| 5490 | at::Tensor tmp_output; |
| 5491 | { |
| 5492 | at::AutoDispatchSkipFunctionalize guard; |
| 5493 | tmp_output = at::_ops::isin_Tensor_Scalar::call(elements_, test_element, assume_unique, invert); |
| 5494 | } |
| 5495 | at::functionalization::impl::replace_(out, tmp_output); |
| 5496 | at::functionalization::impl::commit_update(out); |
| 5497 | at::functionalization::impl::sync(out); |
| 5498 | return out; |
| 5499 | } |
| 5500 | } |
| 5501 | |
| 5502 | at::Tensor & isin_out_Scalar_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Scalar & element, const at::Tensor & test_elements, bool assume_unique, bool invert, at::Tensor & out) { |
| 5503 | if (false) { |
| 5504 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5505 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5506 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5507 | auto test_elements_meta = to_meta(test_elements); |
| 5508 | auto out_meta = to_meta(out); |
| 5509 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5510 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5511 | at::_ops::isin_Scalar_Tensor_out::call(element, test_elements_meta, assume_unique, invert, out_meta); |
| 5512 | } |
| 5513 | |
| 5514 | at::Tensor test_elements_; |
| 5515 | if (at::functionalization::impl::isFunctionalTensor(test_elements)) { |
| 5516 | at::functionalization::impl::sync(test_elements); |
| 5517 | test_elements_ = at::functionalization::impl::from_functional_tensor(test_elements); |
| 5518 | } else { |
| 5519 | test_elements_ = test_elements; |
| 5520 | } |
| 5521 | |
| 5522 | at::Tensor out_; |
| 5523 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5524 | at::functionalization::impl::sync(out); |
| 5525 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5526 | } else { |
| 5527 | out_ = out; |
| 5528 | } |
| 5529 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5530 | if ((false || at::functionalization::impl::isFunctionalTensor(test_elements))) { |
| 5531 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5532 | TORCH_INTERNAL_ASSERT(false, |
| 5533 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5534 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5535 | } else { |
| 5536 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5537 | at::AutoDispatchSkipFunctionalize guard; |
| 5538 | at::Tensor tmp_output = at::_ops::isin_Scalar_Tensor_out::call(element, test_elements_, assume_unique, invert, out_); |
| 5539 | return out;; |
| 5540 | } |
| 5541 | } else { |
| 5542 | at::Tensor tmp_output; |
| 5543 | { |
| 5544 | at::AutoDispatchSkipFunctionalize guard; |
| 5545 | tmp_output = at::_ops::isin_Scalar_Tensor::call(element, test_elements_, assume_unique, invert); |
| 5546 | } |
| 5547 | at::functionalization::impl::replace_(out, tmp_output); |
| 5548 | at::functionalization::impl::commit_update(out); |
| 5549 | at::functionalization::impl::sync(out); |
| 5550 | return out; |
| 5551 | } |
| 5552 | } |
| 5553 | |
| 5554 | at::Tensor & kron_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 5555 | if (false) { |
| 5556 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5557 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5558 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5559 | auto self_meta = to_meta(self); |
| 5560 | auto other_meta = to_meta(other); |
| 5561 | auto out_meta = to_meta(out); |
| 5562 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5563 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5564 | at::_ops::kron_out::call(self_meta, other_meta, out_meta); |
| 5565 | } |
| 5566 | |
| 5567 | at::Tensor self_; |
| 5568 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5569 | at::functionalization::impl::sync(self); |
| 5570 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5571 | } else { |
| 5572 | self_ = self; |
| 5573 | } |
| 5574 | |
| 5575 | at::Tensor other_; |
| 5576 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 5577 | at::functionalization::impl::sync(other); |
| 5578 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 5579 | } else { |
| 5580 | other_ = other; |
| 5581 | } |
| 5582 | |
| 5583 | at::Tensor out_; |
| 5584 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5585 | at::functionalization::impl::sync(out); |
| 5586 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5587 | } else { |
| 5588 | out_ = out; |
| 5589 | } |
| 5590 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5591 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 5592 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5593 | TORCH_INTERNAL_ASSERT(false, |
| 5594 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5595 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5596 | } else { |
| 5597 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5598 | at::AutoDispatchSkipFunctionalize guard; |
| 5599 | at::Tensor tmp_output = at::_ops::kron_out::call(self_, other_, out_); |
| 5600 | return out;; |
| 5601 | } |
| 5602 | } else { |
| 5603 | at::Tensor tmp_output; |
| 5604 | { |
| 5605 | at::AutoDispatchSkipFunctionalize guard; |
| 5606 | tmp_output = at::_ops::kron::call(self_, other_); |
| 5607 | } |
| 5608 | at::functionalization::impl::replace_(out, tmp_output); |
| 5609 | at::functionalization::impl::commit_update(out); |
| 5610 | at::functionalization::impl::sync(out); |
| 5611 | return out; |
| 5612 | } |
| 5613 | } |
| 5614 | |
| 5615 | at::Tensor & nan_to_num_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<double> nan, c10::optional<double> posinf, c10::optional<double> neginf, at::Tensor & out) { |
| 5616 | if (false) { |
| 5617 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5618 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5619 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5620 | auto self_meta = to_meta(self); |
| 5621 | auto out_meta = to_meta(out); |
| 5622 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5623 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5624 | at::_ops::nan_to_num_out::call(self_meta, nan, posinf, neginf, out_meta); |
| 5625 | } |
| 5626 | |
| 5627 | at::Tensor self_; |
| 5628 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5629 | at::functionalization::impl::sync(self); |
| 5630 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5631 | } else { |
| 5632 | self_ = self; |
| 5633 | } |
| 5634 | |
| 5635 | at::Tensor out_; |
| 5636 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5637 | at::functionalization::impl::sync(out); |
| 5638 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5639 | } else { |
| 5640 | out_ = out; |
| 5641 | } |
| 5642 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5643 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 5644 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5645 | TORCH_INTERNAL_ASSERT(false, |
| 5646 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5647 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5648 | } else { |
| 5649 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5650 | at::AutoDispatchSkipFunctionalize guard; |
| 5651 | at::Tensor tmp_output = at::_ops::nan_to_num_out::call(self_, nan, posinf, neginf, out_); |
| 5652 | return out;; |
| 5653 | } |
| 5654 | } else { |
| 5655 | at::Tensor tmp_output; |
| 5656 | { |
| 5657 | at::AutoDispatchSkipFunctionalize guard; |
| 5658 | tmp_output = at::_ops::nan_to_num::call(self_, nan, posinf, neginf); |
| 5659 | } |
| 5660 | at::functionalization::impl::replace_(out, tmp_output); |
| 5661 | at::functionalization::impl::commit_update(out); |
| 5662 | at::functionalization::impl::sync(out); |
| 5663 | return out; |
| 5664 | } |
| 5665 | } |
| 5666 | |
| 5667 | at::Tensor & nan_to_num_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, c10::optional<double> nan, c10::optional<double> posinf, c10::optional<double> neginf) { |
| 5668 | if (true) { |
| 5669 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5670 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5671 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5672 | auto self_meta = to_meta(self); |
| 5673 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5674 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5675 | at::_ops::nan_to_num_::call(self_meta, nan, posinf, neginf); |
| 5676 | } |
| 5677 | |
| 5678 | at::Tensor self_; |
| 5679 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5680 | at::functionalization::impl::sync(self); |
| 5681 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5682 | } else { |
| 5683 | self_ = self; |
| 5684 | } |
| 5685 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 5686 | if ((false)) { |
| 5687 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5688 | TORCH_INTERNAL_ASSERT(false, |
| 5689 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5690 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5691 | } else { |
| 5692 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5693 | at::AutoDispatchSkipFunctionalize guard; |
| 5694 | at::Tensor tmp_output = at::_ops::nan_to_num_::call(self_, nan, posinf, neginf); |
| 5695 | return self;; |
| 5696 | } |
| 5697 | } else { |
| 5698 | at::Tensor tmp_output; |
| 5699 | { |
| 5700 | at::AutoDispatchSkipFunctionalize guard; |
| 5701 | tmp_output = at::_ops::nan_to_num::call(self_, nan, posinf, neginf); |
| 5702 | } |
| 5703 | at::functionalization::impl::replace_(self, tmp_output); |
| 5704 | at::functionalization::impl::commit_update(self); |
| 5705 | at::functionalization::impl::sync(self); |
| 5706 | return self; |
| 5707 | } |
| 5708 | } |
| 5709 | |
| 5710 | at::Tensor & linear_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, at::Tensor & out) { |
| 5711 | if (false) { |
| 5712 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5713 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5714 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5715 | auto input_meta = to_meta(input); |
| 5716 | auto weight_meta = to_meta(weight); |
| 5717 | auto bias_meta = to_meta(bias); |
| 5718 | auto out_meta = to_meta(out); |
| 5719 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5720 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5721 | at::_ops::linear_out::call(input_meta, weight_meta, bias_meta, out_meta); |
| 5722 | } |
| 5723 | |
| 5724 | at::Tensor input_; |
| 5725 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 5726 | at::functionalization::impl::sync(input); |
| 5727 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 5728 | } else { |
| 5729 | input_ = input; |
| 5730 | } |
| 5731 | |
| 5732 | at::Tensor weight_; |
| 5733 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 5734 | at::functionalization::impl::sync(weight); |
| 5735 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 5736 | } else { |
| 5737 | weight_ = weight; |
| 5738 | } |
| 5739 | |
| 5740 | c10::optional<at::Tensor> bias_; |
| 5741 | if (at::functionalization::impl::isFunctionalTensor(bias)) { |
| 5742 | at::functionalization::impl::sync(bias); |
| 5743 | bias_ = at::functionalization::impl::from_functional_tensor(bias); |
| 5744 | } else { |
| 5745 | bias_ = bias; |
| 5746 | } |
| 5747 | |
| 5748 | at::Tensor out_; |
| 5749 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5750 | at::functionalization::impl::sync(out); |
| 5751 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5752 | } else { |
| 5753 | out_ = out; |
| 5754 | } |
| 5755 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5756 | if ((false || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(bias))) { |
| 5757 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5758 | TORCH_INTERNAL_ASSERT(false, |
| 5759 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5760 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5761 | } else { |
| 5762 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5763 | at::AutoDispatchSkipFunctionalize guard; |
| 5764 | at::Tensor tmp_output = at::_ops::linear_out::call(input_, weight_, bias_, out_); |
| 5765 | return out;; |
| 5766 | } |
| 5767 | } else { |
| 5768 | at::Tensor tmp_output; |
| 5769 | { |
| 5770 | at::AutoDispatchSkipFunctionalize guard; |
| 5771 | tmp_output = at::_ops::linear::call(input_, weight_, bias_); |
| 5772 | } |
| 5773 | at::functionalization::impl::replace_(out, tmp_output); |
| 5774 | at::functionalization::impl::commit_update(out); |
| 5775 | at::functionalization::impl::sync(out); |
| 5776 | return out; |
| 5777 | } |
| 5778 | } |
| 5779 | |
| 5780 | at::Tensor & mkldnn_linear_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, at::Tensor & out) { |
| 5781 | if (false) { |
| 5782 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5783 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5784 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5785 | auto self_meta = to_meta(self); |
| 5786 | auto weight_meta = to_meta(weight); |
| 5787 | auto bias_meta = to_meta(bias); |
| 5788 | auto out_meta = to_meta(out); |
| 5789 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5790 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5791 | at::_ops::mkldnn_linear_out::call(self_meta, weight_meta, bias_meta, out_meta); |
| 5792 | } |
| 5793 | |
| 5794 | at::Tensor self_; |
| 5795 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5796 | at::functionalization::impl::sync(self); |
| 5797 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5798 | } else { |
| 5799 | self_ = self; |
| 5800 | } |
| 5801 | |
| 5802 | at::Tensor weight_; |
| 5803 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 5804 | at::functionalization::impl::sync(weight); |
| 5805 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 5806 | } else { |
| 5807 | weight_ = weight; |
| 5808 | } |
| 5809 | |
| 5810 | c10::optional<at::Tensor> bias_; |
| 5811 | if (at::functionalization::impl::isFunctionalTensor(bias)) { |
| 5812 | at::functionalization::impl::sync(bias); |
| 5813 | bias_ = at::functionalization::impl::from_functional_tensor(bias); |
| 5814 | } else { |
| 5815 | bias_ = bias; |
| 5816 | } |
| 5817 | |
| 5818 | at::Tensor out_; |
| 5819 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5820 | at::functionalization::impl::sync(out); |
| 5821 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5822 | } else { |
| 5823 | out_ = out; |
| 5824 | } |
| 5825 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5826 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(bias))) { |
| 5827 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5828 | TORCH_INTERNAL_ASSERT(false, |
| 5829 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5830 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5831 | } else { |
| 5832 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5833 | at::AutoDispatchSkipFunctionalize guard; |
| 5834 | at::Tensor tmp_output = at::_ops::mkldnn_linear_out::call(self_, weight_, bias_, out_); |
| 5835 | return out;; |
| 5836 | } |
| 5837 | } else { |
| 5838 | at::Tensor tmp_output; |
| 5839 | { |
| 5840 | at::AutoDispatchSkipFunctionalize guard; |
| 5841 | tmp_output = at::_ops::mkldnn_linear::call(self_, weight_, bias_); |
| 5842 | } |
| 5843 | at::functionalization::impl::replace_(out, tmp_output); |
| 5844 | at::functionalization::impl::commit_update(out); |
| 5845 | at::functionalization::impl::sync(out); |
| 5846 | return out; |
| 5847 | } |
| 5848 | } |
| 5849 | |
| 5850 | at::Tensor & linspace_out_out(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Scalar & end, int64_t steps, at::Tensor & out) { |
| 5851 | if (false) { |
| 5852 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5853 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5854 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5855 | auto out_meta = to_meta(out); |
| 5856 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5857 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5858 | at::_ops::linspace_out::call(start, end, steps, out_meta); |
| 5859 | } |
| 5860 | |
| 5861 | at::Tensor out_; |
| 5862 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5863 | at::functionalization::impl::sync(out); |
| 5864 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5865 | } else { |
| 5866 | out_ = out; |
| 5867 | } |
| 5868 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5869 | if ((false)) { |
| 5870 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5871 | TORCH_INTERNAL_ASSERT(false, |
| 5872 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5873 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5874 | } else { |
| 5875 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5876 | at::AutoDispatchSkipFunctionalize guard; |
| 5877 | at::Tensor tmp_output = at::_ops::linspace_out::call(start, end, steps, out_); |
| 5878 | return out;; |
| 5879 | } |
| 5880 | } else { |
| 5881 | at::Tensor tmp_output; |
| 5882 | { |
| 5883 | at::AutoDispatchSkipFunctionalize guard; |
| 5884 | tmp_output = at::_ops::linspace::call(start, end, steps, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 5885 | } |
| 5886 | at::functionalization::impl::replace_(out, tmp_output); |
| 5887 | at::functionalization::impl::commit_update(out); |
| 5888 | at::functionalization::impl::sync(out); |
| 5889 | return out; |
| 5890 | } |
| 5891 | } |
| 5892 | |
| 5893 | at::Tensor & log_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 5894 | if (false) { |
| 5895 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5896 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5897 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5898 | auto self_meta = to_meta(self); |
| 5899 | auto out_meta = to_meta(out); |
| 5900 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5901 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5902 | at::_ops::log_out::call(self_meta, out_meta); |
| 5903 | } |
| 5904 | |
| 5905 | at::Tensor self_; |
| 5906 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5907 | at::functionalization::impl::sync(self); |
| 5908 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5909 | } else { |
| 5910 | self_ = self; |
| 5911 | } |
| 5912 | |
| 5913 | at::Tensor out_; |
| 5914 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5915 | at::functionalization::impl::sync(out); |
| 5916 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5917 | } else { |
| 5918 | out_ = out; |
| 5919 | } |
| 5920 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5921 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 5922 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5923 | TORCH_INTERNAL_ASSERT(false, |
| 5924 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5925 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5926 | } else { |
| 5927 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5928 | at::AutoDispatchSkipFunctionalize guard; |
| 5929 | at::Tensor tmp_output = at::_ops::log_out::call(self_, out_); |
| 5930 | return out;; |
| 5931 | } |
| 5932 | } else { |
| 5933 | at::Tensor tmp_output; |
| 5934 | { |
| 5935 | at::AutoDispatchSkipFunctionalize guard; |
| 5936 | tmp_output = at::_ops::log::call(self_); |
| 5937 | } |
| 5938 | at::functionalization::impl::replace_(out, tmp_output); |
| 5939 | at::functionalization::impl::commit_update(out); |
| 5940 | at::functionalization::impl::sync(out); |
| 5941 | return out; |
| 5942 | } |
| 5943 | } |
| 5944 | |
| 5945 | at::Tensor & log_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 5946 | if (true) { |
| 5947 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5948 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5949 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5950 | auto self_meta = to_meta(self); |
| 5951 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5952 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5953 | at::_ops::log_::call(self_meta); |
| 5954 | } |
| 5955 | |
| 5956 | at::Tensor self_; |
| 5957 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5958 | at::functionalization::impl::sync(self); |
| 5959 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5960 | } else { |
| 5961 | self_ = self; |
| 5962 | } |
| 5963 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 5964 | if ((false)) { |
| 5965 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5966 | TORCH_INTERNAL_ASSERT(false, |
| 5967 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5968 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5969 | } else { |
| 5970 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5971 | at::AutoDispatchSkipFunctionalize guard; |
| 5972 | at::Tensor tmp_output = at::_ops::log_::call(self_); |
| 5973 | return self;; |
| 5974 | } |
| 5975 | } else { |
| 5976 | at::Tensor tmp_output; |
| 5977 | { |
| 5978 | at::AutoDispatchSkipFunctionalize guard; |
| 5979 | tmp_output = at::_ops::log::call(self_); |
| 5980 | } |
| 5981 | at::functionalization::impl::replace_(self, tmp_output); |
| 5982 | at::functionalization::impl::commit_update(self); |
| 5983 | at::functionalization::impl::sync(self); |
| 5984 | return self; |
| 5985 | } |
| 5986 | } |
| 5987 | |
| 5988 | at::Tensor & log_softmax_out_int_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 5989 | if (false) { |
| 5990 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5991 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5992 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5993 | auto self_meta = to_meta(self); |
| 5994 | auto out_meta = to_meta(out); |
| 5995 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5996 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5997 | at::_ops::log_softmax_int_out::call(self_meta, dim, dtype, out_meta); |
| 5998 | } |
| 5999 | |
| 6000 | at::Tensor self_; |
| 6001 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6002 | at::functionalization::impl::sync(self); |
| 6003 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6004 | } else { |
| 6005 | self_ = self; |
| 6006 | } |
| 6007 | |
| 6008 | at::Tensor out_; |
| 6009 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 6010 | at::functionalization::impl::sync(out); |
| 6011 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 6012 | } else { |
| 6013 | out_ = out; |
| 6014 | } |
| 6015 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 6016 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 6017 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6018 | TORCH_INTERNAL_ASSERT(false, |
| 6019 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6020 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6021 | } else { |
| 6022 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6023 | at::AutoDispatchSkipFunctionalize guard; |
| 6024 | at::Tensor tmp_output = at::_ops::log_softmax_int_out::call(self_, dim, dtype, out_); |
| 6025 | return out;; |
| 6026 | } |
| 6027 | } else { |
| 6028 | at::Tensor tmp_output; |
| 6029 | { |
| 6030 | at::AutoDispatchSkipFunctionalize guard; |
| 6031 | tmp_output = at::_ops::log_softmax_int::call(self_, dim, dtype); |
| 6032 | } |
| 6033 | at::functionalization::impl::replace_(out, tmp_output); |
| 6034 | at::functionalization::impl::commit_update(out); |
| 6035 | at::functionalization::impl::sync(out); |
| 6036 | return out; |
| 6037 | } |
| 6038 | } |
| 6039 | |
| 6040 | at::Tensor & _log_softmax_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool half_to_float, at::Tensor & out) { |
| 6041 | if (false) { |
| 6042 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6043 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6044 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6045 | auto self_meta = to_meta(self); |
| 6046 | auto out_meta = to_meta(out); |
| 6047 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6048 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6049 | at::_ops::_log_softmax_out::call(self_meta, dim, half_to_float, out_meta); |
| 6050 | } |
| 6051 | |
| 6052 | at::Tensor self_; |
| 6053 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6054 | at::functionalization::impl::sync(self); |
| 6055 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6056 | } else { |
| 6057 | self_ = self; |
| 6058 | } |
| 6059 | |
| 6060 | at::Tensor out_; |
| 6061 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 6062 | at::functionalization::impl::sync(out); |
| 6063 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 6064 | } else { |
| 6065 | out_ = out; |
| 6066 | } |
| 6067 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 6068 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 6069 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6070 | TORCH_INTERNAL_ASSERT(false, |
| 6071 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6072 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6073 | } else { |
| 6074 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6075 | at::AutoDispatchSkipFunctionalize guard; |
| 6076 | at::Tensor tmp_output = at::_ops::_log_softmax_out::call(self_, dim, half_to_float, out_); |
| 6077 | return out;; |
| 6078 | } |
| 6079 | } else { |
| 6080 | at::Tensor tmp_output; |
| 6081 | { |
| 6082 | at::AutoDispatchSkipFunctionalize guard; |
| 6083 | tmp_output = at::_ops::_log_softmax::call(self_, dim, half_to_float); |
| 6084 | } |
| 6085 | at::functionalization::impl::replace_(out, tmp_output); |
| 6086 | at::functionalization::impl::commit_update(out); |
| 6087 | at::functionalization::impl::sync(out); |
| 6088 | return out; |
| 6089 | } |
| 6090 | } |
| 6091 | |
| 6092 | at::Tensor & _log_softmax_backward_data_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype, at::Tensor & out) { |
| 6093 | if (false) { |
| 6094 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6095 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6096 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6097 | auto grad_output_meta = to_meta(grad_output); |
| 6098 | auto output_meta = to_meta(output); |
| 6099 | auto out_meta = to_meta(out); |
| 6100 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6101 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6102 | at::_ops::_log_softmax_backward_data_out::call(grad_output_meta, output_meta, dim, input_dtype, out_meta); |
| 6103 | } |
| 6104 | |
| 6105 | at::Tensor grad_output_; |
| 6106 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 6107 | at::functionalization::impl::sync(grad_output); |
| 6108 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 6109 | } else { |
| 6110 | grad_output_ = grad_output; |
| 6111 | } |
| 6112 | |
| 6113 | at::Tensor output_; |
| 6114 | if (at::functionalization::impl::isFunctionalTensor(output)) { |
| 6115 | at::functionalization::impl::sync(output); |
| 6116 | output_ = at::functionalization::impl::from_functional_tensor(output); |
| 6117 | } else { |
| 6118 | output_ = output; |
| 6119 | } |
| 6120 | |
| 6121 | at::Tensor out_; |
| 6122 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 6123 | at::functionalization::impl::sync(out); |
| 6124 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 6125 | } else { |
| 6126 | out_ = out; |
| 6127 | } |
| 6128 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 6129 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(output))) { |
| 6130 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6131 | TORCH_INTERNAL_ASSERT(false, |
| 6132 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6133 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6134 | } else { |
| 6135 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6136 | at::AutoDispatchSkipFunctionalize guard; |
| 6137 | at::Tensor tmp_output = at::_ops::_log_softmax_backward_data_out::call(grad_output_, output_, dim, input_dtype, out_); |
| 6138 | return out;; |
| 6139 | } |
| 6140 | } else { |
| 6141 | at::Tensor tmp_output; |
| 6142 | { |
| 6143 | at::AutoDispatchSkipFunctionalize guard; |
| 6144 | tmp_output = at::_ops::_log_softmax_backward_data::call(grad_output_, output_, dim, input_dtype); |
| 6145 | } |
| 6146 | at::functionalization::impl::replace_(out, tmp_output); |
| 6147 | at::functionalization::impl::commit_update(out); |
| 6148 | at::functionalization::impl::sync(out); |
| 6149 | return out; |
| 6150 | } |
| 6151 | } |
| 6152 | |
| 6153 | ::std::tuple<at::Tensor &,at::Tensor &> max_out_dim_max(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & max, at::Tensor & max_values) { |
| 6154 | if (false) { |
| 6155 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6156 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6157 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6158 | auto self_meta = to_meta(self); |
| 6159 | auto max_meta = to_meta(max); |
| 6160 | auto max_values_meta = to_meta(max_values); |
| 6161 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6162 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6163 | at::_ops::max_dim_max::call(self_meta, dim, keepdim, max_meta, max_values_meta); |
| 6164 | } |
| 6165 | |
| 6166 | at::Tensor self_; |
| 6167 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6168 | at::functionalization::impl::sync(self); |
| 6169 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6170 | } else { |
| 6171 | self_ = self; |
| 6172 | } |
| 6173 | |
| 6174 | at::Tensor max_; |
| 6175 | if (at::functionalization::impl::isFunctionalTensor(max)) { |
| 6176 | at::functionalization::impl::sync(max); |
| 6177 | max_ = at::functionalization::impl::from_functional_tensor(max); |
| 6178 | } else { |
| 6179 | max_ = max; |
| 6180 | } |
| 6181 | |
| 6182 | at::Tensor max_values_; |
| 6183 | if (at::functionalization::impl::isFunctionalTensor(max_values)) { |
| 6184 | at::functionalization::impl::sync(max_values); |
| 6185 | max_values_ = at::functionalization::impl::from_functional_tensor(max_values); |
| 6186 | } else { |
| 6187 | max_values_ = max_values; |
| 6188 | } |
| 6189 | if (!(true && at::functionalization::impl::isFunctionalTensor(max) && at::functionalization::impl::isFunctionalTensor(max_values))) { |
| 6190 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 6191 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6192 | TORCH_INTERNAL_ASSERT(false, |
| 6193 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6194 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6195 | } else { |
| 6196 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6197 | at::AutoDispatchSkipFunctionalize guard; |
| 6198 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::max_dim_max::call(self_, dim, keepdim, max_, max_values_); |
| 6199 | return ::std::tuple<at::Tensor &,at::Tensor &>(max, max_values);; |
| 6200 | } |
| 6201 | } else { |
| 6202 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 6203 | { |
| 6204 | at::AutoDispatchSkipFunctionalize guard; |
| 6205 | tmp_output = at::_ops::max_dim::call(self_, dim, keepdim); |
| 6206 | } |
| 6207 | at::functionalization::impl::replace_(max, std::get<0>(tmp_output)); |
| 6208 | at::functionalization::impl::commit_update(max); |
| 6209 | at::functionalization::impl::sync(max); |
| 6210 | at::functionalization::impl::replace_(max_values, std::get<1>(tmp_output)); |
| 6211 | at::functionalization::impl::commit_update(max_values); |
| 6212 | at::functionalization::impl::sync(max_values); |
| 6213 | return ::std::tuple<at::Tensor &,at::Tensor &>(max, max_values); |
| 6214 | } |
| 6215 | } |
| 6216 | |
| 6217 | ::std::tuple<at::Tensor &,at::Tensor &> max_out_names_dim_max(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & max, at::Tensor & max_values) { |
| 6218 | if (false) { |
| 6219 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6220 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6221 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6222 | auto self_meta = to_meta(self); |
| 6223 | auto max_meta = to_meta(max); |
| 6224 | auto max_values_meta = to_meta(max_values); |
| 6225 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6226 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6227 | at::_ops::max_names_dim_max::call(self_meta, dim, keepdim, max_meta, max_values_meta); |
| 6228 | } |
| 6229 | |
| 6230 | at::Tensor self_; |
| 6231 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6232 | at::functionalization::impl::sync(self); |
| 6233 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6234 | } else { |
| 6235 | self_ = self; |
| 6236 | } |
| 6237 | |
| 6238 | at::Tensor max_; |
| 6239 | if (at::functionalization::impl::isFunctionalTensor(max)) { |
| 6240 | at::functionalization::impl::sync(max); |
| 6241 | max_ = at::functionalization::impl::from_functional_tensor(max); |
| 6242 | } else { |
| 6243 | max_ = max; |
| 6244 | } |
| 6245 | |
| 6246 | at::Tensor max_values_; |
| 6247 | if (at::functionalization::impl::isFunctionalTensor(max_values)) { |
| 6248 | at::functionalization::impl::sync(max_values); |
| 6249 | max_values_ = at::functionalization::impl::from_functional_tensor(max_values); |
| 6250 | } else { |
| 6251 | max_values_ = max_values; |
| 6252 | } |
| 6253 | if (!(true && at::functionalization::impl::isFunctionalTensor(max) && at::functionalization::impl::isFunctionalTensor(max_values))) { |
| 6254 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 6255 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6256 | TORCH_INTERNAL_ASSERT(false, |
| 6257 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6258 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6259 | } else { |
| 6260 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6261 | at::AutoDispatchSkipFunctionalize guard; |
| 6262 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::max_names_dim_max::call(self_, dim, keepdim, max_, max_values_); |
| 6263 | return ::std::tuple<at::Tensor &,at::Tensor &>(max, max_values);; |
| 6264 | } |
| 6265 | } else { |
| 6266 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 6267 | { |
| 6268 | at::AutoDispatchSkipFunctionalize guard; |
| 6269 | tmp_output = at::_ops::max_names_dim::call(self_, dim, keepdim); |
| 6270 | } |
| 6271 | at::functionalization::impl::replace_(max, std::get<0>(tmp_output)); |
| 6272 | at::functionalization::impl::commit_update(max); |
| 6273 | at::functionalization::impl::sync(max); |
| 6274 | at::functionalization::impl::replace_(max_values, std::get<1>(tmp_output)); |
| 6275 | at::functionalization::impl::commit_update(max_values); |
| 6276 | at::functionalization::impl::sync(max_values); |
| 6277 | return ::std::tuple<at::Tensor &,at::Tensor &>(max, max_values); |
| 6278 | } |
| 6279 | } |
| 6280 | |
| 6281 | at::Tensor & amax_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { |
| 6282 | if (false) { |
| 6283 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6284 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6285 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6286 | auto self_meta = to_meta(self); |
| 6287 | auto out_meta = to_meta(out); |
| 6288 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6289 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6290 | at::_ops::amax_out::call(self_meta, dim, keepdim, out_meta); |
| 6291 | } |
| 6292 | |
| 6293 | at::Tensor self_; |
| 6294 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6295 | at::functionalization::impl::sync(self); |
| 6296 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6297 | } else { |
| 6298 | self_ = self; |
| 6299 | } |
| 6300 | |
| 6301 | at::Tensor out_; |
| 6302 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 6303 | at::functionalization::impl::sync(out); |
| 6304 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 6305 | } else { |
| 6306 | out_ = out; |
| 6307 | } |
| 6308 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 6309 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 6310 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6311 | TORCH_INTERNAL_ASSERT(false, |
| 6312 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6313 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6314 | } else { |
| 6315 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6316 | at::AutoDispatchSkipFunctionalize guard; |
| 6317 | at::Tensor tmp_output = at::_ops::amax_out::call(self_, dim, keepdim, out_); |
| 6318 | return out;; |
| 6319 | } |
| 6320 | } else { |
| 6321 | at::Tensor tmp_output; |
| 6322 | { |
| 6323 | at::AutoDispatchSkipFunctionalize guard; |
| 6324 | tmp_output = at::_ops::amax::call(self_, dim, keepdim); |
| 6325 | } |
| 6326 | at::functionalization::impl::replace_(out, tmp_output); |
| 6327 | at::functionalization::impl::commit_update(out); |
| 6328 | at::functionalization::impl::sync(out); |
| 6329 | return out; |
| 6330 | } |
| 6331 | } |
| 6332 | |
| 6333 | at::Tensor & mkldnn_max_pool2d_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & input, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out) { |
| 6334 | if (false) { |
| 6335 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6336 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6337 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6338 | auto grad_output_meta = to_meta(grad_output); |
| 6339 | auto output_meta = to_meta(output); |
| 6340 | auto input_meta = to_meta(input); |
| 6341 | auto out_meta = to_meta(out); |
| 6342 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6343 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6344 | at::_ops::mkldnn_max_pool2d_backward_out::call(grad_output_meta, output_meta, input_meta, kernel_size, stride, padding, dilation, ceil_mode, out_meta); |
| 6345 | } |
| 6346 | |
| 6347 | at::Tensor grad_output_; |
| 6348 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 6349 | at::functionalization::impl::sync(grad_output); |
| 6350 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 6351 | } else { |
| 6352 | grad_output_ = grad_output; |
| 6353 | } |
| 6354 | |
| 6355 | at::Tensor output_; |
| 6356 | if (at::functionalization::impl::isFunctionalTensor(output)) { |
| 6357 | at::functionalization::impl::sync(output); |
| 6358 | output_ = at::functionalization::impl::from_functional_tensor(output); |
| 6359 | } else { |
| 6360 | output_ = output; |
| 6361 | } |
| 6362 | |
| 6363 | at::Tensor input_; |
| 6364 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 6365 | at::functionalization::impl::sync(input); |
| 6366 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 6367 | } else { |
| 6368 | input_ = input; |
| 6369 | } |
| 6370 | |
| 6371 | at::Tensor out_; |
| 6372 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 6373 | at::functionalization::impl::sync(out); |
| 6374 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 6375 | } else { |
| 6376 | out_ = out; |
| 6377 | } |
| 6378 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 6379 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(output) || at::functionalization::impl::isFunctionalTensor(input))) { |
| 6380 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6381 | TORCH_INTERNAL_ASSERT(false, |
| 6382 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6383 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6384 | } else { |
| 6385 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6386 | at::AutoDispatchSkipFunctionalize guard; |
| 6387 | at::Tensor tmp_output = at::_ops::mkldnn_max_pool2d_backward_out::call(grad_output_, output_, input_, kernel_size, stride, padding, dilation, ceil_mode, out_); |
| 6388 | return out;; |
| 6389 | } |
| 6390 | } else { |
| 6391 | at::Tensor tmp_output; |
| 6392 | { |
| 6393 | at::AutoDispatchSkipFunctionalize guard; |
| 6394 | tmp_output = at::_ops::mkldnn_max_pool2d_backward::call(grad_output_, output_, input_, kernel_size, stride, padding, dilation, ceil_mode); |
| 6395 | } |
| 6396 | at::functionalization::impl::replace_(out, tmp_output); |
| 6397 | at::functionalization::impl::commit_update(out); |
| 6398 | at::functionalization::impl::sync(out); |
| 6399 | return out; |
| 6400 | } |
| 6401 | } |
| 6402 | |
| 6403 | at::Tensor & mkldnn_max_pool3d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out) { |
| 6404 | if (false) { |
| 6405 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6406 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6407 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6408 | auto self_meta = to_meta(self); |
| 6409 | auto out_meta = to_meta(out); |
| 6410 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6411 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6412 | at::_ops::mkldnn_max_pool3d_out::call(self_meta, kernel_size, stride, padding, dilation, ceil_mode, out_meta); |
| 6413 | } |
| 6414 | |
| 6415 | at::Tensor self_; |
| 6416 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6417 | at::functionalization::impl::sync(self); |
| 6418 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6419 | } else { |
| 6420 | self_ = self; |
| 6421 | } |
| 6422 | |
| 6423 | at::Tensor out_; |
| 6424 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 6425 | at::functionalization::impl::sync(out); |
| 6426 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 6427 | } else { |
| 6428 | out_ = out; |
| 6429 | } |
| 6430 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 6431 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 6432 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6433 | TORCH_INTERNAL_ASSERT(false, |
| 6434 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6435 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6436 | } else { |
| 6437 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6438 | at::AutoDispatchSkipFunctionalize guard; |
| 6439 | at::Tensor tmp_output = at::_ops::mkldnn_max_pool3d_out::call(self_, kernel_size, stride, padding, dilation, ceil_mode, out_); |
| 6440 | return out;; |
| 6441 | } |
| 6442 | } else { |
| 6443 | at::Tensor tmp_output; |
| 6444 | { |
| 6445 | at::AutoDispatchSkipFunctionalize guard; |
| 6446 | tmp_output = at::_ops::mkldnn_max_pool3d::call(self_, kernel_size, stride, padding, dilation, ceil_mode); |
| 6447 | } |
| 6448 | at::functionalization::impl::replace_(out, tmp_output); |
| 6449 | at::functionalization::impl::commit_update(out); |
| 6450 | at::functionalization::impl::sync(out); |
| 6451 | return out; |
| 6452 | } |
| 6453 | } |
| 6454 | |
| 6455 | at::Tensor & quantized_max_pool2d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out) { |
| 6456 | if (false) { |
| 6457 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6458 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6459 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6460 | auto self_meta = to_meta(self); |
| 6461 | auto out_meta = to_meta(out); |
| 6462 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6463 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6464 | at::_ops::quantized_max_pool2d_out::call(self_meta, kernel_size, stride, padding, dilation, ceil_mode, out_meta); |
| 6465 | } |
| 6466 | |
| 6467 | at::Tensor self_; |
| 6468 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6469 | at::functionalization::impl::sync(self); |
| 6470 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6471 | } else { |
| 6472 | self_ = self; |
| 6473 | } |
| 6474 | |
| 6475 | at::Tensor out_; |
| 6476 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 6477 | at::functionalization::impl::sync(out); |
| 6478 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 6479 | } else { |
| 6480 | out_ = out; |
| 6481 | } |
| 6482 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 6483 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 6484 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6485 | TORCH_INTERNAL_ASSERT(false, |
| 6486 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6487 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6488 | } else { |
| 6489 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6490 | at::AutoDispatchSkipFunctionalize guard; |
| 6491 | at::Tensor tmp_output = at::_ops::quantized_max_pool2d_out::call(self_, kernel_size, stride, padding, dilation, ceil_mode, out_); |
| 6492 | return out;; |
| 6493 | } |
| 6494 | } else { |
| 6495 | at::Tensor tmp_output; |
| 6496 | { |
| 6497 | at::AutoDispatchSkipFunctionalize guard; |
| 6498 | tmp_output = at::_ops::quantized_max_pool2d::call(self_, kernel_size, stride, padding, dilation, ceil_mode); |
| 6499 | } |
| 6500 | at::functionalization::impl::replace_(out, tmp_output); |
| 6501 | at::functionalization::impl::commit_update(out); |
| 6502 | at::functionalization::impl::sync(out); |
| 6503 | return out; |
| 6504 | } |
| 6505 | } |
| 6506 | |
| 6507 | at::Tensor & mean_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 6508 | if (false) { |
| 6509 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6510 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6511 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6512 | auto self_meta = to_meta(self); |
| 6513 | auto out_meta = to_meta(out); |
| 6514 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6515 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6516 | at::_ops::mean_out::call(self_meta, dim, keepdim, dtype, out_meta); |
| 6517 | } |
| 6518 | |
| 6519 | at::Tensor self_; |
| 6520 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6521 | at::functionalization::impl::sync(self); |
| 6522 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6523 | } else { |
| 6524 | self_ = self; |
| 6525 | } |
| 6526 | |
| 6527 | at::Tensor out_; |
| 6528 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 6529 | at::functionalization::impl::sync(out); |
| 6530 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 6531 | } else { |
| 6532 | out_ = out; |
| 6533 | } |
| 6534 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 6535 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 6536 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6537 | TORCH_INTERNAL_ASSERT(false, |
| 6538 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6539 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6540 | } else { |
| 6541 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6542 | at::AutoDispatchSkipFunctionalize guard; |
| 6543 | at::Tensor tmp_output = at::_ops::mean_out::call(self_, dim, keepdim, dtype, out_); |
| 6544 | return out;; |
| 6545 | } |
| 6546 | } else { |
| 6547 | at::Tensor tmp_output; |
| 6548 | { |
| 6549 | at::AutoDispatchSkipFunctionalize guard; |
| 6550 | tmp_output = at::_ops::mean_dim::call(self_, dim, keepdim, dtype); |
| 6551 | } |
| 6552 | at::functionalization::impl::replace_(out, tmp_output); |
| 6553 | at::functionalization::impl::commit_update(out); |
| 6554 | at::functionalization::impl::sync(out); |
| 6555 | return out; |
| 6556 | } |
| 6557 | } |
| 6558 | |
| 6559 | at::Tensor & mean_out_names_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 6560 | if (false) { |
| 6561 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6562 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6563 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6564 | auto self_meta = to_meta(self); |
| 6565 | auto out_meta = to_meta(out); |
| 6566 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6567 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6568 | at::_ops::mean_names_out::call(self_meta, dim, keepdim, dtype, out_meta); |
| 6569 | } |
| 6570 | |
| 6571 | at::Tensor self_; |
| 6572 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6573 | at::functionalization::impl::sync(self); |
| 6574 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6575 | } else { |
| 6576 | self_ = self; |
| 6577 | } |
| 6578 | |
| 6579 | at::Tensor out_; |
| 6580 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 6581 | at::functionalization::impl::sync(out); |
| 6582 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 6583 | } else { |
| 6584 | out_ = out; |
| 6585 | } |
| 6586 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 6587 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 6588 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6589 | TORCH_INTERNAL_ASSERT(false, |
| 6590 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6591 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6592 | } else { |
| 6593 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6594 | at::AutoDispatchSkipFunctionalize guard; |
| 6595 | at::Tensor tmp_output = at::_ops::mean_names_out::call(self_, dim, keepdim, dtype, out_); |
| 6596 | return out;; |
| 6597 | } |
| 6598 | } else { |
| 6599 | at::Tensor tmp_output; |
| 6600 | { |
| 6601 | at::AutoDispatchSkipFunctionalize guard; |
| 6602 | tmp_output = at::_ops::mean_names_dim::call(self_, dim, keepdim, dtype); |
| 6603 | } |
| 6604 | at::functionalization::impl::replace_(out, tmp_output); |
| 6605 | at::functionalization::impl::commit_update(out); |
| 6606 | at::functionalization::impl::sync(out); |
| 6607 | return out; |
| 6608 | } |
| 6609 | } |
| 6610 | |
| 6611 | at::Tensor & nanmedian_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 6612 | if (false) { |
| 6613 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6614 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6615 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6616 | auto self_meta = to_meta(self); |
| 6617 | auto out_meta = to_meta(out); |
| 6618 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6619 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6620 | at::_ops::nanmedian_out::call(self_meta, out_meta); |
| 6621 | } |
| 6622 | |
| 6623 | at::Tensor self_; |
| 6624 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6625 | at::functionalization::impl::sync(self); |
| 6626 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6627 | } else { |
| 6628 | self_ = self; |
| 6629 | } |
| 6630 | |
| 6631 | at::Tensor out_; |
| 6632 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 6633 | at::functionalization::impl::sync(out); |
| 6634 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 6635 | } else { |
| 6636 | out_ = out; |
| 6637 | } |
| 6638 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 6639 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 6640 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6641 | TORCH_INTERNAL_ASSERT(false, |
| 6642 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6643 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6644 | } else { |
| 6645 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6646 | at::AutoDispatchSkipFunctionalize guard; |
| 6647 | at::Tensor tmp_output = at::_ops::nanmedian_out::call(self_, out_); |
| 6648 | return out;; |
| 6649 | } |
| 6650 | } else { |
| 6651 | at::Tensor tmp_output; |
| 6652 | { |
| 6653 | at::AutoDispatchSkipFunctionalize guard; |
| 6654 | tmp_output = at::_ops::nanmedian::call(self_); |
| 6655 | } |
| 6656 | at::functionalization::impl::replace_(out, tmp_output); |
| 6657 | at::functionalization::impl::commit_update(out); |
| 6658 | at::functionalization::impl::sync(out); |
| 6659 | return out; |
| 6660 | } |
| 6661 | } |
| 6662 | |
| 6663 | ::std::tuple<at::Tensor &,at::Tensor &> nanmedian_out_dim_values(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & values, at::Tensor & indices) { |
| 6664 | if (false) { |
| 6665 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6666 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6667 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6668 | auto self_meta = to_meta(self); |
| 6669 | auto values_meta = to_meta(values); |
| 6670 | auto indices_meta = to_meta(indices); |
| 6671 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6672 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6673 | at::_ops::nanmedian_dim_values::call(self_meta, dim, keepdim, values_meta, indices_meta); |
| 6674 | } |
| 6675 | |
| 6676 | at::Tensor self_; |
| 6677 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6678 | at::functionalization::impl::sync(self); |
| 6679 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6680 | } else { |
| 6681 | self_ = self; |
| 6682 | } |
| 6683 | |
| 6684 | at::Tensor values_; |
| 6685 | if (at::functionalization::impl::isFunctionalTensor(values)) { |
| 6686 | at::functionalization::impl::sync(values); |
| 6687 | values_ = at::functionalization::impl::from_functional_tensor(values); |
| 6688 | } else { |
| 6689 | values_ = values; |
| 6690 | } |
| 6691 | |
| 6692 | at::Tensor indices_; |
| 6693 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 6694 | at::functionalization::impl::sync(indices); |
| 6695 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 6696 | } else { |
| 6697 | indices_ = indices; |
| 6698 | } |
| 6699 | if (!(true && at::functionalization::impl::isFunctionalTensor(values) && at::functionalization::impl::isFunctionalTensor(indices))) { |
| 6700 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 6701 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6702 | TORCH_INTERNAL_ASSERT(false, |
| 6703 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6704 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6705 | } else { |
| 6706 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6707 | at::AutoDispatchSkipFunctionalize guard; |
| 6708 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::nanmedian_dim_values::call(self_, dim, keepdim, values_, indices_); |
| 6709 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices);; |
| 6710 | } |
| 6711 | } else { |
| 6712 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 6713 | { |
| 6714 | at::AutoDispatchSkipFunctionalize guard; |
| 6715 | tmp_output = at::_ops::nanmedian_dim::call(self_, dim, keepdim); |
| 6716 | } |
| 6717 | at::functionalization::impl::replace_(values, std::get<0>(tmp_output)); |
| 6718 | at::functionalization::impl::commit_update(values); |
| 6719 | at::functionalization::impl::sync(values); |
| 6720 | at::functionalization::impl::replace_(indices, std::get<1>(tmp_output)); |
| 6721 | at::functionalization::impl::commit_update(indices); |
| 6722 | at::functionalization::impl::sync(indices); |
| 6723 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices); |
| 6724 | } |
| 6725 | } |
| 6726 | |
| 6727 | ::std::tuple<at::Tensor &,at::Tensor &> nanmedian_out_names_dim_values(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & values, at::Tensor & indices) { |
| 6728 | if (false) { |
| 6729 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6730 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6731 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6732 | auto self_meta = to_meta(self); |
| 6733 | auto values_meta = to_meta(values); |
| 6734 | auto indices_meta = to_meta(indices); |
| 6735 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6736 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6737 | at::_ops::nanmedian_names_dim_values::call(self_meta, dim, keepdim, values_meta, indices_meta); |
| 6738 | } |
| 6739 | |
| 6740 | at::Tensor self_; |
| 6741 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6742 | at::functionalization::impl::sync(self); |
| 6743 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6744 | } else { |
| 6745 | self_ = self; |
| 6746 | } |
| 6747 | |
| 6748 | at::Tensor values_; |
| 6749 | if (at::functionalization::impl::isFunctionalTensor(values)) { |
| 6750 | at::functionalization::impl::sync(values); |
| 6751 | values_ = at::functionalization::impl::from_functional_tensor(values); |
| 6752 | } else { |
| 6753 | values_ = values; |
| 6754 | } |
| 6755 | |
| 6756 | at::Tensor indices_; |
| 6757 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 6758 | at::functionalization::impl::sync(indices); |
| 6759 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 6760 | } else { |
| 6761 | indices_ = indices; |
| 6762 | } |
| 6763 | if (!(true && at::functionalization::impl::isFunctionalTensor(values) && at::functionalization::impl::isFunctionalTensor(indices))) { |
| 6764 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 6765 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6766 | TORCH_INTERNAL_ASSERT(false, |
| 6767 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6768 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6769 | } else { |
| 6770 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6771 | at::AutoDispatchSkipFunctionalize guard; |
| 6772 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::nanmedian_names_dim_values::call(self_, dim, keepdim, values_, indices_); |
| 6773 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices);; |
| 6774 | } |
| 6775 | } else { |
| 6776 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 6777 | { |
| 6778 | at::AutoDispatchSkipFunctionalize guard; |
| 6779 | tmp_output = at::_ops::nanmedian_names_dim::call(self_, dim, keepdim); |
| 6780 | } |
| 6781 | at::functionalization::impl::replace_(values, std::get<0>(tmp_output)); |
| 6782 | at::functionalization::impl::commit_update(values); |
| 6783 | at::functionalization::impl::sync(values); |
| 6784 | at::functionalization::impl::replace_(indices, std::get<1>(tmp_output)); |
| 6785 | at::functionalization::impl::commit_update(indices); |
| 6786 | at::functionalization::impl::sync(indices); |
| 6787 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices); |
| 6788 | } |
| 6789 | } |
| 6790 | |
| 6791 | ::std::tuple<at::Tensor &,at::Tensor &> mode_out_values(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & values, at::Tensor & indices) { |
| 6792 | if (false) { |
| 6793 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6794 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6795 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6796 | auto self_meta = to_meta(self); |
| 6797 | auto values_meta = to_meta(values); |
| 6798 | auto indices_meta = to_meta(indices); |
| 6799 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6800 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6801 | at::_ops::mode_values::call(self_meta, dim, keepdim, values_meta, indices_meta); |
| 6802 | } |
| 6803 | |
| 6804 | at::Tensor self_; |
| 6805 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6806 | at::functionalization::impl::sync(self); |
| 6807 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6808 | } else { |
| 6809 | self_ = self; |
| 6810 | } |
| 6811 | |
| 6812 | at::Tensor values_; |
| 6813 | if (at::functionalization::impl::isFunctionalTensor(values)) { |
| 6814 | at::functionalization::impl::sync(values); |
| 6815 | values_ = at::functionalization::impl::from_functional_tensor(values); |
| 6816 | } else { |
| 6817 | values_ = values; |
| 6818 | } |
| 6819 | |
| 6820 | at::Tensor indices_; |
| 6821 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 6822 | at::functionalization::impl::sync(indices); |
| 6823 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 6824 | } else { |
| 6825 | indices_ = indices; |
| 6826 | } |
| 6827 | if (!(true && at::functionalization::impl::isFunctionalTensor(values) && at::functionalization::impl::isFunctionalTensor(indices))) { |
| 6828 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 6829 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6830 | TORCH_INTERNAL_ASSERT(false, |
| 6831 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6832 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6833 | } else { |
| 6834 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6835 | at::AutoDispatchSkipFunctionalize guard; |
| 6836 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::mode_values::call(self_, dim, keepdim, values_, indices_); |
| 6837 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices);; |
| 6838 | } |
| 6839 | } else { |
| 6840 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 6841 | { |
| 6842 | at::AutoDispatchSkipFunctionalize guard; |
| 6843 | tmp_output = at::_ops::mode::call(self_, dim, keepdim); |
| 6844 | } |
| 6845 | at::functionalization::impl::replace_(values, std::get<0>(tmp_output)); |
| 6846 | at::functionalization::impl::commit_update(values); |
| 6847 | at::functionalization::impl::sync(values); |
| 6848 | at::functionalization::impl::replace_(indices, std::get<1>(tmp_output)); |
| 6849 | at::functionalization::impl::commit_update(indices); |
| 6850 | at::functionalization::impl::sync(indices); |
| 6851 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices); |
| 6852 | } |
| 6853 | } |
| 6854 | |
| 6855 | ::std::tuple<at::Tensor &,at::Tensor &> mode_out_dimname_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & values, at::Tensor & indices) { |
| 6856 | if (false) { |
| 6857 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6858 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6859 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6860 | auto self_meta = to_meta(self); |
| 6861 | auto values_meta = to_meta(values); |
| 6862 | auto indices_meta = to_meta(indices); |
| 6863 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6864 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6865 | at::_ops::mode_dimname_out::call(self_meta, dim, keepdim, values_meta, indices_meta); |
| 6866 | } |
| 6867 | |
| 6868 | at::Tensor self_; |
| 6869 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6870 | at::functionalization::impl::sync(self); |
| 6871 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6872 | } else { |
| 6873 | self_ = self; |
| 6874 | } |
| 6875 | |
| 6876 | at::Tensor values_; |
| 6877 | if (at::functionalization::impl::isFunctionalTensor(values)) { |
| 6878 | at::functionalization::impl::sync(values); |
| 6879 | values_ = at::functionalization::impl::from_functional_tensor(values); |
| 6880 | } else { |
| 6881 | values_ = values; |
| 6882 | } |
| 6883 | |
| 6884 | at::Tensor indices_; |
| 6885 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 6886 | at::functionalization::impl::sync(indices); |
| 6887 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 6888 | } else { |
| 6889 | indices_ = indices; |
| 6890 | } |
| 6891 | if (!(true && at::functionalization::impl::isFunctionalTensor(values) && at::functionalization::impl::isFunctionalTensor(indices))) { |
| 6892 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 6893 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6894 | TORCH_INTERNAL_ASSERT(false, |
| 6895 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6896 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6897 | } else { |
| 6898 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6899 | at::AutoDispatchSkipFunctionalize guard; |
| 6900 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::mode_dimname_out::call(self_, dim, keepdim, values_, indices_); |
| 6901 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices);; |
| 6902 | } |
| 6903 | } else { |
| 6904 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 6905 | { |
| 6906 | at::AutoDispatchSkipFunctionalize guard; |
| 6907 | tmp_output = at::_ops::mode_dimname::call(self_, dim, keepdim); |
| 6908 | } |
| 6909 | at::functionalization::impl::replace_(values, std::get<0>(tmp_output)); |
| 6910 | at::functionalization::impl::commit_update(values); |
| 6911 | at::functionalization::impl::sync(values); |
| 6912 | at::functionalization::impl::replace_(indices, std::get<1>(tmp_output)); |
| 6913 | at::functionalization::impl::commit_update(indices); |
| 6914 | at::functionalization::impl::sync(indices); |
| 6915 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices); |
| 6916 | } |
| 6917 | } |
| 6918 | |
| 6919 | at::Tensor & multiply_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 6920 | if (false) { |
| 6921 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6922 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6923 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6924 | auto self_meta = to_meta(self); |
| 6925 | auto other_meta = to_meta(other); |
| 6926 | auto out_meta = to_meta(out); |
| 6927 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6928 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6929 | at::_ops::multiply_out::call(self_meta, other_meta, out_meta); |
| 6930 | } |
| 6931 | |
| 6932 | at::Tensor self_; |
| 6933 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6934 | at::functionalization::impl::sync(self); |
| 6935 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6936 | } else { |
| 6937 | self_ = self; |
| 6938 | } |
| 6939 | |
| 6940 | at::Tensor other_; |
| 6941 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 6942 | at::functionalization::impl::sync(other); |
| 6943 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 6944 | } else { |
| 6945 | other_ = other; |
| 6946 | } |
| 6947 | |
| 6948 | at::Tensor out_; |
| 6949 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 6950 | at::functionalization::impl::sync(out); |
| 6951 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 6952 | } else { |
| 6953 | out_ = out; |
| 6954 | } |
| 6955 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 6956 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 6957 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6958 | TORCH_INTERNAL_ASSERT(false, |
| 6959 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6960 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6961 | } else { |
| 6962 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6963 | at::AutoDispatchSkipFunctionalize guard; |
| 6964 | at::Tensor tmp_output = at::_ops::multiply_out::call(self_, other_, out_); |
| 6965 | return out;; |
| 6966 | } |
| 6967 | } else { |
| 6968 | at::Tensor tmp_output; |
| 6969 | { |
| 6970 | at::AutoDispatchSkipFunctionalize guard; |
| 6971 | tmp_output = at::_ops::multiply_Tensor::call(self_, other_); |
| 6972 | } |
| 6973 | at::functionalization::impl::replace_(out, tmp_output); |
| 6974 | at::functionalization::impl::commit_update(out); |
| 6975 | at::functionalization::impl::sync(out); |
| 6976 | return out; |
| 6977 | } |
| 6978 | } |
| 6979 | |
| 6980 | at::Tensor & multiply__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 6981 | if (true) { |
| 6982 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6983 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6984 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6985 | auto self_meta = to_meta(self); |
| 6986 | auto other_meta = to_meta(other); |
| 6987 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6988 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6989 | at::_ops::multiply__Tensor::call(self_meta, other_meta); |
| 6990 | } |
| 6991 | |
| 6992 | at::Tensor self_; |
| 6993 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6994 | at::functionalization::impl::sync(self); |
| 6995 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6996 | } else { |
| 6997 | self_ = self; |
| 6998 | } |
| 6999 | |
| 7000 | at::Tensor other_; |
| 7001 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 7002 | at::functionalization::impl::sync(other); |
| 7003 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 7004 | } else { |
| 7005 | other_ = other; |
| 7006 | } |
| 7007 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 7008 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 7009 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7010 | TORCH_INTERNAL_ASSERT(false, |
| 7011 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7012 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7013 | } else { |
| 7014 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7015 | at::AutoDispatchSkipFunctionalize guard; |
| 7016 | at::Tensor tmp_output = at::_ops::multiply__Tensor::call(self_, other_); |
| 7017 | return self;; |
| 7018 | } |
| 7019 | } else { |
| 7020 | at::Tensor tmp_output; |
| 7021 | { |
| 7022 | at::AutoDispatchSkipFunctionalize guard; |
| 7023 | tmp_output = at::_ops::multiply_Tensor::call(self_, other_); |
| 7024 | } |
| 7025 | at::functionalization::impl::replace_(self, tmp_output); |
| 7026 | at::functionalization::impl::commit_update(self); |
| 7027 | at::functionalization::impl::sync(self); |
| 7028 | return self; |
| 7029 | } |
| 7030 | } |
| 7031 | |
| 7032 | at::Tensor & narrow_copy_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length, at::Tensor & out) { |
| 7033 | if (false) { |
| 7034 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7035 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7036 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7037 | auto self_meta = to_meta(self); |
| 7038 | auto out_meta = to_meta(out); |
| 7039 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7040 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7041 | at::_ops::narrow_copy_out::call(self_meta, dim, start, length, out_meta); |
| 7042 | } |
| 7043 | |
| 7044 | at::Tensor self_; |
| 7045 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7046 | at::functionalization::impl::sync(self); |
| 7047 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7048 | } else { |
| 7049 | self_ = self; |
| 7050 | } |
| 7051 | |
| 7052 | at::Tensor out_; |
| 7053 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7054 | at::functionalization::impl::sync(out); |
| 7055 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7056 | } else { |
| 7057 | out_ = out; |
| 7058 | } |
| 7059 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7060 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 7061 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7062 | TORCH_INTERNAL_ASSERT(false, |
| 7063 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7064 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7065 | } else { |
| 7066 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7067 | at::AutoDispatchSkipFunctionalize guard; |
| 7068 | at::Tensor tmp_output = at::_ops::narrow_copy_out::call(self_, dim, start, length, out_); |
| 7069 | return out;; |
| 7070 | } |
| 7071 | } else { |
| 7072 | at::Tensor tmp_output; |
| 7073 | { |
| 7074 | at::AutoDispatchSkipFunctionalize guard; |
| 7075 | tmp_output = at::_ops::narrow_copy::call(self_, dim, start, length); |
| 7076 | } |
| 7077 | at::functionalization::impl::replace_(out, tmp_output); |
| 7078 | at::functionalization::impl::commit_update(out); |
| 7079 | at::functionalization::impl::sync(out); |
| 7080 | return out; |
| 7081 | } |
| 7082 | } |
| 7083 | |
| 7084 | ::std::tuple<at::Tensor &,at::Tensor &> batch_norm_gather_stats_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, double momentum, double eps, int64_t count, at::Tensor & out0, at::Tensor & out1) { |
| 7085 | if (false) { |
| 7086 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7087 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7088 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7089 | auto input_meta = to_meta(input); |
| 7090 | auto mean_meta = to_meta(mean); |
| 7091 | auto invstd_meta = to_meta(invstd); |
| 7092 | auto running_mean_meta = to_meta(running_mean); |
| 7093 | auto running_var_meta = to_meta(running_var); |
| 7094 | auto out0_meta = to_meta(out0); |
| 7095 | auto out1_meta = to_meta(out1); |
| 7096 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7097 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7098 | at::_ops::batch_norm_gather_stats_out::call(input_meta, mean_meta, invstd_meta, running_mean_meta, running_var_meta, momentum, eps, count, out0_meta, out1_meta); |
| 7099 | } |
| 7100 | |
| 7101 | at::Tensor input_; |
| 7102 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 7103 | at::functionalization::impl::sync(input); |
| 7104 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 7105 | } else { |
| 7106 | input_ = input; |
| 7107 | } |
| 7108 | |
| 7109 | at::Tensor mean_; |
| 7110 | if (at::functionalization::impl::isFunctionalTensor(mean)) { |
| 7111 | at::functionalization::impl::sync(mean); |
| 7112 | mean_ = at::functionalization::impl::from_functional_tensor(mean); |
| 7113 | } else { |
| 7114 | mean_ = mean; |
| 7115 | } |
| 7116 | |
| 7117 | at::Tensor invstd_; |
| 7118 | if (at::functionalization::impl::isFunctionalTensor(invstd)) { |
| 7119 | at::functionalization::impl::sync(invstd); |
| 7120 | invstd_ = at::functionalization::impl::from_functional_tensor(invstd); |
| 7121 | } else { |
| 7122 | invstd_ = invstd; |
| 7123 | } |
| 7124 | |
| 7125 | c10::optional<at::Tensor> running_mean_; |
| 7126 | if (at::functionalization::impl::isFunctionalTensor(running_mean)) { |
| 7127 | at::functionalization::impl::sync(running_mean); |
| 7128 | running_mean_ = at::functionalization::impl::from_functional_tensor(running_mean); |
| 7129 | } else { |
| 7130 | running_mean_ = running_mean; |
| 7131 | } |
| 7132 | |
| 7133 | c10::optional<at::Tensor> running_var_; |
| 7134 | if (at::functionalization::impl::isFunctionalTensor(running_var)) { |
| 7135 | at::functionalization::impl::sync(running_var); |
| 7136 | running_var_ = at::functionalization::impl::from_functional_tensor(running_var); |
| 7137 | } else { |
| 7138 | running_var_ = running_var; |
| 7139 | } |
| 7140 | |
| 7141 | at::Tensor out0_; |
| 7142 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 7143 | at::functionalization::impl::sync(out0); |
| 7144 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 7145 | } else { |
| 7146 | out0_ = out0; |
| 7147 | } |
| 7148 | |
| 7149 | at::Tensor out1_; |
| 7150 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 7151 | at::functionalization::impl::sync(out1); |
| 7152 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 7153 | } else { |
| 7154 | out1_ = out1; |
| 7155 | } |
| 7156 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1))) { |
| 7157 | if ((false || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(mean) || at::functionalization::impl::isFunctionalTensor(invstd) || at::functionalization::impl::isFunctionalTensor(running_mean) || at::functionalization::impl::isFunctionalTensor(running_var))) { |
| 7158 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7159 | TORCH_INTERNAL_ASSERT(false, |
| 7160 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7161 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7162 | } else { |
| 7163 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7164 | at::AutoDispatchSkipFunctionalize guard; |
| 7165 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::batch_norm_gather_stats_out::call(input_, mean_, invstd_, running_mean_, running_var_, momentum, eps, count, out0_, out1_); |
| 7166 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1);; |
| 7167 | } |
| 7168 | } else { |
| 7169 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 7170 | { |
| 7171 | at::AutoDispatchSkipFunctionalize guard; |
| 7172 | tmp_output = at::_ops::batch_norm_gather_stats::call(input_, mean_, invstd_, running_mean_, running_var_, momentum, eps, count); |
| 7173 | } |
| 7174 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 7175 | at::functionalization::impl::commit_update(out0); |
| 7176 | at::functionalization::impl::sync(out0); |
| 7177 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 7178 | at::functionalization::impl::commit_update(out1); |
| 7179 | at::functionalization::impl::sync(out1); |
| 7180 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1); |
| 7181 | } |
| 7182 | } |
| 7183 | |
| 7184 | ::std::tuple<at::Tensor &,at::Tensor &> batch_norm_gather_stats_with_counts_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, double momentum, double eps, const at::Tensor & counts, at::Tensor & out0, at::Tensor & out1) { |
| 7185 | if (false) { |
| 7186 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7187 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7188 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7189 | auto input_meta = to_meta(input); |
| 7190 | auto mean_meta = to_meta(mean); |
| 7191 | auto invstd_meta = to_meta(invstd); |
| 7192 | auto running_mean_meta = to_meta(running_mean); |
| 7193 | auto running_var_meta = to_meta(running_var); |
| 7194 | auto counts_meta = to_meta(counts); |
| 7195 | auto out0_meta = to_meta(out0); |
| 7196 | auto out1_meta = to_meta(out1); |
| 7197 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7198 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7199 | at::_ops::batch_norm_gather_stats_with_counts_out::call(input_meta, mean_meta, invstd_meta, running_mean_meta, running_var_meta, momentum, eps, counts_meta, out0_meta, out1_meta); |
| 7200 | } |
| 7201 | |
| 7202 | at::Tensor input_; |
| 7203 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 7204 | at::functionalization::impl::sync(input); |
| 7205 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 7206 | } else { |
| 7207 | input_ = input; |
| 7208 | } |
| 7209 | |
| 7210 | at::Tensor mean_; |
| 7211 | if (at::functionalization::impl::isFunctionalTensor(mean)) { |
| 7212 | at::functionalization::impl::sync(mean); |
| 7213 | mean_ = at::functionalization::impl::from_functional_tensor(mean); |
| 7214 | } else { |
| 7215 | mean_ = mean; |
| 7216 | } |
| 7217 | |
| 7218 | at::Tensor invstd_; |
| 7219 | if (at::functionalization::impl::isFunctionalTensor(invstd)) { |
| 7220 | at::functionalization::impl::sync(invstd); |
| 7221 | invstd_ = at::functionalization::impl::from_functional_tensor(invstd); |
| 7222 | } else { |
| 7223 | invstd_ = invstd; |
| 7224 | } |
| 7225 | |
| 7226 | c10::optional<at::Tensor> running_mean_; |
| 7227 | if (at::functionalization::impl::isFunctionalTensor(running_mean)) { |
| 7228 | at::functionalization::impl::sync(running_mean); |
| 7229 | running_mean_ = at::functionalization::impl::from_functional_tensor(running_mean); |
| 7230 | } else { |
| 7231 | running_mean_ = running_mean; |
| 7232 | } |
| 7233 | |
| 7234 | c10::optional<at::Tensor> running_var_; |
| 7235 | if (at::functionalization::impl::isFunctionalTensor(running_var)) { |
| 7236 | at::functionalization::impl::sync(running_var); |
| 7237 | running_var_ = at::functionalization::impl::from_functional_tensor(running_var); |
| 7238 | } else { |
| 7239 | running_var_ = running_var; |
| 7240 | } |
| 7241 | |
| 7242 | at::Tensor counts_; |
| 7243 | if (at::functionalization::impl::isFunctionalTensor(counts)) { |
| 7244 | at::functionalization::impl::sync(counts); |
| 7245 | counts_ = at::functionalization::impl::from_functional_tensor(counts); |
| 7246 | } else { |
| 7247 | counts_ = counts; |
| 7248 | } |
| 7249 | |
| 7250 | at::Tensor out0_; |
| 7251 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 7252 | at::functionalization::impl::sync(out0); |
| 7253 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 7254 | } else { |
| 7255 | out0_ = out0; |
| 7256 | } |
| 7257 | |
| 7258 | at::Tensor out1_; |
| 7259 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 7260 | at::functionalization::impl::sync(out1); |
| 7261 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 7262 | } else { |
| 7263 | out1_ = out1; |
| 7264 | } |
| 7265 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1))) { |
| 7266 | if ((false || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(mean) || at::functionalization::impl::isFunctionalTensor(invstd) || at::functionalization::impl::isFunctionalTensor(running_mean) || at::functionalization::impl::isFunctionalTensor(running_var) || at::functionalization::impl::isFunctionalTensor(counts))) { |
| 7267 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7268 | TORCH_INTERNAL_ASSERT(false, |
| 7269 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7270 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7271 | } else { |
| 7272 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7273 | at::AutoDispatchSkipFunctionalize guard; |
| 7274 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::batch_norm_gather_stats_with_counts_out::call(input_, mean_, invstd_, running_mean_, running_var_, momentum, eps, counts_, out0_, out1_); |
| 7275 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1);; |
| 7276 | } |
| 7277 | } else { |
| 7278 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 7279 | { |
| 7280 | at::AutoDispatchSkipFunctionalize guard; |
| 7281 | tmp_output = at::_ops::batch_norm_gather_stats_with_counts::call(input_, mean_, invstd_, running_mean_, running_var_, momentum, eps, counts_); |
| 7282 | } |
| 7283 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 7284 | at::functionalization::impl::commit_update(out0); |
| 7285 | at::functionalization::impl::sync(out0); |
| 7286 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 7287 | at::functionalization::impl::commit_update(out1); |
| 7288 | at::functionalization::impl::sync(out1); |
| 7289 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1); |
| 7290 | } |
| 7291 | } |
| 7292 | |
| 7293 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_batch_norm_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & input, const c10::optional<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_invstd, bool train, double eps, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 7294 | if (false) { |
| 7295 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7296 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7297 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7298 | auto grad_out_meta = to_meta(grad_out); |
| 7299 | auto input_meta = to_meta(input); |
| 7300 | auto weight_meta = to_meta(weight); |
| 7301 | auto running_mean_meta = to_meta(running_mean); |
| 7302 | auto running_var_meta = to_meta(running_var); |
| 7303 | auto save_mean_meta = to_meta(save_mean); |
| 7304 | auto save_invstd_meta = to_meta(save_invstd); |
| 7305 | auto out0_meta = to_meta(out0); |
| 7306 | auto out1_meta = to_meta(out1); |
| 7307 | auto out2_meta = to_meta(out2); |
| 7308 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7309 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7310 | at::_ops::native_batch_norm_backward_out::call(grad_out_meta, input_meta, weight_meta, running_mean_meta, running_var_meta, save_mean_meta, save_invstd_meta, train, eps, output_mask, out0_meta, out1_meta, out2_meta); |
| 7311 | } |
| 7312 | |
| 7313 | at::Tensor grad_out_; |
| 7314 | if (at::functionalization::impl::isFunctionalTensor(grad_out)) { |
| 7315 | at::functionalization::impl::sync(grad_out); |
| 7316 | grad_out_ = at::functionalization::impl::from_functional_tensor(grad_out); |
| 7317 | } else { |
| 7318 | grad_out_ = grad_out; |
| 7319 | } |
| 7320 | |
| 7321 | at::Tensor input_; |
| 7322 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 7323 | at::functionalization::impl::sync(input); |
| 7324 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 7325 | } else { |
| 7326 | input_ = input; |
| 7327 | } |
| 7328 | |
| 7329 | c10::optional<at::Tensor> weight_; |
| 7330 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 7331 | at::functionalization::impl::sync(weight); |
| 7332 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 7333 | } else { |
| 7334 | weight_ = weight; |
| 7335 | } |
| 7336 | |
| 7337 | c10::optional<at::Tensor> running_mean_; |
| 7338 | if (at::functionalization::impl::isFunctionalTensor(running_mean)) { |
| 7339 | at::functionalization::impl::sync(running_mean); |
| 7340 | running_mean_ = at::functionalization::impl::from_functional_tensor(running_mean); |
| 7341 | } else { |
| 7342 | running_mean_ = running_mean; |
| 7343 | } |
| 7344 | |
| 7345 | c10::optional<at::Tensor> running_var_; |
| 7346 | if (at::functionalization::impl::isFunctionalTensor(running_var)) { |
| 7347 | at::functionalization::impl::sync(running_var); |
| 7348 | running_var_ = at::functionalization::impl::from_functional_tensor(running_var); |
| 7349 | } else { |
| 7350 | running_var_ = running_var; |
| 7351 | } |
| 7352 | |
| 7353 | c10::optional<at::Tensor> save_mean_; |
| 7354 | if (at::functionalization::impl::isFunctionalTensor(save_mean)) { |
| 7355 | at::functionalization::impl::sync(save_mean); |
| 7356 | save_mean_ = at::functionalization::impl::from_functional_tensor(save_mean); |
| 7357 | } else { |
| 7358 | save_mean_ = save_mean; |
| 7359 | } |
| 7360 | |
| 7361 | c10::optional<at::Tensor> save_invstd_; |
| 7362 | if (at::functionalization::impl::isFunctionalTensor(save_invstd)) { |
| 7363 | at::functionalization::impl::sync(save_invstd); |
| 7364 | save_invstd_ = at::functionalization::impl::from_functional_tensor(save_invstd); |
| 7365 | } else { |
| 7366 | save_invstd_ = save_invstd; |
| 7367 | } |
| 7368 | |
| 7369 | at::Tensor out0_; |
| 7370 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 7371 | at::functionalization::impl::sync(out0); |
| 7372 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 7373 | } else { |
| 7374 | out0_ = out0; |
| 7375 | } |
| 7376 | |
| 7377 | at::Tensor out1_; |
| 7378 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 7379 | at::functionalization::impl::sync(out1); |
| 7380 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 7381 | } else { |
| 7382 | out1_ = out1; |
| 7383 | } |
| 7384 | |
| 7385 | at::Tensor out2_; |
| 7386 | if (at::functionalization::impl::isFunctionalTensor(out2)) { |
| 7387 | at::functionalization::impl::sync(out2); |
| 7388 | out2_ = at::functionalization::impl::from_functional_tensor(out2); |
| 7389 | } else { |
| 7390 | out2_ = out2; |
| 7391 | } |
| 7392 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1) && at::functionalization::impl::isFunctionalTensor(out2))) { |
| 7393 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_out) || at::functionalization::impl::isFunctionalTensor(input) || 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_invstd))) { |
| 7394 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7395 | TORCH_INTERNAL_ASSERT(false, |
| 7396 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7397 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7398 | } else { |
| 7399 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7400 | at::AutoDispatchSkipFunctionalize guard; |
| 7401 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::native_batch_norm_backward_out::call(grad_out_, input_, weight_, running_mean_, running_var_, save_mean_, save_invstd_, train, eps, output_mask, out0_, out1_, out2_); |
| 7402 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2);; |
| 7403 | } |
| 7404 | } else { |
| 7405 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 7406 | { |
| 7407 | at::AutoDispatchSkipFunctionalize guard; |
| 7408 | tmp_output = at::_ops::native_batch_norm_backward::call(grad_out_, input_, weight_, running_mean_, running_var_, save_mean_, save_invstd_, train, eps, output_mask); |
| 7409 | } |
| 7410 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 7411 | at::functionalization::impl::commit_update(out0); |
| 7412 | at::functionalization::impl::sync(out0); |
| 7413 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 7414 | at::functionalization::impl::commit_update(out1); |
| 7415 | at::functionalization::impl::sync(out1); |
| 7416 | at::functionalization::impl::replace_(out2, std::get<2>(tmp_output)); |
| 7417 | at::functionalization::impl::commit_update(out2); |
| 7418 | at::functionalization::impl::sync(out2); |
| 7419 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2); |
| 7420 | } |
| 7421 | } |
| 7422 | |
| 7423 | at::Tensor & ones_out_names_out(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional<at::DimnameList> names, at::Tensor & out) { |
| 7424 | if (false) { |
| 7425 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7426 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7427 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7428 | auto out_meta = to_meta(out); |
| 7429 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7430 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7431 | at::_ops::ones_names_out::call(size, names, out_meta); |
| 7432 | } |
| 7433 | |
| 7434 | at::Tensor out_; |
| 7435 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7436 | at::functionalization::impl::sync(out); |
| 7437 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7438 | } else { |
| 7439 | out_ = out; |
| 7440 | } |
| 7441 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7442 | if ((false)) { |
| 7443 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7444 | TORCH_INTERNAL_ASSERT(false, |
| 7445 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7446 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7447 | } else { |
| 7448 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7449 | at::AutoDispatchSkipFunctionalize guard; |
| 7450 | at::Tensor tmp_output = at::_ops::ones_names_out::call(size, names, out_); |
| 7451 | return out;; |
| 7452 | } |
| 7453 | } else { |
| 7454 | at::Tensor tmp_output; |
| 7455 | { |
| 7456 | at::AutoDispatchSkipFunctionalize guard; |
| 7457 | tmp_output = at::_ops::ones_names::call(size, names, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 7458 | } |
| 7459 | at::functionalization::impl::replace_(out, tmp_output); |
| 7460 | at::functionalization::impl::commit_update(out); |
| 7461 | at::functionalization::impl::sync(out); |
| 7462 | return out; |
| 7463 | } |
| 7464 | } |
| 7465 | |
| 7466 | at::Tensor & ones_out_out(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, at::Tensor & out) { |
| 7467 | if (false) { |
| 7468 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7469 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7470 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7471 | auto out_meta = to_meta(out); |
| 7472 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7473 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7474 | at::_ops::ones_out::call(size, out_meta); |
| 7475 | } |
| 7476 | |
| 7477 | at::Tensor out_; |
| 7478 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7479 | at::functionalization::impl::sync(out); |
| 7480 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7481 | } else { |
| 7482 | out_ = out; |
| 7483 | } |
| 7484 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7485 | if ((false)) { |
| 7486 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7487 | TORCH_INTERNAL_ASSERT(false, |
| 7488 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7489 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7490 | } else { |
| 7491 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7492 | at::AutoDispatchSkipFunctionalize guard; |
| 7493 | at::Tensor tmp_output = at::_ops::ones_out::call(size, out_); |
| 7494 | return out;; |
| 7495 | } |
| 7496 | } else { |
| 7497 | at::Tensor tmp_output; |
| 7498 | { |
| 7499 | at::AutoDispatchSkipFunctionalize guard; |
| 7500 | tmp_output = at::_ops::ones::call(size, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 7501 | } |
| 7502 | at::functionalization::impl::replace_(out, tmp_output); |
| 7503 | at::functionalization::impl::commit_update(out); |
| 7504 | at::functionalization::impl::sync(out); |
| 7505 | return out; |
| 7506 | } |
| 7507 | } |
| 7508 | |
| 7509 | at::Tensor & _pdist_forward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double p, at::Tensor & out) { |
| 7510 | if (false) { |
| 7511 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7512 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7513 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7514 | auto self_meta = to_meta(self); |
| 7515 | auto out_meta = to_meta(out); |
| 7516 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7517 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7518 | at::_ops::_pdist_forward_out::call(self_meta, p, out_meta); |
| 7519 | } |
| 7520 | |
| 7521 | at::Tensor self_; |
| 7522 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7523 | at::functionalization::impl::sync(self); |
| 7524 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7525 | } else { |
| 7526 | self_ = self; |
| 7527 | } |
| 7528 | |
| 7529 | at::Tensor out_; |
| 7530 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7531 | at::functionalization::impl::sync(out); |
| 7532 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7533 | } else { |
| 7534 | out_ = out; |
| 7535 | } |
| 7536 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7537 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 7538 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7539 | TORCH_INTERNAL_ASSERT(false, |
| 7540 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7541 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7542 | } else { |
| 7543 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7544 | at::AutoDispatchSkipFunctionalize guard; |
| 7545 | at::Tensor tmp_output = at::_ops::_pdist_forward_out::call(self_, p, out_); |
| 7546 | return out;; |
| 7547 | } |
| 7548 | } else { |
| 7549 | at::Tensor tmp_output; |
| 7550 | { |
| 7551 | at::AutoDispatchSkipFunctionalize guard; |
| 7552 | tmp_output = at::_ops::_pdist_forward::call(self_, p); |
| 7553 | } |
| 7554 | at::functionalization::impl::replace_(out, tmp_output); |
| 7555 | at::functionalization::impl::commit_update(out); |
| 7556 | at::functionalization::impl::sync(out); |
| 7557 | return out; |
| 7558 | } |
| 7559 | } |
| 7560 | |
| 7561 | at::Tensor & _pdist_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & self, double p, const at::Tensor & pdist, at::Tensor & out) { |
| 7562 | if (false) { |
| 7563 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7564 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7565 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7566 | auto grad_meta = to_meta(grad); |
| 7567 | auto self_meta = to_meta(self); |
| 7568 | auto pdist_meta = to_meta(pdist); |
| 7569 | auto out_meta = to_meta(out); |
| 7570 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7571 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7572 | at::_ops::_pdist_backward_out::call(grad_meta, self_meta, p, pdist_meta, out_meta); |
| 7573 | } |
| 7574 | |
| 7575 | at::Tensor grad_; |
| 7576 | if (at::functionalization::impl::isFunctionalTensor(grad)) { |
| 7577 | at::functionalization::impl::sync(grad); |
| 7578 | grad_ = at::functionalization::impl::from_functional_tensor(grad); |
| 7579 | } else { |
| 7580 | grad_ = grad; |
| 7581 | } |
| 7582 | |
| 7583 | at::Tensor self_; |
| 7584 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7585 | at::functionalization::impl::sync(self); |
| 7586 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7587 | } else { |
| 7588 | self_ = self; |
| 7589 | } |
| 7590 | |
| 7591 | at::Tensor pdist_; |
| 7592 | if (at::functionalization::impl::isFunctionalTensor(pdist)) { |
| 7593 | at::functionalization::impl::sync(pdist); |
| 7594 | pdist_ = at::functionalization::impl::from_functional_tensor(pdist); |
| 7595 | } else { |
| 7596 | pdist_ = pdist; |
| 7597 | } |
| 7598 | |
| 7599 | at::Tensor out_; |
| 7600 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7601 | at::functionalization::impl::sync(out); |
| 7602 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7603 | } else { |
| 7604 | out_ = out; |
| 7605 | } |
| 7606 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7607 | if ((false || at::functionalization::impl::isFunctionalTensor(grad) || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(pdist))) { |
| 7608 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7609 | TORCH_INTERNAL_ASSERT(false, |
| 7610 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7611 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7612 | } else { |
| 7613 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7614 | at::AutoDispatchSkipFunctionalize guard; |
| 7615 | at::Tensor tmp_output = at::_ops::_pdist_backward_out::call(grad_, self_, p, pdist_, out_); |
| 7616 | return out;; |
| 7617 | } |
| 7618 | } else { |
| 7619 | at::Tensor tmp_output; |
| 7620 | { |
| 7621 | at::AutoDispatchSkipFunctionalize guard; |
| 7622 | tmp_output = at::_ops::_pdist_backward::call(grad_, self_, p, pdist_); |
| 7623 | } |
| 7624 | at::functionalization::impl::replace_(out, tmp_output); |
| 7625 | at::functionalization::impl::commit_update(out); |
| 7626 | at::functionalization::impl::sync(out); |
| 7627 | return out; |
| 7628 | } |
| 7629 | } |
| 7630 | |
| 7631 | at::Tensor & pixel_shuffle_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t upscale_factor, at::Tensor & out) { |
| 7632 | if (false) { |
| 7633 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7634 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7635 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7636 | auto self_meta = to_meta(self); |
| 7637 | auto out_meta = to_meta(out); |
| 7638 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7639 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7640 | at::_ops::pixel_shuffle_out::call(self_meta, upscale_factor, out_meta); |
| 7641 | } |
| 7642 | |
| 7643 | at::Tensor self_; |
| 7644 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7645 | at::functionalization::impl::sync(self); |
| 7646 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7647 | } else { |
| 7648 | self_ = self; |
| 7649 | } |
| 7650 | |
| 7651 | at::Tensor out_; |
| 7652 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7653 | at::functionalization::impl::sync(out); |
| 7654 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7655 | } else { |
| 7656 | out_ = out; |
| 7657 | } |
| 7658 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7659 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 7660 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7661 | TORCH_INTERNAL_ASSERT(false, |
| 7662 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7663 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7664 | } else { |
| 7665 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7666 | at::AutoDispatchSkipFunctionalize guard; |
| 7667 | at::Tensor tmp_output = at::_ops::pixel_shuffle_out::call(self_, upscale_factor, out_); |
| 7668 | return out;; |
| 7669 | } |
| 7670 | } else { |
| 7671 | at::Tensor tmp_output; |
| 7672 | { |
| 7673 | at::AutoDispatchSkipFunctionalize guard; |
| 7674 | tmp_output = at::_ops::pixel_shuffle::call(self_, upscale_factor); |
| 7675 | } |
| 7676 | at::functionalization::impl::replace_(out, tmp_output); |
| 7677 | at::functionalization::impl::commit_update(out); |
| 7678 | at::functionalization::impl::sync(out); |
| 7679 | return out; |
| 7680 | } |
| 7681 | } |
| 7682 | |
| 7683 | at::Tensor & _pin_memory_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::Device> device, at::Tensor & out) { |
| 7684 | if (false) { |
| 7685 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7686 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7687 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7688 | auto self_meta = to_meta(self); |
| 7689 | auto out_meta = to_meta(out); |
| 7690 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7691 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7692 | at::_ops::_pin_memory_out::call(self_meta, device, out_meta); |
| 7693 | } |
| 7694 | |
| 7695 | at::Tensor self_; |
| 7696 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7697 | at::functionalization::impl::sync(self); |
| 7698 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7699 | } else { |
| 7700 | self_ = self; |
| 7701 | } |
| 7702 | |
| 7703 | at::Tensor out_; |
| 7704 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7705 | at::functionalization::impl::sync(out); |
| 7706 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7707 | } else { |
| 7708 | out_ = out; |
| 7709 | } |
| 7710 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7711 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 7712 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7713 | TORCH_INTERNAL_ASSERT(false, |
| 7714 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7715 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7716 | } else { |
| 7717 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7718 | at::AutoDispatchSkipFunctionalize guard; |
| 7719 | at::Tensor tmp_output = at::_ops::_pin_memory_out::call(self_, device, out_); |
| 7720 | return out;; |
| 7721 | } |
| 7722 | } else { |
| 7723 | at::Tensor tmp_output; |
| 7724 | { |
| 7725 | at::AutoDispatchSkipFunctionalize guard; |
| 7726 | tmp_output = at::_ops::_pin_memory::call(self_, device); |
| 7727 | } |
| 7728 | at::functionalization::impl::replace_(out, tmp_output); |
| 7729 | at::functionalization::impl::commit_update(out); |
| 7730 | at::functionalization::impl::sync(out); |
| 7731 | return out; |
| 7732 | } |
| 7733 | } |
| 7734 | |
| 7735 | at::Tensor & randn_out_out(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, at::Tensor & out) { |
| 7736 | if (false) { |
| 7737 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7738 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7739 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7740 | auto out_meta = to_meta(out); |
| 7741 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7742 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7743 | at::_ops::randn_out::call(size, out_meta); |
| 7744 | } |
| 7745 | |
| 7746 | at::Tensor out_; |
| 7747 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7748 | at::functionalization::impl::sync(out); |
| 7749 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7750 | } else { |
| 7751 | out_ = out; |
| 7752 | } |
| 7753 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7754 | if ((false)) { |
| 7755 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7756 | TORCH_INTERNAL_ASSERT(false, |
| 7757 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7758 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7759 | } else { |
| 7760 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7761 | at::AutoDispatchSkipFunctionalize guard; |
| 7762 | at::Tensor tmp_output = at::_ops::randn_out::call(size, out_); |
| 7763 | return out;; |
| 7764 | } |
| 7765 | } else { |
| 7766 | at::Tensor tmp_output; |
| 7767 | { |
| 7768 | at::AutoDispatchSkipFunctionalize guard; |
| 7769 | tmp_output = at::_ops::randn::call(size, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 7770 | } |
| 7771 | at::functionalization::impl::replace_(out, tmp_output); |
| 7772 | at::functionalization::impl::commit_update(out); |
| 7773 | at::functionalization::impl::sync(out); |
| 7774 | return out; |
| 7775 | } |
| 7776 | } |
| 7777 | |
| 7778 | at::Tensor & randn_out_generator_out(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 7779 | if (false) { |
| 7780 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7781 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7782 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7783 | auto out_meta = to_meta(out); |
| 7784 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7785 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7786 | at::_ops::randn_generator_out::call(size, generator, out_meta); |
| 7787 | } |
| 7788 | |
| 7789 | at::Tensor out_; |
| 7790 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7791 | at::functionalization::impl::sync(out); |
| 7792 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7793 | } else { |
| 7794 | out_ = out; |
| 7795 | } |
| 7796 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7797 | if ((false)) { |
| 7798 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7799 | TORCH_INTERNAL_ASSERT(false, |
| 7800 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7801 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7802 | } else { |
| 7803 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7804 | at::AutoDispatchSkipFunctionalize guard; |
| 7805 | at::Tensor tmp_output = at::_ops::randn_generator_out::call(size, generator, out_); |
| 7806 | return out;; |
| 7807 | } |
| 7808 | } else { |
| 7809 | at::Tensor tmp_output; |
| 7810 | { |
| 7811 | at::AutoDispatchSkipFunctionalize guard; |
| 7812 | tmp_output = at::_ops::randn_generator::call(size, generator, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 7813 | } |
| 7814 | at::functionalization::impl::replace_(out, tmp_output); |
| 7815 | at::functionalization::impl::commit_update(out); |
| 7816 | at::functionalization::impl::sync(out); |
| 7817 | return out; |
| 7818 | } |
| 7819 | } |
| 7820 | |
| 7821 | at::Tensor & randn_out_names_out(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional<at::DimnameList> names, at::Tensor & out) { |
| 7822 | if (false) { |
| 7823 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7824 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7825 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7826 | auto out_meta = to_meta(out); |
| 7827 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7828 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7829 | at::_ops::randn_names_out::call(size, names, out_meta); |
| 7830 | } |
| 7831 | |
| 7832 | at::Tensor out_; |
| 7833 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7834 | at::functionalization::impl::sync(out); |
| 7835 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7836 | } else { |
| 7837 | out_ = out; |
| 7838 | } |
| 7839 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7840 | if ((false)) { |
| 7841 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7842 | TORCH_INTERNAL_ASSERT(false, |
| 7843 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7844 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7845 | } else { |
| 7846 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7847 | at::AutoDispatchSkipFunctionalize guard; |
| 7848 | at::Tensor tmp_output = at::_ops::randn_names_out::call(size, names, out_); |
| 7849 | return out;; |
| 7850 | } |
| 7851 | } else { |
| 7852 | at::Tensor tmp_output; |
| 7853 | { |
| 7854 | at::AutoDispatchSkipFunctionalize guard; |
| 7855 | tmp_output = at::_ops::randn_names::call(size, names, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 7856 | } |
| 7857 | at::functionalization::impl::replace_(out, tmp_output); |
| 7858 | at::functionalization::impl::commit_update(out); |
| 7859 | at::functionalization::impl::sync(out); |
| 7860 | return out; |
| 7861 | } |
| 7862 | } |
| 7863 | |
| 7864 | at::Tensor & randn_out_generator_with_names_out(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional<at::Generator> generator, c10::optional<at::DimnameList> names, at::Tensor & out) { |
| 7865 | if (false) { |
| 7866 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7867 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7868 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7869 | auto out_meta = to_meta(out); |
| 7870 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7871 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7872 | at::_ops::randn_generator_with_names_out::call(size, generator, names, out_meta); |
| 7873 | } |
| 7874 | |
| 7875 | at::Tensor out_; |
| 7876 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7877 | at::functionalization::impl::sync(out); |
| 7878 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7879 | } else { |
| 7880 | out_ = out; |
| 7881 | } |
| 7882 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7883 | if ((false)) { |
| 7884 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7885 | TORCH_INTERNAL_ASSERT(false, |
| 7886 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7887 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7888 | } else { |
| 7889 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7890 | at::AutoDispatchSkipFunctionalize guard; |
| 7891 | at::Tensor tmp_output = at::_ops::randn_generator_with_names_out::call(size, generator, names, out_); |
| 7892 | return out;; |
| 7893 | } |
| 7894 | } else { |
| 7895 | at::Tensor tmp_output; |
| 7896 | { |
| 7897 | at::AutoDispatchSkipFunctionalize guard; |
| 7898 | tmp_output = at::_ops::randn_generator_with_names::call(size, generator, names, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 7899 | } |
| 7900 | at::functionalization::impl::replace_(out, tmp_output); |
| 7901 | at::functionalization::impl::commit_update(out); |
| 7902 | at::functionalization::impl::sync(out); |
| 7903 | return out; |
| 7904 | } |
| 7905 | } |
| 7906 | |
| 7907 | at::Tensor & randn_like_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out) { |
| 7908 | if (false) { |
| 7909 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7910 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7911 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7912 | auto self_meta = to_meta(self); |
| 7913 | auto out_meta = to_meta(out); |
| 7914 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7915 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7916 | at::_ops::randn_like_out::call(self_meta, memory_format, out_meta); |
| 7917 | } |
| 7918 | |
| 7919 | at::Tensor self_; |
| 7920 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7921 | at::functionalization::impl::sync(self); |
| 7922 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7923 | } else { |
| 7924 | self_ = self; |
| 7925 | } |
| 7926 | |
| 7927 | at::Tensor out_; |
| 7928 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7929 | at::functionalization::impl::sync(out); |
| 7930 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7931 | } else { |
| 7932 | out_ = out; |
| 7933 | } |
| 7934 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7935 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 7936 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7937 | TORCH_INTERNAL_ASSERT(false, |
| 7938 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7939 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7940 | } else { |
| 7941 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7942 | at::AutoDispatchSkipFunctionalize guard; |
| 7943 | at::Tensor tmp_output = at::_ops::randn_like_out::call(self_, memory_format, out_); |
| 7944 | return out;; |
| 7945 | } |
| 7946 | } else { |
| 7947 | at::Tensor tmp_output; |
| 7948 | { |
| 7949 | at::AutoDispatchSkipFunctionalize guard; |
| 7950 | tmp_output = at::_ops::randn_like::call(self_, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt, memory_format); |
| 7951 | } |
| 7952 | at::functionalization::impl::replace_(out, tmp_output); |
| 7953 | at::functionalization::impl::commit_update(out); |
| 7954 | at::functionalization::impl::sync(out); |
| 7955 | return out; |
| 7956 | } |
| 7957 | } |
| 7958 | |
| 7959 | at::Tensor & neg_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 7960 | if (false) { |
| 7961 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7962 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7963 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7964 | auto self_meta = to_meta(self); |
| 7965 | auto out_meta = to_meta(out); |
| 7966 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7967 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7968 | at::_ops::neg_out::call(self_meta, out_meta); |
| 7969 | } |
| 7970 | |
| 7971 | at::Tensor self_; |
| 7972 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7973 | at::functionalization::impl::sync(self); |
| 7974 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7975 | } else { |
| 7976 | self_ = self; |
| 7977 | } |
| 7978 | |
| 7979 | at::Tensor out_; |
| 7980 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7981 | at::functionalization::impl::sync(out); |
| 7982 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7983 | } else { |
| 7984 | out_ = out; |
| 7985 | } |
| 7986 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7987 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 7988 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7989 | TORCH_INTERNAL_ASSERT(false, |
| 7990 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7991 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7992 | } else { |
| 7993 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7994 | at::AutoDispatchSkipFunctionalize guard; |
| 7995 | at::Tensor tmp_output = at::_ops::neg_out::call(self_, out_); |
| 7996 | return out;; |
| 7997 | } |
| 7998 | } else { |
| 7999 | at::Tensor tmp_output; |
| 8000 | { |
| 8001 | at::AutoDispatchSkipFunctionalize guard; |
| 8002 | tmp_output = at::_ops::neg::call(self_); |
| 8003 | } |
| 8004 | at::functionalization::impl::replace_(out, tmp_output); |
| 8005 | at::functionalization::impl::commit_update(out); |
| 8006 | at::functionalization::impl::sync(out); |
| 8007 | return out; |
| 8008 | } |
| 8009 | } |
| 8010 | |
| 8011 | at::Tensor & neg_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 8012 | if (true) { |
| 8013 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8014 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8015 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8016 | auto self_meta = to_meta(self); |
| 8017 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8018 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8019 | at::_ops::neg_::call(self_meta); |
| 8020 | } |
| 8021 | |
| 8022 | at::Tensor self_; |
| 8023 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8024 | at::functionalization::impl::sync(self); |
| 8025 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8026 | } else { |
| 8027 | self_ = self; |
| 8028 | } |
| 8029 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 8030 | if ((false)) { |
| 8031 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8032 | TORCH_INTERNAL_ASSERT(false, |
| 8033 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8034 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8035 | } else { |
| 8036 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8037 | at::AutoDispatchSkipFunctionalize guard; |
| 8038 | at::Tensor tmp_output = at::_ops::neg_::call(self_); |
| 8039 | return self;; |
| 8040 | } |
| 8041 | } else { |
| 8042 | at::Tensor tmp_output; |
| 8043 | { |
| 8044 | at::AutoDispatchSkipFunctionalize guard; |
| 8045 | tmp_output = at::_ops::neg::call(self_); |
| 8046 | } |
| 8047 | at::functionalization::impl::replace_(self, tmp_output); |
| 8048 | at::functionalization::impl::commit_update(self); |
| 8049 | at::functionalization::impl::sync(self); |
| 8050 | return self; |
| 8051 | } |
| 8052 | } |
| 8053 | |
| 8054 | at::Tensor & negative_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 8055 | if (false) { |
| 8056 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8057 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8058 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8059 | auto self_meta = to_meta(self); |
| 8060 | auto out_meta = to_meta(out); |
| 8061 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8062 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8063 | at::_ops::negative_out::call(self_meta, out_meta); |
| 8064 | } |
| 8065 | |
| 8066 | at::Tensor self_; |
| 8067 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8068 | at::functionalization::impl::sync(self); |
| 8069 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8070 | } else { |
| 8071 | self_ = self; |
| 8072 | } |
| 8073 | |
| 8074 | at::Tensor out_; |
| 8075 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8076 | at::functionalization::impl::sync(out); |
| 8077 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8078 | } else { |
| 8079 | out_ = out; |
| 8080 | } |
| 8081 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8082 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8083 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8084 | TORCH_INTERNAL_ASSERT(false, |
| 8085 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8086 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8087 | } else { |
| 8088 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8089 | at::AutoDispatchSkipFunctionalize guard; |
| 8090 | at::Tensor tmp_output = at::_ops::negative_out::call(self_, out_); |
| 8091 | return out;; |
| 8092 | } |
| 8093 | } else { |
| 8094 | at::Tensor tmp_output; |
| 8095 | { |
| 8096 | at::AutoDispatchSkipFunctionalize guard; |
| 8097 | tmp_output = at::_ops::negative::call(self_); |
| 8098 | } |
| 8099 | at::functionalization::impl::replace_(out, tmp_output); |
| 8100 | at::functionalization::impl::commit_update(out); |
| 8101 | at::functionalization::impl::sync(out); |
| 8102 | return out; |
| 8103 | } |
| 8104 | } |
| 8105 | |
| 8106 | at::Tensor & negative_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 8107 | if (true) { |
| 8108 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8109 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8110 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8111 | auto self_meta = to_meta(self); |
| 8112 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8113 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8114 | at::_ops::negative_::call(self_meta); |
| 8115 | } |
| 8116 | |
| 8117 | at::Tensor self_; |
| 8118 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8119 | at::functionalization::impl::sync(self); |
| 8120 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8121 | } else { |
| 8122 | self_ = self; |
| 8123 | } |
| 8124 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 8125 | if ((false)) { |
| 8126 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8127 | TORCH_INTERNAL_ASSERT(false, |
| 8128 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8129 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8130 | } else { |
| 8131 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8132 | at::AutoDispatchSkipFunctionalize guard; |
| 8133 | at::Tensor tmp_output = at::_ops::negative_::call(self_); |
| 8134 | return self;; |
| 8135 | } |
| 8136 | } else { |
| 8137 | at::Tensor tmp_output; |
| 8138 | { |
| 8139 | at::AutoDispatchSkipFunctionalize guard; |
| 8140 | tmp_output = at::_ops::negative::call(self_); |
| 8141 | } |
| 8142 | at::functionalization::impl::replace_(self, tmp_output); |
| 8143 | at::functionalization::impl::commit_update(self); |
| 8144 | at::functionalization::impl::sync(self); |
| 8145 | return self; |
| 8146 | } |
| 8147 | } |
| 8148 | |
| 8149 | at::Tensor & repeat_interleave_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & repeats, c10::optional<int64_t> output_size, at::Tensor & out) { |
| 8150 | if (false) { |
| 8151 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8152 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8153 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8154 | auto repeats_meta = to_meta(repeats); |
| 8155 | auto out_meta = to_meta(out); |
| 8156 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8157 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8158 | at::_ops::repeat_interleave_Tensor_out::call(repeats_meta, output_size, out_meta); |
| 8159 | } |
| 8160 | |
| 8161 | at::Tensor repeats_; |
| 8162 | if (at::functionalization::impl::isFunctionalTensor(repeats)) { |
| 8163 | at::functionalization::impl::sync(repeats); |
| 8164 | repeats_ = at::functionalization::impl::from_functional_tensor(repeats); |
| 8165 | } else { |
| 8166 | repeats_ = repeats; |
| 8167 | } |
| 8168 | |
| 8169 | at::Tensor out_; |
| 8170 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8171 | at::functionalization::impl::sync(out); |
| 8172 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8173 | } else { |
| 8174 | out_ = out; |
| 8175 | } |
| 8176 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8177 | if ((false || at::functionalization::impl::isFunctionalTensor(repeats))) { |
| 8178 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8179 | TORCH_INTERNAL_ASSERT(false, |
| 8180 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8181 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8182 | } else { |
| 8183 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8184 | at::AutoDispatchSkipFunctionalize guard; |
| 8185 | at::Tensor tmp_output = at::_ops::repeat_interleave_Tensor_out::call(repeats_, output_size, out_); |
| 8186 | return out;; |
| 8187 | } |
| 8188 | } else { |
| 8189 | at::Tensor tmp_output; |
| 8190 | { |
| 8191 | at::AutoDispatchSkipFunctionalize guard; |
| 8192 | tmp_output = at::_ops::repeat_interleave_Tensor::call(repeats_, output_size); |
| 8193 | } |
| 8194 | at::functionalization::impl::replace_(out, tmp_output); |
| 8195 | at::functionalization::impl::commit_update(out); |
| 8196 | at::functionalization::impl::sync(out); |
| 8197 | return out; |
| 8198 | } |
| 8199 | } |
| 8200 | |
| 8201 | at::Tensor & gelu_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::string_view approximate, at::Tensor & out) { |
| 8202 | if (false) { |
| 8203 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8204 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8205 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8206 | auto self_meta = to_meta(self); |
| 8207 | auto out_meta = to_meta(out); |
| 8208 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8209 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8210 | at::_ops::gelu_out::call(self_meta, approximate, out_meta); |
| 8211 | } |
| 8212 | |
| 8213 | at::Tensor self_; |
| 8214 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8215 | at::functionalization::impl::sync(self); |
| 8216 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8217 | } else { |
| 8218 | self_ = self; |
| 8219 | } |
| 8220 | |
| 8221 | at::Tensor out_; |
| 8222 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8223 | at::functionalization::impl::sync(out); |
| 8224 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8225 | } else { |
| 8226 | out_ = out; |
| 8227 | } |
| 8228 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8229 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8230 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8231 | TORCH_INTERNAL_ASSERT(false, |
| 8232 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8233 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8234 | } else { |
| 8235 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8236 | at::AutoDispatchSkipFunctionalize guard; |
| 8237 | at::Tensor tmp_output = at::_ops::gelu_out::call(self_, approximate, out_); |
| 8238 | return out;; |
| 8239 | } |
| 8240 | } else { |
| 8241 | at::Tensor tmp_output; |
| 8242 | { |
| 8243 | at::AutoDispatchSkipFunctionalize guard; |
| 8244 | tmp_output = at::_ops::gelu::call(self_, approximate); |
| 8245 | } |
| 8246 | at::functionalization::impl::replace_(out, tmp_output); |
| 8247 | at::functionalization::impl::commit_update(out); |
| 8248 | at::functionalization::impl::sync(out); |
| 8249 | return out; |
| 8250 | } |
| 8251 | } |
| 8252 | |
| 8253 | at::Tensor & gelu_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, c10::string_view approximate) { |
| 8254 | if (true) { |
| 8255 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8256 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8257 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8258 | auto self_meta = to_meta(self); |
| 8259 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8260 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8261 | at::_ops::gelu_::call(self_meta, approximate); |
| 8262 | } |
| 8263 | |
| 8264 | at::Tensor self_; |
| 8265 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8266 | at::functionalization::impl::sync(self); |
| 8267 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8268 | } else { |
| 8269 | self_ = self; |
| 8270 | } |
| 8271 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 8272 | if ((false)) { |
| 8273 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8274 | TORCH_INTERNAL_ASSERT(false, |
| 8275 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8276 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8277 | } else { |
| 8278 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8279 | at::AutoDispatchSkipFunctionalize guard; |
| 8280 | at::Tensor tmp_output = at::_ops::gelu_::call(self_, approximate); |
| 8281 | return self;; |
| 8282 | } |
| 8283 | } else { |
| 8284 | at::Tensor tmp_output; |
| 8285 | { |
| 8286 | at::AutoDispatchSkipFunctionalize guard; |
| 8287 | tmp_output = at::_ops::gelu::call(self_, approximate); |
| 8288 | } |
| 8289 | at::functionalization::impl::replace_(self, tmp_output); |
| 8290 | at::functionalization::impl::commit_update(self); |
| 8291 | at::functionalization::impl::sync(self); |
| 8292 | return self; |
| 8293 | } |
| 8294 | } |
| 8295 | |
| 8296 | at::Tensor & select_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt index, at::Tensor & out) { |
| 8297 | if (false) { |
| 8298 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8299 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8300 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8301 | auto grad_output_meta = to_meta(grad_output); |
| 8302 | auto out_meta = to_meta(out); |
| 8303 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8304 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8305 | at::_ops::select_backward_out::call(grad_output_meta, input_sizes, dim, index, out_meta); |
| 8306 | } |
| 8307 | |
| 8308 | at::Tensor grad_output_; |
| 8309 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 8310 | at::functionalization::impl::sync(grad_output); |
| 8311 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 8312 | } else { |
| 8313 | grad_output_ = grad_output; |
| 8314 | } |
| 8315 | |
| 8316 | at::Tensor out_; |
| 8317 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8318 | at::functionalization::impl::sync(out); |
| 8319 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8320 | } else { |
| 8321 | out_ = out; |
| 8322 | } |
| 8323 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8324 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output))) { |
| 8325 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8326 | TORCH_INTERNAL_ASSERT(false, |
| 8327 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8328 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8329 | } else { |
| 8330 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8331 | at::AutoDispatchSkipFunctionalize guard; |
| 8332 | at::Tensor tmp_output = at::_ops::select_backward_out::call(grad_output_, input_sizes, dim, index, out_); |
| 8333 | return out;; |
| 8334 | } |
| 8335 | } else { |
| 8336 | at::Tensor tmp_output; |
| 8337 | { |
| 8338 | at::AutoDispatchSkipFunctionalize guard; |
| 8339 | tmp_output = at::_ops::select_backward::call(grad_output_, input_sizes, dim, index); |
| 8340 | } |
| 8341 | at::functionalization::impl::replace_(out, tmp_output); |
| 8342 | at::functionalization::impl::commit_update(out); |
| 8343 | at::functionalization::impl::sync(out); |
| 8344 | return out; |
| 8345 | } |
| 8346 | } |
| 8347 | |
| 8348 | at::Tensor & mish_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 8349 | if (false) { |
| 8350 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8351 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8352 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8353 | auto self_meta = to_meta(self); |
| 8354 | auto out_meta = to_meta(out); |
| 8355 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8356 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8357 | at::_ops::mish_out::call(self_meta, out_meta); |
| 8358 | } |
| 8359 | |
| 8360 | at::Tensor self_; |
| 8361 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8362 | at::functionalization::impl::sync(self); |
| 8363 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8364 | } else { |
| 8365 | self_ = self; |
| 8366 | } |
| 8367 | |
| 8368 | at::Tensor out_; |
| 8369 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8370 | at::functionalization::impl::sync(out); |
| 8371 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8372 | } else { |
| 8373 | out_ = out; |
| 8374 | } |
| 8375 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8376 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8377 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8378 | TORCH_INTERNAL_ASSERT(false, |
| 8379 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8380 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8381 | } else { |
| 8382 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8383 | at::AutoDispatchSkipFunctionalize guard; |
| 8384 | at::Tensor tmp_output = at::_ops::mish_out::call(self_, out_); |
| 8385 | return out;; |
| 8386 | } |
| 8387 | } else { |
| 8388 | at::Tensor tmp_output; |
| 8389 | { |
| 8390 | at::AutoDispatchSkipFunctionalize guard; |
| 8391 | tmp_output = at::_ops::mish::call(self_); |
| 8392 | } |
| 8393 | at::functionalization::impl::replace_(out, tmp_output); |
| 8394 | at::functionalization::impl::commit_update(out); |
| 8395 | at::functionalization::impl::sync(out); |
| 8396 | return out; |
| 8397 | } |
| 8398 | } |
| 8399 | |
| 8400 | at::Tensor & mish_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 8401 | if (true) { |
| 8402 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8403 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8404 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8405 | auto self_meta = to_meta(self); |
| 8406 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8407 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8408 | at::_ops::mish_::call(self_meta); |
| 8409 | } |
| 8410 | |
| 8411 | at::Tensor self_; |
| 8412 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8413 | at::functionalization::impl::sync(self); |
| 8414 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8415 | } else { |
| 8416 | self_ = self; |
| 8417 | } |
| 8418 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 8419 | if ((false)) { |
| 8420 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8421 | TORCH_INTERNAL_ASSERT(false, |
| 8422 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8423 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8424 | } else { |
| 8425 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8426 | at::AutoDispatchSkipFunctionalize guard; |
| 8427 | at::Tensor tmp_output = at::_ops::mish_::call(self_); |
| 8428 | return self;; |
| 8429 | } |
| 8430 | } else { |
| 8431 | at::Tensor tmp_output; |
| 8432 | { |
| 8433 | at::AutoDispatchSkipFunctionalize guard; |
| 8434 | tmp_output = at::_ops::mish::call(self_); |
| 8435 | } |
| 8436 | at::functionalization::impl::replace_(self, tmp_output); |
| 8437 | at::functionalization::impl::commit_update(self); |
| 8438 | at::functionalization::impl::sync(self); |
| 8439 | return self; |
| 8440 | } |
| 8441 | } |
| 8442 | |
| 8443 | at::Tensor & slice_scatter_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, int64_t dim, c10::optional<c10::SymInt> start, c10::optional<c10::SymInt> end, c10::SymInt step, at::Tensor & out) { |
| 8444 | if (false) { |
| 8445 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8446 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8447 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8448 | auto self_meta = to_meta(self); |
| 8449 | auto src_meta = to_meta(src); |
| 8450 | auto out_meta = to_meta(out); |
| 8451 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8452 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8453 | at::_ops::slice_scatter_out::call(self_meta, src_meta, dim, start, end, step, out_meta); |
| 8454 | } |
| 8455 | |
| 8456 | at::Tensor self_; |
| 8457 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8458 | at::functionalization::impl::sync(self); |
| 8459 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8460 | } else { |
| 8461 | self_ = self; |
| 8462 | } |
| 8463 | |
| 8464 | at::Tensor src_; |
| 8465 | if (at::functionalization::impl::isFunctionalTensor(src)) { |
| 8466 | at::functionalization::impl::sync(src); |
| 8467 | src_ = at::functionalization::impl::from_functional_tensor(src); |
| 8468 | } else { |
| 8469 | src_ = src; |
| 8470 | } |
| 8471 | |
| 8472 | at::Tensor out_; |
| 8473 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8474 | at::functionalization::impl::sync(out); |
| 8475 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8476 | } else { |
| 8477 | out_ = out; |
| 8478 | } |
| 8479 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8480 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(src))) { |
| 8481 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8482 | TORCH_INTERNAL_ASSERT(false, |
| 8483 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8484 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8485 | } else { |
| 8486 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8487 | at::AutoDispatchSkipFunctionalize guard; |
| 8488 | at::Tensor tmp_output = at::_ops::slice_scatter_out::call(self_, src_, dim, start, end, step, out_); |
| 8489 | return out;; |
| 8490 | } |
| 8491 | } else { |
| 8492 | at::Tensor tmp_output; |
| 8493 | { |
| 8494 | at::AutoDispatchSkipFunctionalize guard; |
| 8495 | tmp_output = at::_ops::slice_scatter::call(self_, src_, dim, start, end, step); |
| 8496 | } |
| 8497 | at::functionalization::impl::replace_(out, tmp_output); |
| 8498 | at::functionalization::impl::commit_update(out); |
| 8499 | at::functionalization::impl::sync(out); |
| 8500 | return out; |
| 8501 | } |
| 8502 | } |
| 8503 | |
| 8504 | at::Tensor & diagonal_scatter_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, int64_t offset, int64_t dim1, int64_t dim2, at::Tensor & out) { |
| 8505 | if (false) { |
| 8506 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8507 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8508 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8509 | auto self_meta = to_meta(self); |
| 8510 | auto src_meta = to_meta(src); |
| 8511 | auto out_meta = to_meta(out); |
| 8512 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8513 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8514 | at::_ops::diagonal_scatter_out::call(self_meta, src_meta, offset, dim1, dim2, out_meta); |
| 8515 | } |
| 8516 | |
| 8517 | at::Tensor self_; |
| 8518 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8519 | at::functionalization::impl::sync(self); |
| 8520 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8521 | } else { |
| 8522 | self_ = self; |
| 8523 | } |
| 8524 | |
| 8525 | at::Tensor src_; |
| 8526 | if (at::functionalization::impl::isFunctionalTensor(src)) { |
| 8527 | at::functionalization::impl::sync(src); |
| 8528 | src_ = at::functionalization::impl::from_functional_tensor(src); |
| 8529 | } else { |
| 8530 | src_ = src; |
| 8531 | } |
| 8532 | |
| 8533 | at::Tensor out_; |
| 8534 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8535 | at::functionalization::impl::sync(out); |
| 8536 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8537 | } else { |
| 8538 | out_ = out; |
| 8539 | } |
| 8540 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8541 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(src))) { |
| 8542 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8543 | TORCH_INTERNAL_ASSERT(false, |
| 8544 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8545 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8546 | } else { |
| 8547 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8548 | at::AutoDispatchSkipFunctionalize guard; |
| 8549 | at::Tensor tmp_output = at::_ops::diagonal_scatter_out::call(self_, src_, offset, dim1, dim2, out_); |
| 8550 | return out;; |
| 8551 | } |
| 8552 | } else { |
| 8553 | at::Tensor tmp_output; |
| 8554 | { |
| 8555 | at::AutoDispatchSkipFunctionalize guard; |
| 8556 | tmp_output = at::_ops::diagonal_scatter::call(self_, src_, offset, dim1, dim2); |
| 8557 | } |
| 8558 | at::functionalization::impl::replace_(out, tmp_output); |
| 8559 | at::functionalization::impl::commit_update(out); |
| 8560 | at::functionalization::impl::sync(out); |
| 8561 | return out; |
| 8562 | } |
| 8563 | } |
| 8564 | |
| 8565 | at::Tensor & _softmax_backward_data_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype, at::Tensor & grad_input) { |
| 8566 | if (false) { |
| 8567 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8568 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8569 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8570 | auto grad_output_meta = to_meta(grad_output); |
| 8571 | auto output_meta = to_meta(output); |
| 8572 | auto grad_input_meta = to_meta(grad_input); |
| 8573 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8574 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8575 | at::_ops::_softmax_backward_data_out::call(grad_output_meta, output_meta, dim, input_dtype, grad_input_meta); |
| 8576 | } |
| 8577 | |
| 8578 | at::Tensor grad_output_; |
| 8579 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 8580 | at::functionalization::impl::sync(grad_output); |
| 8581 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 8582 | } else { |
| 8583 | grad_output_ = grad_output; |
| 8584 | } |
| 8585 | |
| 8586 | at::Tensor output_; |
| 8587 | if (at::functionalization::impl::isFunctionalTensor(output)) { |
| 8588 | at::functionalization::impl::sync(output); |
| 8589 | output_ = at::functionalization::impl::from_functional_tensor(output); |
| 8590 | } else { |
| 8591 | output_ = output; |
| 8592 | } |
| 8593 | |
| 8594 | at::Tensor grad_input_; |
| 8595 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 8596 | at::functionalization::impl::sync(grad_input); |
| 8597 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 8598 | } else { |
| 8599 | grad_input_ = grad_input; |
| 8600 | } |
| 8601 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 8602 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(output))) { |
| 8603 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8604 | TORCH_INTERNAL_ASSERT(false, |
| 8605 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8606 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8607 | } else { |
| 8608 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8609 | at::AutoDispatchSkipFunctionalize guard; |
| 8610 | at::Tensor tmp_output = at::_ops::_softmax_backward_data_out::call(grad_output_, output_, dim, input_dtype, grad_input_); |
| 8611 | return grad_input;; |
| 8612 | } |
| 8613 | } else { |
| 8614 | at::Tensor tmp_output; |
| 8615 | { |
| 8616 | at::AutoDispatchSkipFunctionalize guard; |
| 8617 | tmp_output = at::_ops::_softmax_backward_data::call(grad_output_, output_, dim, input_dtype); |
| 8618 | } |
| 8619 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 8620 | at::functionalization::impl::commit_update(grad_input); |
| 8621 | at::functionalization::impl::sync(grad_input); |
| 8622 | return grad_input; |
| 8623 | } |
| 8624 | } |
| 8625 | |
| 8626 | void unsafe_split_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt split_size, int64_t dim, at::TensorList out) { |
| 8627 | if (false) { |
| 8628 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8629 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8630 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8631 | auto self_meta = to_meta(self); |
| 8632 | auto out_meta = to_meta(out); |
| 8633 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8634 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8635 | at::_ops::unsafe_split_Tensor_out::call(self_meta, split_size, dim, out_meta); |
| 8636 | } |
| 8637 | |
| 8638 | at::Tensor self_; |
| 8639 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8640 | at::functionalization::impl::sync(self); |
| 8641 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8642 | } else { |
| 8643 | self_ = self; |
| 8644 | } |
| 8645 | |
| 8646 | ::std::vector<at::Tensor> out_; |
| 8647 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8648 | at::functionalization::impl::sync(out); |
| 8649 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8650 | } else { |
| 8651 | out_ = out.vec(); |
| 8652 | } |
| 8653 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8654 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8655 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8656 | TORCH_INTERNAL_ASSERT(false, |
| 8657 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8658 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8659 | } else { |
| 8660 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8661 | at::AutoDispatchSkipFunctionalize guard; |
| 8662 | at::_ops::unsafe_split_Tensor_out::call(self_, split_size, dim, out_); |
| 8663 | ; |
| 8664 | } |
| 8665 | } else { |
| 8666 | ::std::vector<at::Tensor> tmp_output; |
| 8667 | { |
| 8668 | at::AutoDispatchSkipFunctionalize guard; |
| 8669 | tmp_output = at::_ops::unsafe_split_Tensor::call(self_, split_size, dim); |
| 8670 | } |
| 8671 | at::functionalization::impl::replace_(out, tmp_output); |
| 8672 | at::functionalization::impl::commit_update(out); |
| 8673 | at::functionalization::impl::sync(out); |
| 8674 | |
| 8675 | } |
| 8676 | } |
| 8677 | |
| 8678 | void unsafe_split_with_sizes_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim, at::TensorList out) { |
| 8679 | if (false) { |
| 8680 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8681 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8682 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8683 | auto self_meta = to_meta(self); |
| 8684 | auto out_meta = to_meta(out); |
| 8685 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8686 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8687 | at::_ops::unsafe_split_with_sizes_out::call(self_meta, split_sizes, dim, out_meta); |
| 8688 | } |
| 8689 | |
| 8690 | at::Tensor self_; |
| 8691 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8692 | at::functionalization::impl::sync(self); |
| 8693 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8694 | } else { |
| 8695 | self_ = self; |
| 8696 | } |
| 8697 | |
| 8698 | ::std::vector<at::Tensor> out_; |
| 8699 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8700 | at::functionalization::impl::sync(out); |
| 8701 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8702 | } else { |
| 8703 | out_ = out.vec(); |
| 8704 | } |
| 8705 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8706 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8707 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8708 | TORCH_INTERNAL_ASSERT(false, |
| 8709 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8710 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8711 | } else { |
| 8712 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8713 | at::AutoDispatchSkipFunctionalize guard; |
| 8714 | at::_ops::unsafe_split_with_sizes_out::call(self_, split_sizes, dim, out_); |
| 8715 | ; |
| 8716 | } |
| 8717 | } else { |
| 8718 | ::std::vector<at::Tensor> tmp_output; |
| 8719 | { |
| 8720 | at::AutoDispatchSkipFunctionalize guard; |
| 8721 | tmp_output = at::_ops::unsafe_split_with_sizes::call(self_, split_sizes, dim); |
| 8722 | } |
| 8723 | at::functionalization::impl::replace_(out, tmp_output); |
| 8724 | at::functionalization::impl::commit_update(out); |
| 8725 | at::functionalization::impl::sync(out); |
| 8726 | |
| 8727 | } |
| 8728 | } |
| 8729 | |
| 8730 | at::Tensor & square_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 8731 | if (false) { |
| 8732 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8733 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8734 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8735 | auto self_meta = to_meta(self); |
| 8736 | auto out_meta = to_meta(out); |
| 8737 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8738 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8739 | at::_ops::square_out::call(self_meta, out_meta); |
| 8740 | } |
| 8741 | |
| 8742 | at::Tensor self_; |
| 8743 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8744 | at::functionalization::impl::sync(self); |
| 8745 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8746 | } else { |
| 8747 | self_ = self; |
| 8748 | } |
| 8749 | |
| 8750 | at::Tensor out_; |
| 8751 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8752 | at::functionalization::impl::sync(out); |
| 8753 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8754 | } else { |
| 8755 | out_ = out; |
| 8756 | } |
| 8757 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8758 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8759 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8760 | TORCH_INTERNAL_ASSERT(false, |
| 8761 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8762 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8763 | } else { |
| 8764 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8765 | at::AutoDispatchSkipFunctionalize guard; |
| 8766 | at::Tensor tmp_output = at::_ops::square_out::call(self_, out_); |
| 8767 | return out;; |
| 8768 | } |
| 8769 | } else { |
| 8770 | at::Tensor tmp_output; |
| 8771 | { |
| 8772 | at::AutoDispatchSkipFunctionalize guard; |
| 8773 | tmp_output = at::_ops::square::call(self_); |
| 8774 | } |
| 8775 | at::functionalization::impl::replace_(out, tmp_output); |
| 8776 | at::functionalization::impl::commit_update(out); |
| 8777 | at::functionalization::impl::sync(out); |
| 8778 | return out; |
| 8779 | } |
| 8780 | } |
| 8781 | |
| 8782 | at::Tensor & square_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 8783 | if (true) { |
| 8784 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8785 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8786 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8787 | auto self_meta = to_meta(self); |
| 8788 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8789 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8790 | at::_ops::square_::call(self_meta); |
| 8791 | } |
| 8792 | |
| 8793 | at::Tensor self_; |
| 8794 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8795 | at::functionalization::impl::sync(self); |
| 8796 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8797 | } else { |
| 8798 | self_ = self; |
| 8799 | } |
| 8800 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 8801 | if ((false)) { |
| 8802 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8803 | TORCH_INTERNAL_ASSERT(false, |
| 8804 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8805 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8806 | } else { |
| 8807 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8808 | at::AutoDispatchSkipFunctionalize guard; |
| 8809 | at::Tensor tmp_output = at::_ops::square_::call(self_); |
| 8810 | return self;; |
| 8811 | } |
| 8812 | } else { |
| 8813 | at::Tensor tmp_output; |
| 8814 | { |
| 8815 | at::AutoDispatchSkipFunctionalize guard; |
| 8816 | tmp_output = at::_ops::square::call(self_); |
| 8817 | } |
| 8818 | at::functionalization::impl::replace_(self, tmp_output); |
| 8819 | at::functionalization::impl::commit_update(self); |
| 8820 | at::functionalization::impl::sync(self); |
| 8821 | return self; |
| 8822 | } |
| 8823 | } |
| 8824 | |
| 8825 | at::Tensor & tanh_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 8826 | if (false) { |
| 8827 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8828 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8829 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8830 | auto self_meta = to_meta(self); |
| 8831 | auto out_meta = to_meta(out); |
| 8832 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8833 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8834 | at::_ops::tanh_out::call(self_meta, out_meta); |
| 8835 | } |
| 8836 | |
| 8837 | at::Tensor self_; |
| 8838 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8839 | at::functionalization::impl::sync(self); |
| 8840 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8841 | } else { |
| 8842 | self_ = self; |
| 8843 | } |
| 8844 | |
| 8845 | at::Tensor out_; |
| 8846 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8847 | at::functionalization::impl::sync(out); |
| 8848 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8849 | } else { |
| 8850 | out_ = out; |
| 8851 | } |
| 8852 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8853 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8854 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8855 | TORCH_INTERNAL_ASSERT(false, |
| 8856 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8857 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8858 | } else { |
| 8859 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8860 | at::AutoDispatchSkipFunctionalize guard; |
| 8861 | at::Tensor tmp_output = at::_ops::tanh_out::call(self_, out_); |
| 8862 | return out;; |
| 8863 | } |
| 8864 | } else { |
| 8865 | at::Tensor tmp_output; |
| 8866 | { |
| 8867 | at::AutoDispatchSkipFunctionalize guard; |
| 8868 | tmp_output = at::_ops::tanh::call(self_); |
| 8869 | } |
| 8870 | at::functionalization::impl::replace_(out, tmp_output); |
| 8871 | at::functionalization::impl::commit_update(out); |
| 8872 | at::functionalization::impl::sync(out); |
| 8873 | return out; |
| 8874 | } |
| 8875 | } |
| 8876 | |
| 8877 | at::Tensor & tanh_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 8878 | if (true) { |
| 8879 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8880 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8881 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8882 | auto self_meta = to_meta(self); |
| 8883 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8884 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8885 | at::_ops::tanh_::call(self_meta); |
| 8886 | } |
| 8887 | |
| 8888 | at::Tensor self_; |
| 8889 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8890 | at::functionalization::impl::sync(self); |
| 8891 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8892 | } else { |
| 8893 | self_ = self; |
| 8894 | } |
| 8895 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 8896 | if ((false)) { |
| 8897 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8898 | TORCH_INTERNAL_ASSERT(false, |
| 8899 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8900 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8901 | } else { |
| 8902 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8903 | at::AutoDispatchSkipFunctionalize guard; |
| 8904 | at::Tensor tmp_output = at::_ops::tanh_::call(self_); |
| 8905 | return self;; |
| 8906 | } |
| 8907 | } else { |
| 8908 | at::Tensor tmp_output; |
| 8909 | { |
| 8910 | at::AutoDispatchSkipFunctionalize guard; |
| 8911 | tmp_output = at::_ops::tanh::call(self_); |
| 8912 | } |
| 8913 | at::functionalization::impl::replace_(self, tmp_output); |
| 8914 | at::functionalization::impl::commit_update(self); |
| 8915 | at::functionalization::impl::sync(self); |
| 8916 | return self; |
| 8917 | } |
| 8918 | } |
| 8919 | |
| 8920 | at::Tensor & roll_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef shifts, at::IntArrayRef dims, at::Tensor & out) { |
| 8921 | if (false) { |
| 8922 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8923 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8924 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8925 | auto self_meta = to_meta(self); |
| 8926 | auto out_meta = to_meta(out); |
| 8927 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8928 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8929 | at::_ops::roll_out::call(self_meta, shifts, dims, out_meta); |
| 8930 | } |
| 8931 | |
| 8932 | at::Tensor self_; |
| 8933 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8934 | at::functionalization::impl::sync(self); |
| 8935 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8936 | } else { |
| 8937 | self_ = self; |
| 8938 | } |
| 8939 | |
| 8940 | at::Tensor out_; |
| 8941 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8942 | at::functionalization::impl::sync(out); |
| 8943 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8944 | } else { |
| 8945 | out_ = out; |
| 8946 | } |
| 8947 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8948 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8949 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8950 | TORCH_INTERNAL_ASSERT(false, |
| 8951 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8952 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8953 | } else { |
| 8954 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8955 | at::AutoDispatchSkipFunctionalize guard; |
| 8956 | at::Tensor tmp_output = at::_ops::roll_out::call(self_, shifts, dims, out_); |
| 8957 | return out;; |
| 8958 | } |
| 8959 | } else { |
| 8960 | at::Tensor tmp_output; |
| 8961 | { |
| 8962 | at::AutoDispatchSkipFunctionalize guard; |
| 8963 | tmp_output = at::_ops::roll::call(self_, shifts, dims); |
| 8964 | } |
| 8965 | at::functionalization::impl::replace_(out, tmp_output); |
| 8966 | at::functionalization::impl::commit_update(out); |
| 8967 | at::functionalization::impl::sync(out); |
| 8968 | return out; |
| 8969 | } |
| 8970 | } |
| 8971 | |
| 8972 | at::Tensor & rot90_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t k, at::IntArrayRef dims, at::Tensor & out) { |
| 8973 | if (false) { |
| 8974 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8975 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8976 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8977 | auto self_meta = to_meta(self); |
| 8978 | auto out_meta = to_meta(out); |
| 8979 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8980 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8981 | at::_ops::rot90_out::call(self_meta, k, dims, out_meta); |
| 8982 | } |
| 8983 | |
| 8984 | at::Tensor self_; |
| 8985 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8986 | at::functionalization::impl::sync(self); |
| 8987 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8988 | } else { |
| 8989 | self_ = self; |
| 8990 | } |
| 8991 | |
| 8992 | at::Tensor out_; |
| 8993 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8994 | at::functionalization::impl::sync(out); |
| 8995 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8996 | } else { |
| 8997 | out_ = out; |
| 8998 | } |
| 8999 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9000 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9001 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9002 | TORCH_INTERNAL_ASSERT(false, |
| 9003 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9004 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9005 | } else { |
| 9006 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9007 | at::AutoDispatchSkipFunctionalize guard; |
| 9008 | at::Tensor tmp_output = at::_ops::rot90_out::call(self_, k, dims, out_); |
| 9009 | return out;; |
| 9010 | } |
| 9011 | } else { |
| 9012 | at::Tensor tmp_output; |
| 9013 | { |
| 9014 | at::AutoDispatchSkipFunctionalize guard; |
| 9015 | tmp_output = at::_ops::rot90::call(self_, k, dims); |
| 9016 | } |
| 9017 | at::functionalization::impl::replace_(out, tmp_output); |
| 9018 | at::functionalization::impl::commit_update(out); |
| 9019 | at::functionalization::impl::sync(out); |
| 9020 | return out; |
| 9021 | } |
| 9022 | } |
| 9023 | |
| 9024 | at::Tensor & _trilinear_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & i1, const at::Tensor & i2, const at::Tensor & i3, at::IntArrayRef expand1, at::IntArrayRef expand2, at::IntArrayRef expand3, at::IntArrayRef sumdim, int64_t unroll_dim, at::Tensor & out) { |
| 9025 | if (false) { |
| 9026 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9027 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9028 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9029 | auto i1_meta = to_meta(i1); |
| 9030 | auto i2_meta = to_meta(i2); |
| 9031 | auto i3_meta = to_meta(i3); |
| 9032 | auto out_meta = to_meta(out); |
| 9033 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9034 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9035 | at::_ops::_trilinear_out::call(i1_meta, i2_meta, i3_meta, expand1, expand2, expand3, sumdim, unroll_dim, out_meta); |
| 9036 | } |
| 9037 | |
| 9038 | at::Tensor i1_; |
| 9039 | if (at::functionalization::impl::isFunctionalTensor(i1)) { |
| 9040 | at::functionalization::impl::sync(i1); |
| 9041 | i1_ = at::functionalization::impl::from_functional_tensor(i1); |
| 9042 | } else { |
| 9043 | i1_ = i1; |
| 9044 | } |
| 9045 | |
| 9046 | at::Tensor i2_; |
| 9047 | if (at::functionalization::impl::isFunctionalTensor(i2)) { |
| 9048 | at::functionalization::impl::sync(i2); |
| 9049 | i2_ = at::functionalization::impl::from_functional_tensor(i2); |
| 9050 | } else { |
| 9051 | i2_ = i2; |
| 9052 | } |
| 9053 | |
| 9054 | at::Tensor i3_; |
| 9055 | if (at::functionalization::impl::isFunctionalTensor(i3)) { |
| 9056 | at::functionalization::impl::sync(i3); |
| 9057 | i3_ = at::functionalization::impl::from_functional_tensor(i3); |
| 9058 | } else { |
| 9059 | i3_ = i3; |
| 9060 | } |
| 9061 | |
| 9062 | at::Tensor out_; |
| 9063 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9064 | at::functionalization::impl::sync(out); |
| 9065 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9066 | } else { |
| 9067 | out_ = out; |
| 9068 | } |
| 9069 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9070 | if ((false || at::functionalization::impl::isFunctionalTensor(i1) || at::functionalization::impl::isFunctionalTensor(i2) || at::functionalization::impl::isFunctionalTensor(i3))) { |
| 9071 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9072 | TORCH_INTERNAL_ASSERT(false, |
| 9073 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9074 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9075 | } else { |
| 9076 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9077 | at::AutoDispatchSkipFunctionalize guard; |
| 9078 | at::Tensor tmp_output = at::_ops::_trilinear_out::call(i1_, i2_, i3_, expand1, expand2, expand3, sumdim, unroll_dim, out_); |
| 9079 | return out;; |
| 9080 | } |
| 9081 | } else { |
| 9082 | at::Tensor tmp_output; |
| 9083 | { |
| 9084 | at::AutoDispatchSkipFunctionalize guard; |
| 9085 | tmp_output = at::_ops::_trilinear::call(i1_, i2_, i3_, expand1, expand2, expand3, sumdim, unroll_dim); |
| 9086 | } |
| 9087 | at::functionalization::impl::replace_(out, tmp_output); |
| 9088 | at::functionalization::impl::commit_update(out); |
| 9089 | at::functionalization::impl::sync(out); |
| 9090 | return out; |
| 9091 | } |
| 9092 | } |
| 9093 | |
| 9094 | ::std::tuple<at::Tensor &,at::Tensor &> _unique_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool sorted, bool return_inverse, at::Tensor & out0, at::Tensor & out1) { |
| 9095 | if (false) { |
| 9096 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9097 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9098 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9099 | auto self_meta = to_meta(self); |
| 9100 | auto out0_meta = to_meta(out0); |
| 9101 | auto out1_meta = to_meta(out1); |
| 9102 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9103 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9104 | at::_ops::_unique_out::call(self_meta, sorted, return_inverse, out0_meta, out1_meta); |
| 9105 | } |
| 9106 | |
| 9107 | at::Tensor self_; |
| 9108 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9109 | at::functionalization::impl::sync(self); |
| 9110 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9111 | } else { |
| 9112 | self_ = self; |
| 9113 | } |
| 9114 | |
| 9115 | at::Tensor out0_; |
| 9116 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 9117 | at::functionalization::impl::sync(out0); |
| 9118 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 9119 | } else { |
| 9120 | out0_ = out0; |
| 9121 | } |
| 9122 | |
| 9123 | at::Tensor out1_; |
| 9124 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 9125 | at::functionalization::impl::sync(out1); |
| 9126 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 9127 | } else { |
| 9128 | out1_ = out1; |
| 9129 | } |
| 9130 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1))) { |
| 9131 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9132 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9133 | TORCH_INTERNAL_ASSERT(false, |
| 9134 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9135 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9136 | } else { |
| 9137 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9138 | at::AutoDispatchSkipFunctionalize guard; |
| 9139 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::_unique_out::call(self_, sorted, return_inverse, out0_, out1_); |
| 9140 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1);; |
| 9141 | } |
| 9142 | } else { |
| 9143 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 9144 | { |
| 9145 | at::AutoDispatchSkipFunctionalize guard; |
| 9146 | tmp_output = at::_ops::_unique::call(self_, sorted, return_inverse); |
| 9147 | } |
| 9148 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 9149 | at::functionalization::impl::commit_update(out0); |
| 9150 | at::functionalization::impl::sync(out0); |
| 9151 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 9152 | at::functionalization::impl::commit_update(out1); |
| 9153 | at::functionalization::impl::sync(out1); |
| 9154 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1); |
| 9155 | } |
| 9156 | } |
| 9157 | |
| 9158 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _unique2_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool sorted, bool return_inverse, bool return_counts, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 9159 | if (false) { |
| 9160 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9161 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9162 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9163 | auto self_meta = to_meta(self); |
| 9164 | auto out0_meta = to_meta(out0); |
| 9165 | auto out1_meta = to_meta(out1); |
| 9166 | auto out2_meta = to_meta(out2); |
| 9167 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9168 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9169 | at::_ops::_unique2_out::call(self_meta, sorted, return_inverse, return_counts, out0_meta, out1_meta, out2_meta); |
| 9170 | } |
| 9171 | |
| 9172 | at::Tensor self_; |
| 9173 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9174 | at::functionalization::impl::sync(self); |
| 9175 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9176 | } else { |
| 9177 | self_ = self; |
| 9178 | } |
| 9179 | |
| 9180 | at::Tensor out0_; |
| 9181 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 9182 | at::functionalization::impl::sync(out0); |
| 9183 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 9184 | } else { |
| 9185 | out0_ = out0; |
| 9186 | } |
| 9187 | |
| 9188 | at::Tensor out1_; |
| 9189 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 9190 | at::functionalization::impl::sync(out1); |
| 9191 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 9192 | } else { |
| 9193 | out1_ = out1; |
| 9194 | } |
| 9195 | |
| 9196 | at::Tensor out2_; |
| 9197 | if (at::functionalization::impl::isFunctionalTensor(out2)) { |
| 9198 | at::functionalization::impl::sync(out2); |
| 9199 | out2_ = at::functionalization::impl::from_functional_tensor(out2); |
| 9200 | } else { |
| 9201 | out2_ = out2; |
| 9202 | } |
| 9203 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1) && at::functionalization::impl::isFunctionalTensor(out2))) { |
| 9204 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9205 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9206 | TORCH_INTERNAL_ASSERT(false, |
| 9207 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9208 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9209 | } else { |
| 9210 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9211 | at::AutoDispatchSkipFunctionalize guard; |
| 9212 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::_unique2_out::call(self_, sorted, return_inverse, return_counts, out0_, out1_, out2_); |
| 9213 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2);; |
| 9214 | } |
| 9215 | } else { |
| 9216 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 9217 | { |
| 9218 | at::AutoDispatchSkipFunctionalize guard; |
| 9219 | tmp_output = at::_ops::_unique2::call(self_, sorted, return_inverse, return_counts); |
| 9220 | } |
| 9221 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 9222 | at::functionalization::impl::commit_update(out0); |
| 9223 | at::functionalization::impl::sync(out0); |
| 9224 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 9225 | at::functionalization::impl::commit_update(out1); |
| 9226 | at::functionalization::impl::sync(out1); |
| 9227 | at::functionalization::impl::replace_(out2, std::get<2>(tmp_output)); |
| 9228 | at::functionalization::impl::commit_update(out2); |
| 9229 | at::functionalization::impl::sync(out2); |
| 9230 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2); |
| 9231 | } |
| 9232 | } |
| 9233 | |
| 9234 | ::std::tuple<at::Tensor &,at::Tensor &> _weight_norm_interface_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & v, const at::Tensor & g, int64_t dim, at::Tensor & out0, at::Tensor & out1) { |
| 9235 | if (false) { |
| 9236 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9237 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9238 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9239 | auto v_meta = to_meta(v); |
| 9240 | auto g_meta = to_meta(g); |
| 9241 | auto out0_meta = to_meta(out0); |
| 9242 | auto out1_meta = to_meta(out1); |
| 9243 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9244 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9245 | at::_ops::_weight_norm_interface_out::call(v_meta, g_meta, dim, out0_meta, out1_meta); |
| 9246 | } |
| 9247 | |
| 9248 | at::Tensor v_; |
| 9249 | if (at::functionalization::impl::isFunctionalTensor(v)) { |
| 9250 | at::functionalization::impl::sync(v); |
| 9251 | v_ = at::functionalization::impl::from_functional_tensor(v); |
| 9252 | } else { |
| 9253 | v_ = v; |
| 9254 | } |
| 9255 | |
| 9256 | at::Tensor g_; |
| 9257 | if (at::functionalization::impl::isFunctionalTensor(g)) { |
| 9258 | at::functionalization::impl::sync(g); |
| 9259 | g_ = at::functionalization::impl::from_functional_tensor(g); |
| 9260 | } else { |
| 9261 | g_ = g; |
| 9262 | } |
| 9263 | |
| 9264 | at::Tensor out0_; |
| 9265 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 9266 | at::functionalization::impl::sync(out0); |
| 9267 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 9268 | } else { |
| 9269 | out0_ = out0; |
| 9270 | } |
| 9271 | |
| 9272 | at::Tensor out1_; |
| 9273 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 9274 | at::functionalization::impl::sync(out1); |
| 9275 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 9276 | } else { |
| 9277 | out1_ = out1; |
| 9278 | } |
| 9279 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1))) { |
| 9280 | if ((false || at::functionalization::impl::isFunctionalTensor(v) || at::functionalization::impl::isFunctionalTensor(g))) { |
| 9281 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9282 | TORCH_INTERNAL_ASSERT(false, |
| 9283 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9284 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9285 | } else { |
| 9286 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9287 | at::AutoDispatchSkipFunctionalize guard; |
| 9288 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::_weight_norm_interface_out::call(v_, g_, dim, out0_, out1_); |
| 9289 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1);; |
| 9290 | } |
| 9291 | } else { |
| 9292 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 9293 | { |
| 9294 | at::AutoDispatchSkipFunctionalize guard; |
| 9295 | tmp_output = at::_ops::_weight_norm_interface::call(v_, g_, dim); |
| 9296 | } |
| 9297 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 9298 | at::functionalization::impl::commit_update(out0); |
| 9299 | at::functionalization::impl::sync(out0); |
| 9300 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 9301 | at::functionalization::impl::commit_update(out1); |
| 9302 | at::functionalization::impl::sync(out1); |
| 9303 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1); |
| 9304 | } |
| 9305 | } |
| 9306 | |
| 9307 | at::Tensor & _efficientzerotensor_out_out(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, at::Tensor & out) { |
| 9308 | if (false) { |
| 9309 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9310 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9311 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9312 | auto out_meta = to_meta(out); |
| 9313 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9314 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9315 | at::_ops::_efficientzerotensor_out::call(size, out_meta); |
| 9316 | } |
| 9317 | |
| 9318 | at::Tensor out_; |
| 9319 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9320 | at::functionalization::impl::sync(out); |
| 9321 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9322 | } else { |
| 9323 | out_ = out; |
| 9324 | } |
| 9325 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9326 | if ((false)) { |
| 9327 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9328 | TORCH_INTERNAL_ASSERT(false, |
| 9329 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9330 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9331 | } else { |
| 9332 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9333 | at::AutoDispatchSkipFunctionalize guard; |
| 9334 | at::Tensor tmp_output = at::_ops::_efficientzerotensor_out::call(size, out_); |
| 9335 | return out;; |
| 9336 | } |
| 9337 | } else { |
| 9338 | at::Tensor tmp_output; |
| 9339 | { |
| 9340 | at::AutoDispatchSkipFunctionalize guard; |
| 9341 | tmp_output = at::_ops::_efficientzerotensor::call(size, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 9342 | } |
| 9343 | at::functionalization::impl::replace_(out, tmp_output); |
| 9344 | at::functionalization::impl::commit_update(out); |
| 9345 | at::functionalization::impl::sync(out); |
| 9346 | return out; |
| 9347 | } |
| 9348 | } |
| 9349 | |
| 9350 | at::Tensor & _standard_gamma_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 9351 | if (false) { |
| 9352 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9353 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9354 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9355 | auto self_meta = to_meta(self); |
| 9356 | auto out_meta = to_meta(out); |
| 9357 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9358 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9359 | at::_ops::_standard_gamma_out::call(self_meta, generator, out_meta); |
| 9360 | } |
| 9361 | |
| 9362 | at::Tensor self_; |
| 9363 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9364 | at::functionalization::impl::sync(self); |
| 9365 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9366 | } else { |
| 9367 | self_ = self; |
| 9368 | } |
| 9369 | |
| 9370 | at::Tensor out_; |
| 9371 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9372 | at::functionalization::impl::sync(out); |
| 9373 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9374 | } else { |
| 9375 | out_ = out; |
| 9376 | } |
| 9377 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9378 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9379 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9380 | TORCH_INTERNAL_ASSERT(false, |
| 9381 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9382 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9383 | } else { |
| 9384 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9385 | at::AutoDispatchSkipFunctionalize guard; |
| 9386 | at::Tensor tmp_output = at::_ops::_standard_gamma_out::call(self_, generator, out_); |
| 9387 | return out;; |
| 9388 | } |
| 9389 | } else { |
| 9390 | at::Tensor tmp_output; |
| 9391 | { |
| 9392 | at::AutoDispatchSkipFunctionalize guard; |
| 9393 | tmp_output = at::_ops::_standard_gamma::call(self_, generator); |
| 9394 | } |
| 9395 | at::functionalization::impl::replace_(out, tmp_output); |
| 9396 | at::functionalization::impl::commit_update(out); |
| 9397 | at::functionalization::impl::sync(out); |
| 9398 | return out; |
| 9399 | } |
| 9400 | } |
| 9401 | |
| 9402 | at::Tensor & _dirichlet_grad_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & alpha, const at::Tensor & total, at::Tensor & out) { |
| 9403 | if (false) { |
| 9404 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9405 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9406 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9407 | auto x_meta = to_meta(x); |
| 9408 | auto alpha_meta = to_meta(alpha); |
| 9409 | auto total_meta = to_meta(total); |
| 9410 | auto out_meta = to_meta(out); |
| 9411 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9412 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9413 | at::_ops::_dirichlet_grad_out::call(x_meta, alpha_meta, total_meta, out_meta); |
| 9414 | } |
| 9415 | |
| 9416 | at::Tensor x_; |
| 9417 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 9418 | at::functionalization::impl::sync(x); |
| 9419 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 9420 | } else { |
| 9421 | x_ = x; |
| 9422 | } |
| 9423 | |
| 9424 | at::Tensor alpha_; |
| 9425 | if (at::functionalization::impl::isFunctionalTensor(alpha)) { |
| 9426 | at::functionalization::impl::sync(alpha); |
| 9427 | alpha_ = at::functionalization::impl::from_functional_tensor(alpha); |
| 9428 | } else { |
| 9429 | alpha_ = alpha; |
| 9430 | } |
| 9431 | |
| 9432 | at::Tensor total_; |
| 9433 | if (at::functionalization::impl::isFunctionalTensor(total)) { |
| 9434 | at::functionalization::impl::sync(total); |
| 9435 | total_ = at::functionalization::impl::from_functional_tensor(total); |
| 9436 | } else { |
| 9437 | total_ = total; |
| 9438 | } |
| 9439 | |
| 9440 | at::Tensor out_; |
| 9441 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9442 | at::functionalization::impl::sync(out); |
| 9443 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9444 | } else { |
| 9445 | out_ = out; |
| 9446 | } |
| 9447 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9448 | if ((false || at::functionalization::impl::isFunctionalTensor(x) || at::functionalization::impl::isFunctionalTensor(alpha) || at::functionalization::impl::isFunctionalTensor(total))) { |
| 9449 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9450 | TORCH_INTERNAL_ASSERT(false, |
| 9451 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9452 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9453 | } else { |
| 9454 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9455 | at::AutoDispatchSkipFunctionalize guard; |
| 9456 | at::Tensor tmp_output = at::_ops::_dirichlet_grad_out::call(x_, alpha_, total_, out_); |
| 9457 | return out;; |
| 9458 | } |
| 9459 | } else { |
| 9460 | at::Tensor tmp_output; |
| 9461 | { |
| 9462 | at::AutoDispatchSkipFunctionalize guard; |
| 9463 | tmp_output = at::_ops::_dirichlet_grad::call(x_, alpha_, total_); |
| 9464 | } |
| 9465 | at::functionalization::impl::replace_(out, tmp_output); |
| 9466 | at::functionalization::impl::commit_update(out); |
| 9467 | at::functionalization::impl::sync(out); |
| 9468 | return out; |
| 9469 | } |
| 9470 | } |
| 9471 | |
| 9472 | at::Tensor & norm_out_ScalarOpt_dtype_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional<at::Scalar> & p, at::ScalarType dtype, at::Tensor & out) { |
| 9473 | if (false) { |
| 9474 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9475 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9476 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9477 | auto self_meta = to_meta(self); |
| 9478 | auto out_meta = to_meta(out); |
| 9479 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9480 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9481 | at::_ops::norm_ScalarOpt_dtype_out::call(self_meta, p, dtype, out_meta); |
| 9482 | } |
| 9483 | |
| 9484 | at::Tensor self_; |
| 9485 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9486 | at::functionalization::impl::sync(self); |
| 9487 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9488 | } else { |
| 9489 | self_ = self; |
| 9490 | } |
| 9491 | |
| 9492 | at::Tensor out_; |
| 9493 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9494 | at::functionalization::impl::sync(out); |
| 9495 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9496 | } else { |
| 9497 | out_ = out; |
| 9498 | } |
| 9499 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9500 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9501 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9502 | TORCH_INTERNAL_ASSERT(false, |
| 9503 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9504 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9505 | } else { |
| 9506 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9507 | at::AutoDispatchSkipFunctionalize guard; |
| 9508 | at::Tensor tmp_output = at::_ops::norm_ScalarOpt_dtype_out::call(self_, p, dtype, out_); |
| 9509 | return out;; |
| 9510 | } |
| 9511 | } else { |
| 9512 | at::Tensor tmp_output; |
| 9513 | { |
| 9514 | at::AutoDispatchSkipFunctionalize guard; |
| 9515 | tmp_output = at::_ops::norm_ScalarOpt_dtype::call(self_, p, dtype); |
| 9516 | } |
| 9517 | at::functionalization::impl::replace_(out, tmp_output); |
| 9518 | at::functionalization::impl::commit_update(out); |
| 9519 | at::functionalization::impl::sync(out); |
| 9520 | return out; |
| 9521 | } |
| 9522 | } |
| 9523 | |
| 9524 | at::Tensor & norm_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & p, at::Tensor & out) { |
| 9525 | if (false) { |
| 9526 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9527 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9528 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9529 | auto self_meta = to_meta(self); |
| 9530 | auto out_meta = to_meta(out); |
| 9531 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9532 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9533 | at::_ops::norm_Scalar_out::call(self_meta, p, out_meta); |
| 9534 | } |
| 9535 | |
| 9536 | at::Tensor self_; |
| 9537 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9538 | at::functionalization::impl::sync(self); |
| 9539 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9540 | } else { |
| 9541 | self_ = self; |
| 9542 | } |
| 9543 | |
| 9544 | at::Tensor out_; |
| 9545 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9546 | at::functionalization::impl::sync(out); |
| 9547 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9548 | } else { |
| 9549 | out_ = out; |
| 9550 | } |
| 9551 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9552 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9553 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9554 | TORCH_INTERNAL_ASSERT(false, |
| 9555 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9556 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9557 | } else { |
| 9558 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9559 | at::AutoDispatchSkipFunctionalize guard; |
| 9560 | at::Tensor tmp_output = at::_ops::norm_Scalar_out::call(self_, p, out_); |
| 9561 | return out;; |
| 9562 | } |
| 9563 | } else { |
| 9564 | at::Tensor tmp_output; |
| 9565 | { |
| 9566 | at::AutoDispatchSkipFunctionalize guard; |
| 9567 | tmp_output = at::_ops::norm_Scalar::call(self_, p); |
| 9568 | } |
| 9569 | at::functionalization::impl::replace_(out, tmp_output); |
| 9570 | at::functionalization::impl::commit_update(out); |
| 9571 | at::functionalization::impl::sync(out); |
| 9572 | return out; |
| 9573 | } |
| 9574 | } |
| 9575 | |
| 9576 | at::Tensor & norm_out_dtype_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim, at::ScalarType dtype, at::Tensor & out) { |
| 9577 | if (false) { |
| 9578 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9579 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9580 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9581 | auto self_meta = to_meta(self); |
| 9582 | auto out_meta = to_meta(out); |
| 9583 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9584 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9585 | at::_ops::norm_dtype_out::call(self_meta, p, dim, keepdim, dtype, out_meta); |
| 9586 | } |
| 9587 | |
| 9588 | at::Tensor self_; |
| 9589 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9590 | at::functionalization::impl::sync(self); |
| 9591 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9592 | } else { |
| 9593 | self_ = self; |
| 9594 | } |
| 9595 | |
| 9596 | at::Tensor out_; |
| 9597 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9598 | at::functionalization::impl::sync(out); |
| 9599 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9600 | } else { |
| 9601 | out_ = out; |
| 9602 | } |
| 9603 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9604 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9605 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9606 | TORCH_INTERNAL_ASSERT(false, |
| 9607 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9608 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9609 | } else { |
| 9610 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9611 | at::AutoDispatchSkipFunctionalize guard; |
| 9612 | at::Tensor tmp_output = at::_ops::norm_dtype_out::call(self_, p, dim, keepdim, dtype, out_); |
| 9613 | return out;; |
| 9614 | } |
| 9615 | } else { |
| 9616 | at::Tensor tmp_output; |
| 9617 | { |
| 9618 | at::AutoDispatchSkipFunctionalize guard; |
| 9619 | tmp_output = at::_ops::norm_ScalarOpt_dim_dtype::call(self_, p, dim, keepdim, dtype); |
| 9620 | } |
| 9621 | at::functionalization::impl::replace_(out, tmp_output); |
| 9622 | at::functionalization::impl::commit_update(out); |
| 9623 | at::functionalization::impl::sync(out); |
| 9624 | return out; |
| 9625 | } |
| 9626 | } |
| 9627 | |
| 9628 | at::Tensor & norm_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { |
| 9629 | if (false) { |
| 9630 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9631 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9632 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9633 | auto self_meta = to_meta(self); |
| 9634 | auto out_meta = to_meta(out); |
| 9635 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9636 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9637 | at::_ops::norm_out::call(self_meta, p, dim, keepdim, out_meta); |
| 9638 | } |
| 9639 | |
| 9640 | at::Tensor self_; |
| 9641 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9642 | at::functionalization::impl::sync(self); |
| 9643 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9644 | } else { |
| 9645 | self_ = self; |
| 9646 | } |
| 9647 | |
| 9648 | at::Tensor out_; |
| 9649 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9650 | at::functionalization::impl::sync(out); |
| 9651 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9652 | } else { |
| 9653 | out_ = out; |
| 9654 | } |
| 9655 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9656 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9657 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9658 | TORCH_INTERNAL_ASSERT(false, |
| 9659 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9660 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9661 | } else { |
| 9662 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9663 | at::AutoDispatchSkipFunctionalize guard; |
| 9664 | at::Tensor tmp_output = at::_ops::norm_out::call(self_, p, dim, keepdim, out_); |
| 9665 | return out;; |
| 9666 | } |
| 9667 | } else { |
| 9668 | at::Tensor tmp_output; |
| 9669 | { |
| 9670 | at::AutoDispatchSkipFunctionalize guard; |
| 9671 | tmp_output = at::_ops::norm_ScalarOpt_dim::call(self_, p, dim, keepdim); |
| 9672 | } |
| 9673 | at::functionalization::impl::replace_(out, tmp_output); |
| 9674 | at::functionalization::impl::commit_update(out); |
| 9675 | at::functionalization::impl::sync(out); |
| 9676 | return out; |
| 9677 | } |
| 9678 | } |
| 9679 | |
| 9680 | at::Tensor & norm_out_names_dtype_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional<at::Scalar> & p, at::DimnameList dim, bool keepdim, at::ScalarType dtype, at::Tensor & out) { |
| 9681 | if (false) { |
| 9682 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9683 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9684 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9685 | auto self_meta = to_meta(self); |
| 9686 | auto out_meta = to_meta(out); |
| 9687 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9688 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9689 | at::_ops::norm_names_dtype_out::call(self_meta, p, dim, keepdim, dtype, out_meta); |
| 9690 | } |
| 9691 | |
| 9692 | at::Tensor self_; |
| 9693 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9694 | at::functionalization::impl::sync(self); |
| 9695 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9696 | } else { |
| 9697 | self_ = self; |
| 9698 | } |
| 9699 | |
| 9700 | at::Tensor out_; |
| 9701 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9702 | at::functionalization::impl::sync(out); |
| 9703 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9704 | } else { |
| 9705 | out_ = out; |
| 9706 | } |
| 9707 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9708 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9709 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9710 | TORCH_INTERNAL_ASSERT(false, |
| 9711 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9712 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9713 | } else { |
| 9714 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9715 | at::AutoDispatchSkipFunctionalize guard; |
| 9716 | at::Tensor tmp_output = at::_ops::norm_names_dtype_out::call(self_, p, dim, keepdim, dtype, out_); |
| 9717 | return out;; |
| 9718 | } |
| 9719 | } else { |
| 9720 | at::Tensor tmp_output; |
| 9721 | { |
| 9722 | at::AutoDispatchSkipFunctionalize guard; |
| 9723 | tmp_output = at::_ops::norm_names_ScalarOpt_dim_dtype::call(self_, p, dim, keepdim, dtype); |
| 9724 | } |
| 9725 | at::functionalization::impl::replace_(out, tmp_output); |
| 9726 | at::functionalization::impl::commit_update(out); |
| 9727 | at::functionalization::impl::sync(out); |
| 9728 | return out; |
| 9729 | } |
| 9730 | } |
| 9731 | |
| 9732 | at::Tensor & norm_out_names_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional<at::Scalar> & p, at::DimnameList dim, bool keepdim, at::Tensor & out) { |
| 9733 | if (false) { |
| 9734 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9735 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9736 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9737 | auto self_meta = to_meta(self); |
| 9738 | auto out_meta = to_meta(out); |
| 9739 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9740 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9741 | at::_ops::norm_names_out::call(self_meta, p, dim, keepdim, out_meta); |
| 9742 | } |
| 9743 | |
| 9744 | at::Tensor self_; |
| 9745 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9746 | at::functionalization::impl::sync(self); |
| 9747 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9748 | } else { |
| 9749 | self_ = self; |
| 9750 | } |
| 9751 | |
| 9752 | at::Tensor out_; |
| 9753 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9754 | at::functionalization::impl::sync(out); |
| 9755 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9756 | } else { |
| 9757 | out_ = out; |
| 9758 | } |
| 9759 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9760 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9761 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9762 | TORCH_INTERNAL_ASSERT(false, |
| 9763 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9764 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9765 | } else { |
| 9766 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9767 | at::AutoDispatchSkipFunctionalize guard; |
| 9768 | at::Tensor tmp_output = at::_ops::norm_names_out::call(self_, p, dim, keepdim, out_); |
| 9769 | return out;; |
| 9770 | } |
| 9771 | } else { |
| 9772 | at::Tensor tmp_output; |
| 9773 | { |
| 9774 | at::AutoDispatchSkipFunctionalize guard; |
| 9775 | tmp_output = at::_ops::norm_names_ScalarOpt_dim::call(self_, p, dim, keepdim); |
| 9776 | } |
| 9777 | at::functionalization::impl::replace_(out, tmp_output); |
| 9778 | at::functionalization::impl::commit_update(out); |
| 9779 | at::functionalization::impl::sync(out); |
| 9780 | return out; |
| 9781 | } |
| 9782 | } |
| 9783 | |
| 9784 | ::std::tuple<at::Tensor &,at::Tensor &> frexp_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & mantissa, at::Tensor & exponent) { |
| 9785 | if (false) { |
| 9786 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9787 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9788 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9789 | auto self_meta = to_meta(self); |
| 9790 | auto mantissa_meta = to_meta(mantissa); |
| 9791 | auto exponent_meta = to_meta(exponent); |
| 9792 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9793 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9794 | at::_ops::frexp_Tensor_out::call(self_meta, mantissa_meta, exponent_meta); |
| 9795 | } |
| 9796 | |
| 9797 | at::Tensor self_; |
| 9798 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9799 | at::functionalization::impl::sync(self); |
| 9800 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9801 | } else { |
| 9802 | self_ = self; |
| 9803 | } |
| 9804 | |
| 9805 | at::Tensor mantissa_; |
| 9806 | if (at::functionalization::impl::isFunctionalTensor(mantissa)) { |
| 9807 | at::functionalization::impl::sync(mantissa); |
| 9808 | mantissa_ = at::functionalization::impl::from_functional_tensor(mantissa); |
| 9809 | } else { |
| 9810 | mantissa_ = mantissa; |
| 9811 | } |
| 9812 | |
| 9813 | at::Tensor exponent_; |
| 9814 | if (at::functionalization::impl::isFunctionalTensor(exponent)) { |
| 9815 | at::functionalization::impl::sync(exponent); |
| 9816 | exponent_ = at::functionalization::impl::from_functional_tensor(exponent); |
| 9817 | } else { |
| 9818 | exponent_ = exponent; |
| 9819 | } |
| 9820 | if (!(true && at::functionalization::impl::isFunctionalTensor(mantissa) && at::functionalization::impl::isFunctionalTensor(exponent))) { |
| 9821 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9822 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9823 | TORCH_INTERNAL_ASSERT(false, |
| 9824 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9825 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9826 | } else { |
| 9827 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9828 | at::AutoDispatchSkipFunctionalize guard; |
| 9829 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::frexp_Tensor_out::call(self_, mantissa_, exponent_); |
| 9830 | return ::std::tuple<at::Tensor &,at::Tensor &>(mantissa, exponent);; |
| 9831 | } |
| 9832 | } else { |
| 9833 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 9834 | { |
| 9835 | at::AutoDispatchSkipFunctionalize guard; |
| 9836 | tmp_output = at::_ops::frexp_Tensor::call(self_); |
| 9837 | } |
| 9838 | at::functionalization::impl::replace_(mantissa, std::get<0>(tmp_output)); |
| 9839 | at::functionalization::impl::commit_update(mantissa); |
| 9840 | at::functionalization::impl::sync(mantissa); |
| 9841 | at::functionalization::impl::replace_(exponent, std::get<1>(tmp_output)); |
| 9842 | at::functionalization::impl::commit_update(exponent); |
| 9843 | at::functionalization::impl::sync(exponent); |
| 9844 | return ::std::tuple<at::Tensor &,at::Tensor &>(mantissa, exponent); |
| 9845 | } |
| 9846 | } |
| 9847 | |
| 9848 | at::Tensor & frobenius_norm_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { |
| 9849 | if (false) { |
| 9850 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9851 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9852 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9853 | auto self_meta = to_meta(self); |
| 9854 | auto out_meta = to_meta(out); |
| 9855 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9856 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9857 | at::_ops::frobenius_norm_out::call(self_meta, dim, keepdim, out_meta); |
| 9858 | } |
| 9859 | |
| 9860 | at::Tensor self_; |
| 9861 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9862 | at::functionalization::impl::sync(self); |
| 9863 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9864 | } else { |
| 9865 | self_ = self; |
| 9866 | } |
| 9867 | |
| 9868 | at::Tensor out_; |
| 9869 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9870 | at::functionalization::impl::sync(out); |
| 9871 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9872 | } else { |
| 9873 | out_ = out; |
| 9874 | } |
| 9875 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9876 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9877 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9878 | TORCH_INTERNAL_ASSERT(false, |
| 9879 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9880 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9881 | } else { |
| 9882 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9883 | at::AutoDispatchSkipFunctionalize guard; |
| 9884 | at::Tensor tmp_output = at::_ops::frobenius_norm_out::call(self_, dim, keepdim, out_); |
| 9885 | return out;; |
| 9886 | } |
| 9887 | } else { |
| 9888 | at::Tensor tmp_output; |
| 9889 | { |
| 9890 | at::AutoDispatchSkipFunctionalize guard; |
| 9891 | tmp_output = at::_ops::frobenius_norm_dim::call(self_, dim, keepdim); |
| 9892 | } |
| 9893 | at::functionalization::impl::replace_(out, tmp_output); |
| 9894 | at::functionalization::impl::commit_update(out); |
| 9895 | at::functionalization::impl::sync(out); |
| 9896 | return out; |
| 9897 | } |
| 9898 | } |
| 9899 | |
| 9900 | at::Tensor & nuclear_norm_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool keepdim, at::Tensor & out) { |
| 9901 | if (false) { |
| 9902 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9903 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9904 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9905 | auto self_meta = to_meta(self); |
| 9906 | auto out_meta = to_meta(out); |
| 9907 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9908 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9909 | at::_ops::nuclear_norm_out::call(self_meta, keepdim, out_meta); |
| 9910 | } |
| 9911 | |
| 9912 | at::Tensor self_; |
| 9913 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9914 | at::functionalization::impl::sync(self); |
| 9915 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9916 | } else { |
| 9917 | self_ = self; |
| 9918 | } |
| 9919 | |
| 9920 | at::Tensor out_; |
| 9921 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9922 | at::functionalization::impl::sync(out); |
| 9923 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9924 | } else { |
| 9925 | out_ = out; |
| 9926 | } |
| 9927 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9928 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9929 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9930 | TORCH_INTERNAL_ASSERT(false, |
| 9931 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9932 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9933 | } else { |
| 9934 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9935 | at::AutoDispatchSkipFunctionalize guard; |
| 9936 | at::Tensor tmp_output = at::_ops::nuclear_norm_out::call(self_, keepdim, out_); |
| 9937 | return out;; |
| 9938 | } |
| 9939 | } else { |
| 9940 | at::Tensor tmp_output; |
| 9941 | { |
| 9942 | at::AutoDispatchSkipFunctionalize guard; |
| 9943 | tmp_output = at::_ops::nuclear_norm::call(self_, keepdim); |
| 9944 | } |
| 9945 | at::functionalization::impl::replace_(out, tmp_output); |
| 9946 | at::functionalization::impl::commit_update(out); |
| 9947 | at::functionalization::impl::sync(out); |
| 9948 | return out; |
| 9949 | } |
| 9950 | } |
| 9951 | |
| 9952 | at::Tensor & nuclear_norm_out_dim_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { |
| 9953 | if (false) { |
| 9954 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9955 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9956 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9957 | auto self_meta = to_meta(self); |
| 9958 | auto out_meta = to_meta(out); |
| 9959 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9960 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9961 | at::_ops::nuclear_norm_dim_out::call(self_meta, dim, keepdim, out_meta); |
| 9962 | } |
| 9963 | |
| 9964 | at::Tensor self_; |
| 9965 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9966 | at::functionalization::impl::sync(self); |
| 9967 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9968 | } else { |
| 9969 | self_ = self; |
| 9970 | } |
| 9971 | |
| 9972 | at::Tensor out_; |
| 9973 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9974 | at::functionalization::impl::sync(out); |
| 9975 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9976 | } else { |
| 9977 | out_ = out; |
| 9978 | } |
| 9979 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9980 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9981 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9982 | TORCH_INTERNAL_ASSERT(false, |
| 9983 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9984 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9985 | } else { |
| 9986 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9987 | at::AutoDispatchSkipFunctionalize guard; |
| 9988 | at::Tensor tmp_output = at::_ops::nuclear_norm_dim_out::call(self_, dim, keepdim, out_); |
| 9989 | return out;; |
| 9990 | } |
| 9991 | } else { |
| 9992 | at::Tensor tmp_output; |
| 9993 | { |
| 9994 | at::AutoDispatchSkipFunctionalize guard; |
| 9995 | tmp_output = at::_ops::nuclear_norm_dim::call(self_, dim, keepdim); |
| 9996 | } |
| 9997 | at::functionalization::impl::replace_(out, tmp_output); |
| 9998 | at::functionalization::impl::commit_update(out); |
| 9999 | at::functionalization::impl::sync(out); |
| 10000 | return out; |
| 10001 | } |
| 10002 | } |
| 10003 | |
| 10004 | at::Tensor & subtract_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) { |
| 10005 | if (false) { |
| 10006 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10007 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10008 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10009 | auto self_meta = to_meta(self); |
| 10010 | auto other_meta = to_meta(other); |
| 10011 | auto out_meta = to_meta(out); |
| 10012 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10013 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10014 | at::_ops::subtract_out::call(self_meta, other_meta, alpha, out_meta); |
| 10015 | } |
| 10016 | |
| 10017 | at::Tensor self_; |
| 10018 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10019 | at::functionalization::impl::sync(self); |
| 10020 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10021 | } else { |
| 10022 | self_ = self; |
| 10023 | } |
| 10024 | |
| 10025 | at::Tensor other_; |
| 10026 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 10027 | at::functionalization::impl::sync(other); |
| 10028 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 10029 | } else { |
| 10030 | other_ = other; |
| 10031 | } |
| 10032 | |
| 10033 | at::Tensor out_; |
| 10034 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10035 | at::functionalization::impl::sync(out); |
| 10036 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10037 | } else { |
| 10038 | out_ = out; |
| 10039 | } |
| 10040 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10041 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 10042 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10043 | TORCH_INTERNAL_ASSERT(false, |
| 10044 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10045 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10046 | } else { |
| 10047 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10048 | at::AutoDispatchSkipFunctionalize guard; |
| 10049 | at::Tensor tmp_output = at::_ops::subtract_out::call(self_, other_, alpha, out_); |
| 10050 | return out;; |
| 10051 | } |
| 10052 | } else { |
| 10053 | at::Tensor tmp_output; |
| 10054 | { |
| 10055 | at::AutoDispatchSkipFunctionalize guard; |
| 10056 | tmp_output = at::_ops::subtract_Tensor::call(self_, other_, alpha); |
| 10057 | } |
| 10058 | at::functionalization::impl::replace_(out, tmp_output); |
| 10059 | at::functionalization::impl::commit_update(out); |
| 10060 | at::functionalization::impl::sync(out); |
| 10061 | return out; |
| 10062 | } |
| 10063 | } |
| 10064 | |
| 10065 | at::Tensor & subtract__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 10066 | if (true) { |
| 10067 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10068 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10069 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10070 | auto self_meta = to_meta(self); |
| 10071 | auto other_meta = to_meta(other); |
| 10072 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10073 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10074 | at::_ops::subtract__Tensor::call(self_meta, other_meta, alpha); |
| 10075 | } |
| 10076 | |
| 10077 | at::Tensor self_; |
| 10078 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10079 | at::functionalization::impl::sync(self); |
| 10080 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10081 | } else { |
| 10082 | self_ = self; |
| 10083 | } |
| 10084 | |
| 10085 | at::Tensor other_; |
| 10086 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 10087 | at::functionalization::impl::sync(other); |
| 10088 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 10089 | } else { |
| 10090 | other_ = other; |
| 10091 | } |
| 10092 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 10093 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 10094 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10095 | TORCH_INTERNAL_ASSERT(false, |
| 10096 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10097 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10098 | } else { |
| 10099 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10100 | at::AutoDispatchSkipFunctionalize guard; |
| 10101 | at::Tensor tmp_output = at::_ops::subtract__Tensor::call(self_, other_, alpha); |
| 10102 | return self;; |
| 10103 | } |
| 10104 | } else { |
| 10105 | at::Tensor tmp_output; |
| 10106 | { |
| 10107 | at::AutoDispatchSkipFunctionalize guard; |
| 10108 | tmp_output = at::_ops::subtract_Tensor::call(self_, other_, alpha); |
| 10109 | } |
| 10110 | at::functionalization::impl::replace_(self, tmp_output); |
| 10111 | at::functionalization::impl::commit_update(self); |
| 10112 | at::functionalization::impl::sync(self); |
| 10113 | return self; |
| 10114 | } |
| 10115 | } |
| 10116 | |
| 10117 | at::Tensor & sparse_sampled_addmm_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) { |
| 10118 | if (false) { |
| 10119 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10120 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10121 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10122 | auto self_meta = to_meta(self); |
| 10123 | auto mat1_meta = to_meta(mat1); |
| 10124 | auto mat2_meta = to_meta(mat2); |
| 10125 | auto out_meta = to_meta(out); |
| 10126 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10127 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10128 | at::_ops::sparse_sampled_addmm_out::call(self_meta, mat1_meta, mat2_meta, beta, alpha, out_meta); |
| 10129 | } |
| 10130 | |
| 10131 | at::Tensor self_; |
| 10132 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10133 | at::functionalization::impl::sync(self); |
| 10134 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10135 | } else { |
| 10136 | self_ = self; |
| 10137 | } |
| 10138 | |
| 10139 | at::Tensor mat1_; |
| 10140 | if (at::functionalization::impl::isFunctionalTensor(mat1)) { |
| 10141 | at::functionalization::impl::sync(mat1); |
| 10142 | mat1_ = at::functionalization::impl::from_functional_tensor(mat1); |
| 10143 | } else { |
| 10144 | mat1_ = mat1; |
| 10145 | } |
| 10146 | |
| 10147 | at::Tensor mat2_; |
| 10148 | if (at::functionalization::impl::isFunctionalTensor(mat2)) { |
| 10149 | at::functionalization::impl::sync(mat2); |
| 10150 | mat2_ = at::functionalization::impl::from_functional_tensor(mat2); |
| 10151 | } else { |
| 10152 | mat2_ = mat2; |
| 10153 | } |
| 10154 | |
| 10155 | at::Tensor out_; |
| 10156 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10157 | at::functionalization::impl::sync(out); |
| 10158 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10159 | } else { |
| 10160 | out_ = out; |
| 10161 | } |
| 10162 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10163 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(mat1) || at::functionalization::impl::isFunctionalTensor(mat2))) { |
| 10164 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10165 | TORCH_INTERNAL_ASSERT(false, |
| 10166 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10167 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10168 | } else { |
| 10169 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10170 | at::AutoDispatchSkipFunctionalize guard; |
| 10171 | at::Tensor tmp_output = at::_ops::sparse_sampled_addmm_out::call(self_, mat1_, mat2_, beta, alpha, out_); |
| 10172 | return out;; |
| 10173 | } |
| 10174 | } else { |
| 10175 | at::Tensor tmp_output; |
| 10176 | { |
| 10177 | at::AutoDispatchSkipFunctionalize guard; |
| 10178 | tmp_output = at::_ops::sparse_sampled_addmm::call(self_, mat1_, mat2_, beta, alpha); |
| 10179 | } |
| 10180 | at::functionalization::impl::replace_(out, tmp_output); |
| 10181 | at::functionalization::impl::commit_update(out); |
| 10182 | at::functionalization::impl::sync(out); |
| 10183 | return out; |
| 10184 | } |
| 10185 | } |
| 10186 | |
| 10187 | at::Tensor & _addmm_activation_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, bool use_gelu, at::Tensor & out) { |
| 10188 | if (false) { |
| 10189 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10190 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10191 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10192 | auto self_meta = to_meta(self); |
| 10193 | auto mat1_meta = to_meta(mat1); |
| 10194 | auto mat2_meta = to_meta(mat2); |
| 10195 | auto out_meta = to_meta(out); |
| 10196 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10197 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10198 | at::_ops::_addmm_activation_out::call(self_meta, mat1_meta, mat2_meta, beta, alpha, use_gelu, out_meta); |
| 10199 | } |
| 10200 | |
| 10201 | at::Tensor self_; |
| 10202 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10203 | at::functionalization::impl::sync(self); |
| 10204 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10205 | } else { |
| 10206 | self_ = self; |
| 10207 | } |
| 10208 | |
| 10209 | at::Tensor mat1_; |
| 10210 | if (at::functionalization::impl::isFunctionalTensor(mat1)) { |
| 10211 | at::functionalization::impl::sync(mat1); |
| 10212 | mat1_ = at::functionalization::impl::from_functional_tensor(mat1); |
| 10213 | } else { |
| 10214 | mat1_ = mat1; |
| 10215 | } |
| 10216 | |
| 10217 | at::Tensor mat2_; |
| 10218 | if (at::functionalization::impl::isFunctionalTensor(mat2)) { |
| 10219 | at::functionalization::impl::sync(mat2); |
| 10220 | mat2_ = at::functionalization::impl::from_functional_tensor(mat2); |
| 10221 | } else { |
| 10222 | mat2_ = mat2; |
| 10223 | } |
| 10224 | |
| 10225 | at::Tensor out_; |
| 10226 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10227 | at::functionalization::impl::sync(out); |
| 10228 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10229 | } else { |
| 10230 | out_ = out; |
| 10231 | } |
| 10232 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10233 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(mat1) || at::functionalization::impl::isFunctionalTensor(mat2))) { |
| 10234 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10235 | TORCH_INTERNAL_ASSERT(false, |
| 10236 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10237 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10238 | } else { |
| 10239 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10240 | at::AutoDispatchSkipFunctionalize guard; |
| 10241 | at::Tensor tmp_output = at::_ops::_addmm_activation_out::call(self_, mat1_, mat2_, beta, alpha, use_gelu, out_); |
| 10242 | return out;; |
| 10243 | } |
| 10244 | } else { |
| 10245 | at::Tensor tmp_output; |
| 10246 | { |
| 10247 | at::AutoDispatchSkipFunctionalize guard; |
| 10248 | tmp_output = at::_ops::_addmm_activation::call(self_, mat1_, mat2_, beta, alpha, use_gelu); |
| 10249 | } |
| 10250 | at::functionalization::impl::replace_(out, tmp_output); |
| 10251 | at::functionalization::impl::commit_update(out); |
| 10252 | at::functionalization::impl::sync(out); |
| 10253 | return out; |
| 10254 | } |
| 10255 | } |
| 10256 | |
| 10257 | at::Tensor & _to_dense_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 10258 | if (false) { |
| 10259 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10260 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10261 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10262 | auto self_meta = to_meta(self); |
| 10263 | auto out_meta = to_meta(out); |
| 10264 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10265 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10266 | at::_ops::_to_dense_out::call(self_meta, dtype, out_meta); |
| 10267 | } |
| 10268 | |
| 10269 | at::Tensor self_; |
| 10270 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10271 | at::functionalization::impl::sync(self); |
| 10272 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10273 | } else { |
| 10274 | self_ = self; |
| 10275 | } |
| 10276 | |
| 10277 | at::Tensor out_; |
| 10278 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10279 | at::functionalization::impl::sync(out); |
| 10280 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10281 | } else { |
| 10282 | out_ = out; |
| 10283 | } |
| 10284 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10285 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 10286 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10287 | TORCH_INTERNAL_ASSERT(false, |
| 10288 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10289 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10290 | } else { |
| 10291 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10292 | at::AutoDispatchSkipFunctionalize guard; |
| 10293 | at::Tensor tmp_output = at::_ops::_to_dense_out::call(self_, dtype, out_); |
| 10294 | return out;; |
| 10295 | } |
| 10296 | } else { |
| 10297 | at::Tensor tmp_output; |
| 10298 | { |
| 10299 | at::AutoDispatchSkipFunctionalize guard; |
| 10300 | tmp_output = at::_ops::_to_dense::call(self_, dtype); |
| 10301 | } |
| 10302 | at::functionalization::impl::replace_(out, tmp_output); |
| 10303 | at::functionalization::impl::commit_update(out); |
| 10304 | at::functionalization::impl::sync(out); |
| 10305 | return out; |
| 10306 | } |
| 10307 | } |
| 10308 | |
| 10309 | at::Tensor & _coalesced_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool coalesced, at::Tensor & out) { |
| 10310 | if (false) { |
| 10311 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10312 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10313 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10314 | auto self_meta = to_meta(self); |
| 10315 | auto out_meta = to_meta(out); |
| 10316 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10317 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10318 | at::_ops::_coalesced_out::call(self_meta, coalesced, out_meta); |
| 10319 | } |
| 10320 | |
| 10321 | at::Tensor self_; |
| 10322 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10323 | at::functionalization::impl::sync(self); |
| 10324 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10325 | } else { |
| 10326 | self_ = self; |
| 10327 | } |
| 10328 | |
| 10329 | at::Tensor out_; |
| 10330 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10331 | at::functionalization::impl::sync(out); |
| 10332 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10333 | } else { |
| 10334 | out_ = out; |
| 10335 | } |
| 10336 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10337 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 10338 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10339 | TORCH_INTERNAL_ASSERT(false, |
| 10340 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10341 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10342 | } else { |
| 10343 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10344 | at::AutoDispatchSkipFunctionalize guard; |
| 10345 | at::Tensor tmp_output = at::_ops::_coalesced_out::call(self_, coalesced, out_); |
| 10346 | return out;; |
| 10347 | } |
| 10348 | } else { |
| 10349 | at::Tensor tmp_output; |
| 10350 | { |
| 10351 | at::AutoDispatchSkipFunctionalize guard; |
| 10352 | tmp_output = at::_ops::_coalesced::call(self_, coalesced); |
| 10353 | } |
| 10354 | at::functionalization::impl::replace_(out, tmp_output); |
| 10355 | at::functionalization::impl::commit_update(out); |
| 10356 | at::functionalization::impl::sync(out); |
| 10357 | return out; |
| 10358 | } |
| 10359 | } |
| 10360 | |
| 10361 | at::Tensor & _coalesced_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, bool coalesced) { |
| 10362 | if (true) { |
| 10363 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10364 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10365 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10366 | auto self_meta = to_meta(self); |
| 10367 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10368 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10369 | at::_ops::_coalesced_::call(self_meta, coalesced); |
| 10370 | } |
| 10371 | |
| 10372 | at::Tensor self_; |
| 10373 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10374 | at::functionalization::impl::sync(self); |
| 10375 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10376 | } else { |
| 10377 | self_ = self; |
| 10378 | } |
| 10379 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 10380 | if ((false)) { |
| 10381 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10382 | TORCH_INTERNAL_ASSERT(false, |
| 10383 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10384 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10385 | } else { |
| 10386 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10387 | at::AutoDispatchSkipFunctionalize guard; |
| 10388 | at::Tensor tmp_output = at::_ops::_coalesced_::call(self_, coalesced); |
| 10389 | return self;; |
| 10390 | } |
| 10391 | } else { |
| 10392 | at::Tensor tmp_output; |
| 10393 | { |
| 10394 | at::AutoDispatchSkipFunctionalize guard; |
| 10395 | tmp_output = at::_ops::_coalesced::call(self_, coalesced); |
| 10396 | } |
| 10397 | at::functionalization::impl::replace_(self, tmp_output); |
| 10398 | at::functionalization::impl::commit_update(self); |
| 10399 | at::functionalization::impl::sync(self); |
| 10400 | return self; |
| 10401 | } |
| 10402 | } |
| 10403 | |
| 10404 | at::Tensor & to_sparse_csr_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<int64_t> dense_dim, at::Tensor & out) { |
| 10405 | if (false) { |
| 10406 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10407 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10408 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10409 | auto self_meta = to_meta(self); |
| 10410 | auto out_meta = to_meta(out); |
| 10411 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10412 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10413 | at::_ops::to_sparse_csr_out::call(self_meta, dense_dim, out_meta); |
| 10414 | } |
| 10415 | |
| 10416 | at::Tensor self_; |
| 10417 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10418 | at::functionalization::impl::sync(self); |
| 10419 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10420 | } else { |
| 10421 | self_ = self; |
| 10422 | } |
| 10423 | |
| 10424 | at::Tensor out_; |
| 10425 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10426 | at::functionalization::impl::sync(out); |
| 10427 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10428 | } else { |
| 10429 | out_ = out; |
| 10430 | } |
| 10431 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10432 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 10433 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10434 | TORCH_INTERNAL_ASSERT(false, |
| 10435 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10436 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10437 | } else { |
| 10438 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10439 | at::AutoDispatchSkipFunctionalize guard; |
| 10440 | at::Tensor tmp_output = at::_ops::to_sparse_csr_out::call(self_, dense_dim, out_); |
| 10441 | return out;; |
| 10442 | } |
| 10443 | } else { |
| 10444 | at::Tensor tmp_output; |
| 10445 | { |
| 10446 | at::AutoDispatchSkipFunctionalize guard; |
| 10447 | tmp_output = at::_ops::to_sparse_csr::call(self_, dense_dim); |
| 10448 | } |
| 10449 | at::functionalization::impl::replace_(out, tmp_output); |
| 10450 | at::functionalization::impl::commit_update(out); |
| 10451 | at::functionalization::impl::sync(out); |
| 10452 | return out; |
| 10453 | } |
| 10454 | } |
| 10455 | |
| 10456 | at::Tensor & to_sparse_csc_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<int64_t> dense_dim, at::Tensor & out) { |
| 10457 | if (false) { |
| 10458 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10459 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10460 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10461 | auto self_meta = to_meta(self); |
| 10462 | auto out_meta = to_meta(out); |
| 10463 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10464 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10465 | at::_ops::to_sparse_csc_out::call(self_meta, dense_dim, out_meta); |
| 10466 | } |
| 10467 | |
| 10468 | at::Tensor self_; |
| 10469 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10470 | at::functionalization::impl::sync(self); |
| 10471 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10472 | } else { |
| 10473 | self_ = self; |
| 10474 | } |
| 10475 | |
| 10476 | at::Tensor out_; |
| 10477 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10478 | at::functionalization::impl::sync(out); |
| 10479 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10480 | } else { |
| 10481 | out_ = out; |
| 10482 | } |
| 10483 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10484 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 10485 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10486 | TORCH_INTERNAL_ASSERT(false, |
| 10487 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10488 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10489 | } else { |
| 10490 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10491 | at::AutoDispatchSkipFunctionalize guard; |
| 10492 | at::Tensor tmp_output = at::_ops::to_sparse_csc_out::call(self_, dense_dim, out_); |
| 10493 | return out;; |
| 10494 | } |
| 10495 | } else { |
| 10496 | at::Tensor tmp_output; |
| 10497 | { |
| 10498 | at::AutoDispatchSkipFunctionalize guard; |
| 10499 | tmp_output = at::_ops::to_sparse_csc::call(self_, dense_dim); |
| 10500 | } |
| 10501 | at::functionalization::impl::replace_(out, tmp_output); |
| 10502 | at::functionalization::impl::commit_update(out); |
| 10503 | at::functionalization::impl::sync(out); |
| 10504 | return out; |
| 10505 | } |
| 10506 | } |
| 10507 | |
| 10508 | at::Tensor & to_sparse_bsc_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef blocksize, c10::optional<int64_t> dense_dim, at::Tensor & out) { |
| 10509 | if (false) { |
| 10510 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10511 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10512 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10513 | auto self_meta = to_meta(self); |
| 10514 | auto out_meta = to_meta(out); |
| 10515 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10516 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10517 | at::_ops::to_sparse_bsc_out::call(self_meta, blocksize, dense_dim, out_meta); |
| 10518 | } |
| 10519 | |
| 10520 | at::Tensor self_; |
| 10521 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10522 | at::functionalization::impl::sync(self); |
| 10523 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10524 | } else { |
| 10525 | self_ = self; |
| 10526 | } |
| 10527 | |
| 10528 | at::Tensor out_; |
| 10529 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10530 | at::functionalization::impl::sync(out); |
| 10531 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10532 | } else { |
| 10533 | out_ = out; |
| 10534 | } |
| 10535 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10536 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 10537 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10538 | TORCH_INTERNAL_ASSERT(false, |
| 10539 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10540 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10541 | } else { |
| 10542 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10543 | at::AutoDispatchSkipFunctionalize guard; |
| 10544 | at::Tensor tmp_output = at::_ops::to_sparse_bsc_out::call(self_, blocksize, dense_dim, out_); |
| 10545 | return out;; |
| 10546 | } |
| 10547 | } else { |
| 10548 | at::Tensor tmp_output; |
| 10549 | { |
| 10550 | at::AutoDispatchSkipFunctionalize guard; |
| 10551 | tmp_output = at::_ops::to_sparse_bsc::call(self_, blocksize, dense_dim); |
| 10552 | } |
| 10553 | at::functionalization::impl::replace_(out, tmp_output); |
| 10554 | at::functionalization::impl::commit_update(out); |
| 10555 | at::functionalization::impl::sync(out); |
| 10556 | return out; |
| 10557 | } |
| 10558 | } |
| 10559 | |
| 10560 | at::Tensor & quantize_per_tensor_dynamic_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::ScalarType dtype, bool reduce_range, at::Tensor & out) { |
| 10561 | if (false) { |
| 10562 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10563 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10564 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10565 | auto self_meta = to_meta(self); |
| 10566 | auto out_meta = to_meta(out); |
| 10567 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10568 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10569 | at::_ops::quantize_per_tensor_dynamic_out::call(self_meta, dtype, reduce_range, out_meta); |
| 10570 | } |
| 10571 | |
| 10572 | at::Tensor self_; |
| 10573 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10574 | at::functionalization::impl::sync(self); |
| 10575 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10576 | } else { |
| 10577 | self_ = self; |
| 10578 | } |
| 10579 | |
| 10580 | at::Tensor out_; |
| 10581 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10582 | at::functionalization::impl::sync(out); |
| 10583 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10584 | } else { |
| 10585 | out_ = out; |
| 10586 | } |
| 10587 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10588 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 10589 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10590 | TORCH_INTERNAL_ASSERT(false, |
| 10591 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10592 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10593 | } else { |
| 10594 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10595 | at::AutoDispatchSkipFunctionalize guard; |
| 10596 | at::Tensor tmp_output = at::_ops::quantize_per_tensor_dynamic_out::call(self_, dtype, reduce_range, out_); |
| 10597 | return out;; |
| 10598 | } |
| 10599 | } else { |
| 10600 | at::Tensor tmp_output; |
| 10601 | { |
| 10602 | at::AutoDispatchSkipFunctionalize guard; |
| 10603 | tmp_output = at::_ops::quantize_per_tensor_dynamic::call(self_, dtype, reduce_range); |
| 10604 | } |
| 10605 | at::functionalization::impl::replace_(out, tmp_output); |
| 10606 | at::functionalization::impl::commit_update(out); |
| 10607 | at::functionalization::impl::sync(out); |
| 10608 | return out; |
| 10609 | } |
| 10610 | } |
| 10611 | |
| 10612 | at::Tensor & quantize_per_tensor_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double scale, int64_t zero_point, at::ScalarType dtype, at::Tensor & out) { |
| 10613 | if (false) { |
| 10614 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10615 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10616 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10617 | auto self_meta = to_meta(self); |
| 10618 | auto out_meta = to_meta(out); |
| 10619 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10620 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10621 | at::_ops::quantize_per_tensor_out::call(self_meta, scale, zero_point, dtype, out_meta); |
| 10622 | } |
| 10623 | |
| 10624 | at::Tensor self_; |
| 10625 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10626 | at::functionalization::impl::sync(self); |
| 10627 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10628 | } else { |
| 10629 | self_ = self; |
| 10630 | } |
| 10631 | |
| 10632 | at::Tensor out_; |
| 10633 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10634 | at::functionalization::impl::sync(out); |
| 10635 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10636 | } else { |
| 10637 | out_ = out; |
| 10638 | } |
| 10639 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10640 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 10641 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10642 | TORCH_INTERNAL_ASSERT(false, |
| 10643 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10644 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10645 | } else { |
| 10646 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10647 | at::AutoDispatchSkipFunctionalize guard; |
| 10648 | at::Tensor tmp_output = at::_ops::quantize_per_tensor_out::call(self_, scale, zero_point, dtype, out_); |
| 10649 | return out;; |
| 10650 | } |
| 10651 | } else { |
| 10652 | at::Tensor tmp_output; |
| 10653 | { |
| 10654 | at::AutoDispatchSkipFunctionalize guard; |
| 10655 | tmp_output = at::_ops::quantize_per_tensor::call(self_, scale, zero_point, dtype); |
| 10656 | } |
| 10657 | at::functionalization::impl::replace_(out, tmp_output); |
| 10658 | at::functionalization::impl::commit_update(out); |
| 10659 | at::functionalization::impl::sync(out); |
| 10660 | return out; |
| 10661 | } |
| 10662 | } |
| 10663 | |
| 10664 | at::Tensor & quantize_per_tensor_out_tensor_qparams_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, at::ScalarType dtype, at::Tensor & out) { |
| 10665 | if (false) { |
| 10666 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10667 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10668 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10669 | auto self_meta = to_meta(self); |
| 10670 | auto scale_meta = to_meta(scale); |
| 10671 | auto zero_point_meta = to_meta(zero_point); |
| 10672 | auto out_meta = to_meta(out); |
| 10673 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10674 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10675 | at::_ops::quantize_per_tensor_tensor_qparams_out::call(self_meta, scale_meta, zero_point_meta, dtype, out_meta); |
| 10676 | } |
| 10677 | |
| 10678 | at::Tensor self_; |
| 10679 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10680 | at::functionalization::impl::sync(self); |
| 10681 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10682 | } else { |
| 10683 | self_ = self; |
| 10684 | } |
| 10685 | |
| 10686 | at::Tensor scale_; |
| 10687 | if (at::functionalization::impl::isFunctionalTensor(scale)) { |
| 10688 | at::functionalization::impl::sync(scale); |
| 10689 | scale_ = at::functionalization::impl::from_functional_tensor(scale); |
| 10690 | } else { |
| 10691 | scale_ = scale; |
| 10692 | } |
| 10693 | |
| 10694 | at::Tensor zero_point_; |
| 10695 | if (at::functionalization::impl::isFunctionalTensor(zero_point)) { |
| 10696 | at::functionalization::impl::sync(zero_point); |
| 10697 | zero_point_ = at::functionalization::impl::from_functional_tensor(zero_point); |
| 10698 | } else { |
| 10699 | zero_point_ = zero_point; |
| 10700 | } |
| 10701 | |
| 10702 | at::Tensor out_; |
| 10703 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10704 | at::functionalization::impl::sync(out); |
| 10705 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10706 | } else { |
| 10707 | out_ = out; |
| 10708 | } |
| 10709 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10710 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(scale) || at::functionalization::impl::isFunctionalTensor(zero_point))) { |
| 10711 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10712 | TORCH_INTERNAL_ASSERT(false, |
| 10713 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10714 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10715 | } else { |
| 10716 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10717 | at::AutoDispatchSkipFunctionalize guard; |
| 10718 | at::Tensor tmp_output = at::_ops::quantize_per_tensor_tensor_qparams_out::call(self_, scale_, zero_point_, dtype, out_); |
| 10719 | return out;; |
| 10720 | } |
| 10721 | } else { |
| 10722 | at::Tensor tmp_output; |
| 10723 | { |
| 10724 | at::AutoDispatchSkipFunctionalize guard; |
| 10725 | tmp_output = at::_ops::quantize_per_tensor_tensor_qparams::call(self_, scale_, zero_point_, dtype); |
| 10726 | } |
| 10727 | at::functionalization::impl::replace_(out, tmp_output); |
| 10728 | at::functionalization::impl::commit_update(out); |
| 10729 | at::functionalization::impl::sync(out); |
| 10730 | return out; |
| 10731 | } |
| 10732 | } |
| 10733 | |
| 10734 | void quantize_per_tensor_out_tensors_out(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, const at::Tensor & scales, const at::Tensor & zero_points, at::ScalarType dtype, at::TensorList out) { |
| 10735 | if (false) { |
| 10736 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10737 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10738 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10739 | auto tensors_meta = to_meta(tensors); |
| 10740 | auto scales_meta = to_meta(scales); |
| 10741 | auto zero_points_meta = to_meta(zero_points); |
| 10742 | auto out_meta = to_meta(out); |
| 10743 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10744 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10745 | at::_ops::quantize_per_tensor_tensors_out::call(tensors_meta, scales_meta, zero_points_meta, dtype, out_meta); |
| 10746 | } |
| 10747 | |
| 10748 | ::std::vector<at::Tensor> tensors_; |
| 10749 | if (at::functionalization::impl::isFunctionalTensor(tensors)) { |
| 10750 | at::functionalization::impl::sync(tensors); |
| 10751 | tensors_ = at::functionalization::impl::from_functional_tensor(tensors); |
| 10752 | } else { |
| 10753 | tensors_ = tensors.vec(); |
| 10754 | } |
| 10755 | |
| 10756 | at::Tensor scales_; |
| 10757 | if (at::functionalization::impl::isFunctionalTensor(scales)) { |
| 10758 | at::functionalization::impl::sync(scales); |
| 10759 | scales_ = at::functionalization::impl::from_functional_tensor(scales); |
| 10760 | } else { |
| 10761 | scales_ = scales; |
| 10762 | } |
| 10763 | |
| 10764 | at::Tensor zero_points_; |
| 10765 | if (at::functionalization::impl::isFunctionalTensor(zero_points)) { |
| 10766 | at::functionalization::impl::sync(zero_points); |
| 10767 | zero_points_ = at::functionalization::impl::from_functional_tensor(zero_points); |
| 10768 | } else { |
| 10769 | zero_points_ = zero_points; |
| 10770 | } |
| 10771 | |
| 10772 | ::std::vector<at::Tensor> out_; |
| 10773 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10774 | at::functionalization::impl::sync(out); |
| 10775 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10776 | } else { |
| 10777 | out_ = out.vec(); |
| 10778 | } |
| 10779 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10780 | if ((false || at::functionalization::impl::isFunctionalTensor(tensors) || at::functionalization::impl::isFunctionalTensor(scales) || at::functionalization::impl::isFunctionalTensor(zero_points))) { |
| 10781 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10782 | TORCH_INTERNAL_ASSERT(false, |
| 10783 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10784 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10785 | } else { |
| 10786 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10787 | at::AutoDispatchSkipFunctionalize guard; |
| 10788 | at::_ops::quantize_per_tensor_tensors_out::call(tensors_, scales_, zero_points_, dtype, out_); |
| 10789 | ; |
| 10790 | } |
| 10791 | } else { |
| 10792 | ::std::vector<at::Tensor> tmp_output; |
| 10793 | { |
| 10794 | at::AutoDispatchSkipFunctionalize guard; |
| 10795 | tmp_output = at::_ops::quantize_per_tensor_tensors::call(tensors_, scales_, zero_points_, dtype); |
| 10796 | } |
| 10797 | at::functionalization::impl::replace_(out, tmp_output); |
| 10798 | at::functionalization::impl::commit_update(out); |
| 10799 | at::functionalization::impl::sync(out); |
| 10800 | |
| 10801 | } |
| 10802 | } |
| 10803 | |
| 10804 | at::Tensor & quantize_per_channel_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::ScalarType dtype, at::Tensor & out) { |
| 10805 | if (false) { |
| 10806 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10807 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10808 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10809 | auto self_meta = to_meta(self); |
| 10810 | auto scales_meta = to_meta(scales); |
| 10811 | auto zero_points_meta = to_meta(zero_points); |
| 10812 | auto out_meta = to_meta(out); |
| 10813 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10814 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10815 | at::_ops::quantize_per_channel_out::call(self_meta, scales_meta, zero_points_meta, axis, dtype, out_meta); |
| 10816 | } |
| 10817 | |
| 10818 | at::Tensor self_; |
| 10819 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10820 | at::functionalization::impl::sync(self); |
| 10821 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10822 | } else { |
| 10823 | self_ = self; |
| 10824 | } |
| 10825 | |
| 10826 | at::Tensor scales_; |
| 10827 | if (at::functionalization::impl::isFunctionalTensor(scales)) { |
| 10828 | at::functionalization::impl::sync(scales); |
| 10829 | scales_ = at::functionalization::impl::from_functional_tensor(scales); |
| 10830 | } else { |
| 10831 | scales_ = scales; |
| 10832 | } |
| 10833 | |
| 10834 | at::Tensor zero_points_; |
| 10835 | if (at::functionalization::impl::isFunctionalTensor(zero_points)) { |
| 10836 | at::functionalization::impl::sync(zero_points); |
| 10837 | zero_points_ = at::functionalization::impl::from_functional_tensor(zero_points); |
| 10838 | } else { |
| 10839 | zero_points_ = zero_points; |
| 10840 | } |
| 10841 | |
| 10842 | at::Tensor out_; |
| 10843 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10844 | at::functionalization::impl::sync(out); |
| 10845 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10846 | } else { |
| 10847 | out_ = out; |
| 10848 | } |
| 10849 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10850 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(scales) || at::functionalization::impl::isFunctionalTensor(zero_points))) { |
| 10851 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10852 | TORCH_INTERNAL_ASSERT(false, |
| 10853 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10854 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10855 | } else { |
| 10856 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10857 | at::AutoDispatchSkipFunctionalize guard; |
| 10858 | at::Tensor tmp_output = at::_ops::quantize_per_channel_out::call(self_, scales_, zero_points_, axis, dtype, out_); |
| 10859 | return out;; |
| 10860 | } |
| 10861 | } else { |
| 10862 | at::Tensor tmp_output; |
| 10863 | { |
| 10864 | at::AutoDispatchSkipFunctionalize guard; |
| 10865 | tmp_output = at::_ops::quantize_per_channel::call(self_, scales_, zero_points_, axis, dtype); |
| 10866 | } |
| 10867 | at::functionalization::impl::replace_(out, tmp_output); |
| 10868 | at::functionalization::impl::commit_update(out); |
| 10869 | at::functionalization::impl::sync(out); |
| 10870 | return out; |
| 10871 | } |
| 10872 | } |
| 10873 | |
| 10874 | at::Tensor & _make_per_channel_quantized_tensor_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, at::Tensor & out) { |
| 10875 | if (false) { |
| 10876 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10877 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10878 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10879 | auto self_meta = to_meta(self); |
| 10880 | auto scale_meta = to_meta(scale); |
| 10881 | auto zero_point_meta = to_meta(zero_point); |
| 10882 | auto out_meta = to_meta(out); |
| 10883 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10884 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10885 | at::_ops::_make_per_channel_quantized_tensor_out::call(self_meta, scale_meta, zero_point_meta, axis, out_meta); |
| 10886 | } |
| 10887 | |
| 10888 | at::Tensor self_; |
| 10889 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10890 | at::functionalization::impl::sync(self); |
| 10891 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10892 | } else { |
| 10893 | self_ = self; |
| 10894 | } |
| 10895 | |
| 10896 | at::Tensor scale_; |
| 10897 | if (at::functionalization::impl::isFunctionalTensor(scale)) { |
| 10898 | at::functionalization::impl::sync(scale); |
| 10899 | scale_ = at::functionalization::impl::from_functional_tensor(scale); |
| 10900 | } else { |
| 10901 | scale_ = scale; |
| 10902 | } |
| 10903 | |
| 10904 | at::Tensor zero_point_; |
| 10905 | if (at::functionalization::impl::isFunctionalTensor(zero_point)) { |
| 10906 | at::functionalization::impl::sync(zero_point); |
| 10907 | zero_point_ = at::functionalization::impl::from_functional_tensor(zero_point); |
| 10908 | } else { |
| 10909 | zero_point_ = zero_point; |
| 10910 | } |
| 10911 | |
| 10912 | at::Tensor out_; |
| 10913 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10914 | at::functionalization::impl::sync(out); |
| 10915 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10916 | } else { |
| 10917 | out_ = out; |
| 10918 | } |
| 10919 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10920 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(scale) || at::functionalization::impl::isFunctionalTensor(zero_point))) { |
| 10921 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10922 | TORCH_INTERNAL_ASSERT(false, |
| 10923 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10924 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10925 | } else { |
| 10926 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10927 | at::AutoDispatchSkipFunctionalize guard; |
| 10928 | at::Tensor tmp_output = at::_ops::_make_per_channel_quantized_tensor_out::call(self_, scale_, zero_point_, axis, out_); |
| 10929 | return out;; |
| 10930 | } |
| 10931 | } else { |
| 10932 | at::Tensor tmp_output; |
| 10933 | { |
| 10934 | at::AutoDispatchSkipFunctionalize guard; |
| 10935 | tmp_output = at::_ops::_make_per_channel_quantized_tensor::call(self_, scale_, zero_point_, axis); |
| 10936 | } |
| 10937 | at::functionalization::impl::replace_(out, tmp_output); |
| 10938 | at::functionalization::impl::commit_update(out); |
| 10939 | at::functionalization::impl::sync(out); |
| 10940 | return out; |
| 10941 | } |
| 10942 | } |
| 10943 | |
| 10944 | ::std::tuple<at::Tensor &,at::Tensor &> fake_quantize_per_tensor_affine_cachemask_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max, at::Tensor & out0, at::Tensor & out1) { |
| 10945 | if (false) { |
| 10946 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10947 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10948 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10949 | auto self_meta = to_meta(self); |
| 10950 | auto out0_meta = to_meta(out0); |
| 10951 | auto out1_meta = to_meta(out1); |
| 10952 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10953 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10954 | at::_ops::fake_quantize_per_tensor_affine_cachemask_out::call(self_meta, scale, zero_point, quant_min, quant_max, out0_meta, out1_meta); |
| 10955 | } |
| 10956 | |
| 10957 | at::Tensor self_; |
| 10958 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10959 | at::functionalization::impl::sync(self); |
| 10960 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10961 | } else { |
| 10962 | self_ = self; |
| 10963 | } |
| 10964 | |
| 10965 | at::Tensor out0_; |
| 10966 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 10967 | at::functionalization::impl::sync(out0); |
| 10968 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 10969 | } else { |
| 10970 | out0_ = out0; |
| 10971 | } |
| 10972 | |
| 10973 | at::Tensor out1_; |
| 10974 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 10975 | at::functionalization::impl::sync(out1); |
| 10976 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 10977 | } else { |
| 10978 | out1_ = out1; |
| 10979 | } |
| 10980 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1))) { |
| 10981 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 10982 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10983 | TORCH_INTERNAL_ASSERT(false, |
| 10984 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10985 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10986 | } else { |
| 10987 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10988 | at::AutoDispatchSkipFunctionalize guard; |
| 10989 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::fake_quantize_per_tensor_affine_cachemask_out::call(self_, scale, zero_point, quant_min, quant_max, out0_, out1_); |
| 10990 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1);; |
| 10991 | } |
| 10992 | } else { |
| 10993 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 10994 | { |
| 10995 | at::AutoDispatchSkipFunctionalize guard; |
| 10996 | tmp_output = at::_ops::fake_quantize_per_tensor_affine_cachemask::call(self_, scale, zero_point, quant_min, quant_max); |
| 10997 | } |
| 10998 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 10999 | at::functionalization::impl::commit_update(out0); |
| 11000 | at::functionalization::impl::sync(out0); |
| 11001 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 11002 | at::functionalization::impl::commit_update(out1); |
| 11003 | at::functionalization::impl::sync(out1); |
| 11004 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1); |
| 11005 | } |
| 11006 | } |
| 11007 | |
| 11008 | ::std::tuple<at::Tensor &,at::Tensor &> _fake_quantize_per_tensor_affine_cachemask_tensor_qparams_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, const at::Tensor & fake_quant_enabled, int64_t quant_min, int64_t quant_max, at::Tensor & out0, at::Tensor & out1) { |
| 11009 | if (false) { |
| 11010 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11011 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11012 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11013 | auto self_meta = to_meta(self); |
| 11014 | auto scale_meta = to_meta(scale); |
| 11015 | auto zero_point_meta = to_meta(zero_point); |
| 11016 | auto fake_quant_enabled_meta = to_meta(fake_quant_enabled); |
| 11017 | auto out0_meta = to_meta(out0); |
| 11018 | auto out1_meta = to_meta(out1); |
| 11019 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11020 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11021 | at::_ops::_fake_quantize_per_tensor_affine_cachemask_tensor_qparams_out::call(self_meta, scale_meta, zero_point_meta, fake_quant_enabled_meta, quant_min, quant_max, out0_meta, out1_meta); |
| 11022 | } |
| 11023 | |
| 11024 | at::Tensor self_; |
| 11025 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11026 | at::functionalization::impl::sync(self); |
| 11027 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11028 | } else { |
| 11029 | self_ = self; |
| 11030 | } |
| 11031 | |
| 11032 | at::Tensor scale_; |
| 11033 | if (at::functionalization::impl::isFunctionalTensor(scale)) { |
| 11034 | at::functionalization::impl::sync(scale); |
| 11035 | scale_ = at::functionalization::impl::from_functional_tensor(scale); |
| 11036 | } else { |
| 11037 | scale_ = scale; |
| 11038 | } |
| 11039 | |
| 11040 | at::Tensor zero_point_; |
| 11041 | if (at::functionalization::impl::isFunctionalTensor(zero_point)) { |
| 11042 | at::functionalization::impl::sync(zero_point); |
| 11043 | zero_point_ = at::functionalization::impl::from_functional_tensor(zero_point); |
| 11044 | } else { |
| 11045 | zero_point_ = zero_point; |
| 11046 | } |
| 11047 | |
| 11048 | at::Tensor fake_quant_enabled_; |
| 11049 | if (at::functionalization::impl::isFunctionalTensor(fake_quant_enabled)) { |
| 11050 | at::functionalization::impl::sync(fake_quant_enabled); |
| 11051 | fake_quant_enabled_ = at::functionalization::impl::from_functional_tensor(fake_quant_enabled); |
| 11052 | } else { |
| 11053 | fake_quant_enabled_ = fake_quant_enabled; |
| 11054 | } |
| 11055 | |
| 11056 | at::Tensor out0_; |
| 11057 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 11058 | at::functionalization::impl::sync(out0); |
| 11059 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 11060 | } else { |
| 11061 | out0_ = out0; |
| 11062 | } |
| 11063 | |
| 11064 | at::Tensor out1_; |
| 11065 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 11066 | at::functionalization::impl::sync(out1); |
| 11067 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 11068 | } else { |
| 11069 | out1_ = out1; |
| 11070 | } |
| 11071 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1))) { |
| 11072 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(scale) || at::functionalization::impl::isFunctionalTensor(zero_point) || at::functionalization::impl::isFunctionalTensor(fake_quant_enabled))) { |
| 11073 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11074 | TORCH_INTERNAL_ASSERT(false, |
| 11075 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11076 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11077 | } else { |
| 11078 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11079 | at::AutoDispatchSkipFunctionalize guard; |
| 11080 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::_fake_quantize_per_tensor_affine_cachemask_tensor_qparams_out::call(self_, scale_, zero_point_, fake_quant_enabled_, quant_min, quant_max, out0_, out1_); |
| 11081 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1);; |
| 11082 | } |
| 11083 | } else { |
| 11084 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 11085 | { |
| 11086 | at::AutoDispatchSkipFunctionalize guard; |
| 11087 | tmp_output = at::_ops::_fake_quantize_per_tensor_affine_cachemask_tensor_qparams::call(self_, scale_, zero_point_, fake_quant_enabled_, quant_min, quant_max); |
| 11088 | } |
| 11089 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 11090 | at::functionalization::impl::commit_update(out0); |
| 11091 | at::functionalization::impl::sync(out0); |
| 11092 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 11093 | at::functionalization::impl::commit_update(out1); |
| 11094 | at::functionalization::impl::sync(out1); |
| 11095 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1); |
| 11096 | } |
| 11097 | } |
| 11098 | |
| 11099 | at::Tensor & _fake_quantize_learnable_per_tensor_affine_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t quant_min, int64_t quant_max, double grad_factor, at::Tensor & out) { |
| 11100 | if (false) { |
| 11101 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11102 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11103 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11104 | auto self_meta = to_meta(self); |
| 11105 | auto scale_meta = to_meta(scale); |
| 11106 | auto zero_point_meta = to_meta(zero_point); |
| 11107 | auto out_meta = to_meta(out); |
| 11108 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11109 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11110 | at::_ops::_fake_quantize_learnable_per_tensor_affine_out::call(self_meta, scale_meta, zero_point_meta, quant_min, quant_max, grad_factor, out_meta); |
| 11111 | } |
| 11112 | |
| 11113 | at::Tensor self_; |
| 11114 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11115 | at::functionalization::impl::sync(self); |
| 11116 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11117 | } else { |
| 11118 | self_ = self; |
| 11119 | } |
| 11120 | |
| 11121 | at::Tensor scale_; |
| 11122 | if (at::functionalization::impl::isFunctionalTensor(scale)) { |
| 11123 | at::functionalization::impl::sync(scale); |
| 11124 | scale_ = at::functionalization::impl::from_functional_tensor(scale); |
| 11125 | } else { |
| 11126 | scale_ = scale; |
| 11127 | } |
| 11128 | |
| 11129 | at::Tensor zero_point_; |
| 11130 | if (at::functionalization::impl::isFunctionalTensor(zero_point)) { |
| 11131 | at::functionalization::impl::sync(zero_point); |
| 11132 | zero_point_ = at::functionalization::impl::from_functional_tensor(zero_point); |
| 11133 | } else { |
| 11134 | zero_point_ = zero_point; |
| 11135 | } |
| 11136 | |
| 11137 | at::Tensor out_; |
| 11138 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11139 | at::functionalization::impl::sync(out); |
| 11140 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11141 | } else { |
| 11142 | out_ = out; |
| 11143 | } |
| 11144 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11145 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(scale) || at::functionalization::impl::isFunctionalTensor(zero_point))) { |
| 11146 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11147 | TORCH_INTERNAL_ASSERT(false, |
| 11148 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11149 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11150 | } else { |
| 11151 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11152 | at::AutoDispatchSkipFunctionalize guard; |
| 11153 | at::Tensor tmp_output = at::_ops::_fake_quantize_learnable_per_tensor_affine_out::call(self_, scale_, zero_point_, quant_min, quant_max, grad_factor, out_); |
| 11154 | return out;; |
| 11155 | } |
| 11156 | } else { |
| 11157 | at::Tensor tmp_output; |
| 11158 | { |
| 11159 | at::AutoDispatchSkipFunctionalize guard; |
| 11160 | tmp_output = at::_ops::_fake_quantize_learnable_per_tensor_affine::call(self_, scale_, zero_point_, quant_min, quant_max, grad_factor); |
| 11161 | } |
| 11162 | at::functionalization::impl::replace_(out, tmp_output); |
| 11163 | at::functionalization::impl::commit_update(out); |
| 11164 | at::functionalization::impl::sync(out); |
| 11165 | return out; |
| 11166 | } |
| 11167 | } |
| 11168 | |
| 11169 | ::std::tuple<at::Tensor &,at::Tensor &> fake_quantize_per_channel_affine_cachemask_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max, at::Tensor & out0, at::Tensor & out1) { |
| 11170 | if (false) { |
| 11171 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11172 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11173 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11174 | auto self_meta = to_meta(self); |
| 11175 | auto scale_meta = to_meta(scale); |
| 11176 | auto zero_point_meta = to_meta(zero_point); |
| 11177 | auto out0_meta = to_meta(out0); |
| 11178 | auto out1_meta = to_meta(out1); |
| 11179 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11180 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11181 | at::_ops::fake_quantize_per_channel_affine_cachemask_out::call(self_meta, scale_meta, zero_point_meta, axis, quant_min, quant_max, out0_meta, out1_meta); |
| 11182 | } |
| 11183 | |
| 11184 | at::Tensor self_; |
| 11185 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11186 | at::functionalization::impl::sync(self); |
| 11187 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11188 | } else { |
| 11189 | self_ = self; |
| 11190 | } |
| 11191 | |
| 11192 | at::Tensor scale_; |
| 11193 | if (at::functionalization::impl::isFunctionalTensor(scale)) { |
| 11194 | at::functionalization::impl::sync(scale); |
| 11195 | scale_ = at::functionalization::impl::from_functional_tensor(scale); |
| 11196 | } else { |
| 11197 | scale_ = scale; |
| 11198 | } |
| 11199 | |
| 11200 | at::Tensor zero_point_; |
| 11201 | if (at::functionalization::impl::isFunctionalTensor(zero_point)) { |
| 11202 | at::functionalization::impl::sync(zero_point); |
| 11203 | zero_point_ = at::functionalization::impl::from_functional_tensor(zero_point); |
| 11204 | } else { |
| 11205 | zero_point_ = zero_point; |
| 11206 | } |
| 11207 | |
| 11208 | at::Tensor out0_; |
| 11209 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 11210 | at::functionalization::impl::sync(out0); |
| 11211 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 11212 | } else { |
| 11213 | out0_ = out0; |
| 11214 | } |
| 11215 | |
| 11216 | at::Tensor out1_; |
| 11217 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 11218 | at::functionalization::impl::sync(out1); |
| 11219 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 11220 | } else { |
| 11221 | out1_ = out1; |
| 11222 | } |
| 11223 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1))) { |
| 11224 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(scale) || at::functionalization::impl::isFunctionalTensor(zero_point))) { |
| 11225 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11226 | TORCH_INTERNAL_ASSERT(false, |
| 11227 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11228 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11229 | } else { |
| 11230 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11231 | at::AutoDispatchSkipFunctionalize guard; |
| 11232 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::fake_quantize_per_channel_affine_cachemask_out::call(self_, scale_, zero_point_, axis, quant_min, quant_max, out0_, out1_); |
| 11233 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1);; |
| 11234 | } |
| 11235 | } else { |
| 11236 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 11237 | { |
| 11238 | at::AutoDispatchSkipFunctionalize guard; |
| 11239 | tmp_output = at::_ops::fake_quantize_per_channel_affine_cachemask::call(self_, scale_, zero_point_, axis, quant_min, quant_max); |
| 11240 | } |
| 11241 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 11242 | at::functionalization::impl::commit_update(out0); |
| 11243 | at::functionalization::impl::sync(out0); |
| 11244 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 11245 | at::functionalization::impl::commit_update(out1); |
| 11246 | at::functionalization::impl::sync(out1); |
| 11247 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1); |
| 11248 | } |
| 11249 | } |
| 11250 | |
| 11251 | void lstm_mps_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_y, const c10::optional<at::Tensor> & grad_hy, const c10::optional<at::Tensor> & grad_cy, const at::Tensor & z_state, const at::Tensor & cell_state_fwd, 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::TensorList out1, at::TensorList out2) { |
| 11252 | if (false) { |
| 11253 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11254 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11255 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11256 | auto grad_y_meta = to_meta(grad_y); |
| 11257 | auto grad_hy_meta = to_meta(grad_hy); |
| 11258 | auto grad_cy_meta = to_meta(grad_cy); |
| 11259 | auto z_state_meta = to_meta(z_state); |
| 11260 | auto cell_state_fwd_meta = to_meta(cell_state_fwd); |
| 11261 | auto input_meta = to_meta(input); |
| 11262 | auto hx_meta = to_meta(hx); |
| 11263 | auto params_meta = to_meta(params); |
| 11264 | auto out0_meta = to_meta(out0); |
| 11265 | auto out1_meta = to_meta(out1); |
| 11266 | auto out2_meta = to_meta(out2); |
| 11267 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11268 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11269 | at::_ops::lstm_mps_backward_out::call(grad_y_meta, grad_hy_meta, grad_cy_meta, z_state_meta, cell_state_fwd_meta, input_meta, hx_meta, params_meta, has_biases, num_layers, dropout, train, bidirectional, batch_first, out0_meta, out1_meta, out2_meta); |
| 11270 | } |
| 11271 | |
| 11272 | at::Tensor grad_y_; |
| 11273 | if (at::functionalization::impl::isFunctionalTensor(grad_y)) { |
| 11274 | at::functionalization::impl::sync(grad_y); |
| 11275 | grad_y_ = at::functionalization::impl::from_functional_tensor(grad_y); |
| 11276 | } else { |
| 11277 | grad_y_ = grad_y; |
| 11278 | } |
| 11279 | |
| 11280 | c10::optional<at::Tensor> grad_hy_; |
| 11281 | if (at::functionalization::impl::isFunctionalTensor(grad_hy)) { |
| 11282 | at::functionalization::impl::sync(grad_hy); |
| 11283 | grad_hy_ = at::functionalization::impl::from_functional_tensor(grad_hy); |
| 11284 | } else { |
| 11285 | grad_hy_ = grad_hy; |
| 11286 | } |
| 11287 | |
| 11288 | c10::optional<at::Tensor> grad_cy_; |
| 11289 | if (at::functionalization::impl::isFunctionalTensor(grad_cy)) { |
| 11290 | at::functionalization::impl::sync(grad_cy); |
| 11291 | grad_cy_ = at::functionalization::impl::from_functional_tensor(grad_cy); |
| 11292 | } else { |
| 11293 | grad_cy_ = grad_cy; |
| 11294 | } |
| 11295 | |
| 11296 | at::Tensor z_state_; |
| 11297 | if (at::functionalization::impl::isFunctionalTensor(z_state)) { |
| 11298 | at::functionalization::impl::sync(z_state); |
| 11299 | z_state_ = at::functionalization::impl::from_functional_tensor(z_state); |
| 11300 | } else { |
| 11301 | z_state_ = z_state; |
| 11302 | } |
| 11303 | |
| 11304 | at::Tensor cell_state_fwd_; |
| 11305 | if (at::functionalization::impl::isFunctionalTensor(cell_state_fwd)) { |
| 11306 | at::functionalization::impl::sync(cell_state_fwd); |
| 11307 | cell_state_fwd_ = at::functionalization::impl::from_functional_tensor(cell_state_fwd); |
| 11308 | } else { |
| 11309 | cell_state_fwd_ = cell_state_fwd; |
| 11310 | } |
| 11311 | |
| 11312 | at::Tensor input_; |
| 11313 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 11314 | at::functionalization::impl::sync(input); |
| 11315 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 11316 | } else { |
| 11317 | input_ = input; |
| 11318 | } |
| 11319 | |
| 11320 | ::std::vector<at::Tensor> hx_; |
| 11321 | if (at::functionalization::impl::isFunctionalTensor(hx)) { |
| 11322 | at::functionalization::impl::sync(hx); |
| 11323 | hx_ = at::functionalization::impl::from_functional_tensor(hx); |
| 11324 | } else { |
| 11325 | hx_ = hx.vec(); |
| 11326 | } |
| 11327 | |
| 11328 | ::std::vector<at::Tensor> params_; |
| 11329 | if (at::functionalization::impl::isFunctionalTensor(params)) { |
| 11330 | at::functionalization::impl::sync(params); |
| 11331 | params_ = at::functionalization::impl::from_functional_tensor(params); |
| 11332 | } else { |
| 11333 | params_ = params.vec(); |
| 11334 | } |
| 11335 | |
| 11336 | at::Tensor out0_; |
| 11337 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 11338 | at::functionalization::impl::sync(out0); |
| 11339 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 11340 | } else { |
| 11341 | out0_ = out0; |
| 11342 | } |
| 11343 | |
| 11344 | ::std::vector<at::Tensor> out1_; |
| 11345 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 11346 | at::functionalization::impl::sync(out1); |
| 11347 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 11348 | } else { |
| 11349 | out1_ = out1.vec(); |
| 11350 | } |
| 11351 | |
| 11352 | ::std::vector<at::Tensor> out2_; |
| 11353 | if (at::functionalization::impl::isFunctionalTensor(out2)) { |
| 11354 | at::functionalization::impl::sync(out2); |
| 11355 | out2_ = at::functionalization::impl::from_functional_tensor(out2); |
| 11356 | } else { |
| 11357 | out2_ = out2.vec(); |
| 11358 | } |
| 11359 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1) && at::functionalization::impl::isFunctionalTensor(out2))) { |
| 11360 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_y) || at::functionalization::impl::isFunctionalTensor(grad_hy) || at::functionalization::impl::isFunctionalTensor(grad_cy) || at::functionalization::impl::isFunctionalTensor(z_state) || at::functionalization::impl::isFunctionalTensor(cell_state_fwd) || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(hx) || at::functionalization::impl::isFunctionalTensor(params))) { |
| 11361 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11362 | TORCH_INTERNAL_ASSERT(false, |
| 11363 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11364 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11365 | } else { |
| 11366 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11367 | at::AutoDispatchSkipFunctionalize guard; |
| 11368 | at::_ops::lstm_mps_backward_out::call(grad_y_, grad_hy_, grad_cy_, z_state_, cell_state_fwd_, input_, hx_, params_, has_biases, num_layers, dropout, train, bidirectional, batch_first, out0_, out1_, out2_); |
| 11369 | ; |
| 11370 | } |
| 11371 | } else { |
| 11372 | ::std::tuple<at::Tensor,::std::vector<at::Tensor>,::std::vector<at::Tensor>> tmp_output; |
| 11373 | { |
| 11374 | at::AutoDispatchSkipFunctionalize guard; |
| 11375 | tmp_output = at::_ops::lstm_mps_backward::call(grad_y_, grad_hy_, grad_cy_, z_state_, cell_state_fwd_, input_, hx_, params_, has_biases, num_layers, dropout, train, bidirectional, batch_first); |
| 11376 | } |
| 11377 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 11378 | at::functionalization::impl::commit_update(out0); |
| 11379 | at::functionalization::impl::sync(out0); |
| 11380 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 11381 | at::functionalization::impl::commit_update(out1); |
| 11382 | at::functionalization::impl::sync(out1); |
| 11383 | at::functionalization::impl::replace_(out2, std::get<2>(tmp_output)); |
| 11384 | at::functionalization::impl::commit_update(out2); |
| 11385 | at::functionalization::impl::sync(out2); |
| 11386 | |
| 11387 | } |
| 11388 | } |
| 11389 | |
| 11390 | at::Tensor & lift_fresh_copy_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 11391 | if (false) { |
| 11392 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11393 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11394 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11395 | auto self_meta = to_meta(self); |
| 11396 | auto out_meta = to_meta(out); |
| 11397 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11398 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11399 | at::_ops::lift_fresh_copy_out::call(self_meta, out_meta); |
| 11400 | } |
| 11401 | |
| 11402 | at::Tensor self_; |
| 11403 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11404 | at::functionalization::impl::sync(self); |
| 11405 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11406 | } else { |
| 11407 | self_ = self; |
| 11408 | } |
| 11409 | |
| 11410 | at::Tensor out_; |
| 11411 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11412 | at::functionalization::impl::sync(out); |
| 11413 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11414 | } else { |
| 11415 | out_ = out; |
| 11416 | } |
| 11417 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11418 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 11419 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11420 | TORCH_INTERNAL_ASSERT(false, |
| 11421 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11422 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11423 | } else { |
| 11424 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11425 | at::AutoDispatchSkipFunctionalize guard; |
| 11426 | at::Tensor tmp_output = at::_ops::lift_fresh_copy_out::call(self_, out_); |
| 11427 | return out;; |
| 11428 | } |
| 11429 | } else { |
| 11430 | at::Tensor tmp_output; |
| 11431 | { |
| 11432 | at::AutoDispatchSkipFunctionalize guard; |
| 11433 | tmp_output = at::_ops::lift_fresh_copy::call(self_); |
| 11434 | } |
| 11435 | at::functionalization::impl::replace_(out, tmp_output); |
| 11436 | at::functionalization::impl::commit_update(out); |
| 11437 | at::functionalization::impl::sync(out); |
| 11438 | return out; |
| 11439 | } |
| 11440 | } |
| 11441 | |
| 11442 | at::Tensor & _masked_softmax_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & mask, c10::optional<int64_t> dim, at::Tensor & out) { |
| 11443 | if (false) { |
| 11444 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11445 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11446 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11447 | auto grad_output_meta = to_meta(grad_output); |
| 11448 | auto output_meta = to_meta(output); |
| 11449 | auto mask_meta = to_meta(mask); |
| 11450 | auto out_meta = to_meta(out); |
| 11451 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11452 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11453 | at::_ops::_masked_softmax_backward_out::call(grad_output_meta, output_meta, mask_meta, dim, out_meta); |
| 11454 | } |
| 11455 | |
| 11456 | at::Tensor grad_output_; |
| 11457 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 11458 | at::functionalization::impl::sync(grad_output); |
| 11459 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 11460 | } else { |
| 11461 | grad_output_ = grad_output; |
| 11462 | } |
| 11463 | |
| 11464 | at::Tensor output_; |
| 11465 | if (at::functionalization::impl::isFunctionalTensor(output)) { |
| 11466 | at::functionalization::impl::sync(output); |
| 11467 | output_ = at::functionalization::impl::from_functional_tensor(output); |
| 11468 | } else { |
| 11469 | output_ = output; |
| 11470 | } |
| 11471 | |
| 11472 | at::Tensor mask_; |
| 11473 | if (at::functionalization::impl::isFunctionalTensor(mask)) { |
| 11474 | at::functionalization::impl::sync(mask); |
| 11475 | mask_ = at::functionalization::impl::from_functional_tensor(mask); |
| 11476 | } else { |
| 11477 | mask_ = mask; |
| 11478 | } |
| 11479 | |
| 11480 | at::Tensor out_; |
| 11481 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11482 | at::functionalization::impl::sync(out); |
| 11483 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11484 | } else { |
| 11485 | out_ = out; |
| 11486 | } |
| 11487 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11488 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(output) || at::functionalization::impl::isFunctionalTensor(mask))) { |
| 11489 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11490 | TORCH_INTERNAL_ASSERT(false, |
| 11491 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11492 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11493 | } else { |
| 11494 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11495 | at::AutoDispatchSkipFunctionalize guard; |
| 11496 | at::Tensor tmp_output = at::_ops::_masked_softmax_backward_out::call(grad_output_, output_, mask_, dim, out_); |
| 11497 | return out;; |
| 11498 | } |
| 11499 | } else { |
| 11500 | at::Tensor tmp_output; |
| 11501 | { |
| 11502 | at::AutoDispatchSkipFunctionalize guard; |
| 11503 | tmp_output = at::_ops::_masked_softmax_backward::call(grad_output_, output_, mask_, dim); |
| 11504 | } |
| 11505 | at::functionalization::impl::replace_(out, tmp_output); |
| 11506 | at::functionalization::impl::commit_update(out); |
| 11507 | at::functionalization::impl::sync(out); |
| 11508 | return out; |
| 11509 | } |
| 11510 | } |
| 11511 | |
| 11512 | at::Tensor & put_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & index, const at::Tensor & source, bool accumulate, at::Tensor & out) { |
| 11513 | if (false) { |
| 11514 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11515 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11516 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11517 | auto self_meta = to_meta(self); |
| 11518 | auto index_meta = to_meta(index); |
| 11519 | auto source_meta = to_meta(source); |
| 11520 | auto out_meta = to_meta(out); |
| 11521 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11522 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11523 | at::_ops::put_out::call(self_meta, index_meta, source_meta, accumulate, out_meta); |
| 11524 | } |
| 11525 | |
| 11526 | at::Tensor self_; |
| 11527 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11528 | at::functionalization::impl::sync(self); |
| 11529 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11530 | } else { |
| 11531 | self_ = self; |
| 11532 | } |
| 11533 | |
| 11534 | at::Tensor index_; |
| 11535 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 11536 | at::functionalization::impl::sync(index); |
| 11537 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 11538 | } else { |
| 11539 | index_ = index; |
| 11540 | } |
| 11541 | |
| 11542 | at::Tensor source_; |
| 11543 | if (at::functionalization::impl::isFunctionalTensor(source)) { |
| 11544 | at::functionalization::impl::sync(source); |
| 11545 | source_ = at::functionalization::impl::from_functional_tensor(source); |
| 11546 | } else { |
| 11547 | source_ = source; |
| 11548 | } |
| 11549 | |
| 11550 | at::Tensor out_; |
| 11551 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11552 | at::functionalization::impl::sync(out); |
| 11553 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11554 | } else { |
| 11555 | out_ = out; |
| 11556 | } |
| 11557 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11558 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(index) || at::functionalization::impl::isFunctionalTensor(source))) { |
| 11559 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11560 | TORCH_INTERNAL_ASSERT(false, |
| 11561 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11562 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11563 | } else { |
| 11564 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11565 | at::AutoDispatchSkipFunctionalize guard; |
| 11566 | at::Tensor tmp_output = at::_ops::put_out::call(self_, index_, source_, accumulate, out_); |
| 11567 | return out;; |
| 11568 | } |
| 11569 | } else { |
| 11570 | at::Tensor tmp_output; |
| 11571 | { |
| 11572 | at::AutoDispatchSkipFunctionalize guard; |
| 11573 | tmp_output = at::_ops::put::call(self_, index_, source_, accumulate); |
| 11574 | } |
| 11575 | at::functionalization::impl::replace_(out, tmp_output); |
| 11576 | at::functionalization::impl::commit_update(out); |
| 11577 | at::functionalization::impl::sync(out); |
| 11578 | return out; |
| 11579 | } |
| 11580 | } |
| 11581 | |
| 11582 | at::Tensor & put_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & index, const at::Tensor & source, bool accumulate) { |
| 11583 | if (true) { |
| 11584 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11585 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11586 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11587 | auto self_meta = to_meta(self); |
| 11588 | auto index_meta = to_meta(index); |
| 11589 | auto source_meta = to_meta(source); |
| 11590 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11591 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11592 | at::_ops::put_::call(self_meta, index_meta, source_meta, accumulate); |
| 11593 | } |
| 11594 | |
| 11595 | at::Tensor self_; |
| 11596 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11597 | at::functionalization::impl::sync(self); |
| 11598 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11599 | } else { |
| 11600 | self_ = self; |
| 11601 | } |
| 11602 | |
| 11603 | at::Tensor index_; |
| 11604 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 11605 | at::functionalization::impl::sync(index); |
| 11606 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 11607 | } else { |
| 11608 | index_ = index; |
| 11609 | } |
| 11610 | |
| 11611 | at::Tensor source_; |
| 11612 | if (at::functionalization::impl::isFunctionalTensor(source)) { |
| 11613 | at::functionalization::impl::sync(source); |
| 11614 | source_ = at::functionalization::impl::from_functional_tensor(source); |
| 11615 | } else { |
| 11616 | source_ = source; |
| 11617 | } |
| 11618 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 11619 | if ((false || at::functionalization::impl::isFunctionalTensor(index) || at::functionalization::impl::isFunctionalTensor(source))) { |
| 11620 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11621 | TORCH_INTERNAL_ASSERT(false, |
| 11622 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11623 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11624 | } else { |
| 11625 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11626 | at::AutoDispatchSkipFunctionalize guard; |
| 11627 | at::Tensor tmp_output = at::_ops::put_::call(self_, index_, source_, accumulate); |
| 11628 | return self;; |
| 11629 | } |
| 11630 | } else { |
| 11631 | at::Tensor tmp_output; |
| 11632 | { |
| 11633 | at::AutoDispatchSkipFunctionalize guard; |
| 11634 | tmp_output = at::_ops::put::call(self_, index_, source_, accumulate); |
| 11635 | } |
| 11636 | at::functionalization::impl::replace_(self, tmp_output); |
| 11637 | at::functionalization::impl::commit_update(self); |
| 11638 | at::functionalization::impl::sync(self); |
| 11639 | return self; |
| 11640 | } |
| 11641 | } |
| 11642 | |
| 11643 | at::Tensor & index_add_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha, at::Tensor & out) { |
| 11644 | if (false) { |
| 11645 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11646 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11647 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11648 | auto self_meta = to_meta(self); |
| 11649 | auto index_meta = to_meta(index); |
| 11650 | auto source_meta = to_meta(source); |
| 11651 | auto out_meta = to_meta(out); |
| 11652 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11653 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11654 | at::_ops::index_add_out::call(self_meta, dim, index_meta, source_meta, alpha, out_meta); |
| 11655 | } |
| 11656 | |
| 11657 | at::Tensor self_; |
| 11658 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11659 | at::functionalization::impl::sync(self); |
| 11660 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11661 | } else { |
| 11662 | self_ = self; |
| 11663 | } |
| 11664 | |
| 11665 | at::Tensor index_; |
| 11666 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 11667 | at::functionalization::impl::sync(index); |
| 11668 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 11669 | } else { |
| 11670 | index_ = index; |
| 11671 | } |
| 11672 | |
| 11673 | at::Tensor source_; |
| 11674 | if (at::functionalization::impl::isFunctionalTensor(source)) { |
| 11675 | at::functionalization::impl::sync(source); |
| 11676 | source_ = at::functionalization::impl::from_functional_tensor(source); |
| 11677 | } else { |
| 11678 | source_ = source; |
| 11679 | } |
| 11680 | |
| 11681 | at::Tensor out_; |
| 11682 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11683 | at::functionalization::impl::sync(out); |
| 11684 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11685 | } else { |
| 11686 | out_ = out; |
| 11687 | } |
| 11688 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11689 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(index) || at::functionalization::impl::isFunctionalTensor(source))) { |
| 11690 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11691 | TORCH_INTERNAL_ASSERT(false, |
| 11692 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11693 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11694 | } else { |
| 11695 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11696 | at::AutoDispatchSkipFunctionalize guard; |
| 11697 | at::Tensor tmp_output = at::_ops::index_add_out::call(self_, dim, index_, source_, alpha, out_); |
| 11698 | return out;; |
| 11699 | } |
| 11700 | } else { |
| 11701 | at::Tensor tmp_output; |
| 11702 | { |
| 11703 | at::AutoDispatchSkipFunctionalize guard; |
| 11704 | tmp_output = at::_ops::index_add::call(self_, dim, index_, source_, alpha); |
| 11705 | } |
| 11706 | at::functionalization::impl::replace_(out, tmp_output); |
| 11707 | at::functionalization::impl::commit_update(out); |
| 11708 | at::functionalization::impl::sync(out); |
| 11709 | return out; |
| 11710 | } |
| 11711 | } |
| 11712 | |
| 11713 | at::Tensor & index_add_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha) { |
| 11714 | if (true) { |
| 11715 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11716 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11717 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11718 | auto self_meta = to_meta(self); |
| 11719 | auto index_meta = to_meta(index); |
| 11720 | auto source_meta = to_meta(source); |
| 11721 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11722 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11723 | at::_ops::index_add_::call(self_meta, dim, index_meta, source_meta, alpha); |
| 11724 | } |
| 11725 | |
| 11726 | at::Tensor self_; |
| 11727 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11728 | at::functionalization::impl::sync(self); |
| 11729 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11730 | } else { |
| 11731 | self_ = self; |
| 11732 | } |
| 11733 | |
| 11734 | at::Tensor index_; |
| 11735 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 11736 | at::functionalization::impl::sync(index); |
| 11737 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 11738 | } else { |
| 11739 | index_ = index; |
| 11740 | } |
| 11741 | |
| 11742 | at::Tensor source_; |
| 11743 | if (at::functionalization::impl::isFunctionalTensor(source)) { |
| 11744 | at::functionalization::impl::sync(source); |
| 11745 | source_ = at::functionalization::impl::from_functional_tensor(source); |
| 11746 | } else { |
| 11747 | source_ = source; |
| 11748 | } |
| 11749 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 11750 | if ((false || at::functionalization::impl::isFunctionalTensor(index) || at::functionalization::impl::isFunctionalTensor(source))) { |
| 11751 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11752 | TORCH_INTERNAL_ASSERT(false, |
| 11753 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11754 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11755 | } else { |
| 11756 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11757 | at::AutoDispatchSkipFunctionalize guard; |
| 11758 | at::Tensor tmp_output = at::_ops::index_add_::call(self_, dim, index_, source_, alpha); |
| 11759 | return self;; |
| 11760 | } |
| 11761 | } else { |
| 11762 | at::Tensor tmp_output; |
| 11763 | { |
| 11764 | at::AutoDispatchSkipFunctionalize guard; |
| 11765 | tmp_output = at::_ops::index_add::call(self_, dim, index_, source_, alpha); |
| 11766 | } |
| 11767 | at::functionalization::impl::replace_(self, tmp_output); |
| 11768 | at::functionalization::impl::commit_update(self); |
| 11769 | at::functionalization::impl::sync(self); |
| 11770 | return self; |
| 11771 | } |
| 11772 | } |
| 11773 | |
| 11774 | at::Tensor & index_fill_out_int_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, at::Tensor & out) { |
| 11775 | if (false) { |
| 11776 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11777 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11778 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11779 | auto self_meta = to_meta(self); |
| 11780 | auto index_meta = to_meta(index); |
| 11781 | auto out_meta = to_meta(out); |
| 11782 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11783 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11784 | at::_ops::index_fill_int_Scalar_out::call(self_meta, dim, index_meta, value, out_meta); |
| 11785 | } |
| 11786 | |
| 11787 | at::Tensor self_; |
| 11788 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11789 | at::functionalization::impl::sync(self); |
| 11790 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11791 | } else { |
| 11792 | self_ = self; |
| 11793 | } |
| 11794 | |
| 11795 | at::Tensor index_; |
| 11796 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 11797 | at::functionalization::impl::sync(index); |
| 11798 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 11799 | } else { |
| 11800 | index_ = index; |
| 11801 | } |
| 11802 | |
| 11803 | at::Tensor out_; |
| 11804 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11805 | at::functionalization::impl::sync(out); |
| 11806 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11807 | } else { |
| 11808 | out_ = out; |
| 11809 | } |
| 11810 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11811 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(index))) { |
| 11812 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11813 | TORCH_INTERNAL_ASSERT(false, |
| 11814 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11815 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11816 | } else { |
| 11817 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11818 | at::AutoDispatchSkipFunctionalize guard; |
| 11819 | at::Tensor tmp_output = at::_ops::index_fill_int_Scalar_out::call(self_, dim, index_, value, out_); |
| 11820 | return out;; |
| 11821 | } |
| 11822 | } else { |
| 11823 | at::Tensor tmp_output; |
| 11824 | { |
| 11825 | at::AutoDispatchSkipFunctionalize guard; |
| 11826 | tmp_output = at::_ops::index_fill_int_Scalar::call(self_, dim, index_, value); |
| 11827 | } |
| 11828 | at::functionalization::impl::replace_(out, tmp_output); |
| 11829 | at::functionalization::impl::commit_update(out); |
| 11830 | at::functionalization::impl::sync(out); |
| 11831 | return out; |
| 11832 | } |
| 11833 | } |
| 11834 | |
| 11835 | at::Tensor & index_fill__int_Scalar(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) { |
| 11836 | if (true) { |
| 11837 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11838 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11839 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11840 | auto self_meta = to_meta(self); |
| 11841 | auto index_meta = to_meta(index); |
| 11842 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11843 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11844 | at::_ops::index_fill__int_Scalar::call(self_meta, dim, index_meta, value); |
| 11845 | } |
| 11846 | |
| 11847 | at::Tensor self_; |
| 11848 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11849 | at::functionalization::impl::sync(self); |
| 11850 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11851 | } else { |
| 11852 | self_ = self; |
| 11853 | } |
| 11854 | |
| 11855 | at::Tensor index_; |
| 11856 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 11857 | at::functionalization::impl::sync(index); |
| 11858 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 11859 | } else { |
| 11860 | index_ = index; |
| 11861 | } |
| 11862 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 11863 | if ((false || at::functionalization::impl::isFunctionalTensor(index))) { |
| 11864 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11865 | TORCH_INTERNAL_ASSERT(false, |
| 11866 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11867 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11868 | } else { |
| 11869 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11870 | at::AutoDispatchSkipFunctionalize guard; |
| 11871 | at::Tensor tmp_output = at::_ops::index_fill__int_Scalar::call(self_, dim, index_, value); |
| 11872 | return self;; |
| 11873 | } |
| 11874 | } else { |
| 11875 | at::Tensor tmp_output; |
| 11876 | { |
| 11877 | at::AutoDispatchSkipFunctionalize guard; |
| 11878 | tmp_output = at::_ops::index_fill_int_Scalar::call(self_, dim, index_, value); |
| 11879 | } |
| 11880 | at::functionalization::impl::replace_(self, tmp_output); |
| 11881 | at::functionalization::impl::commit_update(self); |
| 11882 | at::functionalization::impl::sync(self); |
| 11883 | return self; |
| 11884 | } |
| 11885 | } |
| 11886 | |
| 11887 | at::Tensor & index_fill_out_int_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & value, at::Tensor & out) { |
| 11888 | if (false) { |
| 11889 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11890 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11891 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11892 | auto self_meta = to_meta(self); |
| 11893 | auto index_meta = to_meta(index); |
| 11894 | auto value_meta = to_meta(value); |
| 11895 | auto out_meta = to_meta(out); |
| 11896 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11897 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11898 | at::_ops::index_fill_int_Tensor_out::call(self_meta, dim, index_meta, value_meta, out_meta); |
| 11899 | } |
| 11900 | |
| 11901 | at::Tensor self_; |
| 11902 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11903 | at::functionalization::impl::sync(self); |
| 11904 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11905 | } else { |
| 11906 | self_ = self; |
| 11907 | } |
| 11908 | |
| 11909 | at::Tensor index_; |
| 11910 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 11911 | at::functionalization::impl::sync(index); |
| 11912 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 11913 | } else { |
| 11914 | index_ = index; |
| 11915 | } |
| 11916 | |
| 11917 | at::Tensor value_; |
| 11918 | if (at::functionalization::impl::isFunctionalTensor(value)) { |
| 11919 | at::functionalization::impl::sync(value); |
| 11920 | value_ = at::functionalization::impl::from_functional_tensor(value); |
| 11921 | } else { |
| 11922 | value_ = value; |
| 11923 | } |
| 11924 | |
| 11925 | at::Tensor out_; |
| 11926 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11927 | at::functionalization::impl::sync(out); |
| 11928 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11929 | } else { |
| 11930 | out_ = out; |
| 11931 | } |
| 11932 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11933 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(index) || at::functionalization::impl::isFunctionalTensor(value))) { |
| 11934 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11935 | TORCH_INTERNAL_ASSERT(false, |
| 11936 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11937 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11938 | } else { |
| 11939 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11940 | at::AutoDispatchSkipFunctionalize guard; |
| 11941 | at::Tensor tmp_output = at::_ops::index_fill_int_Tensor_out::call(self_, dim, index_, value_, out_); |
| 11942 | return out;; |
| 11943 | } |
| 11944 | } else { |
| 11945 | at::Tensor tmp_output; |
| 11946 | { |
| 11947 | at::AutoDispatchSkipFunctionalize guard; |
| 11948 | tmp_output = at::_ops::index_fill_int_Tensor::call(self_, dim, index_, value_); |
| 11949 | } |
| 11950 | at::functionalization::impl::replace_(out, tmp_output); |
| 11951 | at::functionalization::impl::commit_update(out); |
| 11952 | at::functionalization::impl::sync(out); |
| 11953 | return out; |
| 11954 | } |
| 11955 | } |
| 11956 | |
| 11957 | at::Tensor & index_fill__int_Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & value) { |
| 11958 | if (true) { |
| 11959 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11960 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11961 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11962 | auto self_meta = to_meta(self); |
| 11963 | auto index_meta = to_meta(index); |
| 11964 | auto value_meta = to_meta(value); |
| 11965 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11966 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11967 | at::_ops::index_fill__int_Tensor::call(self_meta, dim, index_meta, value_meta); |
| 11968 | } |
| 11969 | |
| 11970 | at::Tensor self_; |
| 11971 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11972 | at::functionalization::impl::sync(self); |
| 11973 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11974 | } else { |
| 11975 | self_ = self; |
| 11976 | } |
| 11977 | |
| 11978 | at::Tensor index_; |
| 11979 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 11980 | at::functionalization::impl::sync(index); |
| 11981 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 11982 | } else { |
| 11983 | index_ = index; |
| 11984 | } |
| 11985 | |
| 11986 | at::Tensor value_; |
| 11987 | if (at::functionalization::impl::isFunctionalTensor(value)) { |
| 11988 | at::functionalization::impl::sync(value); |
| 11989 | value_ = at::functionalization::impl::from_functional_tensor(value); |
| 11990 | } else { |
| 11991 | value_ = value; |
| 11992 | } |
| 11993 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 11994 | if ((false || at::functionalization::impl::isFunctionalTensor(index) || at::functionalization::impl::isFunctionalTensor(value))) { |
| 11995 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11996 | TORCH_INTERNAL_ASSERT(false, |
| 11997 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11998 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11999 | } else { |
| 12000 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12001 | at::AutoDispatchSkipFunctionalize guard; |
| 12002 | at::Tensor tmp_output = at::_ops::index_fill__int_Tensor::call(self_, dim, index_, value_); |
| 12003 | return self;; |
| 12004 | } |
| 12005 | } else { |
| 12006 | at::Tensor tmp_output; |
| 12007 | { |
| 12008 | at::AutoDispatchSkipFunctionalize guard; |
| 12009 | tmp_output = at::_ops::index_fill_int_Tensor::call(self_, dim, index_, value_); |
| 12010 | } |
| 12011 | at::functionalization::impl::replace_(self, tmp_output); |
| 12012 | at::functionalization::impl::commit_update(self); |
| 12013 | at::functionalization::impl::sync(self); |
| 12014 | return self; |
| 12015 | } |
| 12016 | } |
| 12017 | |
| 12018 | at::Tensor & scatter_out_src_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, at::Tensor & out) { |
| 12019 | if (false) { |
| 12020 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12021 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12022 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12023 | auto self_meta = to_meta(self); |
| 12024 | auto index_meta = to_meta(index); |
| 12025 | auto src_meta = to_meta(src); |
| 12026 | auto out_meta = to_meta(out); |
| 12027 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12028 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12029 | at::_ops::scatter_src_out::call(self_meta, dim, index_meta, src_meta, out_meta); |
| 12030 | } |
| 12031 | |
| 12032 | at::Tensor self_; |
| 12033 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12034 | at::functionalization::impl::sync(self); |
| 12035 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12036 | } else { |
| 12037 | self_ = self; |
| 12038 | } |
| 12039 | |
| 12040 | at::Tensor index_; |
| 12041 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 12042 | at::functionalization::impl::sync(index); |
| 12043 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 12044 | } else { |
| 12045 | index_ = index; |
| 12046 | } |
| 12047 | |
| 12048 | at::Tensor src_; |
| 12049 | if (at::functionalization::impl::isFunctionalTensor(src)) { |
| 12050 | at::functionalization::impl::sync(src); |
| 12051 | src_ = at::functionalization::impl::from_functional_tensor(src); |
| 12052 | } else { |
| 12053 | src_ = src; |
| 12054 | } |
| 12055 | |
| 12056 | at::Tensor out_; |
| 12057 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12058 | at::functionalization::impl::sync(out); |
| 12059 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12060 | } else { |
| 12061 | out_ = out; |
| 12062 | } |
| 12063 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12064 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(index) || at::functionalization::impl::isFunctionalTensor(src))) { |
| 12065 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12066 | TORCH_INTERNAL_ASSERT(false, |
| 12067 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12068 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12069 | } else { |
| 12070 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12071 | at::AutoDispatchSkipFunctionalize guard; |
| 12072 | at::Tensor tmp_output = at::_ops::scatter_src_out::call(self_, dim, index_, src_, out_); |
| 12073 | return out;; |
| 12074 | } |
| 12075 | } else { |
| 12076 | at::Tensor tmp_output; |
| 12077 | { |
| 12078 | at::AutoDispatchSkipFunctionalize guard; |
| 12079 | tmp_output = at::_ops::scatter_src::call(self_, dim, index_, src_); |
| 12080 | } |
| 12081 | at::functionalization::impl::replace_(out, tmp_output); |
| 12082 | at::functionalization::impl::commit_update(out); |
| 12083 | at::functionalization::impl::sync(out); |
| 12084 | return out; |
| 12085 | } |
| 12086 | } |
| 12087 | |
| 12088 | at::Tensor & scatter__src(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { |
| 12089 | if (true) { |
| 12090 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12091 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12092 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12093 | auto self_meta = to_meta(self); |
| 12094 | auto index_meta = to_meta(index); |
| 12095 | auto src_meta = to_meta(src); |
| 12096 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12097 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12098 | at::_ops::scatter__src::call(self_meta, dim, index_meta, src_meta); |
| 12099 | } |
| 12100 | |
| 12101 | at::Tensor self_; |
| 12102 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12103 | at::functionalization::impl::sync(self); |
| 12104 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12105 | } else { |
| 12106 | self_ = self; |
| 12107 | } |
| 12108 | |
| 12109 | at::Tensor index_; |
| 12110 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 12111 | at::functionalization::impl::sync(index); |
| 12112 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 12113 | } else { |
| 12114 | index_ = index; |
| 12115 | } |
| 12116 | |
| 12117 | at::Tensor src_; |
| 12118 | if (at::functionalization::impl::isFunctionalTensor(src)) { |
| 12119 | at::functionalization::impl::sync(src); |
| 12120 | src_ = at::functionalization::impl::from_functional_tensor(src); |
| 12121 | } else { |
| 12122 | src_ = src; |
| 12123 | } |
| 12124 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 12125 | if ((false || at::functionalization::impl::isFunctionalTensor(index) || at::functionalization::impl::isFunctionalTensor(src))) { |
| 12126 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12127 | TORCH_INTERNAL_ASSERT(false, |
| 12128 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12129 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12130 | } else { |
| 12131 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12132 | at::AutoDispatchSkipFunctionalize guard; |
| 12133 | at::Tensor tmp_output = at::_ops::scatter__src::call(self_, dim, index_, src_); |
| 12134 | return self;; |
| 12135 | } |
| 12136 | } else { |
| 12137 | at::Tensor tmp_output; |
| 12138 | { |
| 12139 | at::AutoDispatchSkipFunctionalize guard; |
| 12140 | tmp_output = at::_ops::scatter_src::call(self_, dim, index_, src_); |
| 12141 | } |
| 12142 | at::functionalization::impl::replace_(self, tmp_output); |
| 12143 | at::functionalization::impl::commit_update(self); |
| 12144 | at::functionalization::impl::sync(self); |
| 12145 | return self; |
| 12146 | } |
| 12147 | } |
| 12148 | |
| 12149 | at::Tensor & scatter_out_value_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, at::Tensor & out) { |
| 12150 | if (false) { |
| 12151 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12152 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12153 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12154 | auto self_meta = to_meta(self); |
| 12155 | auto index_meta = to_meta(index); |
| 12156 | auto out_meta = to_meta(out); |
| 12157 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12158 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12159 | at::_ops::scatter_value_out::call(self_meta, dim, index_meta, value, out_meta); |
| 12160 | } |
| 12161 | |
| 12162 | at::Tensor self_; |
| 12163 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12164 | at::functionalization::impl::sync(self); |
| 12165 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12166 | } else { |
| 12167 | self_ = self; |
| 12168 | } |
| 12169 | |
| 12170 | at::Tensor index_; |
| 12171 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 12172 | at::functionalization::impl::sync(index); |
| 12173 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 12174 | } else { |
| 12175 | index_ = index; |
| 12176 | } |
| 12177 | |
| 12178 | at::Tensor out_; |
| 12179 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12180 | at::functionalization::impl::sync(out); |
| 12181 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12182 | } else { |
| 12183 | out_ = out; |
| 12184 | } |
| 12185 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12186 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(index))) { |
| 12187 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12188 | TORCH_INTERNAL_ASSERT(false, |
| 12189 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12190 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12191 | } else { |
| 12192 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12193 | at::AutoDispatchSkipFunctionalize guard; |
| 12194 | at::Tensor tmp_output = at::_ops::scatter_value_out::call(self_, dim, index_, value, out_); |
| 12195 | return out;; |
| 12196 | } |
| 12197 | } else { |
| 12198 | at::Tensor tmp_output; |
| 12199 | { |
| 12200 | at::AutoDispatchSkipFunctionalize guard; |
| 12201 | tmp_output = at::_ops::scatter_value::call(self_, dim, index_, value); |
| 12202 | } |
| 12203 | at::functionalization::impl::replace_(out, tmp_output); |
| 12204 | at::functionalization::impl::commit_update(out); |
| 12205 | at::functionalization::impl::sync(out); |
| 12206 | return out; |
| 12207 | } |
| 12208 | } |
| 12209 | |
| 12210 | at::Tensor & scatter__value(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) { |
| 12211 | if (true) { |
| 12212 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12213 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12214 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12215 | auto self_meta = to_meta(self); |
| 12216 | auto index_meta = to_meta(index); |
| 12217 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12218 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12219 | at::_ops::scatter__value::call(self_meta, dim, index_meta, value); |
| 12220 | } |
| 12221 | |
| 12222 | at::Tensor self_; |
| 12223 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12224 | at::functionalization::impl::sync(self); |
| 12225 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12226 | } else { |
| 12227 | self_ = self; |
| 12228 | } |
| 12229 | |
| 12230 | at::Tensor index_; |
| 12231 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 12232 | at::functionalization::impl::sync(index); |
| 12233 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 12234 | } else { |
| 12235 | index_ = index; |
| 12236 | } |
| 12237 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 12238 | if ((false || at::functionalization::impl::isFunctionalTensor(index))) { |
| 12239 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12240 | TORCH_INTERNAL_ASSERT(false, |
| 12241 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12242 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12243 | } else { |
| 12244 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12245 | at::AutoDispatchSkipFunctionalize guard; |
| 12246 | at::Tensor tmp_output = at::_ops::scatter__value::call(self_, dim, index_, value); |
| 12247 | return self;; |
| 12248 | } |
| 12249 | } else { |
| 12250 | at::Tensor tmp_output; |
| 12251 | { |
| 12252 | at::AutoDispatchSkipFunctionalize guard; |
| 12253 | tmp_output = at::_ops::scatter_value::call(self_, dim, index_, value); |
| 12254 | } |
| 12255 | at::functionalization::impl::replace_(self, tmp_output); |
| 12256 | at::functionalization::impl::commit_update(self); |
| 12257 | at::functionalization::impl::sync(self); |
| 12258 | return self; |
| 12259 | } |
| 12260 | } |
| 12261 | |
| 12262 | at::Tensor & scatter_out_reduce_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, at::Tensor & out) { |
| 12263 | if (false) { |
| 12264 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12265 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12266 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12267 | auto self_meta = to_meta(self); |
| 12268 | auto index_meta = to_meta(index); |
| 12269 | auto src_meta = to_meta(src); |
| 12270 | auto out_meta = to_meta(out); |
| 12271 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12272 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12273 | at::_ops::scatter_reduce_out::call(self_meta, dim, index_meta, src_meta, reduce, out_meta); |
| 12274 | } |
| 12275 | |
| 12276 | at::Tensor self_; |
| 12277 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12278 | at::functionalization::impl::sync(self); |
| 12279 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12280 | } else { |
| 12281 | self_ = self; |
| 12282 | } |
| 12283 | |
| 12284 | at::Tensor index_; |
| 12285 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 12286 | at::functionalization::impl::sync(index); |
| 12287 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 12288 | } else { |
| 12289 | index_ = index; |
| 12290 | } |
| 12291 | |
| 12292 | at::Tensor src_; |
| 12293 | if (at::functionalization::impl::isFunctionalTensor(src)) { |
| 12294 | at::functionalization::impl::sync(src); |
| 12295 | src_ = at::functionalization::impl::from_functional_tensor(src); |
| 12296 | } else { |
| 12297 | src_ = src; |
| 12298 | } |
| 12299 | |
| 12300 | at::Tensor out_; |
| 12301 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12302 | at::functionalization::impl::sync(out); |
| 12303 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12304 | } else { |
| 12305 | out_ = out; |
| 12306 | } |
| 12307 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12308 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(index) || at::functionalization::impl::isFunctionalTensor(src))) { |
| 12309 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12310 | TORCH_INTERNAL_ASSERT(false, |
| 12311 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12312 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12313 | } else { |
| 12314 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12315 | at::AutoDispatchSkipFunctionalize guard; |
| 12316 | at::Tensor tmp_output = at::_ops::scatter_reduce_out::call(self_, dim, index_, src_, reduce, out_); |
| 12317 | return out;; |
| 12318 | } |
| 12319 | } else { |
| 12320 | at::Tensor tmp_output; |
| 12321 | { |
| 12322 | at::AutoDispatchSkipFunctionalize guard; |
| 12323 | tmp_output = at::_ops::scatter_reduce::call(self_, dim, index_, src_, reduce); |
| 12324 | } |
| 12325 | at::functionalization::impl::replace_(out, tmp_output); |
| 12326 | at::functionalization::impl::commit_update(out); |
| 12327 | at::functionalization::impl::sync(out); |
| 12328 | return out; |
| 12329 | } |
| 12330 | } |
| 12331 | |
| 12332 | at::Tensor & scatter__reduce(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce) { |
| 12333 | if (true) { |
| 12334 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12335 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12336 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12337 | auto self_meta = to_meta(self); |
| 12338 | auto index_meta = to_meta(index); |
| 12339 | auto src_meta = to_meta(src); |
| 12340 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12341 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12342 | at::_ops::scatter__reduce::call(self_meta, dim, index_meta, src_meta, reduce); |
| 12343 | } |
| 12344 | |
| 12345 | at::Tensor self_; |
| 12346 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12347 | at::functionalization::impl::sync(self); |
| 12348 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12349 | } else { |
| 12350 | self_ = self; |
| 12351 | } |
| 12352 | |
| 12353 | at::Tensor index_; |
| 12354 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 12355 | at::functionalization::impl::sync(index); |
| 12356 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 12357 | } else { |
| 12358 | index_ = index; |
| 12359 | } |
| 12360 | |
| 12361 | at::Tensor src_; |
| 12362 | if (at::functionalization::impl::isFunctionalTensor(src)) { |
| 12363 | at::functionalization::impl::sync(src); |
| 12364 | src_ = at::functionalization::impl::from_functional_tensor(src); |
| 12365 | } else { |
| 12366 | src_ = src; |
| 12367 | } |
| 12368 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 12369 | if ((false || at::functionalization::impl::isFunctionalTensor(index) || at::functionalization::impl::isFunctionalTensor(src))) { |
| 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::scatter__reduce::call(self_, dim, index_, src_, reduce); |
| 12378 | return self;; |
| 12379 | } |
| 12380 | } else { |
| 12381 | at::Tensor tmp_output; |
| 12382 | { |
| 12383 | at::AutoDispatchSkipFunctionalize guard; |
| 12384 | tmp_output = at::_ops::scatter_reduce::call(self_, dim, index_, src_, reduce); |
| 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 & scatter_out_value_reduce_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce, 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 index_meta = to_meta(index); |
| 12400 | auto out_meta = to_meta(out); |
| 12401 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12402 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12403 | at::_ops::scatter_value_reduce_out::call(self_meta, dim, index_meta, value, reduce, out_meta); |
| 12404 | } |
| 12405 | |
| 12406 | at::Tensor self_; |
| 12407 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12408 | at::functionalization::impl::sync(self); |
| 12409 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12410 | } else { |
| 12411 | self_ = self; |
| 12412 | } |
| 12413 | |
| 12414 | at::Tensor index_; |
| 12415 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 12416 | at::functionalization::impl::sync(index); |
| 12417 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 12418 | } else { |
| 12419 | index_ = index; |
| 12420 | } |
| 12421 | |
| 12422 | at::Tensor out_; |
| 12423 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12424 | at::functionalization::impl::sync(out); |
| 12425 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12426 | } else { |
| 12427 | out_ = out; |
| 12428 | } |
| 12429 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12430 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(index))) { |
| 12431 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12432 | TORCH_INTERNAL_ASSERT(false, |
| 12433 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12434 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12435 | } else { |
| 12436 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12437 | at::AutoDispatchSkipFunctionalize guard; |
| 12438 | at::Tensor tmp_output = at::_ops::scatter_value_reduce_out::call(self_, dim, index_, value, reduce, out_); |
| 12439 | return out;; |
| 12440 | } |
| 12441 | } else { |
| 12442 | at::Tensor tmp_output; |
| 12443 | { |
| 12444 | at::AutoDispatchSkipFunctionalize guard; |
| 12445 | tmp_output = at::_ops::scatter_value_reduce::call(self_, dim, index_, value, reduce); |
| 12446 | } |
| 12447 | at::functionalization::impl::replace_(out, tmp_output); |
| 12448 | at::functionalization::impl::commit_update(out); |
| 12449 | at::functionalization::impl::sync(out); |
| 12450 | return out; |
| 12451 | } |
| 12452 | } |
| 12453 | |
| 12454 | at::Tensor & scatter__value_reduce(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce) { |
| 12455 | if (true) { |
| 12456 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12457 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12458 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12459 | auto self_meta = to_meta(self); |
| 12460 | auto index_meta = to_meta(index); |
| 12461 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12462 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12463 | at::_ops::scatter__value_reduce::call(self_meta, dim, index_meta, value, reduce); |
| 12464 | } |
| 12465 | |
| 12466 | at::Tensor self_; |
| 12467 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12468 | at::functionalization::impl::sync(self); |
| 12469 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12470 | } else { |
| 12471 | self_ = self; |
| 12472 | } |
| 12473 | |
| 12474 | at::Tensor index_; |
| 12475 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 12476 | at::functionalization::impl::sync(index); |
| 12477 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 12478 | } else { |
| 12479 | index_ = index; |
| 12480 | } |
| 12481 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 12482 | if ((false || at::functionalization::impl::isFunctionalTensor(index))) { |
| 12483 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12484 | TORCH_INTERNAL_ASSERT(false, |
| 12485 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12486 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12487 | } else { |
| 12488 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12489 | at::AutoDispatchSkipFunctionalize guard; |
| 12490 | at::Tensor tmp_output = at::_ops::scatter__value_reduce::call(self_, dim, index_, value, reduce); |
| 12491 | return self;; |
| 12492 | } |
| 12493 | } else { |
| 12494 | at::Tensor tmp_output; |
| 12495 | { |
| 12496 | at::AutoDispatchSkipFunctionalize guard; |
| 12497 | tmp_output = at::_ops::scatter_value_reduce::call(self_, dim, index_, value, reduce); |
| 12498 | } |
| 12499 | at::functionalization::impl::replace_(self, tmp_output); |
| 12500 | at::functionalization::impl::commit_update(self); |
| 12501 | at::functionalization::impl::sync(self); |
| 12502 | return self; |
| 12503 | } |
| 12504 | } |
| 12505 | |
| 12506 | at::Tensor & scatter_add_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, at::Tensor & out) { |
| 12507 | if (false) { |
| 12508 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12509 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12510 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12511 | auto self_meta = to_meta(self); |
| 12512 | auto index_meta = to_meta(index); |
| 12513 | auto src_meta = to_meta(src); |
| 12514 | auto out_meta = to_meta(out); |
| 12515 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12516 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12517 | at::_ops::scatter_add_out::call(self_meta, dim, index_meta, src_meta, out_meta); |
| 12518 | } |
| 12519 | |
| 12520 | at::Tensor self_; |
| 12521 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12522 | at::functionalization::impl::sync(self); |
| 12523 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12524 | } else { |
| 12525 | self_ = self; |
| 12526 | } |
| 12527 | |
| 12528 | at::Tensor index_; |
| 12529 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 12530 | at::functionalization::impl::sync(index); |
| 12531 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 12532 | } else { |
| 12533 | index_ = index; |
| 12534 | } |
| 12535 | |
| 12536 | at::Tensor src_; |
| 12537 | if (at::functionalization::impl::isFunctionalTensor(src)) { |
| 12538 | at::functionalization::impl::sync(src); |
| 12539 | src_ = at::functionalization::impl::from_functional_tensor(src); |
| 12540 | } else { |
| 12541 | src_ = src; |
| 12542 | } |
| 12543 | |
| 12544 | at::Tensor out_; |
| 12545 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12546 | at::functionalization::impl::sync(out); |
| 12547 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12548 | } else { |
| 12549 | out_ = out; |
| 12550 | } |
| 12551 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12552 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(index) || at::functionalization::impl::isFunctionalTensor(src))) { |
| 12553 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12554 | TORCH_INTERNAL_ASSERT(false, |
| 12555 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12556 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12557 | } else { |
| 12558 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12559 | at::AutoDispatchSkipFunctionalize guard; |
| 12560 | at::Tensor tmp_output = at::_ops::scatter_add_out::call(self_, dim, index_, src_, out_); |
| 12561 | return out;; |
| 12562 | } |
| 12563 | } else { |
| 12564 | at::Tensor tmp_output; |
| 12565 | { |
| 12566 | at::AutoDispatchSkipFunctionalize guard; |
| 12567 | tmp_output = at::_ops::scatter_add::call(self_, dim, index_, src_); |
| 12568 | } |
| 12569 | at::functionalization::impl::replace_(out, tmp_output); |
| 12570 | at::functionalization::impl::commit_update(out); |
| 12571 | at::functionalization::impl::sync(out); |
| 12572 | return out; |
| 12573 | } |
| 12574 | } |
| 12575 | |
| 12576 | at::Tensor & scatter_add_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { |
| 12577 | if (true) { |
| 12578 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12579 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12580 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12581 | auto self_meta = to_meta(self); |
| 12582 | auto index_meta = to_meta(index); |
| 12583 | auto src_meta = to_meta(src); |
| 12584 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12585 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12586 | at::_ops::scatter_add_::call(self_meta, dim, index_meta, src_meta); |
| 12587 | } |
| 12588 | |
| 12589 | at::Tensor self_; |
| 12590 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12591 | at::functionalization::impl::sync(self); |
| 12592 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12593 | } else { |
| 12594 | self_ = self; |
| 12595 | } |
| 12596 | |
| 12597 | at::Tensor index_; |
| 12598 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 12599 | at::functionalization::impl::sync(index); |
| 12600 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 12601 | } else { |
| 12602 | index_ = index; |
| 12603 | } |
| 12604 | |
| 12605 | at::Tensor src_; |
| 12606 | if (at::functionalization::impl::isFunctionalTensor(src)) { |
| 12607 | at::functionalization::impl::sync(src); |
| 12608 | src_ = at::functionalization::impl::from_functional_tensor(src); |
| 12609 | } else { |
| 12610 | src_ = src; |
| 12611 | } |
| 12612 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 12613 | if ((false || at::functionalization::impl::isFunctionalTensor(index) || at::functionalization::impl::isFunctionalTensor(src))) { |
| 12614 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12615 | TORCH_INTERNAL_ASSERT(false, |
| 12616 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12617 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12618 | } else { |
| 12619 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12620 | at::AutoDispatchSkipFunctionalize guard; |
| 12621 | at::Tensor tmp_output = at::_ops::scatter_add_::call(self_, dim, index_, src_); |
| 12622 | return self;; |
| 12623 | } |
| 12624 | } else { |
| 12625 | at::Tensor tmp_output; |
| 12626 | { |
| 12627 | at::AutoDispatchSkipFunctionalize guard; |
| 12628 | tmp_output = at::_ops::scatter_add::call(self_, dim, index_, src_); |
| 12629 | } |
| 12630 | at::functionalization::impl::replace_(self, tmp_output); |
| 12631 | at::functionalization::impl::commit_update(self); |
| 12632 | at::functionalization::impl::sync(self); |
| 12633 | return self; |
| 12634 | } |
| 12635 | } |
| 12636 | |
| 12637 | at::Tensor & __lshift___out_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 12638 | if (false) { |
| 12639 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12640 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12641 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12642 | auto self_meta = to_meta(self); |
| 12643 | auto out_meta = to_meta(out); |
| 12644 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12645 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12646 | at::_ops::__lshift___Scalar_out::call(self_meta, other, out_meta); |
| 12647 | } |
| 12648 | |
| 12649 | at::Tensor self_; |
| 12650 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12651 | at::functionalization::impl::sync(self); |
| 12652 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12653 | } else { |
| 12654 | self_ = self; |
| 12655 | } |
| 12656 | |
| 12657 | at::Tensor out_; |
| 12658 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12659 | at::functionalization::impl::sync(out); |
| 12660 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12661 | } else { |
| 12662 | out_ = out; |
| 12663 | } |
| 12664 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12665 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 12666 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12667 | TORCH_INTERNAL_ASSERT(false, |
| 12668 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12669 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12670 | } else { |
| 12671 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12672 | at::AutoDispatchSkipFunctionalize guard; |
| 12673 | at::Tensor tmp_output = at::_ops::__lshift___Scalar_out::call(self_, other, out_); |
| 12674 | return out;; |
| 12675 | } |
| 12676 | } else { |
| 12677 | at::Tensor tmp_output; |
| 12678 | { |
| 12679 | at::AutoDispatchSkipFunctionalize guard; |
| 12680 | tmp_output = at::_ops::__lshift___Scalar::call(self_, other); |
| 12681 | } |
| 12682 | at::functionalization::impl::replace_(out, tmp_output); |
| 12683 | at::functionalization::impl::commit_update(out); |
| 12684 | at::functionalization::impl::sync(out); |
| 12685 | return out; |
| 12686 | } |
| 12687 | } |
| 12688 | |
| 12689 | at::Tensor & __ilshift___Scalar(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { |
| 12690 | if (true) { |
| 12691 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12692 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12693 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12694 | auto self_meta = to_meta(self); |
| 12695 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12696 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12697 | at::_ops::__ilshift___Scalar::call(self_meta, other); |
| 12698 | } |
| 12699 | |
| 12700 | at::Tensor self_; |
| 12701 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12702 | at::functionalization::impl::sync(self); |
| 12703 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12704 | } else { |
| 12705 | self_ = self; |
| 12706 | } |
| 12707 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 12708 | if ((false)) { |
| 12709 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12710 | TORCH_INTERNAL_ASSERT(false, |
| 12711 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12712 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12713 | } else { |
| 12714 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12715 | at::AutoDispatchSkipFunctionalize guard; |
| 12716 | at::Tensor tmp_output = at::_ops::__ilshift___Scalar::call(self_, other); |
| 12717 | return self;; |
| 12718 | } |
| 12719 | } else { |
| 12720 | at::Tensor tmp_output; |
| 12721 | { |
| 12722 | at::AutoDispatchSkipFunctionalize guard; |
| 12723 | tmp_output = at::_ops::__lshift___Scalar::call(self_, other); |
| 12724 | } |
| 12725 | at::functionalization::impl::replace_(self, tmp_output); |
| 12726 | at::functionalization::impl::commit_update(self); |
| 12727 | at::functionalization::impl::sync(self); |
| 12728 | return self; |
| 12729 | } |
| 12730 | } |
| 12731 | |
| 12732 | at::Tensor & __lshift___out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 12733 | if (false) { |
| 12734 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12735 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12736 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12737 | auto self_meta = to_meta(self); |
| 12738 | auto other_meta = to_meta(other); |
| 12739 | auto out_meta = to_meta(out); |
| 12740 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12741 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12742 | at::_ops::__lshift___Tensor_out::call(self_meta, other_meta, out_meta); |
| 12743 | } |
| 12744 | |
| 12745 | at::Tensor self_; |
| 12746 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12747 | at::functionalization::impl::sync(self); |
| 12748 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12749 | } else { |
| 12750 | self_ = self; |
| 12751 | } |
| 12752 | |
| 12753 | at::Tensor other_; |
| 12754 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 12755 | at::functionalization::impl::sync(other); |
| 12756 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 12757 | } else { |
| 12758 | other_ = other; |
| 12759 | } |
| 12760 | |
| 12761 | at::Tensor out_; |
| 12762 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12763 | at::functionalization::impl::sync(out); |
| 12764 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12765 | } else { |
| 12766 | out_ = out; |
| 12767 | } |
| 12768 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12769 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 12770 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12771 | TORCH_INTERNAL_ASSERT(false, |
| 12772 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12773 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12774 | } else { |
| 12775 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12776 | at::AutoDispatchSkipFunctionalize guard; |
| 12777 | at::Tensor tmp_output = at::_ops::__lshift___Tensor_out::call(self_, other_, out_); |
| 12778 | return out;; |
| 12779 | } |
| 12780 | } else { |
| 12781 | at::Tensor tmp_output; |
| 12782 | { |
| 12783 | at::AutoDispatchSkipFunctionalize guard; |
| 12784 | tmp_output = at::_ops::__lshift___Tensor::call(self_, other_); |
| 12785 | } |
| 12786 | at::functionalization::impl::replace_(out, tmp_output); |
| 12787 | at::functionalization::impl::commit_update(out); |
| 12788 | at::functionalization::impl::sync(out); |
| 12789 | return out; |
| 12790 | } |
| 12791 | } |
| 12792 | |
| 12793 | at::Tensor & __ilshift___Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 12794 | if (true) { |
| 12795 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12796 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12797 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12798 | auto self_meta = to_meta(self); |
| 12799 | auto other_meta = to_meta(other); |
| 12800 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12801 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12802 | at::_ops::__ilshift___Tensor::call(self_meta, other_meta); |
| 12803 | } |
| 12804 | |
| 12805 | at::Tensor self_; |
| 12806 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12807 | at::functionalization::impl::sync(self); |
| 12808 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12809 | } else { |
| 12810 | self_ = self; |
| 12811 | } |
| 12812 | |
| 12813 | at::Tensor other_; |
| 12814 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 12815 | at::functionalization::impl::sync(other); |
| 12816 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 12817 | } else { |
| 12818 | other_ = other; |
| 12819 | } |
| 12820 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 12821 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 12822 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12823 | TORCH_INTERNAL_ASSERT(false, |
| 12824 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12825 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12826 | } else { |
| 12827 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12828 | at::AutoDispatchSkipFunctionalize guard; |
| 12829 | at::Tensor tmp_output = at::_ops::__ilshift___Tensor::call(self_, other_); |
| 12830 | return self;; |
| 12831 | } |
| 12832 | } else { |
| 12833 | at::Tensor tmp_output; |
| 12834 | { |
| 12835 | at::AutoDispatchSkipFunctionalize guard; |
| 12836 | tmp_output = at::_ops::__lshift___Tensor::call(self_, other_); |
| 12837 | } |
| 12838 | at::functionalization::impl::replace_(self, tmp_output); |
| 12839 | at::functionalization::impl::commit_update(self); |
| 12840 | at::functionalization::impl::sync(self); |
| 12841 | return self; |
| 12842 | } |
| 12843 | } |
| 12844 | |
| 12845 | at::Tensor & greater_equal_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 12846 | if (false) { |
| 12847 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12848 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12849 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12850 | auto self_meta = to_meta(self); |
| 12851 | auto out_meta = to_meta(out); |
| 12852 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12853 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12854 | at::_ops::greater_equal_Scalar_out::call(self_meta, other, out_meta); |
| 12855 | } |
| 12856 | |
| 12857 | at::Tensor self_; |
| 12858 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12859 | at::functionalization::impl::sync(self); |
| 12860 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12861 | } else { |
| 12862 | self_ = self; |
| 12863 | } |
| 12864 | |
| 12865 | at::Tensor out_; |
| 12866 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12867 | at::functionalization::impl::sync(out); |
| 12868 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12869 | } else { |
| 12870 | out_ = out; |
| 12871 | } |
| 12872 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12873 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 12874 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12875 | TORCH_INTERNAL_ASSERT(false, |
| 12876 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12877 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12878 | } else { |
| 12879 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12880 | at::AutoDispatchSkipFunctionalize guard; |
| 12881 | at::Tensor tmp_output = at::_ops::greater_equal_Scalar_out::call(self_, other, out_); |
| 12882 | return out;; |
| 12883 | } |
| 12884 | } else { |
| 12885 | at::Tensor tmp_output; |
| 12886 | { |
| 12887 | at::AutoDispatchSkipFunctionalize guard; |
| 12888 | tmp_output = at::_ops::greater_equal_Scalar::call(self_, other); |
| 12889 | } |
| 12890 | at::functionalization::impl::replace_(out, tmp_output); |
| 12891 | at::functionalization::impl::commit_update(out); |
| 12892 | at::functionalization::impl::sync(out); |
| 12893 | return out; |
| 12894 | } |
| 12895 | } |
| 12896 | |
| 12897 | at::Tensor & greater_equal__Scalar(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { |
| 12898 | if (true) { |
| 12899 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12900 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12901 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12902 | auto self_meta = to_meta(self); |
| 12903 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12904 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12905 | at::_ops::greater_equal__Scalar::call(self_meta, other); |
| 12906 | } |
| 12907 | |
| 12908 | at::Tensor self_; |
| 12909 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12910 | at::functionalization::impl::sync(self); |
| 12911 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12912 | } else { |
| 12913 | self_ = self; |
| 12914 | } |
| 12915 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 12916 | if ((false)) { |
| 12917 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12918 | TORCH_INTERNAL_ASSERT(false, |
| 12919 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12920 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12921 | } else { |
| 12922 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12923 | at::AutoDispatchSkipFunctionalize guard; |
| 12924 | at::Tensor tmp_output = at::_ops::greater_equal__Scalar::call(self_, other); |
| 12925 | return self;; |
| 12926 | } |
| 12927 | } else { |
| 12928 | at::Tensor tmp_output; |
| 12929 | { |
| 12930 | at::AutoDispatchSkipFunctionalize guard; |
| 12931 | tmp_output = at::_ops::greater_equal_Scalar::call(self_, other); |
| 12932 | } |
| 12933 | at::functionalization::impl::replace_(self, tmp_output); |
| 12934 | at::functionalization::impl::commit_update(self); |
| 12935 | at::functionalization::impl::sync(self); |
| 12936 | return self; |
| 12937 | } |
| 12938 | } |
| 12939 | |
| 12940 | at::Tensor & greater_equal_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 12941 | if (false) { |
| 12942 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12943 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12944 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12945 | auto self_meta = to_meta(self); |
| 12946 | auto other_meta = to_meta(other); |
| 12947 | auto out_meta = to_meta(out); |
| 12948 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12949 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12950 | at::_ops::greater_equal_Tensor_out::call(self_meta, other_meta, out_meta); |
| 12951 | } |
| 12952 | |
| 12953 | at::Tensor self_; |
| 12954 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12955 | at::functionalization::impl::sync(self); |
| 12956 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12957 | } else { |
| 12958 | self_ = self; |
| 12959 | } |
| 12960 | |
| 12961 | at::Tensor other_; |
| 12962 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 12963 | at::functionalization::impl::sync(other); |
| 12964 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 12965 | } else { |
| 12966 | other_ = other; |
| 12967 | } |
| 12968 | |
| 12969 | at::Tensor out_; |
| 12970 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12971 | at::functionalization::impl::sync(out); |
| 12972 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12973 | } else { |
| 12974 | out_ = out; |
| 12975 | } |
| 12976 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12977 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 12978 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12979 | TORCH_INTERNAL_ASSERT(false, |
| 12980 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12981 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12982 | } else { |
| 12983 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12984 | at::AutoDispatchSkipFunctionalize guard; |
| 12985 | at::Tensor tmp_output = at::_ops::greater_equal_Tensor_out::call(self_, other_, out_); |
| 12986 | return out;; |
| 12987 | } |
| 12988 | } else { |
| 12989 | at::Tensor tmp_output; |
| 12990 | { |
| 12991 | at::AutoDispatchSkipFunctionalize guard; |
| 12992 | tmp_output = at::_ops::greater_equal_Tensor::call(self_, other_); |
| 12993 | } |
| 12994 | at::functionalization::impl::replace_(out, tmp_output); |
| 12995 | at::functionalization::impl::commit_update(out); |
| 12996 | at::functionalization::impl::sync(out); |
| 12997 | return out; |
| 12998 | } |
| 12999 | } |
| 13000 | |
| 13001 | at::Tensor & greater_equal__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 13002 | if (true) { |
| 13003 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13004 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13005 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13006 | auto self_meta = to_meta(self); |
| 13007 | auto other_meta = to_meta(other); |
| 13008 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13009 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13010 | at::_ops::greater_equal__Tensor::call(self_meta, other_meta); |
| 13011 | } |
| 13012 | |
| 13013 | at::Tensor self_; |
| 13014 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13015 | at::functionalization::impl::sync(self); |
| 13016 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13017 | } else { |
| 13018 | self_ = self; |
| 13019 | } |
| 13020 | |
| 13021 | at::Tensor other_; |
| 13022 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 13023 | at::functionalization::impl::sync(other); |
| 13024 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 13025 | } else { |
| 13026 | other_ = other; |
| 13027 | } |
| 13028 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 13029 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 13030 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13031 | TORCH_INTERNAL_ASSERT(false, |
| 13032 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13033 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13034 | } else { |
| 13035 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13036 | at::AutoDispatchSkipFunctionalize guard; |
| 13037 | at::Tensor tmp_output = at::_ops::greater_equal__Tensor::call(self_, other_); |
| 13038 | return self;; |
| 13039 | } |
| 13040 | } else { |
| 13041 | at::Tensor tmp_output; |
| 13042 | { |
| 13043 | at::AutoDispatchSkipFunctionalize guard; |
| 13044 | tmp_output = at::_ops::greater_equal_Tensor::call(self_, other_); |
| 13045 | } |
| 13046 | at::functionalization::impl::replace_(self, tmp_output); |
| 13047 | at::functionalization::impl::commit_update(self); |
| 13048 | at::functionalization::impl::sync(self); |
| 13049 | return self; |
| 13050 | } |
| 13051 | } |
| 13052 | |
| 13053 | at::Tensor & less_equal_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 13054 | if (false) { |
| 13055 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13056 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13057 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13058 | auto self_meta = to_meta(self); |
| 13059 | auto out_meta = to_meta(out); |
| 13060 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13061 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13062 | at::_ops::less_equal_Scalar_out::call(self_meta, other, out_meta); |
| 13063 | } |
| 13064 | |
| 13065 | at::Tensor self_; |
| 13066 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13067 | at::functionalization::impl::sync(self); |
| 13068 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13069 | } else { |
| 13070 | self_ = self; |
| 13071 | } |
| 13072 | |
| 13073 | at::Tensor out_; |
| 13074 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13075 | at::functionalization::impl::sync(out); |
| 13076 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13077 | } else { |
| 13078 | out_ = out; |
| 13079 | } |
| 13080 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13081 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 13082 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13083 | TORCH_INTERNAL_ASSERT(false, |
| 13084 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13085 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13086 | } else { |
| 13087 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13088 | at::AutoDispatchSkipFunctionalize guard; |
| 13089 | at::Tensor tmp_output = at::_ops::less_equal_Scalar_out::call(self_, other, out_); |
| 13090 | return out;; |
| 13091 | } |
| 13092 | } else { |
| 13093 | at::Tensor tmp_output; |
| 13094 | { |
| 13095 | at::AutoDispatchSkipFunctionalize guard; |
| 13096 | tmp_output = at::_ops::less_equal_Scalar::call(self_, other); |
| 13097 | } |
| 13098 | at::functionalization::impl::replace_(out, tmp_output); |
| 13099 | at::functionalization::impl::commit_update(out); |
| 13100 | at::functionalization::impl::sync(out); |
| 13101 | return out; |
| 13102 | } |
| 13103 | } |
| 13104 | |
| 13105 | at::Tensor & less_equal__Scalar(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { |
| 13106 | if (true) { |
| 13107 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13108 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13109 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13110 | auto self_meta = to_meta(self); |
| 13111 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13112 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13113 | at::_ops::less_equal__Scalar::call(self_meta, other); |
| 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 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 13124 | if ((false)) { |
| 13125 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13126 | TORCH_INTERNAL_ASSERT(false, |
| 13127 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13128 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13129 | } else { |
| 13130 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13131 | at::AutoDispatchSkipFunctionalize guard; |
| 13132 | at::Tensor tmp_output = at::_ops::less_equal__Scalar::call(self_, other); |
| 13133 | return self;; |
| 13134 | } |
| 13135 | } else { |
| 13136 | at::Tensor tmp_output; |
| 13137 | { |
| 13138 | at::AutoDispatchSkipFunctionalize guard; |
| 13139 | tmp_output = at::_ops::less_equal_Scalar::call(self_, other); |
| 13140 | } |
| 13141 | at::functionalization::impl::replace_(self, tmp_output); |
| 13142 | at::functionalization::impl::commit_update(self); |
| 13143 | at::functionalization::impl::sync(self); |
| 13144 | return self; |
| 13145 | } |
| 13146 | } |
| 13147 | |
| 13148 | at::Tensor & less_equal_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 13149 | if (false) { |
| 13150 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13151 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13152 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13153 | auto self_meta = to_meta(self); |
| 13154 | auto other_meta = to_meta(other); |
| 13155 | auto out_meta = to_meta(out); |
| 13156 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13157 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13158 | at::_ops::less_equal_Tensor_out::call(self_meta, other_meta, out_meta); |
| 13159 | } |
| 13160 | |
| 13161 | at::Tensor self_; |
| 13162 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13163 | at::functionalization::impl::sync(self); |
| 13164 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13165 | } else { |
| 13166 | self_ = self; |
| 13167 | } |
| 13168 | |
| 13169 | at::Tensor other_; |
| 13170 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 13171 | at::functionalization::impl::sync(other); |
| 13172 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 13173 | } else { |
| 13174 | other_ = other; |
| 13175 | } |
| 13176 | |
| 13177 | at::Tensor out_; |
| 13178 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13179 | at::functionalization::impl::sync(out); |
| 13180 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13181 | } else { |
| 13182 | out_ = out; |
| 13183 | } |
| 13184 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13185 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 13186 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13187 | TORCH_INTERNAL_ASSERT(false, |
| 13188 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13189 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13190 | } else { |
| 13191 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13192 | at::AutoDispatchSkipFunctionalize guard; |
| 13193 | at::Tensor tmp_output = at::_ops::less_equal_Tensor_out::call(self_, other_, out_); |
| 13194 | return out;; |
| 13195 | } |
| 13196 | } else { |
| 13197 | at::Tensor tmp_output; |
| 13198 | { |
| 13199 | at::AutoDispatchSkipFunctionalize guard; |
| 13200 | tmp_output = at::_ops::less_equal_Tensor::call(self_, other_); |
| 13201 | } |
| 13202 | at::functionalization::impl::replace_(out, tmp_output); |
| 13203 | at::functionalization::impl::commit_update(out); |
| 13204 | at::functionalization::impl::sync(out); |
| 13205 | return out; |
| 13206 | } |
| 13207 | } |
| 13208 | |
| 13209 | at::Tensor & less_equal__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 13210 | if (true) { |
| 13211 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13212 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13213 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13214 | auto self_meta = to_meta(self); |
| 13215 | auto other_meta = to_meta(other); |
| 13216 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13217 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13218 | at::_ops::less_equal__Tensor::call(self_meta, other_meta); |
| 13219 | } |
| 13220 | |
| 13221 | at::Tensor self_; |
| 13222 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13223 | at::functionalization::impl::sync(self); |
| 13224 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13225 | } else { |
| 13226 | self_ = self; |
| 13227 | } |
| 13228 | |
| 13229 | at::Tensor other_; |
| 13230 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 13231 | at::functionalization::impl::sync(other); |
| 13232 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 13233 | } else { |
| 13234 | other_ = other; |
| 13235 | } |
| 13236 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 13237 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 13238 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13239 | TORCH_INTERNAL_ASSERT(false, |
| 13240 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13241 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13242 | } else { |
| 13243 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13244 | at::AutoDispatchSkipFunctionalize guard; |
| 13245 | at::Tensor tmp_output = at::_ops::less_equal__Tensor::call(self_, other_); |
| 13246 | return self;; |
| 13247 | } |
| 13248 | } else { |
| 13249 | at::Tensor tmp_output; |
| 13250 | { |
| 13251 | at::AutoDispatchSkipFunctionalize guard; |
| 13252 | tmp_output = at::_ops::less_equal_Tensor::call(self_, other_); |
| 13253 | } |
| 13254 | at::functionalization::impl::replace_(self, tmp_output); |
| 13255 | at::functionalization::impl::commit_update(self); |
| 13256 | at::functionalization::impl::sync(self); |
| 13257 | return self; |
| 13258 | } |
| 13259 | } |
| 13260 | |
| 13261 | at::Tensor & greater_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 13262 | if (false) { |
| 13263 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13264 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13265 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13266 | auto self_meta = to_meta(self); |
| 13267 | auto out_meta = to_meta(out); |
| 13268 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13269 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13270 | at::_ops::greater_Scalar_out::call(self_meta, other, out_meta); |
| 13271 | } |
| 13272 | |
| 13273 | at::Tensor self_; |
| 13274 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13275 | at::functionalization::impl::sync(self); |
| 13276 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13277 | } else { |
| 13278 | self_ = self; |
| 13279 | } |
| 13280 | |
| 13281 | at::Tensor out_; |
| 13282 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13283 | at::functionalization::impl::sync(out); |
| 13284 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13285 | } else { |
| 13286 | out_ = out; |
| 13287 | } |
| 13288 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13289 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 13290 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13291 | TORCH_INTERNAL_ASSERT(false, |
| 13292 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13293 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13294 | } else { |
| 13295 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13296 | at::AutoDispatchSkipFunctionalize guard; |
| 13297 | at::Tensor tmp_output = at::_ops::greater_Scalar_out::call(self_, other, out_); |
| 13298 | return out;; |
| 13299 | } |
| 13300 | } else { |
| 13301 | at::Tensor tmp_output; |
| 13302 | { |
| 13303 | at::AutoDispatchSkipFunctionalize guard; |
| 13304 | tmp_output = at::_ops::greater_Scalar::call(self_, other); |
| 13305 | } |
| 13306 | at::functionalization::impl::replace_(out, tmp_output); |
| 13307 | at::functionalization::impl::commit_update(out); |
| 13308 | at::functionalization::impl::sync(out); |
| 13309 | return out; |
| 13310 | } |
| 13311 | } |
| 13312 | |
| 13313 | at::Tensor & greater__Scalar(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { |
| 13314 | if (true) { |
| 13315 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13316 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13317 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13318 | auto self_meta = to_meta(self); |
| 13319 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13320 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13321 | at::_ops::greater__Scalar::call(self_meta, other); |
| 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 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 13332 | if ((false)) { |
| 13333 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13334 | TORCH_INTERNAL_ASSERT(false, |
| 13335 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13336 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13337 | } else { |
| 13338 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13339 | at::AutoDispatchSkipFunctionalize guard; |
| 13340 | at::Tensor tmp_output = at::_ops::greater__Scalar::call(self_, other); |
| 13341 | return self;; |
| 13342 | } |
| 13343 | } else { |
| 13344 | at::Tensor tmp_output; |
| 13345 | { |
| 13346 | at::AutoDispatchSkipFunctionalize guard; |
| 13347 | tmp_output = at::_ops::greater_Scalar::call(self_, other); |
| 13348 | } |
| 13349 | at::functionalization::impl::replace_(self, tmp_output); |
| 13350 | at::functionalization::impl::commit_update(self); |
| 13351 | at::functionalization::impl::sync(self); |
| 13352 | return self; |
| 13353 | } |
| 13354 | } |
| 13355 | |
| 13356 | at::Tensor & greater_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 13357 | if (false) { |
| 13358 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13359 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13360 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13361 | auto self_meta = to_meta(self); |
| 13362 | auto other_meta = to_meta(other); |
| 13363 | auto out_meta = to_meta(out); |
| 13364 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13365 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13366 | at::_ops::greater_Tensor_out::call(self_meta, other_meta, out_meta); |
| 13367 | } |
| 13368 | |
| 13369 | at::Tensor self_; |
| 13370 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13371 | at::functionalization::impl::sync(self); |
| 13372 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13373 | } else { |
| 13374 | self_ = self; |
| 13375 | } |
| 13376 | |
| 13377 | at::Tensor other_; |
| 13378 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 13379 | at::functionalization::impl::sync(other); |
| 13380 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 13381 | } else { |
| 13382 | other_ = other; |
| 13383 | } |
| 13384 | |
| 13385 | at::Tensor out_; |
| 13386 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13387 | at::functionalization::impl::sync(out); |
| 13388 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13389 | } else { |
| 13390 | out_ = out; |
| 13391 | } |
| 13392 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13393 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 13394 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13395 | TORCH_INTERNAL_ASSERT(false, |
| 13396 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13397 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13398 | } else { |
| 13399 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13400 | at::AutoDispatchSkipFunctionalize guard; |
| 13401 | at::Tensor tmp_output = at::_ops::greater_Tensor_out::call(self_, other_, out_); |
| 13402 | return out;; |
| 13403 | } |
| 13404 | } else { |
| 13405 | at::Tensor tmp_output; |
| 13406 | { |
| 13407 | at::AutoDispatchSkipFunctionalize guard; |
| 13408 | tmp_output = at::_ops::greater_Tensor::call(self_, other_); |
| 13409 | } |
| 13410 | at::functionalization::impl::replace_(out, tmp_output); |
| 13411 | at::functionalization::impl::commit_update(out); |
| 13412 | at::functionalization::impl::sync(out); |
| 13413 | return out; |
| 13414 | } |
| 13415 | } |
| 13416 | |
| 13417 | at::Tensor & greater__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 13418 | if (true) { |
| 13419 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13420 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13421 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13422 | auto self_meta = to_meta(self); |
| 13423 | auto other_meta = to_meta(other); |
| 13424 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13425 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13426 | at::_ops::greater__Tensor::call(self_meta, other_meta); |
| 13427 | } |
| 13428 | |
| 13429 | at::Tensor self_; |
| 13430 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13431 | at::functionalization::impl::sync(self); |
| 13432 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13433 | } else { |
| 13434 | self_ = self; |
| 13435 | } |
| 13436 | |
| 13437 | at::Tensor other_; |
| 13438 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 13439 | at::functionalization::impl::sync(other); |
| 13440 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 13441 | } else { |
| 13442 | other_ = other; |
| 13443 | } |
| 13444 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 13445 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 13446 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13447 | TORCH_INTERNAL_ASSERT(false, |
| 13448 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13449 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13450 | } else { |
| 13451 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13452 | at::AutoDispatchSkipFunctionalize guard; |
| 13453 | at::Tensor tmp_output = at::_ops::greater__Tensor::call(self_, other_); |
| 13454 | return self;; |
| 13455 | } |
| 13456 | } else { |
| 13457 | at::Tensor tmp_output; |
| 13458 | { |
| 13459 | at::AutoDispatchSkipFunctionalize guard; |
| 13460 | tmp_output = at::_ops::greater_Tensor::call(self_, other_); |
| 13461 | } |
| 13462 | at::functionalization::impl::replace_(self, tmp_output); |
| 13463 | at::functionalization::impl::commit_update(self); |
| 13464 | at::functionalization::impl::sync(self); |
| 13465 | return self; |
| 13466 | } |
| 13467 | } |
| 13468 | |
| 13469 | at::Tensor & lt_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 13470 | if (false) { |
| 13471 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13472 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13473 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13474 | auto self_meta = to_meta(self); |
| 13475 | auto out_meta = to_meta(out); |
| 13476 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13477 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13478 | at::_ops::lt_Scalar_out::call(self_meta, other, out_meta); |
| 13479 | } |
| 13480 | |
| 13481 | at::Tensor self_; |
| 13482 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13483 | at::functionalization::impl::sync(self); |
| 13484 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13485 | } else { |
| 13486 | self_ = self; |
| 13487 | } |
| 13488 | |
| 13489 | at::Tensor out_; |
| 13490 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13491 | at::functionalization::impl::sync(out); |
| 13492 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13493 | } else { |
| 13494 | out_ = out; |
| 13495 | } |
| 13496 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13497 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 13498 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13499 | TORCH_INTERNAL_ASSERT(false, |
| 13500 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13501 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13502 | } else { |
| 13503 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13504 | at::AutoDispatchSkipFunctionalize guard; |
| 13505 | at::Tensor tmp_output = at::_ops::lt_Scalar_out::call(self_, other, out_); |
| 13506 | return out;; |
| 13507 | } |
| 13508 | } else { |
| 13509 | at::Tensor tmp_output; |
| 13510 | { |
| 13511 | at::AutoDispatchSkipFunctionalize guard; |
| 13512 | tmp_output = at::_ops::lt_Scalar::call(self_, other); |
| 13513 | } |
| 13514 | at::functionalization::impl::replace_(out, tmp_output); |
| 13515 | at::functionalization::impl::commit_update(out); |
| 13516 | at::functionalization::impl::sync(out); |
| 13517 | return out; |
| 13518 | } |
| 13519 | } |
| 13520 | |
| 13521 | at::Tensor & lt__Scalar(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { |
| 13522 | if (true) { |
| 13523 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13524 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13525 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13526 | auto self_meta = to_meta(self); |
| 13527 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13528 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13529 | at::_ops::lt__Scalar::call(self_meta, other); |
| 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 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 13540 | if ((false)) { |
| 13541 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13542 | TORCH_INTERNAL_ASSERT(false, |
| 13543 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13544 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13545 | } else { |
| 13546 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13547 | at::AutoDispatchSkipFunctionalize guard; |
| 13548 | at::Tensor tmp_output = at::_ops::lt__Scalar::call(self_, other); |
| 13549 | return self;; |
| 13550 | } |
| 13551 | } else { |
| 13552 | at::Tensor tmp_output; |
| 13553 | { |
| 13554 | at::AutoDispatchSkipFunctionalize guard; |
| 13555 | tmp_output = at::_ops::lt_Scalar::call(self_, other); |
| 13556 | } |
| 13557 | at::functionalization::impl::replace_(self, tmp_output); |
| 13558 | at::functionalization::impl::commit_update(self); |
| 13559 | at::functionalization::impl::sync(self); |
| 13560 | return self; |
| 13561 | } |
| 13562 | } |
| 13563 | |
| 13564 | at::Tensor & lt_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 13565 | if (false) { |
| 13566 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13567 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13568 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13569 | auto self_meta = to_meta(self); |
| 13570 | auto other_meta = to_meta(other); |
| 13571 | auto out_meta = to_meta(out); |
| 13572 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13573 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13574 | at::_ops::lt_Tensor_out::call(self_meta, other_meta, out_meta); |
| 13575 | } |
| 13576 | |
| 13577 | at::Tensor self_; |
| 13578 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13579 | at::functionalization::impl::sync(self); |
| 13580 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13581 | } else { |
| 13582 | self_ = self; |
| 13583 | } |
| 13584 | |
| 13585 | at::Tensor other_; |
| 13586 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 13587 | at::functionalization::impl::sync(other); |
| 13588 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 13589 | } else { |
| 13590 | other_ = other; |
| 13591 | } |
| 13592 | |
| 13593 | at::Tensor out_; |
| 13594 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13595 | at::functionalization::impl::sync(out); |
| 13596 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13597 | } else { |
| 13598 | out_ = out; |
| 13599 | } |
| 13600 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13601 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 13602 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13603 | TORCH_INTERNAL_ASSERT(false, |
| 13604 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13605 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13606 | } else { |
| 13607 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13608 | at::AutoDispatchSkipFunctionalize guard; |
| 13609 | at::Tensor tmp_output = at::_ops::lt_Tensor_out::call(self_, other_, out_); |
| 13610 | return out;; |
| 13611 | } |
| 13612 | } else { |
| 13613 | at::Tensor tmp_output; |
| 13614 | { |
| 13615 | at::AutoDispatchSkipFunctionalize guard; |
| 13616 | tmp_output = at::_ops::lt_Tensor::call(self_, other_); |
| 13617 | } |
| 13618 | at::functionalization::impl::replace_(out, tmp_output); |
| 13619 | at::functionalization::impl::commit_update(out); |
| 13620 | at::functionalization::impl::sync(out); |
| 13621 | return out; |
| 13622 | } |
| 13623 | } |
| 13624 | |
| 13625 | at::Tensor & lt__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 13626 | if (true) { |
| 13627 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13628 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13629 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13630 | auto self_meta = to_meta(self); |
| 13631 | auto other_meta = to_meta(other); |
| 13632 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13633 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13634 | at::_ops::lt__Tensor::call(self_meta, other_meta); |
| 13635 | } |
| 13636 | |
| 13637 | at::Tensor self_; |
| 13638 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13639 | at::functionalization::impl::sync(self); |
| 13640 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13641 | } else { |
| 13642 | self_ = self; |
| 13643 | } |
| 13644 | |
| 13645 | at::Tensor other_; |
| 13646 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 13647 | at::functionalization::impl::sync(other); |
| 13648 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 13649 | } else { |
| 13650 | other_ = other; |
| 13651 | } |
| 13652 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 13653 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 13654 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13655 | TORCH_INTERNAL_ASSERT(false, |
| 13656 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13657 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13658 | } else { |
| 13659 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13660 | at::AutoDispatchSkipFunctionalize guard; |
| 13661 | at::Tensor tmp_output = at::_ops::lt__Tensor::call(self_, other_); |
| 13662 | return self;; |
| 13663 | } |
| 13664 | } else { |
| 13665 | at::Tensor tmp_output; |
| 13666 | { |
| 13667 | at::AutoDispatchSkipFunctionalize guard; |
| 13668 | tmp_output = at::_ops::lt_Tensor::call(self_, other_); |
| 13669 | } |
| 13670 | at::functionalization::impl::replace_(self, tmp_output); |
| 13671 | at::functionalization::impl::commit_update(self); |
| 13672 | at::functionalization::impl::sync(self); |
| 13673 | return self; |
| 13674 | } |
| 13675 | } |
| 13676 | |
| 13677 | at::Tensor & less_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 13678 | if (false) { |
| 13679 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13680 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13681 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13682 | auto self_meta = to_meta(self); |
| 13683 | auto out_meta = to_meta(out); |
| 13684 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13685 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13686 | at::_ops::less_Scalar_out::call(self_meta, other, out_meta); |
| 13687 | } |
| 13688 | |
| 13689 | at::Tensor self_; |
| 13690 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13691 | at::functionalization::impl::sync(self); |
| 13692 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13693 | } else { |
| 13694 | self_ = self; |
| 13695 | } |
| 13696 | |
| 13697 | at::Tensor out_; |
| 13698 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13699 | at::functionalization::impl::sync(out); |
| 13700 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13701 | } else { |
| 13702 | out_ = out; |
| 13703 | } |
| 13704 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13705 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 13706 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13707 | TORCH_INTERNAL_ASSERT(false, |
| 13708 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13709 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13710 | } else { |
| 13711 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13712 | at::AutoDispatchSkipFunctionalize guard; |
| 13713 | at::Tensor tmp_output = at::_ops::less_Scalar_out::call(self_, other, out_); |
| 13714 | return out;; |
| 13715 | } |
| 13716 | } else { |
| 13717 | at::Tensor tmp_output; |
| 13718 | { |
| 13719 | at::AutoDispatchSkipFunctionalize guard; |
| 13720 | tmp_output = at::_ops::less_Scalar::call(self_, other); |
| 13721 | } |
| 13722 | at::functionalization::impl::replace_(out, tmp_output); |
| 13723 | at::functionalization::impl::commit_update(out); |
| 13724 | at::functionalization::impl::sync(out); |
| 13725 | return out; |
| 13726 | } |
| 13727 | } |
| 13728 | |
| 13729 | at::Tensor & less__Scalar(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { |
| 13730 | if (true) { |
| 13731 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13732 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13733 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13734 | auto self_meta = to_meta(self); |
| 13735 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13736 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13737 | at::_ops::less__Scalar::call(self_meta, other); |
| 13738 | } |
| 13739 | |
| 13740 | at::Tensor self_; |
| 13741 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13742 | at::functionalization::impl::sync(self); |
| 13743 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13744 | } else { |
| 13745 | self_ = self; |
| 13746 | } |
| 13747 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 13748 | if ((false)) { |
| 13749 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13750 | TORCH_INTERNAL_ASSERT(false, |
| 13751 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13752 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13753 | } else { |
| 13754 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13755 | at::AutoDispatchSkipFunctionalize guard; |
| 13756 | at::Tensor tmp_output = at::_ops::less__Scalar::call(self_, other); |
| 13757 | return self;; |
| 13758 | } |
| 13759 | } else { |
| 13760 | at::Tensor tmp_output; |
| 13761 | { |
| 13762 | at::AutoDispatchSkipFunctionalize guard; |
| 13763 | tmp_output = at::_ops::less_Scalar::call(self_, other); |
| 13764 | } |
| 13765 | at::functionalization::impl::replace_(self, tmp_output); |
| 13766 | at::functionalization::impl::commit_update(self); |
| 13767 | at::functionalization::impl::sync(self); |
| 13768 | return self; |
| 13769 | } |
| 13770 | } |
| 13771 | |
| 13772 | at::Tensor & less_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 13773 | if (false) { |
| 13774 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13775 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13776 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13777 | auto self_meta = to_meta(self); |
| 13778 | auto other_meta = to_meta(other); |
| 13779 | auto out_meta = to_meta(out); |
| 13780 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13781 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13782 | at::_ops::less_Tensor_out::call(self_meta, other_meta, out_meta); |
| 13783 | } |
| 13784 | |
| 13785 | at::Tensor self_; |
| 13786 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13787 | at::functionalization::impl::sync(self); |
| 13788 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13789 | } else { |
| 13790 | self_ = self; |
| 13791 | } |
| 13792 | |
| 13793 | at::Tensor other_; |
| 13794 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 13795 | at::functionalization::impl::sync(other); |
| 13796 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 13797 | } else { |
| 13798 | other_ = other; |
| 13799 | } |
| 13800 | |
| 13801 | at::Tensor out_; |
| 13802 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13803 | at::functionalization::impl::sync(out); |
| 13804 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13805 | } else { |
| 13806 | out_ = out; |
| 13807 | } |
| 13808 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13809 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 13810 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13811 | TORCH_INTERNAL_ASSERT(false, |
| 13812 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13813 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13814 | } else { |
| 13815 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13816 | at::AutoDispatchSkipFunctionalize guard; |
| 13817 | at::Tensor tmp_output = at::_ops::less_Tensor_out::call(self_, other_, out_); |
| 13818 | return out;; |
| 13819 | } |
| 13820 | } else { |
| 13821 | at::Tensor tmp_output; |
| 13822 | { |
| 13823 | at::AutoDispatchSkipFunctionalize guard; |
| 13824 | tmp_output = at::_ops::less_Tensor::call(self_, other_); |
| 13825 | } |
| 13826 | at::functionalization::impl::replace_(out, tmp_output); |
| 13827 | at::functionalization::impl::commit_update(out); |
| 13828 | at::functionalization::impl::sync(out); |
| 13829 | return out; |
| 13830 | } |
| 13831 | } |
| 13832 | |
| 13833 | at::Tensor & less__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 13834 | if (true) { |
| 13835 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13836 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13837 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13838 | auto self_meta = to_meta(self); |
| 13839 | auto other_meta = to_meta(other); |
| 13840 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13841 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13842 | at::_ops::less__Tensor::call(self_meta, other_meta); |
| 13843 | } |
| 13844 | |
| 13845 | at::Tensor self_; |
| 13846 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13847 | at::functionalization::impl::sync(self); |
| 13848 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13849 | } else { |
| 13850 | self_ = self; |
| 13851 | } |
| 13852 | |
| 13853 | at::Tensor other_; |
| 13854 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 13855 | at::functionalization::impl::sync(other); |
| 13856 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 13857 | } else { |
| 13858 | other_ = other; |
| 13859 | } |
| 13860 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 13861 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 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 | at::Tensor tmp_output = at::_ops::less__Tensor::call(self_, other_); |
| 13870 | return self;; |
| 13871 | } |
| 13872 | } else { |
| 13873 | at::Tensor tmp_output; |
| 13874 | { |
| 13875 | at::AutoDispatchSkipFunctionalize guard; |
| 13876 | tmp_output = at::_ops::less_Tensor::call(self_, other_); |
| 13877 | } |
| 13878 | at::functionalization::impl::replace_(self, tmp_output); |
| 13879 | at::functionalization::impl::commit_update(self); |
| 13880 | at::functionalization::impl::sync(self); |
| 13881 | return self; |
| 13882 | } |
| 13883 | } |
| 13884 | |
| 13885 | at::Tensor & take_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & index, at::Tensor & out) { |
| 13886 | if (false) { |
| 13887 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13888 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13889 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13890 | auto self_meta = to_meta(self); |
| 13891 | auto index_meta = to_meta(index); |
| 13892 | auto out_meta = to_meta(out); |
| 13893 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13894 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13895 | at::_ops::take_out::call(self_meta, index_meta, out_meta); |
| 13896 | } |
| 13897 | |
| 13898 | at::Tensor self_; |
| 13899 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13900 | at::functionalization::impl::sync(self); |
| 13901 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13902 | } else { |
| 13903 | self_ = self; |
| 13904 | } |
| 13905 | |
| 13906 | at::Tensor index_; |
| 13907 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 13908 | at::functionalization::impl::sync(index); |
| 13909 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 13910 | } else { |
| 13911 | index_ = index; |
| 13912 | } |
| 13913 | |
| 13914 | at::Tensor out_; |
| 13915 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13916 | at::functionalization::impl::sync(out); |
| 13917 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13918 | } else { |
| 13919 | out_ = out; |
| 13920 | } |
| 13921 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13922 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(index))) { |
| 13923 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13924 | TORCH_INTERNAL_ASSERT(false, |
| 13925 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13926 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13927 | } else { |
| 13928 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13929 | at::AutoDispatchSkipFunctionalize guard; |
| 13930 | at::Tensor tmp_output = at::_ops::take_out::call(self_, index_, out_); |
| 13931 | return out;; |
| 13932 | } |
| 13933 | } else { |
| 13934 | at::Tensor tmp_output; |
| 13935 | { |
| 13936 | at::AutoDispatchSkipFunctionalize guard; |
| 13937 | tmp_output = at::_ops::take::call(self_, index_); |
| 13938 | } |
| 13939 | at::functionalization::impl::replace_(out, tmp_output); |
| 13940 | at::functionalization::impl::commit_update(out); |
| 13941 | at::functionalization::impl::sync(out); |
| 13942 | return out; |
| 13943 | } |
| 13944 | } |
| 13945 | |
| 13946 | at::Tensor & gather_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad, at::Tensor & out) { |
| 13947 | if (false) { |
| 13948 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13949 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13950 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13951 | auto self_meta = to_meta(self); |
| 13952 | auto index_meta = to_meta(index); |
| 13953 | auto out_meta = to_meta(out); |
| 13954 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13955 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13956 | at::_ops::gather_out::call(self_meta, dim, index_meta, sparse_grad, out_meta); |
| 13957 | } |
| 13958 | |
| 13959 | at::Tensor self_; |
| 13960 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13961 | at::functionalization::impl::sync(self); |
| 13962 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13963 | } else { |
| 13964 | self_ = self; |
| 13965 | } |
| 13966 | |
| 13967 | at::Tensor index_; |
| 13968 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 13969 | at::functionalization::impl::sync(index); |
| 13970 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 13971 | } else { |
| 13972 | index_ = index; |
| 13973 | } |
| 13974 | |
| 13975 | at::Tensor out_; |
| 13976 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13977 | at::functionalization::impl::sync(out); |
| 13978 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13979 | } else { |
| 13980 | out_ = out; |
| 13981 | } |
| 13982 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13983 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(index))) { |
| 13984 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13985 | TORCH_INTERNAL_ASSERT(false, |
| 13986 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13987 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13988 | } else { |
| 13989 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13990 | at::AutoDispatchSkipFunctionalize guard; |
| 13991 | at::Tensor tmp_output = at::_ops::gather_out::call(self_, dim, index_, sparse_grad, out_); |
| 13992 | return out;; |
| 13993 | } |
| 13994 | } else { |
| 13995 | at::Tensor tmp_output; |
| 13996 | { |
| 13997 | at::AutoDispatchSkipFunctionalize guard; |
| 13998 | tmp_output = at::_ops::gather::call(self_, dim, index_, sparse_grad); |
| 13999 | } |
| 14000 | at::functionalization::impl::replace_(out, tmp_output); |
| 14001 | at::functionalization::impl::commit_update(out); |
| 14002 | at::functionalization::impl::sync(out); |
| 14003 | return out; |
| 14004 | } |
| 14005 | } |
| 14006 | |
| 14007 | at::Tensor & gather_out_dimname_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, const at::Tensor & index, bool sparse_grad, at::Tensor & out) { |
| 14008 | if (false) { |
| 14009 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14010 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14011 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14012 | auto self_meta = to_meta(self); |
| 14013 | auto index_meta = to_meta(index); |
| 14014 | auto out_meta = to_meta(out); |
| 14015 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14016 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14017 | at::_ops::gather_dimname_out::call(self_meta, dim, index_meta, sparse_grad, out_meta); |
| 14018 | } |
| 14019 | |
| 14020 | at::Tensor self_; |
| 14021 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14022 | at::functionalization::impl::sync(self); |
| 14023 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14024 | } else { |
| 14025 | self_ = self; |
| 14026 | } |
| 14027 | |
| 14028 | at::Tensor index_; |
| 14029 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 14030 | at::functionalization::impl::sync(index); |
| 14031 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 14032 | } else { |
| 14033 | index_ = index; |
| 14034 | } |
| 14035 | |
| 14036 | at::Tensor out_; |
| 14037 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14038 | at::functionalization::impl::sync(out); |
| 14039 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14040 | } else { |
| 14041 | out_ = out; |
| 14042 | } |
| 14043 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14044 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(index))) { |
| 14045 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14046 | TORCH_INTERNAL_ASSERT(false, |
| 14047 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14048 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14049 | } else { |
| 14050 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14051 | at::AutoDispatchSkipFunctionalize guard; |
| 14052 | at::Tensor tmp_output = at::_ops::gather_dimname_out::call(self_, dim, index_, sparse_grad, out_); |
| 14053 | return out;; |
| 14054 | } |
| 14055 | } else { |
| 14056 | at::Tensor tmp_output; |
| 14057 | { |
| 14058 | at::AutoDispatchSkipFunctionalize guard; |
| 14059 | tmp_output = at::_ops::gather_dimname::call(self_, dim, index_, sparse_grad); |
| 14060 | } |
| 14061 | at::functionalization::impl::replace_(out, tmp_output); |
| 14062 | at::functionalization::impl::commit_update(out); |
| 14063 | at::functionalization::impl::sync(out); |
| 14064 | return out; |
| 14065 | } |
| 14066 | } |
| 14067 | |
| 14068 | at::Tensor & cholesky_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool upper, at::Tensor & out) { |
| 14069 | if (false) { |
| 14070 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14071 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14072 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14073 | auto self_meta = to_meta(self); |
| 14074 | auto out_meta = to_meta(out); |
| 14075 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14076 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14077 | at::_ops::cholesky_out::call(self_meta, upper, out_meta); |
| 14078 | } |
| 14079 | |
| 14080 | at::Tensor self_; |
| 14081 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14082 | at::functionalization::impl::sync(self); |
| 14083 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14084 | } else { |
| 14085 | self_ = self; |
| 14086 | } |
| 14087 | |
| 14088 | at::Tensor out_; |
| 14089 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14090 | at::functionalization::impl::sync(out); |
| 14091 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14092 | } else { |
| 14093 | out_ = out; |
| 14094 | } |
| 14095 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14096 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 14097 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14098 | TORCH_INTERNAL_ASSERT(false, |
| 14099 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14100 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14101 | } else { |
| 14102 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14103 | at::AutoDispatchSkipFunctionalize guard; |
| 14104 | at::Tensor tmp_output = at::_ops::cholesky_out::call(self_, upper, out_); |
| 14105 | return out;; |
| 14106 | } |
| 14107 | } else { |
| 14108 | at::Tensor tmp_output; |
| 14109 | { |
| 14110 | at::AutoDispatchSkipFunctionalize guard; |
| 14111 | tmp_output = at::_ops::cholesky::call(self_, upper); |
| 14112 | } |
| 14113 | at::functionalization::impl::replace_(out, tmp_output); |
| 14114 | at::functionalization::impl::commit_update(out); |
| 14115 | at::functionalization::impl::sync(out); |
| 14116 | return out; |
| 14117 | } |
| 14118 | } |
| 14119 | |
| 14120 | at::Tensor & _cholesky_solve_helper_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & A, bool upper, at::Tensor & out) { |
| 14121 | if (false) { |
| 14122 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14123 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14124 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14125 | auto self_meta = to_meta(self); |
| 14126 | auto A_meta = to_meta(A); |
| 14127 | auto out_meta = to_meta(out); |
| 14128 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14129 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14130 | at::_ops::_cholesky_solve_helper_out::call(self_meta, A_meta, upper, out_meta); |
| 14131 | } |
| 14132 | |
| 14133 | at::Tensor self_; |
| 14134 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14135 | at::functionalization::impl::sync(self); |
| 14136 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14137 | } else { |
| 14138 | self_ = self; |
| 14139 | } |
| 14140 | |
| 14141 | at::Tensor A_; |
| 14142 | if (at::functionalization::impl::isFunctionalTensor(A)) { |
| 14143 | at::functionalization::impl::sync(A); |
| 14144 | A_ = at::functionalization::impl::from_functional_tensor(A); |
| 14145 | } else { |
| 14146 | A_ = A; |
| 14147 | } |
| 14148 | |
| 14149 | at::Tensor out_; |
| 14150 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14151 | at::functionalization::impl::sync(out); |
| 14152 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14153 | } else { |
| 14154 | out_ = out; |
| 14155 | } |
| 14156 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14157 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(A))) { |
| 14158 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14159 | TORCH_INTERNAL_ASSERT(false, |
| 14160 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14161 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14162 | } else { |
| 14163 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14164 | at::AutoDispatchSkipFunctionalize guard; |
| 14165 | at::Tensor tmp_output = at::_ops::_cholesky_solve_helper_out::call(self_, A_, upper, out_); |
| 14166 | return out;; |
| 14167 | } |
| 14168 | } else { |
| 14169 | at::Tensor tmp_output; |
| 14170 | { |
| 14171 | at::AutoDispatchSkipFunctionalize guard; |
| 14172 | tmp_output = at::_ops::_cholesky_solve_helper::call(self_, A_, upper); |
| 14173 | } |
| 14174 | at::functionalization::impl::replace_(out, tmp_output); |
| 14175 | at::functionalization::impl::commit_update(out); |
| 14176 | at::functionalization::impl::sync(out); |
| 14177 | return out; |
| 14178 | } |
| 14179 | } |
| 14180 | |
| 14181 | at::Tensor & polygamma_out_out(c10::DispatchKeySet dispatchKeySet, int64_t n, const at::Tensor & self, at::Tensor & out) { |
| 14182 | if (false) { |
| 14183 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14184 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14185 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14186 | auto self_meta = to_meta(self); |
| 14187 | auto out_meta = to_meta(out); |
| 14188 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14189 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14190 | at::_ops::polygamma_out::call(n, self_meta, out_meta); |
| 14191 | } |
| 14192 | |
| 14193 | at::Tensor self_; |
| 14194 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14195 | at::functionalization::impl::sync(self); |
| 14196 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14197 | } else { |
| 14198 | self_ = self; |
| 14199 | } |
| 14200 | |
| 14201 | at::Tensor out_; |
| 14202 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14203 | at::functionalization::impl::sync(out); |
| 14204 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14205 | } else { |
| 14206 | out_ = out; |
| 14207 | } |
| 14208 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14209 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 14210 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14211 | TORCH_INTERNAL_ASSERT(false, |
| 14212 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14213 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14214 | } else { |
| 14215 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14216 | at::AutoDispatchSkipFunctionalize guard; |
| 14217 | at::Tensor tmp_output = at::_ops::polygamma_out::call(n, self_, out_); |
| 14218 | return out;; |
| 14219 | } |
| 14220 | } else { |
| 14221 | at::Tensor tmp_output; |
| 14222 | { |
| 14223 | at::AutoDispatchSkipFunctionalize guard; |
| 14224 | tmp_output = at::_ops::polygamma::call(n, self_); |
| 14225 | } |
| 14226 | at::functionalization::impl::replace_(out, tmp_output); |
| 14227 | at::functionalization::impl::commit_update(out); |
| 14228 | at::functionalization::impl::sync(out); |
| 14229 | return out; |
| 14230 | } |
| 14231 | } |
| 14232 | |
| 14233 | at::Tensor & igamma_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 14234 | if (false) { |
| 14235 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14236 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14237 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14238 | auto self_meta = to_meta(self); |
| 14239 | auto other_meta = to_meta(other); |
| 14240 | auto out_meta = to_meta(out); |
| 14241 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14242 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14243 | at::_ops::igamma_out::call(self_meta, other_meta, out_meta); |
| 14244 | } |
| 14245 | |
| 14246 | at::Tensor self_; |
| 14247 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14248 | at::functionalization::impl::sync(self); |
| 14249 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14250 | } else { |
| 14251 | self_ = self; |
| 14252 | } |
| 14253 | |
| 14254 | at::Tensor other_; |
| 14255 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 14256 | at::functionalization::impl::sync(other); |
| 14257 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 14258 | } else { |
| 14259 | other_ = other; |
| 14260 | } |
| 14261 | |
| 14262 | at::Tensor out_; |
| 14263 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14264 | at::functionalization::impl::sync(out); |
| 14265 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14266 | } else { |
| 14267 | out_ = out; |
| 14268 | } |
| 14269 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14270 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 14271 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14272 | TORCH_INTERNAL_ASSERT(false, |
| 14273 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14274 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14275 | } else { |
| 14276 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14277 | at::AutoDispatchSkipFunctionalize guard; |
| 14278 | at::Tensor tmp_output = at::_ops::igamma_out::call(self_, other_, out_); |
| 14279 | return out;; |
| 14280 | } |
| 14281 | } else { |
| 14282 | at::Tensor tmp_output; |
| 14283 | { |
| 14284 | at::AutoDispatchSkipFunctionalize guard; |
| 14285 | tmp_output = at::_ops::igamma::call(self_, other_); |
| 14286 | } |
| 14287 | at::functionalization::impl::replace_(out, tmp_output); |
| 14288 | at::functionalization::impl::commit_update(out); |
| 14289 | at::functionalization::impl::sync(out); |
| 14290 | return out; |
| 14291 | } |
| 14292 | } |
| 14293 | |
| 14294 | at::Tensor & igamma_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 14295 | if (true) { |
| 14296 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14297 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14298 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14299 | auto self_meta = to_meta(self); |
| 14300 | auto other_meta = to_meta(other); |
| 14301 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14302 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14303 | at::_ops::igamma_::call(self_meta, other_meta); |
| 14304 | } |
| 14305 | |
| 14306 | at::Tensor self_; |
| 14307 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14308 | at::functionalization::impl::sync(self); |
| 14309 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14310 | } else { |
| 14311 | self_ = self; |
| 14312 | } |
| 14313 | |
| 14314 | at::Tensor other_; |
| 14315 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 14316 | at::functionalization::impl::sync(other); |
| 14317 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 14318 | } else { |
| 14319 | other_ = other; |
| 14320 | } |
| 14321 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 14322 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 14323 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14324 | TORCH_INTERNAL_ASSERT(false, |
| 14325 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14326 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14327 | } else { |
| 14328 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14329 | at::AutoDispatchSkipFunctionalize guard; |
| 14330 | at::Tensor tmp_output = at::_ops::igamma_::call(self_, other_); |
| 14331 | return self;; |
| 14332 | } |
| 14333 | } else { |
| 14334 | at::Tensor tmp_output; |
| 14335 | { |
| 14336 | at::AutoDispatchSkipFunctionalize guard; |
| 14337 | tmp_output = at::_ops::igamma::call(self_, other_); |
| 14338 | } |
| 14339 | at::functionalization::impl::replace_(self, tmp_output); |
| 14340 | at::functionalization::impl::commit_update(self); |
| 14341 | at::functionalization::impl::sync(self); |
| 14342 | return self; |
| 14343 | } |
| 14344 | } |
| 14345 | |
| 14346 | at::Tensor & fmin_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 14347 | if (false) { |
| 14348 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14349 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14350 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14351 | auto self_meta = to_meta(self); |
| 14352 | auto other_meta = to_meta(other); |
| 14353 | auto out_meta = to_meta(out); |
| 14354 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14355 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14356 | at::_ops::fmin_out::call(self_meta, other_meta, out_meta); |
| 14357 | } |
| 14358 | |
| 14359 | at::Tensor self_; |
| 14360 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14361 | at::functionalization::impl::sync(self); |
| 14362 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14363 | } else { |
| 14364 | self_ = self; |
| 14365 | } |
| 14366 | |
| 14367 | at::Tensor other_; |
| 14368 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 14369 | at::functionalization::impl::sync(other); |
| 14370 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 14371 | } else { |
| 14372 | other_ = other; |
| 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) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 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::fmin_out::call(self_, other_, out_); |
| 14392 | return out;; |
| 14393 | } |
| 14394 | } else { |
| 14395 | at::Tensor tmp_output; |
| 14396 | { |
| 14397 | at::AutoDispatchSkipFunctionalize guard; |
| 14398 | tmp_output = at::_ops::fmin::call(self_, other_); |
| 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 & max_out_unary_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 14408 | if (false) { |
| 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 | auto out_meta = to_meta(out); |
| 14414 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14415 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14416 | at::_ops::max_unary_out::call(self_meta, out_meta); |
| 14417 | } |
| 14418 | |
| 14419 | at::Tensor self_; |
| 14420 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14421 | at::functionalization::impl::sync(self); |
| 14422 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14423 | } else { |
| 14424 | self_ = self; |
| 14425 | } |
| 14426 | |
| 14427 | at::Tensor out_; |
| 14428 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14429 | at::functionalization::impl::sync(out); |
| 14430 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14431 | } else { |
| 14432 | out_ = out; |
| 14433 | } |
| 14434 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14435 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 14436 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14437 | TORCH_INTERNAL_ASSERT(false, |
| 14438 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14439 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14440 | } else { |
| 14441 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14442 | at::AutoDispatchSkipFunctionalize guard; |
| 14443 | at::Tensor tmp_output = at::_ops::max_unary_out::call(self_, out_); |
| 14444 | return out;; |
| 14445 | } |
| 14446 | } else { |
| 14447 | at::Tensor tmp_output; |
| 14448 | { |
| 14449 | at::AutoDispatchSkipFunctionalize guard; |
| 14450 | tmp_output = at::_ops::max::call(self_); |
| 14451 | } |
| 14452 | at::functionalization::impl::replace_(out, tmp_output); |
| 14453 | at::functionalization::impl::commit_update(out); |
| 14454 | at::functionalization::impl::sync(out); |
| 14455 | return out; |
| 14456 | } |
| 14457 | } |
| 14458 | |
| 14459 | at::Tensor & fmax_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 14460 | if (false) { |
| 14461 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14462 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14463 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14464 | auto self_meta = to_meta(self); |
| 14465 | auto other_meta = to_meta(other); |
| 14466 | auto out_meta = to_meta(out); |
| 14467 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14468 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14469 | at::_ops::fmax_out::call(self_meta, other_meta, out_meta); |
| 14470 | } |
| 14471 | |
| 14472 | at::Tensor self_; |
| 14473 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14474 | at::functionalization::impl::sync(self); |
| 14475 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14476 | } else { |
| 14477 | self_ = self; |
| 14478 | } |
| 14479 | |
| 14480 | at::Tensor other_; |
| 14481 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 14482 | at::functionalization::impl::sync(other); |
| 14483 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 14484 | } else { |
| 14485 | other_ = other; |
| 14486 | } |
| 14487 | |
| 14488 | at::Tensor out_; |
| 14489 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14490 | at::functionalization::impl::sync(out); |
| 14491 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14492 | } else { |
| 14493 | out_ = out; |
| 14494 | } |
| 14495 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14496 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 14497 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14498 | TORCH_INTERNAL_ASSERT(false, |
| 14499 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14500 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14501 | } else { |
| 14502 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14503 | at::AutoDispatchSkipFunctionalize guard; |
| 14504 | at::Tensor tmp_output = at::_ops::fmax_out::call(self_, other_, out_); |
| 14505 | return out;; |
| 14506 | } |
| 14507 | } else { |
| 14508 | at::Tensor tmp_output; |
| 14509 | { |
| 14510 | at::AutoDispatchSkipFunctionalize guard; |
| 14511 | tmp_output = at::_ops::fmax::call(self_, other_); |
| 14512 | } |
| 14513 | at::functionalization::impl::replace_(out, tmp_output); |
| 14514 | at::functionalization::impl::commit_update(out); |
| 14515 | at::functionalization::impl::sync(out); |
| 14516 | return out; |
| 14517 | } |
| 14518 | } |
| 14519 | |
| 14520 | at::Tensor & maximum_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 14521 | if (false) { |
| 14522 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14523 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14524 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14525 | auto self_meta = to_meta(self); |
| 14526 | auto other_meta = to_meta(other); |
| 14527 | auto out_meta = to_meta(out); |
| 14528 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14529 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14530 | at::_ops::maximum_out::call(self_meta, other_meta, out_meta); |
| 14531 | } |
| 14532 | |
| 14533 | at::Tensor self_; |
| 14534 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14535 | at::functionalization::impl::sync(self); |
| 14536 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14537 | } else { |
| 14538 | self_ = self; |
| 14539 | } |
| 14540 | |
| 14541 | at::Tensor other_; |
| 14542 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 14543 | at::functionalization::impl::sync(other); |
| 14544 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 14545 | } else { |
| 14546 | other_ = other; |
| 14547 | } |
| 14548 | |
| 14549 | at::Tensor out_; |
| 14550 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14551 | at::functionalization::impl::sync(out); |
| 14552 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14553 | } else { |
| 14554 | out_ = out; |
| 14555 | } |
| 14556 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14557 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 14558 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14559 | TORCH_INTERNAL_ASSERT(false, |
| 14560 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14561 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14562 | } else { |
| 14563 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14564 | at::AutoDispatchSkipFunctionalize guard; |
| 14565 | at::Tensor tmp_output = at::_ops::maximum_out::call(self_, other_, out_); |
| 14566 | return out;; |
| 14567 | } |
| 14568 | } else { |
| 14569 | at::Tensor tmp_output; |
| 14570 | { |
| 14571 | at::AutoDispatchSkipFunctionalize guard; |
| 14572 | tmp_output = at::_ops::maximum::call(self_, other_); |
| 14573 | } |
| 14574 | at::functionalization::impl::replace_(out, tmp_output); |
| 14575 | at::functionalization::impl::commit_update(out); |
| 14576 | at::functionalization::impl::sync(out); |
| 14577 | return out; |
| 14578 | } |
| 14579 | } |
| 14580 | |
| 14581 | at::Tensor & max_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 14582 | if (false) { |
| 14583 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14584 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14585 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14586 | auto self_meta = to_meta(self); |
| 14587 | auto other_meta = to_meta(other); |
| 14588 | auto out_meta = to_meta(out); |
| 14589 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14590 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14591 | at::_ops::max_out::call(self_meta, other_meta, out_meta); |
| 14592 | } |
| 14593 | |
| 14594 | at::Tensor self_; |
| 14595 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14596 | at::functionalization::impl::sync(self); |
| 14597 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14598 | } else { |
| 14599 | self_ = self; |
| 14600 | } |
| 14601 | |
| 14602 | at::Tensor other_; |
| 14603 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 14604 | at::functionalization::impl::sync(other); |
| 14605 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 14606 | } else { |
| 14607 | other_ = other; |
| 14608 | } |
| 14609 | |
| 14610 | at::Tensor out_; |
| 14611 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14612 | at::functionalization::impl::sync(out); |
| 14613 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14614 | } else { |
| 14615 | out_ = out; |
| 14616 | } |
| 14617 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14618 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 14619 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14620 | TORCH_INTERNAL_ASSERT(false, |
| 14621 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14622 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14623 | } else { |
| 14624 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14625 | at::AutoDispatchSkipFunctionalize guard; |
| 14626 | at::Tensor tmp_output = at::_ops::max_out::call(self_, other_, out_); |
| 14627 | return out;; |
| 14628 | } |
| 14629 | } else { |
| 14630 | at::Tensor tmp_output; |
| 14631 | { |
| 14632 | at::AutoDispatchSkipFunctionalize guard; |
| 14633 | tmp_output = at::_ops::max_other::call(self_, other_); |
| 14634 | } |
| 14635 | at::functionalization::impl::replace_(out, tmp_output); |
| 14636 | at::functionalization::impl::commit_update(out); |
| 14637 | at::functionalization::impl::sync(out); |
| 14638 | return out; |
| 14639 | } |
| 14640 | } |
| 14641 | |
| 14642 | void _amp_foreach_non_finite_check_and_unscale_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::Tensor & found_inf, const at::Tensor & inv_scale, at::TensorList out) { |
| 14643 | if (false) { |
| 14644 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14645 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14646 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14647 | auto self_meta = to_meta(self); |
| 14648 | auto found_inf_meta = to_meta(found_inf); |
| 14649 | auto inv_scale_meta = to_meta(inv_scale); |
| 14650 | auto out_meta = to_meta(out); |
| 14651 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14652 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14653 | at::_ops::_amp_foreach_non_finite_check_and_unscale_out::call(self_meta, found_inf_meta, inv_scale_meta, out_meta); |
| 14654 | } |
| 14655 | |
| 14656 | ::std::vector<at::Tensor> self_; |
| 14657 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14658 | at::functionalization::impl::sync(self); |
| 14659 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14660 | } else { |
| 14661 | self_ = self.vec(); |
| 14662 | } |
| 14663 | |
| 14664 | at::Tensor found_inf_; |
| 14665 | if (at::functionalization::impl::isFunctionalTensor(found_inf)) { |
| 14666 | at::functionalization::impl::sync(found_inf); |
| 14667 | found_inf_ = at::functionalization::impl::from_functional_tensor(found_inf); |
| 14668 | } else { |
| 14669 | found_inf_ = found_inf; |
| 14670 | } |
| 14671 | |
| 14672 | at::Tensor inv_scale_; |
| 14673 | if (at::functionalization::impl::isFunctionalTensor(inv_scale)) { |
| 14674 | at::functionalization::impl::sync(inv_scale); |
| 14675 | inv_scale_ = at::functionalization::impl::from_functional_tensor(inv_scale); |
| 14676 | } else { |
| 14677 | inv_scale_ = inv_scale; |
| 14678 | } |
| 14679 | |
| 14680 | ::std::vector<at::Tensor> out_; |
| 14681 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14682 | at::functionalization::impl::sync(out); |
| 14683 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14684 | } else { |
| 14685 | out_ = out.vec(); |
| 14686 | } |
| 14687 | if (!(true && at::functionalization::impl::isFunctionalTensor(found_inf) && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14688 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(inv_scale))) { |
| 14689 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14690 | TORCH_INTERNAL_ASSERT(false, |
| 14691 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14692 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14693 | } else { |
| 14694 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14695 | at::AutoDispatchSkipFunctionalize guard; |
| 14696 | at::_ops::_amp_foreach_non_finite_check_and_unscale_out::call(self_, found_inf_, inv_scale_, out_); |
| 14697 | ; |
| 14698 | } |
| 14699 | } else { |
| 14700 | ::std::tuple<::std::vector<at::Tensor>,at::Tensor> tmp_output; |
| 14701 | { |
| 14702 | at::AutoDispatchSkipFunctionalize guard; |
| 14703 | tmp_output = at::_ops::_amp_foreach_non_finite_check_and_unscale::call(self_, found_inf_, inv_scale_); |
| 14704 | } |
| 14705 | at::functionalization::impl::replace_(found_inf, std::get<0>(tmp_output)); |
| 14706 | at::functionalization::impl::commit_update(found_inf); |
| 14707 | at::functionalization::impl::sync(found_inf); |
| 14708 | at::functionalization::impl::replace_(out, std::get<1>(tmp_output)); |
| 14709 | at::functionalization::impl::commit_update(out); |
| 14710 | at::functionalization::impl::sync(out); |
| 14711 | |
| 14712 | } |
| 14713 | } |
| 14714 | |
| 14715 | void _amp_foreach_non_finite_check_and_unscale_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::Tensor & found_inf, const at::Tensor & inv_scale) { |
| 14716 | if (true) { |
| 14717 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14718 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14719 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14720 | auto self_meta = to_meta(self); |
| 14721 | auto found_inf_meta = to_meta(found_inf); |
| 14722 | auto inv_scale_meta = to_meta(inv_scale); |
| 14723 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14724 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14725 | at::_ops::_amp_foreach_non_finite_check_and_unscale_::call(self_meta, found_inf_meta, inv_scale_meta); |
| 14726 | } |
| 14727 | |
| 14728 | ::std::vector<at::Tensor> self_; |
| 14729 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14730 | at::functionalization::impl::sync(self); |
| 14731 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14732 | } else { |
| 14733 | self_ = self.vec(); |
| 14734 | } |
| 14735 | |
| 14736 | at::Tensor found_inf_; |
| 14737 | if (at::functionalization::impl::isFunctionalTensor(found_inf)) { |
| 14738 | at::functionalization::impl::sync(found_inf); |
| 14739 | found_inf_ = at::functionalization::impl::from_functional_tensor(found_inf); |
| 14740 | } else { |
| 14741 | found_inf_ = found_inf; |
| 14742 | } |
| 14743 | |
| 14744 | at::Tensor inv_scale_; |
| 14745 | if (at::functionalization::impl::isFunctionalTensor(inv_scale)) { |
| 14746 | at::functionalization::impl::sync(inv_scale); |
| 14747 | inv_scale_ = at::functionalization::impl::from_functional_tensor(inv_scale); |
| 14748 | } else { |
| 14749 | inv_scale_ = inv_scale; |
| 14750 | } |
| 14751 | if (!(true && at::functionalization::impl::isFunctionalTensor(self) && at::functionalization::impl::isFunctionalTensor(found_inf))) { |
| 14752 | if ((false || at::functionalization::impl::isFunctionalTensor(inv_scale))) { |
| 14753 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14754 | TORCH_INTERNAL_ASSERT(false, |
| 14755 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14756 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14757 | } else { |
| 14758 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14759 | at::AutoDispatchSkipFunctionalize guard; |
| 14760 | at::_ops::_amp_foreach_non_finite_check_and_unscale_::call(self_, found_inf_, inv_scale_); |
| 14761 | ; |
| 14762 | } |
| 14763 | } else { |
| 14764 | ::std::tuple<::std::vector<at::Tensor>,at::Tensor> tmp_output; |
| 14765 | { |
| 14766 | at::AutoDispatchSkipFunctionalize guard; |
| 14767 | tmp_output = at::_ops::_amp_foreach_non_finite_check_and_unscale::call(self_, found_inf_, inv_scale_); |
| 14768 | } |
| 14769 | at::functionalization::impl::replace_(self, std::get<0>(tmp_output)); |
| 14770 | at::functionalization::impl::commit_update(self); |
| 14771 | at::functionalization::impl::sync(self); |
| 14772 | at::functionalization::impl::replace_(found_inf, std::get<1>(tmp_output)); |
| 14773 | at::functionalization::impl::commit_update(found_inf); |
| 14774 | at::functionalization::impl::sync(found_inf); |
| 14775 | |
| 14776 | } |
| 14777 | } |
| 14778 | |
| 14779 | void _foreach_sub_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar, at::TensorList out) { |
| 14780 | if (false) { |
| 14781 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14782 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14783 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14784 | auto self_meta = to_meta(self); |
| 14785 | auto out_meta = to_meta(out); |
| 14786 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14787 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14788 | at::_ops::_foreach_sub_Scalar_out::call(self_meta, scalar, out_meta); |
| 14789 | } |
| 14790 | |
| 14791 | ::std::vector<at::Tensor> self_; |
| 14792 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14793 | at::functionalization::impl::sync(self); |
| 14794 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14795 | } else { |
| 14796 | self_ = self.vec(); |
| 14797 | } |
| 14798 | |
| 14799 | ::std::vector<at::Tensor> out_; |
| 14800 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14801 | at::functionalization::impl::sync(out); |
| 14802 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14803 | } else { |
| 14804 | out_ = out.vec(); |
| 14805 | } |
| 14806 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14807 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 14808 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14809 | TORCH_INTERNAL_ASSERT(false, |
| 14810 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14811 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14812 | } else { |
| 14813 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14814 | at::AutoDispatchSkipFunctionalize guard; |
| 14815 | at::_ops::_foreach_sub_Scalar_out::call(self_, scalar, out_); |
| 14816 | ; |
| 14817 | } |
| 14818 | } else { |
| 14819 | ::std::vector<at::Tensor> tmp_output; |
| 14820 | { |
| 14821 | at::AutoDispatchSkipFunctionalize guard; |
| 14822 | tmp_output = at::_ops::_foreach_sub_Scalar::call(self_, scalar); |
| 14823 | } |
| 14824 | at::functionalization::impl::replace_(out, tmp_output); |
| 14825 | at::functionalization::impl::commit_update(out); |
| 14826 | at::functionalization::impl::sync(out); |
| 14827 | |
| 14828 | } |
| 14829 | } |
| 14830 | |
| 14831 | void _foreach_sub__Scalar(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { |
| 14832 | if (true) { |
| 14833 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14834 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14835 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14836 | auto self_meta = to_meta(self); |
| 14837 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14838 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14839 | at::_ops::_foreach_sub__Scalar::call(self_meta, scalar); |
| 14840 | } |
| 14841 | |
| 14842 | ::std::vector<at::Tensor> self_; |
| 14843 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14844 | at::functionalization::impl::sync(self); |
| 14845 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14846 | } else { |
| 14847 | self_ = self.vec(); |
| 14848 | } |
| 14849 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 14850 | if ((false)) { |
| 14851 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14852 | TORCH_INTERNAL_ASSERT(false, |
| 14853 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14854 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14855 | } else { |
| 14856 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14857 | at::AutoDispatchSkipFunctionalize guard; |
| 14858 | at::_ops::_foreach_sub__Scalar::call(self_, scalar); |
| 14859 | ; |
| 14860 | } |
| 14861 | } else { |
| 14862 | ::std::vector<at::Tensor> tmp_output; |
| 14863 | { |
| 14864 | at::AutoDispatchSkipFunctionalize guard; |
| 14865 | tmp_output = at::_ops::_foreach_sub_Scalar::call(self_, scalar); |
| 14866 | } |
| 14867 | at::functionalization::impl::replace_(self, tmp_output); |
| 14868 | at::functionalization::impl::commit_update(self); |
| 14869 | at::functionalization::impl::sync(self); |
| 14870 | |
| 14871 | } |
| 14872 | } |
| 14873 | |
| 14874 | void _foreach_maximum_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar, at::TensorList out) { |
| 14875 | if (false) { |
| 14876 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14877 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14878 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14879 | auto self_meta = to_meta(self); |
| 14880 | auto out_meta = to_meta(out); |
| 14881 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14882 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14883 | at::_ops::_foreach_maximum_Scalar_out::call(self_meta, scalar, out_meta); |
| 14884 | } |
| 14885 | |
| 14886 | ::std::vector<at::Tensor> self_; |
| 14887 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14888 | at::functionalization::impl::sync(self); |
| 14889 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14890 | } else { |
| 14891 | self_ = self.vec(); |
| 14892 | } |
| 14893 | |
| 14894 | ::std::vector<at::Tensor> out_; |
| 14895 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14896 | at::functionalization::impl::sync(out); |
| 14897 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14898 | } else { |
| 14899 | out_ = out.vec(); |
| 14900 | } |
| 14901 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14902 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 14903 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14904 | TORCH_INTERNAL_ASSERT(false, |
| 14905 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14906 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14907 | } else { |
| 14908 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14909 | at::AutoDispatchSkipFunctionalize guard; |
| 14910 | at::_ops::_foreach_maximum_Scalar_out::call(self_, scalar, out_); |
| 14911 | ; |
| 14912 | } |
| 14913 | } else { |
| 14914 | ::std::vector<at::Tensor> tmp_output; |
| 14915 | { |
| 14916 | at::AutoDispatchSkipFunctionalize guard; |
| 14917 | tmp_output = at::_ops::_foreach_maximum_Scalar::call(self_, scalar); |
| 14918 | } |
| 14919 | at::functionalization::impl::replace_(out, tmp_output); |
| 14920 | at::functionalization::impl::commit_update(out); |
| 14921 | at::functionalization::impl::sync(out); |
| 14922 | |
| 14923 | } |
| 14924 | } |
| 14925 | |
| 14926 | void _foreach_maximum__Scalar(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { |
| 14927 | if (true) { |
| 14928 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14929 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14930 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14931 | auto self_meta = to_meta(self); |
| 14932 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14933 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14934 | at::_ops::_foreach_maximum__Scalar::call(self_meta, scalar); |
| 14935 | } |
| 14936 | |
| 14937 | ::std::vector<at::Tensor> self_; |
| 14938 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14939 | at::functionalization::impl::sync(self); |
| 14940 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14941 | } else { |
| 14942 | self_ = self.vec(); |
| 14943 | } |
| 14944 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 14945 | if ((false)) { |
| 14946 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14947 | TORCH_INTERNAL_ASSERT(false, |
| 14948 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14949 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14950 | } else { |
| 14951 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14952 | at::AutoDispatchSkipFunctionalize guard; |
| 14953 | at::_ops::_foreach_maximum__Scalar::call(self_, scalar); |
| 14954 | ; |
| 14955 | } |
| 14956 | } else { |
| 14957 | ::std::vector<at::Tensor> tmp_output; |
| 14958 | { |
| 14959 | at::AutoDispatchSkipFunctionalize guard; |
| 14960 | tmp_output = at::_ops::_foreach_maximum_Scalar::call(self_, scalar); |
| 14961 | } |
| 14962 | at::functionalization::impl::replace_(self, tmp_output); |
| 14963 | at::functionalization::impl::commit_update(self); |
| 14964 | at::functionalization::impl::sync(self); |
| 14965 | |
| 14966 | } |
| 14967 | } |
| 14968 | |
| 14969 | void _foreach_sub_out_List_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other, const at::Scalar & alpha, at::TensorList out) { |
| 14970 | if (false) { |
| 14971 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14972 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14973 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14974 | auto self_meta = to_meta(self); |
| 14975 | auto other_meta = to_meta(other); |
| 14976 | auto out_meta = to_meta(out); |
| 14977 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14978 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14979 | at::_ops::_foreach_sub_List_out::call(self_meta, other_meta, alpha, out_meta); |
| 14980 | } |
| 14981 | |
| 14982 | ::std::vector<at::Tensor> self_; |
| 14983 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14984 | at::functionalization::impl::sync(self); |
| 14985 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14986 | } else { |
| 14987 | self_ = self.vec(); |
| 14988 | } |
| 14989 | |
| 14990 | ::std::vector<at::Tensor> other_; |
| 14991 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 14992 | at::functionalization::impl::sync(other); |
| 14993 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 14994 | } else { |
| 14995 | other_ = other.vec(); |
| 14996 | } |
| 14997 | |
| 14998 | ::std::vector<at::Tensor> out_; |
| 14999 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15000 | at::functionalization::impl::sync(out); |
| 15001 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15002 | } else { |
| 15003 | out_ = out.vec(); |
| 15004 | } |
| 15005 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15006 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 15007 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15008 | TORCH_INTERNAL_ASSERT(false, |
| 15009 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15010 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15011 | } else { |
| 15012 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15013 | at::AutoDispatchSkipFunctionalize guard; |
| 15014 | at::_ops::_foreach_sub_List_out::call(self_, other_, alpha, out_); |
| 15015 | ; |
| 15016 | } |
| 15017 | } else { |
| 15018 | ::std::vector<at::Tensor> tmp_output; |
| 15019 | { |
| 15020 | at::AutoDispatchSkipFunctionalize guard; |
| 15021 | tmp_output = at::_ops::_foreach_sub_List::call(self_, other_, alpha); |
| 15022 | } |
| 15023 | at::functionalization::impl::replace_(out, tmp_output); |
| 15024 | at::functionalization::impl::commit_update(out); |
| 15025 | at::functionalization::impl::sync(out); |
| 15026 | |
| 15027 | } |
| 15028 | } |
| 15029 | |
| 15030 | void _foreach_sub__List(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other, const at::Scalar & alpha) { |
| 15031 | if (true) { |
| 15032 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15033 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15034 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15035 | auto self_meta = to_meta(self); |
| 15036 | auto other_meta = to_meta(other); |
| 15037 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15038 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15039 | at::_ops::_foreach_sub__List::call(self_meta, other_meta, alpha); |
| 15040 | } |
| 15041 | |
| 15042 | ::std::vector<at::Tensor> self_; |
| 15043 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15044 | at::functionalization::impl::sync(self); |
| 15045 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15046 | } else { |
| 15047 | self_ = self.vec(); |
| 15048 | } |
| 15049 | |
| 15050 | ::std::vector<at::Tensor> other_; |
| 15051 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 15052 | at::functionalization::impl::sync(other); |
| 15053 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 15054 | } else { |
| 15055 | other_ = other.vec(); |
| 15056 | } |
| 15057 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 15058 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 15059 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15060 | TORCH_INTERNAL_ASSERT(false, |
| 15061 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15062 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15063 | } else { |
| 15064 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15065 | at::AutoDispatchSkipFunctionalize guard; |
| 15066 | at::_ops::_foreach_sub__List::call(self_, other_, alpha); |
| 15067 | ; |
| 15068 | } |
| 15069 | } else { |
| 15070 | ::std::vector<at::Tensor> tmp_output; |
| 15071 | { |
| 15072 | at::AutoDispatchSkipFunctionalize guard; |
| 15073 | tmp_output = at::_ops::_foreach_sub_List::call(self_, other_, alpha); |
| 15074 | } |
| 15075 | at::functionalization::impl::replace_(self, tmp_output); |
| 15076 | at::functionalization::impl::commit_update(self); |
| 15077 | at::functionalization::impl::sync(self); |
| 15078 | |
| 15079 | } |
| 15080 | } |
| 15081 | |
| 15082 | void _foreach_maximum_out_List_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other, at::TensorList out) { |
| 15083 | if (false) { |
| 15084 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15085 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15086 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15087 | auto self_meta = to_meta(self); |
| 15088 | auto other_meta = to_meta(other); |
| 15089 | auto out_meta = to_meta(out); |
| 15090 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15091 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15092 | at::_ops::_foreach_maximum_List_out::call(self_meta, other_meta, out_meta); |
| 15093 | } |
| 15094 | |
| 15095 | ::std::vector<at::Tensor> self_; |
| 15096 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15097 | at::functionalization::impl::sync(self); |
| 15098 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15099 | } else { |
| 15100 | self_ = self.vec(); |
| 15101 | } |
| 15102 | |
| 15103 | ::std::vector<at::Tensor> other_; |
| 15104 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 15105 | at::functionalization::impl::sync(other); |
| 15106 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 15107 | } else { |
| 15108 | other_ = other.vec(); |
| 15109 | } |
| 15110 | |
| 15111 | ::std::vector<at::Tensor> out_; |
| 15112 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15113 | at::functionalization::impl::sync(out); |
| 15114 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15115 | } else { |
| 15116 | out_ = out.vec(); |
| 15117 | } |
| 15118 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15119 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 15120 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15121 | TORCH_INTERNAL_ASSERT(false, |
| 15122 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15123 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15124 | } else { |
| 15125 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15126 | at::AutoDispatchSkipFunctionalize guard; |
| 15127 | at::_ops::_foreach_maximum_List_out::call(self_, other_, out_); |
| 15128 | ; |
| 15129 | } |
| 15130 | } else { |
| 15131 | ::std::vector<at::Tensor> tmp_output; |
| 15132 | { |
| 15133 | at::AutoDispatchSkipFunctionalize guard; |
| 15134 | tmp_output = at::_ops::_foreach_maximum_List::call(self_, other_); |
| 15135 | } |
| 15136 | at::functionalization::impl::replace_(out, tmp_output); |
| 15137 | at::functionalization::impl::commit_update(out); |
| 15138 | at::functionalization::impl::sync(out); |
| 15139 | |
| 15140 | } |
| 15141 | } |
| 15142 | |
| 15143 | void _foreach_maximum__List(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other) { |
| 15144 | if (true) { |
| 15145 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15146 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15147 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15148 | auto self_meta = to_meta(self); |
| 15149 | auto other_meta = to_meta(other); |
| 15150 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15151 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15152 | at::_ops::_foreach_maximum__List::call(self_meta, other_meta); |
| 15153 | } |
| 15154 | |
| 15155 | ::std::vector<at::Tensor> self_; |
| 15156 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15157 | at::functionalization::impl::sync(self); |
| 15158 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15159 | } else { |
| 15160 | self_ = self.vec(); |
| 15161 | } |
| 15162 | |
| 15163 | ::std::vector<at::Tensor> other_; |
| 15164 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 15165 | at::functionalization::impl::sync(other); |
| 15166 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 15167 | } else { |
| 15168 | other_ = other.vec(); |
| 15169 | } |
| 15170 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 15171 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 15172 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15173 | TORCH_INTERNAL_ASSERT(false, |
| 15174 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15175 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15176 | } else { |
| 15177 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15178 | at::AutoDispatchSkipFunctionalize guard; |
| 15179 | at::_ops::_foreach_maximum__List::call(self_, other_); |
| 15180 | ; |
| 15181 | } |
| 15182 | } else { |
| 15183 | ::std::vector<at::Tensor> tmp_output; |
| 15184 | { |
| 15185 | at::AutoDispatchSkipFunctionalize guard; |
| 15186 | tmp_output = at::_ops::_foreach_maximum_List::call(self_, other_); |
| 15187 | } |
| 15188 | at::functionalization::impl::replace_(self, tmp_output); |
| 15189 | at::functionalization::impl::commit_update(self); |
| 15190 | at::functionalization::impl::sync(self); |
| 15191 | |
| 15192 | } |
| 15193 | } |
| 15194 | |
| 15195 | void _foreach_sub_out_ScalarList_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out) { |
| 15196 | if (false) { |
| 15197 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15198 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15199 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15200 | auto self_meta = to_meta(self); |
| 15201 | auto out_meta = to_meta(out); |
| 15202 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15203 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15204 | at::_ops::_foreach_sub_ScalarList_out::call(self_meta, scalars, out_meta); |
| 15205 | } |
| 15206 | |
| 15207 | ::std::vector<at::Tensor> self_; |
| 15208 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15209 | at::functionalization::impl::sync(self); |
| 15210 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15211 | } else { |
| 15212 | self_ = self.vec(); |
| 15213 | } |
| 15214 | |
| 15215 | ::std::vector<at::Tensor> out_; |
| 15216 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15217 | at::functionalization::impl::sync(out); |
| 15218 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15219 | } else { |
| 15220 | out_ = out.vec(); |
| 15221 | } |
| 15222 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15223 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 15224 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15225 | TORCH_INTERNAL_ASSERT(false, |
| 15226 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15227 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15228 | } else { |
| 15229 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15230 | at::AutoDispatchSkipFunctionalize guard; |
| 15231 | at::_ops::_foreach_sub_ScalarList_out::call(self_, scalars, out_); |
| 15232 | ; |
| 15233 | } |
| 15234 | } else { |
| 15235 | ::std::vector<at::Tensor> tmp_output; |
| 15236 | { |
| 15237 | at::AutoDispatchSkipFunctionalize guard; |
| 15238 | tmp_output = at::_ops::_foreach_sub_ScalarList::call(self_, scalars); |
| 15239 | } |
| 15240 | at::functionalization::impl::replace_(out, tmp_output); |
| 15241 | at::functionalization::impl::commit_update(out); |
| 15242 | at::functionalization::impl::sync(out); |
| 15243 | |
| 15244 | } |
| 15245 | } |
| 15246 | |
| 15247 | void _foreach_sub__ScalarList(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 15248 | if (true) { |
| 15249 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15250 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15251 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15252 | auto self_meta = to_meta(self); |
| 15253 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15254 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15255 | at::_ops::_foreach_sub__ScalarList::call(self_meta, scalars); |
| 15256 | } |
| 15257 | |
| 15258 | ::std::vector<at::Tensor> self_; |
| 15259 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15260 | at::functionalization::impl::sync(self); |
| 15261 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15262 | } else { |
| 15263 | self_ = self.vec(); |
| 15264 | } |
| 15265 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 15266 | if ((false)) { |
| 15267 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15268 | TORCH_INTERNAL_ASSERT(false, |
| 15269 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15270 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15271 | } else { |
| 15272 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15273 | at::AutoDispatchSkipFunctionalize guard; |
| 15274 | at::_ops::_foreach_sub__ScalarList::call(self_, scalars); |
| 15275 | ; |
| 15276 | } |
| 15277 | } else { |
| 15278 | ::std::vector<at::Tensor> tmp_output; |
| 15279 | { |
| 15280 | at::AutoDispatchSkipFunctionalize guard; |
| 15281 | tmp_output = at::_ops::_foreach_sub_ScalarList::call(self_, scalars); |
| 15282 | } |
| 15283 | at::functionalization::impl::replace_(self, tmp_output); |
| 15284 | at::functionalization::impl::commit_update(self); |
| 15285 | at::functionalization::impl::sync(self); |
| 15286 | |
| 15287 | } |
| 15288 | } |
| 15289 | |
| 15290 | void _foreach_maximum_out_ScalarList_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out) { |
| 15291 | if (false) { |
| 15292 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15293 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15294 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15295 | auto self_meta = to_meta(self); |
| 15296 | auto out_meta = to_meta(out); |
| 15297 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15298 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15299 | at::_ops::_foreach_maximum_ScalarList_out::call(self_meta, scalars, out_meta); |
| 15300 | } |
| 15301 | |
| 15302 | ::std::vector<at::Tensor> self_; |
| 15303 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15304 | at::functionalization::impl::sync(self); |
| 15305 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15306 | } else { |
| 15307 | self_ = self.vec(); |
| 15308 | } |
| 15309 | |
| 15310 | ::std::vector<at::Tensor> out_; |
| 15311 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15312 | at::functionalization::impl::sync(out); |
| 15313 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15314 | } else { |
| 15315 | out_ = out.vec(); |
| 15316 | } |
| 15317 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15318 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 15319 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15320 | TORCH_INTERNAL_ASSERT(false, |
| 15321 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15322 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15323 | } else { |
| 15324 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15325 | at::AutoDispatchSkipFunctionalize guard; |
| 15326 | at::_ops::_foreach_maximum_ScalarList_out::call(self_, scalars, out_); |
| 15327 | ; |
| 15328 | } |
| 15329 | } else { |
| 15330 | ::std::vector<at::Tensor> tmp_output; |
| 15331 | { |
| 15332 | at::AutoDispatchSkipFunctionalize guard; |
| 15333 | tmp_output = at::_ops::_foreach_maximum_ScalarList::call(self_, scalars); |
| 15334 | } |
| 15335 | at::functionalization::impl::replace_(out, tmp_output); |
| 15336 | at::functionalization::impl::commit_update(out); |
| 15337 | at::functionalization::impl::sync(out); |
| 15338 | |
| 15339 | } |
| 15340 | } |
| 15341 | |
| 15342 | void _foreach_maximum__ScalarList(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 15343 | if (true) { |
| 15344 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15345 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15346 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15347 | auto self_meta = to_meta(self); |
| 15348 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15349 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15350 | at::_ops::_foreach_maximum__ScalarList::call(self_meta, scalars); |
| 15351 | } |
| 15352 | |
| 15353 | ::std::vector<at::Tensor> self_; |
| 15354 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15355 | at::functionalization::impl::sync(self); |
| 15356 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15357 | } else { |
| 15358 | self_ = self.vec(); |
| 15359 | } |
| 15360 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 15361 | if ((false)) { |
| 15362 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15363 | TORCH_INTERNAL_ASSERT(false, |
| 15364 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15365 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15366 | } else { |
| 15367 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15368 | at::AutoDispatchSkipFunctionalize guard; |
| 15369 | at::_ops::_foreach_maximum__ScalarList::call(self_, scalars); |
| 15370 | ; |
| 15371 | } |
| 15372 | } else { |
| 15373 | ::std::vector<at::Tensor> tmp_output; |
| 15374 | { |
| 15375 | at::AutoDispatchSkipFunctionalize guard; |
| 15376 | tmp_output = at::_ops::_foreach_maximum_ScalarList::call(self_, scalars); |
| 15377 | } |
| 15378 | at::functionalization::impl::replace_(self, tmp_output); |
| 15379 | at::functionalization::impl::commit_update(self); |
| 15380 | at::functionalization::impl::sync(self); |
| 15381 | |
| 15382 | } |
| 15383 | } |
| 15384 | |
| 15385 | void _foreach_exp_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 15386 | if (false) { |
| 15387 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15388 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15389 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15390 | auto self_meta = to_meta(self); |
| 15391 | auto out_meta = to_meta(out); |
| 15392 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15393 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15394 | at::_ops::_foreach_exp_out::call(self_meta, out_meta); |
| 15395 | } |
| 15396 | |
| 15397 | ::std::vector<at::Tensor> self_; |
| 15398 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15399 | at::functionalization::impl::sync(self); |
| 15400 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15401 | } else { |
| 15402 | self_ = self.vec(); |
| 15403 | } |
| 15404 | |
| 15405 | ::std::vector<at::Tensor> out_; |
| 15406 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15407 | at::functionalization::impl::sync(out); |
| 15408 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15409 | } else { |
| 15410 | out_ = out.vec(); |
| 15411 | } |
| 15412 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15413 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 15414 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15415 | TORCH_INTERNAL_ASSERT(false, |
| 15416 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15417 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15418 | } else { |
| 15419 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15420 | at::AutoDispatchSkipFunctionalize guard; |
| 15421 | at::_ops::_foreach_exp_out::call(self_, out_); |
| 15422 | ; |
| 15423 | } |
| 15424 | } else { |
| 15425 | ::std::vector<at::Tensor> tmp_output; |
| 15426 | { |
| 15427 | at::AutoDispatchSkipFunctionalize guard; |
| 15428 | tmp_output = at::_ops::_foreach_exp::call(self_); |
| 15429 | } |
| 15430 | at::functionalization::impl::replace_(out, tmp_output); |
| 15431 | at::functionalization::impl::commit_update(out); |
| 15432 | at::functionalization::impl::sync(out); |
| 15433 | |
| 15434 | } |
| 15435 | } |
| 15436 | |
| 15437 | void _foreach_exp_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 15438 | if (true) { |
| 15439 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15440 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15441 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15442 | auto self_meta = to_meta(self); |
| 15443 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15444 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15445 | at::_ops::_foreach_exp_::call(self_meta); |
| 15446 | } |
| 15447 | |
| 15448 | ::std::vector<at::Tensor> self_; |
| 15449 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15450 | at::functionalization::impl::sync(self); |
| 15451 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15452 | } else { |
| 15453 | self_ = self.vec(); |
| 15454 | } |
| 15455 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 15456 | if ((false)) { |
| 15457 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15458 | TORCH_INTERNAL_ASSERT(false, |
| 15459 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15460 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15461 | } else { |
| 15462 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15463 | at::AutoDispatchSkipFunctionalize guard; |
| 15464 | at::_ops::_foreach_exp_::call(self_); |
| 15465 | ; |
| 15466 | } |
| 15467 | } else { |
| 15468 | ::std::vector<at::Tensor> tmp_output; |
| 15469 | { |
| 15470 | at::AutoDispatchSkipFunctionalize guard; |
| 15471 | tmp_output = at::_ops::_foreach_exp::call(self_); |
| 15472 | } |
| 15473 | at::functionalization::impl::replace_(self, tmp_output); |
| 15474 | at::functionalization::impl::commit_update(self); |
| 15475 | at::functionalization::impl::sync(self); |
| 15476 | |
| 15477 | } |
| 15478 | } |
| 15479 | |
| 15480 | void _foreach_sqrt_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 15481 | if (false) { |
| 15482 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15483 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15484 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15485 | auto self_meta = to_meta(self); |
| 15486 | auto out_meta = to_meta(out); |
| 15487 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15488 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15489 | at::_ops::_foreach_sqrt_out::call(self_meta, out_meta); |
| 15490 | } |
| 15491 | |
| 15492 | ::std::vector<at::Tensor> self_; |
| 15493 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15494 | at::functionalization::impl::sync(self); |
| 15495 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15496 | } else { |
| 15497 | self_ = self.vec(); |
| 15498 | } |
| 15499 | |
| 15500 | ::std::vector<at::Tensor> out_; |
| 15501 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15502 | at::functionalization::impl::sync(out); |
| 15503 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15504 | } else { |
| 15505 | out_ = out.vec(); |
| 15506 | } |
| 15507 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15508 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 15509 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15510 | TORCH_INTERNAL_ASSERT(false, |
| 15511 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15512 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15513 | } else { |
| 15514 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15515 | at::AutoDispatchSkipFunctionalize guard; |
| 15516 | at::_ops::_foreach_sqrt_out::call(self_, out_); |
| 15517 | ; |
| 15518 | } |
| 15519 | } else { |
| 15520 | ::std::vector<at::Tensor> tmp_output; |
| 15521 | { |
| 15522 | at::AutoDispatchSkipFunctionalize guard; |
| 15523 | tmp_output = at::_ops::_foreach_sqrt::call(self_); |
| 15524 | } |
| 15525 | at::functionalization::impl::replace_(out, tmp_output); |
| 15526 | at::functionalization::impl::commit_update(out); |
| 15527 | at::functionalization::impl::sync(out); |
| 15528 | |
| 15529 | } |
| 15530 | } |
| 15531 | |
| 15532 | void _foreach_sqrt_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 15533 | if (true) { |
| 15534 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15535 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15536 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15537 | auto self_meta = to_meta(self); |
| 15538 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15539 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15540 | at::_ops::_foreach_sqrt_::call(self_meta); |
| 15541 | } |
| 15542 | |
| 15543 | ::std::vector<at::Tensor> self_; |
| 15544 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15545 | at::functionalization::impl::sync(self); |
| 15546 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15547 | } else { |
| 15548 | self_ = self.vec(); |
| 15549 | } |
| 15550 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 15551 | if ((false)) { |
| 15552 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15553 | TORCH_INTERNAL_ASSERT(false, |
| 15554 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15555 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15556 | } else { |
| 15557 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15558 | at::AutoDispatchSkipFunctionalize guard; |
| 15559 | at::_ops::_foreach_sqrt_::call(self_); |
| 15560 | ; |
| 15561 | } |
| 15562 | } else { |
| 15563 | ::std::vector<at::Tensor> tmp_output; |
| 15564 | { |
| 15565 | at::AutoDispatchSkipFunctionalize guard; |
| 15566 | tmp_output = at::_ops::_foreach_sqrt::call(self_); |
| 15567 | } |
| 15568 | at::functionalization::impl::replace_(self, tmp_output); |
| 15569 | at::functionalization::impl::commit_update(self); |
| 15570 | at::functionalization::impl::sync(self); |
| 15571 | |
| 15572 | } |
| 15573 | } |
| 15574 | |
| 15575 | void _foreach_abs_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 15576 | if (false) { |
| 15577 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15578 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15579 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15580 | auto self_meta = to_meta(self); |
| 15581 | auto out_meta = to_meta(out); |
| 15582 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15583 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15584 | at::_ops::_foreach_abs_out::call(self_meta, out_meta); |
| 15585 | } |
| 15586 | |
| 15587 | ::std::vector<at::Tensor> self_; |
| 15588 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15589 | at::functionalization::impl::sync(self); |
| 15590 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15591 | } else { |
| 15592 | self_ = self.vec(); |
| 15593 | } |
| 15594 | |
| 15595 | ::std::vector<at::Tensor> out_; |
| 15596 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15597 | at::functionalization::impl::sync(out); |
| 15598 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15599 | } else { |
| 15600 | out_ = out.vec(); |
| 15601 | } |
| 15602 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15603 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 15604 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15605 | TORCH_INTERNAL_ASSERT(false, |
| 15606 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15607 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15608 | } else { |
| 15609 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15610 | at::AutoDispatchSkipFunctionalize guard; |
| 15611 | at::_ops::_foreach_abs_out::call(self_, out_); |
| 15612 | ; |
| 15613 | } |
| 15614 | } else { |
| 15615 | ::std::vector<at::Tensor> tmp_output; |
| 15616 | { |
| 15617 | at::AutoDispatchSkipFunctionalize guard; |
| 15618 | tmp_output = at::_ops::_foreach_abs::call(self_); |
| 15619 | } |
| 15620 | at::functionalization::impl::replace_(out, tmp_output); |
| 15621 | at::functionalization::impl::commit_update(out); |
| 15622 | at::functionalization::impl::sync(out); |
| 15623 | |
| 15624 | } |
| 15625 | } |
| 15626 | |
| 15627 | void _foreach_abs_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 15628 | if (true) { |
| 15629 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15630 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15631 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15632 | auto self_meta = to_meta(self); |
| 15633 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15634 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15635 | at::_ops::_foreach_abs_::call(self_meta); |
| 15636 | } |
| 15637 | |
| 15638 | ::std::vector<at::Tensor> self_; |
| 15639 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15640 | at::functionalization::impl::sync(self); |
| 15641 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15642 | } else { |
| 15643 | self_ = self.vec(); |
| 15644 | } |
| 15645 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 15646 | if ((false)) { |
| 15647 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15648 | TORCH_INTERNAL_ASSERT(false, |
| 15649 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15650 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15651 | } else { |
| 15652 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15653 | at::AutoDispatchSkipFunctionalize guard; |
| 15654 | at::_ops::_foreach_abs_::call(self_); |
| 15655 | ; |
| 15656 | } |
| 15657 | } else { |
| 15658 | ::std::vector<at::Tensor> tmp_output; |
| 15659 | { |
| 15660 | at::AutoDispatchSkipFunctionalize guard; |
| 15661 | tmp_output = at::_ops::_foreach_abs::call(self_); |
| 15662 | } |
| 15663 | at::functionalization::impl::replace_(self, tmp_output); |
| 15664 | at::functionalization::impl::commit_update(self); |
| 15665 | at::functionalization::impl::sync(self); |
| 15666 | |
| 15667 | } |
| 15668 | } |
| 15669 | |
| 15670 | void _foreach_acos_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 15671 | if (false) { |
| 15672 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15673 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15674 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15675 | auto self_meta = to_meta(self); |
| 15676 | auto out_meta = to_meta(out); |
| 15677 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15678 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15679 | at::_ops::_foreach_acos_out::call(self_meta, out_meta); |
| 15680 | } |
| 15681 | |
| 15682 | ::std::vector<at::Tensor> self_; |
| 15683 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15684 | at::functionalization::impl::sync(self); |
| 15685 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15686 | } else { |
| 15687 | self_ = self.vec(); |
| 15688 | } |
| 15689 | |
| 15690 | ::std::vector<at::Tensor> out_; |
| 15691 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15692 | at::functionalization::impl::sync(out); |
| 15693 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15694 | } else { |
| 15695 | out_ = out.vec(); |
| 15696 | } |
| 15697 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15698 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 15699 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15700 | TORCH_INTERNAL_ASSERT(false, |
| 15701 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15702 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15703 | } else { |
| 15704 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15705 | at::AutoDispatchSkipFunctionalize guard; |
| 15706 | at::_ops::_foreach_acos_out::call(self_, out_); |
| 15707 | ; |
| 15708 | } |
| 15709 | } else { |
| 15710 | ::std::vector<at::Tensor> tmp_output; |
| 15711 | { |
| 15712 | at::AutoDispatchSkipFunctionalize guard; |
| 15713 | tmp_output = at::_ops::_foreach_acos::call(self_); |
| 15714 | } |
| 15715 | at::functionalization::impl::replace_(out, tmp_output); |
| 15716 | at::functionalization::impl::commit_update(out); |
| 15717 | at::functionalization::impl::sync(out); |
| 15718 | |
| 15719 | } |
| 15720 | } |
| 15721 | |
| 15722 | void _foreach_acos_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 15723 | if (true) { |
| 15724 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15725 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15726 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15727 | auto self_meta = to_meta(self); |
| 15728 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15729 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15730 | at::_ops::_foreach_acos_::call(self_meta); |
| 15731 | } |
| 15732 | |
| 15733 | ::std::vector<at::Tensor> self_; |
| 15734 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15735 | at::functionalization::impl::sync(self); |
| 15736 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15737 | } else { |
| 15738 | self_ = self.vec(); |
| 15739 | } |
| 15740 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 15741 | if ((false)) { |
| 15742 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15743 | TORCH_INTERNAL_ASSERT(false, |
| 15744 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15745 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15746 | } else { |
| 15747 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15748 | at::AutoDispatchSkipFunctionalize guard; |
| 15749 | at::_ops::_foreach_acos_::call(self_); |
| 15750 | ; |
| 15751 | } |
| 15752 | } else { |
| 15753 | ::std::vector<at::Tensor> tmp_output; |
| 15754 | { |
| 15755 | at::AutoDispatchSkipFunctionalize guard; |
| 15756 | tmp_output = at::_ops::_foreach_acos::call(self_); |
| 15757 | } |
| 15758 | at::functionalization::impl::replace_(self, tmp_output); |
| 15759 | at::functionalization::impl::commit_update(self); |
| 15760 | at::functionalization::impl::sync(self); |
| 15761 | |
| 15762 | } |
| 15763 | } |
| 15764 | |
| 15765 | void _foreach_cos_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 15766 | if (false) { |
| 15767 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15768 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15769 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15770 | auto self_meta = to_meta(self); |
| 15771 | auto out_meta = to_meta(out); |
| 15772 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15773 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15774 | at::_ops::_foreach_cos_out::call(self_meta, out_meta); |
| 15775 | } |
| 15776 | |
| 15777 | ::std::vector<at::Tensor> self_; |
| 15778 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15779 | at::functionalization::impl::sync(self); |
| 15780 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15781 | } else { |
| 15782 | self_ = self.vec(); |
| 15783 | } |
| 15784 | |
| 15785 | ::std::vector<at::Tensor> out_; |
| 15786 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15787 | at::functionalization::impl::sync(out); |
| 15788 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15789 | } else { |
| 15790 | out_ = out.vec(); |
| 15791 | } |
| 15792 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15793 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 15794 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15795 | TORCH_INTERNAL_ASSERT(false, |
| 15796 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15797 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15798 | } else { |
| 15799 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15800 | at::AutoDispatchSkipFunctionalize guard; |
| 15801 | at::_ops::_foreach_cos_out::call(self_, out_); |
| 15802 | ; |
| 15803 | } |
| 15804 | } else { |
| 15805 | ::std::vector<at::Tensor> tmp_output; |
| 15806 | { |
| 15807 | at::AutoDispatchSkipFunctionalize guard; |
| 15808 | tmp_output = at::_ops::_foreach_cos::call(self_); |
| 15809 | } |
| 15810 | at::functionalization::impl::replace_(out, tmp_output); |
| 15811 | at::functionalization::impl::commit_update(out); |
| 15812 | at::functionalization::impl::sync(out); |
| 15813 | |
| 15814 | } |
| 15815 | } |
| 15816 | |
| 15817 | void _foreach_cos_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 15818 | if (true) { |
| 15819 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15820 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15821 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15822 | auto self_meta = to_meta(self); |
| 15823 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15824 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15825 | at::_ops::_foreach_cos_::call(self_meta); |
| 15826 | } |
| 15827 | |
| 15828 | ::std::vector<at::Tensor> self_; |
| 15829 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15830 | at::functionalization::impl::sync(self); |
| 15831 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15832 | } else { |
| 15833 | self_ = self.vec(); |
| 15834 | } |
| 15835 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 15836 | if ((false)) { |
| 15837 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15838 | TORCH_INTERNAL_ASSERT(false, |
| 15839 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15840 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15841 | } else { |
| 15842 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15843 | at::AutoDispatchSkipFunctionalize guard; |
| 15844 | at::_ops::_foreach_cos_::call(self_); |
| 15845 | ; |
| 15846 | } |
| 15847 | } else { |
| 15848 | ::std::vector<at::Tensor> tmp_output; |
| 15849 | { |
| 15850 | at::AutoDispatchSkipFunctionalize guard; |
| 15851 | tmp_output = at::_ops::_foreach_cos::call(self_); |
| 15852 | } |
| 15853 | at::functionalization::impl::replace_(self, tmp_output); |
| 15854 | at::functionalization::impl::commit_update(self); |
| 15855 | at::functionalization::impl::sync(self); |
| 15856 | |
| 15857 | } |
| 15858 | } |
| 15859 | |
| 15860 | void _foreach_floor_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 15861 | if (false) { |
| 15862 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15863 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15864 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15865 | auto self_meta = to_meta(self); |
| 15866 | auto out_meta = to_meta(out); |
| 15867 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15868 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15869 | at::_ops::_foreach_floor_out::call(self_meta, out_meta); |
| 15870 | } |
| 15871 | |
| 15872 | ::std::vector<at::Tensor> self_; |
| 15873 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15874 | at::functionalization::impl::sync(self); |
| 15875 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15876 | } else { |
| 15877 | self_ = self.vec(); |
| 15878 | } |
| 15879 | |
| 15880 | ::std::vector<at::Tensor> out_; |
| 15881 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15882 | at::functionalization::impl::sync(out); |
| 15883 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15884 | } else { |
| 15885 | out_ = out.vec(); |
| 15886 | } |
| 15887 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15888 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 15889 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15890 | TORCH_INTERNAL_ASSERT(false, |
| 15891 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15892 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15893 | } else { |
| 15894 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15895 | at::AutoDispatchSkipFunctionalize guard; |
| 15896 | at::_ops::_foreach_floor_out::call(self_, out_); |
| 15897 | ; |
| 15898 | } |
| 15899 | } else { |
| 15900 | ::std::vector<at::Tensor> tmp_output; |
| 15901 | { |
| 15902 | at::AutoDispatchSkipFunctionalize guard; |
| 15903 | tmp_output = at::_ops::_foreach_floor::call(self_); |
| 15904 | } |
| 15905 | at::functionalization::impl::replace_(out, tmp_output); |
| 15906 | at::functionalization::impl::commit_update(out); |
| 15907 | at::functionalization::impl::sync(out); |
| 15908 | |
| 15909 | } |
| 15910 | } |
| 15911 | |
| 15912 | void _foreach_floor_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 15913 | if (true) { |
| 15914 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15915 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15916 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15917 | auto self_meta = to_meta(self); |
| 15918 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15919 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15920 | at::_ops::_foreach_floor_::call(self_meta); |
| 15921 | } |
| 15922 | |
| 15923 | ::std::vector<at::Tensor> self_; |
| 15924 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15925 | at::functionalization::impl::sync(self); |
| 15926 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15927 | } else { |
| 15928 | self_ = self.vec(); |
| 15929 | } |
| 15930 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 15931 | if ((false)) { |
| 15932 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15933 | TORCH_INTERNAL_ASSERT(false, |
| 15934 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15935 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15936 | } else { |
| 15937 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15938 | at::AutoDispatchSkipFunctionalize guard; |
| 15939 | at::_ops::_foreach_floor_::call(self_); |
| 15940 | ; |
| 15941 | } |
| 15942 | } else { |
| 15943 | ::std::vector<at::Tensor> tmp_output; |
| 15944 | { |
| 15945 | at::AutoDispatchSkipFunctionalize guard; |
| 15946 | tmp_output = at::_ops::_foreach_floor::call(self_); |
| 15947 | } |
| 15948 | at::functionalization::impl::replace_(self, tmp_output); |
| 15949 | at::functionalization::impl::commit_update(self); |
| 15950 | at::functionalization::impl::sync(self); |
| 15951 | |
| 15952 | } |
| 15953 | } |
| 15954 | |
| 15955 | void _foreach_log10_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 15956 | if (false) { |
| 15957 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15958 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15959 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15960 | auto self_meta = to_meta(self); |
| 15961 | auto out_meta = to_meta(out); |
| 15962 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15963 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15964 | at::_ops::_foreach_log10_out::call(self_meta, out_meta); |
| 15965 | } |
| 15966 | |
| 15967 | ::std::vector<at::Tensor> self_; |
| 15968 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15969 | at::functionalization::impl::sync(self); |
| 15970 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15971 | } else { |
| 15972 | self_ = self.vec(); |
| 15973 | } |
| 15974 | |
| 15975 | ::std::vector<at::Tensor> out_; |
| 15976 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15977 | at::functionalization::impl::sync(out); |
| 15978 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15979 | } else { |
| 15980 | out_ = out.vec(); |
| 15981 | } |
| 15982 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15983 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 15984 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15985 | TORCH_INTERNAL_ASSERT(false, |
| 15986 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15987 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15988 | } else { |
| 15989 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15990 | at::AutoDispatchSkipFunctionalize guard; |
| 15991 | at::_ops::_foreach_log10_out::call(self_, out_); |
| 15992 | ; |
| 15993 | } |
| 15994 | } else { |
| 15995 | ::std::vector<at::Tensor> tmp_output; |
| 15996 | { |
| 15997 | at::AutoDispatchSkipFunctionalize guard; |
| 15998 | tmp_output = at::_ops::_foreach_log10::call(self_); |
| 15999 | } |
| 16000 | at::functionalization::impl::replace_(out, tmp_output); |
| 16001 | at::functionalization::impl::commit_update(out); |
| 16002 | at::functionalization::impl::sync(out); |
| 16003 | |
| 16004 | } |
| 16005 | } |
| 16006 | |
| 16007 | void _foreach_log10_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 16008 | if (true) { |
| 16009 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16010 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16011 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16012 | auto self_meta = to_meta(self); |
| 16013 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16014 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16015 | at::_ops::_foreach_log10_::call(self_meta); |
| 16016 | } |
| 16017 | |
| 16018 | ::std::vector<at::Tensor> self_; |
| 16019 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16020 | at::functionalization::impl::sync(self); |
| 16021 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16022 | } else { |
| 16023 | self_ = self.vec(); |
| 16024 | } |
| 16025 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 16026 | if ((false)) { |
| 16027 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16028 | TORCH_INTERNAL_ASSERT(false, |
| 16029 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16030 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16031 | } else { |
| 16032 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16033 | at::AutoDispatchSkipFunctionalize guard; |
| 16034 | at::_ops::_foreach_log10_::call(self_); |
| 16035 | ; |
| 16036 | } |
| 16037 | } else { |
| 16038 | ::std::vector<at::Tensor> tmp_output; |
| 16039 | { |
| 16040 | at::AutoDispatchSkipFunctionalize guard; |
| 16041 | tmp_output = at::_ops::_foreach_log10::call(self_); |
| 16042 | } |
| 16043 | at::functionalization::impl::replace_(self, tmp_output); |
| 16044 | at::functionalization::impl::commit_update(self); |
| 16045 | at::functionalization::impl::sync(self); |
| 16046 | |
| 16047 | } |
| 16048 | } |
| 16049 | |
| 16050 | void _foreach_neg_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 16051 | if (false) { |
| 16052 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16053 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16054 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16055 | auto self_meta = to_meta(self); |
| 16056 | auto out_meta = to_meta(out); |
| 16057 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16058 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16059 | at::_ops::_foreach_neg_out::call(self_meta, out_meta); |
| 16060 | } |
| 16061 | |
| 16062 | ::std::vector<at::Tensor> self_; |
| 16063 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16064 | at::functionalization::impl::sync(self); |
| 16065 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16066 | } else { |
| 16067 | self_ = self.vec(); |
| 16068 | } |
| 16069 | |
| 16070 | ::std::vector<at::Tensor> out_; |
| 16071 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16072 | at::functionalization::impl::sync(out); |
| 16073 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16074 | } else { |
| 16075 | out_ = out.vec(); |
| 16076 | } |
| 16077 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16078 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16079 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16080 | TORCH_INTERNAL_ASSERT(false, |
| 16081 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16082 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16083 | } else { |
| 16084 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16085 | at::AutoDispatchSkipFunctionalize guard; |
| 16086 | at::_ops::_foreach_neg_out::call(self_, out_); |
| 16087 | ; |
| 16088 | } |
| 16089 | } else { |
| 16090 | ::std::vector<at::Tensor> tmp_output; |
| 16091 | { |
| 16092 | at::AutoDispatchSkipFunctionalize guard; |
| 16093 | tmp_output = at::_ops::_foreach_neg::call(self_); |
| 16094 | } |
| 16095 | at::functionalization::impl::replace_(out, tmp_output); |
| 16096 | at::functionalization::impl::commit_update(out); |
| 16097 | at::functionalization::impl::sync(out); |
| 16098 | |
| 16099 | } |
| 16100 | } |
| 16101 | |
| 16102 | void _foreach_neg_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 16103 | if (true) { |
| 16104 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16105 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16106 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16107 | auto self_meta = to_meta(self); |
| 16108 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16109 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16110 | at::_ops::_foreach_neg_::call(self_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 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 16121 | if ((false)) { |
| 16122 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16123 | TORCH_INTERNAL_ASSERT(false, |
| 16124 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16125 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16126 | } else { |
| 16127 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16128 | at::AutoDispatchSkipFunctionalize guard; |
| 16129 | at::_ops::_foreach_neg_::call(self_); |
| 16130 | ; |
| 16131 | } |
| 16132 | } else { |
| 16133 | ::std::vector<at::Tensor> tmp_output; |
| 16134 | { |
| 16135 | at::AutoDispatchSkipFunctionalize guard; |
| 16136 | tmp_output = at::_ops::_foreach_neg::call(self_); |
| 16137 | } |
| 16138 | at::functionalization::impl::replace_(self, tmp_output); |
| 16139 | at::functionalization::impl::commit_update(self); |
| 16140 | at::functionalization::impl::sync(self); |
| 16141 | |
| 16142 | } |
| 16143 | } |
| 16144 | |
| 16145 | void _foreach_tan_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 16146 | if (false) { |
| 16147 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16148 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16149 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16150 | auto self_meta = to_meta(self); |
| 16151 | auto out_meta = to_meta(out); |
| 16152 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16153 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16154 | at::_ops::_foreach_tan_out::call(self_meta, out_meta); |
| 16155 | } |
| 16156 | |
| 16157 | ::std::vector<at::Tensor> self_; |
| 16158 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16159 | at::functionalization::impl::sync(self); |
| 16160 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16161 | } else { |
| 16162 | self_ = self.vec(); |
| 16163 | } |
| 16164 | |
| 16165 | ::std::vector<at::Tensor> out_; |
| 16166 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16167 | at::functionalization::impl::sync(out); |
| 16168 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16169 | } else { |
| 16170 | out_ = out.vec(); |
| 16171 | } |
| 16172 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16173 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16174 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16175 | TORCH_INTERNAL_ASSERT(false, |
| 16176 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16177 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16178 | } else { |
| 16179 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16180 | at::AutoDispatchSkipFunctionalize guard; |
| 16181 | at::_ops::_foreach_tan_out::call(self_, out_); |
| 16182 | ; |
| 16183 | } |
| 16184 | } else { |
| 16185 | ::std::vector<at::Tensor> tmp_output; |
| 16186 | { |
| 16187 | at::AutoDispatchSkipFunctionalize guard; |
| 16188 | tmp_output = at::_ops::_foreach_tan::call(self_); |
| 16189 | } |
| 16190 | at::functionalization::impl::replace_(out, tmp_output); |
| 16191 | at::functionalization::impl::commit_update(out); |
| 16192 | at::functionalization::impl::sync(out); |
| 16193 | |
| 16194 | } |
| 16195 | } |
| 16196 | |
| 16197 | void _foreach_tan_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 16198 | if (true) { |
| 16199 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16200 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16201 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16202 | auto self_meta = to_meta(self); |
| 16203 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16204 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16205 | at::_ops::_foreach_tan_::call(self_meta); |
| 16206 | } |
| 16207 | |
| 16208 | ::std::vector<at::Tensor> self_; |
| 16209 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16210 | at::functionalization::impl::sync(self); |
| 16211 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16212 | } else { |
| 16213 | self_ = self.vec(); |
| 16214 | } |
| 16215 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 16216 | if ((false)) { |
| 16217 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16218 | TORCH_INTERNAL_ASSERT(false, |
| 16219 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16220 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16221 | } else { |
| 16222 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16223 | at::AutoDispatchSkipFunctionalize guard; |
| 16224 | at::_ops::_foreach_tan_::call(self_); |
| 16225 | ; |
| 16226 | } |
| 16227 | } else { |
| 16228 | ::std::vector<at::Tensor> tmp_output; |
| 16229 | { |
| 16230 | at::AutoDispatchSkipFunctionalize guard; |
| 16231 | tmp_output = at::_ops::_foreach_tan::call(self_); |
| 16232 | } |
| 16233 | at::functionalization::impl::replace_(self, tmp_output); |
| 16234 | at::functionalization::impl::commit_update(self); |
| 16235 | at::functionalization::impl::sync(self); |
| 16236 | |
| 16237 | } |
| 16238 | } |
| 16239 | |
| 16240 | void _foreach_sigmoid_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 16241 | if (false) { |
| 16242 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16243 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16244 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16245 | auto self_meta = to_meta(self); |
| 16246 | auto out_meta = to_meta(out); |
| 16247 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16248 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16249 | at::_ops::_foreach_sigmoid_out::call(self_meta, out_meta); |
| 16250 | } |
| 16251 | |
| 16252 | ::std::vector<at::Tensor> self_; |
| 16253 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16254 | at::functionalization::impl::sync(self); |
| 16255 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16256 | } else { |
| 16257 | self_ = self.vec(); |
| 16258 | } |
| 16259 | |
| 16260 | ::std::vector<at::Tensor> out_; |
| 16261 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16262 | at::functionalization::impl::sync(out); |
| 16263 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16264 | } else { |
| 16265 | out_ = out.vec(); |
| 16266 | } |
| 16267 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16268 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16269 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16270 | TORCH_INTERNAL_ASSERT(false, |
| 16271 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16272 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16273 | } else { |
| 16274 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16275 | at::AutoDispatchSkipFunctionalize guard; |
| 16276 | at::_ops::_foreach_sigmoid_out::call(self_, out_); |
| 16277 | ; |
| 16278 | } |
| 16279 | } else { |
| 16280 | ::std::vector<at::Tensor> tmp_output; |
| 16281 | { |
| 16282 | at::AutoDispatchSkipFunctionalize guard; |
| 16283 | tmp_output = at::_ops::_foreach_sigmoid::call(self_); |
| 16284 | } |
| 16285 | at::functionalization::impl::replace_(out, tmp_output); |
| 16286 | at::functionalization::impl::commit_update(out); |
| 16287 | at::functionalization::impl::sync(out); |
| 16288 | |
| 16289 | } |
| 16290 | } |
| 16291 | |
| 16292 | void _foreach_sigmoid_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 16293 | if (true) { |
| 16294 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16295 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16296 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16297 | auto self_meta = to_meta(self); |
| 16298 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16299 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16300 | at::_ops::_foreach_sigmoid_::call(self_meta); |
| 16301 | } |
| 16302 | |
| 16303 | ::std::vector<at::Tensor> self_; |
| 16304 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16305 | at::functionalization::impl::sync(self); |
| 16306 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16307 | } else { |
| 16308 | self_ = self.vec(); |
| 16309 | } |
| 16310 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 16311 | if ((false)) { |
| 16312 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16313 | TORCH_INTERNAL_ASSERT(false, |
| 16314 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16315 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16316 | } else { |
| 16317 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16318 | at::AutoDispatchSkipFunctionalize guard; |
| 16319 | at::_ops::_foreach_sigmoid_::call(self_); |
| 16320 | ; |
| 16321 | } |
| 16322 | } else { |
| 16323 | ::std::vector<at::Tensor> tmp_output; |
| 16324 | { |
| 16325 | at::AutoDispatchSkipFunctionalize guard; |
| 16326 | tmp_output = at::_ops::_foreach_sigmoid::call(self_); |
| 16327 | } |
| 16328 | at::functionalization::impl::replace_(self, tmp_output); |
| 16329 | at::functionalization::impl::commit_update(self); |
| 16330 | at::functionalization::impl::sync(self); |
| 16331 | |
| 16332 | } |
| 16333 | } |
| 16334 | |
| 16335 | void _foreach_norm_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & ord, at::TensorList out) { |
| 16336 | if (false) { |
| 16337 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16338 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16339 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16340 | auto self_meta = to_meta(self); |
| 16341 | auto out_meta = to_meta(out); |
| 16342 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16343 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16344 | at::_ops::_foreach_norm_Scalar_out::call(self_meta, ord, out_meta); |
| 16345 | } |
| 16346 | |
| 16347 | ::std::vector<at::Tensor> self_; |
| 16348 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16349 | at::functionalization::impl::sync(self); |
| 16350 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16351 | } else { |
| 16352 | self_ = self.vec(); |
| 16353 | } |
| 16354 | |
| 16355 | ::std::vector<at::Tensor> out_; |
| 16356 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16357 | at::functionalization::impl::sync(out); |
| 16358 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16359 | } else { |
| 16360 | out_ = out.vec(); |
| 16361 | } |
| 16362 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16363 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16364 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16365 | TORCH_INTERNAL_ASSERT(false, |
| 16366 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16367 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16368 | } else { |
| 16369 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16370 | at::AutoDispatchSkipFunctionalize guard; |
| 16371 | at::_ops::_foreach_norm_Scalar_out::call(self_, ord, out_); |
| 16372 | ; |
| 16373 | } |
| 16374 | } else { |
| 16375 | ::std::vector<at::Tensor> tmp_output; |
| 16376 | { |
| 16377 | at::AutoDispatchSkipFunctionalize guard; |
| 16378 | tmp_output = at::_ops::_foreach_norm_Scalar::call(self_, ord); |
| 16379 | } |
| 16380 | at::functionalization::impl::replace_(out, tmp_output); |
| 16381 | at::functionalization::impl::commit_update(out); |
| 16382 | at::functionalization::impl::sync(out); |
| 16383 | |
| 16384 | } |
| 16385 | } |
| 16386 | |
| 16387 | at::Tensor & searchsorted_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & sorted_sequence, const at::Tensor & self, bool out_int32, bool right, c10::optional<c10::string_view> side, const c10::optional<at::Tensor> & sorter, at::Tensor & out) { |
| 16388 | if (false) { |
| 16389 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16390 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16391 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16392 | auto sorted_sequence_meta = to_meta(sorted_sequence); |
| 16393 | auto self_meta = to_meta(self); |
| 16394 | auto sorter_meta = to_meta(sorter); |
| 16395 | auto out_meta = to_meta(out); |
| 16396 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16397 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16398 | at::_ops::searchsorted_Tensor_out::call(sorted_sequence_meta, self_meta, out_int32, right, side, sorter_meta, out_meta); |
| 16399 | } |
| 16400 | |
| 16401 | at::Tensor sorted_sequence_; |
| 16402 | if (at::functionalization::impl::isFunctionalTensor(sorted_sequence)) { |
| 16403 | at::functionalization::impl::sync(sorted_sequence); |
| 16404 | sorted_sequence_ = at::functionalization::impl::from_functional_tensor(sorted_sequence); |
| 16405 | } else { |
| 16406 | sorted_sequence_ = sorted_sequence; |
| 16407 | } |
| 16408 | |
| 16409 | at::Tensor self_; |
| 16410 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16411 | at::functionalization::impl::sync(self); |
| 16412 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16413 | } else { |
| 16414 | self_ = self; |
| 16415 | } |
| 16416 | |
| 16417 | c10::optional<at::Tensor> sorter_; |
| 16418 | if (at::functionalization::impl::isFunctionalTensor(sorter)) { |
| 16419 | at::functionalization::impl::sync(sorter); |
| 16420 | sorter_ = at::functionalization::impl::from_functional_tensor(sorter); |
| 16421 | } else { |
| 16422 | sorter_ = sorter; |
| 16423 | } |
| 16424 | |
| 16425 | at::Tensor out_; |
| 16426 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16427 | at::functionalization::impl::sync(out); |
| 16428 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16429 | } else { |
| 16430 | out_ = out; |
| 16431 | } |
| 16432 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16433 | if ((false || at::functionalization::impl::isFunctionalTensor(sorted_sequence) || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(sorter))) { |
| 16434 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16435 | TORCH_INTERNAL_ASSERT(false, |
| 16436 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16437 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16438 | } else { |
| 16439 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16440 | at::AutoDispatchSkipFunctionalize guard; |
| 16441 | at::Tensor tmp_output = at::_ops::searchsorted_Tensor_out::call(sorted_sequence_, self_, out_int32, right, side, sorter_, out_); |
| 16442 | return out;; |
| 16443 | } |
| 16444 | } else { |
| 16445 | at::Tensor tmp_output; |
| 16446 | { |
| 16447 | at::AutoDispatchSkipFunctionalize guard; |
| 16448 | tmp_output = at::_ops::searchsorted_Tensor::call(sorted_sequence_, self_, out_int32, right, side, sorter_); |
| 16449 | } |
| 16450 | at::functionalization::impl::replace_(out, tmp_output); |
| 16451 | at::functionalization::impl::commit_update(out); |
| 16452 | at::functionalization::impl::sync(out); |
| 16453 | return out; |
| 16454 | } |
| 16455 | } |
| 16456 | |
| 16457 | at::Tensor & searchsorted_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & sorted_sequence, const at::Scalar & self, bool out_int32, bool right, c10::optional<c10::string_view> side, const c10::optional<at::Tensor> & sorter, at::Tensor & out) { |
| 16458 | if (false) { |
| 16459 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16460 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16461 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16462 | auto sorted_sequence_meta = to_meta(sorted_sequence); |
| 16463 | auto sorter_meta = to_meta(sorter); |
| 16464 | auto out_meta = to_meta(out); |
| 16465 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16466 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16467 | at::_ops::searchsorted_Scalar_out::call(sorted_sequence_meta, self, out_int32, right, side, sorter_meta, out_meta); |
| 16468 | } |
| 16469 | |
| 16470 | at::Tensor sorted_sequence_; |
| 16471 | if (at::functionalization::impl::isFunctionalTensor(sorted_sequence)) { |
| 16472 | at::functionalization::impl::sync(sorted_sequence); |
| 16473 | sorted_sequence_ = at::functionalization::impl::from_functional_tensor(sorted_sequence); |
| 16474 | } else { |
| 16475 | sorted_sequence_ = sorted_sequence; |
| 16476 | } |
| 16477 | |
| 16478 | c10::optional<at::Tensor> sorter_; |
| 16479 | if (at::functionalization::impl::isFunctionalTensor(sorter)) { |
| 16480 | at::functionalization::impl::sync(sorter); |
| 16481 | sorter_ = at::functionalization::impl::from_functional_tensor(sorter); |
| 16482 | } else { |
| 16483 | sorter_ = sorter; |
| 16484 | } |
| 16485 | |
| 16486 | at::Tensor out_; |
| 16487 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16488 | at::functionalization::impl::sync(out); |
| 16489 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16490 | } else { |
| 16491 | out_ = out; |
| 16492 | } |
| 16493 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16494 | if ((false || at::functionalization::impl::isFunctionalTensor(sorted_sequence) || at::functionalization::impl::isFunctionalTensor(sorter))) { |
| 16495 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16496 | TORCH_INTERNAL_ASSERT(false, |
| 16497 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16498 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16499 | } else { |
| 16500 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16501 | at::AutoDispatchSkipFunctionalize guard; |
| 16502 | at::Tensor tmp_output = at::_ops::searchsorted_Scalar_out::call(sorted_sequence_, self, out_int32, right, side, sorter_, out_); |
| 16503 | return out;; |
| 16504 | } |
| 16505 | } else { |
| 16506 | at::Tensor tmp_output; |
| 16507 | { |
| 16508 | at::AutoDispatchSkipFunctionalize guard; |
| 16509 | tmp_output = at::_ops::searchsorted_Scalar::call(sorted_sequence_, self, out_int32, right, side, sorter_); |
| 16510 | } |
| 16511 | at::functionalization::impl::replace_(out, tmp_output); |
| 16512 | at::functionalization::impl::commit_update(out); |
| 16513 | at::functionalization::impl::sync(out); |
| 16514 | return out; |
| 16515 | } |
| 16516 | } |
| 16517 | |
| 16518 | at::Tensor & mse_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) { |
| 16519 | if (false) { |
| 16520 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16521 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16522 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16523 | auto grad_output_meta = to_meta(grad_output); |
| 16524 | auto self_meta = to_meta(self); |
| 16525 | auto target_meta = to_meta(target); |
| 16526 | auto grad_input_meta = to_meta(grad_input); |
| 16527 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16528 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16529 | at::_ops::mse_loss_backward_grad_input::call(grad_output_meta, self_meta, target_meta, reduction, grad_input_meta); |
| 16530 | } |
| 16531 | |
| 16532 | at::Tensor grad_output_; |
| 16533 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 16534 | at::functionalization::impl::sync(grad_output); |
| 16535 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 16536 | } else { |
| 16537 | grad_output_ = grad_output; |
| 16538 | } |
| 16539 | |
| 16540 | at::Tensor self_; |
| 16541 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16542 | at::functionalization::impl::sync(self); |
| 16543 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16544 | } else { |
| 16545 | self_ = self; |
| 16546 | } |
| 16547 | |
| 16548 | at::Tensor target_; |
| 16549 | if (at::functionalization::impl::isFunctionalTensor(target)) { |
| 16550 | at::functionalization::impl::sync(target); |
| 16551 | target_ = at::functionalization::impl::from_functional_tensor(target); |
| 16552 | } else { |
| 16553 | target_ = target; |
| 16554 | } |
| 16555 | |
| 16556 | at::Tensor grad_input_; |
| 16557 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 16558 | at::functionalization::impl::sync(grad_input); |
| 16559 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 16560 | } else { |
| 16561 | grad_input_ = grad_input; |
| 16562 | } |
| 16563 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 16564 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(target))) { |
| 16565 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16566 | TORCH_INTERNAL_ASSERT(false, |
| 16567 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16568 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16569 | } else { |
| 16570 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16571 | at::AutoDispatchSkipFunctionalize guard; |
| 16572 | at::Tensor tmp_output = at::_ops::mse_loss_backward_grad_input::call(grad_output_, self_, target_, reduction, grad_input_); |
| 16573 | return grad_input;; |
| 16574 | } |
| 16575 | } else { |
| 16576 | at::Tensor tmp_output; |
| 16577 | { |
| 16578 | at::AutoDispatchSkipFunctionalize guard; |
| 16579 | tmp_output = at::_ops::mse_loss_backward::call(grad_output_, self_, target_, reduction); |
| 16580 | } |
| 16581 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 16582 | at::functionalization::impl::commit_update(grad_input); |
| 16583 | at::functionalization::impl::sync(grad_input); |
| 16584 | return grad_input; |
| 16585 | } |
| 16586 | } |
| 16587 | |
| 16588 | at::Tensor & smooth_l1_loss_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta, at::Tensor & grad_input) { |
| 16589 | if (false) { |
| 16590 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16591 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16592 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16593 | auto grad_output_meta = to_meta(grad_output); |
| 16594 | auto self_meta = to_meta(self); |
| 16595 | auto target_meta = to_meta(target); |
| 16596 | auto grad_input_meta = to_meta(grad_input); |
| 16597 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16598 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16599 | at::_ops::smooth_l1_loss_backward_grad_input::call(grad_output_meta, self_meta, target_meta, reduction, beta, grad_input_meta); |
| 16600 | } |
| 16601 | |
| 16602 | at::Tensor grad_output_; |
| 16603 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 16604 | at::functionalization::impl::sync(grad_output); |
| 16605 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 16606 | } else { |
| 16607 | grad_output_ = grad_output; |
| 16608 | } |
| 16609 | |
| 16610 | at::Tensor self_; |
| 16611 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16612 | at::functionalization::impl::sync(self); |
| 16613 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16614 | } else { |
| 16615 | self_ = self; |
| 16616 | } |
| 16617 | |
| 16618 | at::Tensor target_; |
| 16619 | if (at::functionalization::impl::isFunctionalTensor(target)) { |
| 16620 | at::functionalization::impl::sync(target); |
| 16621 | target_ = at::functionalization::impl::from_functional_tensor(target); |
| 16622 | } else { |
| 16623 | target_ = target; |
| 16624 | } |
| 16625 | |
| 16626 | at::Tensor grad_input_; |
| 16627 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 16628 | at::functionalization::impl::sync(grad_input); |
| 16629 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 16630 | } else { |
| 16631 | grad_input_ = grad_input; |
| 16632 | } |
| 16633 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 16634 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(target))) { |
| 16635 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16636 | TORCH_INTERNAL_ASSERT(false, |
| 16637 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16638 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16639 | } else { |
| 16640 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16641 | at::AutoDispatchSkipFunctionalize guard; |
| 16642 | at::Tensor tmp_output = at::_ops::smooth_l1_loss_backward_grad_input::call(grad_output_, self_, target_, reduction, beta, grad_input_); |
| 16643 | return grad_input;; |
| 16644 | } |
| 16645 | } else { |
| 16646 | at::Tensor tmp_output; |
| 16647 | { |
| 16648 | at::AutoDispatchSkipFunctionalize guard; |
| 16649 | tmp_output = at::_ops::smooth_l1_loss_backward::call(grad_output_, self_, target_, reduction, beta); |
| 16650 | } |
| 16651 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 16652 | at::functionalization::impl::commit_update(grad_input); |
| 16653 | at::functionalization::impl::sync(grad_input); |
| 16654 | return grad_input; |
| 16655 | } |
| 16656 | } |
| 16657 | |
| 16658 | at::Tensor & huber_loss_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta, at::Tensor & grad_input) { |
| 16659 | if (false) { |
| 16660 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16661 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16662 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16663 | auto grad_output_meta = to_meta(grad_output); |
| 16664 | auto self_meta = to_meta(self); |
| 16665 | auto target_meta = to_meta(target); |
| 16666 | auto grad_input_meta = to_meta(grad_input); |
| 16667 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16668 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16669 | at::_ops::huber_loss_backward_out::call(grad_output_meta, self_meta, target_meta, reduction, delta, grad_input_meta); |
| 16670 | } |
| 16671 | |
| 16672 | at::Tensor grad_output_; |
| 16673 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 16674 | at::functionalization::impl::sync(grad_output); |
| 16675 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 16676 | } else { |
| 16677 | grad_output_ = grad_output; |
| 16678 | } |
| 16679 | |
| 16680 | at::Tensor self_; |
| 16681 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16682 | at::functionalization::impl::sync(self); |
| 16683 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16684 | } else { |
| 16685 | self_ = self; |
| 16686 | } |
| 16687 | |
| 16688 | at::Tensor target_; |
| 16689 | if (at::functionalization::impl::isFunctionalTensor(target)) { |
| 16690 | at::functionalization::impl::sync(target); |
| 16691 | target_ = at::functionalization::impl::from_functional_tensor(target); |
| 16692 | } else { |
| 16693 | target_ = target; |
| 16694 | } |
| 16695 | |
| 16696 | at::Tensor grad_input_; |
| 16697 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 16698 | at::functionalization::impl::sync(grad_input); |
| 16699 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 16700 | } else { |
| 16701 | grad_input_ = grad_input; |
| 16702 | } |
| 16703 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 16704 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(target))) { |
| 16705 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16706 | TORCH_INTERNAL_ASSERT(false, |
| 16707 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16708 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16709 | } else { |
| 16710 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16711 | at::AutoDispatchSkipFunctionalize guard; |
| 16712 | at::Tensor tmp_output = at::_ops::huber_loss_backward_out::call(grad_output_, self_, target_, reduction, delta, grad_input_); |
| 16713 | return grad_input;; |
| 16714 | } |
| 16715 | } else { |
| 16716 | at::Tensor tmp_output; |
| 16717 | { |
| 16718 | at::AutoDispatchSkipFunctionalize guard; |
| 16719 | tmp_output = at::_ops::huber_loss_backward::call(grad_output_, self_, target_, reduction, delta); |
| 16720 | } |
| 16721 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 16722 | at::functionalization::impl::commit_update(grad_input); |
| 16723 | at::functionalization::impl::sync(grad_input); |
| 16724 | return grad_input; |
| 16725 | } |
| 16726 | } |
| 16727 | |
| 16728 | at::Tensor & elu_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale, bool is_result, const at::Tensor & self_or_result, at::Tensor & grad_input) { |
| 16729 | if (false) { |
| 16730 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16731 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16732 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16733 | auto grad_output_meta = to_meta(grad_output); |
| 16734 | auto self_or_result_meta = to_meta(self_or_result); |
| 16735 | auto grad_input_meta = to_meta(grad_input); |
| 16736 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16737 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16738 | at::_ops::elu_backward_grad_input::call(grad_output_meta, alpha, scale, input_scale, is_result, self_or_result_meta, grad_input_meta); |
| 16739 | } |
| 16740 | |
| 16741 | at::Tensor grad_output_; |
| 16742 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 16743 | at::functionalization::impl::sync(grad_output); |
| 16744 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 16745 | } else { |
| 16746 | grad_output_ = grad_output; |
| 16747 | } |
| 16748 | |
| 16749 | at::Tensor self_or_result_; |
| 16750 | if (at::functionalization::impl::isFunctionalTensor(self_or_result)) { |
| 16751 | at::functionalization::impl::sync(self_or_result); |
| 16752 | self_or_result_ = at::functionalization::impl::from_functional_tensor(self_or_result); |
| 16753 | } else { |
| 16754 | self_or_result_ = self_or_result; |
| 16755 | } |
| 16756 | |
| 16757 | at::Tensor grad_input_; |
| 16758 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 16759 | at::functionalization::impl::sync(grad_input); |
| 16760 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 16761 | } else { |
| 16762 | grad_input_ = grad_input; |
| 16763 | } |
| 16764 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 16765 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self_or_result))) { |
| 16766 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16767 | TORCH_INTERNAL_ASSERT(false, |
| 16768 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16769 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16770 | } else { |
| 16771 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16772 | at::AutoDispatchSkipFunctionalize guard; |
| 16773 | at::Tensor tmp_output = at::_ops::elu_backward_grad_input::call(grad_output_, alpha, scale, input_scale, is_result, self_or_result_, grad_input_); |
| 16774 | return grad_input;; |
| 16775 | } |
| 16776 | } else { |
| 16777 | at::Tensor tmp_output; |
| 16778 | { |
| 16779 | at::AutoDispatchSkipFunctionalize guard; |
| 16780 | tmp_output = at::_ops::elu_backward::call(grad_output_, alpha, scale, input_scale, is_result, self_or_result_); |
| 16781 | } |
| 16782 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 16783 | at::functionalization::impl::commit_update(grad_input); |
| 16784 | at::functionalization::impl::sync(grad_input); |
| 16785 | return grad_input; |
| 16786 | } |
| 16787 | } |
| 16788 | |
| 16789 | at::Tensor & glu_jvp_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & glu, const at::Tensor & x, const at::Tensor & dx, int64_t dim, at::Tensor & out) { |
| 16790 | if (false) { |
| 16791 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16792 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16793 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16794 | auto glu_meta = to_meta(glu); |
| 16795 | auto x_meta = to_meta(x); |
| 16796 | auto dx_meta = to_meta(dx); |
| 16797 | auto out_meta = to_meta(out); |
| 16798 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16799 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16800 | at::_ops::glu_jvp_out::call(glu_meta, x_meta, dx_meta, dim, out_meta); |
| 16801 | } |
| 16802 | |
| 16803 | at::Tensor glu_; |
| 16804 | if (at::functionalization::impl::isFunctionalTensor(glu)) { |
| 16805 | at::functionalization::impl::sync(glu); |
| 16806 | glu_ = at::functionalization::impl::from_functional_tensor(glu); |
| 16807 | } else { |
| 16808 | glu_ = glu; |
| 16809 | } |
| 16810 | |
| 16811 | at::Tensor x_; |
| 16812 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 16813 | at::functionalization::impl::sync(x); |
| 16814 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 16815 | } else { |
| 16816 | x_ = x; |
| 16817 | } |
| 16818 | |
| 16819 | at::Tensor dx_; |
| 16820 | if (at::functionalization::impl::isFunctionalTensor(dx)) { |
| 16821 | at::functionalization::impl::sync(dx); |
| 16822 | dx_ = at::functionalization::impl::from_functional_tensor(dx); |
| 16823 | } else { |
| 16824 | dx_ = dx; |
| 16825 | } |
| 16826 | |
| 16827 | at::Tensor out_; |
| 16828 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16829 | at::functionalization::impl::sync(out); |
| 16830 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16831 | } else { |
| 16832 | out_ = out; |
| 16833 | } |
| 16834 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16835 | if ((false || at::functionalization::impl::isFunctionalTensor(glu) || at::functionalization::impl::isFunctionalTensor(x) || at::functionalization::impl::isFunctionalTensor(dx))) { |
| 16836 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16837 | TORCH_INTERNAL_ASSERT(false, |
| 16838 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16839 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16840 | } else { |
| 16841 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16842 | at::AutoDispatchSkipFunctionalize guard; |
| 16843 | at::Tensor tmp_output = at::_ops::glu_jvp_out::call(glu_, x_, dx_, dim, out_); |
| 16844 | return out;; |
| 16845 | } |
| 16846 | } else { |
| 16847 | at::Tensor tmp_output; |
| 16848 | { |
| 16849 | at::AutoDispatchSkipFunctionalize guard; |
| 16850 | tmp_output = at::_ops::glu_jvp::call(glu_, x_, dx_, dim); |
| 16851 | } |
| 16852 | at::functionalization::impl::replace_(out, tmp_output); |
| 16853 | at::functionalization::impl::commit_update(out); |
| 16854 | at::functionalization::impl::sync(out); |
| 16855 | return out; |
| 16856 | } |
| 16857 | } |
| 16858 | |
| 16859 | at::Tensor & hardsigmoid_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & grad_input) { |
| 16860 | if (false) { |
| 16861 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16862 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16863 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16864 | auto grad_output_meta = to_meta(grad_output); |
| 16865 | auto self_meta = to_meta(self); |
| 16866 | auto grad_input_meta = to_meta(grad_input); |
| 16867 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16868 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16869 | at::_ops::hardsigmoid_backward_grad_input::call(grad_output_meta, self_meta, grad_input_meta); |
| 16870 | } |
| 16871 | |
| 16872 | at::Tensor grad_output_; |
| 16873 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 16874 | at::functionalization::impl::sync(grad_output); |
| 16875 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 16876 | } else { |
| 16877 | grad_output_ = grad_output; |
| 16878 | } |
| 16879 | |
| 16880 | at::Tensor self_; |
| 16881 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16882 | at::functionalization::impl::sync(self); |
| 16883 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16884 | } else { |
| 16885 | self_ = self; |
| 16886 | } |
| 16887 | |
| 16888 | at::Tensor grad_input_; |
| 16889 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 16890 | at::functionalization::impl::sync(grad_input); |
| 16891 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 16892 | } else { |
| 16893 | grad_input_ = grad_input; |
| 16894 | } |
| 16895 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 16896 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16897 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16898 | TORCH_INTERNAL_ASSERT(false, |
| 16899 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16900 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16901 | } else { |
| 16902 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16903 | at::AutoDispatchSkipFunctionalize guard; |
| 16904 | at::Tensor tmp_output = at::_ops::hardsigmoid_backward_grad_input::call(grad_output_, self_, grad_input_); |
| 16905 | return grad_input;; |
| 16906 | } |
| 16907 | } else { |
| 16908 | at::Tensor tmp_output; |
| 16909 | { |
| 16910 | at::AutoDispatchSkipFunctionalize guard; |
| 16911 | tmp_output = at::_ops::hardsigmoid_backward::call(grad_output_, self_); |
| 16912 | } |
| 16913 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 16914 | at::functionalization::impl::commit_update(grad_input); |
| 16915 | at::functionalization::impl::sync(grad_input); |
| 16916 | return grad_input; |
| 16917 | } |
| 16918 | } |
| 16919 | |
| 16920 | at::Tensor & log_sigmoid_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 16921 | if (false) { |
| 16922 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16923 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16924 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16925 | auto self_meta = to_meta(self); |
| 16926 | auto out_meta = to_meta(out); |
| 16927 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16928 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16929 | at::_ops::log_sigmoid_out::call(self_meta, out_meta); |
| 16930 | } |
| 16931 | |
| 16932 | at::Tensor self_; |
| 16933 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16934 | at::functionalization::impl::sync(self); |
| 16935 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16936 | } else { |
| 16937 | self_ = self; |
| 16938 | } |
| 16939 | |
| 16940 | at::Tensor out_; |
| 16941 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16942 | at::functionalization::impl::sync(out); |
| 16943 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16944 | } else { |
| 16945 | out_ = out; |
| 16946 | } |
| 16947 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16948 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16949 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16950 | TORCH_INTERNAL_ASSERT(false, |
| 16951 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16952 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16953 | } else { |
| 16954 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16955 | at::AutoDispatchSkipFunctionalize guard; |
| 16956 | at::Tensor tmp_output = at::_ops::log_sigmoid_out::call(self_, out_); |
| 16957 | return out;; |
| 16958 | } |
| 16959 | } else { |
| 16960 | at::Tensor tmp_output; |
| 16961 | { |
| 16962 | at::AutoDispatchSkipFunctionalize guard; |
| 16963 | tmp_output = at::_ops::log_sigmoid::call(self_); |
| 16964 | } |
| 16965 | at::functionalization::impl::replace_(out, tmp_output); |
| 16966 | at::functionalization::impl::commit_update(out); |
| 16967 | at::functionalization::impl::sync(out); |
| 16968 | return out; |
| 16969 | } |
| 16970 | } |
| 16971 | |
| 16972 | ::std::tuple<at::Tensor &,at::Tensor &> log_sigmoid_forward_out_output(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & output, at::Tensor & buffer) { |
| 16973 | if (false) { |
| 16974 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16975 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16976 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16977 | auto self_meta = to_meta(self); |
| 16978 | auto output_meta = to_meta(output); |
| 16979 | auto buffer_meta = to_meta(buffer); |
| 16980 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16981 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16982 | at::_ops::log_sigmoid_forward_output::call(self_meta, output_meta, buffer_meta); |
| 16983 | } |
| 16984 | |
| 16985 | at::Tensor self_; |
| 16986 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16987 | at::functionalization::impl::sync(self); |
| 16988 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16989 | } else { |
| 16990 | self_ = self; |
| 16991 | } |
| 16992 | |
| 16993 | at::Tensor output_; |
| 16994 | if (at::functionalization::impl::isFunctionalTensor(output)) { |
| 16995 | at::functionalization::impl::sync(output); |
| 16996 | output_ = at::functionalization::impl::from_functional_tensor(output); |
| 16997 | } else { |
| 16998 | output_ = output; |
| 16999 | } |
| 17000 | |
| 17001 | at::Tensor buffer_; |
| 17002 | if (at::functionalization::impl::isFunctionalTensor(buffer)) { |
| 17003 | at::functionalization::impl::sync(buffer); |
| 17004 | buffer_ = at::functionalization::impl::from_functional_tensor(buffer); |
| 17005 | } else { |
| 17006 | buffer_ = buffer; |
| 17007 | } |
| 17008 | if (!(true && at::functionalization::impl::isFunctionalTensor(output) && at::functionalization::impl::isFunctionalTensor(buffer))) { |
| 17009 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 17010 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17011 | TORCH_INTERNAL_ASSERT(false, |
| 17012 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17013 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17014 | } else { |
| 17015 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17016 | at::AutoDispatchSkipFunctionalize guard; |
| 17017 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::log_sigmoid_forward_output::call(self_, output_, buffer_); |
| 17018 | return ::std::tuple<at::Tensor &,at::Tensor &>(output, buffer);; |
| 17019 | } |
| 17020 | } else { |
| 17021 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 17022 | { |
| 17023 | at::AutoDispatchSkipFunctionalize guard; |
| 17024 | tmp_output = at::_ops::log_sigmoid_forward::call(self_); |
| 17025 | } |
| 17026 | at::functionalization::impl::replace_(output, std::get<0>(tmp_output)); |
| 17027 | at::functionalization::impl::commit_update(output); |
| 17028 | at::functionalization::impl::sync(output); |
| 17029 | at::functionalization::impl::replace_(buffer, std::get<1>(tmp_output)); |
| 17030 | at::functionalization::impl::commit_update(buffer); |
| 17031 | at::functionalization::impl::sync(buffer); |
| 17032 | return ::std::tuple<at::Tensor &,at::Tensor &>(output, buffer); |
| 17033 | } |
| 17034 | } |
| 17035 | |
| 17036 | at::Tensor & rrelu_with_noise_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower, const at::Scalar & upper, bool training, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 17037 | if (false) { |
| 17038 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17039 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17040 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17041 | auto self_meta = to_meta(self); |
| 17042 | auto noise_meta = to_meta(noise); |
| 17043 | auto out_meta = to_meta(out); |
| 17044 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17045 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17046 | at::_ops::rrelu_with_noise_out::call(self_meta, noise_meta, lower, upper, training, generator, out_meta); |
| 17047 | } |
| 17048 | |
| 17049 | at::Tensor self_; |
| 17050 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17051 | at::functionalization::impl::sync(self); |
| 17052 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17053 | } else { |
| 17054 | self_ = self; |
| 17055 | } |
| 17056 | |
| 17057 | at::Tensor noise_; |
| 17058 | if (at::functionalization::impl::isFunctionalTensor(noise)) { |
| 17059 | at::functionalization::impl::sync(noise); |
| 17060 | noise_ = at::functionalization::impl::from_functional_tensor(noise); |
| 17061 | } else { |
| 17062 | noise_ = noise; |
| 17063 | } |
| 17064 | |
| 17065 | at::Tensor out_; |
| 17066 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17067 | at::functionalization::impl::sync(out); |
| 17068 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17069 | } else { |
| 17070 | out_ = out; |
| 17071 | } |
| 17072 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 17073 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(noise))) { |
| 17074 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17075 | TORCH_INTERNAL_ASSERT(false, |
| 17076 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17077 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17078 | } else { |
| 17079 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17080 | at::AutoDispatchSkipFunctionalize guard; |
| 17081 | at::Tensor tmp_output = at::_ops::rrelu_with_noise_out::call(self_, noise_, lower, upper, training, generator, out_); |
| 17082 | return out;; |
| 17083 | } |
| 17084 | } else { |
| 17085 | at::Tensor tmp_output; |
| 17086 | { |
| 17087 | at::AutoDispatchSkipFunctionalize guard; |
| 17088 | tmp_output = at::_ops::rrelu_with_noise::call(self_, noise_, lower, upper, training, generator); |
| 17089 | } |
| 17090 | at::functionalization::impl::replace_(out, tmp_output); |
| 17091 | at::functionalization::impl::commit_update(out); |
| 17092 | at::functionalization::impl::sync(out); |
| 17093 | return out; |
| 17094 | } |
| 17095 | } |
| 17096 | |
| 17097 | at::Tensor & rrelu_with_noise_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower, const at::Scalar & upper, bool training, c10::optional<at::Generator> generator) { |
| 17098 | if (true) { |
| 17099 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17100 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17101 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17102 | auto self_meta = to_meta(self); |
| 17103 | auto noise_meta = to_meta(noise); |
| 17104 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17105 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17106 | at::_ops::rrelu_with_noise_::call(self_meta, noise_meta, lower, upper, training, generator); |
| 17107 | } |
| 17108 | |
| 17109 | at::Tensor self_; |
| 17110 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17111 | at::functionalization::impl::sync(self); |
| 17112 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17113 | } else { |
| 17114 | self_ = self; |
| 17115 | } |
| 17116 | |
| 17117 | at::Tensor noise_; |
| 17118 | if (at::functionalization::impl::isFunctionalTensor(noise)) { |
| 17119 | at::functionalization::impl::sync(noise); |
| 17120 | noise_ = at::functionalization::impl::from_functional_tensor(noise); |
| 17121 | } else { |
| 17122 | noise_ = noise; |
| 17123 | } |
| 17124 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 17125 | if ((false || at::functionalization::impl::isFunctionalTensor(noise))) { |
| 17126 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17127 | TORCH_INTERNAL_ASSERT(false, |
| 17128 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17129 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17130 | } else { |
| 17131 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17132 | at::AutoDispatchSkipFunctionalize guard; |
| 17133 | at::Tensor tmp_output = at::_ops::rrelu_with_noise_::call(self_, noise_, lower, upper, training, generator); |
| 17134 | return self;; |
| 17135 | } |
| 17136 | } else { |
| 17137 | at::Tensor tmp_output; |
| 17138 | { |
| 17139 | at::AutoDispatchSkipFunctionalize guard; |
| 17140 | tmp_output = at::_ops::rrelu_with_noise::call(self_, noise_, lower, upper, training, generator); |
| 17141 | } |
| 17142 | at::functionalization::impl::replace_(self, tmp_output); |
| 17143 | at::functionalization::impl::commit_update(self); |
| 17144 | at::functionalization::impl::sync(self); |
| 17145 | return self; |
| 17146 | } |
| 17147 | } |
| 17148 | |
| 17149 | at::Tensor & rrelu_with_noise_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower, const at::Scalar & upper, bool training, bool self_is_result, at::Tensor & out) { |
| 17150 | if (false) { |
| 17151 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17152 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17153 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17154 | auto grad_output_meta = to_meta(grad_output); |
| 17155 | auto self_meta = to_meta(self); |
| 17156 | auto noise_meta = to_meta(noise); |
| 17157 | auto out_meta = to_meta(out); |
| 17158 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17159 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17160 | at::_ops::rrelu_with_noise_backward_out::call(grad_output_meta, self_meta, noise_meta, lower, upper, training, self_is_result, out_meta); |
| 17161 | } |
| 17162 | |
| 17163 | at::Tensor grad_output_; |
| 17164 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 17165 | at::functionalization::impl::sync(grad_output); |
| 17166 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 17167 | } else { |
| 17168 | grad_output_ = grad_output; |
| 17169 | } |
| 17170 | |
| 17171 | at::Tensor self_; |
| 17172 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17173 | at::functionalization::impl::sync(self); |
| 17174 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17175 | } else { |
| 17176 | self_ = self; |
| 17177 | } |
| 17178 | |
| 17179 | at::Tensor noise_; |
| 17180 | if (at::functionalization::impl::isFunctionalTensor(noise)) { |
| 17181 | at::functionalization::impl::sync(noise); |
| 17182 | noise_ = at::functionalization::impl::from_functional_tensor(noise); |
| 17183 | } else { |
| 17184 | noise_ = noise; |
| 17185 | } |
| 17186 | |
| 17187 | at::Tensor out_; |
| 17188 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17189 | at::functionalization::impl::sync(out); |
| 17190 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17191 | } else { |
| 17192 | out_ = out; |
| 17193 | } |
| 17194 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 17195 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(noise))) { |
| 17196 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17197 | TORCH_INTERNAL_ASSERT(false, |
| 17198 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17199 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17200 | } else { |
| 17201 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17202 | at::AutoDispatchSkipFunctionalize guard; |
| 17203 | at::Tensor tmp_output = at::_ops::rrelu_with_noise_backward_out::call(grad_output_, self_, noise_, lower, upper, training, self_is_result, out_); |
| 17204 | return out;; |
| 17205 | } |
| 17206 | } else { |
| 17207 | at::Tensor tmp_output; |
| 17208 | { |
| 17209 | at::AutoDispatchSkipFunctionalize guard; |
| 17210 | tmp_output = at::_ops::rrelu_with_noise_backward::call(grad_output_, self_, noise_, lower, upper, training, self_is_result); |
| 17211 | } |
| 17212 | at::functionalization::impl::replace_(out, tmp_output); |
| 17213 | at::functionalization::impl::commit_update(out); |
| 17214 | at::functionalization::impl::sync(out); |
| 17215 | return out; |
| 17216 | } |
| 17217 | } |
| 17218 | |
| 17219 | at::Tensor & softplus_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold, at::Tensor & grad_input) { |
| 17220 | if (false) { |
| 17221 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17222 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17223 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17224 | auto grad_output_meta = to_meta(grad_output); |
| 17225 | auto self_meta = to_meta(self); |
| 17226 | auto grad_input_meta = to_meta(grad_input); |
| 17227 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17228 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17229 | at::_ops::softplus_backward_grad_input::call(grad_output_meta, self_meta, beta, threshold, grad_input_meta); |
| 17230 | } |
| 17231 | |
| 17232 | at::Tensor grad_output_; |
| 17233 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 17234 | at::functionalization::impl::sync(grad_output); |
| 17235 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 17236 | } else { |
| 17237 | grad_output_ = grad_output; |
| 17238 | } |
| 17239 | |
| 17240 | at::Tensor self_; |
| 17241 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17242 | at::functionalization::impl::sync(self); |
| 17243 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17244 | } else { |
| 17245 | self_ = self; |
| 17246 | } |
| 17247 | |
| 17248 | at::Tensor grad_input_; |
| 17249 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 17250 | at::functionalization::impl::sync(grad_input); |
| 17251 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 17252 | } else { |
| 17253 | grad_input_ = grad_input; |
| 17254 | } |
| 17255 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 17256 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self))) { |
| 17257 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17258 | TORCH_INTERNAL_ASSERT(false, |
| 17259 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17260 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17261 | } else { |
| 17262 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17263 | at::AutoDispatchSkipFunctionalize guard; |
| 17264 | at::Tensor tmp_output = at::_ops::softplus_backward_grad_input::call(grad_output_, self_, beta, threshold, grad_input_); |
| 17265 | return grad_input;; |
| 17266 | } |
| 17267 | } else { |
| 17268 | at::Tensor tmp_output; |
| 17269 | { |
| 17270 | at::AutoDispatchSkipFunctionalize guard; |
| 17271 | tmp_output = at::_ops::softplus_backward::call(grad_output_, self_, beta, threshold); |
| 17272 | } |
| 17273 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 17274 | at::functionalization::impl::commit_update(grad_input); |
| 17275 | at::functionalization::impl::sync(grad_input); |
| 17276 | return grad_input; |
| 17277 | } |
| 17278 | } |
| 17279 | |
| 17280 | at::Tensor & mkldnn_adaptive_avg_pool2d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out) { |
| 17281 | if (false) { |
| 17282 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17283 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17284 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17285 | auto self_meta = to_meta(self); |
| 17286 | auto out_meta = to_meta(out); |
| 17287 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17288 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17289 | at::_ops::mkldnn_adaptive_avg_pool2d_out::call(self_meta, output_size, out_meta); |
| 17290 | } |
| 17291 | |
| 17292 | at::Tensor self_; |
| 17293 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17294 | at::functionalization::impl::sync(self); |
| 17295 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17296 | } else { |
| 17297 | self_ = self; |
| 17298 | } |
| 17299 | |
| 17300 | at::Tensor out_; |
| 17301 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17302 | at::functionalization::impl::sync(out); |
| 17303 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17304 | } else { |
| 17305 | out_ = out; |
| 17306 | } |
| 17307 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 17308 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 17309 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17310 | TORCH_INTERNAL_ASSERT(false, |
| 17311 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17312 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17313 | } else { |
| 17314 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17315 | at::AutoDispatchSkipFunctionalize guard; |
| 17316 | at::Tensor tmp_output = at::_ops::mkldnn_adaptive_avg_pool2d_out::call(self_, output_size, out_); |
| 17317 | return out;; |
| 17318 | } |
| 17319 | } else { |
| 17320 | at::Tensor tmp_output; |
| 17321 | { |
| 17322 | at::AutoDispatchSkipFunctionalize guard; |
| 17323 | tmp_output = at::_ops::mkldnn_adaptive_avg_pool2d::call(self_, output_size); |
| 17324 | } |
| 17325 | at::functionalization::impl::replace_(out, tmp_output); |
| 17326 | at::functionalization::impl::commit_update(out); |
| 17327 | at::functionalization::impl::sync(out); |
| 17328 | return out; |
| 17329 | } |
| 17330 | } |
| 17331 | |
| 17332 | ::std::tuple<at::Tensor &,at::Tensor &> adaptive_max_pool2d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out, at::Tensor & indices) { |
| 17333 | if (false) { |
| 17334 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17335 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17336 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17337 | auto self_meta = to_meta(self); |
| 17338 | auto out_meta = to_meta(out); |
| 17339 | auto indices_meta = to_meta(indices); |
| 17340 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17341 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17342 | at::_ops::adaptive_max_pool2d_out::call(self_meta, output_size, out_meta, indices_meta); |
| 17343 | } |
| 17344 | |
| 17345 | at::Tensor self_; |
| 17346 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17347 | at::functionalization::impl::sync(self); |
| 17348 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17349 | } else { |
| 17350 | self_ = self; |
| 17351 | } |
| 17352 | |
| 17353 | at::Tensor out_; |
| 17354 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17355 | at::functionalization::impl::sync(out); |
| 17356 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17357 | } else { |
| 17358 | out_ = out; |
| 17359 | } |
| 17360 | |
| 17361 | at::Tensor indices_; |
| 17362 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 17363 | at::functionalization::impl::sync(indices); |
| 17364 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 17365 | } else { |
| 17366 | indices_ = indices; |
| 17367 | } |
| 17368 | if (!(true && at::functionalization::impl::isFunctionalTensor(out) && at::functionalization::impl::isFunctionalTensor(indices))) { |
| 17369 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 17370 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17371 | TORCH_INTERNAL_ASSERT(false, |
| 17372 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17373 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17374 | } else { |
| 17375 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17376 | at::AutoDispatchSkipFunctionalize guard; |
| 17377 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::adaptive_max_pool2d_out::call(self_, output_size, out_, indices_); |
| 17378 | return ::std::tuple<at::Tensor &,at::Tensor &>(out, indices);; |
| 17379 | } |
| 17380 | } else { |
| 17381 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 17382 | { |
| 17383 | at::AutoDispatchSkipFunctionalize guard; |
| 17384 | tmp_output = at::_ops::adaptive_max_pool2d::call(self_, output_size); |
| 17385 | } |
| 17386 | at::functionalization::impl::replace_(out, std::get<0>(tmp_output)); |
| 17387 | at::functionalization::impl::commit_update(out); |
| 17388 | at::functionalization::impl::sync(out); |
| 17389 | at::functionalization::impl::replace_(indices, std::get<1>(tmp_output)); |
| 17390 | at::functionalization::impl::commit_update(indices); |
| 17391 | at::functionalization::impl::sync(indices); |
| 17392 | return ::std::tuple<at::Tensor &,at::Tensor &>(out, indices); |
| 17393 | } |
| 17394 | } |
| 17395 | |
| 17396 | ::std::tuple<at::Tensor &,at::Tensor &> adaptive_max_pool3d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out, at::Tensor & indices) { |
| 17397 | if (false) { |
| 17398 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17399 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17400 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17401 | auto self_meta = to_meta(self); |
| 17402 | auto out_meta = to_meta(out); |
| 17403 | auto indices_meta = to_meta(indices); |
| 17404 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17405 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17406 | at::_ops::adaptive_max_pool3d_out::call(self_meta, output_size, out_meta, indices_meta); |
| 17407 | } |
| 17408 | |
| 17409 | at::Tensor self_; |
| 17410 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17411 | at::functionalization::impl::sync(self); |
| 17412 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17413 | } else { |
| 17414 | self_ = self; |
| 17415 | } |
| 17416 | |
| 17417 | at::Tensor out_; |
| 17418 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17419 | at::functionalization::impl::sync(out); |
| 17420 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17421 | } else { |
| 17422 | out_ = out; |
| 17423 | } |
| 17424 | |
| 17425 | at::Tensor indices_; |
| 17426 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 17427 | at::functionalization::impl::sync(indices); |
| 17428 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 17429 | } else { |
| 17430 | indices_ = indices; |
| 17431 | } |
| 17432 | if (!(true && at::functionalization::impl::isFunctionalTensor(out) && at::functionalization::impl::isFunctionalTensor(indices))) { |
| 17433 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 17434 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17435 | TORCH_INTERNAL_ASSERT(false, |
| 17436 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17437 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17438 | } else { |
| 17439 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17440 | at::AutoDispatchSkipFunctionalize guard; |
| 17441 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::adaptive_max_pool3d_out::call(self_, output_size, out_, indices_); |
| 17442 | return ::std::tuple<at::Tensor &,at::Tensor &>(out, indices);; |
| 17443 | } |
| 17444 | } else { |
| 17445 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 17446 | { |
| 17447 | at::AutoDispatchSkipFunctionalize guard; |
| 17448 | tmp_output = at::_ops::adaptive_max_pool3d::call(self_, output_size); |
| 17449 | } |
| 17450 | at::functionalization::impl::replace_(out, std::get<0>(tmp_output)); |
| 17451 | at::functionalization::impl::commit_update(out); |
| 17452 | at::functionalization::impl::sync(out); |
| 17453 | at::functionalization::impl::replace_(indices, std::get<1>(tmp_output)); |
| 17454 | at::functionalization::impl::commit_update(indices); |
| 17455 | at::functionalization::impl::sync(indices); |
| 17456 | return ::std::tuple<at::Tensor &,at::Tensor &>(out, indices); |
| 17457 | } |
| 17458 | } |
| 17459 | |
| 17460 | at::Tensor & avg_pool2d_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, bool ceil_mode, bool count_include_pad, c10::optional<int64_t> divisor_override, at::Tensor & grad_input) { |
| 17461 | if (false) { |
| 17462 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17463 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17464 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17465 | auto grad_output_meta = to_meta(grad_output); |
| 17466 | auto self_meta = to_meta(self); |
| 17467 | auto grad_input_meta = to_meta(grad_input); |
| 17468 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17469 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17470 | at::_ops::avg_pool2d_backward_grad_input::call(grad_output_meta, self_meta, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, grad_input_meta); |
| 17471 | } |
| 17472 | |
| 17473 | at::Tensor grad_output_; |
| 17474 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 17475 | at::functionalization::impl::sync(grad_output); |
| 17476 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 17477 | } else { |
| 17478 | grad_output_ = grad_output; |
| 17479 | } |
| 17480 | |
| 17481 | at::Tensor self_; |
| 17482 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17483 | at::functionalization::impl::sync(self); |
| 17484 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17485 | } else { |
| 17486 | self_ = self; |
| 17487 | } |
| 17488 | |
| 17489 | at::Tensor grad_input_; |
| 17490 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 17491 | at::functionalization::impl::sync(grad_input); |
| 17492 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 17493 | } else { |
| 17494 | grad_input_ = grad_input; |
| 17495 | } |
| 17496 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 17497 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self))) { |
| 17498 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17499 | TORCH_INTERNAL_ASSERT(false, |
| 17500 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17501 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17502 | } else { |
| 17503 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17504 | at::AutoDispatchSkipFunctionalize guard; |
| 17505 | at::Tensor tmp_output = at::_ops::avg_pool2d_backward_grad_input::call(grad_output_, self_, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, grad_input_); |
| 17506 | return grad_input;; |
| 17507 | } |
| 17508 | } else { |
| 17509 | at::Tensor tmp_output; |
| 17510 | { |
| 17511 | at::AutoDispatchSkipFunctionalize guard; |
| 17512 | tmp_output = at::_ops::avg_pool2d_backward::call(grad_output_, self_, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); |
| 17513 | } |
| 17514 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 17515 | at::functionalization::impl::commit_update(grad_input); |
| 17516 | at::functionalization::impl::sync(grad_input); |
| 17517 | return grad_input; |
| 17518 | } |
| 17519 | } |
| 17520 | |
| 17521 | ::std::tuple<at::Tensor &,at::Tensor &> max_pool2d_with_indices_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out, at::Tensor & indices) { |
| 17522 | if (false) { |
| 17523 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17524 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17525 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17526 | auto self_meta = to_meta(self); |
| 17527 | auto out_meta = to_meta(out); |
| 17528 | auto indices_meta = to_meta(indices); |
| 17529 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17530 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17531 | at::_ops::max_pool2d_with_indices_out::call(self_meta, kernel_size, stride, padding, dilation, ceil_mode, out_meta, indices_meta); |
| 17532 | } |
| 17533 | |
| 17534 | at::Tensor self_; |
| 17535 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17536 | at::functionalization::impl::sync(self); |
| 17537 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17538 | } else { |
| 17539 | self_ = self; |
| 17540 | } |
| 17541 | |
| 17542 | at::Tensor out_; |
| 17543 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17544 | at::functionalization::impl::sync(out); |
| 17545 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17546 | } else { |
| 17547 | out_ = out; |
| 17548 | } |
| 17549 | |
| 17550 | at::Tensor indices_; |
| 17551 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 17552 | at::functionalization::impl::sync(indices); |
| 17553 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 17554 | } else { |
| 17555 | indices_ = indices; |
| 17556 | } |
| 17557 | if (!(true && at::functionalization::impl::isFunctionalTensor(out) && at::functionalization::impl::isFunctionalTensor(indices))) { |
| 17558 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 17559 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17560 | TORCH_INTERNAL_ASSERT(false, |
| 17561 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17562 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17563 | } else { |
| 17564 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17565 | at::AutoDispatchSkipFunctionalize guard; |
| 17566 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::max_pool2d_with_indices_out::call(self_, kernel_size, stride, padding, dilation, ceil_mode, out_, indices_); |
| 17567 | return ::std::tuple<at::Tensor &,at::Tensor &>(out, indices);; |
| 17568 | } |
| 17569 | } else { |
| 17570 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 17571 | { |
| 17572 | at::AutoDispatchSkipFunctionalize guard; |
| 17573 | tmp_output = at::_ops::max_pool2d_with_indices::call(self_, kernel_size, stride, padding, dilation, ceil_mode); |
| 17574 | } |
| 17575 | at::functionalization::impl::replace_(out, std::get<0>(tmp_output)); |
| 17576 | at::functionalization::impl::commit_update(out); |
| 17577 | at::functionalization::impl::sync(out); |
| 17578 | at::functionalization::impl::replace_(indices, std::get<1>(tmp_output)); |
| 17579 | at::functionalization::impl::commit_update(indices); |
| 17580 | at::functionalization::impl::sync(indices); |
| 17581 | return ::std::tuple<at::Tensor &,at::Tensor &>(out, indices); |
| 17582 | } |
| 17583 | } |
| 17584 | |
| 17585 | at::Tensor & upsample_linear1d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales, at::Tensor & out) { |
| 17586 | if (false) { |
| 17587 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17588 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17589 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17590 | auto self_meta = to_meta(self); |
| 17591 | auto out_meta = to_meta(out); |
| 17592 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17593 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17594 | at::_ops::upsample_linear1d_out::call(self_meta, output_size, align_corners, scales, out_meta); |
| 17595 | } |
| 17596 | |
| 17597 | at::Tensor self_; |
| 17598 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17599 | at::functionalization::impl::sync(self); |
| 17600 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17601 | } else { |
| 17602 | self_ = self; |
| 17603 | } |
| 17604 | |
| 17605 | at::Tensor out_; |
| 17606 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17607 | at::functionalization::impl::sync(out); |
| 17608 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17609 | } else { |
| 17610 | out_ = out; |
| 17611 | } |
| 17612 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 17613 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 17614 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17615 | TORCH_INTERNAL_ASSERT(false, |
| 17616 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17617 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17618 | } else { |
| 17619 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17620 | at::AutoDispatchSkipFunctionalize guard; |
| 17621 | at::Tensor tmp_output = at::_ops::upsample_linear1d_out::call(self_, output_size, align_corners, scales, out_); |
| 17622 | return out;; |
| 17623 | } |
| 17624 | } else { |
| 17625 | at::Tensor tmp_output; |
| 17626 | { |
| 17627 | at::AutoDispatchSkipFunctionalize guard; |
| 17628 | tmp_output = at::_ops::upsample_linear1d::call(self_, output_size, align_corners, scales); |
| 17629 | } |
| 17630 | at::functionalization::impl::replace_(out, tmp_output); |
| 17631 | at::functionalization::impl::commit_update(out); |
| 17632 | at::functionalization::impl::sync(out); |
| 17633 | return out; |
| 17634 | } |
| 17635 | } |
| 17636 | |
| 17637 | at::Tensor & upsample_linear1d_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional<double> scales, at::Tensor & grad_input) { |
| 17638 | if (false) { |
| 17639 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17640 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17641 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17642 | auto grad_output_meta = to_meta(grad_output); |
| 17643 | auto grad_input_meta = to_meta(grad_input); |
| 17644 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17645 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17646 | at::_ops::upsample_linear1d_backward_grad_input::call(grad_output_meta, output_size, input_size, align_corners, scales, grad_input_meta); |
| 17647 | } |
| 17648 | |
| 17649 | at::Tensor grad_output_; |
| 17650 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 17651 | at::functionalization::impl::sync(grad_output); |
| 17652 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 17653 | } else { |
| 17654 | grad_output_ = grad_output; |
| 17655 | } |
| 17656 | |
| 17657 | at::Tensor grad_input_; |
| 17658 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 17659 | at::functionalization::impl::sync(grad_input); |
| 17660 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 17661 | } else { |
| 17662 | grad_input_ = grad_input; |
| 17663 | } |
| 17664 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 17665 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output))) { |
| 17666 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17667 | TORCH_INTERNAL_ASSERT(false, |
| 17668 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17669 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17670 | } else { |
| 17671 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17672 | at::AutoDispatchSkipFunctionalize guard; |
| 17673 | at::Tensor tmp_output = at::_ops::upsample_linear1d_backward_grad_input::call(grad_output_, output_size, input_size, align_corners, scales, grad_input_); |
| 17674 | return grad_input;; |
| 17675 | } |
| 17676 | } else { |
| 17677 | at::Tensor tmp_output; |
| 17678 | { |
| 17679 | at::AutoDispatchSkipFunctionalize guard; |
| 17680 | tmp_output = at::_ops::upsample_linear1d_backward::call(grad_output_, output_size, input_size, align_corners, scales); |
| 17681 | } |
| 17682 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 17683 | at::functionalization::impl::commit_update(grad_input); |
| 17684 | at::functionalization::impl::sync(grad_input); |
| 17685 | return grad_input; |
| 17686 | } |
| 17687 | } |
| 17688 | |
| 17689 | at::Tensor & upsample_bicubic2d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 17690 | if (false) { |
| 17691 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17692 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17693 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17694 | auto self_meta = to_meta(self); |
| 17695 | auto out_meta = to_meta(out); |
| 17696 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17697 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17698 | at::_ops::upsample_bicubic2d_out::call(self_meta, output_size, align_corners, scales_h, scales_w, out_meta); |
| 17699 | } |
| 17700 | |
| 17701 | at::Tensor self_; |
| 17702 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17703 | at::functionalization::impl::sync(self); |
| 17704 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17705 | } else { |
| 17706 | self_ = self; |
| 17707 | } |
| 17708 | |
| 17709 | at::Tensor out_; |
| 17710 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17711 | at::functionalization::impl::sync(out); |
| 17712 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17713 | } else { |
| 17714 | out_ = out; |
| 17715 | } |
| 17716 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 17717 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 17718 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17719 | TORCH_INTERNAL_ASSERT(false, |
| 17720 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17721 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17722 | } else { |
| 17723 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17724 | at::AutoDispatchSkipFunctionalize guard; |
| 17725 | at::Tensor tmp_output = at::_ops::upsample_bicubic2d_out::call(self_, output_size, align_corners, scales_h, scales_w, out_); |
| 17726 | return out;; |
| 17727 | } |
| 17728 | } else { |
| 17729 | at::Tensor tmp_output; |
| 17730 | { |
| 17731 | at::AutoDispatchSkipFunctionalize guard; |
| 17732 | tmp_output = at::_ops::upsample_bicubic2d::call(self_, output_size, align_corners, scales_h, scales_w); |
| 17733 | } |
| 17734 | at::functionalization::impl::replace_(out, tmp_output); |
| 17735 | at::functionalization::impl::commit_update(out); |
| 17736 | at::functionalization::impl::sync(out); |
| 17737 | return out; |
| 17738 | } |
| 17739 | } |
| 17740 | |
| 17741 | at::Tensor & upsample_bicubic2d_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) { |
| 17742 | if (false) { |
| 17743 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17744 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17745 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17746 | auto grad_output_meta = to_meta(grad_output); |
| 17747 | auto grad_input_meta = to_meta(grad_input); |
| 17748 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17749 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17750 | at::_ops::upsample_bicubic2d_backward_grad_input::call(grad_output_meta, output_size, input_size, align_corners, scales_h, scales_w, grad_input_meta); |
| 17751 | } |
| 17752 | |
| 17753 | at::Tensor grad_output_; |
| 17754 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 17755 | at::functionalization::impl::sync(grad_output); |
| 17756 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 17757 | } else { |
| 17758 | grad_output_ = grad_output; |
| 17759 | } |
| 17760 | |
| 17761 | at::Tensor grad_input_; |
| 17762 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 17763 | at::functionalization::impl::sync(grad_input); |
| 17764 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 17765 | } else { |
| 17766 | grad_input_ = grad_input; |
| 17767 | } |
| 17768 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 17769 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output))) { |
| 17770 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17771 | TORCH_INTERNAL_ASSERT(false, |
| 17772 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17773 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17774 | } else { |
| 17775 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17776 | at::AutoDispatchSkipFunctionalize guard; |
| 17777 | at::Tensor tmp_output = at::_ops::upsample_bicubic2d_backward_grad_input::call(grad_output_, output_size, input_size, align_corners, scales_h, scales_w, grad_input_); |
| 17778 | return grad_input;; |
| 17779 | } |
| 17780 | } else { |
| 17781 | at::Tensor tmp_output; |
| 17782 | { |
| 17783 | at::AutoDispatchSkipFunctionalize guard; |
| 17784 | tmp_output = at::_ops::upsample_bicubic2d_backward::call(grad_output_, output_size, input_size, align_corners, scales_h, scales_w); |
| 17785 | } |
| 17786 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 17787 | at::functionalization::impl::commit_update(grad_input); |
| 17788 | at::functionalization::impl::sync(grad_input); |
| 17789 | return grad_input; |
| 17790 | } |
| 17791 | } |
| 17792 | |
| 17793 | at::Tensor & _upsample_bicubic2d_aa_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) { |
| 17794 | if (false) { |
| 17795 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17796 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17797 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17798 | auto grad_output_meta = to_meta(grad_output); |
| 17799 | auto grad_input_meta = to_meta(grad_input); |
| 17800 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17801 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17802 | at::_ops::_upsample_bicubic2d_aa_backward_grad_input::call(grad_output_meta, output_size, input_size, align_corners, scales_h, scales_w, grad_input_meta); |
| 17803 | } |
| 17804 | |
| 17805 | at::Tensor grad_output_; |
| 17806 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 17807 | at::functionalization::impl::sync(grad_output); |
| 17808 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 17809 | } else { |
| 17810 | grad_output_ = grad_output; |
| 17811 | } |
| 17812 | |
| 17813 | at::Tensor grad_input_; |
| 17814 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 17815 | at::functionalization::impl::sync(grad_input); |
| 17816 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 17817 | } else { |
| 17818 | grad_input_ = grad_input; |
| 17819 | } |
| 17820 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 17821 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output))) { |
| 17822 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17823 | TORCH_INTERNAL_ASSERT(false, |
| 17824 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17825 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17826 | } else { |
| 17827 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17828 | at::AutoDispatchSkipFunctionalize guard; |
| 17829 | at::Tensor tmp_output = at::_ops::_upsample_bicubic2d_aa_backward_grad_input::call(grad_output_, output_size, input_size, align_corners, scales_h, scales_w, grad_input_); |
| 17830 | return grad_input;; |
| 17831 | } |
| 17832 | } else { |
| 17833 | at::Tensor tmp_output; |
| 17834 | { |
| 17835 | at::AutoDispatchSkipFunctionalize guard; |
| 17836 | tmp_output = at::_ops::_upsample_bicubic2d_aa_backward::call(grad_output_, output_size, input_size, align_corners, scales_h, scales_w); |
| 17837 | } |
| 17838 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 17839 | at::functionalization::impl::commit_update(grad_input); |
| 17840 | at::functionalization::impl::sync(grad_input); |
| 17841 | return grad_input; |
| 17842 | } |
| 17843 | } |
| 17844 | |
| 17845 | at::Tensor & upsample_nearest1d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales, at::Tensor & out) { |
| 17846 | if (false) { |
| 17847 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17848 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17849 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17850 | auto self_meta = to_meta(self); |
| 17851 | auto out_meta = to_meta(out); |
| 17852 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17853 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17854 | at::_ops::upsample_nearest1d_out::call(self_meta, output_size, scales, out_meta); |
| 17855 | } |
| 17856 | |
| 17857 | at::Tensor self_; |
| 17858 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17859 | at::functionalization::impl::sync(self); |
| 17860 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17861 | } else { |
| 17862 | self_ = self; |
| 17863 | } |
| 17864 | |
| 17865 | at::Tensor out_; |
| 17866 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17867 | at::functionalization::impl::sync(out); |
| 17868 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17869 | } else { |
| 17870 | out_ = out; |
| 17871 | } |
| 17872 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 17873 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 17874 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17875 | TORCH_INTERNAL_ASSERT(false, |
| 17876 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17877 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17878 | } else { |
| 17879 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17880 | at::AutoDispatchSkipFunctionalize guard; |
| 17881 | at::Tensor tmp_output = at::_ops::upsample_nearest1d_out::call(self_, output_size, scales, out_); |
| 17882 | return out;; |
| 17883 | } |
| 17884 | } else { |
| 17885 | at::Tensor tmp_output; |
| 17886 | { |
| 17887 | at::AutoDispatchSkipFunctionalize guard; |
| 17888 | tmp_output = at::_ops::upsample_nearest1d::call(self_, output_size, scales); |
| 17889 | } |
| 17890 | at::functionalization::impl::replace_(out, tmp_output); |
| 17891 | at::functionalization::impl::commit_update(out); |
| 17892 | at::functionalization::impl::sync(out); |
| 17893 | return out; |
| 17894 | } |
| 17895 | } |
| 17896 | |
| 17897 | at::Tensor & _upsample_nearest_exact1d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales, at::Tensor & out) { |
| 17898 | if (false) { |
| 17899 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17900 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17901 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17902 | auto self_meta = to_meta(self); |
| 17903 | auto out_meta = to_meta(out); |
| 17904 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17905 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17906 | at::_ops::_upsample_nearest_exact1d_out::call(self_meta, output_size, scales, out_meta); |
| 17907 | } |
| 17908 | |
| 17909 | at::Tensor self_; |
| 17910 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17911 | at::functionalization::impl::sync(self); |
| 17912 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17913 | } else { |
| 17914 | self_ = self; |
| 17915 | } |
| 17916 | |
| 17917 | at::Tensor out_; |
| 17918 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17919 | at::functionalization::impl::sync(out); |
| 17920 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17921 | } else { |
| 17922 | out_ = out; |
| 17923 | } |
| 17924 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 17925 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 17926 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17927 | TORCH_INTERNAL_ASSERT(false, |
| 17928 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17929 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17930 | } else { |
| 17931 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17932 | at::AutoDispatchSkipFunctionalize guard; |
| 17933 | at::Tensor tmp_output = at::_ops::_upsample_nearest_exact1d_out::call(self_, output_size, scales, out_); |
| 17934 | return out;; |
| 17935 | } |
| 17936 | } else { |
| 17937 | at::Tensor tmp_output; |
| 17938 | { |
| 17939 | at::AutoDispatchSkipFunctionalize guard; |
| 17940 | tmp_output = at::_ops::_upsample_nearest_exact1d::call(self_, output_size, scales); |
| 17941 | } |
| 17942 | at::functionalization::impl::replace_(out, tmp_output); |
| 17943 | at::functionalization::impl::commit_update(out); |
| 17944 | at::functionalization::impl::sync(out); |
| 17945 | return out; |
| 17946 | } |
| 17947 | } |
| 17948 | |
| 17949 | at::Tensor & upsample_nearest1d_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales, at::Tensor & grad_input) { |
| 17950 | if (false) { |
| 17951 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17952 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17953 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17954 | auto grad_output_meta = to_meta(grad_output); |
| 17955 | auto grad_input_meta = to_meta(grad_input); |
| 17956 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17957 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17958 | at::_ops::upsample_nearest1d_backward_grad_input::call(grad_output_meta, output_size, input_size, scales, grad_input_meta); |
| 17959 | } |
| 17960 | |
| 17961 | at::Tensor grad_output_; |
| 17962 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 17963 | at::functionalization::impl::sync(grad_output); |
| 17964 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 17965 | } else { |
| 17966 | grad_output_ = grad_output; |
| 17967 | } |
| 17968 | |
| 17969 | at::Tensor grad_input_; |
| 17970 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 17971 | at::functionalization::impl::sync(grad_input); |
| 17972 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 17973 | } else { |
| 17974 | grad_input_ = grad_input; |
| 17975 | } |
| 17976 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 17977 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output))) { |
| 17978 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17979 | TORCH_INTERNAL_ASSERT(false, |
| 17980 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17981 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17982 | } else { |
| 17983 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17984 | at::AutoDispatchSkipFunctionalize guard; |
| 17985 | at::Tensor tmp_output = at::_ops::upsample_nearest1d_backward_grad_input::call(grad_output_, output_size, input_size, scales, grad_input_); |
| 17986 | return grad_input;; |
| 17987 | } |
| 17988 | } else { |
| 17989 | at::Tensor tmp_output; |
| 17990 | { |
| 17991 | at::AutoDispatchSkipFunctionalize guard; |
| 17992 | tmp_output = at::_ops::upsample_nearest1d_backward::call(grad_output_, output_size, input_size, scales); |
| 17993 | } |
| 17994 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 17995 | at::functionalization::impl::commit_update(grad_input); |
| 17996 | at::functionalization::impl::sync(grad_input); |
| 17997 | return grad_input; |
| 17998 | } |
| 17999 | } |
| 18000 | |
| 18001 | at::Tensor & upsample_nearest3d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 18002 | if (false) { |
| 18003 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18004 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18005 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18006 | auto self_meta = to_meta(self); |
| 18007 | auto out_meta = to_meta(out); |
| 18008 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18009 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18010 | at::_ops::upsample_nearest3d_out::call(self_meta, output_size, scales_d, scales_h, scales_w, out_meta); |
| 18011 | } |
| 18012 | |
| 18013 | at::Tensor self_; |
| 18014 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18015 | at::functionalization::impl::sync(self); |
| 18016 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18017 | } else { |
| 18018 | self_ = self; |
| 18019 | } |
| 18020 | |
| 18021 | at::Tensor out_; |
| 18022 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18023 | at::functionalization::impl::sync(out); |
| 18024 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18025 | } else { |
| 18026 | out_ = out; |
| 18027 | } |
| 18028 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18029 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18030 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18031 | TORCH_INTERNAL_ASSERT(false, |
| 18032 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18033 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18034 | } else { |
| 18035 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18036 | at::AutoDispatchSkipFunctionalize guard; |
| 18037 | at::Tensor tmp_output = at::_ops::upsample_nearest3d_out::call(self_, output_size, scales_d, scales_h, scales_w, out_); |
| 18038 | return out;; |
| 18039 | } |
| 18040 | } else { |
| 18041 | at::Tensor tmp_output; |
| 18042 | { |
| 18043 | at::AutoDispatchSkipFunctionalize guard; |
| 18044 | tmp_output = at::_ops::upsample_nearest3d::call(self_, output_size, scales_d, scales_h, scales_w); |
| 18045 | } |
| 18046 | at::functionalization::impl::replace_(out, tmp_output); |
| 18047 | at::functionalization::impl::commit_update(out); |
| 18048 | at::functionalization::impl::sync(out); |
| 18049 | return out; |
| 18050 | } |
| 18051 | } |
| 18052 | |
| 18053 | at::Tensor & slow_conv_transpose3d_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) { |
| 18054 | if (false) { |
| 18055 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18056 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18057 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18058 | auto self_meta = to_meta(self); |
| 18059 | auto weight_meta = to_meta(weight); |
| 18060 | auto bias_meta = to_meta(bias); |
| 18061 | auto out_meta = to_meta(out); |
| 18062 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18063 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18064 | at::_ops::slow_conv_transpose3d_out::call(self_meta, weight_meta, kernel_size, bias_meta, stride, padding, output_padding, dilation, out_meta); |
| 18065 | } |
| 18066 | |
| 18067 | at::Tensor self_; |
| 18068 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18069 | at::functionalization::impl::sync(self); |
| 18070 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18071 | } else { |
| 18072 | self_ = self; |
| 18073 | } |
| 18074 | |
| 18075 | at::Tensor weight_; |
| 18076 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 18077 | at::functionalization::impl::sync(weight); |
| 18078 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 18079 | } else { |
| 18080 | weight_ = weight; |
| 18081 | } |
| 18082 | |
| 18083 | c10::optional<at::Tensor> bias_; |
| 18084 | if (at::functionalization::impl::isFunctionalTensor(bias)) { |
| 18085 | at::functionalization::impl::sync(bias); |
| 18086 | bias_ = at::functionalization::impl::from_functional_tensor(bias); |
| 18087 | } else { |
| 18088 | bias_ = bias; |
| 18089 | } |
| 18090 | |
| 18091 | at::Tensor out_; |
| 18092 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18093 | at::functionalization::impl::sync(out); |
| 18094 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18095 | } else { |
| 18096 | out_ = out; |
| 18097 | } |
| 18098 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18099 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(bias))) { |
| 18100 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18101 | TORCH_INTERNAL_ASSERT(false, |
| 18102 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18103 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18104 | } else { |
| 18105 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18106 | at::AutoDispatchSkipFunctionalize guard; |
| 18107 | at::Tensor tmp_output = at::_ops::slow_conv_transpose3d_out::call(self_, weight_, kernel_size, bias_, stride, padding, output_padding, dilation, out_); |
| 18108 | return out;; |
| 18109 | } |
| 18110 | } else { |
| 18111 | at::Tensor tmp_output; |
| 18112 | { |
| 18113 | at::AutoDispatchSkipFunctionalize guard; |
| 18114 | tmp_output = at::_ops::slow_conv_transpose3d::call(self_, weight_, kernel_size, bias_, stride, padding, output_padding, dilation); |
| 18115 | } |
| 18116 | at::functionalization::impl::replace_(out, tmp_output); |
| 18117 | at::functionalization::impl::commit_update(out); |
| 18118 | at::functionalization::impl::sync(out); |
| 18119 | return out; |
| 18120 | } |
| 18121 | } |
| 18122 | |
| 18123 | at::Tensor & isposinf_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 18124 | if (false) { |
| 18125 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18126 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18127 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18128 | auto self_meta = to_meta(self); |
| 18129 | auto out_meta = to_meta(out); |
| 18130 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18131 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18132 | at::_ops::isposinf_out::call(self_meta, out_meta); |
| 18133 | } |
| 18134 | |
| 18135 | at::Tensor self_; |
| 18136 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18137 | at::functionalization::impl::sync(self); |
| 18138 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18139 | } else { |
| 18140 | self_ = self; |
| 18141 | } |
| 18142 | |
| 18143 | at::Tensor out_; |
| 18144 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18145 | at::functionalization::impl::sync(out); |
| 18146 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18147 | } else { |
| 18148 | out_ = out; |
| 18149 | } |
| 18150 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18151 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18152 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18153 | TORCH_INTERNAL_ASSERT(false, |
| 18154 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18155 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18156 | } else { |
| 18157 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18158 | at::AutoDispatchSkipFunctionalize guard; |
| 18159 | at::Tensor tmp_output = at::_ops::isposinf_out::call(self_, out_); |
| 18160 | return out;; |
| 18161 | } |
| 18162 | } else { |
| 18163 | at::Tensor tmp_output; |
| 18164 | { |
| 18165 | at::AutoDispatchSkipFunctionalize guard; |
| 18166 | tmp_output = at::_ops::isposinf::call(self_); |
| 18167 | } |
| 18168 | at::functionalization::impl::replace_(out, tmp_output); |
| 18169 | at::functionalization::impl::commit_update(out); |
| 18170 | at::functionalization::impl::sync(out); |
| 18171 | return out; |
| 18172 | } |
| 18173 | } |
| 18174 | |
| 18175 | at::Tensor & special_entr_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 18176 | if (false) { |
| 18177 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18178 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18179 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18180 | auto self_meta = to_meta(self); |
| 18181 | auto out_meta = to_meta(out); |
| 18182 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18183 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18184 | at::_ops::special_entr_out::call(self_meta, out_meta); |
| 18185 | } |
| 18186 | |
| 18187 | at::Tensor self_; |
| 18188 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18189 | at::functionalization::impl::sync(self); |
| 18190 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18191 | } else { |
| 18192 | self_ = self; |
| 18193 | } |
| 18194 | |
| 18195 | at::Tensor out_; |
| 18196 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18197 | at::functionalization::impl::sync(out); |
| 18198 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18199 | } else { |
| 18200 | out_ = out; |
| 18201 | } |
| 18202 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18203 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18204 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18205 | TORCH_INTERNAL_ASSERT(false, |
| 18206 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18207 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18208 | } else { |
| 18209 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18210 | at::AutoDispatchSkipFunctionalize guard; |
| 18211 | at::Tensor tmp_output = at::_ops::special_entr_out::call(self_, out_); |
| 18212 | return out;; |
| 18213 | } |
| 18214 | } else { |
| 18215 | at::Tensor tmp_output; |
| 18216 | { |
| 18217 | at::AutoDispatchSkipFunctionalize guard; |
| 18218 | tmp_output = at::_ops::special_entr::call(self_); |
| 18219 | } |
| 18220 | at::functionalization::impl::replace_(out, tmp_output); |
| 18221 | at::functionalization::impl::commit_update(out); |
| 18222 | at::functionalization::impl::sync(out); |
| 18223 | return out; |
| 18224 | } |
| 18225 | } |
| 18226 | |
| 18227 | at::Tensor & special_psi_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 18228 | if (false) { |
| 18229 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18230 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18231 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18232 | auto self_meta = to_meta(self); |
| 18233 | auto out_meta = to_meta(out); |
| 18234 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18235 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18236 | at::_ops::special_psi_out::call(self_meta, out_meta); |
| 18237 | } |
| 18238 | |
| 18239 | at::Tensor self_; |
| 18240 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18241 | at::functionalization::impl::sync(self); |
| 18242 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18243 | } else { |
| 18244 | self_ = self; |
| 18245 | } |
| 18246 | |
| 18247 | at::Tensor out_; |
| 18248 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18249 | at::functionalization::impl::sync(out); |
| 18250 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18251 | } else { |
| 18252 | out_ = out; |
| 18253 | } |
| 18254 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18255 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18256 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18257 | TORCH_INTERNAL_ASSERT(false, |
| 18258 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18259 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18260 | } else { |
| 18261 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18262 | at::AutoDispatchSkipFunctionalize guard; |
| 18263 | at::Tensor tmp_output = at::_ops::special_psi_out::call(self_, out_); |
| 18264 | return out;; |
| 18265 | } |
| 18266 | } else { |
| 18267 | at::Tensor tmp_output; |
| 18268 | { |
| 18269 | at::AutoDispatchSkipFunctionalize guard; |
| 18270 | tmp_output = at::_ops::special_psi::call(self_); |
| 18271 | } |
| 18272 | at::functionalization::impl::replace_(out, tmp_output); |
| 18273 | at::functionalization::impl::commit_update(out); |
| 18274 | at::functionalization::impl::sync(out); |
| 18275 | return out; |
| 18276 | } |
| 18277 | } |
| 18278 | |
| 18279 | at::Tensor & special_erfinv_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 18280 | if (false) { |
| 18281 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18282 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18283 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18284 | auto self_meta = to_meta(self); |
| 18285 | auto out_meta = to_meta(out); |
| 18286 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18287 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18288 | at::_ops::special_erfinv_out::call(self_meta, out_meta); |
| 18289 | } |
| 18290 | |
| 18291 | at::Tensor self_; |
| 18292 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18293 | at::functionalization::impl::sync(self); |
| 18294 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18295 | } else { |
| 18296 | self_ = self; |
| 18297 | } |
| 18298 | |
| 18299 | at::Tensor out_; |
| 18300 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18301 | at::functionalization::impl::sync(out); |
| 18302 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18303 | } else { |
| 18304 | out_ = out; |
| 18305 | } |
| 18306 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18307 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18308 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18309 | TORCH_INTERNAL_ASSERT(false, |
| 18310 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18311 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18312 | } else { |
| 18313 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18314 | at::AutoDispatchSkipFunctionalize guard; |
| 18315 | at::Tensor tmp_output = at::_ops::special_erfinv_out::call(self_, out_); |
| 18316 | return out;; |
| 18317 | } |
| 18318 | } else { |
| 18319 | at::Tensor tmp_output; |
| 18320 | { |
| 18321 | at::AutoDispatchSkipFunctionalize guard; |
| 18322 | tmp_output = at::_ops::special_erfinv::call(self_); |
| 18323 | } |
| 18324 | at::functionalization::impl::replace_(out, tmp_output); |
| 18325 | at::functionalization::impl::commit_update(out); |
| 18326 | at::functionalization::impl::sync(out); |
| 18327 | return out; |
| 18328 | } |
| 18329 | } |
| 18330 | |
| 18331 | at::Tensor & special_ndtr_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 18332 | if (false) { |
| 18333 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18334 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18335 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18336 | auto self_meta = to_meta(self); |
| 18337 | auto out_meta = to_meta(out); |
| 18338 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18339 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18340 | at::_ops::special_ndtr_out::call(self_meta, out_meta); |
| 18341 | } |
| 18342 | |
| 18343 | at::Tensor self_; |
| 18344 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18345 | at::functionalization::impl::sync(self); |
| 18346 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18347 | } else { |
| 18348 | self_ = self; |
| 18349 | } |
| 18350 | |
| 18351 | at::Tensor out_; |
| 18352 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18353 | at::functionalization::impl::sync(out); |
| 18354 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18355 | } else { |
| 18356 | out_ = out; |
| 18357 | } |
| 18358 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18359 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18360 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18361 | TORCH_INTERNAL_ASSERT(false, |
| 18362 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18363 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18364 | } else { |
| 18365 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18366 | at::AutoDispatchSkipFunctionalize guard; |
| 18367 | at::Tensor tmp_output = at::_ops::special_ndtr_out::call(self_, out_); |
| 18368 | return out;; |
| 18369 | } |
| 18370 | } else { |
| 18371 | at::Tensor tmp_output; |
| 18372 | { |
| 18373 | at::AutoDispatchSkipFunctionalize guard; |
| 18374 | tmp_output = at::_ops::special_ndtr::call(self_); |
| 18375 | } |
| 18376 | at::functionalization::impl::replace_(out, tmp_output); |
| 18377 | at::functionalization::impl::commit_update(out); |
| 18378 | at::functionalization::impl::sync(out); |
| 18379 | return out; |
| 18380 | } |
| 18381 | } |
| 18382 | |
| 18383 | at::Tensor & special_xlogy_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 18384 | if (false) { |
| 18385 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18386 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18387 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18388 | auto self_meta = to_meta(self); |
| 18389 | auto other_meta = to_meta(other); |
| 18390 | auto out_meta = to_meta(out); |
| 18391 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18392 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18393 | at::_ops::special_xlogy_out::call(self_meta, other_meta, out_meta); |
| 18394 | } |
| 18395 | |
| 18396 | at::Tensor self_; |
| 18397 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18398 | at::functionalization::impl::sync(self); |
| 18399 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18400 | } else { |
| 18401 | self_ = self; |
| 18402 | } |
| 18403 | |
| 18404 | at::Tensor other_; |
| 18405 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 18406 | at::functionalization::impl::sync(other); |
| 18407 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 18408 | } else { |
| 18409 | other_ = other; |
| 18410 | } |
| 18411 | |
| 18412 | at::Tensor out_; |
| 18413 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18414 | at::functionalization::impl::sync(out); |
| 18415 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18416 | } else { |
| 18417 | out_ = out; |
| 18418 | } |
| 18419 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18420 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 18421 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18422 | TORCH_INTERNAL_ASSERT(false, |
| 18423 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18424 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18425 | } else { |
| 18426 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18427 | at::AutoDispatchSkipFunctionalize guard; |
| 18428 | at::Tensor tmp_output = at::_ops::special_xlogy_out::call(self_, other_, out_); |
| 18429 | return out;; |
| 18430 | } |
| 18431 | } else { |
| 18432 | at::Tensor tmp_output; |
| 18433 | { |
| 18434 | at::AutoDispatchSkipFunctionalize guard; |
| 18435 | tmp_output = at::_ops::special_xlogy::call(self_, other_); |
| 18436 | } |
| 18437 | at::functionalization::impl::replace_(out, tmp_output); |
| 18438 | at::functionalization::impl::commit_update(out); |
| 18439 | at::functionalization::impl::sync(out); |
| 18440 | return out; |
| 18441 | } |
| 18442 | } |
| 18443 | |
| 18444 | at::Tensor & special_xlogy_out_self_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other, at::Tensor & out) { |
| 18445 | if (false) { |
| 18446 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18447 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18448 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18449 | auto other_meta = to_meta(other); |
| 18450 | auto out_meta = to_meta(out); |
| 18451 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18452 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18453 | at::_ops::special_xlogy_self_scalar_out::call(self, other_meta, out_meta); |
| 18454 | } |
| 18455 | |
| 18456 | at::Tensor other_; |
| 18457 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 18458 | at::functionalization::impl::sync(other); |
| 18459 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 18460 | } else { |
| 18461 | other_ = other; |
| 18462 | } |
| 18463 | |
| 18464 | at::Tensor out_; |
| 18465 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18466 | at::functionalization::impl::sync(out); |
| 18467 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18468 | } else { |
| 18469 | out_ = out; |
| 18470 | } |
| 18471 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18472 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 18473 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18474 | TORCH_INTERNAL_ASSERT(false, |
| 18475 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18476 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18477 | } else { |
| 18478 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18479 | at::AutoDispatchSkipFunctionalize guard; |
| 18480 | at::Tensor tmp_output = at::_ops::special_xlogy_self_scalar_out::call(self, other_, out_); |
| 18481 | return out;; |
| 18482 | } |
| 18483 | } else { |
| 18484 | at::Tensor tmp_output; |
| 18485 | { |
| 18486 | at::AutoDispatchSkipFunctionalize guard; |
| 18487 | tmp_output = at::_ops::special_xlogy_self_scalar::call(self, other_); |
| 18488 | } |
| 18489 | at::functionalization::impl::replace_(out, tmp_output); |
| 18490 | at::functionalization::impl::commit_update(out); |
| 18491 | at::functionalization::impl::sync(out); |
| 18492 | return out; |
| 18493 | } |
| 18494 | } |
| 18495 | |
| 18496 | at::Tensor & special_xlogy_out_other_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 18497 | if (false) { |
| 18498 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18499 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18500 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18501 | auto self_meta = to_meta(self); |
| 18502 | auto out_meta = to_meta(out); |
| 18503 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18504 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18505 | at::_ops::special_xlogy_other_scalar_out::call(self_meta, other, out_meta); |
| 18506 | } |
| 18507 | |
| 18508 | at::Tensor self_; |
| 18509 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18510 | at::functionalization::impl::sync(self); |
| 18511 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18512 | } else { |
| 18513 | self_ = self; |
| 18514 | } |
| 18515 | |
| 18516 | at::Tensor out_; |
| 18517 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18518 | at::functionalization::impl::sync(out); |
| 18519 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18520 | } else { |
| 18521 | out_ = out; |
| 18522 | } |
| 18523 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18524 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18525 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18526 | TORCH_INTERNAL_ASSERT(false, |
| 18527 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18528 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18529 | } else { |
| 18530 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18531 | at::AutoDispatchSkipFunctionalize guard; |
| 18532 | at::Tensor tmp_output = at::_ops::special_xlogy_other_scalar_out::call(self_, other, out_); |
| 18533 | return out;; |
| 18534 | } |
| 18535 | } else { |
| 18536 | at::Tensor tmp_output; |
| 18537 | { |
| 18538 | at::AutoDispatchSkipFunctionalize guard; |
| 18539 | tmp_output = at::_ops::special_xlogy_other_scalar::call(self_, other); |
| 18540 | } |
| 18541 | at::functionalization::impl::replace_(out, tmp_output); |
| 18542 | at::functionalization::impl::commit_update(out); |
| 18543 | at::functionalization::impl::sync(out); |
| 18544 | return out; |
| 18545 | } |
| 18546 | } |
| 18547 | |
| 18548 | at::Tensor & special_zeta_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 18549 | if (false) { |
| 18550 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18551 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18552 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18553 | auto self_meta = to_meta(self); |
| 18554 | auto other_meta = to_meta(other); |
| 18555 | auto out_meta = to_meta(out); |
| 18556 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18557 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18558 | at::_ops::special_zeta_out::call(self_meta, other_meta, out_meta); |
| 18559 | } |
| 18560 | |
| 18561 | at::Tensor self_; |
| 18562 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18563 | at::functionalization::impl::sync(self); |
| 18564 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18565 | } else { |
| 18566 | self_ = self; |
| 18567 | } |
| 18568 | |
| 18569 | at::Tensor other_; |
| 18570 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 18571 | at::functionalization::impl::sync(other); |
| 18572 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 18573 | } else { |
| 18574 | other_ = other; |
| 18575 | } |
| 18576 | |
| 18577 | at::Tensor out_; |
| 18578 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18579 | at::functionalization::impl::sync(out); |
| 18580 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18581 | } else { |
| 18582 | out_ = out; |
| 18583 | } |
| 18584 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18585 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 18586 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18587 | TORCH_INTERNAL_ASSERT(false, |
| 18588 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18589 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18590 | } else { |
| 18591 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18592 | at::AutoDispatchSkipFunctionalize guard; |
| 18593 | at::Tensor tmp_output = at::_ops::special_zeta_out::call(self_, other_, out_); |
| 18594 | return out;; |
| 18595 | } |
| 18596 | } else { |
| 18597 | at::Tensor tmp_output; |
| 18598 | { |
| 18599 | at::AutoDispatchSkipFunctionalize guard; |
| 18600 | tmp_output = at::_ops::special_zeta::call(self_, other_); |
| 18601 | } |
| 18602 | at::functionalization::impl::replace_(out, tmp_output); |
| 18603 | at::functionalization::impl::commit_update(out); |
| 18604 | at::functionalization::impl::sync(out); |
| 18605 | return out; |
| 18606 | } |
| 18607 | } |
| 18608 | |
| 18609 | at::Tensor & special_zeta_out_self_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other, at::Tensor & out) { |
| 18610 | if (false) { |
| 18611 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18612 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18613 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18614 | auto other_meta = to_meta(other); |
| 18615 | auto out_meta = to_meta(out); |
| 18616 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18617 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18618 | at::_ops::special_zeta_self_scalar_out::call(self, other_meta, out_meta); |
| 18619 | } |
| 18620 | |
| 18621 | at::Tensor other_; |
| 18622 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 18623 | at::functionalization::impl::sync(other); |
| 18624 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 18625 | } else { |
| 18626 | other_ = other; |
| 18627 | } |
| 18628 | |
| 18629 | at::Tensor out_; |
| 18630 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18631 | at::functionalization::impl::sync(out); |
| 18632 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18633 | } else { |
| 18634 | out_ = out; |
| 18635 | } |
| 18636 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18637 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 18638 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18639 | TORCH_INTERNAL_ASSERT(false, |
| 18640 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18641 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18642 | } else { |
| 18643 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18644 | at::AutoDispatchSkipFunctionalize guard; |
| 18645 | at::Tensor tmp_output = at::_ops::special_zeta_self_scalar_out::call(self, other_, out_); |
| 18646 | return out;; |
| 18647 | } |
| 18648 | } else { |
| 18649 | at::Tensor tmp_output; |
| 18650 | { |
| 18651 | at::AutoDispatchSkipFunctionalize guard; |
| 18652 | tmp_output = at::_ops::special_zeta_self_scalar::call(self, other_); |
| 18653 | } |
| 18654 | at::functionalization::impl::replace_(out, tmp_output); |
| 18655 | at::functionalization::impl::commit_update(out); |
| 18656 | at::functionalization::impl::sync(out); |
| 18657 | return out; |
| 18658 | } |
| 18659 | } |
| 18660 | |
| 18661 | at::Tensor & special_zeta_out_other_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 18662 | if (false) { |
| 18663 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18664 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18665 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18666 | auto self_meta = to_meta(self); |
| 18667 | auto out_meta = to_meta(out); |
| 18668 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18669 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18670 | at::_ops::special_zeta_other_scalar_out::call(self_meta, other, out_meta); |
| 18671 | } |
| 18672 | |
| 18673 | at::Tensor self_; |
| 18674 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18675 | at::functionalization::impl::sync(self); |
| 18676 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18677 | } else { |
| 18678 | self_ = self; |
| 18679 | } |
| 18680 | |
| 18681 | at::Tensor out_; |
| 18682 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18683 | at::functionalization::impl::sync(out); |
| 18684 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18685 | } else { |
| 18686 | out_ = out; |
| 18687 | } |
| 18688 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18689 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18690 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18691 | TORCH_INTERNAL_ASSERT(false, |
| 18692 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18693 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18694 | } else { |
| 18695 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18696 | at::AutoDispatchSkipFunctionalize guard; |
| 18697 | at::Tensor tmp_output = at::_ops::special_zeta_other_scalar_out::call(self_, other, out_); |
| 18698 | return out;; |
| 18699 | } |
| 18700 | } else { |
| 18701 | at::Tensor tmp_output; |
| 18702 | { |
| 18703 | at::AutoDispatchSkipFunctionalize guard; |
| 18704 | tmp_output = at::_ops::special_zeta_other_scalar::call(self_, other); |
| 18705 | } |
| 18706 | at::functionalization::impl::replace_(out, tmp_output); |
| 18707 | at::functionalization::impl::commit_update(out); |
| 18708 | at::functionalization::impl::sync(out); |
| 18709 | return out; |
| 18710 | } |
| 18711 | } |
| 18712 | |
| 18713 | at::Tensor & special_i0_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 18714 | if (false) { |
| 18715 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18716 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18717 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18718 | auto self_meta = to_meta(self); |
| 18719 | auto out_meta = to_meta(out); |
| 18720 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18721 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18722 | at::_ops::special_i0_out::call(self_meta, out_meta); |
| 18723 | } |
| 18724 | |
| 18725 | at::Tensor self_; |
| 18726 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18727 | at::functionalization::impl::sync(self); |
| 18728 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18729 | } else { |
| 18730 | self_ = self; |
| 18731 | } |
| 18732 | |
| 18733 | at::Tensor out_; |
| 18734 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18735 | at::functionalization::impl::sync(out); |
| 18736 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18737 | } else { |
| 18738 | out_ = out; |
| 18739 | } |
| 18740 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18741 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18742 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18743 | TORCH_INTERNAL_ASSERT(false, |
| 18744 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18745 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18746 | } else { |
| 18747 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18748 | at::AutoDispatchSkipFunctionalize guard; |
| 18749 | at::Tensor tmp_output = at::_ops::special_i0_out::call(self_, out_); |
| 18750 | return out;; |
| 18751 | } |
| 18752 | } else { |
| 18753 | at::Tensor tmp_output; |
| 18754 | { |
| 18755 | at::AutoDispatchSkipFunctionalize guard; |
| 18756 | tmp_output = at::_ops::special_i0::call(self_); |
| 18757 | } |
| 18758 | at::functionalization::impl::replace_(out, tmp_output); |
| 18759 | at::functionalization::impl::commit_update(out); |
| 18760 | at::functionalization::impl::sync(out); |
| 18761 | return out; |
| 18762 | } |
| 18763 | } |
| 18764 | |
| 18765 | at::Tensor & special_i0e_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 18766 | if (false) { |
| 18767 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18768 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18769 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18770 | auto self_meta = to_meta(self); |
| 18771 | auto out_meta = to_meta(out); |
| 18772 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18773 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18774 | at::_ops::special_i0e_out::call(self_meta, out_meta); |
| 18775 | } |
| 18776 | |
| 18777 | at::Tensor self_; |
| 18778 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18779 | at::functionalization::impl::sync(self); |
| 18780 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18781 | } else { |
| 18782 | self_ = self; |
| 18783 | } |
| 18784 | |
| 18785 | at::Tensor out_; |
| 18786 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18787 | at::functionalization::impl::sync(out); |
| 18788 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18789 | } else { |
| 18790 | out_ = out; |
| 18791 | } |
| 18792 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18793 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18794 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18795 | TORCH_INTERNAL_ASSERT(false, |
| 18796 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18797 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18798 | } else { |
| 18799 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18800 | at::AutoDispatchSkipFunctionalize guard; |
| 18801 | at::Tensor tmp_output = at::_ops::special_i0e_out::call(self_, out_); |
| 18802 | return out;; |
| 18803 | } |
| 18804 | } else { |
| 18805 | at::Tensor tmp_output; |
| 18806 | { |
| 18807 | at::AutoDispatchSkipFunctionalize guard; |
| 18808 | tmp_output = at::_ops::special_i0e::call(self_); |
| 18809 | } |
| 18810 | at::functionalization::impl::replace_(out, tmp_output); |
| 18811 | at::functionalization::impl::commit_update(out); |
| 18812 | at::functionalization::impl::sync(out); |
| 18813 | return out; |
| 18814 | } |
| 18815 | } |
| 18816 | |
| 18817 | at::Tensor & special_expit_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 18818 | if (false) { |
| 18819 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18820 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18821 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18822 | auto self_meta = to_meta(self); |
| 18823 | auto out_meta = to_meta(out); |
| 18824 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18825 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18826 | at::_ops::special_expit_out::call(self_meta, out_meta); |
| 18827 | } |
| 18828 | |
| 18829 | at::Tensor self_; |
| 18830 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18831 | at::functionalization::impl::sync(self); |
| 18832 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18833 | } else { |
| 18834 | self_ = self; |
| 18835 | } |
| 18836 | |
| 18837 | at::Tensor out_; |
| 18838 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18839 | at::functionalization::impl::sync(out); |
| 18840 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18841 | } else { |
| 18842 | out_ = out; |
| 18843 | } |
| 18844 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18845 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18846 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18847 | TORCH_INTERNAL_ASSERT(false, |
| 18848 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18849 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18850 | } else { |
| 18851 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18852 | at::AutoDispatchSkipFunctionalize guard; |
| 18853 | at::Tensor tmp_output = at::_ops::special_expit_out::call(self_, out_); |
| 18854 | return out;; |
| 18855 | } |
| 18856 | } else { |
| 18857 | at::Tensor tmp_output; |
| 18858 | { |
| 18859 | at::AutoDispatchSkipFunctionalize guard; |
| 18860 | tmp_output = at::_ops::special_expit::call(self_); |
| 18861 | } |
| 18862 | at::functionalization::impl::replace_(out, tmp_output); |
| 18863 | at::functionalization::impl::commit_update(out); |
| 18864 | at::functionalization::impl::sync(out); |
| 18865 | return out; |
| 18866 | } |
| 18867 | } |
| 18868 | |
| 18869 | at::Tensor & special_round_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t decimals, at::Tensor & out) { |
| 18870 | if (false) { |
| 18871 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18872 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18873 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18874 | auto self_meta = to_meta(self); |
| 18875 | auto out_meta = to_meta(out); |
| 18876 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18877 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18878 | at::_ops::special_round_out::call(self_meta, decimals, out_meta); |
| 18879 | } |
| 18880 | |
| 18881 | at::Tensor self_; |
| 18882 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18883 | at::functionalization::impl::sync(self); |
| 18884 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18885 | } else { |
| 18886 | self_ = self; |
| 18887 | } |
| 18888 | |
| 18889 | at::Tensor out_; |
| 18890 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18891 | at::functionalization::impl::sync(out); |
| 18892 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18893 | } else { |
| 18894 | out_ = out; |
| 18895 | } |
| 18896 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18897 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18898 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18899 | TORCH_INTERNAL_ASSERT(false, |
| 18900 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18901 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18902 | } else { |
| 18903 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18904 | at::AutoDispatchSkipFunctionalize guard; |
| 18905 | at::Tensor tmp_output = at::_ops::special_round_out::call(self_, decimals, out_); |
| 18906 | return out;; |
| 18907 | } |
| 18908 | } else { |
| 18909 | at::Tensor tmp_output; |
| 18910 | { |
| 18911 | at::AutoDispatchSkipFunctionalize guard; |
| 18912 | tmp_output = at::_ops::special_round::call(self_, decimals); |
| 18913 | } |
| 18914 | at::functionalization::impl::replace_(out, tmp_output); |
| 18915 | at::functionalization::impl::commit_update(out); |
| 18916 | at::functionalization::impl::sync(out); |
| 18917 | return out; |
| 18918 | } |
| 18919 | } |
| 18920 | |
| 18921 | at::Tensor & special_gammainc_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 18922 | if (false) { |
| 18923 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18924 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18925 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18926 | auto self_meta = to_meta(self); |
| 18927 | auto other_meta = to_meta(other); |
| 18928 | auto out_meta = to_meta(out); |
| 18929 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18930 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18931 | at::_ops::special_gammainc_out::call(self_meta, other_meta, out_meta); |
| 18932 | } |
| 18933 | |
| 18934 | at::Tensor self_; |
| 18935 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18936 | at::functionalization::impl::sync(self); |
| 18937 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18938 | } else { |
| 18939 | self_ = self; |
| 18940 | } |
| 18941 | |
| 18942 | at::Tensor other_; |
| 18943 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 18944 | at::functionalization::impl::sync(other); |
| 18945 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 18946 | } else { |
| 18947 | other_ = other; |
| 18948 | } |
| 18949 | |
| 18950 | at::Tensor out_; |
| 18951 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18952 | at::functionalization::impl::sync(out); |
| 18953 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18954 | } else { |
| 18955 | out_ = out; |
| 18956 | } |
| 18957 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18958 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 18959 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18960 | TORCH_INTERNAL_ASSERT(false, |
| 18961 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18962 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18963 | } else { |
| 18964 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18965 | at::AutoDispatchSkipFunctionalize guard; |
| 18966 | at::Tensor tmp_output = at::_ops::special_gammainc_out::call(self_, other_, out_); |
| 18967 | return out;; |
| 18968 | } |
| 18969 | } else { |
| 18970 | at::Tensor tmp_output; |
| 18971 | { |
| 18972 | at::AutoDispatchSkipFunctionalize guard; |
| 18973 | tmp_output = at::_ops::special_gammainc::call(self_, other_); |
| 18974 | } |
| 18975 | at::functionalization::impl::replace_(out, tmp_output); |
| 18976 | at::functionalization::impl::commit_update(out); |
| 18977 | at::functionalization::impl::sync(out); |
| 18978 | return out; |
| 18979 | } |
| 18980 | } |
| 18981 | |
| 18982 | at::Tensor & fft_irfft_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) { |
| 18983 | if (false) { |
| 18984 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18985 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18986 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18987 | auto self_meta = to_meta(self); |
| 18988 | auto out_meta = to_meta(out); |
| 18989 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18990 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18991 | at::_ops::fft_irfft_out::call(self_meta, n, dim, norm, out_meta); |
| 18992 | } |
| 18993 | |
| 18994 | at::Tensor self_; |
| 18995 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18996 | at::functionalization::impl::sync(self); |
| 18997 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18998 | } else { |
| 18999 | self_ = self; |
| 19000 | } |
| 19001 | |
| 19002 | at::Tensor out_; |
| 19003 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19004 | at::functionalization::impl::sync(out); |
| 19005 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19006 | } else { |
| 19007 | out_ = out; |
| 19008 | } |
| 19009 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19010 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 19011 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19012 | TORCH_INTERNAL_ASSERT(false, |
| 19013 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19014 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19015 | } else { |
| 19016 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19017 | at::AutoDispatchSkipFunctionalize guard; |
| 19018 | at::Tensor tmp_output = at::_ops::fft_irfft_out::call(self_, n, dim, norm, out_); |
| 19019 | return out;; |
| 19020 | } |
| 19021 | } else { |
| 19022 | at::Tensor tmp_output; |
| 19023 | { |
| 19024 | at::AutoDispatchSkipFunctionalize guard; |
| 19025 | tmp_output = at::_ops::fft_irfft::call(self_, n, dim, norm); |
| 19026 | } |
| 19027 | at::functionalization::impl::replace_(out, tmp_output); |
| 19028 | at::functionalization::impl::commit_update(out); |
| 19029 | at::functionalization::impl::sync(out); |
| 19030 | return out; |
| 19031 | } |
| 19032 | } |
| 19033 | |
| 19034 | at::Tensor & fft_fft2_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional<c10::string_view> norm, at::Tensor & out) { |
| 19035 | if (false) { |
| 19036 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19037 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19038 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19039 | auto self_meta = to_meta(self); |
| 19040 | auto out_meta = to_meta(out); |
| 19041 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19042 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19043 | at::_ops::fft_fft2_out::call(self_meta, s, dim, norm, out_meta); |
| 19044 | } |
| 19045 | |
| 19046 | at::Tensor self_; |
| 19047 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19048 | at::functionalization::impl::sync(self); |
| 19049 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19050 | } else { |
| 19051 | self_ = self; |
| 19052 | } |
| 19053 | |
| 19054 | at::Tensor out_; |
| 19055 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19056 | at::functionalization::impl::sync(out); |
| 19057 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19058 | } else { |
| 19059 | out_ = out; |
| 19060 | } |
| 19061 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19062 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 19063 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19064 | TORCH_INTERNAL_ASSERT(false, |
| 19065 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19066 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19067 | } else { |
| 19068 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19069 | at::AutoDispatchSkipFunctionalize guard; |
| 19070 | at::Tensor tmp_output = at::_ops::fft_fft2_out::call(self_, s, dim, norm, out_); |
| 19071 | return out;; |
| 19072 | } |
| 19073 | } else { |
| 19074 | at::Tensor tmp_output; |
| 19075 | { |
| 19076 | at::AutoDispatchSkipFunctionalize guard; |
| 19077 | tmp_output = at::_ops::fft_fft2::call(self_, s, dim, norm); |
| 19078 | } |
| 19079 | at::functionalization::impl::replace_(out, tmp_output); |
| 19080 | at::functionalization::impl::commit_update(out); |
| 19081 | at::functionalization::impl::sync(out); |
| 19082 | return out; |
| 19083 | } |
| 19084 | } |
| 19085 | |
| 19086 | at::Tensor & fft_rfftfreq_out_out(c10::DispatchKeySet dispatchKeySet, int64_t n, double d, at::Tensor & out) { |
| 19087 | if (false) { |
| 19088 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19089 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19090 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19091 | auto out_meta = to_meta(out); |
| 19092 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19093 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19094 | at::_ops::fft_rfftfreq_out::call(n, d, out_meta); |
| 19095 | } |
| 19096 | |
| 19097 | at::Tensor out_; |
| 19098 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19099 | at::functionalization::impl::sync(out); |
| 19100 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19101 | } else { |
| 19102 | out_ = out; |
| 19103 | } |
| 19104 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19105 | if ((false)) { |
| 19106 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19107 | TORCH_INTERNAL_ASSERT(false, |
| 19108 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19109 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19110 | } else { |
| 19111 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19112 | at::AutoDispatchSkipFunctionalize guard; |
| 19113 | at::Tensor tmp_output = at::_ops::fft_rfftfreq_out::call(n, d, out_); |
| 19114 | return out;; |
| 19115 | } |
| 19116 | } else { |
| 19117 | at::Tensor tmp_output; |
| 19118 | { |
| 19119 | at::AutoDispatchSkipFunctionalize guard; |
| 19120 | tmp_output = at::_ops::fft_rfftfreq::call(n, d, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 19121 | } |
| 19122 | at::functionalization::impl::replace_(out, tmp_output); |
| 19123 | at::functionalization::impl::commit_update(out); |
| 19124 | at::functionalization::impl::sync(out); |
| 19125 | return out; |
| 19126 | } |
| 19127 | } |
| 19128 | |
| 19129 | at::Tensor & linalg_cholesky_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool upper, at::Tensor & out) { |
| 19130 | if (false) { |
| 19131 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19132 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19133 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19134 | auto self_meta = to_meta(self); |
| 19135 | auto out_meta = to_meta(out); |
| 19136 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19137 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19138 | at::_ops::linalg_cholesky_out::call(self_meta, upper, out_meta); |
| 19139 | } |
| 19140 | |
| 19141 | at::Tensor self_; |
| 19142 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19143 | at::functionalization::impl::sync(self); |
| 19144 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19145 | } else { |
| 19146 | self_ = self; |
| 19147 | } |
| 19148 | |
| 19149 | at::Tensor out_; |
| 19150 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19151 | at::functionalization::impl::sync(out); |
| 19152 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19153 | } else { |
| 19154 | out_ = out; |
| 19155 | } |
| 19156 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19157 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 19158 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19159 | TORCH_INTERNAL_ASSERT(false, |
| 19160 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19161 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19162 | } else { |
| 19163 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19164 | at::AutoDispatchSkipFunctionalize guard; |
| 19165 | at::Tensor tmp_output = at::_ops::linalg_cholesky_out::call(self_, upper, out_); |
| 19166 | return out;; |
| 19167 | } |
| 19168 | } else { |
| 19169 | at::Tensor tmp_output; |
| 19170 | { |
| 19171 | at::AutoDispatchSkipFunctionalize guard; |
| 19172 | tmp_output = at::_ops::linalg_cholesky::call(self_, upper); |
| 19173 | } |
| 19174 | at::functionalization::impl::replace_(out, tmp_output); |
| 19175 | at::functionalization::impl::commit_update(out); |
| 19176 | at::functionalization::impl::sync(out); |
| 19177 | return out; |
| 19178 | } |
| 19179 | } |
| 19180 | |
| 19181 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linalg_lu_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, bool pivot, at::Tensor & P, at::Tensor & L, at::Tensor & U) { |
| 19182 | if (false) { |
| 19183 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19184 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19185 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19186 | auto A_meta = to_meta(A); |
| 19187 | auto P_meta = to_meta(P); |
| 19188 | auto L_meta = to_meta(L); |
| 19189 | auto U_meta = to_meta(U); |
| 19190 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19191 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19192 | at::_ops::linalg_lu_out::call(A_meta, pivot, P_meta, L_meta, U_meta); |
| 19193 | } |
| 19194 | |
| 19195 | at::Tensor A_; |
| 19196 | if (at::functionalization::impl::isFunctionalTensor(A)) { |
| 19197 | at::functionalization::impl::sync(A); |
| 19198 | A_ = at::functionalization::impl::from_functional_tensor(A); |
| 19199 | } else { |
| 19200 | A_ = A; |
| 19201 | } |
| 19202 | |
| 19203 | at::Tensor P_; |
| 19204 | if (at::functionalization::impl::isFunctionalTensor(P)) { |
| 19205 | at::functionalization::impl::sync(P); |
| 19206 | P_ = at::functionalization::impl::from_functional_tensor(P); |
| 19207 | } else { |
| 19208 | P_ = P; |
| 19209 | } |
| 19210 | |
| 19211 | at::Tensor L_; |
| 19212 | if (at::functionalization::impl::isFunctionalTensor(L)) { |
| 19213 | at::functionalization::impl::sync(L); |
| 19214 | L_ = at::functionalization::impl::from_functional_tensor(L); |
| 19215 | } else { |
| 19216 | L_ = L; |
| 19217 | } |
| 19218 | |
| 19219 | at::Tensor U_; |
| 19220 | if (at::functionalization::impl::isFunctionalTensor(U)) { |
| 19221 | at::functionalization::impl::sync(U); |
| 19222 | U_ = at::functionalization::impl::from_functional_tensor(U); |
| 19223 | } else { |
| 19224 | U_ = U; |
| 19225 | } |
| 19226 | if (!(true && at::functionalization::impl::isFunctionalTensor(P) && at::functionalization::impl::isFunctionalTensor(L) && at::functionalization::impl::isFunctionalTensor(U))) { |
| 19227 | if ((false || at::functionalization::impl::isFunctionalTensor(A))) { |
| 19228 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19229 | TORCH_INTERNAL_ASSERT(false, |
| 19230 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19231 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19232 | } else { |
| 19233 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19234 | at::AutoDispatchSkipFunctionalize guard; |
| 19235 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::linalg_lu_out::call(A_, pivot, P_, L_, U_); |
| 19236 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(P, L, U);; |
| 19237 | } |
| 19238 | } else { |
| 19239 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 19240 | { |
| 19241 | at::AutoDispatchSkipFunctionalize guard; |
| 19242 | tmp_output = at::_ops::linalg_lu::call(A_, pivot); |
| 19243 | } |
| 19244 | at::functionalization::impl::replace_(P, std::get<0>(tmp_output)); |
| 19245 | at::functionalization::impl::commit_update(P); |
| 19246 | at::functionalization::impl::sync(P); |
| 19247 | at::functionalization::impl::replace_(L, std::get<1>(tmp_output)); |
| 19248 | at::functionalization::impl::commit_update(L); |
| 19249 | at::functionalization::impl::sync(L); |
| 19250 | at::functionalization::impl::replace_(U, std::get<2>(tmp_output)); |
| 19251 | at::functionalization::impl::commit_update(U); |
| 19252 | at::functionalization::impl::sync(U); |
| 19253 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(P, L, U); |
| 19254 | } |
| 19255 | } |
| 19256 | |
| 19257 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _linalg_det_out_result(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, at::Tensor & result, at::Tensor & LU, at::Tensor & pivots) { |
| 19258 | if (false) { |
| 19259 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19260 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19261 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19262 | auto A_meta = to_meta(A); |
| 19263 | auto result_meta = to_meta(result); |
| 19264 | auto LU_meta = to_meta(LU); |
| 19265 | auto pivots_meta = to_meta(pivots); |
| 19266 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19267 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19268 | at::_ops::_linalg_det_result::call(A_meta, result_meta, LU_meta, pivots_meta); |
| 19269 | } |
| 19270 | |
| 19271 | at::Tensor A_; |
| 19272 | if (at::functionalization::impl::isFunctionalTensor(A)) { |
| 19273 | at::functionalization::impl::sync(A); |
| 19274 | A_ = at::functionalization::impl::from_functional_tensor(A); |
| 19275 | } else { |
| 19276 | A_ = A; |
| 19277 | } |
| 19278 | |
| 19279 | at::Tensor result_; |
| 19280 | if (at::functionalization::impl::isFunctionalTensor(result)) { |
| 19281 | at::functionalization::impl::sync(result); |
| 19282 | result_ = at::functionalization::impl::from_functional_tensor(result); |
| 19283 | } else { |
| 19284 | result_ = result; |
| 19285 | } |
| 19286 | |
| 19287 | at::Tensor LU_; |
| 19288 | if (at::functionalization::impl::isFunctionalTensor(LU)) { |
| 19289 | at::functionalization::impl::sync(LU); |
| 19290 | LU_ = at::functionalization::impl::from_functional_tensor(LU); |
| 19291 | } else { |
| 19292 | LU_ = LU; |
| 19293 | } |
| 19294 | |
| 19295 | at::Tensor pivots_; |
| 19296 | if (at::functionalization::impl::isFunctionalTensor(pivots)) { |
| 19297 | at::functionalization::impl::sync(pivots); |
| 19298 | pivots_ = at::functionalization::impl::from_functional_tensor(pivots); |
| 19299 | } else { |
| 19300 | pivots_ = pivots; |
| 19301 | } |
| 19302 | if (!(true && at::functionalization::impl::isFunctionalTensor(result) && at::functionalization::impl::isFunctionalTensor(LU) && at::functionalization::impl::isFunctionalTensor(pivots))) { |
| 19303 | if ((false || at::functionalization::impl::isFunctionalTensor(A))) { |
| 19304 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19305 | TORCH_INTERNAL_ASSERT(false, |
| 19306 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19307 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19308 | } else { |
| 19309 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19310 | at::AutoDispatchSkipFunctionalize guard; |
| 19311 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::_linalg_det_result::call(A_, result_, LU_, pivots_); |
| 19312 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(result, LU, pivots);; |
| 19313 | } |
| 19314 | } else { |
| 19315 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 19316 | { |
| 19317 | at::AutoDispatchSkipFunctionalize guard; |
| 19318 | tmp_output = at::_ops::_linalg_det::call(A_); |
| 19319 | } |
| 19320 | at::functionalization::impl::replace_(result, std::get<0>(tmp_output)); |
| 19321 | at::functionalization::impl::commit_update(result); |
| 19322 | at::functionalization::impl::sync(result); |
| 19323 | at::functionalization::impl::replace_(LU, std::get<1>(tmp_output)); |
| 19324 | at::functionalization::impl::commit_update(LU); |
| 19325 | at::functionalization::impl::sync(LU); |
| 19326 | at::functionalization::impl::replace_(pivots, std::get<2>(tmp_output)); |
| 19327 | at::functionalization::impl::commit_update(pivots); |
| 19328 | at::functionalization::impl::sync(pivots); |
| 19329 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(result, LU, pivots); |
| 19330 | } |
| 19331 | } |
| 19332 | |
| 19333 | ::std::tuple<at::Tensor &,at::Tensor &> slogdet_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & sign, at::Tensor & logabsdet) { |
| 19334 | if (false) { |
| 19335 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19336 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19337 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19338 | auto self_meta = to_meta(self); |
| 19339 | auto sign_meta = to_meta(sign); |
| 19340 | auto logabsdet_meta = to_meta(logabsdet); |
| 19341 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19342 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19343 | at::_ops::slogdet_out::call(self_meta, sign_meta, logabsdet_meta); |
| 19344 | } |
| 19345 | |
| 19346 | at::Tensor self_; |
| 19347 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19348 | at::functionalization::impl::sync(self); |
| 19349 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19350 | } else { |
| 19351 | self_ = self; |
| 19352 | } |
| 19353 | |
| 19354 | at::Tensor sign_; |
| 19355 | if (at::functionalization::impl::isFunctionalTensor(sign)) { |
| 19356 | at::functionalization::impl::sync(sign); |
| 19357 | sign_ = at::functionalization::impl::from_functional_tensor(sign); |
| 19358 | } else { |
| 19359 | sign_ = sign; |
| 19360 | } |
| 19361 | |
| 19362 | at::Tensor logabsdet_; |
| 19363 | if (at::functionalization::impl::isFunctionalTensor(logabsdet)) { |
| 19364 | at::functionalization::impl::sync(logabsdet); |
| 19365 | logabsdet_ = at::functionalization::impl::from_functional_tensor(logabsdet); |
| 19366 | } else { |
| 19367 | logabsdet_ = logabsdet; |
| 19368 | } |
| 19369 | if (!(true && at::functionalization::impl::isFunctionalTensor(sign) && at::functionalization::impl::isFunctionalTensor(logabsdet))) { |
| 19370 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 19371 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19372 | TORCH_INTERNAL_ASSERT(false, |
| 19373 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19374 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19375 | } else { |
| 19376 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19377 | at::AutoDispatchSkipFunctionalize guard; |
| 19378 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::slogdet_out::call(self_, sign_, logabsdet_); |
| 19379 | return ::std::tuple<at::Tensor &,at::Tensor &>(sign, logabsdet);; |
| 19380 | } |
| 19381 | } else { |
| 19382 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 19383 | { |
| 19384 | at::AutoDispatchSkipFunctionalize guard; |
| 19385 | tmp_output = at::_ops::slogdet::call(self_); |
| 19386 | } |
| 19387 | at::functionalization::impl::replace_(sign, std::get<0>(tmp_output)); |
| 19388 | at::functionalization::impl::commit_update(sign); |
| 19389 | at::functionalization::impl::sync(sign); |
| 19390 | at::functionalization::impl::replace_(logabsdet, std::get<1>(tmp_output)); |
| 19391 | at::functionalization::impl::commit_update(logabsdet); |
| 19392 | at::functionalization::impl::sync(logabsdet); |
| 19393 | return ::std::tuple<at::Tensor &,at::Tensor &>(sign, logabsdet); |
| 19394 | } |
| 19395 | } |
| 19396 | |
| 19397 | ::std::tuple<at::Tensor &,at::Tensor &> linalg_eig_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & eigenvalues, at::Tensor & eigenvectors) { |
| 19398 | if (false) { |
| 19399 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19400 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19401 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19402 | auto self_meta = to_meta(self); |
| 19403 | auto eigenvalues_meta = to_meta(eigenvalues); |
| 19404 | auto eigenvectors_meta = to_meta(eigenvectors); |
| 19405 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19406 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19407 | at::_ops::linalg_eig_out::call(self_meta, eigenvalues_meta, eigenvectors_meta); |
| 19408 | } |
| 19409 | |
| 19410 | at::Tensor self_; |
| 19411 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19412 | at::functionalization::impl::sync(self); |
| 19413 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19414 | } else { |
| 19415 | self_ = self; |
| 19416 | } |
| 19417 | |
| 19418 | at::Tensor eigenvalues_; |
| 19419 | if (at::functionalization::impl::isFunctionalTensor(eigenvalues)) { |
| 19420 | at::functionalization::impl::sync(eigenvalues); |
| 19421 | eigenvalues_ = at::functionalization::impl::from_functional_tensor(eigenvalues); |
| 19422 | } else { |
| 19423 | eigenvalues_ = eigenvalues; |
| 19424 | } |
| 19425 | |
| 19426 | at::Tensor eigenvectors_; |
| 19427 | if (at::functionalization::impl::isFunctionalTensor(eigenvectors)) { |
| 19428 | at::functionalization::impl::sync(eigenvectors); |
| 19429 | eigenvectors_ = at::functionalization::impl::from_functional_tensor(eigenvectors); |
| 19430 | } else { |
| 19431 | eigenvectors_ = eigenvectors; |
| 19432 | } |
| 19433 | if (!(true && at::functionalization::impl::isFunctionalTensor(eigenvalues) && at::functionalization::impl::isFunctionalTensor(eigenvectors))) { |
| 19434 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 19435 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19436 | TORCH_INTERNAL_ASSERT(false, |
| 19437 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19438 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19439 | } else { |
| 19440 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19441 | at::AutoDispatchSkipFunctionalize guard; |
| 19442 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::linalg_eig_out::call(self_, eigenvalues_, eigenvectors_); |
| 19443 | return ::std::tuple<at::Tensor &,at::Tensor &>(eigenvalues, eigenvectors);; |
| 19444 | } |
| 19445 | } else { |
| 19446 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 19447 | { |
| 19448 | at::AutoDispatchSkipFunctionalize guard; |
| 19449 | tmp_output = at::_ops::linalg_eig::call(self_); |
| 19450 | } |
| 19451 | at::functionalization::impl::replace_(eigenvalues, std::get<0>(tmp_output)); |
| 19452 | at::functionalization::impl::commit_update(eigenvalues); |
| 19453 | at::functionalization::impl::sync(eigenvalues); |
| 19454 | at::functionalization::impl::replace_(eigenvectors, std::get<1>(tmp_output)); |
| 19455 | at::functionalization::impl::commit_update(eigenvectors); |
| 19456 | at::functionalization::impl::sync(eigenvectors); |
| 19457 | return ::std::tuple<at::Tensor &,at::Tensor &>(eigenvalues, eigenvectors); |
| 19458 | } |
| 19459 | } |
| 19460 | |
| 19461 | at::Tensor & linalg_inv_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, at::Tensor & out) { |
| 19462 | if (false) { |
| 19463 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19464 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19465 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19466 | auto A_meta = to_meta(A); |
| 19467 | auto out_meta = to_meta(out); |
| 19468 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19469 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19470 | at::_ops::linalg_inv_out::call(A_meta, out_meta); |
| 19471 | } |
| 19472 | |
| 19473 | at::Tensor A_; |
| 19474 | if (at::functionalization::impl::isFunctionalTensor(A)) { |
| 19475 | at::functionalization::impl::sync(A); |
| 19476 | A_ = at::functionalization::impl::from_functional_tensor(A); |
| 19477 | } else { |
| 19478 | A_ = A; |
| 19479 | } |
| 19480 | |
| 19481 | at::Tensor out_; |
| 19482 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19483 | at::functionalization::impl::sync(out); |
| 19484 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19485 | } else { |
| 19486 | out_ = out; |
| 19487 | } |
| 19488 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19489 | if ((false || at::functionalization::impl::isFunctionalTensor(A))) { |
| 19490 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19491 | TORCH_INTERNAL_ASSERT(false, |
| 19492 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19493 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19494 | } else { |
| 19495 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19496 | at::AutoDispatchSkipFunctionalize guard; |
| 19497 | at::Tensor tmp_output = at::_ops::linalg_inv_out::call(A_, out_); |
| 19498 | return out;; |
| 19499 | } |
| 19500 | } else { |
| 19501 | at::Tensor tmp_output; |
| 19502 | { |
| 19503 | at::AutoDispatchSkipFunctionalize guard; |
| 19504 | tmp_output = at::_ops::linalg_inv::call(A_); |
| 19505 | } |
| 19506 | at::functionalization::impl::replace_(out, tmp_output); |
| 19507 | at::functionalization::impl::commit_update(out); |
| 19508 | at::functionalization::impl::sync(out); |
| 19509 | return out; |
| 19510 | } |
| 19511 | } |
| 19512 | |
| 19513 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _linalg_svd_out_U(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, bool full_matrices, bool compute_uv, c10::optional<c10::string_view> driver, at::Tensor & U, at::Tensor & S, at::Tensor & Vh) { |
| 19514 | if (false) { |
| 19515 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19516 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19517 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19518 | auto A_meta = to_meta(A); |
| 19519 | auto U_meta = to_meta(U); |
| 19520 | auto S_meta = to_meta(S); |
| 19521 | auto Vh_meta = to_meta(Vh); |
| 19522 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19523 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19524 | at::_ops::_linalg_svd_U::call(A_meta, full_matrices, compute_uv, driver, U_meta, S_meta, Vh_meta); |
| 19525 | } |
| 19526 | |
| 19527 | at::Tensor A_; |
| 19528 | if (at::functionalization::impl::isFunctionalTensor(A)) { |
| 19529 | at::functionalization::impl::sync(A); |
| 19530 | A_ = at::functionalization::impl::from_functional_tensor(A); |
| 19531 | } else { |
| 19532 | A_ = A; |
| 19533 | } |
| 19534 | |
| 19535 | at::Tensor U_; |
| 19536 | if (at::functionalization::impl::isFunctionalTensor(U)) { |
| 19537 | at::functionalization::impl::sync(U); |
| 19538 | U_ = at::functionalization::impl::from_functional_tensor(U); |
| 19539 | } else { |
| 19540 | U_ = U; |
| 19541 | } |
| 19542 | |
| 19543 | at::Tensor S_; |
| 19544 | if (at::functionalization::impl::isFunctionalTensor(S)) { |
| 19545 | at::functionalization::impl::sync(S); |
| 19546 | S_ = at::functionalization::impl::from_functional_tensor(S); |
| 19547 | } else { |
| 19548 | S_ = S; |
| 19549 | } |
| 19550 | |
| 19551 | at::Tensor Vh_; |
| 19552 | if (at::functionalization::impl::isFunctionalTensor(Vh)) { |
| 19553 | at::functionalization::impl::sync(Vh); |
| 19554 | Vh_ = at::functionalization::impl::from_functional_tensor(Vh); |
| 19555 | } else { |
| 19556 | Vh_ = Vh; |
| 19557 | } |
| 19558 | if (!(true && at::functionalization::impl::isFunctionalTensor(U) && at::functionalization::impl::isFunctionalTensor(S) && at::functionalization::impl::isFunctionalTensor(Vh))) { |
| 19559 | if ((false || at::functionalization::impl::isFunctionalTensor(A))) { |
| 19560 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19561 | TORCH_INTERNAL_ASSERT(false, |
| 19562 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19563 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19564 | } else { |
| 19565 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19566 | at::AutoDispatchSkipFunctionalize guard; |
| 19567 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::_linalg_svd_U::call(A_, full_matrices, compute_uv, driver, U_, S_, Vh_); |
| 19568 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(U, S, Vh);; |
| 19569 | } |
| 19570 | } else { |
| 19571 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 19572 | { |
| 19573 | at::AutoDispatchSkipFunctionalize guard; |
| 19574 | tmp_output = at::_ops::_linalg_svd::call(A_, full_matrices, compute_uv, driver); |
| 19575 | } |
| 19576 | at::functionalization::impl::replace_(U, std::get<0>(tmp_output)); |
| 19577 | at::functionalization::impl::commit_update(U); |
| 19578 | at::functionalization::impl::sync(U); |
| 19579 | at::functionalization::impl::replace_(S, std::get<1>(tmp_output)); |
| 19580 | at::functionalization::impl::commit_update(S); |
| 19581 | at::functionalization::impl::sync(S); |
| 19582 | at::functionalization::impl::replace_(Vh, std::get<2>(tmp_output)); |
| 19583 | at::functionalization::impl::commit_update(Vh); |
| 19584 | at::functionalization::impl::sync(Vh); |
| 19585 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(U, S, Vh); |
| 19586 | } |
| 19587 | } |
| 19588 | |
| 19589 | at::Tensor & linalg_svdvals_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, c10::optional<c10::string_view> driver, at::Tensor & out) { |
| 19590 | if (false) { |
| 19591 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19592 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19593 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19594 | auto A_meta = to_meta(A); |
| 19595 | auto out_meta = to_meta(out); |
| 19596 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19597 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19598 | at::_ops::linalg_svdvals_out::call(A_meta, driver, out_meta); |
| 19599 | } |
| 19600 | |
| 19601 | at::Tensor A_; |
| 19602 | if (at::functionalization::impl::isFunctionalTensor(A)) { |
| 19603 | at::functionalization::impl::sync(A); |
| 19604 | A_ = at::functionalization::impl::from_functional_tensor(A); |
| 19605 | } else { |
| 19606 | A_ = A; |
| 19607 | } |
| 19608 | |
| 19609 | at::Tensor out_; |
| 19610 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19611 | at::functionalization::impl::sync(out); |
| 19612 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19613 | } else { |
| 19614 | out_ = out; |
| 19615 | } |
| 19616 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19617 | if ((false || at::functionalization::impl::isFunctionalTensor(A))) { |
| 19618 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19619 | TORCH_INTERNAL_ASSERT(false, |
| 19620 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19621 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19622 | } else { |
| 19623 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19624 | at::AutoDispatchSkipFunctionalize guard; |
| 19625 | at::Tensor tmp_output = at::_ops::linalg_svdvals_out::call(A_, driver, out_); |
| 19626 | return out;; |
| 19627 | } |
| 19628 | } else { |
| 19629 | at::Tensor tmp_output; |
| 19630 | { |
| 19631 | at::AutoDispatchSkipFunctionalize guard; |
| 19632 | tmp_output = at::_ops::linalg_svdvals::call(A_, driver); |
| 19633 | } |
| 19634 | at::functionalization::impl::replace_(out, tmp_output); |
| 19635 | at::functionalization::impl::commit_update(out); |
| 19636 | at::functionalization::impl::sync(out); |
| 19637 | return out; |
| 19638 | } |
| 19639 | } |
| 19640 | |
| 19641 | at::Tensor & linalg_pinv_out_atol_rtol_tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional<at::Tensor> & atol, const c10::optional<at::Tensor> & rtol, bool hermitian, at::Tensor & out) { |
| 19642 | if (false) { |
| 19643 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19644 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19645 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19646 | auto self_meta = to_meta(self); |
| 19647 | auto atol_meta = to_meta(atol); |
| 19648 | auto rtol_meta = to_meta(rtol); |
| 19649 | auto out_meta = to_meta(out); |
| 19650 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19651 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19652 | at::_ops::linalg_pinv_atol_rtol_tensor_out::call(self_meta, atol_meta, rtol_meta, hermitian, out_meta); |
| 19653 | } |
| 19654 | |
| 19655 | at::Tensor self_; |
| 19656 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19657 | at::functionalization::impl::sync(self); |
| 19658 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19659 | } else { |
| 19660 | self_ = self; |
| 19661 | } |
| 19662 | |
| 19663 | c10::optional<at::Tensor> atol_; |
| 19664 | if (at::functionalization::impl::isFunctionalTensor(atol)) { |
| 19665 | at::functionalization::impl::sync(atol); |
| 19666 | atol_ = at::functionalization::impl::from_functional_tensor(atol); |
| 19667 | } else { |
| 19668 | atol_ = atol; |
| 19669 | } |
| 19670 | |
| 19671 | c10::optional<at::Tensor> rtol_; |
| 19672 | if (at::functionalization::impl::isFunctionalTensor(rtol)) { |
| 19673 | at::functionalization::impl::sync(rtol); |
| 19674 | rtol_ = at::functionalization::impl::from_functional_tensor(rtol); |
| 19675 | } else { |
| 19676 | rtol_ = rtol; |
| 19677 | } |
| 19678 | |
| 19679 | at::Tensor out_; |
| 19680 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19681 | at::functionalization::impl::sync(out); |
| 19682 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19683 | } else { |
| 19684 | out_ = out; |
| 19685 | } |
| 19686 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19687 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(atol) || at::functionalization::impl::isFunctionalTensor(rtol))) { |
| 19688 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19689 | TORCH_INTERNAL_ASSERT(false, |
| 19690 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19691 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19692 | } else { |
| 19693 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19694 | at::AutoDispatchSkipFunctionalize guard; |
| 19695 | at::Tensor tmp_output = at::_ops::linalg_pinv_atol_rtol_tensor_out::call(self_, atol_, rtol_, hermitian, out_); |
| 19696 | return out;; |
| 19697 | } |
| 19698 | } else { |
| 19699 | at::Tensor tmp_output; |
| 19700 | { |
| 19701 | at::AutoDispatchSkipFunctionalize guard; |
| 19702 | tmp_output = at::_ops::linalg_pinv_atol_rtol_tensor::call(self_, atol_, rtol_, hermitian); |
| 19703 | } |
| 19704 | at::functionalization::impl::replace_(out, tmp_output); |
| 19705 | at::functionalization::impl::commit_update(out); |
| 19706 | at::functionalization::impl::sync(out); |
| 19707 | return out; |
| 19708 | } |
| 19709 | } |
| 19710 | |
| 19711 | at::Tensor & linalg_pinv_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) { |
| 19712 | if (false) { |
| 19713 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19714 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19715 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19716 | auto self_meta = to_meta(self); |
| 19717 | auto out_meta = to_meta(out); |
| 19718 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19719 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19720 | at::_ops::linalg_pinv_atol_rtol_float_out::call(self_meta, atol, rtol, hermitian, out_meta); |
| 19721 | } |
| 19722 | |
| 19723 | at::Tensor self_; |
| 19724 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19725 | at::functionalization::impl::sync(self); |
| 19726 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19727 | } else { |
| 19728 | self_ = self; |
| 19729 | } |
| 19730 | |
| 19731 | at::Tensor out_; |
| 19732 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19733 | at::functionalization::impl::sync(out); |
| 19734 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19735 | } else { |
| 19736 | out_ = out; |
| 19737 | } |
| 19738 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19739 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 19740 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19741 | TORCH_INTERNAL_ASSERT(false, |
| 19742 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19743 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19744 | } else { |
| 19745 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19746 | at::AutoDispatchSkipFunctionalize guard; |
| 19747 | at::Tensor tmp_output = at::_ops::linalg_pinv_atol_rtol_float_out::call(self_, atol, rtol, hermitian, out_); |
| 19748 | return out;; |
| 19749 | } |
| 19750 | } else { |
| 19751 | at::Tensor tmp_output; |
| 19752 | { |
| 19753 | at::AutoDispatchSkipFunctionalize guard; |
| 19754 | tmp_output = at::_ops::linalg_pinv_atol_rtol_float::call(self_, atol, rtol, hermitian); |
| 19755 | } |
| 19756 | at::functionalization::impl::replace_(out, tmp_output); |
| 19757 | at::functionalization::impl::commit_update(out); |
| 19758 | at::functionalization::impl::sync(out); |
| 19759 | return out; |
| 19760 | } |
| 19761 | } |
| 19762 | |
| 19763 | at::Tensor & linalg_pinv_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double rcond, bool hermitian, at::Tensor & out) { |
| 19764 | if (false) { |
| 19765 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19766 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19767 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19768 | auto self_meta = to_meta(self); |
| 19769 | auto out_meta = to_meta(out); |
| 19770 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19771 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19772 | at::_ops::linalg_pinv_out::call(self_meta, rcond, hermitian, out_meta); |
| 19773 | } |
| 19774 | |
| 19775 | at::Tensor self_; |
| 19776 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19777 | at::functionalization::impl::sync(self); |
| 19778 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19779 | } else { |
| 19780 | self_ = self; |
| 19781 | } |
| 19782 | |
| 19783 | at::Tensor out_; |
| 19784 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19785 | at::functionalization::impl::sync(out); |
| 19786 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19787 | } else { |
| 19788 | out_ = out; |
| 19789 | } |
| 19790 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19791 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 19792 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19793 | TORCH_INTERNAL_ASSERT(false, |
| 19794 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19795 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19796 | } else { |
| 19797 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19798 | at::AutoDispatchSkipFunctionalize guard; |
| 19799 | at::Tensor tmp_output = at::_ops::linalg_pinv_out::call(self_, rcond, hermitian, out_); |
| 19800 | return out;; |
| 19801 | } |
| 19802 | } else { |
| 19803 | at::Tensor tmp_output; |
| 19804 | { |
| 19805 | at::AutoDispatchSkipFunctionalize guard; |
| 19806 | tmp_output = at::_ops::linalg_pinv::call(self_, rcond, hermitian); |
| 19807 | } |
| 19808 | at::functionalization::impl::replace_(out, tmp_output); |
| 19809 | at::functionalization::impl::commit_update(out); |
| 19810 | at::functionalization::impl::sync(out); |
| 19811 | return out; |
| 19812 | } |
| 19813 | } |
| 19814 | |
| 19815 | at::Tensor & linalg_pinv_out_out_rcond_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & rcond, bool hermitian, at::Tensor & out) { |
| 19816 | if (false) { |
| 19817 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19818 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19819 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19820 | auto self_meta = to_meta(self); |
| 19821 | auto rcond_meta = to_meta(rcond); |
| 19822 | auto out_meta = to_meta(out); |
| 19823 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19824 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19825 | at::_ops::linalg_pinv_out_rcond_tensor::call(self_meta, rcond_meta, hermitian, out_meta); |
| 19826 | } |
| 19827 | |
| 19828 | at::Tensor self_; |
| 19829 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19830 | at::functionalization::impl::sync(self); |
| 19831 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19832 | } else { |
| 19833 | self_ = self; |
| 19834 | } |
| 19835 | |
| 19836 | at::Tensor rcond_; |
| 19837 | if (at::functionalization::impl::isFunctionalTensor(rcond)) { |
| 19838 | at::functionalization::impl::sync(rcond); |
| 19839 | rcond_ = at::functionalization::impl::from_functional_tensor(rcond); |
| 19840 | } else { |
| 19841 | rcond_ = rcond; |
| 19842 | } |
| 19843 | |
| 19844 | at::Tensor out_; |
| 19845 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19846 | at::functionalization::impl::sync(out); |
| 19847 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19848 | } else { |
| 19849 | out_ = out; |
| 19850 | } |
| 19851 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19852 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(rcond))) { |
| 19853 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19854 | TORCH_INTERNAL_ASSERT(false, |
| 19855 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19856 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19857 | } else { |
| 19858 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19859 | at::AutoDispatchSkipFunctionalize guard; |
| 19860 | at::Tensor tmp_output = at::_ops::linalg_pinv_out_rcond_tensor::call(self_, rcond_, hermitian, out_); |
| 19861 | return out;; |
| 19862 | } |
| 19863 | } else { |
| 19864 | at::Tensor tmp_output; |
| 19865 | { |
| 19866 | at::AutoDispatchSkipFunctionalize guard; |
| 19867 | tmp_output = at::_ops::linalg_pinv_rcond_tensor::call(self_, rcond_, hermitian); |
| 19868 | } |
| 19869 | at::functionalization::impl::replace_(out, tmp_output); |
| 19870 | at::functionalization::impl::commit_update(out); |
| 19871 | at::functionalization::impl::sync(out); |
| 19872 | return out; |
| 19873 | } |
| 19874 | } |
| 19875 | |
| 19876 | ::std::tuple<at::Tensor &,at::Tensor &> linalg_qr_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, c10::string_view mode, at::Tensor & Q, at::Tensor & R) { |
| 19877 | if (false) { |
| 19878 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19879 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19880 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19881 | auto A_meta = to_meta(A); |
| 19882 | auto Q_meta = to_meta(Q); |
| 19883 | auto R_meta = to_meta(R); |
| 19884 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19885 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19886 | at::_ops::linalg_qr_out::call(A_meta, mode, Q_meta, R_meta); |
| 19887 | } |
| 19888 | |
| 19889 | at::Tensor A_; |
| 19890 | if (at::functionalization::impl::isFunctionalTensor(A)) { |
| 19891 | at::functionalization::impl::sync(A); |
| 19892 | A_ = at::functionalization::impl::from_functional_tensor(A); |
| 19893 | } else { |
| 19894 | A_ = A; |
| 19895 | } |
| 19896 | |
| 19897 | at::Tensor Q_; |
| 19898 | if (at::functionalization::impl::isFunctionalTensor(Q)) { |
| 19899 | at::functionalization::impl::sync(Q); |
| 19900 | Q_ = at::functionalization::impl::from_functional_tensor(Q); |
| 19901 | } else { |
| 19902 | Q_ = Q; |
| 19903 | } |
| 19904 | |
| 19905 | at::Tensor R_; |
| 19906 | if (at::functionalization::impl::isFunctionalTensor(R)) { |
| 19907 | at::functionalization::impl::sync(R); |
| 19908 | R_ = at::functionalization::impl::from_functional_tensor(R); |
| 19909 | } else { |
| 19910 | R_ = R; |
| 19911 | } |
| 19912 | if (!(true && at::functionalization::impl::isFunctionalTensor(Q) && at::functionalization::impl::isFunctionalTensor(R))) { |
| 19913 | if ((false || at::functionalization::impl::isFunctionalTensor(A))) { |
| 19914 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19915 | TORCH_INTERNAL_ASSERT(false, |
| 19916 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19917 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19918 | } else { |
| 19919 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19920 | at::AutoDispatchSkipFunctionalize guard; |
| 19921 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::linalg_qr_out::call(A_, mode, Q_, R_); |
| 19922 | return ::std::tuple<at::Tensor &,at::Tensor &>(Q, R);; |
| 19923 | } |
| 19924 | } else { |
| 19925 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 19926 | { |
| 19927 | at::AutoDispatchSkipFunctionalize guard; |
| 19928 | tmp_output = at::_ops::linalg_qr::call(A_, mode); |
| 19929 | } |
| 19930 | at::functionalization::impl::replace_(Q, std::get<0>(tmp_output)); |
| 19931 | at::functionalization::impl::commit_update(Q); |
| 19932 | at::functionalization::impl::sync(Q); |
| 19933 | at::functionalization::impl::replace_(R, std::get<1>(tmp_output)); |
| 19934 | at::functionalization::impl::commit_update(R); |
| 19935 | at::functionalization::impl::sync(R); |
| 19936 | return ::std::tuple<at::Tensor &,at::Tensor &>(Q, R); |
| 19937 | } |
| 19938 | } |
| 19939 | |
| 19940 | at::Tensor & _test_optional_intlist_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & values, at::OptionalIntArrayRef addends, at::Tensor & out) { |
| 19941 | if (false) { |
| 19942 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19943 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19944 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19945 | auto values_meta = to_meta(values); |
| 19946 | auto out_meta = to_meta(out); |
| 19947 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19948 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19949 | at::_ops::_test_optional_intlist_out::call(values_meta, addends, out_meta); |
| 19950 | } |
| 19951 | |
| 19952 | at::Tensor values_; |
| 19953 | if (at::functionalization::impl::isFunctionalTensor(values)) { |
| 19954 | at::functionalization::impl::sync(values); |
| 19955 | values_ = at::functionalization::impl::from_functional_tensor(values); |
| 19956 | } else { |
| 19957 | values_ = values; |
| 19958 | } |
| 19959 | |
| 19960 | at::Tensor out_; |
| 19961 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19962 | at::functionalization::impl::sync(out); |
| 19963 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19964 | } else { |
| 19965 | out_ = out; |
| 19966 | } |
| 19967 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19968 | if ((false || at::functionalization::impl::isFunctionalTensor(values))) { |
| 19969 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19970 | TORCH_INTERNAL_ASSERT(false, |
| 19971 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19972 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19973 | } else { |
| 19974 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19975 | at::AutoDispatchSkipFunctionalize guard; |
| 19976 | at::Tensor tmp_output = at::_ops::_test_optional_intlist_out::call(values_, addends, out_); |
| 19977 | return out;; |
| 19978 | } |
| 19979 | } else { |
| 19980 | at::Tensor tmp_output; |
| 19981 | { |
| 19982 | at::AutoDispatchSkipFunctionalize guard; |
| 19983 | tmp_output = at::_ops::_test_optional_intlist::call(values_, addends); |
| 19984 | } |
| 19985 | at::functionalization::impl::replace_(out, tmp_output); |
| 19986 | at::functionalization::impl::commit_update(out); |
| 19987 | at::functionalization::impl::sync(out); |
| 19988 | return out; |
| 19989 | } |
| 19990 | } |
| 19991 | |
| 19992 | at::Tensor & _fw_primal_copy_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t level, at::Tensor & out) { |
| 19993 | if (false) { |
| 19994 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19995 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19996 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19997 | auto self_meta = to_meta(self); |
| 19998 | auto out_meta = to_meta(out); |
| 19999 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20000 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20001 | at::_ops::_fw_primal_copy_out::call(self_meta, level, out_meta); |
| 20002 | } |
| 20003 | |
| 20004 | at::Tensor self_; |
| 20005 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20006 | at::functionalization::impl::sync(self); |
| 20007 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20008 | } else { |
| 20009 | self_ = self; |
| 20010 | } |
| 20011 | |
| 20012 | at::Tensor out_; |
| 20013 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20014 | at::functionalization::impl::sync(out); |
| 20015 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20016 | } else { |
| 20017 | out_ = out; |
| 20018 | } |
| 20019 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20020 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 20021 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20022 | TORCH_INTERNAL_ASSERT(false, |
| 20023 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20024 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20025 | } else { |
| 20026 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20027 | at::AutoDispatchSkipFunctionalize guard; |
| 20028 | at::Tensor tmp_output = at::_ops::_fw_primal_copy_out::call(self_, level, out_); |
| 20029 | return out;; |
| 20030 | } |
| 20031 | } else { |
| 20032 | at::Tensor tmp_output; |
| 20033 | { |
| 20034 | at::AutoDispatchSkipFunctionalize guard; |
| 20035 | tmp_output = at::_ops::_fw_primal_copy::call(self_, level); |
| 20036 | } |
| 20037 | at::functionalization::impl::replace_(out, tmp_output); |
| 20038 | at::functionalization::impl::commit_update(out); |
| 20039 | at::functionalization::impl::sync(out); |
| 20040 | return out; |
| 20041 | } |
| 20042 | } |
| 20043 | |
| 20044 | at::Tensor & as_strided_copy_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional<c10::SymInt> storage_offset, at::Tensor & out) { |
| 20045 | if (false) { |
| 20046 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20047 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20048 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20049 | auto self_meta = to_meta(self); |
| 20050 | auto out_meta = to_meta(out); |
| 20051 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20052 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20053 | at::_ops::as_strided_copy_out::call(self_meta, size, stride, storage_offset, out_meta); |
| 20054 | } |
| 20055 | |
| 20056 | at::Tensor self_; |
| 20057 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20058 | at::functionalization::impl::sync(self); |
| 20059 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20060 | } else { |
| 20061 | self_ = self; |
| 20062 | } |
| 20063 | |
| 20064 | at::Tensor out_; |
| 20065 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20066 | at::functionalization::impl::sync(out); |
| 20067 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20068 | } else { |
| 20069 | out_ = out; |
| 20070 | } |
| 20071 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20072 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 20073 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20074 | TORCH_INTERNAL_ASSERT(false, |
| 20075 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20076 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20077 | } else { |
| 20078 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20079 | at::AutoDispatchSkipFunctionalize guard; |
| 20080 | at::Tensor tmp_output = at::_ops::as_strided_copy_out::call(self_, size, stride, storage_offset, out_); |
| 20081 | return out;; |
| 20082 | } |
| 20083 | } else { |
| 20084 | at::Tensor tmp_output; |
| 20085 | { |
| 20086 | at::AutoDispatchSkipFunctionalize guard; |
| 20087 | tmp_output = at::_ops::as_strided_copy::call(self_, size, stride, storage_offset); |
| 20088 | } |
| 20089 | at::functionalization::impl::replace_(out, tmp_output); |
| 20090 | at::functionalization::impl::commit_update(out); |
| 20091 | at::functionalization::impl::sync(out); |
| 20092 | return out; |
| 20093 | } |
| 20094 | } |
| 20095 | |
| 20096 | at::Tensor & expand_copy_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, bool implicit, at::Tensor & out) { |
| 20097 | if (false) { |
| 20098 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20099 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20100 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20101 | auto self_meta = to_meta(self); |
| 20102 | auto out_meta = to_meta(out); |
| 20103 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20104 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20105 | at::_ops::expand_copy_out::call(self_meta, size, implicit, out_meta); |
| 20106 | } |
| 20107 | |
| 20108 | at::Tensor self_; |
| 20109 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20110 | at::functionalization::impl::sync(self); |
| 20111 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20112 | } else { |
| 20113 | self_ = self; |
| 20114 | } |
| 20115 | |
| 20116 | at::Tensor out_; |
| 20117 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20118 | at::functionalization::impl::sync(out); |
| 20119 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20120 | } else { |
| 20121 | out_ = out; |
| 20122 | } |
| 20123 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20124 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 20125 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20126 | TORCH_INTERNAL_ASSERT(false, |
| 20127 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20128 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20129 | } else { |
| 20130 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20131 | at::AutoDispatchSkipFunctionalize guard; |
| 20132 | at::Tensor tmp_output = at::_ops::expand_copy_out::call(self_, size, implicit, out_); |
| 20133 | return out;; |
| 20134 | } |
| 20135 | } else { |
| 20136 | at::Tensor tmp_output; |
| 20137 | { |
| 20138 | at::AutoDispatchSkipFunctionalize guard; |
| 20139 | tmp_output = at::_ops::expand_copy::call(self_, size, implicit); |
| 20140 | } |
| 20141 | at::functionalization::impl::replace_(out, tmp_output); |
| 20142 | at::functionalization::impl::commit_update(out); |
| 20143 | at::functionalization::impl::sync(out); |
| 20144 | return out; |
| 20145 | } |
| 20146 | } |
| 20147 | |
| 20148 | at::Tensor & _reshape_alias_copy_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::Tensor & out) { |
| 20149 | if (false) { |
| 20150 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20151 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20152 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20153 | auto self_meta = to_meta(self); |
| 20154 | auto out_meta = to_meta(out); |
| 20155 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20156 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20157 | at::_ops::_reshape_alias_copy_out::call(self_meta, size, stride, out_meta); |
| 20158 | } |
| 20159 | |
| 20160 | at::Tensor self_; |
| 20161 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20162 | at::functionalization::impl::sync(self); |
| 20163 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20164 | } else { |
| 20165 | self_ = self; |
| 20166 | } |
| 20167 | |
| 20168 | at::Tensor out_; |
| 20169 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20170 | at::functionalization::impl::sync(out); |
| 20171 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20172 | } else { |
| 20173 | out_ = out; |
| 20174 | } |
| 20175 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20176 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 20177 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20178 | TORCH_INTERNAL_ASSERT(false, |
| 20179 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20180 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20181 | } else { |
| 20182 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20183 | at::AutoDispatchSkipFunctionalize guard; |
| 20184 | at::Tensor tmp_output = at::_ops::_reshape_alias_copy_out::call(self_, size, stride, out_); |
| 20185 | return out;; |
| 20186 | } |
| 20187 | } else { |
| 20188 | at::Tensor tmp_output; |
| 20189 | { |
| 20190 | at::AutoDispatchSkipFunctionalize guard; |
| 20191 | tmp_output = at::_ops::_reshape_alias_copy::call(self_, size, stride); |
| 20192 | } |
| 20193 | at::functionalization::impl::replace_(out, tmp_output); |
| 20194 | at::functionalization::impl::commit_update(out); |
| 20195 | at::functionalization::impl::sync(out); |
| 20196 | return out; |
| 20197 | } |
| 20198 | } |
| 20199 | |
| 20200 | at::Tensor & select_copy_out_int_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::SymInt index, at::Tensor & out) { |
| 20201 | if (false) { |
| 20202 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20203 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20204 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20205 | auto self_meta = to_meta(self); |
| 20206 | auto out_meta = to_meta(out); |
| 20207 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20208 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20209 | at::_ops::select_copy_int_out::call(self_meta, dim, index, out_meta); |
| 20210 | } |
| 20211 | |
| 20212 | at::Tensor self_; |
| 20213 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20214 | at::functionalization::impl::sync(self); |
| 20215 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20216 | } else { |
| 20217 | self_ = self; |
| 20218 | } |
| 20219 | |
| 20220 | at::Tensor out_; |
| 20221 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20222 | at::functionalization::impl::sync(out); |
| 20223 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20224 | } else { |
| 20225 | out_ = out; |
| 20226 | } |
| 20227 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20228 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 20229 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20230 | TORCH_INTERNAL_ASSERT(false, |
| 20231 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20232 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20233 | } else { |
| 20234 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20235 | at::AutoDispatchSkipFunctionalize guard; |
| 20236 | at::Tensor tmp_output = at::_ops::select_copy_int_out::call(self_, dim, index, out_); |
| 20237 | return out;; |
| 20238 | } |
| 20239 | } else { |
| 20240 | at::Tensor tmp_output; |
| 20241 | { |
| 20242 | at::AutoDispatchSkipFunctionalize guard; |
| 20243 | tmp_output = at::_ops::select_copy_int::call(self_, dim, index); |
| 20244 | } |
| 20245 | at::functionalization::impl::replace_(out, tmp_output); |
| 20246 | at::functionalization::impl::commit_update(out); |
| 20247 | at::functionalization::impl::sync(out); |
| 20248 | return out; |
| 20249 | } |
| 20250 | } |
| 20251 | |
| 20252 | void split_with_sizes_copy_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim, at::TensorList out) { |
| 20253 | if (false) { |
| 20254 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20255 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20256 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20257 | auto self_meta = to_meta(self); |
| 20258 | auto out_meta = to_meta(out); |
| 20259 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20260 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20261 | at::_ops::split_with_sizes_copy_out::call(self_meta, split_sizes, dim, out_meta); |
| 20262 | } |
| 20263 | |
| 20264 | at::Tensor self_; |
| 20265 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20266 | at::functionalization::impl::sync(self); |
| 20267 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20268 | } else { |
| 20269 | self_ = self; |
| 20270 | } |
| 20271 | |
| 20272 | ::std::vector<at::Tensor> out_; |
| 20273 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20274 | at::functionalization::impl::sync(out); |
| 20275 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20276 | } else { |
| 20277 | out_ = out.vec(); |
| 20278 | } |
| 20279 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20280 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 20281 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20282 | TORCH_INTERNAL_ASSERT(false, |
| 20283 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20284 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20285 | } else { |
| 20286 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20287 | at::AutoDispatchSkipFunctionalize guard; |
| 20288 | at::_ops::split_with_sizes_copy_out::call(self_, split_sizes, dim, out_); |
| 20289 | ; |
| 20290 | } |
| 20291 | } else { |
| 20292 | ::std::vector<at::Tensor> tmp_output; |
| 20293 | { |
| 20294 | at::AutoDispatchSkipFunctionalize guard; |
| 20295 | tmp_output = at::_ops::split_with_sizes_copy::call(self_, split_sizes, dim); |
| 20296 | } |
| 20297 | at::functionalization::impl::replace_(out, tmp_output); |
| 20298 | at::functionalization::impl::commit_update(out); |
| 20299 | at::functionalization::impl::sync(out); |
| 20300 | |
| 20301 | } |
| 20302 | } |
| 20303 | |
| 20304 | at::Tensor & squeeze_copy_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 20305 | if (false) { |
| 20306 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20307 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20308 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20309 | auto self_meta = to_meta(self); |
| 20310 | auto out_meta = to_meta(out); |
| 20311 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20312 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20313 | at::_ops::squeeze_copy_out::call(self_meta, out_meta); |
| 20314 | } |
| 20315 | |
| 20316 | at::Tensor self_; |
| 20317 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20318 | at::functionalization::impl::sync(self); |
| 20319 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20320 | } else { |
| 20321 | self_ = self; |
| 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))) { |
| 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::squeeze_copy_out::call(self_, out_); |
| 20341 | return out;; |
| 20342 | } |
| 20343 | } else { |
| 20344 | at::Tensor tmp_output; |
| 20345 | { |
| 20346 | at::AutoDispatchSkipFunctionalize guard; |
| 20347 | tmp_output = at::_ops::squeeze_copy::call(self_); |
| 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 & squeeze_copy_out_dim_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, 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 self_meta = to_meta(self); |
| 20362 | auto out_meta = to_meta(out); |
| 20363 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20364 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20365 | at::_ops::squeeze_copy_dim_out::call(self_meta, dim, out_meta); |
| 20366 | } |
| 20367 | |
| 20368 | at::Tensor self_; |
| 20369 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20370 | at::functionalization::impl::sync(self); |
| 20371 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20372 | } else { |
| 20373 | self_ = self; |
| 20374 | } |
| 20375 | |
| 20376 | at::Tensor out_; |
| 20377 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20378 | at::functionalization::impl::sync(out); |
| 20379 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20380 | } else { |
| 20381 | out_ = out; |
| 20382 | } |
| 20383 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20384 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 20385 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20386 | TORCH_INTERNAL_ASSERT(false, |
| 20387 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20388 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20389 | } else { |
| 20390 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20391 | at::AutoDispatchSkipFunctionalize guard; |
| 20392 | at::Tensor tmp_output = at::_ops::squeeze_copy_dim_out::call(self_, dim, out_); |
| 20393 | return out;; |
| 20394 | } |
| 20395 | } else { |
| 20396 | at::Tensor tmp_output; |
| 20397 | { |
| 20398 | at::AutoDispatchSkipFunctionalize guard; |
| 20399 | tmp_output = at::_ops::squeeze_copy_dim::call(self_, dim); |
| 20400 | } |
| 20401 | at::functionalization::impl::replace_(out, tmp_output); |
| 20402 | at::functionalization::impl::commit_update(out); |
| 20403 | at::functionalization::impl::sync(out); |
| 20404 | return out; |
| 20405 | } |
| 20406 | } |
| 20407 | |
| 20408 | at::Tensor & squeeze_copy_out_dims_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, at::Tensor & out) { |
| 20409 | if (false) { |
| 20410 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20411 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20412 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20413 | auto self_meta = to_meta(self); |
| 20414 | auto out_meta = to_meta(out); |
| 20415 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20416 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20417 | at::_ops::squeeze_copy_dims_out::call(self_meta, dim, out_meta); |
| 20418 | } |
| 20419 | |
| 20420 | at::Tensor self_; |
| 20421 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20422 | at::functionalization::impl::sync(self); |
| 20423 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20424 | } else { |
| 20425 | self_ = self; |
| 20426 | } |
| 20427 | |
| 20428 | at::Tensor out_; |
| 20429 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20430 | at::functionalization::impl::sync(out); |
| 20431 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20432 | } else { |
| 20433 | out_ = out; |
| 20434 | } |
| 20435 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20436 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 20437 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20438 | TORCH_INTERNAL_ASSERT(false, |
| 20439 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20440 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20441 | } else { |
| 20442 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20443 | at::AutoDispatchSkipFunctionalize guard; |
| 20444 | at::Tensor tmp_output = at::_ops::squeeze_copy_dims_out::call(self_, dim, out_); |
| 20445 | return out;; |
| 20446 | } |
| 20447 | } else { |
| 20448 | at::Tensor tmp_output; |
| 20449 | { |
| 20450 | at::AutoDispatchSkipFunctionalize guard; |
| 20451 | tmp_output = at::_ops::squeeze_copy_dims::call(self_, dim); |
| 20452 | } |
| 20453 | at::functionalization::impl::replace_(out, tmp_output); |
| 20454 | at::functionalization::impl::commit_update(out); |
| 20455 | at::functionalization::impl::sync(out); |
| 20456 | return out; |
| 20457 | } |
| 20458 | } |
| 20459 | |
| 20460 | at::Tensor & _indices_copy_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 20461 | if (false) { |
| 20462 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20463 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20464 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20465 | auto self_meta = to_meta(self); |
| 20466 | auto out_meta = to_meta(out); |
| 20467 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20468 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20469 | at::_ops::_indices_copy_out::call(self_meta, out_meta); |
| 20470 | } |
| 20471 | |
| 20472 | at::Tensor self_; |
| 20473 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20474 | at::functionalization::impl::sync(self); |
| 20475 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20476 | } else { |
| 20477 | self_ = self; |
| 20478 | } |
| 20479 | |
| 20480 | at::Tensor out_; |
| 20481 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20482 | at::functionalization::impl::sync(out); |
| 20483 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20484 | } else { |
| 20485 | out_ = out; |
| 20486 | } |
| 20487 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20488 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 20489 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20490 | TORCH_INTERNAL_ASSERT(false, |
| 20491 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20492 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20493 | } else { |
| 20494 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20495 | at::AutoDispatchSkipFunctionalize guard; |
| 20496 | at::Tensor tmp_output = at::_ops::_indices_copy_out::call(self_, out_); |
| 20497 | return out;; |
| 20498 | } |
| 20499 | } else { |
| 20500 | at::Tensor tmp_output; |
| 20501 | { |
| 20502 | at::AutoDispatchSkipFunctionalize guard; |
| 20503 | tmp_output = at::_ops::_indices_copy::call(self_); |
| 20504 | } |
| 20505 | at::functionalization::impl::replace_(out, tmp_output); |
| 20506 | at::functionalization::impl::commit_update(out); |
| 20507 | at::functionalization::impl::sync(out); |
| 20508 | return out; |
| 20509 | } |
| 20510 | } |
| 20511 | |
| 20512 | at::Tensor & _values_copy_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 20513 | if (false) { |
| 20514 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20515 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20516 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20517 | auto self_meta = to_meta(self); |
| 20518 | auto out_meta = to_meta(out); |
| 20519 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20520 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20521 | at::_ops::_values_copy_out::call(self_meta, out_meta); |
| 20522 | } |
| 20523 | |
| 20524 | at::Tensor self_; |
| 20525 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20526 | at::functionalization::impl::sync(self); |
| 20527 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20528 | } else { |
| 20529 | self_ = self; |
| 20530 | } |
| 20531 | |
| 20532 | at::Tensor out_; |
| 20533 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20534 | at::functionalization::impl::sync(out); |
| 20535 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20536 | } else { |
| 20537 | out_ = out; |
| 20538 | } |
| 20539 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20540 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 20541 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20542 | TORCH_INTERNAL_ASSERT(false, |
| 20543 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20544 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20545 | } else { |
| 20546 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20547 | at::AutoDispatchSkipFunctionalize guard; |
| 20548 | at::Tensor tmp_output = at::_ops::_values_copy_out::call(self_, out_); |
| 20549 | return out;; |
| 20550 | } |
| 20551 | } else { |
| 20552 | at::Tensor tmp_output; |
| 20553 | { |
| 20554 | at::AutoDispatchSkipFunctionalize guard; |
| 20555 | tmp_output = at::_ops::_values_copy::call(self_); |
| 20556 | } |
| 20557 | at::functionalization::impl::replace_(out, tmp_output); |
| 20558 | at::functionalization::impl::commit_update(out); |
| 20559 | at::functionalization::impl::sync(out); |
| 20560 | return out; |
| 20561 | } |
| 20562 | } |
| 20563 | |
| 20564 | at::Tensor & crow_indices_copy_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 20565 | if (false) { |
| 20566 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20567 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20568 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20569 | auto self_meta = to_meta(self); |
| 20570 | auto out_meta = to_meta(out); |
| 20571 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20572 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20573 | at::_ops::crow_indices_copy_out::call(self_meta, out_meta); |
| 20574 | } |
| 20575 | |
| 20576 | at::Tensor self_; |
| 20577 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20578 | at::functionalization::impl::sync(self); |
| 20579 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20580 | } else { |
| 20581 | self_ = self; |
| 20582 | } |
| 20583 | |
| 20584 | at::Tensor out_; |
| 20585 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20586 | at::functionalization::impl::sync(out); |
| 20587 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20588 | } else { |
| 20589 | out_ = out; |
| 20590 | } |
| 20591 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20592 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 20593 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20594 | TORCH_INTERNAL_ASSERT(false, |
| 20595 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20596 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20597 | } else { |
| 20598 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20599 | at::AutoDispatchSkipFunctionalize guard; |
| 20600 | at::Tensor tmp_output = at::_ops::crow_indices_copy_out::call(self_, out_); |
| 20601 | return out;; |
| 20602 | } |
| 20603 | } else { |
| 20604 | at::Tensor tmp_output; |
| 20605 | { |
| 20606 | at::AutoDispatchSkipFunctionalize guard; |
| 20607 | tmp_output = at::_ops::crow_indices_copy::call(self_); |
| 20608 | } |
| 20609 | at::functionalization::impl::replace_(out, tmp_output); |
| 20610 | at::functionalization::impl::commit_update(out); |
| 20611 | at::functionalization::impl::sync(out); |
| 20612 | return out; |
| 20613 | } |
| 20614 | } |
| 20615 | |
| 20616 | at::Tensor & col_indices_copy_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 20617 | if (false) { |
| 20618 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20619 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20620 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20621 | auto self_meta = to_meta(self); |
| 20622 | auto out_meta = to_meta(out); |
| 20623 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20624 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20625 | at::_ops::col_indices_copy_out::call(self_meta, out_meta); |
| 20626 | } |
| 20627 | |
| 20628 | at::Tensor self_; |
| 20629 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20630 | at::functionalization::impl::sync(self); |
| 20631 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20632 | } else { |
| 20633 | self_ = self; |
| 20634 | } |
| 20635 | |
| 20636 | at::Tensor out_; |
| 20637 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20638 | at::functionalization::impl::sync(out); |
| 20639 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20640 | } else { |
| 20641 | out_ = out; |
| 20642 | } |
| 20643 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20644 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 20645 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20646 | TORCH_INTERNAL_ASSERT(false, |
| 20647 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20648 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20649 | } else { |
| 20650 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20651 | at::AutoDispatchSkipFunctionalize guard; |
| 20652 | at::Tensor tmp_output = at::_ops::col_indices_copy_out::call(self_, out_); |
| 20653 | return out;; |
| 20654 | } |
| 20655 | } else { |
| 20656 | at::Tensor tmp_output; |
| 20657 | { |
| 20658 | at::AutoDispatchSkipFunctionalize guard; |
| 20659 | tmp_output = at::_ops::col_indices_copy::call(self_); |
| 20660 | } |
| 20661 | at::functionalization::impl::replace_(out, tmp_output); |
| 20662 | at::functionalization::impl::commit_update(out); |
| 20663 | at::functionalization::impl::sync(out); |
| 20664 | return out; |
| 20665 | } |
| 20666 | } |
| 20667 | |
| 20668 | void unbind_copy_out_int_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, at::TensorList out) { |
| 20669 | if (false) { |
| 20670 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20671 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20672 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20673 | auto self_meta = to_meta(self); |
| 20674 | auto out_meta = to_meta(out); |
| 20675 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20676 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20677 | at::_ops::unbind_copy_int_out::call(self_meta, dim, out_meta); |
| 20678 | } |
| 20679 | |
| 20680 | at::Tensor self_; |
| 20681 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20682 | at::functionalization::impl::sync(self); |
| 20683 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20684 | } else { |
| 20685 | self_ = self; |
| 20686 | } |
| 20687 | |
| 20688 | ::std::vector<at::Tensor> out_; |
| 20689 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20690 | at::functionalization::impl::sync(out); |
| 20691 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20692 | } else { |
| 20693 | out_ = out.vec(); |
| 20694 | } |
| 20695 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20696 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 20697 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20698 | TORCH_INTERNAL_ASSERT(false, |
| 20699 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20700 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20701 | } else { |
| 20702 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20703 | at::AutoDispatchSkipFunctionalize guard; |
| 20704 | at::_ops::unbind_copy_int_out::call(self_, dim, out_); |
| 20705 | ; |
| 20706 | } |
| 20707 | } else { |
| 20708 | ::std::vector<at::Tensor> tmp_output; |
| 20709 | { |
| 20710 | at::AutoDispatchSkipFunctionalize guard; |
| 20711 | tmp_output = at::_ops::unbind_copy_int::call(self_, dim); |
| 20712 | } |
| 20713 | at::functionalization::impl::replace_(out, tmp_output); |
| 20714 | at::functionalization::impl::commit_update(out); |
| 20715 | at::functionalization::impl::sync(out); |
| 20716 | |
| 20717 | } |
| 20718 | } |
| 20719 | |
| 20720 | at::Tensor & view_copy_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out) { |
| 20721 | if (false) { |
| 20722 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20723 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20724 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20725 | auto self_meta = to_meta(self); |
| 20726 | auto out_meta = to_meta(out); |
| 20727 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20728 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20729 | at::_ops::view_copy_out::call(self_meta, size, out_meta); |
| 20730 | } |
| 20731 | |
| 20732 | at::Tensor self_; |
| 20733 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20734 | at::functionalization::impl::sync(self); |
| 20735 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20736 | } else { |
| 20737 | self_ = self; |
| 20738 | } |
| 20739 | |
| 20740 | at::Tensor out_; |
| 20741 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20742 | at::functionalization::impl::sync(out); |
| 20743 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20744 | } else { |
| 20745 | out_ = out; |
| 20746 | } |
| 20747 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20748 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 20749 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20750 | TORCH_INTERNAL_ASSERT(false, |
| 20751 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20752 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20753 | } else { |
| 20754 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20755 | at::AutoDispatchSkipFunctionalize guard; |
| 20756 | at::Tensor tmp_output = at::_ops::view_copy_out::call(self_, size, out_); |
| 20757 | return out;; |
| 20758 | } |
| 20759 | } else { |
| 20760 | at::Tensor tmp_output; |
| 20761 | { |
| 20762 | at::AutoDispatchSkipFunctionalize guard; |
| 20763 | tmp_output = at::_ops::view_copy::call(self_, size); |
| 20764 | } |
| 20765 | at::functionalization::impl::replace_(out, tmp_output); |
| 20766 | at::functionalization::impl::commit_update(out); |
| 20767 | at::functionalization::impl::sync(out); |
| 20768 | return out; |
| 20769 | } |
| 20770 | } |
| 20771 | |
| 20772 | at::Tensor & view_copy_out_dtype_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::ScalarType dtype, at::Tensor & out) { |
| 20773 | if (false) { |
| 20774 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20775 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20776 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20777 | auto self_meta = to_meta(self); |
| 20778 | auto out_meta = to_meta(out); |
| 20779 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20780 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20781 | at::_ops::view_copy_dtype_out::call(self_meta, dtype, out_meta); |
| 20782 | } |
| 20783 | |
| 20784 | at::Tensor self_; |
| 20785 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20786 | at::functionalization::impl::sync(self); |
| 20787 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20788 | } else { |
| 20789 | self_ = self; |
| 20790 | } |
| 20791 | |
| 20792 | at::Tensor out_; |
| 20793 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20794 | at::functionalization::impl::sync(out); |
| 20795 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20796 | } else { |
| 20797 | out_ = out; |
| 20798 | } |
| 20799 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20800 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 20801 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20802 | TORCH_INTERNAL_ASSERT(false, |
| 20803 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20804 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20805 | } else { |
| 20806 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20807 | at::AutoDispatchSkipFunctionalize guard; |
| 20808 | at::Tensor tmp_output = at::_ops::view_copy_dtype_out::call(self_, dtype, out_); |
| 20809 | return out;; |
| 20810 | } |
| 20811 | } else { |
| 20812 | at::Tensor tmp_output; |
| 20813 | { |
| 20814 | at::AutoDispatchSkipFunctionalize guard; |
| 20815 | tmp_output = at::_ops::view_copy_dtype::call(self_, dtype); |
| 20816 | } |
| 20817 | at::functionalization::impl::replace_(out, tmp_output); |
| 20818 | at::functionalization::impl::commit_update(out); |
| 20819 | at::functionalization::impl::sync(out); |
| 20820 | return out; |
| 20821 | } |
| 20822 | } |
| 20823 | |
| 20824 | at::Tensor & alias_copy_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 20825 | if (false) { |
| 20826 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20827 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20828 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20829 | auto self_meta = to_meta(self); |
| 20830 | auto out_meta = to_meta(out); |
| 20831 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20832 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20833 | at::_ops::alias_copy_out::call(self_meta, out_meta); |
| 20834 | } |
| 20835 | |
| 20836 | at::Tensor self_; |
| 20837 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20838 | at::functionalization::impl::sync(self); |
| 20839 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20840 | } else { |
| 20841 | self_ = self; |
| 20842 | } |
| 20843 | |
| 20844 | at::Tensor out_; |
| 20845 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20846 | at::functionalization::impl::sync(out); |
| 20847 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20848 | } else { |
| 20849 | out_ = out; |
| 20850 | } |
| 20851 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20852 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 20853 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20854 | TORCH_INTERNAL_ASSERT(false, |
| 20855 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20856 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20857 | } else { |
| 20858 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20859 | at::AutoDispatchSkipFunctionalize guard; |
| 20860 | at::Tensor tmp_output = at::_ops::alias_copy_out::call(self_, out_); |
| 20861 | return out;; |
| 20862 | } |
| 20863 | } else { |
| 20864 | at::Tensor tmp_output; |
| 20865 | { |
| 20866 | at::AutoDispatchSkipFunctionalize guard; |
| 20867 | tmp_output = at::_ops::alias_copy::call(self_); |
| 20868 | } |
| 20869 | at::functionalization::impl::replace_(out, tmp_output); |
| 20870 | at::functionalization::impl::commit_update(out); |
| 20871 | at::functionalization::impl::sync(out); |
| 20872 | return out; |
| 20873 | } |
| 20874 | } |
| 20875 | |
| 20876 | at::Tensor & special_airy_ai_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, at::Tensor & out) { |
| 20877 | if (false) { |
| 20878 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20879 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20880 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20881 | auto x_meta = to_meta(x); |
| 20882 | auto out_meta = to_meta(out); |
| 20883 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20884 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20885 | at::_ops::special_airy_ai_out::call(x_meta, out_meta); |
| 20886 | } |
| 20887 | |
| 20888 | at::Tensor x_; |
| 20889 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 20890 | at::functionalization::impl::sync(x); |
| 20891 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 20892 | } else { |
| 20893 | x_ = x; |
| 20894 | } |
| 20895 | |
| 20896 | at::Tensor out_; |
| 20897 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20898 | at::functionalization::impl::sync(out); |
| 20899 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20900 | } else { |
| 20901 | out_ = out; |
| 20902 | } |
| 20903 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20904 | if ((false || at::functionalization::impl::isFunctionalTensor(x))) { |
| 20905 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20906 | TORCH_INTERNAL_ASSERT(false, |
| 20907 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20908 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20909 | } else { |
| 20910 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20911 | at::AutoDispatchSkipFunctionalize guard; |
| 20912 | at::Tensor tmp_output = at::_ops::special_airy_ai_out::call(x_, out_); |
| 20913 | return out;; |
| 20914 | } |
| 20915 | } else { |
| 20916 | at::Tensor tmp_output; |
| 20917 | { |
| 20918 | at::AutoDispatchSkipFunctionalize guard; |
| 20919 | tmp_output = at::_ops::special_airy_ai::call(x_); |
| 20920 | } |
| 20921 | at::functionalization::impl::replace_(out, tmp_output); |
| 20922 | at::functionalization::impl::commit_update(out); |
| 20923 | at::functionalization::impl::sync(out); |
| 20924 | return out; |
| 20925 | } |
| 20926 | } |
| 20927 | |
| 20928 | at::Tensor & special_bessel_j0_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 20929 | if (false) { |
| 20930 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20931 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20932 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20933 | auto self_meta = to_meta(self); |
| 20934 | auto out_meta = to_meta(out); |
| 20935 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20936 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20937 | at::_ops::special_bessel_j0_out::call(self_meta, out_meta); |
| 20938 | } |
| 20939 | |
| 20940 | at::Tensor self_; |
| 20941 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20942 | at::functionalization::impl::sync(self); |
| 20943 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20944 | } else { |
| 20945 | self_ = self; |
| 20946 | } |
| 20947 | |
| 20948 | at::Tensor out_; |
| 20949 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 20950 | at::functionalization::impl::sync(out); |
| 20951 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 20952 | } else { |
| 20953 | out_ = out; |
| 20954 | } |
| 20955 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 20956 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 20957 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 20958 | TORCH_INTERNAL_ASSERT(false, |
| 20959 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 20960 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 20961 | } else { |
| 20962 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 20963 | at::AutoDispatchSkipFunctionalize guard; |
| 20964 | at::Tensor tmp_output = at::_ops::special_bessel_j0_out::call(self_, out_); |
| 20965 | return out;; |
| 20966 | } |
| 20967 | } else { |
| 20968 | at::Tensor tmp_output; |
| 20969 | { |
| 20970 | at::AutoDispatchSkipFunctionalize guard; |
| 20971 | tmp_output = at::_ops::special_bessel_j0::call(self_); |
| 20972 | } |
| 20973 | at::functionalization::impl::replace_(out, tmp_output); |
| 20974 | at::functionalization::impl::commit_update(out); |
| 20975 | at::functionalization::impl::sync(out); |
| 20976 | return out; |
| 20977 | } |
| 20978 | } |
| 20979 | |
| 20980 | at::Tensor & special_chebyshev_polynomial_v_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 20981 | if (false) { |
| 20982 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 20983 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 20984 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 20985 | auto x_meta = to_meta(x); |
| 20986 | auto n_meta = to_meta(n); |
| 20987 | auto out_meta = to_meta(out); |
| 20988 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20989 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20990 | at::_ops::special_chebyshev_polynomial_v_out::call(x_meta, n_meta, out_meta); |
| 20991 | } |
| 20992 | |
| 20993 | at::Tensor x_; |
| 20994 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 20995 | at::functionalization::impl::sync(x); |
| 20996 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 20997 | } else { |
| 20998 | x_ = x; |
| 20999 | } |
| 21000 | |
| 21001 | at::Tensor n_; |
| 21002 | if (at::functionalization::impl::isFunctionalTensor(n)) { |
| 21003 | at::functionalization::impl::sync(n); |
| 21004 | n_ = at::functionalization::impl::from_functional_tensor(n); |
| 21005 | } else { |
| 21006 | n_ = n; |
| 21007 | } |
| 21008 | |
| 21009 | at::Tensor out_; |
| 21010 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21011 | at::functionalization::impl::sync(out); |
| 21012 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21013 | } else { |
| 21014 | out_ = out; |
| 21015 | } |
| 21016 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21017 | if ((false || at::functionalization::impl::isFunctionalTensor(x) || at::functionalization::impl::isFunctionalTensor(n))) { |
| 21018 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21019 | TORCH_INTERNAL_ASSERT(false, |
| 21020 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21021 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21022 | } else { |
| 21023 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21024 | at::AutoDispatchSkipFunctionalize guard; |
| 21025 | at::Tensor tmp_output = at::_ops::special_chebyshev_polynomial_v_out::call(x_, n_, out_); |
| 21026 | return out;; |
| 21027 | } |
| 21028 | } else { |
| 21029 | at::Tensor tmp_output; |
| 21030 | { |
| 21031 | at::AutoDispatchSkipFunctionalize guard; |
| 21032 | tmp_output = at::_ops::special_chebyshev_polynomial_v::call(x_, n_); |
| 21033 | } |
| 21034 | at::functionalization::impl::replace_(out, tmp_output); |
| 21035 | at::functionalization::impl::commit_update(out); |
| 21036 | at::functionalization::impl::sync(out); |
| 21037 | return out; |
| 21038 | } |
| 21039 | } |
| 21040 | |
| 21041 | at::Tensor & special_chebyshev_polynomial_v_out_x_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { |
| 21042 | if (false) { |
| 21043 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21044 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21045 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21046 | auto n_meta = to_meta(n); |
| 21047 | auto out_meta = to_meta(out); |
| 21048 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21049 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21050 | at::_ops::special_chebyshev_polynomial_v_x_scalar_out::call(x, n_meta, out_meta); |
| 21051 | } |
| 21052 | |
| 21053 | at::Tensor n_; |
| 21054 | if (at::functionalization::impl::isFunctionalTensor(n)) { |
| 21055 | at::functionalization::impl::sync(n); |
| 21056 | n_ = at::functionalization::impl::from_functional_tensor(n); |
| 21057 | } else { |
| 21058 | n_ = n; |
| 21059 | } |
| 21060 | |
| 21061 | at::Tensor out_; |
| 21062 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21063 | at::functionalization::impl::sync(out); |
| 21064 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21065 | } else { |
| 21066 | out_ = out; |
| 21067 | } |
| 21068 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21069 | if ((false || at::functionalization::impl::isFunctionalTensor(n))) { |
| 21070 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21071 | TORCH_INTERNAL_ASSERT(false, |
| 21072 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21073 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21074 | } else { |
| 21075 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21076 | at::AutoDispatchSkipFunctionalize guard; |
| 21077 | at::Tensor tmp_output = at::_ops::special_chebyshev_polynomial_v_x_scalar_out::call(x, n_, out_); |
| 21078 | return out;; |
| 21079 | } |
| 21080 | } else { |
| 21081 | at::Tensor tmp_output; |
| 21082 | { |
| 21083 | at::AutoDispatchSkipFunctionalize guard; |
| 21084 | tmp_output = at::_ops::special_chebyshev_polynomial_v_x_scalar::call(x, n_); |
| 21085 | } |
| 21086 | at::functionalization::impl::replace_(out, tmp_output); |
| 21087 | at::functionalization::impl::commit_update(out); |
| 21088 | at::functionalization::impl::sync(out); |
| 21089 | return out; |
| 21090 | } |
| 21091 | } |
| 21092 | |
| 21093 | at::Tensor & special_chebyshev_polynomial_v_out_n_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { |
| 21094 | if (false) { |
| 21095 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21096 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21097 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21098 | auto x_meta = to_meta(x); |
| 21099 | auto out_meta = to_meta(out); |
| 21100 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21101 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21102 | at::_ops::special_chebyshev_polynomial_v_n_scalar_out::call(x_meta, n, out_meta); |
| 21103 | } |
| 21104 | |
| 21105 | at::Tensor x_; |
| 21106 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 21107 | at::functionalization::impl::sync(x); |
| 21108 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 21109 | } else { |
| 21110 | x_ = x; |
| 21111 | } |
| 21112 | |
| 21113 | at::Tensor out_; |
| 21114 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21115 | at::functionalization::impl::sync(out); |
| 21116 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21117 | } else { |
| 21118 | out_ = out; |
| 21119 | } |
| 21120 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21121 | if ((false || at::functionalization::impl::isFunctionalTensor(x))) { |
| 21122 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21123 | TORCH_INTERNAL_ASSERT(false, |
| 21124 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21125 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21126 | } else { |
| 21127 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21128 | at::AutoDispatchSkipFunctionalize guard; |
| 21129 | at::Tensor tmp_output = at::_ops::special_chebyshev_polynomial_v_n_scalar_out::call(x_, n, out_); |
| 21130 | return out;; |
| 21131 | } |
| 21132 | } else { |
| 21133 | at::Tensor tmp_output; |
| 21134 | { |
| 21135 | at::AutoDispatchSkipFunctionalize guard; |
| 21136 | tmp_output = at::_ops::special_chebyshev_polynomial_v_n_scalar::call(x_, n); |
| 21137 | } |
| 21138 | at::functionalization::impl::replace_(out, tmp_output); |
| 21139 | at::functionalization::impl::commit_update(out); |
| 21140 | at::functionalization::impl::sync(out); |
| 21141 | return out; |
| 21142 | } |
| 21143 | } |
| 21144 | |
| 21145 | at::Tensor & special_chebyshev_polynomial_w_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 21146 | if (false) { |
| 21147 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21148 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21149 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21150 | auto x_meta = to_meta(x); |
| 21151 | auto n_meta = to_meta(n); |
| 21152 | auto out_meta = to_meta(out); |
| 21153 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21154 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21155 | at::_ops::special_chebyshev_polynomial_w_out::call(x_meta, n_meta, out_meta); |
| 21156 | } |
| 21157 | |
| 21158 | at::Tensor x_; |
| 21159 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 21160 | at::functionalization::impl::sync(x); |
| 21161 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 21162 | } else { |
| 21163 | x_ = x; |
| 21164 | } |
| 21165 | |
| 21166 | at::Tensor n_; |
| 21167 | if (at::functionalization::impl::isFunctionalTensor(n)) { |
| 21168 | at::functionalization::impl::sync(n); |
| 21169 | n_ = at::functionalization::impl::from_functional_tensor(n); |
| 21170 | } else { |
| 21171 | n_ = n; |
| 21172 | } |
| 21173 | |
| 21174 | at::Tensor out_; |
| 21175 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21176 | at::functionalization::impl::sync(out); |
| 21177 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21178 | } else { |
| 21179 | out_ = out; |
| 21180 | } |
| 21181 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21182 | if ((false || at::functionalization::impl::isFunctionalTensor(x) || at::functionalization::impl::isFunctionalTensor(n))) { |
| 21183 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21184 | TORCH_INTERNAL_ASSERT(false, |
| 21185 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21186 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21187 | } else { |
| 21188 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21189 | at::AutoDispatchSkipFunctionalize guard; |
| 21190 | at::Tensor tmp_output = at::_ops::special_chebyshev_polynomial_w_out::call(x_, n_, out_); |
| 21191 | return out;; |
| 21192 | } |
| 21193 | } else { |
| 21194 | at::Tensor tmp_output; |
| 21195 | { |
| 21196 | at::AutoDispatchSkipFunctionalize guard; |
| 21197 | tmp_output = at::_ops::special_chebyshev_polynomial_w::call(x_, n_); |
| 21198 | } |
| 21199 | at::functionalization::impl::replace_(out, tmp_output); |
| 21200 | at::functionalization::impl::commit_update(out); |
| 21201 | at::functionalization::impl::sync(out); |
| 21202 | return out; |
| 21203 | } |
| 21204 | } |
| 21205 | |
| 21206 | at::Tensor & special_chebyshev_polynomial_w_out_x_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { |
| 21207 | if (false) { |
| 21208 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21209 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21210 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21211 | auto n_meta = to_meta(n); |
| 21212 | auto out_meta = to_meta(out); |
| 21213 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21214 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21215 | at::_ops::special_chebyshev_polynomial_w_x_scalar_out::call(x, n_meta, out_meta); |
| 21216 | } |
| 21217 | |
| 21218 | at::Tensor n_; |
| 21219 | if (at::functionalization::impl::isFunctionalTensor(n)) { |
| 21220 | at::functionalization::impl::sync(n); |
| 21221 | n_ = at::functionalization::impl::from_functional_tensor(n); |
| 21222 | } else { |
| 21223 | n_ = n; |
| 21224 | } |
| 21225 | |
| 21226 | at::Tensor out_; |
| 21227 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21228 | at::functionalization::impl::sync(out); |
| 21229 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21230 | } else { |
| 21231 | out_ = out; |
| 21232 | } |
| 21233 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21234 | if ((false || at::functionalization::impl::isFunctionalTensor(n))) { |
| 21235 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21236 | TORCH_INTERNAL_ASSERT(false, |
| 21237 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21238 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21239 | } else { |
| 21240 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21241 | at::AutoDispatchSkipFunctionalize guard; |
| 21242 | at::Tensor tmp_output = at::_ops::special_chebyshev_polynomial_w_x_scalar_out::call(x, n_, out_); |
| 21243 | return out;; |
| 21244 | } |
| 21245 | } else { |
| 21246 | at::Tensor tmp_output; |
| 21247 | { |
| 21248 | at::AutoDispatchSkipFunctionalize guard; |
| 21249 | tmp_output = at::_ops::special_chebyshev_polynomial_w_x_scalar::call(x, n_); |
| 21250 | } |
| 21251 | at::functionalization::impl::replace_(out, tmp_output); |
| 21252 | at::functionalization::impl::commit_update(out); |
| 21253 | at::functionalization::impl::sync(out); |
| 21254 | return out; |
| 21255 | } |
| 21256 | } |
| 21257 | |
| 21258 | at::Tensor & special_chebyshev_polynomial_w_out_n_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { |
| 21259 | if (false) { |
| 21260 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21261 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21262 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21263 | auto x_meta = to_meta(x); |
| 21264 | auto out_meta = to_meta(out); |
| 21265 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21266 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21267 | at::_ops::special_chebyshev_polynomial_w_n_scalar_out::call(x_meta, n, out_meta); |
| 21268 | } |
| 21269 | |
| 21270 | at::Tensor x_; |
| 21271 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 21272 | at::functionalization::impl::sync(x); |
| 21273 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 21274 | } else { |
| 21275 | x_ = x; |
| 21276 | } |
| 21277 | |
| 21278 | at::Tensor out_; |
| 21279 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21280 | at::functionalization::impl::sync(out); |
| 21281 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21282 | } else { |
| 21283 | out_ = out; |
| 21284 | } |
| 21285 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21286 | if ((false || at::functionalization::impl::isFunctionalTensor(x))) { |
| 21287 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21288 | TORCH_INTERNAL_ASSERT(false, |
| 21289 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21290 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21291 | } else { |
| 21292 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21293 | at::AutoDispatchSkipFunctionalize guard; |
| 21294 | at::Tensor tmp_output = at::_ops::special_chebyshev_polynomial_w_n_scalar_out::call(x_, n, out_); |
| 21295 | return out;; |
| 21296 | } |
| 21297 | } else { |
| 21298 | at::Tensor tmp_output; |
| 21299 | { |
| 21300 | at::AutoDispatchSkipFunctionalize guard; |
| 21301 | tmp_output = at::_ops::special_chebyshev_polynomial_w_n_scalar::call(x_, n); |
| 21302 | } |
| 21303 | at::functionalization::impl::replace_(out, tmp_output); |
| 21304 | at::functionalization::impl::commit_update(out); |
| 21305 | at::functionalization::impl::sync(out); |
| 21306 | return out; |
| 21307 | } |
| 21308 | } |
| 21309 | |
| 21310 | at::Tensor & special_hermite_polynomial_he_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 21311 | if (false) { |
| 21312 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21313 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21314 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21315 | auto x_meta = to_meta(x); |
| 21316 | auto n_meta = to_meta(n); |
| 21317 | auto out_meta = to_meta(out); |
| 21318 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21319 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21320 | at::_ops::special_hermite_polynomial_he_out::call(x_meta, n_meta, out_meta); |
| 21321 | } |
| 21322 | |
| 21323 | at::Tensor x_; |
| 21324 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 21325 | at::functionalization::impl::sync(x); |
| 21326 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 21327 | } else { |
| 21328 | x_ = x; |
| 21329 | } |
| 21330 | |
| 21331 | at::Tensor n_; |
| 21332 | if (at::functionalization::impl::isFunctionalTensor(n)) { |
| 21333 | at::functionalization::impl::sync(n); |
| 21334 | n_ = at::functionalization::impl::from_functional_tensor(n); |
| 21335 | } else { |
| 21336 | n_ = n; |
| 21337 | } |
| 21338 | |
| 21339 | at::Tensor out_; |
| 21340 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21341 | at::functionalization::impl::sync(out); |
| 21342 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21343 | } else { |
| 21344 | out_ = out; |
| 21345 | } |
| 21346 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21347 | if ((false || at::functionalization::impl::isFunctionalTensor(x) || at::functionalization::impl::isFunctionalTensor(n))) { |
| 21348 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21349 | TORCH_INTERNAL_ASSERT(false, |
| 21350 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21351 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21352 | } else { |
| 21353 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21354 | at::AutoDispatchSkipFunctionalize guard; |
| 21355 | at::Tensor tmp_output = at::_ops::special_hermite_polynomial_he_out::call(x_, n_, out_); |
| 21356 | return out;; |
| 21357 | } |
| 21358 | } else { |
| 21359 | at::Tensor tmp_output; |
| 21360 | { |
| 21361 | at::AutoDispatchSkipFunctionalize guard; |
| 21362 | tmp_output = at::_ops::special_hermite_polynomial_he::call(x_, n_); |
| 21363 | } |
| 21364 | at::functionalization::impl::replace_(out, tmp_output); |
| 21365 | at::functionalization::impl::commit_update(out); |
| 21366 | at::functionalization::impl::sync(out); |
| 21367 | return out; |
| 21368 | } |
| 21369 | } |
| 21370 | |
| 21371 | at::Tensor & special_hermite_polynomial_he_out_x_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { |
| 21372 | if (false) { |
| 21373 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21374 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21375 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21376 | auto n_meta = to_meta(n); |
| 21377 | auto out_meta = to_meta(out); |
| 21378 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21379 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21380 | at::_ops::special_hermite_polynomial_he_x_scalar_out::call(x, n_meta, out_meta); |
| 21381 | } |
| 21382 | |
| 21383 | at::Tensor n_; |
| 21384 | if (at::functionalization::impl::isFunctionalTensor(n)) { |
| 21385 | at::functionalization::impl::sync(n); |
| 21386 | n_ = at::functionalization::impl::from_functional_tensor(n); |
| 21387 | } else { |
| 21388 | n_ = n; |
| 21389 | } |
| 21390 | |
| 21391 | at::Tensor out_; |
| 21392 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21393 | at::functionalization::impl::sync(out); |
| 21394 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21395 | } else { |
| 21396 | out_ = out; |
| 21397 | } |
| 21398 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21399 | if ((false || at::functionalization::impl::isFunctionalTensor(n))) { |
| 21400 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21401 | TORCH_INTERNAL_ASSERT(false, |
| 21402 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21403 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21404 | } else { |
| 21405 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21406 | at::AutoDispatchSkipFunctionalize guard; |
| 21407 | at::Tensor tmp_output = at::_ops::special_hermite_polynomial_he_x_scalar_out::call(x, n_, out_); |
| 21408 | return out;; |
| 21409 | } |
| 21410 | } else { |
| 21411 | at::Tensor tmp_output; |
| 21412 | { |
| 21413 | at::AutoDispatchSkipFunctionalize guard; |
| 21414 | tmp_output = at::_ops::special_hermite_polynomial_he_x_scalar::call(x, n_); |
| 21415 | } |
| 21416 | at::functionalization::impl::replace_(out, tmp_output); |
| 21417 | at::functionalization::impl::commit_update(out); |
| 21418 | at::functionalization::impl::sync(out); |
| 21419 | return out; |
| 21420 | } |
| 21421 | } |
| 21422 | |
| 21423 | at::Tensor & special_hermite_polynomial_he_out_n_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { |
| 21424 | if (false) { |
| 21425 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21426 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21427 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21428 | auto x_meta = to_meta(x); |
| 21429 | auto out_meta = to_meta(out); |
| 21430 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21431 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21432 | at::_ops::special_hermite_polynomial_he_n_scalar_out::call(x_meta, n, out_meta); |
| 21433 | } |
| 21434 | |
| 21435 | at::Tensor x_; |
| 21436 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 21437 | at::functionalization::impl::sync(x); |
| 21438 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 21439 | } else { |
| 21440 | x_ = x; |
| 21441 | } |
| 21442 | |
| 21443 | at::Tensor out_; |
| 21444 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21445 | at::functionalization::impl::sync(out); |
| 21446 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21447 | } else { |
| 21448 | out_ = out; |
| 21449 | } |
| 21450 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21451 | if ((false || at::functionalization::impl::isFunctionalTensor(x))) { |
| 21452 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21453 | TORCH_INTERNAL_ASSERT(false, |
| 21454 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21455 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21456 | } else { |
| 21457 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21458 | at::AutoDispatchSkipFunctionalize guard; |
| 21459 | at::Tensor tmp_output = at::_ops::special_hermite_polynomial_he_n_scalar_out::call(x_, n, out_); |
| 21460 | return out;; |
| 21461 | } |
| 21462 | } else { |
| 21463 | at::Tensor tmp_output; |
| 21464 | { |
| 21465 | at::AutoDispatchSkipFunctionalize guard; |
| 21466 | tmp_output = at::_ops::special_hermite_polynomial_he_n_scalar::call(x_, n); |
| 21467 | } |
| 21468 | at::functionalization::impl::replace_(out, tmp_output); |
| 21469 | at::functionalization::impl::commit_update(out); |
| 21470 | at::functionalization::impl::sync(out); |
| 21471 | return out; |
| 21472 | } |
| 21473 | } |
| 21474 | |
| 21475 | at::Tensor & special_laguerre_polynomial_l_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 21476 | if (false) { |
| 21477 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21478 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21479 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21480 | auto x_meta = to_meta(x); |
| 21481 | auto n_meta = to_meta(n); |
| 21482 | auto out_meta = to_meta(out); |
| 21483 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21484 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21485 | at::_ops::special_laguerre_polynomial_l_out::call(x_meta, n_meta, out_meta); |
| 21486 | } |
| 21487 | |
| 21488 | at::Tensor x_; |
| 21489 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 21490 | at::functionalization::impl::sync(x); |
| 21491 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 21492 | } else { |
| 21493 | x_ = x; |
| 21494 | } |
| 21495 | |
| 21496 | at::Tensor n_; |
| 21497 | if (at::functionalization::impl::isFunctionalTensor(n)) { |
| 21498 | at::functionalization::impl::sync(n); |
| 21499 | n_ = at::functionalization::impl::from_functional_tensor(n); |
| 21500 | } else { |
| 21501 | n_ = n; |
| 21502 | } |
| 21503 | |
| 21504 | at::Tensor out_; |
| 21505 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21506 | at::functionalization::impl::sync(out); |
| 21507 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21508 | } else { |
| 21509 | out_ = out; |
| 21510 | } |
| 21511 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21512 | if ((false || at::functionalization::impl::isFunctionalTensor(x) || at::functionalization::impl::isFunctionalTensor(n))) { |
| 21513 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21514 | TORCH_INTERNAL_ASSERT(false, |
| 21515 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21516 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21517 | } else { |
| 21518 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21519 | at::AutoDispatchSkipFunctionalize guard; |
| 21520 | at::Tensor tmp_output = at::_ops::special_laguerre_polynomial_l_out::call(x_, n_, out_); |
| 21521 | return out;; |
| 21522 | } |
| 21523 | } else { |
| 21524 | at::Tensor tmp_output; |
| 21525 | { |
| 21526 | at::AutoDispatchSkipFunctionalize guard; |
| 21527 | tmp_output = at::_ops::special_laguerre_polynomial_l::call(x_, n_); |
| 21528 | } |
| 21529 | at::functionalization::impl::replace_(out, tmp_output); |
| 21530 | at::functionalization::impl::commit_update(out); |
| 21531 | at::functionalization::impl::sync(out); |
| 21532 | return out; |
| 21533 | } |
| 21534 | } |
| 21535 | |
| 21536 | at::Tensor & special_laguerre_polynomial_l_out_x_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { |
| 21537 | if (false) { |
| 21538 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21539 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21540 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21541 | auto n_meta = to_meta(n); |
| 21542 | auto out_meta = to_meta(out); |
| 21543 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21544 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21545 | at::_ops::special_laguerre_polynomial_l_x_scalar_out::call(x, n_meta, out_meta); |
| 21546 | } |
| 21547 | |
| 21548 | at::Tensor n_; |
| 21549 | if (at::functionalization::impl::isFunctionalTensor(n)) { |
| 21550 | at::functionalization::impl::sync(n); |
| 21551 | n_ = at::functionalization::impl::from_functional_tensor(n); |
| 21552 | } else { |
| 21553 | n_ = n; |
| 21554 | } |
| 21555 | |
| 21556 | at::Tensor out_; |
| 21557 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21558 | at::functionalization::impl::sync(out); |
| 21559 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21560 | } else { |
| 21561 | out_ = out; |
| 21562 | } |
| 21563 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21564 | if ((false || at::functionalization::impl::isFunctionalTensor(n))) { |
| 21565 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21566 | TORCH_INTERNAL_ASSERT(false, |
| 21567 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21568 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21569 | } else { |
| 21570 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21571 | at::AutoDispatchSkipFunctionalize guard; |
| 21572 | at::Tensor tmp_output = at::_ops::special_laguerre_polynomial_l_x_scalar_out::call(x, n_, out_); |
| 21573 | return out;; |
| 21574 | } |
| 21575 | } else { |
| 21576 | at::Tensor tmp_output; |
| 21577 | { |
| 21578 | at::AutoDispatchSkipFunctionalize guard; |
| 21579 | tmp_output = at::_ops::special_laguerre_polynomial_l_x_scalar::call(x, n_); |
| 21580 | } |
| 21581 | at::functionalization::impl::replace_(out, tmp_output); |
| 21582 | at::functionalization::impl::commit_update(out); |
| 21583 | at::functionalization::impl::sync(out); |
| 21584 | return out; |
| 21585 | } |
| 21586 | } |
| 21587 | |
| 21588 | at::Tensor & special_laguerre_polynomial_l_out_n_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { |
| 21589 | if (false) { |
| 21590 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21591 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21592 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21593 | auto x_meta = to_meta(x); |
| 21594 | auto out_meta = to_meta(out); |
| 21595 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21596 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21597 | at::_ops::special_laguerre_polynomial_l_n_scalar_out::call(x_meta, n, out_meta); |
| 21598 | } |
| 21599 | |
| 21600 | at::Tensor x_; |
| 21601 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 21602 | at::functionalization::impl::sync(x); |
| 21603 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 21604 | } else { |
| 21605 | x_ = x; |
| 21606 | } |
| 21607 | |
| 21608 | at::Tensor out_; |
| 21609 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21610 | at::functionalization::impl::sync(out); |
| 21611 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21612 | } else { |
| 21613 | out_ = out; |
| 21614 | } |
| 21615 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21616 | if ((false || at::functionalization::impl::isFunctionalTensor(x))) { |
| 21617 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21618 | TORCH_INTERNAL_ASSERT(false, |
| 21619 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21620 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21621 | } else { |
| 21622 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21623 | at::AutoDispatchSkipFunctionalize guard; |
| 21624 | at::Tensor tmp_output = at::_ops::special_laguerre_polynomial_l_n_scalar_out::call(x_, n, out_); |
| 21625 | return out;; |
| 21626 | } |
| 21627 | } else { |
| 21628 | at::Tensor tmp_output; |
| 21629 | { |
| 21630 | at::AutoDispatchSkipFunctionalize guard; |
| 21631 | tmp_output = at::_ops::special_laguerre_polynomial_l_n_scalar::call(x_, n); |
| 21632 | } |
| 21633 | at::functionalization::impl::replace_(out, tmp_output); |
| 21634 | at::functionalization::impl::commit_update(out); |
| 21635 | at::functionalization::impl::sync(out); |
| 21636 | return out; |
| 21637 | } |
| 21638 | } |
| 21639 | |
| 21640 | at::Tensor & special_shifted_chebyshev_polynomial_v_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 21641 | if (false) { |
| 21642 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21643 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21644 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21645 | auto x_meta = to_meta(x); |
| 21646 | auto n_meta = to_meta(n); |
| 21647 | auto out_meta = to_meta(out); |
| 21648 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21649 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21650 | at::_ops::special_shifted_chebyshev_polynomial_v_out::call(x_meta, n_meta, out_meta); |
| 21651 | } |
| 21652 | |
| 21653 | at::Tensor x_; |
| 21654 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 21655 | at::functionalization::impl::sync(x); |
| 21656 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 21657 | } else { |
| 21658 | x_ = x; |
| 21659 | } |
| 21660 | |
| 21661 | at::Tensor n_; |
| 21662 | if (at::functionalization::impl::isFunctionalTensor(n)) { |
| 21663 | at::functionalization::impl::sync(n); |
| 21664 | n_ = at::functionalization::impl::from_functional_tensor(n); |
| 21665 | } else { |
| 21666 | n_ = n; |
| 21667 | } |
| 21668 | |
| 21669 | at::Tensor out_; |
| 21670 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21671 | at::functionalization::impl::sync(out); |
| 21672 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21673 | } else { |
| 21674 | out_ = out; |
| 21675 | } |
| 21676 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21677 | if ((false || at::functionalization::impl::isFunctionalTensor(x) || at::functionalization::impl::isFunctionalTensor(n))) { |
| 21678 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21679 | TORCH_INTERNAL_ASSERT(false, |
| 21680 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21681 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21682 | } else { |
| 21683 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21684 | at::AutoDispatchSkipFunctionalize guard; |
| 21685 | at::Tensor tmp_output = at::_ops::special_shifted_chebyshev_polynomial_v_out::call(x_, n_, out_); |
| 21686 | return out;; |
| 21687 | } |
| 21688 | } else { |
| 21689 | at::Tensor tmp_output; |
| 21690 | { |
| 21691 | at::AutoDispatchSkipFunctionalize guard; |
| 21692 | tmp_output = at::_ops::special_shifted_chebyshev_polynomial_v::call(x_, n_); |
| 21693 | } |
| 21694 | at::functionalization::impl::replace_(out, tmp_output); |
| 21695 | at::functionalization::impl::commit_update(out); |
| 21696 | at::functionalization::impl::sync(out); |
| 21697 | return out; |
| 21698 | } |
| 21699 | } |
| 21700 | |
| 21701 | at::Tensor & special_shifted_chebyshev_polynomial_v_out_x_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { |
| 21702 | if (false) { |
| 21703 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21704 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21705 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21706 | auto n_meta = to_meta(n); |
| 21707 | auto out_meta = to_meta(out); |
| 21708 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21709 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21710 | at::_ops::special_shifted_chebyshev_polynomial_v_x_scalar_out::call(x, n_meta, out_meta); |
| 21711 | } |
| 21712 | |
| 21713 | at::Tensor n_; |
| 21714 | if (at::functionalization::impl::isFunctionalTensor(n)) { |
| 21715 | at::functionalization::impl::sync(n); |
| 21716 | n_ = at::functionalization::impl::from_functional_tensor(n); |
| 21717 | } else { |
| 21718 | n_ = n; |
| 21719 | } |
| 21720 | |
| 21721 | at::Tensor out_; |
| 21722 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21723 | at::functionalization::impl::sync(out); |
| 21724 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21725 | } else { |
| 21726 | out_ = out; |
| 21727 | } |
| 21728 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21729 | if ((false || at::functionalization::impl::isFunctionalTensor(n))) { |
| 21730 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21731 | TORCH_INTERNAL_ASSERT(false, |
| 21732 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21733 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21734 | } else { |
| 21735 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21736 | at::AutoDispatchSkipFunctionalize guard; |
| 21737 | at::Tensor tmp_output = at::_ops::special_shifted_chebyshev_polynomial_v_x_scalar_out::call(x, n_, out_); |
| 21738 | return out;; |
| 21739 | } |
| 21740 | } else { |
| 21741 | at::Tensor tmp_output; |
| 21742 | { |
| 21743 | at::AutoDispatchSkipFunctionalize guard; |
| 21744 | tmp_output = at::_ops::special_shifted_chebyshev_polynomial_v_x_scalar::call(x, n_); |
| 21745 | } |
| 21746 | at::functionalization::impl::replace_(out, tmp_output); |
| 21747 | at::functionalization::impl::commit_update(out); |
| 21748 | at::functionalization::impl::sync(out); |
| 21749 | return out; |
| 21750 | } |
| 21751 | } |
| 21752 | |
| 21753 | at::Tensor & special_shifted_chebyshev_polynomial_v_out_n_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { |
| 21754 | if (false) { |
| 21755 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21756 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21757 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21758 | auto x_meta = to_meta(x); |
| 21759 | auto out_meta = to_meta(out); |
| 21760 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21761 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21762 | at::_ops::special_shifted_chebyshev_polynomial_v_n_scalar_out::call(x_meta, n, out_meta); |
| 21763 | } |
| 21764 | |
| 21765 | at::Tensor x_; |
| 21766 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 21767 | at::functionalization::impl::sync(x); |
| 21768 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 21769 | } else { |
| 21770 | x_ = x; |
| 21771 | } |
| 21772 | |
| 21773 | at::Tensor out_; |
| 21774 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21775 | at::functionalization::impl::sync(out); |
| 21776 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21777 | } else { |
| 21778 | out_ = out; |
| 21779 | } |
| 21780 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21781 | if ((false || at::functionalization::impl::isFunctionalTensor(x))) { |
| 21782 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21783 | TORCH_INTERNAL_ASSERT(false, |
| 21784 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21785 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21786 | } else { |
| 21787 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21788 | at::AutoDispatchSkipFunctionalize guard; |
| 21789 | at::Tensor tmp_output = at::_ops::special_shifted_chebyshev_polynomial_v_n_scalar_out::call(x_, n, out_); |
| 21790 | return out;; |
| 21791 | } |
| 21792 | } else { |
| 21793 | at::Tensor tmp_output; |
| 21794 | { |
| 21795 | at::AutoDispatchSkipFunctionalize guard; |
| 21796 | tmp_output = at::_ops::special_shifted_chebyshev_polynomial_v_n_scalar::call(x_, n); |
| 21797 | } |
| 21798 | at::functionalization::impl::replace_(out, tmp_output); |
| 21799 | at::functionalization::impl::commit_update(out); |
| 21800 | at::functionalization::impl::sync(out); |
| 21801 | return out; |
| 21802 | } |
| 21803 | } |
| 21804 | |
| 21805 | void _fused_adamw_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional<at::Tensor> & grad_scale, const c10::optional<at::Tensor> & found_inf, at::TensorList out) { |
| 21806 | if (false) { |
| 21807 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21808 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21809 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21810 | auto self_meta = to_meta(self); |
| 21811 | auto grads_meta = to_meta(grads); |
| 21812 | auto exp_avgs_meta = to_meta(exp_avgs); |
| 21813 | auto exp_avg_sqs_meta = to_meta(exp_avg_sqs); |
| 21814 | auto max_exp_avg_sqs_meta = to_meta(max_exp_avg_sqs); |
| 21815 | auto state_steps_meta = to_meta(state_steps); |
| 21816 | auto grad_scale_meta = to_meta(grad_scale); |
| 21817 | auto found_inf_meta = to_meta(found_inf); |
| 21818 | auto out_meta = to_meta(out); |
| 21819 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21820 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21821 | at::_ops::_fused_adamw_out::call(self_meta, grads_meta, exp_avgs_meta, exp_avg_sqs_meta, max_exp_avg_sqs_meta, state_steps_meta, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale_meta, found_inf_meta, out_meta); |
| 21822 | } |
| 21823 | |
| 21824 | ::std::vector<at::Tensor> self_; |
| 21825 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 21826 | at::functionalization::impl::sync(self); |
| 21827 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 21828 | } else { |
| 21829 | self_ = self.vec(); |
| 21830 | } |
| 21831 | |
| 21832 | ::std::vector<at::Tensor> grads_; |
| 21833 | if (at::functionalization::impl::isFunctionalTensor(grads)) { |
| 21834 | at::functionalization::impl::sync(grads); |
| 21835 | grads_ = at::functionalization::impl::from_functional_tensor(grads); |
| 21836 | } else { |
| 21837 | grads_ = grads.vec(); |
| 21838 | } |
| 21839 | |
| 21840 | ::std::vector<at::Tensor> exp_avgs_; |
| 21841 | if (at::functionalization::impl::isFunctionalTensor(exp_avgs)) { |
| 21842 | at::functionalization::impl::sync(exp_avgs); |
| 21843 | exp_avgs_ = at::functionalization::impl::from_functional_tensor(exp_avgs); |
| 21844 | } else { |
| 21845 | exp_avgs_ = exp_avgs.vec(); |
| 21846 | } |
| 21847 | |
| 21848 | ::std::vector<at::Tensor> exp_avg_sqs_; |
| 21849 | if (at::functionalization::impl::isFunctionalTensor(exp_avg_sqs)) { |
| 21850 | at::functionalization::impl::sync(exp_avg_sqs); |
| 21851 | exp_avg_sqs_ = at::functionalization::impl::from_functional_tensor(exp_avg_sqs); |
| 21852 | } else { |
| 21853 | exp_avg_sqs_ = exp_avg_sqs.vec(); |
| 21854 | } |
| 21855 | |
| 21856 | ::std::vector<at::Tensor> max_exp_avg_sqs_; |
| 21857 | if (at::functionalization::impl::isFunctionalTensor(max_exp_avg_sqs)) { |
| 21858 | at::functionalization::impl::sync(max_exp_avg_sqs); |
| 21859 | max_exp_avg_sqs_ = at::functionalization::impl::from_functional_tensor(max_exp_avg_sqs); |
| 21860 | } else { |
| 21861 | max_exp_avg_sqs_ = max_exp_avg_sqs.vec(); |
| 21862 | } |
| 21863 | |
| 21864 | ::std::vector<at::Tensor> state_steps_; |
| 21865 | if (at::functionalization::impl::isFunctionalTensor(state_steps)) { |
| 21866 | at::functionalization::impl::sync(state_steps); |
| 21867 | state_steps_ = at::functionalization::impl::from_functional_tensor(state_steps); |
| 21868 | } else { |
| 21869 | state_steps_ = state_steps.vec(); |
| 21870 | } |
| 21871 | |
| 21872 | c10::optional<at::Tensor> grad_scale_; |
| 21873 | if (at::functionalization::impl::isFunctionalTensor(grad_scale)) { |
| 21874 | at::functionalization::impl::sync(grad_scale); |
| 21875 | grad_scale_ = at::functionalization::impl::from_functional_tensor(grad_scale); |
| 21876 | } else { |
| 21877 | grad_scale_ = grad_scale; |
| 21878 | } |
| 21879 | |
| 21880 | c10::optional<at::Tensor> found_inf_; |
| 21881 | if (at::functionalization::impl::isFunctionalTensor(found_inf)) { |
| 21882 | at::functionalization::impl::sync(found_inf); |
| 21883 | found_inf_ = at::functionalization::impl::from_functional_tensor(found_inf); |
| 21884 | } else { |
| 21885 | found_inf_ = found_inf; |
| 21886 | } |
| 21887 | |
| 21888 | ::std::vector<at::Tensor> out_; |
| 21889 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 21890 | at::functionalization::impl::sync(out); |
| 21891 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 21892 | } else { |
| 21893 | out_ = out.vec(); |
| 21894 | } |
| 21895 | if (!(true && at::functionalization::impl::isFunctionalTensor(grads) && at::functionalization::impl::isFunctionalTensor(exp_avgs) && at::functionalization::impl::isFunctionalTensor(exp_avg_sqs) && at::functionalization::impl::isFunctionalTensor(max_exp_avg_sqs) && at::functionalization::impl::isFunctionalTensor(out))) { |
| 21896 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(state_steps) || at::functionalization::impl::isFunctionalTensor(grad_scale) || at::functionalization::impl::isFunctionalTensor(found_inf))) { |
| 21897 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 21898 | TORCH_INTERNAL_ASSERT(false, |
| 21899 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 21900 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 21901 | } else { |
| 21902 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 21903 | at::AutoDispatchSkipFunctionalize guard; |
| 21904 | at::_ops::_fused_adamw_out::call(self_, grads_, exp_avgs_, exp_avg_sqs_, max_exp_avg_sqs_, state_steps_, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale_, found_inf_, out_); |
| 21905 | ; |
| 21906 | } |
| 21907 | } else { |
| 21908 | ::std::tuple<::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>> tmp_output; |
| 21909 | { |
| 21910 | at::AutoDispatchSkipFunctionalize guard; |
| 21911 | tmp_output = at::_ops::_fused_adamw::call(self_, grads_, exp_avgs_, exp_avg_sqs_, max_exp_avg_sqs_, state_steps_, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale_, found_inf_); |
| 21912 | } |
| 21913 | at::functionalization::impl::replace_(grads, std::get<0>(tmp_output)); |
| 21914 | at::functionalization::impl::commit_update(grads); |
| 21915 | at::functionalization::impl::sync(grads); |
| 21916 | at::functionalization::impl::replace_(exp_avgs, std::get<1>(tmp_output)); |
| 21917 | at::functionalization::impl::commit_update(exp_avgs); |
| 21918 | at::functionalization::impl::sync(exp_avgs); |
| 21919 | at::functionalization::impl::replace_(exp_avg_sqs, std::get<2>(tmp_output)); |
| 21920 | at::functionalization::impl::commit_update(exp_avg_sqs); |
| 21921 | at::functionalization::impl::sync(exp_avg_sqs); |
| 21922 | at::functionalization::impl::replace_(max_exp_avg_sqs, std::get<3>(tmp_output)); |
| 21923 | at::functionalization::impl::commit_update(max_exp_avg_sqs); |
| 21924 | at::functionalization::impl::sync(max_exp_avg_sqs); |
| 21925 | at::functionalization::impl::replace_(out, std::get<4>(tmp_output)); |
| 21926 | at::functionalization::impl::commit_update(out); |
| 21927 | at::functionalization::impl::sync(out); |
| 21928 | |
| 21929 | } |
| 21930 | } |
| 21931 | |
| 21932 | void _fused_adamw_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional<at::Tensor> & grad_scale, const c10::optional<at::Tensor> & found_inf) { |
| 21933 | if (true) { |
| 21934 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 21935 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 21936 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 21937 | auto self_meta = to_meta(self); |
| 21938 | auto grads_meta = to_meta(grads); |
| 21939 | auto exp_avgs_meta = to_meta(exp_avgs); |
| 21940 | auto exp_avg_sqs_meta = to_meta(exp_avg_sqs); |
| 21941 | auto max_exp_avg_sqs_meta = to_meta(max_exp_avg_sqs); |
| 21942 | auto state_steps_meta = to_meta(state_steps); |
| 21943 | auto grad_scale_meta = to_meta(grad_scale); |
| 21944 | auto found_inf_meta = to_meta(found_inf); |
| 21945 | at::AutoDispatchSkipFunctionalize func_guard; |
| 21946 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 21947 | at::_ops::_fused_adamw_::call(self_meta, grads_meta, exp_avgs_meta, exp_avg_sqs_meta, max_exp_avg_sqs_meta, state_steps_meta, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale_meta, found_inf_meta); |
| 21948 | } |
| 21949 | |
| 21950 | ::std::vector<at::Tensor> self_; |
| 21951 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 21952 | at::functionalization::impl::sync(self); |
| 21953 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 21954 | } else { |
| 21955 | self_ = self.vec(); |
| 21956 | } |
| 21957 | |
| 21958 | ::std::vector<at::Tensor> grads_; |
| 21959 | if (at::functionalization::impl::isFunctionalTensor(grads)) { |
| 21960 | at::functionalization::impl::sync(grads); |
| 21961 | grads_ = at::functionalization::impl::from_functional_tensor(grads); |
| 21962 | } else { |
| 21963 | grads_ = grads.vec(); |
| 21964 | } |
| 21965 | |
| 21966 | ::std::vector<at::Tensor> exp_avgs_; |
| 21967 | if (at::functionalization::impl::isFunctionalTensor(exp_avgs)) { |
| 21968 | at::functionalization::impl::sync(exp_avgs); |
| 21969 | exp_avgs_ = at::functionalization::impl::from_functional_tensor(exp_avgs); |
| 21970 | } else { |
| 21971 | exp_avgs_ = exp_avgs.vec(); |
| 21972 | } |
| 21973 | |
| 21974 | ::std::vector<at::Tensor> exp_avg_sqs_; |
| 21975 | if (at::functionalization::impl::isFunctionalTensor(exp_avg_sqs)) { |
| 21976 | at::functionalization::impl::sync(exp_avg_sqs); |
| 21977 | exp_avg_sqs_ = at::functionalization::impl::from_functional_tensor(exp_avg_sqs); |
| 21978 | } else { |
| 21979 | exp_avg_sqs_ = exp_avg_sqs.vec(); |
| 21980 | } |
| 21981 | |
| 21982 | ::std::vector<at::Tensor> max_exp_avg_sqs_; |
| 21983 | if (at::functionalization::impl::isFunctionalTensor(max_exp_avg_sqs)) { |
| 21984 | at::functionalization::impl::sync(max_exp_avg_sqs); |
| 21985 | max_exp_avg_sqs_ = at::functionalization::impl::from_functional_tensor(max_exp_avg_sqs); |
| 21986 | } else { |
| 21987 | max_exp_avg_sqs_ = max_exp_avg_sqs.vec(); |
| 21988 | } |
| 21989 | |
| 21990 | ::std::vector<at::Tensor> state_steps_; |
| 21991 | if (at::functionalization::impl::isFunctionalTensor(state_steps)) { |
| 21992 | at::functionalization::impl::sync(state_steps); |
| 21993 | state_steps_ = at::functionalization::impl::from_functional_tensor(state_steps); |
| 21994 | } else { |
| 21995 | state_steps_ = state_steps.vec(); |
| 21996 | } |
| 21997 | |
| 21998 | c10::optional<at::Tensor> grad_scale_; |
| 21999 | if (at::functionalization::impl::isFunctionalTensor(grad_scale)) { |
| 22000 | at::functionalization::impl::sync(grad_scale); |
| 22001 | grad_scale_ = at::functionalization::impl::from_functional_tensor(grad_scale); |
| 22002 | } else { |
| 22003 | grad_scale_ = grad_scale; |
| 22004 | } |
| 22005 | |
| 22006 | c10::optional<at::Tensor> found_inf_; |
| 22007 | if (at::functionalization::impl::isFunctionalTensor(found_inf)) { |
| 22008 | at::functionalization::impl::sync(found_inf); |
| 22009 | found_inf_ = at::functionalization::impl::from_functional_tensor(found_inf); |
| 22010 | } else { |
| 22011 | found_inf_ = found_inf; |
| 22012 | } |
| 22013 | if (!(true && at::functionalization::impl::isFunctionalTensor(self) && at::functionalization::impl::isFunctionalTensor(grads) && at::functionalization::impl::isFunctionalTensor(exp_avgs) && at::functionalization::impl::isFunctionalTensor(exp_avg_sqs) && at::functionalization::impl::isFunctionalTensor(max_exp_avg_sqs))) { |
| 22014 | if ((false || at::functionalization::impl::isFunctionalTensor(state_steps) || at::functionalization::impl::isFunctionalTensor(grad_scale) || at::functionalization::impl::isFunctionalTensor(found_inf))) { |
| 22015 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 22016 | TORCH_INTERNAL_ASSERT(false, |
| 22017 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 22018 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 22019 | } else { |
| 22020 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 22021 | at::AutoDispatchSkipFunctionalize guard; |
| 22022 | at::_ops::_fused_adamw_::call(self_, grads_, exp_avgs_, exp_avg_sqs_, max_exp_avg_sqs_, state_steps_, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale_, found_inf_); |
| 22023 | ; |
| 22024 | } |
| 22025 | } else { |
| 22026 | ::std::tuple<::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>> tmp_output; |
| 22027 | { |
| 22028 | at::AutoDispatchSkipFunctionalize guard; |
| 22029 | tmp_output = at::_ops::_fused_adamw::call(self_, grads_, exp_avgs_, exp_avg_sqs_, max_exp_avg_sqs_, state_steps_, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale_, found_inf_); |
| 22030 | } |
| 22031 | at::functionalization::impl::replace_(self, std::get<0>(tmp_output)); |
| 22032 | at::functionalization::impl::commit_update(self); |
| 22033 | at::functionalization::impl::sync(self); |
| 22034 | at::functionalization::impl::replace_(grads, std::get<1>(tmp_output)); |
| 22035 | at::functionalization::impl::commit_update(grads); |
| 22036 | at::functionalization::impl::sync(grads); |
| 22037 | at::functionalization::impl::replace_(exp_avgs, std::get<2>(tmp_output)); |
| 22038 | at::functionalization::impl::commit_update(exp_avgs); |
| 22039 | at::functionalization::impl::sync(exp_avgs); |
| 22040 | at::functionalization::impl::replace_(exp_avg_sqs, std::get<3>(tmp_output)); |
| 22041 | at::functionalization::impl::commit_update(exp_avg_sqs); |
| 22042 | at::functionalization::impl::sync(exp_avg_sqs); |
| 22043 | at::functionalization::impl::replace_(max_exp_avg_sqs, std::get<4>(tmp_output)); |
| 22044 | at::functionalization::impl::commit_update(max_exp_avg_sqs); |
| 22045 | at::functionalization::impl::sync(max_exp_avg_sqs); |
| 22046 | |
| 22047 | } |
| 22048 | } |
| 22049 | |
| 22050 | at::Tensor _fw_primal(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t level) { |
| 22051 | |
| 22052 | at::Tensor self_; |
| 22053 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 22054 | |
| 22055 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 22056 | } else { |
| 22057 | self_ = self; |
| 22058 | } |
| 22059 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 22060 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 22061 | at::AutoDispatchSkipFunctionalize guard; |
| 22062 | return at::_ops::_fw_primal::call(self_, level); |
| 22063 | } |
| 22064 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 22065 | auto compute_reference_meta = |
| 22066 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 22067 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 22068 | at::Tensor reference_tensor_output; |
| 22069 | if (compute_reference_meta) { |
| 22070 | auto self_meta = to_meta(self); |
| 22071 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22072 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22073 | reference_tensor_output = at::_ops::_fw_primal::call(self_meta, level); |
| 22074 | } |
| 22075 | at::Tensor tmp_output; |
| 22076 | { |
| 22077 | at::AutoDispatchSkipFunctionalize guard; |
| 22078 | if (reapply_views) { |
| 22079 | tmp_output = at::_ops::_fw_primal::call(self_, level); |
| 22080 | } else { |
| 22081 | tmp_output = at::_ops::_fw_primal_copy::call(self_, level); |
| 22082 | } |
| 22083 | } |
| 22084 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 22085 | [reapply_views = reapply_views, level = level](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 22086 | if (reapply_views) { |
| 22087 | return at::_ops::_fw_primal::call(base, level); |
| 22088 | } else { |
| 22089 | return at::_ops::_fw_primal_copy::call(base, level); |
| 22090 | } |
| 22091 | }, |
| 22092 | [reapply_views = reapply_views, level = level](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 22093 | return at::functionalization::FunctionalInverses::_fw_primal_copy_inverse(base, mutated_view, reapply_views, level); |
| 22094 | } |
| 22095 | ); |
| 22096 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 22097 | // See Note [Propagating strides in the functionalization pass] |
| 22098 | if (compute_reference_meta) { |
| 22099 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 22100 | } |
| 22101 | return out; |
| 22102 | } |
| 22103 | |
| 22104 | at::Tensor _make_dual(c10::DispatchKeySet dispatchKeySet, const at::Tensor & primal, const at::Tensor & tangent, int64_t level) { |
| 22105 | |
| 22106 | at::Tensor primal_; |
| 22107 | if (at::functionalization::impl::isFunctionalTensor(primal)) { |
| 22108 | |
| 22109 | primal_ = at::functionalization::impl::from_functional_tensor(primal); |
| 22110 | } else { |
| 22111 | primal_ = primal; |
| 22112 | } |
| 22113 | |
| 22114 | at::Tensor tangent_; |
| 22115 | if (at::functionalization::impl::isFunctionalTensor(tangent)) { |
| 22116 | |
| 22117 | tangent_ = at::functionalization::impl::from_functional_tensor(tangent); |
| 22118 | } else { |
| 22119 | tangent_ = tangent; |
| 22120 | } |
| 22121 | if (!at::functionalization::impl::isFunctionalTensor(primal)) { |
| 22122 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 22123 | at::AutoDispatchSkipFunctionalize guard; |
| 22124 | return at::_ops::_make_dual::call(primal_, tangent_, level); |
| 22125 | } |
| 22126 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 22127 | auto compute_reference_meta = |
| 22128 | primal.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 22129 | primal.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 22130 | at::Tensor reference_tensor_output; |
| 22131 | if (compute_reference_meta) { |
| 22132 | auto primal_meta = to_meta(primal); |
| 22133 | auto tangent_meta = to_meta(tangent); |
| 22134 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22135 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22136 | reference_tensor_output = at::_ops::_make_dual::call(primal_meta, tangent_meta, level); |
| 22137 | } |
| 22138 | at::Tensor tmp_output; |
| 22139 | { |
| 22140 | at::AutoDispatchSkipFunctionalize guard; |
| 22141 | if (reapply_views) { |
| 22142 | tmp_output = at::_ops::_make_dual::call(primal_, tangent_, level); |
| 22143 | } else { |
| 22144 | tmp_output = at::_ops::_make_dual_copy::call(primal_, tangent_, level); |
| 22145 | } |
| 22146 | } |
| 22147 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 22148 | [reapply_views = reapply_views, tangent = tangent, level = level](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 22149 | if (reapply_views) { |
| 22150 | return at::_ops::_make_dual::call(base, tangent, level); |
| 22151 | } else { |
| 22152 | return at::_ops::_make_dual_copy::call(base, tangent, level); |
| 22153 | } |
| 22154 | }, |
| 22155 | [reapply_views = reapply_views, tangent = tangent, level = level](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 22156 | return at::functionalization::FunctionalInverses::_make_dual_copy_inverse(base, mutated_view, reapply_views, tangent, level); |
| 22157 | } |
| 22158 | ); |
| 22159 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, primal, view_meta); |
| 22160 | // See Note [Propagating strides in the functionalization pass] |
| 22161 | if (compute_reference_meta) { |
| 22162 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 22163 | } |
| 22164 | return out; |
| 22165 | } |
| 22166 | |
| 22167 | at::Tensor view_as_complex(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 22168 | |
| 22169 | at::Tensor self_; |
| 22170 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 22171 | |
| 22172 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 22173 | } else { |
| 22174 | self_ = self; |
| 22175 | } |
| 22176 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 22177 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 22178 | at::AutoDispatchSkipFunctionalize guard; |
| 22179 | return at::_ops::view_as_complex::call(self_); |
| 22180 | } |
| 22181 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 22182 | auto compute_reference_meta = |
| 22183 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 22184 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 22185 | at::Tensor reference_tensor_output; |
| 22186 | if (compute_reference_meta) { |
| 22187 | auto self_meta = to_meta(self); |
| 22188 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22189 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22190 | reference_tensor_output = at::_ops::view_as_complex::call(self_meta); |
| 22191 | } |
| 22192 | at::Tensor tmp_output; |
| 22193 | { |
| 22194 | at::AutoDispatchSkipFunctionalize guard; |
| 22195 | if (reapply_views) { |
| 22196 | tmp_output = at::_ops::view_as_complex::call(self_); |
| 22197 | } else { |
| 22198 | tmp_output = at::_ops::view_as_complex_copy::call(self_); |
| 22199 | } |
| 22200 | } |
| 22201 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 22202 | [reapply_views = reapply_views](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 22203 | if (reapply_views) { |
| 22204 | return at::_ops::view_as_complex::call(base); |
| 22205 | } else { |
| 22206 | return at::_ops::view_as_complex_copy::call(base); |
| 22207 | } |
| 22208 | }, |
| 22209 | [reapply_views = reapply_views](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 22210 | return at::functionalization::FunctionalInverses::view_as_complex_copy_inverse(base, mutated_view, reapply_views); |
| 22211 | } |
| 22212 | ); |
| 22213 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 22214 | // See Note [Propagating strides in the functionalization pass] |
| 22215 | if (compute_reference_meta) { |
| 22216 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 22217 | } |
| 22218 | return out; |
| 22219 | } |
| 22220 | |
| 22221 | at::Tensor expand(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, bool implicit) { |
| 22222 | |
| 22223 | at::Tensor self_; |
| 22224 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 22225 | |
| 22226 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 22227 | } else { |
| 22228 | self_ = self; |
| 22229 | } |
| 22230 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 22231 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 22232 | at::AutoDispatchSkipFunctionalize guard; |
| 22233 | return at::_ops::expand::call(self_, size, implicit); |
| 22234 | } |
| 22235 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 22236 | auto compute_reference_meta = |
| 22237 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 22238 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 22239 | at::Tensor reference_tensor_output; |
| 22240 | if (compute_reference_meta) { |
| 22241 | auto self_meta = to_meta(self); |
| 22242 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22243 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22244 | reference_tensor_output = at::_ops::expand::call(self_meta, size, implicit); |
| 22245 | } |
| 22246 | at::Tensor tmp_output; |
| 22247 | { |
| 22248 | at::AutoDispatchSkipFunctionalize guard; |
| 22249 | if (reapply_views) { |
| 22250 | tmp_output = at::_ops::expand::call(self_, size, implicit); |
| 22251 | } else { |
| 22252 | tmp_output = at::_ops::expand_copy::call(self_, size, implicit); |
| 22253 | } |
| 22254 | } |
| 22255 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 22256 | [reapply_views = reapply_views, size = size.vec(), implicit = implicit](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 22257 | if (reapply_views) { |
| 22258 | return at::_ops::expand::call(base, size, implicit); |
| 22259 | } else { |
| 22260 | return at::_ops::expand_copy::call(base, size, implicit); |
| 22261 | } |
| 22262 | }, |
| 22263 | [reapply_views = reapply_views, size = size.vec(), implicit = implicit](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 22264 | return at::functionalization::FunctionalInverses::expand_copy_inverse(base, mutated_view, reapply_views, size, implicit); |
| 22265 | } |
| 22266 | ); |
| 22267 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 22268 | // See Note [Propagating strides in the functionalization pass] |
| 22269 | if (compute_reference_meta) { |
| 22270 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 22271 | } |
| 22272 | return out; |
| 22273 | } |
| 22274 | |
| 22275 | at::Tensor transpose_int(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim0, int64_t dim1) { |
| 22276 | |
| 22277 | at::Tensor self_; |
| 22278 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 22279 | |
| 22280 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 22281 | } else { |
| 22282 | self_ = self; |
| 22283 | } |
| 22284 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 22285 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 22286 | at::AutoDispatchSkipFunctionalize guard; |
| 22287 | return at::_ops::transpose_int::call(self_, dim0, dim1); |
| 22288 | } |
| 22289 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 22290 | auto compute_reference_meta = |
| 22291 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 22292 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 22293 | at::Tensor reference_tensor_output; |
| 22294 | if (compute_reference_meta) { |
| 22295 | auto self_meta = to_meta(self); |
| 22296 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22297 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22298 | reference_tensor_output = at::_ops::transpose_int::call(self_meta, dim0, dim1); |
| 22299 | } |
| 22300 | at::Tensor tmp_output; |
| 22301 | { |
| 22302 | at::AutoDispatchSkipFunctionalize guard; |
| 22303 | if (reapply_views) { |
| 22304 | tmp_output = at::_ops::transpose_int::call(self_, dim0, dim1); |
| 22305 | } else { |
| 22306 | tmp_output = at::_ops::transpose_copy_int::call(self_, dim0, dim1); |
| 22307 | } |
| 22308 | } |
| 22309 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 22310 | [reapply_views = reapply_views, dim0 = dim0, dim1 = dim1](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 22311 | if (reapply_views) { |
| 22312 | return at::_ops::transpose_int::call(base, dim0, dim1); |
| 22313 | } else { |
| 22314 | return at::_ops::transpose_copy_int::call(base, dim0, dim1); |
| 22315 | } |
| 22316 | }, |
| 22317 | [reapply_views = reapply_views, dim0 = dim0, dim1 = dim1](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 22318 | return at::functionalization::FunctionalInverses::transpose_copy_int_inverse(base, mutated_view, reapply_views, dim0, dim1); |
| 22319 | } |
| 22320 | ); |
| 22321 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 22322 | // See Note [Propagating strides in the functionalization pass] |
| 22323 | if (compute_reference_meta) { |
| 22324 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 22325 | } |
| 22326 | return out; |
| 22327 | } |
| 22328 | |
| 22329 | at::Tensor & transpose_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim0, int64_t dim1) { |
| 22330 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 22331 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 22332 | |
| 22333 | at::Tensor self_; |
| 22334 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 22335 | |
| 22336 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 22337 | } else { |
| 22338 | self_ = self; |
| 22339 | } |
| 22340 | at::AutoDispatchSkipFunctionalize guard; |
| 22341 | return at::_ops::transpose_::call(self_, dim0, dim1); |
| 22342 | } |
| 22343 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 22344 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 22345 | [reapply_views = reapply_views, dim0 = dim0, dim1 = dim1](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 22346 | if (reapply_views) { |
| 22347 | return at::_ops::transpose_int::call(base, dim0, dim1); |
| 22348 | } else { |
| 22349 | return at::_ops::transpose_copy_int::call(base, dim0, dim1); |
| 22350 | } |
| 22351 | }, |
| 22352 | [reapply_views = reapply_views, dim0 = dim0, dim1 = dim1](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 22353 | return at::functionalization::FunctionalInverses::transpose_copy_int_inverse(base, mutated_view, reapply_views, dim0, dim1); |
| 22354 | } |
| 22355 | ); |
| 22356 | auto compute_reference_meta = |
| 22357 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 22358 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 22359 | at::Tensor reference_tensor_output; |
| 22360 | if (compute_reference_meta) { |
| 22361 | auto self_meta = to_meta(self); |
| 22362 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22363 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22364 | reference_tensor_output = at::_ops::transpose_::call(self_meta, dim0, dim1); |
| 22365 | } |
| 22366 | // This function adds the above view meta to the current tensor and replays them off the base, |
| 22367 | // mutating the size/stride info of the current FunctionalTensorWrapper. |
| 22368 | // Because of this, we need to make sure to run the reference shape function above, |
| 22369 | // BEFORE doing this (otherwise we'll end up runnin the reference function using the wrong sizes/strides) |
| 22370 | at::functionalization::impl::mutate_view_meta(self, view_meta); |
| 22371 | // See Note [Propagating strides in the functionalization pass] |
| 22372 | // XLA/LTC don't implement the logic to propagate strides correctly, so we need to rely |
| 22373 | // on a reference implementation here (instead of relying on the output from the forward lambda |
| 22374 | // having the correct stride info) |
| 22375 | if (compute_reference_meta) { |
| 22376 | at::functionalization::impl::set_sizes_strides_offset(self, reference_tensor_output); |
| 22377 | } |
| 22378 | return self; |
| 22379 | } |
| 22380 | |
| 22381 | at::Tensor _nested_view_from_buffer(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & nested_size, const at::Tensor & nested_strides, at::IntArrayRef offsets) { |
| 22382 | |
| 22383 | at::Tensor self_; |
| 22384 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 22385 | |
| 22386 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 22387 | } else { |
| 22388 | self_ = self; |
| 22389 | } |
| 22390 | |
| 22391 | at::Tensor nested_size_; |
| 22392 | if (at::functionalization::impl::isFunctionalTensor(nested_size)) { |
| 22393 | |
| 22394 | nested_size_ = at::functionalization::impl::from_functional_tensor(nested_size); |
| 22395 | } else { |
| 22396 | nested_size_ = nested_size; |
| 22397 | } |
| 22398 | |
| 22399 | at::Tensor nested_strides_; |
| 22400 | if (at::functionalization::impl::isFunctionalTensor(nested_strides)) { |
| 22401 | |
| 22402 | nested_strides_ = at::functionalization::impl::from_functional_tensor(nested_strides); |
| 22403 | } else { |
| 22404 | nested_strides_ = nested_strides; |
| 22405 | } |
| 22406 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 22407 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 22408 | at::AutoDispatchSkipFunctionalize guard; |
| 22409 | return at::_ops::_nested_view_from_buffer::call(self_, nested_size_, nested_strides_, offsets); |
| 22410 | } |
| 22411 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 22412 | auto compute_reference_meta = |
| 22413 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 22414 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 22415 | at::Tensor reference_tensor_output; |
| 22416 | if (compute_reference_meta) { |
| 22417 | auto self_meta = to_meta(self); |
| 22418 | auto nested_size_meta = to_meta(nested_size); |
| 22419 | auto nested_strides_meta = to_meta(nested_strides); |
| 22420 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22421 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22422 | reference_tensor_output = at::_ops::_nested_view_from_buffer::call(self_meta, nested_size_meta, nested_strides_meta, offsets); |
| 22423 | } |
| 22424 | at::Tensor tmp_output; |
| 22425 | { |
| 22426 | at::AutoDispatchSkipFunctionalize guard; |
| 22427 | if (reapply_views) { |
| 22428 | tmp_output = at::_ops::_nested_view_from_buffer::call(self_, nested_size_, nested_strides_, offsets); |
| 22429 | } else { |
| 22430 | tmp_output = at::_ops::_nested_view_from_buffer_copy::call(self_, nested_size_, nested_strides_, offsets); |
| 22431 | } |
| 22432 | } |
| 22433 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 22434 | [reapply_views = reapply_views, nested_size = nested_size, nested_strides = nested_strides, offsets = offsets.vec()](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 22435 | if (reapply_views) { |
| 22436 | return at::_ops::_nested_view_from_buffer::call(base, nested_size, nested_strides, offsets); |
| 22437 | } else { |
| 22438 | return at::_ops::_nested_view_from_buffer_copy::call(base, nested_size, nested_strides, offsets); |
| 22439 | } |
| 22440 | }, |
| 22441 | [reapply_views = reapply_views, nested_size = nested_size, nested_strides = nested_strides, offsets = offsets.vec()](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 22442 | return at::functionalization::FunctionalInverses::_nested_view_from_buffer_copy_inverse(base, mutated_view, reapply_views, nested_size, nested_strides, offsets); |
| 22443 | } |
| 22444 | ); |
| 22445 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 22446 | // See Note [Propagating strides in the functionalization pass] |
| 22447 | if (compute_reference_meta) { |
| 22448 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 22449 | } |
| 22450 | return out; |
| 22451 | } |
| 22452 | |
| 22453 | at::Tensor unsqueeze(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim) { |
| 22454 | |
| 22455 | at::Tensor self_; |
| 22456 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 22457 | |
| 22458 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 22459 | } else { |
| 22460 | self_ = self; |
| 22461 | } |
| 22462 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 22463 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 22464 | at::AutoDispatchSkipFunctionalize guard; |
| 22465 | return at::_ops::unsqueeze::call(self_, dim); |
| 22466 | } |
| 22467 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 22468 | auto compute_reference_meta = |
| 22469 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 22470 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 22471 | at::Tensor reference_tensor_output; |
| 22472 | if (compute_reference_meta) { |
| 22473 | auto self_meta = to_meta(self); |
| 22474 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22475 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22476 | reference_tensor_output = at::_ops::unsqueeze::call(self_meta, dim); |
| 22477 | } |
| 22478 | at::Tensor tmp_output; |
| 22479 | { |
| 22480 | at::AutoDispatchSkipFunctionalize guard; |
| 22481 | if (reapply_views) { |
| 22482 | tmp_output = at::_ops::unsqueeze::call(self_, dim); |
| 22483 | } else { |
| 22484 | tmp_output = at::_ops::unsqueeze_copy::call(self_, dim); |
| 22485 | } |
| 22486 | } |
| 22487 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 22488 | [reapply_views = reapply_views, dim = dim](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 22489 | if (reapply_views) { |
| 22490 | return at::_ops::unsqueeze::call(base, dim); |
| 22491 | } else { |
| 22492 | return at::_ops::unsqueeze_copy::call(base, dim); |
| 22493 | } |
| 22494 | }, |
| 22495 | [reapply_views = reapply_views, dim = dim](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 22496 | return at::functionalization::FunctionalInverses::unsqueeze_copy_inverse(base, mutated_view, reapply_views, dim); |
| 22497 | } |
| 22498 | ); |
| 22499 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 22500 | // See Note [Propagating strides in the functionalization pass] |
| 22501 | if (compute_reference_meta) { |
| 22502 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 22503 | } |
| 22504 | return out; |
| 22505 | } |
| 22506 | |
| 22507 | at::Tensor & unsqueeze_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim) { |
| 22508 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 22509 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 22510 | |
| 22511 | at::Tensor self_; |
| 22512 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 22513 | |
| 22514 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 22515 | } else { |
| 22516 | self_ = self; |
| 22517 | } |
| 22518 | at::AutoDispatchSkipFunctionalize guard; |
| 22519 | return at::_ops::unsqueeze_::call(self_, dim); |
| 22520 | } |
| 22521 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 22522 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 22523 | [reapply_views = reapply_views, dim = dim](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 22524 | if (reapply_views) { |
| 22525 | return at::_ops::unsqueeze::call(base, dim); |
| 22526 | } else { |
| 22527 | return at::_ops::unsqueeze_copy::call(base, dim); |
| 22528 | } |
| 22529 | }, |
| 22530 | [reapply_views = reapply_views, dim = dim](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 22531 | return at::functionalization::FunctionalInverses::unsqueeze_copy_inverse(base, mutated_view, reapply_views, dim); |
| 22532 | } |
| 22533 | ); |
| 22534 | auto compute_reference_meta = |
| 22535 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 22536 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 22537 | at::Tensor reference_tensor_output; |
| 22538 | if (compute_reference_meta) { |
| 22539 | auto self_meta = to_meta(self); |
| 22540 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22541 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22542 | reference_tensor_output = at::_ops::unsqueeze_::call(self_meta, dim); |
| 22543 | } |
| 22544 | // This function adds the above view meta to the current tensor and replays them off the base, |
| 22545 | // mutating the size/stride info of the current FunctionalTensorWrapper. |
| 22546 | // Because of this, we need to make sure to run the reference shape function above, |
| 22547 | // BEFORE doing this (otherwise we'll end up runnin the reference function using the wrong sizes/strides) |
| 22548 | at::functionalization::impl::mutate_view_meta(self, view_meta); |
| 22549 | // See Note [Propagating strides in the functionalization pass] |
| 22550 | // XLA/LTC don't implement the logic to propagate strides correctly, so we need to rely |
| 22551 | // on a reference implementation here (instead of relying on the output from the forward lambda |
| 22552 | // having the correct stride info) |
| 22553 | if (compute_reference_meta) { |
| 22554 | at::functionalization::impl::set_sizes_strides_offset(self, reference_tensor_output); |
| 22555 | } |
| 22556 | return self; |
| 22557 | } |
| 22558 | |
| 22559 | at::Tensor _values(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 22560 | |
| 22561 | at::Tensor self_; |
| 22562 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 22563 | |
| 22564 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 22565 | } else { |
| 22566 | self_ = self; |
| 22567 | } |
| 22568 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 22569 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 22570 | at::AutoDispatchSkipFunctionalize guard; |
| 22571 | return at::_ops::_values::call(self_); |
| 22572 | } |
| 22573 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 22574 | auto compute_reference_meta = |
| 22575 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 22576 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 22577 | at::Tensor reference_tensor_output; |
| 22578 | if (compute_reference_meta) { |
| 22579 | auto self_meta = to_meta(self); |
| 22580 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22581 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22582 | reference_tensor_output = at::_ops::_values::call(self_meta); |
| 22583 | } |
| 22584 | at::Tensor tmp_output; |
| 22585 | { |
| 22586 | at::AutoDispatchSkipFunctionalize guard; |
| 22587 | if (reapply_views) { |
| 22588 | tmp_output = at::_ops::_values::call(self_); |
| 22589 | } else { |
| 22590 | tmp_output = at::_ops::_values_copy::call(self_); |
| 22591 | } |
| 22592 | } |
| 22593 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 22594 | [reapply_views = reapply_views](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 22595 | if (reapply_views) { |
| 22596 | return at::_ops::_values::call(base); |
| 22597 | } else { |
| 22598 | return at::_ops::_values_copy::call(base); |
| 22599 | } |
| 22600 | }, |
| 22601 | [reapply_views = reapply_views](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 22602 | return at::functionalization::FunctionalInverses::_values_copy_inverse(base, mutated_view, reapply_views); |
| 22603 | } |
| 22604 | ); |
| 22605 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 22606 | // See Note [Propagating strides in the functionalization pass] |
| 22607 | if (compute_reference_meta) { |
| 22608 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 22609 | } |
| 22610 | return out; |
| 22611 | } |
| 22612 | |
| 22613 | at::Tensor ccol_indices(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 22614 | |
| 22615 | at::Tensor self_; |
| 22616 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 22617 | |
| 22618 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 22619 | } else { |
| 22620 | self_ = self; |
| 22621 | } |
| 22622 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 22623 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 22624 | at::AutoDispatchSkipFunctionalize guard; |
| 22625 | return at::_ops::ccol_indices::call(self_); |
| 22626 | } |
| 22627 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 22628 | auto compute_reference_meta = |
| 22629 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 22630 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 22631 | at::Tensor reference_tensor_output; |
| 22632 | if (compute_reference_meta) { |
| 22633 | auto self_meta = to_meta(self); |
| 22634 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22635 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22636 | reference_tensor_output = at::_ops::ccol_indices::call(self_meta); |
| 22637 | } |
| 22638 | at::Tensor tmp_output; |
| 22639 | { |
| 22640 | at::AutoDispatchSkipFunctionalize guard; |
| 22641 | if (reapply_views) { |
| 22642 | tmp_output = at::_ops::ccol_indices::call(self_); |
| 22643 | } else { |
| 22644 | tmp_output = at::_ops::ccol_indices_copy::call(self_); |
| 22645 | } |
| 22646 | } |
| 22647 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 22648 | [reapply_views = reapply_views](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 22649 | if (reapply_views) { |
| 22650 | return at::_ops::ccol_indices::call(base); |
| 22651 | } else { |
| 22652 | return at::_ops::ccol_indices_copy::call(base); |
| 22653 | } |
| 22654 | }, |
| 22655 | [reapply_views = reapply_views](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 22656 | return at::functionalization::FunctionalInverses::ccol_indices_copy_inverse(base, mutated_view, reapply_views); |
| 22657 | } |
| 22658 | ); |
| 22659 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 22660 | // See Note [Propagating strides in the functionalization pass] |
| 22661 | if (compute_reference_meta) { |
| 22662 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 22663 | } |
| 22664 | return out; |
| 22665 | } |
| 22666 | |
| 22667 | at::Tensor view(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size) { |
| 22668 | |
| 22669 | at::Tensor self_; |
| 22670 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 22671 | |
| 22672 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 22673 | } else { |
| 22674 | self_ = self; |
| 22675 | } |
| 22676 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 22677 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 22678 | at::AutoDispatchSkipFunctionalize guard; |
| 22679 | return at::_ops::view::call(self_, size); |
| 22680 | } |
| 22681 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 22682 | auto compute_reference_meta = |
| 22683 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 22684 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 22685 | at::Tensor reference_tensor_output; |
| 22686 | if (compute_reference_meta) { |
| 22687 | auto self_meta = to_meta(self); |
| 22688 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22689 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22690 | reference_tensor_output = at::_ops::view::call(self_meta, size); |
| 22691 | } |
| 22692 | at::Tensor tmp_output; |
| 22693 | { |
| 22694 | at::AutoDispatchSkipFunctionalize guard; |
| 22695 | if (reapply_views) { |
| 22696 | tmp_output = at::_ops::view::call(self_, size); |
| 22697 | } else { |
| 22698 | tmp_output = at::_ops::view_copy::call(self_, size); |
| 22699 | } |
| 22700 | } |
| 22701 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 22702 | [reapply_views = reapply_views, size = size.vec()](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 22703 | if (reapply_views) { |
| 22704 | return at::_ops::view::call(base, size); |
| 22705 | } else { |
| 22706 | return at::_ops::view_copy::call(base, size); |
| 22707 | } |
| 22708 | }, |
| 22709 | [reapply_views = reapply_views, size = size.vec()](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 22710 | return at::functionalization::FunctionalInverses::view_copy_inverse(base, mutated_view, reapply_views, size); |
| 22711 | } |
| 22712 | ); |
| 22713 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 22714 | // See Note [Propagating strides in the functionalization pass] |
| 22715 | if (compute_reference_meta) { |
| 22716 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 22717 | } |
| 22718 | return out; |
| 22719 | } |
| 22720 | |
| 22721 | at::Tensor view_dtype(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::ScalarType dtype) { |
| 22722 | |
| 22723 | at::Tensor self_; |
| 22724 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 22725 | |
| 22726 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 22727 | } else { |
| 22728 | self_ = self; |
| 22729 | } |
| 22730 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 22731 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 22732 | at::AutoDispatchSkipFunctionalize guard; |
| 22733 | return at::_ops::view_dtype::call(self_, dtype); |
| 22734 | } |
| 22735 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 22736 | auto compute_reference_meta = |
| 22737 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 22738 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 22739 | at::Tensor reference_tensor_output; |
| 22740 | if (compute_reference_meta) { |
| 22741 | auto self_meta = to_meta(self); |
| 22742 | at::AutoDispatchSkipFunctionalize func_guard; |
| 22743 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 22744 | reference_tensor_output = at::_ops::view_dtype::call(self_meta, dtype); |
| 22745 | } |
| 22746 | at::Tensor tmp_output; |
| 22747 | { |
| 22748 | at::AutoDispatchSkipFunctionalize guard; |
| 22749 | if (reapply_views) { |
| 22750 | tmp_output = at::_ops::view_dtype::call(self_, dtype); |
| 22751 | } else { |
| 22752 | tmp_output = at::_ops::view_copy_dtype::call(self_, dtype); |
| 22753 | } |
| 22754 | } |
| 22755 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 22756 | [reapply_views = reapply_views, dtype = dtype](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 22757 | if (reapply_views) { |
| 22758 | return at::_ops::view_dtype::call(base, dtype); |
| 22759 | } else { |
| 22760 | return at::_ops::view_copy_dtype::call(base, dtype); |
| 22761 | } |
| 22762 | }, |
| 22763 | [reapply_views = reapply_views, dtype = dtype](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 22764 | return at::functionalization::FunctionalInverses::view_copy_dtype_inverse(base, mutated_view, reapply_views, dtype); |
| 22765 | } |
| 22766 | ); |
| 22767 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 22768 | // See Note [Propagating strides in the functionalization pass] |
| 22769 | if (compute_reference_meta) { |
| 22770 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 22771 | } |
| 22772 | return out; |
| 22773 | } |
| 22774 | |
| 22775 | } // namespace functionalization |
| 22776 | |
| 22777 | namespace { |
| 22778 | |
| 22779 | TORCH_LIBRARY_IMPL(aten, Functionalize, m) { |
| 22780 | m.impl("_masked_scale.out", TORCH_FN(functionalization::_masked_scale_out_out)); |
| 22781 | m.impl("native_dropout.out", TORCH_FN(functionalization::native_dropout_out_out)); |
| 22782 | m.impl("native_dropout_backward.out", TORCH_FN(functionalization::native_dropout_backward_out_out)); |
| 22783 | m.impl("asinh.out", TORCH_FN(functionalization::asinh_out_out)); |
| 22784 | m.impl("asinh_", TORCH_FN(functionalization::asinh_)); |
| 22785 | m.impl("arctanh", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::arctanh)); |
| 22786 | m.impl("arctanh.out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::Tensor & out)>(at::native::arctanh_out)); |
| 22787 | m.impl("arctanh_", static_cast<at::Tensor & (*)(at::Tensor & self)>(at::native::arctanh_)); |
| 22788 | m.impl("baddbmm.out", TORCH_FN(functionalization::baddbmm_out_out)); |
| 22789 | m.impl("baddbmm_", TORCH_FN(functionalization::baddbmm_)); |
| 22790 | m.impl("quantized_batch_norm.out", TORCH_FN(functionalization::quantized_batch_norm_out_out)); |
| 22791 | m.impl("bernoulli.out", TORCH_FN(functionalization::bernoulli_out_out)); |
| 22792 | m.impl("bernoulli.Tensor_out", TORCH_FN(functionalization::bernoulli_out_Tensor_out)); |
| 22793 | m.impl("bernoulli_.Tensor", TORCH_FN(functionalization::bernoulli__Tensor)); |
| 22794 | m.impl("bernoulli.float_out", TORCH_FN(functionalization::bernoulli_out_float_out)); |
| 22795 | m.impl("bernoulli_.float", TORCH_FN(functionalization::bernoulli__float)); |
| 22796 | m.impl("bmm.out", TORCH_FN(functionalization::bmm_out_out)); |
| 22797 | m.impl("clamp_max.out", TORCH_FN(functionalization::clamp_max_out_out)); |
| 22798 | m.impl("clamp_max_", TORCH_FN(functionalization::clamp_max_)); |
| 22799 | m.impl("clamp_max.Tensor_out", TORCH_FN(functionalization::clamp_max_out_Tensor_out)); |
| 22800 | m.impl("clamp_max_.Tensor", TORCH_FN(functionalization::clamp_max__Tensor)); |
| 22801 | m.impl("clamp_min.out", TORCH_FN(functionalization::clamp_min_out_out)); |
| 22802 | m.impl("clamp_min_", TORCH_FN(functionalization::clamp_min_)); |
| 22803 | m.impl("clamp_min.Tensor_out", TORCH_FN(functionalization::clamp_min_out_Tensor_out)); |
| 22804 | m.impl("clamp_min_.Tensor", TORCH_FN(functionalization::clamp_min__Tensor)); |
| 22805 | m.impl("clip", static_cast<at::Tensor (*)(const at::Tensor & self, const c10::optional<at::Scalar> & min, const c10::optional<at::Scalar> & max)>(at::native::clip)); |
| 22806 | m.impl("clip.out", static_cast<at::Tensor & (*)(const at::Tensor & self, const c10::optional<at::Scalar> & min, const c10::optional<at::Scalar> & max, at::Tensor & out)>(at::native::clip_out)); |
| 22807 | m.impl("clip_", static_cast<at::Tensor & (*)(at::Tensor & self, const c10::optional<at::Scalar> & min, const c10::optional<at::Scalar> & max)>(at::native::clip_)); |
| 22808 | m.impl("clip.Tensor", static_cast<at::Tensor (*)(const at::Tensor & self, const c10::optional<at::Tensor> & min, const c10::optional<at::Tensor> & max)>(at::native::clip)); |
| 22809 | m.impl("clip.Tensor_out", static_cast<at::Tensor & (*)(const at::Tensor & self, const c10::optional<at::Tensor> & min, const c10::optional<at::Tensor> & max, at::Tensor & out)>(at::native::clip_out)); |
| 22810 | m.impl("clip_.Tensor", static_cast<at::Tensor & (*)(at::Tensor & self, const c10::optional<at::Tensor> & min, const c10::optional<at::Tensor> & max)>(at::native::clip_)); |
| 22811 | m.impl("complex.out", TORCH_FN(functionalization::complex_out_out)); |
| 22812 | m.impl("constant_pad_nd.out", TORCH_FN(functionalization::constant_pad_nd_out_out)); |
| 22813 | m.impl("conv_tbc.out", TORCH_FN(functionalization::conv_tbc_out_out)); |
| 22814 | m.impl("_copy_from_and_resize.out", TORCH_FN(functionalization::_copy_from_and_resize_out_out)); |
| 22815 | m.impl("cos.out", TORCH_FN(functionalization::cos_out_out)); |
| 22816 | m.impl("cos_", TORCH_FN(functionalization::cos_)); |
| 22817 | m.impl("count_nonzero.dim_IntList_out", TORCH_FN(functionalization::count_nonzero_out_dim_IntList_out)); |
| 22818 | m.impl("count_nonzero.out", TORCH_FN(functionalization::count_nonzero_out_out)); |
| 22819 | m.impl("cudnn_affine_grid_generator_backward.out", TORCH_FN(functionalization::cudnn_affine_grid_generator_backward_out_out)); |
| 22820 | m.impl("cudnn_batch_norm.out", TORCH_FN(functionalization::cudnn_batch_norm_out_out)); |
| 22821 | m.impl("mps_convolution_transpose_backward.out", TORCH_FN(functionalization::mps_convolution_transpose_backward_out_out)); |
| 22822 | m.impl("cudnn_grid_sampler_backward.out", TORCH_FN(functionalization::cudnn_grid_sampler_backward_out_out)); |
| 22823 | m.impl("cummin.out", TORCH_FN(functionalization::cummin_out_out)); |
| 22824 | m.impl("cummin.dimname", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & self, at::Dimname dim)>(at::native::cummin)); |
| 22825 | m.impl("cummin.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::cummin_out)); |
| 22826 | m.impl("cumsum.out", TORCH_FN(functionalization::cumsum_out_out)); |
| 22827 | m.impl("cumsum_", TORCH_FN(functionalization::cumsum_)); |
| 22828 | m.impl("cumsum.dimname", static_cast<at::Tensor (*)(const at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype)>(at::native::cumsum)); |
| 22829 | m.impl("cumsum.dimname_out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype, at::Tensor & out)>(at::native::cumsum_out)); |
| 22830 | m.impl("cumsum_.dimname", static_cast<at::Tensor & (*)(at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype)>(at::native::cumsum_)); |
| 22831 | m.impl("_ctc_loss.out", TORCH_FN(functionalization::_ctc_loss_out_out)); |
| 22832 | m.impl("_ctc_loss.Tensor_out", TORCH_FN(functionalization::_ctc_loss_out_Tensor_out)); |
| 22833 | m.impl("_ctc_loss_backward.out", TORCH_FN(functionalization::_ctc_loss_backward_out_out)); |
| 22834 | m.impl("embedding.out", TORCH_FN(functionalization::embedding_out_out)); |
| 22835 | m.impl("embedding_dense_backward.out", TORCH_FN(functionalization::embedding_dense_backward_out_out)); |
| 22836 | m.impl("new_zeros.out", TORCH_FN(functionalization::new_zeros_out_out)); |
| 22837 | m.impl("new_ones.out", TORCH_FN(functionalization::new_ones_out_out)); |
| 22838 | m.impl("_empty_per_channel_affine_quantized.out", TORCH_FN(functionalization::_empty_per_channel_affine_quantized_out_out)); |
| 22839 | m.impl("exp2.out", TORCH_FN(functionalization::exp2_out_out)); |
| 22840 | m.impl("exp2_", TORCH_FN(functionalization::exp2_)); |
| 22841 | m.impl("_grid_sampler_2d_cpu_fallback.out", TORCH_FN(functionalization::_grid_sampler_2d_cpu_fallback_out_out)); |
| 22842 | m.impl("grid_sampler_3d_backward.out", TORCH_FN(functionalization::grid_sampler_3d_backward_out_out)); |
| 22843 | m.impl("_fft_c2c.out", TORCH_FN(functionalization::_fft_c2c_out_out)); |
| 22844 | m.impl("index_copy.out", TORCH_FN(functionalization::index_copy_out_out)); |
| 22845 | m.impl("index_copy_", TORCH_FN(functionalization::index_copy_)); |
| 22846 | m.impl("isin.Tensor_Tensor_out", TORCH_FN(functionalization::isin_out_Tensor_Tensor_out)); |
| 22847 | m.impl("isin.Tensor_Scalar_out", TORCH_FN(functionalization::isin_out_Tensor_Scalar_out)); |
| 22848 | m.impl("isin.Scalar_Tensor_out", TORCH_FN(functionalization::isin_out_Scalar_Tensor_out)); |
| 22849 | m.impl("kron", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other)>(at::native::kron)); |
| 22850 | m.impl("kron.out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & other, at::Tensor & out)>(at::native::kron_out)); |
| 22851 | m.impl("nan_to_num.out", TORCH_FN(functionalization::nan_to_num_out_out)); |
| 22852 | m.impl("nan_to_num_", TORCH_FN(functionalization::nan_to_num_)); |
| 22853 | m.impl("linear", static_cast<at::Tensor (*)(const at::Tensor & input, const at::Tensor & weight, const c10::optional<at::Tensor> & bias)>(at::native::linear)); |
| 22854 | m.impl("linear.out", TORCH_FN(functionalization::linear_out_out)); |
| 22855 | m.impl("mkldnn_linear.out", TORCH_FN(functionalization::mkldnn_linear_out_out)); |
| 22856 | m.impl("linspace.out", TORCH_FN(functionalization::linspace_out_out)); |
| 22857 | m.impl("log.out", TORCH_FN(functionalization::log_out_out)); |
| 22858 | m.impl("log_", TORCH_FN(functionalization::log_)); |
| 22859 | m.impl("log_softmax.int", static_cast<at::Tensor (*)(const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype)>(at::native::log_softmax)); |
| 22860 | m.impl("log_softmax.int_out", TORCH_FN(functionalization::log_softmax_out_int_out)); |
| 22861 | m.impl("_log_softmax.out", TORCH_FN(functionalization::_log_softmax_out_out)); |
| 22862 | m.impl("_log_softmax_backward_data.out", TORCH_FN(functionalization::_log_softmax_backward_data_out_out)); |
| 22863 | m.impl("max.dim_max", TORCH_FN(functionalization::max_out_dim_max)); |
| 22864 | m.impl("max.names_dim", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & self, at::Dimname dim, bool keepdim)>(at::native::max)); |
| 22865 | m.impl("max.names_dim_max", static_cast<::std::tuple<at::Tensor &,at::Tensor &> (*)(const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & max, at::Tensor & max_values)>(at::native::max_out)); |
| 22866 | m.impl("amax.out", TORCH_FN(functionalization::amax_out_out)); |
| 22867 | m.impl("mkldnn_max_pool2d_backward.out", TORCH_FN(functionalization::mkldnn_max_pool2d_backward_out_out)); |
| 22868 | m.impl("mkldnn_max_pool3d.out", TORCH_FN(functionalization::mkldnn_max_pool3d_out_out)); |
| 22869 | m.impl("quantized_max_pool2d.out", TORCH_FN(functionalization::quantized_max_pool2d_out_out)); |
| 22870 | m.impl("mean.out", TORCH_FN(functionalization::mean_out_out)); |
| 22871 | m.impl("mean.names_dim", static_cast<at::Tensor (*)(const at::Tensor & self, at::DimnameList dim, bool keepdim, c10::optional<at::ScalarType> dtype)>(at::native::mean)); |
| 22872 | m.impl("mean.names_out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::DimnameList dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out)>(at::native::mean_out)); |
| 22873 | m.impl("nanmedian.out", TORCH_FN(functionalization::nanmedian_out_out)); |
| 22874 | m.impl("nanmedian.dim_values", TORCH_FN(functionalization::nanmedian_out_dim_values)); |
| 22875 | m.impl("nanmedian.names_dim", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & self, at::Dimname dim, bool keepdim)>(at::native::nanmedian)); |
| 22876 | m.impl("nanmedian.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::nanmedian_out)); |
| 22877 | m.impl("mode.values", TORCH_FN(functionalization::mode_out_values)); |
| 22878 | m.impl("mode.dimname", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & self, at::Dimname dim, bool keepdim)>(at::native::mode)); |
| 22879 | m.impl("mode.dimname_out", 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::mode_out)); |
| 22880 | m.impl("multiply.Tensor", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other)>(at::native::multiply)); |
| 22881 | m.impl("multiply.out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & other, at::Tensor & out)>(at::native::multiply_out)); |
| 22882 | m.impl("multiply_.Tensor", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Tensor & other)>(at::native::multiply_)); |
| 22883 | m.impl("narrow_copy.out", TORCH_FN(functionalization::narrow_copy_out_out)); |
| 22884 | m.impl("batch_norm_gather_stats.out", TORCH_FN(functionalization::batch_norm_gather_stats_out_out)); |
| 22885 | m.impl("batch_norm_gather_stats_with_counts.out", TORCH_FN(functionalization::batch_norm_gather_stats_with_counts_out_out)); |
| 22886 | m.impl("native_batch_norm_backward.out", TORCH_FN(functionalization::native_batch_norm_backward_out_out)); |
| 22887 | m.impl("ones.names_out", TORCH_FN(functionalization::ones_out_names_out)); |
| 22888 | m.impl("ones.out", TORCH_FN(functionalization::ones_out_out)); |
| 22889 | m.impl("_pdist_forward.out", TORCH_FN(functionalization::_pdist_forward_out_out)); |
| 22890 | m.impl("_pdist_backward.out", TORCH_FN(functionalization::_pdist_backward_out_out)); |
| 22891 | m.impl("pixel_shuffle.out", TORCH_FN(functionalization::pixel_shuffle_out_out)); |
| 22892 | m.impl("_pin_memory.out", TORCH_FN(functionalization::_pin_memory_out_out)); |
| 22893 | m.impl("randn.out", static_cast<at::Tensor & (*)(at::IntArrayRef size, at::Tensor & out)>(at::native::randn_out)); |
| 22894 | m.impl("randn.generator_out", static_cast<at::Tensor & (*)(at::IntArrayRef size, c10::optional<at::Generator> generator, at::Tensor & out)>(at::native::randn_out)); |
| 22895 | m.impl("randn.names_out", TORCH_FN(functionalization::randn_out_names_out)); |
| 22896 | m.impl("randn.generator_with_names_out", TORCH_FN(functionalization::randn_out_generator_with_names_out)); |
| 22897 | m.impl("randn_like.out", TORCH_FN(functionalization::randn_like_out_out)); |
| 22898 | m.impl("neg.out", TORCH_FN(functionalization::neg_out_out)); |
| 22899 | m.impl("neg_", TORCH_FN(functionalization::neg_)); |
| 22900 | m.impl("negative", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::negative)); |
| 22901 | m.impl("negative.out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::Tensor & out)>(at::native::negative_out)); |
| 22902 | m.impl("negative_", static_cast<at::Tensor & (*)(at::Tensor & self)>(at::native::negative_)); |
| 22903 | m.impl("repeat_interleave.Tensor_out", TORCH_FN(functionalization::repeat_interleave_out_Tensor_out)); |
| 22904 | m.impl("gelu.out", TORCH_FN(functionalization::gelu_out_out)); |
| 22905 | m.impl("gelu_", TORCH_FN(functionalization::gelu_)); |
| 22906 | m.impl("select_backward.out", TORCH_FN(functionalization::select_backward_out_out)); |
| 22907 | m.impl("mish.out", TORCH_FN(functionalization::mish_out_out)); |
| 22908 | m.impl("mish_", TORCH_FN(functionalization::mish_)); |
| 22909 | m.impl("slice_scatter.out", TORCH_FN(functionalization::slice_scatter_out_out)); |
| 22910 | m.impl("diagonal_scatter.out", TORCH_FN(functionalization::diagonal_scatter_out_out)); |
| 22911 | m.impl("_softmax_backward_data.out", TORCH_FN(functionalization::_softmax_backward_data_out_out)); |
| 22912 | m.impl("unsafe_split.Tensor_out", TORCH_FN(functionalization::unsafe_split_out_Tensor_out)); |
| 22913 | m.impl("unsafe_split_with_sizes.out", TORCH_FN(functionalization::unsafe_split_with_sizes_out_out)); |
| 22914 | m.impl("square", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::square)); |
| 22915 | m.impl("square.out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::Tensor & out)>(at::native::square_out)); |
| 22916 | m.impl("square_", static_cast<at::Tensor & (*)(at::Tensor & self)>(at::native::square_)); |
| 22917 | m.impl("tanh.out", TORCH_FN(functionalization::tanh_out_out)); |
| 22918 | m.impl("tanh_", TORCH_FN(functionalization::tanh_)); |
| 22919 | m.impl("roll.out", TORCH_FN(functionalization::roll_out_out)); |
| 22920 | m.impl("rot90.out", TORCH_FN(functionalization::rot90_out_out)); |
| 22921 | m.impl("_trilinear.out", TORCH_FN(functionalization::_trilinear_out_out)); |
| 22922 | m.impl("_unique.out", TORCH_FN(functionalization::_unique_out_out)); |
| 22923 | m.impl("_unique2.out", TORCH_FN(functionalization::_unique2_out_out)); |
| 22924 | m.impl("_weight_norm_interface.out", TORCH_FN(functionalization::_weight_norm_interface_out_out)); |
| 22925 | m.impl("_efficientzerotensor.out", TORCH_FN(functionalization::_efficientzerotensor_out_out)); |
| 22926 | m.impl("_standard_gamma.out", TORCH_FN(functionalization::_standard_gamma_out_out)); |
| 22927 | m.impl("_dirichlet_grad.out", TORCH_FN(functionalization::_dirichlet_grad_out_out)); |
| 22928 | m.impl("norm.ScalarOpt_dtype_out", TORCH_FN(functionalization::norm_out_ScalarOpt_dtype_out)); |
| 22929 | m.impl("norm.Scalar_out", TORCH_FN(functionalization::norm_out_Scalar_out)); |
| 22930 | m.impl("norm.dtype_out", TORCH_FN(functionalization::norm_out_dtype_out)); |
| 22931 | m.impl("norm.out", TORCH_FN(functionalization::norm_out_out)); |
| 22932 | m.impl("norm.names_ScalarOpt_dim_dtype", static_cast<at::Tensor (*)(const at::Tensor & self, const c10::optional<at::Scalar> & p, at::DimnameList dim, bool keepdim, at::ScalarType dtype)>(at::native::norm)); |
| 22933 | m.impl("norm.names_dtype_out", static_cast<at::Tensor & (*)(const at::Tensor & self, const c10::optional<at::Scalar> & p, at::DimnameList dim, bool keepdim, at::ScalarType dtype, at::Tensor & out)>(at::native::norm_out)); |
| 22934 | m.impl("norm.names_ScalarOpt_dim", static_cast<at::Tensor (*)(const at::Tensor & self, const c10::optional<at::Scalar> & p, at::DimnameList dim, bool keepdim)>(at::native::norm)); |
| 22935 | m.impl("norm.names_out", static_cast<at::Tensor & (*)(const at::Tensor & self, const c10::optional<at::Scalar> & p, at::DimnameList dim, bool keepdim, at::Tensor & out)>(at::native::norm_out)); |
| 22936 | m.impl("frexp.Tensor_out", TORCH_FN(functionalization::frexp_out_Tensor_out)); |
| 22937 | m.impl("frobenius_norm.dim", static_cast<at::Tensor (*)(const at::Tensor & self, at::IntArrayRef dim, bool keepdim)>(at::native::frobenius_norm)); |
| 22938 | m.impl("frobenius_norm.out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out)>(at::native::frobenius_norm_out)); |
| 22939 | m.impl("nuclear_norm", static_cast<at::Tensor (*)(const at::Tensor & self, bool keepdim)>(at::native::nuclear_norm)); |
| 22940 | m.impl("nuclear_norm.out", static_cast<at::Tensor & (*)(const at::Tensor & self, bool keepdim, at::Tensor & out)>(at::native::nuclear_norm_out)); |
| 22941 | m.impl("nuclear_norm.dim", static_cast<at::Tensor (*)(const at::Tensor & self, at::IntArrayRef dim, bool keepdim)>(at::native::nuclear_norm)); |
| 22942 | m.impl("nuclear_norm.dim_out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out)>(at::native::nuclear_norm_out)); |
| 22943 | m.impl("subtract.Tensor", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha)>(at::native::subtract)); |
| 22944 | m.impl("subtract.out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out)>(at::native::subtract_out)); |
| 22945 | m.impl("subtract_.Tensor", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha)>(at::native::subtract_)); |
| 22946 | m.impl("sparse_sampled_addmm.out", TORCH_FN(functionalization::sparse_sampled_addmm_out_out)); |
| 22947 | m.impl("_addmm_activation.out", TORCH_FN(functionalization::_addmm_activation_out_out)); |
| 22948 | m.impl("_to_dense.out", TORCH_FN(functionalization::_to_dense_out_out)); |
| 22949 | m.impl("_coalesced.out", TORCH_FN(functionalization::_coalesced_out_out)); |
| 22950 | m.impl("_coalesced_", TORCH_FN(functionalization::_coalesced_)); |
| 22951 | m.impl("to_sparse_csr.out", TORCH_FN(functionalization::to_sparse_csr_out_out)); |
| 22952 | m.impl("to_sparse_csc.out", TORCH_FN(functionalization::to_sparse_csc_out_out)); |
| 22953 | m.impl("to_sparse_bsc.out", TORCH_FN(functionalization::to_sparse_bsc_out_out)); |
| 22954 | m.impl("quantize_per_tensor_dynamic.out", TORCH_FN(functionalization::quantize_per_tensor_dynamic_out_out)); |
| 22955 | m.impl("quantize_per_tensor.out", TORCH_FN(functionalization::quantize_per_tensor_out_out)); |
| 22956 | m.impl("quantize_per_tensor.tensor_qparams_out", TORCH_FN(functionalization::quantize_per_tensor_out_tensor_qparams_out)); |
| 22957 | m.impl("quantize_per_tensor.tensors_out", TORCH_FN(functionalization::quantize_per_tensor_out_tensors_out)); |
| 22958 | m.impl("quantize_per_channel.out", TORCH_FN(functionalization::quantize_per_channel_out_out)); |
| 22959 | m.impl("_make_per_channel_quantized_tensor.out", TORCH_FN(functionalization::_make_per_channel_quantized_tensor_out_out)); |
| 22960 | m.impl("fake_quantize_per_tensor_affine_cachemask.out", TORCH_FN(functionalization::fake_quantize_per_tensor_affine_cachemask_out_out)); |
| 22961 | m.impl("_fake_quantize_per_tensor_affine_cachemask_tensor_qparams.out", TORCH_FN(functionalization::_fake_quantize_per_tensor_affine_cachemask_tensor_qparams_out_out)); |
| 22962 | m.impl("_fake_quantize_learnable_per_tensor_affine.out", TORCH_FN(functionalization::_fake_quantize_learnable_per_tensor_affine_out_out)); |
| 22963 | m.impl("fake_quantize_per_channel_affine_cachemask.out", TORCH_FN(functionalization::fake_quantize_per_channel_affine_cachemask_out_out)); |
| 22964 | m.impl("lstm_mps_backward.out", TORCH_FN(functionalization::lstm_mps_backward_out_out)); |
| 22965 | m.impl("lift_fresh_copy.out", TORCH_FN(functionalization::lift_fresh_copy_out_out)); |
| 22966 | m.impl("_masked_softmax_backward.out", TORCH_FN(functionalization::_masked_softmax_backward_out_out)); |
| 22967 | m.impl("put.out", TORCH_FN(functionalization::put_out_out)); |
| 22968 | m.impl("put_", TORCH_FN(functionalization::put_)); |
| 22969 | m.impl("index_add.out", TORCH_FN(functionalization::index_add_out_out)); |
| 22970 | m.impl("index_add_", TORCH_FN(functionalization::index_add_)); |
| 22971 | m.impl("index_fill.int_Scalar_out", TORCH_FN(functionalization::index_fill_out_int_Scalar_out)); |
| 22972 | m.impl("index_fill_.int_Scalar", TORCH_FN(functionalization::index_fill__int_Scalar)); |
| 22973 | m.impl("index_fill.int_Tensor_out", TORCH_FN(functionalization::index_fill_out_int_Tensor_out)); |
| 22974 | m.impl("index_fill_.int_Tensor", TORCH_FN(functionalization::index_fill__int_Tensor)); |
| 22975 | m.impl("scatter.src_out", TORCH_FN(functionalization::scatter_out_src_out)); |
| 22976 | m.impl("scatter_.src", TORCH_FN(functionalization::scatter__src)); |
| 22977 | m.impl("scatter.value_out", TORCH_FN(functionalization::scatter_out_value_out)); |
| 22978 | m.impl("scatter_.value", TORCH_FN(functionalization::scatter__value)); |
| 22979 | m.impl("scatter.reduce_out", TORCH_FN(functionalization::scatter_out_reduce_out)); |
| 22980 | m.impl("scatter_.reduce", TORCH_FN(functionalization::scatter__reduce)); |
| 22981 | m.impl("scatter.value_reduce_out", TORCH_FN(functionalization::scatter_out_value_reduce_out)); |
| 22982 | m.impl("scatter_.value_reduce", TORCH_FN(functionalization::scatter__value_reduce)); |
| 22983 | m.impl("scatter_add.out", TORCH_FN(functionalization::scatter_add_out_out)); |
| 22984 | m.impl("scatter_add_", TORCH_FN(functionalization::scatter_add_)); |
| 22985 | m.impl("__lshift__.Scalar_out", TORCH_FN(functionalization::__lshift___out_Scalar_out)); |
| 22986 | m.impl("__ilshift__.Scalar", TORCH_FN(functionalization::__ilshift___Scalar)); |
| 22987 | m.impl("__lshift__.Tensor_out", TORCH_FN(functionalization::__lshift___out_Tensor_out)); |
| 22988 | m.impl("__ilshift__.Tensor", TORCH_FN(functionalization::__ilshift___Tensor)); |
| 22989 | m.impl("greater_equal.Scalar", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Scalar & other)>(at::native::greater_equal)); |
| 22990 | m.impl("greater_equal.Scalar_out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Scalar & other, at::Tensor & out)>(at::native::greater_equal_out)); |
| 22991 | m.impl("greater_equal_.Scalar", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Scalar & other)>(at::native::greater_equal_)); |
| 22992 | m.impl("greater_equal.Tensor", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other)>(at::native::greater_equal)); |
| 22993 | m.impl("greater_equal.Tensor_out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & other, at::Tensor & out)>(at::native::greater_equal_out)); |
| 22994 | m.impl("greater_equal_.Tensor", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Tensor & other)>(at::native::greater_equal_)); |
| 22995 | m.impl("less_equal.Scalar", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Scalar & other)>(at::native::less_equal)); |
| 22996 | m.impl("less_equal.Scalar_out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Scalar & other, at::Tensor & out)>(at::native::less_equal_out)); |
| 22997 | m.impl("less_equal_.Scalar", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Scalar & other)>(at::native::less_equal_)); |
| 22998 | m.impl("less_equal.Tensor", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other)>(at::native::less_equal)); |
| 22999 | m.impl("less_equal.Tensor_out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & other, at::Tensor & out)>(at::native::less_equal_out)); |
| 23000 | m.impl("less_equal_.Tensor", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Tensor & other)>(at::native::less_equal_)); |
| 23001 | m.impl("greater.Scalar", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Scalar & other)>(at::native::greater)); |
| 23002 | m.impl("greater.Scalar_out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Scalar & other, at::Tensor & out)>(at::native::greater_out)); |
| 23003 | m.impl("greater_.Scalar", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Scalar & other)>(at::native::greater_)); |
| 23004 | m.impl("greater.Tensor", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other)>(at::native::greater)); |
| 23005 | m.impl("greater.Tensor_out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & other, at::Tensor & out)>(at::native::greater_out)); |
| 23006 | m.impl("greater_.Tensor", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Tensor & other)>(at::native::greater_)); |
| 23007 | m.impl("lt.Scalar_out", TORCH_FN(functionalization::lt_out_Scalar_out)); |
| 23008 | m.impl("lt_.Scalar", TORCH_FN(functionalization::lt__Scalar)); |
| 23009 | m.impl("lt.Tensor_out", TORCH_FN(functionalization::lt_out_Tensor_out)); |
| 23010 | m.impl("lt_.Tensor", TORCH_FN(functionalization::lt__Tensor)); |
| 23011 | m.impl("less.Scalar", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Scalar & other)>(at::native::less)); |
| 23012 | m.impl("less.Scalar_out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Scalar & other, at::Tensor & out)>(at::native::less_out)); |
| 23013 | m.impl("less_.Scalar", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Scalar & other)>(at::native::less_)); |
| 23014 | m.impl("less.Tensor", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other)>(at::native::less)); |
| 23015 | m.impl("less.Tensor_out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & other, at::Tensor & out)>(at::native::less_out)); |
| 23016 | m.impl("less_.Tensor", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Tensor & other)>(at::native::less_)); |
| 23017 | m.impl("take.out", TORCH_FN(functionalization::take_out_out)); |
| 23018 | m.impl("gather.out", TORCH_FN(functionalization::gather_out_out)); |
| 23019 | m.impl("gather.dimname", static_cast<at::Tensor (*)(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, bool sparse_grad)>(at::native::gather)); |
| 23020 | m.impl("gather.dimname_out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, bool sparse_grad, at::Tensor & out)>(at::native::gather_out)); |
| 23021 | m.impl("cholesky.out", TORCH_FN(functionalization::cholesky_out_out)); |
| 23022 | m.impl("_cholesky_solve_helper.out", TORCH_FN(functionalization::_cholesky_solve_helper_out_out)); |
| 23023 | m.impl("polygamma.out", TORCH_FN(functionalization::polygamma_out_out)); |
| 23024 | m.impl("igamma.out", TORCH_FN(functionalization::igamma_out_out)); |
| 23025 | m.impl("igamma_", TORCH_FN(functionalization::igamma_)); |
| 23026 | m.impl("fmin.out", TORCH_FN(functionalization::fmin_out_out)); |
| 23027 | m.impl("max.unary_out", TORCH_FN(functionalization::max_out_unary_out)); |
| 23028 | m.impl("fmax.out", TORCH_FN(functionalization::fmax_out_out)); |
| 23029 | m.impl("maximum.out", TORCH_FN(functionalization::maximum_out_out)); |
| 23030 | m.impl("max.other", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other)>(at::native::max)); |
| 23031 | m.impl("max.out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & other, at::Tensor & out)>(at::native::max_out)); |
| 23032 | m.impl("_amp_foreach_non_finite_check_and_unscale.out", TORCH_FN(functionalization::_amp_foreach_non_finite_check_and_unscale_out_out)); |
| 23033 | m.impl("_amp_foreach_non_finite_check_and_unscale_", TORCH_FN(functionalization::_amp_foreach_non_finite_check_and_unscale_)); |
| 23034 | m.impl("_foreach_sub.Scalar_out", TORCH_FN(functionalization::_foreach_sub_out_Scalar_out)); |
| 23035 | m.impl("_foreach_sub_.Scalar", TORCH_FN(functionalization::_foreach_sub__Scalar)); |
| 23036 | m.impl("_foreach_maximum.Scalar_out", TORCH_FN(functionalization::_foreach_maximum_out_Scalar_out)); |
| 23037 | m.impl("_foreach_maximum_.Scalar", TORCH_FN(functionalization::_foreach_maximum__Scalar)); |
| 23038 | m.impl("_foreach_sub.List_out", TORCH_FN(functionalization::_foreach_sub_out_List_out)); |
| 23039 | m.impl("_foreach_sub_.List", TORCH_FN(functionalization::_foreach_sub__List)); |
| 23040 | m.impl("_foreach_maximum.List_out", TORCH_FN(functionalization::_foreach_maximum_out_List_out)); |
| 23041 | m.impl("_foreach_maximum_.List", TORCH_FN(functionalization::_foreach_maximum__List)); |
| 23042 | m.impl("_foreach_sub.ScalarList_out", TORCH_FN(functionalization::_foreach_sub_out_ScalarList_out)); |
| 23043 | m.impl("_foreach_sub_.ScalarList", TORCH_FN(functionalization::_foreach_sub__ScalarList)); |
| 23044 | m.impl("_foreach_maximum.ScalarList_out", TORCH_FN(functionalization::_foreach_maximum_out_ScalarList_out)); |
| 23045 | m.impl("_foreach_maximum_.ScalarList", TORCH_FN(functionalization::_foreach_maximum__ScalarList)); |
| 23046 | m.impl("_foreach_exp.out", TORCH_FN(functionalization::_foreach_exp_out_out)); |
| 23047 | m.impl("_foreach_exp_", TORCH_FN(functionalization::_foreach_exp_)); |
| 23048 | m.impl("_foreach_sqrt.out", TORCH_FN(functionalization::_foreach_sqrt_out_out)); |
| 23049 | m.impl("_foreach_sqrt_", TORCH_FN(functionalization::_foreach_sqrt_)); |
| 23050 | m.impl("_foreach_abs.out", TORCH_FN(functionalization::_foreach_abs_out_out)); |
| 23051 | m.impl("_foreach_abs_", TORCH_FN(functionalization::_foreach_abs_)); |
| 23052 | m.impl("_foreach_acos.out", TORCH_FN(functionalization::_foreach_acos_out_out)); |
| 23053 | m.impl("_foreach_acos_", TORCH_FN(functionalization::_foreach_acos_)); |
| 23054 | m.impl("_foreach_cos.out", TORCH_FN(functionalization::_foreach_cos_out_out)); |
| 23055 | m.impl("_foreach_cos_", TORCH_FN(functionalization::_foreach_cos_)); |
| 23056 | m.impl("_foreach_floor.out", TORCH_FN(functionalization::_foreach_floor_out_out)); |
| 23057 | m.impl("_foreach_floor_", TORCH_FN(functionalization::_foreach_floor_)); |
| 23058 | m.impl("_foreach_log10.out", TORCH_FN(functionalization::_foreach_log10_out_out)); |
| 23059 | m.impl("_foreach_log10_", TORCH_FN(functionalization::_foreach_log10_)); |
| 23060 | m.impl("_foreach_neg.out", TORCH_FN(functionalization::_foreach_neg_out_out)); |
| 23061 | m.impl("_foreach_neg_", TORCH_FN(functionalization::_foreach_neg_)); |
| 23062 | m.impl("_foreach_tan.out", TORCH_FN(functionalization::_foreach_tan_out_out)); |
| 23063 | m.impl("_foreach_tan_", TORCH_FN(functionalization::_foreach_tan_)); |
| 23064 | m.impl("_foreach_sigmoid.out", TORCH_FN(functionalization::_foreach_sigmoid_out_out)); |
| 23065 | m.impl("_foreach_sigmoid_", TORCH_FN(functionalization::_foreach_sigmoid_)); |
| 23066 | m.impl("_foreach_norm.Scalar_out", TORCH_FN(functionalization::_foreach_norm_out_Scalar_out)); |
| 23067 | m.impl("searchsorted.Tensor_out", TORCH_FN(functionalization::searchsorted_out_Tensor_out)); |
| 23068 | m.impl("searchsorted.Scalar_out", TORCH_FN(functionalization::searchsorted_out_Scalar_out)); |
| 23069 | m.impl("mse_loss_backward.grad_input", TORCH_FN(functionalization::mse_loss_backward_out_grad_input)); |
| 23070 | m.impl("smooth_l1_loss_backward.grad_input", TORCH_FN(functionalization::smooth_l1_loss_backward_out_grad_input)); |
| 23071 | m.impl("huber_loss_backward.out", TORCH_FN(functionalization::huber_loss_backward_out_out)); |
| 23072 | m.impl("elu_backward.grad_input", TORCH_FN(functionalization::elu_backward_out_grad_input)); |
| 23073 | m.impl("glu_jvp.out", TORCH_FN(functionalization::glu_jvp_out_out)); |
| 23074 | m.impl("hardsigmoid_backward.grad_input", TORCH_FN(functionalization::hardsigmoid_backward_out_grad_input)); |
| 23075 | m.impl("log_sigmoid", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::log_sigmoid)); |
| 23076 | m.impl("log_sigmoid.out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::Tensor & out)>(at::native::log_sigmoid_out)); |
| 23077 | m.impl("log_sigmoid_forward.output", TORCH_FN(functionalization::log_sigmoid_forward_out_output)); |
| 23078 | m.impl("rrelu_with_noise.out", TORCH_FN(functionalization::rrelu_with_noise_out_out)); |
| 23079 | m.impl("rrelu_with_noise_", TORCH_FN(functionalization::rrelu_with_noise_)); |
| 23080 | m.impl("rrelu_with_noise_backward.out", TORCH_FN(functionalization::rrelu_with_noise_backward_out_out)); |
| 23081 | m.impl("softplus_backward.grad_input", TORCH_FN(functionalization::softplus_backward_out_grad_input)); |
| 23082 | m.impl("mkldnn_adaptive_avg_pool2d.out", TORCH_FN(functionalization::mkldnn_adaptive_avg_pool2d_out_out)); |
| 23083 | m.impl("adaptive_max_pool2d.out", TORCH_FN(functionalization::adaptive_max_pool2d_out_out)); |
| 23084 | m.impl("adaptive_max_pool3d.out", TORCH_FN(functionalization::adaptive_max_pool3d_out_out)); |
| 23085 | m.impl("avg_pool2d_backward.grad_input", TORCH_FN(functionalization::avg_pool2d_backward_out_grad_input)); |
| 23086 | m.impl("max_pool2d_with_indices.out", TORCH_FN(functionalization::max_pool2d_with_indices_out_out)); |
| 23087 | m.impl("upsample_linear1d.out", TORCH_FN(functionalization::upsample_linear1d_out_out)); |
| 23088 | m.impl("upsample_linear1d_backward.grad_input", TORCH_FN(functionalization::upsample_linear1d_backward_out_grad_input)); |
| 23089 | m.impl("upsample_bicubic2d.out", TORCH_FN(functionalization::upsample_bicubic2d_out_out)); |
| 23090 | m.impl("upsample_bicubic2d_backward.grad_input", TORCH_FN(functionalization::upsample_bicubic2d_backward_out_grad_input)); |
| 23091 | m.impl("_upsample_bicubic2d_aa_backward.grad_input", TORCH_FN(functionalization::_upsample_bicubic2d_aa_backward_out_grad_input)); |
| 23092 | m.impl("upsample_nearest1d.out", TORCH_FN(functionalization::upsample_nearest1d_out_out)); |
| 23093 | m.impl("_upsample_nearest_exact1d.out", TORCH_FN(functionalization::_upsample_nearest_exact1d_out_out)); |
| 23094 | m.impl("upsample_nearest1d_backward.grad_input", TORCH_FN(functionalization::upsample_nearest1d_backward_out_grad_input)); |
| 23095 | m.impl("upsample_nearest3d.out", TORCH_FN(functionalization::upsample_nearest3d_out_out)); |
| 23096 | m.impl("slow_conv_transpose3d.out", TORCH_FN(functionalization::slow_conv_transpose3d_out_out)); |
| 23097 | m.impl("isposinf.out", TORCH_FN(functionalization::isposinf_out_out)); |
| 23098 | m.impl("special_entr.out", TORCH_FN(functionalization::special_entr_out_out)); |
| 23099 | m.impl("special_psi", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::special_psi)); |
| 23100 | m.impl("special_psi.out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::Tensor & out)>(at::native::special_psi_out)); |
| 23101 | m.impl("special_erfinv", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::special_erfinv)); |
| 23102 | m.impl("special_erfinv.out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::Tensor & out)>(at::native::special_erfinv_out)); |
| 23103 | m.impl("special_ndtr", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::special_ndtr)); |
| 23104 | m.impl("special_ndtr.out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::Tensor & out)>(at::native::special_ndtr_out)); |
| 23105 | m.impl("special_xlogy", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other)>(at::native::special_xlogy)); |
| 23106 | m.impl("special_xlogy.out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & other, at::Tensor & out)>(at::native::special_xlogy_out)); |
| 23107 | m.impl("special_xlogy.self_scalar", static_cast<at::Tensor (*)(const at::Scalar & self, const at::Tensor & other)>(at::native::special_xlogy)); |
| 23108 | m.impl("special_xlogy.self_scalar_out", static_cast<at::Tensor & (*)(const at::Scalar & self, const at::Tensor & other, at::Tensor & out)>(at::native::special_xlogy_out)); |
| 23109 | m.impl("special_xlogy.other_scalar", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Scalar & other)>(at::native::special_xlogy)); |
| 23110 | m.impl("special_xlogy.other_scalar_out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Scalar & other, at::Tensor & out)>(at::native::special_xlogy_out)); |
| 23111 | m.impl("special_zeta.out", TORCH_FN(functionalization::special_zeta_out_out)); |
| 23112 | m.impl("special_zeta.self_scalar_out", TORCH_FN(functionalization::special_zeta_out_self_scalar_out)); |
| 23113 | m.impl("special_zeta.other_scalar_out", TORCH_FN(functionalization::special_zeta_out_other_scalar_out)); |
| 23114 | m.impl("special_i0", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::special_i0)); |
| 23115 | m.impl("special_i0.out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::Tensor & out)>(at::native::special_i0_out)); |
| 23116 | m.impl("special_i0e.out", TORCH_FN(functionalization::special_i0e_out_out)); |
| 23117 | m.impl("special_expit", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::special_expit)); |
| 23118 | m.impl("special_expit.out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::Tensor & out)>(at::native::special_expit_out)); |
| 23119 | m.impl("special_round", static_cast<at::Tensor (*)(const at::Tensor & self, int64_t decimals)>(at::native::special_round)); |
| 23120 | m.impl("special_round.out", static_cast<at::Tensor & (*)(const at::Tensor & self, int64_t decimals, at::Tensor & out)>(at::native::special_round_out)); |
| 23121 | m.impl("special_gammainc", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other)>(at::native::special_gammainc)); |
| 23122 | m.impl("special_gammainc.out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & other, at::Tensor & out)>(at::native::special_gammainc_out)); |
| 23123 | m.impl("fft_irfft", 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_irfft)); |
| 23124 | m.impl("fft_irfft.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_irfft_out)); |
| 23125 | m.impl("fft_fft2", static_cast<at::Tensor (*)(const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional<c10::string_view> norm)>(at::native::fft_fft2)); |
| 23126 | m.impl("fft_fft2.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_fft2_out)); |
| 23127 | m.impl("fft_rfftfreq.out", TORCH_FN(functionalization::fft_rfftfreq_out_out)); |
| 23128 | m.impl("linalg_cholesky", static_cast<at::Tensor (*)(const at::Tensor & self, bool upper)>(at::native::linalg_cholesky)); |
| 23129 | m.impl("linalg_cholesky.out", static_cast<at::Tensor & (*)(const at::Tensor & self, bool upper, at::Tensor & out)>(at::native::linalg_cholesky_out)); |
| 23130 | m.impl("linalg_lu.out", TORCH_FN(functionalization::linalg_lu_out_out)); |
| 23131 | m.impl("_linalg_det.result", TORCH_FN(functionalization::_linalg_det_out_result)); |
| 23132 | m.impl("slogdet", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & self)>(at::native::slogdet)); |
| 23133 | m.impl("slogdet.out", static_cast<::std::tuple<at::Tensor &,at::Tensor &> (*)(const at::Tensor & self, at::Tensor & sign, at::Tensor & logabsdet)>(at::native::slogdet_out)); |
| 23134 | m.impl("linalg_eig.out", TORCH_FN(functionalization::linalg_eig_out_out)); |
| 23135 | m.impl("linalg_inv", static_cast<at::Tensor (*)(const at::Tensor & A)>(at::native::linalg_inv)); |
| 23136 | m.impl("linalg_inv.out", static_cast<at::Tensor & (*)(const at::Tensor & A, at::Tensor & out)>(at::native::linalg_inv_out)); |
| 23137 | m.impl("_linalg_svd.U", TORCH_FN(functionalization::_linalg_svd_out_U)); |
| 23138 | m.impl("linalg_svdvals", static_cast<at::Tensor (*)(const at::Tensor & A, c10::optional<c10::string_view> driver)>(at::native::linalg_svdvals)); |
| 23139 | m.impl("linalg_svdvals.out", static_cast<at::Tensor & (*)(const at::Tensor & A, c10::optional<c10::string_view> driver, at::Tensor & out)>(at::native::linalg_svdvals_out)); |
| 23140 | m.impl("linalg_pinv.atol_rtol_tensor_out", TORCH_FN(functionalization::linalg_pinv_out_atol_rtol_tensor_out)); |
| 23141 | m.impl("linalg_pinv.atol_rtol_float", static_cast<at::Tensor (*)(const at::Tensor & self, c10::optional<double> atol, c10::optional<double> rtol, bool hermitian)>(at::native::linalg_pinv)); |
| 23142 | m.impl("linalg_pinv.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_pinv_out)); |
| 23143 | m.impl("linalg_pinv", static_cast<at::Tensor (*)(const at::Tensor & self, double rcond, bool hermitian)>(at::native::linalg_pinv)); |
| 23144 | m.impl("linalg_pinv.out", static_cast<at::Tensor & (*)(const at::Tensor & self, double rcond, bool hermitian, at::Tensor & out)>(at::native::linalg_pinv_out)); |
| 23145 | m.impl("linalg_pinv.rcond_tensor", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & rcond, bool hermitian)>(at::native::linalg_pinv)); |
| 23146 | m.impl("linalg_pinv.out_rcond_tensor", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & rcond, bool hermitian, at::Tensor & out)>(at::native::linalg_pinv_out)); |
| 23147 | m.impl("linalg_qr.out", TORCH_FN(functionalization::linalg_qr_out_out)); |
| 23148 | m.impl("_test_optional_intlist.out", TORCH_FN(functionalization::_test_optional_intlist_out_out)); |
| 23149 | m.impl("_fw_primal_copy.out", TORCH_FN(functionalization::_fw_primal_copy_out_out)); |
| 23150 | m.impl("as_strided_copy.out", TORCH_FN(functionalization::as_strided_copy_out_out)); |
| 23151 | m.impl("expand_copy.out", TORCH_FN(functionalization::expand_copy_out_out)); |
| 23152 | m.impl("_reshape_alias_copy.out", TORCH_FN(functionalization::_reshape_alias_copy_out_out)); |
| 23153 | m.impl("select_copy.int_out", TORCH_FN(functionalization::select_copy_out_int_out)); |
| 23154 | m.impl("split_with_sizes_copy.out", TORCH_FN(functionalization::split_with_sizes_copy_out_out)); |
| 23155 | m.impl("squeeze_copy.out", TORCH_FN(functionalization::squeeze_copy_out_out)); |
| 23156 | m.impl("squeeze_copy.dim_out", TORCH_FN(functionalization::squeeze_copy_out_dim_out)); |
| 23157 | m.impl("squeeze_copy.dims_out", TORCH_FN(functionalization::squeeze_copy_out_dims_out)); |
| 23158 | m.impl("_indices_copy.out", TORCH_FN(functionalization::_indices_copy_out_out)); |
| 23159 | m.impl("_values_copy.out", TORCH_FN(functionalization::_values_copy_out_out)); |
| 23160 | m.impl("crow_indices_copy.out", TORCH_FN(functionalization::crow_indices_copy_out_out)); |
| 23161 | m.impl("col_indices_copy.out", TORCH_FN(functionalization::col_indices_copy_out_out)); |
| 23162 | m.impl("unbind_copy.int_out", TORCH_FN(functionalization::unbind_copy_out_int_out)); |
| 23163 | m.impl("view_copy.out", TORCH_FN(functionalization::view_copy_out_out)); |
| 23164 | m.impl("view_copy.dtype_out", TORCH_FN(functionalization::view_copy_out_dtype_out)); |
| 23165 | m.impl("alias_copy.out", TORCH_FN(functionalization::alias_copy_out_out)); |
| 23166 | m.impl("special_airy_ai.out", TORCH_FN(functionalization::special_airy_ai_out_out)); |
| 23167 | m.impl("special_bessel_j0.out", TORCH_FN(functionalization::special_bessel_j0_out_out)); |
| 23168 | m.impl("special_chebyshev_polynomial_v.out", TORCH_FN(functionalization::special_chebyshev_polynomial_v_out_out)); |
| 23169 | m.impl("special_chebyshev_polynomial_v.x_scalar", static_cast<at::Tensor (*)(const at::Scalar & x, const at::Tensor & n)>(at::native::special_chebyshev_polynomial_v)); |
| 23170 | m.impl("special_chebyshev_polynomial_v.x_scalar_out", static_cast<at::Tensor & (*)(const at::Scalar & x, const at::Tensor & n, at::Tensor & out)>(at::native::special_chebyshev_polynomial_v_out)); |
| 23171 | m.impl("special_chebyshev_polynomial_v.n_scalar", static_cast<at::Tensor (*)(const at::Tensor & x, const at::Scalar & n)>(at::native::special_chebyshev_polynomial_v)); |
| 23172 | m.impl("special_chebyshev_polynomial_v.n_scalar_out", TORCH_FN(functionalization::special_chebyshev_polynomial_v_out_n_scalar_out)); |
| 23173 | m.impl("special_chebyshev_polynomial_w.out", TORCH_FN(functionalization::special_chebyshev_polynomial_w_out_out)); |
| 23174 | m.impl("special_chebyshev_polynomial_w.x_scalar", static_cast<at::Tensor (*)(const at::Scalar & x, const at::Tensor & n)>(at::native::special_chebyshev_polynomial_w)); |
| 23175 | m.impl("special_chebyshev_polynomial_w.x_scalar_out", static_cast<at::Tensor & (*)(const at::Scalar & x, const at::Tensor & n, at::Tensor & out)>(at::native::special_chebyshev_polynomial_w_out)); |
| 23176 | m.impl("special_chebyshev_polynomial_w.n_scalar", static_cast<at::Tensor (*)(const at::Tensor & x, const at::Scalar & n)>(at::native::special_chebyshev_polynomial_w)); |
| 23177 | m.impl("special_chebyshev_polynomial_w.n_scalar_out", TORCH_FN(functionalization::special_chebyshev_polynomial_w_out_n_scalar_out)); |
| 23178 | m.impl("special_hermite_polynomial_he.out", TORCH_FN(functionalization::special_hermite_polynomial_he_out_out)); |
| 23179 | m.impl("special_hermite_polynomial_he.x_scalar", static_cast<at::Tensor (*)(const at::Scalar & x, const at::Tensor & n)>(at::native::special_hermite_polynomial_he)); |
| 23180 | m.impl("special_hermite_polynomial_he.x_scalar_out", static_cast<at::Tensor & (*)(const at::Scalar & x, const at::Tensor & n, at::Tensor & out)>(at::native::special_hermite_polynomial_he_out)); |
| 23181 | m.impl("special_hermite_polynomial_he.n_scalar", static_cast<at::Tensor (*)(const at::Tensor & x, const at::Scalar & n)>(at::native::special_hermite_polynomial_he)); |
| 23182 | m.impl("special_hermite_polynomial_he.n_scalar_out", TORCH_FN(functionalization::special_hermite_polynomial_he_out_n_scalar_out)); |
| 23183 | m.impl("special_laguerre_polynomial_l.out", TORCH_FN(functionalization::special_laguerre_polynomial_l_out_out)); |
| 23184 | m.impl("special_laguerre_polynomial_l.x_scalar", static_cast<at::Tensor (*)(const at::Scalar & x, const at::Tensor & n)>(at::native::special_laguerre_polynomial_l)); |
| 23185 | m.impl("special_laguerre_polynomial_l.x_scalar_out", static_cast<at::Tensor & (*)(const at::Scalar & x, const at::Tensor & n, at::Tensor & out)>(at::native::special_laguerre_polynomial_l_out)); |
| 23186 | m.impl("special_laguerre_polynomial_l.n_scalar", static_cast<at::Tensor (*)(const at::Tensor & x, const at::Scalar & n)>(at::native::special_laguerre_polynomial_l)); |
| 23187 | m.impl("special_laguerre_polynomial_l.n_scalar_out", TORCH_FN(functionalization::special_laguerre_polynomial_l_out_n_scalar_out)); |
| 23188 | m.impl("special_shifted_chebyshev_polynomial_v.out", TORCH_FN(functionalization::special_shifted_chebyshev_polynomial_v_out_out)); |
| 23189 | m.impl("special_shifted_chebyshev_polynomial_v.x_scalar", static_cast<at::Tensor (*)(const at::Scalar & x, const at::Tensor & n)>(at::native::special_shifted_chebyshev_polynomial_v)); |
| 23190 | m.impl("special_shifted_chebyshev_polynomial_v.x_scalar_out", static_cast<at::Tensor & (*)(const at::Scalar & x, const at::Tensor & n, at::Tensor & out)>(at::native::special_shifted_chebyshev_polynomial_v_out)); |
| 23191 | m.impl("special_shifted_chebyshev_polynomial_v.n_scalar", static_cast<at::Tensor (*)(const at::Tensor & x, const at::Scalar & n)>(at::native::special_shifted_chebyshev_polynomial_v)); |
| 23192 | m.impl("special_shifted_chebyshev_polynomial_v.n_scalar_out", TORCH_FN(functionalization::special_shifted_chebyshev_polynomial_v_out_n_scalar_out)); |
| 23193 | m.impl("_fused_adamw.out", TORCH_FN(functionalization::_fused_adamw_out_out)); |
| 23194 | m.impl("_fused_adamw_", TORCH_FN(functionalization::_fused_adamw_)); |
| 23195 | m.impl("_fw_primal", TORCH_FN(functionalization::_fw_primal)); |
| 23196 | m.impl("_make_dual", TORCH_FN(functionalization::_make_dual)); |
| 23197 | m.impl("_unpack_dual", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & dual, int64_t level)>(at::native::_unpack_dual)); |
| 23198 | m.impl("align_to", static_cast<at::Tensor (*)(const at::Tensor & self, at::DimnameList names)>(at::native::align_to)); |
| 23199 | m.impl("align_to.ellipsis_idx", static_cast<at::Tensor (*)(const at::Tensor & self, at::DimnameList order, int64_t ellipsis_idx)>(at::native::align_to)); |
| 23200 | m.impl("view_as_complex", TORCH_FN(functionalization::view_as_complex)); |
| 23201 | m.impl("resolve_neg", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::resolve_neg)); |
| 23202 | m.impl("linalg_diagonal", static_cast<at::Tensor (*)(const at::Tensor & A, int64_t offset, int64_t dim1, int64_t dim2)>(at::native::linalg_diagonal)); |
| 23203 | m.impl("expand", TORCH_FN(functionalization::expand)); |
| 23204 | m.impl("matrix_H", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::matrix_H)); |
| 23205 | m.impl("adjoint", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::adjoint)); |
| 23206 | m.impl("reshape_as", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other)>(at::native::reshape_as)); |
| 23207 | m.impl("transpose.int", TORCH_FN(functionalization::transpose_int)); |
| 23208 | m.impl("transpose_", TORCH_FN(functionalization::transpose_)); |
| 23209 | m.impl("transpose.Dimname", static_cast<at::Tensor (*)(const at::Tensor & self, at::Dimname dim0, at::Dimname dim1)>(at::native::transpose)); |
| 23210 | m.impl("_nested_view_from_buffer", TORCH_FN(functionalization::_nested_view_from_buffer)); |
| 23211 | m.impl("unsqueeze", TORCH_FN(functionalization::unsqueeze)); |
| 23212 | m.impl("unsqueeze_", TORCH_FN(functionalization::unsqueeze_)); |
| 23213 | m.impl("_values", TORCH_FN(functionalization::_values)); |
| 23214 | m.impl("ccol_indices", TORCH_FN(functionalization::ccol_indices)); |
| 23215 | m.impl("_autocast_to_full_precision", static_cast<at::Tensor (*)(const at::Tensor & self, bool cuda_enabled, bool cpu_enabled)>(at::native::_autocast_to_full_precision)); |
| 23216 | m.impl("to.dtype_layout", static_cast<at::Tensor (*)(const at::Tensor & self, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory, bool non_blocking, bool copy, c10::optional<at::MemoryFormat> memory_format)>(at::native::to)); |
| 23217 | m.impl("to.device", static_cast<at::Tensor (*)(const at::Tensor & self, at::Device device, at::ScalarType dtype, bool non_blocking, bool copy, c10::optional<at::MemoryFormat> memory_format)>(at::native::to)); |
| 23218 | m.impl("to.dtype", static_cast<at::Tensor (*)(const at::Tensor & self, at::ScalarType dtype, bool non_blocking, bool copy, c10::optional<at::MemoryFormat> memory_format)>(at::native::to)); |
| 23219 | m.impl("to.other", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other, bool non_blocking, bool copy, c10::optional<at::MemoryFormat> memory_format)>(at::native::to)); |
| 23220 | m.impl("view", TORCH_FN(functionalization::view)); |
| 23221 | m.impl("view.dtype", TORCH_FN(functionalization::view_dtype)); |
| 23222 | m.impl("_cast_Long", static_cast<at::Tensor (*)(const at::Tensor & self, bool non_blocking)>(at::native::_cast_Long)); |
| 23223 | m.impl("_version", static_cast<int64_t (*)(const at::Tensor & self)>(at::native::_version)); |
| 23224 | m.impl("retain_grad", static_cast<void (*)(at::Tensor & self)>(at::native::retain_grad)); |
| 23225 | m.impl("retains_grad", static_cast<bool (*)(const at::Tensor & self)>(at::native::retains_grad)); |
| 23226 | m.impl("align_tensors", static_cast<::std::vector<at::Tensor> (*)(at::TensorList tensors)>(at::native::align_tensors)); |
| 23227 | m.impl("_assert_tensor_metadata", static_cast<void (*)(const at::Tensor & a, at::OptionalIntArrayRef size, at::OptionalIntArrayRef stride, c10::optional<at::ScalarType> dtype)>(at::native::_assert_tensor_metadata)); |
| 23228 | m.impl("_debug_has_internal_overlap", static_cast<int64_t (*)(const at::Tensor & self)>(at::native::_debug_has_internal_overlap)); |
| 23229 | m.impl("_sobol_engine_draw", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & quasi, int64_t n, const at::Tensor & sobolstate, int64_t dimension, int64_t num_generated, c10::optional<at::ScalarType> dtype)>(at::native::_sobol_engine_draw)); |
| 23230 | m.impl("_sobol_engine_scramble_", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Tensor & ltm, int64_t dimension)>(at::native::_sobol_engine_scramble_)); |
| 23231 | m.impl("feature_dropout", static_cast<at::Tensor (*)(const at::Tensor & input, double p, bool train)>(at::native::feature_dropout)); |
| 23232 | m.impl("feature_dropout_", static_cast<at::Tensor & (*)(at::Tensor & self, double p, bool train)>(at::native::feature_dropout_)); |
| 23233 | m.impl("alpha_dropout", static_cast<at::Tensor (*)(const at::Tensor & input, double p, bool train)>(at::native::alpha_dropout)); |
| 23234 | m.impl("alpha_dropout_", static_cast<at::Tensor & (*)(at::Tensor & self, double p, bool train)>(at::native::alpha_dropout_)); |
| 23235 | m.impl("chalf", static_cast<at::Tensor (*)(const at::Tensor & self, c10::optional<at::MemoryFormat> memory_format)>(at::native::chalf)); |
| 23236 | m.impl("avg_pool1d", static_cast<at::Tensor (*)(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad)>(at::native::avg_pool1d)); |
| 23237 | m.impl("adaptive_avg_pool1d", static_cast<at::Tensor (*)(const at::Tensor & self, at::IntArrayRef output_size)>(at::native::adaptive_avg_pool1d)); |
| 23238 | m.impl("affine_grid_generator_backward", static_cast<at::Tensor (*)(const at::Tensor & grad, at::IntArrayRef size, bool align_corners)>(at::native::affine_grid_generator_backward)); |
| 23239 | m.impl("_test_check_tensor", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::_test_check_tensor)); |
| 23240 | m.impl("atleast_3d", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::atleast_3d)); |
| 23241 | m.impl("atleast_3d.Sequence", static_cast<::std::vector<at::Tensor> (*)(at::TensorList tensors)>(at::native::atleast_3d)); |
| 23242 | m.impl("batch_norm", static_cast<at::Tensor (*)(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)); |
| 23243 | m.impl("broadcast_tensors", static_cast<::std::vector<at::Tensor> (*)(at::TensorList tensors)>(at::native::broadcast_tensors)); |
| 23244 | m.impl("_convolution_mode", static_cast<at::Tensor (*)(const at::Tensor & input, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::string_view padding, at::IntArrayRef dilation, int64_t groups)>(at::native::_convolution_mode)); |
| 23245 | m.impl("conv3d", 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 dilation, int64_t groups)>(at::native::conv3d)); |
| 23246 | m.impl("conv3d.padding", static_cast<at::Tensor (*)(const at::Tensor & input, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::string_view padding, at::IntArrayRef dilation, int64_t groups)>(at::native::conv3d)); |
| 23247 | m.impl("diagflat", static_cast<at::Tensor (*)(const at::Tensor & self, int64_t offset)>(at::native::diagflat)); |
| 23248 | m.impl("fill_diagonal_", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Scalar & fill_value, bool wrap)>(at::native::fill_diagonal_)); |
| 23249 | m.impl("index_copy_.dimname", static_cast<at::Tensor & (*)(at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & source)>(at::native::index_copy_)); |
| 23250 | m.impl("index_copy.dimname", static_cast<at::Tensor (*)(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & source)>(at::native::index_copy)); |
| 23251 | m.impl("instance_norm", static_cast<at::Tensor (*)(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 use_input_stats, double momentum, double eps, bool cudnn_enabled)>(at::native::instance_norm)); |
| 23252 | m.impl("is_complex", static_cast<bool (*)(const at::Tensor & self)>(at::native::is_complex)); |
| 23253 | m.impl("fbgemm_linear_int8_weight_fp32_activation", static_cast<at::Tensor (*)(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & packed, const at::Tensor & col_offsets, const at::Scalar & weight_scale, const at::Scalar & weight_zero_point, const at::Tensor & bias)>(at::native::fbgemm_linear_int8_weight_fp32_activation)); |
| 23254 | m.impl("fbgemm_linear_int8_weight", static_cast<at::Tensor (*)(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & packed, const at::Tensor & col_offsets, const at::Scalar & weight_scale, const at::Scalar & weight_zero_point, const at::Tensor & bias)>(at::native::fbgemm_linear_int8_weight)); |
| 23255 | m.impl("fbgemm_linear_quantize_weight", static_cast<::std::tuple<at::Tensor,at::Tensor,double,int64_t> (*)(const at::Tensor & input)>(at::native::fbgemm_linear_quantize_weight)); |
| 23256 | m.impl("fbgemm_linear_fp16_weight", static_cast<at::Tensor (*)(const at::Tensor & input, const at::Tensor & packed_weight, const at::Tensor & bias)>(at::native::fbgemm_linear_fp16_weight)); |
| 23257 | m.impl("log_softmax.Dimname", static_cast<at::Tensor (*)(const at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype)>(at::native::log_softmax)); |
| 23258 | m.impl("max_pool1d_with_indices", static_cast<::std::tuple<at::Tensor,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_with_indices)); |
| 23259 | m.impl("max_pool3d", 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_pool3d)); |
| 23260 | m.impl("multiply.Scalar", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Scalar & other)>(at::native::multiply)); |
| 23261 | m.impl("multiply_.Scalar", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Scalar & other)>(at::native::multiply_)); |
| 23262 | m.impl("is_vulkan_available", static_cast<bool (*)()>(at::native::is_vulkan_available)); |
| 23263 | m.impl("cdist", static_cast<at::Tensor (*)(const at::Tensor & x1, const at::Tensor & x2, double p, c10::optional<int64_t> compute_mode)>(at::native::cdist)); |
| 23264 | m.impl("poisson_nll_loss", static_cast<at::Tensor (*)(const at::Tensor & input, const at::Tensor & target, bool log_input, bool full, double eps, int64_t reduction)>(at::native::poisson_nll_loss)); |
| 23265 | m.impl("repeat_interleave.self_Tensor", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & repeats, c10::optional<int64_t> dim, c10::optional<int64_t> output_size)>(at::native::repeat_interleave)); |
| 23266 | m.impl("repeat_interleave.self_int", static_cast<at::Tensor (*)(const at::Tensor & self, c10::SymInt repeats, c10::optional<int64_t> dim, c10::optional<int64_t> output_size)>(at::native::repeat_interleave_symint)); |
| 23267 | m.impl("infinitely_differentiable_gelu_backward", static_cast<at::Tensor (*)(const at::Tensor & grad, const at::Tensor & self)>(at::native::infinitely_differentiable_gelu_backward)); |
| 23268 | m.impl("mish_backward", static_cast<at::Tensor (*)(const at::Tensor & grad_output, const at::Tensor & self)>(at::native::math_mish_backward)); |
| 23269 | m.impl("subtract.Scalar", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha)>(at::native::subtract)); |
| 23270 | m.impl("subtract_.Scalar", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha)>(at::native::subtract_)); |
| 23271 | m.impl("sparse_compressed_tensor.comp_plain_value_size", static_cast<at::Tensor (*)(const at::Tensor & compressed_indices, const at::Tensor & plain_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_compressed_tensor)); |
| 23272 | m.impl("sparse_bsr_tensor.crow_col_value_size", 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)); |
| 23273 | m.impl("sparse_compressed_tensor.comp_plain_value", static_cast<at::Tensor (*)(const at::Tensor & compressed_indices, const at::Tensor & plain_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_compressed_tensor)); |
| 23274 | m.impl("sparse_bsr_tensor.crow_col_value", static_cast<at::Tensor (*)(const at::Tensor & crow_indices, const at::Tensor & col_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_bsr_tensor)); |
| 23275 | m.impl("_sparse_csc_tensor_unsafe", 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_unsafe)); |
| 23276 | m.impl("_sparse_coo_tensor_unsafe", static_cast<at::Tensor (*)(const at::Tensor & indices, const at::Tensor & values, c10::SymIntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory)>(at::native::_sparse_coo_tensor_unsafe_symint)); |
| 23277 | m.impl("_validate_sparse_coo_tensor_args", static_cast<void (*)(const at::Tensor & indices, const at::Tensor & values, at::IntArrayRef size)>(at::native::_validate_sparse_coo_tensor_args)); |
| 23278 | m.impl("to_dense", static_cast<at::Tensor (*)(const at::Tensor & self, c10::optional<at::ScalarType> dtype)>(at::native::to_dense)); |
| 23279 | m.impl("to_dense_backward", static_cast<at::Tensor (*)(const at::Tensor & grad, const at::Tensor & input)>(at::native::to_dense_backward)); |
| 23280 | m.impl("fake_quantize_per_channel_affine", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max)>(at::native::fake_quantize_per_channel_affine)); |
| 23281 | m.impl("choose_qparams_optimized", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & input, int64_t numel, int64_t n_bins, double ratio, int64_t bit_width)>(at::native::choose_qparams_optimized)); |
| 23282 | m.impl("combinations", static_cast<at::Tensor (*)(const at::Tensor & self, int64_t r, bool with_replacement)>(at::native::combinations)); |
| 23283 | m.impl("result_type.Tensor", static_cast<at::ScalarType (*)(const at::Tensor & tensor, const at::Tensor & other)>(at::native::result_type)); |
| 23284 | m.impl("result_type.Scalar", static_cast<at::ScalarType (*)(const at::Tensor & tensor, const at::Scalar & other)>(at::native::result_type)); |
| 23285 | m.impl("result_type.Scalar_Tensor", static_cast<at::ScalarType (*)(const at::Scalar & scalar, const at::Tensor & tensor)>(at::native::result_type)); |
| 23286 | m.impl("result_type.Scalar_Scalar", static_cast<at::ScalarType (*)(const at::Scalar & scalar1, const at::Scalar & scalar2)>(at::native::result_type)); |
| 23287 | m.impl("_thnn_differentiable_gru_cell_backward", static_cast<::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> (*)(const at::Tensor & grad_hy, const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & hx, const c10::optional<at::Tensor> & input_bias, const c10::optional<at::Tensor> & hidden_bias)>(at::native::_thnn_differentiable_gru_cell_backward)); |
| 23288 | m.impl("lstm.input", static_cast<::std::tuple<at::Tensor,at::Tensor,at::Tensor> (*)(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::native::lstm)); |
| 23289 | m.impl("lstm.data", static_cast<::std::tuple<at::Tensor,at::Tensor,at::Tensor> (*)(const at::Tensor & data, const at::Tensor & batch_sizes, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional)>(at::native::lstm)); |
| 23290 | m.impl("gru.input", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first)>(at::native::gru)); |
| 23291 | m.impl("gru.data", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional)>(at::native::gru)); |
| 23292 | m.impl("lstm_cell", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & input, at::TensorList 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::lstm_cell)); |
| 23293 | m.impl("quantized_gru_cell", static_cast<at::Tensor (*)(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh)>(at::native::quantized_gru_cell)); |
| 23294 | m.impl("quantized_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 at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh)>(at::native::quantized_rnn_tanh_cell)); |
| 23295 | m.impl("index_add.dimname", static_cast<at::Tensor (*)(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha)>(at::native::index_add)); |
| 23296 | m.impl("index_fill_.Dimname_Scalar", static_cast<at::Tensor & (*)(at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Scalar & value)>(at::native::index_fill_)); |
| 23297 | m.impl("index_fill_.Dimname_Tensor", static_cast<at::Tensor & (*)(at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & value)>(at::native::index_fill_)); |
| 23298 | m.impl("index_fill.Dimname_Scalar", static_cast<at::Tensor (*)(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Scalar & value)>(at::native::index_fill)); |
| 23299 | m.impl("index_fill.Dimname_Tensor", static_cast<at::Tensor (*)(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & value)>(at::native::index_fill)); |
| 23300 | m.impl("scatter.dimname_src", static_cast<at::Tensor (*)(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & src)>(at::native::scatter)); |
| 23301 | m.impl("scatter.dimname_value", static_cast<at::Tensor (*)(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Scalar & value)>(at::native::scatter)); |
| 23302 | m.impl("scatter_add.dimname", static_cast<at::Tensor (*)(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & src)>(at::native::scatter_add)); |
| 23303 | m.impl("__and__.Scalar", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Scalar & other)>(at::native::__and__)); |
| 23304 | m.impl("__and__.Tensor", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other)>(at::native::__and__)); |
| 23305 | m.impl("__iand__.Scalar", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Scalar & other)>(at::native::__iand__)); |
| 23306 | m.impl("__iand__.Tensor", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Tensor & other)>(at::native::__iand__)); |
| 23307 | m.impl("__or__.Scalar", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Scalar & other)>(at::native::__or__)); |
| 23308 | m.impl("__or__.Tensor", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other)>(at::native::__or__)); |
| 23309 | m.impl("__ior__.Scalar", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Scalar & other)>(at::native::__ior__)); |
| 23310 | m.impl("__ior__.Tensor", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Tensor & other)>(at::native::__ior__)); |
| 23311 | m.impl("index_select_backward", static_cast<at::Tensor (*)(const at::Tensor & grad, c10::SymIntArrayRef self_sizes, int64_t dim, const at::Tensor & index)>(at::native::index_select_backward_symint)); |
| 23312 | m.impl("nonzero_numpy", static_cast<::std::vector<at::Tensor> (*)(const at::Tensor & self)>(at::native::nonzero_numpy)); |
| 23313 | m.impl("_lu_with_info", static_cast<::std::tuple<at::Tensor,at::Tensor,at::Tensor> (*)(const at::Tensor & self, bool pivot, bool check_errors)>(at::native::_lu_with_info)); |
| 23314 | m.impl("nll_loss_nd", 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_nd_symint)); |
| 23315 | m.impl("upsample_linear1d.vec", static_cast<at::Tensor (*)(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, c10::optional<at::ArrayRef<double>> scale_factors)>(at::native::upsample_linear1d)); |
| 23316 | m.impl("upsample_bicubic2d.vec", static_cast<at::Tensor (*)(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, c10::optional<at::ArrayRef<double>> scale_factors)>(at::native::upsample_bicubic2d)); |
| 23317 | m.impl("upsample_nearest1d.vec", static_cast<at::Tensor (*)(const at::Tensor & input, at::OptionalIntArrayRef output_size, c10::optional<at::ArrayRef<double>> scale_factors)>(at::native::upsample_nearest1d)); |
| 23318 | m.impl("_upsample_nearest_exact1d.vec", static_cast<at::Tensor (*)(const at::Tensor & input, at::OptionalIntArrayRef output_size, c10::optional<at::ArrayRef<double>> scale_factors)>(at::native::_upsample_nearest_exact1d)); |
| 23319 | m.impl("upsample_nearest3d.vec", static_cast<at::Tensor (*)(const at::Tensor & input, at::OptionalIntArrayRef output_size, c10::optional<at::ArrayRef<double>> scale_factors)>(at::native::upsample_nearest3d)); |
| 23320 | m.impl("det", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::det)); |
| 23321 | m.impl("logdet", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::logdet)); |
| 23322 | m.impl("_test_string_default", static_cast<at::Tensor (*)(const at::Tensor & dummy, c10::string_view a, c10::string_view b)>(at::native::_test_string_default));; |
| 23323 | } |
| 23324 | |
| 23325 | } // namespace |
| 23326 | |
| 23327 | } // namespace at |
| 23328 |
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