| 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/_cudnn_ctc_loss_native.h> |
| 22 | #include <ATen/ops/_cudnn_ctc_loss_ops.h> |
| 23 | #include <ATen/ops/_cudnn_ctc_loss_native.h> |
| 24 | #include <ATen/ops/_cudnn_ctc_loss_ops.h> |
| 25 | #include <ATen/ops/_cudnn_rnn_native.h> |
| 26 | #include <ATen/ops/_cudnn_rnn_ops.h> |
| 27 | #include <ATen/ops/_cudnn_rnn_native.h> |
| 28 | #include <ATen/ops/_cudnn_rnn_ops.h> |
| 29 | #include <ATen/ops/_cudnn_rnn_backward_native.h> |
| 30 | #include <ATen/ops/_cudnn_rnn_backward_ops.h> |
| 31 | #include <ATen/ops/_cudnn_rnn_backward_native.h> |
| 32 | #include <ATen/ops/_cudnn_rnn_backward_ops.h> |
| 33 | #include <ATen/ops/_fused_dropout_native.h> |
| 34 | #include <ATen/ops/_fused_dropout_ops.h> |
| 35 | #include <ATen/ops/_fused_dropout_native.h> |
| 36 | #include <ATen/ops/_fused_dropout_ops.h> |
| 37 | #include <ATen/ops/conj_physical_native.h> |
| 38 | #include <ATen/ops/conj_physical_ops.h> |
| 39 | #include <ATen/ops/conj_physical_native.h> |
| 40 | #include <ATen/ops/conj_physical_ops.h> |
| 41 | #include <ATen/ops/conj_physical_native.h> |
| 42 | #include <ATen/ops/conj_physical_ops.h> |
| 43 | #include <ATen/ops/add_native.h> |
| 44 | #include <ATen/ops/add_ops.h> |
| 45 | #include <ATen/ops/add_native.h> |
| 46 | #include <ATen/ops/add_ops.h> |
| 47 | #include <ATen/ops/add_native.h> |
| 48 | #include <ATen/ops/add_ops.h> |
| 49 | #include <ATen/ops/add_native.h> |
| 50 | #include <ATen/ops/add_ops.h> |
| 51 | #include <ATen/ops/add_native.h> |
| 52 | #include <ATen/ops/add_ops.h> |
| 53 | #include <ATen/ops/add_native.h> |
| 54 | #include <ATen/ops/add_ops.h> |
| 55 | #include <ATen/ops/addmv_native.h> |
| 56 | #include <ATen/ops/addmv_ops.h> |
| 57 | #include <ATen/ops/addmv_native.h> |
| 58 | #include <ATen/ops/addmv_ops.h> |
| 59 | #include <ATen/ops/addmv_native.h> |
| 60 | #include <ATen/ops/addmv_ops.h> |
| 61 | #include <ATen/ops/addr_native.h> |
| 62 | #include <ATen/ops/addr_ops.h> |
| 63 | #include <ATen/ops/addr_native.h> |
| 64 | #include <ATen/ops/addr_ops.h> |
| 65 | #include <ATen/ops/addr_native.h> |
| 66 | #include <ATen/ops/addr_ops.h> |
| 67 | #include <ATen/ops/all_native.h> |
| 68 | #include <ATen/ops/all_ops.h> |
| 69 | #include <ATen/ops/all_native.h> |
| 70 | #include <ATen/ops/all_ops.h> |
| 71 | #include <ATen/ops/all_native.h> |
| 72 | #include <ATen/ops/all_ops.h> |
| 73 | #include <ATen/ops/all_native.h> |
| 74 | #include <ATen/ops/all_ops.h> |
| 75 | #include <ATen/ops/argmax_native.h> |
| 76 | #include <ATen/ops/argmax_ops.h> |
| 77 | #include <ATen/ops/argmax_native.h> |
| 78 | #include <ATen/ops/argmax_ops.h> |
| 79 | #include <ATen/ops/atan_native.h> |
| 80 | #include <ATen/ops/atan_ops.h> |
| 81 | #include <ATen/ops/atan_native.h> |
| 82 | #include <ATen/ops/atan_ops.h> |
| 83 | #include <ATen/ops/atan_native.h> |
| 84 | #include <ATen/ops/atan_ops.h> |
| 85 | #include <ATen/ops/bartlett_window_native.h> |
| 86 | #include <ATen/ops/bartlett_window_ops.h> |
| 87 | #include <ATen/ops/bartlett_window_native.h> |
| 88 | #include <ATen/ops/bartlett_window_ops.h> |
| 89 | #include <ATen/ops/bartlett_window_native.h> |
| 90 | #include <ATen/ops/bartlett_window_ops.h> |
| 91 | #include <ATen/ops/bartlett_window_native.h> |
| 92 | #include <ATen/ops/bartlett_window_ops.h> |
| 93 | #include <ATen/ops/binary_cross_entropy_native.h> |
| 94 | #include <ATen/ops/binary_cross_entropy_ops.h> |
| 95 | #include <ATen/ops/binary_cross_entropy_native.h> |
| 96 | #include <ATen/ops/binary_cross_entropy_ops.h> |
| 97 | #include <ATen/ops/bitwise_not_native.h> |
| 98 | #include <ATen/ops/bitwise_not_ops.h> |
| 99 | #include <ATen/ops/bitwise_not_native.h> |
| 100 | #include <ATen/ops/bitwise_not_ops.h> |
| 101 | #include <ATen/ops/bitwise_not_native.h> |
| 102 | #include <ATen/ops/bitwise_not_ops.h> |
| 103 | #include <ATen/ops/logical_xor_native.h> |
| 104 | #include <ATen/ops/logical_xor_ops.h> |
| 105 | #include <ATen/ops/logical_xor_native.h> |
| 106 | #include <ATen/ops/logical_xor_ops.h> |
| 107 | #include <ATen/ops/logical_xor_native.h> |
| 108 | #include <ATen/ops/logical_xor_ops.h> |
| 109 | #include <ATen/ops/blackman_window_native.h> |
| 110 | #include <ATen/ops/blackman_window_ops.h> |
| 111 | #include <ATen/ops/blackman_window_native.h> |
| 112 | #include <ATen/ops/blackman_window_ops.h> |
| 113 | #include <ATen/ops/blackman_window_native.h> |
| 114 | #include <ATen/ops/blackman_window_ops.h> |
| 115 | #include <ATen/ops/blackman_window_native.h> |
| 116 | #include <ATen/ops/blackman_window_ops.h> |
| 117 | #include <ATen/ops/clamp_native.h> |
| 118 | #include <ATen/ops/clamp_ops.h> |
| 119 | #include <ATen/ops/clamp_native.h> |
| 120 | #include <ATen/ops/clamp_ops.h> |
| 121 | #include <ATen/ops/clamp_native.h> |
| 122 | #include <ATen/ops/clamp_ops.h> |
| 123 | #include <ATen/ops/clamp_native.h> |
| 124 | #include <ATen/ops/clamp_ops.h> |
| 125 | #include <ATen/ops/clamp_native.h> |
| 126 | #include <ATen/ops/clamp_ops.h> |
| 127 | #include <ATen/ops/clamp_native.h> |
| 128 | #include <ATen/ops/clamp_ops.h> |
| 129 | #include <ATen/ops/_convolution_native.h> |
| 130 | #include <ATen/ops/_convolution_ops.h> |
| 131 | #include <ATen/ops/_convolution_native.h> |
| 132 | #include <ATen/ops/_convolution_ops.h> |
| 133 | #include <ATen/ops/copy_native.h> |
| 134 | #include <ATen/ops/copy_ops.h> |
| 135 | #include <ATen/ops/copy_native.h> |
| 136 | #include <ATen/ops/copy_ops.h> |
| 137 | #include <ATen/ops/copy_native.h> |
| 138 | #include <ATen/ops/copy_ops.h> |
| 139 | #include <ATen/ops/cudnn_convolution_native.h> |
| 140 | #include <ATen/ops/cudnn_convolution_ops.h> |
| 141 | #include <ATen/ops/cudnn_convolution_native.h> |
| 142 | #include <ATen/ops/cudnn_convolution_ops.h> |
| 143 | #include <ATen/ops/divide_native.h> |
| 144 | #include <ATen/ops/divide_ops.h> |
| 145 | #include <ATen/ops/divide_native.h> |
| 146 | #include <ATen/ops/divide_ops.h> |
| 147 | #include <ATen/ops/divide_native.h> |
| 148 | #include <ATen/ops/divide_ops.h> |
| 149 | #include <ATen/ops/divide_native.h> |
| 150 | #include <ATen/ops/divide_ops.h> |
| 151 | #include <ATen/ops/divide_native.h> |
| 152 | #include <ATen/ops/divide_ops.h> |
| 153 | #include <ATen/ops/divide_native.h> |
| 154 | #include <ATen/ops/divide_ops.h> |
| 155 | #include <ATen/ops/true_divide_native.h> |
| 156 | #include <ATen/ops/true_divide_ops.h> |
| 157 | #include <ATen/ops/true_divide_native.h> |
| 158 | #include <ATen/ops/true_divide_ops.h> |
| 159 | #include <ATen/ops/true_divide_native.h> |
| 160 | #include <ATen/ops/true_divide_ops.h> |
| 161 | #include <ATen/ops/dot_native.h> |
| 162 | #include <ATen/ops/dot_ops.h> |
| 163 | #include <ATen/ops/dot_native.h> |
| 164 | #include <ATen/ops/dot_ops.h> |
| 165 | #include <ATen/ops/vdot_native.h> |
| 166 | #include <ATen/ops/vdot_ops.h> |
| 167 | #include <ATen/ops/vdot_native.h> |
| 168 | #include <ATen/ops/vdot_ops.h> |
| 169 | #include <ATen/ops/row_stack_native.h> |
| 170 | #include <ATen/ops/row_stack_ops.h> |
| 171 | #include <ATen/ops/row_stack_native.h> |
| 172 | #include <ATen/ops/row_stack_ops.h> |
| 173 | #include <ATen/ops/new_empty_native.h> |
| 174 | #include <ATen/ops/new_empty_ops.h> |
| 175 | #include <ATen/ops/new_empty_native.h> |
| 176 | #include <ATen/ops/new_empty_ops.h> |
| 177 | #include <ATen/ops/new_empty_strided_native.h> |
| 178 | #include <ATen/ops/new_empty_strided_ops.h> |
| 179 | #include <ATen/ops/new_empty_strided_native.h> |
| 180 | #include <ATen/ops/new_empty_strided_ops.h> |
| 181 | #include <ATen/ops/_empty_affine_quantized_native.h> |
| 182 | #include <ATen/ops/_empty_affine_quantized_ops.h> |
| 183 | #include <ATen/ops/_empty_affine_quantized_native.h> |
| 184 | #include <ATen/ops/_empty_affine_quantized_ops.h> |
| 185 | #include <ATen/ops/_resize_output_native.h> |
| 186 | #include <ATen/ops/_resize_output_ops.h> |
| 187 | #include <ATen/ops/_resize_output_native.h> |
| 188 | #include <ATen/ops/_resize_output_ops.h> |
| 189 | #include <ATen/ops/_resize_output_native.h> |
| 190 | #include <ATen/ops/_resize_output_ops.h> |
| 191 | #include <ATen/ops/frac_native.h> |
| 192 | #include <ATen/ops/frac_ops.h> |
| 193 | #include <ATen/ops/frac_native.h> |
| 194 | #include <ATen/ops/frac_ops.h> |
| 195 | #include <ATen/ops/frac_native.h> |
| 196 | #include <ATen/ops/frac_ops.h> |
| 197 | #include <ATen/ops/full_like_native.h> |
| 198 | #include <ATen/ops/full_like_ops.h> |
| 199 | #include <ATen/ops/full_like_native.h> |
| 200 | #include <ATen/ops/full_like_ops.h> |
| 201 | #include <ATen/ops/lcm_native.h> |
| 202 | #include <ATen/ops/lcm_ops.h> |
| 203 | #include <ATen/ops/lcm_native.h> |
| 204 | #include <ATen/ops/lcm_ops.h> |
| 205 | #include <ATen/ops/lcm_native.h> |
| 206 | #include <ATen/ops/lcm_ops.h> |
| 207 | #include <ATen/ops/hann_window_native.h> |
| 208 | #include <ATen/ops/hann_window_ops.h> |
| 209 | #include <ATen/ops/hann_window_native.h> |
| 210 | #include <ATen/ops/hann_window_ops.h> |
| 211 | #include <ATen/ops/hann_window_native.h> |
| 212 | #include <ATen/ops/hann_window_ops.h> |
| 213 | #include <ATen/ops/hann_window_native.h> |
| 214 | #include <ATen/ops/hann_window_ops.h> |
| 215 | #include <ATen/ops/kaiser_window_native.h> |
| 216 | #include <ATen/ops/kaiser_window_ops.h> |
| 217 | #include <ATen/ops/kaiser_window_native.h> |
| 218 | #include <ATen/ops/kaiser_window_ops.h> |
| 219 | #include <ATen/ops/kaiser_window_native.h> |
| 220 | #include <ATen/ops/kaiser_window_ops.h> |
| 221 | #include <ATen/ops/kaiser_window_native.h> |
| 222 | #include <ATen/ops/kaiser_window_ops.h> |
| 223 | #include <ATen/ops/kaiser_window_native.h> |
| 224 | #include <ATen/ops/kaiser_window_ops.h> |
| 225 | #include <ATen/ops/kaiser_window_native.h> |
| 226 | #include <ATen/ops/kaiser_window_ops.h> |
| 227 | #include <ATen/ops/_index_put_impl_native.h> |
| 228 | #include <ATen/ops/_index_put_impl_ops.h> |
| 229 | #include <ATen/ops/_index_put_impl_native.h> |
| 230 | #include <ATen/ops/_index_put_impl_ops.h> |
| 231 | #include <ATen/ops/_index_put_impl_native.h> |
| 232 | #include <ATen/ops/_index_put_impl_ops.h> |
| 233 | #include <ATen/ops/kthvalue_native.h> |
| 234 | #include <ATen/ops/kthvalue_ops.h> |
| 235 | #include <ATen/ops/kthvalue_native.h> |
| 236 | #include <ATen/ops/kthvalue_ops.h> |
| 237 | #include <ATen/ops/kthvalue_native.h> |
| 238 | #include <ATen/ops/kthvalue_ops.h> |
| 239 | #include <ATen/ops/kthvalue_native.h> |
| 240 | #include <ATen/ops/kthvalue_ops.h> |
| 241 | #include <ATen/ops/native_layer_norm_native.h> |
| 242 | #include <ATen/ops/native_layer_norm_ops.h> |
| 243 | #include <ATen/ops/native_layer_norm_native.h> |
| 244 | #include <ATen/ops/native_layer_norm_ops.h> |
| 245 | #include <ATen/ops/native_layer_norm_backward_native.h> |
| 246 | #include <ATen/ops/native_layer_norm_backward_ops.h> |
| 247 | #include <ATen/ops/native_layer_norm_backward_native.h> |
| 248 | #include <ATen/ops/native_layer_norm_backward_ops.h> |
| 249 | #include <ATen/ops/mkldnn_linear_backward_input_native.h> |
| 250 | #include <ATen/ops/mkldnn_linear_backward_input_ops.h> |
| 251 | #include <ATen/ops/mkldnn_linear_backward_input_native.h> |
| 252 | #include <ATen/ops/mkldnn_linear_backward_input_ops.h> |
| 253 | #include <ATen/ops/mkldnn_linear_backward_native.h> |
| 254 | #include <ATen/ops/mkldnn_linear_backward_ops.h> |
| 255 | #include <ATen/ops/mkldnn_linear_backward_native.h> |
| 256 | #include <ATen/ops/mkldnn_linear_backward_ops.h> |
| 257 | #include <ATen/ops/log10_native.h> |
| 258 | #include <ATen/ops/log10_ops.h> |
| 259 | #include <ATen/ops/log10_native.h> |
| 260 | #include <ATen/ops/log10_ops.h> |
| 261 | #include <ATen/ops/log10_native.h> |
| 262 | #include <ATen/ops/log10_ops.h> |
| 263 | #include <ATen/ops/log1p_native.h> |
| 264 | #include <ATen/ops/log1p_ops.h> |
| 265 | #include <ATen/ops/log1p_native.h> |
| 266 | #include <ATen/ops/log1p_ops.h> |
| 267 | #include <ATen/ops/log1p_native.h> |
| 268 | #include <ATen/ops/log1p_ops.h> |
| 269 | #include <ATen/ops/logsumexp_native.h> |
| 270 | #include <ATen/ops/logsumexp_ops.h> |
| 271 | #include <ATen/ops/logsumexp_native.h> |
| 272 | #include <ATen/ops/logsumexp_ops.h> |
| 273 | #include <ATen/ops/logsumexp_native.h> |
| 274 | #include <ATen/ops/logsumexp_ops.h> |
| 275 | #include <ATen/ops/logsumexp_native.h> |
| 276 | #include <ATen/ops/logsumexp_ops.h> |
| 277 | #include <ATen/ops/matmul_native.h> |
| 278 | #include <ATen/ops/matmul_ops.h> |
| 279 | #include <ATen/ops/matmul_native.h> |
| 280 | #include <ATen/ops/matmul_ops.h> |
| 281 | #include <ATen/ops/mkldnn_max_pool2d_native.h> |
| 282 | #include <ATen/ops/mkldnn_max_pool2d_ops.h> |
| 283 | #include <ATen/ops/mkldnn_max_pool2d_native.h> |
| 284 | #include <ATen/ops/mkldnn_max_pool2d_ops.h> |
| 285 | #include <ATen/ops/quantized_max_pool1d_native.h> |
| 286 | #include <ATen/ops/quantized_max_pool1d_ops.h> |
| 287 | #include <ATen/ops/quantized_max_pool1d_native.h> |
| 288 | #include <ATen/ops/quantized_max_pool1d_ops.h> |
| 289 | #include <ATen/ops/nanmean_native.h> |
| 290 | #include <ATen/ops/nanmean_ops.h> |
| 291 | #include <ATen/ops/nanmean_native.h> |
| 292 | #include <ATen/ops/nanmean_ops.h> |
| 293 | #include <ATen/ops/_mps_convolution_native.h> |
| 294 | #include <ATen/ops/_mps_convolution_ops.h> |
| 295 | #include <ATen/ops/_mps_convolution_native.h> |
| 296 | #include <ATen/ops/_mps_convolution_ops.h> |
| 297 | #include <ATen/ops/mkldnn_convolution_native.h> |
| 298 | #include <ATen/ops/mkldnn_convolution_ops.h> |
| 299 | #include <ATen/ops/mkldnn_convolution_native.h> |
| 300 | #include <ATen/ops/mkldnn_convolution_ops.h> |
| 301 | #include <ATen/ops/mkldnn_rnn_layer_native.h> |
| 302 | #include <ATen/ops/mkldnn_rnn_layer_ops.h> |
| 303 | #include <ATen/ops/mkldnn_rnn_layer_native.h> |
| 304 | #include <ATen/ops/mkldnn_rnn_layer_ops.h> |
| 305 | #include <ATen/ops/miopen_batch_norm_native.h> |
| 306 | #include <ATen/ops/miopen_batch_norm_ops.h> |
| 307 | #include <ATen/ops/miopen_batch_norm_native.h> |
| 308 | #include <ATen/ops/miopen_batch_norm_ops.h> |
| 309 | #include <ATen/ops/miopen_batch_norm_backward_native.h> |
| 310 | #include <ATen/ops/miopen_batch_norm_backward_ops.h> |
| 311 | #include <ATen/ops/miopen_batch_norm_backward_native.h> |
| 312 | #include <ATen/ops/miopen_batch_norm_backward_ops.h> |
| 313 | #include <ATen/ops/miopen_convolution_transpose_native.h> |
| 314 | #include <ATen/ops/miopen_convolution_transpose_ops.h> |
| 315 | #include <ATen/ops/miopen_convolution_transpose_native.h> |
| 316 | #include <ATen/ops/miopen_convolution_transpose_ops.h> |
| 317 | #include <ATen/ops/mm_native.h> |
| 318 | #include <ATen/ops/mm_ops.h> |
| 319 | #include <ATen/ops/mm_native.h> |
| 320 | #include <ATen/ops/mm_ops.h> |
| 321 | #include <ATen/ops/_sparse_sparse_matmul_native.h> |
| 322 | #include <ATen/ops/_sparse_sparse_matmul_ops.h> |
| 323 | #include <ATen/ops/_sparse_sparse_matmul_native.h> |
| 324 | #include <ATen/ops/_sparse_sparse_matmul_ops.h> |
| 325 | #include <ATen/ops/mul_native.h> |
| 326 | #include <ATen/ops/mul_ops.h> |
| 327 | #include <ATen/ops/mul_native.h> |
| 328 | #include <ATen/ops/mul_ops.h> |
| 329 | #include <ATen/ops/mul_native.h> |
| 330 | #include <ATen/ops/mul_ops.h> |
| 331 | #include <ATen/ops/mul_native.h> |
| 332 | #include <ATen/ops/mul_ops.h> |
| 333 | #include <ATen/ops/mul_native.h> |
| 334 | #include <ATen/ops/mul_ops.h> |
| 335 | #include <ATen/ops/mul_native.h> |
| 336 | #include <ATen/ops/mul_ops.h> |
| 337 | #include <ATen/ops/mvlgamma_native.h> |
| 338 | #include <ATen/ops/mvlgamma_ops.h> |
| 339 | #include <ATen/ops/mvlgamma_native.h> |
| 340 | #include <ATen/ops/mvlgamma_ops.h> |
| 341 | #include <ATen/ops/mvlgamma_native.h> |
| 342 | #include <ATen/ops/mvlgamma_ops.h> |
| 343 | #include <ATen/ops/batch_norm_backward_reduce_native.h> |
| 344 | #include <ATen/ops/batch_norm_backward_reduce_ops.h> |
| 345 | #include <ATen/ops/batch_norm_backward_reduce_native.h> |
| 346 | #include <ATen/ops/batch_norm_backward_reduce_ops.h> |
| 347 | #include <ATen/ops/deg2rad_native.h> |
| 348 | #include <ATen/ops/deg2rad_ops.h> |
| 349 | #include <ATen/ops/deg2rad_native.h> |
| 350 | #include <ATen/ops/deg2rad_ops.h> |
| 351 | #include <ATen/ops/deg2rad_native.h> |
| 352 | #include <ATen/ops/deg2rad_ops.h> |
| 353 | #include <ATen/ops/randint_like_native.h> |
| 354 | #include <ATen/ops/randint_like_ops.h> |
| 355 | #include <ATen/ops/randint_like_native.h> |
| 356 | #include <ATen/ops/randint_like_ops.h> |
| 357 | #include <ATen/ops/randint_like_native.h> |
| 358 | #include <ATen/ops/randint_like_ops.h> |
| 359 | #include <ATen/ops/randint_like_native.h> |
| 360 | #include <ATen/ops/randint_like_ops.h> |
| 361 | #include <ATen/ops/repeat_native.h> |
| 362 | #include <ATen/ops/repeat_ops.h> |
| 363 | #include <ATen/ops/repeat_native.h> |
| 364 | #include <ATen/ops/repeat_ops.h> |
| 365 | #include <ATen/ops/_mkldnn_reshape_native.h> |
| 366 | #include <ATen/ops/_mkldnn_reshape_ops.h> |
| 367 | #include <ATen/ops/_mkldnn_reshape_native.h> |
| 368 | #include <ATen/ops/_mkldnn_reshape_ops.h> |
| 369 | #include <ATen/ops/round_native.h> |
| 370 | #include <ATen/ops/round_ops.h> |
| 371 | #include <ATen/ops/round_native.h> |
| 372 | #include <ATen/ops/round_ops.h> |
| 373 | #include <ATen/ops/round_native.h> |
| 374 | #include <ATen/ops/round_ops.h> |
| 375 | #include <ATen/ops/round_native.h> |
| 376 | #include <ATen/ops/round_ops.h> |
| 377 | #include <ATen/ops/round_native.h> |
| 378 | #include <ATen/ops/round_ops.h> |
| 379 | #include <ATen/ops/round_native.h> |
| 380 | #include <ATen/ops/round_ops.h> |
| 381 | #include <ATen/ops/rsqrt_native.h> |
| 382 | #include <ATen/ops/rsqrt_ops.h> |
| 383 | #include <ATen/ops/rsqrt_native.h> |
| 384 | #include <ATen/ops/rsqrt_ops.h> |
| 385 | #include <ATen/ops/rsqrt_native.h> |
| 386 | #include <ATen/ops/rsqrt_ops.h> |
| 387 | #include <ATen/ops/celu_native.h> |
| 388 | #include <ATen/ops/celu_ops.h> |
| 389 | #include <ATen/ops/celu_native.h> |
| 390 | #include <ATen/ops/celu_ops.h> |
| 391 | #include <ATen/ops/celu_native.h> |
| 392 | #include <ATen/ops/celu_ops.h> |
| 393 | #include <ATen/ops/sigmoid_native.h> |
| 394 | #include <ATen/ops/sigmoid_ops.h> |
| 395 | #include <ATen/ops/sigmoid_native.h> |
| 396 | #include <ATen/ops/sigmoid_ops.h> |
| 397 | #include <ATen/ops/sigmoid_native.h> |
| 398 | #include <ATen/ops/sigmoid_ops.h> |
| 399 | #include <ATen/ops/sinc_native.h> |
| 400 | #include <ATen/ops/sinc_ops.h> |
| 401 | #include <ATen/ops/sinc_native.h> |
| 402 | #include <ATen/ops/sinc_ops.h> |
| 403 | #include <ATen/ops/sinc_native.h> |
| 404 | #include <ATen/ops/sinc_ops.h> |
| 405 | #include <ATen/ops/sinh_native.h> |
| 406 | #include <ATen/ops/sinh_ops.h> |
| 407 | #include <ATen/ops/sinh_native.h> |
| 408 | #include <ATen/ops/sinh_ops.h> |
| 409 | #include <ATen/ops/sinh_native.h> |
| 410 | #include <ATen/ops/sinh_ops.h> |
| 411 | #include <ATen/ops/slice_backward_native.h> |
| 412 | #include <ATen/ops/slice_backward_ops.h> |
| 413 | #include <ATen/ops/slice_backward_native.h> |
| 414 | #include <ATen/ops/slice_backward_ops.h> |
| 415 | #include <ATen/ops/as_strided_scatter_native.h> |
| 416 | #include <ATen/ops/as_strided_scatter_ops.h> |
| 417 | #include <ATen/ops/as_strided_scatter_native.h> |
| 418 | #include <ATen/ops/as_strided_scatter_ops.h> |
| 419 | #include <ATen/ops/std_mean_native.h> |
| 420 | #include <ATen/ops/std_mean_ops.h> |
| 421 | #include <ATen/ops/std_mean_native.h> |
| 422 | #include <ATen/ops/std_mean_ops.h> |
| 423 | #include <ATen/ops/_mkldnn_transpose_native.h> |
| 424 | #include <ATen/ops/_mkldnn_transpose_ops.h> |
| 425 | #include <ATen/ops/_mkldnn_transpose_native.h> |
| 426 | #include <ATen/ops/_mkldnn_transpose_ops.h> |
| 427 | #include <ATen/ops/_mkldnn_transpose_native.h> |
| 428 | #include <ATen/ops/_mkldnn_transpose_ops.h> |
| 429 | #include <ATen/ops/flip_native.h> |
| 430 | #include <ATen/ops/flip_ops.h> |
| 431 | #include <ATen/ops/flip_native.h> |
| 432 | #include <ATen/ops/flip_ops.h> |
| 433 | #include <ATen/ops/_nested_tensor_from_mask_native.h> |
| 434 | #include <ATen/ops/_nested_tensor_from_mask_ops.h> |
| 435 | #include <ATen/ops/_nested_tensor_from_mask_native.h> |
| 436 | #include <ATen/ops/_nested_tensor_from_mask_ops.h> |
| 437 | #include <ATen/ops/_nested_from_padded_and_nested_example_native.h> |
| 438 | #include <ATen/ops/_nested_from_padded_and_nested_example_ops.h> |
| 439 | #include <ATen/ops/_nested_from_padded_and_nested_example_native.h> |
| 440 | #include <ATen/ops/_nested_from_padded_and_nested_example_ops.h> |
| 441 | #include <ATen/ops/unique_dim_native.h> |
| 442 | #include <ATen/ops/unique_dim_ops.h> |
| 443 | #include <ATen/ops/unique_dim_native.h> |
| 444 | #include <ATen/ops/unique_dim_ops.h> |
| 445 | #include <ATen/ops/_unsafe_view_native.h> |
| 446 | #include <ATen/ops/_unsafe_view_ops.h> |
| 447 | #include <ATen/ops/_unsafe_view_native.h> |
| 448 | #include <ATen/ops/_unsafe_view_ops.h> |
| 449 | #include <ATen/ops/var_mean_native.h> |
| 450 | #include <ATen/ops/var_mean_ops.h> |
| 451 | #include <ATen/ops/var_mean_native.h> |
| 452 | #include <ATen/ops/var_mean_ops.h> |
| 453 | #include <ATen/ops/zeros_native.h> |
| 454 | #include <ATen/ops/zeros_ops.h> |
| 455 | #include <ATen/ops/zeros_native.h> |
| 456 | #include <ATen/ops/zeros_ops.h> |
| 457 | #include <ATen/ops/zeros_native.h> |
| 458 | #include <ATen/ops/zeros_ops.h> |
| 459 | #include <ATen/ops/zeros_native.h> |
| 460 | #include <ATen/ops/zeros_ops.h> |
| 461 | #include <ATen/ops/zeros_like_native.h> |
| 462 | #include <ATen/ops/zeros_like_ops.h> |
| 463 | #include <ATen/ops/zeros_like_native.h> |
| 464 | #include <ATen/ops/zeros_like_ops.h> |
| 465 | #include <ATen/ops/_sparse_csr_prod_native.h> |
| 466 | #include <ATen/ops/_sparse_csr_prod_ops.h> |
| 467 | #include <ATen/ops/_sparse_csr_prod_native.h> |
| 468 | #include <ATen/ops/_sparse_csr_prod_ops.h> |
| 469 | #include <ATen/ops/_spdiags_native.h> |
| 470 | #include <ATen/ops/_spdiags_ops.h> |
| 471 | #include <ATen/ops/_spdiags_native.h> |
| 472 | #include <ATen/ops/_spdiags_ops.h> |
| 473 | #include <ATen/ops/rsub_native.h> |
| 474 | #include <ATen/ops/rsub_ops.h> |
| 475 | #include <ATen/ops/rsub_native.h> |
| 476 | #include <ATen/ops/rsub_ops.h> |
| 477 | #include <ATen/ops/rsub_native.h> |
| 478 | #include <ATen/ops/rsub_ops.h> |
| 479 | #include <ATen/ops/rsub_native.h> |
| 480 | #include <ATen/ops/rsub_ops.h> |
| 481 | #include <ATen/ops/_sparse_addmm_native.h> |
| 482 | #include <ATen/ops/_sparse_addmm_ops.h> |
| 483 | #include <ATen/ops/_sparse_addmm_native.h> |
| 484 | #include <ATen/ops/_sparse_addmm_ops.h> |
| 485 | #include <ATen/ops/sparse_coo_tensor_native.h> |
| 486 | #include <ATen/ops/sparse_coo_tensor_ops.h> |
| 487 | #include <ATen/ops/sparse_coo_tensor_native.h> |
| 488 | #include <ATen/ops/sparse_coo_tensor_ops.h> |
| 489 | #include <ATen/ops/sparse_resize_native.h> |
| 490 | #include <ATen/ops/sparse_resize_ops.h> |
| 491 | #include <ATen/ops/sparse_resize_native.h> |
| 492 | #include <ATen/ops/sparse_resize_ops.h> |
| 493 | #include <ATen/ops/sparse_resize_native.h> |
| 494 | #include <ATen/ops/sparse_resize_ops.h> |
| 495 | #include <ATen/ops/sparse_mask_native.h> |
| 496 | #include <ATen/ops/sparse_mask_ops.h> |
| 497 | #include <ATen/ops/sparse_mask_native.h> |
| 498 | #include <ATen/ops/sparse_mask_ops.h> |
| 499 | #include <ATen/ops/_coalesce_native.h> |
| 500 | #include <ATen/ops/_coalesce_ops.h> |
| 501 | #include <ATen/ops/_coalesce_native.h> |
| 502 | #include <ATen/ops/_coalesce_ops.h> |
| 503 | #include <ATen/ops/dequantize_native.h> |
| 504 | #include <ATen/ops/dequantize_ops.h> |
| 505 | #include <ATen/ops/dequantize_native.h> |
| 506 | #include <ATen/ops/dequantize_ops.h> |
| 507 | #include <ATen/ops/dequantize_native.h> |
| 508 | #include <ATen/ops/dequantize_ops.h> |
| 509 | #include <ATen/ops/dequantize_native.h> |
| 510 | #include <ATen/ops/dequantize_ops.h> |
| 511 | #include <ATen/ops/q_per_channel_zero_points_native.h> |
| 512 | #include <ATen/ops/q_per_channel_zero_points_ops.h> |
| 513 | #include <ATen/ops/q_per_channel_zero_points_native.h> |
| 514 | #include <ATen/ops/q_per_channel_zero_points_ops.h> |
| 515 | #include <ATen/ops/_fake_quantize_learnable_per_channel_affine_native.h> |
| 516 | #include <ATen/ops/_fake_quantize_learnable_per_channel_affine_ops.h> |
| 517 | #include <ATen/ops/_fake_quantize_learnable_per_channel_affine_native.h> |
| 518 | #include <ATen/ops/_fake_quantize_learnable_per_channel_affine_ops.h> |
| 519 | #include <ATen/ops/_fused_moving_avg_obs_fq_helper_native.h> |
| 520 | #include <ATen/ops/_fused_moving_avg_obs_fq_helper_ops.h> |
| 521 | #include <ATen/ops/_fused_moving_avg_obs_fq_helper_native.h> |
| 522 | #include <ATen/ops/_fused_moving_avg_obs_fq_helper_ops.h> |
| 523 | #include <ATen/ops/_fused_moving_avg_obs_fq_helper_native.h> |
| 524 | #include <ATen/ops/_fused_moving_avg_obs_fq_helper_ops.h> |
| 525 | #include <ATen/ops/_to_copy_native.h> |
| 526 | #include <ATen/ops/_to_copy_ops.h> |
| 527 | #include <ATen/ops/_to_copy_native.h> |
| 528 | #include <ATen/ops/_to_copy_ops.h> |
| 529 | #include <ATen/ops/_thnn_fused_gru_cell_native.h> |
| 530 | #include <ATen/ops/_thnn_fused_gru_cell_ops.h> |
| 531 | #include <ATen/ops/_thnn_fused_gru_cell_native.h> |
| 532 | #include <ATen/ops/_thnn_fused_gru_cell_ops.h> |
| 533 | #include <ATen/ops/_pack_padded_sequence_native.h> |
| 534 | #include <ATen/ops/_pack_padded_sequence_ops.h> |
| 535 | #include <ATen/ops/_pack_padded_sequence_native.h> |
| 536 | #include <ATen/ops/_pack_padded_sequence_ops.h> |
| 537 | #include <ATen/ops/scatter_reduce_native.h> |
| 538 | #include <ATen/ops/scatter_reduce_ops.h> |
| 539 | #include <ATen/ops/scatter_reduce_native.h> |
| 540 | #include <ATen/ops/scatter_reduce_ops.h> |
| 541 | #include <ATen/ops/scatter_reduce_native.h> |
| 542 | #include <ATen/ops/scatter_reduce_ops.h> |
| 543 | #include <ATen/ops/bitwise_xor_native.h> |
| 544 | #include <ATen/ops/bitwise_xor_ops.h> |
| 545 | #include <ATen/ops/bitwise_xor_native.h> |
| 546 | #include <ATen/ops/bitwise_xor_ops.h> |
| 547 | #include <ATen/ops/bitwise_xor_native.h> |
| 548 | #include <ATen/ops/bitwise_xor_ops.h> |
| 549 | #include <ATen/ops/bitwise_xor_native.h> |
| 550 | #include <ATen/ops/bitwise_xor_ops.h> |
| 551 | #include <ATen/ops/bitwise_xor_native.h> |
| 552 | #include <ATen/ops/bitwise_xor_ops.h> |
| 553 | #include <ATen/ops/bitwise_xor_native.h> |
| 554 | #include <ATen/ops/bitwise_xor_ops.h> |
| 555 | #include <ATen/ops/bitwise_xor_native.h> |
| 556 | #include <ATen/ops/bitwise_xor_ops.h> |
| 557 | #include <ATen/ops/bitwise_xor_native.h> |
| 558 | #include <ATen/ops/bitwise_xor_ops.h> |
| 559 | #include <ATen/ops/addbmm_native.h> |
| 560 | #include <ATen/ops/addbmm_ops.h> |
| 561 | #include <ATen/ops/addbmm_native.h> |
| 562 | #include <ATen/ops/addbmm_ops.h> |
| 563 | #include <ATen/ops/addbmm_native.h> |
| 564 | #include <ATen/ops/addbmm_ops.h> |
| 565 | #include <ATen/ops/random_native.h> |
| 566 | #include <ATen/ops/random_ops.h> |
| 567 | #include <ATen/ops/random_native.h> |
| 568 | #include <ATen/ops/random_ops.h> |
| 569 | #include <ATen/ops/random_native.h> |
| 570 | #include <ATen/ops/random_ops.h> |
| 571 | #include <ATen/ops/random_native.h> |
| 572 | #include <ATen/ops/random_ops.h> |
| 573 | #include <ATen/ops/random_native.h> |
| 574 | #include <ATen/ops/random_ops.h> |
| 575 | #include <ATen/ops/random_native.h> |
| 576 | #include <ATen/ops/random_ops.h> |
| 577 | #include <ATen/ops/random_native.h> |
| 578 | #include <ATen/ops/random_ops.h> |
| 579 | #include <ATen/ops/random_native.h> |
| 580 | #include <ATen/ops/random_ops.h> |
| 581 | #include <ATen/ops/random_native.h> |
| 582 | #include <ATen/ops/random_ops.h> |
| 583 | #include <ATen/ops/exponential_native.h> |
| 584 | #include <ATen/ops/exponential_ops.h> |
| 585 | #include <ATen/ops/exponential_native.h> |
| 586 | #include <ATen/ops/exponential_ops.h> |
| 587 | #include <ATen/ops/exponential_native.h> |
| 588 | #include <ATen/ops/exponential_ops.h> |
| 589 | #include <ATen/ops/geometric_native.h> |
| 590 | #include <ATen/ops/geometric_ops.h> |
| 591 | #include <ATen/ops/geometric_native.h> |
| 592 | #include <ATen/ops/geometric_ops.h> |
| 593 | #include <ATen/ops/geometric_native.h> |
| 594 | #include <ATen/ops/geometric_ops.h> |
| 595 | #include <ATen/ops/cross_native.h> |
| 596 | #include <ATen/ops/cross_ops.h> |
| 597 | #include <ATen/ops/cross_native.h> |
| 598 | #include <ATen/ops/cross_ops.h> |
| 599 | #include <ATen/ops/trace_native.h> |
| 600 | #include <ATen/ops/trace_ops.h> |
| 601 | #include <ATen/ops/trace_native.h> |
| 602 | #include <ATen/ops/trace_ops.h> |
| 603 | #include <ATen/ops/take_along_dim_native.h> |
| 604 | #include <ATen/ops/take_along_dim_ops.h> |
| 605 | #include <ATen/ops/take_along_dim_native.h> |
| 606 | #include <ATen/ops/take_along_dim_ops.h> |
| 607 | #include <ATen/ops/index_select_native.h> |
| 608 | #include <ATen/ops/index_select_ops.h> |
| 609 | #include <ATen/ops/index_select_native.h> |
| 610 | #include <ATen/ops/index_select_ops.h> |
| 611 | #include <ATen/ops/index_select_native.h> |
| 612 | #include <ATen/ops/index_select_ops.h> |
| 613 | #include <ATen/ops/index_select_native.h> |
| 614 | #include <ATen/ops/index_select_ops.h> |
| 615 | #include <ATen/ops/masked_select_native.h> |
| 616 | #include <ATen/ops/masked_select_ops.h> |
| 617 | #include <ATen/ops/masked_select_native.h> |
| 618 | #include <ATen/ops/masked_select_ops.h> |
| 619 | #include <ATen/ops/linalg_solve_triangular_native.h> |
| 620 | #include <ATen/ops/linalg_solve_triangular_ops.h> |
| 621 | #include <ATen/ops/linalg_solve_triangular_native.h> |
| 622 | #include <ATen/ops/linalg_solve_triangular_ops.h> |
| 623 | #include <ATen/ops/svd_native.h> |
| 624 | #include <ATen/ops/svd_ops.h> |
| 625 | #include <ATen/ops/svd_native.h> |
| 626 | #include <ATen/ops/svd_ops.h> |
| 627 | #include <ATen/ops/multinomial_native.h> |
| 628 | #include <ATen/ops/multinomial_ops.h> |
| 629 | #include <ATen/ops/multinomial_native.h> |
| 630 | #include <ATen/ops/multinomial_ops.h> |
| 631 | #include <ATen/ops/histogram_native.h> |
| 632 | #include <ATen/ops/histogram_ops.h> |
| 633 | #include <ATen/ops/histogram_native.h> |
| 634 | #include <ATen/ops/histogram_ops.h> |
| 635 | #include <ATen/ops/histogram_native.h> |
| 636 | #include <ATen/ops/histogram_ops.h> |
| 637 | #include <ATen/ops/histogram_native.h> |
| 638 | #include <ATen/ops/histogram_ops.h> |
| 639 | #include <ATen/ops/igammac_native.h> |
| 640 | #include <ATen/ops/igammac_ops.h> |
| 641 | #include <ATen/ops/igammac_native.h> |
| 642 | #include <ATen/ops/igammac_ops.h> |
| 643 | #include <ATen/ops/igammac_native.h> |
| 644 | #include <ATen/ops/igammac_ops.h> |
| 645 | #include <ATen/ops/remainder_native.h> |
| 646 | #include <ATen/ops/remainder_ops.h> |
| 647 | #include <ATen/ops/remainder_native.h> |
| 648 | #include <ATen/ops/remainder_ops.h> |
| 649 | #include <ATen/ops/remainder_native.h> |
| 650 | #include <ATen/ops/remainder_ops.h> |
| 651 | #include <ATen/ops/remainder_native.h> |
| 652 | #include <ATen/ops/remainder_ops.h> |
| 653 | #include <ATen/ops/remainder_native.h> |
| 654 | #include <ATen/ops/remainder_ops.h> |
| 655 | #include <ATen/ops/remainder_native.h> |
| 656 | #include <ATen/ops/remainder_ops.h> |
| 657 | #include <ATen/ops/remainder_native.h> |
| 658 | #include <ATen/ops/remainder_ops.h> |
| 659 | #include <ATen/ops/remainder_native.h> |
| 660 | #include <ATen/ops/remainder_ops.h> |
| 661 | #include <ATen/ops/quantile_native.h> |
| 662 | #include <ATen/ops/quantile_ops.h> |
| 663 | #include <ATen/ops/quantile_native.h> |
| 664 | #include <ATen/ops/quantile_ops.h> |
| 665 | #include <ATen/ops/quantile_native.h> |
| 666 | #include <ATen/ops/quantile_ops.h> |
| 667 | #include <ATen/ops/quantile_native.h> |
| 668 | #include <ATen/ops/quantile_ops.h> |
| 669 | #include <ATen/ops/nanquantile_native.h> |
| 670 | #include <ATen/ops/nanquantile_ops.h> |
| 671 | #include <ATen/ops/nanquantile_native.h> |
| 672 | #include <ATen/ops/nanquantile_ops.h> |
| 673 | #include <ATen/ops/nanquantile_native.h> |
| 674 | #include <ATen/ops/nanquantile_ops.h> |
| 675 | #include <ATen/ops/nanquantile_native.h> |
| 676 | #include <ATen/ops/nanquantile_ops.h> |
| 677 | #include <ATen/ops/sort_native.h> |
| 678 | #include <ATen/ops/sort_ops.h> |
| 679 | #include <ATen/ops/sort_native.h> |
| 680 | #include <ATen/ops/sort_ops.h> |
| 681 | #include <ATen/ops/sort_native.h> |
| 682 | #include <ATen/ops/sort_ops.h> |
| 683 | #include <ATen/ops/sort_native.h> |
| 684 | #include <ATen/ops/sort_ops.h> |
| 685 | #include <ATen/ops/sort_native.h> |
| 686 | #include <ATen/ops/sort_ops.h> |
| 687 | #include <ATen/ops/sort_native.h> |
| 688 | #include <ATen/ops/sort_ops.h> |
| 689 | #include <ATen/ops/sort_native.h> |
| 690 | #include <ATen/ops/sort_ops.h> |
| 691 | #include <ATen/ops/sort_native.h> |
| 692 | #include <ATen/ops/sort_ops.h> |
| 693 | #include <ATen/ops/argsort_native.h> |
| 694 | #include <ATen/ops/argsort_ops.h> |
| 695 | #include <ATen/ops/argsort_native.h> |
| 696 | #include <ATen/ops/argsort_ops.h> |
| 697 | #include <ATen/ops/all_native.h> |
| 698 | #include <ATen/ops/all_ops.h> |
| 699 | #include <ATen/ops/all_native.h> |
| 700 | #include <ATen/ops/all_ops.h> |
| 701 | #include <ATen/ops/renorm_native.h> |
| 702 | #include <ATen/ops/renorm_ops.h> |
| 703 | #include <ATen/ops/renorm_native.h> |
| 704 | #include <ATen/ops/renorm_ops.h> |
| 705 | #include <ATen/ops/renorm_native.h> |
| 706 | #include <ATen/ops/renorm_ops.h> |
| 707 | #include <ATen/ops/unfold_backward_native.h> |
| 708 | #include <ATen/ops/unfold_backward_ops.h> |
| 709 | #include <ATen/ops/unfold_backward_native.h> |
| 710 | #include <ATen/ops/unfold_backward_ops.h> |
| 711 | #include <ATen/ops/pow_native.h> |
| 712 | #include <ATen/ops/pow_ops.h> |
| 713 | #include <ATen/ops/pow_native.h> |
| 714 | #include <ATen/ops/pow_ops.h> |
| 715 | #include <ATen/ops/pow_native.h> |
| 716 | #include <ATen/ops/pow_ops.h> |
| 717 | #include <ATen/ops/pow_native.h> |
| 718 | #include <ATen/ops/pow_ops.h> |
| 719 | #include <ATen/ops/pow_native.h> |
| 720 | #include <ATen/ops/pow_ops.h> |
| 721 | #include <ATen/ops/pow_native.h> |
| 722 | #include <ATen/ops/pow_ops.h> |
| 723 | #include <ATen/ops/pow_native.h> |
| 724 | #include <ATen/ops/pow_ops.h> |
| 725 | #include <ATen/ops/pow_native.h> |
| 726 | #include <ATen/ops/pow_ops.h> |
| 727 | #include <ATen/ops/float_power_native.h> |
| 728 | #include <ATen/ops/float_power_ops.h> |
| 729 | #include <ATen/ops/float_power_native.h> |
| 730 | #include <ATen/ops/float_power_ops.h> |
| 731 | #include <ATen/ops/float_power_native.h> |
| 732 | #include <ATen/ops/float_power_ops.h> |
| 733 | #include <ATen/ops/float_power_native.h> |
| 734 | #include <ATen/ops/float_power_ops.h> |
| 735 | #include <ATen/ops/float_power_native.h> |
| 736 | #include <ATen/ops/float_power_ops.h> |
| 737 | #include <ATen/ops/float_power_native.h> |
| 738 | #include <ATen/ops/float_power_ops.h> |
| 739 | #include <ATen/ops/float_power_native.h> |
| 740 | #include <ATen/ops/float_power_ops.h> |
| 741 | #include <ATen/ops/float_power_native.h> |
| 742 | #include <ATen/ops/float_power_ops.h> |
| 743 | #include <ATen/ops/normal_native.h> |
| 744 | #include <ATen/ops/normal_ops.h> |
| 745 | #include <ATen/ops/normal_native.h> |
| 746 | #include <ATen/ops/normal_ops.h> |
| 747 | #include <ATen/ops/normal_native.h> |
| 748 | #include <ATen/ops/normal_ops.h> |
| 749 | #include <ATen/ops/normal_native.h> |
| 750 | #include <ATen/ops/normal_ops.h> |
| 751 | #include <ATen/ops/normal_native.h> |
| 752 | #include <ATen/ops/normal_ops.h> |
| 753 | #include <ATen/ops/normal_native.h> |
| 754 | #include <ATen/ops/normal_ops.h> |
| 755 | #include <ATen/ops/normal_native.h> |
| 756 | #include <ATen/ops/normal_ops.h> |
| 757 | #include <ATen/ops/normal_native.h> |
| 758 | #include <ATen/ops/normal_ops.h> |
| 759 | #include <ATen/ops/normal_native.h> |
| 760 | #include <ATen/ops/normal_ops.h> |
| 761 | #include <ATen/ops/normal_native.h> |
| 762 | #include <ATen/ops/normal_ops.h> |
| 763 | #include <ATen/ops/normal_native.h> |
| 764 | #include <ATen/ops/normal_ops.h> |
| 765 | #include <ATen/ops/_amp_update_scale_native.h> |
| 766 | #include <ATen/ops/_amp_update_scale_ops.h> |
| 767 | #include <ATen/ops/_amp_update_scale_native.h> |
| 768 | #include <ATen/ops/_amp_update_scale_ops.h> |
| 769 | #include <ATen/ops/_amp_update_scale_native.h> |
| 770 | #include <ATen/ops/_amp_update_scale_ops.h> |
| 771 | #include <ATen/ops/_foreach_atan_native.h> |
| 772 | #include <ATen/ops/_foreach_atan_ops.h> |
| 773 | #include <ATen/ops/_foreach_atan_native.h> |
| 774 | #include <ATen/ops/_foreach_atan_ops.h> |
| 775 | #include <ATen/ops/_foreach_atan_native.h> |
| 776 | #include <ATen/ops/_foreach_atan_ops.h> |
| 777 | #include <ATen/ops/_foreach_erf_native.h> |
| 778 | #include <ATen/ops/_foreach_erf_ops.h> |
| 779 | #include <ATen/ops/_foreach_erf_native.h> |
| 780 | #include <ATen/ops/_foreach_erf_ops.h> |
| 781 | #include <ATen/ops/_foreach_erf_native.h> |
| 782 | #include <ATen/ops/_foreach_erf_ops.h> |
| 783 | #include <ATen/ops/_foreach_erfc_native.h> |
| 784 | #include <ATen/ops/_foreach_erfc_ops.h> |
| 785 | #include <ATen/ops/_foreach_erfc_native.h> |
| 786 | #include <ATen/ops/_foreach_erfc_ops.h> |
| 787 | #include <ATen/ops/_foreach_erfc_native.h> |
| 788 | #include <ATen/ops/_foreach_erfc_ops.h> |
| 789 | #include <ATen/ops/_foreach_log_native.h> |
| 790 | #include <ATen/ops/_foreach_log_ops.h> |
| 791 | #include <ATen/ops/_foreach_log_native.h> |
| 792 | #include <ATen/ops/_foreach_log_ops.h> |
| 793 | #include <ATen/ops/_foreach_log_native.h> |
| 794 | #include <ATen/ops/_foreach_log_ops.h> |
| 795 | #include <ATen/ops/_foreach_sinh_native.h> |
| 796 | #include <ATen/ops/_foreach_sinh_ops.h> |
| 797 | #include <ATen/ops/_foreach_sinh_native.h> |
| 798 | #include <ATen/ops/_foreach_sinh_ops.h> |
| 799 | #include <ATen/ops/_foreach_sinh_native.h> |
| 800 | #include <ATen/ops/_foreach_sinh_ops.h> |
| 801 | #include <ATen/ops/_foreach_lgamma_native.h> |
| 802 | #include <ATen/ops/_foreach_lgamma_ops.h> |
| 803 | #include <ATen/ops/_foreach_lgamma_native.h> |
| 804 | #include <ATen/ops/_foreach_lgamma_ops.h> |
| 805 | #include <ATen/ops/_foreach_lgamma_native.h> |
| 806 | #include <ATen/ops/_foreach_lgamma_ops.h> |
| 807 | #include <ATen/ops/_foreach_lerp_native.h> |
| 808 | #include <ATen/ops/_foreach_lerp_ops.h> |
| 809 | #include <ATen/ops/_foreach_lerp_native.h> |
| 810 | #include <ATen/ops/_foreach_lerp_ops.h> |
| 811 | #include <ATen/ops/_foreach_lerp_native.h> |
| 812 | #include <ATen/ops/_foreach_lerp_ops.h> |
| 813 | #include <ATen/ops/_foreach_lerp_native.h> |
| 814 | #include <ATen/ops/_foreach_lerp_ops.h> |
| 815 | #include <ATen/ops/_foreach_lerp_native.h> |
| 816 | #include <ATen/ops/_foreach_lerp_ops.h> |
| 817 | #include <ATen/ops/_foreach_lerp_native.h> |
| 818 | #include <ATen/ops/_foreach_lerp_ops.h> |
| 819 | #include <ATen/ops/_convert_indices_from_coo_to_csr_native.h> |
| 820 | #include <ATen/ops/_convert_indices_from_coo_to_csr_ops.h> |
| 821 | #include <ATen/ops/_convert_indices_from_coo_to_csr_native.h> |
| 822 | #include <ATen/ops/_convert_indices_from_coo_to_csr_ops.h> |
| 823 | #include <ATen/ops/multi_margin_loss_backward_native.h> |
| 824 | #include <ATen/ops/multi_margin_loss_backward_ops.h> |
| 825 | #include <ATen/ops/multi_margin_loss_backward_native.h> |
| 826 | #include <ATen/ops/multi_margin_loss_backward_ops.h> |
| 827 | #include <ATen/ops/nll_loss_forward_native.h> |
| 828 | #include <ATen/ops/nll_loss_forward_ops.h> |
| 829 | #include <ATen/ops/nll_loss_forward_native.h> |
| 830 | #include <ATen/ops/nll_loss_forward_ops.h> |
| 831 | #include <ATen/ops/hardswish_native.h> |
| 832 | #include <ATen/ops/hardswish_ops.h> |
| 833 | #include <ATen/ops/hardswish_native.h> |
| 834 | #include <ATen/ops/hardswish_ops.h> |
| 835 | #include <ATen/ops/hardswish_native.h> |
| 836 | #include <ATen/ops/hardswish_ops.h> |
| 837 | #include <ATen/ops/hardswish_backward_native.h> |
| 838 | #include <ATen/ops/hardswish_backward_ops.h> |
| 839 | #include <ATen/ops/hardswish_backward_native.h> |
| 840 | #include <ATen/ops/hardswish_backward_ops.h> |
| 841 | #include <ATen/ops/softshrink_backward_native.h> |
| 842 | #include <ATen/ops/softshrink_backward_ops.h> |
| 843 | #include <ATen/ops/softshrink_backward_native.h> |
| 844 | #include <ATen/ops/softshrink_backward_ops.h> |
| 845 | #include <ATen/ops/mkldnn_adaptive_avg_pool2d_backward_native.h> |
| 846 | #include <ATen/ops/mkldnn_adaptive_avg_pool2d_backward_ops.h> |
| 847 | #include <ATen/ops/mkldnn_adaptive_avg_pool2d_backward_native.h> |
| 848 | #include <ATen/ops/mkldnn_adaptive_avg_pool2d_backward_ops.h> |
| 849 | #include <ATen/ops/_adaptive_avg_pool2d_backward_native.h> |
| 850 | #include <ATen/ops/_adaptive_avg_pool2d_backward_ops.h> |
| 851 | #include <ATen/ops/_adaptive_avg_pool2d_backward_native.h> |
| 852 | #include <ATen/ops/_adaptive_avg_pool2d_backward_ops.h> |
| 853 | #include <ATen/ops/adaptive_avg_pool3d_native.h> |
| 854 | #include <ATen/ops/adaptive_avg_pool3d_ops.h> |
| 855 | #include <ATen/ops/adaptive_avg_pool3d_native.h> |
| 856 | #include <ATen/ops/adaptive_avg_pool3d_ops.h> |
| 857 | #include <ATen/ops/adaptive_max_pool3d_backward_native.h> |
| 858 | #include <ATen/ops/adaptive_max_pool3d_backward_ops.h> |
| 859 | #include <ATen/ops/adaptive_max_pool3d_backward_native.h> |
| 860 | #include <ATen/ops/adaptive_max_pool3d_backward_ops.h> |
| 861 | #include <ATen/ops/avg_pool2d_native.h> |
| 862 | #include <ATen/ops/avg_pool2d_ops.h> |
| 863 | #include <ATen/ops/avg_pool2d_native.h> |
| 864 | #include <ATen/ops/avg_pool2d_ops.h> |
| 865 | #include <ATen/ops/avg_pool3d_backward_native.h> |
| 866 | #include <ATen/ops/avg_pool3d_backward_ops.h> |
| 867 | #include <ATen/ops/avg_pool3d_backward_native.h> |
| 868 | #include <ATen/ops/avg_pool3d_backward_ops.h> |
| 869 | #include <ATen/ops/fractional_max_pool2d_native.h> |
| 870 | #include <ATen/ops/fractional_max_pool2d_ops.h> |
| 871 | #include <ATen/ops/fractional_max_pool2d_native.h> |
| 872 | #include <ATen/ops/fractional_max_pool2d_ops.h> |
| 873 | #include <ATen/ops/reflection_pad3d_backward_native.h> |
| 874 | #include <ATen/ops/reflection_pad3d_backward_ops.h> |
| 875 | #include <ATen/ops/reflection_pad3d_backward_native.h> |
| 876 | #include <ATen/ops/reflection_pad3d_backward_ops.h> |
| 877 | #include <ATen/ops/replication_pad2d_native.h> |
| 878 | #include <ATen/ops/replication_pad2d_ops.h> |
| 879 | #include <ATen/ops/replication_pad2d_native.h> |
| 880 | #include <ATen/ops/replication_pad2d_ops.h> |
| 881 | #include <ATen/ops/_upsample_bilinear2d_aa_backward_native.h> |
| 882 | #include <ATen/ops/_upsample_bilinear2d_aa_backward_ops.h> |
| 883 | #include <ATen/ops/_upsample_bilinear2d_aa_backward_native.h> |
| 884 | #include <ATen/ops/_upsample_bilinear2d_aa_backward_ops.h> |
| 885 | #include <ATen/ops/_upsample_bicubic2d_aa_native.h> |
| 886 | #include <ATen/ops/_upsample_bicubic2d_aa_ops.h> |
| 887 | #include <ATen/ops/_upsample_bicubic2d_aa_native.h> |
| 888 | #include <ATen/ops/_upsample_bicubic2d_aa_ops.h> |
| 889 | #include <ATen/ops/upsample_trilinear3d_native.h> |
| 890 | #include <ATen/ops/upsample_trilinear3d_ops.h> |
| 891 | #include <ATen/ops/upsample_trilinear3d_native.h> |
| 892 | #include <ATen/ops/upsample_trilinear3d_ops.h> |
| 893 | #include <ATen/ops/_upsample_nearest_exact1d_backward_native.h> |
| 894 | #include <ATen/ops/_upsample_nearest_exact1d_backward_ops.h> |
| 895 | #include <ATen/ops/_upsample_nearest_exact1d_backward_native.h> |
| 896 | #include <ATen/ops/_upsample_nearest_exact1d_backward_ops.h> |
| 897 | #include <ATen/ops/upsample_nearest2d_backward_native.h> |
| 898 | #include <ATen/ops/upsample_nearest2d_backward_ops.h> |
| 899 | #include <ATen/ops/upsample_nearest2d_backward_native.h> |
| 900 | #include <ATen/ops/upsample_nearest2d_backward_ops.h> |
| 901 | #include <ATen/ops/tanh_backward_native.h> |
| 902 | #include <ATen/ops/tanh_backward_ops.h> |
| 903 | #include <ATen/ops/tanh_backward_native.h> |
| 904 | #include <ATen/ops/tanh_backward_ops.h> |
| 905 | #include <ATen/ops/_conv_depthwise2d_native.h> |
| 906 | #include <ATen/ops/_conv_depthwise2d_ops.h> |
| 907 | #include <ATen/ops/_conv_depthwise2d_native.h> |
| 908 | #include <ATen/ops/_conv_depthwise2d_ops.h> |
| 909 | #include <ATen/ops/col2im_native.h> |
| 910 | #include <ATen/ops/col2im_ops.h> |
| 911 | #include <ATen/ops/col2im_native.h> |
| 912 | #include <ATen/ops/col2im_ops.h> |
| 913 | #include <ATen/ops/column_stack_native.h> |
| 914 | #include <ATen/ops/column_stack_ops.h> |
| 915 | #include <ATen/ops/column_stack_native.h> |
| 916 | #include <ATen/ops/column_stack_ops.h> |
| 917 | #include <ATen/ops/im2col_native.h> |
| 918 | #include <ATen/ops/im2col_ops.h> |
| 919 | #include <ATen/ops/im2col_native.h> |
| 920 | #include <ATen/ops/im2col_ops.h> |
| 921 | #include <ATen/ops/isinf_native.h> |
| 922 | #include <ATen/ops/isinf_ops.h> |
| 923 | #include <ATen/ops/isinf_native.h> |
| 924 | #include <ATen/ops/isinf_ops.h> |
| 925 | #include <ATen/ops/isneginf_native.h> |
| 926 | #include <ATen/ops/isneginf_ops.h> |
| 927 | #include <ATen/ops/isneginf_native.h> |
| 928 | #include <ATen/ops/isneginf_ops.h> |
| 929 | #include <ATen/ops/special_expm1_native.h> |
| 930 | #include <ATen/ops/special_expm1_ops.h> |
| 931 | #include <ATen/ops/special_expm1_native.h> |
| 932 | #include <ATen/ops/special_expm1_ops.h> |
| 933 | #include <ATen/ops/special_erf_native.h> |
| 934 | #include <ATen/ops/special_erf_ops.h> |
| 935 | #include <ATen/ops/special_erf_native.h> |
| 936 | #include <ATen/ops/special_erf_ops.h> |
| 937 | #include <ATen/ops/special_logsumexp_native.h> |
| 938 | #include <ATen/ops/special_logsumexp_ops.h> |
| 939 | #include <ATen/ops/special_logsumexp_native.h> |
| 940 | #include <ATen/ops/special_logsumexp_ops.h> |
| 941 | #include <ATen/ops/special_log1p_native.h> |
| 942 | #include <ATen/ops/special_log1p_ops.h> |
| 943 | #include <ATen/ops/special_log1p_native.h> |
| 944 | #include <ATen/ops/special_log1p_ops.h> |
| 945 | #include <ATen/ops/special_gammaincc_native.h> |
| 946 | #include <ATen/ops/special_gammaincc_ops.h> |
| 947 | #include <ATen/ops/special_gammaincc_native.h> |
| 948 | #include <ATen/ops/special_gammaincc_ops.h> |
| 949 | #include <ATen/ops/special_multigammaln_native.h> |
| 950 | #include <ATen/ops/special_multigammaln_ops.h> |
| 951 | #include <ATen/ops/special_multigammaln_native.h> |
| 952 | #include <ATen/ops/special_multigammaln_ops.h> |
| 953 | #include <ATen/ops/fft_rfft2_native.h> |
| 954 | #include <ATen/ops/fft_rfft2_ops.h> |
| 955 | #include <ATen/ops/fft_rfft2_native.h> |
| 956 | #include <ATen/ops/fft_rfft2_ops.h> |
| 957 | #include <ATen/ops/fft_irfft2_native.h> |
| 958 | #include <ATen/ops/fft_irfft2_ops.h> |
| 959 | #include <ATen/ops/fft_irfft2_native.h> |
| 960 | #include <ATen/ops/fft_irfft2_ops.h> |
| 961 | #include <ATen/ops/fft_ihfft2_native.h> |
| 962 | #include <ATen/ops/fft_ihfft2_ops.h> |
| 963 | #include <ATen/ops/fft_ihfft2_native.h> |
| 964 | #include <ATen/ops/fft_ihfft2_ops.h> |
| 965 | #include <ATen/ops/fft_fftn_native.h> |
| 966 | #include <ATen/ops/fft_fftn_ops.h> |
| 967 | #include <ATen/ops/fft_fftn_native.h> |
| 968 | #include <ATen/ops/fft_fftn_ops.h> |
| 969 | #include <ATen/ops/fft_irfftn_native.h> |
| 970 | #include <ATen/ops/fft_irfftn_ops.h> |
| 971 | #include <ATen/ops/fft_irfftn_native.h> |
| 972 | #include <ATen/ops/fft_irfftn_ops.h> |
| 973 | #include <ATen/ops/linalg_lu_factor_native.h> |
| 974 | #include <ATen/ops/linalg_lu_factor_ops.h> |
| 975 | #include <ATen/ops/linalg_lu_factor_native.h> |
| 976 | #include <ATen/ops/linalg_lu_factor_ops.h> |
| 977 | #include <ATen/ops/linalg_lu_factor_ex_native.h> |
| 978 | #include <ATen/ops/linalg_lu_factor_ex_ops.h> |
| 979 | #include <ATen/ops/linalg_lu_factor_ex_native.h> |
| 980 | #include <ATen/ops/linalg_lu_factor_ex_ops.h> |
| 981 | #include <ATen/ops/linalg_ldl_factor_native.h> |
| 982 | #include <ATen/ops/linalg_ldl_factor_ops.h> |
| 983 | #include <ATen/ops/linalg_ldl_factor_native.h> |
| 984 | #include <ATen/ops/linalg_ldl_factor_ops.h> |
| 985 | #include <ATen/ops/linalg_ldl_solve_native.h> |
| 986 | #include <ATen/ops/linalg_ldl_solve_ops.h> |
| 987 | #include <ATen/ops/linalg_ldl_solve_native.h> |
| 988 | #include <ATen/ops/linalg_ldl_solve_ops.h> |
| 989 | #include <ATen/ops/_linalg_slogdet_native.h> |
| 990 | #include <ATen/ops/_linalg_slogdet_ops.h> |
| 991 | #include <ATen/ops/_linalg_slogdet_native.h> |
| 992 | #include <ATen/ops/_linalg_slogdet_ops.h> |
| 993 | #include <ATen/ops/linalg_eigvals_native.h> |
| 994 | #include <ATen/ops/linalg_eigvals_ops.h> |
| 995 | #include <ATen/ops/linalg_eigvals_native.h> |
| 996 | #include <ATen/ops/linalg_eigvals_ops.h> |
| 997 | #include <ATen/ops/linalg_eigh_native.h> |
| 998 | #include <ATen/ops/linalg_eigh_ops.h> |
| 999 | #include <ATen/ops/linalg_eigh_native.h> |
| 1000 | #include <ATen/ops/linalg_eigh_ops.h> |
| 1001 | #include <ATen/ops/linalg_householder_product_native.h> |
| 1002 | #include <ATen/ops/linalg_householder_product_ops.h> |
| 1003 | #include <ATen/ops/linalg_householder_product_native.h> |
| 1004 | #include <ATen/ops/linalg_householder_product_ops.h> |
| 1005 | #include <ATen/ops/ger_native.h> |
| 1006 | #include <ATen/ops/ger_ops.h> |
| 1007 | #include <ATen/ops/ger_native.h> |
| 1008 | #include <ATen/ops/ger_ops.h> |
| 1009 | #include <ATen/ops/linalg_norm_native.h> |
| 1010 | #include <ATen/ops/linalg_norm_ops.h> |
| 1011 | #include <ATen/ops/linalg_norm_native.h> |
| 1012 | #include <ATen/ops/linalg_norm_ops.h> |
| 1013 | #include <ATen/ops/linalg_norm_native.h> |
| 1014 | #include <ATen/ops/linalg_norm_ops.h> |
| 1015 | #include <ATen/ops/linalg_norm_native.h> |
| 1016 | #include <ATen/ops/linalg_norm_ops.h> |
| 1017 | #include <ATen/ops/linalg_vector_norm_native.h> |
| 1018 | #include <ATen/ops/linalg_vector_norm_ops.h> |
| 1019 | #include <ATen/ops/linalg_vector_norm_native.h> |
| 1020 | #include <ATen/ops/linalg_vector_norm_ops.h> |
| 1021 | #include <ATen/ops/_linalg_solve_ex_native.h> |
| 1022 | #include <ATen/ops/_linalg_solve_ex_ops.h> |
| 1023 | #include <ATen/ops/_linalg_solve_ex_native.h> |
| 1024 | #include <ATen/ops/_linalg_solve_ex_ops.h> |
| 1025 | #include <ATen/ops/linalg_solve_native.h> |
| 1026 | #include <ATen/ops/linalg_solve_ops.h> |
| 1027 | #include <ATen/ops/linalg_solve_native.h> |
| 1028 | #include <ATen/ops/linalg_solve_ops.h> |
| 1029 | #include <ATen/ops/linalg_multi_dot_native.h> |
| 1030 | #include <ATen/ops/linalg_multi_dot_ops.h> |
| 1031 | #include <ATen/ops/linalg_multi_dot_native.h> |
| 1032 | #include <ATen/ops/linalg_multi_dot_ops.h> |
| 1033 | #include <ATen/ops/_test_optional_filled_intlist_native.h> |
| 1034 | #include <ATen/ops/_test_optional_filled_intlist_ops.h> |
| 1035 | #include <ATen/ops/_test_optional_filled_intlist_native.h> |
| 1036 | #include <ATen/ops/_test_optional_filled_intlist_ops.h> |
| 1037 | #include <ATen/ops/_test_autograd_multiple_dispatch_native.h> |
| 1038 | #include <ATen/ops/_test_autograd_multiple_dispatch_ops.h> |
| 1039 | #include <ATen/ops/_test_autograd_multiple_dispatch_native.h> |
| 1040 | #include <ATen/ops/_test_autograd_multiple_dispatch_ops.h> |
| 1041 | #include <ATen/ops/_test_autograd_multiple_dispatch_view_copy_native.h> |
| 1042 | #include <ATen/ops/_test_autograd_multiple_dispatch_view_copy_ops.h> |
| 1043 | #include <ATen/ops/_test_autograd_multiple_dispatch_view_copy_native.h> |
| 1044 | #include <ATen/ops/_test_autograd_multiple_dispatch_view_copy_ops.h> |
| 1045 | #include <ATen/ops/segment_reduce_native.h> |
| 1046 | #include <ATen/ops/segment_reduce_ops.h> |
| 1047 | #include <ATen/ops/segment_reduce_native.h> |
| 1048 | #include <ATen/ops/segment_reduce_ops.h> |
| 1049 | #include <ATen/ops/_nested_tensor_from_tensor_list_native.h> |
| 1050 | #include <ATen/ops/_nested_tensor_from_tensor_list_ops.h> |
| 1051 | #include <ATen/ops/_nested_tensor_from_tensor_list_native.h> |
| 1052 | #include <ATen/ops/_nested_tensor_from_tensor_list_ops.h> |
| 1053 | #include <ATen/ops/diagonal_copy_native.h> |
| 1054 | #include <ATen/ops/diagonal_copy_ops.h> |
| 1055 | #include <ATen/ops/diagonal_copy_native.h> |
| 1056 | #include <ATen/ops/diagonal_copy_ops.h> |
| 1057 | #include <ATen/ops/detach_copy_native.h> |
| 1058 | #include <ATen/ops/detach_copy_ops.h> |
| 1059 | #include <ATen/ops/detach_copy_native.h> |
| 1060 | #include <ATen/ops/detach_copy_ops.h> |
| 1061 | #include <ATen/ops/slice_copy_native.h> |
| 1062 | #include <ATen/ops/slice_copy_ops.h> |
| 1063 | #include <ATen/ops/slice_copy_native.h> |
| 1064 | #include <ATen/ops/slice_copy_ops.h> |
| 1065 | #include <ATen/ops/transpose_copy_native.h> |
| 1066 | #include <ATen/ops/transpose_copy_ops.h> |
| 1067 | #include <ATen/ops/transpose_copy_native.h> |
| 1068 | #include <ATen/ops/transpose_copy_ops.h> |
| 1069 | #include <ATen/ops/indices_copy_native.h> |
| 1070 | #include <ATen/ops/indices_copy_ops.h> |
| 1071 | #include <ATen/ops/indices_copy_native.h> |
| 1072 | #include <ATen/ops/indices_copy_ops.h> |
| 1073 | #include <ATen/ops/row_indices_copy_native.h> |
| 1074 | #include <ATen/ops/row_indices_copy_ops.h> |
| 1075 | #include <ATen/ops/row_indices_copy_native.h> |
| 1076 | #include <ATen/ops/row_indices_copy_ops.h> |
| 1077 | #include <ATen/ops/_triton_multi_head_attention_native.h> |
| 1078 | #include <ATen/ops/_triton_multi_head_attention_ops.h> |
| 1079 | #include <ATen/ops/_triton_multi_head_attention_native.h> |
| 1080 | #include <ATen/ops/_triton_multi_head_attention_ops.h> |
| 1081 | #include <ATen/ops/special_bessel_j1_native.h> |
| 1082 | #include <ATen/ops/special_bessel_j1_ops.h> |
| 1083 | #include <ATen/ops/special_bessel_j1_native.h> |
| 1084 | #include <ATen/ops/special_bessel_j1_ops.h> |
| 1085 | #include <ATen/ops/special_bessel_y1_native.h> |
| 1086 | #include <ATen/ops/special_bessel_y1_ops.h> |
| 1087 | #include <ATen/ops/special_bessel_y1_native.h> |
| 1088 | #include <ATen/ops/special_bessel_y1_ops.h> |
| 1089 | #include <ATen/ops/special_legendre_polynomial_p_native.h> |
| 1090 | #include <ATen/ops/special_legendre_polynomial_p_ops.h> |
| 1091 | #include <ATen/ops/special_legendre_polynomial_p_native.h> |
| 1092 | #include <ATen/ops/special_legendre_polynomial_p_ops.h> |
| 1093 | #include <ATen/ops/special_legendre_polynomial_p_native.h> |
| 1094 | #include <ATen/ops/special_legendre_polynomial_p_ops.h> |
| 1095 | #include <ATen/ops/special_legendre_polynomial_p_native.h> |
| 1096 | #include <ATen/ops/special_legendre_polynomial_p_ops.h> |
| 1097 | #include <ATen/ops/special_legendre_polynomial_p_native.h> |
| 1098 | #include <ATen/ops/special_legendre_polynomial_p_ops.h> |
| 1099 | #include <ATen/ops/special_legendre_polynomial_p_native.h> |
| 1100 | #include <ATen/ops/special_legendre_polynomial_p_ops.h> |
| 1101 | #include <ATen/ops/special_modified_bessel_i0_native.h> |
| 1102 | #include <ATen/ops/special_modified_bessel_i0_ops.h> |
| 1103 | #include <ATen/ops/special_modified_bessel_i0_native.h> |
| 1104 | #include <ATen/ops/special_modified_bessel_i0_ops.h> |
| 1105 | #include <ATen/ops/special_shifted_chebyshev_polynomial_t_native.h> |
| 1106 | #include <ATen/ops/special_shifted_chebyshev_polynomial_t_ops.h> |
| 1107 | #include <ATen/ops/special_shifted_chebyshev_polynomial_t_native.h> |
| 1108 | #include <ATen/ops/special_shifted_chebyshev_polynomial_t_ops.h> |
| 1109 | #include <ATen/ops/special_shifted_chebyshev_polynomial_t_native.h> |
| 1110 | #include <ATen/ops/special_shifted_chebyshev_polynomial_t_ops.h> |
| 1111 | #include <ATen/ops/special_shifted_chebyshev_polynomial_t_native.h> |
| 1112 | #include <ATen/ops/special_shifted_chebyshev_polynomial_t_ops.h> |
| 1113 | #include <ATen/ops/special_shifted_chebyshev_polynomial_t_native.h> |
| 1114 | #include <ATen/ops/special_shifted_chebyshev_polynomial_t_ops.h> |
| 1115 | #include <ATen/ops/special_shifted_chebyshev_polynomial_t_native.h> |
| 1116 | #include <ATen/ops/special_shifted_chebyshev_polynomial_t_ops.h> |
| 1117 | #include <ATen/ops/special_shifted_chebyshev_polynomial_u_native.h> |
| 1118 | #include <ATen/ops/special_shifted_chebyshev_polynomial_u_ops.h> |
| 1119 | #include <ATen/ops/special_shifted_chebyshev_polynomial_u_native.h> |
| 1120 | #include <ATen/ops/special_shifted_chebyshev_polynomial_u_ops.h> |
| 1121 | #include <ATen/ops/special_shifted_chebyshev_polynomial_u_native.h> |
| 1122 | #include <ATen/ops/special_shifted_chebyshev_polynomial_u_ops.h> |
| 1123 | #include <ATen/ops/special_shifted_chebyshev_polynomial_u_native.h> |
| 1124 | #include <ATen/ops/special_shifted_chebyshev_polynomial_u_ops.h> |
| 1125 | #include <ATen/ops/special_shifted_chebyshev_polynomial_u_native.h> |
| 1126 | #include <ATen/ops/special_shifted_chebyshev_polynomial_u_ops.h> |
| 1127 | #include <ATen/ops/special_shifted_chebyshev_polynomial_u_native.h> |
| 1128 | #include <ATen/ops/special_shifted_chebyshev_polynomial_u_ops.h> |
| 1129 | #include <ATen/ops/special_shifted_chebyshev_polynomial_w_native.h> |
| 1130 | #include <ATen/ops/special_shifted_chebyshev_polynomial_w_ops.h> |
| 1131 | #include <ATen/ops/special_shifted_chebyshev_polynomial_w_native.h> |
| 1132 | #include <ATen/ops/special_shifted_chebyshev_polynomial_w_ops.h> |
| 1133 | #include <ATen/ops/special_shifted_chebyshev_polynomial_w_native.h> |
| 1134 | #include <ATen/ops/special_shifted_chebyshev_polynomial_w_ops.h> |
| 1135 | #include <ATen/ops/special_shifted_chebyshev_polynomial_w_native.h> |
| 1136 | #include <ATen/ops/special_shifted_chebyshev_polynomial_w_ops.h> |
| 1137 | #include <ATen/ops/special_shifted_chebyshev_polynomial_w_native.h> |
| 1138 | #include <ATen/ops/special_shifted_chebyshev_polynomial_w_ops.h> |
| 1139 | #include <ATen/ops/special_shifted_chebyshev_polynomial_w_native.h> |
| 1140 | #include <ATen/ops/special_shifted_chebyshev_polynomial_w_ops.h> |
| 1141 | #include <ATen/ops/_fused_adam_native.h> |
| 1142 | #include <ATen/ops/_fused_adam_ops.h> |
| 1143 | #include <ATen/ops/_fused_adam_native.h> |
| 1144 | #include <ATen/ops/_fused_adam_ops.h> |
| 1145 | #include <ATen/ops/_fused_adam_native.h> |
| 1146 | #include <ATen/ops/_fused_adam_ops.h> |
| 1147 | #include <ATen/ops/rename_native.h> |
| 1148 | #include <ATen/ops/rename_ops.h> |
| 1149 | #include <ATen/ops/imag_native.h> |
| 1150 | #include <ATen/ops/imag_ops.h> |
| 1151 | #include <ATen/ops/_conj_native.h> |
| 1152 | #include <ATen/ops/_conj_ops.h> |
| 1153 | #include <ATen/ops/_conj_copy_native.h> |
| 1154 | #include <ATen/ops/_conj_copy_ops.h> |
| 1155 | #include <ATen/ops/conj_native.h> |
| 1156 | #include <ATen/ops/conj_ops.h> |
| 1157 | #include <ATen/ops/resolve_conj_native.h> |
| 1158 | #include <ATen/ops/resolve_conj_ops.h> |
| 1159 | #include <ATen/ops/as_strided_native.h> |
| 1160 | #include <ATen/ops/as_strided_ops.h> |
| 1161 | #include <ATen/ops/as_strided_copy_native.h> |
| 1162 | #include <ATen/ops/as_strided_copy_ops.h> |
| 1163 | #include <ATen/ops/_sparse_broadcast_to_native.h> |
| 1164 | #include <ATen/ops/_sparse_broadcast_to_ops.h> |
| 1165 | #include <ATen/ops/_sparse_broadcast_to_copy_native.h> |
| 1166 | #include <ATen/ops/_sparse_broadcast_to_copy_ops.h> |
| 1167 | #include <ATen/ops/chunk_native.h> |
| 1168 | #include <ATen/ops/chunk_ops.h> |
| 1169 | #include <ATen/ops/tensor_split_native.h> |
| 1170 | #include <ATen/ops/tensor_split_ops.h> |
| 1171 | #include <ATen/ops/tensor_split_native.h> |
| 1172 | #include <ATen/ops/tensor_split_ops.h> |
| 1173 | #include <ATen/ops/tensor_split_native.h> |
| 1174 | #include <ATen/ops/tensor_split_ops.h> |
| 1175 | #include <ATen/ops/expand_as_native.h> |
| 1176 | #include <ATen/ops/expand_as_ops.h> |
| 1177 | #include <ATen/ops/unflatten_native.h> |
| 1178 | #include <ATen/ops/unflatten_ops.h> |
| 1179 | #include <ATen/ops/unflatten_native.h> |
| 1180 | #include <ATen/ops/unflatten_ops.h> |
| 1181 | #include <ATen/ops/permute_native.h> |
| 1182 | #include <ATen/ops/permute_ops.h> |
| 1183 | #include <ATen/ops/permute_copy_native.h> |
| 1184 | #include <ATen/ops/permute_copy_ops.h> |
| 1185 | #include <ATen/ops/movedim_native.h> |
| 1186 | #include <ATen/ops/movedim_ops.h> |
| 1187 | #include <ATen/ops/movedim_native.h> |
| 1188 | #include <ATen/ops/movedim_ops.h> |
| 1189 | #include <ATen/ops/mH_native.h> |
| 1190 | #include <ATen/ops/mH_ops.h> |
| 1191 | #include <ATen/ops/pin_memory_native.h> |
| 1192 | #include <ATen/ops/pin_memory_ops.h> |
| 1193 | #include <ATen/ops/_reshape_alias_native.h> |
| 1194 | #include <ATen/ops/_reshape_alias_ops.h> |
| 1195 | #include <ATen/ops/_reshape_alias_copy_native.h> |
| 1196 | #include <ATen/ops/_reshape_alias_copy_ops.h> |
| 1197 | #include <ATen/ops/detach_native.h> |
| 1198 | #include <ATen/ops/detach_ops.h> |
| 1199 | #include <ATen/ops/detach_copy_native.h> |
| 1200 | #include <ATen/ops/detach_copy_ops.h> |
| 1201 | #include <ATen/ops/split_native.h> |
| 1202 | #include <ATen/ops/split_ops.h> |
| 1203 | #include <ATen/ops/split_copy_native.h> |
| 1204 | #include <ATen/ops/split_copy_ops.h> |
| 1205 | #include <ATen/ops/split_native.h> |
| 1206 | #include <ATen/ops/split_ops.h> |
| 1207 | #include <ATen/ops/dsplit_native.h> |
| 1208 | #include <ATen/ops/dsplit_ops.h> |
| 1209 | #include <ATen/ops/dsplit_native.h> |
| 1210 | #include <ATen/ops/dsplit_ops.h> |
| 1211 | #include <ATen/ops/positive_native.h> |
| 1212 | #include <ATen/ops/positive_ops.h> |
| 1213 | #include <ATen/ops/values_native.h> |
| 1214 | #include <ATen/ops/values_ops.h> |
| 1215 | #include <ATen/ops/values_copy_native.h> |
| 1216 | #include <ATen/ops/values_copy_ops.h> |
| 1217 | #include <ATen/ops/row_indices_native.h> |
| 1218 | #include <ATen/ops/row_indices_ops.h> |
| 1219 | #include <ATen/ops/row_indices_copy_native.h> |
| 1220 | #include <ATen/ops/row_indices_copy_ops.h> |
| 1221 | #include <ATen/ops/lift_fresh_native.h> |
| 1222 | #include <ATen/ops/lift_fresh_ops.h> |
| 1223 | #include <ATen/ops/lift_fresh_copy_native.h> |
| 1224 | #include <ATen/ops/lift_fresh_copy_ops.h> |
| 1225 | #include <ATen/ops/swapdims_native.h> |
| 1226 | #include <ATen/ops/swapdims_ops.h> |
| 1227 | #include <ATen/ops/_test_autograd_multiple_dispatch_view_native.h> |
| 1228 | #include <ATen/ops/_test_autograd_multiple_dispatch_view_ops.h> |
| 1229 | #include <ATen/ops/_test_autograd_multiple_dispatch_view_copy_native.h> |
| 1230 | #include <ATen/ops/_test_autograd_multiple_dispatch_view_copy_ops.h> |
| 1231 | #include <ATen/ops/_cast_Double_native.h> |
| 1232 | #include <ATen/ops/_cast_Double_ops.h> |
| 1233 | #include <ATen/ops/_cast_Int_native.h> |
| 1234 | #include <ATen/ops/_cast_Int_ops.h> |
| 1235 | #include <ATen/ops/_cast_Short_native.h> |
| 1236 | #include <ATen/ops/_cast_Short_ops.h> |
| 1237 | #include <ATen/ops/output_nr_native.h> |
| 1238 | #include <ATen/ops/output_nr_ops.h> |
| 1239 | #include <ATen/ops/_cudnn_ctc_loss_native.h> |
| 1240 | #include <ATen/ops/_cudnn_ctc_loss_ops.h> |
| 1241 | #include <ATen/ops/_use_cudnn_rnn_flatten_weight_native.h> |
| 1242 | #include <ATen/ops/_use_cudnn_rnn_flatten_weight_ops.h> |
| 1243 | #include <ATen/ops/_sobol_engine_initialize_state_native.h> |
| 1244 | #include <ATen/ops/_sobol_engine_initialize_state_ops.h> |
| 1245 | #include <ATen/ops/_shape_as_tensor_native.h> |
| 1246 | #include <ATen/ops/_shape_as_tensor_ops.h> |
| 1247 | #include <ATen/ops/_dim_arange_native.h> |
| 1248 | #include <ATen/ops/_dim_arange_ops.h> |
| 1249 | #include <ATen/ops/cudnn_is_acceptable_native.h> |
| 1250 | #include <ATen/ops/cudnn_is_acceptable_ops.h> |
| 1251 | #include <ATen/ops/_convolution_native.h> |
| 1252 | #include <ATen/ops/_convolution_ops.h> |
| 1253 | #include <ATen/ops/conv2d_native.h> |
| 1254 | #include <ATen/ops/conv2d_ops.h> |
| 1255 | #include <ATen/ops/conv2d_native.h> |
| 1256 | #include <ATen/ops/conv2d_ops.h> |
| 1257 | #include <ATen/ops/conv_tbc_backward_native.h> |
| 1258 | #include <ATen/ops/conv_tbc_backward_ops.h> |
| 1259 | #include <ATen/ops/cov_native.h> |
| 1260 | #include <ATen/ops/cov_ops.h> |
| 1261 | #include <ATen/ops/_cummin_helper_native.h> |
| 1262 | #include <ATen/ops/_cummin_helper_ops.h> |
| 1263 | #include <ATen/ops/divide_native.h> |
| 1264 | #include <ATen/ops/divide_ops.h> |
| 1265 | #include <ATen/ops/divide_native.h> |
| 1266 | #include <ATen/ops/divide_ops.h> |
| 1267 | #include <ATen/ops/divide_native.h> |
| 1268 | #include <ATen/ops/divide_ops.h> |
| 1269 | #include <ATen/ops/divide_native.h> |
| 1270 | #include <ATen/ops/divide_ops.h> |
| 1271 | #include <ATen/ops/true_divide_native.h> |
| 1272 | #include <ATen/ops/true_divide_ops.h> |
| 1273 | #include <ATen/ops/true_divide_native.h> |
| 1274 | #include <ATen/ops/true_divide_ops.h> |
| 1275 | #include <ATen/ops/embedding_backward_native.h> |
| 1276 | #include <ATen/ops/embedding_backward_ops.h> |
| 1277 | #include <ATen/ops/embedding_bag_native.h> |
| 1278 | #include <ATen/ops/embedding_bag_ops.h> |
| 1279 | #include <ATen/ops/embedding_bag_native.h> |
| 1280 | #include <ATen/ops/embedding_bag_ops.h> |
| 1281 | #include <ATen/ops/_embedding_bag_backward_native.h> |
| 1282 | #include <ATen/ops/_embedding_bag_backward_ops.h> |
| 1283 | #include <ATen/ops/_cufft_get_plan_cache_size_native.h> |
| 1284 | #include <ATen/ops/_cufft_get_plan_cache_size_ops.h> |
| 1285 | #include <ATen/ops/_cufft_get_plan_cache_max_size_native.h> |
| 1286 | #include <ATen/ops/_cufft_get_plan_cache_max_size_ops.h> |
| 1287 | #include <ATen/ops/_is_zerotensor_native.h> |
| 1288 | #include <ATen/ops/_is_zerotensor_ops.h> |
| 1289 | #include <ATen/ops/is_inference_native.h> |
| 1290 | #include <ATen/ops/is_inference_ops.h> |
| 1291 | #include <ATen/ops/kl_div_native.h> |
| 1292 | #include <ATen/ops/kl_div_ops.h> |
| 1293 | #include <ATen/ops/margin_ranking_loss_native.h> |
| 1294 | #include <ATen/ops/margin_ranking_loss_ops.h> |
| 1295 | #include <ATen/ops/matrix_exp_native.h> |
| 1296 | #include <ATen/ops/matrix_exp_ops.h> |
| 1297 | #include <ATen/ops/miopen_convolution_relu_native.h> |
| 1298 | #include <ATen/ops/miopen_convolution_relu_ops.h> |
| 1299 | #include <ATen/ops/_sparse_mm_native.h> |
| 1300 | #include <ATen/ops/_sparse_mm_ops.h> |
| 1301 | #include <ATen/ops/_sparse_mm_native.h> |
| 1302 | #include <ATen/ops/_sparse_mm_ops.h> |
| 1303 | #include <ATen/ops/_nnpack_available_native.h> |
| 1304 | #include <ATen/ops/_nnpack_available_ops.h> |
| 1305 | #include <ATen/ops/_reshape_copy_native.h> |
| 1306 | #include <ATen/ops/_reshape_copy_ops.h> |
| 1307 | #include <ATen/ops/relu6_native.h> |
| 1308 | #include <ATen/ops/relu6_ops.h> |
| 1309 | #include <ATen/ops/relu6_native.h> |
| 1310 | #include <ATen/ops/relu6_ops.h> |
| 1311 | #include <ATen/ops/_prelu_kernel_backward_native.h> |
| 1312 | #include <ATen/ops/_prelu_kernel_backward_ops.h> |
| 1313 | #include <ATen/ops/selu_native.h> |
| 1314 | #include <ATen/ops/selu_ops.h> |
| 1315 | #include <ATen/ops/selu_native.h> |
| 1316 | #include <ATen/ops/selu_ops.h> |
| 1317 | #include <ATen/ops/istft_native.h> |
| 1318 | #include <ATen/ops/istft_ops.h> |
| 1319 | #include <ATen/ops/_nested_sum_backward_native.h> |
| 1320 | #include <ATen/ops/_nested_sum_backward_ops.h> |
| 1321 | #include <ATen/ops/std_mean_native.h> |
| 1322 | #include <ATen/ops/std_mean_ops.h> |
| 1323 | #include <ATen/ops/std_mean_native.h> |
| 1324 | #include <ATen/ops/std_mean_ops.h> |
| 1325 | #include <ATen/ops/std_mean_native.h> |
| 1326 | #include <ATen/ops/std_mean_ops.h> |
| 1327 | #include <ATen/ops/std_mean_native.h> |
| 1328 | #include <ATen/ops/std_mean_ops.h> |
| 1329 | #include <ATen/ops/flipud_native.h> |
| 1330 | #include <ATen/ops/flipud_ops.h> |
| 1331 | #include <ATen/ops/_nested_tensor_from_mask_left_aligned_native.h> |
| 1332 | #include <ATen/ops/_nested_tensor_from_mask_left_aligned_ops.h> |
| 1333 | #include <ATen/ops/var_mean_native.h> |
| 1334 | #include <ATen/ops/var_mean_ops.h> |
| 1335 | #include <ATen/ops/var_mean_native.h> |
| 1336 | #include <ATen/ops/var_mean_ops.h> |
| 1337 | #include <ATen/ops/var_mean_native.h> |
| 1338 | #include <ATen/ops/var_mean_ops.h> |
| 1339 | #include <ATen/ops/var_mean_native.h> |
| 1340 | #include <ATen/ops/var_mean_ops.h> |
| 1341 | #include <ATen/ops/norm_except_dim_native.h> |
| 1342 | #include <ATen/ops/norm_except_dim_ops.h> |
| 1343 | #include <ATen/ops/_sparse_compressed_tensor_unsafe_native.h> |
| 1344 | #include <ATen/ops/_sparse_compressed_tensor_unsafe_ops.h> |
| 1345 | #include <ATen/ops/sparse_coo_tensor_native.h> |
| 1346 | #include <ATen/ops/sparse_coo_tensor_ops.h> |
| 1347 | #include <ATen/ops/sparse_coo_tensor_native.h> |
| 1348 | #include <ATen/ops/sparse_coo_tensor_ops.h> |
| 1349 | #include <ATen/ops/_validate_sparse_bsc_tensor_args_native.h> |
| 1350 | #include <ATen/ops/_validate_sparse_bsc_tensor_args_ops.h> |
| 1351 | #include <ATen/ops/dense_dim_native.h> |
| 1352 | #include <ATen/ops/dense_dim_ops.h> |
| 1353 | #include <ATen/ops/_dimV_native.h> |
| 1354 | #include <ATen/ops/_dimV_ops.h> |
| 1355 | #include <ATen/ops/to_mkldnn_backward_native.h> |
| 1356 | #include <ATen/ops/to_mkldnn_backward_ops.h> |
| 1357 | #include <ATen/ops/q_zero_point_native.h> |
| 1358 | #include <ATen/ops/q_zero_point_ops.h> |
| 1359 | #include <ATen/ops/qscheme_native.h> |
| 1360 | #include <ATen/ops/qscheme_ops.h> |
| 1361 | #include <ATen/ops/fake_quantize_per_tensor_affine_native.h> |
| 1362 | #include <ATen/ops/fake_quantize_per_tensor_affine_ops.h> |
| 1363 | #include <ATen/ops/fake_quantize_per_tensor_affine_native.h> |
| 1364 | #include <ATen/ops/fake_quantize_per_tensor_affine_ops.h> |
| 1365 | #include <ATen/ops/fused_moving_avg_obs_fake_quant_native.h> |
| 1366 | #include <ATen/ops/fused_moving_avg_obs_fake_quant_ops.h> |
| 1367 | #include <ATen/ops/_choose_qparams_per_tensor_native.h> |
| 1368 | #include <ATen/ops/_choose_qparams_per_tensor_ops.h> |
| 1369 | #include <ATen/ops/meshgrid_native.h> |
| 1370 | #include <ATen/ops/meshgrid_ops.h> |
| 1371 | #include <ATen/ops/meshgrid_native.h> |
| 1372 | #include <ATen/ops/meshgrid_ops.h> |
| 1373 | #include <ATen/ops/cartesian_prod_native.h> |
| 1374 | #include <ATen/ops/cartesian_prod_ops.h> |
| 1375 | #include <ATen/ops/can_cast_native.h> |
| 1376 | #include <ATen/ops/can_cast_ops.h> |
| 1377 | #include <ATen/ops/promote_types_native.h> |
| 1378 | #include <ATen/ops/promote_types_ops.h> |
| 1379 | #include <ATen/ops/_local_scalar_dense_native.h> |
| 1380 | #include <ATen/ops/_local_scalar_dense_ops.h> |
| 1381 | #include <ATen/ops/rnn_tanh_native.h> |
| 1382 | #include <ATen/ops/rnn_tanh_ops.h> |
| 1383 | #include <ATen/ops/rnn_tanh_native.h> |
| 1384 | #include <ATen/ops/rnn_tanh_ops.h> |
| 1385 | #include <ATen/ops/gru_cell_native.h> |
| 1386 | #include <ATen/ops/gru_cell_ops.h> |
| 1387 | #include <ATen/ops/rnn_relu_cell_native.h> |
| 1388 | #include <ATen/ops/rnn_relu_cell_ops.h> |
| 1389 | #include <ATen/ops/_pad_packed_sequence_native.h> |
| 1390 | #include <ATen/ops/_pad_packed_sequence_ops.h> |
| 1391 | #include <ATen/ops/is_set_to_native.h> |
| 1392 | #include <ATen/ops/is_set_to_ops.h> |
| 1393 | #include <ATen/ops/gather_backward_native.h> |
| 1394 | #include <ATen/ops/gather_backward_ops.h> |
| 1395 | #include <ATen/ops/_gather_sparse_backward_native.h> |
| 1396 | #include <ATen/ops/_gather_sparse_backward_ops.h> |
| 1397 | #include <ATen/ops/linalg_vander_native.h> |
| 1398 | #include <ATen/ops/linalg_vander_ops.h> |
| 1399 | #include <ATen/ops/argsort_native.h> |
| 1400 | #include <ATen/ops/argsort_ops.h> |
| 1401 | #include <ATen/ops/argsort_native.h> |
| 1402 | #include <ATen/ops/argsort_ops.h> |
| 1403 | #include <ATen/ops/_pad_circular_native.h> |
| 1404 | #include <ATen/ops/_pad_circular_ops.h> |
| 1405 | #include <ATen/ops/upsample_trilinear3d_native.h> |
| 1406 | #include <ATen/ops/upsample_trilinear3d_ops.h> |
| 1407 | #include <ATen/ops/_upsample_bicubic2d_aa_native.h> |
| 1408 | #include <ATen/ops/_upsample_bicubic2d_aa_ops.h> |
| 1409 | #include <ATen/ops/record_stream_native.h> |
| 1410 | #include <ATen/ops/record_stream_ops.h> |
| 1411 | #include <ATen/ops/special_softmax_native.h> |
| 1412 | #include <ATen/ops/special_softmax_ops.h> |
| 1413 | #include <ATen/ops/fft_fftshift_native.h> |
| 1414 | #include <ATen/ops/fft_fftshift_ops.h> |
| 1415 | #include <ATen/ops/nested_to_padded_tensor_native.h> |
| 1416 | #include <ATen/ops/nested_to_padded_tensor_ops.h> |
| 1417 | #include <ATen/ops/_test_ambiguous_defaults_native.h> |
| 1418 | #include <ATen/ops/_test_ambiguous_defaults_ops.h> |
| 1419 | #include <ATen/ops/_test_ambiguous_defaults_native.h> |
| 1420 | #include <ATen/ops/_test_ambiguous_defaults_ops.h> |
| 1421 | #include <ATen/ops/_test_autograd_multiple_dispatch_native.h> |
| 1422 | #include <ATen/ops/_test_autograd_multiple_dispatch_ops.h> |
| 1423 | #include <ATen/ops/pad_sequence_native.h> |
| 1424 | #include <ATen/ops/pad_sequence_ops.h> |
| 1425 | #include <ATen/ops/flatten_dense_tensors_native.h> |
| 1426 | #include <ATen/ops/flatten_dense_tensors_ops.h> |
| 1427 | #include <ATen/ops/_scaled_dot_product_attention_native.h> |
| 1428 | #include <ATen/ops/_scaled_dot_product_attention_ops.h> |
| 1429 | #include <ATen/ops/_scaled_dot_product_efficient_attention_backward_native.h> |
| 1430 | #include <ATen/ops/_scaled_dot_product_efficient_attention_backward_ops.h> |
| 1431 | #include <ATen/ops/_chunk_grad_outputs_efficient_attention_native.h> |
| 1432 | #include <ATen/ops/_chunk_grad_outputs_efficient_attention_ops.h> |
| 1433 | #endif |
| 1434 | |
| 1435 | namespace at { |
| 1436 | namespace functionalization { |
| 1437 | |
| 1438 | // This keyset is used by functionalization when it calls into meta kernels |
| 1439 | // to accurately propagate stride metadata. |
| 1440 | // Exclude any modes: the purpose of calling into meta kernels is only as an implementation |
| 1441 | // detail to perform shape inference, and we don't want any modal keys to run. |
| 1442 | // Specifically, we want to prevent functionalization and Python modes from running. |
| 1443 | constexpr auto exclude_keys_for_meta_dispatch = |
| 1444 | c10::functorch_transforms_ks | |
| 1445 | c10::DispatchKeySet({ |
| 1446 | c10::DispatchKey::FuncTorchDynamicLayerBackMode, |
| 1447 | c10::DispatchKey::FuncTorchDynamicLayerFrontMode, |
| 1448 | c10::DispatchKey::Python |
| 1449 | }); |
| 1450 | |
| 1451 | |
| 1452 | inline Tensor to_meta(const Tensor& t) { |
| 1453 | if (!t.defined()) return t; |
| 1454 | return at::native::empty_strided_meta_symint(t.sym_sizes(), t.sym_strides(), |
| 1455 | /*dtype=*/c10::make_optional(t.scalar_type()), /*layout=*/c10::make_optional(t.layout()), |
| 1456 | /*device=*/c10::make_optional(c10::Device(kMeta)), /*pin_memory=*/c10::nullopt); |
| 1457 | } |
| 1458 | |
| 1459 | inline c10::optional<Tensor> to_meta(const c10::optional<Tensor>& t) { |
| 1460 | if (t.has_value()) { |
| 1461 | return c10::make_optional<Tensor>(to_meta(*t)); |
| 1462 | } |
| 1463 | return c10::nullopt; |
| 1464 | } |
| 1465 | |
| 1466 | inline std::vector<Tensor> to_meta(at::ITensorListRef t_list) { |
| 1467 | std::vector<Tensor> outputs; |
| 1468 | outputs.reserve(t_list.size()); |
| 1469 | for (const auto& tensor : t_list) { |
| 1470 | outputs.push_back(to_meta(tensor)); |
| 1471 | } |
| 1472 | return outputs; |
| 1473 | } |
| 1474 | |
| 1475 | inline c10::List<Tensor> to_meta(const c10::List<Tensor>& t_list) { |
| 1476 | c10::List<Tensor> outputs; |
| 1477 | outputs.reserve(t_list.size()); |
| 1478 | for (const auto i : c10::irange(t_list.size())) { |
| 1479 | outputs.push_back(to_meta(t_list[i])); |
| 1480 | } |
| 1481 | return outputs; |
| 1482 | } |
| 1483 | |
| 1484 | inline c10::List<c10::optional<Tensor>> to_meta(const c10::List<c10::optional<Tensor>>& t_list) { |
| 1485 | c10::List<c10::optional<Tensor>> outputs; |
| 1486 | outputs.reserve(t_list.size()); |
| 1487 | for (const auto i : c10::irange(t_list.size())) { |
| 1488 | outputs.push_back(to_meta(t_list[i])); |
| 1489 | } |
| 1490 | return outputs; |
| 1491 | } |
| 1492 | |
| 1493 | |
| 1494 | |
| 1495 | ::std::tuple<at::Tensor &,at::Tensor &> _cudnn_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 deterministic, bool zero_infinity, at::Tensor & out0, at::Tensor & out1) { |
| 1496 | if (false) { |
| 1497 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 1498 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 1499 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 1500 | auto log_probs_meta = to_meta(log_probs); |
| 1501 | auto targets_meta = to_meta(targets); |
| 1502 | auto out0_meta = to_meta(out0); |
| 1503 | auto out1_meta = to_meta(out1); |
| 1504 | at::AutoDispatchSkipFunctionalize func_guard; |
| 1505 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 1506 | at::_ops::_cudnn_ctc_loss_out::call(log_probs_meta, targets_meta, input_lengths, target_lengths, blank, deterministic, zero_infinity, out0_meta, out1_meta); |
| 1507 | } |
| 1508 | |
| 1509 | at::Tensor log_probs_; |
| 1510 | if (at::functionalization::impl::isFunctionalTensor(log_probs)) { |
| 1511 | at::functionalization::impl::sync(log_probs); |
| 1512 | log_probs_ = at::functionalization::impl::from_functional_tensor(log_probs); |
| 1513 | } else { |
| 1514 | log_probs_ = log_probs; |
| 1515 | } |
| 1516 | |
| 1517 | at::Tensor targets_; |
| 1518 | if (at::functionalization::impl::isFunctionalTensor(targets)) { |
| 1519 | at::functionalization::impl::sync(targets); |
| 1520 | targets_ = at::functionalization::impl::from_functional_tensor(targets); |
| 1521 | } else { |
| 1522 | targets_ = targets; |
| 1523 | } |
| 1524 | |
| 1525 | at::Tensor out0_; |
| 1526 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 1527 | at::functionalization::impl::sync(out0); |
| 1528 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 1529 | } else { |
| 1530 | out0_ = out0; |
| 1531 | } |
| 1532 | |
| 1533 | at::Tensor out1_; |
| 1534 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 1535 | at::functionalization::impl::sync(out1); |
| 1536 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 1537 | } else { |
| 1538 | out1_ = out1; |
| 1539 | } |
| 1540 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1))) { |
| 1541 | if ((false || at::functionalization::impl::isFunctionalTensor(log_probs) || at::functionalization::impl::isFunctionalTensor(targets))) { |
| 1542 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 1543 | TORCH_INTERNAL_ASSERT(false, |
| 1544 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 1545 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 1546 | } else { |
| 1547 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 1548 | at::AutoDispatchSkipFunctionalize guard; |
| 1549 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::_cudnn_ctc_loss_out::call(log_probs_, targets_, input_lengths, target_lengths, blank, deterministic, zero_infinity, out0_, out1_); |
| 1550 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1);; |
| 1551 | } |
| 1552 | } else { |
| 1553 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 1554 | { |
| 1555 | at::AutoDispatchSkipFunctionalize guard; |
| 1556 | tmp_output = at::_ops::_cudnn_ctc_loss::call(log_probs_, targets_, input_lengths, target_lengths, blank, deterministic, zero_infinity); |
| 1557 | } |
| 1558 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 1559 | at::functionalization::impl::commit_update(out0); |
| 1560 | at::functionalization::impl::sync(out0); |
| 1561 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 1562 | at::functionalization::impl::commit_update(out1); |
| 1563 | at::functionalization::impl::sync(out1); |
| 1564 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1); |
| 1565 | } |
| 1566 | } |
| 1567 | |
| 1568 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _cudnn_rnn_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const c10::optional<at::Tensor> & weight_buf, const at::Tensor & hx, const c10::optional<at::Tensor> & cx, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const c10::optional<at::Tensor> & dropout_state, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4) { |
| 1569 | if (false) { |
| 1570 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 1571 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 1572 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 1573 | auto input_meta = to_meta(input); |
| 1574 | auto weight_meta = to_meta(weight); |
| 1575 | auto weight_buf_meta = to_meta(weight_buf); |
| 1576 | auto hx_meta = to_meta(hx); |
| 1577 | auto cx_meta = to_meta(cx); |
| 1578 | auto dropout_state_meta = to_meta(dropout_state); |
| 1579 | auto out0_meta = to_meta(out0); |
| 1580 | auto out1_meta = to_meta(out1); |
| 1581 | auto out2_meta = to_meta(out2); |
| 1582 | auto out3_meta = to_meta(out3); |
| 1583 | auto out4_meta = to_meta(out4); |
| 1584 | at::AutoDispatchSkipFunctionalize func_guard; |
| 1585 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 1586 | at::_ops::_cudnn_rnn_out::call(input_meta, weight_meta, weight_stride0, weight_buf_meta, hx_meta, cx_meta, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state_meta, out0_meta, out1_meta, out2_meta, out3_meta, out4_meta); |
| 1587 | } |
| 1588 | |
| 1589 | at::Tensor input_; |
| 1590 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 1591 | at::functionalization::impl::sync(input); |
| 1592 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 1593 | } else { |
| 1594 | input_ = input; |
| 1595 | } |
| 1596 | |
| 1597 | ::std::vector<at::Tensor> weight_; |
| 1598 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 1599 | at::functionalization::impl::sync(weight); |
| 1600 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 1601 | } else { |
| 1602 | weight_ = weight.vec(); |
| 1603 | } |
| 1604 | |
| 1605 | c10::optional<at::Tensor> weight_buf_; |
| 1606 | if (at::functionalization::impl::isFunctionalTensor(weight_buf)) { |
| 1607 | at::functionalization::impl::sync(weight_buf); |
| 1608 | weight_buf_ = at::functionalization::impl::from_functional_tensor(weight_buf); |
| 1609 | } else { |
| 1610 | weight_buf_ = weight_buf; |
| 1611 | } |
| 1612 | |
| 1613 | at::Tensor hx_; |
| 1614 | if (at::functionalization::impl::isFunctionalTensor(hx)) { |
| 1615 | at::functionalization::impl::sync(hx); |
| 1616 | hx_ = at::functionalization::impl::from_functional_tensor(hx); |
| 1617 | } else { |
| 1618 | hx_ = hx; |
| 1619 | } |
| 1620 | |
| 1621 | c10::optional<at::Tensor> cx_; |
| 1622 | if (at::functionalization::impl::isFunctionalTensor(cx)) { |
| 1623 | at::functionalization::impl::sync(cx); |
| 1624 | cx_ = at::functionalization::impl::from_functional_tensor(cx); |
| 1625 | } else { |
| 1626 | cx_ = cx; |
| 1627 | } |
| 1628 | |
| 1629 | c10::optional<at::Tensor> dropout_state_; |
| 1630 | if (at::functionalization::impl::isFunctionalTensor(dropout_state)) { |
| 1631 | at::functionalization::impl::sync(dropout_state); |
| 1632 | dropout_state_ = at::functionalization::impl::from_functional_tensor(dropout_state); |
| 1633 | } else { |
| 1634 | dropout_state_ = dropout_state; |
| 1635 | } |
| 1636 | |
| 1637 | at::Tensor out0_; |
| 1638 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 1639 | at::functionalization::impl::sync(out0); |
| 1640 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 1641 | } else { |
| 1642 | out0_ = out0; |
| 1643 | } |
| 1644 | |
| 1645 | at::Tensor out1_; |
| 1646 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 1647 | at::functionalization::impl::sync(out1); |
| 1648 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 1649 | } else { |
| 1650 | out1_ = out1; |
| 1651 | } |
| 1652 | |
| 1653 | at::Tensor out2_; |
| 1654 | if (at::functionalization::impl::isFunctionalTensor(out2)) { |
| 1655 | at::functionalization::impl::sync(out2); |
| 1656 | out2_ = at::functionalization::impl::from_functional_tensor(out2); |
| 1657 | } else { |
| 1658 | out2_ = out2; |
| 1659 | } |
| 1660 | |
| 1661 | at::Tensor out3_; |
| 1662 | if (at::functionalization::impl::isFunctionalTensor(out3)) { |
| 1663 | at::functionalization::impl::sync(out3); |
| 1664 | out3_ = at::functionalization::impl::from_functional_tensor(out3); |
| 1665 | } else { |
| 1666 | out3_ = out3; |
| 1667 | } |
| 1668 | |
| 1669 | at::Tensor out4_; |
| 1670 | if (at::functionalization::impl::isFunctionalTensor(out4)) { |
| 1671 | at::functionalization::impl::sync(out4); |
| 1672 | out4_ = at::functionalization::impl::from_functional_tensor(out4); |
| 1673 | } else { |
| 1674 | out4_ = out4; |
| 1675 | } |
| 1676 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1) && at::functionalization::impl::isFunctionalTensor(out2) && at::functionalization::impl::isFunctionalTensor(out3) && at::functionalization::impl::isFunctionalTensor(out4))) { |
| 1677 | if ((false || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(weight_buf) || at::functionalization::impl::isFunctionalTensor(hx) || at::functionalization::impl::isFunctionalTensor(cx) || at::functionalization::impl::isFunctionalTensor(dropout_state))) { |
| 1678 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 1679 | TORCH_INTERNAL_ASSERT(false, |
| 1680 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 1681 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 1682 | } else { |
| 1683 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 1684 | at::AutoDispatchSkipFunctionalize guard; |
| 1685 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::_cudnn_rnn_out::call(input_, weight_, weight_stride0, weight_buf_, hx_, cx_, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state_, out0_, out1_, out2_, out3_, out4_); |
| 1686 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2, out3, out4);; |
| 1687 | } |
| 1688 | } else { |
| 1689 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 1690 | { |
| 1691 | at::AutoDispatchSkipFunctionalize guard; |
| 1692 | tmp_output = at::_ops::_cudnn_rnn::call(input_, weight_, weight_stride0, weight_buf_, hx_, cx_, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state_); |
| 1693 | } |
| 1694 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 1695 | at::functionalization::impl::commit_update(out0); |
| 1696 | at::functionalization::impl::sync(out0); |
| 1697 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 1698 | at::functionalization::impl::commit_update(out1); |
| 1699 | at::functionalization::impl::sync(out1); |
| 1700 | at::functionalization::impl::replace_(out2, std::get<2>(tmp_output)); |
| 1701 | at::functionalization::impl::commit_update(out2); |
| 1702 | at::functionalization::impl::sync(out2); |
| 1703 | at::functionalization::impl::replace_(out3, std::get<3>(tmp_output)); |
| 1704 | at::functionalization::impl::commit_update(out3); |
| 1705 | at::functionalization::impl::sync(out3); |
| 1706 | at::functionalization::impl::replace_(out4, std::get<4>(tmp_output)); |
| 1707 | at::functionalization::impl::commit_update(out4); |
| 1708 | at::functionalization::impl::sync(out4); |
| 1709 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2, out3, out4); |
| 1710 | } |
| 1711 | } |
| 1712 | |
| 1713 | void _cudnn_rnn_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const c10::optional<at::Tensor> & cx, const at::Tensor & output, const c10::optional<at::Tensor> & grad_output, const c10::optional<at::Tensor> & grad_hy, const c10::optional<at::Tensor> & grad_cy, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const c10::optional<at::Tensor> & dropout_state, const at::Tensor & reserve, ::std::array<bool,4> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::TensorList out3) { |
| 1714 | if (false) { |
| 1715 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 1716 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 1717 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 1718 | auto input_meta = to_meta(input); |
| 1719 | auto weight_meta = to_meta(weight); |
| 1720 | auto weight_buf_meta = to_meta(weight_buf); |
| 1721 | auto hx_meta = to_meta(hx); |
| 1722 | auto cx_meta = to_meta(cx); |
| 1723 | auto output_meta = to_meta(output); |
| 1724 | auto grad_output_meta = to_meta(grad_output); |
| 1725 | auto grad_hy_meta = to_meta(grad_hy); |
| 1726 | auto grad_cy_meta = to_meta(grad_cy); |
| 1727 | auto dropout_state_meta = to_meta(dropout_state); |
| 1728 | auto reserve_meta = to_meta(reserve); |
| 1729 | auto out0_meta = to_meta(out0); |
| 1730 | auto out1_meta = to_meta(out1); |
| 1731 | auto out2_meta = to_meta(out2); |
| 1732 | auto out3_meta = to_meta(out3); |
| 1733 | at::AutoDispatchSkipFunctionalize func_guard; |
| 1734 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 1735 | at::_ops::_cudnn_rnn_backward_out::call(input_meta, weight_meta, weight_stride0, weight_buf_meta, hx_meta, cx_meta, output_meta, grad_output_meta, grad_hy_meta, grad_cy_meta, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state_meta, reserve_meta, output_mask, out0_meta, out1_meta, out2_meta, out3_meta); |
| 1736 | } |
| 1737 | |
| 1738 | at::Tensor input_; |
| 1739 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 1740 | at::functionalization::impl::sync(input); |
| 1741 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 1742 | } else { |
| 1743 | input_ = input; |
| 1744 | } |
| 1745 | |
| 1746 | ::std::vector<at::Tensor> weight_; |
| 1747 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 1748 | at::functionalization::impl::sync(weight); |
| 1749 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 1750 | } else { |
| 1751 | weight_ = weight.vec(); |
| 1752 | } |
| 1753 | |
| 1754 | at::Tensor weight_buf_; |
| 1755 | if (at::functionalization::impl::isFunctionalTensor(weight_buf)) { |
| 1756 | at::functionalization::impl::sync(weight_buf); |
| 1757 | weight_buf_ = at::functionalization::impl::from_functional_tensor(weight_buf); |
| 1758 | } else { |
| 1759 | weight_buf_ = weight_buf; |
| 1760 | } |
| 1761 | |
| 1762 | at::Tensor hx_; |
| 1763 | if (at::functionalization::impl::isFunctionalTensor(hx)) { |
| 1764 | at::functionalization::impl::sync(hx); |
| 1765 | hx_ = at::functionalization::impl::from_functional_tensor(hx); |
| 1766 | } else { |
| 1767 | hx_ = hx; |
| 1768 | } |
| 1769 | |
| 1770 | c10::optional<at::Tensor> cx_; |
| 1771 | if (at::functionalization::impl::isFunctionalTensor(cx)) { |
| 1772 | at::functionalization::impl::sync(cx); |
| 1773 | cx_ = at::functionalization::impl::from_functional_tensor(cx); |
| 1774 | } else { |
| 1775 | cx_ = cx; |
| 1776 | } |
| 1777 | |
| 1778 | at::Tensor output_; |
| 1779 | if (at::functionalization::impl::isFunctionalTensor(output)) { |
| 1780 | at::functionalization::impl::sync(output); |
| 1781 | output_ = at::functionalization::impl::from_functional_tensor(output); |
| 1782 | } else { |
| 1783 | output_ = output; |
| 1784 | } |
| 1785 | |
| 1786 | c10::optional<at::Tensor> grad_output_; |
| 1787 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 1788 | at::functionalization::impl::sync(grad_output); |
| 1789 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 1790 | } else { |
| 1791 | grad_output_ = grad_output; |
| 1792 | } |
| 1793 | |
| 1794 | c10::optional<at::Tensor> grad_hy_; |
| 1795 | if (at::functionalization::impl::isFunctionalTensor(grad_hy)) { |
| 1796 | at::functionalization::impl::sync(grad_hy); |
| 1797 | grad_hy_ = at::functionalization::impl::from_functional_tensor(grad_hy); |
| 1798 | } else { |
| 1799 | grad_hy_ = grad_hy; |
| 1800 | } |
| 1801 | |
| 1802 | c10::optional<at::Tensor> grad_cy_; |
| 1803 | if (at::functionalization::impl::isFunctionalTensor(grad_cy)) { |
| 1804 | at::functionalization::impl::sync(grad_cy); |
| 1805 | grad_cy_ = at::functionalization::impl::from_functional_tensor(grad_cy); |
| 1806 | } else { |
| 1807 | grad_cy_ = grad_cy; |
| 1808 | } |
| 1809 | |
| 1810 | c10::optional<at::Tensor> dropout_state_; |
| 1811 | if (at::functionalization::impl::isFunctionalTensor(dropout_state)) { |
| 1812 | at::functionalization::impl::sync(dropout_state); |
| 1813 | dropout_state_ = at::functionalization::impl::from_functional_tensor(dropout_state); |
| 1814 | } else { |
| 1815 | dropout_state_ = dropout_state; |
| 1816 | } |
| 1817 | |
| 1818 | at::Tensor reserve_; |
| 1819 | if (at::functionalization::impl::isFunctionalTensor(reserve)) { |
| 1820 | at::functionalization::impl::sync(reserve); |
| 1821 | reserve_ = at::functionalization::impl::from_functional_tensor(reserve); |
| 1822 | } else { |
| 1823 | reserve_ = reserve; |
| 1824 | } |
| 1825 | |
| 1826 | at::Tensor out0_; |
| 1827 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 1828 | at::functionalization::impl::sync(out0); |
| 1829 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 1830 | } else { |
| 1831 | out0_ = out0; |
| 1832 | } |
| 1833 | |
| 1834 | at::Tensor out1_; |
| 1835 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 1836 | at::functionalization::impl::sync(out1); |
| 1837 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 1838 | } else { |
| 1839 | out1_ = out1; |
| 1840 | } |
| 1841 | |
| 1842 | at::Tensor out2_; |
| 1843 | if (at::functionalization::impl::isFunctionalTensor(out2)) { |
| 1844 | at::functionalization::impl::sync(out2); |
| 1845 | out2_ = at::functionalization::impl::from_functional_tensor(out2); |
| 1846 | } else { |
| 1847 | out2_ = out2; |
| 1848 | } |
| 1849 | |
| 1850 | ::std::vector<at::Tensor> out3_; |
| 1851 | if (at::functionalization::impl::isFunctionalTensor(out3)) { |
| 1852 | at::functionalization::impl::sync(out3); |
| 1853 | out3_ = at::functionalization::impl::from_functional_tensor(out3); |
| 1854 | } else { |
| 1855 | out3_ = out3.vec(); |
| 1856 | } |
| 1857 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1) && at::functionalization::impl::isFunctionalTensor(out2) && at::functionalization::impl::isFunctionalTensor(out3))) { |
| 1858 | if ((false || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(weight_buf) || at::functionalization::impl::isFunctionalTensor(hx) || at::functionalization::impl::isFunctionalTensor(cx) || at::functionalization::impl::isFunctionalTensor(output) || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(grad_hy) || at::functionalization::impl::isFunctionalTensor(grad_cy) || at::functionalization::impl::isFunctionalTensor(dropout_state) || at::functionalization::impl::isFunctionalTensor(reserve))) { |
| 1859 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 1860 | TORCH_INTERNAL_ASSERT(false, |
| 1861 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 1862 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 1863 | } else { |
| 1864 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 1865 | at::AutoDispatchSkipFunctionalize guard; |
| 1866 | at::_ops::_cudnn_rnn_backward_out::call(input_, weight_, weight_stride0, weight_buf_, hx_, cx_, output_, grad_output_, grad_hy_, grad_cy_, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state_, reserve_, output_mask, out0_, out1_, out2_, out3_); |
| 1867 | ; |
| 1868 | } |
| 1869 | } else { |
| 1870 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,::std::vector<at::Tensor>> tmp_output; |
| 1871 | { |
| 1872 | at::AutoDispatchSkipFunctionalize guard; |
| 1873 | tmp_output = at::_ops::_cudnn_rnn_backward::call(input_, weight_, weight_stride0, weight_buf_, hx_, cx_, output_, grad_output_, grad_hy_, grad_cy_, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state_, reserve_, output_mask); |
| 1874 | } |
| 1875 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 1876 | at::functionalization::impl::commit_update(out0); |
| 1877 | at::functionalization::impl::sync(out0); |
| 1878 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 1879 | at::functionalization::impl::commit_update(out1); |
| 1880 | at::functionalization::impl::sync(out1); |
| 1881 | at::functionalization::impl::replace_(out2, std::get<2>(tmp_output)); |
| 1882 | at::functionalization::impl::commit_update(out2); |
| 1883 | at::functionalization::impl::sync(out2); |
| 1884 | at::functionalization::impl::replace_(out3, std::get<3>(tmp_output)); |
| 1885 | at::functionalization::impl::commit_update(out3); |
| 1886 | at::functionalization::impl::sync(out3); |
| 1887 | |
| 1888 | } |
| 1889 | } |
| 1890 | |
| 1891 | ::std::tuple<at::Tensor &,at::Tensor &> _fused_dropout_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double p, c10::optional<at::Generator> generator, at::Tensor & out0, at::Tensor & out1) { |
| 1892 | if (false) { |
| 1893 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 1894 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 1895 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 1896 | auto self_meta = to_meta(self); |
| 1897 | auto out0_meta = to_meta(out0); |
| 1898 | auto out1_meta = to_meta(out1); |
| 1899 | at::AutoDispatchSkipFunctionalize func_guard; |
| 1900 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 1901 | at::_ops::_fused_dropout_out::call(self_meta, p, generator, out0_meta, out1_meta); |
| 1902 | } |
| 1903 | |
| 1904 | at::Tensor self_; |
| 1905 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 1906 | at::functionalization::impl::sync(self); |
| 1907 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 1908 | } else { |
| 1909 | self_ = self; |
| 1910 | } |
| 1911 | |
| 1912 | at::Tensor out0_; |
| 1913 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 1914 | at::functionalization::impl::sync(out0); |
| 1915 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 1916 | } else { |
| 1917 | out0_ = out0; |
| 1918 | } |
| 1919 | |
| 1920 | at::Tensor out1_; |
| 1921 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 1922 | at::functionalization::impl::sync(out1); |
| 1923 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 1924 | } else { |
| 1925 | out1_ = out1; |
| 1926 | } |
| 1927 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1))) { |
| 1928 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 1929 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 1930 | TORCH_INTERNAL_ASSERT(false, |
| 1931 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 1932 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 1933 | } else { |
| 1934 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 1935 | at::AutoDispatchSkipFunctionalize guard; |
| 1936 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::_fused_dropout_out::call(self_, p, generator, out0_, out1_); |
| 1937 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1);; |
| 1938 | } |
| 1939 | } else { |
| 1940 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 1941 | { |
| 1942 | at::AutoDispatchSkipFunctionalize guard; |
| 1943 | tmp_output = at::_ops::_fused_dropout::call(self_, p, generator); |
| 1944 | } |
| 1945 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 1946 | at::functionalization::impl::commit_update(out0); |
| 1947 | at::functionalization::impl::sync(out0); |
| 1948 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 1949 | at::functionalization::impl::commit_update(out1); |
| 1950 | at::functionalization::impl::sync(out1); |
| 1951 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1); |
| 1952 | } |
| 1953 | } |
| 1954 | |
| 1955 | at::Tensor & conj_physical_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 1956 | if (false) { |
| 1957 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 1958 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 1959 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 1960 | auto self_meta = to_meta(self); |
| 1961 | auto out_meta = to_meta(out); |
| 1962 | at::AutoDispatchSkipFunctionalize func_guard; |
| 1963 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 1964 | at::_ops::conj_physical_out::call(self_meta, out_meta); |
| 1965 | } |
| 1966 | |
| 1967 | at::Tensor self_; |
| 1968 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 1969 | at::functionalization::impl::sync(self); |
| 1970 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 1971 | } else { |
| 1972 | self_ = self; |
| 1973 | } |
| 1974 | |
| 1975 | at::Tensor out_; |
| 1976 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 1977 | at::functionalization::impl::sync(out); |
| 1978 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 1979 | } else { |
| 1980 | out_ = out; |
| 1981 | } |
| 1982 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 1983 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 1984 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 1985 | TORCH_INTERNAL_ASSERT(false, |
| 1986 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 1987 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 1988 | } else { |
| 1989 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 1990 | at::AutoDispatchSkipFunctionalize guard; |
| 1991 | at::Tensor tmp_output = at::_ops::conj_physical_out::call(self_, out_); |
| 1992 | return out;; |
| 1993 | } |
| 1994 | } else { |
| 1995 | at::Tensor tmp_output; |
| 1996 | { |
| 1997 | at::AutoDispatchSkipFunctionalize guard; |
| 1998 | tmp_output = at::_ops::conj_physical::call(self_); |
| 1999 | } |
| 2000 | at::functionalization::impl::replace_(out, tmp_output); |
| 2001 | at::functionalization::impl::commit_update(out); |
| 2002 | at::functionalization::impl::sync(out); |
| 2003 | return out; |
| 2004 | } |
| 2005 | } |
| 2006 | |
| 2007 | at::Tensor & conj_physical_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 2008 | if (true) { |
| 2009 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2010 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2011 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2012 | auto self_meta = to_meta(self); |
| 2013 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2014 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2015 | at::_ops::conj_physical_::call(self_meta); |
| 2016 | } |
| 2017 | |
| 2018 | at::Tensor self_; |
| 2019 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2020 | at::functionalization::impl::sync(self); |
| 2021 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2022 | } else { |
| 2023 | self_ = self; |
| 2024 | } |
| 2025 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 2026 | if ((false)) { |
| 2027 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2028 | TORCH_INTERNAL_ASSERT(false, |
| 2029 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2030 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2031 | } else { |
| 2032 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2033 | at::AutoDispatchSkipFunctionalize guard; |
| 2034 | at::Tensor tmp_output = at::_ops::conj_physical_::call(self_); |
| 2035 | return self;; |
| 2036 | } |
| 2037 | } else { |
| 2038 | at::Tensor tmp_output; |
| 2039 | { |
| 2040 | at::AutoDispatchSkipFunctionalize guard; |
| 2041 | tmp_output = at::_ops::conj_physical::call(self_); |
| 2042 | } |
| 2043 | at::functionalization::impl::replace_(self, tmp_output); |
| 2044 | at::functionalization::impl::commit_update(self); |
| 2045 | at::functionalization::impl::sync(self); |
| 2046 | return self; |
| 2047 | } |
| 2048 | } |
| 2049 | |
| 2050 | at::Tensor & add_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) { |
| 2051 | if (false) { |
| 2052 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2053 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2054 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2055 | auto self_meta = to_meta(self); |
| 2056 | auto other_meta = to_meta(other); |
| 2057 | auto out_meta = to_meta(out); |
| 2058 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2059 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2060 | at::_ops::add_out::call(self_meta, other_meta, alpha, out_meta); |
| 2061 | } |
| 2062 | |
| 2063 | at::Tensor self_; |
| 2064 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2065 | at::functionalization::impl::sync(self); |
| 2066 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2067 | } else { |
| 2068 | self_ = self; |
| 2069 | } |
| 2070 | |
| 2071 | at::Tensor other_; |
| 2072 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 2073 | at::functionalization::impl::sync(other); |
| 2074 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 2075 | } else { |
| 2076 | other_ = other; |
| 2077 | } |
| 2078 | |
| 2079 | at::Tensor out_; |
| 2080 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2081 | at::functionalization::impl::sync(out); |
| 2082 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2083 | } else { |
| 2084 | out_ = out; |
| 2085 | } |
| 2086 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2087 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 2088 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2089 | TORCH_INTERNAL_ASSERT(false, |
| 2090 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2091 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2092 | } else { |
| 2093 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2094 | at::AutoDispatchSkipFunctionalize guard; |
| 2095 | at::Tensor tmp_output = at::_ops::add_out::call(self_, other_, alpha, out_); |
| 2096 | return out;; |
| 2097 | } |
| 2098 | } else { |
| 2099 | at::Tensor tmp_output; |
| 2100 | { |
| 2101 | at::AutoDispatchSkipFunctionalize guard; |
| 2102 | tmp_output = at::_ops::add_Tensor::call(self_, other_, alpha); |
| 2103 | } |
| 2104 | at::functionalization::impl::replace_(out, tmp_output); |
| 2105 | at::functionalization::impl::commit_update(out); |
| 2106 | at::functionalization::impl::sync(out); |
| 2107 | return out; |
| 2108 | } |
| 2109 | } |
| 2110 | |
| 2111 | at::Tensor & add__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 2112 | if (true) { |
| 2113 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2114 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2115 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2116 | auto self_meta = to_meta(self); |
| 2117 | auto other_meta = to_meta(other); |
| 2118 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2119 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2120 | at::_ops::add__Tensor::call(self_meta, other_meta, alpha); |
| 2121 | } |
| 2122 | |
| 2123 | at::Tensor self_; |
| 2124 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2125 | at::functionalization::impl::sync(self); |
| 2126 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2127 | } else { |
| 2128 | self_ = self; |
| 2129 | } |
| 2130 | |
| 2131 | at::Tensor other_; |
| 2132 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 2133 | at::functionalization::impl::sync(other); |
| 2134 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 2135 | } else { |
| 2136 | other_ = other; |
| 2137 | } |
| 2138 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 2139 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 2140 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2141 | TORCH_INTERNAL_ASSERT(false, |
| 2142 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2143 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2144 | } else { |
| 2145 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2146 | at::AutoDispatchSkipFunctionalize guard; |
| 2147 | at::Tensor tmp_output = at::_ops::add__Tensor::call(self_, other_, alpha); |
| 2148 | return self;; |
| 2149 | } |
| 2150 | } else { |
| 2151 | at::Tensor tmp_output; |
| 2152 | { |
| 2153 | at::AutoDispatchSkipFunctionalize guard; |
| 2154 | tmp_output = at::_ops::add_Tensor::call(self_, other_, alpha); |
| 2155 | } |
| 2156 | at::functionalization::impl::replace_(self, tmp_output); |
| 2157 | at::functionalization::impl::commit_update(self); |
| 2158 | at::functionalization::impl::sync(self); |
| 2159 | return self; |
| 2160 | } |
| 2161 | } |
| 2162 | |
| 2163 | at::Tensor & add_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha, at::Tensor & out) { |
| 2164 | if (false) { |
| 2165 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2166 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2167 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2168 | auto self_meta = to_meta(self); |
| 2169 | auto out_meta = to_meta(out); |
| 2170 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2171 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2172 | at::_ops::add_Scalar_out::call(self_meta, other, alpha, out_meta); |
| 2173 | } |
| 2174 | |
| 2175 | at::Tensor self_; |
| 2176 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2177 | at::functionalization::impl::sync(self); |
| 2178 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2179 | } else { |
| 2180 | self_ = self; |
| 2181 | } |
| 2182 | |
| 2183 | at::Tensor out_; |
| 2184 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2185 | at::functionalization::impl::sync(out); |
| 2186 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2187 | } else { |
| 2188 | out_ = out; |
| 2189 | } |
| 2190 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2191 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 2192 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2193 | TORCH_INTERNAL_ASSERT(false, |
| 2194 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2195 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2196 | } else { |
| 2197 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2198 | at::AutoDispatchSkipFunctionalize guard; |
| 2199 | at::Tensor tmp_output = at::_ops::add_Scalar_out::call(self_, other, alpha, out_); |
| 2200 | return out;; |
| 2201 | } |
| 2202 | } else { |
| 2203 | at::Tensor tmp_output; |
| 2204 | { |
| 2205 | at::AutoDispatchSkipFunctionalize guard; |
| 2206 | tmp_output = at::_ops::add_Scalar::call(self_, other, alpha); |
| 2207 | } |
| 2208 | at::functionalization::impl::replace_(out, tmp_output); |
| 2209 | at::functionalization::impl::commit_update(out); |
| 2210 | at::functionalization::impl::sync(out); |
| 2211 | return out; |
| 2212 | } |
| 2213 | } |
| 2214 | |
| 2215 | at::Tensor & add__Scalar(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha) { |
| 2216 | if (true) { |
| 2217 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2218 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2219 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2220 | auto self_meta = to_meta(self); |
| 2221 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2222 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2223 | at::_ops::add__Scalar::call(self_meta, other, alpha); |
| 2224 | } |
| 2225 | |
| 2226 | at::Tensor self_; |
| 2227 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2228 | at::functionalization::impl::sync(self); |
| 2229 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2230 | } else { |
| 2231 | self_ = self; |
| 2232 | } |
| 2233 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 2234 | if ((false)) { |
| 2235 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2236 | TORCH_INTERNAL_ASSERT(false, |
| 2237 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2238 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2239 | } else { |
| 2240 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2241 | at::AutoDispatchSkipFunctionalize guard; |
| 2242 | at::Tensor tmp_output = at::_ops::add__Scalar::call(self_, other, alpha); |
| 2243 | return self;; |
| 2244 | } |
| 2245 | } else { |
| 2246 | at::Tensor tmp_output; |
| 2247 | { |
| 2248 | at::AutoDispatchSkipFunctionalize guard; |
| 2249 | tmp_output = at::_ops::add_Scalar::call(self_, other, alpha); |
| 2250 | } |
| 2251 | at::functionalization::impl::replace_(self, tmp_output); |
| 2252 | at::functionalization::impl::commit_update(self); |
| 2253 | at::functionalization::impl::sync(self); |
| 2254 | return self; |
| 2255 | } |
| 2256 | } |
| 2257 | |
| 2258 | at::Tensor & addmv_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { |
| 2259 | if (false) { |
| 2260 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2261 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2262 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2263 | auto self_meta = to_meta(self); |
| 2264 | auto mat_meta = to_meta(mat); |
| 2265 | auto vec_meta = to_meta(vec); |
| 2266 | auto out_meta = to_meta(out); |
| 2267 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2268 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2269 | at::_ops::addmv_out::call(self_meta, mat_meta, vec_meta, beta, alpha, out_meta); |
| 2270 | } |
| 2271 | |
| 2272 | at::Tensor self_; |
| 2273 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2274 | at::functionalization::impl::sync(self); |
| 2275 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2276 | } else { |
| 2277 | self_ = self; |
| 2278 | } |
| 2279 | |
| 2280 | at::Tensor mat_; |
| 2281 | if (at::functionalization::impl::isFunctionalTensor(mat)) { |
| 2282 | at::functionalization::impl::sync(mat); |
| 2283 | mat_ = at::functionalization::impl::from_functional_tensor(mat); |
| 2284 | } else { |
| 2285 | mat_ = mat; |
| 2286 | } |
| 2287 | |
| 2288 | at::Tensor vec_; |
| 2289 | if (at::functionalization::impl::isFunctionalTensor(vec)) { |
| 2290 | at::functionalization::impl::sync(vec); |
| 2291 | vec_ = at::functionalization::impl::from_functional_tensor(vec); |
| 2292 | } else { |
| 2293 | vec_ = vec; |
| 2294 | } |
| 2295 | |
| 2296 | at::Tensor out_; |
| 2297 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2298 | at::functionalization::impl::sync(out); |
| 2299 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2300 | } else { |
| 2301 | out_ = out; |
| 2302 | } |
| 2303 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2304 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(mat) || at::functionalization::impl::isFunctionalTensor(vec))) { |
| 2305 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2306 | TORCH_INTERNAL_ASSERT(false, |
| 2307 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2308 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2309 | } else { |
| 2310 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2311 | at::AutoDispatchSkipFunctionalize guard; |
| 2312 | at::Tensor tmp_output = at::_ops::addmv_out::call(self_, mat_, vec_, beta, alpha, out_); |
| 2313 | return out;; |
| 2314 | } |
| 2315 | } else { |
| 2316 | at::Tensor tmp_output; |
| 2317 | { |
| 2318 | at::AutoDispatchSkipFunctionalize guard; |
| 2319 | tmp_output = at::_ops::addmv::call(self_, mat_, vec_, beta, alpha); |
| 2320 | } |
| 2321 | at::functionalization::impl::replace_(out, tmp_output); |
| 2322 | at::functionalization::impl::commit_update(out); |
| 2323 | at::functionalization::impl::sync(out); |
| 2324 | return out; |
| 2325 | } |
| 2326 | } |
| 2327 | |
| 2328 | at::Tensor & addmv_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha) { |
| 2329 | if (true) { |
| 2330 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2331 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2332 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2333 | auto self_meta = to_meta(self); |
| 2334 | auto mat_meta = to_meta(mat); |
| 2335 | auto vec_meta = to_meta(vec); |
| 2336 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2337 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2338 | at::_ops::addmv_::call(self_meta, mat_meta, vec_meta, beta, alpha); |
| 2339 | } |
| 2340 | |
| 2341 | at::Tensor self_; |
| 2342 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2343 | at::functionalization::impl::sync(self); |
| 2344 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2345 | } else { |
| 2346 | self_ = self; |
| 2347 | } |
| 2348 | |
| 2349 | at::Tensor mat_; |
| 2350 | if (at::functionalization::impl::isFunctionalTensor(mat)) { |
| 2351 | at::functionalization::impl::sync(mat); |
| 2352 | mat_ = at::functionalization::impl::from_functional_tensor(mat); |
| 2353 | } else { |
| 2354 | mat_ = mat; |
| 2355 | } |
| 2356 | |
| 2357 | at::Tensor vec_; |
| 2358 | if (at::functionalization::impl::isFunctionalTensor(vec)) { |
| 2359 | at::functionalization::impl::sync(vec); |
| 2360 | vec_ = at::functionalization::impl::from_functional_tensor(vec); |
| 2361 | } else { |
| 2362 | vec_ = vec; |
| 2363 | } |
| 2364 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 2365 | if ((false || at::functionalization::impl::isFunctionalTensor(mat) || at::functionalization::impl::isFunctionalTensor(vec))) { |
| 2366 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2367 | TORCH_INTERNAL_ASSERT(false, |
| 2368 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2369 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2370 | } else { |
| 2371 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2372 | at::AutoDispatchSkipFunctionalize guard; |
| 2373 | at::Tensor tmp_output = at::_ops::addmv_::call(self_, mat_, vec_, beta, alpha); |
| 2374 | return self;; |
| 2375 | } |
| 2376 | } else { |
| 2377 | at::Tensor tmp_output; |
| 2378 | { |
| 2379 | at::AutoDispatchSkipFunctionalize guard; |
| 2380 | tmp_output = at::_ops::addmv::call(self_, mat_, vec_, beta, alpha); |
| 2381 | } |
| 2382 | at::functionalization::impl::replace_(self, tmp_output); |
| 2383 | at::functionalization::impl::commit_update(self); |
| 2384 | at::functionalization::impl::sync(self); |
| 2385 | return self; |
| 2386 | } |
| 2387 | } |
| 2388 | |
| 2389 | at::Tensor & addr_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { |
| 2390 | if (false) { |
| 2391 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2392 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2393 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2394 | auto self_meta = to_meta(self); |
| 2395 | auto vec1_meta = to_meta(vec1); |
| 2396 | auto vec2_meta = to_meta(vec2); |
| 2397 | auto out_meta = to_meta(out); |
| 2398 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2399 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2400 | at::_ops::addr_out::call(self_meta, vec1_meta, vec2_meta, beta, alpha, out_meta); |
| 2401 | } |
| 2402 | |
| 2403 | at::Tensor self_; |
| 2404 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2405 | at::functionalization::impl::sync(self); |
| 2406 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2407 | } else { |
| 2408 | self_ = self; |
| 2409 | } |
| 2410 | |
| 2411 | at::Tensor vec1_; |
| 2412 | if (at::functionalization::impl::isFunctionalTensor(vec1)) { |
| 2413 | at::functionalization::impl::sync(vec1); |
| 2414 | vec1_ = at::functionalization::impl::from_functional_tensor(vec1); |
| 2415 | } else { |
| 2416 | vec1_ = vec1; |
| 2417 | } |
| 2418 | |
| 2419 | at::Tensor vec2_; |
| 2420 | if (at::functionalization::impl::isFunctionalTensor(vec2)) { |
| 2421 | at::functionalization::impl::sync(vec2); |
| 2422 | vec2_ = at::functionalization::impl::from_functional_tensor(vec2); |
| 2423 | } else { |
| 2424 | vec2_ = vec2; |
| 2425 | } |
| 2426 | |
| 2427 | at::Tensor out_; |
| 2428 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2429 | at::functionalization::impl::sync(out); |
| 2430 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2431 | } else { |
| 2432 | out_ = out; |
| 2433 | } |
| 2434 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2435 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(vec1) || at::functionalization::impl::isFunctionalTensor(vec2))) { |
| 2436 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2437 | TORCH_INTERNAL_ASSERT(false, |
| 2438 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2439 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2440 | } else { |
| 2441 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2442 | at::AutoDispatchSkipFunctionalize guard; |
| 2443 | at::Tensor tmp_output = at::_ops::addr_out::call(self_, vec1_, vec2_, beta, alpha, out_); |
| 2444 | return out;; |
| 2445 | } |
| 2446 | } else { |
| 2447 | at::Tensor tmp_output; |
| 2448 | { |
| 2449 | at::AutoDispatchSkipFunctionalize guard; |
| 2450 | tmp_output = at::_ops::addr::call(self_, vec1_, vec2_, beta, alpha); |
| 2451 | } |
| 2452 | at::functionalization::impl::replace_(out, tmp_output); |
| 2453 | at::functionalization::impl::commit_update(out); |
| 2454 | at::functionalization::impl::sync(out); |
| 2455 | return out; |
| 2456 | } |
| 2457 | } |
| 2458 | |
| 2459 | at::Tensor & addr_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 2460 | if (true) { |
| 2461 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2462 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2463 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2464 | auto self_meta = to_meta(self); |
| 2465 | auto vec1_meta = to_meta(vec1); |
| 2466 | auto vec2_meta = to_meta(vec2); |
| 2467 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2468 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2469 | at::_ops::addr_::call(self_meta, vec1_meta, vec2_meta, beta, alpha); |
| 2470 | } |
| 2471 | |
| 2472 | at::Tensor self_; |
| 2473 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2474 | at::functionalization::impl::sync(self); |
| 2475 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2476 | } else { |
| 2477 | self_ = self; |
| 2478 | } |
| 2479 | |
| 2480 | at::Tensor vec1_; |
| 2481 | if (at::functionalization::impl::isFunctionalTensor(vec1)) { |
| 2482 | at::functionalization::impl::sync(vec1); |
| 2483 | vec1_ = at::functionalization::impl::from_functional_tensor(vec1); |
| 2484 | } else { |
| 2485 | vec1_ = vec1; |
| 2486 | } |
| 2487 | |
| 2488 | at::Tensor vec2_; |
| 2489 | if (at::functionalization::impl::isFunctionalTensor(vec2)) { |
| 2490 | at::functionalization::impl::sync(vec2); |
| 2491 | vec2_ = at::functionalization::impl::from_functional_tensor(vec2); |
| 2492 | } else { |
| 2493 | vec2_ = vec2; |
| 2494 | } |
| 2495 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 2496 | if ((false || at::functionalization::impl::isFunctionalTensor(vec1) || at::functionalization::impl::isFunctionalTensor(vec2))) { |
| 2497 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2498 | TORCH_INTERNAL_ASSERT(false, |
| 2499 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2500 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2501 | } else { |
| 2502 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2503 | at::AutoDispatchSkipFunctionalize guard; |
| 2504 | at::Tensor tmp_output = at::_ops::addr_::call(self_, vec1_, vec2_, beta, alpha); |
| 2505 | return self;; |
| 2506 | } |
| 2507 | } else { |
| 2508 | at::Tensor tmp_output; |
| 2509 | { |
| 2510 | at::AutoDispatchSkipFunctionalize guard; |
| 2511 | tmp_output = at::_ops::addr::call(self_, vec1_, vec2_, beta, alpha); |
| 2512 | } |
| 2513 | at::functionalization::impl::replace_(self, tmp_output); |
| 2514 | at::functionalization::impl::commit_update(self); |
| 2515 | at::functionalization::impl::sync(self); |
| 2516 | return self; |
| 2517 | } |
| 2518 | } |
| 2519 | |
| 2520 | at::Tensor & all_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & out) { |
| 2521 | if (false) { |
| 2522 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2523 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2524 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2525 | auto self_meta = to_meta(self); |
| 2526 | auto out_meta = to_meta(out); |
| 2527 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2528 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2529 | at::_ops::all_out::call(self_meta, dim, keepdim, out_meta); |
| 2530 | } |
| 2531 | |
| 2532 | at::Tensor self_; |
| 2533 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2534 | at::functionalization::impl::sync(self); |
| 2535 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2536 | } else { |
| 2537 | self_ = self; |
| 2538 | } |
| 2539 | |
| 2540 | at::Tensor out_; |
| 2541 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2542 | at::functionalization::impl::sync(out); |
| 2543 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2544 | } else { |
| 2545 | out_ = out; |
| 2546 | } |
| 2547 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2548 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 2549 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2550 | TORCH_INTERNAL_ASSERT(false, |
| 2551 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2552 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2553 | } else { |
| 2554 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2555 | at::AutoDispatchSkipFunctionalize guard; |
| 2556 | at::Tensor tmp_output = at::_ops::all_out::call(self_, dim, keepdim, out_); |
| 2557 | return out;; |
| 2558 | } |
| 2559 | } else { |
| 2560 | at::Tensor tmp_output; |
| 2561 | { |
| 2562 | at::AutoDispatchSkipFunctionalize guard; |
| 2563 | tmp_output = at::_ops::all_dim::call(self_, dim, keepdim); |
| 2564 | } |
| 2565 | at::functionalization::impl::replace_(out, tmp_output); |
| 2566 | at::functionalization::impl::commit_update(out); |
| 2567 | at::functionalization::impl::sync(out); |
| 2568 | return out; |
| 2569 | } |
| 2570 | } |
| 2571 | |
| 2572 | at::Tensor & all_out_dimname_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & out) { |
| 2573 | if (false) { |
| 2574 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2575 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2576 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2577 | auto self_meta = to_meta(self); |
| 2578 | auto out_meta = to_meta(out); |
| 2579 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2580 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2581 | at::_ops::all_dimname_out::call(self_meta, dim, keepdim, out_meta); |
| 2582 | } |
| 2583 | |
| 2584 | at::Tensor self_; |
| 2585 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2586 | at::functionalization::impl::sync(self); |
| 2587 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2588 | } else { |
| 2589 | self_ = self; |
| 2590 | } |
| 2591 | |
| 2592 | at::Tensor out_; |
| 2593 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2594 | at::functionalization::impl::sync(out); |
| 2595 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2596 | } else { |
| 2597 | out_ = out; |
| 2598 | } |
| 2599 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2600 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 2601 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2602 | TORCH_INTERNAL_ASSERT(false, |
| 2603 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2604 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2605 | } else { |
| 2606 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2607 | at::AutoDispatchSkipFunctionalize guard; |
| 2608 | at::Tensor tmp_output = at::_ops::all_dimname_out::call(self_, dim, keepdim, out_); |
| 2609 | return out;; |
| 2610 | } |
| 2611 | } else { |
| 2612 | at::Tensor tmp_output; |
| 2613 | { |
| 2614 | at::AutoDispatchSkipFunctionalize guard; |
| 2615 | tmp_output = at::_ops::all_dimname::call(self_, dim, keepdim); |
| 2616 | } |
| 2617 | at::functionalization::impl::replace_(out, tmp_output); |
| 2618 | at::functionalization::impl::commit_update(out); |
| 2619 | at::functionalization::impl::sync(out); |
| 2620 | return out; |
| 2621 | } |
| 2622 | } |
| 2623 | |
| 2624 | at::Tensor & argmax_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim, at::Tensor & out) { |
| 2625 | if (false) { |
| 2626 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2627 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2628 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2629 | auto self_meta = to_meta(self); |
| 2630 | auto out_meta = to_meta(out); |
| 2631 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2632 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2633 | at::_ops::argmax_out::call(self_meta, dim, keepdim, out_meta); |
| 2634 | } |
| 2635 | |
| 2636 | at::Tensor self_; |
| 2637 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2638 | at::functionalization::impl::sync(self); |
| 2639 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2640 | } else { |
| 2641 | self_ = self; |
| 2642 | } |
| 2643 | |
| 2644 | at::Tensor out_; |
| 2645 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2646 | at::functionalization::impl::sync(out); |
| 2647 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2648 | } else { |
| 2649 | out_ = out; |
| 2650 | } |
| 2651 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2652 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 2653 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2654 | TORCH_INTERNAL_ASSERT(false, |
| 2655 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2656 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2657 | } else { |
| 2658 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2659 | at::AutoDispatchSkipFunctionalize guard; |
| 2660 | at::Tensor tmp_output = at::_ops::argmax_out::call(self_, dim, keepdim, out_); |
| 2661 | return out;; |
| 2662 | } |
| 2663 | } else { |
| 2664 | at::Tensor tmp_output; |
| 2665 | { |
| 2666 | at::AutoDispatchSkipFunctionalize guard; |
| 2667 | tmp_output = at::_ops::argmax::call(self_, dim, keepdim); |
| 2668 | } |
| 2669 | at::functionalization::impl::replace_(out, tmp_output); |
| 2670 | at::functionalization::impl::commit_update(out); |
| 2671 | at::functionalization::impl::sync(out); |
| 2672 | return out; |
| 2673 | } |
| 2674 | } |
| 2675 | |
| 2676 | at::Tensor & atan_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 2677 | if (false) { |
| 2678 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2679 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2680 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2681 | auto self_meta = to_meta(self); |
| 2682 | auto out_meta = to_meta(out); |
| 2683 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2684 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2685 | at::_ops::atan_out::call(self_meta, out_meta); |
| 2686 | } |
| 2687 | |
| 2688 | at::Tensor self_; |
| 2689 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2690 | at::functionalization::impl::sync(self); |
| 2691 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2692 | } else { |
| 2693 | self_ = self; |
| 2694 | } |
| 2695 | |
| 2696 | at::Tensor out_; |
| 2697 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2698 | at::functionalization::impl::sync(out); |
| 2699 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2700 | } else { |
| 2701 | out_ = out; |
| 2702 | } |
| 2703 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2704 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 2705 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2706 | TORCH_INTERNAL_ASSERT(false, |
| 2707 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2708 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2709 | } else { |
| 2710 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2711 | at::AutoDispatchSkipFunctionalize guard; |
| 2712 | at::Tensor tmp_output = at::_ops::atan_out::call(self_, out_); |
| 2713 | return out;; |
| 2714 | } |
| 2715 | } else { |
| 2716 | at::Tensor tmp_output; |
| 2717 | { |
| 2718 | at::AutoDispatchSkipFunctionalize guard; |
| 2719 | tmp_output = at::_ops::atan::call(self_); |
| 2720 | } |
| 2721 | at::functionalization::impl::replace_(out, tmp_output); |
| 2722 | at::functionalization::impl::commit_update(out); |
| 2723 | at::functionalization::impl::sync(out); |
| 2724 | return out; |
| 2725 | } |
| 2726 | } |
| 2727 | |
| 2728 | at::Tensor & atan_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 2729 | if (true) { |
| 2730 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2731 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2732 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2733 | auto self_meta = to_meta(self); |
| 2734 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2735 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2736 | at::_ops::atan_::call(self_meta); |
| 2737 | } |
| 2738 | |
| 2739 | at::Tensor self_; |
| 2740 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2741 | at::functionalization::impl::sync(self); |
| 2742 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2743 | } else { |
| 2744 | self_ = self; |
| 2745 | } |
| 2746 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 2747 | if ((false)) { |
| 2748 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2749 | TORCH_INTERNAL_ASSERT(false, |
| 2750 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2751 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2752 | } else { |
| 2753 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2754 | at::AutoDispatchSkipFunctionalize guard; |
| 2755 | at::Tensor tmp_output = at::_ops::atan_::call(self_); |
| 2756 | return self;; |
| 2757 | } |
| 2758 | } else { |
| 2759 | at::Tensor tmp_output; |
| 2760 | { |
| 2761 | at::AutoDispatchSkipFunctionalize guard; |
| 2762 | tmp_output = at::_ops::atan::call(self_); |
| 2763 | } |
| 2764 | at::functionalization::impl::replace_(self, tmp_output); |
| 2765 | at::functionalization::impl::commit_update(self); |
| 2766 | at::functionalization::impl::sync(self); |
| 2767 | return self; |
| 2768 | } |
| 2769 | } |
| 2770 | |
| 2771 | at::Tensor & bartlett_window_out_out(c10::DispatchKeySet dispatchKeySet, int64_t window_length, at::Tensor & out) { |
| 2772 | if (false) { |
| 2773 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2774 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2775 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2776 | auto out_meta = to_meta(out); |
| 2777 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2778 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2779 | at::_ops::bartlett_window_out::call(window_length, out_meta); |
| 2780 | } |
| 2781 | |
| 2782 | at::Tensor out_; |
| 2783 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2784 | at::functionalization::impl::sync(out); |
| 2785 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2786 | } else { |
| 2787 | out_ = out; |
| 2788 | } |
| 2789 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2790 | if ((false)) { |
| 2791 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2792 | TORCH_INTERNAL_ASSERT(false, |
| 2793 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2794 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2795 | } else { |
| 2796 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2797 | at::AutoDispatchSkipFunctionalize guard; |
| 2798 | at::Tensor tmp_output = at::_ops::bartlett_window_out::call(window_length, out_); |
| 2799 | return out;; |
| 2800 | } |
| 2801 | } else { |
| 2802 | at::Tensor tmp_output; |
| 2803 | { |
| 2804 | at::AutoDispatchSkipFunctionalize guard; |
| 2805 | tmp_output = at::_ops::bartlett_window::call(window_length, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 2806 | } |
| 2807 | at::functionalization::impl::replace_(out, tmp_output); |
| 2808 | at::functionalization::impl::commit_update(out); |
| 2809 | at::functionalization::impl::sync(out); |
| 2810 | return out; |
| 2811 | } |
| 2812 | } |
| 2813 | |
| 2814 | at::Tensor & bartlett_window_out_periodic_out(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, at::Tensor & out) { |
| 2815 | if (false) { |
| 2816 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2817 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2818 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2819 | auto out_meta = to_meta(out); |
| 2820 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2821 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2822 | at::_ops::bartlett_window_periodic_out::call(window_length, periodic, out_meta); |
| 2823 | } |
| 2824 | |
| 2825 | at::Tensor out_; |
| 2826 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2827 | at::functionalization::impl::sync(out); |
| 2828 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2829 | } else { |
| 2830 | out_ = out; |
| 2831 | } |
| 2832 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2833 | if ((false)) { |
| 2834 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2835 | TORCH_INTERNAL_ASSERT(false, |
| 2836 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2837 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2838 | } else { |
| 2839 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2840 | at::AutoDispatchSkipFunctionalize guard; |
| 2841 | at::Tensor tmp_output = at::_ops::bartlett_window_periodic_out::call(window_length, periodic, out_); |
| 2842 | return out;; |
| 2843 | } |
| 2844 | } else { |
| 2845 | at::Tensor tmp_output; |
| 2846 | { |
| 2847 | at::AutoDispatchSkipFunctionalize guard; |
| 2848 | tmp_output = at::_ops::bartlett_window_periodic::call(window_length, periodic, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 2849 | } |
| 2850 | at::functionalization::impl::replace_(out, tmp_output); |
| 2851 | at::functionalization::impl::commit_update(out); |
| 2852 | at::functionalization::impl::sync(out); |
| 2853 | return out; |
| 2854 | } |
| 2855 | } |
| 2856 | |
| 2857 | at::Tensor & binary_cross_entropy_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, at::Tensor & out) { |
| 2858 | if (false) { |
| 2859 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2860 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2861 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2862 | auto self_meta = to_meta(self); |
| 2863 | auto target_meta = to_meta(target); |
| 2864 | auto weight_meta = to_meta(weight); |
| 2865 | auto out_meta = to_meta(out); |
| 2866 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2867 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2868 | at::_ops::binary_cross_entropy_out::call(self_meta, target_meta, weight_meta, reduction, out_meta); |
| 2869 | } |
| 2870 | |
| 2871 | at::Tensor self_; |
| 2872 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2873 | at::functionalization::impl::sync(self); |
| 2874 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2875 | } else { |
| 2876 | self_ = self; |
| 2877 | } |
| 2878 | |
| 2879 | at::Tensor target_; |
| 2880 | if (at::functionalization::impl::isFunctionalTensor(target)) { |
| 2881 | at::functionalization::impl::sync(target); |
| 2882 | target_ = at::functionalization::impl::from_functional_tensor(target); |
| 2883 | } else { |
| 2884 | target_ = target; |
| 2885 | } |
| 2886 | |
| 2887 | c10::optional<at::Tensor> weight_; |
| 2888 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 2889 | at::functionalization::impl::sync(weight); |
| 2890 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 2891 | } else { |
| 2892 | weight_ = weight; |
| 2893 | } |
| 2894 | |
| 2895 | at::Tensor out_; |
| 2896 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2897 | at::functionalization::impl::sync(out); |
| 2898 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2899 | } else { |
| 2900 | out_ = out; |
| 2901 | } |
| 2902 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2903 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(target) || at::functionalization::impl::isFunctionalTensor(weight))) { |
| 2904 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2905 | TORCH_INTERNAL_ASSERT(false, |
| 2906 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2907 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2908 | } else { |
| 2909 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2910 | at::AutoDispatchSkipFunctionalize guard; |
| 2911 | at::Tensor tmp_output = at::_ops::binary_cross_entropy_out::call(self_, target_, weight_, reduction, out_); |
| 2912 | return out;; |
| 2913 | } |
| 2914 | } else { |
| 2915 | at::Tensor tmp_output; |
| 2916 | { |
| 2917 | at::AutoDispatchSkipFunctionalize guard; |
| 2918 | tmp_output = at::_ops::binary_cross_entropy::call(self_, target_, weight_, reduction); |
| 2919 | } |
| 2920 | at::functionalization::impl::replace_(out, tmp_output); |
| 2921 | at::functionalization::impl::commit_update(out); |
| 2922 | at::functionalization::impl::sync(out); |
| 2923 | return out; |
| 2924 | } |
| 2925 | } |
| 2926 | |
| 2927 | at::Tensor & bitwise_not_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 2928 | if (false) { |
| 2929 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2930 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2931 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2932 | auto self_meta = to_meta(self); |
| 2933 | auto out_meta = to_meta(out); |
| 2934 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2935 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2936 | at::_ops::bitwise_not_out::call(self_meta, out_meta); |
| 2937 | } |
| 2938 | |
| 2939 | at::Tensor self_; |
| 2940 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2941 | at::functionalization::impl::sync(self); |
| 2942 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2943 | } else { |
| 2944 | self_ = self; |
| 2945 | } |
| 2946 | |
| 2947 | at::Tensor out_; |
| 2948 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 2949 | at::functionalization::impl::sync(out); |
| 2950 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 2951 | } else { |
| 2952 | out_ = out; |
| 2953 | } |
| 2954 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 2955 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 2956 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 2957 | TORCH_INTERNAL_ASSERT(false, |
| 2958 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 2959 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 2960 | } else { |
| 2961 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 2962 | at::AutoDispatchSkipFunctionalize guard; |
| 2963 | at::Tensor tmp_output = at::_ops::bitwise_not_out::call(self_, out_); |
| 2964 | return out;; |
| 2965 | } |
| 2966 | } else { |
| 2967 | at::Tensor tmp_output; |
| 2968 | { |
| 2969 | at::AutoDispatchSkipFunctionalize guard; |
| 2970 | tmp_output = at::_ops::bitwise_not::call(self_); |
| 2971 | } |
| 2972 | at::functionalization::impl::replace_(out, tmp_output); |
| 2973 | at::functionalization::impl::commit_update(out); |
| 2974 | at::functionalization::impl::sync(out); |
| 2975 | return out; |
| 2976 | } |
| 2977 | } |
| 2978 | |
| 2979 | at::Tensor & bitwise_not_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 2980 | if (true) { |
| 2981 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 2982 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 2983 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 2984 | auto self_meta = to_meta(self); |
| 2985 | at::AutoDispatchSkipFunctionalize func_guard; |
| 2986 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 2987 | at::_ops::bitwise_not_::call(self_meta); |
| 2988 | } |
| 2989 | |
| 2990 | at::Tensor self_; |
| 2991 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 2992 | at::functionalization::impl::sync(self); |
| 2993 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 2994 | } else { |
| 2995 | self_ = self; |
| 2996 | } |
| 2997 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 2998 | if ((false)) { |
| 2999 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3000 | TORCH_INTERNAL_ASSERT(false, |
| 3001 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3002 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3003 | } else { |
| 3004 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3005 | at::AutoDispatchSkipFunctionalize guard; |
| 3006 | at::Tensor tmp_output = at::_ops::bitwise_not_::call(self_); |
| 3007 | return self;; |
| 3008 | } |
| 3009 | } else { |
| 3010 | at::Tensor tmp_output; |
| 3011 | { |
| 3012 | at::AutoDispatchSkipFunctionalize guard; |
| 3013 | tmp_output = at::_ops::bitwise_not::call(self_); |
| 3014 | } |
| 3015 | at::functionalization::impl::replace_(self, tmp_output); |
| 3016 | at::functionalization::impl::commit_update(self); |
| 3017 | at::functionalization::impl::sync(self); |
| 3018 | return self; |
| 3019 | } |
| 3020 | } |
| 3021 | |
| 3022 | at::Tensor & logical_xor_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 3023 | if (false) { |
| 3024 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3025 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3026 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3027 | auto self_meta = to_meta(self); |
| 3028 | auto other_meta = to_meta(other); |
| 3029 | auto out_meta = to_meta(out); |
| 3030 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3031 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3032 | at::_ops::logical_xor_out::call(self_meta, other_meta, out_meta); |
| 3033 | } |
| 3034 | |
| 3035 | at::Tensor self_; |
| 3036 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3037 | at::functionalization::impl::sync(self); |
| 3038 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3039 | } else { |
| 3040 | self_ = self; |
| 3041 | } |
| 3042 | |
| 3043 | at::Tensor other_; |
| 3044 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 3045 | at::functionalization::impl::sync(other); |
| 3046 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 3047 | } else { |
| 3048 | other_ = other; |
| 3049 | } |
| 3050 | |
| 3051 | at::Tensor out_; |
| 3052 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3053 | at::functionalization::impl::sync(out); |
| 3054 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3055 | } else { |
| 3056 | out_ = out; |
| 3057 | } |
| 3058 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3059 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 3060 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3061 | TORCH_INTERNAL_ASSERT(false, |
| 3062 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3063 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3064 | } else { |
| 3065 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3066 | at::AutoDispatchSkipFunctionalize guard; |
| 3067 | at::Tensor tmp_output = at::_ops::logical_xor_out::call(self_, other_, out_); |
| 3068 | return out;; |
| 3069 | } |
| 3070 | } else { |
| 3071 | at::Tensor tmp_output; |
| 3072 | { |
| 3073 | at::AutoDispatchSkipFunctionalize guard; |
| 3074 | tmp_output = at::_ops::logical_xor::call(self_, other_); |
| 3075 | } |
| 3076 | at::functionalization::impl::replace_(out, tmp_output); |
| 3077 | at::functionalization::impl::commit_update(out); |
| 3078 | at::functionalization::impl::sync(out); |
| 3079 | return out; |
| 3080 | } |
| 3081 | } |
| 3082 | |
| 3083 | at::Tensor & logical_xor_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 3084 | if (true) { |
| 3085 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3086 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3087 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3088 | auto self_meta = to_meta(self); |
| 3089 | auto other_meta = to_meta(other); |
| 3090 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3091 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3092 | at::_ops::logical_xor_::call(self_meta, other_meta); |
| 3093 | } |
| 3094 | |
| 3095 | at::Tensor self_; |
| 3096 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3097 | at::functionalization::impl::sync(self); |
| 3098 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3099 | } else { |
| 3100 | self_ = self; |
| 3101 | } |
| 3102 | |
| 3103 | at::Tensor other_; |
| 3104 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 3105 | at::functionalization::impl::sync(other); |
| 3106 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 3107 | } else { |
| 3108 | other_ = other; |
| 3109 | } |
| 3110 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 3111 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 3112 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3113 | TORCH_INTERNAL_ASSERT(false, |
| 3114 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3115 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3116 | } else { |
| 3117 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3118 | at::AutoDispatchSkipFunctionalize guard; |
| 3119 | at::Tensor tmp_output = at::_ops::logical_xor_::call(self_, other_); |
| 3120 | return self;; |
| 3121 | } |
| 3122 | } else { |
| 3123 | at::Tensor tmp_output; |
| 3124 | { |
| 3125 | at::AutoDispatchSkipFunctionalize guard; |
| 3126 | tmp_output = at::_ops::logical_xor::call(self_, other_); |
| 3127 | } |
| 3128 | at::functionalization::impl::replace_(self, tmp_output); |
| 3129 | at::functionalization::impl::commit_update(self); |
| 3130 | at::functionalization::impl::sync(self); |
| 3131 | return self; |
| 3132 | } |
| 3133 | } |
| 3134 | |
| 3135 | at::Tensor & blackman_window_out_out(c10::DispatchKeySet dispatchKeySet, int64_t window_length, at::Tensor & out) { |
| 3136 | if (false) { |
| 3137 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3138 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3139 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3140 | auto out_meta = to_meta(out); |
| 3141 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3142 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3143 | at::_ops::blackman_window_out::call(window_length, out_meta); |
| 3144 | } |
| 3145 | |
| 3146 | at::Tensor out_; |
| 3147 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3148 | at::functionalization::impl::sync(out); |
| 3149 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3150 | } else { |
| 3151 | out_ = out; |
| 3152 | } |
| 3153 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3154 | if ((false)) { |
| 3155 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3156 | TORCH_INTERNAL_ASSERT(false, |
| 3157 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3158 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3159 | } else { |
| 3160 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3161 | at::AutoDispatchSkipFunctionalize guard; |
| 3162 | at::Tensor tmp_output = at::_ops::blackman_window_out::call(window_length, out_); |
| 3163 | return out;; |
| 3164 | } |
| 3165 | } else { |
| 3166 | at::Tensor tmp_output; |
| 3167 | { |
| 3168 | at::AutoDispatchSkipFunctionalize guard; |
| 3169 | tmp_output = at::_ops::blackman_window::call(window_length, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 3170 | } |
| 3171 | at::functionalization::impl::replace_(out, tmp_output); |
| 3172 | at::functionalization::impl::commit_update(out); |
| 3173 | at::functionalization::impl::sync(out); |
| 3174 | return out; |
| 3175 | } |
| 3176 | } |
| 3177 | |
| 3178 | at::Tensor & blackman_window_out_periodic_out(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, at::Tensor & out) { |
| 3179 | if (false) { |
| 3180 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3181 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3182 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3183 | auto out_meta = to_meta(out); |
| 3184 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3185 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3186 | at::_ops::blackman_window_periodic_out::call(window_length, periodic, out_meta); |
| 3187 | } |
| 3188 | |
| 3189 | at::Tensor out_; |
| 3190 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3191 | at::functionalization::impl::sync(out); |
| 3192 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3193 | } else { |
| 3194 | out_ = out; |
| 3195 | } |
| 3196 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3197 | if ((false)) { |
| 3198 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3199 | TORCH_INTERNAL_ASSERT(false, |
| 3200 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3201 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3202 | } else { |
| 3203 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3204 | at::AutoDispatchSkipFunctionalize guard; |
| 3205 | at::Tensor tmp_output = at::_ops::blackman_window_periodic_out::call(window_length, periodic, out_); |
| 3206 | return out;; |
| 3207 | } |
| 3208 | } else { |
| 3209 | at::Tensor tmp_output; |
| 3210 | { |
| 3211 | at::AutoDispatchSkipFunctionalize guard; |
| 3212 | tmp_output = at::_ops::blackman_window_periodic::call(window_length, periodic, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 3213 | } |
| 3214 | at::functionalization::impl::replace_(out, tmp_output); |
| 3215 | at::functionalization::impl::commit_update(out); |
| 3216 | at::functionalization::impl::sync(out); |
| 3217 | return out; |
| 3218 | } |
| 3219 | } |
| 3220 | |
| 3221 | at::Tensor & clamp_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional<at::Scalar> & min, const c10::optional<at::Scalar> & max, at::Tensor & out) { |
| 3222 | if (false) { |
| 3223 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3224 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3225 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3226 | auto self_meta = to_meta(self); |
| 3227 | auto out_meta = to_meta(out); |
| 3228 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3229 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3230 | at::_ops::clamp_out::call(self_meta, min, max, out_meta); |
| 3231 | } |
| 3232 | |
| 3233 | at::Tensor self_; |
| 3234 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3235 | at::functionalization::impl::sync(self); |
| 3236 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3237 | } else { |
| 3238 | self_ = self; |
| 3239 | } |
| 3240 | |
| 3241 | at::Tensor out_; |
| 3242 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3243 | at::functionalization::impl::sync(out); |
| 3244 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3245 | } else { |
| 3246 | out_ = out; |
| 3247 | } |
| 3248 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3249 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 3250 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3251 | TORCH_INTERNAL_ASSERT(false, |
| 3252 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3253 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3254 | } else { |
| 3255 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3256 | at::AutoDispatchSkipFunctionalize guard; |
| 3257 | at::Tensor tmp_output = at::_ops::clamp_out::call(self_, min, max, out_); |
| 3258 | return out;; |
| 3259 | } |
| 3260 | } else { |
| 3261 | at::Tensor tmp_output; |
| 3262 | { |
| 3263 | at::AutoDispatchSkipFunctionalize guard; |
| 3264 | tmp_output = at::_ops::clamp::call(self_, min, max); |
| 3265 | } |
| 3266 | at::functionalization::impl::replace_(out, tmp_output); |
| 3267 | at::functionalization::impl::commit_update(out); |
| 3268 | at::functionalization::impl::sync(out); |
| 3269 | return out; |
| 3270 | } |
| 3271 | } |
| 3272 | |
| 3273 | at::Tensor & clamp_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const c10::optional<at::Scalar> & min, const c10::optional<at::Scalar> & max) { |
| 3274 | if (true) { |
| 3275 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3276 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3277 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3278 | auto self_meta = to_meta(self); |
| 3279 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3280 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3281 | at::_ops::clamp_::call(self_meta, min, max); |
| 3282 | } |
| 3283 | |
| 3284 | at::Tensor self_; |
| 3285 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3286 | at::functionalization::impl::sync(self); |
| 3287 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3288 | } else { |
| 3289 | self_ = self; |
| 3290 | } |
| 3291 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 3292 | if ((false)) { |
| 3293 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3294 | TORCH_INTERNAL_ASSERT(false, |
| 3295 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3296 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3297 | } else { |
| 3298 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3299 | at::AutoDispatchSkipFunctionalize guard; |
| 3300 | at::Tensor tmp_output = at::_ops::clamp_::call(self_, min, max); |
| 3301 | return self;; |
| 3302 | } |
| 3303 | } else { |
| 3304 | at::Tensor tmp_output; |
| 3305 | { |
| 3306 | at::AutoDispatchSkipFunctionalize guard; |
| 3307 | tmp_output = at::_ops::clamp::call(self_, min, max); |
| 3308 | } |
| 3309 | at::functionalization::impl::replace_(self, tmp_output); |
| 3310 | at::functionalization::impl::commit_update(self); |
| 3311 | at::functionalization::impl::sync(self); |
| 3312 | return self; |
| 3313 | } |
| 3314 | } |
| 3315 | |
| 3316 | at::Tensor & clamp_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) { |
| 3317 | if (false) { |
| 3318 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3319 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3320 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3321 | auto self_meta = to_meta(self); |
| 3322 | auto min_meta = to_meta(min); |
| 3323 | auto max_meta = to_meta(max); |
| 3324 | auto out_meta = to_meta(out); |
| 3325 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3326 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3327 | at::_ops::clamp_Tensor_out::call(self_meta, min_meta, max_meta, out_meta); |
| 3328 | } |
| 3329 | |
| 3330 | at::Tensor self_; |
| 3331 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3332 | at::functionalization::impl::sync(self); |
| 3333 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3334 | } else { |
| 3335 | self_ = self; |
| 3336 | } |
| 3337 | |
| 3338 | c10::optional<at::Tensor> min_; |
| 3339 | if (at::functionalization::impl::isFunctionalTensor(min)) { |
| 3340 | at::functionalization::impl::sync(min); |
| 3341 | min_ = at::functionalization::impl::from_functional_tensor(min); |
| 3342 | } else { |
| 3343 | min_ = min; |
| 3344 | } |
| 3345 | |
| 3346 | c10::optional<at::Tensor> max_; |
| 3347 | if (at::functionalization::impl::isFunctionalTensor(max)) { |
| 3348 | at::functionalization::impl::sync(max); |
| 3349 | max_ = at::functionalization::impl::from_functional_tensor(max); |
| 3350 | } else { |
| 3351 | max_ = max; |
| 3352 | } |
| 3353 | |
| 3354 | at::Tensor out_; |
| 3355 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3356 | at::functionalization::impl::sync(out); |
| 3357 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3358 | } else { |
| 3359 | out_ = out; |
| 3360 | } |
| 3361 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3362 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(min) || at::functionalization::impl::isFunctionalTensor(max))) { |
| 3363 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3364 | TORCH_INTERNAL_ASSERT(false, |
| 3365 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3366 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3367 | } else { |
| 3368 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3369 | at::AutoDispatchSkipFunctionalize guard; |
| 3370 | at::Tensor tmp_output = at::_ops::clamp_Tensor_out::call(self_, min_, max_, out_); |
| 3371 | return out;; |
| 3372 | } |
| 3373 | } else { |
| 3374 | at::Tensor tmp_output; |
| 3375 | { |
| 3376 | at::AutoDispatchSkipFunctionalize guard; |
| 3377 | tmp_output = at::_ops::clamp_Tensor::call(self_, min_, max_); |
| 3378 | } |
| 3379 | at::functionalization::impl::replace_(out, tmp_output); |
| 3380 | at::functionalization::impl::commit_update(out); |
| 3381 | at::functionalization::impl::sync(out); |
| 3382 | return out; |
| 3383 | } |
| 3384 | } |
| 3385 | |
| 3386 | at::Tensor & clamp__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const c10::optional<at::Tensor> & min, const c10::optional<at::Tensor> & max) { |
| 3387 | if (true) { |
| 3388 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3389 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3390 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3391 | auto self_meta = to_meta(self); |
| 3392 | auto min_meta = to_meta(min); |
| 3393 | auto max_meta = to_meta(max); |
| 3394 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3395 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3396 | at::_ops::clamp__Tensor::call(self_meta, min_meta, max_meta); |
| 3397 | } |
| 3398 | |
| 3399 | at::Tensor self_; |
| 3400 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3401 | at::functionalization::impl::sync(self); |
| 3402 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3403 | } else { |
| 3404 | self_ = self; |
| 3405 | } |
| 3406 | |
| 3407 | c10::optional<at::Tensor> min_; |
| 3408 | if (at::functionalization::impl::isFunctionalTensor(min)) { |
| 3409 | at::functionalization::impl::sync(min); |
| 3410 | min_ = at::functionalization::impl::from_functional_tensor(min); |
| 3411 | } else { |
| 3412 | min_ = min; |
| 3413 | } |
| 3414 | |
| 3415 | c10::optional<at::Tensor> max_; |
| 3416 | if (at::functionalization::impl::isFunctionalTensor(max)) { |
| 3417 | at::functionalization::impl::sync(max); |
| 3418 | max_ = at::functionalization::impl::from_functional_tensor(max); |
| 3419 | } else { |
| 3420 | max_ = max; |
| 3421 | } |
| 3422 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 3423 | if ((false || at::functionalization::impl::isFunctionalTensor(min) || at::functionalization::impl::isFunctionalTensor(max))) { |
| 3424 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3425 | TORCH_INTERNAL_ASSERT(false, |
| 3426 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3427 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3428 | } else { |
| 3429 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3430 | at::AutoDispatchSkipFunctionalize guard; |
| 3431 | at::Tensor tmp_output = at::_ops::clamp__Tensor::call(self_, min_, max_); |
| 3432 | return self;; |
| 3433 | } |
| 3434 | } else { |
| 3435 | at::Tensor tmp_output; |
| 3436 | { |
| 3437 | at::AutoDispatchSkipFunctionalize guard; |
| 3438 | tmp_output = at::_ops::clamp_Tensor::call(self_, min_, max_); |
| 3439 | } |
| 3440 | at::functionalization::impl::replace_(self, tmp_output); |
| 3441 | at::functionalization::impl::commit_update(self); |
| 3442 | at::functionalization::impl::sync(self); |
| 3443 | return self; |
| 3444 | } |
| 3445 | } |
| 3446 | |
| 3447 | at::Tensor & _convolution_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::SymIntArrayRef padding, at::IntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32, at::Tensor & out) { |
| 3448 | if (false) { |
| 3449 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3450 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3451 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3452 | auto input_meta = to_meta(input); |
| 3453 | auto weight_meta = to_meta(weight); |
| 3454 | auto bias_meta = to_meta(bias); |
| 3455 | auto out_meta = to_meta(out); |
| 3456 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3457 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3458 | at::_ops::_convolution_out::call(input_meta, weight_meta, bias_meta, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled, allow_tf32, out_meta); |
| 3459 | } |
| 3460 | |
| 3461 | at::Tensor input_; |
| 3462 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 3463 | at::functionalization::impl::sync(input); |
| 3464 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 3465 | } else { |
| 3466 | input_ = input; |
| 3467 | } |
| 3468 | |
| 3469 | at::Tensor weight_; |
| 3470 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 3471 | at::functionalization::impl::sync(weight); |
| 3472 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 3473 | } else { |
| 3474 | weight_ = weight; |
| 3475 | } |
| 3476 | |
| 3477 | c10::optional<at::Tensor> bias_; |
| 3478 | if (at::functionalization::impl::isFunctionalTensor(bias)) { |
| 3479 | at::functionalization::impl::sync(bias); |
| 3480 | bias_ = at::functionalization::impl::from_functional_tensor(bias); |
| 3481 | } else { |
| 3482 | bias_ = bias; |
| 3483 | } |
| 3484 | |
| 3485 | at::Tensor out_; |
| 3486 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3487 | at::functionalization::impl::sync(out); |
| 3488 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3489 | } else { |
| 3490 | out_ = out; |
| 3491 | } |
| 3492 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3493 | if ((false || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(bias))) { |
| 3494 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3495 | TORCH_INTERNAL_ASSERT(false, |
| 3496 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3497 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3498 | } else { |
| 3499 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3500 | at::AutoDispatchSkipFunctionalize guard; |
| 3501 | at::Tensor tmp_output = at::_ops::_convolution_out::call(input_, weight_, bias_, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled, allow_tf32, out_); |
| 3502 | return out;; |
| 3503 | } |
| 3504 | } else { |
| 3505 | at::Tensor tmp_output; |
| 3506 | { |
| 3507 | at::AutoDispatchSkipFunctionalize guard; |
| 3508 | tmp_output = at::_ops::_convolution::call(input_, weight_, bias_, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled, allow_tf32); |
| 3509 | } |
| 3510 | at::functionalization::impl::replace_(out, tmp_output); |
| 3511 | at::functionalization::impl::commit_update(out); |
| 3512 | at::functionalization::impl::sync(out); |
| 3513 | return out; |
| 3514 | } |
| 3515 | } |
| 3516 | |
| 3517 | at::Tensor & copy_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, bool non_blocking, at::Tensor & out) { |
| 3518 | if (false) { |
| 3519 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3520 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3521 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3522 | auto self_meta = to_meta(self); |
| 3523 | auto src_meta = to_meta(src); |
| 3524 | auto out_meta = to_meta(out); |
| 3525 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3526 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3527 | at::_ops::copy_out::call(self_meta, src_meta, non_blocking, out_meta); |
| 3528 | } |
| 3529 | |
| 3530 | at::Tensor self_; |
| 3531 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3532 | at::functionalization::impl::sync(self); |
| 3533 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3534 | } else { |
| 3535 | self_ = self; |
| 3536 | } |
| 3537 | |
| 3538 | at::Tensor src_; |
| 3539 | if (at::functionalization::impl::isFunctionalTensor(src)) { |
| 3540 | at::functionalization::impl::sync(src); |
| 3541 | src_ = at::functionalization::impl::from_functional_tensor(src); |
| 3542 | } else { |
| 3543 | src_ = src; |
| 3544 | } |
| 3545 | |
| 3546 | at::Tensor out_; |
| 3547 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3548 | at::functionalization::impl::sync(out); |
| 3549 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3550 | } else { |
| 3551 | out_ = out; |
| 3552 | } |
| 3553 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3554 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(src))) { |
| 3555 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3556 | TORCH_INTERNAL_ASSERT(false, |
| 3557 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3558 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3559 | } else { |
| 3560 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3561 | at::AutoDispatchSkipFunctionalize guard; |
| 3562 | at::Tensor tmp_output = at::_ops::copy_out::call(self_, src_, non_blocking, out_); |
| 3563 | return out;; |
| 3564 | } |
| 3565 | } else { |
| 3566 | at::Tensor tmp_output; |
| 3567 | { |
| 3568 | at::AutoDispatchSkipFunctionalize guard; |
| 3569 | tmp_output = at::_ops::copy::call(self_, src_, non_blocking); |
| 3570 | } |
| 3571 | at::functionalization::impl::replace_(out, tmp_output); |
| 3572 | at::functionalization::impl::commit_update(out); |
| 3573 | at::functionalization::impl::sync(out); |
| 3574 | return out; |
| 3575 | } |
| 3576 | } |
| 3577 | |
| 3578 | at::Tensor & copy_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & src, bool non_blocking) { |
| 3579 | if (true) { |
| 3580 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3581 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3582 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3583 | auto self_meta = to_meta(self); |
| 3584 | auto src_meta = to_meta(src); |
| 3585 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3586 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3587 | at::_ops::copy_::call(self_meta, src_meta, non_blocking); |
| 3588 | } |
| 3589 | |
| 3590 | at::Tensor self_; |
| 3591 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3592 | at::functionalization::impl::sync(self); |
| 3593 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3594 | } else { |
| 3595 | self_ = self; |
| 3596 | } |
| 3597 | |
| 3598 | at::Tensor src_; |
| 3599 | if (at::functionalization::impl::isFunctionalTensor(src)) { |
| 3600 | at::functionalization::impl::sync(src); |
| 3601 | src_ = at::functionalization::impl::from_functional_tensor(src); |
| 3602 | } else { |
| 3603 | src_ = src; |
| 3604 | } |
| 3605 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 3606 | if ((false || at::functionalization::impl::isFunctionalTensor(src))) { |
| 3607 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3608 | TORCH_INTERNAL_ASSERT(false, |
| 3609 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3610 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3611 | } else { |
| 3612 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3613 | at::AutoDispatchSkipFunctionalize guard; |
| 3614 | at::Tensor tmp_output = at::_ops::copy_::call(self_, src_, non_blocking); |
| 3615 | return self;; |
| 3616 | } |
| 3617 | } else { |
| 3618 | at::Tensor tmp_output; |
| 3619 | { |
| 3620 | at::AutoDispatchSkipFunctionalize guard; |
| 3621 | tmp_output = at::_ops::copy::call(self_, src_, non_blocking); |
| 3622 | } |
| 3623 | at::functionalization::impl::replace_(self, tmp_output); |
| 3624 | at::functionalization::impl::commit_update(self); |
| 3625 | at::functionalization::impl::sync(self); |
| 3626 | return self; |
| 3627 | } |
| 3628 | } |
| 3629 | |
| 3630 | at::Tensor & cudnn_convolution_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, bool allow_tf32, at::Tensor & out) { |
| 3631 | if (false) { |
| 3632 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3633 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3634 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3635 | auto self_meta = to_meta(self); |
| 3636 | auto weight_meta = to_meta(weight); |
| 3637 | auto out_meta = to_meta(out); |
| 3638 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3639 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3640 | at::_ops::cudnn_convolution_out::call(self_meta, weight_meta, padding, stride, dilation, groups, benchmark, deterministic, allow_tf32, out_meta); |
| 3641 | } |
| 3642 | |
| 3643 | at::Tensor self_; |
| 3644 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3645 | at::functionalization::impl::sync(self); |
| 3646 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3647 | } else { |
| 3648 | self_ = self; |
| 3649 | } |
| 3650 | |
| 3651 | at::Tensor weight_; |
| 3652 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 3653 | at::functionalization::impl::sync(weight); |
| 3654 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 3655 | } else { |
| 3656 | weight_ = weight; |
| 3657 | } |
| 3658 | |
| 3659 | at::Tensor out_; |
| 3660 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3661 | at::functionalization::impl::sync(out); |
| 3662 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3663 | } else { |
| 3664 | out_ = out; |
| 3665 | } |
| 3666 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3667 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(weight))) { |
| 3668 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3669 | TORCH_INTERNAL_ASSERT(false, |
| 3670 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3671 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3672 | } else { |
| 3673 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3674 | at::AutoDispatchSkipFunctionalize guard; |
| 3675 | at::Tensor tmp_output = at::_ops::cudnn_convolution_out::call(self_, weight_, padding, stride, dilation, groups, benchmark, deterministic, allow_tf32, out_); |
| 3676 | return out;; |
| 3677 | } |
| 3678 | } else { |
| 3679 | at::Tensor tmp_output; |
| 3680 | { |
| 3681 | at::AutoDispatchSkipFunctionalize guard; |
| 3682 | tmp_output = at::_ops::cudnn_convolution::call(self_, weight_, padding, stride, dilation, groups, benchmark, deterministic, allow_tf32); |
| 3683 | } |
| 3684 | at::functionalization::impl::replace_(out, tmp_output); |
| 3685 | at::functionalization::impl::commit_update(out); |
| 3686 | at::functionalization::impl::sync(out); |
| 3687 | return out; |
| 3688 | } |
| 3689 | } |
| 3690 | |
| 3691 | at::Tensor & divide_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 3692 | if (false) { |
| 3693 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3694 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3695 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3696 | auto self_meta = to_meta(self); |
| 3697 | auto other_meta = to_meta(other); |
| 3698 | auto out_meta = to_meta(out); |
| 3699 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3700 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3701 | at::_ops::divide_out::call(self_meta, other_meta, out_meta); |
| 3702 | } |
| 3703 | |
| 3704 | at::Tensor self_; |
| 3705 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3706 | at::functionalization::impl::sync(self); |
| 3707 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3708 | } else { |
| 3709 | self_ = self; |
| 3710 | } |
| 3711 | |
| 3712 | at::Tensor other_; |
| 3713 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 3714 | at::functionalization::impl::sync(other); |
| 3715 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 3716 | } else { |
| 3717 | other_ = other; |
| 3718 | } |
| 3719 | |
| 3720 | at::Tensor out_; |
| 3721 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3722 | at::functionalization::impl::sync(out); |
| 3723 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3724 | } else { |
| 3725 | out_ = out; |
| 3726 | } |
| 3727 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3728 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 3729 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3730 | TORCH_INTERNAL_ASSERT(false, |
| 3731 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3732 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3733 | } else { |
| 3734 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3735 | at::AutoDispatchSkipFunctionalize guard; |
| 3736 | at::Tensor tmp_output = at::_ops::divide_out::call(self_, other_, out_); |
| 3737 | return out;; |
| 3738 | } |
| 3739 | } else { |
| 3740 | at::Tensor tmp_output; |
| 3741 | { |
| 3742 | at::AutoDispatchSkipFunctionalize guard; |
| 3743 | tmp_output = at::_ops::divide_Tensor::call(self_, other_); |
| 3744 | } |
| 3745 | at::functionalization::impl::replace_(out, tmp_output); |
| 3746 | at::functionalization::impl::commit_update(out); |
| 3747 | at::functionalization::impl::sync(out); |
| 3748 | return out; |
| 3749 | } |
| 3750 | } |
| 3751 | |
| 3752 | at::Tensor & divide__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 3753 | if (true) { |
| 3754 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3755 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3756 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3757 | auto self_meta = to_meta(self); |
| 3758 | auto other_meta = to_meta(other); |
| 3759 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3760 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3761 | at::_ops::divide__Tensor::call(self_meta, other_meta); |
| 3762 | } |
| 3763 | |
| 3764 | at::Tensor self_; |
| 3765 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3766 | at::functionalization::impl::sync(self); |
| 3767 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3768 | } else { |
| 3769 | self_ = self; |
| 3770 | } |
| 3771 | |
| 3772 | at::Tensor other_; |
| 3773 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 3774 | at::functionalization::impl::sync(other); |
| 3775 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 3776 | } else { |
| 3777 | other_ = other; |
| 3778 | } |
| 3779 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 3780 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 3781 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3782 | TORCH_INTERNAL_ASSERT(false, |
| 3783 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3784 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3785 | } else { |
| 3786 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3787 | at::AutoDispatchSkipFunctionalize guard; |
| 3788 | at::Tensor tmp_output = at::_ops::divide__Tensor::call(self_, other_); |
| 3789 | return self;; |
| 3790 | } |
| 3791 | } else { |
| 3792 | at::Tensor tmp_output; |
| 3793 | { |
| 3794 | at::AutoDispatchSkipFunctionalize guard; |
| 3795 | tmp_output = at::_ops::divide_Tensor::call(self_, other_); |
| 3796 | } |
| 3797 | at::functionalization::impl::replace_(self, tmp_output); |
| 3798 | at::functionalization::impl::commit_update(self); |
| 3799 | at::functionalization::impl::sync(self); |
| 3800 | return self; |
| 3801 | } |
| 3802 | } |
| 3803 | |
| 3804 | at::Tensor & divide_out_out_mode(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode, at::Tensor & out) { |
| 3805 | if (false) { |
| 3806 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3807 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3808 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3809 | auto self_meta = to_meta(self); |
| 3810 | auto other_meta = to_meta(other); |
| 3811 | auto out_meta = to_meta(out); |
| 3812 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3813 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3814 | at::_ops::divide_out_mode::call(self_meta, other_meta, rounding_mode, out_meta); |
| 3815 | } |
| 3816 | |
| 3817 | at::Tensor self_; |
| 3818 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3819 | at::functionalization::impl::sync(self); |
| 3820 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3821 | } else { |
| 3822 | self_ = self; |
| 3823 | } |
| 3824 | |
| 3825 | at::Tensor other_; |
| 3826 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 3827 | at::functionalization::impl::sync(other); |
| 3828 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 3829 | } else { |
| 3830 | other_ = other; |
| 3831 | } |
| 3832 | |
| 3833 | at::Tensor out_; |
| 3834 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3835 | at::functionalization::impl::sync(out); |
| 3836 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3837 | } else { |
| 3838 | out_ = out; |
| 3839 | } |
| 3840 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3841 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 3842 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3843 | TORCH_INTERNAL_ASSERT(false, |
| 3844 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3845 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3846 | } else { |
| 3847 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3848 | at::AutoDispatchSkipFunctionalize guard; |
| 3849 | at::Tensor tmp_output = at::_ops::divide_out_mode::call(self_, other_, rounding_mode, out_); |
| 3850 | return out;; |
| 3851 | } |
| 3852 | } else { |
| 3853 | at::Tensor tmp_output; |
| 3854 | { |
| 3855 | at::AutoDispatchSkipFunctionalize guard; |
| 3856 | tmp_output = at::_ops::divide_Tensor_mode::call(self_, other_, rounding_mode); |
| 3857 | } |
| 3858 | at::functionalization::impl::replace_(out, tmp_output); |
| 3859 | at::functionalization::impl::commit_update(out); |
| 3860 | at::functionalization::impl::sync(out); |
| 3861 | return out; |
| 3862 | } |
| 3863 | } |
| 3864 | |
| 3865 | at::Tensor & divide__Tensor_mode(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode) { |
| 3866 | if (true) { |
| 3867 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3868 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3869 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3870 | auto self_meta = to_meta(self); |
| 3871 | auto other_meta = to_meta(other); |
| 3872 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3873 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3874 | at::_ops::divide__Tensor_mode::call(self_meta, other_meta, rounding_mode); |
| 3875 | } |
| 3876 | |
| 3877 | at::Tensor self_; |
| 3878 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3879 | at::functionalization::impl::sync(self); |
| 3880 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3881 | } else { |
| 3882 | self_ = self; |
| 3883 | } |
| 3884 | |
| 3885 | at::Tensor other_; |
| 3886 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 3887 | at::functionalization::impl::sync(other); |
| 3888 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 3889 | } else { |
| 3890 | other_ = other; |
| 3891 | } |
| 3892 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 3893 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 3894 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3895 | TORCH_INTERNAL_ASSERT(false, |
| 3896 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3897 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3898 | } else { |
| 3899 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3900 | at::AutoDispatchSkipFunctionalize guard; |
| 3901 | at::Tensor tmp_output = at::_ops::divide__Tensor_mode::call(self_, other_, rounding_mode); |
| 3902 | return self;; |
| 3903 | } |
| 3904 | } else { |
| 3905 | at::Tensor tmp_output; |
| 3906 | { |
| 3907 | at::AutoDispatchSkipFunctionalize guard; |
| 3908 | tmp_output = at::_ops::divide_Tensor_mode::call(self_, other_, rounding_mode); |
| 3909 | } |
| 3910 | at::functionalization::impl::replace_(self, tmp_output); |
| 3911 | at::functionalization::impl::commit_update(self); |
| 3912 | at::functionalization::impl::sync(self); |
| 3913 | return self; |
| 3914 | } |
| 3915 | } |
| 3916 | |
| 3917 | at::Tensor & true_divide_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 3918 | if (false) { |
| 3919 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3920 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3921 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3922 | auto self_meta = to_meta(self); |
| 3923 | auto other_meta = to_meta(other); |
| 3924 | auto out_meta = to_meta(out); |
| 3925 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3926 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3927 | at::_ops::true_divide_out::call(self_meta, other_meta, out_meta); |
| 3928 | } |
| 3929 | |
| 3930 | at::Tensor self_; |
| 3931 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3932 | at::functionalization::impl::sync(self); |
| 3933 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3934 | } else { |
| 3935 | self_ = self; |
| 3936 | } |
| 3937 | |
| 3938 | at::Tensor other_; |
| 3939 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 3940 | at::functionalization::impl::sync(other); |
| 3941 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 3942 | } else { |
| 3943 | other_ = other; |
| 3944 | } |
| 3945 | |
| 3946 | at::Tensor out_; |
| 3947 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 3948 | at::functionalization::impl::sync(out); |
| 3949 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 3950 | } else { |
| 3951 | out_ = out; |
| 3952 | } |
| 3953 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 3954 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 3955 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 3956 | TORCH_INTERNAL_ASSERT(false, |
| 3957 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 3958 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 3959 | } else { |
| 3960 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 3961 | at::AutoDispatchSkipFunctionalize guard; |
| 3962 | at::Tensor tmp_output = at::_ops::true_divide_out::call(self_, other_, out_); |
| 3963 | return out;; |
| 3964 | } |
| 3965 | } else { |
| 3966 | at::Tensor tmp_output; |
| 3967 | { |
| 3968 | at::AutoDispatchSkipFunctionalize guard; |
| 3969 | tmp_output = at::_ops::true_divide_Tensor::call(self_, other_); |
| 3970 | } |
| 3971 | at::functionalization::impl::replace_(out, tmp_output); |
| 3972 | at::functionalization::impl::commit_update(out); |
| 3973 | at::functionalization::impl::sync(out); |
| 3974 | return out; |
| 3975 | } |
| 3976 | } |
| 3977 | |
| 3978 | at::Tensor & true_divide__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 3979 | if (true) { |
| 3980 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 3981 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 3982 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 3983 | auto self_meta = to_meta(self); |
| 3984 | auto other_meta = to_meta(other); |
| 3985 | at::AutoDispatchSkipFunctionalize func_guard; |
| 3986 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 3987 | at::_ops::true_divide__Tensor::call(self_meta, other_meta); |
| 3988 | } |
| 3989 | |
| 3990 | at::Tensor self_; |
| 3991 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 3992 | at::functionalization::impl::sync(self); |
| 3993 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 3994 | } else { |
| 3995 | self_ = self; |
| 3996 | } |
| 3997 | |
| 3998 | at::Tensor other_; |
| 3999 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 4000 | at::functionalization::impl::sync(other); |
| 4001 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 4002 | } else { |
| 4003 | other_ = other; |
| 4004 | } |
| 4005 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 4006 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 4007 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4008 | TORCH_INTERNAL_ASSERT(false, |
| 4009 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4010 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4011 | } else { |
| 4012 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4013 | at::AutoDispatchSkipFunctionalize guard; |
| 4014 | at::Tensor tmp_output = at::_ops::true_divide__Tensor::call(self_, other_); |
| 4015 | return self;; |
| 4016 | } |
| 4017 | } else { |
| 4018 | at::Tensor tmp_output; |
| 4019 | { |
| 4020 | at::AutoDispatchSkipFunctionalize guard; |
| 4021 | tmp_output = at::_ops::true_divide_Tensor::call(self_, other_); |
| 4022 | } |
| 4023 | at::functionalization::impl::replace_(self, tmp_output); |
| 4024 | at::functionalization::impl::commit_update(self); |
| 4025 | at::functionalization::impl::sync(self); |
| 4026 | return self; |
| 4027 | } |
| 4028 | } |
| 4029 | |
| 4030 | at::Tensor & dot_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & tensor, at::Tensor & out) { |
| 4031 | if (false) { |
| 4032 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4033 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4034 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4035 | auto self_meta = to_meta(self); |
| 4036 | auto tensor_meta = to_meta(tensor); |
| 4037 | auto out_meta = to_meta(out); |
| 4038 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4039 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4040 | at::_ops::dot_out::call(self_meta, tensor_meta, out_meta); |
| 4041 | } |
| 4042 | |
| 4043 | at::Tensor self_; |
| 4044 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4045 | at::functionalization::impl::sync(self); |
| 4046 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4047 | } else { |
| 4048 | self_ = self; |
| 4049 | } |
| 4050 | |
| 4051 | at::Tensor tensor_; |
| 4052 | if (at::functionalization::impl::isFunctionalTensor(tensor)) { |
| 4053 | at::functionalization::impl::sync(tensor); |
| 4054 | tensor_ = at::functionalization::impl::from_functional_tensor(tensor); |
| 4055 | } else { |
| 4056 | tensor_ = tensor; |
| 4057 | } |
| 4058 | |
| 4059 | at::Tensor out_; |
| 4060 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4061 | at::functionalization::impl::sync(out); |
| 4062 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4063 | } else { |
| 4064 | out_ = out; |
| 4065 | } |
| 4066 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4067 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(tensor))) { |
| 4068 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4069 | TORCH_INTERNAL_ASSERT(false, |
| 4070 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4071 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4072 | } else { |
| 4073 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4074 | at::AutoDispatchSkipFunctionalize guard; |
| 4075 | at::Tensor tmp_output = at::_ops::dot_out::call(self_, tensor_, out_); |
| 4076 | return out;; |
| 4077 | } |
| 4078 | } else { |
| 4079 | at::Tensor tmp_output; |
| 4080 | { |
| 4081 | at::AutoDispatchSkipFunctionalize guard; |
| 4082 | tmp_output = at::_ops::dot::call(self_, tensor_); |
| 4083 | } |
| 4084 | at::functionalization::impl::replace_(out, tmp_output); |
| 4085 | at::functionalization::impl::commit_update(out); |
| 4086 | at::functionalization::impl::sync(out); |
| 4087 | return out; |
| 4088 | } |
| 4089 | } |
| 4090 | |
| 4091 | at::Tensor & vdot_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 4092 | if (false) { |
| 4093 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4094 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4095 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4096 | auto self_meta = to_meta(self); |
| 4097 | auto other_meta = to_meta(other); |
| 4098 | auto out_meta = to_meta(out); |
| 4099 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4100 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4101 | at::_ops::vdot_out::call(self_meta, other_meta, out_meta); |
| 4102 | } |
| 4103 | |
| 4104 | at::Tensor self_; |
| 4105 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4106 | at::functionalization::impl::sync(self); |
| 4107 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4108 | } else { |
| 4109 | self_ = self; |
| 4110 | } |
| 4111 | |
| 4112 | at::Tensor other_; |
| 4113 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 4114 | at::functionalization::impl::sync(other); |
| 4115 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 4116 | } else { |
| 4117 | other_ = other; |
| 4118 | } |
| 4119 | |
| 4120 | at::Tensor out_; |
| 4121 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4122 | at::functionalization::impl::sync(out); |
| 4123 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4124 | } else { |
| 4125 | out_ = out; |
| 4126 | } |
| 4127 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4128 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 4129 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4130 | TORCH_INTERNAL_ASSERT(false, |
| 4131 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4132 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4133 | } else { |
| 4134 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4135 | at::AutoDispatchSkipFunctionalize guard; |
| 4136 | at::Tensor tmp_output = at::_ops::vdot_out::call(self_, other_, out_); |
| 4137 | return out;; |
| 4138 | } |
| 4139 | } else { |
| 4140 | at::Tensor tmp_output; |
| 4141 | { |
| 4142 | at::AutoDispatchSkipFunctionalize guard; |
| 4143 | tmp_output = at::_ops::vdot::call(self_, other_); |
| 4144 | } |
| 4145 | at::functionalization::impl::replace_(out, tmp_output); |
| 4146 | at::functionalization::impl::commit_update(out); |
| 4147 | at::functionalization::impl::sync(out); |
| 4148 | return out; |
| 4149 | } |
| 4150 | } |
| 4151 | |
| 4152 | at::Tensor & row_stack_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Tensor & out) { |
| 4153 | if (false) { |
| 4154 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4155 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4156 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4157 | auto tensors_meta = to_meta(tensors); |
| 4158 | auto out_meta = to_meta(out); |
| 4159 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4160 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4161 | at::_ops::row_stack_out::call(tensors_meta, out_meta); |
| 4162 | } |
| 4163 | |
| 4164 | ::std::vector<at::Tensor> tensors_; |
| 4165 | if (at::functionalization::impl::isFunctionalTensor(tensors)) { |
| 4166 | at::functionalization::impl::sync(tensors); |
| 4167 | tensors_ = at::functionalization::impl::from_functional_tensor(tensors); |
| 4168 | } else { |
| 4169 | tensors_ = tensors.vec(); |
| 4170 | } |
| 4171 | |
| 4172 | at::Tensor out_; |
| 4173 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4174 | at::functionalization::impl::sync(out); |
| 4175 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4176 | } else { |
| 4177 | out_ = out; |
| 4178 | } |
| 4179 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4180 | if ((false || at::functionalization::impl::isFunctionalTensor(tensors))) { |
| 4181 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4182 | TORCH_INTERNAL_ASSERT(false, |
| 4183 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4184 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4185 | } else { |
| 4186 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4187 | at::AutoDispatchSkipFunctionalize guard; |
| 4188 | at::Tensor tmp_output = at::_ops::row_stack_out::call(tensors_, out_); |
| 4189 | return out;; |
| 4190 | } |
| 4191 | } else { |
| 4192 | at::Tensor tmp_output; |
| 4193 | { |
| 4194 | at::AutoDispatchSkipFunctionalize guard; |
| 4195 | tmp_output = at::_ops::row_stack::call(tensors_); |
| 4196 | } |
| 4197 | at::functionalization::impl::replace_(out, tmp_output); |
| 4198 | at::functionalization::impl::commit_update(out); |
| 4199 | at::functionalization::impl::sync(out); |
| 4200 | return out; |
| 4201 | } |
| 4202 | } |
| 4203 | |
| 4204 | at::Tensor & new_empty_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out) { |
| 4205 | if (false) { |
| 4206 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4207 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4208 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4209 | auto self_meta = to_meta(self); |
| 4210 | auto out_meta = to_meta(out); |
| 4211 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4212 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4213 | at::_ops::new_empty_out::call(self_meta, size, out_meta); |
| 4214 | } |
| 4215 | |
| 4216 | at::Tensor self_; |
| 4217 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4218 | at::functionalization::impl::sync(self); |
| 4219 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4220 | } else { |
| 4221 | self_ = self; |
| 4222 | } |
| 4223 | |
| 4224 | at::Tensor out_; |
| 4225 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4226 | at::functionalization::impl::sync(out); |
| 4227 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4228 | } else { |
| 4229 | out_ = out; |
| 4230 | } |
| 4231 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4232 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 4233 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4234 | TORCH_INTERNAL_ASSERT(false, |
| 4235 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4236 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4237 | } else { |
| 4238 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4239 | at::AutoDispatchSkipFunctionalize guard; |
| 4240 | at::Tensor tmp_output = at::_ops::new_empty_out::call(self_, size, out_); |
| 4241 | return out;; |
| 4242 | } |
| 4243 | } else { |
| 4244 | at::Tensor tmp_output; |
| 4245 | { |
| 4246 | at::AutoDispatchSkipFunctionalize guard; |
| 4247 | tmp_output = at::_ops::new_empty::call(self_, size, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 4248 | } |
| 4249 | at::functionalization::impl::replace_(out, tmp_output); |
| 4250 | at::functionalization::impl::commit_update(out); |
| 4251 | at::functionalization::impl::sync(out); |
| 4252 | return out; |
| 4253 | } |
| 4254 | } |
| 4255 | |
| 4256 | at::Tensor & new_empty_strided_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::Tensor & out) { |
| 4257 | if (false) { |
| 4258 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4259 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4260 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4261 | auto self_meta = to_meta(self); |
| 4262 | auto out_meta = to_meta(out); |
| 4263 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4264 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4265 | at::_ops::new_empty_strided_out::call(self_meta, size, stride, out_meta); |
| 4266 | } |
| 4267 | |
| 4268 | at::Tensor self_; |
| 4269 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4270 | at::functionalization::impl::sync(self); |
| 4271 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4272 | } else { |
| 4273 | self_ = self; |
| 4274 | } |
| 4275 | |
| 4276 | at::Tensor out_; |
| 4277 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4278 | at::functionalization::impl::sync(out); |
| 4279 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4280 | } else { |
| 4281 | out_ = out; |
| 4282 | } |
| 4283 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4284 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 4285 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4286 | TORCH_INTERNAL_ASSERT(false, |
| 4287 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4288 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4289 | } else { |
| 4290 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4291 | at::AutoDispatchSkipFunctionalize guard; |
| 4292 | at::Tensor tmp_output = at::_ops::new_empty_strided_out::call(self_, size, stride, out_); |
| 4293 | return out;; |
| 4294 | } |
| 4295 | } else { |
| 4296 | at::Tensor tmp_output; |
| 4297 | { |
| 4298 | at::AutoDispatchSkipFunctionalize guard; |
| 4299 | tmp_output = at::_ops::new_empty_strided::call(self_, size, stride, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 4300 | } |
| 4301 | at::functionalization::impl::replace_(out, tmp_output); |
| 4302 | at::functionalization::impl::commit_update(out); |
| 4303 | at::functionalization::impl::sync(out); |
| 4304 | return out; |
| 4305 | } |
| 4306 | } |
| 4307 | |
| 4308 | at::Tensor & _empty_affine_quantized_out_out(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, double scale, int64_t zero_point, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out) { |
| 4309 | if (false) { |
| 4310 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4311 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4312 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4313 | auto out_meta = to_meta(out); |
| 4314 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4315 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4316 | at::_ops::_empty_affine_quantized_out::call(size, scale, zero_point, memory_format, out_meta); |
| 4317 | } |
| 4318 | |
| 4319 | at::Tensor out_; |
| 4320 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4321 | at::functionalization::impl::sync(out); |
| 4322 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4323 | } else { |
| 4324 | out_ = out; |
| 4325 | } |
| 4326 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4327 | if ((false)) { |
| 4328 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4329 | TORCH_INTERNAL_ASSERT(false, |
| 4330 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4331 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4332 | } else { |
| 4333 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4334 | at::AutoDispatchSkipFunctionalize guard; |
| 4335 | at::Tensor tmp_output = at::_ops::_empty_affine_quantized_out::call(size, scale, zero_point, memory_format, out_); |
| 4336 | return out;; |
| 4337 | } |
| 4338 | } else { |
| 4339 | at::Tensor tmp_output; |
| 4340 | { |
| 4341 | at::AutoDispatchSkipFunctionalize guard; |
| 4342 | tmp_output = at::_ops::_empty_affine_quantized::call(size, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt, scale, zero_point, memory_format); |
| 4343 | } |
| 4344 | at::functionalization::impl::replace_(out, tmp_output); |
| 4345 | at::functionalization::impl::commit_update(out); |
| 4346 | at::functionalization::impl::sync(out); |
| 4347 | return out; |
| 4348 | } |
| 4349 | } |
| 4350 | |
| 4351 | const at::Tensor & _resize_output_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::Device device, const at::Tensor & out) { |
| 4352 | if (false) { |
| 4353 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4354 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4355 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4356 | auto self_meta = to_meta(self); |
| 4357 | auto out_meta = to_meta(out); |
| 4358 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4359 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4360 | at::_ops::_resize_output_out::call(self_meta, size, device, out_meta); |
| 4361 | } |
| 4362 | |
| 4363 | at::Tensor self_; |
| 4364 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4365 | at::functionalization::impl::sync(self); |
| 4366 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4367 | } else { |
| 4368 | self_ = self; |
| 4369 | } |
| 4370 | |
| 4371 | at::Tensor out_; |
| 4372 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4373 | at::functionalization::impl::sync(out); |
| 4374 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4375 | } else { |
| 4376 | out_ = out; |
| 4377 | } |
| 4378 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4379 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 4380 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4381 | TORCH_INTERNAL_ASSERT(false, |
| 4382 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4383 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4384 | } else { |
| 4385 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4386 | at::AutoDispatchSkipFunctionalize guard; |
| 4387 | at::Tensor tmp_output = at::_ops::_resize_output_out::call(self_, size, device, out_); |
| 4388 | return out;; |
| 4389 | } |
| 4390 | } else { |
| 4391 | at::Tensor tmp_output; |
| 4392 | { |
| 4393 | at::AutoDispatchSkipFunctionalize guard; |
| 4394 | tmp_output = at::_ops::_resize_output::call(self_, size, device); |
| 4395 | } |
| 4396 | at::functionalization::impl::replace_(out, tmp_output); |
| 4397 | at::functionalization::impl::commit_update(out); |
| 4398 | at::functionalization::impl::sync(out); |
| 4399 | return out; |
| 4400 | } |
| 4401 | } |
| 4402 | |
| 4403 | const at::Tensor & _resize_output_(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::Device device) { |
| 4404 | if (true) { |
| 4405 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4406 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4407 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4408 | auto self_meta = to_meta(self); |
| 4409 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4410 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4411 | at::_ops::_resize_output_::call(self_meta, size, device); |
| 4412 | } |
| 4413 | |
| 4414 | at::Tensor self_; |
| 4415 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4416 | at::functionalization::impl::sync(self); |
| 4417 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4418 | } else { |
| 4419 | self_ = self; |
| 4420 | } |
| 4421 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 4422 | if ((false)) { |
| 4423 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4424 | TORCH_INTERNAL_ASSERT(false, |
| 4425 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4426 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4427 | } else { |
| 4428 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4429 | at::AutoDispatchSkipFunctionalize guard; |
| 4430 | at::Tensor tmp_output = at::_ops::_resize_output_::call(self_, size, device); |
| 4431 | return self;; |
| 4432 | } |
| 4433 | } else { |
| 4434 | at::Tensor tmp_output; |
| 4435 | { |
| 4436 | at::AutoDispatchSkipFunctionalize guard; |
| 4437 | tmp_output = at::_ops::_resize_output::call(self_, size, device); |
| 4438 | } |
| 4439 | at::functionalization::impl::replace_(self, tmp_output); |
| 4440 | at::functionalization::impl::commit_update(self); |
| 4441 | at::functionalization::impl::sync(self); |
| 4442 | return self; |
| 4443 | } |
| 4444 | } |
| 4445 | |
| 4446 | at::Tensor & frac_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 4447 | if (false) { |
| 4448 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4449 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4450 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4451 | auto self_meta = to_meta(self); |
| 4452 | auto out_meta = to_meta(out); |
| 4453 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4454 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4455 | at::_ops::frac_out::call(self_meta, out_meta); |
| 4456 | } |
| 4457 | |
| 4458 | at::Tensor self_; |
| 4459 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4460 | at::functionalization::impl::sync(self); |
| 4461 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4462 | } else { |
| 4463 | self_ = self; |
| 4464 | } |
| 4465 | |
| 4466 | at::Tensor out_; |
| 4467 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4468 | at::functionalization::impl::sync(out); |
| 4469 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4470 | } else { |
| 4471 | out_ = out; |
| 4472 | } |
| 4473 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4474 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 4475 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4476 | TORCH_INTERNAL_ASSERT(false, |
| 4477 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4478 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4479 | } else { |
| 4480 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4481 | at::AutoDispatchSkipFunctionalize guard; |
| 4482 | at::Tensor tmp_output = at::_ops::frac_out::call(self_, out_); |
| 4483 | return out;; |
| 4484 | } |
| 4485 | } else { |
| 4486 | at::Tensor tmp_output; |
| 4487 | { |
| 4488 | at::AutoDispatchSkipFunctionalize guard; |
| 4489 | tmp_output = at::_ops::frac::call(self_); |
| 4490 | } |
| 4491 | at::functionalization::impl::replace_(out, tmp_output); |
| 4492 | at::functionalization::impl::commit_update(out); |
| 4493 | at::functionalization::impl::sync(out); |
| 4494 | return out; |
| 4495 | } |
| 4496 | } |
| 4497 | |
| 4498 | at::Tensor & frac_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 4499 | if (true) { |
| 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 self_meta = to_meta(self); |
| 4504 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4505 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4506 | at::_ops::frac_::call(self_meta); |
| 4507 | } |
| 4508 | |
| 4509 | at::Tensor self_; |
| 4510 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4511 | at::functionalization::impl::sync(self); |
| 4512 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4513 | } else { |
| 4514 | self_ = self; |
| 4515 | } |
| 4516 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 4517 | if ((false)) { |
| 4518 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4519 | TORCH_INTERNAL_ASSERT(false, |
| 4520 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4521 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4522 | } else { |
| 4523 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4524 | at::AutoDispatchSkipFunctionalize guard; |
| 4525 | at::Tensor tmp_output = at::_ops::frac_::call(self_); |
| 4526 | return self;; |
| 4527 | } |
| 4528 | } else { |
| 4529 | at::Tensor tmp_output; |
| 4530 | { |
| 4531 | at::AutoDispatchSkipFunctionalize guard; |
| 4532 | tmp_output = at::_ops::frac::call(self_); |
| 4533 | } |
| 4534 | at::functionalization::impl::replace_(self, tmp_output); |
| 4535 | at::functionalization::impl::commit_update(self); |
| 4536 | at::functionalization::impl::sync(self); |
| 4537 | return self; |
| 4538 | } |
| 4539 | } |
| 4540 | |
| 4541 | at::Tensor & full_like_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & fill_value, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out) { |
| 4542 | if (false) { |
| 4543 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4544 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4545 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4546 | auto self_meta = to_meta(self); |
| 4547 | auto out_meta = to_meta(out); |
| 4548 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4549 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4550 | at::_ops::full_like_out::call(self_meta, fill_value, memory_format, out_meta); |
| 4551 | } |
| 4552 | |
| 4553 | at::Tensor self_; |
| 4554 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4555 | at::functionalization::impl::sync(self); |
| 4556 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4557 | } else { |
| 4558 | self_ = self; |
| 4559 | } |
| 4560 | |
| 4561 | at::Tensor out_; |
| 4562 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4563 | at::functionalization::impl::sync(out); |
| 4564 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4565 | } else { |
| 4566 | out_ = out; |
| 4567 | } |
| 4568 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4569 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 4570 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4571 | TORCH_INTERNAL_ASSERT(false, |
| 4572 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4573 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4574 | } else { |
| 4575 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4576 | at::AutoDispatchSkipFunctionalize guard; |
| 4577 | at::Tensor tmp_output = at::_ops::full_like_out::call(self_, fill_value, memory_format, out_); |
| 4578 | return out;; |
| 4579 | } |
| 4580 | } else { |
| 4581 | at::Tensor tmp_output; |
| 4582 | { |
| 4583 | at::AutoDispatchSkipFunctionalize guard; |
| 4584 | tmp_output = at::_ops::full_like::call(self_, fill_value, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt, memory_format); |
| 4585 | } |
| 4586 | at::functionalization::impl::replace_(out, tmp_output); |
| 4587 | at::functionalization::impl::commit_update(out); |
| 4588 | at::functionalization::impl::sync(out); |
| 4589 | return out; |
| 4590 | } |
| 4591 | } |
| 4592 | |
| 4593 | at::Tensor & lcm_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 4594 | if (false) { |
| 4595 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4596 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4597 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4598 | auto self_meta = to_meta(self); |
| 4599 | auto other_meta = to_meta(other); |
| 4600 | auto out_meta = to_meta(out); |
| 4601 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4602 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4603 | at::_ops::lcm_out::call(self_meta, other_meta, out_meta); |
| 4604 | } |
| 4605 | |
| 4606 | at::Tensor self_; |
| 4607 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4608 | at::functionalization::impl::sync(self); |
| 4609 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4610 | } else { |
| 4611 | self_ = self; |
| 4612 | } |
| 4613 | |
| 4614 | at::Tensor other_; |
| 4615 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 4616 | at::functionalization::impl::sync(other); |
| 4617 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 4618 | } else { |
| 4619 | other_ = other; |
| 4620 | } |
| 4621 | |
| 4622 | at::Tensor out_; |
| 4623 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4624 | at::functionalization::impl::sync(out); |
| 4625 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4626 | } else { |
| 4627 | out_ = out; |
| 4628 | } |
| 4629 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4630 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 4631 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4632 | TORCH_INTERNAL_ASSERT(false, |
| 4633 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4634 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4635 | } else { |
| 4636 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4637 | at::AutoDispatchSkipFunctionalize guard; |
| 4638 | at::Tensor tmp_output = at::_ops::lcm_out::call(self_, other_, out_); |
| 4639 | return out;; |
| 4640 | } |
| 4641 | } else { |
| 4642 | at::Tensor tmp_output; |
| 4643 | { |
| 4644 | at::AutoDispatchSkipFunctionalize guard; |
| 4645 | tmp_output = at::_ops::lcm::call(self_, other_); |
| 4646 | } |
| 4647 | at::functionalization::impl::replace_(out, tmp_output); |
| 4648 | at::functionalization::impl::commit_update(out); |
| 4649 | at::functionalization::impl::sync(out); |
| 4650 | return out; |
| 4651 | } |
| 4652 | } |
| 4653 | |
| 4654 | at::Tensor & lcm_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 4655 | if (true) { |
| 4656 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4657 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4658 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4659 | auto self_meta = to_meta(self); |
| 4660 | auto other_meta = to_meta(other); |
| 4661 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4662 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4663 | at::_ops::lcm_::call(self_meta, other_meta); |
| 4664 | } |
| 4665 | |
| 4666 | at::Tensor self_; |
| 4667 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4668 | at::functionalization::impl::sync(self); |
| 4669 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4670 | } else { |
| 4671 | self_ = self; |
| 4672 | } |
| 4673 | |
| 4674 | at::Tensor other_; |
| 4675 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 4676 | at::functionalization::impl::sync(other); |
| 4677 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 4678 | } else { |
| 4679 | other_ = other; |
| 4680 | } |
| 4681 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 4682 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 4683 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4684 | TORCH_INTERNAL_ASSERT(false, |
| 4685 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4686 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4687 | } else { |
| 4688 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4689 | at::AutoDispatchSkipFunctionalize guard; |
| 4690 | at::Tensor tmp_output = at::_ops::lcm_::call(self_, other_); |
| 4691 | return self;; |
| 4692 | } |
| 4693 | } else { |
| 4694 | at::Tensor tmp_output; |
| 4695 | { |
| 4696 | at::AutoDispatchSkipFunctionalize guard; |
| 4697 | tmp_output = at::_ops::lcm::call(self_, other_); |
| 4698 | } |
| 4699 | at::functionalization::impl::replace_(self, tmp_output); |
| 4700 | at::functionalization::impl::commit_update(self); |
| 4701 | at::functionalization::impl::sync(self); |
| 4702 | return self; |
| 4703 | } |
| 4704 | } |
| 4705 | |
| 4706 | at::Tensor & hann_window_out_out(c10::DispatchKeySet dispatchKeySet, int64_t window_length, at::Tensor & out) { |
| 4707 | if (false) { |
| 4708 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4709 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4710 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4711 | auto out_meta = to_meta(out); |
| 4712 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4713 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4714 | at::_ops::hann_window_out::call(window_length, out_meta); |
| 4715 | } |
| 4716 | |
| 4717 | at::Tensor out_; |
| 4718 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4719 | at::functionalization::impl::sync(out); |
| 4720 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4721 | } else { |
| 4722 | out_ = out; |
| 4723 | } |
| 4724 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4725 | if ((false)) { |
| 4726 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4727 | TORCH_INTERNAL_ASSERT(false, |
| 4728 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4729 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4730 | } else { |
| 4731 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4732 | at::AutoDispatchSkipFunctionalize guard; |
| 4733 | at::Tensor tmp_output = at::_ops::hann_window_out::call(window_length, out_); |
| 4734 | return out;; |
| 4735 | } |
| 4736 | } else { |
| 4737 | at::Tensor tmp_output; |
| 4738 | { |
| 4739 | at::AutoDispatchSkipFunctionalize guard; |
| 4740 | tmp_output = at::_ops::hann_window::call(window_length, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 4741 | } |
| 4742 | at::functionalization::impl::replace_(out, tmp_output); |
| 4743 | at::functionalization::impl::commit_update(out); |
| 4744 | at::functionalization::impl::sync(out); |
| 4745 | return out; |
| 4746 | } |
| 4747 | } |
| 4748 | |
| 4749 | at::Tensor & hann_window_out_periodic_out(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, at::Tensor & out) { |
| 4750 | if (false) { |
| 4751 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4752 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4753 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4754 | auto out_meta = to_meta(out); |
| 4755 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4756 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4757 | at::_ops::hann_window_periodic_out::call(window_length, periodic, out_meta); |
| 4758 | } |
| 4759 | |
| 4760 | at::Tensor out_; |
| 4761 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4762 | at::functionalization::impl::sync(out); |
| 4763 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4764 | } else { |
| 4765 | out_ = out; |
| 4766 | } |
| 4767 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4768 | if ((false)) { |
| 4769 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4770 | TORCH_INTERNAL_ASSERT(false, |
| 4771 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4772 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4773 | } else { |
| 4774 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4775 | at::AutoDispatchSkipFunctionalize guard; |
| 4776 | at::Tensor tmp_output = at::_ops::hann_window_periodic_out::call(window_length, periodic, out_); |
| 4777 | return out;; |
| 4778 | } |
| 4779 | } else { |
| 4780 | at::Tensor tmp_output; |
| 4781 | { |
| 4782 | at::AutoDispatchSkipFunctionalize guard; |
| 4783 | tmp_output = at::_ops::hann_window_periodic::call(window_length, periodic, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 4784 | } |
| 4785 | at::functionalization::impl::replace_(out, tmp_output); |
| 4786 | at::functionalization::impl::commit_update(out); |
| 4787 | at::functionalization::impl::sync(out); |
| 4788 | return out; |
| 4789 | } |
| 4790 | } |
| 4791 | |
| 4792 | at::Tensor & kaiser_window_out_out(c10::DispatchKeySet dispatchKeySet, int64_t window_length, at::Tensor & out) { |
| 4793 | if (false) { |
| 4794 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4795 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4796 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4797 | auto out_meta = to_meta(out); |
| 4798 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4799 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4800 | at::_ops::kaiser_window_out::call(window_length, out_meta); |
| 4801 | } |
| 4802 | |
| 4803 | at::Tensor out_; |
| 4804 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4805 | at::functionalization::impl::sync(out); |
| 4806 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4807 | } else { |
| 4808 | out_ = out; |
| 4809 | } |
| 4810 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4811 | if ((false)) { |
| 4812 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4813 | TORCH_INTERNAL_ASSERT(false, |
| 4814 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4815 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4816 | } else { |
| 4817 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4818 | at::AutoDispatchSkipFunctionalize guard; |
| 4819 | at::Tensor tmp_output = at::_ops::kaiser_window_out::call(window_length, out_); |
| 4820 | return out;; |
| 4821 | } |
| 4822 | } else { |
| 4823 | at::Tensor tmp_output; |
| 4824 | { |
| 4825 | at::AutoDispatchSkipFunctionalize guard; |
| 4826 | tmp_output = at::_ops::kaiser_window::call(window_length, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 4827 | } |
| 4828 | at::functionalization::impl::replace_(out, tmp_output); |
| 4829 | at::functionalization::impl::commit_update(out); |
| 4830 | at::functionalization::impl::sync(out); |
| 4831 | return out; |
| 4832 | } |
| 4833 | } |
| 4834 | |
| 4835 | at::Tensor & kaiser_window_out_periodic_out(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, at::Tensor & out) { |
| 4836 | if (false) { |
| 4837 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4838 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4839 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4840 | auto out_meta = to_meta(out); |
| 4841 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4842 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4843 | at::_ops::kaiser_window_periodic_out::call(window_length, periodic, out_meta); |
| 4844 | } |
| 4845 | |
| 4846 | at::Tensor out_; |
| 4847 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4848 | at::functionalization::impl::sync(out); |
| 4849 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4850 | } else { |
| 4851 | out_ = out; |
| 4852 | } |
| 4853 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4854 | if ((false)) { |
| 4855 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4856 | TORCH_INTERNAL_ASSERT(false, |
| 4857 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4858 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4859 | } else { |
| 4860 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4861 | at::AutoDispatchSkipFunctionalize guard; |
| 4862 | at::Tensor tmp_output = at::_ops::kaiser_window_periodic_out::call(window_length, periodic, out_); |
| 4863 | return out;; |
| 4864 | } |
| 4865 | } else { |
| 4866 | at::Tensor tmp_output; |
| 4867 | { |
| 4868 | at::AutoDispatchSkipFunctionalize guard; |
| 4869 | tmp_output = at::_ops::kaiser_window_periodic::call(window_length, periodic, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 4870 | } |
| 4871 | at::functionalization::impl::replace_(out, tmp_output); |
| 4872 | at::functionalization::impl::commit_update(out); |
| 4873 | at::functionalization::impl::sync(out); |
| 4874 | return out; |
| 4875 | } |
| 4876 | } |
| 4877 | |
| 4878 | at::Tensor & kaiser_window_out_beta_out(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, double beta, at::Tensor & out) { |
| 4879 | if (false) { |
| 4880 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4881 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4882 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4883 | auto out_meta = to_meta(out); |
| 4884 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4885 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4886 | at::_ops::kaiser_window_beta_out::call(window_length, periodic, beta, out_meta); |
| 4887 | } |
| 4888 | |
| 4889 | at::Tensor out_; |
| 4890 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4891 | at::functionalization::impl::sync(out); |
| 4892 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4893 | } else { |
| 4894 | out_ = out; |
| 4895 | } |
| 4896 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4897 | if ((false)) { |
| 4898 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4899 | TORCH_INTERNAL_ASSERT(false, |
| 4900 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4901 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4902 | } else { |
| 4903 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4904 | at::AutoDispatchSkipFunctionalize guard; |
| 4905 | at::Tensor tmp_output = at::_ops::kaiser_window_beta_out::call(window_length, periodic, beta, out_); |
| 4906 | return out;; |
| 4907 | } |
| 4908 | } else { |
| 4909 | at::Tensor tmp_output; |
| 4910 | { |
| 4911 | at::AutoDispatchSkipFunctionalize guard; |
| 4912 | tmp_output = at::_ops::kaiser_window_beta::call(window_length, periodic, beta, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 4913 | } |
| 4914 | at::functionalization::impl::replace_(out, tmp_output); |
| 4915 | at::functionalization::impl::commit_update(out); |
| 4916 | at::functionalization::impl::sync(out); |
| 4917 | return out; |
| 4918 | } |
| 4919 | } |
| 4920 | |
| 4921 | at::Tensor & _index_put_impl_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::List<c10::optional<at::Tensor>> & indices, const at::Tensor & values, bool accumulate, bool unsafe, at::Tensor & out) { |
| 4922 | if (false) { |
| 4923 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4924 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4925 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4926 | auto self_meta = to_meta(self); |
| 4927 | auto indices_meta = to_meta(indices); |
| 4928 | auto values_meta = to_meta(values); |
| 4929 | auto out_meta = to_meta(out); |
| 4930 | at::AutoDispatchSkipFunctionalize func_guard; |
| 4931 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 4932 | at::_ops::_index_put_impl_out::call(self_meta, indices_meta, values_meta, accumulate, unsafe, out_meta); |
| 4933 | } |
| 4934 | |
| 4935 | at::Tensor self_; |
| 4936 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 4937 | at::functionalization::impl::sync(self); |
| 4938 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 4939 | } else { |
| 4940 | self_ = self; |
| 4941 | } |
| 4942 | |
| 4943 | c10::List<c10::optional<at::Tensor>> indices_; |
| 4944 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 4945 | at::functionalization::impl::sync(indices); |
| 4946 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 4947 | } else { |
| 4948 | indices_ = indices; |
| 4949 | } |
| 4950 | |
| 4951 | at::Tensor values_; |
| 4952 | if (at::functionalization::impl::isFunctionalTensor(values)) { |
| 4953 | at::functionalization::impl::sync(values); |
| 4954 | values_ = at::functionalization::impl::from_functional_tensor(values); |
| 4955 | } else { |
| 4956 | values_ = values; |
| 4957 | } |
| 4958 | |
| 4959 | at::Tensor out_; |
| 4960 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 4961 | at::functionalization::impl::sync(out); |
| 4962 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 4963 | } else { |
| 4964 | out_ = out; |
| 4965 | } |
| 4966 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 4967 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(indices) || at::functionalization::impl::isFunctionalTensor(values))) { |
| 4968 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 4969 | TORCH_INTERNAL_ASSERT(false, |
| 4970 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 4971 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 4972 | } else { |
| 4973 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 4974 | at::AutoDispatchSkipFunctionalize guard; |
| 4975 | at::Tensor tmp_output = at::_ops::_index_put_impl_out::call(self_, indices_, values_, accumulate, unsafe, out_); |
| 4976 | return out;; |
| 4977 | } |
| 4978 | } else { |
| 4979 | at::Tensor tmp_output; |
| 4980 | { |
| 4981 | at::AutoDispatchSkipFunctionalize guard; |
| 4982 | tmp_output = at::_ops::_index_put_impl::call(self_, indices_, values_, accumulate, unsafe); |
| 4983 | } |
| 4984 | at::functionalization::impl::replace_(out, tmp_output); |
| 4985 | at::functionalization::impl::commit_update(out); |
| 4986 | at::functionalization::impl::sync(out); |
| 4987 | return out; |
| 4988 | } |
| 4989 | } |
| 4990 | |
| 4991 | at::Tensor & _index_put_impl_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const c10::List<c10::optional<at::Tensor>> & indices, const at::Tensor & values, bool accumulate, bool unsafe) { |
| 4992 | if (true) { |
| 4993 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 4994 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 4995 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 4996 | auto self_meta = to_meta(self); |
| 4997 | auto indices_meta = to_meta(indices); |
| 4998 | auto values_meta = to_meta(values); |
| 4999 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5000 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5001 | at::_ops::_index_put_impl_::call(self_meta, indices_meta, values_meta, accumulate, unsafe); |
| 5002 | } |
| 5003 | |
| 5004 | at::Tensor self_; |
| 5005 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5006 | at::functionalization::impl::sync(self); |
| 5007 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5008 | } else { |
| 5009 | self_ = self; |
| 5010 | } |
| 5011 | |
| 5012 | c10::List<c10::optional<at::Tensor>> indices_; |
| 5013 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 5014 | at::functionalization::impl::sync(indices); |
| 5015 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 5016 | } else { |
| 5017 | indices_ = indices; |
| 5018 | } |
| 5019 | |
| 5020 | at::Tensor values_; |
| 5021 | if (at::functionalization::impl::isFunctionalTensor(values)) { |
| 5022 | at::functionalization::impl::sync(values); |
| 5023 | values_ = at::functionalization::impl::from_functional_tensor(values); |
| 5024 | } else { |
| 5025 | values_ = values; |
| 5026 | } |
| 5027 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 5028 | if ((false || at::functionalization::impl::isFunctionalTensor(indices) || at::functionalization::impl::isFunctionalTensor(values))) { |
| 5029 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5030 | TORCH_INTERNAL_ASSERT(false, |
| 5031 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5032 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5033 | } else { |
| 5034 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5035 | at::AutoDispatchSkipFunctionalize guard; |
| 5036 | at::Tensor tmp_output = at::_ops::_index_put_impl_::call(self_, indices_, values_, accumulate, unsafe); |
| 5037 | return self;; |
| 5038 | } |
| 5039 | } else { |
| 5040 | at::Tensor tmp_output; |
| 5041 | { |
| 5042 | at::AutoDispatchSkipFunctionalize guard; |
| 5043 | tmp_output = at::_ops::_index_put_impl::call(self_, indices_, values_, accumulate, unsafe); |
| 5044 | } |
| 5045 | at::functionalization::impl::replace_(self, tmp_output); |
| 5046 | at::functionalization::impl::commit_update(self); |
| 5047 | at::functionalization::impl::sync(self); |
| 5048 | return self; |
| 5049 | } |
| 5050 | } |
| 5051 | |
| 5052 | ::std::tuple<at::Tensor &,at::Tensor &> kthvalue_out_values(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t k, int64_t dim, bool keepdim, at::Tensor & values, at::Tensor & indices) { |
| 5053 | if (false) { |
| 5054 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5055 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5056 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5057 | auto self_meta = to_meta(self); |
| 5058 | auto values_meta = to_meta(values); |
| 5059 | auto indices_meta = to_meta(indices); |
| 5060 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5061 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5062 | at::_ops::kthvalue_values::call(self_meta, k, dim, keepdim, values_meta, indices_meta); |
| 5063 | } |
| 5064 | |
| 5065 | at::Tensor self_; |
| 5066 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5067 | at::functionalization::impl::sync(self); |
| 5068 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5069 | } else { |
| 5070 | self_ = self; |
| 5071 | } |
| 5072 | |
| 5073 | at::Tensor values_; |
| 5074 | if (at::functionalization::impl::isFunctionalTensor(values)) { |
| 5075 | at::functionalization::impl::sync(values); |
| 5076 | values_ = at::functionalization::impl::from_functional_tensor(values); |
| 5077 | } else { |
| 5078 | values_ = values; |
| 5079 | } |
| 5080 | |
| 5081 | at::Tensor indices_; |
| 5082 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 5083 | at::functionalization::impl::sync(indices); |
| 5084 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 5085 | } else { |
| 5086 | indices_ = indices; |
| 5087 | } |
| 5088 | if (!(true && at::functionalization::impl::isFunctionalTensor(values) && at::functionalization::impl::isFunctionalTensor(indices))) { |
| 5089 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 5090 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5091 | TORCH_INTERNAL_ASSERT(false, |
| 5092 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5093 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5094 | } else { |
| 5095 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5096 | at::AutoDispatchSkipFunctionalize guard; |
| 5097 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::kthvalue_values::call(self_, k, dim, keepdim, values_, indices_); |
| 5098 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices);; |
| 5099 | } |
| 5100 | } else { |
| 5101 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 5102 | { |
| 5103 | at::AutoDispatchSkipFunctionalize guard; |
| 5104 | tmp_output = at::_ops::kthvalue::call(self_, k, dim, keepdim); |
| 5105 | } |
| 5106 | at::functionalization::impl::replace_(values, std::get<0>(tmp_output)); |
| 5107 | at::functionalization::impl::commit_update(values); |
| 5108 | at::functionalization::impl::sync(values); |
| 5109 | at::functionalization::impl::replace_(indices, std::get<1>(tmp_output)); |
| 5110 | at::functionalization::impl::commit_update(indices); |
| 5111 | at::functionalization::impl::sync(indices); |
| 5112 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices); |
| 5113 | } |
| 5114 | } |
| 5115 | |
| 5116 | ::std::tuple<at::Tensor &,at::Tensor &> kthvalue_out_dimname_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t k, at::Dimname dim, bool keepdim, at::Tensor & values, at::Tensor & indices) { |
| 5117 | if (false) { |
| 5118 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5119 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5120 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5121 | auto self_meta = to_meta(self); |
| 5122 | auto values_meta = to_meta(values); |
| 5123 | auto indices_meta = to_meta(indices); |
| 5124 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5125 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5126 | at::_ops::kthvalue_dimname_out::call(self_meta, k, dim, keepdim, values_meta, indices_meta); |
| 5127 | } |
| 5128 | |
| 5129 | at::Tensor self_; |
| 5130 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5131 | at::functionalization::impl::sync(self); |
| 5132 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5133 | } else { |
| 5134 | self_ = self; |
| 5135 | } |
| 5136 | |
| 5137 | at::Tensor values_; |
| 5138 | if (at::functionalization::impl::isFunctionalTensor(values)) { |
| 5139 | at::functionalization::impl::sync(values); |
| 5140 | values_ = at::functionalization::impl::from_functional_tensor(values); |
| 5141 | } else { |
| 5142 | values_ = values; |
| 5143 | } |
| 5144 | |
| 5145 | at::Tensor indices_; |
| 5146 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 5147 | at::functionalization::impl::sync(indices); |
| 5148 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 5149 | } else { |
| 5150 | indices_ = indices; |
| 5151 | } |
| 5152 | if (!(true && at::functionalization::impl::isFunctionalTensor(values) && at::functionalization::impl::isFunctionalTensor(indices))) { |
| 5153 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 5154 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5155 | TORCH_INTERNAL_ASSERT(false, |
| 5156 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5157 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5158 | } else { |
| 5159 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5160 | at::AutoDispatchSkipFunctionalize guard; |
| 5161 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::kthvalue_dimname_out::call(self_, k, dim, keepdim, values_, indices_); |
| 5162 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices);; |
| 5163 | } |
| 5164 | } else { |
| 5165 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 5166 | { |
| 5167 | at::AutoDispatchSkipFunctionalize guard; |
| 5168 | tmp_output = at::_ops::kthvalue_dimname::call(self_, k, dim, keepdim); |
| 5169 | } |
| 5170 | at::functionalization::impl::replace_(values, std::get<0>(tmp_output)); |
| 5171 | at::functionalization::impl::commit_update(values); |
| 5172 | at::functionalization::impl::sync(values); |
| 5173 | at::functionalization::impl::replace_(indices, std::get<1>(tmp_output)); |
| 5174 | at::functionalization::impl::commit_update(indices); |
| 5175 | at::functionalization::impl::sync(indices); |
| 5176 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices); |
| 5177 | } |
| 5178 | } |
| 5179 | |
| 5180 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_layer_norm_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, double eps, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 5181 | if (false) { |
| 5182 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5183 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5184 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5185 | auto input_meta = to_meta(input); |
| 5186 | auto weight_meta = to_meta(weight); |
| 5187 | auto bias_meta = to_meta(bias); |
| 5188 | auto out0_meta = to_meta(out0); |
| 5189 | auto out1_meta = to_meta(out1); |
| 5190 | auto out2_meta = to_meta(out2); |
| 5191 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5192 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5193 | at::_ops::native_layer_norm_out::call(input_meta, normalized_shape, weight_meta, bias_meta, eps, out0_meta, out1_meta, out2_meta); |
| 5194 | } |
| 5195 | |
| 5196 | at::Tensor input_; |
| 5197 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 5198 | at::functionalization::impl::sync(input); |
| 5199 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 5200 | } else { |
| 5201 | input_ = input; |
| 5202 | } |
| 5203 | |
| 5204 | c10::optional<at::Tensor> weight_; |
| 5205 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 5206 | at::functionalization::impl::sync(weight); |
| 5207 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 5208 | } else { |
| 5209 | weight_ = weight; |
| 5210 | } |
| 5211 | |
| 5212 | c10::optional<at::Tensor> bias_; |
| 5213 | if (at::functionalization::impl::isFunctionalTensor(bias)) { |
| 5214 | at::functionalization::impl::sync(bias); |
| 5215 | bias_ = at::functionalization::impl::from_functional_tensor(bias); |
| 5216 | } else { |
| 5217 | bias_ = bias; |
| 5218 | } |
| 5219 | |
| 5220 | at::Tensor out0_; |
| 5221 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 5222 | at::functionalization::impl::sync(out0); |
| 5223 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 5224 | } else { |
| 5225 | out0_ = out0; |
| 5226 | } |
| 5227 | |
| 5228 | at::Tensor out1_; |
| 5229 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 5230 | at::functionalization::impl::sync(out1); |
| 5231 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 5232 | } else { |
| 5233 | out1_ = out1; |
| 5234 | } |
| 5235 | |
| 5236 | at::Tensor out2_; |
| 5237 | if (at::functionalization::impl::isFunctionalTensor(out2)) { |
| 5238 | at::functionalization::impl::sync(out2); |
| 5239 | out2_ = at::functionalization::impl::from_functional_tensor(out2); |
| 5240 | } else { |
| 5241 | out2_ = out2; |
| 5242 | } |
| 5243 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1) && at::functionalization::impl::isFunctionalTensor(out2))) { |
| 5244 | if ((false || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(bias))) { |
| 5245 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5246 | TORCH_INTERNAL_ASSERT(false, |
| 5247 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5248 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5249 | } else { |
| 5250 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5251 | at::AutoDispatchSkipFunctionalize guard; |
| 5252 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::native_layer_norm_out::call(input_, normalized_shape, weight_, bias_, eps, out0_, out1_, out2_); |
| 5253 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2);; |
| 5254 | } |
| 5255 | } else { |
| 5256 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 5257 | { |
| 5258 | at::AutoDispatchSkipFunctionalize guard; |
| 5259 | tmp_output = at::_ops::native_layer_norm::call(input_, normalized_shape, weight_, bias_, eps); |
| 5260 | } |
| 5261 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 5262 | at::functionalization::impl::commit_update(out0); |
| 5263 | at::functionalization::impl::sync(out0); |
| 5264 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 5265 | at::functionalization::impl::commit_update(out1); |
| 5266 | at::functionalization::impl::sync(out1); |
| 5267 | at::functionalization::impl::replace_(out2, std::get<2>(tmp_output)); |
| 5268 | at::functionalization::impl::commit_update(out2); |
| 5269 | at::functionalization::impl::sync(out2); |
| 5270 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2); |
| 5271 | } |
| 5272 | } |
| 5273 | |
| 5274 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_layer_norm_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const at::Tensor & mean, const at::Tensor & rstd, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 5275 | if (false) { |
| 5276 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5277 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5278 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5279 | auto grad_out_meta = to_meta(grad_out); |
| 5280 | auto input_meta = to_meta(input); |
| 5281 | auto mean_meta = to_meta(mean); |
| 5282 | auto rstd_meta = to_meta(rstd); |
| 5283 | auto weight_meta = to_meta(weight); |
| 5284 | auto bias_meta = to_meta(bias); |
| 5285 | auto out0_meta = to_meta(out0); |
| 5286 | auto out1_meta = to_meta(out1); |
| 5287 | auto out2_meta = to_meta(out2); |
| 5288 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5289 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5290 | at::_ops::native_layer_norm_backward_out::call(grad_out_meta, input_meta, normalized_shape, mean_meta, rstd_meta, weight_meta, bias_meta, output_mask, out0_meta, out1_meta, out2_meta); |
| 5291 | } |
| 5292 | |
| 5293 | at::Tensor grad_out_; |
| 5294 | if (at::functionalization::impl::isFunctionalTensor(grad_out)) { |
| 5295 | at::functionalization::impl::sync(grad_out); |
| 5296 | grad_out_ = at::functionalization::impl::from_functional_tensor(grad_out); |
| 5297 | } else { |
| 5298 | grad_out_ = grad_out; |
| 5299 | } |
| 5300 | |
| 5301 | at::Tensor input_; |
| 5302 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 5303 | at::functionalization::impl::sync(input); |
| 5304 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 5305 | } else { |
| 5306 | input_ = input; |
| 5307 | } |
| 5308 | |
| 5309 | at::Tensor mean_; |
| 5310 | if (at::functionalization::impl::isFunctionalTensor(mean)) { |
| 5311 | at::functionalization::impl::sync(mean); |
| 5312 | mean_ = at::functionalization::impl::from_functional_tensor(mean); |
| 5313 | } else { |
| 5314 | mean_ = mean; |
| 5315 | } |
| 5316 | |
| 5317 | at::Tensor rstd_; |
| 5318 | if (at::functionalization::impl::isFunctionalTensor(rstd)) { |
| 5319 | at::functionalization::impl::sync(rstd); |
| 5320 | rstd_ = at::functionalization::impl::from_functional_tensor(rstd); |
| 5321 | } else { |
| 5322 | rstd_ = rstd; |
| 5323 | } |
| 5324 | |
| 5325 | c10::optional<at::Tensor> weight_; |
| 5326 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 5327 | at::functionalization::impl::sync(weight); |
| 5328 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 5329 | } else { |
| 5330 | weight_ = weight; |
| 5331 | } |
| 5332 | |
| 5333 | c10::optional<at::Tensor> bias_; |
| 5334 | if (at::functionalization::impl::isFunctionalTensor(bias)) { |
| 5335 | at::functionalization::impl::sync(bias); |
| 5336 | bias_ = at::functionalization::impl::from_functional_tensor(bias); |
| 5337 | } else { |
| 5338 | bias_ = bias; |
| 5339 | } |
| 5340 | |
| 5341 | at::Tensor out0_; |
| 5342 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 5343 | at::functionalization::impl::sync(out0); |
| 5344 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 5345 | } else { |
| 5346 | out0_ = out0; |
| 5347 | } |
| 5348 | |
| 5349 | at::Tensor out1_; |
| 5350 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 5351 | at::functionalization::impl::sync(out1); |
| 5352 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 5353 | } else { |
| 5354 | out1_ = out1; |
| 5355 | } |
| 5356 | |
| 5357 | at::Tensor out2_; |
| 5358 | if (at::functionalization::impl::isFunctionalTensor(out2)) { |
| 5359 | at::functionalization::impl::sync(out2); |
| 5360 | out2_ = at::functionalization::impl::from_functional_tensor(out2); |
| 5361 | } else { |
| 5362 | out2_ = out2; |
| 5363 | } |
| 5364 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1) && at::functionalization::impl::isFunctionalTensor(out2))) { |
| 5365 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_out) || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(mean) || at::functionalization::impl::isFunctionalTensor(rstd) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(bias))) { |
| 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 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::native_layer_norm_backward_out::call(grad_out_, input_, normalized_shape, mean_, rstd_, weight_, bias_, output_mask, out0_, out1_, out2_); |
| 5374 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2);; |
| 5375 | } |
| 5376 | } else { |
| 5377 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 5378 | { |
| 5379 | at::AutoDispatchSkipFunctionalize guard; |
| 5380 | tmp_output = at::_ops::native_layer_norm_backward::call(grad_out_, input_, normalized_shape, mean_, rstd_, weight_, bias_, output_mask); |
| 5381 | } |
| 5382 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 5383 | at::functionalization::impl::commit_update(out0); |
| 5384 | at::functionalization::impl::sync(out0); |
| 5385 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 5386 | at::functionalization::impl::commit_update(out1); |
| 5387 | at::functionalization::impl::sync(out1); |
| 5388 | at::functionalization::impl::replace_(out2, std::get<2>(tmp_output)); |
| 5389 | at::functionalization::impl::commit_update(out2); |
| 5390 | at::functionalization::impl::sync(out2); |
| 5391 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2); |
| 5392 | } |
| 5393 | } |
| 5394 | |
| 5395 | at::Tensor & mkldnn_linear_backward_input_out_out(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef input_size, const at::Tensor & grad_output, const at::Tensor & weight, at::Tensor & out) { |
| 5396 | if (false) { |
| 5397 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5398 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5399 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5400 | auto grad_output_meta = to_meta(grad_output); |
| 5401 | auto weight_meta = to_meta(weight); |
| 5402 | auto out_meta = to_meta(out); |
| 5403 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5404 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5405 | at::_ops::mkldnn_linear_backward_input_out::call(input_size, grad_output_meta, weight_meta, out_meta); |
| 5406 | } |
| 5407 | |
| 5408 | at::Tensor grad_output_; |
| 5409 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 5410 | at::functionalization::impl::sync(grad_output); |
| 5411 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 5412 | } else { |
| 5413 | grad_output_ = grad_output; |
| 5414 | } |
| 5415 | |
| 5416 | at::Tensor weight_; |
| 5417 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 5418 | at::functionalization::impl::sync(weight); |
| 5419 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 5420 | } else { |
| 5421 | weight_ = weight; |
| 5422 | } |
| 5423 | |
| 5424 | at::Tensor out_; |
| 5425 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5426 | at::functionalization::impl::sync(out); |
| 5427 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5428 | } else { |
| 5429 | out_ = out; |
| 5430 | } |
| 5431 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5432 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(weight))) { |
| 5433 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5434 | TORCH_INTERNAL_ASSERT(false, |
| 5435 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5436 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5437 | } else { |
| 5438 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5439 | at::AutoDispatchSkipFunctionalize guard; |
| 5440 | at::Tensor tmp_output = at::_ops::mkldnn_linear_backward_input_out::call(input_size, grad_output_, weight_, out_); |
| 5441 | return out;; |
| 5442 | } |
| 5443 | } else { |
| 5444 | at::Tensor tmp_output; |
| 5445 | { |
| 5446 | at::AutoDispatchSkipFunctionalize guard; |
| 5447 | tmp_output = at::_ops::mkldnn_linear_backward_input::call(input_size, grad_output_, weight_); |
| 5448 | } |
| 5449 | at::functionalization::impl::replace_(out, tmp_output); |
| 5450 | at::functionalization::impl::commit_update(out); |
| 5451 | at::functionalization::impl::sync(out); |
| 5452 | return out; |
| 5453 | } |
| 5454 | } |
| 5455 | |
| 5456 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> mkldnn_linear_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 5457 | if (false) { |
| 5458 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5459 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5460 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5461 | auto self_meta = to_meta(self); |
| 5462 | auto grad_output_meta = to_meta(grad_output); |
| 5463 | auto weight_meta = to_meta(weight); |
| 5464 | auto out0_meta = to_meta(out0); |
| 5465 | auto out1_meta = to_meta(out1); |
| 5466 | auto out2_meta = to_meta(out2); |
| 5467 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5468 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5469 | at::_ops::mkldnn_linear_backward_out::call(self_meta, grad_output_meta, weight_meta, output_mask, out0_meta, out1_meta, out2_meta); |
| 5470 | } |
| 5471 | |
| 5472 | at::Tensor self_; |
| 5473 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5474 | at::functionalization::impl::sync(self); |
| 5475 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5476 | } else { |
| 5477 | self_ = self; |
| 5478 | } |
| 5479 | |
| 5480 | at::Tensor grad_output_; |
| 5481 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 5482 | at::functionalization::impl::sync(grad_output); |
| 5483 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 5484 | } else { |
| 5485 | grad_output_ = grad_output; |
| 5486 | } |
| 5487 | |
| 5488 | at::Tensor weight_; |
| 5489 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 5490 | at::functionalization::impl::sync(weight); |
| 5491 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 5492 | } else { |
| 5493 | weight_ = weight; |
| 5494 | } |
| 5495 | |
| 5496 | at::Tensor out0_; |
| 5497 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 5498 | at::functionalization::impl::sync(out0); |
| 5499 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 5500 | } else { |
| 5501 | out0_ = out0; |
| 5502 | } |
| 5503 | |
| 5504 | at::Tensor out1_; |
| 5505 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 5506 | at::functionalization::impl::sync(out1); |
| 5507 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 5508 | } else { |
| 5509 | out1_ = out1; |
| 5510 | } |
| 5511 | |
| 5512 | at::Tensor out2_; |
| 5513 | if (at::functionalization::impl::isFunctionalTensor(out2)) { |
| 5514 | at::functionalization::impl::sync(out2); |
| 5515 | out2_ = at::functionalization::impl::from_functional_tensor(out2); |
| 5516 | } else { |
| 5517 | out2_ = out2; |
| 5518 | } |
| 5519 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1) && at::functionalization::impl::isFunctionalTensor(out2))) { |
| 5520 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(weight))) { |
| 5521 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5522 | TORCH_INTERNAL_ASSERT(false, |
| 5523 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5524 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5525 | } else { |
| 5526 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5527 | at::AutoDispatchSkipFunctionalize guard; |
| 5528 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::mkldnn_linear_backward_out::call(self_, grad_output_, weight_, output_mask, out0_, out1_, out2_); |
| 5529 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2);; |
| 5530 | } |
| 5531 | } else { |
| 5532 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 5533 | { |
| 5534 | at::AutoDispatchSkipFunctionalize guard; |
| 5535 | tmp_output = at::_ops::mkldnn_linear_backward::call(self_, grad_output_, weight_, output_mask); |
| 5536 | } |
| 5537 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 5538 | at::functionalization::impl::commit_update(out0); |
| 5539 | at::functionalization::impl::sync(out0); |
| 5540 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 5541 | at::functionalization::impl::commit_update(out1); |
| 5542 | at::functionalization::impl::sync(out1); |
| 5543 | at::functionalization::impl::replace_(out2, std::get<2>(tmp_output)); |
| 5544 | at::functionalization::impl::commit_update(out2); |
| 5545 | at::functionalization::impl::sync(out2); |
| 5546 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2); |
| 5547 | } |
| 5548 | } |
| 5549 | |
| 5550 | at::Tensor & log10_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 5551 | if (false) { |
| 5552 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5553 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5554 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5555 | auto self_meta = to_meta(self); |
| 5556 | auto out_meta = to_meta(out); |
| 5557 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5558 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5559 | at::_ops::log10_out::call(self_meta, out_meta); |
| 5560 | } |
| 5561 | |
| 5562 | at::Tensor self_; |
| 5563 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5564 | at::functionalization::impl::sync(self); |
| 5565 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5566 | } else { |
| 5567 | self_ = self; |
| 5568 | } |
| 5569 | |
| 5570 | at::Tensor out_; |
| 5571 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5572 | at::functionalization::impl::sync(out); |
| 5573 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5574 | } else { |
| 5575 | out_ = out; |
| 5576 | } |
| 5577 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5578 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 5579 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5580 | TORCH_INTERNAL_ASSERT(false, |
| 5581 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5582 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5583 | } else { |
| 5584 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5585 | at::AutoDispatchSkipFunctionalize guard; |
| 5586 | at::Tensor tmp_output = at::_ops::log10_out::call(self_, out_); |
| 5587 | return out;; |
| 5588 | } |
| 5589 | } else { |
| 5590 | at::Tensor tmp_output; |
| 5591 | { |
| 5592 | at::AutoDispatchSkipFunctionalize guard; |
| 5593 | tmp_output = at::_ops::log10::call(self_); |
| 5594 | } |
| 5595 | at::functionalization::impl::replace_(out, tmp_output); |
| 5596 | at::functionalization::impl::commit_update(out); |
| 5597 | at::functionalization::impl::sync(out); |
| 5598 | return out; |
| 5599 | } |
| 5600 | } |
| 5601 | |
| 5602 | at::Tensor & log10_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 5603 | if (true) { |
| 5604 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5605 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5606 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5607 | auto self_meta = to_meta(self); |
| 5608 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5609 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5610 | at::_ops::log10_::call(self_meta); |
| 5611 | } |
| 5612 | |
| 5613 | at::Tensor self_; |
| 5614 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5615 | at::functionalization::impl::sync(self); |
| 5616 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5617 | } else { |
| 5618 | self_ = self; |
| 5619 | } |
| 5620 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 5621 | if ((false)) { |
| 5622 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5623 | TORCH_INTERNAL_ASSERT(false, |
| 5624 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5625 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5626 | } else { |
| 5627 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5628 | at::AutoDispatchSkipFunctionalize guard; |
| 5629 | at::Tensor tmp_output = at::_ops::log10_::call(self_); |
| 5630 | return self;; |
| 5631 | } |
| 5632 | } else { |
| 5633 | at::Tensor tmp_output; |
| 5634 | { |
| 5635 | at::AutoDispatchSkipFunctionalize guard; |
| 5636 | tmp_output = at::_ops::log10::call(self_); |
| 5637 | } |
| 5638 | at::functionalization::impl::replace_(self, tmp_output); |
| 5639 | at::functionalization::impl::commit_update(self); |
| 5640 | at::functionalization::impl::sync(self); |
| 5641 | return self; |
| 5642 | } |
| 5643 | } |
| 5644 | |
| 5645 | at::Tensor & log1p_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 5646 | if (false) { |
| 5647 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5648 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5649 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5650 | auto self_meta = to_meta(self); |
| 5651 | auto out_meta = to_meta(out); |
| 5652 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5653 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5654 | at::_ops::log1p_out::call(self_meta, out_meta); |
| 5655 | } |
| 5656 | |
| 5657 | at::Tensor self_; |
| 5658 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5659 | at::functionalization::impl::sync(self); |
| 5660 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5661 | } else { |
| 5662 | self_ = self; |
| 5663 | } |
| 5664 | |
| 5665 | at::Tensor out_; |
| 5666 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5667 | at::functionalization::impl::sync(out); |
| 5668 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5669 | } else { |
| 5670 | out_ = out; |
| 5671 | } |
| 5672 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5673 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 5674 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5675 | TORCH_INTERNAL_ASSERT(false, |
| 5676 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5677 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5678 | } else { |
| 5679 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5680 | at::AutoDispatchSkipFunctionalize guard; |
| 5681 | at::Tensor tmp_output = at::_ops::log1p_out::call(self_, out_); |
| 5682 | return out;; |
| 5683 | } |
| 5684 | } else { |
| 5685 | at::Tensor tmp_output; |
| 5686 | { |
| 5687 | at::AutoDispatchSkipFunctionalize guard; |
| 5688 | tmp_output = at::_ops::log1p::call(self_); |
| 5689 | } |
| 5690 | at::functionalization::impl::replace_(out, tmp_output); |
| 5691 | at::functionalization::impl::commit_update(out); |
| 5692 | at::functionalization::impl::sync(out); |
| 5693 | return out; |
| 5694 | } |
| 5695 | } |
| 5696 | |
| 5697 | at::Tensor & log1p_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 5698 | if (true) { |
| 5699 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5700 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5701 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5702 | auto self_meta = to_meta(self); |
| 5703 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5704 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5705 | at::_ops::log1p_::call(self_meta); |
| 5706 | } |
| 5707 | |
| 5708 | at::Tensor self_; |
| 5709 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5710 | at::functionalization::impl::sync(self); |
| 5711 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5712 | } else { |
| 5713 | self_ = self; |
| 5714 | } |
| 5715 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 5716 | if ((false)) { |
| 5717 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5718 | TORCH_INTERNAL_ASSERT(false, |
| 5719 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5720 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5721 | } else { |
| 5722 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5723 | at::AutoDispatchSkipFunctionalize guard; |
| 5724 | at::Tensor tmp_output = at::_ops::log1p_::call(self_); |
| 5725 | return self;; |
| 5726 | } |
| 5727 | } else { |
| 5728 | at::Tensor tmp_output; |
| 5729 | { |
| 5730 | at::AutoDispatchSkipFunctionalize guard; |
| 5731 | tmp_output = at::_ops::log1p::call(self_); |
| 5732 | } |
| 5733 | at::functionalization::impl::replace_(self, tmp_output); |
| 5734 | at::functionalization::impl::commit_update(self); |
| 5735 | at::functionalization::impl::sync(self); |
| 5736 | return self; |
| 5737 | } |
| 5738 | } |
| 5739 | |
| 5740 | at::Tensor & logsumexp_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { |
| 5741 | if (false) { |
| 5742 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5743 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5744 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5745 | auto self_meta = to_meta(self); |
| 5746 | auto out_meta = to_meta(out); |
| 5747 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5748 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5749 | at::_ops::logsumexp_out::call(self_meta, dim, keepdim, out_meta); |
| 5750 | } |
| 5751 | |
| 5752 | at::Tensor self_; |
| 5753 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5754 | at::functionalization::impl::sync(self); |
| 5755 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5756 | } else { |
| 5757 | self_ = self; |
| 5758 | } |
| 5759 | |
| 5760 | at::Tensor out_; |
| 5761 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5762 | at::functionalization::impl::sync(out); |
| 5763 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5764 | } else { |
| 5765 | out_ = out; |
| 5766 | } |
| 5767 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5768 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 5769 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5770 | TORCH_INTERNAL_ASSERT(false, |
| 5771 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5772 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5773 | } else { |
| 5774 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5775 | at::AutoDispatchSkipFunctionalize guard; |
| 5776 | at::Tensor tmp_output = at::_ops::logsumexp_out::call(self_, dim, keepdim, out_); |
| 5777 | return out;; |
| 5778 | } |
| 5779 | } else { |
| 5780 | at::Tensor tmp_output; |
| 5781 | { |
| 5782 | at::AutoDispatchSkipFunctionalize guard; |
| 5783 | tmp_output = at::_ops::logsumexp::call(self_, dim, keepdim); |
| 5784 | } |
| 5785 | at::functionalization::impl::replace_(out, tmp_output); |
| 5786 | at::functionalization::impl::commit_update(out); |
| 5787 | at::functionalization::impl::sync(out); |
| 5788 | return out; |
| 5789 | } |
| 5790 | } |
| 5791 | |
| 5792 | at::Tensor & logsumexp_out_names_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool keepdim, at::Tensor & out) { |
| 5793 | if (false) { |
| 5794 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5795 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5796 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5797 | auto self_meta = to_meta(self); |
| 5798 | auto out_meta = to_meta(out); |
| 5799 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5800 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5801 | at::_ops::logsumexp_names_out::call(self_meta, dim, keepdim, out_meta); |
| 5802 | } |
| 5803 | |
| 5804 | at::Tensor self_; |
| 5805 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5806 | at::functionalization::impl::sync(self); |
| 5807 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5808 | } else { |
| 5809 | self_ = self; |
| 5810 | } |
| 5811 | |
| 5812 | at::Tensor out_; |
| 5813 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5814 | at::functionalization::impl::sync(out); |
| 5815 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5816 | } else { |
| 5817 | out_ = out; |
| 5818 | } |
| 5819 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5820 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 5821 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5822 | TORCH_INTERNAL_ASSERT(false, |
| 5823 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5824 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5825 | } else { |
| 5826 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5827 | at::AutoDispatchSkipFunctionalize guard; |
| 5828 | at::Tensor tmp_output = at::_ops::logsumexp_names_out::call(self_, dim, keepdim, out_); |
| 5829 | return out;; |
| 5830 | } |
| 5831 | } else { |
| 5832 | at::Tensor tmp_output; |
| 5833 | { |
| 5834 | at::AutoDispatchSkipFunctionalize guard; |
| 5835 | tmp_output = at::_ops::logsumexp_names::call(self_, dim, keepdim); |
| 5836 | } |
| 5837 | at::functionalization::impl::replace_(out, tmp_output); |
| 5838 | at::functionalization::impl::commit_update(out); |
| 5839 | at::functionalization::impl::sync(out); |
| 5840 | return out; |
| 5841 | } |
| 5842 | } |
| 5843 | |
| 5844 | at::Tensor & matmul_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 5845 | if (false) { |
| 5846 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5847 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5848 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5849 | auto self_meta = to_meta(self); |
| 5850 | auto other_meta = to_meta(other); |
| 5851 | auto out_meta = to_meta(out); |
| 5852 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5853 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5854 | at::_ops::matmul_out::call(self_meta, other_meta, out_meta); |
| 5855 | } |
| 5856 | |
| 5857 | at::Tensor self_; |
| 5858 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5859 | at::functionalization::impl::sync(self); |
| 5860 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5861 | } else { |
| 5862 | self_ = self; |
| 5863 | } |
| 5864 | |
| 5865 | at::Tensor other_; |
| 5866 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 5867 | at::functionalization::impl::sync(other); |
| 5868 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 5869 | } else { |
| 5870 | other_ = other; |
| 5871 | } |
| 5872 | |
| 5873 | at::Tensor out_; |
| 5874 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5875 | at::functionalization::impl::sync(out); |
| 5876 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5877 | } else { |
| 5878 | out_ = out; |
| 5879 | } |
| 5880 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5881 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 5882 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5883 | TORCH_INTERNAL_ASSERT(false, |
| 5884 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5885 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5886 | } else { |
| 5887 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5888 | at::AutoDispatchSkipFunctionalize guard; |
| 5889 | at::Tensor tmp_output = at::_ops::matmul_out::call(self_, other_, out_); |
| 5890 | return out;; |
| 5891 | } |
| 5892 | } else { |
| 5893 | at::Tensor tmp_output; |
| 5894 | { |
| 5895 | at::AutoDispatchSkipFunctionalize guard; |
| 5896 | tmp_output = at::_ops::matmul::call(self_, other_); |
| 5897 | } |
| 5898 | at::functionalization::impl::replace_(out, tmp_output); |
| 5899 | at::functionalization::impl::commit_update(out); |
| 5900 | at::functionalization::impl::sync(out); |
| 5901 | return out; |
| 5902 | } |
| 5903 | } |
| 5904 | |
| 5905 | at::Tensor & mkldnn_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) { |
| 5906 | if (false) { |
| 5907 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5908 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5909 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5910 | auto self_meta = to_meta(self); |
| 5911 | auto out_meta = to_meta(out); |
| 5912 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5913 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5914 | at::_ops::mkldnn_max_pool2d_out::call(self_meta, kernel_size, stride, padding, dilation, ceil_mode, out_meta); |
| 5915 | } |
| 5916 | |
| 5917 | at::Tensor self_; |
| 5918 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5919 | at::functionalization::impl::sync(self); |
| 5920 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5921 | } else { |
| 5922 | self_ = self; |
| 5923 | } |
| 5924 | |
| 5925 | at::Tensor out_; |
| 5926 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5927 | at::functionalization::impl::sync(out); |
| 5928 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5929 | } else { |
| 5930 | out_ = out; |
| 5931 | } |
| 5932 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5933 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 5934 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5935 | TORCH_INTERNAL_ASSERT(false, |
| 5936 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5937 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5938 | } else { |
| 5939 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5940 | at::AutoDispatchSkipFunctionalize guard; |
| 5941 | at::Tensor tmp_output = at::_ops::mkldnn_max_pool2d_out::call(self_, kernel_size, stride, padding, dilation, ceil_mode, out_); |
| 5942 | return out;; |
| 5943 | } |
| 5944 | } else { |
| 5945 | at::Tensor tmp_output; |
| 5946 | { |
| 5947 | at::AutoDispatchSkipFunctionalize guard; |
| 5948 | tmp_output = at::_ops::mkldnn_max_pool2d::call(self_, kernel_size, stride, padding, dilation, ceil_mode); |
| 5949 | } |
| 5950 | at::functionalization::impl::replace_(out, tmp_output); |
| 5951 | at::functionalization::impl::commit_update(out); |
| 5952 | at::functionalization::impl::sync(out); |
| 5953 | return out; |
| 5954 | } |
| 5955 | } |
| 5956 | |
| 5957 | at::Tensor & quantized_max_pool1d_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) { |
| 5958 | if (false) { |
| 5959 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 5960 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 5961 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 5962 | auto self_meta = to_meta(self); |
| 5963 | auto out_meta = to_meta(out); |
| 5964 | at::AutoDispatchSkipFunctionalize func_guard; |
| 5965 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 5966 | at::_ops::quantized_max_pool1d_out::call(self_meta, kernel_size, stride, padding, dilation, ceil_mode, out_meta); |
| 5967 | } |
| 5968 | |
| 5969 | at::Tensor self_; |
| 5970 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 5971 | at::functionalization::impl::sync(self); |
| 5972 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 5973 | } else { |
| 5974 | self_ = self; |
| 5975 | } |
| 5976 | |
| 5977 | at::Tensor out_; |
| 5978 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 5979 | at::functionalization::impl::sync(out); |
| 5980 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 5981 | } else { |
| 5982 | out_ = out; |
| 5983 | } |
| 5984 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 5985 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 5986 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 5987 | TORCH_INTERNAL_ASSERT(false, |
| 5988 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 5989 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 5990 | } else { |
| 5991 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 5992 | at::AutoDispatchSkipFunctionalize guard; |
| 5993 | at::Tensor tmp_output = at::_ops::quantized_max_pool1d_out::call(self_, kernel_size, stride, padding, dilation, ceil_mode, out_); |
| 5994 | return out;; |
| 5995 | } |
| 5996 | } else { |
| 5997 | at::Tensor tmp_output; |
| 5998 | { |
| 5999 | at::AutoDispatchSkipFunctionalize guard; |
| 6000 | tmp_output = at::_ops::quantized_max_pool1d::call(self_, kernel_size, stride, padding, dilation, ceil_mode); |
| 6001 | } |
| 6002 | at::functionalization::impl::replace_(out, tmp_output); |
| 6003 | at::functionalization::impl::commit_update(out); |
| 6004 | at::functionalization::impl::sync(out); |
| 6005 | return out; |
| 6006 | } |
| 6007 | } |
| 6008 | |
| 6009 | at::Tensor & nanmean_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 6010 | if (false) { |
| 6011 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6012 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6013 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6014 | auto self_meta = to_meta(self); |
| 6015 | auto out_meta = to_meta(out); |
| 6016 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6017 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6018 | at::_ops::nanmean_out::call(self_meta, dim, keepdim, dtype, out_meta); |
| 6019 | } |
| 6020 | |
| 6021 | at::Tensor self_; |
| 6022 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6023 | at::functionalization::impl::sync(self); |
| 6024 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6025 | } else { |
| 6026 | self_ = self; |
| 6027 | } |
| 6028 | |
| 6029 | at::Tensor out_; |
| 6030 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 6031 | at::functionalization::impl::sync(out); |
| 6032 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 6033 | } else { |
| 6034 | out_ = out; |
| 6035 | } |
| 6036 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 6037 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 6038 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6039 | TORCH_INTERNAL_ASSERT(false, |
| 6040 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6041 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6042 | } else { |
| 6043 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6044 | at::AutoDispatchSkipFunctionalize guard; |
| 6045 | at::Tensor tmp_output = at::_ops::nanmean_out::call(self_, dim, keepdim, dtype, out_); |
| 6046 | return out;; |
| 6047 | } |
| 6048 | } else { |
| 6049 | at::Tensor tmp_output; |
| 6050 | { |
| 6051 | at::AutoDispatchSkipFunctionalize guard; |
| 6052 | tmp_output = at::_ops::nanmean::call(self_, dim, keepdim, dtype); |
| 6053 | } |
| 6054 | at::functionalization::impl::replace_(out, tmp_output); |
| 6055 | at::functionalization::impl::commit_update(out); |
| 6056 | at::functionalization::impl::sync(out); |
| 6057 | return out; |
| 6058 | } |
| 6059 | } |
| 6060 | |
| 6061 | at::Tensor & _mps_convolution_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, at::Tensor & out) { |
| 6062 | if (false) { |
| 6063 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6064 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6065 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6066 | auto self_meta = to_meta(self); |
| 6067 | auto weight_meta = to_meta(weight); |
| 6068 | auto bias_meta = to_meta(bias); |
| 6069 | auto out_meta = to_meta(out); |
| 6070 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6071 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6072 | at::_ops::_mps_convolution_out::call(self_meta, weight_meta, bias_meta, padding, stride, dilation, groups, out_meta); |
| 6073 | } |
| 6074 | |
| 6075 | at::Tensor self_; |
| 6076 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6077 | at::functionalization::impl::sync(self); |
| 6078 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6079 | } else { |
| 6080 | self_ = self; |
| 6081 | } |
| 6082 | |
| 6083 | at::Tensor weight_; |
| 6084 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 6085 | at::functionalization::impl::sync(weight); |
| 6086 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 6087 | } else { |
| 6088 | weight_ = weight; |
| 6089 | } |
| 6090 | |
| 6091 | c10::optional<at::Tensor> bias_; |
| 6092 | if (at::functionalization::impl::isFunctionalTensor(bias)) { |
| 6093 | at::functionalization::impl::sync(bias); |
| 6094 | bias_ = at::functionalization::impl::from_functional_tensor(bias); |
| 6095 | } else { |
| 6096 | bias_ = bias; |
| 6097 | } |
| 6098 | |
| 6099 | at::Tensor out_; |
| 6100 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 6101 | at::functionalization::impl::sync(out); |
| 6102 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 6103 | } else { |
| 6104 | out_ = out; |
| 6105 | } |
| 6106 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 6107 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(bias))) { |
| 6108 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6109 | TORCH_INTERNAL_ASSERT(false, |
| 6110 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6111 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6112 | } else { |
| 6113 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6114 | at::AutoDispatchSkipFunctionalize guard; |
| 6115 | at::Tensor tmp_output = at::_ops::_mps_convolution_out::call(self_, weight_, bias_, padding, stride, dilation, groups, out_); |
| 6116 | return out;; |
| 6117 | } |
| 6118 | } else { |
| 6119 | at::Tensor tmp_output; |
| 6120 | { |
| 6121 | at::AutoDispatchSkipFunctionalize guard; |
| 6122 | tmp_output = at::_ops::_mps_convolution::call(self_, weight_, bias_, padding, stride, dilation, groups); |
| 6123 | } |
| 6124 | at::functionalization::impl::replace_(out, tmp_output); |
| 6125 | at::functionalization::impl::commit_update(out); |
| 6126 | at::functionalization::impl::sync(out); |
| 6127 | return out; |
| 6128 | } |
| 6129 | } |
| 6130 | |
| 6131 | at::Tensor & mkldnn_convolution_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, at::Tensor & out) { |
| 6132 | if (false) { |
| 6133 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6134 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6135 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6136 | auto self_meta = to_meta(self); |
| 6137 | auto weight_meta = to_meta(weight); |
| 6138 | auto bias_meta = to_meta(bias); |
| 6139 | auto out_meta = to_meta(out); |
| 6140 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6141 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6142 | at::_ops::mkldnn_convolution_out::call(self_meta, weight_meta, bias_meta, padding, stride, dilation, groups, out_meta); |
| 6143 | } |
| 6144 | |
| 6145 | at::Tensor self_; |
| 6146 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6147 | at::functionalization::impl::sync(self); |
| 6148 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6149 | } else { |
| 6150 | self_ = self; |
| 6151 | } |
| 6152 | |
| 6153 | at::Tensor weight_; |
| 6154 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 6155 | at::functionalization::impl::sync(weight); |
| 6156 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 6157 | } else { |
| 6158 | weight_ = weight; |
| 6159 | } |
| 6160 | |
| 6161 | c10::optional<at::Tensor> bias_; |
| 6162 | if (at::functionalization::impl::isFunctionalTensor(bias)) { |
| 6163 | at::functionalization::impl::sync(bias); |
| 6164 | bias_ = at::functionalization::impl::from_functional_tensor(bias); |
| 6165 | } else { |
| 6166 | bias_ = bias; |
| 6167 | } |
| 6168 | |
| 6169 | at::Tensor out_; |
| 6170 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 6171 | at::functionalization::impl::sync(out); |
| 6172 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 6173 | } else { |
| 6174 | out_ = out; |
| 6175 | } |
| 6176 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 6177 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(bias))) { |
| 6178 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6179 | TORCH_INTERNAL_ASSERT(false, |
| 6180 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6181 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6182 | } else { |
| 6183 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6184 | at::AutoDispatchSkipFunctionalize guard; |
| 6185 | at::Tensor tmp_output = at::_ops::mkldnn_convolution_out::call(self_, weight_, bias_, padding, stride, dilation, groups, out_); |
| 6186 | return out;; |
| 6187 | } |
| 6188 | } else { |
| 6189 | at::Tensor tmp_output; |
| 6190 | { |
| 6191 | at::AutoDispatchSkipFunctionalize guard; |
| 6192 | tmp_output = at::_ops::mkldnn_convolution::call(self_, weight_, bias_, padding, stride, dilation, groups); |
| 6193 | } |
| 6194 | at::functionalization::impl::replace_(out, tmp_output); |
| 6195 | at::functionalization::impl::commit_update(out); |
| 6196 | at::functionalization::impl::sync(out); |
| 6197 | return out; |
| 6198 | } |
| 6199 | } |
| 6200 | |
| 6201 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> mkldnn_rnn_layer_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight0, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & hx_, const at::Tensor & cx_, bool reverse, at::IntArrayRef batch_sizes, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool bidirectional, bool batch_first, bool train, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3) { |
| 6202 | if (false) { |
| 6203 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6204 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6205 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6206 | auto input_meta = to_meta(input); |
| 6207 | auto weight0_meta = to_meta(weight0); |
| 6208 | auto weight1_meta = to_meta(weight1); |
| 6209 | auto weight2_meta = to_meta(weight2); |
| 6210 | auto weight3_meta = to_meta(weight3); |
| 6211 | auto hx__meta = to_meta(hx_); |
| 6212 | auto cx__meta = to_meta(cx_); |
| 6213 | auto out0_meta = to_meta(out0); |
| 6214 | auto out1_meta = to_meta(out1); |
| 6215 | auto out2_meta = to_meta(out2); |
| 6216 | auto out3_meta = to_meta(out3); |
| 6217 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6218 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6219 | at::_ops::mkldnn_rnn_layer_out::call(input_meta, weight0_meta, weight1_meta, weight2_meta, weight3_meta, hx__meta, cx__meta, reverse, batch_sizes, mode, hidden_size, num_layers, has_biases, bidirectional, batch_first, train, out0_meta, out1_meta, out2_meta, out3_meta); |
| 6220 | } |
| 6221 | |
| 6222 | at::Tensor input_; |
| 6223 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 6224 | at::functionalization::impl::sync(input); |
| 6225 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 6226 | } else { |
| 6227 | input_ = input; |
| 6228 | } |
| 6229 | |
| 6230 | at::Tensor weight0_; |
| 6231 | if (at::functionalization::impl::isFunctionalTensor(weight0)) { |
| 6232 | at::functionalization::impl::sync(weight0); |
| 6233 | weight0_ = at::functionalization::impl::from_functional_tensor(weight0); |
| 6234 | } else { |
| 6235 | weight0_ = weight0; |
| 6236 | } |
| 6237 | |
| 6238 | at::Tensor weight1_; |
| 6239 | if (at::functionalization::impl::isFunctionalTensor(weight1)) { |
| 6240 | at::functionalization::impl::sync(weight1); |
| 6241 | weight1_ = at::functionalization::impl::from_functional_tensor(weight1); |
| 6242 | } else { |
| 6243 | weight1_ = weight1; |
| 6244 | } |
| 6245 | |
| 6246 | at::Tensor weight2_; |
| 6247 | if (at::functionalization::impl::isFunctionalTensor(weight2)) { |
| 6248 | at::functionalization::impl::sync(weight2); |
| 6249 | weight2_ = at::functionalization::impl::from_functional_tensor(weight2); |
| 6250 | } else { |
| 6251 | weight2_ = weight2; |
| 6252 | } |
| 6253 | |
| 6254 | at::Tensor weight3_; |
| 6255 | if (at::functionalization::impl::isFunctionalTensor(weight3)) { |
| 6256 | at::functionalization::impl::sync(weight3); |
| 6257 | weight3_ = at::functionalization::impl::from_functional_tensor(weight3); |
| 6258 | } else { |
| 6259 | weight3_ = weight3; |
| 6260 | } |
| 6261 | |
| 6262 | at::Tensor hx__; |
| 6263 | if (at::functionalization::impl::isFunctionalTensor(hx_)) { |
| 6264 | at::functionalization::impl::sync(hx_); |
| 6265 | hx__ = at::functionalization::impl::from_functional_tensor(hx_); |
| 6266 | } else { |
| 6267 | hx__ = hx_; |
| 6268 | } |
| 6269 | |
| 6270 | at::Tensor cx__; |
| 6271 | if (at::functionalization::impl::isFunctionalTensor(cx_)) { |
| 6272 | at::functionalization::impl::sync(cx_); |
| 6273 | cx__ = at::functionalization::impl::from_functional_tensor(cx_); |
| 6274 | } else { |
| 6275 | cx__ = cx_; |
| 6276 | } |
| 6277 | |
| 6278 | at::Tensor out0_; |
| 6279 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 6280 | at::functionalization::impl::sync(out0); |
| 6281 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 6282 | } else { |
| 6283 | out0_ = out0; |
| 6284 | } |
| 6285 | |
| 6286 | at::Tensor out1_; |
| 6287 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 6288 | at::functionalization::impl::sync(out1); |
| 6289 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 6290 | } else { |
| 6291 | out1_ = out1; |
| 6292 | } |
| 6293 | |
| 6294 | at::Tensor out2_; |
| 6295 | if (at::functionalization::impl::isFunctionalTensor(out2)) { |
| 6296 | at::functionalization::impl::sync(out2); |
| 6297 | out2_ = at::functionalization::impl::from_functional_tensor(out2); |
| 6298 | } else { |
| 6299 | out2_ = out2; |
| 6300 | } |
| 6301 | |
| 6302 | at::Tensor out3_; |
| 6303 | if (at::functionalization::impl::isFunctionalTensor(out3)) { |
| 6304 | at::functionalization::impl::sync(out3); |
| 6305 | out3_ = at::functionalization::impl::from_functional_tensor(out3); |
| 6306 | } else { |
| 6307 | out3_ = out3; |
| 6308 | } |
| 6309 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1) && at::functionalization::impl::isFunctionalTensor(out2) && at::functionalization::impl::isFunctionalTensor(out3))) { |
| 6310 | if ((false || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(weight0) || at::functionalization::impl::isFunctionalTensor(weight1) || at::functionalization::impl::isFunctionalTensor(weight2) || at::functionalization::impl::isFunctionalTensor(weight3) || at::functionalization::impl::isFunctionalTensor(hx_) || at::functionalization::impl::isFunctionalTensor(cx_))) { |
| 6311 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6312 | TORCH_INTERNAL_ASSERT(false, |
| 6313 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6314 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6315 | } else { |
| 6316 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6317 | at::AutoDispatchSkipFunctionalize guard; |
| 6318 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::mkldnn_rnn_layer_out::call(input_, weight0_, weight1_, weight2_, weight3_, hx__, cx__, reverse, batch_sizes, mode, hidden_size, num_layers, has_biases, bidirectional, batch_first, train, out0_, out1_, out2_, out3_); |
| 6319 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2, out3);; |
| 6320 | } |
| 6321 | } else { |
| 6322 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 6323 | { |
| 6324 | at::AutoDispatchSkipFunctionalize guard; |
| 6325 | tmp_output = at::_ops::mkldnn_rnn_layer::call(input_, weight0_, weight1_, weight2_, weight3_, hx__, cx__, reverse, batch_sizes, mode, hidden_size, num_layers, has_biases, bidirectional, batch_first, train); |
| 6326 | } |
| 6327 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 6328 | at::functionalization::impl::commit_update(out0); |
| 6329 | at::functionalization::impl::sync(out0); |
| 6330 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 6331 | at::functionalization::impl::commit_update(out1); |
| 6332 | at::functionalization::impl::sync(out1); |
| 6333 | at::functionalization::impl::replace_(out2, std::get<2>(tmp_output)); |
| 6334 | at::functionalization::impl::commit_update(out2); |
| 6335 | at::functionalization::impl::sync(out2); |
| 6336 | at::functionalization::impl::replace_(out3, std::get<3>(tmp_output)); |
| 6337 | at::functionalization::impl::commit_update(out3); |
| 6338 | at::functionalization::impl::sync(out3); |
| 6339 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2, out3); |
| 6340 | } |
| 6341 | } |
| 6342 | |
| 6343 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> miopen_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) { |
| 6344 | if (false) { |
| 6345 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6346 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6347 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6348 | auto input_meta = to_meta(input); |
| 6349 | auto weight_meta = to_meta(weight); |
| 6350 | auto bias_meta = to_meta(bias); |
| 6351 | auto running_mean_meta = to_meta(running_mean); |
| 6352 | auto running_var_meta = to_meta(running_var); |
| 6353 | auto out0_meta = to_meta(out0); |
| 6354 | auto out1_meta = to_meta(out1); |
| 6355 | auto out2_meta = to_meta(out2); |
| 6356 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6357 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6358 | at::_ops::miopen_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); |
| 6359 | } |
| 6360 | |
| 6361 | at::Tensor input_; |
| 6362 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 6363 | at::functionalization::impl::sync(input); |
| 6364 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 6365 | } else { |
| 6366 | input_ = input; |
| 6367 | } |
| 6368 | |
| 6369 | at::Tensor weight_; |
| 6370 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 6371 | at::functionalization::impl::sync(weight); |
| 6372 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 6373 | } else { |
| 6374 | weight_ = weight; |
| 6375 | } |
| 6376 | |
| 6377 | c10::optional<at::Tensor> bias_; |
| 6378 | if (at::functionalization::impl::isFunctionalTensor(bias)) { |
| 6379 | at::functionalization::impl::sync(bias); |
| 6380 | bias_ = at::functionalization::impl::from_functional_tensor(bias); |
| 6381 | } else { |
| 6382 | bias_ = bias; |
| 6383 | } |
| 6384 | |
| 6385 | c10::optional<at::Tensor> running_mean_; |
| 6386 | if (at::functionalization::impl::isFunctionalTensor(running_mean)) { |
| 6387 | at::functionalization::impl::sync(running_mean); |
| 6388 | running_mean_ = at::functionalization::impl::from_functional_tensor(running_mean); |
| 6389 | } else { |
| 6390 | running_mean_ = running_mean; |
| 6391 | } |
| 6392 | |
| 6393 | c10::optional<at::Tensor> running_var_; |
| 6394 | if (at::functionalization::impl::isFunctionalTensor(running_var)) { |
| 6395 | at::functionalization::impl::sync(running_var); |
| 6396 | running_var_ = at::functionalization::impl::from_functional_tensor(running_var); |
| 6397 | } else { |
| 6398 | running_var_ = running_var; |
| 6399 | } |
| 6400 | |
| 6401 | at::Tensor out0_; |
| 6402 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 6403 | at::functionalization::impl::sync(out0); |
| 6404 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 6405 | } else { |
| 6406 | out0_ = out0; |
| 6407 | } |
| 6408 | |
| 6409 | at::Tensor out1_; |
| 6410 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 6411 | at::functionalization::impl::sync(out1); |
| 6412 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 6413 | } else { |
| 6414 | out1_ = out1; |
| 6415 | } |
| 6416 | |
| 6417 | at::Tensor out2_; |
| 6418 | if (at::functionalization::impl::isFunctionalTensor(out2)) { |
| 6419 | at::functionalization::impl::sync(out2); |
| 6420 | out2_ = at::functionalization::impl::from_functional_tensor(out2); |
| 6421 | } else { |
| 6422 | out2_ = out2; |
| 6423 | } |
| 6424 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1) && at::functionalization::impl::isFunctionalTensor(out2))) { |
| 6425 | 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))) { |
| 6426 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6427 | TORCH_INTERNAL_ASSERT(false, |
| 6428 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6429 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6430 | } else { |
| 6431 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6432 | at::AutoDispatchSkipFunctionalize guard; |
| 6433 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::miopen_batch_norm_out::call(input_, weight_, bias_, running_mean_, running_var_, training, exponential_average_factor, epsilon, out0_, out1_, out2_); |
| 6434 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2);; |
| 6435 | } |
| 6436 | } else { |
| 6437 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 6438 | { |
| 6439 | at::AutoDispatchSkipFunctionalize guard; |
| 6440 | tmp_output = at::_ops::miopen_batch_norm::call(input_, weight_, bias_, running_mean_, running_var_, training, exponential_average_factor, epsilon); |
| 6441 | } |
| 6442 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 6443 | at::functionalization::impl::commit_update(out0); |
| 6444 | at::functionalization::impl::sync(out0); |
| 6445 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 6446 | at::functionalization::impl::commit_update(out1); |
| 6447 | at::functionalization::impl::sync(out1); |
| 6448 | at::functionalization::impl::replace_(out2, std::get<2>(tmp_output)); |
| 6449 | at::functionalization::impl::commit_update(out2); |
| 6450 | at::functionalization::impl::sync(out2); |
| 6451 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2); |
| 6452 | } |
| 6453 | } |
| 6454 | |
| 6455 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> miopen_batch_norm_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & grad_output, const at::Tensor & weight, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, const c10::optional<at::Tensor> & save_mean, const c10::optional<at::Tensor> & save_var, double epsilon, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 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 input_meta = to_meta(input); |
| 6461 | auto grad_output_meta = to_meta(grad_output); |
| 6462 | auto weight_meta = to_meta(weight); |
| 6463 | auto running_mean_meta = to_meta(running_mean); |
| 6464 | auto running_var_meta = to_meta(running_var); |
| 6465 | auto save_mean_meta = to_meta(save_mean); |
| 6466 | auto save_var_meta = to_meta(save_var); |
| 6467 | auto out0_meta = to_meta(out0); |
| 6468 | auto out1_meta = to_meta(out1); |
| 6469 | auto out2_meta = to_meta(out2); |
| 6470 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6471 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6472 | at::_ops::miopen_batch_norm_backward_out::call(input_meta, grad_output_meta, weight_meta, running_mean_meta, running_var_meta, save_mean_meta, save_var_meta, epsilon, out0_meta, out1_meta, out2_meta); |
| 6473 | } |
| 6474 | |
| 6475 | at::Tensor input_; |
| 6476 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 6477 | at::functionalization::impl::sync(input); |
| 6478 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 6479 | } else { |
| 6480 | input_ = input; |
| 6481 | } |
| 6482 | |
| 6483 | at::Tensor grad_output_; |
| 6484 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 6485 | at::functionalization::impl::sync(grad_output); |
| 6486 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 6487 | } else { |
| 6488 | grad_output_ = grad_output; |
| 6489 | } |
| 6490 | |
| 6491 | at::Tensor weight_; |
| 6492 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 6493 | at::functionalization::impl::sync(weight); |
| 6494 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 6495 | } else { |
| 6496 | weight_ = weight; |
| 6497 | } |
| 6498 | |
| 6499 | c10::optional<at::Tensor> running_mean_; |
| 6500 | if (at::functionalization::impl::isFunctionalTensor(running_mean)) { |
| 6501 | at::functionalization::impl::sync(running_mean); |
| 6502 | running_mean_ = at::functionalization::impl::from_functional_tensor(running_mean); |
| 6503 | } else { |
| 6504 | running_mean_ = running_mean; |
| 6505 | } |
| 6506 | |
| 6507 | c10::optional<at::Tensor> running_var_; |
| 6508 | if (at::functionalization::impl::isFunctionalTensor(running_var)) { |
| 6509 | at::functionalization::impl::sync(running_var); |
| 6510 | running_var_ = at::functionalization::impl::from_functional_tensor(running_var); |
| 6511 | } else { |
| 6512 | running_var_ = running_var; |
| 6513 | } |
| 6514 | |
| 6515 | c10::optional<at::Tensor> save_mean_; |
| 6516 | if (at::functionalization::impl::isFunctionalTensor(save_mean)) { |
| 6517 | at::functionalization::impl::sync(save_mean); |
| 6518 | save_mean_ = at::functionalization::impl::from_functional_tensor(save_mean); |
| 6519 | } else { |
| 6520 | save_mean_ = save_mean; |
| 6521 | } |
| 6522 | |
| 6523 | c10::optional<at::Tensor> save_var_; |
| 6524 | if (at::functionalization::impl::isFunctionalTensor(save_var)) { |
| 6525 | at::functionalization::impl::sync(save_var); |
| 6526 | save_var_ = at::functionalization::impl::from_functional_tensor(save_var); |
| 6527 | } else { |
| 6528 | save_var_ = save_var; |
| 6529 | } |
| 6530 | |
| 6531 | at::Tensor out0_; |
| 6532 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 6533 | at::functionalization::impl::sync(out0); |
| 6534 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 6535 | } else { |
| 6536 | out0_ = out0; |
| 6537 | } |
| 6538 | |
| 6539 | at::Tensor out1_; |
| 6540 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 6541 | at::functionalization::impl::sync(out1); |
| 6542 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 6543 | } else { |
| 6544 | out1_ = out1; |
| 6545 | } |
| 6546 | |
| 6547 | at::Tensor out2_; |
| 6548 | if (at::functionalization::impl::isFunctionalTensor(out2)) { |
| 6549 | at::functionalization::impl::sync(out2); |
| 6550 | out2_ = at::functionalization::impl::from_functional_tensor(out2); |
| 6551 | } else { |
| 6552 | out2_ = out2; |
| 6553 | } |
| 6554 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1) && at::functionalization::impl::isFunctionalTensor(out2))) { |
| 6555 | if ((false || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(running_mean) || at::functionalization::impl::isFunctionalTensor(running_var) || at::functionalization::impl::isFunctionalTensor(save_mean) || at::functionalization::impl::isFunctionalTensor(save_var))) { |
| 6556 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6557 | TORCH_INTERNAL_ASSERT(false, |
| 6558 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6559 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6560 | } else { |
| 6561 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6562 | at::AutoDispatchSkipFunctionalize guard; |
| 6563 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::miopen_batch_norm_backward_out::call(input_, grad_output_, weight_, running_mean_, running_var_, save_mean_, save_var_, epsilon, out0_, out1_, out2_); |
| 6564 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2);; |
| 6565 | } |
| 6566 | } else { |
| 6567 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 6568 | { |
| 6569 | at::AutoDispatchSkipFunctionalize guard; |
| 6570 | tmp_output = at::_ops::miopen_batch_norm_backward::call(input_, grad_output_, weight_, running_mean_, running_var_, save_mean_, save_var_, epsilon); |
| 6571 | } |
| 6572 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 6573 | at::functionalization::impl::commit_update(out0); |
| 6574 | at::functionalization::impl::sync(out0); |
| 6575 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 6576 | at::functionalization::impl::commit_update(out1); |
| 6577 | at::functionalization::impl::sync(out1); |
| 6578 | at::functionalization::impl::replace_(out2, std::get<2>(tmp_output)); |
| 6579 | at::functionalization::impl::commit_update(out2); |
| 6580 | at::functionalization::impl::sync(out2); |
| 6581 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2); |
| 6582 | } |
| 6583 | } |
| 6584 | |
| 6585 | at::Tensor & miopen_convolution_transpose_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, at::Tensor & out) { |
| 6586 | if (false) { |
| 6587 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6588 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6589 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6590 | auto self_meta = to_meta(self); |
| 6591 | auto weight_meta = to_meta(weight); |
| 6592 | auto bias_meta = to_meta(bias); |
| 6593 | auto out_meta = to_meta(out); |
| 6594 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6595 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6596 | at::_ops::miopen_convolution_transpose_out::call(self_meta, weight_meta, bias_meta, padding, output_padding, stride, dilation, groups, benchmark, deterministic, out_meta); |
| 6597 | } |
| 6598 | |
| 6599 | at::Tensor self_; |
| 6600 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6601 | at::functionalization::impl::sync(self); |
| 6602 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6603 | } else { |
| 6604 | self_ = self; |
| 6605 | } |
| 6606 | |
| 6607 | at::Tensor weight_; |
| 6608 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 6609 | at::functionalization::impl::sync(weight); |
| 6610 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 6611 | } else { |
| 6612 | weight_ = weight; |
| 6613 | } |
| 6614 | |
| 6615 | c10::optional<at::Tensor> bias_; |
| 6616 | if (at::functionalization::impl::isFunctionalTensor(bias)) { |
| 6617 | at::functionalization::impl::sync(bias); |
| 6618 | bias_ = at::functionalization::impl::from_functional_tensor(bias); |
| 6619 | } else { |
| 6620 | bias_ = bias; |
| 6621 | } |
| 6622 | |
| 6623 | at::Tensor out_; |
| 6624 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 6625 | at::functionalization::impl::sync(out); |
| 6626 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 6627 | } else { |
| 6628 | out_ = out; |
| 6629 | } |
| 6630 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 6631 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(bias))) { |
| 6632 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6633 | TORCH_INTERNAL_ASSERT(false, |
| 6634 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6635 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6636 | } else { |
| 6637 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6638 | at::AutoDispatchSkipFunctionalize guard; |
| 6639 | at::Tensor tmp_output = at::_ops::miopen_convolution_transpose_out::call(self_, weight_, bias_, padding, output_padding, stride, dilation, groups, benchmark, deterministic, out_); |
| 6640 | return out;; |
| 6641 | } |
| 6642 | } else { |
| 6643 | at::Tensor tmp_output; |
| 6644 | { |
| 6645 | at::AutoDispatchSkipFunctionalize guard; |
| 6646 | tmp_output = at::_ops::miopen_convolution_transpose::call(self_, weight_, bias_, padding, output_padding, stride, dilation, groups, benchmark, deterministic); |
| 6647 | } |
| 6648 | at::functionalization::impl::replace_(out, tmp_output); |
| 6649 | at::functionalization::impl::commit_update(out); |
| 6650 | at::functionalization::impl::sync(out); |
| 6651 | return out; |
| 6652 | } |
| 6653 | } |
| 6654 | |
| 6655 | at::Tensor & mm_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat2, at::Tensor & out) { |
| 6656 | if (false) { |
| 6657 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6658 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6659 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6660 | auto self_meta = to_meta(self); |
| 6661 | auto mat2_meta = to_meta(mat2); |
| 6662 | auto out_meta = to_meta(out); |
| 6663 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6664 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6665 | at::_ops::mm_out::call(self_meta, mat2_meta, out_meta); |
| 6666 | } |
| 6667 | |
| 6668 | at::Tensor self_; |
| 6669 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6670 | at::functionalization::impl::sync(self); |
| 6671 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6672 | } else { |
| 6673 | self_ = self; |
| 6674 | } |
| 6675 | |
| 6676 | at::Tensor mat2_; |
| 6677 | if (at::functionalization::impl::isFunctionalTensor(mat2)) { |
| 6678 | at::functionalization::impl::sync(mat2); |
| 6679 | mat2_ = at::functionalization::impl::from_functional_tensor(mat2); |
| 6680 | } else { |
| 6681 | mat2_ = mat2; |
| 6682 | } |
| 6683 | |
| 6684 | at::Tensor out_; |
| 6685 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 6686 | at::functionalization::impl::sync(out); |
| 6687 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 6688 | } else { |
| 6689 | out_ = out; |
| 6690 | } |
| 6691 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 6692 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(mat2))) { |
| 6693 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6694 | TORCH_INTERNAL_ASSERT(false, |
| 6695 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6696 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6697 | } else { |
| 6698 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6699 | at::AutoDispatchSkipFunctionalize guard; |
| 6700 | at::Tensor tmp_output = at::_ops::mm_out::call(self_, mat2_, out_); |
| 6701 | return out;; |
| 6702 | } |
| 6703 | } else { |
| 6704 | at::Tensor tmp_output; |
| 6705 | { |
| 6706 | at::AutoDispatchSkipFunctionalize guard; |
| 6707 | tmp_output = at::_ops::mm::call(self_, mat2_); |
| 6708 | } |
| 6709 | at::functionalization::impl::replace_(out, tmp_output); |
| 6710 | at::functionalization::impl::commit_update(out); |
| 6711 | at::functionalization::impl::sync(out); |
| 6712 | return out; |
| 6713 | } |
| 6714 | } |
| 6715 | |
| 6716 | at::Tensor & _sparse_sparse_matmul_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 6717 | if (false) { |
| 6718 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6719 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6720 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6721 | auto self_meta = to_meta(self); |
| 6722 | auto other_meta = to_meta(other); |
| 6723 | auto out_meta = to_meta(out); |
| 6724 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6725 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6726 | at::_ops::_sparse_sparse_matmul_out::call(self_meta, other_meta, out_meta); |
| 6727 | } |
| 6728 | |
| 6729 | at::Tensor self_; |
| 6730 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6731 | at::functionalization::impl::sync(self); |
| 6732 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6733 | } else { |
| 6734 | self_ = self; |
| 6735 | } |
| 6736 | |
| 6737 | at::Tensor other_; |
| 6738 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 6739 | at::functionalization::impl::sync(other); |
| 6740 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 6741 | } else { |
| 6742 | other_ = other; |
| 6743 | } |
| 6744 | |
| 6745 | at::Tensor out_; |
| 6746 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 6747 | at::functionalization::impl::sync(out); |
| 6748 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 6749 | } else { |
| 6750 | out_ = out; |
| 6751 | } |
| 6752 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 6753 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 6754 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6755 | TORCH_INTERNAL_ASSERT(false, |
| 6756 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6757 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6758 | } else { |
| 6759 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6760 | at::AutoDispatchSkipFunctionalize guard; |
| 6761 | at::Tensor tmp_output = at::_ops::_sparse_sparse_matmul_out::call(self_, other_, out_); |
| 6762 | return out;; |
| 6763 | } |
| 6764 | } else { |
| 6765 | at::Tensor tmp_output; |
| 6766 | { |
| 6767 | at::AutoDispatchSkipFunctionalize guard; |
| 6768 | tmp_output = at::_ops::_sparse_sparse_matmul::call(self_, other_); |
| 6769 | } |
| 6770 | at::functionalization::impl::replace_(out, tmp_output); |
| 6771 | at::functionalization::impl::commit_update(out); |
| 6772 | at::functionalization::impl::sync(out); |
| 6773 | return out; |
| 6774 | } |
| 6775 | } |
| 6776 | |
| 6777 | at::Tensor & mul_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 6778 | if (false) { |
| 6779 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6780 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6781 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6782 | auto self_meta = to_meta(self); |
| 6783 | auto other_meta = to_meta(other); |
| 6784 | auto out_meta = to_meta(out); |
| 6785 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6786 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6787 | at::_ops::mul_out::call(self_meta, other_meta, out_meta); |
| 6788 | } |
| 6789 | |
| 6790 | at::Tensor self_; |
| 6791 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6792 | at::functionalization::impl::sync(self); |
| 6793 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6794 | } else { |
| 6795 | self_ = self; |
| 6796 | } |
| 6797 | |
| 6798 | at::Tensor other_; |
| 6799 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 6800 | at::functionalization::impl::sync(other); |
| 6801 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 6802 | } else { |
| 6803 | other_ = other; |
| 6804 | } |
| 6805 | |
| 6806 | at::Tensor out_; |
| 6807 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 6808 | at::functionalization::impl::sync(out); |
| 6809 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 6810 | } else { |
| 6811 | out_ = out; |
| 6812 | } |
| 6813 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 6814 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 6815 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6816 | TORCH_INTERNAL_ASSERT(false, |
| 6817 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6818 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6819 | } else { |
| 6820 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6821 | at::AutoDispatchSkipFunctionalize guard; |
| 6822 | at::Tensor tmp_output = at::_ops::mul_out::call(self_, other_, out_); |
| 6823 | return out;; |
| 6824 | } |
| 6825 | } else { |
| 6826 | at::Tensor tmp_output; |
| 6827 | { |
| 6828 | at::AutoDispatchSkipFunctionalize guard; |
| 6829 | tmp_output = at::_ops::mul_Tensor::call(self_, other_); |
| 6830 | } |
| 6831 | at::functionalization::impl::replace_(out, tmp_output); |
| 6832 | at::functionalization::impl::commit_update(out); |
| 6833 | at::functionalization::impl::sync(out); |
| 6834 | return out; |
| 6835 | } |
| 6836 | } |
| 6837 | |
| 6838 | at::Tensor & mul__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 6839 | if (true) { |
| 6840 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6841 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6842 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6843 | auto self_meta = to_meta(self); |
| 6844 | auto other_meta = to_meta(other); |
| 6845 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6846 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6847 | at::_ops::mul__Tensor::call(self_meta, other_meta); |
| 6848 | } |
| 6849 | |
| 6850 | at::Tensor self_; |
| 6851 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6852 | at::functionalization::impl::sync(self); |
| 6853 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6854 | } else { |
| 6855 | self_ = self; |
| 6856 | } |
| 6857 | |
| 6858 | at::Tensor other_; |
| 6859 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 6860 | at::functionalization::impl::sync(other); |
| 6861 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 6862 | } else { |
| 6863 | other_ = other; |
| 6864 | } |
| 6865 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 6866 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 6867 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6868 | TORCH_INTERNAL_ASSERT(false, |
| 6869 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6870 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6871 | } else { |
| 6872 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6873 | at::AutoDispatchSkipFunctionalize guard; |
| 6874 | at::Tensor tmp_output = at::_ops::mul__Tensor::call(self_, other_); |
| 6875 | return self;; |
| 6876 | } |
| 6877 | } else { |
| 6878 | at::Tensor tmp_output; |
| 6879 | { |
| 6880 | at::AutoDispatchSkipFunctionalize guard; |
| 6881 | tmp_output = at::_ops::mul_Tensor::call(self_, other_); |
| 6882 | } |
| 6883 | at::functionalization::impl::replace_(self, tmp_output); |
| 6884 | at::functionalization::impl::commit_update(self); |
| 6885 | at::functionalization::impl::sync(self); |
| 6886 | return self; |
| 6887 | } |
| 6888 | } |
| 6889 | |
| 6890 | at::Tensor & mul_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 6891 | if (false) { |
| 6892 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6893 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6894 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6895 | auto self_meta = to_meta(self); |
| 6896 | auto out_meta = to_meta(out); |
| 6897 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6898 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6899 | at::_ops::mul_Scalar_out::call(self_meta, other, out_meta); |
| 6900 | } |
| 6901 | |
| 6902 | at::Tensor self_; |
| 6903 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6904 | at::functionalization::impl::sync(self); |
| 6905 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6906 | } else { |
| 6907 | self_ = self; |
| 6908 | } |
| 6909 | |
| 6910 | at::Tensor out_; |
| 6911 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 6912 | at::functionalization::impl::sync(out); |
| 6913 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 6914 | } else { |
| 6915 | out_ = out; |
| 6916 | } |
| 6917 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 6918 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 6919 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6920 | TORCH_INTERNAL_ASSERT(false, |
| 6921 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6922 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6923 | } else { |
| 6924 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6925 | at::AutoDispatchSkipFunctionalize guard; |
| 6926 | at::Tensor tmp_output = at::_ops::mul_Scalar_out::call(self_, other, out_); |
| 6927 | return out;; |
| 6928 | } |
| 6929 | } else { |
| 6930 | at::Tensor tmp_output; |
| 6931 | { |
| 6932 | at::AutoDispatchSkipFunctionalize guard; |
| 6933 | tmp_output = at::_ops::mul_Scalar::call(self_, other); |
| 6934 | } |
| 6935 | at::functionalization::impl::replace_(out, tmp_output); |
| 6936 | at::functionalization::impl::commit_update(out); |
| 6937 | at::functionalization::impl::sync(out); |
| 6938 | return out; |
| 6939 | } |
| 6940 | } |
| 6941 | |
| 6942 | at::Tensor & mul__Scalar(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { |
| 6943 | if (true) { |
| 6944 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6945 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6946 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6947 | auto self_meta = to_meta(self); |
| 6948 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6949 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6950 | at::_ops::mul__Scalar::call(self_meta, other); |
| 6951 | } |
| 6952 | |
| 6953 | at::Tensor self_; |
| 6954 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6955 | at::functionalization::impl::sync(self); |
| 6956 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 6957 | } else { |
| 6958 | self_ = self; |
| 6959 | } |
| 6960 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 6961 | if ((false)) { |
| 6962 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 6963 | TORCH_INTERNAL_ASSERT(false, |
| 6964 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 6965 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 6966 | } else { |
| 6967 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 6968 | at::AutoDispatchSkipFunctionalize guard; |
| 6969 | at::Tensor tmp_output = at::_ops::mul__Scalar::call(self_, other); |
| 6970 | return self;; |
| 6971 | } |
| 6972 | } else { |
| 6973 | at::Tensor tmp_output; |
| 6974 | { |
| 6975 | at::AutoDispatchSkipFunctionalize guard; |
| 6976 | tmp_output = at::_ops::mul_Scalar::call(self_, other); |
| 6977 | } |
| 6978 | at::functionalization::impl::replace_(self, tmp_output); |
| 6979 | at::functionalization::impl::commit_update(self); |
| 6980 | at::functionalization::impl::sync(self); |
| 6981 | return self; |
| 6982 | } |
| 6983 | } |
| 6984 | |
| 6985 | at::Tensor & mvlgamma_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t p, at::Tensor & out) { |
| 6986 | if (false) { |
| 6987 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 6988 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 6989 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 6990 | auto self_meta = to_meta(self); |
| 6991 | auto out_meta = to_meta(out); |
| 6992 | at::AutoDispatchSkipFunctionalize func_guard; |
| 6993 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 6994 | at::_ops::mvlgamma_out::call(self_meta, p, out_meta); |
| 6995 | } |
| 6996 | |
| 6997 | at::Tensor self_; |
| 6998 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 6999 | at::functionalization::impl::sync(self); |
| 7000 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7001 | } else { |
| 7002 | self_ = self; |
| 7003 | } |
| 7004 | |
| 7005 | at::Tensor out_; |
| 7006 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7007 | at::functionalization::impl::sync(out); |
| 7008 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7009 | } else { |
| 7010 | out_ = out; |
| 7011 | } |
| 7012 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7013 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 7014 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7015 | TORCH_INTERNAL_ASSERT(false, |
| 7016 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7017 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7018 | } else { |
| 7019 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7020 | at::AutoDispatchSkipFunctionalize guard; |
| 7021 | at::Tensor tmp_output = at::_ops::mvlgamma_out::call(self_, p, out_); |
| 7022 | return out;; |
| 7023 | } |
| 7024 | } else { |
| 7025 | at::Tensor tmp_output; |
| 7026 | { |
| 7027 | at::AutoDispatchSkipFunctionalize guard; |
| 7028 | tmp_output = at::_ops::mvlgamma::call(self_, p); |
| 7029 | } |
| 7030 | at::functionalization::impl::replace_(out, tmp_output); |
| 7031 | at::functionalization::impl::commit_update(out); |
| 7032 | at::functionalization::impl::sync(out); |
| 7033 | return out; |
| 7034 | } |
| 7035 | } |
| 7036 | |
| 7037 | at::Tensor & mvlgamma_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t p) { |
| 7038 | if (true) { |
| 7039 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7040 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7041 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7042 | auto self_meta = to_meta(self); |
| 7043 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7044 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7045 | at::_ops::mvlgamma_::call(self_meta, p); |
| 7046 | } |
| 7047 | |
| 7048 | at::Tensor self_; |
| 7049 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7050 | at::functionalization::impl::sync(self); |
| 7051 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7052 | } else { |
| 7053 | self_ = self; |
| 7054 | } |
| 7055 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 7056 | if ((false)) { |
| 7057 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7058 | TORCH_INTERNAL_ASSERT(false, |
| 7059 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7060 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7061 | } else { |
| 7062 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7063 | at::AutoDispatchSkipFunctionalize guard; |
| 7064 | at::Tensor tmp_output = at::_ops::mvlgamma_::call(self_, p); |
| 7065 | return self;; |
| 7066 | } |
| 7067 | } else { |
| 7068 | at::Tensor tmp_output; |
| 7069 | { |
| 7070 | at::AutoDispatchSkipFunctionalize guard; |
| 7071 | tmp_output = at::_ops::mvlgamma::call(self_, p); |
| 7072 | } |
| 7073 | at::functionalization::impl::replace_(self, tmp_output); |
| 7074 | at::functionalization::impl::commit_update(self); |
| 7075 | at::functionalization::impl::sync(self); |
| 7076 | return self; |
| 7077 | } |
| 7078 | } |
| 7079 | |
| 7080 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> batch_norm_backward_reduce_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional<at::Tensor> & weight, bool input_g, bool weight_g, bool bias_g, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3) { |
| 7081 | if (false) { |
| 7082 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7083 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7084 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7085 | auto grad_out_meta = to_meta(grad_out); |
| 7086 | auto input_meta = to_meta(input); |
| 7087 | auto mean_meta = to_meta(mean); |
| 7088 | auto invstd_meta = to_meta(invstd); |
| 7089 | auto weight_meta = to_meta(weight); |
| 7090 | auto out0_meta = to_meta(out0); |
| 7091 | auto out1_meta = to_meta(out1); |
| 7092 | auto out2_meta = to_meta(out2); |
| 7093 | auto out3_meta = to_meta(out3); |
| 7094 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7095 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7096 | at::_ops::batch_norm_backward_reduce_out::call(grad_out_meta, input_meta, mean_meta, invstd_meta, weight_meta, input_g, weight_g, bias_g, out0_meta, out1_meta, out2_meta, out3_meta); |
| 7097 | } |
| 7098 | |
| 7099 | at::Tensor grad_out_; |
| 7100 | if (at::functionalization::impl::isFunctionalTensor(grad_out)) { |
| 7101 | at::functionalization::impl::sync(grad_out); |
| 7102 | grad_out_ = at::functionalization::impl::from_functional_tensor(grad_out); |
| 7103 | } else { |
| 7104 | grad_out_ = grad_out; |
| 7105 | } |
| 7106 | |
| 7107 | at::Tensor input_; |
| 7108 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 7109 | at::functionalization::impl::sync(input); |
| 7110 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 7111 | } else { |
| 7112 | input_ = input; |
| 7113 | } |
| 7114 | |
| 7115 | at::Tensor mean_; |
| 7116 | if (at::functionalization::impl::isFunctionalTensor(mean)) { |
| 7117 | at::functionalization::impl::sync(mean); |
| 7118 | mean_ = at::functionalization::impl::from_functional_tensor(mean); |
| 7119 | } else { |
| 7120 | mean_ = mean; |
| 7121 | } |
| 7122 | |
| 7123 | at::Tensor invstd_; |
| 7124 | if (at::functionalization::impl::isFunctionalTensor(invstd)) { |
| 7125 | at::functionalization::impl::sync(invstd); |
| 7126 | invstd_ = at::functionalization::impl::from_functional_tensor(invstd); |
| 7127 | } else { |
| 7128 | invstd_ = invstd; |
| 7129 | } |
| 7130 | |
| 7131 | c10::optional<at::Tensor> weight_; |
| 7132 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 7133 | at::functionalization::impl::sync(weight); |
| 7134 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 7135 | } else { |
| 7136 | weight_ = weight; |
| 7137 | } |
| 7138 | |
| 7139 | at::Tensor out0_; |
| 7140 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 7141 | at::functionalization::impl::sync(out0); |
| 7142 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 7143 | } else { |
| 7144 | out0_ = out0; |
| 7145 | } |
| 7146 | |
| 7147 | at::Tensor out1_; |
| 7148 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 7149 | at::functionalization::impl::sync(out1); |
| 7150 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 7151 | } else { |
| 7152 | out1_ = out1; |
| 7153 | } |
| 7154 | |
| 7155 | at::Tensor out2_; |
| 7156 | if (at::functionalization::impl::isFunctionalTensor(out2)) { |
| 7157 | at::functionalization::impl::sync(out2); |
| 7158 | out2_ = at::functionalization::impl::from_functional_tensor(out2); |
| 7159 | } else { |
| 7160 | out2_ = out2; |
| 7161 | } |
| 7162 | |
| 7163 | at::Tensor out3_; |
| 7164 | if (at::functionalization::impl::isFunctionalTensor(out3)) { |
| 7165 | at::functionalization::impl::sync(out3); |
| 7166 | out3_ = at::functionalization::impl::from_functional_tensor(out3); |
| 7167 | } else { |
| 7168 | out3_ = out3; |
| 7169 | } |
| 7170 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1) && at::functionalization::impl::isFunctionalTensor(out2) && at::functionalization::impl::isFunctionalTensor(out3))) { |
| 7171 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_out) || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(mean) || at::functionalization::impl::isFunctionalTensor(invstd) || at::functionalization::impl::isFunctionalTensor(weight))) { |
| 7172 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7173 | TORCH_INTERNAL_ASSERT(false, |
| 7174 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7175 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7176 | } else { |
| 7177 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7178 | at::AutoDispatchSkipFunctionalize guard; |
| 7179 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::batch_norm_backward_reduce_out::call(grad_out_, input_, mean_, invstd_, weight_, input_g, weight_g, bias_g, out0_, out1_, out2_, out3_); |
| 7180 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2, out3);; |
| 7181 | } |
| 7182 | } else { |
| 7183 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 7184 | { |
| 7185 | at::AutoDispatchSkipFunctionalize guard; |
| 7186 | tmp_output = at::_ops::batch_norm_backward_reduce::call(grad_out_, input_, mean_, invstd_, weight_, input_g, weight_g, bias_g); |
| 7187 | } |
| 7188 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 7189 | at::functionalization::impl::commit_update(out0); |
| 7190 | at::functionalization::impl::sync(out0); |
| 7191 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 7192 | at::functionalization::impl::commit_update(out1); |
| 7193 | at::functionalization::impl::sync(out1); |
| 7194 | at::functionalization::impl::replace_(out2, std::get<2>(tmp_output)); |
| 7195 | at::functionalization::impl::commit_update(out2); |
| 7196 | at::functionalization::impl::sync(out2); |
| 7197 | at::functionalization::impl::replace_(out3, std::get<3>(tmp_output)); |
| 7198 | at::functionalization::impl::commit_update(out3); |
| 7199 | at::functionalization::impl::sync(out3); |
| 7200 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2, out3); |
| 7201 | } |
| 7202 | } |
| 7203 | |
| 7204 | at::Tensor & deg2rad_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 7205 | if (false) { |
| 7206 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7207 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7208 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7209 | auto self_meta = to_meta(self); |
| 7210 | auto out_meta = to_meta(out); |
| 7211 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7212 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7213 | at::_ops::deg2rad_out::call(self_meta, out_meta); |
| 7214 | } |
| 7215 | |
| 7216 | at::Tensor self_; |
| 7217 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7218 | at::functionalization::impl::sync(self); |
| 7219 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7220 | } else { |
| 7221 | self_ = self; |
| 7222 | } |
| 7223 | |
| 7224 | at::Tensor out_; |
| 7225 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7226 | at::functionalization::impl::sync(out); |
| 7227 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7228 | } else { |
| 7229 | out_ = out; |
| 7230 | } |
| 7231 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7232 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 7233 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7234 | TORCH_INTERNAL_ASSERT(false, |
| 7235 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7236 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7237 | } else { |
| 7238 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7239 | at::AutoDispatchSkipFunctionalize guard; |
| 7240 | at::Tensor tmp_output = at::_ops::deg2rad_out::call(self_, out_); |
| 7241 | return out;; |
| 7242 | } |
| 7243 | } else { |
| 7244 | at::Tensor tmp_output; |
| 7245 | { |
| 7246 | at::AutoDispatchSkipFunctionalize guard; |
| 7247 | tmp_output = at::_ops::deg2rad::call(self_); |
| 7248 | } |
| 7249 | at::functionalization::impl::replace_(out, tmp_output); |
| 7250 | at::functionalization::impl::commit_update(out); |
| 7251 | at::functionalization::impl::sync(out); |
| 7252 | return out; |
| 7253 | } |
| 7254 | } |
| 7255 | |
| 7256 | at::Tensor & deg2rad_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 7257 | if (true) { |
| 7258 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7259 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7260 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7261 | auto self_meta = to_meta(self); |
| 7262 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7263 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7264 | at::_ops::deg2rad_::call(self_meta); |
| 7265 | } |
| 7266 | |
| 7267 | at::Tensor self_; |
| 7268 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7269 | at::functionalization::impl::sync(self); |
| 7270 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7271 | } else { |
| 7272 | self_ = self; |
| 7273 | } |
| 7274 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 7275 | if ((false)) { |
| 7276 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7277 | TORCH_INTERNAL_ASSERT(false, |
| 7278 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7279 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7280 | } else { |
| 7281 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7282 | at::AutoDispatchSkipFunctionalize guard; |
| 7283 | at::Tensor tmp_output = at::_ops::deg2rad_::call(self_); |
| 7284 | return self;; |
| 7285 | } |
| 7286 | } else { |
| 7287 | at::Tensor tmp_output; |
| 7288 | { |
| 7289 | at::AutoDispatchSkipFunctionalize guard; |
| 7290 | tmp_output = at::_ops::deg2rad::call(self_); |
| 7291 | } |
| 7292 | at::functionalization::impl::replace_(self, tmp_output); |
| 7293 | at::functionalization::impl::commit_update(self); |
| 7294 | at::functionalization::impl::sync(self); |
| 7295 | return self; |
| 7296 | } |
| 7297 | } |
| 7298 | |
| 7299 | at::Tensor & randint_like_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t high, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out) { |
| 7300 | if (false) { |
| 7301 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7302 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7303 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7304 | auto self_meta = to_meta(self); |
| 7305 | auto out_meta = to_meta(out); |
| 7306 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7307 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7308 | at::_ops::randint_like_out::call(self_meta, high, memory_format, out_meta); |
| 7309 | } |
| 7310 | |
| 7311 | at::Tensor self_; |
| 7312 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7313 | at::functionalization::impl::sync(self); |
| 7314 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7315 | } else { |
| 7316 | self_ = self; |
| 7317 | } |
| 7318 | |
| 7319 | at::Tensor out_; |
| 7320 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7321 | at::functionalization::impl::sync(out); |
| 7322 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7323 | } else { |
| 7324 | out_ = out; |
| 7325 | } |
| 7326 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7327 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 7328 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7329 | TORCH_INTERNAL_ASSERT(false, |
| 7330 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7331 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7332 | } else { |
| 7333 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7334 | at::AutoDispatchSkipFunctionalize guard; |
| 7335 | at::Tensor tmp_output = at::_ops::randint_like_out::call(self_, high, memory_format, out_); |
| 7336 | return out;; |
| 7337 | } |
| 7338 | } else { |
| 7339 | at::Tensor tmp_output; |
| 7340 | { |
| 7341 | at::AutoDispatchSkipFunctionalize guard; |
| 7342 | tmp_output = at::_ops::randint_like::call(self_, high, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt, memory_format); |
| 7343 | } |
| 7344 | at::functionalization::impl::replace_(out, tmp_output); |
| 7345 | at::functionalization::impl::commit_update(out); |
| 7346 | at::functionalization::impl::sync(out); |
| 7347 | return out; |
| 7348 | } |
| 7349 | } |
| 7350 | |
| 7351 | at::Tensor & randint_like_out_low_dtype_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t low, int64_t high, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out) { |
| 7352 | if (false) { |
| 7353 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7354 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7355 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7356 | auto self_meta = to_meta(self); |
| 7357 | auto out_meta = to_meta(out); |
| 7358 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7359 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7360 | at::_ops::randint_like_low_dtype_out::call(self_meta, low, high, memory_format, out_meta); |
| 7361 | } |
| 7362 | |
| 7363 | at::Tensor self_; |
| 7364 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7365 | at::functionalization::impl::sync(self); |
| 7366 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7367 | } else { |
| 7368 | self_ = self; |
| 7369 | } |
| 7370 | |
| 7371 | at::Tensor out_; |
| 7372 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7373 | at::functionalization::impl::sync(out); |
| 7374 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7375 | } else { |
| 7376 | out_ = out; |
| 7377 | } |
| 7378 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7379 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 7380 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7381 | TORCH_INTERNAL_ASSERT(false, |
| 7382 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7383 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7384 | } else { |
| 7385 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7386 | at::AutoDispatchSkipFunctionalize guard; |
| 7387 | at::Tensor tmp_output = at::_ops::randint_like_low_dtype_out::call(self_, low, high, memory_format, out_); |
| 7388 | return out;; |
| 7389 | } |
| 7390 | } else { |
| 7391 | at::Tensor tmp_output; |
| 7392 | { |
| 7393 | at::AutoDispatchSkipFunctionalize guard; |
| 7394 | tmp_output = at::_ops::randint_like_low_dtype::call(self_, low, high, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt, memory_format); |
| 7395 | } |
| 7396 | at::functionalization::impl::replace_(out, tmp_output); |
| 7397 | at::functionalization::impl::commit_update(out); |
| 7398 | at::functionalization::impl::sync(out); |
| 7399 | return out; |
| 7400 | } |
| 7401 | } |
| 7402 | |
| 7403 | at::Tensor & repeat_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef repeats, at::Tensor & out) { |
| 7404 | if (false) { |
| 7405 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7406 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7407 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7408 | auto self_meta = to_meta(self); |
| 7409 | auto out_meta = to_meta(out); |
| 7410 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7411 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7412 | at::_ops::repeat_out::call(self_meta, repeats, out_meta); |
| 7413 | } |
| 7414 | |
| 7415 | at::Tensor self_; |
| 7416 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7417 | at::functionalization::impl::sync(self); |
| 7418 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7419 | } else { |
| 7420 | self_ = self; |
| 7421 | } |
| 7422 | |
| 7423 | at::Tensor out_; |
| 7424 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7425 | at::functionalization::impl::sync(out); |
| 7426 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7427 | } else { |
| 7428 | out_ = out; |
| 7429 | } |
| 7430 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7431 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 7432 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7433 | TORCH_INTERNAL_ASSERT(false, |
| 7434 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7435 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7436 | } else { |
| 7437 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7438 | at::AutoDispatchSkipFunctionalize guard; |
| 7439 | at::Tensor tmp_output = at::_ops::repeat_out::call(self_, repeats, out_); |
| 7440 | return out;; |
| 7441 | } |
| 7442 | } else { |
| 7443 | at::Tensor tmp_output; |
| 7444 | { |
| 7445 | at::AutoDispatchSkipFunctionalize guard; |
| 7446 | tmp_output = at::_ops::repeat::call(self_, repeats); |
| 7447 | } |
| 7448 | at::functionalization::impl::replace_(out, tmp_output); |
| 7449 | at::functionalization::impl::commit_update(out); |
| 7450 | at::functionalization::impl::sync(out); |
| 7451 | return out; |
| 7452 | } |
| 7453 | } |
| 7454 | |
| 7455 | at::Tensor & _mkldnn_reshape_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef shape, at::Tensor & out) { |
| 7456 | if (false) { |
| 7457 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7458 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7459 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7460 | auto self_meta = to_meta(self); |
| 7461 | auto out_meta = to_meta(out); |
| 7462 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7463 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7464 | at::_ops::_mkldnn_reshape_out::call(self_meta, shape, out_meta); |
| 7465 | } |
| 7466 | |
| 7467 | at::Tensor self_; |
| 7468 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7469 | at::functionalization::impl::sync(self); |
| 7470 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7471 | } else { |
| 7472 | self_ = self; |
| 7473 | } |
| 7474 | |
| 7475 | at::Tensor out_; |
| 7476 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7477 | at::functionalization::impl::sync(out); |
| 7478 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7479 | } else { |
| 7480 | out_ = out; |
| 7481 | } |
| 7482 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7483 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 7484 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7485 | TORCH_INTERNAL_ASSERT(false, |
| 7486 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7487 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7488 | } else { |
| 7489 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7490 | at::AutoDispatchSkipFunctionalize guard; |
| 7491 | at::Tensor tmp_output = at::_ops::_mkldnn_reshape_out::call(self_, shape, out_); |
| 7492 | return out;; |
| 7493 | } |
| 7494 | } else { |
| 7495 | at::Tensor tmp_output; |
| 7496 | { |
| 7497 | at::AutoDispatchSkipFunctionalize guard; |
| 7498 | tmp_output = at::_ops::_mkldnn_reshape::call(self_, shape); |
| 7499 | } |
| 7500 | at::functionalization::impl::replace_(out, tmp_output); |
| 7501 | at::functionalization::impl::commit_update(out); |
| 7502 | at::functionalization::impl::sync(out); |
| 7503 | return out; |
| 7504 | } |
| 7505 | } |
| 7506 | |
| 7507 | at::Tensor & round_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 7508 | if (false) { |
| 7509 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7510 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7511 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7512 | auto self_meta = to_meta(self); |
| 7513 | auto out_meta = to_meta(out); |
| 7514 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7515 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7516 | at::_ops::round_out::call(self_meta, out_meta); |
| 7517 | } |
| 7518 | |
| 7519 | at::Tensor self_; |
| 7520 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7521 | at::functionalization::impl::sync(self); |
| 7522 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7523 | } else { |
| 7524 | self_ = self; |
| 7525 | } |
| 7526 | |
| 7527 | at::Tensor out_; |
| 7528 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7529 | at::functionalization::impl::sync(out); |
| 7530 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7531 | } else { |
| 7532 | out_ = out; |
| 7533 | } |
| 7534 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7535 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 7536 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7537 | TORCH_INTERNAL_ASSERT(false, |
| 7538 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7539 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7540 | } else { |
| 7541 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7542 | at::AutoDispatchSkipFunctionalize guard; |
| 7543 | at::Tensor tmp_output = at::_ops::round_out::call(self_, out_); |
| 7544 | return out;; |
| 7545 | } |
| 7546 | } else { |
| 7547 | at::Tensor tmp_output; |
| 7548 | { |
| 7549 | at::AutoDispatchSkipFunctionalize guard; |
| 7550 | tmp_output = at::_ops::round::call(self_); |
| 7551 | } |
| 7552 | at::functionalization::impl::replace_(out, tmp_output); |
| 7553 | at::functionalization::impl::commit_update(out); |
| 7554 | at::functionalization::impl::sync(out); |
| 7555 | return out; |
| 7556 | } |
| 7557 | } |
| 7558 | |
| 7559 | at::Tensor & round_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 7560 | if (true) { |
| 7561 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7562 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7563 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7564 | auto self_meta = to_meta(self); |
| 7565 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7566 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7567 | at::_ops::round_::call(self_meta); |
| 7568 | } |
| 7569 | |
| 7570 | at::Tensor self_; |
| 7571 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7572 | at::functionalization::impl::sync(self); |
| 7573 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7574 | } else { |
| 7575 | self_ = self; |
| 7576 | } |
| 7577 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 7578 | if ((false)) { |
| 7579 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7580 | TORCH_INTERNAL_ASSERT(false, |
| 7581 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7582 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7583 | } else { |
| 7584 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7585 | at::AutoDispatchSkipFunctionalize guard; |
| 7586 | at::Tensor tmp_output = at::_ops::round_::call(self_); |
| 7587 | return self;; |
| 7588 | } |
| 7589 | } else { |
| 7590 | at::Tensor tmp_output; |
| 7591 | { |
| 7592 | at::AutoDispatchSkipFunctionalize guard; |
| 7593 | tmp_output = at::_ops::round::call(self_); |
| 7594 | } |
| 7595 | at::functionalization::impl::replace_(self, tmp_output); |
| 7596 | at::functionalization::impl::commit_update(self); |
| 7597 | at::functionalization::impl::sync(self); |
| 7598 | return self; |
| 7599 | } |
| 7600 | } |
| 7601 | |
| 7602 | at::Tensor & round_out_decimals_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t decimals, at::Tensor & out) { |
| 7603 | if (false) { |
| 7604 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7605 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7606 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7607 | auto self_meta = to_meta(self); |
| 7608 | auto out_meta = to_meta(out); |
| 7609 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7610 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7611 | at::_ops::round_decimals_out::call(self_meta, decimals, out_meta); |
| 7612 | } |
| 7613 | |
| 7614 | at::Tensor self_; |
| 7615 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7616 | at::functionalization::impl::sync(self); |
| 7617 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7618 | } else { |
| 7619 | self_ = self; |
| 7620 | } |
| 7621 | |
| 7622 | at::Tensor out_; |
| 7623 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7624 | at::functionalization::impl::sync(out); |
| 7625 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7626 | } else { |
| 7627 | out_ = out; |
| 7628 | } |
| 7629 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7630 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 7631 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7632 | TORCH_INTERNAL_ASSERT(false, |
| 7633 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7634 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7635 | } else { |
| 7636 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7637 | at::AutoDispatchSkipFunctionalize guard; |
| 7638 | at::Tensor tmp_output = at::_ops::round_decimals_out::call(self_, decimals, out_); |
| 7639 | return out;; |
| 7640 | } |
| 7641 | } else { |
| 7642 | at::Tensor tmp_output; |
| 7643 | { |
| 7644 | at::AutoDispatchSkipFunctionalize guard; |
| 7645 | tmp_output = at::_ops::round_decimals::call(self_, decimals); |
| 7646 | } |
| 7647 | at::functionalization::impl::replace_(out, tmp_output); |
| 7648 | at::functionalization::impl::commit_update(out); |
| 7649 | at::functionalization::impl::sync(out); |
| 7650 | return out; |
| 7651 | } |
| 7652 | } |
| 7653 | |
| 7654 | at::Tensor & round__decimals(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t decimals) { |
| 7655 | if (true) { |
| 7656 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7657 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7658 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7659 | auto self_meta = to_meta(self); |
| 7660 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7661 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7662 | at::_ops::round__decimals::call(self_meta, decimals); |
| 7663 | } |
| 7664 | |
| 7665 | at::Tensor self_; |
| 7666 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7667 | at::functionalization::impl::sync(self); |
| 7668 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7669 | } else { |
| 7670 | self_ = self; |
| 7671 | } |
| 7672 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 7673 | if ((false)) { |
| 7674 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7675 | TORCH_INTERNAL_ASSERT(false, |
| 7676 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7677 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7678 | } else { |
| 7679 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7680 | at::AutoDispatchSkipFunctionalize guard; |
| 7681 | at::Tensor tmp_output = at::_ops::round__decimals::call(self_, decimals); |
| 7682 | return self;; |
| 7683 | } |
| 7684 | } else { |
| 7685 | at::Tensor tmp_output; |
| 7686 | { |
| 7687 | at::AutoDispatchSkipFunctionalize guard; |
| 7688 | tmp_output = at::_ops::round_decimals::call(self_, decimals); |
| 7689 | } |
| 7690 | at::functionalization::impl::replace_(self, tmp_output); |
| 7691 | at::functionalization::impl::commit_update(self); |
| 7692 | at::functionalization::impl::sync(self); |
| 7693 | return self; |
| 7694 | } |
| 7695 | } |
| 7696 | |
| 7697 | at::Tensor & rsqrt_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 7698 | if (false) { |
| 7699 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7700 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7701 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7702 | auto self_meta = to_meta(self); |
| 7703 | auto out_meta = to_meta(out); |
| 7704 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7705 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7706 | at::_ops::rsqrt_out::call(self_meta, out_meta); |
| 7707 | } |
| 7708 | |
| 7709 | at::Tensor self_; |
| 7710 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7711 | at::functionalization::impl::sync(self); |
| 7712 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7713 | } else { |
| 7714 | self_ = self; |
| 7715 | } |
| 7716 | |
| 7717 | at::Tensor out_; |
| 7718 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7719 | at::functionalization::impl::sync(out); |
| 7720 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7721 | } else { |
| 7722 | out_ = out; |
| 7723 | } |
| 7724 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7725 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 7726 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7727 | TORCH_INTERNAL_ASSERT(false, |
| 7728 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7729 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7730 | } else { |
| 7731 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7732 | at::AutoDispatchSkipFunctionalize guard; |
| 7733 | at::Tensor tmp_output = at::_ops::rsqrt_out::call(self_, out_); |
| 7734 | return out;; |
| 7735 | } |
| 7736 | } else { |
| 7737 | at::Tensor tmp_output; |
| 7738 | { |
| 7739 | at::AutoDispatchSkipFunctionalize guard; |
| 7740 | tmp_output = at::_ops::rsqrt::call(self_); |
| 7741 | } |
| 7742 | at::functionalization::impl::replace_(out, tmp_output); |
| 7743 | at::functionalization::impl::commit_update(out); |
| 7744 | at::functionalization::impl::sync(out); |
| 7745 | return out; |
| 7746 | } |
| 7747 | } |
| 7748 | |
| 7749 | at::Tensor & rsqrt_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 7750 | if (true) { |
| 7751 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7752 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7753 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7754 | auto self_meta = to_meta(self); |
| 7755 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7756 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7757 | at::_ops::rsqrt_::call(self_meta); |
| 7758 | } |
| 7759 | |
| 7760 | at::Tensor self_; |
| 7761 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7762 | at::functionalization::impl::sync(self); |
| 7763 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7764 | } else { |
| 7765 | self_ = self; |
| 7766 | } |
| 7767 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 7768 | if ((false)) { |
| 7769 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7770 | TORCH_INTERNAL_ASSERT(false, |
| 7771 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7772 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7773 | } else { |
| 7774 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7775 | at::AutoDispatchSkipFunctionalize guard; |
| 7776 | at::Tensor tmp_output = at::_ops::rsqrt_::call(self_); |
| 7777 | return self;; |
| 7778 | } |
| 7779 | } else { |
| 7780 | at::Tensor tmp_output; |
| 7781 | { |
| 7782 | at::AutoDispatchSkipFunctionalize guard; |
| 7783 | tmp_output = at::_ops::rsqrt::call(self_); |
| 7784 | } |
| 7785 | at::functionalization::impl::replace_(self, tmp_output); |
| 7786 | at::functionalization::impl::commit_update(self); |
| 7787 | at::functionalization::impl::sync(self); |
| 7788 | return self; |
| 7789 | } |
| 7790 | } |
| 7791 | |
| 7792 | at::Tensor & celu_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & alpha, at::Tensor & out) { |
| 7793 | if (false) { |
| 7794 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7795 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7796 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7797 | auto self_meta = to_meta(self); |
| 7798 | auto out_meta = to_meta(out); |
| 7799 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7800 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7801 | at::_ops::celu_out::call(self_meta, alpha, out_meta); |
| 7802 | } |
| 7803 | |
| 7804 | at::Tensor self_; |
| 7805 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7806 | at::functionalization::impl::sync(self); |
| 7807 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7808 | } else { |
| 7809 | self_ = self; |
| 7810 | } |
| 7811 | |
| 7812 | at::Tensor out_; |
| 7813 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7814 | at::functionalization::impl::sync(out); |
| 7815 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7816 | } else { |
| 7817 | out_ = out; |
| 7818 | } |
| 7819 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7820 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 7821 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7822 | TORCH_INTERNAL_ASSERT(false, |
| 7823 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7824 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7825 | } else { |
| 7826 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7827 | at::AutoDispatchSkipFunctionalize guard; |
| 7828 | at::Tensor tmp_output = at::_ops::celu_out::call(self_, alpha, out_); |
| 7829 | return out;; |
| 7830 | } |
| 7831 | } else { |
| 7832 | at::Tensor tmp_output; |
| 7833 | { |
| 7834 | at::AutoDispatchSkipFunctionalize guard; |
| 7835 | tmp_output = at::_ops::celu::call(self_, alpha); |
| 7836 | } |
| 7837 | at::functionalization::impl::replace_(out, tmp_output); |
| 7838 | at::functionalization::impl::commit_update(out); |
| 7839 | at::functionalization::impl::sync(out); |
| 7840 | return out; |
| 7841 | } |
| 7842 | } |
| 7843 | |
| 7844 | at::Tensor & celu_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & alpha) { |
| 7845 | if (true) { |
| 7846 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7847 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7848 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7849 | auto self_meta = to_meta(self); |
| 7850 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7851 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7852 | at::_ops::celu_::call(self_meta, alpha); |
| 7853 | } |
| 7854 | |
| 7855 | at::Tensor self_; |
| 7856 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7857 | at::functionalization::impl::sync(self); |
| 7858 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7859 | } else { |
| 7860 | self_ = self; |
| 7861 | } |
| 7862 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 7863 | if ((false)) { |
| 7864 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7865 | TORCH_INTERNAL_ASSERT(false, |
| 7866 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7867 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7868 | } else { |
| 7869 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7870 | at::AutoDispatchSkipFunctionalize guard; |
| 7871 | at::Tensor tmp_output = at::_ops::celu_::call(self_, alpha); |
| 7872 | return self;; |
| 7873 | } |
| 7874 | } else { |
| 7875 | at::Tensor tmp_output; |
| 7876 | { |
| 7877 | at::AutoDispatchSkipFunctionalize guard; |
| 7878 | tmp_output = at::_ops::celu::call(self_, alpha); |
| 7879 | } |
| 7880 | at::functionalization::impl::replace_(self, tmp_output); |
| 7881 | at::functionalization::impl::commit_update(self); |
| 7882 | at::functionalization::impl::sync(self); |
| 7883 | return self; |
| 7884 | } |
| 7885 | } |
| 7886 | |
| 7887 | at::Tensor & sigmoid_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 7888 | if (false) { |
| 7889 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7890 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7891 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7892 | auto self_meta = to_meta(self); |
| 7893 | auto out_meta = to_meta(out); |
| 7894 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7895 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7896 | at::_ops::sigmoid_out::call(self_meta, out_meta); |
| 7897 | } |
| 7898 | |
| 7899 | at::Tensor self_; |
| 7900 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7901 | at::functionalization::impl::sync(self); |
| 7902 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7903 | } else { |
| 7904 | self_ = self; |
| 7905 | } |
| 7906 | |
| 7907 | at::Tensor out_; |
| 7908 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 7909 | at::functionalization::impl::sync(out); |
| 7910 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 7911 | } else { |
| 7912 | out_ = out; |
| 7913 | } |
| 7914 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 7915 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 7916 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7917 | TORCH_INTERNAL_ASSERT(false, |
| 7918 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7919 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7920 | } else { |
| 7921 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7922 | at::AutoDispatchSkipFunctionalize guard; |
| 7923 | at::Tensor tmp_output = at::_ops::sigmoid_out::call(self_, out_); |
| 7924 | return out;; |
| 7925 | } |
| 7926 | } else { |
| 7927 | at::Tensor tmp_output; |
| 7928 | { |
| 7929 | at::AutoDispatchSkipFunctionalize guard; |
| 7930 | tmp_output = at::_ops::sigmoid::call(self_); |
| 7931 | } |
| 7932 | at::functionalization::impl::replace_(out, tmp_output); |
| 7933 | at::functionalization::impl::commit_update(out); |
| 7934 | at::functionalization::impl::sync(out); |
| 7935 | return out; |
| 7936 | } |
| 7937 | } |
| 7938 | |
| 7939 | at::Tensor & sigmoid_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 7940 | if (true) { |
| 7941 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7942 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7943 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7944 | auto self_meta = to_meta(self); |
| 7945 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7946 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7947 | at::_ops::sigmoid_::call(self_meta); |
| 7948 | } |
| 7949 | |
| 7950 | at::Tensor self_; |
| 7951 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7952 | at::functionalization::impl::sync(self); |
| 7953 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7954 | } else { |
| 7955 | self_ = self; |
| 7956 | } |
| 7957 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 7958 | if ((false)) { |
| 7959 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 7960 | TORCH_INTERNAL_ASSERT(false, |
| 7961 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 7962 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 7963 | } else { |
| 7964 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 7965 | at::AutoDispatchSkipFunctionalize guard; |
| 7966 | at::Tensor tmp_output = at::_ops::sigmoid_::call(self_); |
| 7967 | return self;; |
| 7968 | } |
| 7969 | } else { |
| 7970 | at::Tensor tmp_output; |
| 7971 | { |
| 7972 | at::AutoDispatchSkipFunctionalize guard; |
| 7973 | tmp_output = at::_ops::sigmoid::call(self_); |
| 7974 | } |
| 7975 | at::functionalization::impl::replace_(self, tmp_output); |
| 7976 | at::functionalization::impl::commit_update(self); |
| 7977 | at::functionalization::impl::sync(self); |
| 7978 | return self; |
| 7979 | } |
| 7980 | } |
| 7981 | |
| 7982 | at::Tensor & sinc_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 7983 | if (false) { |
| 7984 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 7985 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 7986 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 7987 | auto self_meta = to_meta(self); |
| 7988 | auto out_meta = to_meta(out); |
| 7989 | at::AutoDispatchSkipFunctionalize func_guard; |
| 7990 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 7991 | at::_ops::sinc_out::call(self_meta, out_meta); |
| 7992 | } |
| 7993 | |
| 7994 | at::Tensor self_; |
| 7995 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 7996 | at::functionalization::impl::sync(self); |
| 7997 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 7998 | } else { |
| 7999 | self_ = self; |
| 8000 | } |
| 8001 | |
| 8002 | at::Tensor out_; |
| 8003 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8004 | at::functionalization::impl::sync(out); |
| 8005 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8006 | } else { |
| 8007 | out_ = out; |
| 8008 | } |
| 8009 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8010 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8011 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8012 | TORCH_INTERNAL_ASSERT(false, |
| 8013 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8014 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8015 | } else { |
| 8016 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8017 | at::AutoDispatchSkipFunctionalize guard; |
| 8018 | at::Tensor tmp_output = at::_ops::sinc_out::call(self_, out_); |
| 8019 | return out;; |
| 8020 | } |
| 8021 | } else { |
| 8022 | at::Tensor tmp_output; |
| 8023 | { |
| 8024 | at::AutoDispatchSkipFunctionalize guard; |
| 8025 | tmp_output = at::_ops::sinc::call(self_); |
| 8026 | } |
| 8027 | at::functionalization::impl::replace_(out, tmp_output); |
| 8028 | at::functionalization::impl::commit_update(out); |
| 8029 | at::functionalization::impl::sync(out); |
| 8030 | return out; |
| 8031 | } |
| 8032 | } |
| 8033 | |
| 8034 | at::Tensor & sinc_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 8035 | if (true) { |
| 8036 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8037 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8038 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8039 | auto self_meta = to_meta(self); |
| 8040 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8041 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8042 | at::_ops::sinc_::call(self_meta); |
| 8043 | } |
| 8044 | |
| 8045 | at::Tensor self_; |
| 8046 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8047 | at::functionalization::impl::sync(self); |
| 8048 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8049 | } else { |
| 8050 | self_ = self; |
| 8051 | } |
| 8052 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 8053 | if ((false)) { |
| 8054 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8055 | TORCH_INTERNAL_ASSERT(false, |
| 8056 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8057 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8058 | } else { |
| 8059 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8060 | at::AutoDispatchSkipFunctionalize guard; |
| 8061 | at::Tensor tmp_output = at::_ops::sinc_::call(self_); |
| 8062 | return self;; |
| 8063 | } |
| 8064 | } else { |
| 8065 | at::Tensor tmp_output; |
| 8066 | { |
| 8067 | at::AutoDispatchSkipFunctionalize guard; |
| 8068 | tmp_output = at::_ops::sinc::call(self_); |
| 8069 | } |
| 8070 | at::functionalization::impl::replace_(self, tmp_output); |
| 8071 | at::functionalization::impl::commit_update(self); |
| 8072 | at::functionalization::impl::sync(self); |
| 8073 | return self; |
| 8074 | } |
| 8075 | } |
| 8076 | |
| 8077 | at::Tensor & sinh_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 8078 | if (false) { |
| 8079 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8080 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8081 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8082 | auto self_meta = to_meta(self); |
| 8083 | auto out_meta = to_meta(out); |
| 8084 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8085 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8086 | at::_ops::sinh_out::call(self_meta, out_meta); |
| 8087 | } |
| 8088 | |
| 8089 | at::Tensor self_; |
| 8090 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8091 | at::functionalization::impl::sync(self); |
| 8092 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8093 | } else { |
| 8094 | self_ = self; |
| 8095 | } |
| 8096 | |
| 8097 | at::Tensor out_; |
| 8098 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8099 | at::functionalization::impl::sync(out); |
| 8100 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8101 | } else { |
| 8102 | out_ = out; |
| 8103 | } |
| 8104 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8105 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8106 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8107 | TORCH_INTERNAL_ASSERT(false, |
| 8108 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8109 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8110 | } else { |
| 8111 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8112 | at::AutoDispatchSkipFunctionalize guard; |
| 8113 | at::Tensor tmp_output = at::_ops::sinh_out::call(self_, out_); |
| 8114 | return out;; |
| 8115 | } |
| 8116 | } else { |
| 8117 | at::Tensor tmp_output; |
| 8118 | { |
| 8119 | at::AutoDispatchSkipFunctionalize guard; |
| 8120 | tmp_output = at::_ops::sinh::call(self_); |
| 8121 | } |
| 8122 | at::functionalization::impl::replace_(out, tmp_output); |
| 8123 | at::functionalization::impl::commit_update(out); |
| 8124 | at::functionalization::impl::sync(out); |
| 8125 | return out; |
| 8126 | } |
| 8127 | } |
| 8128 | |
| 8129 | at::Tensor & sinh_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 8130 | if (true) { |
| 8131 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8132 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8133 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8134 | auto self_meta = to_meta(self); |
| 8135 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8136 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8137 | at::_ops::sinh_::call(self_meta); |
| 8138 | } |
| 8139 | |
| 8140 | at::Tensor self_; |
| 8141 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8142 | at::functionalization::impl::sync(self); |
| 8143 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8144 | } else { |
| 8145 | self_ = self; |
| 8146 | } |
| 8147 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 8148 | if ((false)) { |
| 8149 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8150 | TORCH_INTERNAL_ASSERT(false, |
| 8151 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8152 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8153 | } else { |
| 8154 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8155 | at::AutoDispatchSkipFunctionalize guard; |
| 8156 | at::Tensor tmp_output = at::_ops::sinh_::call(self_); |
| 8157 | return self;; |
| 8158 | } |
| 8159 | } else { |
| 8160 | at::Tensor tmp_output; |
| 8161 | { |
| 8162 | at::AutoDispatchSkipFunctionalize guard; |
| 8163 | tmp_output = at::_ops::sinh::call(self_); |
| 8164 | } |
| 8165 | at::functionalization::impl::replace_(self, tmp_output); |
| 8166 | at::functionalization::impl::commit_update(self); |
| 8167 | at::functionalization::impl::sync(self); |
| 8168 | return self; |
| 8169 | } |
| 8170 | } |
| 8171 | |
| 8172 | at::Tensor & slice_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step, at::Tensor & out) { |
| 8173 | if (false) { |
| 8174 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8175 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8176 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8177 | auto grad_output_meta = to_meta(grad_output); |
| 8178 | auto out_meta = to_meta(out); |
| 8179 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8180 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8181 | at::_ops::slice_backward_out::call(grad_output_meta, input_sizes, dim, start, end, step, out_meta); |
| 8182 | } |
| 8183 | |
| 8184 | at::Tensor grad_output_; |
| 8185 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 8186 | at::functionalization::impl::sync(grad_output); |
| 8187 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 8188 | } else { |
| 8189 | grad_output_ = grad_output; |
| 8190 | } |
| 8191 | |
| 8192 | at::Tensor out_; |
| 8193 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8194 | at::functionalization::impl::sync(out); |
| 8195 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8196 | } else { |
| 8197 | out_ = out; |
| 8198 | } |
| 8199 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8200 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output))) { |
| 8201 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8202 | TORCH_INTERNAL_ASSERT(false, |
| 8203 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8204 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8205 | } else { |
| 8206 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8207 | at::AutoDispatchSkipFunctionalize guard; |
| 8208 | at::Tensor tmp_output = at::_ops::slice_backward_out::call(grad_output_, input_sizes, dim, start, end, step, out_); |
| 8209 | return out;; |
| 8210 | } |
| 8211 | } else { |
| 8212 | at::Tensor tmp_output; |
| 8213 | { |
| 8214 | at::AutoDispatchSkipFunctionalize guard; |
| 8215 | tmp_output = at::_ops::slice_backward::call(grad_output_, input_sizes, dim, start, end, step); |
| 8216 | } |
| 8217 | at::functionalization::impl::replace_(out, tmp_output); |
| 8218 | at::functionalization::impl::commit_update(out); |
| 8219 | at::functionalization::impl::sync(out); |
| 8220 | return out; |
| 8221 | } |
| 8222 | } |
| 8223 | |
| 8224 | at::Tensor & as_strided_scatter_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional<c10::SymInt> storage_offset, at::Tensor & out) { |
| 8225 | if (false) { |
| 8226 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8227 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8228 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8229 | auto self_meta = to_meta(self); |
| 8230 | auto src_meta = to_meta(src); |
| 8231 | auto out_meta = to_meta(out); |
| 8232 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8233 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8234 | at::_ops::as_strided_scatter_out::call(self_meta, src_meta, size, stride, storage_offset, out_meta); |
| 8235 | } |
| 8236 | |
| 8237 | at::Tensor self_; |
| 8238 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8239 | at::functionalization::impl::sync(self); |
| 8240 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8241 | } else { |
| 8242 | self_ = self; |
| 8243 | } |
| 8244 | |
| 8245 | at::Tensor src_; |
| 8246 | if (at::functionalization::impl::isFunctionalTensor(src)) { |
| 8247 | at::functionalization::impl::sync(src); |
| 8248 | src_ = at::functionalization::impl::from_functional_tensor(src); |
| 8249 | } else { |
| 8250 | src_ = src; |
| 8251 | } |
| 8252 | |
| 8253 | at::Tensor out_; |
| 8254 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8255 | at::functionalization::impl::sync(out); |
| 8256 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8257 | } else { |
| 8258 | out_ = out; |
| 8259 | } |
| 8260 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8261 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(src))) { |
| 8262 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8263 | TORCH_INTERNAL_ASSERT(false, |
| 8264 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8265 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8266 | } else { |
| 8267 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8268 | at::AutoDispatchSkipFunctionalize guard; |
| 8269 | at::Tensor tmp_output = at::_ops::as_strided_scatter_out::call(self_, src_, size, stride, storage_offset, out_); |
| 8270 | return out;; |
| 8271 | } |
| 8272 | } else { |
| 8273 | at::Tensor tmp_output; |
| 8274 | { |
| 8275 | at::AutoDispatchSkipFunctionalize guard; |
| 8276 | tmp_output = at::_ops::as_strided_scatter::call(self_, src_, size, stride, storage_offset); |
| 8277 | } |
| 8278 | at::functionalization::impl::replace_(out, tmp_output); |
| 8279 | at::functionalization::impl::commit_update(out); |
| 8280 | at::functionalization::impl::sync(out); |
| 8281 | return out; |
| 8282 | } |
| 8283 | } |
| 8284 | |
| 8285 | ::std::tuple<at::Tensor &,at::Tensor &> std_mean_out_correction_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, c10::optional<int64_t> correction, bool keepdim, at::Tensor & out0, at::Tensor & out1) { |
| 8286 | if (false) { |
| 8287 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8288 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8289 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8290 | auto self_meta = to_meta(self); |
| 8291 | auto out0_meta = to_meta(out0); |
| 8292 | auto out1_meta = to_meta(out1); |
| 8293 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8294 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8295 | at::_ops::std_mean_correction_out::call(self_meta, dim, correction, keepdim, out0_meta, out1_meta); |
| 8296 | } |
| 8297 | |
| 8298 | at::Tensor self_; |
| 8299 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8300 | at::functionalization::impl::sync(self); |
| 8301 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8302 | } else { |
| 8303 | self_ = self; |
| 8304 | } |
| 8305 | |
| 8306 | at::Tensor out0_; |
| 8307 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 8308 | at::functionalization::impl::sync(out0); |
| 8309 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 8310 | } else { |
| 8311 | out0_ = out0; |
| 8312 | } |
| 8313 | |
| 8314 | at::Tensor out1_; |
| 8315 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 8316 | at::functionalization::impl::sync(out1); |
| 8317 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 8318 | } else { |
| 8319 | out1_ = out1; |
| 8320 | } |
| 8321 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1))) { |
| 8322 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8323 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8324 | TORCH_INTERNAL_ASSERT(false, |
| 8325 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8326 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8327 | } else { |
| 8328 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8329 | at::AutoDispatchSkipFunctionalize guard; |
| 8330 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::std_mean_correction_out::call(self_, dim, correction, keepdim, out0_, out1_); |
| 8331 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1);; |
| 8332 | } |
| 8333 | } else { |
| 8334 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 8335 | { |
| 8336 | at::AutoDispatchSkipFunctionalize guard; |
| 8337 | tmp_output = at::_ops::std_mean_correction::call(self_, dim, correction, keepdim); |
| 8338 | } |
| 8339 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 8340 | at::functionalization::impl::commit_update(out0); |
| 8341 | at::functionalization::impl::sync(out0); |
| 8342 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 8343 | at::functionalization::impl::commit_update(out1); |
| 8344 | at::functionalization::impl::sync(out1); |
| 8345 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1); |
| 8346 | } |
| 8347 | } |
| 8348 | |
| 8349 | at::Tensor & _mkldnn_transpose_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim0, int64_t dim1, at::Tensor & out) { |
| 8350 | if (false) { |
| 8351 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8352 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8353 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8354 | auto self_meta = to_meta(self); |
| 8355 | auto out_meta = to_meta(out); |
| 8356 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8357 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8358 | at::_ops::_mkldnn_transpose_out::call(self_meta, dim0, dim1, out_meta); |
| 8359 | } |
| 8360 | |
| 8361 | at::Tensor self_; |
| 8362 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8363 | at::functionalization::impl::sync(self); |
| 8364 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8365 | } else { |
| 8366 | self_ = self; |
| 8367 | } |
| 8368 | |
| 8369 | at::Tensor out_; |
| 8370 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8371 | at::functionalization::impl::sync(out); |
| 8372 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8373 | } else { |
| 8374 | out_ = out; |
| 8375 | } |
| 8376 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8377 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8378 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8379 | TORCH_INTERNAL_ASSERT(false, |
| 8380 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8381 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8382 | } else { |
| 8383 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8384 | at::AutoDispatchSkipFunctionalize guard; |
| 8385 | at::Tensor tmp_output = at::_ops::_mkldnn_transpose_out::call(self_, dim0, dim1, out_); |
| 8386 | return out;; |
| 8387 | } |
| 8388 | } else { |
| 8389 | at::Tensor tmp_output; |
| 8390 | { |
| 8391 | at::AutoDispatchSkipFunctionalize guard; |
| 8392 | tmp_output = at::_ops::_mkldnn_transpose::call(self_, dim0, dim1); |
| 8393 | } |
| 8394 | at::functionalization::impl::replace_(out, tmp_output); |
| 8395 | at::functionalization::impl::commit_update(out); |
| 8396 | at::functionalization::impl::sync(out); |
| 8397 | return out; |
| 8398 | } |
| 8399 | } |
| 8400 | |
| 8401 | at::Tensor & _mkldnn_transpose_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim0, int64_t dim1) { |
| 8402 | if (true) { |
| 8403 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8404 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8405 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8406 | auto self_meta = to_meta(self); |
| 8407 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8408 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8409 | at::_ops::_mkldnn_transpose_::call(self_meta, dim0, dim1); |
| 8410 | } |
| 8411 | |
| 8412 | at::Tensor self_; |
| 8413 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8414 | at::functionalization::impl::sync(self); |
| 8415 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8416 | } else { |
| 8417 | self_ = self; |
| 8418 | } |
| 8419 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 8420 | if ((false)) { |
| 8421 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8422 | TORCH_INTERNAL_ASSERT(false, |
| 8423 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8424 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8425 | } else { |
| 8426 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8427 | at::AutoDispatchSkipFunctionalize guard; |
| 8428 | at::Tensor tmp_output = at::_ops::_mkldnn_transpose_::call(self_, dim0, dim1); |
| 8429 | return self;; |
| 8430 | } |
| 8431 | } else { |
| 8432 | at::Tensor tmp_output; |
| 8433 | { |
| 8434 | at::AutoDispatchSkipFunctionalize guard; |
| 8435 | tmp_output = at::_ops::_mkldnn_transpose::call(self_, dim0, dim1); |
| 8436 | } |
| 8437 | at::functionalization::impl::replace_(self, tmp_output); |
| 8438 | at::functionalization::impl::commit_update(self); |
| 8439 | at::functionalization::impl::sync(self); |
| 8440 | return self; |
| 8441 | } |
| 8442 | } |
| 8443 | |
| 8444 | at::Tensor & flip_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dims, at::Tensor & out) { |
| 8445 | if (false) { |
| 8446 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8447 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8448 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8449 | auto self_meta = to_meta(self); |
| 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::flip_out::call(self_meta, dims, 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 out_; |
| 8465 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8466 | at::functionalization::impl::sync(out); |
| 8467 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8468 | } else { |
| 8469 | out_ = out; |
| 8470 | } |
| 8471 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8472 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8473 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8474 | TORCH_INTERNAL_ASSERT(false, |
| 8475 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8476 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8477 | } else { |
| 8478 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8479 | at::AutoDispatchSkipFunctionalize guard; |
| 8480 | at::Tensor tmp_output = at::_ops::flip_out::call(self_, dims, out_); |
| 8481 | return out;; |
| 8482 | } |
| 8483 | } else { |
| 8484 | at::Tensor tmp_output; |
| 8485 | { |
| 8486 | at::AutoDispatchSkipFunctionalize guard; |
| 8487 | tmp_output = at::_ops::flip::call(self_, dims); |
| 8488 | } |
| 8489 | at::functionalization::impl::replace_(out, tmp_output); |
| 8490 | at::functionalization::impl::commit_update(out); |
| 8491 | at::functionalization::impl::sync(out); |
| 8492 | return out; |
| 8493 | } |
| 8494 | } |
| 8495 | |
| 8496 | at::Tensor & _nested_tensor_from_mask_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & t, const at::Tensor & mask, bool mask_check, at::Tensor & out) { |
| 8497 | if (false) { |
| 8498 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8499 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8500 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8501 | auto t_meta = to_meta(t); |
| 8502 | auto mask_meta = to_meta(mask); |
| 8503 | auto out_meta = to_meta(out); |
| 8504 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8505 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8506 | at::_ops::_nested_tensor_from_mask_out::call(t_meta, mask_meta, mask_check, out_meta); |
| 8507 | } |
| 8508 | |
| 8509 | at::Tensor t_; |
| 8510 | if (at::functionalization::impl::isFunctionalTensor(t)) { |
| 8511 | at::functionalization::impl::sync(t); |
| 8512 | t_ = at::functionalization::impl::from_functional_tensor(t); |
| 8513 | } else { |
| 8514 | t_ = t; |
| 8515 | } |
| 8516 | |
| 8517 | at::Tensor mask_; |
| 8518 | if (at::functionalization::impl::isFunctionalTensor(mask)) { |
| 8519 | at::functionalization::impl::sync(mask); |
| 8520 | mask_ = at::functionalization::impl::from_functional_tensor(mask); |
| 8521 | } else { |
| 8522 | mask_ = mask; |
| 8523 | } |
| 8524 | |
| 8525 | at::Tensor out_; |
| 8526 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8527 | at::functionalization::impl::sync(out); |
| 8528 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8529 | } else { |
| 8530 | out_ = out; |
| 8531 | } |
| 8532 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8533 | if ((false || at::functionalization::impl::isFunctionalTensor(t) || at::functionalization::impl::isFunctionalTensor(mask))) { |
| 8534 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8535 | TORCH_INTERNAL_ASSERT(false, |
| 8536 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8537 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8538 | } else { |
| 8539 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8540 | at::AutoDispatchSkipFunctionalize guard; |
| 8541 | at::Tensor tmp_output = at::_ops::_nested_tensor_from_mask_out::call(t_, mask_, mask_check, out_); |
| 8542 | return out;; |
| 8543 | } |
| 8544 | } else { |
| 8545 | at::Tensor tmp_output; |
| 8546 | { |
| 8547 | at::AutoDispatchSkipFunctionalize guard; |
| 8548 | tmp_output = at::_ops::_nested_tensor_from_mask::call(t_, mask_, mask_check); |
| 8549 | } |
| 8550 | at::functionalization::impl::replace_(out, tmp_output); |
| 8551 | at::functionalization::impl::commit_update(out); |
| 8552 | at::functionalization::impl::sync(out); |
| 8553 | return out; |
| 8554 | } |
| 8555 | } |
| 8556 | |
| 8557 | at::Tensor & _nested_from_padded_and_nested_example_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & padded, const at::Tensor & nt_example, at::Tensor & out) { |
| 8558 | if (false) { |
| 8559 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8560 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8561 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8562 | auto padded_meta = to_meta(padded); |
| 8563 | auto nt_example_meta = to_meta(nt_example); |
| 8564 | auto out_meta = to_meta(out); |
| 8565 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8566 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8567 | at::_ops::_nested_from_padded_and_nested_example_out::call(padded_meta, nt_example_meta, out_meta); |
| 8568 | } |
| 8569 | |
| 8570 | at::Tensor padded_; |
| 8571 | if (at::functionalization::impl::isFunctionalTensor(padded)) { |
| 8572 | at::functionalization::impl::sync(padded); |
| 8573 | padded_ = at::functionalization::impl::from_functional_tensor(padded); |
| 8574 | } else { |
| 8575 | padded_ = padded; |
| 8576 | } |
| 8577 | |
| 8578 | at::Tensor nt_example_; |
| 8579 | if (at::functionalization::impl::isFunctionalTensor(nt_example)) { |
| 8580 | at::functionalization::impl::sync(nt_example); |
| 8581 | nt_example_ = at::functionalization::impl::from_functional_tensor(nt_example); |
| 8582 | } else { |
| 8583 | nt_example_ = nt_example; |
| 8584 | } |
| 8585 | |
| 8586 | at::Tensor out_; |
| 8587 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8588 | at::functionalization::impl::sync(out); |
| 8589 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8590 | } else { |
| 8591 | out_ = out; |
| 8592 | } |
| 8593 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8594 | if ((false || at::functionalization::impl::isFunctionalTensor(padded) || at::functionalization::impl::isFunctionalTensor(nt_example))) { |
| 8595 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8596 | TORCH_INTERNAL_ASSERT(false, |
| 8597 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8598 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8599 | } else { |
| 8600 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8601 | at::AutoDispatchSkipFunctionalize guard; |
| 8602 | at::Tensor tmp_output = at::_ops::_nested_from_padded_and_nested_example_out::call(padded_, nt_example_, out_); |
| 8603 | return out;; |
| 8604 | } |
| 8605 | } else { |
| 8606 | at::Tensor tmp_output; |
| 8607 | { |
| 8608 | at::AutoDispatchSkipFunctionalize guard; |
| 8609 | tmp_output = at::_ops::_nested_from_padded_and_nested_example::call(padded_, nt_example_); |
| 8610 | } |
| 8611 | at::functionalization::impl::replace_(out, tmp_output); |
| 8612 | at::functionalization::impl::commit_update(out); |
| 8613 | at::functionalization::impl::sync(out); |
| 8614 | return out; |
| 8615 | } |
| 8616 | } |
| 8617 | |
| 8618 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_dim_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool sorted, bool return_inverse, bool return_counts, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 8619 | if (false) { |
| 8620 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8621 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8622 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8623 | auto self_meta = to_meta(self); |
| 8624 | auto out0_meta = to_meta(out0); |
| 8625 | auto out1_meta = to_meta(out1); |
| 8626 | auto out2_meta = to_meta(out2); |
| 8627 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8628 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8629 | at::_ops::unique_dim_out::call(self_meta, dim, sorted, return_inverse, return_counts, out0_meta, out1_meta, out2_meta); |
| 8630 | } |
| 8631 | |
| 8632 | at::Tensor self_; |
| 8633 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8634 | at::functionalization::impl::sync(self); |
| 8635 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8636 | } else { |
| 8637 | self_ = self; |
| 8638 | } |
| 8639 | |
| 8640 | at::Tensor out0_; |
| 8641 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 8642 | at::functionalization::impl::sync(out0); |
| 8643 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 8644 | } else { |
| 8645 | out0_ = out0; |
| 8646 | } |
| 8647 | |
| 8648 | at::Tensor out1_; |
| 8649 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 8650 | at::functionalization::impl::sync(out1); |
| 8651 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 8652 | } else { |
| 8653 | out1_ = out1; |
| 8654 | } |
| 8655 | |
| 8656 | at::Tensor out2_; |
| 8657 | if (at::functionalization::impl::isFunctionalTensor(out2)) { |
| 8658 | at::functionalization::impl::sync(out2); |
| 8659 | out2_ = at::functionalization::impl::from_functional_tensor(out2); |
| 8660 | } else { |
| 8661 | out2_ = out2; |
| 8662 | } |
| 8663 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1) && at::functionalization::impl::isFunctionalTensor(out2))) { |
| 8664 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8665 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8666 | TORCH_INTERNAL_ASSERT(false, |
| 8667 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8668 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8669 | } else { |
| 8670 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8671 | at::AutoDispatchSkipFunctionalize guard; |
| 8672 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::unique_dim_out::call(self_, dim, sorted, return_inverse, return_counts, out0_, out1_, out2_); |
| 8673 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2);; |
| 8674 | } |
| 8675 | } else { |
| 8676 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 8677 | { |
| 8678 | at::AutoDispatchSkipFunctionalize guard; |
| 8679 | tmp_output = at::_ops::unique_dim::call(self_, dim, sorted, return_inverse, return_counts); |
| 8680 | } |
| 8681 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 8682 | at::functionalization::impl::commit_update(out0); |
| 8683 | at::functionalization::impl::sync(out0); |
| 8684 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 8685 | at::functionalization::impl::commit_update(out1); |
| 8686 | at::functionalization::impl::sync(out1); |
| 8687 | at::functionalization::impl::replace_(out2, std::get<2>(tmp_output)); |
| 8688 | at::functionalization::impl::commit_update(out2); |
| 8689 | at::functionalization::impl::sync(out2); |
| 8690 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(out0, out1, out2); |
| 8691 | } |
| 8692 | } |
| 8693 | |
| 8694 | at::Tensor & _unsafe_view_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out) { |
| 8695 | if (false) { |
| 8696 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8697 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8698 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8699 | auto self_meta = to_meta(self); |
| 8700 | auto out_meta = to_meta(out); |
| 8701 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8702 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8703 | at::_ops::_unsafe_view_out::call(self_meta, size, out_meta); |
| 8704 | } |
| 8705 | |
| 8706 | at::Tensor self_; |
| 8707 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8708 | at::functionalization::impl::sync(self); |
| 8709 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8710 | } else { |
| 8711 | self_ = self; |
| 8712 | } |
| 8713 | |
| 8714 | at::Tensor out_; |
| 8715 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8716 | at::functionalization::impl::sync(out); |
| 8717 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8718 | } else { |
| 8719 | out_ = out; |
| 8720 | } |
| 8721 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8722 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8723 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8724 | TORCH_INTERNAL_ASSERT(false, |
| 8725 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8726 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8727 | } else { |
| 8728 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8729 | at::AutoDispatchSkipFunctionalize guard; |
| 8730 | at::Tensor tmp_output = at::_ops::_unsafe_view_out::call(self_, size, out_); |
| 8731 | return out;; |
| 8732 | } |
| 8733 | } else { |
| 8734 | at::Tensor tmp_output; |
| 8735 | { |
| 8736 | at::AutoDispatchSkipFunctionalize guard; |
| 8737 | tmp_output = at::_ops::_unsafe_view::call(self_, size); |
| 8738 | } |
| 8739 | at::functionalization::impl::replace_(out, tmp_output); |
| 8740 | at::functionalization::impl::commit_update(out); |
| 8741 | at::functionalization::impl::sync(out); |
| 8742 | return out; |
| 8743 | } |
| 8744 | } |
| 8745 | |
| 8746 | ::std::tuple<at::Tensor &,at::Tensor &> var_mean_out_correction_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, c10::optional<int64_t> correction, bool keepdim, at::Tensor & out0, at::Tensor & out1) { |
| 8747 | if (false) { |
| 8748 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8749 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8750 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8751 | auto self_meta = to_meta(self); |
| 8752 | auto out0_meta = to_meta(out0); |
| 8753 | auto out1_meta = to_meta(out1); |
| 8754 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8755 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8756 | at::_ops::var_mean_correction_out::call(self_meta, dim, correction, keepdim, out0_meta, out1_meta); |
| 8757 | } |
| 8758 | |
| 8759 | at::Tensor self_; |
| 8760 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8761 | at::functionalization::impl::sync(self); |
| 8762 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8763 | } else { |
| 8764 | self_ = self; |
| 8765 | } |
| 8766 | |
| 8767 | at::Tensor out0_; |
| 8768 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 8769 | at::functionalization::impl::sync(out0); |
| 8770 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 8771 | } else { |
| 8772 | out0_ = out0; |
| 8773 | } |
| 8774 | |
| 8775 | at::Tensor out1_; |
| 8776 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 8777 | at::functionalization::impl::sync(out1); |
| 8778 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 8779 | } else { |
| 8780 | out1_ = out1; |
| 8781 | } |
| 8782 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1))) { |
| 8783 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8784 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8785 | TORCH_INTERNAL_ASSERT(false, |
| 8786 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8787 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8788 | } else { |
| 8789 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8790 | at::AutoDispatchSkipFunctionalize guard; |
| 8791 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::var_mean_correction_out::call(self_, dim, correction, keepdim, out0_, out1_); |
| 8792 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1);; |
| 8793 | } |
| 8794 | } else { |
| 8795 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 8796 | { |
| 8797 | at::AutoDispatchSkipFunctionalize guard; |
| 8798 | tmp_output = at::_ops::var_mean_correction::call(self_, dim, correction, keepdim); |
| 8799 | } |
| 8800 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 8801 | at::functionalization::impl::commit_update(out0); |
| 8802 | at::functionalization::impl::sync(out0); |
| 8803 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 8804 | at::functionalization::impl::commit_update(out1); |
| 8805 | at::functionalization::impl::sync(out1); |
| 8806 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1); |
| 8807 | } |
| 8808 | } |
| 8809 | |
| 8810 | at::Tensor & zeros_out_names_out(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional<at::DimnameList> names, at::Tensor & out) { |
| 8811 | if (false) { |
| 8812 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8813 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8814 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8815 | auto out_meta = to_meta(out); |
| 8816 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8817 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8818 | at::_ops::zeros_names_out::call(size, names, out_meta); |
| 8819 | } |
| 8820 | |
| 8821 | at::Tensor out_; |
| 8822 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8823 | at::functionalization::impl::sync(out); |
| 8824 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8825 | } else { |
| 8826 | out_ = out; |
| 8827 | } |
| 8828 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8829 | if ((false)) { |
| 8830 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8831 | TORCH_INTERNAL_ASSERT(false, |
| 8832 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8833 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8834 | } else { |
| 8835 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8836 | at::AutoDispatchSkipFunctionalize guard; |
| 8837 | at::Tensor tmp_output = at::_ops::zeros_names_out::call(size, names, out_); |
| 8838 | return out;; |
| 8839 | } |
| 8840 | } else { |
| 8841 | at::Tensor tmp_output; |
| 8842 | { |
| 8843 | at::AutoDispatchSkipFunctionalize guard; |
| 8844 | tmp_output = at::_ops::zeros_names::call(size, names, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 8845 | } |
| 8846 | at::functionalization::impl::replace_(out, tmp_output); |
| 8847 | at::functionalization::impl::commit_update(out); |
| 8848 | at::functionalization::impl::sync(out); |
| 8849 | return out; |
| 8850 | } |
| 8851 | } |
| 8852 | |
| 8853 | at::Tensor & zeros_out_out(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, at::Tensor & out) { |
| 8854 | if (false) { |
| 8855 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8856 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8857 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8858 | auto out_meta = to_meta(out); |
| 8859 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8860 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8861 | at::_ops::zeros_out::call(size, out_meta); |
| 8862 | } |
| 8863 | |
| 8864 | at::Tensor out_; |
| 8865 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8866 | at::functionalization::impl::sync(out); |
| 8867 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8868 | } else { |
| 8869 | out_ = out; |
| 8870 | } |
| 8871 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8872 | if ((false)) { |
| 8873 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8874 | TORCH_INTERNAL_ASSERT(false, |
| 8875 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8876 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8877 | } else { |
| 8878 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8879 | at::AutoDispatchSkipFunctionalize guard; |
| 8880 | at::Tensor tmp_output = at::_ops::zeros_out::call(size, out_); |
| 8881 | return out;; |
| 8882 | } |
| 8883 | } else { |
| 8884 | at::Tensor tmp_output; |
| 8885 | { |
| 8886 | at::AutoDispatchSkipFunctionalize guard; |
| 8887 | tmp_output = at::_ops::zeros::call(size, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 8888 | } |
| 8889 | at::functionalization::impl::replace_(out, tmp_output); |
| 8890 | at::functionalization::impl::commit_update(out); |
| 8891 | at::functionalization::impl::sync(out); |
| 8892 | return out; |
| 8893 | } |
| 8894 | } |
| 8895 | |
| 8896 | at::Tensor & zeros_like_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out) { |
| 8897 | if (false) { |
| 8898 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8899 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8900 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8901 | auto self_meta = to_meta(self); |
| 8902 | auto out_meta = to_meta(out); |
| 8903 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8904 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8905 | at::_ops::zeros_like_out::call(self_meta, memory_format, out_meta); |
| 8906 | } |
| 8907 | |
| 8908 | at::Tensor self_; |
| 8909 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8910 | at::functionalization::impl::sync(self); |
| 8911 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8912 | } else { |
| 8913 | self_ = self; |
| 8914 | } |
| 8915 | |
| 8916 | at::Tensor out_; |
| 8917 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8918 | at::functionalization::impl::sync(out); |
| 8919 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8920 | } else { |
| 8921 | out_ = out; |
| 8922 | } |
| 8923 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8924 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8925 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8926 | TORCH_INTERNAL_ASSERT(false, |
| 8927 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8928 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8929 | } else { |
| 8930 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8931 | at::AutoDispatchSkipFunctionalize guard; |
| 8932 | at::Tensor tmp_output = at::_ops::zeros_like_out::call(self_, memory_format, out_); |
| 8933 | return out;; |
| 8934 | } |
| 8935 | } else { |
| 8936 | at::Tensor tmp_output; |
| 8937 | { |
| 8938 | at::AutoDispatchSkipFunctionalize guard; |
| 8939 | tmp_output = at::_ops::zeros_like::call(self_, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt, memory_format); |
| 8940 | } |
| 8941 | at::functionalization::impl::replace_(out, tmp_output); |
| 8942 | at::functionalization::impl::commit_update(out); |
| 8943 | at::functionalization::impl::sync(out); |
| 8944 | return out; |
| 8945 | } |
| 8946 | } |
| 8947 | |
| 8948 | at::Tensor & _sparse_csr_prod_out_dim_dtype_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 8949 | if (false) { |
| 8950 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 8951 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 8952 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 8953 | auto self_meta = to_meta(self); |
| 8954 | auto out_meta = to_meta(out); |
| 8955 | at::AutoDispatchSkipFunctionalize func_guard; |
| 8956 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 8957 | at::_ops::_sparse_csr_prod_dim_dtype_out::call(self_meta, dim, keepdim, dtype, out_meta); |
| 8958 | } |
| 8959 | |
| 8960 | at::Tensor self_; |
| 8961 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 8962 | at::functionalization::impl::sync(self); |
| 8963 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 8964 | } else { |
| 8965 | self_ = self; |
| 8966 | } |
| 8967 | |
| 8968 | at::Tensor out_; |
| 8969 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 8970 | at::functionalization::impl::sync(out); |
| 8971 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 8972 | } else { |
| 8973 | out_ = out; |
| 8974 | } |
| 8975 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 8976 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 8977 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 8978 | TORCH_INTERNAL_ASSERT(false, |
| 8979 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 8980 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 8981 | } else { |
| 8982 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 8983 | at::AutoDispatchSkipFunctionalize guard; |
| 8984 | at::Tensor tmp_output = at::_ops::_sparse_csr_prod_dim_dtype_out::call(self_, dim, keepdim, dtype, out_); |
| 8985 | return out;; |
| 8986 | } |
| 8987 | } else { |
| 8988 | at::Tensor tmp_output; |
| 8989 | { |
| 8990 | at::AutoDispatchSkipFunctionalize guard; |
| 8991 | tmp_output = at::_ops::_sparse_csr_prod_dim_dtype::call(self_, dim, keepdim, dtype); |
| 8992 | } |
| 8993 | at::functionalization::impl::replace_(out, tmp_output); |
| 8994 | at::functionalization::impl::commit_update(out); |
| 8995 | at::functionalization::impl::sync(out); |
| 8996 | return out; |
| 8997 | } |
| 8998 | } |
| 8999 | |
| 9000 | at::Tensor & _spdiags_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & diagonals, const at::Tensor & offsets, at::IntArrayRef shape, c10::optional<at::Layout> layout, at::Tensor & out) { |
| 9001 | if (false) { |
| 9002 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9003 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9004 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9005 | auto diagonals_meta = to_meta(diagonals); |
| 9006 | auto offsets_meta = to_meta(offsets); |
| 9007 | auto out_meta = to_meta(out); |
| 9008 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9009 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9010 | at::_ops::_spdiags_out::call(diagonals_meta, offsets_meta, shape, layout, out_meta); |
| 9011 | } |
| 9012 | |
| 9013 | at::Tensor diagonals_; |
| 9014 | if (at::functionalization::impl::isFunctionalTensor(diagonals)) { |
| 9015 | at::functionalization::impl::sync(diagonals); |
| 9016 | diagonals_ = at::functionalization::impl::from_functional_tensor(diagonals); |
| 9017 | } else { |
| 9018 | diagonals_ = diagonals; |
| 9019 | } |
| 9020 | |
| 9021 | at::Tensor offsets_; |
| 9022 | if (at::functionalization::impl::isFunctionalTensor(offsets)) { |
| 9023 | at::functionalization::impl::sync(offsets); |
| 9024 | offsets_ = at::functionalization::impl::from_functional_tensor(offsets); |
| 9025 | } else { |
| 9026 | offsets_ = offsets; |
| 9027 | } |
| 9028 | |
| 9029 | at::Tensor out_; |
| 9030 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9031 | at::functionalization::impl::sync(out); |
| 9032 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9033 | } else { |
| 9034 | out_ = out; |
| 9035 | } |
| 9036 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9037 | if ((false || at::functionalization::impl::isFunctionalTensor(diagonals) || at::functionalization::impl::isFunctionalTensor(offsets))) { |
| 9038 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9039 | TORCH_INTERNAL_ASSERT(false, |
| 9040 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9041 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9042 | } else { |
| 9043 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9044 | at::AutoDispatchSkipFunctionalize guard; |
| 9045 | at::Tensor tmp_output = at::_ops::_spdiags_out::call(diagonals_, offsets_, shape, layout, out_); |
| 9046 | return out;; |
| 9047 | } |
| 9048 | } else { |
| 9049 | at::Tensor tmp_output; |
| 9050 | { |
| 9051 | at::AutoDispatchSkipFunctionalize guard; |
| 9052 | tmp_output = at::_ops::_spdiags::call(diagonals_, offsets_, shape, layout); |
| 9053 | } |
| 9054 | at::functionalization::impl::replace_(out, tmp_output); |
| 9055 | at::functionalization::impl::commit_update(out); |
| 9056 | at::functionalization::impl::sync(out); |
| 9057 | return out; |
| 9058 | } |
| 9059 | } |
| 9060 | |
| 9061 | at::Tensor & rsub_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) { |
| 9062 | if (false) { |
| 9063 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9064 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9065 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9066 | auto self_meta = to_meta(self); |
| 9067 | auto other_meta = to_meta(other); |
| 9068 | auto out_meta = to_meta(out); |
| 9069 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9070 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9071 | at::_ops::rsub_Tensor_out::call(self_meta, other_meta, alpha, out_meta); |
| 9072 | } |
| 9073 | |
| 9074 | at::Tensor self_; |
| 9075 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9076 | at::functionalization::impl::sync(self); |
| 9077 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9078 | } else { |
| 9079 | self_ = self; |
| 9080 | } |
| 9081 | |
| 9082 | at::Tensor other_; |
| 9083 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 9084 | at::functionalization::impl::sync(other); |
| 9085 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 9086 | } else { |
| 9087 | other_ = other; |
| 9088 | } |
| 9089 | |
| 9090 | at::Tensor out_; |
| 9091 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9092 | at::functionalization::impl::sync(out); |
| 9093 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9094 | } else { |
| 9095 | out_ = out; |
| 9096 | } |
| 9097 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9098 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 9099 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9100 | TORCH_INTERNAL_ASSERT(false, |
| 9101 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9102 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9103 | } else { |
| 9104 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9105 | at::AutoDispatchSkipFunctionalize guard; |
| 9106 | at::Tensor tmp_output = at::_ops::rsub_Tensor_out::call(self_, other_, alpha, out_); |
| 9107 | return out;; |
| 9108 | } |
| 9109 | } else { |
| 9110 | at::Tensor tmp_output; |
| 9111 | { |
| 9112 | at::AutoDispatchSkipFunctionalize guard; |
| 9113 | tmp_output = at::_ops::rsub_Tensor::call(self_, other_, alpha); |
| 9114 | } |
| 9115 | at::functionalization::impl::replace_(out, tmp_output); |
| 9116 | at::functionalization::impl::commit_update(out); |
| 9117 | at::functionalization::impl::sync(out); |
| 9118 | return out; |
| 9119 | } |
| 9120 | } |
| 9121 | |
| 9122 | at::Tensor & rsub_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha, at::Tensor & out) { |
| 9123 | if (false) { |
| 9124 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9125 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9126 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9127 | auto self_meta = to_meta(self); |
| 9128 | auto out_meta = to_meta(out); |
| 9129 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9130 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9131 | at::_ops::rsub_Scalar_out::call(self_meta, other, alpha, out_meta); |
| 9132 | } |
| 9133 | |
| 9134 | at::Tensor self_; |
| 9135 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9136 | at::functionalization::impl::sync(self); |
| 9137 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9138 | } else { |
| 9139 | self_ = self; |
| 9140 | } |
| 9141 | |
| 9142 | at::Tensor out_; |
| 9143 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9144 | at::functionalization::impl::sync(out); |
| 9145 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9146 | } else { |
| 9147 | out_ = out; |
| 9148 | } |
| 9149 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9150 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9151 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9152 | TORCH_INTERNAL_ASSERT(false, |
| 9153 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9154 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9155 | } else { |
| 9156 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9157 | at::AutoDispatchSkipFunctionalize guard; |
| 9158 | at::Tensor tmp_output = at::_ops::rsub_Scalar_out::call(self_, other, alpha, out_); |
| 9159 | return out;; |
| 9160 | } |
| 9161 | } else { |
| 9162 | at::Tensor tmp_output; |
| 9163 | { |
| 9164 | at::AutoDispatchSkipFunctionalize guard; |
| 9165 | tmp_output = at::_ops::rsub_Scalar::call(self_, other, alpha); |
| 9166 | } |
| 9167 | at::functionalization::impl::replace_(out, tmp_output); |
| 9168 | at::functionalization::impl::commit_update(out); |
| 9169 | at::functionalization::impl::sync(out); |
| 9170 | return out; |
| 9171 | } |
| 9172 | } |
| 9173 | |
| 9174 | at::Tensor & _sparse_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) { |
| 9175 | if (false) { |
| 9176 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9177 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9178 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9179 | auto self_meta = to_meta(self); |
| 9180 | auto mat1_meta = to_meta(mat1); |
| 9181 | auto mat2_meta = to_meta(mat2); |
| 9182 | auto out_meta = to_meta(out); |
| 9183 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9184 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9185 | at::_ops::_sparse_addmm_out::call(self_meta, mat1_meta, mat2_meta, beta, alpha, out_meta); |
| 9186 | } |
| 9187 | |
| 9188 | at::Tensor self_; |
| 9189 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9190 | at::functionalization::impl::sync(self); |
| 9191 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9192 | } else { |
| 9193 | self_ = self; |
| 9194 | } |
| 9195 | |
| 9196 | at::Tensor mat1_; |
| 9197 | if (at::functionalization::impl::isFunctionalTensor(mat1)) { |
| 9198 | at::functionalization::impl::sync(mat1); |
| 9199 | mat1_ = at::functionalization::impl::from_functional_tensor(mat1); |
| 9200 | } else { |
| 9201 | mat1_ = mat1; |
| 9202 | } |
| 9203 | |
| 9204 | at::Tensor mat2_; |
| 9205 | if (at::functionalization::impl::isFunctionalTensor(mat2)) { |
| 9206 | at::functionalization::impl::sync(mat2); |
| 9207 | mat2_ = at::functionalization::impl::from_functional_tensor(mat2); |
| 9208 | } else { |
| 9209 | mat2_ = mat2; |
| 9210 | } |
| 9211 | |
| 9212 | at::Tensor out_; |
| 9213 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9214 | at::functionalization::impl::sync(out); |
| 9215 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9216 | } else { |
| 9217 | out_ = out; |
| 9218 | } |
| 9219 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9220 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(mat1) || at::functionalization::impl::isFunctionalTensor(mat2))) { |
| 9221 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9222 | TORCH_INTERNAL_ASSERT(false, |
| 9223 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9224 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9225 | } else { |
| 9226 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9227 | at::AutoDispatchSkipFunctionalize guard; |
| 9228 | at::Tensor tmp_output = at::_ops::_sparse_addmm_out::call(self_, mat1_, mat2_, beta, alpha, out_); |
| 9229 | return out;; |
| 9230 | } |
| 9231 | } else { |
| 9232 | at::Tensor tmp_output; |
| 9233 | { |
| 9234 | at::AutoDispatchSkipFunctionalize guard; |
| 9235 | tmp_output = at::_ops::_sparse_addmm::call(self_, mat1_, mat2_, beta, alpha); |
| 9236 | } |
| 9237 | at::functionalization::impl::replace_(out, tmp_output); |
| 9238 | at::functionalization::impl::commit_update(out); |
| 9239 | at::functionalization::impl::sync(out); |
| 9240 | return out; |
| 9241 | } |
| 9242 | } |
| 9243 | |
| 9244 | at::Tensor & sparse_coo_tensor_out_size_out(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, at::Tensor & out) { |
| 9245 | if (false) { |
| 9246 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9247 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9248 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9249 | auto out_meta = to_meta(out); |
| 9250 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9251 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9252 | at::_ops::sparse_coo_tensor_size_out::call(size, out_meta); |
| 9253 | } |
| 9254 | |
| 9255 | at::Tensor out_; |
| 9256 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9257 | at::functionalization::impl::sync(out); |
| 9258 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9259 | } else { |
| 9260 | out_ = out; |
| 9261 | } |
| 9262 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9263 | if ((false)) { |
| 9264 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9265 | TORCH_INTERNAL_ASSERT(false, |
| 9266 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9267 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9268 | } else { |
| 9269 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9270 | at::AutoDispatchSkipFunctionalize guard; |
| 9271 | at::Tensor tmp_output = at::_ops::sparse_coo_tensor_size_out::call(size, out_); |
| 9272 | return out;; |
| 9273 | } |
| 9274 | } else { |
| 9275 | at::Tensor tmp_output; |
| 9276 | { |
| 9277 | at::AutoDispatchSkipFunctionalize guard; |
| 9278 | tmp_output = at::_ops::sparse_coo_tensor_size::call(size, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 9279 | } |
| 9280 | at::functionalization::impl::replace_(out, tmp_output); |
| 9281 | at::functionalization::impl::commit_update(out); |
| 9282 | at::functionalization::impl::sync(out); |
| 9283 | return out; |
| 9284 | } |
| 9285 | } |
| 9286 | |
| 9287 | const at::Tensor & sparse_resize_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, int64_t sparse_dim, int64_t dense_dim, const at::Tensor & out) { |
| 9288 | if (false) { |
| 9289 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9290 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9291 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9292 | auto self_meta = to_meta(self); |
| 9293 | auto out_meta = to_meta(out); |
| 9294 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9295 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9296 | at::_ops::sparse_resize_out::call(self_meta, size, sparse_dim, dense_dim, out_meta); |
| 9297 | } |
| 9298 | |
| 9299 | at::Tensor self_; |
| 9300 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9301 | at::functionalization::impl::sync(self); |
| 9302 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9303 | } else { |
| 9304 | self_ = self; |
| 9305 | } |
| 9306 | |
| 9307 | at::Tensor out_; |
| 9308 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9309 | at::functionalization::impl::sync(out); |
| 9310 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9311 | } else { |
| 9312 | out_ = out; |
| 9313 | } |
| 9314 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9315 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9316 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9317 | TORCH_INTERNAL_ASSERT(false, |
| 9318 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9319 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9320 | } else { |
| 9321 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9322 | at::AutoDispatchSkipFunctionalize guard; |
| 9323 | at::Tensor tmp_output = at::_ops::sparse_resize_out::call(self_, size, sparse_dim, dense_dim, out_); |
| 9324 | return out;; |
| 9325 | } |
| 9326 | } else { |
| 9327 | at::Tensor tmp_output; |
| 9328 | { |
| 9329 | at::AutoDispatchSkipFunctionalize guard; |
| 9330 | tmp_output = at::_ops::sparse_resize::call(self_, size, sparse_dim, dense_dim); |
| 9331 | } |
| 9332 | at::functionalization::impl::replace_(out, tmp_output); |
| 9333 | at::functionalization::impl::commit_update(out); |
| 9334 | at::functionalization::impl::sync(out); |
| 9335 | return out; |
| 9336 | } |
| 9337 | } |
| 9338 | |
| 9339 | const at::Tensor & sparse_resize_(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, int64_t sparse_dim, int64_t dense_dim) { |
| 9340 | if (true) { |
| 9341 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9342 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9343 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9344 | auto self_meta = to_meta(self); |
| 9345 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9346 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9347 | at::_ops::sparse_resize_::call(self_meta, size, sparse_dim, dense_dim); |
| 9348 | } |
| 9349 | |
| 9350 | at::Tensor self_; |
| 9351 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9352 | at::functionalization::impl::sync(self); |
| 9353 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9354 | } else { |
| 9355 | self_ = self; |
| 9356 | } |
| 9357 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 9358 | if ((false)) { |
| 9359 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9360 | TORCH_INTERNAL_ASSERT(false, |
| 9361 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9362 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9363 | } else { |
| 9364 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9365 | at::AutoDispatchSkipFunctionalize guard; |
| 9366 | at::Tensor tmp_output = at::_ops::sparse_resize_::call(self_, size, sparse_dim, dense_dim); |
| 9367 | return self;; |
| 9368 | } |
| 9369 | } else { |
| 9370 | at::Tensor tmp_output; |
| 9371 | { |
| 9372 | at::AutoDispatchSkipFunctionalize guard; |
| 9373 | tmp_output = at::_ops::sparse_resize::call(self_, size, sparse_dim, dense_dim); |
| 9374 | } |
| 9375 | at::functionalization::impl::replace_(self, tmp_output); |
| 9376 | at::functionalization::impl::commit_update(self); |
| 9377 | at::functionalization::impl::sync(self); |
| 9378 | return self; |
| 9379 | } |
| 9380 | } |
| 9381 | |
| 9382 | at::Tensor & sparse_mask_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mask, at::Tensor & out) { |
| 9383 | if (false) { |
| 9384 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9385 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9386 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9387 | auto self_meta = to_meta(self); |
| 9388 | auto mask_meta = to_meta(mask); |
| 9389 | auto out_meta = to_meta(out); |
| 9390 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9391 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9392 | at::_ops::sparse_mask_out::call(self_meta, mask_meta, out_meta); |
| 9393 | } |
| 9394 | |
| 9395 | at::Tensor self_; |
| 9396 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9397 | at::functionalization::impl::sync(self); |
| 9398 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9399 | } else { |
| 9400 | self_ = self; |
| 9401 | } |
| 9402 | |
| 9403 | at::Tensor mask_; |
| 9404 | if (at::functionalization::impl::isFunctionalTensor(mask)) { |
| 9405 | at::functionalization::impl::sync(mask); |
| 9406 | mask_ = at::functionalization::impl::from_functional_tensor(mask); |
| 9407 | } else { |
| 9408 | mask_ = mask; |
| 9409 | } |
| 9410 | |
| 9411 | at::Tensor out_; |
| 9412 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9413 | at::functionalization::impl::sync(out); |
| 9414 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9415 | } else { |
| 9416 | out_ = out; |
| 9417 | } |
| 9418 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9419 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(mask))) { |
| 9420 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9421 | TORCH_INTERNAL_ASSERT(false, |
| 9422 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9423 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9424 | } else { |
| 9425 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9426 | at::AutoDispatchSkipFunctionalize guard; |
| 9427 | at::Tensor tmp_output = at::_ops::sparse_mask_out::call(self_, mask_, out_); |
| 9428 | return out;; |
| 9429 | } |
| 9430 | } else { |
| 9431 | at::Tensor tmp_output; |
| 9432 | { |
| 9433 | at::AutoDispatchSkipFunctionalize guard; |
| 9434 | tmp_output = at::_ops::sparse_mask::call(self_, mask_); |
| 9435 | } |
| 9436 | at::functionalization::impl::replace_(out, tmp_output); |
| 9437 | at::functionalization::impl::commit_update(out); |
| 9438 | at::functionalization::impl::sync(out); |
| 9439 | return out; |
| 9440 | } |
| 9441 | } |
| 9442 | |
| 9443 | at::Tensor & _coalesce_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 9444 | if (false) { |
| 9445 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9446 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9447 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9448 | auto self_meta = to_meta(self); |
| 9449 | auto out_meta = to_meta(out); |
| 9450 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9451 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9452 | at::_ops::_coalesce_out::call(self_meta, out_meta); |
| 9453 | } |
| 9454 | |
| 9455 | at::Tensor self_; |
| 9456 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9457 | at::functionalization::impl::sync(self); |
| 9458 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9459 | } else { |
| 9460 | self_ = self; |
| 9461 | } |
| 9462 | |
| 9463 | at::Tensor out_; |
| 9464 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9465 | at::functionalization::impl::sync(out); |
| 9466 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9467 | } else { |
| 9468 | out_ = out; |
| 9469 | } |
| 9470 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9471 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9472 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9473 | TORCH_INTERNAL_ASSERT(false, |
| 9474 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9475 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9476 | } else { |
| 9477 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9478 | at::AutoDispatchSkipFunctionalize guard; |
| 9479 | at::Tensor tmp_output = at::_ops::_coalesce_out::call(self_, out_); |
| 9480 | return out;; |
| 9481 | } |
| 9482 | } else { |
| 9483 | at::Tensor tmp_output; |
| 9484 | { |
| 9485 | at::AutoDispatchSkipFunctionalize guard; |
| 9486 | tmp_output = at::_ops::_coalesce::call(self_); |
| 9487 | } |
| 9488 | at::functionalization::impl::replace_(out, tmp_output); |
| 9489 | at::functionalization::impl::commit_update(out); |
| 9490 | at::functionalization::impl::sync(out); |
| 9491 | return out; |
| 9492 | } |
| 9493 | } |
| 9494 | |
| 9495 | at::Tensor & dequantize_out_self_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 9496 | if (false) { |
| 9497 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9498 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9499 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9500 | auto self_meta = to_meta(self); |
| 9501 | auto out_meta = to_meta(out); |
| 9502 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9503 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9504 | at::_ops::dequantize_self_out::call(self_meta, out_meta); |
| 9505 | } |
| 9506 | |
| 9507 | at::Tensor self_; |
| 9508 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9509 | at::functionalization::impl::sync(self); |
| 9510 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9511 | } else { |
| 9512 | self_ = self; |
| 9513 | } |
| 9514 | |
| 9515 | at::Tensor out_; |
| 9516 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9517 | at::functionalization::impl::sync(out); |
| 9518 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9519 | } else { |
| 9520 | out_ = out; |
| 9521 | } |
| 9522 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9523 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9524 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9525 | TORCH_INTERNAL_ASSERT(false, |
| 9526 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9527 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9528 | } else { |
| 9529 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9530 | at::AutoDispatchSkipFunctionalize guard; |
| 9531 | at::Tensor tmp_output = at::_ops::dequantize_self_out::call(self_, out_); |
| 9532 | return out;; |
| 9533 | } |
| 9534 | } else { |
| 9535 | at::Tensor tmp_output; |
| 9536 | { |
| 9537 | at::AutoDispatchSkipFunctionalize guard; |
| 9538 | tmp_output = at::_ops::dequantize_self::call(self_); |
| 9539 | } |
| 9540 | at::functionalization::impl::replace_(out, tmp_output); |
| 9541 | at::functionalization::impl::commit_update(out); |
| 9542 | at::functionalization::impl::sync(out); |
| 9543 | return out; |
| 9544 | } |
| 9545 | } |
| 9546 | |
| 9547 | void dequantize_out_tensors_out(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::TensorList out) { |
| 9548 | if (false) { |
| 9549 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9550 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9551 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9552 | auto tensors_meta = to_meta(tensors); |
| 9553 | auto out_meta = to_meta(out); |
| 9554 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9555 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9556 | at::_ops::dequantize_tensors_out::call(tensors_meta, out_meta); |
| 9557 | } |
| 9558 | |
| 9559 | ::std::vector<at::Tensor> tensors_; |
| 9560 | if (at::functionalization::impl::isFunctionalTensor(tensors)) { |
| 9561 | at::functionalization::impl::sync(tensors); |
| 9562 | tensors_ = at::functionalization::impl::from_functional_tensor(tensors); |
| 9563 | } else { |
| 9564 | tensors_ = tensors.vec(); |
| 9565 | } |
| 9566 | |
| 9567 | ::std::vector<at::Tensor> out_; |
| 9568 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9569 | at::functionalization::impl::sync(out); |
| 9570 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9571 | } else { |
| 9572 | out_ = out.vec(); |
| 9573 | } |
| 9574 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9575 | if ((false || at::functionalization::impl::isFunctionalTensor(tensors))) { |
| 9576 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9577 | TORCH_INTERNAL_ASSERT(false, |
| 9578 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9579 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9580 | } else { |
| 9581 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9582 | at::AutoDispatchSkipFunctionalize guard; |
| 9583 | at::_ops::dequantize_tensors_out::call(tensors_, out_); |
| 9584 | ; |
| 9585 | } |
| 9586 | } else { |
| 9587 | ::std::vector<at::Tensor> tmp_output; |
| 9588 | { |
| 9589 | at::AutoDispatchSkipFunctionalize guard; |
| 9590 | tmp_output = at::_ops::dequantize_tensors::call(tensors_); |
| 9591 | } |
| 9592 | at::functionalization::impl::replace_(out, tmp_output); |
| 9593 | at::functionalization::impl::commit_update(out); |
| 9594 | at::functionalization::impl::sync(out); |
| 9595 | |
| 9596 | } |
| 9597 | } |
| 9598 | |
| 9599 | at::Tensor & q_per_channel_zero_points_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 9600 | if (false) { |
| 9601 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9602 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9603 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9604 | auto self_meta = to_meta(self); |
| 9605 | auto out_meta = to_meta(out); |
| 9606 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9607 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9608 | at::_ops::q_per_channel_zero_points_out::call(self_meta, out_meta); |
| 9609 | } |
| 9610 | |
| 9611 | at::Tensor self_; |
| 9612 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9613 | at::functionalization::impl::sync(self); |
| 9614 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9615 | } else { |
| 9616 | self_ = self; |
| 9617 | } |
| 9618 | |
| 9619 | at::Tensor out_; |
| 9620 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9621 | at::functionalization::impl::sync(out); |
| 9622 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9623 | } else { |
| 9624 | out_ = out; |
| 9625 | } |
| 9626 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9627 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9628 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9629 | TORCH_INTERNAL_ASSERT(false, |
| 9630 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9631 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9632 | } else { |
| 9633 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9634 | at::AutoDispatchSkipFunctionalize guard; |
| 9635 | at::Tensor tmp_output = at::_ops::q_per_channel_zero_points_out::call(self_, out_); |
| 9636 | return out;; |
| 9637 | } |
| 9638 | } else { |
| 9639 | at::Tensor tmp_output; |
| 9640 | { |
| 9641 | at::AutoDispatchSkipFunctionalize guard; |
| 9642 | tmp_output = at::_ops::q_per_channel_zero_points::call(self_); |
| 9643 | } |
| 9644 | at::functionalization::impl::replace_(out, tmp_output); |
| 9645 | at::functionalization::impl::commit_update(out); |
| 9646 | at::functionalization::impl::sync(out); |
| 9647 | return out; |
| 9648 | } |
| 9649 | } |
| 9650 | |
| 9651 | at::Tensor & _fake_quantize_learnable_per_channel_affine_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, double grad_factor, at::Tensor & out) { |
| 9652 | if (false) { |
| 9653 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9654 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9655 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9656 | auto self_meta = to_meta(self); |
| 9657 | auto scale_meta = to_meta(scale); |
| 9658 | auto zero_point_meta = to_meta(zero_point); |
| 9659 | auto out_meta = to_meta(out); |
| 9660 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9661 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9662 | at::_ops::_fake_quantize_learnable_per_channel_affine_out::call(self_meta, scale_meta, zero_point_meta, axis, quant_min, quant_max, grad_factor, out_meta); |
| 9663 | } |
| 9664 | |
| 9665 | at::Tensor self_; |
| 9666 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9667 | at::functionalization::impl::sync(self); |
| 9668 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9669 | } else { |
| 9670 | self_ = self; |
| 9671 | } |
| 9672 | |
| 9673 | at::Tensor scale_; |
| 9674 | if (at::functionalization::impl::isFunctionalTensor(scale)) { |
| 9675 | at::functionalization::impl::sync(scale); |
| 9676 | scale_ = at::functionalization::impl::from_functional_tensor(scale); |
| 9677 | } else { |
| 9678 | scale_ = scale; |
| 9679 | } |
| 9680 | |
| 9681 | at::Tensor zero_point_; |
| 9682 | if (at::functionalization::impl::isFunctionalTensor(zero_point)) { |
| 9683 | at::functionalization::impl::sync(zero_point); |
| 9684 | zero_point_ = at::functionalization::impl::from_functional_tensor(zero_point); |
| 9685 | } else { |
| 9686 | zero_point_ = zero_point; |
| 9687 | } |
| 9688 | |
| 9689 | at::Tensor out_; |
| 9690 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9691 | at::functionalization::impl::sync(out); |
| 9692 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9693 | } else { |
| 9694 | out_ = out; |
| 9695 | } |
| 9696 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9697 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(scale) || at::functionalization::impl::isFunctionalTensor(zero_point))) { |
| 9698 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9699 | TORCH_INTERNAL_ASSERT(false, |
| 9700 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9701 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9702 | } else { |
| 9703 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9704 | at::AutoDispatchSkipFunctionalize guard; |
| 9705 | at::Tensor tmp_output = at::_ops::_fake_quantize_learnable_per_channel_affine_out::call(self_, scale_, zero_point_, axis, quant_min, quant_max, grad_factor, out_); |
| 9706 | return out;; |
| 9707 | } |
| 9708 | } else { |
| 9709 | at::Tensor tmp_output; |
| 9710 | { |
| 9711 | at::AutoDispatchSkipFunctionalize guard; |
| 9712 | tmp_output = at::_ops::_fake_quantize_learnable_per_channel_affine::call(self_, scale_, zero_point_, axis, quant_min, quant_max, grad_factor); |
| 9713 | } |
| 9714 | at::functionalization::impl::replace_(out, tmp_output); |
| 9715 | at::functionalization::impl::commit_update(out); |
| 9716 | at::functionalization::impl::sync(out); |
| 9717 | return out; |
| 9718 | } |
| 9719 | } |
| 9720 | |
| 9721 | ::std::tuple<at::Tensor &,at::Tensor &> _fused_moving_avg_obs_fq_helper_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & observer_on, const at::Tensor & fake_quant_on, at::Tensor & running_min, at::Tensor & running_max, at::Tensor & scale, at::Tensor & zero_point, double averaging_const, int64_t quant_min, int64_t quant_max, int64_t ch_axis, bool per_row_fake_quant, bool symmetric_quant, at::Tensor & out0, at::Tensor & out1) { |
| 9722 | if (false) { |
| 9723 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9724 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9725 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9726 | auto self_meta = to_meta(self); |
| 9727 | auto observer_on_meta = to_meta(observer_on); |
| 9728 | auto fake_quant_on_meta = to_meta(fake_quant_on); |
| 9729 | auto running_min_meta = to_meta(running_min); |
| 9730 | auto running_max_meta = to_meta(running_max); |
| 9731 | auto scale_meta = to_meta(scale); |
| 9732 | auto zero_point_meta = to_meta(zero_point); |
| 9733 | auto out0_meta = to_meta(out0); |
| 9734 | auto out1_meta = to_meta(out1); |
| 9735 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9736 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9737 | at::_ops::_fused_moving_avg_obs_fq_helper_out::call(self_meta, observer_on_meta, fake_quant_on_meta, running_min_meta, running_max_meta, scale_meta, zero_point_meta, averaging_const, quant_min, quant_max, ch_axis, per_row_fake_quant, symmetric_quant, out0_meta, out1_meta); |
| 9738 | } |
| 9739 | |
| 9740 | at::Tensor self_; |
| 9741 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9742 | at::functionalization::impl::sync(self); |
| 9743 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9744 | } else { |
| 9745 | self_ = self; |
| 9746 | } |
| 9747 | |
| 9748 | at::Tensor observer_on_; |
| 9749 | if (at::functionalization::impl::isFunctionalTensor(observer_on)) { |
| 9750 | at::functionalization::impl::sync(observer_on); |
| 9751 | observer_on_ = at::functionalization::impl::from_functional_tensor(observer_on); |
| 9752 | } else { |
| 9753 | observer_on_ = observer_on; |
| 9754 | } |
| 9755 | |
| 9756 | at::Tensor fake_quant_on_; |
| 9757 | if (at::functionalization::impl::isFunctionalTensor(fake_quant_on)) { |
| 9758 | at::functionalization::impl::sync(fake_quant_on); |
| 9759 | fake_quant_on_ = at::functionalization::impl::from_functional_tensor(fake_quant_on); |
| 9760 | } else { |
| 9761 | fake_quant_on_ = fake_quant_on; |
| 9762 | } |
| 9763 | |
| 9764 | at::Tensor running_min_; |
| 9765 | if (at::functionalization::impl::isFunctionalTensor(running_min)) { |
| 9766 | at::functionalization::impl::sync(running_min); |
| 9767 | running_min_ = at::functionalization::impl::from_functional_tensor(running_min); |
| 9768 | } else { |
| 9769 | running_min_ = running_min; |
| 9770 | } |
| 9771 | |
| 9772 | at::Tensor running_max_; |
| 9773 | if (at::functionalization::impl::isFunctionalTensor(running_max)) { |
| 9774 | at::functionalization::impl::sync(running_max); |
| 9775 | running_max_ = at::functionalization::impl::from_functional_tensor(running_max); |
| 9776 | } else { |
| 9777 | running_max_ = running_max; |
| 9778 | } |
| 9779 | |
| 9780 | at::Tensor scale_; |
| 9781 | if (at::functionalization::impl::isFunctionalTensor(scale)) { |
| 9782 | at::functionalization::impl::sync(scale); |
| 9783 | scale_ = at::functionalization::impl::from_functional_tensor(scale); |
| 9784 | } else { |
| 9785 | scale_ = scale; |
| 9786 | } |
| 9787 | |
| 9788 | at::Tensor zero_point_; |
| 9789 | if (at::functionalization::impl::isFunctionalTensor(zero_point)) { |
| 9790 | at::functionalization::impl::sync(zero_point); |
| 9791 | zero_point_ = at::functionalization::impl::from_functional_tensor(zero_point); |
| 9792 | } else { |
| 9793 | zero_point_ = zero_point; |
| 9794 | } |
| 9795 | |
| 9796 | at::Tensor out0_; |
| 9797 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 9798 | at::functionalization::impl::sync(out0); |
| 9799 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 9800 | } else { |
| 9801 | out0_ = out0; |
| 9802 | } |
| 9803 | |
| 9804 | at::Tensor out1_; |
| 9805 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 9806 | at::functionalization::impl::sync(out1); |
| 9807 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 9808 | } else { |
| 9809 | out1_ = out1; |
| 9810 | } |
| 9811 | if (!(true && at::functionalization::impl::isFunctionalTensor(running_min) && at::functionalization::impl::isFunctionalTensor(running_max) && at::functionalization::impl::isFunctionalTensor(scale) && at::functionalization::impl::isFunctionalTensor(zero_point) && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1))) { |
| 9812 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(observer_on) || at::functionalization::impl::isFunctionalTensor(fake_quant_on))) { |
| 9813 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9814 | TORCH_INTERNAL_ASSERT(false, |
| 9815 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9816 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9817 | } else { |
| 9818 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9819 | at::AutoDispatchSkipFunctionalize guard; |
| 9820 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::_fused_moving_avg_obs_fq_helper_out::call(self_, observer_on_, fake_quant_on_, running_min_, running_max_, scale_, zero_point_, averaging_const, quant_min, quant_max, ch_axis, per_row_fake_quant, symmetric_quant, out0_, out1_); |
| 9821 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1);; |
| 9822 | } |
| 9823 | } else { |
| 9824 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 9825 | { |
| 9826 | at::AutoDispatchSkipFunctionalize guard; |
| 9827 | tmp_output = at::_ops::_fused_moving_avg_obs_fq_helper_functional::call(self_, observer_on_, fake_quant_on_, running_min_, running_max_, scale_, zero_point_, averaging_const, quant_min, quant_max, ch_axis, per_row_fake_quant, symmetric_quant); |
| 9828 | } |
| 9829 | at::functionalization::impl::replace_(running_min, std::get<0>(tmp_output)); |
| 9830 | at::functionalization::impl::commit_update(running_min); |
| 9831 | at::functionalization::impl::sync(running_min); |
| 9832 | at::functionalization::impl::replace_(running_max, std::get<1>(tmp_output)); |
| 9833 | at::functionalization::impl::commit_update(running_max); |
| 9834 | at::functionalization::impl::sync(running_max); |
| 9835 | at::functionalization::impl::replace_(scale, std::get<2>(tmp_output)); |
| 9836 | at::functionalization::impl::commit_update(scale); |
| 9837 | at::functionalization::impl::sync(scale); |
| 9838 | at::functionalization::impl::replace_(zero_point, std::get<3>(tmp_output)); |
| 9839 | at::functionalization::impl::commit_update(zero_point); |
| 9840 | at::functionalization::impl::sync(zero_point); |
| 9841 | at::functionalization::impl::replace_(out0, std::get<4>(tmp_output)); |
| 9842 | at::functionalization::impl::commit_update(out0); |
| 9843 | at::functionalization::impl::sync(out0); |
| 9844 | at::functionalization::impl::replace_(out1, std::get<5>(tmp_output)); |
| 9845 | at::functionalization::impl::commit_update(out1); |
| 9846 | at::functionalization::impl::sync(out1); |
| 9847 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1); |
| 9848 | } |
| 9849 | } |
| 9850 | |
| 9851 | ::std::tuple<at::Tensor,at::Tensor> _fused_moving_avg_obs_fq_helper(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & observer_on, const at::Tensor & fake_quant_on, at::Tensor & running_min, at::Tensor & running_max, at::Tensor & scale, at::Tensor & zero_point, double averaging_const, int64_t quant_min, int64_t quant_max, int64_t ch_axis, bool per_row_fake_quant, bool symmetric_quant) { |
| 9852 | if (false) { |
| 9853 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9854 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9855 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9856 | auto self_meta = to_meta(self); |
| 9857 | auto observer_on_meta = to_meta(observer_on); |
| 9858 | auto fake_quant_on_meta = to_meta(fake_quant_on); |
| 9859 | auto running_min_meta = to_meta(running_min); |
| 9860 | auto running_max_meta = to_meta(running_max); |
| 9861 | auto scale_meta = to_meta(scale); |
| 9862 | auto zero_point_meta = to_meta(zero_point); |
| 9863 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9864 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9865 | at::_ops::_fused_moving_avg_obs_fq_helper::call(self_meta, observer_on_meta, fake_quant_on_meta, running_min_meta, running_max_meta, scale_meta, zero_point_meta, averaging_const, quant_min, quant_max, ch_axis, per_row_fake_quant, symmetric_quant); |
| 9866 | } |
| 9867 | |
| 9868 | at::Tensor self_; |
| 9869 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9870 | at::functionalization::impl::sync(self); |
| 9871 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9872 | } else { |
| 9873 | self_ = self; |
| 9874 | } |
| 9875 | |
| 9876 | at::Tensor observer_on_; |
| 9877 | if (at::functionalization::impl::isFunctionalTensor(observer_on)) { |
| 9878 | at::functionalization::impl::sync(observer_on); |
| 9879 | observer_on_ = at::functionalization::impl::from_functional_tensor(observer_on); |
| 9880 | } else { |
| 9881 | observer_on_ = observer_on; |
| 9882 | } |
| 9883 | |
| 9884 | at::Tensor fake_quant_on_; |
| 9885 | if (at::functionalization::impl::isFunctionalTensor(fake_quant_on)) { |
| 9886 | at::functionalization::impl::sync(fake_quant_on); |
| 9887 | fake_quant_on_ = at::functionalization::impl::from_functional_tensor(fake_quant_on); |
| 9888 | } else { |
| 9889 | fake_quant_on_ = fake_quant_on; |
| 9890 | } |
| 9891 | |
| 9892 | at::Tensor running_min_; |
| 9893 | if (at::functionalization::impl::isFunctionalTensor(running_min)) { |
| 9894 | at::functionalization::impl::sync(running_min); |
| 9895 | running_min_ = at::functionalization::impl::from_functional_tensor(running_min); |
| 9896 | } else { |
| 9897 | running_min_ = running_min; |
| 9898 | } |
| 9899 | |
| 9900 | at::Tensor running_max_; |
| 9901 | if (at::functionalization::impl::isFunctionalTensor(running_max)) { |
| 9902 | at::functionalization::impl::sync(running_max); |
| 9903 | running_max_ = at::functionalization::impl::from_functional_tensor(running_max); |
| 9904 | } else { |
| 9905 | running_max_ = running_max; |
| 9906 | } |
| 9907 | |
| 9908 | at::Tensor scale_; |
| 9909 | if (at::functionalization::impl::isFunctionalTensor(scale)) { |
| 9910 | at::functionalization::impl::sync(scale); |
| 9911 | scale_ = at::functionalization::impl::from_functional_tensor(scale); |
| 9912 | } else { |
| 9913 | scale_ = scale; |
| 9914 | } |
| 9915 | |
| 9916 | at::Tensor zero_point_; |
| 9917 | if (at::functionalization::impl::isFunctionalTensor(zero_point)) { |
| 9918 | at::functionalization::impl::sync(zero_point); |
| 9919 | zero_point_ = at::functionalization::impl::from_functional_tensor(zero_point); |
| 9920 | } else { |
| 9921 | zero_point_ = zero_point; |
| 9922 | } |
| 9923 | if (!(true && at::functionalization::impl::isFunctionalTensor(running_min) && at::functionalization::impl::isFunctionalTensor(running_max) && at::functionalization::impl::isFunctionalTensor(scale) && at::functionalization::impl::isFunctionalTensor(zero_point))) { |
| 9924 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(observer_on) || at::functionalization::impl::isFunctionalTensor(fake_quant_on))) { |
| 9925 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9926 | TORCH_INTERNAL_ASSERT(false, |
| 9927 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9928 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9929 | } else { |
| 9930 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9931 | at::AutoDispatchSkipFunctionalize guard; |
| 9932 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::_fused_moving_avg_obs_fq_helper::call(self_, observer_on_, fake_quant_on_, running_min_, running_max_, scale_, zero_point_, averaging_const, quant_min, quant_max, ch_axis, per_row_fake_quant, symmetric_quant); |
| 9933 | return ::std::tuple<at::Tensor,at::Tensor>(std::get<0>(tmp_output), std::get<1>(tmp_output));; |
| 9934 | } |
| 9935 | } else { |
| 9936 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 9937 | { |
| 9938 | at::AutoDispatchSkipFunctionalize guard; |
| 9939 | tmp_output = at::_ops::_fused_moving_avg_obs_fq_helper_functional::call(self_, observer_on_, fake_quant_on_, running_min_, running_max_, scale_, zero_point_, averaging_const, quant_min, quant_max, ch_axis, per_row_fake_quant, symmetric_quant); |
| 9940 | } |
| 9941 | auto output_0 = at::functionalization::impl::to_functional_tensor(std::get<0>(tmp_output)); |
| 9942 | auto output_1 = at::functionalization::impl::to_functional_tensor(std::get<1>(tmp_output)); |
| 9943 | at::functionalization::impl::replace_(running_min, std::get<2>(tmp_output)); |
| 9944 | at::functionalization::impl::commit_update(running_min); |
| 9945 | at::functionalization::impl::sync(running_min); |
| 9946 | at::functionalization::impl::replace_(running_max, std::get<3>(tmp_output)); |
| 9947 | at::functionalization::impl::commit_update(running_max); |
| 9948 | at::functionalization::impl::sync(running_max); |
| 9949 | at::functionalization::impl::replace_(scale, std::get<4>(tmp_output)); |
| 9950 | at::functionalization::impl::commit_update(scale); |
| 9951 | at::functionalization::impl::sync(scale); |
| 9952 | at::functionalization::impl::replace_(zero_point, std::get<5>(tmp_output)); |
| 9953 | at::functionalization::impl::commit_update(zero_point); |
| 9954 | at::functionalization::impl::sync(zero_point); |
| 9955 | return ::std::tuple<at::Tensor,at::Tensor>(output_0, output_1); |
| 9956 | } |
| 9957 | } |
| 9958 | |
| 9959 | at::Tensor & _to_copy_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool non_blocking, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out) { |
| 9960 | if (false) { |
| 9961 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 9962 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 9963 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 9964 | auto self_meta = to_meta(self); |
| 9965 | auto out_meta = to_meta(out); |
| 9966 | at::AutoDispatchSkipFunctionalize func_guard; |
| 9967 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 9968 | at::_ops::_to_copy_out::call(self_meta, non_blocking, memory_format, out_meta); |
| 9969 | } |
| 9970 | |
| 9971 | at::Tensor self_; |
| 9972 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 9973 | at::functionalization::impl::sync(self); |
| 9974 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 9975 | } else { |
| 9976 | self_ = self; |
| 9977 | } |
| 9978 | |
| 9979 | at::Tensor out_; |
| 9980 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 9981 | at::functionalization::impl::sync(out); |
| 9982 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 9983 | } else { |
| 9984 | out_ = out; |
| 9985 | } |
| 9986 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 9987 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 9988 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 9989 | TORCH_INTERNAL_ASSERT(false, |
| 9990 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 9991 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 9992 | } else { |
| 9993 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 9994 | at::AutoDispatchSkipFunctionalize guard; |
| 9995 | at::Tensor tmp_output = at::_ops::_to_copy_out::call(self_, non_blocking, memory_format, out_); |
| 9996 | return out;; |
| 9997 | } |
| 9998 | } else { |
| 9999 | at::Tensor tmp_output; |
| 10000 | { |
| 10001 | at::AutoDispatchSkipFunctionalize guard; |
| 10002 | tmp_output = at::_ops::_to_copy::call(self_, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt, non_blocking, memory_format); |
| 10003 | } |
| 10004 | at::functionalization::impl::replace_(out, tmp_output); |
| 10005 | at::functionalization::impl::commit_update(out); |
| 10006 | at::functionalization::impl::sync(out); |
| 10007 | return out; |
| 10008 | } |
| 10009 | } |
| 10010 | |
| 10011 | ::std::tuple<at::Tensor &,at::Tensor &> _thnn_fused_gru_cell_out_out(c10::DispatchKeySet dispatchKeySet, 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::Tensor & out0, at::Tensor & out1) { |
| 10012 | if (false) { |
| 10013 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10014 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10015 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10016 | auto input_gates_meta = to_meta(input_gates); |
| 10017 | auto hidden_gates_meta = to_meta(hidden_gates); |
| 10018 | auto hx_meta = to_meta(hx); |
| 10019 | auto input_bias_meta = to_meta(input_bias); |
| 10020 | auto hidden_bias_meta = to_meta(hidden_bias); |
| 10021 | auto out0_meta = to_meta(out0); |
| 10022 | auto out1_meta = to_meta(out1); |
| 10023 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10024 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10025 | at::_ops::_thnn_fused_gru_cell_out::call(input_gates_meta, hidden_gates_meta, hx_meta, input_bias_meta, hidden_bias_meta, out0_meta, out1_meta); |
| 10026 | } |
| 10027 | |
| 10028 | at::Tensor input_gates_; |
| 10029 | if (at::functionalization::impl::isFunctionalTensor(input_gates)) { |
| 10030 | at::functionalization::impl::sync(input_gates); |
| 10031 | input_gates_ = at::functionalization::impl::from_functional_tensor(input_gates); |
| 10032 | } else { |
| 10033 | input_gates_ = input_gates; |
| 10034 | } |
| 10035 | |
| 10036 | at::Tensor hidden_gates_; |
| 10037 | if (at::functionalization::impl::isFunctionalTensor(hidden_gates)) { |
| 10038 | at::functionalization::impl::sync(hidden_gates); |
| 10039 | hidden_gates_ = at::functionalization::impl::from_functional_tensor(hidden_gates); |
| 10040 | } else { |
| 10041 | hidden_gates_ = hidden_gates; |
| 10042 | } |
| 10043 | |
| 10044 | at::Tensor hx_; |
| 10045 | if (at::functionalization::impl::isFunctionalTensor(hx)) { |
| 10046 | at::functionalization::impl::sync(hx); |
| 10047 | hx_ = at::functionalization::impl::from_functional_tensor(hx); |
| 10048 | } else { |
| 10049 | hx_ = hx; |
| 10050 | } |
| 10051 | |
| 10052 | c10::optional<at::Tensor> input_bias_; |
| 10053 | if (at::functionalization::impl::isFunctionalTensor(input_bias)) { |
| 10054 | at::functionalization::impl::sync(input_bias); |
| 10055 | input_bias_ = at::functionalization::impl::from_functional_tensor(input_bias); |
| 10056 | } else { |
| 10057 | input_bias_ = input_bias; |
| 10058 | } |
| 10059 | |
| 10060 | c10::optional<at::Tensor> hidden_bias_; |
| 10061 | if (at::functionalization::impl::isFunctionalTensor(hidden_bias)) { |
| 10062 | at::functionalization::impl::sync(hidden_bias); |
| 10063 | hidden_bias_ = at::functionalization::impl::from_functional_tensor(hidden_bias); |
| 10064 | } else { |
| 10065 | hidden_bias_ = hidden_bias; |
| 10066 | } |
| 10067 | |
| 10068 | at::Tensor out0_; |
| 10069 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 10070 | at::functionalization::impl::sync(out0); |
| 10071 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 10072 | } else { |
| 10073 | out0_ = out0; |
| 10074 | } |
| 10075 | |
| 10076 | at::Tensor out1_; |
| 10077 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 10078 | at::functionalization::impl::sync(out1); |
| 10079 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 10080 | } else { |
| 10081 | out1_ = out1; |
| 10082 | } |
| 10083 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1))) { |
| 10084 | if ((false || at::functionalization::impl::isFunctionalTensor(input_gates) || at::functionalization::impl::isFunctionalTensor(hidden_gates) || at::functionalization::impl::isFunctionalTensor(hx) || at::functionalization::impl::isFunctionalTensor(input_bias) || at::functionalization::impl::isFunctionalTensor(hidden_bias))) { |
| 10085 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10086 | TORCH_INTERNAL_ASSERT(false, |
| 10087 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10088 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10089 | } else { |
| 10090 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10091 | at::AutoDispatchSkipFunctionalize guard; |
| 10092 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::_thnn_fused_gru_cell_out::call(input_gates_, hidden_gates_, hx_, input_bias_, hidden_bias_, out0_, out1_); |
| 10093 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1);; |
| 10094 | } |
| 10095 | } else { |
| 10096 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 10097 | { |
| 10098 | at::AutoDispatchSkipFunctionalize guard; |
| 10099 | tmp_output = at::_ops::_thnn_fused_gru_cell::call(input_gates_, hidden_gates_, hx_, input_bias_, hidden_bias_); |
| 10100 | } |
| 10101 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 10102 | at::functionalization::impl::commit_update(out0); |
| 10103 | at::functionalization::impl::sync(out0); |
| 10104 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 10105 | at::functionalization::impl::commit_update(out1); |
| 10106 | at::functionalization::impl::sync(out1); |
| 10107 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1); |
| 10108 | } |
| 10109 | } |
| 10110 | |
| 10111 | ::std::tuple<at::Tensor &,at::Tensor &> _pack_padded_sequence_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & lengths, bool batch_first, at::Tensor & out0, at::Tensor & out1) { |
| 10112 | if (false) { |
| 10113 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10114 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10115 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10116 | auto input_meta = to_meta(input); |
| 10117 | auto lengths_meta = to_meta(lengths); |
| 10118 | auto out0_meta = to_meta(out0); |
| 10119 | auto out1_meta = to_meta(out1); |
| 10120 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10121 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10122 | at::_ops::_pack_padded_sequence_out::call(input_meta, lengths_meta, batch_first, out0_meta, out1_meta); |
| 10123 | } |
| 10124 | |
| 10125 | at::Tensor input_; |
| 10126 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 10127 | at::functionalization::impl::sync(input); |
| 10128 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 10129 | } else { |
| 10130 | input_ = input; |
| 10131 | } |
| 10132 | |
| 10133 | at::Tensor lengths_; |
| 10134 | if (at::functionalization::impl::isFunctionalTensor(lengths)) { |
| 10135 | at::functionalization::impl::sync(lengths); |
| 10136 | lengths_ = at::functionalization::impl::from_functional_tensor(lengths); |
| 10137 | } else { |
| 10138 | lengths_ = lengths; |
| 10139 | } |
| 10140 | |
| 10141 | at::Tensor out0_; |
| 10142 | if (at::functionalization::impl::isFunctionalTensor(out0)) { |
| 10143 | at::functionalization::impl::sync(out0); |
| 10144 | out0_ = at::functionalization::impl::from_functional_tensor(out0); |
| 10145 | } else { |
| 10146 | out0_ = out0; |
| 10147 | } |
| 10148 | |
| 10149 | at::Tensor out1_; |
| 10150 | if (at::functionalization::impl::isFunctionalTensor(out1)) { |
| 10151 | at::functionalization::impl::sync(out1); |
| 10152 | out1_ = at::functionalization::impl::from_functional_tensor(out1); |
| 10153 | } else { |
| 10154 | out1_ = out1; |
| 10155 | } |
| 10156 | if (!(true && at::functionalization::impl::isFunctionalTensor(out0) && at::functionalization::impl::isFunctionalTensor(out1))) { |
| 10157 | if ((false || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(lengths))) { |
| 10158 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10159 | TORCH_INTERNAL_ASSERT(false, |
| 10160 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10161 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10162 | } else { |
| 10163 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10164 | at::AutoDispatchSkipFunctionalize guard; |
| 10165 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::_pack_padded_sequence_out::call(input_, lengths_, batch_first, out0_, out1_); |
| 10166 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1);; |
| 10167 | } |
| 10168 | } else { |
| 10169 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 10170 | { |
| 10171 | at::AutoDispatchSkipFunctionalize guard; |
| 10172 | tmp_output = at::_ops::_pack_padded_sequence::call(input_, lengths_, batch_first); |
| 10173 | } |
| 10174 | at::functionalization::impl::replace_(out0, std::get<0>(tmp_output)); |
| 10175 | at::functionalization::impl::commit_update(out0); |
| 10176 | at::functionalization::impl::sync(out0); |
| 10177 | at::functionalization::impl::replace_(out1, std::get<1>(tmp_output)); |
| 10178 | at::functionalization::impl::commit_update(out1); |
| 10179 | at::functionalization::impl::sync(out1); |
| 10180 | return ::std::tuple<at::Tensor &,at::Tensor &>(out0, out1); |
| 10181 | } |
| 10182 | } |
| 10183 | |
| 10184 | at::Tensor & scatter_reduce_out_two_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self, at::Tensor & out) { |
| 10185 | if (false) { |
| 10186 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10187 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10188 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10189 | auto self_meta = to_meta(self); |
| 10190 | auto index_meta = to_meta(index); |
| 10191 | auto src_meta = to_meta(src); |
| 10192 | auto out_meta = to_meta(out); |
| 10193 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10194 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10195 | at::_ops::scatter_reduce_two_out::call(self_meta, dim, index_meta, src_meta, reduce, include_self, out_meta); |
| 10196 | } |
| 10197 | |
| 10198 | at::Tensor self_; |
| 10199 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10200 | at::functionalization::impl::sync(self); |
| 10201 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10202 | } else { |
| 10203 | self_ = self; |
| 10204 | } |
| 10205 | |
| 10206 | at::Tensor index_; |
| 10207 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 10208 | at::functionalization::impl::sync(index); |
| 10209 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 10210 | } else { |
| 10211 | index_ = index; |
| 10212 | } |
| 10213 | |
| 10214 | at::Tensor src_; |
| 10215 | if (at::functionalization::impl::isFunctionalTensor(src)) { |
| 10216 | at::functionalization::impl::sync(src); |
| 10217 | src_ = at::functionalization::impl::from_functional_tensor(src); |
| 10218 | } else { |
| 10219 | src_ = src; |
| 10220 | } |
| 10221 | |
| 10222 | at::Tensor out_; |
| 10223 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10224 | at::functionalization::impl::sync(out); |
| 10225 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10226 | } else { |
| 10227 | out_ = out; |
| 10228 | } |
| 10229 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10230 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(index) || at::functionalization::impl::isFunctionalTensor(src))) { |
| 10231 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10232 | TORCH_INTERNAL_ASSERT(false, |
| 10233 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10234 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10235 | } else { |
| 10236 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10237 | at::AutoDispatchSkipFunctionalize guard; |
| 10238 | at::Tensor tmp_output = at::_ops::scatter_reduce_two_out::call(self_, dim, index_, src_, reduce, include_self, out_); |
| 10239 | return out;; |
| 10240 | } |
| 10241 | } else { |
| 10242 | at::Tensor tmp_output; |
| 10243 | { |
| 10244 | at::AutoDispatchSkipFunctionalize guard; |
| 10245 | tmp_output = at::_ops::scatter_reduce_two::call(self_, dim, index_, src_, reduce, include_self); |
| 10246 | } |
| 10247 | at::functionalization::impl::replace_(out, tmp_output); |
| 10248 | at::functionalization::impl::commit_update(out); |
| 10249 | at::functionalization::impl::sync(out); |
| 10250 | return out; |
| 10251 | } |
| 10252 | } |
| 10253 | |
| 10254 | at::Tensor & scatter_reduce__two(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self) { |
| 10255 | if (true) { |
| 10256 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10257 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10258 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10259 | auto self_meta = to_meta(self); |
| 10260 | auto index_meta = to_meta(index); |
| 10261 | auto src_meta = to_meta(src); |
| 10262 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10263 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10264 | at::_ops::scatter_reduce__two::call(self_meta, dim, index_meta, src_meta, reduce, include_self); |
| 10265 | } |
| 10266 | |
| 10267 | at::Tensor self_; |
| 10268 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10269 | at::functionalization::impl::sync(self); |
| 10270 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10271 | } else { |
| 10272 | self_ = self; |
| 10273 | } |
| 10274 | |
| 10275 | at::Tensor index_; |
| 10276 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 10277 | at::functionalization::impl::sync(index); |
| 10278 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 10279 | } else { |
| 10280 | index_ = index; |
| 10281 | } |
| 10282 | |
| 10283 | at::Tensor src_; |
| 10284 | if (at::functionalization::impl::isFunctionalTensor(src)) { |
| 10285 | at::functionalization::impl::sync(src); |
| 10286 | src_ = at::functionalization::impl::from_functional_tensor(src); |
| 10287 | } else { |
| 10288 | src_ = src; |
| 10289 | } |
| 10290 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 10291 | if ((false || at::functionalization::impl::isFunctionalTensor(index) || at::functionalization::impl::isFunctionalTensor(src))) { |
| 10292 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10293 | TORCH_INTERNAL_ASSERT(false, |
| 10294 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10295 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10296 | } else { |
| 10297 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10298 | at::AutoDispatchSkipFunctionalize guard; |
| 10299 | at::Tensor tmp_output = at::_ops::scatter_reduce__two::call(self_, dim, index_, src_, reduce, include_self); |
| 10300 | return self;; |
| 10301 | } |
| 10302 | } else { |
| 10303 | at::Tensor tmp_output; |
| 10304 | { |
| 10305 | at::AutoDispatchSkipFunctionalize guard; |
| 10306 | tmp_output = at::_ops::scatter_reduce_two::call(self_, dim, index_, src_, reduce, include_self); |
| 10307 | } |
| 10308 | at::functionalization::impl::replace_(self, tmp_output); |
| 10309 | at::functionalization::impl::commit_update(self); |
| 10310 | at::functionalization::impl::sync(self); |
| 10311 | return self; |
| 10312 | } |
| 10313 | } |
| 10314 | |
| 10315 | at::Tensor & bitwise_xor_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 10316 | if (false) { |
| 10317 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10318 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10319 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10320 | auto self_meta = to_meta(self); |
| 10321 | auto other_meta = to_meta(other); |
| 10322 | auto out_meta = to_meta(out); |
| 10323 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10324 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10325 | at::_ops::bitwise_xor_Tensor_out::call(self_meta, other_meta, out_meta); |
| 10326 | } |
| 10327 | |
| 10328 | at::Tensor self_; |
| 10329 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10330 | at::functionalization::impl::sync(self); |
| 10331 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10332 | } else { |
| 10333 | self_ = self; |
| 10334 | } |
| 10335 | |
| 10336 | at::Tensor other_; |
| 10337 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 10338 | at::functionalization::impl::sync(other); |
| 10339 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 10340 | } else { |
| 10341 | other_ = other; |
| 10342 | } |
| 10343 | |
| 10344 | at::Tensor out_; |
| 10345 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10346 | at::functionalization::impl::sync(out); |
| 10347 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10348 | } else { |
| 10349 | out_ = out; |
| 10350 | } |
| 10351 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10352 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 10353 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10354 | TORCH_INTERNAL_ASSERT(false, |
| 10355 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10356 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10357 | } else { |
| 10358 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10359 | at::AutoDispatchSkipFunctionalize guard; |
| 10360 | at::Tensor tmp_output = at::_ops::bitwise_xor_Tensor_out::call(self_, other_, out_); |
| 10361 | return out;; |
| 10362 | } |
| 10363 | } else { |
| 10364 | at::Tensor tmp_output; |
| 10365 | { |
| 10366 | at::AutoDispatchSkipFunctionalize guard; |
| 10367 | tmp_output = at::_ops::bitwise_xor_Tensor::call(self_, other_); |
| 10368 | } |
| 10369 | at::functionalization::impl::replace_(out, tmp_output); |
| 10370 | at::functionalization::impl::commit_update(out); |
| 10371 | at::functionalization::impl::sync(out); |
| 10372 | return out; |
| 10373 | } |
| 10374 | } |
| 10375 | |
| 10376 | at::Tensor & bitwise_xor__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 10377 | if (true) { |
| 10378 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10379 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10380 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10381 | auto self_meta = to_meta(self); |
| 10382 | auto other_meta = to_meta(other); |
| 10383 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10384 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10385 | at::_ops::bitwise_xor__Tensor::call(self_meta, other_meta); |
| 10386 | } |
| 10387 | |
| 10388 | at::Tensor self_; |
| 10389 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10390 | at::functionalization::impl::sync(self); |
| 10391 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10392 | } else { |
| 10393 | self_ = self; |
| 10394 | } |
| 10395 | |
| 10396 | at::Tensor other_; |
| 10397 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 10398 | at::functionalization::impl::sync(other); |
| 10399 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 10400 | } else { |
| 10401 | other_ = other; |
| 10402 | } |
| 10403 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 10404 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 10405 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10406 | TORCH_INTERNAL_ASSERT(false, |
| 10407 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10408 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10409 | } else { |
| 10410 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10411 | at::AutoDispatchSkipFunctionalize guard; |
| 10412 | at::Tensor tmp_output = at::_ops::bitwise_xor__Tensor::call(self_, other_); |
| 10413 | return self;; |
| 10414 | } |
| 10415 | } else { |
| 10416 | at::Tensor tmp_output; |
| 10417 | { |
| 10418 | at::AutoDispatchSkipFunctionalize guard; |
| 10419 | tmp_output = at::_ops::bitwise_xor_Tensor::call(self_, other_); |
| 10420 | } |
| 10421 | at::functionalization::impl::replace_(self, tmp_output); |
| 10422 | at::functionalization::impl::commit_update(self); |
| 10423 | at::functionalization::impl::sync(self); |
| 10424 | return self; |
| 10425 | } |
| 10426 | } |
| 10427 | |
| 10428 | at::Tensor & bitwise_xor_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 10429 | if (false) { |
| 10430 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10431 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10432 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10433 | auto self_meta = to_meta(self); |
| 10434 | auto out_meta = to_meta(out); |
| 10435 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10436 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10437 | at::_ops::bitwise_xor_Scalar_out::call(self_meta, other, out_meta); |
| 10438 | } |
| 10439 | |
| 10440 | at::Tensor self_; |
| 10441 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10442 | at::functionalization::impl::sync(self); |
| 10443 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10444 | } else { |
| 10445 | self_ = self; |
| 10446 | } |
| 10447 | |
| 10448 | at::Tensor out_; |
| 10449 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10450 | at::functionalization::impl::sync(out); |
| 10451 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10452 | } else { |
| 10453 | out_ = out; |
| 10454 | } |
| 10455 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10456 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 10457 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10458 | TORCH_INTERNAL_ASSERT(false, |
| 10459 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10460 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10461 | } else { |
| 10462 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10463 | at::AutoDispatchSkipFunctionalize guard; |
| 10464 | at::Tensor tmp_output = at::_ops::bitwise_xor_Scalar_out::call(self_, other, out_); |
| 10465 | return out;; |
| 10466 | } |
| 10467 | } else { |
| 10468 | at::Tensor tmp_output; |
| 10469 | { |
| 10470 | at::AutoDispatchSkipFunctionalize guard; |
| 10471 | tmp_output = at::_ops::bitwise_xor_Scalar::call(self_, other); |
| 10472 | } |
| 10473 | at::functionalization::impl::replace_(out, tmp_output); |
| 10474 | at::functionalization::impl::commit_update(out); |
| 10475 | at::functionalization::impl::sync(out); |
| 10476 | return out; |
| 10477 | } |
| 10478 | } |
| 10479 | |
| 10480 | at::Tensor & bitwise_xor__Scalar(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { |
| 10481 | if (true) { |
| 10482 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10483 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10484 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10485 | auto self_meta = to_meta(self); |
| 10486 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10487 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10488 | at::_ops::bitwise_xor__Scalar::call(self_meta, other); |
| 10489 | } |
| 10490 | |
| 10491 | at::Tensor self_; |
| 10492 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10493 | at::functionalization::impl::sync(self); |
| 10494 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10495 | } else { |
| 10496 | self_ = self; |
| 10497 | } |
| 10498 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 10499 | if ((false)) { |
| 10500 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10501 | TORCH_INTERNAL_ASSERT(false, |
| 10502 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10503 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10504 | } else { |
| 10505 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10506 | at::AutoDispatchSkipFunctionalize guard; |
| 10507 | at::Tensor tmp_output = at::_ops::bitwise_xor__Scalar::call(self_, other); |
| 10508 | return self;; |
| 10509 | } |
| 10510 | } else { |
| 10511 | at::Tensor tmp_output; |
| 10512 | { |
| 10513 | at::AutoDispatchSkipFunctionalize guard; |
| 10514 | tmp_output = at::_ops::bitwise_xor_Scalar::call(self_, other); |
| 10515 | } |
| 10516 | at::functionalization::impl::replace_(self, tmp_output); |
| 10517 | at::functionalization::impl::commit_update(self); |
| 10518 | at::functionalization::impl::sync(self); |
| 10519 | return self; |
| 10520 | } |
| 10521 | } |
| 10522 | |
| 10523 | at::Tensor & bitwise_xor_out_Scalar_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other, at::Tensor & out) { |
| 10524 | if (false) { |
| 10525 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10526 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10527 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10528 | auto other_meta = to_meta(other); |
| 10529 | auto out_meta = to_meta(out); |
| 10530 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10531 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10532 | at::_ops::bitwise_xor_Scalar_Tensor_out::call(self, other_meta, out_meta); |
| 10533 | } |
| 10534 | |
| 10535 | at::Tensor other_; |
| 10536 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 10537 | at::functionalization::impl::sync(other); |
| 10538 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 10539 | } else { |
| 10540 | other_ = other; |
| 10541 | } |
| 10542 | |
| 10543 | at::Tensor out_; |
| 10544 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10545 | at::functionalization::impl::sync(out); |
| 10546 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10547 | } else { |
| 10548 | out_ = out; |
| 10549 | } |
| 10550 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10551 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 10552 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10553 | TORCH_INTERNAL_ASSERT(false, |
| 10554 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10555 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10556 | } else { |
| 10557 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10558 | at::AutoDispatchSkipFunctionalize guard; |
| 10559 | at::Tensor tmp_output = at::_ops::bitwise_xor_Scalar_Tensor_out::call(self, other_, out_); |
| 10560 | return out;; |
| 10561 | } |
| 10562 | } else { |
| 10563 | at::Tensor tmp_output; |
| 10564 | { |
| 10565 | at::AutoDispatchSkipFunctionalize guard; |
| 10566 | tmp_output = at::_ops::bitwise_xor_Scalar_Tensor::call(self, other_); |
| 10567 | } |
| 10568 | at::functionalization::impl::replace_(out, tmp_output); |
| 10569 | at::functionalization::impl::commit_update(out); |
| 10570 | at::functionalization::impl::sync(out); |
| 10571 | return out; |
| 10572 | } |
| 10573 | } |
| 10574 | |
| 10575 | at::Tensor & addbmm_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) { |
| 10576 | if (false) { |
| 10577 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10578 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10579 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10580 | auto self_meta = to_meta(self); |
| 10581 | auto batch1_meta = to_meta(batch1); |
| 10582 | auto batch2_meta = to_meta(batch2); |
| 10583 | auto out_meta = to_meta(out); |
| 10584 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10585 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10586 | at::_ops::addbmm_out::call(self_meta, batch1_meta, batch2_meta, beta, alpha, out_meta); |
| 10587 | } |
| 10588 | |
| 10589 | at::Tensor self_; |
| 10590 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10591 | at::functionalization::impl::sync(self); |
| 10592 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10593 | } else { |
| 10594 | self_ = self; |
| 10595 | } |
| 10596 | |
| 10597 | at::Tensor batch1_; |
| 10598 | if (at::functionalization::impl::isFunctionalTensor(batch1)) { |
| 10599 | at::functionalization::impl::sync(batch1); |
| 10600 | batch1_ = at::functionalization::impl::from_functional_tensor(batch1); |
| 10601 | } else { |
| 10602 | batch1_ = batch1; |
| 10603 | } |
| 10604 | |
| 10605 | at::Tensor batch2_; |
| 10606 | if (at::functionalization::impl::isFunctionalTensor(batch2)) { |
| 10607 | at::functionalization::impl::sync(batch2); |
| 10608 | batch2_ = at::functionalization::impl::from_functional_tensor(batch2); |
| 10609 | } else { |
| 10610 | batch2_ = batch2; |
| 10611 | } |
| 10612 | |
| 10613 | at::Tensor out_; |
| 10614 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10615 | at::functionalization::impl::sync(out); |
| 10616 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10617 | } else { |
| 10618 | out_ = out; |
| 10619 | } |
| 10620 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10621 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(batch1) || at::functionalization::impl::isFunctionalTensor(batch2))) { |
| 10622 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10623 | TORCH_INTERNAL_ASSERT(false, |
| 10624 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10625 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10626 | } else { |
| 10627 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10628 | at::AutoDispatchSkipFunctionalize guard; |
| 10629 | at::Tensor tmp_output = at::_ops::addbmm_out::call(self_, batch1_, batch2_, beta, alpha, out_); |
| 10630 | return out;; |
| 10631 | } |
| 10632 | } else { |
| 10633 | at::Tensor tmp_output; |
| 10634 | { |
| 10635 | at::AutoDispatchSkipFunctionalize guard; |
| 10636 | tmp_output = at::_ops::addbmm::call(self_, batch1_, batch2_, beta, alpha); |
| 10637 | } |
| 10638 | at::functionalization::impl::replace_(out, tmp_output); |
| 10639 | at::functionalization::impl::commit_update(out); |
| 10640 | at::functionalization::impl::sync(out); |
| 10641 | return out; |
| 10642 | } |
| 10643 | } |
| 10644 | |
| 10645 | at::Tensor & addbmm_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 10646 | if (true) { |
| 10647 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10648 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10649 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10650 | auto self_meta = to_meta(self); |
| 10651 | auto batch1_meta = to_meta(batch1); |
| 10652 | auto batch2_meta = to_meta(batch2); |
| 10653 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10654 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10655 | at::_ops::addbmm_::call(self_meta, batch1_meta, batch2_meta, beta, alpha); |
| 10656 | } |
| 10657 | |
| 10658 | at::Tensor self_; |
| 10659 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10660 | at::functionalization::impl::sync(self); |
| 10661 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10662 | } else { |
| 10663 | self_ = self; |
| 10664 | } |
| 10665 | |
| 10666 | at::Tensor batch1_; |
| 10667 | if (at::functionalization::impl::isFunctionalTensor(batch1)) { |
| 10668 | at::functionalization::impl::sync(batch1); |
| 10669 | batch1_ = at::functionalization::impl::from_functional_tensor(batch1); |
| 10670 | } else { |
| 10671 | batch1_ = batch1; |
| 10672 | } |
| 10673 | |
| 10674 | at::Tensor batch2_; |
| 10675 | if (at::functionalization::impl::isFunctionalTensor(batch2)) { |
| 10676 | at::functionalization::impl::sync(batch2); |
| 10677 | batch2_ = at::functionalization::impl::from_functional_tensor(batch2); |
| 10678 | } else { |
| 10679 | batch2_ = batch2; |
| 10680 | } |
| 10681 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 10682 | if ((false || at::functionalization::impl::isFunctionalTensor(batch1) || at::functionalization::impl::isFunctionalTensor(batch2))) { |
| 10683 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10684 | TORCH_INTERNAL_ASSERT(false, |
| 10685 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10686 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10687 | } else { |
| 10688 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10689 | at::AutoDispatchSkipFunctionalize guard; |
| 10690 | at::Tensor tmp_output = at::_ops::addbmm_::call(self_, batch1_, batch2_, beta, alpha); |
| 10691 | return self;; |
| 10692 | } |
| 10693 | } else { |
| 10694 | at::Tensor tmp_output; |
| 10695 | { |
| 10696 | at::AutoDispatchSkipFunctionalize guard; |
| 10697 | tmp_output = at::_ops::addbmm::call(self_, batch1_, batch2_, beta, alpha); |
| 10698 | } |
| 10699 | at::functionalization::impl::replace_(self, tmp_output); |
| 10700 | at::functionalization::impl::commit_update(self); |
| 10701 | at::functionalization::impl::sync(self); |
| 10702 | return self; |
| 10703 | } |
| 10704 | } |
| 10705 | |
| 10706 | at::Tensor & random_out_from_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t from, c10::optional<int64_t> to, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 10707 | if (false) { |
| 10708 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10709 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10710 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10711 | auto self_meta = to_meta(self); |
| 10712 | auto out_meta = to_meta(out); |
| 10713 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10714 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10715 | at::_ops::random_from_out::call(self_meta, from, to, generator, out_meta); |
| 10716 | } |
| 10717 | |
| 10718 | at::Tensor self_; |
| 10719 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10720 | at::functionalization::impl::sync(self); |
| 10721 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10722 | } else { |
| 10723 | self_ = self; |
| 10724 | } |
| 10725 | |
| 10726 | at::Tensor out_; |
| 10727 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10728 | at::functionalization::impl::sync(out); |
| 10729 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10730 | } else { |
| 10731 | out_ = out; |
| 10732 | } |
| 10733 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10734 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 10735 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10736 | TORCH_INTERNAL_ASSERT(false, |
| 10737 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10738 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10739 | } else { |
| 10740 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10741 | at::AutoDispatchSkipFunctionalize guard; |
| 10742 | at::Tensor tmp_output = at::_ops::random_from_out::call(self_, from, to, generator, out_); |
| 10743 | return out;; |
| 10744 | } |
| 10745 | } else { |
| 10746 | at::Tensor tmp_output; |
| 10747 | { |
| 10748 | at::AutoDispatchSkipFunctionalize guard; |
| 10749 | tmp_output = at::_ops::random_from::call(self_, from, to, generator); |
| 10750 | } |
| 10751 | at::functionalization::impl::replace_(out, tmp_output); |
| 10752 | at::functionalization::impl::commit_update(out); |
| 10753 | at::functionalization::impl::sync(out); |
| 10754 | return out; |
| 10755 | } |
| 10756 | } |
| 10757 | |
| 10758 | at::Tensor & random__from(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t from, c10::optional<int64_t> to, c10::optional<at::Generator> generator) { |
| 10759 | if (true) { |
| 10760 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10761 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10762 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10763 | auto self_meta = to_meta(self); |
| 10764 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10765 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10766 | at::_ops::random__from::call(self_meta, from, to, generator); |
| 10767 | } |
| 10768 | |
| 10769 | at::Tensor self_; |
| 10770 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10771 | at::functionalization::impl::sync(self); |
| 10772 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10773 | } else { |
| 10774 | self_ = self; |
| 10775 | } |
| 10776 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 10777 | if ((false)) { |
| 10778 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10779 | TORCH_INTERNAL_ASSERT(false, |
| 10780 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10781 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10782 | } else { |
| 10783 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10784 | at::AutoDispatchSkipFunctionalize guard; |
| 10785 | at::Tensor tmp_output = at::_ops::random__from::call(self_, from, to, generator); |
| 10786 | return self;; |
| 10787 | } |
| 10788 | } else { |
| 10789 | at::Tensor tmp_output; |
| 10790 | { |
| 10791 | at::AutoDispatchSkipFunctionalize guard; |
| 10792 | tmp_output = at::_ops::random_from::call(self_, from, to, generator); |
| 10793 | } |
| 10794 | at::functionalization::impl::replace_(self, tmp_output); |
| 10795 | at::functionalization::impl::commit_update(self); |
| 10796 | at::functionalization::impl::sync(self); |
| 10797 | return self; |
| 10798 | } |
| 10799 | } |
| 10800 | |
| 10801 | at::Tensor & random_out_to_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t to, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 10802 | if (false) { |
| 10803 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10804 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10805 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10806 | auto self_meta = to_meta(self); |
| 10807 | auto out_meta = to_meta(out); |
| 10808 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10809 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10810 | at::_ops::random_to_out::call(self_meta, to, generator, out_meta); |
| 10811 | } |
| 10812 | |
| 10813 | at::Tensor self_; |
| 10814 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10815 | at::functionalization::impl::sync(self); |
| 10816 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10817 | } else { |
| 10818 | self_ = self; |
| 10819 | } |
| 10820 | |
| 10821 | at::Tensor out_; |
| 10822 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10823 | at::functionalization::impl::sync(out); |
| 10824 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10825 | } else { |
| 10826 | out_ = out; |
| 10827 | } |
| 10828 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10829 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 10830 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10831 | TORCH_INTERNAL_ASSERT(false, |
| 10832 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10833 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10834 | } else { |
| 10835 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10836 | at::AutoDispatchSkipFunctionalize guard; |
| 10837 | at::Tensor tmp_output = at::_ops::random_to_out::call(self_, to, generator, out_); |
| 10838 | return out;; |
| 10839 | } |
| 10840 | } else { |
| 10841 | at::Tensor tmp_output; |
| 10842 | { |
| 10843 | at::AutoDispatchSkipFunctionalize guard; |
| 10844 | tmp_output = at::_ops::random_to::call(self_, to, generator); |
| 10845 | } |
| 10846 | at::functionalization::impl::replace_(out, tmp_output); |
| 10847 | at::functionalization::impl::commit_update(out); |
| 10848 | at::functionalization::impl::sync(out); |
| 10849 | return out; |
| 10850 | } |
| 10851 | } |
| 10852 | |
| 10853 | at::Tensor & random__to(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t to, c10::optional<at::Generator> generator) { |
| 10854 | if (true) { |
| 10855 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10856 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10857 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10858 | auto self_meta = to_meta(self); |
| 10859 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10860 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10861 | at::_ops::random__to::call(self_meta, to, generator); |
| 10862 | } |
| 10863 | |
| 10864 | at::Tensor self_; |
| 10865 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10866 | at::functionalization::impl::sync(self); |
| 10867 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10868 | } else { |
| 10869 | self_ = self; |
| 10870 | } |
| 10871 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 10872 | if ((false)) { |
| 10873 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10874 | TORCH_INTERNAL_ASSERT(false, |
| 10875 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10876 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10877 | } else { |
| 10878 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10879 | at::AutoDispatchSkipFunctionalize guard; |
| 10880 | at::Tensor tmp_output = at::_ops::random__to::call(self_, to, generator); |
| 10881 | return self;; |
| 10882 | } |
| 10883 | } else { |
| 10884 | at::Tensor tmp_output; |
| 10885 | { |
| 10886 | at::AutoDispatchSkipFunctionalize guard; |
| 10887 | tmp_output = at::_ops::random_to::call(self_, to, generator); |
| 10888 | } |
| 10889 | at::functionalization::impl::replace_(self, tmp_output); |
| 10890 | at::functionalization::impl::commit_update(self); |
| 10891 | at::functionalization::impl::sync(self); |
| 10892 | return self; |
| 10893 | } |
| 10894 | } |
| 10895 | |
| 10896 | at::Tensor & random_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 10897 | if (false) { |
| 10898 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10899 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10900 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10901 | auto self_meta = to_meta(self); |
| 10902 | auto out_meta = to_meta(out); |
| 10903 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10904 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10905 | at::_ops::random_out::call(self_meta, generator, out_meta); |
| 10906 | } |
| 10907 | |
| 10908 | at::Tensor self_; |
| 10909 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10910 | at::functionalization::impl::sync(self); |
| 10911 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10912 | } else { |
| 10913 | self_ = self; |
| 10914 | } |
| 10915 | |
| 10916 | at::Tensor out_; |
| 10917 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 10918 | at::functionalization::impl::sync(out); |
| 10919 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 10920 | } else { |
| 10921 | out_ = out; |
| 10922 | } |
| 10923 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 10924 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 10925 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10926 | TORCH_INTERNAL_ASSERT(false, |
| 10927 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10928 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10929 | } else { |
| 10930 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10931 | at::AutoDispatchSkipFunctionalize guard; |
| 10932 | at::Tensor tmp_output = at::_ops::random_out::call(self_, generator, out_); |
| 10933 | return out;; |
| 10934 | } |
| 10935 | } else { |
| 10936 | at::Tensor tmp_output; |
| 10937 | { |
| 10938 | at::AutoDispatchSkipFunctionalize guard; |
| 10939 | tmp_output = at::_ops::random::call(self_, generator); |
| 10940 | } |
| 10941 | at::functionalization::impl::replace_(out, tmp_output); |
| 10942 | at::functionalization::impl::commit_update(out); |
| 10943 | at::functionalization::impl::sync(out); |
| 10944 | return out; |
| 10945 | } |
| 10946 | } |
| 10947 | |
| 10948 | at::Tensor & random_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, c10::optional<at::Generator> generator) { |
| 10949 | if (true) { |
| 10950 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10951 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10952 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10953 | auto self_meta = to_meta(self); |
| 10954 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10955 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 10956 | at::_ops::random_::call(self_meta, generator); |
| 10957 | } |
| 10958 | |
| 10959 | at::Tensor self_; |
| 10960 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 10961 | at::functionalization::impl::sync(self); |
| 10962 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 10963 | } else { |
| 10964 | self_ = self; |
| 10965 | } |
| 10966 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 10967 | if ((false)) { |
| 10968 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 10969 | TORCH_INTERNAL_ASSERT(false, |
| 10970 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 10971 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 10972 | } else { |
| 10973 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 10974 | at::AutoDispatchSkipFunctionalize guard; |
| 10975 | at::Tensor tmp_output = at::_ops::random_::call(self_, generator); |
| 10976 | return self;; |
| 10977 | } |
| 10978 | } else { |
| 10979 | at::Tensor tmp_output; |
| 10980 | { |
| 10981 | at::AutoDispatchSkipFunctionalize guard; |
| 10982 | tmp_output = at::_ops::random::call(self_, generator); |
| 10983 | } |
| 10984 | at::functionalization::impl::replace_(self, tmp_output); |
| 10985 | at::functionalization::impl::commit_update(self); |
| 10986 | at::functionalization::impl::sync(self); |
| 10987 | return self; |
| 10988 | } |
| 10989 | } |
| 10990 | |
| 10991 | at::Tensor & exponential_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double lambd, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 10992 | if (false) { |
| 10993 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 10994 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 10995 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 10996 | auto self_meta = to_meta(self); |
| 10997 | auto out_meta = to_meta(out); |
| 10998 | at::AutoDispatchSkipFunctionalize func_guard; |
| 10999 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11000 | at::_ops::exponential_out::call(self_meta, lambd, generator, out_meta); |
| 11001 | } |
| 11002 | |
| 11003 | at::Tensor self_; |
| 11004 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11005 | at::functionalization::impl::sync(self); |
| 11006 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11007 | } else { |
| 11008 | self_ = self; |
| 11009 | } |
| 11010 | |
| 11011 | at::Tensor out_; |
| 11012 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11013 | at::functionalization::impl::sync(out); |
| 11014 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11015 | } else { |
| 11016 | out_ = out; |
| 11017 | } |
| 11018 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11019 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 11020 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11021 | TORCH_INTERNAL_ASSERT(false, |
| 11022 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11023 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11024 | } else { |
| 11025 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11026 | at::AutoDispatchSkipFunctionalize guard; |
| 11027 | at::Tensor tmp_output = at::_ops::exponential_out::call(self_, lambd, generator, out_); |
| 11028 | return out;; |
| 11029 | } |
| 11030 | } else { |
| 11031 | at::Tensor tmp_output; |
| 11032 | { |
| 11033 | at::AutoDispatchSkipFunctionalize guard; |
| 11034 | tmp_output = at::_ops::exponential::call(self_, lambd, generator); |
| 11035 | } |
| 11036 | at::functionalization::impl::replace_(out, tmp_output); |
| 11037 | at::functionalization::impl::commit_update(out); |
| 11038 | at::functionalization::impl::sync(out); |
| 11039 | return out; |
| 11040 | } |
| 11041 | } |
| 11042 | |
| 11043 | at::Tensor & exponential_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, double lambd, c10::optional<at::Generator> generator) { |
| 11044 | if (true) { |
| 11045 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11046 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11047 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11048 | auto self_meta = to_meta(self); |
| 11049 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11050 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11051 | at::_ops::exponential_::call(self_meta, lambd, generator); |
| 11052 | } |
| 11053 | |
| 11054 | at::Tensor self_; |
| 11055 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11056 | at::functionalization::impl::sync(self); |
| 11057 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11058 | } else { |
| 11059 | self_ = self; |
| 11060 | } |
| 11061 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 11062 | if ((false)) { |
| 11063 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11064 | TORCH_INTERNAL_ASSERT(false, |
| 11065 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11066 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11067 | } else { |
| 11068 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11069 | at::AutoDispatchSkipFunctionalize guard; |
| 11070 | at::Tensor tmp_output = at::_ops::exponential_::call(self_, lambd, generator); |
| 11071 | return self;; |
| 11072 | } |
| 11073 | } else { |
| 11074 | at::Tensor tmp_output; |
| 11075 | { |
| 11076 | at::AutoDispatchSkipFunctionalize guard; |
| 11077 | tmp_output = at::_ops::exponential::call(self_, lambd, generator); |
| 11078 | } |
| 11079 | at::functionalization::impl::replace_(self, tmp_output); |
| 11080 | at::functionalization::impl::commit_update(self); |
| 11081 | at::functionalization::impl::sync(self); |
| 11082 | return self; |
| 11083 | } |
| 11084 | } |
| 11085 | |
| 11086 | at::Tensor & geometric_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double p, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 11087 | if (false) { |
| 11088 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11089 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11090 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11091 | auto self_meta = to_meta(self); |
| 11092 | auto out_meta = to_meta(out); |
| 11093 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11094 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11095 | at::_ops::geometric_out::call(self_meta, p, generator, out_meta); |
| 11096 | } |
| 11097 | |
| 11098 | at::Tensor self_; |
| 11099 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11100 | at::functionalization::impl::sync(self); |
| 11101 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11102 | } else { |
| 11103 | self_ = self; |
| 11104 | } |
| 11105 | |
| 11106 | at::Tensor out_; |
| 11107 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11108 | at::functionalization::impl::sync(out); |
| 11109 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11110 | } else { |
| 11111 | out_ = out; |
| 11112 | } |
| 11113 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11114 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 11115 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11116 | TORCH_INTERNAL_ASSERT(false, |
| 11117 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11118 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11119 | } else { |
| 11120 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11121 | at::AutoDispatchSkipFunctionalize guard; |
| 11122 | at::Tensor tmp_output = at::_ops::geometric_out::call(self_, p, generator, out_); |
| 11123 | return out;; |
| 11124 | } |
| 11125 | } else { |
| 11126 | at::Tensor tmp_output; |
| 11127 | { |
| 11128 | at::AutoDispatchSkipFunctionalize guard; |
| 11129 | tmp_output = at::_ops::geometric::call(self_, p, generator); |
| 11130 | } |
| 11131 | at::functionalization::impl::replace_(out, tmp_output); |
| 11132 | at::functionalization::impl::commit_update(out); |
| 11133 | at::functionalization::impl::sync(out); |
| 11134 | return out; |
| 11135 | } |
| 11136 | } |
| 11137 | |
| 11138 | at::Tensor & geometric_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, double p, c10::optional<at::Generator> generator) { |
| 11139 | if (true) { |
| 11140 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11141 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11142 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11143 | auto self_meta = to_meta(self); |
| 11144 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11145 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11146 | at::_ops::geometric_::call(self_meta, p, generator); |
| 11147 | } |
| 11148 | |
| 11149 | at::Tensor self_; |
| 11150 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11151 | at::functionalization::impl::sync(self); |
| 11152 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11153 | } else { |
| 11154 | self_ = self; |
| 11155 | } |
| 11156 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 11157 | if ((false)) { |
| 11158 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11159 | TORCH_INTERNAL_ASSERT(false, |
| 11160 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11161 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11162 | } else { |
| 11163 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11164 | at::AutoDispatchSkipFunctionalize guard; |
| 11165 | at::Tensor tmp_output = at::_ops::geometric_::call(self_, p, generator); |
| 11166 | return self;; |
| 11167 | } |
| 11168 | } else { |
| 11169 | at::Tensor tmp_output; |
| 11170 | { |
| 11171 | at::AutoDispatchSkipFunctionalize guard; |
| 11172 | tmp_output = at::_ops::geometric::call(self_, p, generator); |
| 11173 | } |
| 11174 | at::functionalization::impl::replace_(self, tmp_output); |
| 11175 | at::functionalization::impl::commit_update(self); |
| 11176 | at::functionalization::impl::sync(self); |
| 11177 | return self; |
| 11178 | } |
| 11179 | } |
| 11180 | |
| 11181 | at::Tensor & cross_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, c10::optional<int64_t> dim, at::Tensor & out) { |
| 11182 | if (false) { |
| 11183 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11184 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11185 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11186 | auto self_meta = to_meta(self); |
| 11187 | auto other_meta = to_meta(other); |
| 11188 | auto out_meta = to_meta(out); |
| 11189 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11190 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11191 | at::_ops::cross_out::call(self_meta, other_meta, dim, out_meta); |
| 11192 | } |
| 11193 | |
| 11194 | at::Tensor self_; |
| 11195 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11196 | at::functionalization::impl::sync(self); |
| 11197 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11198 | } else { |
| 11199 | self_ = self; |
| 11200 | } |
| 11201 | |
| 11202 | at::Tensor other_; |
| 11203 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 11204 | at::functionalization::impl::sync(other); |
| 11205 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 11206 | } else { |
| 11207 | other_ = other; |
| 11208 | } |
| 11209 | |
| 11210 | at::Tensor out_; |
| 11211 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11212 | at::functionalization::impl::sync(out); |
| 11213 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11214 | } else { |
| 11215 | out_ = out; |
| 11216 | } |
| 11217 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11218 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 11219 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11220 | TORCH_INTERNAL_ASSERT(false, |
| 11221 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11222 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11223 | } else { |
| 11224 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11225 | at::AutoDispatchSkipFunctionalize guard; |
| 11226 | at::Tensor tmp_output = at::_ops::cross_out::call(self_, other_, dim, out_); |
| 11227 | return out;; |
| 11228 | } |
| 11229 | } else { |
| 11230 | at::Tensor tmp_output; |
| 11231 | { |
| 11232 | at::AutoDispatchSkipFunctionalize guard; |
| 11233 | tmp_output = at::_ops::cross::call(self_, other_, dim); |
| 11234 | } |
| 11235 | at::functionalization::impl::replace_(out, tmp_output); |
| 11236 | at::functionalization::impl::commit_update(out); |
| 11237 | at::functionalization::impl::sync(out); |
| 11238 | return out; |
| 11239 | } |
| 11240 | } |
| 11241 | |
| 11242 | at::Tensor & trace_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 11243 | if (false) { |
| 11244 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11245 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11246 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11247 | auto self_meta = to_meta(self); |
| 11248 | auto out_meta = to_meta(out); |
| 11249 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11250 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11251 | at::_ops::trace_out::call(self_meta, out_meta); |
| 11252 | } |
| 11253 | |
| 11254 | at::Tensor self_; |
| 11255 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11256 | at::functionalization::impl::sync(self); |
| 11257 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11258 | } else { |
| 11259 | self_ = self; |
| 11260 | } |
| 11261 | |
| 11262 | at::Tensor out_; |
| 11263 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11264 | at::functionalization::impl::sync(out); |
| 11265 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11266 | } else { |
| 11267 | out_ = out; |
| 11268 | } |
| 11269 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11270 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 11271 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11272 | TORCH_INTERNAL_ASSERT(false, |
| 11273 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11274 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11275 | } else { |
| 11276 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11277 | at::AutoDispatchSkipFunctionalize guard; |
| 11278 | at::Tensor tmp_output = at::_ops::trace_out::call(self_, out_); |
| 11279 | return out;; |
| 11280 | } |
| 11281 | } else { |
| 11282 | at::Tensor tmp_output; |
| 11283 | { |
| 11284 | at::AutoDispatchSkipFunctionalize guard; |
| 11285 | tmp_output = at::_ops::trace::call(self_); |
| 11286 | } |
| 11287 | at::functionalization::impl::replace_(out, tmp_output); |
| 11288 | at::functionalization::impl::commit_update(out); |
| 11289 | at::functionalization::impl::sync(out); |
| 11290 | return out; |
| 11291 | } |
| 11292 | } |
| 11293 | |
| 11294 | at::Tensor & take_along_dim_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & indices, c10::optional<int64_t> dim, at::Tensor & out) { |
| 11295 | if (false) { |
| 11296 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11297 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11298 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11299 | auto self_meta = to_meta(self); |
| 11300 | auto indices_meta = to_meta(indices); |
| 11301 | auto out_meta = to_meta(out); |
| 11302 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11303 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11304 | at::_ops::take_along_dim_out::call(self_meta, indices_meta, dim, out_meta); |
| 11305 | } |
| 11306 | |
| 11307 | at::Tensor self_; |
| 11308 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11309 | at::functionalization::impl::sync(self); |
| 11310 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11311 | } else { |
| 11312 | self_ = self; |
| 11313 | } |
| 11314 | |
| 11315 | at::Tensor indices_; |
| 11316 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 11317 | at::functionalization::impl::sync(indices); |
| 11318 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 11319 | } else { |
| 11320 | indices_ = indices; |
| 11321 | } |
| 11322 | |
| 11323 | at::Tensor out_; |
| 11324 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11325 | at::functionalization::impl::sync(out); |
| 11326 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11327 | } else { |
| 11328 | out_ = out; |
| 11329 | } |
| 11330 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11331 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(indices))) { |
| 11332 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11333 | TORCH_INTERNAL_ASSERT(false, |
| 11334 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11335 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11336 | } else { |
| 11337 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11338 | at::AutoDispatchSkipFunctionalize guard; |
| 11339 | at::Tensor tmp_output = at::_ops::take_along_dim_out::call(self_, indices_, dim, out_); |
| 11340 | return out;; |
| 11341 | } |
| 11342 | } else { |
| 11343 | at::Tensor tmp_output; |
| 11344 | { |
| 11345 | at::AutoDispatchSkipFunctionalize guard; |
| 11346 | tmp_output = at::_ops::take_along_dim::call(self_, indices_, dim); |
| 11347 | } |
| 11348 | at::functionalization::impl::replace_(out, tmp_output); |
| 11349 | at::functionalization::impl::commit_update(out); |
| 11350 | at::functionalization::impl::sync(out); |
| 11351 | return out; |
| 11352 | } |
| 11353 | } |
| 11354 | |
| 11355 | at::Tensor & index_select_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, at::Tensor & out) { |
| 11356 | if (false) { |
| 11357 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11358 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11359 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11360 | auto self_meta = to_meta(self); |
| 11361 | auto index_meta = to_meta(index); |
| 11362 | auto out_meta = to_meta(out); |
| 11363 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11364 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11365 | at::_ops::index_select_out::call(self_meta, dim, index_meta, out_meta); |
| 11366 | } |
| 11367 | |
| 11368 | at::Tensor self_; |
| 11369 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11370 | at::functionalization::impl::sync(self); |
| 11371 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11372 | } else { |
| 11373 | self_ = self; |
| 11374 | } |
| 11375 | |
| 11376 | at::Tensor index_; |
| 11377 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 11378 | at::functionalization::impl::sync(index); |
| 11379 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 11380 | } else { |
| 11381 | index_ = index; |
| 11382 | } |
| 11383 | |
| 11384 | at::Tensor out_; |
| 11385 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11386 | at::functionalization::impl::sync(out); |
| 11387 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11388 | } else { |
| 11389 | out_ = out; |
| 11390 | } |
| 11391 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11392 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(index))) { |
| 11393 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11394 | TORCH_INTERNAL_ASSERT(false, |
| 11395 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11396 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11397 | } else { |
| 11398 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11399 | at::AutoDispatchSkipFunctionalize guard; |
| 11400 | at::Tensor tmp_output = at::_ops::index_select_out::call(self_, dim, index_, out_); |
| 11401 | return out;; |
| 11402 | } |
| 11403 | } else { |
| 11404 | at::Tensor tmp_output; |
| 11405 | { |
| 11406 | at::AutoDispatchSkipFunctionalize guard; |
| 11407 | tmp_output = at::_ops::index_select::call(self_, dim, index_); |
| 11408 | } |
| 11409 | at::functionalization::impl::replace_(out, tmp_output); |
| 11410 | at::functionalization::impl::commit_update(out); |
| 11411 | at::functionalization::impl::sync(out); |
| 11412 | return out; |
| 11413 | } |
| 11414 | } |
| 11415 | |
| 11416 | at::Tensor & index_select_out_dimname_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, const at::Tensor & index, at::Tensor & out) { |
| 11417 | if (false) { |
| 11418 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11419 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11420 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11421 | auto self_meta = to_meta(self); |
| 11422 | auto index_meta = to_meta(index); |
| 11423 | auto out_meta = to_meta(out); |
| 11424 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11425 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11426 | at::_ops::index_select_dimname_out::call(self_meta, dim, index_meta, out_meta); |
| 11427 | } |
| 11428 | |
| 11429 | at::Tensor self_; |
| 11430 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11431 | at::functionalization::impl::sync(self); |
| 11432 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11433 | } else { |
| 11434 | self_ = self; |
| 11435 | } |
| 11436 | |
| 11437 | at::Tensor index_; |
| 11438 | if (at::functionalization::impl::isFunctionalTensor(index)) { |
| 11439 | at::functionalization::impl::sync(index); |
| 11440 | index_ = at::functionalization::impl::from_functional_tensor(index); |
| 11441 | } else { |
| 11442 | index_ = index; |
| 11443 | } |
| 11444 | |
| 11445 | at::Tensor out_; |
| 11446 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11447 | at::functionalization::impl::sync(out); |
| 11448 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11449 | } else { |
| 11450 | out_ = out; |
| 11451 | } |
| 11452 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11453 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(index))) { |
| 11454 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11455 | TORCH_INTERNAL_ASSERT(false, |
| 11456 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11457 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11458 | } else { |
| 11459 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11460 | at::AutoDispatchSkipFunctionalize guard; |
| 11461 | at::Tensor tmp_output = at::_ops::index_select_dimname_out::call(self_, dim, index_, out_); |
| 11462 | return out;; |
| 11463 | } |
| 11464 | } else { |
| 11465 | at::Tensor tmp_output; |
| 11466 | { |
| 11467 | at::AutoDispatchSkipFunctionalize guard; |
| 11468 | tmp_output = at::_ops::index_select_dimname::call(self_, dim, index_); |
| 11469 | } |
| 11470 | at::functionalization::impl::replace_(out, tmp_output); |
| 11471 | at::functionalization::impl::commit_update(out); |
| 11472 | at::functionalization::impl::sync(out); |
| 11473 | return out; |
| 11474 | } |
| 11475 | } |
| 11476 | |
| 11477 | at::Tensor & masked_select_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mask, at::Tensor & out) { |
| 11478 | if (false) { |
| 11479 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11480 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11481 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11482 | auto self_meta = to_meta(self); |
| 11483 | auto mask_meta = to_meta(mask); |
| 11484 | auto out_meta = to_meta(out); |
| 11485 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11486 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11487 | at::_ops::masked_select_out::call(self_meta, mask_meta, out_meta); |
| 11488 | } |
| 11489 | |
| 11490 | at::Tensor self_; |
| 11491 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11492 | at::functionalization::impl::sync(self); |
| 11493 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11494 | } else { |
| 11495 | self_ = self; |
| 11496 | } |
| 11497 | |
| 11498 | at::Tensor mask_; |
| 11499 | if (at::functionalization::impl::isFunctionalTensor(mask)) { |
| 11500 | at::functionalization::impl::sync(mask); |
| 11501 | mask_ = at::functionalization::impl::from_functional_tensor(mask); |
| 11502 | } else { |
| 11503 | mask_ = mask; |
| 11504 | } |
| 11505 | |
| 11506 | at::Tensor out_; |
| 11507 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11508 | at::functionalization::impl::sync(out); |
| 11509 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11510 | } else { |
| 11511 | out_ = out; |
| 11512 | } |
| 11513 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11514 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(mask))) { |
| 11515 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11516 | TORCH_INTERNAL_ASSERT(false, |
| 11517 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11518 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11519 | } else { |
| 11520 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11521 | at::AutoDispatchSkipFunctionalize guard; |
| 11522 | at::Tensor tmp_output = at::_ops::masked_select_out::call(self_, mask_, out_); |
| 11523 | return out;; |
| 11524 | } |
| 11525 | } else { |
| 11526 | at::Tensor tmp_output; |
| 11527 | { |
| 11528 | at::AutoDispatchSkipFunctionalize guard; |
| 11529 | tmp_output = at::_ops::masked_select::call(self_, mask_); |
| 11530 | } |
| 11531 | at::functionalization::impl::replace_(out, tmp_output); |
| 11532 | at::functionalization::impl::commit_update(out); |
| 11533 | at::functionalization::impl::sync(out); |
| 11534 | return out; |
| 11535 | } |
| 11536 | } |
| 11537 | |
| 11538 | at::Tensor & linalg_solve_triangular_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & B, bool upper, bool left, bool unitriangular, at::Tensor & out) { |
| 11539 | if (false) { |
| 11540 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11541 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11542 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11543 | auto self_meta = to_meta(self); |
| 11544 | auto B_meta = to_meta(B); |
| 11545 | auto out_meta = to_meta(out); |
| 11546 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11547 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11548 | at::_ops::linalg_solve_triangular_out::call(self_meta, B_meta, upper, left, unitriangular, out_meta); |
| 11549 | } |
| 11550 | |
| 11551 | at::Tensor self_; |
| 11552 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11553 | at::functionalization::impl::sync(self); |
| 11554 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11555 | } else { |
| 11556 | self_ = self; |
| 11557 | } |
| 11558 | |
| 11559 | at::Tensor B_; |
| 11560 | if (at::functionalization::impl::isFunctionalTensor(B)) { |
| 11561 | at::functionalization::impl::sync(B); |
| 11562 | B_ = at::functionalization::impl::from_functional_tensor(B); |
| 11563 | } else { |
| 11564 | B_ = B; |
| 11565 | } |
| 11566 | |
| 11567 | at::Tensor out_; |
| 11568 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11569 | at::functionalization::impl::sync(out); |
| 11570 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11571 | } else { |
| 11572 | out_ = out; |
| 11573 | } |
| 11574 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11575 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(B))) { |
| 11576 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11577 | TORCH_INTERNAL_ASSERT(false, |
| 11578 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11579 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11580 | } else { |
| 11581 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11582 | at::AutoDispatchSkipFunctionalize guard; |
| 11583 | at::Tensor tmp_output = at::_ops::linalg_solve_triangular_out::call(self_, B_, upper, left, unitriangular, out_); |
| 11584 | return out;; |
| 11585 | } |
| 11586 | } else { |
| 11587 | at::Tensor tmp_output; |
| 11588 | { |
| 11589 | at::AutoDispatchSkipFunctionalize guard; |
| 11590 | tmp_output = at::_ops::linalg_solve_triangular::call(self_, B_, upper, left, unitriangular); |
| 11591 | } |
| 11592 | at::functionalization::impl::replace_(out, tmp_output); |
| 11593 | at::functionalization::impl::commit_update(out); |
| 11594 | at::functionalization::impl::sync(out); |
| 11595 | return out; |
| 11596 | } |
| 11597 | } |
| 11598 | |
| 11599 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> svd_out_U(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool some, bool compute_uv, at::Tensor & U, at::Tensor & S, at::Tensor & V) { |
| 11600 | if (false) { |
| 11601 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11602 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11603 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11604 | auto self_meta = to_meta(self); |
| 11605 | auto U_meta = to_meta(U); |
| 11606 | auto S_meta = to_meta(S); |
| 11607 | auto V_meta = to_meta(V); |
| 11608 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11609 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11610 | at::_ops::svd_U::call(self_meta, some, compute_uv, U_meta, S_meta, V_meta); |
| 11611 | } |
| 11612 | |
| 11613 | at::Tensor self_; |
| 11614 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11615 | at::functionalization::impl::sync(self); |
| 11616 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11617 | } else { |
| 11618 | self_ = self; |
| 11619 | } |
| 11620 | |
| 11621 | at::Tensor U_; |
| 11622 | if (at::functionalization::impl::isFunctionalTensor(U)) { |
| 11623 | at::functionalization::impl::sync(U); |
| 11624 | U_ = at::functionalization::impl::from_functional_tensor(U); |
| 11625 | } else { |
| 11626 | U_ = U; |
| 11627 | } |
| 11628 | |
| 11629 | at::Tensor S_; |
| 11630 | if (at::functionalization::impl::isFunctionalTensor(S)) { |
| 11631 | at::functionalization::impl::sync(S); |
| 11632 | S_ = at::functionalization::impl::from_functional_tensor(S); |
| 11633 | } else { |
| 11634 | S_ = S; |
| 11635 | } |
| 11636 | |
| 11637 | at::Tensor V_; |
| 11638 | if (at::functionalization::impl::isFunctionalTensor(V)) { |
| 11639 | at::functionalization::impl::sync(V); |
| 11640 | V_ = at::functionalization::impl::from_functional_tensor(V); |
| 11641 | } else { |
| 11642 | V_ = V; |
| 11643 | } |
| 11644 | if (!(true && at::functionalization::impl::isFunctionalTensor(U) && at::functionalization::impl::isFunctionalTensor(S) && at::functionalization::impl::isFunctionalTensor(V))) { |
| 11645 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 11646 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11647 | TORCH_INTERNAL_ASSERT(false, |
| 11648 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11649 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11650 | } else { |
| 11651 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11652 | at::AutoDispatchSkipFunctionalize guard; |
| 11653 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::svd_U::call(self_, some, compute_uv, U_, S_, V_); |
| 11654 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(U, S, V);; |
| 11655 | } |
| 11656 | } else { |
| 11657 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 11658 | { |
| 11659 | at::AutoDispatchSkipFunctionalize guard; |
| 11660 | tmp_output = at::_ops::svd::call(self_, some, compute_uv); |
| 11661 | } |
| 11662 | at::functionalization::impl::replace_(U, std::get<0>(tmp_output)); |
| 11663 | at::functionalization::impl::commit_update(U); |
| 11664 | at::functionalization::impl::sync(U); |
| 11665 | at::functionalization::impl::replace_(S, std::get<1>(tmp_output)); |
| 11666 | at::functionalization::impl::commit_update(S); |
| 11667 | at::functionalization::impl::sync(S); |
| 11668 | at::functionalization::impl::replace_(V, std::get<2>(tmp_output)); |
| 11669 | at::functionalization::impl::commit_update(V); |
| 11670 | at::functionalization::impl::sync(V); |
| 11671 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(U, S, V); |
| 11672 | } |
| 11673 | } |
| 11674 | |
| 11675 | at::Tensor & multinomial_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t num_samples, bool replacement, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 11676 | if (false) { |
| 11677 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11678 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11679 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11680 | auto self_meta = to_meta(self); |
| 11681 | auto out_meta = to_meta(out); |
| 11682 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11683 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11684 | at::_ops::multinomial_out::call(self_meta, num_samples, replacement, generator, out_meta); |
| 11685 | } |
| 11686 | |
| 11687 | at::Tensor self_; |
| 11688 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11689 | at::functionalization::impl::sync(self); |
| 11690 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11691 | } else { |
| 11692 | self_ = self; |
| 11693 | } |
| 11694 | |
| 11695 | at::Tensor out_; |
| 11696 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11697 | at::functionalization::impl::sync(out); |
| 11698 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11699 | } else { |
| 11700 | out_ = out; |
| 11701 | } |
| 11702 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11703 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 11704 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11705 | TORCH_INTERNAL_ASSERT(false, |
| 11706 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11707 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11708 | } else { |
| 11709 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11710 | at::AutoDispatchSkipFunctionalize guard; |
| 11711 | at::Tensor tmp_output = at::_ops::multinomial_out::call(self_, num_samples, replacement, generator, out_); |
| 11712 | return out;; |
| 11713 | } |
| 11714 | } else { |
| 11715 | at::Tensor tmp_output; |
| 11716 | { |
| 11717 | at::AutoDispatchSkipFunctionalize guard; |
| 11718 | tmp_output = at::_ops::multinomial::call(self_, num_samples, replacement, generator); |
| 11719 | } |
| 11720 | at::functionalization::impl::replace_(out, tmp_output); |
| 11721 | at::functionalization::impl::commit_update(out); |
| 11722 | at::functionalization::impl::sync(out); |
| 11723 | return out; |
| 11724 | } |
| 11725 | } |
| 11726 | |
| 11727 | ::std::tuple<at::Tensor &,at::Tensor &> histogram_out_bins_tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & bins, const c10::optional<at::Tensor> & weight, bool density, at::Tensor & hist, at::Tensor & bin_edges) { |
| 11728 | if (false) { |
| 11729 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11730 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11731 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11732 | auto self_meta = to_meta(self); |
| 11733 | auto bins_meta = to_meta(bins); |
| 11734 | auto weight_meta = to_meta(weight); |
| 11735 | auto hist_meta = to_meta(hist); |
| 11736 | auto bin_edges_meta = to_meta(bin_edges); |
| 11737 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11738 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11739 | at::_ops::histogram_bins_tensor_out::call(self_meta, bins_meta, weight_meta, density, hist_meta, bin_edges_meta); |
| 11740 | } |
| 11741 | |
| 11742 | at::Tensor self_; |
| 11743 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11744 | at::functionalization::impl::sync(self); |
| 11745 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11746 | } else { |
| 11747 | self_ = self; |
| 11748 | } |
| 11749 | |
| 11750 | at::Tensor bins_; |
| 11751 | if (at::functionalization::impl::isFunctionalTensor(bins)) { |
| 11752 | at::functionalization::impl::sync(bins); |
| 11753 | bins_ = at::functionalization::impl::from_functional_tensor(bins); |
| 11754 | } else { |
| 11755 | bins_ = bins; |
| 11756 | } |
| 11757 | |
| 11758 | c10::optional<at::Tensor> weight_; |
| 11759 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 11760 | at::functionalization::impl::sync(weight); |
| 11761 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 11762 | } else { |
| 11763 | weight_ = weight; |
| 11764 | } |
| 11765 | |
| 11766 | at::Tensor hist_; |
| 11767 | if (at::functionalization::impl::isFunctionalTensor(hist)) { |
| 11768 | at::functionalization::impl::sync(hist); |
| 11769 | hist_ = at::functionalization::impl::from_functional_tensor(hist); |
| 11770 | } else { |
| 11771 | hist_ = hist; |
| 11772 | } |
| 11773 | |
| 11774 | at::Tensor bin_edges_; |
| 11775 | if (at::functionalization::impl::isFunctionalTensor(bin_edges)) { |
| 11776 | at::functionalization::impl::sync(bin_edges); |
| 11777 | bin_edges_ = at::functionalization::impl::from_functional_tensor(bin_edges); |
| 11778 | } else { |
| 11779 | bin_edges_ = bin_edges; |
| 11780 | } |
| 11781 | if (!(true && at::functionalization::impl::isFunctionalTensor(hist) && at::functionalization::impl::isFunctionalTensor(bin_edges))) { |
| 11782 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(bins) || at::functionalization::impl::isFunctionalTensor(weight))) { |
| 11783 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11784 | TORCH_INTERNAL_ASSERT(false, |
| 11785 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11786 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11787 | } else { |
| 11788 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11789 | at::AutoDispatchSkipFunctionalize guard; |
| 11790 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::histogram_bins_tensor_out::call(self_, bins_, weight_, density, hist_, bin_edges_); |
| 11791 | return ::std::tuple<at::Tensor &,at::Tensor &>(hist, bin_edges);; |
| 11792 | } |
| 11793 | } else { |
| 11794 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 11795 | { |
| 11796 | at::AutoDispatchSkipFunctionalize guard; |
| 11797 | tmp_output = at::_ops::histogram_bins_tensor::call(self_, bins_, weight_, density); |
| 11798 | } |
| 11799 | at::functionalization::impl::replace_(hist, std::get<0>(tmp_output)); |
| 11800 | at::functionalization::impl::commit_update(hist); |
| 11801 | at::functionalization::impl::sync(hist); |
| 11802 | at::functionalization::impl::replace_(bin_edges, std::get<1>(tmp_output)); |
| 11803 | at::functionalization::impl::commit_update(bin_edges); |
| 11804 | at::functionalization::impl::sync(bin_edges); |
| 11805 | return ::std::tuple<at::Tensor &,at::Tensor &>(hist, bin_edges); |
| 11806 | } |
| 11807 | } |
| 11808 | |
| 11809 | ::std::tuple<at::Tensor &,at::Tensor &> histogram_out_bin_ct_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t bins, c10::optional<at::ArrayRef<double>> range, const c10::optional<at::Tensor> & weight, bool density, at::Tensor & hist, at::Tensor & bin_edges) { |
| 11810 | if (false) { |
| 11811 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11812 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11813 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11814 | auto self_meta = to_meta(self); |
| 11815 | auto weight_meta = to_meta(weight); |
| 11816 | auto hist_meta = to_meta(hist); |
| 11817 | auto bin_edges_meta = to_meta(bin_edges); |
| 11818 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11819 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11820 | at::_ops::histogram_bin_ct_out::call(self_meta, bins, range, weight_meta, density, hist_meta, bin_edges_meta); |
| 11821 | } |
| 11822 | |
| 11823 | at::Tensor self_; |
| 11824 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11825 | at::functionalization::impl::sync(self); |
| 11826 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11827 | } else { |
| 11828 | self_ = self; |
| 11829 | } |
| 11830 | |
| 11831 | c10::optional<at::Tensor> weight_; |
| 11832 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 11833 | at::functionalization::impl::sync(weight); |
| 11834 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 11835 | } else { |
| 11836 | weight_ = weight; |
| 11837 | } |
| 11838 | |
| 11839 | at::Tensor hist_; |
| 11840 | if (at::functionalization::impl::isFunctionalTensor(hist)) { |
| 11841 | at::functionalization::impl::sync(hist); |
| 11842 | hist_ = at::functionalization::impl::from_functional_tensor(hist); |
| 11843 | } else { |
| 11844 | hist_ = hist; |
| 11845 | } |
| 11846 | |
| 11847 | at::Tensor bin_edges_; |
| 11848 | if (at::functionalization::impl::isFunctionalTensor(bin_edges)) { |
| 11849 | at::functionalization::impl::sync(bin_edges); |
| 11850 | bin_edges_ = at::functionalization::impl::from_functional_tensor(bin_edges); |
| 11851 | } else { |
| 11852 | bin_edges_ = bin_edges; |
| 11853 | } |
| 11854 | if (!(true && at::functionalization::impl::isFunctionalTensor(hist) && at::functionalization::impl::isFunctionalTensor(bin_edges))) { |
| 11855 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(weight))) { |
| 11856 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11857 | TORCH_INTERNAL_ASSERT(false, |
| 11858 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11859 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11860 | } else { |
| 11861 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11862 | at::AutoDispatchSkipFunctionalize guard; |
| 11863 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::histogram_bin_ct_out::call(self_, bins, range, weight_, density, hist_, bin_edges_); |
| 11864 | return ::std::tuple<at::Tensor &,at::Tensor &>(hist, bin_edges);; |
| 11865 | } |
| 11866 | } else { |
| 11867 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 11868 | { |
| 11869 | at::AutoDispatchSkipFunctionalize guard; |
| 11870 | tmp_output = at::_ops::histogram_bin_ct::call(self_, bins, range, weight_, density); |
| 11871 | } |
| 11872 | at::functionalization::impl::replace_(hist, std::get<0>(tmp_output)); |
| 11873 | at::functionalization::impl::commit_update(hist); |
| 11874 | at::functionalization::impl::sync(hist); |
| 11875 | at::functionalization::impl::replace_(bin_edges, std::get<1>(tmp_output)); |
| 11876 | at::functionalization::impl::commit_update(bin_edges); |
| 11877 | at::functionalization::impl::sync(bin_edges); |
| 11878 | return ::std::tuple<at::Tensor &,at::Tensor &>(hist, bin_edges); |
| 11879 | } |
| 11880 | } |
| 11881 | |
| 11882 | at::Tensor & igammac_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 11883 | if (false) { |
| 11884 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11885 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11886 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11887 | auto self_meta = to_meta(self); |
| 11888 | auto other_meta = to_meta(other); |
| 11889 | auto out_meta = to_meta(out); |
| 11890 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11891 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11892 | at::_ops::igammac_out::call(self_meta, other_meta, out_meta); |
| 11893 | } |
| 11894 | |
| 11895 | at::Tensor self_; |
| 11896 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11897 | at::functionalization::impl::sync(self); |
| 11898 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11899 | } else { |
| 11900 | self_ = self; |
| 11901 | } |
| 11902 | |
| 11903 | at::Tensor other_; |
| 11904 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 11905 | at::functionalization::impl::sync(other); |
| 11906 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 11907 | } else { |
| 11908 | other_ = other; |
| 11909 | } |
| 11910 | |
| 11911 | at::Tensor out_; |
| 11912 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 11913 | at::functionalization::impl::sync(out); |
| 11914 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 11915 | } else { |
| 11916 | out_ = out; |
| 11917 | } |
| 11918 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 11919 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 11920 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11921 | TORCH_INTERNAL_ASSERT(false, |
| 11922 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11923 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11924 | } else { |
| 11925 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11926 | at::AutoDispatchSkipFunctionalize guard; |
| 11927 | at::Tensor tmp_output = at::_ops::igammac_out::call(self_, other_, out_); |
| 11928 | return out;; |
| 11929 | } |
| 11930 | } else { |
| 11931 | at::Tensor tmp_output; |
| 11932 | { |
| 11933 | at::AutoDispatchSkipFunctionalize guard; |
| 11934 | tmp_output = at::_ops::igammac::call(self_, other_); |
| 11935 | } |
| 11936 | at::functionalization::impl::replace_(out, tmp_output); |
| 11937 | at::functionalization::impl::commit_update(out); |
| 11938 | at::functionalization::impl::sync(out); |
| 11939 | return out; |
| 11940 | } |
| 11941 | } |
| 11942 | |
| 11943 | at::Tensor & igammac_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 11944 | if (true) { |
| 11945 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11946 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11947 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 11948 | auto self_meta = to_meta(self); |
| 11949 | auto other_meta = to_meta(other); |
| 11950 | at::AutoDispatchSkipFunctionalize func_guard; |
| 11951 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 11952 | at::_ops::igammac_::call(self_meta, other_meta); |
| 11953 | } |
| 11954 | |
| 11955 | at::Tensor self_; |
| 11956 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 11957 | at::functionalization::impl::sync(self); |
| 11958 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 11959 | } else { |
| 11960 | self_ = self; |
| 11961 | } |
| 11962 | |
| 11963 | at::Tensor other_; |
| 11964 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 11965 | at::functionalization::impl::sync(other); |
| 11966 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 11967 | } else { |
| 11968 | other_ = other; |
| 11969 | } |
| 11970 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 11971 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 11972 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 11973 | TORCH_INTERNAL_ASSERT(false, |
| 11974 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 11975 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 11976 | } else { |
| 11977 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 11978 | at::AutoDispatchSkipFunctionalize guard; |
| 11979 | at::Tensor tmp_output = at::_ops::igammac_::call(self_, other_); |
| 11980 | return self;; |
| 11981 | } |
| 11982 | } else { |
| 11983 | at::Tensor tmp_output; |
| 11984 | { |
| 11985 | at::AutoDispatchSkipFunctionalize guard; |
| 11986 | tmp_output = at::_ops::igammac::call(self_, other_); |
| 11987 | } |
| 11988 | at::functionalization::impl::replace_(self, tmp_output); |
| 11989 | at::functionalization::impl::commit_update(self); |
| 11990 | at::functionalization::impl::sync(self); |
| 11991 | return self; |
| 11992 | } |
| 11993 | } |
| 11994 | |
| 11995 | at::Tensor & remainder_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 11996 | if (false) { |
| 11997 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 11998 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 11999 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12000 | auto self_meta = to_meta(self); |
| 12001 | auto out_meta = to_meta(out); |
| 12002 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12003 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12004 | at::_ops::remainder_Scalar_out::call(self_meta, other, out_meta); |
| 12005 | } |
| 12006 | |
| 12007 | at::Tensor self_; |
| 12008 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12009 | at::functionalization::impl::sync(self); |
| 12010 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12011 | } else { |
| 12012 | self_ = self; |
| 12013 | } |
| 12014 | |
| 12015 | at::Tensor out_; |
| 12016 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12017 | at::functionalization::impl::sync(out); |
| 12018 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12019 | } else { |
| 12020 | out_ = out; |
| 12021 | } |
| 12022 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12023 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 12024 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12025 | TORCH_INTERNAL_ASSERT(false, |
| 12026 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12027 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12028 | } else { |
| 12029 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12030 | at::AutoDispatchSkipFunctionalize guard; |
| 12031 | at::Tensor tmp_output = at::_ops::remainder_Scalar_out::call(self_, other, out_); |
| 12032 | return out;; |
| 12033 | } |
| 12034 | } else { |
| 12035 | at::Tensor tmp_output; |
| 12036 | { |
| 12037 | at::AutoDispatchSkipFunctionalize guard; |
| 12038 | tmp_output = at::_ops::remainder_Scalar::call(self_, other); |
| 12039 | } |
| 12040 | at::functionalization::impl::replace_(out, tmp_output); |
| 12041 | at::functionalization::impl::commit_update(out); |
| 12042 | at::functionalization::impl::sync(out); |
| 12043 | return out; |
| 12044 | } |
| 12045 | } |
| 12046 | |
| 12047 | at::Tensor & remainder__Scalar(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { |
| 12048 | if (true) { |
| 12049 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12050 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12051 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12052 | auto self_meta = to_meta(self); |
| 12053 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12054 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12055 | at::_ops::remainder__Scalar::call(self_meta, other); |
| 12056 | } |
| 12057 | |
| 12058 | at::Tensor self_; |
| 12059 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12060 | at::functionalization::impl::sync(self); |
| 12061 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12062 | } else { |
| 12063 | self_ = self; |
| 12064 | } |
| 12065 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 12066 | if ((false)) { |
| 12067 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12068 | TORCH_INTERNAL_ASSERT(false, |
| 12069 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12070 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12071 | } else { |
| 12072 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12073 | at::AutoDispatchSkipFunctionalize guard; |
| 12074 | at::Tensor tmp_output = at::_ops::remainder__Scalar::call(self_, other); |
| 12075 | return self;; |
| 12076 | } |
| 12077 | } else { |
| 12078 | at::Tensor tmp_output; |
| 12079 | { |
| 12080 | at::AutoDispatchSkipFunctionalize guard; |
| 12081 | tmp_output = at::_ops::remainder_Scalar::call(self_, other); |
| 12082 | } |
| 12083 | at::functionalization::impl::replace_(self, tmp_output); |
| 12084 | at::functionalization::impl::commit_update(self); |
| 12085 | at::functionalization::impl::sync(self); |
| 12086 | return self; |
| 12087 | } |
| 12088 | } |
| 12089 | |
| 12090 | at::Tensor & remainder_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 12091 | if (false) { |
| 12092 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12093 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12094 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12095 | auto self_meta = to_meta(self); |
| 12096 | auto other_meta = to_meta(other); |
| 12097 | auto out_meta = to_meta(out); |
| 12098 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12099 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12100 | at::_ops::remainder_Tensor_out::call(self_meta, other_meta, out_meta); |
| 12101 | } |
| 12102 | |
| 12103 | at::Tensor self_; |
| 12104 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12105 | at::functionalization::impl::sync(self); |
| 12106 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12107 | } else { |
| 12108 | self_ = self; |
| 12109 | } |
| 12110 | |
| 12111 | at::Tensor other_; |
| 12112 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 12113 | at::functionalization::impl::sync(other); |
| 12114 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 12115 | } else { |
| 12116 | other_ = other; |
| 12117 | } |
| 12118 | |
| 12119 | at::Tensor out_; |
| 12120 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12121 | at::functionalization::impl::sync(out); |
| 12122 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12123 | } else { |
| 12124 | out_ = out; |
| 12125 | } |
| 12126 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12127 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 12128 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12129 | TORCH_INTERNAL_ASSERT(false, |
| 12130 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12131 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12132 | } else { |
| 12133 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12134 | at::AutoDispatchSkipFunctionalize guard; |
| 12135 | at::Tensor tmp_output = at::_ops::remainder_Tensor_out::call(self_, other_, out_); |
| 12136 | return out;; |
| 12137 | } |
| 12138 | } else { |
| 12139 | at::Tensor tmp_output; |
| 12140 | { |
| 12141 | at::AutoDispatchSkipFunctionalize guard; |
| 12142 | tmp_output = at::_ops::remainder_Tensor::call(self_, other_); |
| 12143 | } |
| 12144 | at::functionalization::impl::replace_(out, tmp_output); |
| 12145 | at::functionalization::impl::commit_update(out); |
| 12146 | at::functionalization::impl::sync(out); |
| 12147 | return out; |
| 12148 | } |
| 12149 | } |
| 12150 | |
| 12151 | at::Tensor & remainder__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 12152 | if (true) { |
| 12153 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12154 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12155 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12156 | auto self_meta = to_meta(self); |
| 12157 | auto other_meta = to_meta(other); |
| 12158 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12159 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12160 | at::_ops::remainder__Tensor::call(self_meta, other_meta); |
| 12161 | } |
| 12162 | |
| 12163 | at::Tensor self_; |
| 12164 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12165 | at::functionalization::impl::sync(self); |
| 12166 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12167 | } else { |
| 12168 | self_ = self; |
| 12169 | } |
| 12170 | |
| 12171 | at::Tensor other_; |
| 12172 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 12173 | at::functionalization::impl::sync(other); |
| 12174 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 12175 | } else { |
| 12176 | other_ = other; |
| 12177 | } |
| 12178 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 12179 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 12180 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12181 | TORCH_INTERNAL_ASSERT(false, |
| 12182 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12183 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12184 | } else { |
| 12185 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12186 | at::AutoDispatchSkipFunctionalize guard; |
| 12187 | at::Tensor tmp_output = at::_ops::remainder__Tensor::call(self_, other_); |
| 12188 | return self;; |
| 12189 | } |
| 12190 | } else { |
| 12191 | at::Tensor tmp_output; |
| 12192 | { |
| 12193 | at::AutoDispatchSkipFunctionalize guard; |
| 12194 | tmp_output = at::_ops::remainder_Tensor::call(self_, other_); |
| 12195 | } |
| 12196 | at::functionalization::impl::replace_(self, tmp_output); |
| 12197 | at::functionalization::impl::commit_update(self); |
| 12198 | at::functionalization::impl::sync(self); |
| 12199 | return self; |
| 12200 | } |
| 12201 | } |
| 12202 | |
| 12203 | at::Tensor & remainder_out_Scalar_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other, at::Tensor & out) { |
| 12204 | if (false) { |
| 12205 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12206 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12207 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12208 | auto other_meta = to_meta(other); |
| 12209 | auto out_meta = to_meta(out); |
| 12210 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12211 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12212 | at::_ops::remainder_Scalar_Tensor_out::call(self, other_meta, out_meta); |
| 12213 | } |
| 12214 | |
| 12215 | at::Tensor other_; |
| 12216 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 12217 | at::functionalization::impl::sync(other); |
| 12218 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 12219 | } else { |
| 12220 | other_ = other; |
| 12221 | } |
| 12222 | |
| 12223 | at::Tensor out_; |
| 12224 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12225 | at::functionalization::impl::sync(out); |
| 12226 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12227 | } else { |
| 12228 | out_ = out; |
| 12229 | } |
| 12230 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12231 | if ((false || at::functionalization::impl::isFunctionalTensor(other))) { |
| 12232 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12233 | TORCH_INTERNAL_ASSERT(false, |
| 12234 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12235 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12236 | } else { |
| 12237 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12238 | at::AutoDispatchSkipFunctionalize guard; |
| 12239 | at::Tensor tmp_output = at::_ops::remainder_Scalar_Tensor_out::call(self, other_, out_); |
| 12240 | return out;; |
| 12241 | } |
| 12242 | } else { |
| 12243 | at::Tensor tmp_output; |
| 12244 | { |
| 12245 | at::AutoDispatchSkipFunctionalize guard; |
| 12246 | tmp_output = at::_ops::remainder_Scalar_Tensor::call(self, other_); |
| 12247 | } |
| 12248 | at::functionalization::impl::replace_(out, tmp_output); |
| 12249 | at::functionalization::impl::commit_update(out); |
| 12250 | at::functionalization::impl::sync(out); |
| 12251 | return out; |
| 12252 | } |
| 12253 | } |
| 12254 | |
| 12255 | at::Tensor & quantile_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & q, c10::optional<int64_t> dim, bool keepdim, c10::string_view interpolation, at::Tensor & out) { |
| 12256 | if (false) { |
| 12257 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12258 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12259 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12260 | auto self_meta = to_meta(self); |
| 12261 | auto q_meta = to_meta(q); |
| 12262 | auto out_meta = to_meta(out); |
| 12263 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12264 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12265 | at::_ops::quantile_out::call(self_meta, q_meta, dim, keepdim, interpolation, out_meta); |
| 12266 | } |
| 12267 | |
| 12268 | at::Tensor self_; |
| 12269 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12270 | at::functionalization::impl::sync(self); |
| 12271 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12272 | } else { |
| 12273 | self_ = self; |
| 12274 | } |
| 12275 | |
| 12276 | at::Tensor q_; |
| 12277 | if (at::functionalization::impl::isFunctionalTensor(q)) { |
| 12278 | at::functionalization::impl::sync(q); |
| 12279 | q_ = at::functionalization::impl::from_functional_tensor(q); |
| 12280 | } else { |
| 12281 | q_ = q; |
| 12282 | } |
| 12283 | |
| 12284 | at::Tensor out_; |
| 12285 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12286 | at::functionalization::impl::sync(out); |
| 12287 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12288 | } else { |
| 12289 | out_ = out; |
| 12290 | } |
| 12291 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12292 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(q))) { |
| 12293 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12294 | TORCH_INTERNAL_ASSERT(false, |
| 12295 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12296 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12297 | } else { |
| 12298 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12299 | at::AutoDispatchSkipFunctionalize guard; |
| 12300 | at::Tensor tmp_output = at::_ops::quantile_out::call(self_, q_, dim, keepdim, interpolation, out_); |
| 12301 | return out;; |
| 12302 | } |
| 12303 | } else { |
| 12304 | at::Tensor tmp_output; |
| 12305 | { |
| 12306 | at::AutoDispatchSkipFunctionalize guard; |
| 12307 | tmp_output = at::_ops::quantile::call(self_, q_, dim, keepdim, interpolation); |
| 12308 | } |
| 12309 | at::functionalization::impl::replace_(out, tmp_output); |
| 12310 | at::functionalization::impl::commit_update(out); |
| 12311 | at::functionalization::impl::sync(out); |
| 12312 | return out; |
| 12313 | } |
| 12314 | } |
| 12315 | |
| 12316 | at::Tensor & quantile_out_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double q, c10::optional<int64_t> dim, bool keepdim, c10::string_view interpolation, at::Tensor & out) { |
| 12317 | if (false) { |
| 12318 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12319 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12320 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12321 | auto self_meta = to_meta(self); |
| 12322 | auto out_meta = to_meta(out); |
| 12323 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12324 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12325 | at::_ops::quantile_scalar_out::call(self_meta, q, dim, keepdim, interpolation, out_meta); |
| 12326 | } |
| 12327 | |
| 12328 | at::Tensor self_; |
| 12329 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12330 | at::functionalization::impl::sync(self); |
| 12331 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12332 | } else { |
| 12333 | self_ = self; |
| 12334 | } |
| 12335 | |
| 12336 | at::Tensor out_; |
| 12337 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12338 | at::functionalization::impl::sync(out); |
| 12339 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12340 | } else { |
| 12341 | out_ = out; |
| 12342 | } |
| 12343 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12344 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 12345 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12346 | TORCH_INTERNAL_ASSERT(false, |
| 12347 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12348 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12349 | } else { |
| 12350 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12351 | at::AutoDispatchSkipFunctionalize guard; |
| 12352 | at::Tensor tmp_output = at::_ops::quantile_scalar_out::call(self_, q, dim, keepdim, interpolation, out_); |
| 12353 | return out;; |
| 12354 | } |
| 12355 | } else { |
| 12356 | at::Tensor tmp_output; |
| 12357 | { |
| 12358 | at::AutoDispatchSkipFunctionalize guard; |
| 12359 | tmp_output = at::_ops::quantile_scalar::call(self_, q, dim, keepdim, interpolation); |
| 12360 | } |
| 12361 | at::functionalization::impl::replace_(out, tmp_output); |
| 12362 | at::functionalization::impl::commit_update(out); |
| 12363 | at::functionalization::impl::sync(out); |
| 12364 | return out; |
| 12365 | } |
| 12366 | } |
| 12367 | |
| 12368 | at::Tensor & nanquantile_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & q, c10::optional<int64_t> dim, bool keepdim, c10::string_view interpolation, at::Tensor & out) { |
| 12369 | if (false) { |
| 12370 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12371 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12372 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12373 | auto self_meta = to_meta(self); |
| 12374 | auto q_meta = to_meta(q); |
| 12375 | auto out_meta = to_meta(out); |
| 12376 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12377 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12378 | at::_ops::nanquantile_out::call(self_meta, q_meta, dim, keepdim, interpolation, out_meta); |
| 12379 | } |
| 12380 | |
| 12381 | at::Tensor self_; |
| 12382 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12383 | at::functionalization::impl::sync(self); |
| 12384 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12385 | } else { |
| 12386 | self_ = self; |
| 12387 | } |
| 12388 | |
| 12389 | at::Tensor q_; |
| 12390 | if (at::functionalization::impl::isFunctionalTensor(q)) { |
| 12391 | at::functionalization::impl::sync(q); |
| 12392 | q_ = at::functionalization::impl::from_functional_tensor(q); |
| 12393 | } else { |
| 12394 | q_ = q; |
| 12395 | } |
| 12396 | |
| 12397 | at::Tensor out_; |
| 12398 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12399 | at::functionalization::impl::sync(out); |
| 12400 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12401 | } else { |
| 12402 | out_ = out; |
| 12403 | } |
| 12404 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12405 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(q))) { |
| 12406 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12407 | TORCH_INTERNAL_ASSERT(false, |
| 12408 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12409 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12410 | } else { |
| 12411 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12412 | at::AutoDispatchSkipFunctionalize guard; |
| 12413 | at::Tensor tmp_output = at::_ops::nanquantile_out::call(self_, q_, dim, keepdim, interpolation, out_); |
| 12414 | return out;; |
| 12415 | } |
| 12416 | } else { |
| 12417 | at::Tensor tmp_output; |
| 12418 | { |
| 12419 | at::AutoDispatchSkipFunctionalize guard; |
| 12420 | tmp_output = at::_ops::nanquantile::call(self_, q_, dim, keepdim, interpolation); |
| 12421 | } |
| 12422 | at::functionalization::impl::replace_(out, tmp_output); |
| 12423 | at::functionalization::impl::commit_update(out); |
| 12424 | at::functionalization::impl::sync(out); |
| 12425 | return out; |
| 12426 | } |
| 12427 | } |
| 12428 | |
| 12429 | at::Tensor & nanquantile_out_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double q, c10::optional<int64_t> dim, bool keepdim, c10::string_view interpolation, at::Tensor & out) { |
| 12430 | if (false) { |
| 12431 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12432 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12433 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12434 | auto self_meta = to_meta(self); |
| 12435 | auto out_meta = to_meta(out); |
| 12436 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12437 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12438 | at::_ops::nanquantile_scalar_out::call(self_meta, q, dim, keepdim, interpolation, out_meta); |
| 12439 | } |
| 12440 | |
| 12441 | at::Tensor self_; |
| 12442 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12443 | at::functionalization::impl::sync(self); |
| 12444 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12445 | } else { |
| 12446 | self_ = self; |
| 12447 | } |
| 12448 | |
| 12449 | at::Tensor out_; |
| 12450 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12451 | at::functionalization::impl::sync(out); |
| 12452 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12453 | } else { |
| 12454 | out_ = out; |
| 12455 | } |
| 12456 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12457 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 12458 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12459 | TORCH_INTERNAL_ASSERT(false, |
| 12460 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12461 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12462 | } else { |
| 12463 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12464 | at::AutoDispatchSkipFunctionalize guard; |
| 12465 | at::Tensor tmp_output = at::_ops::nanquantile_scalar_out::call(self_, q, dim, keepdim, interpolation, out_); |
| 12466 | return out;; |
| 12467 | } |
| 12468 | } else { |
| 12469 | at::Tensor tmp_output; |
| 12470 | { |
| 12471 | at::AutoDispatchSkipFunctionalize guard; |
| 12472 | tmp_output = at::_ops::nanquantile_scalar::call(self_, q, dim, keepdim, interpolation); |
| 12473 | } |
| 12474 | at::functionalization::impl::replace_(out, tmp_output); |
| 12475 | at::functionalization::impl::commit_update(out); |
| 12476 | at::functionalization::impl::sync(out); |
| 12477 | return out; |
| 12478 | } |
| 12479 | } |
| 12480 | |
| 12481 | ::std::tuple<at::Tensor &,at::Tensor &> sort_out_values(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool descending, at::Tensor & values, at::Tensor & indices) { |
| 12482 | if (false) { |
| 12483 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12484 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12485 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12486 | auto self_meta = to_meta(self); |
| 12487 | auto values_meta = to_meta(values); |
| 12488 | auto indices_meta = to_meta(indices); |
| 12489 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12490 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12491 | at::_ops::sort_values::call(self_meta, dim, descending, values_meta, indices_meta); |
| 12492 | } |
| 12493 | |
| 12494 | at::Tensor self_; |
| 12495 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12496 | at::functionalization::impl::sync(self); |
| 12497 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12498 | } else { |
| 12499 | self_ = self; |
| 12500 | } |
| 12501 | |
| 12502 | at::Tensor values_; |
| 12503 | if (at::functionalization::impl::isFunctionalTensor(values)) { |
| 12504 | at::functionalization::impl::sync(values); |
| 12505 | values_ = at::functionalization::impl::from_functional_tensor(values); |
| 12506 | } else { |
| 12507 | values_ = values; |
| 12508 | } |
| 12509 | |
| 12510 | at::Tensor indices_; |
| 12511 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 12512 | at::functionalization::impl::sync(indices); |
| 12513 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 12514 | } else { |
| 12515 | indices_ = indices; |
| 12516 | } |
| 12517 | if (!(true && at::functionalization::impl::isFunctionalTensor(values) && at::functionalization::impl::isFunctionalTensor(indices))) { |
| 12518 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 12519 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12520 | TORCH_INTERNAL_ASSERT(false, |
| 12521 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12522 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12523 | } else { |
| 12524 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12525 | at::AutoDispatchSkipFunctionalize guard; |
| 12526 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::sort_values::call(self_, dim, descending, values_, indices_); |
| 12527 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices);; |
| 12528 | } |
| 12529 | } else { |
| 12530 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 12531 | { |
| 12532 | at::AutoDispatchSkipFunctionalize guard; |
| 12533 | tmp_output = at::_ops::sort::call(self_, dim, descending); |
| 12534 | } |
| 12535 | at::functionalization::impl::replace_(values, std::get<0>(tmp_output)); |
| 12536 | at::functionalization::impl::commit_update(values); |
| 12537 | at::functionalization::impl::sync(values); |
| 12538 | at::functionalization::impl::replace_(indices, std::get<1>(tmp_output)); |
| 12539 | at::functionalization::impl::commit_update(indices); |
| 12540 | at::functionalization::impl::sync(indices); |
| 12541 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices); |
| 12542 | } |
| 12543 | } |
| 12544 | |
| 12545 | ::std::tuple<at::Tensor &,at::Tensor &> sort_out_values_stable(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<bool> stable, int64_t dim, bool descending, at::Tensor & values, at::Tensor & indices) { |
| 12546 | if (false) { |
| 12547 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12548 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12549 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12550 | auto self_meta = to_meta(self); |
| 12551 | auto values_meta = to_meta(values); |
| 12552 | auto indices_meta = to_meta(indices); |
| 12553 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12554 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12555 | at::_ops::sort_values_stable::call(self_meta, stable, dim, descending, values_meta, indices_meta); |
| 12556 | } |
| 12557 | |
| 12558 | at::Tensor self_; |
| 12559 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12560 | at::functionalization::impl::sync(self); |
| 12561 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12562 | } else { |
| 12563 | self_ = self; |
| 12564 | } |
| 12565 | |
| 12566 | at::Tensor values_; |
| 12567 | if (at::functionalization::impl::isFunctionalTensor(values)) { |
| 12568 | at::functionalization::impl::sync(values); |
| 12569 | values_ = at::functionalization::impl::from_functional_tensor(values); |
| 12570 | } else { |
| 12571 | values_ = values; |
| 12572 | } |
| 12573 | |
| 12574 | at::Tensor indices_; |
| 12575 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 12576 | at::functionalization::impl::sync(indices); |
| 12577 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 12578 | } else { |
| 12579 | indices_ = indices; |
| 12580 | } |
| 12581 | if (!(true && at::functionalization::impl::isFunctionalTensor(values) && at::functionalization::impl::isFunctionalTensor(indices))) { |
| 12582 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 12583 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12584 | TORCH_INTERNAL_ASSERT(false, |
| 12585 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12586 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12587 | } else { |
| 12588 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12589 | at::AutoDispatchSkipFunctionalize guard; |
| 12590 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::sort_values_stable::call(self_, stable, dim, descending, values_, indices_); |
| 12591 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices);; |
| 12592 | } |
| 12593 | } else { |
| 12594 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 12595 | { |
| 12596 | at::AutoDispatchSkipFunctionalize guard; |
| 12597 | tmp_output = at::_ops::sort_stable::call(self_, stable, dim, descending); |
| 12598 | } |
| 12599 | at::functionalization::impl::replace_(values, std::get<0>(tmp_output)); |
| 12600 | at::functionalization::impl::commit_update(values); |
| 12601 | at::functionalization::impl::sync(values); |
| 12602 | at::functionalization::impl::replace_(indices, std::get<1>(tmp_output)); |
| 12603 | at::functionalization::impl::commit_update(indices); |
| 12604 | at::functionalization::impl::sync(indices); |
| 12605 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices); |
| 12606 | } |
| 12607 | } |
| 12608 | |
| 12609 | ::std::tuple<at::Tensor &,at::Tensor &> sort_out_dimname_values(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool descending, at::Tensor & values, at::Tensor & indices) { |
| 12610 | if (false) { |
| 12611 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12612 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12613 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12614 | auto self_meta = to_meta(self); |
| 12615 | auto values_meta = to_meta(values); |
| 12616 | auto indices_meta = to_meta(indices); |
| 12617 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12618 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12619 | at::_ops::sort_dimname_values::call(self_meta, dim, descending, values_meta, indices_meta); |
| 12620 | } |
| 12621 | |
| 12622 | at::Tensor self_; |
| 12623 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12624 | at::functionalization::impl::sync(self); |
| 12625 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12626 | } else { |
| 12627 | self_ = self; |
| 12628 | } |
| 12629 | |
| 12630 | at::Tensor values_; |
| 12631 | if (at::functionalization::impl::isFunctionalTensor(values)) { |
| 12632 | at::functionalization::impl::sync(values); |
| 12633 | values_ = at::functionalization::impl::from_functional_tensor(values); |
| 12634 | } else { |
| 12635 | values_ = values; |
| 12636 | } |
| 12637 | |
| 12638 | at::Tensor indices_; |
| 12639 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 12640 | at::functionalization::impl::sync(indices); |
| 12641 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 12642 | } else { |
| 12643 | indices_ = indices; |
| 12644 | } |
| 12645 | if (!(true && at::functionalization::impl::isFunctionalTensor(values) && at::functionalization::impl::isFunctionalTensor(indices))) { |
| 12646 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 12647 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12648 | TORCH_INTERNAL_ASSERT(false, |
| 12649 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12650 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12651 | } else { |
| 12652 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12653 | at::AutoDispatchSkipFunctionalize guard; |
| 12654 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::sort_dimname_values::call(self_, dim, descending, values_, indices_); |
| 12655 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices);; |
| 12656 | } |
| 12657 | } else { |
| 12658 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 12659 | { |
| 12660 | at::AutoDispatchSkipFunctionalize guard; |
| 12661 | tmp_output = at::_ops::sort_dimname::call(self_, dim, descending); |
| 12662 | } |
| 12663 | at::functionalization::impl::replace_(values, std::get<0>(tmp_output)); |
| 12664 | at::functionalization::impl::commit_update(values); |
| 12665 | at::functionalization::impl::sync(values); |
| 12666 | at::functionalization::impl::replace_(indices, std::get<1>(tmp_output)); |
| 12667 | at::functionalization::impl::commit_update(indices); |
| 12668 | at::functionalization::impl::sync(indices); |
| 12669 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices); |
| 12670 | } |
| 12671 | } |
| 12672 | |
| 12673 | ::std::tuple<at::Tensor &,at::Tensor &> sort_out_dimname_values_stable(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<bool> stable, at::Dimname dim, bool descending, at::Tensor & values, at::Tensor & indices) { |
| 12674 | if (false) { |
| 12675 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12676 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12677 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12678 | auto self_meta = to_meta(self); |
| 12679 | auto values_meta = to_meta(values); |
| 12680 | auto indices_meta = to_meta(indices); |
| 12681 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12682 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12683 | at::_ops::sort_dimname_values_stable::call(self_meta, stable, dim, descending, values_meta, indices_meta); |
| 12684 | } |
| 12685 | |
| 12686 | at::Tensor self_; |
| 12687 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12688 | at::functionalization::impl::sync(self); |
| 12689 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12690 | } else { |
| 12691 | self_ = self; |
| 12692 | } |
| 12693 | |
| 12694 | at::Tensor values_; |
| 12695 | if (at::functionalization::impl::isFunctionalTensor(values)) { |
| 12696 | at::functionalization::impl::sync(values); |
| 12697 | values_ = at::functionalization::impl::from_functional_tensor(values); |
| 12698 | } else { |
| 12699 | values_ = values; |
| 12700 | } |
| 12701 | |
| 12702 | at::Tensor indices_; |
| 12703 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 12704 | at::functionalization::impl::sync(indices); |
| 12705 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 12706 | } else { |
| 12707 | indices_ = indices; |
| 12708 | } |
| 12709 | if (!(true && at::functionalization::impl::isFunctionalTensor(values) && at::functionalization::impl::isFunctionalTensor(indices))) { |
| 12710 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 12711 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12712 | TORCH_INTERNAL_ASSERT(false, |
| 12713 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12714 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12715 | } else { |
| 12716 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12717 | at::AutoDispatchSkipFunctionalize guard; |
| 12718 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::sort_dimname_values_stable::call(self_, stable, dim, descending, values_, indices_); |
| 12719 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices);; |
| 12720 | } |
| 12721 | } else { |
| 12722 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 12723 | { |
| 12724 | at::AutoDispatchSkipFunctionalize guard; |
| 12725 | tmp_output = at::_ops::sort_dimname_stable::call(self_, stable, dim, descending); |
| 12726 | } |
| 12727 | at::functionalization::impl::replace_(values, std::get<0>(tmp_output)); |
| 12728 | at::functionalization::impl::commit_update(values); |
| 12729 | at::functionalization::impl::sync(values); |
| 12730 | at::functionalization::impl::replace_(indices, std::get<1>(tmp_output)); |
| 12731 | at::functionalization::impl::commit_update(indices); |
| 12732 | at::functionalization::impl::sync(indices); |
| 12733 | return ::std::tuple<at::Tensor &,at::Tensor &>(values, indices); |
| 12734 | } |
| 12735 | } |
| 12736 | |
| 12737 | at::Tensor & argsort_out_stable_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool stable, int64_t dim, bool descending, at::Tensor & out) { |
| 12738 | if (false) { |
| 12739 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12740 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12741 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12742 | auto self_meta = to_meta(self); |
| 12743 | auto out_meta = to_meta(out); |
| 12744 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12745 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12746 | at::_ops::argsort_stable_out::call(self_meta, stable, dim, descending, out_meta); |
| 12747 | } |
| 12748 | |
| 12749 | at::Tensor self_; |
| 12750 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12751 | at::functionalization::impl::sync(self); |
| 12752 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12753 | } else { |
| 12754 | self_ = self; |
| 12755 | } |
| 12756 | |
| 12757 | at::Tensor out_; |
| 12758 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12759 | at::functionalization::impl::sync(out); |
| 12760 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12761 | } else { |
| 12762 | out_ = out; |
| 12763 | } |
| 12764 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12765 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 12766 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12767 | TORCH_INTERNAL_ASSERT(false, |
| 12768 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12769 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12770 | } else { |
| 12771 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12772 | at::AutoDispatchSkipFunctionalize guard; |
| 12773 | at::Tensor tmp_output = at::_ops::argsort_stable_out::call(self_, stable, dim, descending, out_); |
| 12774 | return out;; |
| 12775 | } |
| 12776 | } else { |
| 12777 | at::Tensor tmp_output; |
| 12778 | { |
| 12779 | at::AutoDispatchSkipFunctionalize guard; |
| 12780 | tmp_output = at::_ops::argsort_stable::call(self_, stable, dim, descending); |
| 12781 | } |
| 12782 | at::functionalization::impl::replace_(out, tmp_output); |
| 12783 | at::functionalization::impl::commit_update(out); |
| 12784 | at::functionalization::impl::sync(out); |
| 12785 | return out; |
| 12786 | } |
| 12787 | } |
| 12788 | |
| 12789 | at::Tensor & all_out_all_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 12790 | if (false) { |
| 12791 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12792 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12793 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12794 | auto self_meta = to_meta(self); |
| 12795 | auto out_meta = to_meta(out); |
| 12796 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12797 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12798 | at::_ops::all_all_out::call(self_meta, out_meta); |
| 12799 | } |
| 12800 | |
| 12801 | at::Tensor self_; |
| 12802 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12803 | at::functionalization::impl::sync(self); |
| 12804 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12805 | } else { |
| 12806 | self_ = self; |
| 12807 | } |
| 12808 | |
| 12809 | at::Tensor out_; |
| 12810 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12811 | at::functionalization::impl::sync(out); |
| 12812 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12813 | } else { |
| 12814 | out_ = out; |
| 12815 | } |
| 12816 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12817 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 12818 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12819 | TORCH_INTERNAL_ASSERT(false, |
| 12820 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12821 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12822 | } else { |
| 12823 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12824 | at::AutoDispatchSkipFunctionalize guard; |
| 12825 | at::Tensor tmp_output = at::_ops::all_all_out::call(self_, out_); |
| 12826 | return out;; |
| 12827 | } |
| 12828 | } else { |
| 12829 | at::Tensor tmp_output; |
| 12830 | { |
| 12831 | at::AutoDispatchSkipFunctionalize guard; |
| 12832 | tmp_output = at::_ops::all::call(self_); |
| 12833 | } |
| 12834 | at::functionalization::impl::replace_(out, tmp_output); |
| 12835 | at::functionalization::impl::commit_update(out); |
| 12836 | at::functionalization::impl::sync(out); |
| 12837 | return out; |
| 12838 | } |
| 12839 | } |
| 12840 | |
| 12841 | at::Tensor & renorm_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & p, int64_t dim, const at::Scalar & maxnorm, at::Tensor & out) { |
| 12842 | if (false) { |
| 12843 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12844 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12845 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12846 | auto self_meta = to_meta(self); |
| 12847 | auto out_meta = to_meta(out); |
| 12848 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12849 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12850 | at::_ops::renorm_out::call(self_meta, p, dim, maxnorm, out_meta); |
| 12851 | } |
| 12852 | |
| 12853 | at::Tensor self_; |
| 12854 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12855 | at::functionalization::impl::sync(self); |
| 12856 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12857 | } else { |
| 12858 | self_ = self; |
| 12859 | } |
| 12860 | |
| 12861 | at::Tensor out_; |
| 12862 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12863 | at::functionalization::impl::sync(out); |
| 12864 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12865 | } else { |
| 12866 | out_ = out; |
| 12867 | } |
| 12868 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12869 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 12870 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12871 | TORCH_INTERNAL_ASSERT(false, |
| 12872 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12873 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12874 | } else { |
| 12875 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12876 | at::AutoDispatchSkipFunctionalize guard; |
| 12877 | at::Tensor tmp_output = at::_ops::renorm_out::call(self_, p, dim, maxnorm, out_); |
| 12878 | return out;; |
| 12879 | } |
| 12880 | } else { |
| 12881 | at::Tensor tmp_output; |
| 12882 | { |
| 12883 | at::AutoDispatchSkipFunctionalize guard; |
| 12884 | tmp_output = at::_ops::renorm::call(self_, p, dim, maxnorm); |
| 12885 | } |
| 12886 | at::functionalization::impl::replace_(out, tmp_output); |
| 12887 | at::functionalization::impl::commit_update(out); |
| 12888 | at::functionalization::impl::sync(out); |
| 12889 | return out; |
| 12890 | } |
| 12891 | } |
| 12892 | |
| 12893 | at::Tensor & renorm_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & p, int64_t dim, const at::Scalar & maxnorm) { |
| 12894 | if (true) { |
| 12895 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12896 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12897 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12898 | auto self_meta = to_meta(self); |
| 12899 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12900 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12901 | at::_ops::renorm_::call(self_meta, p, dim, maxnorm); |
| 12902 | } |
| 12903 | |
| 12904 | at::Tensor self_; |
| 12905 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 12906 | at::functionalization::impl::sync(self); |
| 12907 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 12908 | } else { |
| 12909 | self_ = self; |
| 12910 | } |
| 12911 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 12912 | if ((false)) { |
| 12913 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12914 | TORCH_INTERNAL_ASSERT(false, |
| 12915 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12916 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12917 | } else { |
| 12918 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12919 | at::AutoDispatchSkipFunctionalize guard; |
| 12920 | at::Tensor tmp_output = at::_ops::renorm_::call(self_, p, dim, maxnorm); |
| 12921 | return self;; |
| 12922 | } |
| 12923 | } else { |
| 12924 | at::Tensor tmp_output; |
| 12925 | { |
| 12926 | at::AutoDispatchSkipFunctionalize guard; |
| 12927 | tmp_output = at::_ops::renorm::call(self_, p, dim, maxnorm); |
| 12928 | } |
| 12929 | at::functionalization::impl::replace_(self, tmp_output); |
| 12930 | at::functionalization::impl::commit_update(self); |
| 12931 | at::functionalization::impl::sync(self); |
| 12932 | return self; |
| 12933 | } |
| 12934 | } |
| 12935 | |
| 12936 | at::Tensor & unfold_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step, at::Tensor & out) { |
| 12937 | if (false) { |
| 12938 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12939 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12940 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12941 | auto grad_in_meta = to_meta(grad_in); |
| 12942 | auto out_meta = to_meta(out); |
| 12943 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12944 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12945 | at::_ops::unfold_backward_out::call(grad_in_meta, input_sizes, dim, size, step, out_meta); |
| 12946 | } |
| 12947 | |
| 12948 | at::Tensor grad_in_; |
| 12949 | if (at::functionalization::impl::isFunctionalTensor(grad_in)) { |
| 12950 | at::functionalization::impl::sync(grad_in); |
| 12951 | grad_in_ = at::functionalization::impl::from_functional_tensor(grad_in); |
| 12952 | } else { |
| 12953 | grad_in_ = grad_in; |
| 12954 | } |
| 12955 | |
| 12956 | at::Tensor out_; |
| 12957 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 12958 | at::functionalization::impl::sync(out); |
| 12959 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 12960 | } else { |
| 12961 | out_ = out; |
| 12962 | } |
| 12963 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 12964 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_in))) { |
| 12965 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 12966 | TORCH_INTERNAL_ASSERT(false, |
| 12967 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 12968 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 12969 | } else { |
| 12970 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 12971 | at::AutoDispatchSkipFunctionalize guard; |
| 12972 | at::Tensor tmp_output = at::_ops::unfold_backward_out::call(grad_in_, input_sizes, dim, size, step, out_); |
| 12973 | return out;; |
| 12974 | } |
| 12975 | } else { |
| 12976 | at::Tensor tmp_output; |
| 12977 | { |
| 12978 | at::AutoDispatchSkipFunctionalize guard; |
| 12979 | tmp_output = at::_ops::unfold_backward::call(grad_in_, input_sizes, dim, size, step); |
| 12980 | } |
| 12981 | at::functionalization::impl::replace_(out, tmp_output); |
| 12982 | at::functionalization::impl::commit_update(out); |
| 12983 | at::functionalization::impl::sync(out); |
| 12984 | return out; |
| 12985 | } |
| 12986 | } |
| 12987 | |
| 12988 | at::Tensor & pow_out_Tensor_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & exponent, at::Tensor & out) { |
| 12989 | if (false) { |
| 12990 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 12991 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 12992 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 12993 | auto self_meta = to_meta(self); |
| 12994 | auto exponent_meta = to_meta(exponent); |
| 12995 | auto out_meta = to_meta(out); |
| 12996 | at::AutoDispatchSkipFunctionalize func_guard; |
| 12997 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 12998 | at::_ops::pow_Tensor_Tensor_out::call(self_meta, exponent_meta, out_meta); |
| 12999 | } |
| 13000 | |
| 13001 | at::Tensor self_; |
| 13002 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13003 | at::functionalization::impl::sync(self); |
| 13004 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13005 | } else { |
| 13006 | self_ = self; |
| 13007 | } |
| 13008 | |
| 13009 | at::Tensor exponent_; |
| 13010 | if (at::functionalization::impl::isFunctionalTensor(exponent)) { |
| 13011 | at::functionalization::impl::sync(exponent); |
| 13012 | exponent_ = at::functionalization::impl::from_functional_tensor(exponent); |
| 13013 | } else { |
| 13014 | exponent_ = exponent; |
| 13015 | } |
| 13016 | |
| 13017 | at::Tensor out_; |
| 13018 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13019 | at::functionalization::impl::sync(out); |
| 13020 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13021 | } else { |
| 13022 | out_ = out; |
| 13023 | } |
| 13024 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13025 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(exponent))) { |
| 13026 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13027 | TORCH_INTERNAL_ASSERT(false, |
| 13028 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13029 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13030 | } else { |
| 13031 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13032 | at::AutoDispatchSkipFunctionalize guard; |
| 13033 | at::Tensor tmp_output = at::_ops::pow_Tensor_Tensor_out::call(self_, exponent_, out_); |
| 13034 | return out;; |
| 13035 | } |
| 13036 | } else { |
| 13037 | at::Tensor tmp_output; |
| 13038 | { |
| 13039 | at::AutoDispatchSkipFunctionalize guard; |
| 13040 | tmp_output = at::_ops::pow_Tensor_Tensor::call(self_, exponent_); |
| 13041 | } |
| 13042 | at::functionalization::impl::replace_(out, tmp_output); |
| 13043 | at::functionalization::impl::commit_update(out); |
| 13044 | at::functionalization::impl::sync(out); |
| 13045 | return out; |
| 13046 | } |
| 13047 | } |
| 13048 | |
| 13049 | at::Tensor & pow__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & exponent) { |
| 13050 | if (true) { |
| 13051 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13052 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13053 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13054 | auto self_meta = to_meta(self); |
| 13055 | auto exponent_meta = to_meta(exponent); |
| 13056 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13057 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13058 | at::_ops::pow__Tensor::call(self_meta, exponent_meta); |
| 13059 | } |
| 13060 | |
| 13061 | at::Tensor self_; |
| 13062 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13063 | at::functionalization::impl::sync(self); |
| 13064 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13065 | } else { |
| 13066 | self_ = self; |
| 13067 | } |
| 13068 | |
| 13069 | at::Tensor exponent_; |
| 13070 | if (at::functionalization::impl::isFunctionalTensor(exponent)) { |
| 13071 | at::functionalization::impl::sync(exponent); |
| 13072 | exponent_ = at::functionalization::impl::from_functional_tensor(exponent); |
| 13073 | } else { |
| 13074 | exponent_ = exponent; |
| 13075 | } |
| 13076 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 13077 | if ((false || at::functionalization::impl::isFunctionalTensor(exponent))) { |
| 13078 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13079 | TORCH_INTERNAL_ASSERT(false, |
| 13080 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13081 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13082 | } else { |
| 13083 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13084 | at::AutoDispatchSkipFunctionalize guard; |
| 13085 | at::Tensor tmp_output = at::_ops::pow__Tensor::call(self_, exponent_); |
| 13086 | return self;; |
| 13087 | } |
| 13088 | } else { |
| 13089 | at::Tensor tmp_output; |
| 13090 | { |
| 13091 | at::AutoDispatchSkipFunctionalize guard; |
| 13092 | tmp_output = at::_ops::pow_Tensor_Tensor::call(self_, exponent_); |
| 13093 | } |
| 13094 | at::functionalization::impl::replace_(self, tmp_output); |
| 13095 | at::functionalization::impl::commit_update(self); |
| 13096 | at::functionalization::impl::sync(self); |
| 13097 | return self; |
| 13098 | } |
| 13099 | } |
| 13100 | |
| 13101 | at::Tensor & pow_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & exponent, at::Tensor & out) { |
| 13102 | if (false) { |
| 13103 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13104 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13105 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13106 | auto exponent_meta = to_meta(exponent); |
| 13107 | auto out_meta = to_meta(out); |
| 13108 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13109 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13110 | at::_ops::pow_Scalar_out::call(self, exponent_meta, out_meta); |
| 13111 | } |
| 13112 | |
| 13113 | at::Tensor exponent_; |
| 13114 | if (at::functionalization::impl::isFunctionalTensor(exponent)) { |
| 13115 | at::functionalization::impl::sync(exponent); |
| 13116 | exponent_ = at::functionalization::impl::from_functional_tensor(exponent); |
| 13117 | } else { |
| 13118 | exponent_ = exponent; |
| 13119 | } |
| 13120 | |
| 13121 | at::Tensor out_; |
| 13122 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13123 | at::functionalization::impl::sync(out); |
| 13124 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13125 | } else { |
| 13126 | out_ = out; |
| 13127 | } |
| 13128 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13129 | if ((false || at::functionalization::impl::isFunctionalTensor(exponent))) { |
| 13130 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13131 | TORCH_INTERNAL_ASSERT(false, |
| 13132 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13133 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13134 | } else { |
| 13135 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13136 | at::AutoDispatchSkipFunctionalize guard; |
| 13137 | at::Tensor tmp_output = at::_ops::pow_Scalar_out::call(self, exponent_, out_); |
| 13138 | return out;; |
| 13139 | } |
| 13140 | } else { |
| 13141 | at::Tensor tmp_output; |
| 13142 | { |
| 13143 | at::AutoDispatchSkipFunctionalize guard; |
| 13144 | tmp_output = at::_ops::pow_Scalar::call(self, exponent_); |
| 13145 | } |
| 13146 | at::functionalization::impl::replace_(out, tmp_output); |
| 13147 | at::functionalization::impl::commit_update(out); |
| 13148 | at::functionalization::impl::sync(out); |
| 13149 | return out; |
| 13150 | } |
| 13151 | } |
| 13152 | |
| 13153 | at::Tensor & pow_out_Tensor_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & exponent, at::Tensor & out) { |
| 13154 | if (false) { |
| 13155 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13156 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13157 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13158 | auto self_meta = to_meta(self); |
| 13159 | auto out_meta = to_meta(out); |
| 13160 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13161 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13162 | at::_ops::pow_Tensor_Scalar_out::call(self_meta, exponent, out_meta); |
| 13163 | } |
| 13164 | |
| 13165 | at::Tensor self_; |
| 13166 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13167 | at::functionalization::impl::sync(self); |
| 13168 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13169 | } else { |
| 13170 | self_ = self; |
| 13171 | } |
| 13172 | |
| 13173 | at::Tensor out_; |
| 13174 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13175 | at::functionalization::impl::sync(out); |
| 13176 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13177 | } else { |
| 13178 | out_ = out; |
| 13179 | } |
| 13180 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13181 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 13182 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13183 | TORCH_INTERNAL_ASSERT(false, |
| 13184 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13185 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13186 | } else { |
| 13187 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13188 | at::AutoDispatchSkipFunctionalize guard; |
| 13189 | at::Tensor tmp_output = at::_ops::pow_Tensor_Scalar_out::call(self_, exponent, out_); |
| 13190 | return out;; |
| 13191 | } |
| 13192 | } else { |
| 13193 | at::Tensor tmp_output; |
| 13194 | { |
| 13195 | at::AutoDispatchSkipFunctionalize guard; |
| 13196 | tmp_output = at::_ops::pow_Tensor_Scalar::call(self_, exponent); |
| 13197 | } |
| 13198 | at::functionalization::impl::replace_(out, tmp_output); |
| 13199 | at::functionalization::impl::commit_update(out); |
| 13200 | at::functionalization::impl::sync(out); |
| 13201 | return out; |
| 13202 | } |
| 13203 | } |
| 13204 | |
| 13205 | at::Tensor & pow__Scalar(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & exponent) { |
| 13206 | if (true) { |
| 13207 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13208 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13209 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13210 | auto self_meta = to_meta(self); |
| 13211 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13212 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13213 | at::_ops::pow__Scalar::call(self_meta, exponent); |
| 13214 | } |
| 13215 | |
| 13216 | at::Tensor self_; |
| 13217 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13218 | at::functionalization::impl::sync(self); |
| 13219 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13220 | } else { |
| 13221 | self_ = self; |
| 13222 | } |
| 13223 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 13224 | if ((false)) { |
| 13225 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13226 | TORCH_INTERNAL_ASSERT(false, |
| 13227 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13228 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13229 | } else { |
| 13230 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13231 | at::AutoDispatchSkipFunctionalize guard; |
| 13232 | at::Tensor tmp_output = at::_ops::pow__Scalar::call(self_, exponent); |
| 13233 | return self;; |
| 13234 | } |
| 13235 | } else { |
| 13236 | at::Tensor tmp_output; |
| 13237 | { |
| 13238 | at::AutoDispatchSkipFunctionalize guard; |
| 13239 | tmp_output = at::_ops::pow_Tensor_Scalar::call(self_, exponent); |
| 13240 | } |
| 13241 | at::functionalization::impl::replace_(self, tmp_output); |
| 13242 | at::functionalization::impl::commit_update(self); |
| 13243 | at::functionalization::impl::sync(self); |
| 13244 | return self; |
| 13245 | } |
| 13246 | } |
| 13247 | |
| 13248 | at::Tensor & float_power_out_Tensor_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & exponent, at::Tensor & out) { |
| 13249 | if (false) { |
| 13250 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13251 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13252 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13253 | auto self_meta = to_meta(self); |
| 13254 | auto exponent_meta = to_meta(exponent); |
| 13255 | auto out_meta = to_meta(out); |
| 13256 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13257 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13258 | at::_ops::float_power_Tensor_Tensor_out::call(self_meta, exponent_meta, out_meta); |
| 13259 | } |
| 13260 | |
| 13261 | at::Tensor self_; |
| 13262 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13263 | at::functionalization::impl::sync(self); |
| 13264 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13265 | } else { |
| 13266 | self_ = self; |
| 13267 | } |
| 13268 | |
| 13269 | at::Tensor exponent_; |
| 13270 | if (at::functionalization::impl::isFunctionalTensor(exponent)) { |
| 13271 | at::functionalization::impl::sync(exponent); |
| 13272 | exponent_ = at::functionalization::impl::from_functional_tensor(exponent); |
| 13273 | } else { |
| 13274 | exponent_ = exponent; |
| 13275 | } |
| 13276 | |
| 13277 | at::Tensor out_; |
| 13278 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13279 | at::functionalization::impl::sync(out); |
| 13280 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13281 | } else { |
| 13282 | out_ = out; |
| 13283 | } |
| 13284 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13285 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(exponent))) { |
| 13286 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13287 | TORCH_INTERNAL_ASSERT(false, |
| 13288 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13289 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13290 | } else { |
| 13291 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13292 | at::AutoDispatchSkipFunctionalize guard; |
| 13293 | at::Tensor tmp_output = at::_ops::float_power_Tensor_Tensor_out::call(self_, exponent_, out_); |
| 13294 | return out;; |
| 13295 | } |
| 13296 | } else { |
| 13297 | at::Tensor tmp_output; |
| 13298 | { |
| 13299 | at::AutoDispatchSkipFunctionalize guard; |
| 13300 | tmp_output = at::_ops::float_power_Tensor_Tensor::call(self_, exponent_); |
| 13301 | } |
| 13302 | at::functionalization::impl::replace_(out, tmp_output); |
| 13303 | at::functionalization::impl::commit_update(out); |
| 13304 | at::functionalization::impl::sync(out); |
| 13305 | return out; |
| 13306 | } |
| 13307 | } |
| 13308 | |
| 13309 | at::Tensor & float_power__Tensor(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & exponent) { |
| 13310 | if (true) { |
| 13311 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13312 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13313 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13314 | auto self_meta = to_meta(self); |
| 13315 | auto exponent_meta = to_meta(exponent); |
| 13316 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13317 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13318 | at::_ops::float_power__Tensor::call(self_meta, exponent_meta); |
| 13319 | } |
| 13320 | |
| 13321 | at::Tensor self_; |
| 13322 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13323 | at::functionalization::impl::sync(self); |
| 13324 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13325 | } else { |
| 13326 | self_ = self; |
| 13327 | } |
| 13328 | |
| 13329 | at::Tensor exponent_; |
| 13330 | if (at::functionalization::impl::isFunctionalTensor(exponent)) { |
| 13331 | at::functionalization::impl::sync(exponent); |
| 13332 | exponent_ = at::functionalization::impl::from_functional_tensor(exponent); |
| 13333 | } else { |
| 13334 | exponent_ = exponent; |
| 13335 | } |
| 13336 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 13337 | if ((false || at::functionalization::impl::isFunctionalTensor(exponent))) { |
| 13338 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13339 | TORCH_INTERNAL_ASSERT(false, |
| 13340 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13341 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13342 | } else { |
| 13343 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13344 | at::AutoDispatchSkipFunctionalize guard; |
| 13345 | at::Tensor tmp_output = at::_ops::float_power__Tensor::call(self_, exponent_); |
| 13346 | return self;; |
| 13347 | } |
| 13348 | } else { |
| 13349 | at::Tensor tmp_output; |
| 13350 | { |
| 13351 | at::AutoDispatchSkipFunctionalize guard; |
| 13352 | tmp_output = at::_ops::float_power_Tensor_Tensor::call(self_, exponent_); |
| 13353 | } |
| 13354 | at::functionalization::impl::replace_(self, tmp_output); |
| 13355 | at::functionalization::impl::commit_update(self); |
| 13356 | at::functionalization::impl::sync(self); |
| 13357 | return self; |
| 13358 | } |
| 13359 | } |
| 13360 | |
| 13361 | at::Tensor & float_power_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & exponent, at::Tensor & out) { |
| 13362 | if (false) { |
| 13363 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13364 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13365 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13366 | auto exponent_meta = to_meta(exponent); |
| 13367 | auto out_meta = to_meta(out); |
| 13368 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13369 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13370 | at::_ops::float_power_Scalar_out::call(self, exponent_meta, out_meta); |
| 13371 | } |
| 13372 | |
| 13373 | at::Tensor exponent_; |
| 13374 | if (at::functionalization::impl::isFunctionalTensor(exponent)) { |
| 13375 | at::functionalization::impl::sync(exponent); |
| 13376 | exponent_ = at::functionalization::impl::from_functional_tensor(exponent); |
| 13377 | } else { |
| 13378 | exponent_ = exponent; |
| 13379 | } |
| 13380 | |
| 13381 | at::Tensor out_; |
| 13382 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13383 | at::functionalization::impl::sync(out); |
| 13384 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13385 | } else { |
| 13386 | out_ = out; |
| 13387 | } |
| 13388 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13389 | if ((false || at::functionalization::impl::isFunctionalTensor(exponent))) { |
| 13390 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13391 | TORCH_INTERNAL_ASSERT(false, |
| 13392 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13393 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13394 | } else { |
| 13395 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13396 | at::AutoDispatchSkipFunctionalize guard; |
| 13397 | at::Tensor tmp_output = at::_ops::float_power_Scalar_out::call(self, exponent_, out_); |
| 13398 | return out;; |
| 13399 | } |
| 13400 | } else { |
| 13401 | at::Tensor tmp_output; |
| 13402 | { |
| 13403 | at::AutoDispatchSkipFunctionalize guard; |
| 13404 | tmp_output = at::_ops::float_power_Scalar::call(self, exponent_); |
| 13405 | } |
| 13406 | at::functionalization::impl::replace_(out, tmp_output); |
| 13407 | at::functionalization::impl::commit_update(out); |
| 13408 | at::functionalization::impl::sync(out); |
| 13409 | return out; |
| 13410 | } |
| 13411 | } |
| 13412 | |
| 13413 | at::Tensor & float_power_out_Tensor_Scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & exponent, at::Tensor & out) { |
| 13414 | if (false) { |
| 13415 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13416 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13417 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13418 | auto self_meta = to_meta(self); |
| 13419 | auto out_meta = to_meta(out); |
| 13420 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13421 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13422 | at::_ops::float_power_Tensor_Scalar_out::call(self_meta, exponent, out_meta); |
| 13423 | } |
| 13424 | |
| 13425 | at::Tensor self_; |
| 13426 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13427 | at::functionalization::impl::sync(self); |
| 13428 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13429 | } else { |
| 13430 | self_ = self; |
| 13431 | } |
| 13432 | |
| 13433 | at::Tensor out_; |
| 13434 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13435 | at::functionalization::impl::sync(out); |
| 13436 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13437 | } else { |
| 13438 | out_ = out; |
| 13439 | } |
| 13440 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13441 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 13442 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13443 | TORCH_INTERNAL_ASSERT(false, |
| 13444 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13445 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13446 | } else { |
| 13447 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13448 | at::AutoDispatchSkipFunctionalize guard; |
| 13449 | at::Tensor tmp_output = at::_ops::float_power_Tensor_Scalar_out::call(self_, exponent, out_); |
| 13450 | return out;; |
| 13451 | } |
| 13452 | } else { |
| 13453 | at::Tensor tmp_output; |
| 13454 | { |
| 13455 | at::AutoDispatchSkipFunctionalize guard; |
| 13456 | tmp_output = at::_ops::float_power_Tensor_Scalar::call(self_, exponent); |
| 13457 | } |
| 13458 | at::functionalization::impl::replace_(out, tmp_output); |
| 13459 | at::functionalization::impl::commit_update(out); |
| 13460 | at::functionalization::impl::sync(out); |
| 13461 | return out; |
| 13462 | } |
| 13463 | } |
| 13464 | |
| 13465 | at::Tensor & float_power__Scalar(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & exponent) { |
| 13466 | if (true) { |
| 13467 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13468 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13469 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13470 | auto self_meta = to_meta(self); |
| 13471 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13472 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13473 | at::_ops::float_power__Scalar::call(self_meta, exponent); |
| 13474 | } |
| 13475 | |
| 13476 | at::Tensor self_; |
| 13477 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13478 | at::functionalization::impl::sync(self); |
| 13479 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13480 | } else { |
| 13481 | self_ = self; |
| 13482 | } |
| 13483 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 13484 | if ((false)) { |
| 13485 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13486 | TORCH_INTERNAL_ASSERT(false, |
| 13487 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13488 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13489 | } else { |
| 13490 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13491 | at::AutoDispatchSkipFunctionalize guard; |
| 13492 | at::Tensor tmp_output = at::_ops::float_power__Scalar::call(self_, exponent); |
| 13493 | return self;; |
| 13494 | } |
| 13495 | } else { |
| 13496 | at::Tensor tmp_output; |
| 13497 | { |
| 13498 | at::AutoDispatchSkipFunctionalize guard; |
| 13499 | tmp_output = at::_ops::float_power_Tensor_Scalar::call(self_, exponent); |
| 13500 | } |
| 13501 | at::functionalization::impl::replace_(self, tmp_output); |
| 13502 | at::functionalization::impl::commit_update(self); |
| 13503 | at::functionalization::impl::sync(self); |
| 13504 | return self; |
| 13505 | } |
| 13506 | } |
| 13507 | |
| 13508 | at::Tensor & normal_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double mean, double std, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 13509 | if (false) { |
| 13510 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13511 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13512 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13513 | auto self_meta = to_meta(self); |
| 13514 | auto out_meta = to_meta(out); |
| 13515 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13516 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13517 | at::_ops::normal_out::call(self_meta, mean, std, generator, out_meta); |
| 13518 | } |
| 13519 | |
| 13520 | at::Tensor self_; |
| 13521 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13522 | at::functionalization::impl::sync(self); |
| 13523 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13524 | } else { |
| 13525 | self_ = self; |
| 13526 | } |
| 13527 | |
| 13528 | at::Tensor out_; |
| 13529 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13530 | at::functionalization::impl::sync(out); |
| 13531 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13532 | } else { |
| 13533 | out_ = out; |
| 13534 | } |
| 13535 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13536 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 13537 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13538 | TORCH_INTERNAL_ASSERT(false, |
| 13539 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13540 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13541 | } else { |
| 13542 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13543 | at::AutoDispatchSkipFunctionalize guard; |
| 13544 | at::Tensor tmp_output = at::_ops::normal_out::call(self_, mean, std, generator, out_); |
| 13545 | return out;; |
| 13546 | } |
| 13547 | } else { |
| 13548 | at::Tensor tmp_output; |
| 13549 | { |
| 13550 | at::AutoDispatchSkipFunctionalize guard; |
| 13551 | tmp_output = at::_ops::normal_functional::call(self_, mean, std, generator); |
| 13552 | } |
| 13553 | at::functionalization::impl::replace_(out, tmp_output); |
| 13554 | at::functionalization::impl::commit_update(out); |
| 13555 | at::functionalization::impl::sync(out); |
| 13556 | return out; |
| 13557 | } |
| 13558 | } |
| 13559 | |
| 13560 | at::Tensor & normal_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, double mean, double std, c10::optional<at::Generator> generator) { |
| 13561 | if (true) { |
| 13562 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13563 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13564 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13565 | auto self_meta = to_meta(self); |
| 13566 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13567 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13568 | at::_ops::normal_::call(self_meta, mean, std, generator); |
| 13569 | } |
| 13570 | |
| 13571 | at::Tensor self_; |
| 13572 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13573 | at::functionalization::impl::sync(self); |
| 13574 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13575 | } else { |
| 13576 | self_ = self; |
| 13577 | } |
| 13578 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 13579 | if ((false)) { |
| 13580 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13581 | TORCH_INTERNAL_ASSERT(false, |
| 13582 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13583 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13584 | } else { |
| 13585 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13586 | at::AutoDispatchSkipFunctionalize guard; |
| 13587 | at::Tensor tmp_output = at::_ops::normal_::call(self_, mean, std, generator); |
| 13588 | return self;; |
| 13589 | } |
| 13590 | } else { |
| 13591 | at::Tensor tmp_output; |
| 13592 | { |
| 13593 | at::AutoDispatchSkipFunctionalize guard; |
| 13594 | tmp_output = at::_ops::normal_functional::call(self_, mean, std, generator); |
| 13595 | } |
| 13596 | at::functionalization::impl::replace_(self, tmp_output); |
| 13597 | at::functionalization::impl::commit_update(self); |
| 13598 | at::functionalization::impl::sync(self); |
| 13599 | return self; |
| 13600 | } |
| 13601 | } |
| 13602 | |
| 13603 | at::Tensor & normal_out_Tensor_float_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & mean, double std, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 13604 | if (false) { |
| 13605 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13606 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13607 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13608 | auto mean_meta = to_meta(mean); |
| 13609 | auto out_meta = to_meta(out); |
| 13610 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13611 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13612 | at::_ops::normal_Tensor_float_out::call(mean_meta, std, generator, out_meta); |
| 13613 | } |
| 13614 | |
| 13615 | at::Tensor mean_; |
| 13616 | if (at::functionalization::impl::isFunctionalTensor(mean)) { |
| 13617 | at::functionalization::impl::sync(mean); |
| 13618 | mean_ = at::functionalization::impl::from_functional_tensor(mean); |
| 13619 | } else { |
| 13620 | mean_ = mean; |
| 13621 | } |
| 13622 | |
| 13623 | at::Tensor out_; |
| 13624 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13625 | at::functionalization::impl::sync(out); |
| 13626 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13627 | } else { |
| 13628 | out_ = out; |
| 13629 | } |
| 13630 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13631 | if ((false || at::functionalization::impl::isFunctionalTensor(mean))) { |
| 13632 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13633 | TORCH_INTERNAL_ASSERT(false, |
| 13634 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13635 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13636 | } else { |
| 13637 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13638 | at::AutoDispatchSkipFunctionalize guard; |
| 13639 | at::Tensor tmp_output = at::_ops::normal_Tensor_float_out::call(mean_, std, generator, out_); |
| 13640 | return out;; |
| 13641 | } |
| 13642 | } else { |
| 13643 | at::Tensor tmp_output; |
| 13644 | { |
| 13645 | at::AutoDispatchSkipFunctionalize guard; |
| 13646 | tmp_output = at::_ops::normal_Tensor_float::call(mean_, std, generator); |
| 13647 | } |
| 13648 | at::functionalization::impl::replace_(out, tmp_output); |
| 13649 | at::functionalization::impl::commit_update(out); |
| 13650 | at::functionalization::impl::sync(out); |
| 13651 | return out; |
| 13652 | } |
| 13653 | } |
| 13654 | |
| 13655 | at::Tensor & normal_out_float_Tensor_out(c10::DispatchKeySet dispatchKeySet, double mean, const at::Tensor & std, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 13656 | if (false) { |
| 13657 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13658 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13659 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13660 | auto std_meta = to_meta(std); |
| 13661 | auto out_meta = to_meta(out); |
| 13662 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13663 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13664 | at::_ops::normal_float_Tensor_out::call(mean, std_meta, generator, out_meta); |
| 13665 | } |
| 13666 | |
| 13667 | at::Tensor std_; |
| 13668 | if (at::functionalization::impl::isFunctionalTensor(std)) { |
| 13669 | at::functionalization::impl::sync(std); |
| 13670 | std_ = at::functionalization::impl::from_functional_tensor(std); |
| 13671 | } else { |
| 13672 | std_ = std; |
| 13673 | } |
| 13674 | |
| 13675 | at::Tensor out_; |
| 13676 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13677 | at::functionalization::impl::sync(out); |
| 13678 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13679 | } else { |
| 13680 | out_ = out; |
| 13681 | } |
| 13682 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13683 | if ((false || at::functionalization::impl::isFunctionalTensor(std))) { |
| 13684 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13685 | TORCH_INTERNAL_ASSERT(false, |
| 13686 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13687 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13688 | } else { |
| 13689 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13690 | at::AutoDispatchSkipFunctionalize guard; |
| 13691 | at::Tensor tmp_output = at::_ops::normal_float_Tensor_out::call(mean, std_, generator, out_); |
| 13692 | return out;; |
| 13693 | } |
| 13694 | } else { |
| 13695 | at::Tensor tmp_output; |
| 13696 | { |
| 13697 | at::AutoDispatchSkipFunctionalize guard; |
| 13698 | tmp_output = at::_ops::normal_float_Tensor::call(mean, std_, generator); |
| 13699 | } |
| 13700 | at::functionalization::impl::replace_(out, tmp_output); |
| 13701 | at::functionalization::impl::commit_update(out); |
| 13702 | at::functionalization::impl::sync(out); |
| 13703 | return out; |
| 13704 | } |
| 13705 | } |
| 13706 | |
| 13707 | at::Tensor & normal_out_Tensor_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & mean, const at::Tensor & std, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 13708 | if (false) { |
| 13709 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13710 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13711 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13712 | auto mean_meta = to_meta(mean); |
| 13713 | auto std_meta = to_meta(std); |
| 13714 | auto out_meta = to_meta(out); |
| 13715 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13716 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13717 | at::_ops::normal_Tensor_Tensor_out::call(mean_meta, std_meta, generator, out_meta); |
| 13718 | } |
| 13719 | |
| 13720 | at::Tensor mean_; |
| 13721 | if (at::functionalization::impl::isFunctionalTensor(mean)) { |
| 13722 | at::functionalization::impl::sync(mean); |
| 13723 | mean_ = at::functionalization::impl::from_functional_tensor(mean); |
| 13724 | } else { |
| 13725 | mean_ = mean; |
| 13726 | } |
| 13727 | |
| 13728 | at::Tensor std_; |
| 13729 | if (at::functionalization::impl::isFunctionalTensor(std)) { |
| 13730 | at::functionalization::impl::sync(std); |
| 13731 | std_ = at::functionalization::impl::from_functional_tensor(std); |
| 13732 | } else { |
| 13733 | std_ = std; |
| 13734 | } |
| 13735 | |
| 13736 | at::Tensor out_; |
| 13737 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13738 | at::functionalization::impl::sync(out); |
| 13739 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13740 | } else { |
| 13741 | out_ = out; |
| 13742 | } |
| 13743 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13744 | if ((false || at::functionalization::impl::isFunctionalTensor(mean) || at::functionalization::impl::isFunctionalTensor(std))) { |
| 13745 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13746 | TORCH_INTERNAL_ASSERT(false, |
| 13747 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13748 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13749 | } else { |
| 13750 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13751 | at::AutoDispatchSkipFunctionalize guard; |
| 13752 | at::Tensor tmp_output = at::_ops::normal_Tensor_Tensor_out::call(mean_, std_, generator, out_); |
| 13753 | return out;; |
| 13754 | } |
| 13755 | } else { |
| 13756 | at::Tensor tmp_output; |
| 13757 | { |
| 13758 | at::AutoDispatchSkipFunctionalize guard; |
| 13759 | tmp_output = at::_ops::normal_Tensor_Tensor::call(mean_, std_, generator); |
| 13760 | } |
| 13761 | at::functionalization::impl::replace_(out, tmp_output); |
| 13762 | at::functionalization::impl::commit_update(out); |
| 13763 | at::functionalization::impl::sync(out); |
| 13764 | return out; |
| 13765 | } |
| 13766 | } |
| 13767 | |
| 13768 | at::Tensor & normal_out_float_float_out(c10::DispatchKeySet dispatchKeySet, double mean, double std, c10::SymIntArrayRef size, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 13769 | if (false) { |
| 13770 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13771 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13772 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13773 | auto out_meta = to_meta(out); |
| 13774 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13775 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13776 | at::_ops::normal_float_float_out::call(mean, std, size, generator, out_meta); |
| 13777 | } |
| 13778 | |
| 13779 | at::Tensor out_; |
| 13780 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13781 | at::functionalization::impl::sync(out); |
| 13782 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13783 | } else { |
| 13784 | out_ = out; |
| 13785 | } |
| 13786 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13787 | if ((false)) { |
| 13788 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13789 | TORCH_INTERNAL_ASSERT(false, |
| 13790 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13791 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13792 | } else { |
| 13793 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13794 | at::AutoDispatchSkipFunctionalize guard; |
| 13795 | at::Tensor tmp_output = at::_ops::normal_float_float_out::call(mean, std, size, generator, out_); |
| 13796 | return out;; |
| 13797 | } |
| 13798 | } else { |
| 13799 | at::Tensor tmp_output; |
| 13800 | { |
| 13801 | at::AutoDispatchSkipFunctionalize guard; |
| 13802 | tmp_output = at::_ops::normal_float_float::call(mean, std, size, generator, out_.scalar_type(), out_.layout(), out_.device(), c10::nullopt); |
| 13803 | } |
| 13804 | at::functionalization::impl::replace_(out, tmp_output); |
| 13805 | at::functionalization::impl::commit_update(out); |
| 13806 | at::functionalization::impl::sync(out); |
| 13807 | return out; |
| 13808 | } |
| 13809 | } |
| 13810 | |
| 13811 | at::Tensor & _amp_update_scale_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & growth_tracker, const at::Tensor & found_inf, double scale_growth_factor, double scale_backoff_factor, int64_t growth_interval, at::Tensor & out) { |
| 13812 | if (false) { |
| 13813 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13814 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13815 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13816 | auto self_meta = to_meta(self); |
| 13817 | auto growth_tracker_meta = to_meta(growth_tracker); |
| 13818 | auto found_inf_meta = to_meta(found_inf); |
| 13819 | auto out_meta = to_meta(out); |
| 13820 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13821 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13822 | at::_ops::_amp_update_scale_out::call(self_meta, growth_tracker_meta, found_inf_meta, scale_growth_factor, scale_backoff_factor, growth_interval, out_meta); |
| 13823 | } |
| 13824 | |
| 13825 | at::Tensor self_; |
| 13826 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13827 | at::functionalization::impl::sync(self); |
| 13828 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13829 | } else { |
| 13830 | self_ = self; |
| 13831 | } |
| 13832 | |
| 13833 | at::Tensor growth_tracker_; |
| 13834 | if (at::functionalization::impl::isFunctionalTensor(growth_tracker)) { |
| 13835 | at::functionalization::impl::sync(growth_tracker); |
| 13836 | growth_tracker_ = at::functionalization::impl::from_functional_tensor(growth_tracker); |
| 13837 | } else { |
| 13838 | growth_tracker_ = growth_tracker; |
| 13839 | } |
| 13840 | |
| 13841 | at::Tensor found_inf_; |
| 13842 | if (at::functionalization::impl::isFunctionalTensor(found_inf)) { |
| 13843 | at::functionalization::impl::sync(found_inf); |
| 13844 | found_inf_ = at::functionalization::impl::from_functional_tensor(found_inf); |
| 13845 | } else { |
| 13846 | found_inf_ = found_inf; |
| 13847 | } |
| 13848 | |
| 13849 | at::Tensor out_; |
| 13850 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13851 | at::functionalization::impl::sync(out); |
| 13852 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13853 | } else { |
| 13854 | out_ = out; |
| 13855 | } |
| 13856 | if (!(true && at::functionalization::impl::isFunctionalTensor(growth_tracker) && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13857 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(found_inf))) { |
| 13858 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13859 | TORCH_INTERNAL_ASSERT(false, |
| 13860 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13861 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13862 | } else { |
| 13863 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13864 | at::AutoDispatchSkipFunctionalize guard; |
| 13865 | at::Tensor tmp_output = at::_ops::_amp_update_scale_out::call(self_, growth_tracker_, found_inf_, scale_growth_factor, scale_backoff_factor, growth_interval, out_); |
| 13866 | return out;; |
| 13867 | } |
| 13868 | } else { |
| 13869 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 13870 | { |
| 13871 | at::AutoDispatchSkipFunctionalize guard; |
| 13872 | tmp_output = at::_ops::_amp_update_scale::call(self_, growth_tracker_, found_inf_, scale_growth_factor, scale_backoff_factor, growth_interval); |
| 13873 | } |
| 13874 | at::functionalization::impl::replace_(growth_tracker, std::get<0>(tmp_output)); |
| 13875 | at::functionalization::impl::commit_update(growth_tracker); |
| 13876 | at::functionalization::impl::sync(growth_tracker); |
| 13877 | at::functionalization::impl::replace_(out, std::get<1>(tmp_output)); |
| 13878 | at::functionalization::impl::commit_update(out); |
| 13879 | at::functionalization::impl::sync(out); |
| 13880 | return out; |
| 13881 | } |
| 13882 | } |
| 13883 | |
| 13884 | at::Tensor & _amp_update_scale_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, at::Tensor & growth_tracker, const at::Tensor & found_inf, double scale_growth_factor, double scale_backoff_factor, int64_t growth_interval) { |
| 13885 | if (true) { |
| 13886 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13887 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13888 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13889 | auto self_meta = to_meta(self); |
| 13890 | auto growth_tracker_meta = to_meta(growth_tracker); |
| 13891 | auto found_inf_meta = to_meta(found_inf); |
| 13892 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13893 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13894 | at::_ops::_amp_update_scale_::call(self_meta, growth_tracker_meta, found_inf_meta, scale_growth_factor, scale_backoff_factor, growth_interval); |
| 13895 | } |
| 13896 | |
| 13897 | at::Tensor self_; |
| 13898 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13899 | at::functionalization::impl::sync(self); |
| 13900 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13901 | } else { |
| 13902 | self_ = self; |
| 13903 | } |
| 13904 | |
| 13905 | at::Tensor growth_tracker_; |
| 13906 | if (at::functionalization::impl::isFunctionalTensor(growth_tracker)) { |
| 13907 | at::functionalization::impl::sync(growth_tracker); |
| 13908 | growth_tracker_ = at::functionalization::impl::from_functional_tensor(growth_tracker); |
| 13909 | } else { |
| 13910 | growth_tracker_ = growth_tracker; |
| 13911 | } |
| 13912 | |
| 13913 | at::Tensor found_inf_; |
| 13914 | if (at::functionalization::impl::isFunctionalTensor(found_inf)) { |
| 13915 | at::functionalization::impl::sync(found_inf); |
| 13916 | found_inf_ = at::functionalization::impl::from_functional_tensor(found_inf); |
| 13917 | } else { |
| 13918 | found_inf_ = found_inf; |
| 13919 | } |
| 13920 | if (!(true && at::functionalization::impl::isFunctionalTensor(self) && at::functionalization::impl::isFunctionalTensor(growth_tracker))) { |
| 13921 | if ((false || at::functionalization::impl::isFunctionalTensor(found_inf))) { |
| 13922 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13923 | TORCH_INTERNAL_ASSERT(false, |
| 13924 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13925 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13926 | } else { |
| 13927 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13928 | at::AutoDispatchSkipFunctionalize guard; |
| 13929 | at::Tensor tmp_output = at::_ops::_amp_update_scale_::call(self_, growth_tracker_, found_inf_, scale_growth_factor, scale_backoff_factor, growth_interval); |
| 13930 | return self;; |
| 13931 | } |
| 13932 | } else { |
| 13933 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 13934 | { |
| 13935 | at::AutoDispatchSkipFunctionalize guard; |
| 13936 | tmp_output = at::_ops::_amp_update_scale::call(self_, growth_tracker_, found_inf_, scale_growth_factor, scale_backoff_factor, growth_interval); |
| 13937 | } |
| 13938 | at::functionalization::impl::replace_(self, std::get<0>(tmp_output)); |
| 13939 | at::functionalization::impl::commit_update(self); |
| 13940 | at::functionalization::impl::sync(self); |
| 13941 | at::functionalization::impl::replace_(growth_tracker, std::get<1>(tmp_output)); |
| 13942 | at::functionalization::impl::commit_update(growth_tracker); |
| 13943 | at::functionalization::impl::sync(growth_tracker); |
| 13944 | return self; |
| 13945 | } |
| 13946 | } |
| 13947 | |
| 13948 | void _foreach_atan_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 13949 | if (false) { |
| 13950 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 13951 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 13952 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 13953 | auto self_meta = to_meta(self); |
| 13954 | auto out_meta = to_meta(out); |
| 13955 | at::AutoDispatchSkipFunctionalize func_guard; |
| 13956 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 13957 | at::_ops::_foreach_atan_out::call(self_meta, out_meta); |
| 13958 | } |
| 13959 | |
| 13960 | ::std::vector<at::Tensor> self_; |
| 13961 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 13962 | at::functionalization::impl::sync(self); |
| 13963 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 13964 | } else { |
| 13965 | self_ = self.vec(); |
| 13966 | } |
| 13967 | |
| 13968 | ::std::vector<at::Tensor> out_; |
| 13969 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 13970 | at::functionalization::impl::sync(out); |
| 13971 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 13972 | } else { |
| 13973 | out_ = out.vec(); |
| 13974 | } |
| 13975 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 13976 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 13977 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 13978 | TORCH_INTERNAL_ASSERT(false, |
| 13979 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 13980 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 13981 | } else { |
| 13982 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 13983 | at::AutoDispatchSkipFunctionalize guard; |
| 13984 | at::_ops::_foreach_atan_out::call(self_, out_); |
| 13985 | ; |
| 13986 | } |
| 13987 | } else { |
| 13988 | ::std::vector<at::Tensor> tmp_output; |
| 13989 | { |
| 13990 | at::AutoDispatchSkipFunctionalize guard; |
| 13991 | tmp_output = at::_ops::_foreach_atan::call(self_); |
| 13992 | } |
| 13993 | at::functionalization::impl::replace_(out, tmp_output); |
| 13994 | at::functionalization::impl::commit_update(out); |
| 13995 | at::functionalization::impl::sync(out); |
| 13996 | |
| 13997 | } |
| 13998 | } |
| 13999 | |
| 14000 | void _foreach_atan_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 14001 | if (true) { |
| 14002 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14003 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14004 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14005 | auto self_meta = to_meta(self); |
| 14006 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14007 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14008 | at::_ops::_foreach_atan_::call(self_meta); |
| 14009 | } |
| 14010 | |
| 14011 | ::std::vector<at::Tensor> self_; |
| 14012 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14013 | at::functionalization::impl::sync(self); |
| 14014 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14015 | } else { |
| 14016 | self_ = self.vec(); |
| 14017 | } |
| 14018 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 14019 | if ((false)) { |
| 14020 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14021 | TORCH_INTERNAL_ASSERT(false, |
| 14022 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14023 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14024 | } else { |
| 14025 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14026 | at::AutoDispatchSkipFunctionalize guard; |
| 14027 | at::_ops::_foreach_atan_::call(self_); |
| 14028 | ; |
| 14029 | } |
| 14030 | } else { |
| 14031 | ::std::vector<at::Tensor> tmp_output; |
| 14032 | { |
| 14033 | at::AutoDispatchSkipFunctionalize guard; |
| 14034 | tmp_output = at::_ops::_foreach_atan::call(self_); |
| 14035 | } |
| 14036 | at::functionalization::impl::replace_(self, tmp_output); |
| 14037 | at::functionalization::impl::commit_update(self); |
| 14038 | at::functionalization::impl::sync(self); |
| 14039 | |
| 14040 | } |
| 14041 | } |
| 14042 | |
| 14043 | void _foreach_erf_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 14044 | if (false) { |
| 14045 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14046 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14047 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14048 | auto self_meta = to_meta(self); |
| 14049 | auto out_meta = to_meta(out); |
| 14050 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14051 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14052 | at::_ops::_foreach_erf_out::call(self_meta, out_meta); |
| 14053 | } |
| 14054 | |
| 14055 | ::std::vector<at::Tensor> self_; |
| 14056 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14057 | at::functionalization::impl::sync(self); |
| 14058 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14059 | } else { |
| 14060 | self_ = self.vec(); |
| 14061 | } |
| 14062 | |
| 14063 | ::std::vector<at::Tensor> out_; |
| 14064 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14065 | at::functionalization::impl::sync(out); |
| 14066 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14067 | } else { |
| 14068 | out_ = out.vec(); |
| 14069 | } |
| 14070 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14071 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 14072 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14073 | TORCH_INTERNAL_ASSERT(false, |
| 14074 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14075 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14076 | } else { |
| 14077 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14078 | at::AutoDispatchSkipFunctionalize guard; |
| 14079 | at::_ops::_foreach_erf_out::call(self_, out_); |
| 14080 | ; |
| 14081 | } |
| 14082 | } else { |
| 14083 | ::std::vector<at::Tensor> tmp_output; |
| 14084 | { |
| 14085 | at::AutoDispatchSkipFunctionalize guard; |
| 14086 | tmp_output = at::_ops::_foreach_erf::call(self_); |
| 14087 | } |
| 14088 | at::functionalization::impl::replace_(out, tmp_output); |
| 14089 | at::functionalization::impl::commit_update(out); |
| 14090 | at::functionalization::impl::sync(out); |
| 14091 | |
| 14092 | } |
| 14093 | } |
| 14094 | |
| 14095 | void _foreach_erf_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 14096 | if (true) { |
| 14097 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14098 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14099 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14100 | auto self_meta = to_meta(self); |
| 14101 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14102 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14103 | at::_ops::_foreach_erf_::call(self_meta); |
| 14104 | } |
| 14105 | |
| 14106 | ::std::vector<at::Tensor> self_; |
| 14107 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14108 | at::functionalization::impl::sync(self); |
| 14109 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14110 | } else { |
| 14111 | self_ = self.vec(); |
| 14112 | } |
| 14113 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 14114 | if ((false)) { |
| 14115 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14116 | TORCH_INTERNAL_ASSERT(false, |
| 14117 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14118 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14119 | } else { |
| 14120 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14121 | at::AutoDispatchSkipFunctionalize guard; |
| 14122 | at::_ops::_foreach_erf_::call(self_); |
| 14123 | ; |
| 14124 | } |
| 14125 | } else { |
| 14126 | ::std::vector<at::Tensor> tmp_output; |
| 14127 | { |
| 14128 | at::AutoDispatchSkipFunctionalize guard; |
| 14129 | tmp_output = at::_ops::_foreach_erf::call(self_); |
| 14130 | } |
| 14131 | at::functionalization::impl::replace_(self, tmp_output); |
| 14132 | at::functionalization::impl::commit_update(self); |
| 14133 | at::functionalization::impl::sync(self); |
| 14134 | |
| 14135 | } |
| 14136 | } |
| 14137 | |
| 14138 | void _foreach_erfc_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 14139 | if (false) { |
| 14140 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14141 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14142 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14143 | auto self_meta = to_meta(self); |
| 14144 | auto out_meta = to_meta(out); |
| 14145 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14146 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14147 | at::_ops::_foreach_erfc_out::call(self_meta, out_meta); |
| 14148 | } |
| 14149 | |
| 14150 | ::std::vector<at::Tensor> self_; |
| 14151 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14152 | at::functionalization::impl::sync(self); |
| 14153 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14154 | } else { |
| 14155 | self_ = self.vec(); |
| 14156 | } |
| 14157 | |
| 14158 | ::std::vector<at::Tensor> out_; |
| 14159 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14160 | at::functionalization::impl::sync(out); |
| 14161 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14162 | } else { |
| 14163 | out_ = out.vec(); |
| 14164 | } |
| 14165 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14166 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 14167 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14168 | TORCH_INTERNAL_ASSERT(false, |
| 14169 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14170 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14171 | } else { |
| 14172 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14173 | at::AutoDispatchSkipFunctionalize guard; |
| 14174 | at::_ops::_foreach_erfc_out::call(self_, out_); |
| 14175 | ; |
| 14176 | } |
| 14177 | } else { |
| 14178 | ::std::vector<at::Tensor> tmp_output; |
| 14179 | { |
| 14180 | at::AutoDispatchSkipFunctionalize guard; |
| 14181 | tmp_output = at::_ops::_foreach_erfc::call(self_); |
| 14182 | } |
| 14183 | at::functionalization::impl::replace_(out, tmp_output); |
| 14184 | at::functionalization::impl::commit_update(out); |
| 14185 | at::functionalization::impl::sync(out); |
| 14186 | |
| 14187 | } |
| 14188 | } |
| 14189 | |
| 14190 | void _foreach_erfc_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 14191 | if (true) { |
| 14192 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14193 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14194 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14195 | auto self_meta = to_meta(self); |
| 14196 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14197 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14198 | at::_ops::_foreach_erfc_::call(self_meta); |
| 14199 | } |
| 14200 | |
| 14201 | ::std::vector<at::Tensor> self_; |
| 14202 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14203 | at::functionalization::impl::sync(self); |
| 14204 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14205 | } else { |
| 14206 | self_ = self.vec(); |
| 14207 | } |
| 14208 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 14209 | if ((false)) { |
| 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::_ops::_foreach_erfc_::call(self_); |
| 14218 | ; |
| 14219 | } |
| 14220 | } else { |
| 14221 | ::std::vector<at::Tensor> tmp_output; |
| 14222 | { |
| 14223 | at::AutoDispatchSkipFunctionalize guard; |
| 14224 | tmp_output = at::_ops::_foreach_erfc::call(self_); |
| 14225 | } |
| 14226 | at::functionalization::impl::replace_(self, tmp_output); |
| 14227 | at::functionalization::impl::commit_update(self); |
| 14228 | at::functionalization::impl::sync(self); |
| 14229 | |
| 14230 | } |
| 14231 | } |
| 14232 | |
| 14233 | void _foreach_log_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList 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 out_meta = to_meta(out); |
| 14240 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14241 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14242 | at::_ops::_foreach_log_out::call(self_meta, out_meta); |
| 14243 | } |
| 14244 | |
| 14245 | ::std::vector<at::Tensor> self_; |
| 14246 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14247 | at::functionalization::impl::sync(self); |
| 14248 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14249 | } else { |
| 14250 | self_ = self.vec(); |
| 14251 | } |
| 14252 | |
| 14253 | ::std::vector<at::Tensor> out_; |
| 14254 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14255 | at::functionalization::impl::sync(out); |
| 14256 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14257 | } else { |
| 14258 | out_ = out.vec(); |
| 14259 | } |
| 14260 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14261 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 14262 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14263 | TORCH_INTERNAL_ASSERT(false, |
| 14264 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14265 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14266 | } else { |
| 14267 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14268 | at::AutoDispatchSkipFunctionalize guard; |
| 14269 | at::_ops::_foreach_log_out::call(self_, out_); |
| 14270 | ; |
| 14271 | } |
| 14272 | } else { |
| 14273 | ::std::vector<at::Tensor> tmp_output; |
| 14274 | { |
| 14275 | at::AutoDispatchSkipFunctionalize guard; |
| 14276 | tmp_output = at::_ops::_foreach_log::call(self_); |
| 14277 | } |
| 14278 | at::functionalization::impl::replace_(out, tmp_output); |
| 14279 | at::functionalization::impl::commit_update(out); |
| 14280 | at::functionalization::impl::sync(out); |
| 14281 | |
| 14282 | } |
| 14283 | } |
| 14284 | |
| 14285 | void _foreach_log_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 14286 | if (true) { |
| 14287 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14288 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14289 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14290 | auto self_meta = to_meta(self); |
| 14291 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14292 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14293 | at::_ops::_foreach_log_::call(self_meta); |
| 14294 | } |
| 14295 | |
| 14296 | ::std::vector<at::Tensor> self_; |
| 14297 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14298 | at::functionalization::impl::sync(self); |
| 14299 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14300 | } else { |
| 14301 | self_ = self.vec(); |
| 14302 | } |
| 14303 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 14304 | if ((false)) { |
| 14305 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14306 | TORCH_INTERNAL_ASSERT(false, |
| 14307 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14308 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14309 | } else { |
| 14310 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14311 | at::AutoDispatchSkipFunctionalize guard; |
| 14312 | at::_ops::_foreach_log_::call(self_); |
| 14313 | ; |
| 14314 | } |
| 14315 | } else { |
| 14316 | ::std::vector<at::Tensor> tmp_output; |
| 14317 | { |
| 14318 | at::AutoDispatchSkipFunctionalize guard; |
| 14319 | tmp_output = at::_ops::_foreach_log::call(self_); |
| 14320 | } |
| 14321 | at::functionalization::impl::replace_(self, tmp_output); |
| 14322 | at::functionalization::impl::commit_update(self); |
| 14323 | at::functionalization::impl::sync(self); |
| 14324 | |
| 14325 | } |
| 14326 | } |
| 14327 | |
| 14328 | void _foreach_sinh_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 14329 | if (false) { |
| 14330 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14331 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14332 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14333 | auto self_meta = to_meta(self); |
| 14334 | auto out_meta = to_meta(out); |
| 14335 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14336 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14337 | at::_ops::_foreach_sinh_out::call(self_meta, out_meta); |
| 14338 | } |
| 14339 | |
| 14340 | ::std::vector<at::Tensor> self_; |
| 14341 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14342 | at::functionalization::impl::sync(self); |
| 14343 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14344 | } else { |
| 14345 | self_ = self.vec(); |
| 14346 | } |
| 14347 | |
| 14348 | ::std::vector<at::Tensor> out_; |
| 14349 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14350 | at::functionalization::impl::sync(out); |
| 14351 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14352 | } else { |
| 14353 | out_ = out.vec(); |
| 14354 | } |
| 14355 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14356 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 14357 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14358 | TORCH_INTERNAL_ASSERT(false, |
| 14359 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14360 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14361 | } else { |
| 14362 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14363 | at::AutoDispatchSkipFunctionalize guard; |
| 14364 | at::_ops::_foreach_sinh_out::call(self_, out_); |
| 14365 | ; |
| 14366 | } |
| 14367 | } else { |
| 14368 | ::std::vector<at::Tensor> tmp_output; |
| 14369 | { |
| 14370 | at::AutoDispatchSkipFunctionalize guard; |
| 14371 | tmp_output = at::_ops::_foreach_sinh::call(self_); |
| 14372 | } |
| 14373 | at::functionalization::impl::replace_(out, tmp_output); |
| 14374 | at::functionalization::impl::commit_update(out); |
| 14375 | at::functionalization::impl::sync(out); |
| 14376 | |
| 14377 | } |
| 14378 | } |
| 14379 | |
| 14380 | void _foreach_sinh_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 14381 | if (true) { |
| 14382 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14383 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14384 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14385 | auto self_meta = to_meta(self); |
| 14386 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14387 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14388 | at::_ops::_foreach_sinh_::call(self_meta); |
| 14389 | } |
| 14390 | |
| 14391 | ::std::vector<at::Tensor> self_; |
| 14392 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14393 | at::functionalization::impl::sync(self); |
| 14394 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14395 | } else { |
| 14396 | self_ = self.vec(); |
| 14397 | } |
| 14398 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 14399 | if ((false)) { |
| 14400 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14401 | TORCH_INTERNAL_ASSERT(false, |
| 14402 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14403 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14404 | } else { |
| 14405 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14406 | at::AutoDispatchSkipFunctionalize guard; |
| 14407 | at::_ops::_foreach_sinh_::call(self_); |
| 14408 | ; |
| 14409 | } |
| 14410 | } else { |
| 14411 | ::std::vector<at::Tensor> tmp_output; |
| 14412 | { |
| 14413 | at::AutoDispatchSkipFunctionalize guard; |
| 14414 | tmp_output = at::_ops::_foreach_sinh::call(self_); |
| 14415 | } |
| 14416 | at::functionalization::impl::replace_(self, tmp_output); |
| 14417 | at::functionalization::impl::commit_update(self); |
| 14418 | at::functionalization::impl::sync(self); |
| 14419 | |
| 14420 | } |
| 14421 | } |
| 14422 | |
| 14423 | void _foreach_lgamma_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 14424 | if (false) { |
| 14425 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14426 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14427 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14428 | auto self_meta = to_meta(self); |
| 14429 | auto out_meta = to_meta(out); |
| 14430 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14431 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14432 | at::_ops::_foreach_lgamma_out::call(self_meta, out_meta); |
| 14433 | } |
| 14434 | |
| 14435 | ::std::vector<at::Tensor> self_; |
| 14436 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14437 | at::functionalization::impl::sync(self); |
| 14438 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14439 | } else { |
| 14440 | self_ = self.vec(); |
| 14441 | } |
| 14442 | |
| 14443 | ::std::vector<at::Tensor> out_; |
| 14444 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14445 | at::functionalization::impl::sync(out); |
| 14446 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14447 | } else { |
| 14448 | out_ = out.vec(); |
| 14449 | } |
| 14450 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14451 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 14452 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14453 | TORCH_INTERNAL_ASSERT(false, |
| 14454 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14455 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14456 | } else { |
| 14457 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14458 | at::AutoDispatchSkipFunctionalize guard; |
| 14459 | at::_ops::_foreach_lgamma_out::call(self_, out_); |
| 14460 | ; |
| 14461 | } |
| 14462 | } else { |
| 14463 | ::std::vector<at::Tensor> tmp_output; |
| 14464 | { |
| 14465 | at::AutoDispatchSkipFunctionalize guard; |
| 14466 | tmp_output = at::_ops::_foreach_lgamma::call(self_); |
| 14467 | } |
| 14468 | at::functionalization::impl::replace_(out, tmp_output); |
| 14469 | at::functionalization::impl::commit_update(out); |
| 14470 | at::functionalization::impl::sync(out); |
| 14471 | |
| 14472 | } |
| 14473 | } |
| 14474 | |
| 14475 | void _foreach_lgamma_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 14476 | if (true) { |
| 14477 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14478 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14479 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14480 | auto self_meta = to_meta(self); |
| 14481 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14482 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14483 | at::_ops::_foreach_lgamma_::call(self_meta); |
| 14484 | } |
| 14485 | |
| 14486 | ::std::vector<at::Tensor> self_; |
| 14487 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14488 | at::functionalization::impl::sync(self); |
| 14489 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14490 | } else { |
| 14491 | self_ = self.vec(); |
| 14492 | } |
| 14493 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 14494 | if ((false)) { |
| 14495 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14496 | TORCH_INTERNAL_ASSERT(false, |
| 14497 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14498 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14499 | } else { |
| 14500 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14501 | at::AutoDispatchSkipFunctionalize guard; |
| 14502 | at::_ops::_foreach_lgamma_::call(self_); |
| 14503 | ; |
| 14504 | } |
| 14505 | } else { |
| 14506 | ::std::vector<at::Tensor> tmp_output; |
| 14507 | { |
| 14508 | at::AutoDispatchSkipFunctionalize guard; |
| 14509 | tmp_output = at::_ops::_foreach_lgamma::call(self_); |
| 14510 | } |
| 14511 | at::functionalization::impl::replace_(self, tmp_output); |
| 14512 | at::functionalization::impl::commit_update(self); |
| 14513 | at::functionalization::impl::sync(self); |
| 14514 | |
| 14515 | } |
| 14516 | } |
| 14517 | |
| 14518 | void _foreach_lerp_out_List_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensors1, at::TensorList weights, at::TensorList out) { |
| 14519 | if (false) { |
| 14520 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14521 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14522 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14523 | auto self_meta = to_meta(self); |
| 14524 | auto tensors1_meta = to_meta(tensors1); |
| 14525 | auto weights_meta = to_meta(weights); |
| 14526 | auto out_meta = to_meta(out); |
| 14527 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14528 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14529 | at::_ops::_foreach_lerp_List_out::call(self_meta, tensors1_meta, weights_meta, out_meta); |
| 14530 | } |
| 14531 | |
| 14532 | ::std::vector<at::Tensor> self_; |
| 14533 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14534 | at::functionalization::impl::sync(self); |
| 14535 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14536 | } else { |
| 14537 | self_ = self.vec(); |
| 14538 | } |
| 14539 | |
| 14540 | ::std::vector<at::Tensor> tensors1_; |
| 14541 | if (at::functionalization::impl::isFunctionalTensor(tensors1)) { |
| 14542 | at::functionalization::impl::sync(tensors1); |
| 14543 | tensors1_ = at::functionalization::impl::from_functional_tensor(tensors1); |
| 14544 | } else { |
| 14545 | tensors1_ = tensors1.vec(); |
| 14546 | } |
| 14547 | |
| 14548 | ::std::vector<at::Tensor> weights_; |
| 14549 | if (at::functionalization::impl::isFunctionalTensor(weights)) { |
| 14550 | at::functionalization::impl::sync(weights); |
| 14551 | weights_ = at::functionalization::impl::from_functional_tensor(weights); |
| 14552 | } else { |
| 14553 | weights_ = weights.vec(); |
| 14554 | } |
| 14555 | |
| 14556 | ::std::vector<at::Tensor> out_; |
| 14557 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14558 | at::functionalization::impl::sync(out); |
| 14559 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14560 | } else { |
| 14561 | out_ = out.vec(); |
| 14562 | } |
| 14563 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14564 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(tensors1) || at::functionalization::impl::isFunctionalTensor(weights))) { |
| 14565 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14566 | TORCH_INTERNAL_ASSERT(false, |
| 14567 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14568 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14569 | } else { |
| 14570 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14571 | at::AutoDispatchSkipFunctionalize guard; |
| 14572 | at::_ops::_foreach_lerp_List_out::call(self_, tensors1_, weights_, out_); |
| 14573 | ; |
| 14574 | } |
| 14575 | } else { |
| 14576 | ::std::vector<at::Tensor> tmp_output; |
| 14577 | { |
| 14578 | at::AutoDispatchSkipFunctionalize guard; |
| 14579 | tmp_output = at::_ops::_foreach_lerp_List::call(self_, tensors1_, weights_); |
| 14580 | } |
| 14581 | at::functionalization::impl::replace_(out, tmp_output); |
| 14582 | at::functionalization::impl::commit_update(out); |
| 14583 | at::functionalization::impl::sync(out); |
| 14584 | |
| 14585 | } |
| 14586 | } |
| 14587 | |
| 14588 | void _foreach_lerp__List(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensors1, at::TensorList weights) { |
| 14589 | if (true) { |
| 14590 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14591 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14592 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14593 | auto self_meta = to_meta(self); |
| 14594 | auto tensors1_meta = to_meta(tensors1); |
| 14595 | auto weights_meta = to_meta(weights); |
| 14596 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14597 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14598 | at::_ops::_foreach_lerp__List::call(self_meta, tensors1_meta, weights_meta); |
| 14599 | } |
| 14600 | |
| 14601 | ::std::vector<at::Tensor> self_; |
| 14602 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14603 | at::functionalization::impl::sync(self); |
| 14604 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14605 | } else { |
| 14606 | self_ = self.vec(); |
| 14607 | } |
| 14608 | |
| 14609 | ::std::vector<at::Tensor> tensors1_; |
| 14610 | if (at::functionalization::impl::isFunctionalTensor(tensors1)) { |
| 14611 | at::functionalization::impl::sync(tensors1); |
| 14612 | tensors1_ = at::functionalization::impl::from_functional_tensor(tensors1); |
| 14613 | } else { |
| 14614 | tensors1_ = tensors1.vec(); |
| 14615 | } |
| 14616 | |
| 14617 | ::std::vector<at::Tensor> weights_; |
| 14618 | if (at::functionalization::impl::isFunctionalTensor(weights)) { |
| 14619 | at::functionalization::impl::sync(weights); |
| 14620 | weights_ = at::functionalization::impl::from_functional_tensor(weights); |
| 14621 | } else { |
| 14622 | weights_ = weights.vec(); |
| 14623 | } |
| 14624 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 14625 | if ((false || at::functionalization::impl::isFunctionalTensor(tensors1) || at::functionalization::impl::isFunctionalTensor(weights))) { |
| 14626 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14627 | TORCH_INTERNAL_ASSERT(false, |
| 14628 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14629 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14630 | } else { |
| 14631 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14632 | at::AutoDispatchSkipFunctionalize guard; |
| 14633 | at::_ops::_foreach_lerp__List::call(self_, tensors1_, weights_); |
| 14634 | ; |
| 14635 | } |
| 14636 | } else { |
| 14637 | ::std::vector<at::Tensor> tmp_output; |
| 14638 | { |
| 14639 | at::AutoDispatchSkipFunctionalize guard; |
| 14640 | tmp_output = at::_ops::_foreach_lerp_List::call(self_, tensors1_, weights_); |
| 14641 | } |
| 14642 | at::functionalization::impl::replace_(self, tmp_output); |
| 14643 | at::functionalization::impl::commit_update(self); |
| 14644 | at::functionalization::impl::sync(self); |
| 14645 | |
| 14646 | } |
| 14647 | } |
| 14648 | |
| 14649 | void _foreach_lerp_out_Scalar_out(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensors1, const at::Scalar & weight, at::TensorList out) { |
| 14650 | if (false) { |
| 14651 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14652 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14653 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14654 | auto self_meta = to_meta(self); |
| 14655 | auto tensors1_meta = to_meta(tensors1); |
| 14656 | auto out_meta = to_meta(out); |
| 14657 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14658 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14659 | at::_ops::_foreach_lerp_Scalar_out::call(self_meta, tensors1_meta, weight, out_meta); |
| 14660 | } |
| 14661 | |
| 14662 | ::std::vector<at::Tensor> self_; |
| 14663 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14664 | at::functionalization::impl::sync(self); |
| 14665 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14666 | } else { |
| 14667 | self_ = self.vec(); |
| 14668 | } |
| 14669 | |
| 14670 | ::std::vector<at::Tensor> tensors1_; |
| 14671 | if (at::functionalization::impl::isFunctionalTensor(tensors1)) { |
| 14672 | at::functionalization::impl::sync(tensors1); |
| 14673 | tensors1_ = at::functionalization::impl::from_functional_tensor(tensors1); |
| 14674 | } else { |
| 14675 | tensors1_ = tensors1.vec(); |
| 14676 | } |
| 14677 | |
| 14678 | ::std::vector<at::Tensor> out_; |
| 14679 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14680 | at::functionalization::impl::sync(out); |
| 14681 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14682 | } else { |
| 14683 | out_ = out.vec(); |
| 14684 | } |
| 14685 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14686 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(tensors1))) { |
| 14687 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14688 | TORCH_INTERNAL_ASSERT(false, |
| 14689 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14690 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14691 | } else { |
| 14692 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14693 | at::AutoDispatchSkipFunctionalize guard; |
| 14694 | at::_ops::_foreach_lerp_Scalar_out::call(self_, tensors1_, weight, out_); |
| 14695 | ; |
| 14696 | } |
| 14697 | } else { |
| 14698 | ::std::vector<at::Tensor> tmp_output; |
| 14699 | { |
| 14700 | at::AutoDispatchSkipFunctionalize guard; |
| 14701 | tmp_output = at::_ops::_foreach_lerp_Scalar::call(self_, tensors1_, weight); |
| 14702 | } |
| 14703 | at::functionalization::impl::replace_(out, tmp_output); |
| 14704 | at::functionalization::impl::commit_update(out); |
| 14705 | at::functionalization::impl::sync(out); |
| 14706 | |
| 14707 | } |
| 14708 | } |
| 14709 | |
| 14710 | void _foreach_lerp__Scalar(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensors1, const at::Scalar & weight) { |
| 14711 | if (true) { |
| 14712 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14713 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14714 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14715 | auto self_meta = to_meta(self); |
| 14716 | auto tensors1_meta = to_meta(tensors1); |
| 14717 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14718 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14719 | at::_ops::_foreach_lerp__Scalar::call(self_meta, tensors1_meta, weight); |
| 14720 | } |
| 14721 | |
| 14722 | ::std::vector<at::Tensor> self_; |
| 14723 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14724 | at::functionalization::impl::sync(self); |
| 14725 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14726 | } else { |
| 14727 | self_ = self.vec(); |
| 14728 | } |
| 14729 | |
| 14730 | ::std::vector<at::Tensor> tensors1_; |
| 14731 | if (at::functionalization::impl::isFunctionalTensor(tensors1)) { |
| 14732 | at::functionalization::impl::sync(tensors1); |
| 14733 | tensors1_ = at::functionalization::impl::from_functional_tensor(tensors1); |
| 14734 | } else { |
| 14735 | tensors1_ = tensors1.vec(); |
| 14736 | } |
| 14737 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 14738 | if ((false || at::functionalization::impl::isFunctionalTensor(tensors1))) { |
| 14739 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14740 | TORCH_INTERNAL_ASSERT(false, |
| 14741 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14742 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14743 | } else { |
| 14744 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14745 | at::AutoDispatchSkipFunctionalize guard; |
| 14746 | at::_ops::_foreach_lerp__Scalar::call(self_, tensors1_, weight); |
| 14747 | ; |
| 14748 | } |
| 14749 | } else { |
| 14750 | ::std::vector<at::Tensor> tmp_output; |
| 14751 | { |
| 14752 | at::AutoDispatchSkipFunctionalize guard; |
| 14753 | tmp_output = at::_ops::_foreach_lerp_Scalar::call(self_, tensors1_, weight); |
| 14754 | } |
| 14755 | at::functionalization::impl::replace_(self, tmp_output); |
| 14756 | at::functionalization::impl::commit_update(self); |
| 14757 | at::functionalization::impl::sync(self); |
| 14758 | |
| 14759 | } |
| 14760 | } |
| 14761 | |
| 14762 | at::Tensor & _convert_indices_from_coo_to_csr_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t size, bool out_int32, at::Tensor & out) { |
| 14763 | if (false) { |
| 14764 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14765 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14766 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14767 | auto self_meta = to_meta(self); |
| 14768 | auto out_meta = to_meta(out); |
| 14769 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14770 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14771 | at::_ops::_convert_indices_from_coo_to_csr_out::call(self_meta, size, out_int32, out_meta); |
| 14772 | } |
| 14773 | |
| 14774 | at::Tensor self_; |
| 14775 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14776 | at::functionalization::impl::sync(self); |
| 14777 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14778 | } else { |
| 14779 | self_ = self; |
| 14780 | } |
| 14781 | |
| 14782 | at::Tensor out_; |
| 14783 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14784 | at::functionalization::impl::sync(out); |
| 14785 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14786 | } else { |
| 14787 | out_ = out; |
| 14788 | } |
| 14789 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 14790 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 14791 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14792 | TORCH_INTERNAL_ASSERT(false, |
| 14793 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14794 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14795 | } else { |
| 14796 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14797 | at::AutoDispatchSkipFunctionalize guard; |
| 14798 | at::Tensor tmp_output = at::_ops::_convert_indices_from_coo_to_csr_out::call(self_, size, out_int32, out_); |
| 14799 | return out;; |
| 14800 | } |
| 14801 | } else { |
| 14802 | at::Tensor tmp_output; |
| 14803 | { |
| 14804 | at::AutoDispatchSkipFunctionalize guard; |
| 14805 | tmp_output = at::_ops::_convert_indices_from_coo_to_csr::call(self_, size, out_int32); |
| 14806 | } |
| 14807 | at::functionalization::impl::replace_(out, tmp_output); |
| 14808 | at::functionalization::impl::commit_update(out); |
| 14809 | at::functionalization::impl::sync(out); |
| 14810 | return out; |
| 14811 | } |
| 14812 | } |
| 14813 | |
| 14814 | at::Tensor & multi_margin_loss_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Scalar & p, const at::Scalar & margin, const c10::optional<at::Tensor> & weight, int64_t reduction, at::Tensor & grad_input) { |
| 14815 | if (false) { |
| 14816 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14817 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14818 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14819 | auto grad_output_meta = to_meta(grad_output); |
| 14820 | auto self_meta = to_meta(self); |
| 14821 | auto target_meta = to_meta(target); |
| 14822 | auto weight_meta = to_meta(weight); |
| 14823 | auto grad_input_meta = to_meta(grad_input); |
| 14824 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14825 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14826 | at::_ops::multi_margin_loss_backward_grad_input::call(grad_output_meta, self_meta, target_meta, p, margin, weight_meta, reduction, grad_input_meta); |
| 14827 | } |
| 14828 | |
| 14829 | at::Tensor grad_output_; |
| 14830 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 14831 | at::functionalization::impl::sync(grad_output); |
| 14832 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 14833 | } else { |
| 14834 | grad_output_ = grad_output; |
| 14835 | } |
| 14836 | |
| 14837 | at::Tensor self_; |
| 14838 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14839 | at::functionalization::impl::sync(self); |
| 14840 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14841 | } else { |
| 14842 | self_ = self; |
| 14843 | } |
| 14844 | |
| 14845 | at::Tensor target_; |
| 14846 | if (at::functionalization::impl::isFunctionalTensor(target)) { |
| 14847 | at::functionalization::impl::sync(target); |
| 14848 | target_ = at::functionalization::impl::from_functional_tensor(target); |
| 14849 | } else { |
| 14850 | target_ = target; |
| 14851 | } |
| 14852 | |
| 14853 | c10::optional<at::Tensor> weight_; |
| 14854 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 14855 | at::functionalization::impl::sync(weight); |
| 14856 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 14857 | } else { |
| 14858 | weight_ = weight; |
| 14859 | } |
| 14860 | |
| 14861 | at::Tensor grad_input_; |
| 14862 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 14863 | at::functionalization::impl::sync(grad_input); |
| 14864 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 14865 | } else { |
| 14866 | grad_input_ = grad_input; |
| 14867 | } |
| 14868 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 14869 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(target) || at::functionalization::impl::isFunctionalTensor(weight))) { |
| 14870 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14871 | TORCH_INTERNAL_ASSERT(false, |
| 14872 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14873 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14874 | } else { |
| 14875 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14876 | at::AutoDispatchSkipFunctionalize guard; |
| 14877 | at::Tensor tmp_output = at::_ops::multi_margin_loss_backward_grad_input::call(grad_output_, self_, target_, p, margin, weight_, reduction, grad_input_); |
| 14878 | return grad_input;; |
| 14879 | } |
| 14880 | } else { |
| 14881 | at::Tensor tmp_output; |
| 14882 | { |
| 14883 | at::AutoDispatchSkipFunctionalize guard; |
| 14884 | tmp_output = at::_ops::multi_margin_loss_backward::call(grad_output_, self_, target_, p, margin, weight_, reduction); |
| 14885 | } |
| 14886 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 14887 | at::functionalization::impl::commit_update(grad_input); |
| 14888 | at::functionalization::impl::sync(grad_input); |
| 14889 | return grad_input; |
| 14890 | } |
| 14891 | } |
| 14892 | |
| 14893 | ::std::tuple<at::Tensor &,at::Tensor &> nll_loss_forward_out_output(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, at::Tensor & output, at::Tensor & total_weight) { |
| 14894 | if (false) { |
| 14895 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14896 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14897 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14898 | auto self_meta = to_meta(self); |
| 14899 | auto target_meta = to_meta(target); |
| 14900 | auto weight_meta = to_meta(weight); |
| 14901 | auto output_meta = to_meta(output); |
| 14902 | auto total_weight_meta = to_meta(total_weight); |
| 14903 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14904 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14905 | at::_ops::nll_loss_forward_output::call(self_meta, target_meta, weight_meta, reduction, ignore_index, output_meta, total_weight_meta); |
| 14906 | } |
| 14907 | |
| 14908 | at::Tensor self_; |
| 14909 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14910 | at::functionalization::impl::sync(self); |
| 14911 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14912 | } else { |
| 14913 | self_ = self; |
| 14914 | } |
| 14915 | |
| 14916 | at::Tensor target_; |
| 14917 | if (at::functionalization::impl::isFunctionalTensor(target)) { |
| 14918 | at::functionalization::impl::sync(target); |
| 14919 | target_ = at::functionalization::impl::from_functional_tensor(target); |
| 14920 | } else { |
| 14921 | target_ = target; |
| 14922 | } |
| 14923 | |
| 14924 | c10::optional<at::Tensor> weight_; |
| 14925 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 14926 | at::functionalization::impl::sync(weight); |
| 14927 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 14928 | } else { |
| 14929 | weight_ = weight; |
| 14930 | } |
| 14931 | |
| 14932 | at::Tensor output_; |
| 14933 | if (at::functionalization::impl::isFunctionalTensor(output)) { |
| 14934 | at::functionalization::impl::sync(output); |
| 14935 | output_ = at::functionalization::impl::from_functional_tensor(output); |
| 14936 | } else { |
| 14937 | output_ = output; |
| 14938 | } |
| 14939 | |
| 14940 | at::Tensor total_weight_; |
| 14941 | if (at::functionalization::impl::isFunctionalTensor(total_weight)) { |
| 14942 | at::functionalization::impl::sync(total_weight); |
| 14943 | total_weight_ = at::functionalization::impl::from_functional_tensor(total_weight); |
| 14944 | } else { |
| 14945 | total_weight_ = total_weight; |
| 14946 | } |
| 14947 | if (!(true && at::functionalization::impl::isFunctionalTensor(output) && at::functionalization::impl::isFunctionalTensor(total_weight))) { |
| 14948 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(target) || at::functionalization::impl::isFunctionalTensor(weight))) { |
| 14949 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 14950 | TORCH_INTERNAL_ASSERT(false, |
| 14951 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 14952 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 14953 | } else { |
| 14954 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 14955 | at::AutoDispatchSkipFunctionalize guard; |
| 14956 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::nll_loss_forward_output::call(self_, target_, weight_, reduction, ignore_index, output_, total_weight_); |
| 14957 | return ::std::tuple<at::Tensor &,at::Tensor &>(output, total_weight);; |
| 14958 | } |
| 14959 | } else { |
| 14960 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 14961 | { |
| 14962 | at::AutoDispatchSkipFunctionalize guard; |
| 14963 | tmp_output = at::_ops::nll_loss_forward::call(self_, target_, weight_, reduction, ignore_index); |
| 14964 | } |
| 14965 | at::functionalization::impl::replace_(output, std::get<0>(tmp_output)); |
| 14966 | at::functionalization::impl::commit_update(output); |
| 14967 | at::functionalization::impl::sync(output); |
| 14968 | at::functionalization::impl::replace_(total_weight, std::get<1>(tmp_output)); |
| 14969 | at::functionalization::impl::commit_update(total_weight); |
| 14970 | at::functionalization::impl::sync(total_weight); |
| 14971 | return ::std::tuple<at::Tensor &,at::Tensor &>(output, total_weight); |
| 14972 | } |
| 14973 | } |
| 14974 | |
| 14975 | at::Tensor & hardswish_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 14976 | if (false) { |
| 14977 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 14978 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 14979 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 14980 | auto self_meta = to_meta(self); |
| 14981 | auto out_meta = to_meta(out); |
| 14982 | at::AutoDispatchSkipFunctionalize func_guard; |
| 14983 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 14984 | at::_ops::hardswish_out::call(self_meta, out_meta); |
| 14985 | } |
| 14986 | |
| 14987 | at::Tensor self_; |
| 14988 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 14989 | at::functionalization::impl::sync(self); |
| 14990 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 14991 | } else { |
| 14992 | self_ = self; |
| 14993 | } |
| 14994 | |
| 14995 | at::Tensor out_; |
| 14996 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 14997 | at::functionalization::impl::sync(out); |
| 14998 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 14999 | } else { |
| 15000 | out_ = out; |
| 15001 | } |
| 15002 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15003 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 15004 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15005 | TORCH_INTERNAL_ASSERT(false, |
| 15006 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15007 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15008 | } else { |
| 15009 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15010 | at::AutoDispatchSkipFunctionalize guard; |
| 15011 | at::Tensor tmp_output = at::_ops::hardswish_out::call(self_, out_); |
| 15012 | return out;; |
| 15013 | } |
| 15014 | } else { |
| 15015 | at::Tensor tmp_output; |
| 15016 | { |
| 15017 | at::AutoDispatchSkipFunctionalize guard; |
| 15018 | tmp_output = at::_ops::hardswish::call(self_); |
| 15019 | } |
| 15020 | at::functionalization::impl::replace_(out, tmp_output); |
| 15021 | at::functionalization::impl::commit_update(out); |
| 15022 | at::functionalization::impl::sync(out); |
| 15023 | return out; |
| 15024 | } |
| 15025 | } |
| 15026 | |
| 15027 | at::Tensor & hardswish_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 15028 | if (true) { |
| 15029 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15030 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15031 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15032 | auto self_meta = to_meta(self); |
| 15033 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15034 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15035 | at::_ops::hardswish_::call(self_meta); |
| 15036 | } |
| 15037 | |
| 15038 | at::Tensor self_; |
| 15039 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15040 | at::functionalization::impl::sync(self); |
| 15041 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15042 | } else { |
| 15043 | self_ = self; |
| 15044 | } |
| 15045 | if (!(true && at::functionalization::impl::isFunctionalTensor(self))) { |
| 15046 | if ((false)) { |
| 15047 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15048 | TORCH_INTERNAL_ASSERT(false, |
| 15049 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15050 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15051 | } else { |
| 15052 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15053 | at::AutoDispatchSkipFunctionalize guard; |
| 15054 | at::Tensor tmp_output = at::_ops::hardswish_::call(self_); |
| 15055 | return self;; |
| 15056 | } |
| 15057 | } else { |
| 15058 | at::Tensor tmp_output; |
| 15059 | { |
| 15060 | at::AutoDispatchSkipFunctionalize guard; |
| 15061 | tmp_output = at::_ops::hardswish::call(self_); |
| 15062 | } |
| 15063 | at::functionalization::impl::replace_(self, tmp_output); |
| 15064 | at::functionalization::impl::commit_update(self); |
| 15065 | at::functionalization::impl::sync(self); |
| 15066 | return self; |
| 15067 | } |
| 15068 | } |
| 15069 | |
| 15070 | at::Tensor & hardswish_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & out) { |
| 15071 | if (false) { |
| 15072 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15073 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15074 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15075 | auto grad_output_meta = to_meta(grad_output); |
| 15076 | auto self_meta = to_meta(self); |
| 15077 | auto out_meta = to_meta(out); |
| 15078 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15079 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15080 | at::_ops::hardswish_backward_out::call(grad_output_meta, self_meta, out_meta); |
| 15081 | } |
| 15082 | |
| 15083 | at::Tensor grad_output_; |
| 15084 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 15085 | at::functionalization::impl::sync(grad_output); |
| 15086 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 15087 | } else { |
| 15088 | grad_output_ = grad_output; |
| 15089 | } |
| 15090 | |
| 15091 | at::Tensor self_; |
| 15092 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15093 | at::functionalization::impl::sync(self); |
| 15094 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15095 | } else { |
| 15096 | self_ = self; |
| 15097 | } |
| 15098 | |
| 15099 | at::Tensor out_; |
| 15100 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15101 | at::functionalization::impl::sync(out); |
| 15102 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15103 | } else { |
| 15104 | out_ = out; |
| 15105 | } |
| 15106 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15107 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self))) { |
| 15108 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15109 | TORCH_INTERNAL_ASSERT(false, |
| 15110 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15111 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15112 | } else { |
| 15113 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15114 | at::AutoDispatchSkipFunctionalize guard; |
| 15115 | at::Tensor tmp_output = at::_ops::hardswish_backward_out::call(grad_output_, self_, out_); |
| 15116 | return out;; |
| 15117 | } |
| 15118 | } else { |
| 15119 | at::Tensor tmp_output; |
| 15120 | { |
| 15121 | at::AutoDispatchSkipFunctionalize guard; |
| 15122 | tmp_output = at::_ops::hardswish_backward::call(grad_output_, self_); |
| 15123 | } |
| 15124 | at::functionalization::impl::replace_(out, tmp_output); |
| 15125 | at::functionalization::impl::commit_update(out); |
| 15126 | at::functionalization::impl::sync(out); |
| 15127 | return out; |
| 15128 | } |
| 15129 | } |
| 15130 | |
| 15131 | at::Tensor & softshrink_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & lambd, at::Tensor & grad_input) { |
| 15132 | if (false) { |
| 15133 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15134 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15135 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15136 | auto grad_output_meta = to_meta(grad_output); |
| 15137 | auto self_meta = to_meta(self); |
| 15138 | auto grad_input_meta = to_meta(grad_input); |
| 15139 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15140 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15141 | at::_ops::softshrink_backward_grad_input::call(grad_output_meta, self_meta, lambd, grad_input_meta); |
| 15142 | } |
| 15143 | |
| 15144 | at::Tensor grad_output_; |
| 15145 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 15146 | at::functionalization::impl::sync(grad_output); |
| 15147 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 15148 | } else { |
| 15149 | grad_output_ = grad_output; |
| 15150 | } |
| 15151 | |
| 15152 | at::Tensor self_; |
| 15153 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15154 | at::functionalization::impl::sync(self); |
| 15155 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15156 | } else { |
| 15157 | self_ = self; |
| 15158 | } |
| 15159 | |
| 15160 | at::Tensor grad_input_; |
| 15161 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 15162 | at::functionalization::impl::sync(grad_input); |
| 15163 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 15164 | } else { |
| 15165 | grad_input_ = grad_input; |
| 15166 | } |
| 15167 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 15168 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self))) { |
| 15169 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15170 | TORCH_INTERNAL_ASSERT(false, |
| 15171 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15172 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15173 | } else { |
| 15174 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15175 | at::AutoDispatchSkipFunctionalize guard; |
| 15176 | at::Tensor tmp_output = at::_ops::softshrink_backward_grad_input::call(grad_output_, self_, lambd, grad_input_); |
| 15177 | return grad_input;; |
| 15178 | } |
| 15179 | } else { |
| 15180 | at::Tensor tmp_output; |
| 15181 | { |
| 15182 | at::AutoDispatchSkipFunctionalize guard; |
| 15183 | tmp_output = at::_ops::softshrink_backward::call(grad_output_, self_, lambd); |
| 15184 | } |
| 15185 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 15186 | at::functionalization::impl::commit_update(grad_input); |
| 15187 | at::functionalization::impl::sync(grad_input); |
| 15188 | return grad_input; |
| 15189 | } |
| 15190 | } |
| 15191 | |
| 15192 | at::Tensor & mkldnn_adaptive_avg_pool2d_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & out) { |
| 15193 | if (false) { |
| 15194 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15195 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15196 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15197 | auto grad_output_meta = to_meta(grad_output); |
| 15198 | auto self_meta = to_meta(self); |
| 15199 | auto out_meta = to_meta(out); |
| 15200 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15201 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15202 | at::_ops::mkldnn_adaptive_avg_pool2d_backward_out::call(grad_output_meta, self_meta, out_meta); |
| 15203 | } |
| 15204 | |
| 15205 | at::Tensor grad_output_; |
| 15206 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 15207 | at::functionalization::impl::sync(grad_output); |
| 15208 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 15209 | } else { |
| 15210 | grad_output_ = grad_output; |
| 15211 | } |
| 15212 | |
| 15213 | at::Tensor self_; |
| 15214 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15215 | at::functionalization::impl::sync(self); |
| 15216 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15217 | } else { |
| 15218 | self_ = self; |
| 15219 | } |
| 15220 | |
| 15221 | at::Tensor out_; |
| 15222 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15223 | at::functionalization::impl::sync(out); |
| 15224 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15225 | } else { |
| 15226 | out_ = out; |
| 15227 | } |
| 15228 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15229 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self))) { |
| 15230 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15231 | TORCH_INTERNAL_ASSERT(false, |
| 15232 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15233 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15234 | } else { |
| 15235 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15236 | at::AutoDispatchSkipFunctionalize guard; |
| 15237 | at::Tensor tmp_output = at::_ops::mkldnn_adaptive_avg_pool2d_backward_out::call(grad_output_, self_, out_); |
| 15238 | return out;; |
| 15239 | } |
| 15240 | } else { |
| 15241 | at::Tensor tmp_output; |
| 15242 | { |
| 15243 | at::AutoDispatchSkipFunctionalize guard; |
| 15244 | tmp_output = at::_ops::mkldnn_adaptive_avg_pool2d_backward::call(grad_output_, self_); |
| 15245 | } |
| 15246 | at::functionalization::impl::replace_(out, tmp_output); |
| 15247 | at::functionalization::impl::commit_update(out); |
| 15248 | at::functionalization::impl::sync(out); |
| 15249 | return out; |
| 15250 | } |
| 15251 | } |
| 15252 | |
| 15253 | at::Tensor & _adaptive_avg_pool2d_backward_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & out) { |
| 15254 | if (false) { |
| 15255 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15256 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15257 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15258 | auto grad_output_meta = to_meta(grad_output); |
| 15259 | auto self_meta = to_meta(self); |
| 15260 | auto out_meta = to_meta(out); |
| 15261 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15262 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15263 | at::_ops::_adaptive_avg_pool2d_backward_out::call(grad_output_meta, self_meta, out_meta); |
| 15264 | } |
| 15265 | |
| 15266 | at::Tensor grad_output_; |
| 15267 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 15268 | at::functionalization::impl::sync(grad_output); |
| 15269 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 15270 | } else { |
| 15271 | grad_output_ = grad_output; |
| 15272 | } |
| 15273 | |
| 15274 | at::Tensor self_; |
| 15275 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15276 | at::functionalization::impl::sync(self); |
| 15277 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15278 | } else { |
| 15279 | self_ = self; |
| 15280 | } |
| 15281 | |
| 15282 | at::Tensor out_; |
| 15283 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15284 | at::functionalization::impl::sync(out); |
| 15285 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15286 | } else { |
| 15287 | out_ = out; |
| 15288 | } |
| 15289 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15290 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self))) { |
| 15291 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15292 | TORCH_INTERNAL_ASSERT(false, |
| 15293 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15294 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15295 | } else { |
| 15296 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15297 | at::AutoDispatchSkipFunctionalize guard; |
| 15298 | at::Tensor tmp_output = at::_ops::_adaptive_avg_pool2d_backward_out::call(grad_output_, self_, out_); |
| 15299 | return out;; |
| 15300 | } |
| 15301 | } else { |
| 15302 | at::Tensor tmp_output; |
| 15303 | { |
| 15304 | at::AutoDispatchSkipFunctionalize guard; |
| 15305 | tmp_output = at::_ops::_adaptive_avg_pool2d_backward::call(grad_output_, self_); |
| 15306 | } |
| 15307 | at::functionalization::impl::replace_(out, tmp_output); |
| 15308 | at::functionalization::impl::commit_update(out); |
| 15309 | at::functionalization::impl::sync(out); |
| 15310 | return out; |
| 15311 | } |
| 15312 | } |
| 15313 | |
| 15314 | at::Tensor & adaptive_avg_pool3d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out) { |
| 15315 | if (false) { |
| 15316 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15317 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15318 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15319 | auto self_meta = to_meta(self); |
| 15320 | auto out_meta = to_meta(out); |
| 15321 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15322 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15323 | at::_ops::adaptive_avg_pool3d_out::call(self_meta, output_size, out_meta); |
| 15324 | } |
| 15325 | |
| 15326 | at::Tensor self_; |
| 15327 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15328 | at::functionalization::impl::sync(self); |
| 15329 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15330 | } else { |
| 15331 | self_ = self; |
| 15332 | } |
| 15333 | |
| 15334 | at::Tensor out_; |
| 15335 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15336 | at::functionalization::impl::sync(out); |
| 15337 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15338 | } else { |
| 15339 | out_ = out; |
| 15340 | } |
| 15341 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15342 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 15343 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15344 | TORCH_INTERNAL_ASSERT(false, |
| 15345 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15346 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15347 | } else { |
| 15348 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15349 | at::AutoDispatchSkipFunctionalize guard; |
| 15350 | at::Tensor tmp_output = at::_ops::adaptive_avg_pool3d_out::call(self_, output_size, out_); |
| 15351 | return out;; |
| 15352 | } |
| 15353 | } else { |
| 15354 | at::Tensor tmp_output; |
| 15355 | { |
| 15356 | at::AutoDispatchSkipFunctionalize guard; |
| 15357 | tmp_output = at::_ops::adaptive_avg_pool3d::call(self_, output_size); |
| 15358 | } |
| 15359 | at::functionalization::impl::replace_(out, tmp_output); |
| 15360 | at::functionalization::impl::commit_update(out); |
| 15361 | at::functionalization::impl::sync(out); |
| 15362 | return out; |
| 15363 | } |
| 15364 | } |
| 15365 | |
| 15366 | at::Tensor & adaptive_max_pool3d_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::Tensor & grad_input) { |
| 15367 | if (false) { |
| 15368 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15369 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15370 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15371 | auto grad_output_meta = to_meta(grad_output); |
| 15372 | auto self_meta = to_meta(self); |
| 15373 | auto indices_meta = to_meta(indices); |
| 15374 | auto grad_input_meta = to_meta(grad_input); |
| 15375 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15376 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15377 | at::_ops::adaptive_max_pool3d_backward_grad_input::call(grad_output_meta, self_meta, indices_meta, grad_input_meta); |
| 15378 | } |
| 15379 | |
| 15380 | at::Tensor grad_output_; |
| 15381 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 15382 | at::functionalization::impl::sync(grad_output); |
| 15383 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 15384 | } else { |
| 15385 | grad_output_ = grad_output; |
| 15386 | } |
| 15387 | |
| 15388 | at::Tensor self_; |
| 15389 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15390 | at::functionalization::impl::sync(self); |
| 15391 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15392 | } else { |
| 15393 | self_ = self; |
| 15394 | } |
| 15395 | |
| 15396 | at::Tensor indices_; |
| 15397 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 15398 | at::functionalization::impl::sync(indices); |
| 15399 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 15400 | } else { |
| 15401 | indices_ = indices; |
| 15402 | } |
| 15403 | |
| 15404 | at::Tensor grad_input_; |
| 15405 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 15406 | at::functionalization::impl::sync(grad_input); |
| 15407 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 15408 | } else { |
| 15409 | grad_input_ = grad_input; |
| 15410 | } |
| 15411 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 15412 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(indices))) { |
| 15413 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15414 | TORCH_INTERNAL_ASSERT(false, |
| 15415 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15416 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15417 | } else { |
| 15418 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15419 | at::AutoDispatchSkipFunctionalize guard; |
| 15420 | at::Tensor tmp_output = at::_ops::adaptive_max_pool3d_backward_grad_input::call(grad_output_, self_, indices_, grad_input_); |
| 15421 | return grad_input;; |
| 15422 | } |
| 15423 | } else { |
| 15424 | at::Tensor tmp_output; |
| 15425 | { |
| 15426 | at::AutoDispatchSkipFunctionalize guard; |
| 15427 | tmp_output = at::_ops::adaptive_max_pool3d_backward::call(grad_output_, self_, indices_); |
| 15428 | } |
| 15429 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 15430 | at::functionalization::impl::commit_update(grad_input); |
| 15431 | at::functionalization::impl::sync(grad_input); |
| 15432 | return grad_input; |
| 15433 | } |
| 15434 | } |
| 15435 | |
| 15436 | at::Tensor & avg_pool2d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional<int64_t> divisor_override, at::Tensor & out) { |
| 15437 | if (false) { |
| 15438 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15439 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15440 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15441 | auto self_meta = to_meta(self); |
| 15442 | auto out_meta = to_meta(out); |
| 15443 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15444 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15445 | at::_ops::avg_pool2d_out::call(self_meta, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, out_meta); |
| 15446 | } |
| 15447 | |
| 15448 | 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; |
| 15454 | } |
| 15455 | |
| 15456 | at::Tensor out_; |
| 15457 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15458 | at::functionalization::impl::sync(out); |
| 15459 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15460 | } else { |
| 15461 | out_ = out; |
| 15462 | } |
| 15463 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15464 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 15465 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15466 | TORCH_INTERNAL_ASSERT(false, |
| 15467 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15468 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15469 | } else { |
| 15470 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15471 | at::AutoDispatchSkipFunctionalize guard; |
| 15472 | at::Tensor tmp_output = at::_ops::avg_pool2d_out::call(self_, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, out_); |
| 15473 | return out;; |
| 15474 | } |
| 15475 | } else { |
| 15476 | at::Tensor tmp_output; |
| 15477 | { |
| 15478 | at::AutoDispatchSkipFunctionalize guard; |
| 15479 | tmp_output = at::_ops::avg_pool2d::call(self_, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); |
| 15480 | } |
| 15481 | at::functionalization::impl::replace_(out, tmp_output); |
| 15482 | at::functionalization::impl::commit_update(out); |
| 15483 | at::functionalization::impl::sync(out); |
| 15484 | return out; |
| 15485 | } |
| 15486 | } |
| 15487 | |
| 15488 | at::Tensor & avg_pool3d_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) { |
| 15489 | if (false) { |
| 15490 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15491 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15492 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15493 | auto grad_output_meta = to_meta(grad_output); |
| 15494 | auto self_meta = to_meta(self); |
| 15495 | auto grad_input_meta = to_meta(grad_input); |
| 15496 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15497 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15498 | at::_ops::avg_pool3d_backward_grad_input::call(grad_output_meta, self_meta, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, grad_input_meta); |
| 15499 | } |
| 15500 | |
| 15501 | at::Tensor grad_output_; |
| 15502 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 15503 | at::functionalization::impl::sync(grad_output); |
| 15504 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 15505 | } else { |
| 15506 | grad_output_ = grad_output; |
| 15507 | } |
| 15508 | |
| 15509 | at::Tensor self_; |
| 15510 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15511 | at::functionalization::impl::sync(self); |
| 15512 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15513 | } else { |
| 15514 | self_ = self; |
| 15515 | } |
| 15516 | |
| 15517 | at::Tensor grad_input_; |
| 15518 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 15519 | at::functionalization::impl::sync(grad_input); |
| 15520 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 15521 | } else { |
| 15522 | grad_input_ = grad_input; |
| 15523 | } |
| 15524 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 15525 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self))) { |
| 15526 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15527 | TORCH_INTERNAL_ASSERT(false, |
| 15528 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15529 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15530 | } else { |
| 15531 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15532 | at::AutoDispatchSkipFunctionalize guard; |
| 15533 | at::Tensor tmp_output = at::_ops::avg_pool3d_backward_grad_input::call(grad_output_, self_, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, grad_input_); |
| 15534 | return grad_input;; |
| 15535 | } |
| 15536 | } else { |
| 15537 | at::Tensor tmp_output; |
| 15538 | { |
| 15539 | at::AutoDispatchSkipFunctionalize guard; |
| 15540 | tmp_output = at::_ops::avg_pool3d_backward::call(grad_output_, self_, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); |
| 15541 | } |
| 15542 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 15543 | at::functionalization::impl::commit_update(grad_input); |
| 15544 | at::functionalization::impl::sync(grad_input); |
| 15545 | return grad_input; |
| 15546 | } |
| 15547 | } |
| 15548 | |
| 15549 | ::std::tuple<at::Tensor &,at::Tensor &> fractional_max_pool2d_out_output(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples, at::Tensor & output, at::Tensor & indices) { |
| 15550 | if (false) { |
| 15551 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15552 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15553 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15554 | auto self_meta = to_meta(self); |
| 15555 | auto random_samples_meta = to_meta(random_samples); |
| 15556 | auto output_meta = to_meta(output); |
| 15557 | auto indices_meta = to_meta(indices); |
| 15558 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15559 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15560 | at::_ops::fractional_max_pool2d_output::call(self_meta, kernel_size, output_size, random_samples_meta, output_meta, indices_meta); |
| 15561 | } |
| 15562 | |
| 15563 | at::Tensor self_; |
| 15564 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15565 | at::functionalization::impl::sync(self); |
| 15566 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15567 | } else { |
| 15568 | self_ = self; |
| 15569 | } |
| 15570 | |
| 15571 | at::Tensor random_samples_; |
| 15572 | if (at::functionalization::impl::isFunctionalTensor(random_samples)) { |
| 15573 | at::functionalization::impl::sync(random_samples); |
| 15574 | random_samples_ = at::functionalization::impl::from_functional_tensor(random_samples); |
| 15575 | } else { |
| 15576 | random_samples_ = random_samples; |
| 15577 | } |
| 15578 | |
| 15579 | at::Tensor output_; |
| 15580 | if (at::functionalization::impl::isFunctionalTensor(output)) { |
| 15581 | at::functionalization::impl::sync(output); |
| 15582 | output_ = at::functionalization::impl::from_functional_tensor(output); |
| 15583 | } else { |
| 15584 | output_ = output; |
| 15585 | } |
| 15586 | |
| 15587 | at::Tensor indices_; |
| 15588 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 15589 | at::functionalization::impl::sync(indices); |
| 15590 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 15591 | } else { |
| 15592 | indices_ = indices; |
| 15593 | } |
| 15594 | if (!(true && at::functionalization::impl::isFunctionalTensor(output) && at::functionalization::impl::isFunctionalTensor(indices))) { |
| 15595 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(random_samples))) { |
| 15596 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15597 | TORCH_INTERNAL_ASSERT(false, |
| 15598 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15599 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15600 | } else { |
| 15601 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15602 | at::AutoDispatchSkipFunctionalize guard; |
| 15603 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::fractional_max_pool2d_output::call(self_, kernel_size, output_size, random_samples_, output_, indices_); |
| 15604 | return ::std::tuple<at::Tensor &,at::Tensor &>(output, indices);; |
| 15605 | } |
| 15606 | } else { |
| 15607 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 15608 | { |
| 15609 | at::AutoDispatchSkipFunctionalize guard; |
| 15610 | tmp_output = at::_ops::fractional_max_pool2d::call(self_, kernel_size, output_size, random_samples_); |
| 15611 | } |
| 15612 | at::functionalization::impl::replace_(output, std::get<0>(tmp_output)); |
| 15613 | at::functionalization::impl::commit_update(output); |
| 15614 | at::functionalization::impl::sync(output); |
| 15615 | at::functionalization::impl::replace_(indices, std::get<1>(tmp_output)); |
| 15616 | at::functionalization::impl::commit_update(indices); |
| 15617 | at::functionalization::impl::sync(indices); |
| 15618 | return ::std::tuple<at::Tensor &,at::Tensor &>(output, indices); |
| 15619 | } |
| 15620 | } |
| 15621 | |
| 15622 | at::Tensor & reflection_pad3d_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) { |
| 15623 | if (false) { |
| 15624 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15625 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15626 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15627 | auto grad_output_meta = to_meta(grad_output); |
| 15628 | auto self_meta = to_meta(self); |
| 15629 | auto grad_input_meta = to_meta(grad_input); |
| 15630 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15631 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15632 | at::_ops::reflection_pad3d_backward_grad_input::call(grad_output_meta, self_meta, padding, grad_input_meta); |
| 15633 | } |
| 15634 | |
| 15635 | at::Tensor grad_output_; |
| 15636 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 15637 | at::functionalization::impl::sync(grad_output); |
| 15638 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 15639 | } else { |
| 15640 | grad_output_ = grad_output; |
| 15641 | } |
| 15642 | |
| 15643 | at::Tensor self_; |
| 15644 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15645 | at::functionalization::impl::sync(self); |
| 15646 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15647 | } else { |
| 15648 | self_ = self; |
| 15649 | } |
| 15650 | |
| 15651 | at::Tensor grad_input_; |
| 15652 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 15653 | at::functionalization::impl::sync(grad_input); |
| 15654 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 15655 | } else { |
| 15656 | grad_input_ = grad_input; |
| 15657 | } |
| 15658 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 15659 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(self))) { |
| 15660 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15661 | TORCH_INTERNAL_ASSERT(false, |
| 15662 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15663 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15664 | } else { |
| 15665 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15666 | at::AutoDispatchSkipFunctionalize guard; |
| 15667 | at::Tensor tmp_output = at::_ops::reflection_pad3d_backward_grad_input::call(grad_output_, self_, padding, grad_input_); |
| 15668 | return grad_input;; |
| 15669 | } |
| 15670 | } else { |
| 15671 | at::Tensor tmp_output; |
| 15672 | { |
| 15673 | at::AutoDispatchSkipFunctionalize guard; |
| 15674 | tmp_output = at::_ops::reflection_pad3d_backward::call(grad_output_, self_, padding); |
| 15675 | } |
| 15676 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 15677 | at::functionalization::impl::commit_update(grad_input); |
| 15678 | at::functionalization::impl::sync(grad_input); |
| 15679 | return grad_input; |
| 15680 | } |
| 15681 | } |
| 15682 | |
| 15683 | at::Tensor & replication_pad2d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) { |
| 15684 | if (false) { |
| 15685 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15686 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15687 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15688 | auto self_meta = to_meta(self); |
| 15689 | auto out_meta = to_meta(out); |
| 15690 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15691 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15692 | at::_ops::replication_pad2d_out::call(self_meta, padding, out_meta); |
| 15693 | } |
| 15694 | |
| 15695 | at::Tensor self_; |
| 15696 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15697 | at::functionalization::impl::sync(self); |
| 15698 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15699 | } else { |
| 15700 | self_ = self; |
| 15701 | } |
| 15702 | |
| 15703 | at::Tensor out_; |
| 15704 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15705 | at::functionalization::impl::sync(out); |
| 15706 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15707 | } else { |
| 15708 | out_ = out; |
| 15709 | } |
| 15710 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15711 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 15712 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15713 | TORCH_INTERNAL_ASSERT(false, |
| 15714 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15715 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15716 | } else { |
| 15717 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15718 | at::AutoDispatchSkipFunctionalize guard; |
| 15719 | at::Tensor tmp_output = at::_ops::replication_pad2d_out::call(self_, padding, out_); |
| 15720 | return out;; |
| 15721 | } |
| 15722 | } else { |
| 15723 | at::Tensor tmp_output; |
| 15724 | { |
| 15725 | at::AutoDispatchSkipFunctionalize guard; |
| 15726 | tmp_output = at::_ops::replication_pad2d::call(self_, padding); |
| 15727 | } |
| 15728 | at::functionalization::impl::replace_(out, tmp_output); |
| 15729 | at::functionalization::impl::commit_update(out); |
| 15730 | at::functionalization::impl::sync(out); |
| 15731 | return out; |
| 15732 | } |
| 15733 | } |
| 15734 | |
| 15735 | at::Tensor & _upsample_bilinear2d_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) { |
| 15736 | if (false) { |
| 15737 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15738 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15739 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15740 | auto grad_output_meta = to_meta(grad_output); |
| 15741 | auto grad_input_meta = to_meta(grad_input); |
| 15742 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15743 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15744 | at::_ops::_upsample_bilinear2d_aa_backward_grad_input::call(grad_output_meta, output_size, input_size, align_corners, scales_h, scales_w, grad_input_meta); |
| 15745 | } |
| 15746 | |
| 15747 | at::Tensor grad_output_; |
| 15748 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 15749 | at::functionalization::impl::sync(grad_output); |
| 15750 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 15751 | } else { |
| 15752 | grad_output_ = grad_output; |
| 15753 | } |
| 15754 | |
| 15755 | at::Tensor grad_input_; |
| 15756 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 15757 | at::functionalization::impl::sync(grad_input); |
| 15758 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 15759 | } else { |
| 15760 | grad_input_ = grad_input; |
| 15761 | } |
| 15762 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 15763 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output))) { |
| 15764 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15765 | TORCH_INTERNAL_ASSERT(false, |
| 15766 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15767 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15768 | } else { |
| 15769 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15770 | at::AutoDispatchSkipFunctionalize guard; |
| 15771 | at::Tensor tmp_output = at::_ops::_upsample_bilinear2d_aa_backward_grad_input::call(grad_output_, output_size, input_size, align_corners, scales_h, scales_w, grad_input_); |
| 15772 | return grad_input;; |
| 15773 | } |
| 15774 | } else { |
| 15775 | at::Tensor tmp_output; |
| 15776 | { |
| 15777 | at::AutoDispatchSkipFunctionalize guard; |
| 15778 | tmp_output = at::_ops::_upsample_bilinear2d_aa_backward::call(grad_output_, output_size, input_size, align_corners, scales_h, scales_w); |
| 15779 | } |
| 15780 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 15781 | at::functionalization::impl::commit_update(grad_input); |
| 15782 | at::functionalization::impl::sync(grad_input); |
| 15783 | return grad_input; |
| 15784 | } |
| 15785 | } |
| 15786 | |
| 15787 | at::Tensor & _upsample_bicubic2d_aa_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) { |
| 15788 | if (false) { |
| 15789 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15790 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15791 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15792 | auto self_meta = to_meta(self); |
| 15793 | auto out_meta = to_meta(out); |
| 15794 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15795 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15796 | at::_ops::_upsample_bicubic2d_aa_out::call(self_meta, output_size, align_corners, scales_h, scales_w, out_meta); |
| 15797 | } |
| 15798 | |
| 15799 | at::Tensor self_; |
| 15800 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15801 | at::functionalization::impl::sync(self); |
| 15802 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15803 | } else { |
| 15804 | self_ = self; |
| 15805 | } |
| 15806 | |
| 15807 | at::Tensor out_; |
| 15808 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15809 | at::functionalization::impl::sync(out); |
| 15810 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15811 | } else { |
| 15812 | out_ = out; |
| 15813 | } |
| 15814 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15815 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 15816 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15817 | TORCH_INTERNAL_ASSERT(false, |
| 15818 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15819 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15820 | } else { |
| 15821 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15822 | at::AutoDispatchSkipFunctionalize guard; |
| 15823 | at::Tensor tmp_output = at::_ops::_upsample_bicubic2d_aa_out::call(self_, output_size, align_corners, scales_h, scales_w, out_); |
| 15824 | return out;; |
| 15825 | } |
| 15826 | } else { |
| 15827 | at::Tensor tmp_output; |
| 15828 | { |
| 15829 | at::AutoDispatchSkipFunctionalize guard; |
| 15830 | tmp_output = at::_ops::_upsample_bicubic2d_aa::call(self_, output_size, align_corners, scales_h, scales_w); |
| 15831 | } |
| 15832 | at::functionalization::impl::replace_(out, tmp_output); |
| 15833 | at::functionalization::impl::commit_update(out); |
| 15834 | at::functionalization::impl::sync(out); |
| 15835 | return out; |
| 15836 | } |
| 15837 | } |
| 15838 | |
| 15839 | at::Tensor & upsample_trilinear3d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 15840 | if (false) { |
| 15841 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15842 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15843 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15844 | auto self_meta = to_meta(self); |
| 15845 | auto out_meta = to_meta(out); |
| 15846 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15847 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15848 | at::_ops::upsample_trilinear3d_out::call(self_meta, output_size, align_corners, scales_d, scales_h, scales_w, out_meta); |
| 15849 | } |
| 15850 | |
| 15851 | at::Tensor self_; |
| 15852 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 15853 | at::functionalization::impl::sync(self); |
| 15854 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 15855 | } else { |
| 15856 | self_ = self; |
| 15857 | } |
| 15858 | |
| 15859 | at::Tensor out_; |
| 15860 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 15861 | at::functionalization::impl::sync(out); |
| 15862 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 15863 | } else { |
| 15864 | out_ = out; |
| 15865 | } |
| 15866 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 15867 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 15868 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15869 | TORCH_INTERNAL_ASSERT(false, |
| 15870 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15871 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15872 | } else { |
| 15873 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15874 | at::AutoDispatchSkipFunctionalize guard; |
| 15875 | at::Tensor tmp_output = at::_ops::upsample_trilinear3d_out::call(self_, output_size, align_corners, scales_d, scales_h, scales_w, out_); |
| 15876 | return out;; |
| 15877 | } |
| 15878 | } else { |
| 15879 | at::Tensor tmp_output; |
| 15880 | { |
| 15881 | at::AutoDispatchSkipFunctionalize guard; |
| 15882 | tmp_output = at::_ops::upsample_trilinear3d::call(self_, output_size, align_corners, scales_d, scales_h, scales_w); |
| 15883 | } |
| 15884 | at::functionalization::impl::replace_(out, tmp_output); |
| 15885 | at::functionalization::impl::commit_update(out); |
| 15886 | at::functionalization::impl::sync(out); |
| 15887 | return out; |
| 15888 | } |
| 15889 | } |
| 15890 | |
| 15891 | at::Tensor & _upsample_nearest_exact1d_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) { |
| 15892 | if (false) { |
| 15893 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15894 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15895 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15896 | auto grad_output_meta = to_meta(grad_output); |
| 15897 | auto grad_input_meta = to_meta(grad_input); |
| 15898 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15899 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15900 | at::_ops::_upsample_nearest_exact1d_backward_grad_input::call(grad_output_meta, output_size, input_size, scales, grad_input_meta); |
| 15901 | } |
| 15902 | |
| 15903 | at::Tensor grad_output_; |
| 15904 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 15905 | at::functionalization::impl::sync(grad_output); |
| 15906 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 15907 | } else { |
| 15908 | grad_output_ = grad_output; |
| 15909 | } |
| 15910 | |
| 15911 | at::Tensor grad_input_; |
| 15912 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 15913 | at::functionalization::impl::sync(grad_input); |
| 15914 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 15915 | } else { |
| 15916 | grad_input_ = grad_input; |
| 15917 | } |
| 15918 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 15919 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output))) { |
| 15920 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15921 | TORCH_INTERNAL_ASSERT(false, |
| 15922 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15923 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15924 | } else { |
| 15925 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15926 | at::AutoDispatchSkipFunctionalize guard; |
| 15927 | at::Tensor tmp_output = at::_ops::_upsample_nearest_exact1d_backward_grad_input::call(grad_output_, output_size, input_size, scales, grad_input_); |
| 15928 | return grad_input;; |
| 15929 | } |
| 15930 | } else { |
| 15931 | at::Tensor tmp_output; |
| 15932 | { |
| 15933 | at::AutoDispatchSkipFunctionalize guard; |
| 15934 | tmp_output = at::_ops::_upsample_nearest_exact1d_backward::call(grad_output_, output_size, input_size, scales); |
| 15935 | } |
| 15936 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 15937 | at::functionalization::impl::commit_update(grad_input); |
| 15938 | at::functionalization::impl::sync(grad_input); |
| 15939 | return grad_input; |
| 15940 | } |
| 15941 | } |
| 15942 | |
| 15943 | at::Tensor & upsample_nearest2d_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) { |
| 15944 | if (false) { |
| 15945 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15946 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15947 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 15948 | auto grad_output_meta = to_meta(grad_output); |
| 15949 | auto grad_input_meta = to_meta(grad_input); |
| 15950 | at::AutoDispatchSkipFunctionalize func_guard; |
| 15951 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 15952 | at::_ops::upsample_nearest2d_backward_grad_input::call(grad_output_meta, output_size, input_size, scales_h, scales_w, grad_input_meta); |
| 15953 | } |
| 15954 | |
| 15955 | at::Tensor grad_output_; |
| 15956 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 15957 | at::functionalization::impl::sync(grad_output); |
| 15958 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 15959 | } else { |
| 15960 | grad_output_ = grad_output; |
| 15961 | } |
| 15962 | |
| 15963 | at::Tensor grad_input_; |
| 15964 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 15965 | at::functionalization::impl::sync(grad_input); |
| 15966 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 15967 | } else { |
| 15968 | grad_input_ = grad_input; |
| 15969 | } |
| 15970 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 15971 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output))) { |
| 15972 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 15973 | TORCH_INTERNAL_ASSERT(false, |
| 15974 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 15975 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 15976 | } else { |
| 15977 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 15978 | at::AutoDispatchSkipFunctionalize guard; |
| 15979 | at::Tensor tmp_output = at::_ops::upsample_nearest2d_backward_grad_input::call(grad_output_, output_size, input_size, scales_h, scales_w, grad_input_); |
| 15980 | return grad_input;; |
| 15981 | } |
| 15982 | } else { |
| 15983 | at::Tensor tmp_output; |
| 15984 | { |
| 15985 | at::AutoDispatchSkipFunctionalize guard; |
| 15986 | tmp_output = at::_ops::upsample_nearest2d_backward::call(grad_output_, output_size, input_size, scales_h, scales_w); |
| 15987 | } |
| 15988 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 15989 | at::functionalization::impl::commit_update(grad_input); |
| 15990 | at::functionalization::impl::sync(grad_input); |
| 15991 | return grad_input; |
| 15992 | } |
| 15993 | } |
| 15994 | |
| 15995 | at::Tensor & tanh_backward_out_grad_input(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, at::Tensor & grad_input) { |
| 15996 | if (false) { |
| 15997 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 15998 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 15999 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16000 | auto grad_output_meta = to_meta(grad_output); |
| 16001 | auto output_meta = to_meta(output); |
| 16002 | auto grad_input_meta = to_meta(grad_input); |
| 16003 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16004 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16005 | at::_ops::tanh_backward_grad_input::call(grad_output_meta, output_meta, grad_input_meta); |
| 16006 | } |
| 16007 | |
| 16008 | at::Tensor grad_output_; |
| 16009 | if (at::functionalization::impl::isFunctionalTensor(grad_output)) { |
| 16010 | at::functionalization::impl::sync(grad_output); |
| 16011 | grad_output_ = at::functionalization::impl::from_functional_tensor(grad_output); |
| 16012 | } else { |
| 16013 | grad_output_ = grad_output; |
| 16014 | } |
| 16015 | |
| 16016 | at::Tensor output_; |
| 16017 | if (at::functionalization::impl::isFunctionalTensor(output)) { |
| 16018 | at::functionalization::impl::sync(output); |
| 16019 | output_ = at::functionalization::impl::from_functional_tensor(output); |
| 16020 | } else { |
| 16021 | output_ = output; |
| 16022 | } |
| 16023 | |
| 16024 | at::Tensor grad_input_; |
| 16025 | if (at::functionalization::impl::isFunctionalTensor(grad_input)) { |
| 16026 | at::functionalization::impl::sync(grad_input); |
| 16027 | grad_input_ = at::functionalization::impl::from_functional_tensor(grad_input); |
| 16028 | } else { |
| 16029 | grad_input_ = grad_input; |
| 16030 | } |
| 16031 | if (!(true && at::functionalization::impl::isFunctionalTensor(grad_input))) { |
| 16032 | if ((false || at::functionalization::impl::isFunctionalTensor(grad_output) || at::functionalization::impl::isFunctionalTensor(output))) { |
| 16033 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16034 | TORCH_INTERNAL_ASSERT(false, |
| 16035 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16036 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16037 | } else { |
| 16038 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16039 | at::AutoDispatchSkipFunctionalize guard; |
| 16040 | at::Tensor tmp_output = at::_ops::tanh_backward_grad_input::call(grad_output_, output_, grad_input_); |
| 16041 | return grad_input;; |
| 16042 | } |
| 16043 | } else { |
| 16044 | at::Tensor tmp_output; |
| 16045 | { |
| 16046 | at::AutoDispatchSkipFunctionalize guard; |
| 16047 | tmp_output = at::_ops::tanh_backward::call(grad_output_, output_); |
| 16048 | } |
| 16049 | at::functionalization::impl::replace_(grad_input, tmp_output); |
| 16050 | at::functionalization::impl::commit_update(grad_input); |
| 16051 | at::functionalization::impl::sync(grad_input); |
| 16052 | return grad_input; |
| 16053 | } |
| 16054 | } |
| 16055 | |
| 16056 | const at::Tensor & _conv_depthwise2d_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::SymIntArrayRef padding, at::IntArrayRef dilation, const at::Tensor & out) { |
| 16057 | if (false) { |
| 16058 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16059 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16060 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16061 | auto self_meta = to_meta(self); |
| 16062 | auto weight_meta = to_meta(weight); |
| 16063 | auto bias_meta = to_meta(bias); |
| 16064 | auto out_meta = to_meta(out); |
| 16065 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16066 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16067 | at::_ops::_conv_depthwise2d_out::call(self_meta, weight_meta, kernel_size, bias_meta, stride, padding, dilation, out_meta); |
| 16068 | } |
| 16069 | |
| 16070 | at::Tensor self_; |
| 16071 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16072 | at::functionalization::impl::sync(self); |
| 16073 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16074 | } else { |
| 16075 | self_ = self; |
| 16076 | } |
| 16077 | |
| 16078 | at::Tensor weight_; |
| 16079 | if (at::functionalization::impl::isFunctionalTensor(weight)) { |
| 16080 | at::functionalization::impl::sync(weight); |
| 16081 | weight_ = at::functionalization::impl::from_functional_tensor(weight); |
| 16082 | } else { |
| 16083 | weight_ = weight; |
| 16084 | } |
| 16085 | |
| 16086 | c10::optional<at::Tensor> bias_; |
| 16087 | if (at::functionalization::impl::isFunctionalTensor(bias)) { |
| 16088 | at::functionalization::impl::sync(bias); |
| 16089 | bias_ = at::functionalization::impl::from_functional_tensor(bias); |
| 16090 | } else { |
| 16091 | bias_ = bias; |
| 16092 | } |
| 16093 | |
| 16094 | at::Tensor out_; |
| 16095 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16096 | at::functionalization::impl::sync(out); |
| 16097 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16098 | } else { |
| 16099 | out_ = out; |
| 16100 | } |
| 16101 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16102 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(weight) || at::functionalization::impl::isFunctionalTensor(bias))) { |
| 16103 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16104 | TORCH_INTERNAL_ASSERT(false, |
| 16105 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16106 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16107 | } else { |
| 16108 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16109 | at::AutoDispatchSkipFunctionalize guard; |
| 16110 | at::Tensor tmp_output = at::_ops::_conv_depthwise2d_out::call(self_, weight_, kernel_size, bias_, stride, padding, dilation, out_); |
| 16111 | return out;; |
| 16112 | } |
| 16113 | } else { |
| 16114 | at::Tensor tmp_output; |
| 16115 | { |
| 16116 | at::AutoDispatchSkipFunctionalize guard; |
| 16117 | tmp_output = at::_ops::_conv_depthwise2d::call(self_, weight_, kernel_size, bias_, stride, padding, dilation); |
| 16118 | } |
| 16119 | at::functionalization::impl::replace_(out, tmp_output); |
| 16120 | at::functionalization::impl::commit_update(out); |
| 16121 | at::functionalization::impl::sync(out); |
| 16122 | return out; |
| 16123 | } |
| 16124 | } |
| 16125 | |
| 16126 | at::Tensor & col2im_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride, at::Tensor & out) { |
| 16127 | if (false) { |
| 16128 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16129 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16130 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16131 | auto self_meta = to_meta(self); |
| 16132 | auto out_meta = to_meta(out); |
| 16133 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16134 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16135 | at::_ops::col2im_out::call(self_meta, output_size, kernel_size, dilation, padding, stride, out_meta); |
| 16136 | } |
| 16137 | |
| 16138 | at::Tensor self_; |
| 16139 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16140 | at::functionalization::impl::sync(self); |
| 16141 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16142 | } else { |
| 16143 | self_ = self; |
| 16144 | } |
| 16145 | |
| 16146 | at::Tensor out_; |
| 16147 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16148 | at::functionalization::impl::sync(out); |
| 16149 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16150 | } else { |
| 16151 | out_ = out; |
| 16152 | } |
| 16153 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16154 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16155 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16156 | TORCH_INTERNAL_ASSERT(false, |
| 16157 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16158 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16159 | } else { |
| 16160 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16161 | at::AutoDispatchSkipFunctionalize guard; |
| 16162 | at::Tensor tmp_output = at::_ops::col2im_out::call(self_, output_size, kernel_size, dilation, padding, stride, out_); |
| 16163 | return out;; |
| 16164 | } |
| 16165 | } else { |
| 16166 | at::Tensor tmp_output; |
| 16167 | { |
| 16168 | at::AutoDispatchSkipFunctionalize guard; |
| 16169 | tmp_output = at::_ops::col2im::call(self_, output_size, kernel_size, dilation, padding, stride); |
| 16170 | } |
| 16171 | at::functionalization::impl::replace_(out, tmp_output); |
| 16172 | at::functionalization::impl::commit_update(out); |
| 16173 | at::functionalization::impl::sync(out); |
| 16174 | return out; |
| 16175 | } |
| 16176 | } |
| 16177 | |
| 16178 | at::Tensor & column_stack_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Tensor & out) { |
| 16179 | if (false) { |
| 16180 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16181 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16182 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16183 | auto tensors_meta = to_meta(tensors); |
| 16184 | auto out_meta = to_meta(out); |
| 16185 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16186 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16187 | at::_ops::column_stack_out::call(tensors_meta, out_meta); |
| 16188 | } |
| 16189 | |
| 16190 | ::std::vector<at::Tensor> tensors_; |
| 16191 | if (at::functionalization::impl::isFunctionalTensor(tensors)) { |
| 16192 | at::functionalization::impl::sync(tensors); |
| 16193 | tensors_ = at::functionalization::impl::from_functional_tensor(tensors); |
| 16194 | } else { |
| 16195 | tensors_ = tensors.vec(); |
| 16196 | } |
| 16197 | |
| 16198 | at::Tensor out_; |
| 16199 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16200 | at::functionalization::impl::sync(out); |
| 16201 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16202 | } else { |
| 16203 | out_ = out; |
| 16204 | } |
| 16205 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16206 | if ((false || at::functionalization::impl::isFunctionalTensor(tensors))) { |
| 16207 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16208 | TORCH_INTERNAL_ASSERT(false, |
| 16209 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16210 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16211 | } else { |
| 16212 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16213 | at::AutoDispatchSkipFunctionalize guard; |
| 16214 | at::Tensor tmp_output = at::_ops::column_stack_out::call(tensors_, out_); |
| 16215 | return out;; |
| 16216 | } |
| 16217 | } else { |
| 16218 | at::Tensor tmp_output; |
| 16219 | { |
| 16220 | at::AutoDispatchSkipFunctionalize guard; |
| 16221 | tmp_output = at::_ops::column_stack::call(tensors_); |
| 16222 | } |
| 16223 | at::functionalization::impl::replace_(out, tmp_output); |
| 16224 | at::functionalization::impl::commit_update(out); |
| 16225 | at::functionalization::impl::sync(out); |
| 16226 | return out; |
| 16227 | } |
| 16228 | } |
| 16229 | |
| 16230 | at::Tensor & im2col_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride, at::Tensor & out) { |
| 16231 | if (false) { |
| 16232 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16233 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16234 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16235 | auto self_meta = to_meta(self); |
| 16236 | auto out_meta = to_meta(out); |
| 16237 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16238 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16239 | at::_ops::im2col_out::call(self_meta, kernel_size, dilation, padding, stride, out_meta); |
| 16240 | } |
| 16241 | |
| 16242 | at::Tensor self_; |
| 16243 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16244 | at::functionalization::impl::sync(self); |
| 16245 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16246 | } else { |
| 16247 | self_ = self; |
| 16248 | } |
| 16249 | |
| 16250 | at::Tensor out_; |
| 16251 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16252 | at::functionalization::impl::sync(out); |
| 16253 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16254 | } else { |
| 16255 | out_ = out; |
| 16256 | } |
| 16257 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16258 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16259 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16260 | TORCH_INTERNAL_ASSERT(false, |
| 16261 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16262 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16263 | } else { |
| 16264 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16265 | at::AutoDispatchSkipFunctionalize guard; |
| 16266 | at::Tensor tmp_output = at::_ops::im2col_out::call(self_, kernel_size, dilation, padding, stride, out_); |
| 16267 | return out;; |
| 16268 | } |
| 16269 | } else { |
| 16270 | at::Tensor tmp_output; |
| 16271 | { |
| 16272 | at::AutoDispatchSkipFunctionalize guard; |
| 16273 | tmp_output = at::_ops::im2col::call(self_, kernel_size, dilation, padding, stride); |
| 16274 | } |
| 16275 | at::functionalization::impl::replace_(out, tmp_output); |
| 16276 | at::functionalization::impl::commit_update(out); |
| 16277 | at::functionalization::impl::sync(out); |
| 16278 | return out; |
| 16279 | } |
| 16280 | } |
| 16281 | |
| 16282 | at::Tensor & isinf_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 16283 | if (false) { |
| 16284 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16285 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16286 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16287 | auto self_meta = to_meta(self); |
| 16288 | auto out_meta = to_meta(out); |
| 16289 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16290 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16291 | at::_ops::isinf_out::call(self_meta, out_meta); |
| 16292 | } |
| 16293 | |
| 16294 | at::Tensor self_; |
| 16295 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16296 | at::functionalization::impl::sync(self); |
| 16297 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16298 | } else { |
| 16299 | self_ = self; |
| 16300 | } |
| 16301 | |
| 16302 | at::Tensor out_; |
| 16303 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16304 | at::functionalization::impl::sync(out); |
| 16305 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16306 | } else { |
| 16307 | out_ = out; |
| 16308 | } |
| 16309 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16310 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16311 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16312 | TORCH_INTERNAL_ASSERT(false, |
| 16313 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16314 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16315 | } else { |
| 16316 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16317 | at::AutoDispatchSkipFunctionalize guard; |
| 16318 | at::Tensor tmp_output = at::_ops::isinf_out::call(self_, out_); |
| 16319 | return out;; |
| 16320 | } |
| 16321 | } else { |
| 16322 | at::Tensor tmp_output; |
| 16323 | { |
| 16324 | at::AutoDispatchSkipFunctionalize guard; |
| 16325 | tmp_output = at::_ops::isinf::call(self_); |
| 16326 | } |
| 16327 | at::functionalization::impl::replace_(out, tmp_output); |
| 16328 | at::functionalization::impl::commit_update(out); |
| 16329 | at::functionalization::impl::sync(out); |
| 16330 | return out; |
| 16331 | } |
| 16332 | } |
| 16333 | |
| 16334 | at::Tensor & isneginf_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 16335 | if (false) { |
| 16336 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16337 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16338 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16339 | auto self_meta = to_meta(self); |
| 16340 | auto out_meta = to_meta(out); |
| 16341 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16342 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16343 | at::_ops::isneginf_out::call(self_meta, out_meta); |
| 16344 | } |
| 16345 | |
| 16346 | at::Tensor self_; |
| 16347 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16348 | at::functionalization::impl::sync(self); |
| 16349 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16350 | } else { |
| 16351 | self_ = self; |
| 16352 | } |
| 16353 | |
| 16354 | at::Tensor out_; |
| 16355 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16356 | at::functionalization::impl::sync(out); |
| 16357 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16358 | } else { |
| 16359 | out_ = out; |
| 16360 | } |
| 16361 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16362 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16363 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16364 | TORCH_INTERNAL_ASSERT(false, |
| 16365 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16366 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16367 | } else { |
| 16368 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16369 | at::AutoDispatchSkipFunctionalize guard; |
| 16370 | at::Tensor tmp_output = at::_ops::isneginf_out::call(self_, out_); |
| 16371 | return out;; |
| 16372 | } |
| 16373 | } else { |
| 16374 | at::Tensor tmp_output; |
| 16375 | { |
| 16376 | at::AutoDispatchSkipFunctionalize guard; |
| 16377 | tmp_output = at::_ops::isneginf::call(self_); |
| 16378 | } |
| 16379 | at::functionalization::impl::replace_(out, tmp_output); |
| 16380 | at::functionalization::impl::commit_update(out); |
| 16381 | at::functionalization::impl::sync(out); |
| 16382 | return out; |
| 16383 | } |
| 16384 | } |
| 16385 | |
| 16386 | at::Tensor & special_expm1_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 16387 | if (false) { |
| 16388 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16389 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16390 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16391 | auto self_meta = to_meta(self); |
| 16392 | auto out_meta = to_meta(out); |
| 16393 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16394 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16395 | at::_ops::special_expm1_out::call(self_meta, out_meta); |
| 16396 | } |
| 16397 | |
| 16398 | at::Tensor self_; |
| 16399 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16400 | at::functionalization::impl::sync(self); |
| 16401 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16402 | } else { |
| 16403 | self_ = self; |
| 16404 | } |
| 16405 | |
| 16406 | at::Tensor out_; |
| 16407 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16408 | at::functionalization::impl::sync(out); |
| 16409 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16410 | } else { |
| 16411 | out_ = out; |
| 16412 | } |
| 16413 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16414 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16415 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16416 | TORCH_INTERNAL_ASSERT(false, |
| 16417 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16418 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16419 | } else { |
| 16420 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16421 | at::AutoDispatchSkipFunctionalize guard; |
| 16422 | at::Tensor tmp_output = at::_ops::special_expm1_out::call(self_, out_); |
| 16423 | return out;; |
| 16424 | } |
| 16425 | } else { |
| 16426 | at::Tensor tmp_output; |
| 16427 | { |
| 16428 | at::AutoDispatchSkipFunctionalize guard; |
| 16429 | tmp_output = at::_ops::special_expm1::call(self_); |
| 16430 | } |
| 16431 | at::functionalization::impl::replace_(out, tmp_output); |
| 16432 | at::functionalization::impl::commit_update(out); |
| 16433 | at::functionalization::impl::sync(out); |
| 16434 | return out; |
| 16435 | } |
| 16436 | } |
| 16437 | |
| 16438 | at::Tensor & special_erf_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 16439 | if (false) { |
| 16440 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16441 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16442 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16443 | auto self_meta = to_meta(self); |
| 16444 | auto out_meta = to_meta(out); |
| 16445 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16446 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16447 | at::_ops::special_erf_out::call(self_meta, out_meta); |
| 16448 | } |
| 16449 | |
| 16450 | at::Tensor self_; |
| 16451 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16452 | at::functionalization::impl::sync(self); |
| 16453 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16454 | } else { |
| 16455 | self_ = self; |
| 16456 | } |
| 16457 | |
| 16458 | at::Tensor out_; |
| 16459 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16460 | at::functionalization::impl::sync(out); |
| 16461 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16462 | } else { |
| 16463 | out_ = out; |
| 16464 | } |
| 16465 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16466 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16467 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16468 | TORCH_INTERNAL_ASSERT(false, |
| 16469 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16470 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16471 | } else { |
| 16472 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16473 | at::AutoDispatchSkipFunctionalize guard; |
| 16474 | at::Tensor tmp_output = at::_ops::special_erf_out::call(self_, out_); |
| 16475 | return out;; |
| 16476 | } |
| 16477 | } else { |
| 16478 | at::Tensor tmp_output; |
| 16479 | { |
| 16480 | at::AutoDispatchSkipFunctionalize guard; |
| 16481 | tmp_output = at::_ops::special_erf::call(self_); |
| 16482 | } |
| 16483 | at::functionalization::impl::replace_(out, tmp_output); |
| 16484 | at::functionalization::impl::commit_update(out); |
| 16485 | at::functionalization::impl::sync(out); |
| 16486 | return out; |
| 16487 | } |
| 16488 | } |
| 16489 | |
| 16490 | at::Tensor & special_logsumexp_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { |
| 16491 | if (false) { |
| 16492 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16493 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16494 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16495 | auto self_meta = to_meta(self); |
| 16496 | auto out_meta = to_meta(out); |
| 16497 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16498 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16499 | at::_ops::special_logsumexp_out::call(self_meta, dim, keepdim, out_meta); |
| 16500 | } |
| 16501 | |
| 16502 | at::Tensor self_; |
| 16503 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16504 | at::functionalization::impl::sync(self); |
| 16505 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16506 | } else { |
| 16507 | self_ = self; |
| 16508 | } |
| 16509 | |
| 16510 | at::Tensor out_; |
| 16511 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16512 | at::functionalization::impl::sync(out); |
| 16513 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16514 | } else { |
| 16515 | out_ = out; |
| 16516 | } |
| 16517 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16518 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16519 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16520 | TORCH_INTERNAL_ASSERT(false, |
| 16521 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16522 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16523 | } else { |
| 16524 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16525 | at::AutoDispatchSkipFunctionalize guard; |
| 16526 | at::Tensor tmp_output = at::_ops::special_logsumexp_out::call(self_, dim, keepdim, out_); |
| 16527 | return out;; |
| 16528 | } |
| 16529 | } else { |
| 16530 | at::Tensor tmp_output; |
| 16531 | { |
| 16532 | at::AutoDispatchSkipFunctionalize guard; |
| 16533 | tmp_output = at::_ops::special_logsumexp::call(self_, dim, keepdim); |
| 16534 | } |
| 16535 | at::functionalization::impl::replace_(out, tmp_output); |
| 16536 | at::functionalization::impl::commit_update(out); |
| 16537 | at::functionalization::impl::sync(out); |
| 16538 | return out; |
| 16539 | } |
| 16540 | } |
| 16541 | |
| 16542 | at::Tensor & special_log1p_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 16543 | if (false) { |
| 16544 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16545 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16546 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16547 | auto self_meta = to_meta(self); |
| 16548 | auto out_meta = to_meta(out); |
| 16549 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16550 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16551 | at::_ops::special_log1p_out::call(self_meta, out_meta); |
| 16552 | } |
| 16553 | |
| 16554 | at::Tensor self_; |
| 16555 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16556 | at::functionalization::impl::sync(self); |
| 16557 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16558 | } else { |
| 16559 | self_ = self; |
| 16560 | } |
| 16561 | |
| 16562 | at::Tensor out_; |
| 16563 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16564 | at::functionalization::impl::sync(out); |
| 16565 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16566 | } else { |
| 16567 | out_ = out; |
| 16568 | } |
| 16569 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16570 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16571 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16572 | TORCH_INTERNAL_ASSERT(false, |
| 16573 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16574 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16575 | } else { |
| 16576 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16577 | at::AutoDispatchSkipFunctionalize guard; |
| 16578 | at::Tensor tmp_output = at::_ops::special_log1p_out::call(self_, out_); |
| 16579 | return out;; |
| 16580 | } |
| 16581 | } else { |
| 16582 | at::Tensor tmp_output; |
| 16583 | { |
| 16584 | at::AutoDispatchSkipFunctionalize guard; |
| 16585 | tmp_output = at::_ops::special_log1p::call(self_); |
| 16586 | } |
| 16587 | at::functionalization::impl::replace_(out, tmp_output); |
| 16588 | at::functionalization::impl::commit_update(out); |
| 16589 | at::functionalization::impl::sync(out); |
| 16590 | return out; |
| 16591 | } |
| 16592 | } |
| 16593 | |
| 16594 | at::Tensor & special_gammaincc_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 16595 | if (false) { |
| 16596 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16597 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16598 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16599 | auto self_meta = to_meta(self); |
| 16600 | auto other_meta = to_meta(other); |
| 16601 | auto out_meta = to_meta(out); |
| 16602 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16603 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16604 | at::_ops::special_gammaincc_out::call(self_meta, other_meta, out_meta); |
| 16605 | } |
| 16606 | |
| 16607 | at::Tensor self_; |
| 16608 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16609 | at::functionalization::impl::sync(self); |
| 16610 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16611 | } else { |
| 16612 | self_ = self; |
| 16613 | } |
| 16614 | |
| 16615 | at::Tensor other_; |
| 16616 | if (at::functionalization::impl::isFunctionalTensor(other)) { |
| 16617 | at::functionalization::impl::sync(other); |
| 16618 | other_ = at::functionalization::impl::from_functional_tensor(other); |
| 16619 | } else { |
| 16620 | other_ = other; |
| 16621 | } |
| 16622 | |
| 16623 | at::Tensor out_; |
| 16624 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16625 | at::functionalization::impl::sync(out); |
| 16626 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16627 | } else { |
| 16628 | out_ = out; |
| 16629 | } |
| 16630 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16631 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(other))) { |
| 16632 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16633 | TORCH_INTERNAL_ASSERT(false, |
| 16634 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16635 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16636 | } else { |
| 16637 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16638 | at::AutoDispatchSkipFunctionalize guard; |
| 16639 | at::Tensor tmp_output = at::_ops::special_gammaincc_out::call(self_, other_, out_); |
| 16640 | return out;; |
| 16641 | } |
| 16642 | } else { |
| 16643 | at::Tensor tmp_output; |
| 16644 | { |
| 16645 | at::AutoDispatchSkipFunctionalize guard; |
| 16646 | tmp_output = at::_ops::special_gammaincc::call(self_, other_); |
| 16647 | } |
| 16648 | at::functionalization::impl::replace_(out, tmp_output); |
| 16649 | at::functionalization::impl::commit_update(out); |
| 16650 | at::functionalization::impl::sync(out); |
| 16651 | return out; |
| 16652 | } |
| 16653 | } |
| 16654 | |
| 16655 | at::Tensor & special_multigammaln_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t p, at::Tensor & out) { |
| 16656 | if (false) { |
| 16657 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16658 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16659 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16660 | auto self_meta = to_meta(self); |
| 16661 | auto out_meta = to_meta(out); |
| 16662 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16663 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16664 | at::_ops::special_multigammaln_out::call(self_meta, p, out_meta); |
| 16665 | } |
| 16666 | |
| 16667 | at::Tensor self_; |
| 16668 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16669 | at::functionalization::impl::sync(self); |
| 16670 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16671 | } else { |
| 16672 | self_ = self; |
| 16673 | } |
| 16674 | |
| 16675 | at::Tensor out_; |
| 16676 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16677 | at::functionalization::impl::sync(out); |
| 16678 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16679 | } else { |
| 16680 | out_ = out; |
| 16681 | } |
| 16682 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16683 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16684 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16685 | TORCH_INTERNAL_ASSERT(false, |
| 16686 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16687 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16688 | } else { |
| 16689 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16690 | at::AutoDispatchSkipFunctionalize guard; |
| 16691 | at::Tensor tmp_output = at::_ops::special_multigammaln_out::call(self_, p, out_); |
| 16692 | return out;; |
| 16693 | } |
| 16694 | } else { |
| 16695 | at::Tensor tmp_output; |
| 16696 | { |
| 16697 | at::AutoDispatchSkipFunctionalize guard; |
| 16698 | tmp_output = at::_ops::special_multigammaln::call(self_, p); |
| 16699 | } |
| 16700 | at::functionalization::impl::replace_(out, tmp_output); |
| 16701 | at::functionalization::impl::commit_update(out); |
| 16702 | at::functionalization::impl::sync(out); |
| 16703 | return out; |
| 16704 | } |
| 16705 | } |
| 16706 | |
| 16707 | at::Tensor & fft_rfft2_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional<c10::string_view> norm, at::Tensor & out) { |
| 16708 | if (false) { |
| 16709 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16710 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16711 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16712 | auto self_meta = to_meta(self); |
| 16713 | auto out_meta = to_meta(out); |
| 16714 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16715 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16716 | at::_ops::fft_rfft2_out::call(self_meta, s, dim, norm, out_meta); |
| 16717 | } |
| 16718 | |
| 16719 | at::Tensor self_; |
| 16720 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16721 | at::functionalization::impl::sync(self); |
| 16722 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16723 | } else { |
| 16724 | self_ = self; |
| 16725 | } |
| 16726 | |
| 16727 | at::Tensor out_; |
| 16728 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16729 | at::functionalization::impl::sync(out); |
| 16730 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16731 | } else { |
| 16732 | out_ = out; |
| 16733 | } |
| 16734 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16735 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16736 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16737 | TORCH_INTERNAL_ASSERT(false, |
| 16738 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16739 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16740 | } else { |
| 16741 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16742 | at::AutoDispatchSkipFunctionalize guard; |
| 16743 | at::Tensor tmp_output = at::_ops::fft_rfft2_out::call(self_, s, dim, norm, out_); |
| 16744 | return out;; |
| 16745 | } |
| 16746 | } else { |
| 16747 | at::Tensor tmp_output; |
| 16748 | { |
| 16749 | at::AutoDispatchSkipFunctionalize guard; |
| 16750 | tmp_output = at::_ops::fft_rfft2::call(self_, s, dim, norm); |
| 16751 | } |
| 16752 | at::functionalization::impl::replace_(out, tmp_output); |
| 16753 | at::functionalization::impl::commit_update(out); |
| 16754 | at::functionalization::impl::sync(out); |
| 16755 | return out; |
| 16756 | } |
| 16757 | } |
| 16758 | |
| 16759 | at::Tensor & fft_irfft2_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional<c10::string_view> norm, at::Tensor & out) { |
| 16760 | if (false) { |
| 16761 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16762 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16763 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16764 | auto self_meta = to_meta(self); |
| 16765 | auto out_meta = to_meta(out); |
| 16766 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16767 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16768 | at::_ops::fft_irfft2_out::call(self_meta, s, dim, norm, out_meta); |
| 16769 | } |
| 16770 | |
| 16771 | at::Tensor self_; |
| 16772 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16773 | at::functionalization::impl::sync(self); |
| 16774 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16775 | } else { |
| 16776 | self_ = self; |
| 16777 | } |
| 16778 | |
| 16779 | at::Tensor out_; |
| 16780 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16781 | at::functionalization::impl::sync(out); |
| 16782 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16783 | } else { |
| 16784 | out_ = out; |
| 16785 | } |
| 16786 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16787 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16788 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16789 | TORCH_INTERNAL_ASSERT(false, |
| 16790 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16791 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16792 | } else { |
| 16793 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16794 | at::AutoDispatchSkipFunctionalize guard; |
| 16795 | at::Tensor tmp_output = at::_ops::fft_irfft2_out::call(self_, s, dim, norm, out_); |
| 16796 | return out;; |
| 16797 | } |
| 16798 | } else { |
| 16799 | at::Tensor tmp_output; |
| 16800 | { |
| 16801 | at::AutoDispatchSkipFunctionalize guard; |
| 16802 | tmp_output = at::_ops::fft_irfft2::call(self_, s, dim, norm); |
| 16803 | } |
| 16804 | at::functionalization::impl::replace_(out, tmp_output); |
| 16805 | at::functionalization::impl::commit_update(out); |
| 16806 | at::functionalization::impl::sync(out); |
| 16807 | return out; |
| 16808 | } |
| 16809 | } |
| 16810 | |
| 16811 | const at::Tensor & fft_ihfft2_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional<c10::string_view> norm, const at::Tensor & out) { |
| 16812 | if (false) { |
| 16813 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16814 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16815 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16816 | auto self_meta = to_meta(self); |
| 16817 | auto out_meta = to_meta(out); |
| 16818 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16819 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16820 | at::_ops::fft_ihfft2_out::call(self_meta, s, dim, norm, out_meta); |
| 16821 | } |
| 16822 | |
| 16823 | at::Tensor self_; |
| 16824 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16825 | at::functionalization::impl::sync(self); |
| 16826 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16827 | } else { |
| 16828 | self_ = self; |
| 16829 | } |
| 16830 | |
| 16831 | at::Tensor out_; |
| 16832 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16833 | at::functionalization::impl::sync(out); |
| 16834 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16835 | } else { |
| 16836 | out_ = out; |
| 16837 | } |
| 16838 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16839 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16840 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16841 | TORCH_INTERNAL_ASSERT(false, |
| 16842 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16843 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16844 | } else { |
| 16845 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16846 | at::AutoDispatchSkipFunctionalize guard; |
| 16847 | at::Tensor tmp_output = at::_ops::fft_ihfft2_out::call(self_, s, dim, norm, out_); |
| 16848 | return out;; |
| 16849 | } |
| 16850 | } else { |
| 16851 | at::Tensor tmp_output; |
| 16852 | { |
| 16853 | at::AutoDispatchSkipFunctionalize guard; |
| 16854 | tmp_output = at::_ops::fft_ihfft2::call(self_, s, dim, norm); |
| 16855 | } |
| 16856 | at::functionalization::impl::replace_(out, tmp_output); |
| 16857 | at::functionalization::impl::commit_update(out); |
| 16858 | at::functionalization::impl::sync(out); |
| 16859 | return out; |
| 16860 | } |
| 16861 | } |
| 16862 | |
| 16863 | at::Tensor & fft_fftn_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional<c10::string_view> norm, at::Tensor & out) { |
| 16864 | if (false) { |
| 16865 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16866 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16867 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16868 | auto self_meta = to_meta(self); |
| 16869 | auto out_meta = to_meta(out); |
| 16870 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16871 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16872 | at::_ops::fft_fftn_out::call(self_meta, s, dim, norm, out_meta); |
| 16873 | } |
| 16874 | |
| 16875 | at::Tensor self_; |
| 16876 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16877 | at::functionalization::impl::sync(self); |
| 16878 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16879 | } else { |
| 16880 | self_ = self; |
| 16881 | } |
| 16882 | |
| 16883 | at::Tensor out_; |
| 16884 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16885 | at::functionalization::impl::sync(out); |
| 16886 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16887 | } else { |
| 16888 | out_ = out; |
| 16889 | } |
| 16890 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16891 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16892 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16893 | TORCH_INTERNAL_ASSERT(false, |
| 16894 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16895 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16896 | } else { |
| 16897 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16898 | at::AutoDispatchSkipFunctionalize guard; |
| 16899 | at::Tensor tmp_output = at::_ops::fft_fftn_out::call(self_, s, dim, norm, out_); |
| 16900 | return out;; |
| 16901 | } |
| 16902 | } else { |
| 16903 | at::Tensor tmp_output; |
| 16904 | { |
| 16905 | at::AutoDispatchSkipFunctionalize guard; |
| 16906 | tmp_output = at::_ops::fft_fftn::call(self_, s, dim, norm); |
| 16907 | } |
| 16908 | at::functionalization::impl::replace_(out, tmp_output); |
| 16909 | at::functionalization::impl::commit_update(out); |
| 16910 | at::functionalization::impl::sync(out); |
| 16911 | return out; |
| 16912 | } |
| 16913 | } |
| 16914 | |
| 16915 | at::Tensor & fft_irfftn_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional<c10::string_view> norm, at::Tensor & out) { |
| 16916 | if (false) { |
| 16917 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16918 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16919 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16920 | auto self_meta = to_meta(self); |
| 16921 | auto out_meta = to_meta(out); |
| 16922 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16923 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16924 | at::_ops::fft_irfftn_out::call(self_meta, s, dim, norm, out_meta); |
| 16925 | } |
| 16926 | |
| 16927 | at::Tensor self_; |
| 16928 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 16929 | at::functionalization::impl::sync(self); |
| 16930 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 16931 | } else { |
| 16932 | self_ = self; |
| 16933 | } |
| 16934 | |
| 16935 | at::Tensor out_; |
| 16936 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 16937 | at::functionalization::impl::sync(out); |
| 16938 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 16939 | } else { |
| 16940 | out_ = out; |
| 16941 | } |
| 16942 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 16943 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 16944 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 16945 | TORCH_INTERNAL_ASSERT(false, |
| 16946 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 16947 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 16948 | } else { |
| 16949 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 16950 | at::AutoDispatchSkipFunctionalize guard; |
| 16951 | at::Tensor tmp_output = at::_ops::fft_irfftn_out::call(self_, s, dim, norm, out_); |
| 16952 | return out;; |
| 16953 | } |
| 16954 | } else { |
| 16955 | at::Tensor tmp_output; |
| 16956 | { |
| 16957 | at::AutoDispatchSkipFunctionalize guard; |
| 16958 | tmp_output = at::_ops::fft_irfftn::call(self_, s, dim, norm); |
| 16959 | } |
| 16960 | at::functionalization::impl::replace_(out, tmp_output); |
| 16961 | at::functionalization::impl::commit_update(out); |
| 16962 | at::functionalization::impl::sync(out); |
| 16963 | return out; |
| 16964 | } |
| 16965 | } |
| 16966 | |
| 16967 | ::std::tuple<at::Tensor &,at::Tensor &> linalg_lu_factor_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, bool pivot, at::Tensor & LU, at::Tensor & pivots) { |
| 16968 | if (false) { |
| 16969 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 16970 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 16971 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 16972 | auto A_meta = to_meta(A); |
| 16973 | auto LU_meta = to_meta(LU); |
| 16974 | auto pivots_meta = to_meta(pivots); |
| 16975 | at::AutoDispatchSkipFunctionalize func_guard; |
| 16976 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 16977 | at::_ops::linalg_lu_factor_out::call(A_meta, pivot, LU_meta, pivots_meta); |
| 16978 | } |
| 16979 | |
| 16980 | at::Tensor A_; |
| 16981 | if (at::functionalization::impl::isFunctionalTensor(A)) { |
| 16982 | at::functionalization::impl::sync(A); |
| 16983 | A_ = at::functionalization::impl::from_functional_tensor(A); |
| 16984 | } else { |
| 16985 | A_ = A; |
| 16986 | } |
| 16987 | |
| 16988 | at::Tensor LU_; |
| 16989 | if (at::functionalization::impl::isFunctionalTensor(LU)) { |
| 16990 | at::functionalization::impl::sync(LU); |
| 16991 | LU_ = at::functionalization::impl::from_functional_tensor(LU); |
| 16992 | } else { |
| 16993 | LU_ = LU; |
| 16994 | } |
| 16995 | |
| 16996 | at::Tensor pivots_; |
| 16997 | if (at::functionalization::impl::isFunctionalTensor(pivots)) { |
| 16998 | at::functionalization::impl::sync(pivots); |
| 16999 | pivots_ = at::functionalization::impl::from_functional_tensor(pivots); |
| 17000 | } else { |
| 17001 | pivots_ = pivots; |
| 17002 | } |
| 17003 | if (!(true && at::functionalization::impl::isFunctionalTensor(LU) && at::functionalization::impl::isFunctionalTensor(pivots))) { |
| 17004 | if ((false || at::functionalization::impl::isFunctionalTensor(A))) { |
| 17005 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17006 | TORCH_INTERNAL_ASSERT(false, |
| 17007 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17008 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17009 | } else { |
| 17010 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17011 | at::AutoDispatchSkipFunctionalize guard; |
| 17012 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::linalg_lu_factor_out::call(A_, pivot, LU_, pivots_); |
| 17013 | return ::std::tuple<at::Tensor &,at::Tensor &>(LU, pivots);; |
| 17014 | } |
| 17015 | } else { |
| 17016 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 17017 | { |
| 17018 | at::AutoDispatchSkipFunctionalize guard; |
| 17019 | tmp_output = at::_ops::linalg_lu_factor::call(A_, pivot); |
| 17020 | } |
| 17021 | at::functionalization::impl::replace_(LU, std::get<0>(tmp_output)); |
| 17022 | at::functionalization::impl::commit_update(LU); |
| 17023 | at::functionalization::impl::sync(LU); |
| 17024 | at::functionalization::impl::replace_(pivots, std::get<1>(tmp_output)); |
| 17025 | at::functionalization::impl::commit_update(pivots); |
| 17026 | at::functionalization::impl::sync(pivots); |
| 17027 | return ::std::tuple<at::Tensor &,at::Tensor &>(LU, pivots); |
| 17028 | } |
| 17029 | } |
| 17030 | |
| 17031 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linalg_lu_factor_ex_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, bool pivot, bool check_errors, at::Tensor & LU, at::Tensor & pivots, at::Tensor & info) { |
| 17032 | if (false) { |
| 17033 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17034 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17035 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17036 | auto A_meta = to_meta(A); |
| 17037 | auto LU_meta = to_meta(LU); |
| 17038 | auto pivots_meta = to_meta(pivots); |
| 17039 | auto info_meta = to_meta(info); |
| 17040 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17041 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17042 | at::_ops::linalg_lu_factor_ex_out::call(A_meta, pivot, check_errors, LU_meta, pivots_meta, info_meta); |
| 17043 | } |
| 17044 | |
| 17045 | at::Tensor A_; |
| 17046 | if (at::functionalization::impl::isFunctionalTensor(A)) { |
| 17047 | at::functionalization::impl::sync(A); |
| 17048 | A_ = at::functionalization::impl::from_functional_tensor(A); |
| 17049 | } else { |
| 17050 | A_ = A; |
| 17051 | } |
| 17052 | |
| 17053 | at::Tensor LU_; |
| 17054 | if (at::functionalization::impl::isFunctionalTensor(LU)) { |
| 17055 | at::functionalization::impl::sync(LU); |
| 17056 | LU_ = at::functionalization::impl::from_functional_tensor(LU); |
| 17057 | } else { |
| 17058 | LU_ = LU; |
| 17059 | } |
| 17060 | |
| 17061 | at::Tensor pivots_; |
| 17062 | if (at::functionalization::impl::isFunctionalTensor(pivots)) { |
| 17063 | at::functionalization::impl::sync(pivots); |
| 17064 | pivots_ = at::functionalization::impl::from_functional_tensor(pivots); |
| 17065 | } else { |
| 17066 | pivots_ = pivots; |
| 17067 | } |
| 17068 | |
| 17069 | at::Tensor info_; |
| 17070 | if (at::functionalization::impl::isFunctionalTensor(info)) { |
| 17071 | at::functionalization::impl::sync(info); |
| 17072 | info_ = at::functionalization::impl::from_functional_tensor(info); |
| 17073 | } else { |
| 17074 | info_ = info; |
| 17075 | } |
| 17076 | if (!(true && at::functionalization::impl::isFunctionalTensor(LU) && at::functionalization::impl::isFunctionalTensor(pivots) && at::functionalization::impl::isFunctionalTensor(info))) { |
| 17077 | if ((false || at::functionalization::impl::isFunctionalTensor(A))) { |
| 17078 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17079 | TORCH_INTERNAL_ASSERT(false, |
| 17080 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17081 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17082 | } else { |
| 17083 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17084 | at::AutoDispatchSkipFunctionalize guard; |
| 17085 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::linalg_lu_factor_ex_out::call(A_, pivot, check_errors, LU_, pivots_, info_); |
| 17086 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(LU, pivots, info);; |
| 17087 | } |
| 17088 | } else { |
| 17089 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 17090 | { |
| 17091 | at::AutoDispatchSkipFunctionalize guard; |
| 17092 | tmp_output = at::_ops::linalg_lu_factor_ex::call(A_, pivot, check_errors); |
| 17093 | } |
| 17094 | at::functionalization::impl::replace_(LU, std::get<0>(tmp_output)); |
| 17095 | at::functionalization::impl::commit_update(LU); |
| 17096 | at::functionalization::impl::sync(LU); |
| 17097 | at::functionalization::impl::replace_(pivots, std::get<1>(tmp_output)); |
| 17098 | at::functionalization::impl::commit_update(pivots); |
| 17099 | at::functionalization::impl::sync(pivots); |
| 17100 | at::functionalization::impl::replace_(info, std::get<2>(tmp_output)); |
| 17101 | at::functionalization::impl::commit_update(info); |
| 17102 | at::functionalization::impl::sync(info); |
| 17103 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &>(LU, pivots, info); |
| 17104 | } |
| 17105 | } |
| 17106 | |
| 17107 | ::std::tuple<at::Tensor &,at::Tensor &> linalg_ldl_factor_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool hermitian, at::Tensor & LD, at::Tensor & pivots) { |
| 17108 | if (false) { |
| 17109 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17110 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17111 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17112 | auto self_meta = to_meta(self); |
| 17113 | auto LD_meta = to_meta(LD); |
| 17114 | auto pivots_meta = to_meta(pivots); |
| 17115 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17116 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17117 | at::_ops::linalg_ldl_factor_out::call(self_meta, hermitian, LD_meta, pivots_meta); |
| 17118 | } |
| 17119 | |
| 17120 | at::Tensor self_; |
| 17121 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17122 | at::functionalization::impl::sync(self); |
| 17123 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17124 | } else { |
| 17125 | self_ = self; |
| 17126 | } |
| 17127 | |
| 17128 | at::Tensor LD_; |
| 17129 | if (at::functionalization::impl::isFunctionalTensor(LD)) { |
| 17130 | at::functionalization::impl::sync(LD); |
| 17131 | LD_ = at::functionalization::impl::from_functional_tensor(LD); |
| 17132 | } else { |
| 17133 | LD_ = LD; |
| 17134 | } |
| 17135 | |
| 17136 | at::Tensor pivots_; |
| 17137 | if (at::functionalization::impl::isFunctionalTensor(pivots)) { |
| 17138 | at::functionalization::impl::sync(pivots); |
| 17139 | pivots_ = at::functionalization::impl::from_functional_tensor(pivots); |
| 17140 | } else { |
| 17141 | pivots_ = pivots; |
| 17142 | } |
| 17143 | if (!(true && at::functionalization::impl::isFunctionalTensor(LD) && at::functionalization::impl::isFunctionalTensor(pivots))) { |
| 17144 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 17145 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17146 | TORCH_INTERNAL_ASSERT(false, |
| 17147 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17148 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17149 | } else { |
| 17150 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17151 | at::AutoDispatchSkipFunctionalize guard; |
| 17152 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::linalg_ldl_factor_out::call(self_, hermitian, LD_, pivots_); |
| 17153 | return ::std::tuple<at::Tensor &,at::Tensor &>(LD, pivots);; |
| 17154 | } |
| 17155 | } else { |
| 17156 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 17157 | { |
| 17158 | at::AutoDispatchSkipFunctionalize guard; |
| 17159 | tmp_output = at::_ops::linalg_ldl_factor::call(self_, hermitian); |
| 17160 | } |
| 17161 | at::functionalization::impl::replace_(LD, std::get<0>(tmp_output)); |
| 17162 | at::functionalization::impl::commit_update(LD); |
| 17163 | at::functionalization::impl::sync(LD); |
| 17164 | at::functionalization::impl::replace_(pivots, std::get<1>(tmp_output)); |
| 17165 | at::functionalization::impl::commit_update(pivots); |
| 17166 | at::functionalization::impl::sync(pivots); |
| 17167 | return ::std::tuple<at::Tensor &,at::Tensor &>(LD, pivots); |
| 17168 | } |
| 17169 | } |
| 17170 | |
| 17171 | at::Tensor & linalg_ldl_solve_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & LD, const at::Tensor & pivots, const at::Tensor & B, bool hermitian, at::Tensor & out) { |
| 17172 | if (false) { |
| 17173 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17174 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17175 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17176 | auto LD_meta = to_meta(LD); |
| 17177 | auto pivots_meta = to_meta(pivots); |
| 17178 | auto B_meta = to_meta(B); |
| 17179 | auto out_meta = to_meta(out); |
| 17180 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17181 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17182 | at::_ops::linalg_ldl_solve_out::call(LD_meta, pivots_meta, B_meta, hermitian, out_meta); |
| 17183 | } |
| 17184 | |
| 17185 | at::Tensor LD_; |
| 17186 | if (at::functionalization::impl::isFunctionalTensor(LD)) { |
| 17187 | at::functionalization::impl::sync(LD); |
| 17188 | LD_ = at::functionalization::impl::from_functional_tensor(LD); |
| 17189 | } else { |
| 17190 | LD_ = LD; |
| 17191 | } |
| 17192 | |
| 17193 | at::Tensor pivots_; |
| 17194 | if (at::functionalization::impl::isFunctionalTensor(pivots)) { |
| 17195 | at::functionalization::impl::sync(pivots); |
| 17196 | pivots_ = at::functionalization::impl::from_functional_tensor(pivots); |
| 17197 | } else { |
| 17198 | pivots_ = pivots; |
| 17199 | } |
| 17200 | |
| 17201 | at::Tensor B_; |
| 17202 | if (at::functionalization::impl::isFunctionalTensor(B)) { |
| 17203 | at::functionalization::impl::sync(B); |
| 17204 | B_ = at::functionalization::impl::from_functional_tensor(B); |
| 17205 | } else { |
| 17206 | B_ = B; |
| 17207 | } |
| 17208 | |
| 17209 | at::Tensor out_; |
| 17210 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17211 | at::functionalization::impl::sync(out); |
| 17212 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17213 | } else { |
| 17214 | out_ = out; |
| 17215 | } |
| 17216 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 17217 | if ((false || at::functionalization::impl::isFunctionalTensor(LD) || at::functionalization::impl::isFunctionalTensor(pivots) || at::functionalization::impl::isFunctionalTensor(B))) { |
| 17218 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17219 | TORCH_INTERNAL_ASSERT(false, |
| 17220 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17221 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17222 | } else { |
| 17223 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17224 | at::AutoDispatchSkipFunctionalize guard; |
| 17225 | at::Tensor tmp_output = at::_ops::linalg_ldl_solve_out::call(LD_, pivots_, B_, hermitian, out_); |
| 17226 | return out;; |
| 17227 | } |
| 17228 | } else { |
| 17229 | at::Tensor tmp_output; |
| 17230 | { |
| 17231 | at::AutoDispatchSkipFunctionalize guard; |
| 17232 | tmp_output = at::_ops::linalg_ldl_solve::call(LD_, pivots_, B_, hermitian); |
| 17233 | } |
| 17234 | at::functionalization::impl::replace_(out, tmp_output); |
| 17235 | at::functionalization::impl::commit_update(out); |
| 17236 | at::functionalization::impl::sync(out); |
| 17237 | return out; |
| 17238 | } |
| 17239 | } |
| 17240 | |
| 17241 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _linalg_slogdet_out_sign(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, at::Tensor & sign, at::Tensor & logabsdet, at::Tensor & LU, at::Tensor & pivots) { |
| 17242 | if (false) { |
| 17243 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17244 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17245 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17246 | auto A_meta = to_meta(A); |
| 17247 | auto sign_meta = to_meta(sign); |
| 17248 | auto logabsdet_meta = to_meta(logabsdet); |
| 17249 | auto LU_meta = to_meta(LU); |
| 17250 | auto pivots_meta = to_meta(pivots); |
| 17251 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17252 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17253 | at::_ops::_linalg_slogdet_sign::call(A_meta, sign_meta, logabsdet_meta, LU_meta, pivots_meta); |
| 17254 | } |
| 17255 | |
| 17256 | at::Tensor A_; |
| 17257 | if (at::functionalization::impl::isFunctionalTensor(A)) { |
| 17258 | at::functionalization::impl::sync(A); |
| 17259 | A_ = at::functionalization::impl::from_functional_tensor(A); |
| 17260 | } else { |
| 17261 | A_ = A; |
| 17262 | } |
| 17263 | |
| 17264 | at::Tensor sign_; |
| 17265 | if (at::functionalization::impl::isFunctionalTensor(sign)) { |
| 17266 | at::functionalization::impl::sync(sign); |
| 17267 | sign_ = at::functionalization::impl::from_functional_tensor(sign); |
| 17268 | } else { |
| 17269 | sign_ = sign; |
| 17270 | } |
| 17271 | |
| 17272 | at::Tensor logabsdet_; |
| 17273 | if (at::functionalization::impl::isFunctionalTensor(logabsdet)) { |
| 17274 | at::functionalization::impl::sync(logabsdet); |
| 17275 | logabsdet_ = at::functionalization::impl::from_functional_tensor(logabsdet); |
| 17276 | } else { |
| 17277 | logabsdet_ = logabsdet; |
| 17278 | } |
| 17279 | |
| 17280 | at::Tensor LU_; |
| 17281 | if (at::functionalization::impl::isFunctionalTensor(LU)) { |
| 17282 | at::functionalization::impl::sync(LU); |
| 17283 | LU_ = at::functionalization::impl::from_functional_tensor(LU); |
| 17284 | } else { |
| 17285 | LU_ = LU; |
| 17286 | } |
| 17287 | |
| 17288 | at::Tensor pivots_; |
| 17289 | if (at::functionalization::impl::isFunctionalTensor(pivots)) { |
| 17290 | at::functionalization::impl::sync(pivots); |
| 17291 | pivots_ = at::functionalization::impl::from_functional_tensor(pivots); |
| 17292 | } else { |
| 17293 | pivots_ = pivots; |
| 17294 | } |
| 17295 | if (!(true && at::functionalization::impl::isFunctionalTensor(sign) && at::functionalization::impl::isFunctionalTensor(logabsdet) && at::functionalization::impl::isFunctionalTensor(LU) && at::functionalization::impl::isFunctionalTensor(pivots))) { |
| 17296 | if ((false || at::functionalization::impl::isFunctionalTensor(A))) { |
| 17297 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17298 | TORCH_INTERNAL_ASSERT(false, |
| 17299 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17300 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17301 | } else { |
| 17302 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17303 | at::AutoDispatchSkipFunctionalize guard; |
| 17304 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::_linalg_slogdet_sign::call(A_, sign_, logabsdet_, LU_, pivots_); |
| 17305 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &>(sign, logabsdet, LU, pivots);; |
| 17306 | } |
| 17307 | } else { |
| 17308 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 17309 | { |
| 17310 | at::AutoDispatchSkipFunctionalize guard; |
| 17311 | tmp_output = at::_ops::_linalg_slogdet::call(A_); |
| 17312 | } |
| 17313 | at::functionalization::impl::replace_(sign, std::get<0>(tmp_output)); |
| 17314 | at::functionalization::impl::commit_update(sign); |
| 17315 | at::functionalization::impl::sync(sign); |
| 17316 | at::functionalization::impl::replace_(logabsdet, std::get<1>(tmp_output)); |
| 17317 | at::functionalization::impl::commit_update(logabsdet); |
| 17318 | at::functionalization::impl::sync(logabsdet); |
| 17319 | at::functionalization::impl::replace_(LU, std::get<2>(tmp_output)); |
| 17320 | at::functionalization::impl::commit_update(LU); |
| 17321 | at::functionalization::impl::sync(LU); |
| 17322 | at::functionalization::impl::replace_(pivots, std::get<3>(tmp_output)); |
| 17323 | at::functionalization::impl::commit_update(pivots); |
| 17324 | at::functionalization::impl::sync(pivots); |
| 17325 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &>(sign, logabsdet, LU, pivots); |
| 17326 | } |
| 17327 | } |
| 17328 | |
| 17329 | at::Tensor & linalg_eigvals_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 17330 | if (false) { |
| 17331 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17332 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17333 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17334 | auto self_meta = to_meta(self); |
| 17335 | auto out_meta = to_meta(out); |
| 17336 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17337 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17338 | at::_ops::linalg_eigvals_out::call(self_meta, out_meta); |
| 17339 | } |
| 17340 | |
| 17341 | at::Tensor self_; |
| 17342 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17343 | at::functionalization::impl::sync(self); |
| 17344 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17345 | } else { |
| 17346 | self_ = self; |
| 17347 | } |
| 17348 | |
| 17349 | at::Tensor out_; |
| 17350 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17351 | at::functionalization::impl::sync(out); |
| 17352 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17353 | } else { |
| 17354 | out_ = out; |
| 17355 | } |
| 17356 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 17357 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 17358 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17359 | TORCH_INTERNAL_ASSERT(false, |
| 17360 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17361 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17362 | } else { |
| 17363 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17364 | at::AutoDispatchSkipFunctionalize guard; |
| 17365 | at::Tensor tmp_output = at::_ops::linalg_eigvals_out::call(self_, out_); |
| 17366 | return out;; |
| 17367 | } |
| 17368 | } else { |
| 17369 | at::Tensor tmp_output; |
| 17370 | { |
| 17371 | at::AutoDispatchSkipFunctionalize guard; |
| 17372 | tmp_output = at::_ops::linalg_eigvals::call(self_); |
| 17373 | } |
| 17374 | at::functionalization::impl::replace_(out, tmp_output); |
| 17375 | at::functionalization::impl::commit_update(out); |
| 17376 | at::functionalization::impl::sync(out); |
| 17377 | return out; |
| 17378 | } |
| 17379 | } |
| 17380 | |
| 17381 | ::std::tuple<at::Tensor &,at::Tensor &> linalg_eigh_out_eigvals(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::string_view UPLO, at::Tensor & eigvals, at::Tensor & eigvecs) { |
| 17382 | if (false) { |
| 17383 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17384 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17385 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17386 | auto self_meta = to_meta(self); |
| 17387 | auto eigvals_meta = to_meta(eigvals); |
| 17388 | auto eigvecs_meta = to_meta(eigvecs); |
| 17389 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17390 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17391 | at::_ops::linalg_eigh_eigvals::call(self_meta, UPLO, eigvals_meta, eigvecs_meta); |
| 17392 | } |
| 17393 | |
| 17394 | at::Tensor self_; |
| 17395 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17396 | at::functionalization::impl::sync(self); |
| 17397 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17398 | } else { |
| 17399 | self_ = self; |
| 17400 | } |
| 17401 | |
| 17402 | at::Tensor eigvals_; |
| 17403 | if (at::functionalization::impl::isFunctionalTensor(eigvals)) { |
| 17404 | at::functionalization::impl::sync(eigvals); |
| 17405 | eigvals_ = at::functionalization::impl::from_functional_tensor(eigvals); |
| 17406 | } else { |
| 17407 | eigvals_ = eigvals; |
| 17408 | } |
| 17409 | |
| 17410 | at::Tensor eigvecs_; |
| 17411 | if (at::functionalization::impl::isFunctionalTensor(eigvecs)) { |
| 17412 | at::functionalization::impl::sync(eigvecs); |
| 17413 | eigvecs_ = at::functionalization::impl::from_functional_tensor(eigvecs); |
| 17414 | } else { |
| 17415 | eigvecs_ = eigvecs; |
| 17416 | } |
| 17417 | if (!(true && at::functionalization::impl::isFunctionalTensor(eigvals) && at::functionalization::impl::isFunctionalTensor(eigvecs))) { |
| 17418 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 17419 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17420 | TORCH_INTERNAL_ASSERT(false, |
| 17421 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17422 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17423 | } else { |
| 17424 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17425 | at::AutoDispatchSkipFunctionalize guard; |
| 17426 | ::std::tuple<at::Tensor,at::Tensor> tmp_output = at::_ops::linalg_eigh_eigvals::call(self_, UPLO, eigvals_, eigvecs_); |
| 17427 | return ::std::tuple<at::Tensor &,at::Tensor &>(eigvals, eigvecs);; |
| 17428 | } |
| 17429 | } else { |
| 17430 | ::std::tuple<at::Tensor,at::Tensor> tmp_output; |
| 17431 | { |
| 17432 | at::AutoDispatchSkipFunctionalize guard; |
| 17433 | tmp_output = at::_ops::linalg_eigh::call(self_, UPLO); |
| 17434 | } |
| 17435 | at::functionalization::impl::replace_(eigvals, std::get<0>(tmp_output)); |
| 17436 | at::functionalization::impl::commit_update(eigvals); |
| 17437 | at::functionalization::impl::sync(eigvals); |
| 17438 | at::functionalization::impl::replace_(eigvecs, std::get<1>(tmp_output)); |
| 17439 | at::functionalization::impl::commit_update(eigvecs); |
| 17440 | at::functionalization::impl::sync(eigvecs); |
| 17441 | return ::std::tuple<at::Tensor &,at::Tensor &>(eigvals, eigvecs); |
| 17442 | } |
| 17443 | } |
| 17444 | |
| 17445 | at::Tensor & linalg_householder_product_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & tau, at::Tensor & out) { |
| 17446 | if (false) { |
| 17447 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17448 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17449 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17450 | auto input_meta = to_meta(input); |
| 17451 | auto tau_meta = to_meta(tau); |
| 17452 | auto out_meta = to_meta(out); |
| 17453 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17454 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17455 | at::_ops::linalg_householder_product_out::call(input_meta, tau_meta, out_meta); |
| 17456 | } |
| 17457 | |
| 17458 | at::Tensor input_; |
| 17459 | if (at::functionalization::impl::isFunctionalTensor(input)) { |
| 17460 | at::functionalization::impl::sync(input); |
| 17461 | input_ = at::functionalization::impl::from_functional_tensor(input); |
| 17462 | } else { |
| 17463 | input_ = input; |
| 17464 | } |
| 17465 | |
| 17466 | at::Tensor tau_; |
| 17467 | if (at::functionalization::impl::isFunctionalTensor(tau)) { |
| 17468 | at::functionalization::impl::sync(tau); |
| 17469 | tau_ = at::functionalization::impl::from_functional_tensor(tau); |
| 17470 | } else { |
| 17471 | tau_ = tau; |
| 17472 | } |
| 17473 | |
| 17474 | at::Tensor out_; |
| 17475 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17476 | at::functionalization::impl::sync(out); |
| 17477 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17478 | } else { |
| 17479 | out_ = out; |
| 17480 | } |
| 17481 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 17482 | if ((false || at::functionalization::impl::isFunctionalTensor(input) || at::functionalization::impl::isFunctionalTensor(tau))) { |
| 17483 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17484 | TORCH_INTERNAL_ASSERT(false, |
| 17485 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17486 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17487 | } else { |
| 17488 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17489 | at::AutoDispatchSkipFunctionalize guard; |
| 17490 | at::Tensor tmp_output = at::_ops::linalg_householder_product_out::call(input_, tau_, out_); |
| 17491 | return out;; |
| 17492 | } |
| 17493 | } else { |
| 17494 | at::Tensor tmp_output; |
| 17495 | { |
| 17496 | at::AutoDispatchSkipFunctionalize guard; |
| 17497 | tmp_output = at::_ops::linalg_householder_product::call(input_, tau_); |
| 17498 | } |
| 17499 | at::functionalization::impl::replace_(out, tmp_output); |
| 17500 | at::functionalization::impl::commit_update(out); |
| 17501 | at::functionalization::impl::sync(out); |
| 17502 | return out; |
| 17503 | } |
| 17504 | } |
| 17505 | |
| 17506 | at::Tensor & ger_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & vec2, at::Tensor & out) { |
| 17507 | if (false) { |
| 17508 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17509 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17510 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17511 | auto self_meta = to_meta(self); |
| 17512 | auto vec2_meta = to_meta(vec2); |
| 17513 | auto out_meta = to_meta(out); |
| 17514 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17515 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17516 | at::_ops::ger_out::call(self_meta, vec2_meta, out_meta); |
| 17517 | } |
| 17518 | |
| 17519 | at::Tensor self_; |
| 17520 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17521 | at::functionalization::impl::sync(self); |
| 17522 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17523 | } else { |
| 17524 | self_ = self; |
| 17525 | } |
| 17526 | |
| 17527 | at::Tensor vec2_; |
| 17528 | if (at::functionalization::impl::isFunctionalTensor(vec2)) { |
| 17529 | at::functionalization::impl::sync(vec2); |
| 17530 | vec2_ = at::functionalization::impl::from_functional_tensor(vec2); |
| 17531 | } else { |
| 17532 | vec2_ = vec2; |
| 17533 | } |
| 17534 | |
| 17535 | at::Tensor out_; |
| 17536 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17537 | at::functionalization::impl::sync(out); |
| 17538 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17539 | } else { |
| 17540 | out_ = out; |
| 17541 | } |
| 17542 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 17543 | if ((false || at::functionalization::impl::isFunctionalTensor(self) || at::functionalization::impl::isFunctionalTensor(vec2))) { |
| 17544 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17545 | TORCH_INTERNAL_ASSERT(false, |
| 17546 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17547 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17548 | } else { |
| 17549 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17550 | at::AutoDispatchSkipFunctionalize guard; |
| 17551 | at::Tensor tmp_output = at::_ops::ger_out::call(self_, vec2_, out_); |
| 17552 | return out;; |
| 17553 | } |
| 17554 | } else { |
| 17555 | at::Tensor tmp_output; |
| 17556 | { |
| 17557 | at::AutoDispatchSkipFunctionalize guard; |
| 17558 | tmp_output = at::_ops::ger::call(self_, vec2_); |
| 17559 | } |
| 17560 | at::functionalization::impl::replace_(out, tmp_output); |
| 17561 | at::functionalization::impl::commit_update(out); |
| 17562 | at::functionalization::impl::sync(out); |
| 17563 | return out; |
| 17564 | } |
| 17565 | } |
| 17566 | |
| 17567 | at::Tensor & linalg_norm_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional<at::Scalar> & ord, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 17568 | if (false) { |
| 17569 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17570 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17571 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17572 | auto self_meta = to_meta(self); |
| 17573 | auto out_meta = to_meta(out); |
| 17574 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17575 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17576 | at::_ops::linalg_norm_out::call(self_meta, ord, dim, keepdim, dtype, out_meta); |
| 17577 | } |
| 17578 | |
| 17579 | at::Tensor self_; |
| 17580 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17581 | at::functionalization::impl::sync(self); |
| 17582 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17583 | } else { |
| 17584 | self_ = self; |
| 17585 | } |
| 17586 | |
| 17587 | at::Tensor out_; |
| 17588 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17589 | at::functionalization::impl::sync(out); |
| 17590 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17591 | } else { |
| 17592 | out_ = out; |
| 17593 | } |
| 17594 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 17595 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 17596 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17597 | TORCH_INTERNAL_ASSERT(false, |
| 17598 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17599 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17600 | } else { |
| 17601 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17602 | at::AutoDispatchSkipFunctionalize guard; |
| 17603 | at::Tensor tmp_output = at::_ops::linalg_norm_out::call(self_, ord, dim, keepdim, dtype, out_); |
| 17604 | return out;; |
| 17605 | } |
| 17606 | } else { |
| 17607 | at::Tensor tmp_output; |
| 17608 | { |
| 17609 | at::AutoDispatchSkipFunctionalize guard; |
| 17610 | tmp_output = at::_ops::linalg_norm::call(self_, ord, dim, keepdim, dtype); |
| 17611 | } |
| 17612 | at::functionalization::impl::replace_(out, tmp_output); |
| 17613 | at::functionalization::impl::commit_update(out); |
| 17614 | at::functionalization::impl::sync(out); |
| 17615 | return out; |
| 17616 | } |
| 17617 | } |
| 17618 | |
| 17619 | at::Tensor & linalg_norm_out_ord_str_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::string_view ord, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 17620 | if (false) { |
| 17621 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17622 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17623 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17624 | auto self_meta = to_meta(self); |
| 17625 | auto out_meta = to_meta(out); |
| 17626 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17627 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17628 | at::_ops::linalg_norm_ord_str_out::call(self_meta, ord, dim, keepdim, dtype, out_meta); |
| 17629 | } |
| 17630 | |
| 17631 | at::Tensor self_; |
| 17632 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17633 | at::functionalization::impl::sync(self); |
| 17634 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17635 | } else { |
| 17636 | self_ = self; |
| 17637 | } |
| 17638 | |
| 17639 | at::Tensor out_; |
| 17640 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17641 | at::functionalization::impl::sync(out); |
| 17642 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17643 | } else { |
| 17644 | out_ = out; |
| 17645 | } |
| 17646 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 17647 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 17648 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17649 | TORCH_INTERNAL_ASSERT(false, |
| 17650 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17651 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17652 | } else { |
| 17653 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17654 | at::AutoDispatchSkipFunctionalize guard; |
| 17655 | at::Tensor tmp_output = at::_ops::linalg_norm_ord_str_out::call(self_, ord, dim, keepdim, dtype, out_); |
| 17656 | return out;; |
| 17657 | } |
| 17658 | } else { |
| 17659 | at::Tensor tmp_output; |
| 17660 | { |
| 17661 | at::AutoDispatchSkipFunctionalize guard; |
| 17662 | tmp_output = at::_ops::linalg_norm_ord_str::call(self_, ord, dim, keepdim, dtype); |
| 17663 | } |
| 17664 | at::functionalization::impl::replace_(out, tmp_output); |
| 17665 | at::functionalization::impl::commit_update(out); |
| 17666 | at::functionalization::impl::sync(out); |
| 17667 | return out; |
| 17668 | } |
| 17669 | } |
| 17670 | |
| 17671 | at::Tensor & linalg_vector_norm_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & ord, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 17672 | if (false) { |
| 17673 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17674 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17675 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17676 | auto self_meta = to_meta(self); |
| 17677 | auto out_meta = to_meta(out); |
| 17678 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17679 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17680 | at::_ops::linalg_vector_norm_out::call(self_meta, ord, dim, keepdim, dtype, out_meta); |
| 17681 | } |
| 17682 | |
| 17683 | at::Tensor self_; |
| 17684 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17685 | at::functionalization::impl::sync(self); |
| 17686 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 17687 | } else { |
| 17688 | self_ = self; |
| 17689 | } |
| 17690 | |
| 17691 | at::Tensor out_; |
| 17692 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17693 | at::functionalization::impl::sync(out); |
| 17694 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17695 | } else { |
| 17696 | out_ = out; |
| 17697 | } |
| 17698 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 17699 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 17700 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17701 | TORCH_INTERNAL_ASSERT(false, |
| 17702 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17703 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17704 | } else { |
| 17705 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17706 | at::AutoDispatchSkipFunctionalize guard; |
| 17707 | at::Tensor tmp_output = at::_ops::linalg_vector_norm_out::call(self_, ord, dim, keepdim, dtype, out_); |
| 17708 | return out;; |
| 17709 | } |
| 17710 | } else { |
| 17711 | at::Tensor tmp_output; |
| 17712 | { |
| 17713 | at::AutoDispatchSkipFunctionalize guard; |
| 17714 | tmp_output = at::_ops::linalg_vector_norm::call(self_, ord, dim, keepdim, dtype); |
| 17715 | } |
| 17716 | at::functionalization::impl::replace_(out, tmp_output); |
| 17717 | at::functionalization::impl::commit_update(out); |
| 17718 | at::functionalization::impl::sync(out); |
| 17719 | return out; |
| 17720 | } |
| 17721 | } |
| 17722 | |
| 17723 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _linalg_solve_ex_out_result(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, const at::Tensor & B, bool left, bool check_errors, at::Tensor & result, at::Tensor & LU, at::Tensor & pivots, at::Tensor & info) { |
| 17724 | if (false) { |
| 17725 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17726 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17727 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17728 | auto A_meta = to_meta(A); |
| 17729 | auto B_meta = to_meta(B); |
| 17730 | auto result_meta = to_meta(result); |
| 17731 | auto LU_meta = to_meta(LU); |
| 17732 | auto pivots_meta = to_meta(pivots); |
| 17733 | auto info_meta = to_meta(info); |
| 17734 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17735 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17736 | at::_ops::_linalg_solve_ex_result::call(A_meta, B_meta, left, check_errors, result_meta, LU_meta, pivots_meta, info_meta); |
| 17737 | } |
| 17738 | |
| 17739 | at::Tensor A_; |
| 17740 | if (at::functionalization::impl::isFunctionalTensor(A)) { |
| 17741 | at::functionalization::impl::sync(A); |
| 17742 | A_ = at::functionalization::impl::from_functional_tensor(A); |
| 17743 | } else { |
| 17744 | A_ = A; |
| 17745 | } |
| 17746 | |
| 17747 | at::Tensor B_; |
| 17748 | if (at::functionalization::impl::isFunctionalTensor(B)) { |
| 17749 | at::functionalization::impl::sync(B); |
| 17750 | B_ = at::functionalization::impl::from_functional_tensor(B); |
| 17751 | } else { |
| 17752 | B_ = B; |
| 17753 | } |
| 17754 | |
| 17755 | at::Tensor result_; |
| 17756 | if (at::functionalization::impl::isFunctionalTensor(result)) { |
| 17757 | at::functionalization::impl::sync(result); |
| 17758 | result_ = at::functionalization::impl::from_functional_tensor(result); |
| 17759 | } else { |
| 17760 | result_ = result; |
| 17761 | } |
| 17762 | |
| 17763 | at::Tensor LU_; |
| 17764 | if (at::functionalization::impl::isFunctionalTensor(LU)) { |
| 17765 | at::functionalization::impl::sync(LU); |
| 17766 | LU_ = at::functionalization::impl::from_functional_tensor(LU); |
| 17767 | } else { |
| 17768 | LU_ = LU; |
| 17769 | } |
| 17770 | |
| 17771 | at::Tensor pivots_; |
| 17772 | if (at::functionalization::impl::isFunctionalTensor(pivots)) { |
| 17773 | at::functionalization::impl::sync(pivots); |
| 17774 | pivots_ = at::functionalization::impl::from_functional_tensor(pivots); |
| 17775 | } else { |
| 17776 | pivots_ = pivots; |
| 17777 | } |
| 17778 | |
| 17779 | at::Tensor info_; |
| 17780 | if (at::functionalization::impl::isFunctionalTensor(info)) { |
| 17781 | at::functionalization::impl::sync(info); |
| 17782 | info_ = at::functionalization::impl::from_functional_tensor(info); |
| 17783 | } else { |
| 17784 | info_ = info; |
| 17785 | } |
| 17786 | if (!(true && at::functionalization::impl::isFunctionalTensor(result) && at::functionalization::impl::isFunctionalTensor(LU) && at::functionalization::impl::isFunctionalTensor(pivots) && at::functionalization::impl::isFunctionalTensor(info))) { |
| 17787 | if ((false || at::functionalization::impl::isFunctionalTensor(A) || at::functionalization::impl::isFunctionalTensor(B))) { |
| 17788 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17789 | TORCH_INTERNAL_ASSERT(false, |
| 17790 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17791 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17792 | } else { |
| 17793 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17794 | at::AutoDispatchSkipFunctionalize guard; |
| 17795 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> tmp_output = at::_ops::_linalg_solve_ex_result::call(A_, B_, left, check_errors, result_, LU_, pivots_, info_); |
| 17796 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &>(result, LU, pivots, info);; |
| 17797 | } |
| 17798 | } else { |
| 17799 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> tmp_output; |
| 17800 | { |
| 17801 | at::AutoDispatchSkipFunctionalize guard; |
| 17802 | tmp_output = at::_ops::_linalg_solve_ex::call(A_, B_, left, check_errors); |
| 17803 | } |
| 17804 | at::functionalization::impl::replace_(result, std::get<0>(tmp_output)); |
| 17805 | at::functionalization::impl::commit_update(result); |
| 17806 | at::functionalization::impl::sync(result); |
| 17807 | at::functionalization::impl::replace_(LU, std::get<1>(tmp_output)); |
| 17808 | at::functionalization::impl::commit_update(LU); |
| 17809 | at::functionalization::impl::sync(LU); |
| 17810 | at::functionalization::impl::replace_(pivots, std::get<2>(tmp_output)); |
| 17811 | at::functionalization::impl::commit_update(pivots); |
| 17812 | at::functionalization::impl::sync(pivots); |
| 17813 | at::functionalization::impl::replace_(info, std::get<3>(tmp_output)); |
| 17814 | at::functionalization::impl::commit_update(info); |
| 17815 | at::functionalization::impl::sync(info); |
| 17816 | return ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &>(result, LU, pivots, info); |
| 17817 | } |
| 17818 | } |
| 17819 | |
| 17820 | at::Tensor & linalg_solve_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, const at::Tensor & B, bool left, at::Tensor & out) { |
| 17821 | if (false) { |
| 17822 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17823 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17824 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17825 | auto A_meta = to_meta(A); |
| 17826 | auto B_meta = to_meta(B); |
| 17827 | auto out_meta = to_meta(out); |
| 17828 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17829 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17830 | at::_ops::linalg_solve_out::call(A_meta, B_meta, left, out_meta); |
| 17831 | } |
| 17832 | |
| 17833 | at::Tensor A_; |
| 17834 | if (at::functionalization::impl::isFunctionalTensor(A)) { |
| 17835 | at::functionalization::impl::sync(A); |
| 17836 | A_ = at::functionalization::impl::from_functional_tensor(A); |
| 17837 | } else { |
| 17838 | A_ = A; |
| 17839 | } |
| 17840 | |
| 17841 | at::Tensor B_; |
| 17842 | if (at::functionalization::impl::isFunctionalTensor(B)) { |
| 17843 | at::functionalization::impl::sync(B); |
| 17844 | B_ = at::functionalization::impl::from_functional_tensor(B); |
| 17845 | } else { |
| 17846 | B_ = B; |
| 17847 | } |
| 17848 | |
| 17849 | at::Tensor out_; |
| 17850 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17851 | at::functionalization::impl::sync(out); |
| 17852 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17853 | } else { |
| 17854 | out_ = out; |
| 17855 | } |
| 17856 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 17857 | if ((false || at::functionalization::impl::isFunctionalTensor(A) || at::functionalization::impl::isFunctionalTensor(B))) { |
| 17858 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17859 | TORCH_INTERNAL_ASSERT(false, |
| 17860 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17861 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17862 | } else { |
| 17863 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17864 | at::AutoDispatchSkipFunctionalize guard; |
| 17865 | at::Tensor tmp_output = at::_ops::linalg_solve_out::call(A_, B_, left, out_); |
| 17866 | return out;; |
| 17867 | } |
| 17868 | } else { |
| 17869 | at::Tensor tmp_output; |
| 17870 | { |
| 17871 | at::AutoDispatchSkipFunctionalize guard; |
| 17872 | tmp_output = at::_ops::linalg_solve::call(A_, B_, left); |
| 17873 | } |
| 17874 | at::functionalization::impl::replace_(out, tmp_output); |
| 17875 | at::functionalization::impl::commit_update(out); |
| 17876 | at::functionalization::impl::sync(out); |
| 17877 | return out; |
| 17878 | } |
| 17879 | } |
| 17880 | |
| 17881 | at::Tensor & linalg_multi_dot_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Tensor & out) { |
| 17882 | if (false) { |
| 17883 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17884 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17885 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17886 | auto tensors_meta = to_meta(tensors); |
| 17887 | auto out_meta = to_meta(out); |
| 17888 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17889 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17890 | at::_ops::linalg_multi_dot_out::call(tensors_meta, out_meta); |
| 17891 | } |
| 17892 | |
| 17893 | ::std::vector<at::Tensor> tensors_; |
| 17894 | if (at::functionalization::impl::isFunctionalTensor(tensors)) { |
| 17895 | at::functionalization::impl::sync(tensors); |
| 17896 | tensors_ = at::functionalization::impl::from_functional_tensor(tensors); |
| 17897 | } else { |
| 17898 | tensors_ = tensors.vec(); |
| 17899 | } |
| 17900 | |
| 17901 | at::Tensor out_; |
| 17902 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17903 | at::functionalization::impl::sync(out); |
| 17904 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17905 | } else { |
| 17906 | out_ = out; |
| 17907 | } |
| 17908 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 17909 | if ((false || at::functionalization::impl::isFunctionalTensor(tensors))) { |
| 17910 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17911 | TORCH_INTERNAL_ASSERT(false, |
| 17912 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17913 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17914 | } else { |
| 17915 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17916 | at::AutoDispatchSkipFunctionalize guard; |
| 17917 | at::Tensor tmp_output = at::_ops::linalg_multi_dot_out::call(tensors_, out_); |
| 17918 | return out;; |
| 17919 | } |
| 17920 | } else { |
| 17921 | at::Tensor tmp_output; |
| 17922 | { |
| 17923 | at::AutoDispatchSkipFunctionalize guard; |
| 17924 | tmp_output = at::_ops::linalg_multi_dot::call(tensors_); |
| 17925 | } |
| 17926 | at::functionalization::impl::replace_(out, tmp_output); |
| 17927 | at::functionalization::impl::commit_update(out); |
| 17928 | at::functionalization::impl::sync(out); |
| 17929 | return out; |
| 17930 | } |
| 17931 | } |
| 17932 | |
| 17933 | at::Tensor & _test_optional_filled_intlist_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & values, at::OptionalIntArrayRef addends, at::Tensor & out) { |
| 17934 | if (false) { |
| 17935 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17936 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17937 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17938 | auto values_meta = to_meta(values); |
| 17939 | auto out_meta = to_meta(out); |
| 17940 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17941 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17942 | at::_ops::_test_optional_filled_intlist_out::call(values_meta, addends, out_meta); |
| 17943 | } |
| 17944 | |
| 17945 | at::Tensor values_; |
| 17946 | if (at::functionalization::impl::isFunctionalTensor(values)) { |
| 17947 | at::functionalization::impl::sync(values); |
| 17948 | values_ = at::functionalization::impl::from_functional_tensor(values); |
| 17949 | } else { |
| 17950 | values_ = values; |
| 17951 | } |
| 17952 | |
| 17953 | at::Tensor out_; |
| 17954 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 17955 | at::functionalization::impl::sync(out); |
| 17956 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 17957 | } else { |
| 17958 | out_ = out; |
| 17959 | } |
| 17960 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 17961 | if ((false || at::functionalization::impl::isFunctionalTensor(values))) { |
| 17962 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 17963 | TORCH_INTERNAL_ASSERT(false, |
| 17964 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 17965 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 17966 | } else { |
| 17967 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 17968 | at::AutoDispatchSkipFunctionalize guard; |
| 17969 | at::Tensor tmp_output = at::_ops::_test_optional_filled_intlist_out::call(values_, addends, out_); |
| 17970 | return out;; |
| 17971 | } |
| 17972 | } else { |
| 17973 | at::Tensor tmp_output; |
| 17974 | { |
| 17975 | at::AutoDispatchSkipFunctionalize guard; |
| 17976 | tmp_output = at::_ops::_test_optional_filled_intlist::call(values_, addends); |
| 17977 | } |
| 17978 | at::functionalization::impl::replace_(out, tmp_output); |
| 17979 | at::functionalization::impl::commit_update(out); |
| 17980 | at::functionalization::impl::sync(out); |
| 17981 | return out; |
| 17982 | } |
| 17983 | } |
| 17984 | |
| 17985 | at::Tensor & _test_autograd_multiple_dispatch_out_fullcoverage_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 17986 | if (false) { |
| 17987 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 17988 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 17989 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 17990 | auto self_meta = to_meta(self); |
| 17991 | auto out_meta = to_meta(out); |
| 17992 | at::AutoDispatchSkipFunctionalize func_guard; |
| 17993 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 17994 | at::_ops::_test_autograd_multiple_dispatch_fullcoverage_out::call(self_meta, out_meta); |
| 17995 | } |
| 17996 | |
| 17997 | at::Tensor self_; |
| 17998 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 17999 | at::functionalization::impl::sync(self); |
| 18000 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18001 | } else { |
| 18002 | self_ = self; |
| 18003 | } |
| 18004 | |
| 18005 | at::Tensor out_; |
| 18006 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18007 | at::functionalization::impl::sync(out); |
| 18008 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18009 | } else { |
| 18010 | out_ = out; |
| 18011 | } |
| 18012 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18013 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18014 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18015 | TORCH_INTERNAL_ASSERT(false, |
| 18016 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18017 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18018 | } else { |
| 18019 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18020 | at::AutoDispatchSkipFunctionalize guard; |
| 18021 | at::Tensor tmp_output = at::_ops::_test_autograd_multiple_dispatch_fullcoverage_out::call(self_, out_); |
| 18022 | return out;; |
| 18023 | } |
| 18024 | } else { |
| 18025 | at::Tensor tmp_output; |
| 18026 | { |
| 18027 | at::AutoDispatchSkipFunctionalize guard; |
| 18028 | tmp_output = at::_ops::_test_autograd_multiple_dispatch_fullcoverage::call(self_); |
| 18029 | } |
| 18030 | at::functionalization::impl::replace_(out, tmp_output); |
| 18031 | at::functionalization::impl::commit_update(out); |
| 18032 | at::functionalization::impl::sync(out); |
| 18033 | return out; |
| 18034 | } |
| 18035 | } |
| 18036 | |
| 18037 | at::Tensor & _test_autograd_multiple_dispatch_view_copy_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 18038 | if (false) { |
| 18039 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18040 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18041 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18042 | auto self_meta = to_meta(self); |
| 18043 | auto out_meta = to_meta(out); |
| 18044 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18045 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18046 | at::_ops::_test_autograd_multiple_dispatch_view_copy_out::call(self_meta, out_meta); |
| 18047 | } |
| 18048 | |
| 18049 | at::Tensor self_; |
| 18050 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18051 | at::functionalization::impl::sync(self); |
| 18052 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18053 | } else { |
| 18054 | self_ = self; |
| 18055 | } |
| 18056 | |
| 18057 | at::Tensor out_; |
| 18058 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18059 | at::functionalization::impl::sync(out); |
| 18060 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18061 | } else { |
| 18062 | out_ = out; |
| 18063 | } |
| 18064 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18065 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18066 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18067 | TORCH_INTERNAL_ASSERT(false, |
| 18068 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18069 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18070 | } else { |
| 18071 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18072 | at::AutoDispatchSkipFunctionalize guard; |
| 18073 | at::Tensor tmp_output = at::_ops::_test_autograd_multiple_dispatch_view_copy_out::call(self_, out_); |
| 18074 | return out;; |
| 18075 | } |
| 18076 | } else { |
| 18077 | at::Tensor tmp_output; |
| 18078 | { |
| 18079 | at::AutoDispatchSkipFunctionalize guard; |
| 18080 | tmp_output = at::_ops::_test_autograd_multiple_dispatch_view_copy::call(self_); |
| 18081 | } |
| 18082 | at::functionalization::impl::replace_(out, tmp_output); |
| 18083 | at::functionalization::impl::commit_update(out); |
| 18084 | at::functionalization::impl::sync(out); |
| 18085 | return out; |
| 18086 | } |
| 18087 | } |
| 18088 | |
| 18089 | at::Tensor & segment_reduce_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & data, c10::string_view reduce, const c10::optional<at::Tensor> & lengths, const c10::optional<at::Tensor> & indices, const c10::optional<at::Tensor> & offsets, int64_t axis, bool unsafe, const c10::optional<at::Scalar> & initial, at::Tensor & out) { |
| 18090 | if (false) { |
| 18091 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18092 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18093 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18094 | auto data_meta = to_meta(data); |
| 18095 | auto lengths_meta = to_meta(lengths); |
| 18096 | auto indices_meta = to_meta(indices); |
| 18097 | auto offsets_meta = to_meta(offsets); |
| 18098 | auto out_meta = to_meta(out); |
| 18099 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18100 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18101 | at::_ops::segment_reduce_out::call(data_meta, reduce, lengths_meta, indices_meta, offsets_meta, axis, unsafe, initial, out_meta); |
| 18102 | } |
| 18103 | |
| 18104 | at::Tensor data_; |
| 18105 | if (at::functionalization::impl::isFunctionalTensor(data)) { |
| 18106 | at::functionalization::impl::sync(data); |
| 18107 | data_ = at::functionalization::impl::from_functional_tensor(data); |
| 18108 | } else { |
| 18109 | data_ = data; |
| 18110 | } |
| 18111 | |
| 18112 | c10::optional<at::Tensor> lengths_; |
| 18113 | if (at::functionalization::impl::isFunctionalTensor(lengths)) { |
| 18114 | at::functionalization::impl::sync(lengths); |
| 18115 | lengths_ = at::functionalization::impl::from_functional_tensor(lengths); |
| 18116 | } else { |
| 18117 | lengths_ = lengths; |
| 18118 | } |
| 18119 | |
| 18120 | c10::optional<at::Tensor> indices_; |
| 18121 | if (at::functionalization::impl::isFunctionalTensor(indices)) { |
| 18122 | at::functionalization::impl::sync(indices); |
| 18123 | indices_ = at::functionalization::impl::from_functional_tensor(indices); |
| 18124 | } else { |
| 18125 | indices_ = indices; |
| 18126 | } |
| 18127 | |
| 18128 | c10::optional<at::Tensor> offsets_; |
| 18129 | if (at::functionalization::impl::isFunctionalTensor(offsets)) { |
| 18130 | at::functionalization::impl::sync(offsets); |
| 18131 | offsets_ = at::functionalization::impl::from_functional_tensor(offsets); |
| 18132 | } else { |
| 18133 | offsets_ = offsets; |
| 18134 | } |
| 18135 | |
| 18136 | at::Tensor out_; |
| 18137 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18138 | at::functionalization::impl::sync(out); |
| 18139 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18140 | } else { |
| 18141 | out_ = out; |
| 18142 | } |
| 18143 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18144 | if ((false || at::functionalization::impl::isFunctionalTensor(data) || at::functionalization::impl::isFunctionalTensor(lengths) || at::functionalization::impl::isFunctionalTensor(indices) || at::functionalization::impl::isFunctionalTensor(offsets))) { |
| 18145 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18146 | TORCH_INTERNAL_ASSERT(false, |
| 18147 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18148 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18149 | } else { |
| 18150 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18151 | at::AutoDispatchSkipFunctionalize guard; |
| 18152 | at::Tensor tmp_output = at::_ops::segment_reduce_out::call(data_, reduce, lengths_, indices_, offsets_, axis, unsafe, initial, out_); |
| 18153 | return out;; |
| 18154 | } |
| 18155 | } else { |
| 18156 | at::Tensor tmp_output; |
| 18157 | { |
| 18158 | at::AutoDispatchSkipFunctionalize guard; |
| 18159 | tmp_output = at::_ops::segment_reduce::call(data_, reduce, lengths_, indices_, offsets_, axis, unsafe, initial); |
| 18160 | } |
| 18161 | at::functionalization::impl::replace_(out, tmp_output); |
| 18162 | at::functionalization::impl::commit_update(out); |
| 18163 | at::functionalization::impl::sync(out); |
| 18164 | return out; |
| 18165 | } |
| 18166 | } |
| 18167 | |
| 18168 | at::Tensor & _nested_tensor_from_tensor_list_out_out(c10::DispatchKeySet dispatchKeySet, at::TensorList list, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory, at::Tensor & out) { |
| 18169 | if (false) { |
| 18170 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18171 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18172 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18173 | auto list_meta = to_meta(list); |
| 18174 | auto out_meta = to_meta(out); |
| 18175 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18176 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18177 | at::_ops::_nested_tensor_from_tensor_list_out::call(list_meta, dtype, layout, device, pin_memory, out_meta); |
| 18178 | } |
| 18179 | |
| 18180 | ::std::vector<at::Tensor> list_; |
| 18181 | if (at::functionalization::impl::isFunctionalTensor(list)) { |
| 18182 | at::functionalization::impl::sync(list); |
| 18183 | list_ = at::functionalization::impl::from_functional_tensor(list); |
| 18184 | } else { |
| 18185 | list_ = list.vec(); |
| 18186 | } |
| 18187 | |
| 18188 | at::Tensor out_; |
| 18189 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18190 | at::functionalization::impl::sync(out); |
| 18191 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18192 | } else { |
| 18193 | out_ = out; |
| 18194 | } |
| 18195 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18196 | if ((false || at::functionalization::impl::isFunctionalTensor(list))) { |
| 18197 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18198 | TORCH_INTERNAL_ASSERT(false, |
| 18199 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18200 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18201 | } else { |
| 18202 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18203 | at::AutoDispatchSkipFunctionalize guard; |
| 18204 | at::Tensor tmp_output = at::_ops::_nested_tensor_from_tensor_list_out::call(list_, dtype, layout, device, pin_memory, out_); |
| 18205 | return out;; |
| 18206 | } |
| 18207 | } else { |
| 18208 | at::Tensor tmp_output; |
| 18209 | { |
| 18210 | at::AutoDispatchSkipFunctionalize guard; |
| 18211 | tmp_output = at::_ops::_nested_tensor_from_tensor_list::call(list_, dtype, layout, device, pin_memory); |
| 18212 | } |
| 18213 | at::functionalization::impl::replace_(out, tmp_output); |
| 18214 | at::functionalization::impl::commit_update(out); |
| 18215 | at::functionalization::impl::sync(out); |
| 18216 | return out; |
| 18217 | } |
| 18218 | } |
| 18219 | |
| 18220 | at::Tensor & diagonal_copy_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2, at::Tensor & out) { |
| 18221 | if (false) { |
| 18222 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18223 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18224 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18225 | auto self_meta = to_meta(self); |
| 18226 | auto out_meta = to_meta(out); |
| 18227 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18228 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18229 | at::_ops::diagonal_copy_out::call(self_meta, offset, dim1, dim2, out_meta); |
| 18230 | } |
| 18231 | |
| 18232 | at::Tensor self_; |
| 18233 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18234 | at::functionalization::impl::sync(self); |
| 18235 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18236 | } else { |
| 18237 | self_ = self; |
| 18238 | } |
| 18239 | |
| 18240 | at::Tensor out_; |
| 18241 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18242 | at::functionalization::impl::sync(out); |
| 18243 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18244 | } else { |
| 18245 | out_ = out; |
| 18246 | } |
| 18247 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18248 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18249 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18250 | TORCH_INTERNAL_ASSERT(false, |
| 18251 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18252 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18253 | } else { |
| 18254 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18255 | at::AutoDispatchSkipFunctionalize guard; |
| 18256 | at::Tensor tmp_output = at::_ops::diagonal_copy_out::call(self_, offset, dim1, dim2, out_); |
| 18257 | return out;; |
| 18258 | } |
| 18259 | } else { |
| 18260 | at::Tensor tmp_output; |
| 18261 | { |
| 18262 | at::AutoDispatchSkipFunctionalize guard; |
| 18263 | tmp_output = at::_ops::diagonal_copy::call(self_, offset, dim1, dim2); |
| 18264 | } |
| 18265 | at::functionalization::impl::replace_(out, tmp_output); |
| 18266 | at::functionalization::impl::commit_update(out); |
| 18267 | at::functionalization::impl::sync(out); |
| 18268 | return out; |
| 18269 | } |
| 18270 | } |
| 18271 | |
| 18272 | at::Tensor & detach_copy_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 18273 | if (false) { |
| 18274 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18275 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18276 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18277 | auto self_meta = to_meta(self); |
| 18278 | auto out_meta = to_meta(out); |
| 18279 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18280 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18281 | at::_ops::detach_copy_out::call(self_meta, out_meta); |
| 18282 | } |
| 18283 | |
| 18284 | at::Tensor self_; |
| 18285 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18286 | at::functionalization::impl::sync(self); |
| 18287 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18288 | } else { |
| 18289 | self_ = self; |
| 18290 | } |
| 18291 | |
| 18292 | at::Tensor out_; |
| 18293 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18294 | at::functionalization::impl::sync(out); |
| 18295 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18296 | } else { |
| 18297 | out_ = out; |
| 18298 | } |
| 18299 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18300 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18301 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18302 | TORCH_INTERNAL_ASSERT(false, |
| 18303 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18304 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18305 | } else { |
| 18306 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18307 | at::AutoDispatchSkipFunctionalize guard; |
| 18308 | at::Tensor tmp_output = at::_ops::detach_copy_out::call(self_, out_); |
| 18309 | return out;; |
| 18310 | } |
| 18311 | } else { |
| 18312 | at::Tensor tmp_output; |
| 18313 | { |
| 18314 | at::AutoDispatchSkipFunctionalize guard; |
| 18315 | tmp_output = at::_ops::detach_copy::call(self_); |
| 18316 | } |
| 18317 | at::functionalization::impl::replace_(out, tmp_output); |
| 18318 | at::functionalization::impl::commit_update(out); |
| 18319 | at::functionalization::impl::sync(out); |
| 18320 | return out; |
| 18321 | } |
| 18322 | } |
| 18323 | |
| 18324 | at::Tensor & slice_copy_out_Tensor_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::optional<c10::SymInt> start, c10::optional<c10::SymInt> end, c10::SymInt step, at::Tensor & out) { |
| 18325 | if (false) { |
| 18326 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18327 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18328 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18329 | auto self_meta = to_meta(self); |
| 18330 | auto out_meta = to_meta(out); |
| 18331 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18332 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18333 | at::_ops::slice_copy_Tensor_out::call(self_meta, dim, start, end, step, out_meta); |
| 18334 | } |
| 18335 | |
| 18336 | at::Tensor self_; |
| 18337 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18338 | at::functionalization::impl::sync(self); |
| 18339 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18340 | } else { |
| 18341 | self_ = self; |
| 18342 | } |
| 18343 | |
| 18344 | at::Tensor out_; |
| 18345 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18346 | at::functionalization::impl::sync(out); |
| 18347 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18348 | } else { |
| 18349 | out_ = out; |
| 18350 | } |
| 18351 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18352 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18353 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18354 | TORCH_INTERNAL_ASSERT(false, |
| 18355 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18356 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18357 | } else { |
| 18358 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18359 | at::AutoDispatchSkipFunctionalize guard; |
| 18360 | at::Tensor tmp_output = at::_ops::slice_copy_Tensor_out::call(self_, dim, start, end, step, out_); |
| 18361 | return out;; |
| 18362 | } |
| 18363 | } else { |
| 18364 | at::Tensor tmp_output; |
| 18365 | { |
| 18366 | at::AutoDispatchSkipFunctionalize guard; |
| 18367 | tmp_output = at::_ops::slice_copy_Tensor::call(self_, dim, start, end, step); |
| 18368 | } |
| 18369 | at::functionalization::impl::replace_(out, tmp_output); |
| 18370 | at::functionalization::impl::commit_update(out); |
| 18371 | at::functionalization::impl::sync(out); |
| 18372 | return out; |
| 18373 | } |
| 18374 | } |
| 18375 | |
| 18376 | at::Tensor & transpose_copy_out_int_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim0, int64_t dim1, at::Tensor & out) { |
| 18377 | if (false) { |
| 18378 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18379 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18380 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18381 | auto self_meta = to_meta(self); |
| 18382 | auto out_meta = to_meta(out); |
| 18383 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18384 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18385 | at::_ops::transpose_copy_int_out::call(self_meta, dim0, dim1, out_meta); |
| 18386 | } |
| 18387 | |
| 18388 | at::Tensor self_; |
| 18389 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18390 | at::functionalization::impl::sync(self); |
| 18391 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18392 | } else { |
| 18393 | self_ = self; |
| 18394 | } |
| 18395 | |
| 18396 | at::Tensor out_; |
| 18397 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18398 | at::functionalization::impl::sync(out); |
| 18399 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18400 | } else { |
| 18401 | out_ = out; |
| 18402 | } |
| 18403 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18404 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18405 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18406 | TORCH_INTERNAL_ASSERT(false, |
| 18407 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18408 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18409 | } else { |
| 18410 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18411 | at::AutoDispatchSkipFunctionalize guard; |
| 18412 | at::Tensor tmp_output = at::_ops::transpose_copy_int_out::call(self_, dim0, dim1, out_); |
| 18413 | return out;; |
| 18414 | } |
| 18415 | } else { |
| 18416 | at::Tensor tmp_output; |
| 18417 | { |
| 18418 | at::AutoDispatchSkipFunctionalize guard; |
| 18419 | tmp_output = at::_ops::transpose_copy_int::call(self_, dim0, dim1); |
| 18420 | } |
| 18421 | at::functionalization::impl::replace_(out, tmp_output); |
| 18422 | at::functionalization::impl::commit_update(out); |
| 18423 | at::functionalization::impl::sync(out); |
| 18424 | return out; |
| 18425 | } |
| 18426 | } |
| 18427 | |
| 18428 | at::Tensor & indices_copy_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 18429 | if (false) { |
| 18430 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18431 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18432 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18433 | auto self_meta = to_meta(self); |
| 18434 | auto out_meta = to_meta(out); |
| 18435 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18436 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18437 | at::_ops::indices_copy_out::call(self_meta, out_meta); |
| 18438 | } |
| 18439 | |
| 18440 | at::Tensor self_; |
| 18441 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18442 | at::functionalization::impl::sync(self); |
| 18443 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18444 | } else { |
| 18445 | self_ = self; |
| 18446 | } |
| 18447 | |
| 18448 | at::Tensor out_; |
| 18449 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18450 | at::functionalization::impl::sync(out); |
| 18451 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18452 | } else { |
| 18453 | out_ = out; |
| 18454 | } |
| 18455 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18456 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18457 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18458 | TORCH_INTERNAL_ASSERT(false, |
| 18459 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18460 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18461 | } else { |
| 18462 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18463 | at::AutoDispatchSkipFunctionalize guard; |
| 18464 | at::Tensor tmp_output = at::_ops::indices_copy_out::call(self_, out_); |
| 18465 | return out;; |
| 18466 | } |
| 18467 | } else { |
| 18468 | at::Tensor tmp_output; |
| 18469 | { |
| 18470 | at::AutoDispatchSkipFunctionalize guard; |
| 18471 | tmp_output = at::_ops::indices_copy::call(self_); |
| 18472 | } |
| 18473 | at::functionalization::impl::replace_(out, tmp_output); |
| 18474 | at::functionalization::impl::commit_update(out); |
| 18475 | at::functionalization::impl::sync(out); |
| 18476 | return out; |
| 18477 | } |
| 18478 | } |
| 18479 | |
| 18480 | at::Tensor & row_indices_copy_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 18481 | if (false) { |
| 18482 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18483 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18484 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18485 | auto self_meta = to_meta(self); |
| 18486 | auto out_meta = to_meta(out); |
| 18487 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18488 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18489 | at::_ops::row_indices_copy_out::call(self_meta, out_meta); |
| 18490 | } |
| 18491 | |
| 18492 | at::Tensor self_; |
| 18493 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18494 | at::functionalization::impl::sync(self); |
| 18495 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18496 | } else { |
| 18497 | self_ = self; |
| 18498 | } |
| 18499 | |
| 18500 | at::Tensor out_; |
| 18501 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18502 | at::functionalization::impl::sync(out); |
| 18503 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18504 | } else { |
| 18505 | out_ = out; |
| 18506 | } |
| 18507 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18508 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18509 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18510 | TORCH_INTERNAL_ASSERT(false, |
| 18511 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18512 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18513 | } else { |
| 18514 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18515 | at::AutoDispatchSkipFunctionalize guard; |
| 18516 | at::Tensor tmp_output = at::_ops::row_indices_copy_out::call(self_, out_); |
| 18517 | return out;; |
| 18518 | } |
| 18519 | } else { |
| 18520 | at::Tensor tmp_output; |
| 18521 | { |
| 18522 | at::AutoDispatchSkipFunctionalize guard; |
| 18523 | tmp_output = at::_ops::row_indices_copy::call(self_); |
| 18524 | } |
| 18525 | at::functionalization::impl::replace_(out, tmp_output); |
| 18526 | at::functionalization::impl::commit_update(out); |
| 18527 | at::functionalization::impl::sync(out); |
| 18528 | return out; |
| 18529 | } |
| 18530 | } |
| 18531 | |
| 18532 | at::Tensor & _triton_multi_head_attention_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const c10::optional<at::Tensor> & mask, at::Tensor & out) { |
| 18533 | if (false) { |
| 18534 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18535 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18536 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18537 | auto query_meta = to_meta(query); |
| 18538 | auto key_meta = to_meta(key); |
| 18539 | auto value_meta = to_meta(value); |
| 18540 | auto qkv_weight_meta = to_meta(qkv_weight); |
| 18541 | auto qkv_bias_meta = to_meta(qkv_bias); |
| 18542 | auto proj_weight_meta = to_meta(proj_weight); |
| 18543 | auto proj_bias_meta = to_meta(proj_bias); |
| 18544 | auto mask_meta = to_meta(mask); |
| 18545 | auto out_meta = to_meta(out); |
| 18546 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18547 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18548 | at::_ops::_triton_multi_head_attention_out::call(query_meta, key_meta, value_meta, embed_dim, num_head, qkv_weight_meta, qkv_bias_meta, proj_weight_meta, proj_bias_meta, mask_meta, out_meta); |
| 18549 | } |
| 18550 | |
| 18551 | at::Tensor query_; |
| 18552 | if (at::functionalization::impl::isFunctionalTensor(query)) { |
| 18553 | at::functionalization::impl::sync(query); |
| 18554 | query_ = at::functionalization::impl::from_functional_tensor(query); |
| 18555 | } else { |
| 18556 | query_ = query; |
| 18557 | } |
| 18558 | |
| 18559 | at::Tensor key_; |
| 18560 | if (at::functionalization::impl::isFunctionalTensor(key)) { |
| 18561 | at::functionalization::impl::sync(key); |
| 18562 | key_ = at::functionalization::impl::from_functional_tensor(key); |
| 18563 | } else { |
| 18564 | key_ = key; |
| 18565 | } |
| 18566 | |
| 18567 | at::Tensor value_; |
| 18568 | if (at::functionalization::impl::isFunctionalTensor(value)) { |
| 18569 | at::functionalization::impl::sync(value); |
| 18570 | value_ = at::functionalization::impl::from_functional_tensor(value); |
| 18571 | } else { |
| 18572 | value_ = value; |
| 18573 | } |
| 18574 | |
| 18575 | at::Tensor qkv_weight_; |
| 18576 | if (at::functionalization::impl::isFunctionalTensor(qkv_weight)) { |
| 18577 | at::functionalization::impl::sync(qkv_weight); |
| 18578 | qkv_weight_ = at::functionalization::impl::from_functional_tensor(qkv_weight); |
| 18579 | } else { |
| 18580 | qkv_weight_ = qkv_weight; |
| 18581 | } |
| 18582 | |
| 18583 | at::Tensor qkv_bias_; |
| 18584 | if (at::functionalization::impl::isFunctionalTensor(qkv_bias)) { |
| 18585 | at::functionalization::impl::sync(qkv_bias); |
| 18586 | qkv_bias_ = at::functionalization::impl::from_functional_tensor(qkv_bias); |
| 18587 | } else { |
| 18588 | qkv_bias_ = qkv_bias; |
| 18589 | } |
| 18590 | |
| 18591 | at::Tensor proj_weight_; |
| 18592 | if (at::functionalization::impl::isFunctionalTensor(proj_weight)) { |
| 18593 | at::functionalization::impl::sync(proj_weight); |
| 18594 | proj_weight_ = at::functionalization::impl::from_functional_tensor(proj_weight); |
| 18595 | } else { |
| 18596 | proj_weight_ = proj_weight; |
| 18597 | } |
| 18598 | |
| 18599 | at::Tensor proj_bias_; |
| 18600 | if (at::functionalization::impl::isFunctionalTensor(proj_bias)) { |
| 18601 | at::functionalization::impl::sync(proj_bias); |
| 18602 | proj_bias_ = at::functionalization::impl::from_functional_tensor(proj_bias); |
| 18603 | } else { |
| 18604 | proj_bias_ = proj_bias; |
| 18605 | } |
| 18606 | |
| 18607 | c10::optional<at::Tensor> mask_; |
| 18608 | if (at::functionalization::impl::isFunctionalTensor(mask)) { |
| 18609 | at::functionalization::impl::sync(mask); |
| 18610 | mask_ = at::functionalization::impl::from_functional_tensor(mask); |
| 18611 | } else { |
| 18612 | mask_ = mask; |
| 18613 | } |
| 18614 | |
| 18615 | at::Tensor out_; |
| 18616 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18617 | at::functionalization::impl::sync(out); |
| 18618 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18619 | } else { |
| 18620 | out_ = out; |
| 18621 | } |
| 18622 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18623 | if ((false || at::functionalization::impl::isFunctionalTensor(query) || at::functionalization::impl::isFunctionalTensor(key) || at::functionalization::impl::isFunctionalTensor(value) || at::functionalization::impl::isFunctionalTensor(qkv_weight) || at::functionalization::impl::isFunctionalTensor(qkv_bias) || at::functionalization::impl::isFunctionalTensor(proj_weight) || at::functionalization::impl::isFunctionalTensor(proj_bias) || at::functionalization::impl::isFunctionalTensor(mask))) { |
| 18624 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18625 | TORCH_INTERNAL_ASSERT(false, |
| 18626 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18627 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18628 | } else { |
| 18629 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18630 | at::AutoDispatchSkipFunctionalize guard; |
| 18631 | at::Tensor tmp_output = at::_ops::_triton_multi_head_attention_out::call(query_, key_, value_, embed_dim, num_head, qkv_weight_, qkv_bias_, proj_weight_, proj_bias_, mask_, out_); |
| 18632 | return out;; |
| 18633 | } |
| 18634 | } else { |
| 18635 | at::Tensor tmp_output; |
| 18636 | { |
| 18637 | at::AutoDispatchSkipFunctionalize guard; |
| 18638 | tmp_output = at::_ops::_triton_multi_head_attention::call(query_, key_, value_, embed_dim, num_head, qkv_weight_, qkv_bias_, proj_weight_, proj_bias_, mask_); |
| 18639 | } |
| 18640 | at::functionalization::impl::replace_(out, tmp_output); |
| 18641 | at::functionalization::impl::commit_update(out); |
| 18642 | at::functionalization::impl::sync(out); |
| 18643 | return out; |
| 18644 | } |
| 18645 | } |
| 18646 | |
| 18647 | at::Tensor & special_bessel_j1_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 18648 | if (false) { |
| 18649 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18650 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18651 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18652 | auto self_meta = to_meta(self); |
| 18653 | auto out_meta = to_meta(out); |
| 18654 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18655 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18656 | at::_ops::special_bessel_j1_out::call(self_meta, out_meta); |
| 18657 | } |
| 18658 | |
| 18659 | at::Tensor self_; |
| 18660 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18661 | at::functionalization::impl::sync(self); |
| 18662 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18663 | } else { |
| 18664 | self_ = self; |
| 18665 | } |
| 18666 | |
| 18667 | at::Tensor out_; |
| 18668 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18669 | at::functionalization::impl::sync(out); |
| 18670 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18671 | } else { |
| 18672 | out_ = out; |
| 18673 | } |
| 18674 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18675 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18676 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18677 | TORCH_INTERNAL_ASSERT(false, |
| 18678 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18679 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18680 | } else { |
| 18681 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18682 | at::AutoDispatchSkipFunctionalize guard; |
| 18683 | at::Tensor tmp_output = at::_ops::special_bessel_j1_out::call(self_, out_); |
| 18684 | return out;; |
| 18685 | } |
| 18686 | } else { |
| 18687 | at::Tensor tmp_output; |
| 18688 | { |
| 18689 | at::AutoDispatchSkipFunctionalize guard; |
| 18690 | tmp_output = at::_ops::special_bessel_j1::call(self_); |
| 18691 | } |
| 18692 | at::functionalization::impl::replace_(out, tmp_output); |
| 18693 | at::functionalization::impl::commit_update(out); |
| 18694 | at::functionalization::impl::sync(out); |
| 18695 | return out; |
| 18696 | } |
| 18697 | } |
| 18698 | |
| 18699 | at::Tensor & special_bessel_y1_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 18700 | if (false) { |
| 18701 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18702 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18703 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18704 | auto self_meta = to_meta(self); |
| 18705 | auto out_meta = to_meta(out); |
| 18706 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18707 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18708 | at::_ops::special_bessel_y1_out::call(self_meta, out_meta); |
| 18709 | } |
| 18710 | |
| 18711 | at::Tensor self_; |
| 18712 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18713 | at::functionalization::impl::sync(self); |
| 18714 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18715 | } else { |
| 18716 | self_ = self; |
| 18717 | } |
| 18718 | |
| 18719 | at::Tensor out_; |
| 18720 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18721 | at::functionalization::impl::sync(out); |
| 18722 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18723 | } else { |
| 18724 | out_ = out; |
| 18725 | } |
| 18726 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18727 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18728 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18729 | TORCH_INTERNAL_ASSERT(false, |
| 18730 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18731 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18732 | } else { |
| 18733 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18734 | at::AutoDispatchSkipFunctionalize guard; |
| 18735 | at::Tensor tmp_output = at::_ops::special_bessel_y1_out::call(self_, out_); |
| 18736 | return out;; |
| 18737 | } |
| 18738 | } else { |
| 18739 | at::Tensor tmp_output; |
| 18740 | { |
| 18741 | at::AutoDispatchSkipFunctionalize guard; |
| 18742 | tmp_output = at::_ops::special_bessel_y1::call(self_); |
| 18743 | } |
| 18744 | at::functionalization::impl::replace_(out, tmp_output); |
| 18745 | at::functionalization::impl::commit_update(out); |
| 18746 | at::functionalization::impl::sync(out); |
| 18747 | return out; |
| 18748 | } |
| 18749 | } |
| 18750 | |
| 18751 | at::Tensor & special_legendre_polynomial_p_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 18752 | if (false) { |
| 18753 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18754 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18755 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18756 | auto x_meta = to_meta(x); |
| 18757 | auto n_meta = to_meta(n); |
| 18758 | auto out_meta = to_meta(out); |
| 18759 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18760 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18761 | at::_ops::special_legendre_polynomial_p_out::call(x_meta, n_meta, out_meta); |
| 18762 | } |
| 18763 | |
| 18764 | at::Tensor x_; |
| 18765 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 18766 | at::functionalization::impl::sync(x); |
| 18767 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 18768 | } else { |
| 18769 | x_ = x; |
| 18770 | } |
| 18771 | |
| 18772 | at::Tensor n_; |
| 18773 | if (at::functionalization::impl::isFunctionalTensor(n)) { |
| 18774 | at::functionalization::impl::sync(n); |
| 18775 | n_ = at::functionalization::impl::from_functional_tensor(n); |
| 18776 | } else { |
| 18777 | n_ = n; |
| 18778 | } |
| 18779 | |
| 18780 | at::Tensor out_; |
| 18781 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18782 | at::functionalization::impl::sync(out); |
| 18783 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18784 | } else { |
| 18785 | out_ = out; |
| 18786 | } |
| 18787 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18788 | if ((false || at::functionalization::impl::isFunctionalTensor(x) || at::functionalization::impl::isFunctionalTensor(n))) { |
| 18789 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18790 | TORCH_INTERNAL_ASSERT(false, |
| 18791 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18792 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18793 | } else { |
| 18794 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18795 | at::AutoDispatchSkipFunctionalize guard; |
| 18796 | at::Tensor tmp_output = at::_ops::special_legendre_polynomial_p_out::call(x_, n_, out_); |
| 18797 | return out;; |
| 18798 | } |
| 18799 | } else { |
| 18800 | at::Tensor tmp_output; |
| 18801 | { |
| 18802 | at::AutoDispatchSkipFunctionalize guard; |
| 18803 | tmp_output = at::_ops::special_legendre_polynomial_p::call(x_, n_); |
| 18804 | } |
| 18805 | at::functionalization::impl::replace_(out, tmp_output); |
| 18806 | at::functionalization::impl::commit_update(out); |
| 18807 | at::functionalization::impl::sync(out); |
| 18808 | return out; |
| 18809 | } |
| 18810 | } |
| 18811 | |
| 18812 | at::Tensor & special_legendre_polynomial_p_out_x_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { |
| 18813 | if (false) { |
| 18814 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18815 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18816 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18817 | auto n_meta = to_meta(n); |
| 18818 | auto out_meta = to_meta(out); |
| 18819 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18820 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18821 | at::_ops::special_legendre_polynomial_p_x_scalar_out::call(x, n_meta, out_meta); |
| 18822 | } |
| 18823 | |
| 18824 | at::Tensor n_; |
| 18825 | if (at::functionalization::impl::isFunctionalTensor(n)) { |
| 18826 | at::functionalization::impl::sync(n); |
| 18827 | n_ = at::functionalization::impl::from_functional_tensor(n); |
| 18828 | } else { |
| 18829 | n_ = n; |
| 18830 | } |
| 18831 | |
| 18832 | at::Tensor out_; |
| 18833 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18834 | at::functionalization::impl::sync(out); |
| 18835 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18836 | } else { |
| 18837 | out_ = out; |
| 18838 | } |
| 18839 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18840 | if ((false || at::functionalization::impl::isFunctionalTensor(n))) { |
| 18841 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18842 | TORCH_INTERNAL_ASSERT(false, |
| 18843 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18844 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18845 | } else { |
| 18846 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18847 | at::AutoDispatchSkipFunctionalize guard; |
| 18848 | at::Tensor tmp_output = at::_ops::special_legendre_polynomial_p_x_scalar_out::call(x, n_, out_); |
| 18849 | return out;; |
| 18850 | } |
| 18851 | } else { |
| 18852 | at::Tensor tmp_output; |
| 18853 | { |
| 18854 | at::AutoDispatchSkipFunctionalize guard; |
| 18855 | tmp_output = at::_ops::special_legendre_polynomial_p_x_scalar::call(x, n_); |
| 18856 | } |
| 18857 | at::functionalization::impl::replace_(out, tmp_output); |
| 18858 | at::functionalization::impl::commit_update(out); |
| 18859 | at::functionalization::impl::sync(out); |
| 18860 | return out; |
| 18861 | } |
| 18862 | } |
| 18863 | |
| 18864 | at::Tensor & special_legendre_polynomial_p_out_n_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { |
| 18865 | if (false) { |
| 18866 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18867 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18868 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18869 | auto x_meta = to_meta(x); |
| 18870 | auto out_meta = to_meta(out); |
| 18871 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18872 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18873 | at::_ops::special_legendre_polynomial_p_n_scalar_out::call(x_meta, n, out_meta); |
| 18874 | } |
| 18875 | |
| 18876 | at::Tensor x_; |
| 18877 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 18878 | at::functionalization::impl::sync(x); |
| 18879 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 18880 | } else { |
| 18881 | x_ = x; |
| 18882 | } |
| 18883 | |
| 18884 | at::Tensor out_; |
| 18885 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18886 | at::functionalization::impl::sync(out); |
| 18887 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18888 | } else { |
| 18889 | out_ = out; |
| 18890 | } |
| 18891 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18892 | if ((false || at::functionalization::impl::isFunctionalTensor(x))) { |
| 18893 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18894 | TORCH_INTERNAL_ASSERT(false, |
| 18895 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18896 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18897 | } else { |
| 18898 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18899 | at::AutoDispatchSkipFunctionalize guard; |
| 18900 | at::Tensor tmp_output = at::_ops::special_legendre_polynomial_p_n_scalar_out::call(x_, n, out_); |
| 18901 | return out;; |
| 18902 | } |
| 18903 | } else { |
| 18904 | at::Tensor tmp_output; |
| 18905 | { |
| 18906 | at::AutoDispatchSkipFunctionalize guard; |
| 18907 | tmp_output = at::_ops::special_legendre_polynomial_p_n_scalar::call(x_, n); |
| 18908 | } |
| 18909 | at::functionalization::impl::replace_(out, tmp_output); |
| 18910 | at::functionalization::impl::commit_update(out); |
| 18911 | at::functionalization::impl::sync(out); |
| 18912 | return out; |
| 18913 | } |
| 18914 | } |
| 18915 | |
| 18916 | at::Tensor & special_modified_bessel_i0_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 18917 | if (false) { |
| 18918 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18919 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18920 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18921 | auto self_meta = to_meta(self); |
| 18922 | auto out_meta = to_meta(out); |
| 18923 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18924 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18925 | at::_ops::special_modified_bessel_i0_out::call(self_meta, out_meta); |
| 18926 | } |
| 18927 | |
| 18928 | at::Tensor self_; |
| 18929 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 18930 | at::functionalization::impl::sync(self); |
| 18931 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 18932 | } else { |
| 18933 | self_ = self; |
| 18934 | } |
| 18935 | |
| 18936 | at::Tensor out_; |
| 18937 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18938 | at::functionalization::impl::sync(out); |
| 18939 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 18940 | } else { |
| 18941 | out_ = out; |
| 18942 | } |
| 18943 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 18944 | if ((false || at::functionalization::impl::isFunctionalTensor(self))) { |
| 18945 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 18946 | TORCH_INTERNAL_ASSERT(false, |
| 18947 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 18948 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 18949 | } else { |
| 18950 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 18951 | at::AutoDispatchSkipFunctionalize guard; |
| 18952 | at::Tensor tmp_output = at::_ops::special_modified_bessel_i0_out::call(self_, out_); |
| 18953 | return out;; |
| 18954 | } |
| 18955 | } else { |
| 18956 | at::Tensor tmp_output; |
| 18957 | { |
| 18958 | at::AutoDispatchSkipFunctionalize guard; |
| 18959 | tmp_output = at::_ops::special_modified_bessel_i0::call(self_); |
| 18960 | } |
| 18961 | at::functionalization::impl::replace_(out, tmp_output); |
| 18962 | at::functionalization::impl::commit_update(out); |
| 18963 | at::functionalization::impl::sync(out); |
| 18964 | return out; |
| 18965 | } |
| 18966 | } |
| 18967 | |
| 18968 | at::Tensor & special_shifted_chebyshev_polynomial_t_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 18969 | if (false) { |
| 18970 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 18971 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 18972 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 18973 | auto x_meta = to_meta(x); |
| 18974 | auto n_meta = to_meta(n); |
| 18975 | auto out_meta = to_meta(out); |
| 18976 | at::AutoDispatchSkipFunctionalize func_guard; |
| 18977 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 18978 | at::_ops::special_shifted_chebyshev_polynomial_t_out::call(x_meta, n_meta, out_meta); |
| 18979 | } |
| 18980 | |
| 18981 | at::Tensor x_; |
| 18982 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 18983 | at::functionalization::impl::sync(x); |
| 18984 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 18985 | } else { |
| 18986 | x_ = x; |
| 18987 | } |
| 18988 | |
| 18989 | at::Tensor n_; |
| 18990 | if (at::functionalization::impl::isFunctionalTensor(n)) { |
| 18991 | at::functionalization::impl::sync(n); |
| 18992 | n_ = at::functionalization::impl::from_functional_tensor(n); |
| 18993 | } else { |
| 18994 | n_ = n; |
| 18995 | } |
| 18996 | |
| 18997 | at::Tensor out_; |
| 18998 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 18999 | at::functionalization::impl::sync(out); |
| 19000 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19001 | } else { |
| 19002 | out_ = out; |
| 19003 | } |
| 19004 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19005 | if ((false || at::functionalization::impl::isFunctionalTensor(x) || at::functionalization::impl::isFunctionalTensor(n))) { |
| 19006 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19007 | TORCH_INTERNAL_ASSERT(false, |
| 19008 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19009 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19010 | } else { |
| 19011 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19012 | at::AutoDispatchSkipFunctionalize guard; |
| 19013 | at::Tensor tmp_output = at::_ops::special_shifted_chebyshev_polynomial_t_out::call(x_, n_, out_); |
| 19014 | return out;; |
| 19015 | } |
| 19016 | } else { |
| 19017 | at::Tensor tmp_output; |
| 19018 | { |
| 19019 | at::AutoDispatchSkipFunctionalize guard; |
| 19020 | tmp_output = at::_ops::special_shifted_chebyshev_polynomial_t::call(x_, n_); |
| 19021 | } |
| 19022 | at::functionalization::impl::replace_(out, tmp_output); |
| 19023 | at::functionalization::impl::commit_update(out); |
| 19024 | at::functionalization::impl::sync(out); |
| 19025 | return out; |
| 19026 | } |
| 19027 | } |
| 19028 | |
| 19029 | at::Tensor & special_shifted_chebyshev_polynomial_t_out_x_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { |
| 19030 | if (false) { |
| 19031 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19032 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19033 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19034 | auto n_meta = to_meta(n); |
| 19035 | auto out_meta = to_meta(out); |
| 19036 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19037 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19038 | at::_ops::special_shifted_chebyshev_polynomial_t_x_scalar_out::call(x, n_meta, out_meta); |
| 19039 | } |
| 19040 | |
| 19041 | at::Tensor n_; |
| 19042 | if (at::functionalization::impl::isFunctionalTensor(n)) { |
| 19043 | at::functionalization::impl::sync(n); |
| 19044 | n_ = at::functionalization::impl::from_functional_tensor(n); |
| 19045 | } else { |
| 19046 | n_ = n; |
| 19047 | } |
| 19048 | |
| 19049 | at::Tensor out_; |
| 19050 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19051 | at::functionalization::impl::sync(out); |
| 19052 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19053 | } else { |
| 19054 | out_ = out; |
| 19055 | } |
| 19056 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19057 | if ((false || at::functionalization::impl::isFunctionalTensor(n))) { |
| 19058 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19059 | TORCH_INTERNAL_ASSERT(false, |
| 19060 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19061 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19062 | } else { |
| 19063 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19064 | at::AutoDispatchSkipFunctionalize guard; |
| 19065 | at::Tensor tmp_output = at::_ops::special_shifted_chebyshev_polynomial_t_x_scalar_out::call(x, n_, out_); |
| 19066 | return out;; |
| 19067 | } |
| 19068 | } else { |
| 19069 | at::Tensor tmp_output; |
| 19070 | { |
| 19071 | at::AutoDispatchSkipFunctionalize guard; |
| 19072 | tmp_output = at::_ops::special_shifted_chebyshev_polynomial_t_x_scalar::call(x, n_); |
| 19073 | } |
| 19074 | at::functionalization::impl::replace_(out, tmp_output); |
| 19075 | at::functionalization::impl::commit_update(out); |
| 19076 | at::functionalization::impl::sync(out); |
| 19077 | return out; |
| 19078 | } |
| 19079 | } |
| 19080 | |
| 19081 | at::Tensor & special_shifted_chebyshev_polynomial_t_out_n_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { |
| 19082 | if (false) { |
| 19083 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19084 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19085 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19086 | auto x_meta = to_meta(x); |
| 19087 | auto out_meta = to_meta(out); |
| 19088 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19089 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19090 | at::_ops::special_shifted_chebyshev_polynomial_t_n_scalar_out::call(x_meta, n, out_meta); |
| 19091 | } |
| 19092 | |
| 19093 | at::Tensor x_; |
| 19094 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 19095 | at::functionalization::impl::sync(x); |
| 19096 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 19097 | } else { |
| 19098 | x_ = x; |
| 19099 | } |
| 19100 | |
| 19101 | at::Tensor out_; |
| 19102 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19103 | at::functionalization::impl::sync(out); |
| 19104 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19105 | } else { |
| 19106 | out_ = out; |
| 19107 | } |
| 19108 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19109 | if ((false || at::functionalization::impl::isFunctionalTensor(x))) { |
| 19110 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19111 | TORCH_INTERNAL_ASSERT(false, |
| 19112 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19113 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19114 | } else { |
| 19115 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19116 | at::AutoDispatchSkipFunctionalize guard; |
| 19117 | at::Tensor tmp_output = at::_ops::special_shifted_chebyshev_polynomial_t_n_scalar_out::call(x_, n, out_); |
| 19118 | return out;; |
| 19119 | } |
| 19120 | } else { |
| 19121 | at::Tensor tmp_output; |
| 19122 | { |
| 19123 | at::AutoDispatchSkipFunctionalize guard; |
| 19124 | tmp_output = at::_ops::special_shifted_chebyshev_polynomial_t_n_scalar::call(x_, n); |
| 19125 | } |
| 19126 | at::functionalization::impl::replace_(out, tmp_output); |
| 19127 | at::functionalization::impl::commit_update(out); |
| 19128 | at::functionalization::impl::sync(out); |
| 19129 | return out; |
| 19130 | } |
| 19131 | } |
| 19132 | |
| 19133 | at::Tensor & special_shifted_chebyshev_polynomial_u_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 19134 | if (false) { |
| 19135 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19136 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19137 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19138 | auto x_meta = to_meta(x); |
| 19139 | auto n_meta = to_meta(n); |
| 19140 | auto out_meta = to_meta(out); |
| 19141 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19142 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19143 | at::_ops::special_shifted_chebyshev_polynomial_u_out::call(x_meta, n_meta, out_meta); |
| 19144 | } |
| 19145 | |
| 19146 | at::Tensor x_; |
| 19147 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 19148 | at::functionalization::impl::sync(x); |
| 19149 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 19150 | } else { |
| 19151 | x_ = x; |
| 19152 | } |
| 19153 | |
| 19154 | at::Tensor n_; |
| 19155 | if (at::functionalization::impl::isFunctionalTensor(n)) { |
| 19156 | at::functionalization::impl::sync(n); |
| 19157 | n_ = at::functionalization::impl::from_functional_tensor(n); |
| 19158 | } else { |
| 19159 | n_ = n; |
| 19160 | } |
| 19161 | |
| 19162 | at::Tensor out_; |
| 19163 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19164 | at::functionalization::impl::sync(out); |
| 19165 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19166 | } else { |
| 19167 | out_ = out; |
| 19168 | } |
| 19169 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19170 | if ((false || at::functionalization::impl::isFunctionalTensor(x) || at::functionalization::impl::isFunctionalTensor(n))) { |
| 19171 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19172 | TORCH_INTERNAL_ASSERT(false, |
| 19173 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19174 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19175 | } else { |
| 19176 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19177 | at::AutoDispatchSkipFunctionalize guard; |
| 19178 | at::Tensor tmp_output = at::_ops::special_shifted_chebyshev_polynomial_u_out::call(x_, n_, out_); |
| 19179 | return out;; |
| 19180 | } |
| 19181 | } else { |
| 19182 | at::Tensor tmp_output; |
| 19183 | { |
| 19184 | at::AutoDispatchSkipFunctionalize guard; |
| 19185 | tmp_output = at::_ops::special_shifted_chebyshev_polynomial_u::call(x_, n_); |
| 19186 | } |
| 19187 | at::functionalization::impl::replace_(out, tmp_output); |
| 19188 | at::functionalization::impl::commit_update(out); |
| 19189 | at::functionalization::impl::sync(out); |
| 19190 | return out; |
| 19191 | } |
| 19192 | } |
| 19193 | |
| 19194 | at::Tensor & special_shifted_chebyshev_polynomial_u_out_x_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { |
| 19195 | if (false) { |
| 19196 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19197 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19198 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19199 | auto n_meta = to_meta(n); |
| 19200 | auto out_meta = to_meta(out); |
| 19201 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19202 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19203 | at::_ops::special_shifted_chebyshev_polynomial_u_x_scalar_out::call(x, n_meta, out_meta); |
| 19204 | } |
| 19205 | |
| 19206 | at::Tensor n_; |
| 19207 | if (at::functionalization::impl::isFunctionalTensor(n)) { |
| 19208 | at::functionalization::impl::sync(n); |
| 19209 | n_ = at::functionalization::impl::from_functional_tensor(n); |
| 19210 | } else { |
| 19211 | n_ = n; |
| 19212 | } |
| 19213 | |
| 19214 | at::Tensor out_; |
| 19215 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19216 | at::functionalization::impl::sync(out); |
| 19217 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19218 | } else { |
| 19219 | out_ = out; |
| 19220 | } |
| 19221 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19222 | if ((false || at::functionalization::impl::isFunctionalTensor(n))) { |
| 19223 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19224 | TORCH_INTERNAL_ASSERT(false, |
| 19225 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19226 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19227 | } else { |
| 19228 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19229 | at::AutoDispatchSkipFunctionalize guard; |
| 19230 | at::Tensor tmp_output = at::_ops::special_shifted_chebyshev_polynomial_u_x_scalar_out::call(x, n_, out_); |
| 19231 | return out;; |
| 19232 | } |
| 19233 | } else { |
| 19234 | at::Tensor tmp_output; |
| 19235 | { |
| 19236 | at::AutoDispatchSkipFunctionalize guard; |
| 19237 | tmp_output = at::_ops::special_shifted_chebyshev_polynomial_u_x_scalar::call(x, n_); |
| 19238 | } |
| 19239 | at::functionalization::impl::replace_(out, tmp_output); |
| 19240 | at::functionalization::impl::commit_update(out); |
| 19241 | at::functionalization::impl::sync(out); |
| 19242 | return out; |
| 19243 | } |
| 19244 | } |
| 19245 | |
| 19246 | at::Tensor & special_shifted_chebyshev_polynomial_u_out_n_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { |
| 19247 | if (false) { |
| 19248 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19249 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19250 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19251 | auto x_meta = to_meta(x); |
| 19252 | auto out_meta = to_meta(out); |
| 19253 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19254 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19255 | at::_ops::special_shifted_chebyshev_polynomial_u_n_scalar_out::call(x_meta, n, out_meta); |
| 19256 | } |
| 19257 | |
| 19258 | at::Tensor x_; |
| 19259 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 19260 | at::functionalization::impl::sync(x); |
| 19261 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 19262 | } else { |
| 19263 | x_ = x; |
| 19264 | } |
| 19265 | |
| 19266 | at::Tensor out_; |
| 19267 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19268 | at::functionalization::impl::sync(out); |
| 19269 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19270 | } else { |
| 19271 | out_ = out; |
| 19272 | } |
| 19273 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19274 | if ((false || at::functionalization::impl::isFunctionalTensor(x))) { |
| 19275 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19276 | TORCH_INTERNAL_ASSERT(false, |
| 19277 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19278 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19279 | } else { |
| 19280 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19281 | at::AutoDispatchSkipFunctionalize guard; |
| 19282 | at::Tensor tmp_output = at::_ops::special_shifted_chebyshev_polynomial_u_n_scalar_out::call(x_, n, out_); |
| 19283 | return out;; |
| 19284 | } |
| 19285 | } else { |
| 19286 | at::Tensor tmp_output; |
| 19287 | { |
| 19288 | at::AutoDispatchSkipFunctionalize guard; |
| 19289 | tmp_output = at::_ops::special_shifted_chebyshev_polynomial_u_n_scalar::call(x_, n); |
| 19290 | } |
| 19291 | at::functionalization::impl::replace_(out, tmp_output); |
| 19292 | at::functionalization::impl::commit_update(out); |
| 19293 | at::functionalization::impl::sync(out); |
| 19294 | return out; |
| 19295 | } |
| 19296 | } |
| 19297 | |
| 19298 | at::Tensor & special_shifted_chebyshev_polynomial_w_out_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 19299 | if (false) { |
| 19300 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19301 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19302 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19303 | auto x_meta = to_meta(x); |
| 19304 | auto n_meta = to_meta(n); |
| 19305 | auto out_meta = to_meta(out); |
| 19306 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19307 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19308 | at::_ops::special_shifted_chebyshev_polynomial_w_out::call(x_meta, n_meta, out_meta); |
| 19309 | } |
| 19310 | |
| 19311 | at::Tensor x_; |
| 19312 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 19313 | at::functionalization::impl::sync(x); |
| 19314 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 19315 | } else { |
| 19316 | x_ = x; |
| 19317 | } |
| 19318 | |
| 19319 | at::Tensor n_; |
| 19320 | if (at::functionalization::impl::isFunctionalTensor(n)) { |
| 19321 | at::functionalization::impl::sync(n); |
| 19322 | n_ = at::functionalization::impl::from_functional_tensor(n); |
| 19323 | } else { |
| 19324 | n_ = n; |
| 19325 | } |
| 19326 | |
| 19327 | at::Tensor out_; |
| 19328 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19329 | at::functionalization::impl::sync(out); |
| 19330 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19331 | } else { |
| 19332 | out_ = out; |
| 19333 | } |
| 19334 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19335 | if ((false || at::functionalization::impl::isFunctionalTensor(x) || at::functionalization::impl::isFunctionalTensor(n))) { |
| 19336 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19337 | TORCH_INTERNAL_ASSERT(false, |
| 19338 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19339 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19340 | } else { |
| 19341 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19342 | at::AutoDispatchSkipFunctionalize guard; |
| 19343 | at::Tensor tmp_output = at::_ops::special_shifted_chebyshev_polynomial_w_out::call(x_, n_, out_); |
| 19344 | return out;; |
| 19345 | } |
| 19346 | } else { |
| 19347 | at::Tensor tmp_output; |
| 19348 | { |
| 19349 | at::AutoDispatchSkipFunctionalize guard; |
| 19350 | tmp_output = at::_ops::special_shifted_chebyshev_polynomial_w::call(x_, n_); |
| 19351 | } |
| 19352 | at::functionalization::impl::replace_(out, tmp_output); |
| 19353 | at::functionalization::impl::commit_update(out); |
| 19354 | at::functionalization::impl::sync(out); |
| 19355 | return out; |
| 19356 | } |
| 19357 | } |
| 19358 | |
| 19359 | at::Tensor & special_shifted_chebyshev_polynomial_w_out_x_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { |
| 19360 | if (false) { |
| 19361 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19362 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19363 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19364 | auto n_meta = to_meta(n); |
| 19365 | auto out_meta = to_meta(out); |
| 19366 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19367 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19368 | at::_ops::special_shifted_chebyshev_polynomial_w_x_scalar_out::call(x, n_meta, out_meta); |
| 19369 | } |
| 19370 | |
| 19371 | at::Tensor n_; |
| 19372 | if (at::functionalization::impl::isFunctionalTensor(n)) { |
| 19373 | at::functionalization::impl::sync(n); |
| 19374 | n_ = at::functionalization::impl::from_functional_tensor(n); |
| 19375 | } else { |
| 19376 | n_ = n; |
| 19377 | } |
| 19378 | |
| 19379 | at::Tensor out_; |
| 19380 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19381 | at::functionalization::impl::sync(out); |
| 19382 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19383 | } else { |
| 19384 | out_ = out; |
| 19385 | } |
| 19386 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19387 | if ((false || at::functionalization::impl::isFunctionalTensor(n))) { |
| 19388 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19389 | TORCH_INTERNAL_ASSERT(false, |
| 19390 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19391 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19392 | } else { |
| 19393 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19394 | at::AutoDispatchSkipFunctionalize guard; |
| 19395 | at::Tensor tmp_output = at::_ops::special_shifted_chebyshev_polynomial_w_x_scalar_out::call(x, n_, out_); |
| 19396 | return out;; |
| 19397 | } |
| 19398 | } else { |
| 19399 | at::Tensor tmp_output; |
| 19400 | { |
| 19401 | at::AutoDispatchSkipFunctionalize guard; |
| 19402 | tmp_output = at::_ops::special_shifted_chebyshev_polynomial_w_x_scalar::call(x, n_); |
| 19403 | } |
| 19404 | at::functionalization::impl::replace_(out, tmp_output); |
| 19405 | at::functionalization::impl::commit_update(out); |
| 19406 | at::functionalization::impl::sync(out); |
| 19407 | return out; |
| 19408 | } |
| 19409 | } |
| 19410 | |
| 19411 | at::Tensor & special_shifted_chebyshev_polynomial_w_out_n_scalar_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { |
| 19412 | if (false) { |
| 19413 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19414 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19415 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19416 | auto x_meta = to_meta(x); |
| 19417 | auto out_meta = to_meta(out); |
| 19418 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19419 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19420 | at::_ops::special_shifted_chebyshev_polynomial_w_n_scalar_out::call(x_meta, n, out_meta); |
| 19421 | } |
| 19422 | |
| 19423 | at::Tensor x_; |
| 19424 | if (at::functionalization::impl::isFunctionalTensor(x)) { |
| 19425 | at::functionalization::impl::sync(x); |
| 19426 | x_ = at::functionalization::impl::from_functional_tensor(x); |
| 19427 | } else { |
| 19428 | x_ = x; |
| 19429 | } |
| 19430 | |
| 19431 | at::Tensor out_; |
| 19432 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19433 | at::functionalization::impl::sync(out); |
| 19434 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19435 | } else { |
| 19436 | out_ = out; |
| 19437 | } |
| 19438 | if (!(true && at::functionalization::impl::isFunctionalTensor(out))) { |
| 19439 | if ((false || at::functionalization::impl::isFunctionalTensor(x))) { |
| 19440 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19441 | TORCH_INTERNAL_ASSERT(false, |
| 19442 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19443 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19444 | } else { |
| 19445 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19446 | at::AutoDispatchSkipFunctionalize guard; |
| 19447 | at::Tensor tmp_output = at::_ops::special_shifted_chebyshev_polynomial_w_n_scalar_out::call(x_, n, out_); |
| 19448 | return out;; |
| 19449 | } |
| 19450 | } else { |
| 19451 | at::Tensor tmp_output; |
| 19452 | { |
| 19453 | at::AutoDispatchSkipFunctionalize guard; |
| 19454 | tmp_output = at::_ops::special_shifted_chebyshev_polynomial_w_n_scalar::call(x_, n); |
| 19455 | } |
| 19456 | at::functionalization::impl::replace_(out, tmp_output); |
| 19457 | at::functionalization::impl::commit_update(out); |
| 19458 | at::functionalization::impl::sync(out); |
| 19459 | return out; |
| 19460 | } |
| 19461 | } |
| 19462 | |
| 19463 | void _fused_adam_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) { |
| 19464 | if (false) { |
| 19465 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19466 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19467 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19468 | auto self_meta = to_meta(self); |
| 19469 | auto grads_meta = to_meta(grads); |
| 19470 | auto exp_avgs_meta = to_meta(exp_avgs); |
| 19471 | auto exp_avg_sqs_meta = to_meta(exp_avg_sqs); |
| 19472 | auto max_exp_avg_sqs_meta = to_meta(max_exp_avg_sqs); |
| 19473 | auto state_steps_meta = to_meta(state_steps); |
| 19474 | auto grad_scale_meta = to_meta(grad_scale); |
| 19475 | auto found_inf_meta = to_meta(found_inf); |
| 19476 | auto out_meta = to_meta(out); |
| 19477 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19478 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19479 | at::_ops::_fused_adam_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); |
| 19480 | } |
| 19481 | |
| 19482 | ::std::vector<at::Tensor> self_; |
| 19483 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19484 | at::functionalization::impl::sync(self); |
| 19485 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19486 | } else { |
| 19487 | self_ = self.vec(); |
| 19488 | } |
| 19489 | |
| 19490 | ::std::vector<at::Tensor> grads_; |
| 19491 | if (at::functionalization::impl::isFunctionalTensor(grads)) { |
| 19492 | at::functionalization::impl::sync(grads); |
| 19493 | grads_ = at::functionalization::impl::from_functional_tensor(grads); |
| 19494 | } else { |
| 19495 | grads_ = grads.vec(); |
| 19496 | } |
| 19497 | |
| 19498 | ::std::vector<at::Tensor> exp_avgs_; |
| 19499 | if (at::functionalization::impl::isFunctionalTensor(exp_avgs)) { |
| 19500 | at::functionalization::impl::sync(exp_avgs); |
| 19501 | exp_avgs_ = at::functionalization::impl::from_functional_tensor(exp_avgs); |
| 19502 | } else { |
| 19503 | exp_avgs_ = exp_avgs.vec(); |
| 19504 | } |
| 19505 | |
| 19506 | ::std::vector<at::Tensor> exp_avg_sqs_; |
| 19507 | if (at::functionalization::impl::isFunctionalTensor(exp_avg_sqs)) { |
| 19508 | at::functionalization::impl::sync(exp_avg_sqs); |
| 19509 | exp_avg_sqs_ = at::functionalization::impl::from_functional_tensor(exp_avg_sqs); |
| 19510 | } else { |
| 19511 | exp_avg_sqs_ = exp_avg_sqs.vec(); |
| 19512 | } |
| 19513 | |
| 19514 | ::std::vector<at::Tensor> max_exp_avg_sqs_; |
| 19515 | if (at::functionalization::impl::isFunctionalTensor(max_exp_avg_sqs)) { |
| 19516 | at::functionalization::impl::sync(max_exp_avg_sqs); |
| 19517 | max_exp_avg_sqs_ = at::functionalization::impl::from_functional_tensor(max_exp_avg_sqs); |
| 19518 | } else { |
| 19519 | max_exp_avg_sqs_ = max_exp_avg_sqs.vec(); |
| 19520 | } |
| 19521 | |
| 19522 | ::std::vector<at::Tensor> state_steps_; |
| 19523 | if (at::functionalization::impl::isFunctionalTensor(state_steps)) { |
| 19524 | at::functionalization::impl::sync(state_steps); |
| 19525 | state_steps_ = at::functionalization::impl::from_functional_tensor(state_steps); |
| 19526 | } else { |
| 19527 | state_steps_ = state_steps.vec(); |
| 19528 | } |
| 19529 | |
| 19530 | c10::optional<at::Tensor> grad_scale_; |
| 19531 | if (at::functionalization::impl::isFunctionalTensor(grad_scale)) { |
| 19532 | at::functionalization::impl::sync(grad_scale); |
| 19533 | grad_scale_ = at::functionalization::impl::from_functional_tensor(grad_scale); |
| 19534 | } else { |
| 19535 | grad_scale_ = grad_scale; |
| 19536 | } |
| 19537 | |
| 19538 | c10::optional<at::Tensor> found_inf_; |
| 19539 | if (at::functionalization::impl::isFunctionalTensor(found_inf)) { |
| 19540 | at::functionalization::impl::sync(found_inf); |
| 19541 | found_inf_ = at::functionalization::impl::from_functional_tensor(found_inf); |
| 19542 | } else { |
| 19543 | found_inf_ = found_inf; |
| 19544 | } |
| 19545 | |
| 19546 | ::std::vector<at::Tensor> out_; |
| 19547 | if (at::functionalization::impl::isFunctionalTensor(out)) { |
| 19548 | at::functionalization::impl::sync(out); |
| 19549 | out_ = at::functionalization::impl::from_functional_tensor(out); |
| 19550 | } else { |
| 19551 | out_ = out.vec(); |
| 19552 | } |
| 19553 | 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))) { |
| 19554 | 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))) { |
| 19555 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19556 | TORCH_INTERNAL_ASSERT(false, |
| 19557 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19558 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19559 | } else { |
| 19560 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19561 | at::AutoDispatchSkipFunctionalize guard; |
| 19562 | at::_ops::_fused_adam_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_); |
| 19563 | ; |
| 19564 | } |
| 19565 | } else { |
| 19566 | ::std::tuple<::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>> tmp_output; |
| 19567 | { |
| 19568 | at::AutoDispatchSkipFunctionalize guard; |
| 19569 | tmp_output = at::_ops::_fused_adam::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_); |
| 19570 | } |
| 19571 | at::functionalization::impl::replace_(grads, std::get<0>(tmp_output)); |
| 19572 | at::functionalization::impl::commit_update(grads); |
| 19573 | at::functionalization::impl::sync(grads); |
| 19574 | at::functionalization::impl::replace_(exp_avgs, std::get<1>(tmp_output)); |
| 19575 | at::functionalization::impl::commit_update(exp_avgs); |
| 19576 | at::functionalization::impl::sync(exp_avgs); |
| 19577 | at::functionalization::impl::replace_(exp_avg_sqs, std::get<2>(tmp_output)); |
| 19578 | at::functionalization::impl::commit_update(exp_avg_sqs); |
| 19579 | at::functionalization::impl::sync(exp_avg_sqs); |
| 19580 | at::functionalization::impl::replace_(max_exp_avg_sqs, std::get<3>(tmp_output)); |
| 19581 | at::functionalization::impl::commit_update(max_exp_avg_sqs); |
| 19582 | at::functionalization::impl::sync(max_exp_avg_sqs); |
| 19583 | at::functionalization::impl::replace_(out, std::get<4>(tmp_output)); |
| 19584 | at::functionalization::impl::commit_update(out); |
| 19585 | at::functionalization::impl::sync(out); |
| 19586 | |
| 19587 | } |
| 19588 | } |
| 19589 | |
| 19590 | void _fused_adam_(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) { |
| 19591 | if (true) { |
| 19592 | // Before converting the mutable op to its functional variant, run meta tensors through the original op. |
| 19593 | // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants. |
| 19594 | // (We can only do this for inplace ops today though, because they technicaly all support meta tensors). |
| 19595 | auto self_meta = to_meta(self); |
| 19596 | auto grads_meta = to_meta(grads); |
| 19597 | auto exp_avgs_meta = to_meta(exp_avgs); |
| 19598 | auto exp_avg_sqs_meta = to_meta(exp_avg_sqs); |
| 19599 | auto max_exp_avg_sqs_meta = to_meta(max_exp_avg_sqs); |
| 19600 | auto state_steps_meta = to_meta(state_steps); |
| 19601 | auto grad_scale_meta = to_meta(grad_scale); |
| 19602 | auto found_inf_meta = to_meta(found_inf); |
| 19603 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19604 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19605 | at::_ops::_fused_adam_::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); |
| 19606 | } |
| 19607 | |
| 19608 | ::std::vector<at::Tensor> self_; |
| 19609 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19610 | at::functionalization::impl::sync(self); |
| 19611 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19612 | } else { |
| 19613 | self_ = self.vec(); |
| 19614 | } |
| 19615 | |
| 19616 | ::std::vector<at::Tensor> grads_; |
| 19617 | if (at::functionalization::impl::isFunctionalTensor(grads)) { |
| 19618 | at::functionalization::impl::sync(grads); |
| 19619 | grads_ = at::functionalization::impl::from_functional_tensor(grads); |
| 19620 | } else { |
| 19621 | grads_ = grads.vec(); |
| 19622 | } |
| 19623 | |
| 19624 | ::std::vector<at::Tensor> exp_avgs_; |
| 19625 | if (at::functionalization::impl::isFunctionalTensor(exp_avgs)) { |
| 19626 | at::functionalization::impl::sync(exp_avgs); |
| 19627 | exp_avgs_ = at::functionalization::impl::from_functional_tensor(exp_avgs); |
| 19628 | } else { |
| 19629 | exp_avgs_ = exp_avgs.vec(); |
| 19630 | } |
| 19631 | |
| 19632 | ::std::vector<at::Tensor> exp_avg_sqs_; |
| 19633 | if (at::functionalization::impl::isFunctionalTensor(exp_avg_sqs)) { |
| 19634 | at::functionalization::impl::sync(exp_avg_sqs); |
| 19635 | exp_avg_sqs_ = at::functionalization::impl::from_functional_tensor(exp_avg_sqs); |
| 19636 | } else { |
| 19637 | exp_avg_sqs_ = exp_avg_sqs.vec(); |
| 19638 | } |
| 19639 | |
| 19640 | ::std::vector<at::Tensor> max_exp_avg_sqs_; |
| 19641 | if (at::functionalization::impl::isFunctionalTensor(max_exp_avg_sqs)) { |
| 19642 | at::functionalization::impl::sync(max_exp_avg_sqs); |
| 19643 | max_exp_avg_sqs_ = at::functionalization::impl::from_functional_tensor(max_exp_avg_sqs); |
| 19644 | } else { |
| 19645 | max_exp_avg_sqs_ = max_exp_avg_sqs.vec(); |
| 19646 | } |
| 19647 | |
| 19648 | ::std::vector<at::Tensor> state_steps_; |
| 19649 | if (at::functionalization::impl::isFunctionalTensor(state_steps)) { |
| 19650 | at::functionalization::impl::sync(state_steps); |
| 19651 | state_steps_ = at::functionalization::impl::from_functional_tensor(state_steps); |
| 19652 | } else { |
| 19653 | state_steps_ = state_steps.vec(); |
| 19654 | } |
| 19655 | |
| 19656 | c10::optional<at::Tensor> grad_scale_; |
| 19657 | if (at::functionalization::impl::isFunctionalTensor(grad_scale)) { |
| 19658 | at::functionalization::impl::sync(grad_scale); |
| 19659 | grad_scale_ = at::functionalization::impl::from_functional_tensor(grad_scale); |
| 19660 | } else { |
| 19661 | grad_scale_ = grad_scale; |
| 19662 | } |
| 19663 | |
| 19664 | c10::optional<at::Tensor> found_inf_; |
| 19665 | if (at::functionalization::impl::isFunctionalTensor(found_inf)) { |
| 19666 | at::functionalization::impl::sync(found_inf); |
| 19667 | found_inf_ = at::functionalization::impl::from_functional_tensor(found_inf); |
| 19668 | } else { |
| 19669 | found_inf_ = found_inf; |
| 19670 | } |
| 19671 | 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))) { |
| 19672 | if ((false || at::functionalization::impl::isFunctionalTensor(state_steps) || at::functionalization::impl::isFunctionalTensor(grad_scale) || at::functionalization::impl::isFunctionalTensor(found_inf))) { |
| 19673 | // case 1: trying to mutate a non functional tensor with a functional tensor is an error |
| 19674 | TORCH_INTERNAL_ASSERT(false, |
| 19675 | "mutating a non-functional tensor with a functional tensor is not allowed.", |
| 19676 | " Please ensure that all of your inputs are wrapped inside of a functionalize() call."); |
| 19677 | } else { |
| 19678 | // case 2: arguments are not functional tensors, so we no-op and redispatch. |
| 19679 | at::AutoDispatchSkipFunctionalize guard; |
| 19680 | at::_ops::_fused_adam_::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_); |
| 19681 | ; |
| 19682 | } |
| 19683 | } else { |
| 19684 | ::std::tuple<::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>> tmp_output; |
| 19685 | { |
| 19686 | at::AutoDispatchSkipFunctionalize guard; |
| 19687 | tmp_output = at::_ops::_fused_adam::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_); |
| 19688 | } |
| 19689 | at::functionalization::impl::replace_(self, std::get<0>(tmp_output)); |
| 19690 | at::functionalization::impl::commit_update(self); |
| 19691 | at::functionalization::impl::sync(self); |
| 19692 | at::functionalization::impl::replace_(grads, std::get<1>(tmp_output)); |
| 19693 | at::functionalization::impl::commit_update(grads); |
| 19694 | at::functionalization::impl::sync(grads); |
| 19695 | at::functionalization::impl::replace_(exp_avgs, std::get<2>(tmp_output)); |
| 19696 | at::functionalization::impl::commit_update(exp_avgs); |
| 19697 | at::functionalization::impl::sync(exp_avgs); |
| 19698 | at::functionalization::impl::replace_(exp_avg_sqs, std::get<3>(tmp_output)); |
| 19699 | at::functionalization::impl::commit_update(exp_avg_sqs); |
| 19700 | at::functionalization::impl::sync(exp_avg_sqs); |
| 19701 | at::functionalization::impl::replace_(max_exp_avg_sqs, std::get<4>(tmp_output)); |
| 19702 | at::functionalization::impl::commit_update(max_exp_avg_sqs); |
| 19703 | at::functionalization::impl::sync(max_exp_avg_sqs); |
| 19704 | |
| 19705 | } |
| 19706 | } |
| 19707 | |
| 19708 | at::Tensor _conj(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 19709 | |
| 19710 | at::Tensor self_; |
| 19711 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19712 | |
| 19713 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19714 | } else { |
| 19715 | self_ = self; |
| 19716 | } |
| 19717 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 19718 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 19719 | at::AutoDispatchSkipFunctionalize guard; |
| 19720 | return at::_ops::_conj::call(self_); |
| 19721 | } |
| 19722 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 19723 | auto compute_reference_meta = |
| 19724 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 19725 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 19726 | at::Tensor reference_tensor_output; |
| 19727 | if (compute_reference_meta) { |
| 19728 | auto self_meta = to_meta(self); |
| 19729 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19730 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19731 | reference_tensor_output = at::_ops::_conj::call(self_meta); |
| 19732 | } |
| 19733 | at::Tensor tmp_output; |
| 19734 | { |
| 19735 | at::AutoDispatchSkipFunctionalize guard; |
| 19736 | if (reapply_views) { |
| 19737 | tmp_output = at::_ops::_conj::call(self_); |
| 19738 | } else { |
| 19739 | tmp_output = at::_ops::_conj_copy::call(self_); |
| 19740 | } |
| 19741 | } |
| 19742 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 19743 | [reapply_views = reapply_views](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 19744 | if (reapply_views) { |
| 19745 | return at::_ops::_conj::call(base); |
| 19746 | } else { |
| 19747 | return at::_ops::_conj_copy::call(base); |
| 19748 | } |
| 19749 | }, |
| 19750 | [reapply_views = reapply_views](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 19751 | return at::functionalization::FunctionalInverses::_conj_copy_inverse(base, mutated_view, reapply_views); |
| 19752 | } |
| 19753 | ); |
| 19754 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 19755 | // See Note [Propagating strides in the functionalization pass] |
| 19756 | if (compute_reference_meta) { |
| 19757 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 19758 | } |
| 19759 | return out; |
| 19760 | } |
| 19761 | |
| 19762 | at::Tensor as_strided(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional<c10::SymInt> storage_offset) { |
| 19763 | |
| 19764 | at::Tensor self_; |
| 19765 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19766 | |
| 19767 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19768 | } else { |
| 19769 | self_ = self; |
| 19770 | } |
| 19771 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 19772 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 19773 | at::AutoDispatchSkipFunctionalize guard; |
| 19774 | return at::_ops::as_strided::call(self_, size, stride, storage_offset); |
| 19775 | } |
| 19776 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 19777 | auto compute_reference_meta = |
| 19778 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 19779 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 19780 | at::Tensor reference_tensor_output; |
| 19781 | if (compute_reference_meta) { |
| 19782 | auto self_meta = to_meta(self); |
| 19783 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19784 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19785 | reference_tensor_output = at::_ops::as_strided::call(self_meta, size, stride, storage_offset); |
| 19786 | } |
| 19787 | at::Tensor tmp_output; |
| 19788 | { |
| 19789 | at::AutoDispatchSkipFunctionalize guard; |
| 19790 | if (reapply_views) { |
| 19791 | tmp_output = at::_ops::as_strided::call(self_, size, stride, storage_offset); |
| 19792 | } else { |
| 19793 | tmp_output = at::_ops::as_strided_copy::call(self_, size, stride, storage_offset); |
| 19794 | } |
| 19795 | } |
| 19796 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 19797 | [reapply_views = reapply_views, size = size.vec(), stride = stride.vec(), storage_offset = storage_offset](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 19798 | if (reapply_views) { |
| 19799 | return at::_ops::as_strided::call(base, size, stride, storage_offset); |
| 19800 | } else { |
| 19801 | return at::_ops::as_strided_copy::call(base, size, stride, storage_offset); |
| 19802 | } |
| 19803 | }, |
| 19804 | [reapply_views = reapply_views, size = size.vec(), stride = stride.vec(), storage_offset = storage_offset](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 19805 | return at::functionalization::FunctionalInverses::as_strided_copy_inverse(base, mutated_view, reapply_views, size, stride, storage_offset); |
| 19806 | } |
| 19807 | ); |
| 19808 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 19809 | // See Note [Propagating strides in the functionalization pass] |
| 19810 | if (compute_reference_meta) { |
| 19811 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 19812 | } |
| 19813 | return out; |
| 19814 | } |
| 19815 | |
| 19816 | const at::Tensor & as_strided_(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional<c10::SymInt> storage_offset) { |
| 19817 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 19818 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 19819 | |
| 19820 | at::Tensor self_; |
| 19821 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19822 | |
| 19823 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19824 | } else { |
| 19825 | self_ = self; |
| 19826 | } |
| 19827 | at::AutoDispatchSkipFunctionalize guard; |
| 19828 | return at::_ops::as_strided_::call(self_, size, stride, storage_offset); |
| 19829 | } |
| 19830 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 19831 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 19832 | [reapply_views = reapply_views, size = size.vec(), stride = stride.vec(), storage_offset = storage_offset](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 19833 | if (reapply_views) { |
| 19834 | return at::_ops::as_strided::call(base, size, stride, storage_offset); |
| 19835 | } else { |
| 19836 | return at::_ops::as_strided_copy::call(base, size, stride, storage_offset); |
| 19837 | } |
| 19838 | }, |
| 19839 | [reapply_views = reapply_views, size = size.vec(), stride = stride.vec(), storage_offset = storage_offset](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 19840 | return at::functionalization::FunctionalInverses::as_strided_copy_inverse(base, mutated_view, reapply_views, size, stride, storage_offset); |
| 19841 | } |
| 19842 | ); |
| 19843 | auto compute_reference_meta = |
| 19844 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 19845 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 19846 | at::Tensor reference_tensor_output; |
| 19847 | if (compute_reference_meta) { |
| 19848 | auto self_meta = to_meta(self); |
| 19849 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19850 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19851 | reference_tensor_output = at::_ops::as_strided_::call(self_meta, size, stride, storage_offset); |
| 19852 | } |
| 19853 | // This function adds the above view meta to the current tensor and replays them off the base, |
| 19854 | // mutating the size/stride info of the current FunctionalTensorWrapper. |
| 19855 | // Because of this, we need to make sure to run the reference shape function above, |
| 19856 | // BEFORE doing this (otherwise we'll end up runnin the reference function using the wrong sizes/strides) |
| 19857 | at::functionalization::impl::mutate_view_meta(self, view_meta); |
| 19858 | // See Note [Propagating strides in the functionalization pass] |
| 19859 | // XLA/LTC don't implement the logic to propagate strides correctly, so we need to rely |
| 19860 | // on a reference implementation here (instead of relying on the output from the forward lambda |
| 19861 | // having the correct stride info) |
| 19862 | if (compute_reference_meta) { |
| 19863 | at::functionalization::impl::set_sizes_strides_offset(self, reference_tensor_output); |
| 19864 | } |
| 19865 | return self; |
| 19866 | } |
| 19867 | |
| 19868 | at::Tensor _sparse_broadcast_to(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size) { |
| 19869 | |
| 19870 | at::Tensor self_; |
| 19871 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19872 | |
| 19873 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19874 | } else { |
| 19875 | self_ = self; |
| 19876 | } |
| 19877 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 19878 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 19879 | at::AutoDispatchSkipFunctionalize guard; |
| 19880 | return at::_ops::_sparse_broadcast_to::call(self_, size); |
| 19881 | } |
| 19882 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 19883 | auto compute_reference_meta = |
| 19884 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 19885 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 19886 | at::Tensor reference_tensor_output; |
| 19887 | if (compute_reference_meta) { |
| 19888 | auto self_meta = to_meta(self); |
| 19889 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19890 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19891 | reference_tensor_output = at::_ops::_sparse_broadcast_to::call(self_meta, size); |
| 19892 | } |
| 19893 | at::Tensor tmp_output; |
| 19894 | { |
| 19895 | at::AutoDispatchSkipFunctionalize guard; |
| 19896 | if (reapply_views) { |
| 19897 | tmp_output = at::_ops::_sparse_broadcast_to::call(self_, size); |
| 19898 | } else { |
| 19899 | tmp_output = at::_ops::_sparse_broadcast_to_copy::call(self_, size); |
| 19900 | } |
| 19901 | } |
| 19902 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 19903 | [reapply_views = reapply_views, size = size.vec()](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 19904 | if (reapply_views) { |
| 19905 | return at::_ops::_sparse_broadcast_to::call(base, size); |
| 19906 | } else { |
| 19907 | return at::_ops::_sparse_broadcast_to_copy::call(base, size); |
| 19908 | } |
| 19909 | }, |
| 19910 | [reapply_views = reapply_views, size = size.vec()](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 19911 | return at::functionalization::FunctionalInverses::_sparse_broadcast_to_copy_inverse(base, mutated_view, reapply_views, size); |
| 19912 | } |
| 19913 | ); |
| 19914 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 19915 | // See Note [Propagating strides in the functionalization pass] |
| 19916 | if (compute_reference_meta) { |
| 19917 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 19918 | } |
| 19919 | return out; |
| 19920 | } |
| 19921 | |
| 19922 | at::Tensor permute(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dims) { |
| 19923 | |
| 19924 | at::Tensor self_; |
| 19925 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19926 | |
| 19927 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19928 | } else { |
| 19929 | self_ = self; |
| 19930 | } |
| 19931 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 19932 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 19933 | at::AutoDispatchSkipFunctionalize guard; |
| 19934 | return at::_ops::permute::call(self_, dims); |
| 19935 | } |
| 19936 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 19937 | auto compute_reference_meta = |
| 19938 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 19939 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 19940 | at::Tensor reference_tensor_output; |
| 19941 | if (compute_reference_meta) { |
| 19942 | auto self_meta = to_meta(self); |
| 19943 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19944 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19945 | reference_tensor_output = at::_ops::permute::call(self_meta, dims); |
| 19946 | } |
| 19947 | at::Tensor tmp_output; |
| 19948 | { |
| 19949 | at::AutoDispatchSkipFunctionalize guard; |
| 19950 | if (reapply_views) { |
| 19951 | tmp_output = at::_ops::permute::call(self_, dims); |
| 19952 | } else { |
| 19953 | tmp_output = at::_ops::permute_copy::call(self_, dims); |
| 19954 | } |
| 19955 | } |
| 19956 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 19957 | [reapply_views = reapply_views, dims = dims.vec()](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 19958 | if (reapply_views) { |
| 19959 | return at::_ops::permute::call(base, dims); |
| 19960 | } else { |
| 19961 | return at::_ops::permute_copy::call(base, dims); |
| 19962 | } |
| 19963 | }, |
| 19964 | [reapply_views = reapply_views, dims = dims.vec()](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 19965 | return at::functionalization::FunctionalInverses::permute_copy_inverse(base, mutated_view, reapply_views, dims); |
| 19966 | } |
| 19967 | ); |
| 19968 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 19969 | // See Note [Propagating strides in the functionalization pass] |
| 19970 | if (compute_reference_meta) { |
| 19971 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 19972 | } |
| 19973 | return out; |
| 19974 | } |
| 19975 | |
| 19976 | at::Tensor _reshape_alias(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) { |
| 19977 | |
| 19978 | at::Tensor self_; |
| 19979 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 19980 | |
| 19981 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 19982 | } else { |
| 19983 | self_ = self; |
| 19984 | } |
| 19985 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 19986 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 19987 | at::AutoDispatchSkipFunctionalize guard; |
| 19988 | return at::_ops::_reshape_alias::call(self_, size, stride); |
| 19989 | } |
| 19990 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 19991 | auto compute_reference_meta = |
| 19992 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 19993 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 19994 | at::Tensor reference_tensor_output; |
| 19995 | if (compute_reference_meta) { |
| 19996 | auto self_meta = to_meta(self); |
| 19997 | at::AutoDispatchSkipFunctionalize func_guard; |
| 19998 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 19999 | reference_tensor_output = at::_ops::_reshape_alias::call(self_meta, size, stride); |
| 20000 | } |
| 20001 | at::Tensor tmp_output; |
| 20002 | { |
| 20003 | at::AutoDispatchSkipFunctionalize guard; |
| 20004 | if (reapply_views) { |
| 20005 | tmp_output = at::_ops::_reshape_alias::call(self_, size, stride); |
| 20006 | } else { |
| 20007 | tmp_output = at::_ops::_reshape_alias_copy::call(self_, size, stride); |
| 20008 | } |
| 20009 | } |
| 20010 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 20011 | [reapply_views = reapply_views, size = size.vec(), stride = stride.vec()](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 20012 | if (reapply_views) { |
| 20013 | return at::_ops::_reshape_alias::call(base, size, stride); |
| 20014 | } else { |
| 20015 | return at::_ops::_reshape_alias_copy::call(base, size, stride); |
| 20016 | } |
| 20017 | }, |
| 20018 | [reapply_views = reapply_views, size = size.vec(), stride = stride.vec()](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 20019 | return at::functionalization::FunctionalInverses::_reshape_alias_copy_inverse(base, mutated_view, reapply_views, size, stride); |
| 20020 | } |
| 20021 | ); |
| 20022 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 20023 | // See Note [Propagating strides in the functionalization pass] |
| 20024 | if (compute_reference_meta) { |
| 20025 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 20026 | } |
| 20027 | return out; |
| 20028 | } |
| 20029 | |
| 20030 | at::Tensor detach(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 20031 | |
| 20032 | at::Tensor self_; |
| 20033 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20034 | |
| 20035 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20036 | } else { |
| 20037 | self_ = self; |
| 20038 | } |
| 20039 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 20040 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 20041 | at::AutoDispatchSkipFunctionalize guard; |
| 20042 | return at::_ops::detach::call(self_); |
| 20043 | } |
| 20044 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 20045 | auto compute_reference_meta = |
| 20046 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 20047 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 20048 | at::Tensor reference_tensor_output; |
| 20049 | if (compute_reference_meta) { |
| 20050 | auto self_meta = to_meta(self); |
| 20051 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20052 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20053 | reference_tensor_output = at::_ops::detach::call(self_meta); |
| 20054 | } |
| 20055 | at::Tensor tmp_output; |
| 20056 | { |
| 20057 | at::AutoDispatchSkipFunctionalize guard; |
| 20058 | if (reapply_views) { |
| 20059 | tmp_output = at::_ops::detach::call(self_); |
| 20060 | } else { |
| 20061 | tmp_output = at::_ops::detach_copy::call(self_); |
| 20062 | } |
| 20063 | } |
| 20064 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 20065 | [reapply_views = reapply_views](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 20066 | if (reapply_views) { |
| 20067 | return at::_ops::detach::call(base); |
| 20068 | } else { |
| 20069 | return at::_ops::detach_copy::call(base); |
| 20070 | } |
| 20071 | }, |
| 20072 | [reapply_views = reapply_views](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 20073 | return at::functionalization::FunctionalInverses::detach_copy_inverse(base, mutated_view, reapply_views); |
| 20074 | } |
| 20075 | ); |
| 20076 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 20077 | // See Note [Propagating strides in the functionalization pass] |
| 20078 | if (compute_reference_meta) { |
| 20079 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 20080 | } |
| 20081 | return out; |
| 20082 | } |
| 20083 | |
| 20084 | at::Tensor & detach_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 20085 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 20086 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 20087 | |
| 20088 | at::Tensor self_; |
| 20089 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20090 | |
| 20091 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20092 | } else { |
| 20093 | self_ = self; |
| 20094 | } |
| 20095 | at::AutoDispatchSkipFunctionalize guard; |
| 20096 | return at::_ops::detach_::call(self_); |
| 20097 | } |
| 20098 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 20099 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 20100 | [reapply_views = reapply_views](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 20101 | if (reapply_views) { |
| 20102 | return at::_ops::detach::call(base); |
| 20103 | } else { |
| 20104 | return at::_ops::detach_copy::call(base); |
| 20105 | } |
| 20106 | }, |
| 20107 | [reapply_views = reapply_views](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 20108 | return at::functionalization::FunctionalInverses::detach_copy_inverse(base, mutated_view, reapply_views); |
| 20109 | } |
| 20110 | ); |
| 20111 | auto compute_reference_meta = |
| 20112 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 20113 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 20114 | at::Tensor reference_tensor_output; |
| 20115 | if (compute_reference_meta) { |
| 20116 | auto self_meta = to_meta(self); |
| 20117 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20118 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20119 | reference_tensor_output = at::_ops::detach_::call(self_meta); |
| 20120 | } |
| 20121 | // This function adds the above view meta to the current tensor and replays them off the base, |
| 20122 | // mutating the size/stride info of the current FunctionalTensorWrapper. |
| 20123 | // Because of this, we need to make sure to run the reference shape function above, |
| 20124 | // BEFORE doing this (otherwise we'll end up runnin the reference function using the wrong sizes/strides) |
| 20125 | at::functionalization::impl::mutate_view_meta(self, view_meta); |
| 20126 | // See Note [Propagating strides in the functionalization pass] |
| 20127 | // XLA/LTC don't implement the logic to propagate strides correctly, so we need to rely |
| 20128 | // on a reference implementation here (instead of relying on the output from the forward lambda |
| 20129 | // having the correct stride info) |
| 20130 | if (compute_reference_meta) { |
| 20131 | at::functionalization::impl::set_sizes_strides_offset(self, reference_tensor_output); |
| 20132 | } |
| 20133 | return self; |
| 20134 | } |
| 20135 | |
| 20136 | ::std::vector<at::Tensor> split_Tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt split_size, int64_t dim) { |
| 20137 | |
| 20138 | at::Tensor self_; |
| 20139 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20140 | |
| 20141 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20142 | } else { |
| 20143 | self_ = self; |
| 20144 | } |
| 20145 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 20146 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 20147 | at::AutoDispatchSkipFunctionalize guard; |
| 20148 | return at::_ops::split_Tensor::call(self_, split_size, dim); |
| 20149 | } |
| 20150 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 20151 | auto compute_reference_meta = |
| 20152 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 20153 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 20154 | ::std::vector<at::Tensor> reference_tensor_output; |
| 20155 | if (compute_reference_meta) { |
| 20156 | auto self_meta = to_meta(self); |
| 20157 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20158 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20159 | reference_tensor_output = at::_ops::split_Tensor::call(self_meta, split_size, dim); |
| 20160 | } |
| 20161 | ::std::vector<at::Tensor> tmp_output; |
| 20162 | { |
| 20163 | at::AutoDispatchSkipFunctionalize guard; |
| 20164 | if (reapply_views) { |
| 20165 | tmp_output = at::_ops::split_Tensor::call(self_, split_size, dim); |
| 20166 | } else { |
| 20167 | tmp_output = at::_ops::split_copy_Tensor::call(self_, split_size, dim); |
| 20168 | } |
| 20169 | } |
| 20170 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 20171 | [reapply_views = reapply_views, split_size = split_size, dim = dim](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 20172 | if (reapply_views) { |
| 20173 | return at::_ops::split_Tensor::call(base, split_size, dim)[mutated_view_idx]; |
| 20174 | } else { |
| 20175 | return at::_ops::split_copy_Tensor::call(base, split_size, dim)[mutated_view_idx]; |
| 20176 | } |
| 20177 | }, |
| 20178 | [reapply_views = reapply_views, split_size = split_size, dim = dim](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 20179 | return at::functionalization::FunctionalInverses::split_copy_Tensor_inverse(base, mutated_view, reapply_views, mutated_view_idx, split_size, dim); |
| 20180 | } |
| 20181 | ); |
| 20182 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 20183 | // See Note [Propagating strides in the functionalization pass] |
| 20184 | if (compute_reference_meta) { |
| 20185 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 20186 | } |
| 20187 | return out; |
| 20188 | } |
| 20189 | |
| 20190 | at::Tensor values(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 20191 | |
| 20192 | at::Tensor self_; |
| 20193 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20194 | |
| 20195 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20196 | } else { |
| 20197 | self_ = self; |
| 20198 | } |
| 20199 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 20200 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 20201 | at::AutoDispatchSkipFunctionalize guard; |
| 20202 | return at::_ops::values::call(self_); |
| 20203 | } |
| 20204 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 20205 | auto compute_reference_meta = |
| 20206 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 20207 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 20208 | at::Tensor reference_tensor_output; |
| 20209 | if (compute_reference_meta) { |
| 20210 | auto self_meta = to_meta(self); |
| 20211 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20212 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20213 | reference_tensor_output = at::_ops::values::call(self_meta); |
| 20214 | } |
| 20215 | at::Tensor tmp_output; |
| 20216 | { |
| 20217 | at::AutoDispatchSkipFunctionalize guard; |
| 20218 | if (reapply_views) { |
| 20219 | tmp_output = at::_ops::values::call(self_); |
| 20220 | } else { |
| 20221 | tmp_output = at::_ops::values_copy::call(self_); |
| 20222 | } |
| 20223 | } |
| 20224 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 20225 | [reapply_views = reapply_views](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 20226 | if (reapply_views) { |
| 20227 | return at::_ops::values::call(base); |
| 20228 | } else { |
| 20229 | return at::_ops::values_copy::call(base); |
| 20230 | } |
| 20231 | }, |
| 20232 | [reapply_views = reapply_views](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 20233 | return at::functionalization::FunctionalInverses::values_copy_inverse(base, mutated_view, reapply_views); |
| 20234 | } |
| 20235 | ); |
| 20236 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 20237 | // See Note [Propagating strides in the functionalization pass] |
| 20238 | if (compute_reference_meta) { |
| 20239 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 20240 | } |
| 20241 | return out; |
| 20242 | } |
| 20243 | |
| 20244 | at::Tensor row_indices(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 20245 | |
| 20246 | at::Tensor self_; |
| 20247 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20248 | |
| 20249 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20250 | } else { |
| 20251 | self_ = self; |
| 20252 | } |
| 20253 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 20254 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 20255 | at::AutoDispatchSkipFunctionalize guard; |
| 20256 | return at::_ops::row_indices::call(self_); |
| 20257 | } |
| 20258 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 20259 | auto compute_reference_meta = |
| 20260 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 20261 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 20262 | at::Tensor reference_tensor_output; |
| 20263 | if (compute_reference_meta) { |
| 20264 | auto self_meta = to_meta(self); |
| 20265 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20266 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20267 | reference_tensor_output = at::_ops::row_indices::call(self_meta); |
| 20268 | } |
| 20269 | at::Tensor tmp_output; |
| 20270 | { |
| 20271 | at::AutoDispatchSkipFunctionalize guard; |
| 20272 | if (reapply_views) { |
| 20273 | tmp_output = at::_ops::row_indices::call(self_); |
| 20274 | } else { |
| 20275 | tmp_output = at::_ops::row_indices_copy::call(self_); |
| 20276 | } |
| 20277 | } |
| 20278 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 20279 | [reapply_views = reapply_views](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 20280 | if (reapply_views) { |
| 20281 | return at::_ops::row_indices::call(base); |
| 20282 | } else { |
| 20283 | return at::_ops::row_indices_copy::call(base); |
| 20284 | } |
| 20285 | }, |
| 20286 | [reapply_views = reapply_views](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 20287 | return at::functionalization::FunctionalInverses::row_indices_copy_inverse(base, mutated_view, reapply_views); |
| 20288 | } |
| 20289 | ); |
| 20290 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 20291 | // See Note [Propagating strides in the functionalization pass] |
| 20292 | if (compute_reference_meta) { |
| 20293 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 20294 | } |
| 20295 | return out; |
| 20296 | } |
| 20297 | |
| 20298 | at::Tensor lift_fresh(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 20299 | |
| 20300 | at::Tensor self_; |
| 20301 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20302 | |
| 20303 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20304 | } else { |
| 20305 | self_ = self; |
| 20306 | } |
| 20307 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 20308 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 20309 | at::AutoDispatchSkipFunctionalize guard; |
| 20310 | return at::_ops::lift_fresh::call(self_); |
| 20311 | } |
| 20312 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 20313 | auto compute_reference_meta = |
| 20314 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 20315 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 20316 | at::Tensor reference_tensor_output; |
| 20317 | if (compute_reference_meta) { |
| 20318 | auto self_meta = to_meta(self); |
| 20319 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20320 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20321 | reference_tensor_output = at::_ops::lift_fresh::call(self_meta); |
| 20322 | } |
| 20323 | at::Tensor tmp_output; |
| 20324 | { |
| 20325 | at::AutoDispatchSkipFunctionalize guard; |
| 20326 | if (reapply_views) { |
| 20327 | tmp_output = at::_ops::lift_fresh::call(self_); |
| 20328 | } else { |
| 20329 | tmp_output = at::_ops::lift_fresh_copy::call(self_); |
| 20330 | } |
| 20331 | } |
| 20332 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 20333 | [reapply_views = reapply_views](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 20334 | if (reapply_views) { |
| 20335 | return at::_ops::lift_fresh::call(base); |
| 20336 | } else { |
| 20337 | return at::_ops::lift_fresh_copy::call(base); |
| 20338 | } |
| 20339 | }, |
| 20340 | [reapply_views = reapply_views](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 20341 | return at::functionalization::FunctionalInverses::lift_fresh_copy_inverse(base, mutated_view, reapply_views); |
| 20342 | } |
| 20343 | ); |
| 20344 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 20345 | // See Note [Propagating strides in the functionalization pass] |
| 20346 | if (compute_reference_meta) { |
| 20347 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 20348 | } |
| 20349 | return out; |
| 20350 | } |
| 20351 | |
| 20352 | at::Tensor _test_autograd_multiple_dispatch_view(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 20353 | |
| 20354 | at::Tensor self_; |
| 20355 | if (at::functionalization::impl::isFunctionalTensor(self)) { |
| 20356 | |
| 20357 | self_ = at::functionalization::impl::from_functional_tensor(self); |
| 20358 | } else { |
| 20359 | self_ = self; |
| 20360 | } |
| 20361 | if (!at::functionalization::impl::isFunctionalTensor(self)) { |
| 20362 | // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper. |
| 20363 | at::AutoDispatchSkipFunctionalize guard; |
| 20364 | return at::_ops::_test_autograd_multiple_dispatch_view::call(self_); |
| 20365 | } |
| 20366 | auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS(); |
| 20367 | auto compute_reference_meta = |
| 20368 | self.key_set().has_backend(c10::BackendComponent::XLABit) || |
| 20369 | self.key_set().has_backend(c10::BackendComponent::LazyBit); |
| 20370 | at::Tensor reference_tensor_output; |
| 20371 | if (compute_reference_meta) { |
| 20372 | auto self_meta = to_meta(self); |
| 20373 | at::AutoDispatchSkipFunctionalize func_guard; |
| 20374 | c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch); |
| 20375 | reference_tensor_output = at::_ops::_test_autograd_multiple_dispatch_view::call(self_meta); |
| 20376 | } |
| 20377 | at::Tensor tmp_output; |
| 20378 | { |
| 20379 | at::AutoDispatchSkipFunctionalize guard; |
| 20380 | if (reapply_views) { |
| 20381 | tmp_output = at::_ops::_test_autograd_multiple_dispatch_view::call(self_); |
| 20382 | } else { |
| 20383 | tmp_output = at::_ops::_test_autograd_multiple_dispatch_view_copy::call(self_); |
| 20384 | } |
| 20385 | } |
| 20386 | at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta( |
| 20387 | [reapply_views = reapply_views](const at::Tensor & base, int64_t mutated_view_idx) -> at::Tensor { |
| 20388 | if (reapply_views) { |
| 20389 | return at::_ops::_test_autograd_multiple_dispatch_view::call(base); |
| 20390 | } else { |
| 20391 | return at::_ops::_test_autograd_multiple_dispatch_view_copy::call(base); |
| 20392 | } |
| 20393 | }, |
| 20394 | [reapply_views = reapply_views](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx) -> at::Tensor { |
| 20395 | return at::functionalization::FunctionalInverses::_test_autograd_multiple_dispatch_view_copy_inverse(base, mutated_view, reapply_views); |
| 20396 | } |
| 20397 | ); |
| 20398 | auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, view_meta); |
| 20399 | // See Note [Propagating strides in the functionalization pass] |
| 20400 | if (compute_reference_meta) { |
| 20401 | at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output); |
| 20402 | } |
| 20403 | return out; |
| 20404 | } |
| 20405 | |
| 20406 | } // namespace functionalization |
| 20407 | |
| 20408 | namespace { |
| 20409 | |
| 20410 | TORCH_LIBRARY_IMPL(aten, Functionalize, m) { |
| 20411 | m.impl("_cudnn_ctc_loss.out", TORCH_FN(functionalization::_cudnn_ctc_loss_out_out)); |
| 20412 | m.impl("_cudnn_rnn.out", TORCH_FN(functionalization::_cudnn_rnn_out_out)); |
| 20413 | m.impl("_cudnn_rnn_backward.out", TORCH_FN(functionalization::_cudnn_rnn_backward_out_out)); |
| 20414 | m.impl("_fused_dropout.out", TORCH_FN(functionalization::_fused_dropout_out_out)); |
| 20415 | m.impl("conj_physical", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::conj_physical)); |
| 20416 | m.impl("conj_physical.out", TORCH_FN(functionalization::conj_physical_out_out)); |
| 20417 | m.impl("conj_physical_", TORCH_FN(functionalization::conj_physical_)); |
| 20418 | m.impl("add.out", TORCH_FN(functionalization::add_out_out)); |
| 20419 | m.impl("add_.Tensor", TORCH_FN(functionalization::add__Tensor)); |
| 20420 | m.impl("add.Scalar_out", TORCH_FN(functionalization::add_out_Scalar_out)); |
| 20421 | m.impl("add_.Scalar", TORCH_FN(functionalization::add__Scalar)); |
| 20422 | m.impl("addmv.out", TORCH_FN(functionalization::addmv_out_out)); |
| 20423 | m.impl("addmv_", TORCH_FN(functionalization::addmv_)); |
| 20424 | m.impl("addr.out", TORCH_FN(functionalization::addr_out_out)); |
| 20425 | m.impl("addr_", TORCH_FN(functionalization::addr_)); |
| 20426 | m.impl("all.out", TORCH_FN(functionalization::all_out_out)); |
| 20427 | m.impl("all.dimname", static_cast<at::Tensor (*)(const at::Tensor & self, at::Dimname dim, bool keepdim)>(at::native::all)); |
| 20428 | m.impl("all.dimname_out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & out)>(at::native::all_out)); |
| 20429 | m.impl("argmax.out", TORCH_FN(functionalization::argmax_out_out)); |
| 20430 | m.impl("atan.out", TORCH_FN(functionalization::atan_out_out)); |
| 20431 | m.impl("atan_", TORCH_FN(functionalization::atan_)); |
| 20432 | m.impl("bartlett_window.out", TORCH_FN(functionalization::bartlett_window_out_out)); |
| 20433 | m.impl("bartlett_window.periodic_out", TORCH_FN(functionalization::bartlett_window_out_periodic_out)); |
| 20434 | m.impl("binary_cross_entropy.out", TORCH_FN(functionalization::binary_cross_entropy_out_out)); |
| 20435 | m.impl("bitwise_not.out", TORCH_FN(functionalization::bitwise_not_out_out)); |
| 20436 | m.impl("bitwise_not_", TORCH_FN(functionalization::bitwise_not_)); |
| 20437 | m.impl("logical_xor.out", TORCH_FN(functionalization::logical_xor_out_out)); |
| 20438 | m.impl("logical_xor_", TORCH_FN(functionalization::logical_xor_)); |
| 20439 | m.impl("blackman_window.out", TORCH_FN(functionalization::blackman_window_out_out)); |
| 20440 | m.impl("blackman_window.periodic_out", TORCH_FN(functionalization::blackman_window_out_periodic_out)); |
| 20441 | m.impl("clamp.out", TORCH_FN(functionalization::clamp_out_out)); |
| 20442 | m.impl("clamp_", TORCH_FN(functionalization::clamp_)); |
| 20443 | m.impl("clamp.Tensor_out", TORCH_FN(functionalization::clamp_out_Tensor_out)); |
| 20444 | m.impl("clamp_.Tensor", TORCH_FN(functionalization::clamp__Tensor)); |
| 20445 | m.impl("_convolution.out", TORCH_FN(functionalization::_convolution_out_out)); |
| 20446 | m.impl("copy.out", TORCH_FN(functionalization::copy_out_out)); |
| 20447 | m.impl("copy_", TORCH_FN(functionalization::copy_)); |
| 20448 | m.impl("cudnn_convolution.out", TORCH_FN(functionalization::cudnn_convolution_out_out)); |
| 20449 | m.impl("divide.Tensor", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other)>(at::native::divide)); |
| 20450 | m.impl("divide.out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & other, at::Tensor & out)>(at::native::divide_out)); |
| 20451 | m.impl("divide_.Tensor", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Tensor & other)>(at::native::divide_)); |
| 20452 | m.impl("divide.Tensor_mode", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode)>(at::native::divide)); |
| 20453 | m.impl("divide.out_mode", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode, at::Tensor & out)>(at::native::divide_out)); |
| 20454 | m.impl("divide_.Tensor_mode", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode)>(at::native::divide_)); |
| 20455 | m.impl("true_divide.Tensor", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other)>(at::native::true_divide)); |
| 20456 | m.impl("true_divide.out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & other, at::Tensor & out)>(at::native::true_divide_out)); |
| 20457 | m.impl("true_divide_.Tensor", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Tensor & other)>(at::native::true_divide_)); |
| 20458 | m.impl("dot.out", TORCH_FN(functionalization::dot_out_out)); |
| 20459 | m.impl("vdot.out", TORCH_FN(functionalization::vdot_out_out)); |
| 20460 | m.impl("row_stack", static_cast<at::Tensor (*)(at::TensorList tensors)>(at::native::row_stack)); |
| 20461 | m.impl("row_stack.out", static_cast<at::Tensor & (*)(at::TensorList tensors, at::Tensor & out)>(at::native::row_stack_out)); |
| 20462 | m.impl("new_empty.out", TORCH_FN(functionalization::new_empty_out_out)); |
| 20463 | m.impl("new_empty_strided.out", TORCH_FN(functionalization::new_empty_strided_out_out)); |
| 20464 | m.impl("_empty_affine_quantized.out", TORCH_FN(functionalization::_empty_affine_quantized_out_out)); |
| 20465 | m.impl("_resize_output.out", TORCH_FN(functionalization::_resize_output_out_out)); |
| 20466 | m.impl("_resize_output_", TORCH_FN(functionalization::_resize_output_)); |
| 20467 | m.impl("frac.out", TORCH_FN(functionalization::frac_out_out)); |
| 20468 | m.impl("frac_", TORCH_FN(functionalization::frac_)); |
| 20469 | m.impl("full_like.out", TORCH_FN(functionalization::full_like_out_out)); |
| 20470 | m.impl("lcm.out", TORCH_FN(functionalization::lcm_out_out)); |
| 20471 | m.impl("lcm_", TORCH_FN(functionalization::lcm_)); |
| 20472 | m.impl("hann_window.out", TORCH_FN(functionalization::hann_window_out_out)); |
| 20473 | m.impl("hann_window.periodic_out", TORCH_FN(functionalization::hann_window_out_periodic_out)); |
| 20474 | m.impl("kaiser_window.out", TORCH_FN(functionalization::kaiser_window_out_out)); |
| 20475 | m.impl("kaiser_window.periodic_out", TORCH_FN(functionalization::kaiser_window_out_periodic_out)); |
| 20476 | m.impl("kaiser_window.beta_out", TORCH_FN(functionalization::kaiser_window_out_beta_out)); |
| 20477 | m.impl("_index_put_impl.out", TORCH_FN(functionalization::_index_put_impl_out_out)); |
| 20478 | m.impl("_index_put_impl_", TORCH_FN(functionalization::_index_put_impl_)); |
| 20479 | m.impl("kthvalue.values", TORCH_FN(functionalization::kthvalue_out_values)); |
| 20480 | m.impl("kthvalue.dimname", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & self, int64_t k, at::Dimname dim, bool keepdim)>(at::native::kthvalue)); |
| 20481 | m.impl("kthvalue.dimname_out", static_cast<::std::tuple<at::Tensor &,at::Tensor &> (*)(const at::Tensor & self, int64_t k, at::Dimname dim, bool keepdim, at::Tensor & values, at::Tensor & indices)>(at::native::kthvalue_out)); |
| 20482 | m.impl("native_layer_norm.out", TORCH_FN(functionalization::native_layer_norm_out_out)); |
| 20483 | m.impl("native_layer_norm_backward.out", TORCH_FN(functionalization::native_layer_norm_backward_out_out)); |
| 20484 | m.impl("mkldnn_linear_backward_input.out", TORCH_FN(functionalization::mkldnn_linear_backward_input_out_out)); |
| 20485 | m.impl("mkldnn_linear_backward.out", TORCH_FN(functionalization::mkldnn_linear_backward_out_out)); |
| 20486 | m.impl("log10.out", TORCH_FN(functionalization::log10_out_out)); |
| 20487 | m.impl("log10_", TORCH_FN(functionalization::log10_)); |
| 20488 | m.impl("log1p.out", TORCH_FN(functionalization::log1p_out_out)); |
| 20489 | m.impl("log1p_", TORCH_FN(functionalization::log1p_)); |
| 20490 | m.impl("logsumexp.out", TORCH_FN(functionalization::logsumexp_out_out)); |
| 20491 | m.impl("logsumexp.names", static_cast<at::Tensor (*)(const at::Tensor & self, at::DimnameList dim, bool keepdim)>(at::native::logsumexp)); |
| 20492 | m.impl("logsumexp.names_out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::DimnameList dim, bool keepdim, at::Tensor & out)>(at::native::logsumexp_out)); |
| 20493 | m.impl("matmul", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other)>(at::native::matmul)); |
| 20494 | m.impl("matmul.out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & other, at::Tensor & out)>(at::native::matmul_out)); |
| 20495 | m.impl("mkldnn_max_pool2d.out", TORCH_FN(functionalization::mkldnn_max_pool2d_out_out)); |
| 20496 | m.impl("quantized_max_pool1d.out", TORCH_FN(functionalization::quantized_max_pool1d_out_out)); |
| 20497 | m.impl("nanmean", static_cast<at::Tensor (*)(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype)>(at::native::nanmean)); |
| 20498 | m.impl("nanmean.out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out)>(at::native::nanmean_out)); |
| 20499 | m.impl("_mps_convolution.out", TORCH_FN(functionalization::_mps_convolution_out_out)); |
| 20500 | m.impl("mkldnn_convolution.out", TORCH_FN(functionalization::mkldnn_convolution_out_out)); |
| 20501 | m.impl("mkldnn_rnn_layer.out", TORCH_FN(functionalization::mkldnn_rnn_layer_out_out)); |
| 20502 | m.impl("miopen_batch_norm.out", TORCH_FN(functionalization::miopen_batch_norm_out_out)); |
| 20503 | m.impl("miopen_batch_norm_backward.out", TORCH_FN(functionalization::miopen_batch_norm_backward_out_out)); |
| 20504 | m.impl("miopen_convolution_transpose.out", TORCH_FN(functionalization::miopen_convolution_transpose_out_out)); |
| 20505 | m.impl("mm.out", TORCH_FN(functionalization::mm_out_out)); |
| 20506 | m.impl("_sparse_sparse_matmul.out", TORCH_FN(functionalization::_sparse_sparse_matmul_out_out)); |
| 20507 | m.impl("mul.out", TORCH_FN(functionalization::mul_out_out)); |
| 20508 | m.impl("mul_.Tensor", TORCH_FN(functionalization::mul__Tensor)); |
| 20509 | m.impl("mul.Scalar_out", TORCH_FN(functionalization::mul_out_Scalar_out)); |
| 20510 | m.impl("mul_.Scalar", TORCH_FN(functionalization::mul__Scalar)); |
| 20511 | m.impl("mvlgamma.out", TORCH_FN(functionalization::mvlgamma_out_out)); |
| 20512 | m.impl("mvlgamma_", TORCH_FN(functionalization::mvlgamma_)); |
| 20513 | m.impl("batch_norm_backward_reduce.out", TORCH_FN(functionalization::batch_norm_backward_reduce_out_out)); |
| 20514 | m.impl("deg2rad.out", TORCH_FN(functionalization::deg2rad_out_out)); |
| 20515 | m.impl("deg2rad_", TORCH_FN(functionalization::deg2rad_)); |
| 20516 | m.impl("randint_like.out", TORCH_FN(functionalization::randint_like_out_out)); |
| 20517 | m.impl("randint_like.low_dtype_out", TORCH_FN(functionalization::randint_like_out_low_dtype_out)); |
| 20518 | m.impl("repeat.out", TORCH_FN(functionalization::repeat_out_out)); |
| 20519 | m.impl("_mkldnn_reshape.out", TORCH_FN(functionalization::_mkldnn_reshape_out_out)); |
| 20520 | m.impl("round.out", TORCH_FN(functionalization::round_out_out)); |
| 20521 | m.impl("round_", TORCH_FN(functionalization::round_)); |
| 20522 | m.impl("round.decimals_out", TORCH_FN(functionalization::round_out_decimals_out)); |
| 20523 | m.impl("round_.decimals", TORCH_FN(functionalization::round__decimals)); |
| 20524 | m.impl("rsqrt.out", TORCH_FN(functionalization::rsqrt_out_out)); |
| 20525 | m.impl("rsqrt_", TORCH_FN(functionalization::rsqrt_)); |
| 20526 | m.impl("celu.out", TORCH_FN(functionalization::celu_out_out)); |
| 20527 | m.impl("celu_", TORCH_FN(functionalization::celu_)); |
| 20528 | m.impl("sigmoid.out", TORCH_FN(functionalization::sigmoid_out_out)); |
| 20529 | m.impl("sigmoid_", TORCH_FN(functionalization::sigmoid_)); |
| 20530 | m.impl("sinc.out", TORCH_FN(functionalization::sinc_out_out)); |
| 20531 | m.impl("sinc_", TORCH_FN(functionalization::sinc_)); |
| 20532 | m.impl("sinh.out", TORCH_FN(functionalization::sinh_out_out)); |
| 20533 | m.impl("sinh_", TORCH_FN(functionalization::sinh_)); |
| 20534 | m.impl("slice_backward.out", TORCH_FN(functionalization::slice_backward_out_out)); |
| 20535 | m.impl("as_strided_scatter.out", TORCH_FN(functionalization::as_strided_scatter_out_out)); |
| 20536 | m.impl("std_mean.correction_out", TORCH_FN(functionalization::std_mean_out_correction_out)); |
| 20537 | m.impl("_mkldnn_transpose.out", TORCH_FN(functionalization::_mkldnn_transpose_out_out)); |
| 20538 | m.impl("_mkldnn_transpose_", TORCH_FN(functionalization::_mkldnn_transpose_)); |
| 20539 | m.impl("flip.out", TORCH_FN(functionalization::flip_out_out)); |
| 20540 | m.impl("_nested_tensor_from_mask.out", TORCH_FN(functionalization::_nested_tensor_from_mask_out_out)); |
| 20541 | m.impl("_nested_from_padded_and_nested_example.out", TORCH_FN(functionalization::_nested_from_padded_and_nested_example_out_out)); |
| 20542 | m.impl("unique_dim.out", TORCH_FN(functionalization::unique_dim_out_out)); |
| 20543 | m.impl("_unsafe_view.out", TORCH_FN(functionalization::_unsafe_view_out_out)); |
| 20544 | m.impl("var_mean.correction_out", TORCH_FN(functionalization::var_mean_out_correction_out)); |
| 20545 | m.impl("zeros.names_out", TORCH_FN(functionalization::zeros_out_names_out)); |
| 20546 | m.impl("zeros.out", TORCH_FN(functionalization::zeros_out_out)); |
| 20547 | m.impl("zeros_like.out", TORCH_FN(functionalization::zeros_like_out_out)); |
| 20548 | m.impl("_sparse_csr_prod.dim_dtype_out", TORCH_FN(functionalization::_sparse_csr_prod_out_dim_dtype_out)); |
| 20549 | m.impl("_spdiags.out", TORCH_FN(functionalization::_spdiags_out_out)); |
| 20550 | m.impl("rsub.Tensor_out", TORCH_FN(functionalization::rsub_out_Tensor_out)); |
| 20551 | m.impl("rsub.Scalar_out", TORCH_FN(functionalization::rsub_out_Scalar_out)); |
| 20552 | m.impl("_sparse_addmm.out", TORCH_FN(functionalization::_sparse_addmm_out_out)); |
| 20553 | m.impl("sparse_coo_tensor.size_out", TORCH_FN(functionalization::sparse_coo_tensor_out_size_out)); |
| 20554 | m.impl("sparse_resize.out", TORCH_FN(functionalization::sparse_resize_out_out)); |
| 20555 | m.impl("sparse_resize_", TORCH_FN(functionalization::sparse_resize_)); |
| 20556 | m.impl("sparse_mask.out", TORCH_FN(functionalization::sparse_mask_out_out)); |
| 20557 | m.impl("_coalesce.out", TORCH_FN(functionalization::_coalesce_out_out)); |
| 20558 | m.impl("dequantize.self_out", TORCH_FN(functionalization::dequantize_out_self_out)); |
| 20559 | m.impl("dequantize.tensors_out", TORCH_FN(functionalization::dequantize_out_tensors_out)); |
| 20560 | m.impl("q_per_channel_zero_points.out", TORCH_FN(functionalization::q_per_channel_zero_points_out_out)); |
| 20561 | m.impl("_fake_quantize_learnable_per_channel_affine.out", TORCH_FN(functionalization::_fake_quantize_learnable_per_channel_affine_out_out)); |
| 20562 | m.impl("_fused_moving_avg_obs_fq_helper.out", TORCH_FN(functionalization::_fused_moving_avg_obs_fq_helper_out_out)); |
| 20563 | m.impl("_fused_moving_avg_obs_fq_helper", TORCH_FN(functionalization::_fused_moving_avg_obs_fq_helper)); |
| 20564 | m.impl("_to_copy.out", TORCH_FN(functionalization::_to_copy_out_out)); |
| 20565 | m.impl("_thnn_fused_gru_cell.out", TORCH_FN(functionalization::_thnn_fused_gru_cell_out_out)); |
| 20566 | m.impl("_pack_padded_sequence.out", TORCH_FN(functionalization::_pack_padded_sequence_out_out)); |
| 20567 | m.impl("scatter_reduce.two_out", TORCH_FN(functionalization::scatter_reduce_out_two_out)); |
| 20568 | m.impl("scatter_reduce_.two", TORCH_FN(functionalization::scatter_reduce__two)); |
| 20569 | m.impl("bitwise_xor.Tensor_out", TORCH_FN(functionalization::bitwise_xor_out_Tensor_out)); |
| 20570 | m.impl("bitwise_xor_.Tensor", TORCH_FN(functionalization::bitwise_xor__Tensor)); |
| 20571 | m.impl("bitwise_xor.Scalar", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Scalar & other)>(at::native::bitwise_xor)); |
| 20572 | m.impl("bitwise_xor.Scalar_out", TORCH_FN(functionalization::bitwise_xor_out_Scalar_out)); |
| 20573 | m.impl("bitwise_xor_.Scalar", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Scalar & other)>(at::native::bitwise_xor_)); |
| 20574 | m.impl("bitwise_xor.Scalar_Tensor_out", TORCH_FN(functionalization::bitwise_xor_out_Scalar_Tensor_out)); |
| 20575 | m.impl("addbmm.out", TORCH_FN(functionalization::addbmm_out_out)); |
| 20576 | m.impl("addbmm_", TORCH_FN(functionalization::addbmm_)); |
| 20577 | m.impl("random.from_out", TORCH_FN(functionalization::random_out_from_out)); |
| 20578 | m.impl("random_.from", TORCH_FN(functionalization::random__from)); |
| 20579 | m.impl("random.to_out", TORCH_FN(functionalization::random_out_to_out)); |
| 20580 | m.impl("random_.to", TORCH_FN(functionalization::random__to)); |
| 20581 | m.impl("random.out", TORCH_FN(functionalization::random_out_out)); |
| 20582 | m.impl("random_", TORCH_FN(functionalization::random_)); |
| 20583 | m.impl("exponential.out", TORCH_FN(functionalization::exponential_out_out)); |
| 20584 | m.impl("exponential_", TORCH_FN(functionalization::exponential_)); |
| 20585 | m.impl("geometric.out", TORCH_FN(functionalization::geometric_out_out)); |
| 20586 | m.impl("geometric_", TORCH_FN(functionalization::geometric_)); |
| 20587 | m.impl("cross", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other, c10::optional<int64_t> dim)>(at::native::cross)); |
| 20588 | m.impl("cross.out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & other, c10::optional<int64_t> dim, at::Tensor & out)>(at::native::cross_out)); |
| 20589 | m.impl("trace.out", TORCH_FN(functionalization::trace_out_out)); |
| 20590 | m.impl("take_along_dim", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & indices, c10::optional<int64_t> dim)>(at::native::take_along_dim)); |
| 20591 | m.impl("take_along_dim.out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & indices, c10::optional<int64_t> dim, at::Tensor & out)>(at::native::take_along_dim_out)); |
| 20592 | m.impl("index_select.out", TORCH_FN(functionalization::index_select_out_out)); |
| 20593 | m.impl("index_select.dimname", static_cast<at::Tensor (*)(const at::Tensor & self, at::Dimname dim, const at::Tensor & index)>(at::native::index_select)); |
| 20594 | m.impl("index_select.dimname_out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, at::Tensor & out)>(at::native::index_select_out)); |
| 20595 | m.impl("masked_select.out", TORCH_FN(functionalization::masked_select_out_out)); |
| 20596 | m.impl("linalg_solve_triangular.out", TORCH_FN(functionalization::linalg_solve_triangular_out_out)); |
| 20597 | m.impl("svd", static_cast<::std::tuple<at::Tensor,at::Tensor,at::Tensor> (*)(const at::Tensor & self, bool some, bool compute_uv)>(at::native::svd)); |
| 20598 | m.impl("svd.U", static_cast<::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> (*)(const at::Tensor & self, bool some, bool compute_uv, at::Tensor & U, at::Tensor & S, at::Tensor & V)>(at::native::svd_out)); |
| 20599 | m.impl("multinomial.out", TORCH_FN(functionalization::multinomial_out_out)); |
| 20600 | m.impl("histogram.bins_tensor_out", TORCH_FN(functionalization::histogram_out_bins_tensor_out)); |
| 20601 | m.impl("histogram.bin_ct_out", TORCH_FN(functionalization::histogram_out_bin_ct_out)); |
| 20602 | m.impl("igammac.out", TORCH_FN(functionalization::igammac_out_out)); |
| 20603 | m.impl("igammac_", TORCH_FN(functionalization::igammac_)); |
| 20604 | m.impl("remainder.Scalar_out", TORCH_FN(functionalization::remainder_out_Scalar_out)); |
| 20605 | m.impl("remainder_.Scalar", TORCH_FN(functionalization::remainder__Scalar)); |
| 20606 | m.impl("remainder.Tensor_out", TORCH_FN(functionalization::remainder_out_Tensor_out)); |
| 20607 | m.impl("remainder_.Tensor", TORCH_FN(functionalization::remainder__Tensor)); |
| 20608 | m.impl("remainder.Scalar_Tensor_out", TORCH_FN(functionalization::remainder_out_Scalar_Tensor_out)); |
| 20609 | m.impl("quantile", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & q, c10::optional<int64_t> dim, bool keepdim, c10::string_view interpolation)>(at::native::quantile)); |
| 20610 | m.impl("quantile.out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & q, c10::optional<int64_t> dim, bool keepdim, c10::string_view interpolation, at::Tensor & out)>(at::native::quantile_out)); |
| 20611 | m.impl("quantile.scalar", static_cast<at::Tensor (*)(const at::Tensor & self, double q, c10::optional<int64_t> dim, bool keepdim, c10::string_view interpolation)>(at::native::quantile)); |
| 20612 | m.impl("quantile.scalar_out", static_cast<at::Tensor & (*)(const at::Tensor & self, double q, c10::optional<int64_t> dim, bool keepdim, c10::string_view interpolation, at::Tensor & out)>(at::native::quantile_out)); |
| 20613 | m.impl("nanquantile", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & q, c10::optional<int64_t> dim, bool keepdim, c10::string_view interpolation)>(at::native::nanquantile)); |
| 20614 | m.impl("nanquantile.out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & q, c10::optional<int64_t> dim, bool keepdim, c10::string_view interpolation, at::Tensor & out)>(at::native::nanquantile_out)); |
| 20615 | m.impl("nanquantile.scalar", static_cast<at::Tensor (*)(const at::Tensor & self, double q, c10::optional<int64_t> dim, bool keepdim, c10::string_view interpolation)>(at::native::nanquantile)); |
| 20616 | m.impl("nanquantile.scalar_out", static_cast<at::Tensor & (*)(const at::Tensor & self, double q, c10::optional<int64_t> dim, bool keepdim, c10::string_view interpolation, at::Tensor & out)>(at::native::nanquantile_out)); |
| 20617 | m.impl("sort.values", TORCH_FN(functionalization::sort_out_values)); |
| 20618 | m.impl("sort.values_stable", TORCH_FN(functionalization::sort_out_values_stable)); |
| 20619 | m.impl("sort.dimname", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & self, at::Dimname dim, bool descending)>(at::native::sort)); |
| 20620 | m.impl("sort.dimname_values", static_cast<::std::tuple<at::Tensor &,at::Tensor &> (*)(const at::Tensor & self, at::Dimname dim, bool descending, at::Tensor & values, at::Tensor & indices)>(at::native::sort_out)); |
| 20621 | m.impl("sort.dimname_stable", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & self, c10::optional<bool> stable, at::Dimname dim, bool descending)>(at::native::sort)); |
| 20622 | m.impl("sort.dimname_values_stable", static_cast<::std::tuple<at::Tensor &,at::Tensor &> (*)(const at::Tensor & self, c10::optional<bool> stable, at::Dimname dim, bool descending, at::Tensor & values, at::Tensor & indices)>(at::native::sort_out)); |
| 20623 | m.impl("argsort.stable_out", TORCH_FN(functionalization::argsort_out_stable_out)); |
| 20624 | m.impl("all.all_out", TORCH_FN(functionalization::all_out_all_out)); |
| 20625 | m.impl("renorm.out", TORCH_FN(functionalization::renorm_out_out)); |
| 20626 | m.impl("renorm_", TORCH_FN(functionalization::renorm_)); |
| 20627 | m.impl("unfold_backward.out", TORCH_FN(functionalization::unfold_backward_out_out)); |
| 20628 | m.impl("pow.Tensor_Tensor_out", TORCH_FN(functionalization::pow_out_Tensor_Tensor_out)); |
| 20629 | m.impl("pow_.Tensor", TORCH_FN(functionalization::pow__Tensor)); |
| 20630 | m.impl("pow.Scalar_out", TORCH_FN(functionalization::pow_out_Scalar_out)); |
| 20631 | m.impl("pow.Tensor_Scalar_out", TORCH_FN(functionalization::pow_out_Tensor_Scalar_out)); |
| 20632 | m.impl("pow_.Scalar", TORCH_FN(functionalization::pow__Scalar)); |
| 20633 | m.impl("float_power.Tensor_Tensor", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & exponent)>(at::native::float_power)); |
| 20634 | m.impl("float_power.Tensor_Tensor_out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & exponent, at::Tensor & out)>(at::native::float_power_out)); |
| 20635 | m.impl("float_power_.Tensor", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Tensor & exponent)>(at::native::float_power_)); |
| 20636 | m.impl("float_power.Scalar", static_cast<at::Tensor (*)(const at::Scalar & self, const at::Tensor & exponent)>(at::native::float_power)); |
| 20637 | m.impl("float_power.Scalar_out", static_cast<at::Tensor & (*)(const at::Scalar & self, const at::Tensor & exponent, at::Tensor & out)>(at::native::float_power_out)); |
| 20638 | m.impl("float_power.Tensor_Scalar", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Scalar & exponent)>(at::native::float_power)); |
| 20639 | m.impl("float_power.Tensor_Scalar_out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Scalar & exponent, at::Tensor & out)>(at::native::float_power_out)); |
| 20640 | m.impl("float_power_.Scalar", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Scalar & exponent)>(at::native::float_power_)); |
| 20641 | m.impl("normal.out", TORCH_FN(functionalization::normal_out_out)); |
| 20642 | m.impl("normal_", TORCH_FN(functionalization::normal_)); |
| 20643 | m.impl("normal.Tensor_float_out", TORCH_FN(functionalization::normal_out_Tensor_float_out)); |
| 20644 | m.impl("normal.float_Tensor_out", TORCH_FN(functionalization::normal_out_float_Tensor_out)); |
| 20645 | m.impl("normal.Tensor_Tensor_out", TORCH_FN(functionalization::normal_out_Tensor_Tensor_out)); |
| 20646 | m.impl("normal.float_float_out", TORCH_FN(functionalization::normal_out_float_float_out)); |
| 20647 | m.impl("_amp_update_scale.out", TORCH_FN(functionalization::_amp_update_scale_out_out)); |
| 20648 | m.impl("_amp_update_scale_", TORCH_FN(functionalization::_amp_update_scale_)); |
| 20649 | m.impl("_foreach_atan.out", TORCH_FN(functionalization::_foreach_atan_out_out)); |
| 20650 | m.impl("_foreach_atan_", TORCH_FN(functionalization::_foreach_atan_)); |
| 20651 | m.impl("_foreach_erf.out", TORCH_FN(functionalization::_foreach_erf_out_out)); |
| 20652 | m.impl("_foreach_erf_", TORCH_FN(functionalization::_foreach_erf_)); |
| 20653 | m.impl("_foreach_erfc.out", TORCH_FN(functionalization::_foreach_erfc_out_out)); |
| 20654 | m.impl("_foreach_erfc_", TORCH_FN(functionalization::_foreach_erfc_)); |
| 20655 | m.impl("_foreach_log.out", TORCH_FN(functionalization::_foreach_log_out_out)); |
| 20656 | m.impl("_foreach_log_", TORCH_FN(functionalization::_foreach_log_)); |
| 20657 | m.impl("_foreach_sinh.out", TORCH_FN(functionalization::_foreach_sinh_out_out)); |
| 20658 | m.impl("_foreach_sinh_", TORCH_FN(functionalization::_foreach_sinh_)); |
| 20659 | m.impl("_foreach_lgamma.out", TORCH_FN(functionalization::_foreach_lgamma_out_out)); |
| 20660 | m.impl("_foreach_lgamma_", TORCH_FN(functionalization::_foreach_lgamma_)); |
| 20661 | m.impl("_foreach_lerp.List_out", TORCH_FN(functionalization::_foreach_lerp_out_List_out)); |
| 20662 | m.impl("_foreach_lerp_.List", TORCH_FN(functionalization::_foreach_lerp__List)); |
| 20663 | m.impl("_foreach_lerp.Scalar_out", TORCH_FN(functionalization::_foreach_lerp_out_Scalar_out)); |
| 20664 | m.impl("_foreach_lerp_.Scalar", TORCH_FN(functionalization::_foreach_lerp__Scalar)); |
| 20665 | m.impl("_convert_indices_from_coo_to_csr.out", TORCH_FN(functionalization::_convert_indices_from_coo_to_csr_out_out)); |
| 20666 | m.impl("multi_margin_loss_backward.grad_input", TORCH_FN(functionalization::multi_margin_loss_backward_out_grad_input)); |
| 20667 | m.impl("nll_loss_forward.output", TORCH_FN(functionalization::nll_loss_forward_out_output)); |
| 20668 | m.impl("hardswish.out", TORCH_FN(functionalization::hardswish_out_out)); |
| 20669 | m.impl("hardswish_", TORCH_FN(functionalization::hardswish_)); |
| 20670 | m.impl("hardswish_backward.out", TORCH_FN(functionalization::hardswish_backward_out_out)); |
| 20671 | m.impl("softshrink_backward.grad_input", TORCH_FN(functionalization::softshrink_backward_out_grad_input)); |
| 20672 | m.impl("mkldnn_adaptive_avg_pool2d_backward.out", TORCH_FN(functionalization::mkldnn_adaptive_avg_pool2d_backward_out_out)); |
| 20673 | m.impl("_adaptive_avg_pool2d_backward.out", TORCH_FN(functionalization::_adaptive_avg_pool2d_backward_out_out)); |
| 20674 | m.impl("adaptive_avg_pool3d", static_cast<at::Tensor (*)(const at::Tensor & self, c10::SymIntArrayRef output_size)>(at::native::adaptive_avg_pool3d_symint)); |
| 20675 | m.impl("adaptive_avg_pool3d.out", TORCH_FN(functionalization::adaptive_avg_pool3d_out_out)); |
| 20676 | m.impl("adaptive_max_pool3d_backward.grad_input", TORCH_FN(functionalization::adaptive_max_pool3d_backward_out_grad_input)); |
| 20677 | m.impl("avg_pool2d.out", TORCH_FN(functionalization::avg_pool2d_out_out)); |
| 20678 | m.impl("avg_pool3d_backward.grad_input", TORCH_FN(functionalization::avg_pool3d_backward_out_grad_input)); |
| 20679 | m.impl("fractional_max_pool2d.output", TORCH_FN(functionalization::fractional_max_pool2d_out_output)); |
| 20680 | m.impl("reflection_pad3d_backward.grad_input", TORCH_FN(functionalization::reflection_pad3d_backward_out_grad_input)); |
| 20681 | m.impl("replication_pad2d.out", TORCH_FN(functionalization::replication_pad2d_out_out)); |
| 20682 | m.impl("_upsample_bilinear2d_aa_backward.grad_input", TORCH_FN(functionalization::_upsample_bilinear2d_aa_backward_out_grad_input)); |
| 20683 | m.impl("_upsample_bicubic2d_aa.out", TORCH_FN(functionalization::_upsample_bicubic2d_aa_out_out)); |
| 20684 | m.impl("upsample_trilinear3d.out", TORCH_FN(functionalization::upsample_trilinear3d_out_out)); |
| 20685 | m.impl("_upsample_nearest_exact1d_backward.grad_input", TORCH_FN(functionalization::_upsample_nearest_exact1d_backward_out_grad_input)); |
| 20686 | m.impl("upsample_nearest2d_backward.grad_input", TORCH_FN(functionalization::upsample_nearest2d_backward_out_grad_input)); |
| 20687 | m.impl("tanh_backward.grad_input", TORCH_FN(functionalization::tanh_backward_out_grad_input)); |
| 20688 | m.impl("_conv_depthwise2d.out", TORCH_FN(functionalization::_conv_depthwise2d_out_out)); |
| 20689 | m.impl("col2im.out", TORCH_FN(functionalization::col2im_out_out)); |
| 20690 | m.impl("column_stack", static_cast<at::Tensor (*)(at::TensorList tensors)>(at::native::column_stack)); |
| 20691 | m.impl("column_stack.out", static_cast<at::Tensor & (*)(at::TensorList tensors, at::Tensor & out)>(at::native::column_stack_out)); |
| 20692 | m.impl("im2col.out", TORCH_FN(functionalization::im2col_out_out)); |
| 20693 | m.impl("isinf.out", TORCH_FN(functionalization::isinf_out_out)); |
| 20694 | m.impl("isneginf.out", TORCH_FN(functionalization::isneginf_out_out)); |
| 20695 | m.impl("special_expm1", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::special_expm1)); |
| 20696 | m.impl("special_expm1.out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::Tensor & out)>(at::native::special_expm1_out)); |
| 20697 | m.impl("special_erf", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::special_erf)); |
| 20698 | m.impl("special_erf.out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::Tensor & out)>(at::native::special_erf_out)); |
| 20699 | m.impl("special_logsumexp", static_cast<at::Tensor (*)(const at::Tensor & self, at::IntArrayRef dim, bool keepdim)>(at::native::special_logsumexp)); |
| 20700 | m.impl("special_logsumexp.out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out)>(at::native::special_logsumexp_out)); |
| 20701 | m.impl("special_log1p", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::special_log1p)); |
| 20702 | m.impl("special_log1p.out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::Tensor & out)>(at::native::special_log1p_out)); |
| 20703 | m.impl("special_gammaincc", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other)>(at::native::special_gammaincc)); |
| 20704 | m.impl("special_gammaincc.out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & other, at::Tensor & out)>(at::native::special_gammaincc_out)); |
| 20705 | m.impl("special_multigammaln", static_cast<at::Tensor (*)(const at::Tensor & self, int64_t p)>(at::native::special_multigammaln)); |
| 20706 | m.impl("special_multigammaln.out", static_cast<at::Tensor & (*)(const at::Tensor & self, int64_t p, at::Tensor & out)>(at::native::special_multigammaln_out)); |
| 20707 | m.impl("fft_rfft2", static_cast<at::Tensor (*)(const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional<c10::string_view> norm)>(at::native::fft_rfft2)); |
| 20708 | m.impl("fft_rfft2.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_rfft2_out)); |
| 20709 | m.impl("fft_irfft2", static_cast<at::Tensor (*)(const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional<c10::string_view> norm)>(at::native::fft_irfft2)); |
| 20710 | m.impl("fft_irfft2.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_irfft2_out)); |
| 20711 | m.impl("fft_ihfft2", static_cast<at::Tensor (*)(const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional<c10::string_view> norm)>(at::native::fft_ihfft2)); |
| 20712 | m.impl("fft_ihfft2.out", static_cast<const at::Tensor & (*)(const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional<c10::string_view> norm, const at::Tensor & out)>(at::native::fft_ihfft2_out)); |
| 20713 | m.impl("fft_fftn", static_cast<at::Tensor (*)(const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional<c10::string_view> norm)>(at::native::fft_fftn)); |
| 20714 | m.impl("fft_fftn.out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional<c10::string_view> norm, at::Tensor & out)>(at::native::fft_fftn_out)); |
| 20715 | m.impl("fft_irfftn", static_cast<at::Tensor (*)(const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional<c10::string_view> norm)>(at::native::fft_irfftn)); |
| 20716 | m.impl("fft_irfftn.out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional<c10::string_view> norm, at::Tensor & out)>(at::native::fft_irfftn_out)); |
| 20717 | m.impl("linalg_lu_factor", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & A, bool pivot)>(at::native::linalg_lu_factor)); |
| 20718 | m.impl("linalg_lu_factor.out", static_cast<::std::tuple<at::Tensor &,at::Tensor &> (*)(const at::Tensor & A, bool pivot, at::Tensor & LU, at::Tensor & pivots)>(at::native::linalg_lu_factor_out)); |
| 20719 | m.impl("linalg_lu_factor_ex.out", TORCH_FN(functionalization::linalg_lu_factor_ex_out_out)); |
| 20720 | m.impl("linalg_ldl_factor", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & self, bool hermitian)>(at::native::linalg_ldl_factor)); |
| 20721 | m.impl("linalg_ldl_factor.out", static_cast<::std::tuple<at::Tensor &,at::Tensor &> (*)(const at::Tensor & self, bool hermitian, at::Tensor & LD, at::Tensor & pivots)>(at::native::linalg_ldl_factor_out)); |
| 20722 | m.impl("linalg_ldl_solve.out", TORCH_FN(functionalization::linalg_ldl_solve_out_out)); |
| 20723 | m.impl("_linalg_slogdet.sign", TORCH_FN(functionalization::_linalg_slogdet_out_sign)); |
| 20724 | m.impl("linalg_eigvals", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::linalg_eigvals)); |
| 20725 | m.impl("linalg_eigvals.out", static_cast<at::Tensor & (*)(const at::Tensor & self, at::Tensor & out)>(at::native::linalg_eigvals_out)); |
| 20726 | m.impl("linalg_eigh", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & self, c10::string_view UPLO)>(at::native::linalg_eigh)); |
| 20727 | m.impl("linalg_eigh.eigvals", static_cast<::std::tuple<at::Tensor &,at::Tensor &> (*)(const at::Tensor & self, c10::string_view UPLO, at::Tensor & eigvals, at::Tensor & eigvecs)>(at::native::linalg_eigh_out)); |
| 20728 | m.impl("linalg_householder_product.out", TORCH_FN(functionalization::linalg_householder_product_out_out)); |
| 20729 | m.impl("ger", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & vec2)>(at::native::ger)); |
| 20730 | m.impl("ger.out", static_cast<at::Tensor & (*)(const at::Tensor & self, const at::Tensor & vec2, at::Tensor & out)>(at::native::ger_out)); |
| 20731 | m.impl("linalg_norm", static_cast<at::Tensor (*)(const at::Tensor & self, const c10::optional<at::Scalar> & ord, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype)>(at::native::linalg_norm)); |
| 20732 | m.impl("linalg_norm.out", static_cast<at::Tensor & (*)(const at::Tensor & self, const c10::optional<at::Scalar> & ord, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out)>(at::native::linalg_norm_out)); |
| 20733 | m.impl("linalg_norm.ord_str", static_cast<at::Tensor (*)(const at::Tensor & self, c10::string_view ord, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype)>(at::native::linalg_norm)); |
| 20734 | m.impl("linalg_norm.ord_str_out", static_cast<at::Tensor & (*)(const at::Tensor & self, c10::string_view ord, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out)>(at::native::linalg_norm_out)); |
| 20735 | m.impl("linalg_vector_norm.out", TORCH_FN(functionalization::linalg_vector_norm_out_out)); |
| 20736 | m.impl("_linalg_solve_ex.result", TORCH_FN(functionalization::_linalg_solve_ex_out_result)); |
| 20737 | m.impl("linalg_solve", static_cast<at::Tensor (*)(const at::Tensor & A, const at::Tensor & B, bool left)>(at::native::linalg_solve)); |
| 20738 | m.impl("linalg_solve.out", static_cast<at::Tensor & (*)(const at::Tensor & A, const at::Tensor & B, bool left, at::Tensor & out)>(at::native::linalg_solve_out)); |
| 20739 | m.impl("linalg_multi_dot", static_cast<at::Tensor (*)(at::TensorList tensors)>(at::native::linalg_multi_dot)); |
| 20740 | m.impl("linalg_multi_dot.out", static_cast<at::Tensor & (*)(at::TensorList tensors, at::Tensor & out)>(at::native::linalg_multi_dot_out)); |
| 20741 | m.impl("_test_optional_filled_intlist.out", TORCH_FN(functionalization::_test_optional_filled_intlist_out_out)); |
| 20742 | m.impl("_test_autograd_multiple_dispatch.fullcoverage_out", TORCH_FN(functionalization::_test_autograd_multiple_dispatch_out_fullcoverage_out)); |
| 20743 | m.impl("_test_autograd_multiple_dispatch_view_copy.out", TORCH_FN(functionalization::_test_autograd_multiple_dispatch_view_copy_out_out)); |
| 20744 | m.impl("segment_reduce.out", TORCH_FN(functionalization::segment_reduce_out_out)); |
| 20745 | m.impl("_nested_tensor_from_tensor_list.out", TORCH_FN(functionalization::_nested_tensor_from_tensor_list_out_out)); |
| 20746 | m.impl("diagonal_copy.out", TORCH_FN(functionalization::diagonal_copy_out_out)); |
| 20747 | m.impl("detach_copy.out", TORCH_FN(functionalization::detach_copy_out_out)); |
| 20748 | m.impl("slice_copy.Tensor_out", TORCH_FN(functionalization::slice_copy_out_Tensor_out)); |
| 20749 | m.impl("transpose_copy.int_out", TORCH_FN(functionalization::transpose_copy_out_int_out)); |
| 20750 | m.impl("indices_copy.out", TORCH_FN(functionalization::indices_copy_out_out)); |
| 20751 | m.impl("row_indices_copy.out", TORCH_FN(functionalization::row_indices_copy_out_out)); |
| 20752 | m.impl("_triton_multi_head_attention.out", TORCH_FN(functionalization::_triton_multi_head_attention_out_out)); |
| 20753 | m.impl("special_bessel_j1.out", TORCH_FN(functionalization::special_bessel_j1_out_out)); |
| 20754 | m.impl("special_bessel_y1.out", TORCH_FN(functionalization::special_bessel_y1_out_out)); |
| 20755 | m.impl("special_legendre_polynomial_p.out", TORCH_FN(functionalization::special_legendre_polynomial_p_out_out)); |
| 20756 | m.impl("special_legendre_polynomial_p.x_scalar", static_cast<at::Tensor (*)(const at::Scalar & x, const at::Tensor & n)>(at::native::special_legendre_polynomial_p)); |
| 20757 | m.impl("special_legendre_polynomial_p.x_scalar_out", static_cast<at::Tensor & (*)(const at::Scalar & x, const at::Tensor & n, at::Tensor & out)>(at::native::special_legendre_polynomial_p_out)); |
| 20758 | m.impl("special_legendre_polynomial_p.n_scalar", static_cast<at::Tensor (*)(const at::Tensor & x, const at::Scalar & n)>(at::native::special_legendre_polynomial_p)); |
| 20759 | m.impl("special_legendre_polynomial_p.n_scalar_out", TORCH_FN(functionalization::special_legendre_polynomial_p_out_n_scalar_out)); |
| 20760 | m.impl("special_modified_bessel_i0.out", TORCH_FN(functionalization::special_modified_bessel_i0_out_out)); |
| 20761 | m.impl("special_shifted_chebyshev_polynomial_t.out", TORCH_FN(functionalization::special_shifted_chebyshev_polynomial_t_out_out)); |
| 20762 | m.impl("special_shifted_chebyshev_polynomial_t.x_scalar", static_cast<at::Tensor (*)(const at::Scalar & x, const at::Tensor & n)>(at::native::special_shifted_chebyshev_polynomial_t)); |
| 20763 | m.impl("special_shifted_chebyshev_polynomial_t.x_scalar_out", static_cast<at::Tensor & (*)(const at::Scalar & x, const at::Tensor & n, at::Tensor & out)>(at::native::special_shifted_chebyshev_polynomial_t_out)); |
| 20764 | m.impl("special_shifted_chebyshev_polynomial_t.n_scalar", static_cast<at::Tensor (*)(const at::Tensor & x, const at::Scalar & n)>(at::native::special_shifted_chebyshev_polynomial_t)); |
| 20765 | m.impl("special_shifted_chebyshev_polynomial_t.n_scalar_out", TORCH_FN(functionalization::special_shifted_chebyshev_polynomial_t_out_n_scalar_out)); |
| 20766 | m.impl("special_shifted_chebyshev_polynomial_u.out", TORCH_FN(functionalization::special_shifted_chebyshev_polynomial_u_out_out)); |
| 20767 | m.impl("special_shifted_chebyshev_polynomial_u.x_scalar", static_cast<at::Tensor (*)(const at::Scalar & x, const at::Tensor & n)>(at::native::special_shifted_chebyshev_polynomial_u)); |
| 20768 | m.impl("special_shifted_chebyshev_polynomial_u.x_scalar_out", static_cast<at::Tensor & (*)(const at::Scalar & x, const at::Tensor & n, at::Tensor & out)>(at::native::special_shifted_chebyshev_polynomial_u_out)); |
| 20769 | m.impl("special_shifted_chebyshev_polynomial_u.n_scalar", static_cast<at::Tensor (*)(const at::Tensor & x, const at::Scalar & n)>(at::native::special_shifted_chebyshev_polynomial_u)); |
| 20770 | m.impl("special_shifted_chebyshev_polynomial_u.n_scalar_out", TORCH_FN(functionalization::special_shifted_chebyshev_polynomial_u_out_n_scalar_out)); |
| 20771 | m.impl("special_shifted_chebyshev_polynomial_w.out", TORCH_FN(functionalization::special_shifted_chebyshev_polynomial_w_out_out)); |
| 20772 | m.impl("special_shifted_chebyshev_polynomial_w.x_scalar", static_cast<at::Tensor (*)(const at::Scalar & x, const at::Tensor & n)>(at::native::special_shifted_chebyshev_polynomial_w)); |
| 20773 | m.impl("special_shifted_chebyshev_polynomial_w.x_scalar_out", static_cast<at::Tensor & (*)(const at::Scalar & x, const at::Tensor & n, at::Tensor & out)>(at::native::special_shifted_chebyshev_polynomial_w_out)); |
| 20774 | m.impl("special_shifted_chebyshev_polynomial_w.n_scalar", static_cast<at::Tensor (*)(const at::Tensor & x, const at::Scalar & n)>(at::native::special_shifted_chebyshev_polynomial_w)); |
| 20775 | m.impl("special_shifted_chebyshev_polynomial_w.n_scalar_out", TORCH_FN(functionalization::special_shifted_chebyshev_polynomial_w_out_n_scalar_out)); |
| 20776 | m.impl("_fused_adam.out", TORCH_FN(functionalization::_fused_adam_out_out)); |
| 20777 | m.impl("_fused_adam_", TORCH_FN(functionalization::_fused_adam_)); |
| 20778 | m.impl("rename", static_cast<at::Tensor (*)(const at::Tensor & self, c10::optional<at::DimnameList> names)>(at::native::rename)); |
| 20779 | m.impl("rename_", static_cast<at::Tensor & (*)(at::Tensor & self, c10::optional<at::DimnameList> names)>(at::native::rename_)); |
| 20780 | m.impl("imag", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::imag)); |
| 20781 | m.impl("_conj", TORCH_FN(functionalization::_conj)); |
| 20782 | m.impl("conj", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::conj)); |
| 20783 | m.impl("resolve_conj", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::resolve_conj)); |
| 20784 | m.impl("as_strided", TORCH_FN(functionalization::as_strided)); |
| 20785 | m.impl("as_strided_", TORCH_FN(functionalization::as_strided_)); |
| 20786 | m.impl("_sparse_broadcast_to", TORCH_FN(functionalization::_sparse_broadcast_to)); |
| 20787 | m.impl("chunk", static_cast<::std::vector<at::Tensor> (*)(const at::Tensor & self, int64_t chunks, int64_t dim)>(at::native::chunk)); |
| 20788 | m.impl("tensor_split.sections", static_cast<::std::vector<at::Tensor> (*)(const at::Tensor & self, c10::SymInt sections, int64_t dim)>(at::native::tensor_split_sections_symint)); |
| 20789 | m.impl("tensor_split.indices", static_cast<::std::vector<at::Tensor> (*)(const at::Tensor & self, c10::SymIntArrayRef indices, int64_t dim)>(at::native::tensor_split_indices_symint)); |
| 20790 | m.impl("tensor_split.tensor_indices_or_sections", static_cast<::std::vector<at::Tensor> (*)(const at::Tensor & self, const at::Tensor & tensor_indices_or_sections, int64_t dim)>(at::native::tensor_split)); |
| 20791 | m.impl("expand_as", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & other)>(at::native::expand_as)); |
| 20792 | m.impl("unflatten.int", static_cast<at::Tensor (*)(const at::Tensor & self, int64_t dim, at::IntArrayRef sizes)>(at::native::unflatten)); |
| 20793 | m.impl("unflatten.Dimname", static_cast<at::Tensor (*)(const at::Tensor & self, at::Dimname dim, at::IntArrayRef sizes, at::DimnameList names)>(at::native::unflatten)); |
| 20794 | m.impl("permute", TORCH_FN(functionalization::permute)); |
| 20795 | m.impl("movedim.intlist", static_cast<at::Tensor (*)(const at::Tensor & self, at::IntArrayRef source, at::IntArrayRef destination)>(at::native::movedim)); |
| 20796 | m.impl("movedim.int", static_cast<at::Tensor (*)(const at::Tensor & self, int64_t source, int64_t destination)>(at::native::movedim)); |
| 20797 | m.impl("mH", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::mH)); |
| 20798 | m.impl("pin_memory", static_cast<at::Tensor (*)(const at::Tensor & self, c10::optional<at::Device> device)>(at::native::pin_memory)); |
| 20799 | m.impl("_reshape_alias", TORCH_FN(functionalization::_reshape_alias)); |
| 20800 | m.impl("detach", TORCH_FN(functionalization::detach)); |
| 20801 | m.impl("detach_", TORCH_FN(functionalization::detach_)); |
| 20802 | m.impl("split.Tensor", TORCH_FN(functionalization::split_Tensor)); |
| 20803 | m.impl("split.sizes", static_cast<::std::vector<at::Tensor> (*)(const at::Tensor & self, c10::SymIntArrayRef split_size, int64_t dim)>(at::native::split_symint)); |
| 20804 | m.impl("dsplit.int", static_cast<::std::vector<at::Tensor> (*)(const at::Tensor & self, int64_t sections)>(at::native::dsplit)); |
| 20805 | m.impl("dsplit.array", static_cast<::std::vector<at::Tensor> (*)(const at::Tensor & self, at::IntArrayRef indices)>(at::native::dsplit)); |
| 20806 | m.impl("positive", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::positive)); |
| 20807 | m.impl("values", TORCH_FN(functionalization::values)); |
| 20808 | m.impl("row_indices", TORCH_FN(functionalization::row_indices)); |
| 20809 | m.impl("swapdims", static_cast<at::Tensor (*)(const at::Tensor & self, int64_t dim0, int64_t dim1)>(at::native::swapdims)); |
| 20810 | m.impl("swapdims_", static_cast<at::Tensor & (*)(at::Tensor & self, int64_t dim0, int64_t dim1)>(at::native::swapdims_)); |
| 20811 | m.impl("_test_autograd_multiple_dispatch_view", TORCH_FN(functionalization::_test_autograd_multiple_dispatch_view)); |
| 20812 | m.impl("_cast_Double", static_cast<at::Tensor (*)(const at::Tensor & self, bool non_blocking)>(at::native::_cast_Double)); |
| 20813 | m.impl("_cast_Int", static_cast<at::Tensor (*)(const at::Tensor & self, bool non_blocking)>(at::native::_cast_Int)); |
| 20814 | m.impl("_cast_Short", static_cast<at::Tensor (*)(const at::Tensor & self, bool non_blocking)>(at::native::_cast_Short)); |
| 20815 | m.impl("output_nr", static_cast<int64_t (*)(const at::Tensor & self)>(at::native::output_nr)); |
| 20816 | m.impl("_use_cudnn_rnn_flatten_weight", static_cast<bool (*)()>(at::native::_use_cudnn_rnn_flatten_weight)); |
| 20817 | m.impl("_sobol_engine_initialize_state_", static_cast<at::Tensor & (*)(at::Tensor & self, int64_t dimension)>(at::native::_sobol_engine_initialize_state_)); |
| 20818 | m.impl("_shape_as_tensor", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::_shape_as_tensor)); |
| 20819 | m.impl("_dim_arange", static_cast<at::Tensor (*)(const at::Tensor & like, int64_t dim)>(at::native::_dim_arange)); |
| 20820 | m.impl("cudnn_is_acceptable", static_cast<bool (*)(const at::Tensor & self)>(at::native::cudnn_is_acceptable)); |
| 20821 | m.impl("_convolution.deprecated", 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, bool transposed, at::IntArrayRef output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled)>(at::native::_convolution)); |
| 20822 | m.impl("conv2d", 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::conv2d)); |
| 20823 | m.impl("conv2d.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::conv2d)); |
| 20824 | m.impl("conv_tbc_backward", static_cast<::std::tuple<at::Tensor,at::Tensor,at::Tensor> (*)(const at::Tensor & self, const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, int64_t pad)>(at::native::conv_tbc_backward)); |
| 20825 | m.impl("cov", static_cast<at::Tensor (*)(const at::Tensor & self, int64_t correction, const c10::optional<at::Tensor> & fweights, const c10::optional<at::Tensor> & aweights)>(at::native::cov)); |
| 20826 | m.impl("divide.Scalar", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Scalar & other)>(at::native::divide)); |
| 20827 | m.impl("divide_.Scalar", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Scalar & other)>(at::native::divide_)); |
| 20828 | m.impl("divide.Scalar_mode", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Scalar & other, c10::optional<c10::string_view> rounding_mode)>(at::native::divide)); |
| 20829 | m.impl("divide_.Scalar_mode", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Scalar & other, c10::optional<c10::string_view> rounding_mode)>(at::native::divide_)); |
| 20830 | m.impl("true_divide.Scalar", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Scalar & other)>(at::native::true_divide)); |
| 20831 | m.impl("true_divide_.Scalar", static_cast<at::Tensor & (*)(at::Tensor & self, const at::Scalar & other)>(at::native::true_divide_)); |
| 20832 | m.impl("embedding_backward", static_cast<at::Tensor (*)(const at::Tensor & grad, const at::Tensor & indices, c10::SymInt num_weights, c10::SymInt padding_idx, bool scale_grad_by_freq, bool sparse)>(at::native::embedding_backward_symint)); |
| 20833 | m.impl("embedding_bag", static_cast<::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> (*)(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional<at::Tensor> & per_sample_weights, bool include_last_offset)>(at::native::embedding_bag)); |
| 20834 | m.impl("embedding_bag.padding_idx", static_cast<::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> (*)(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional<at::Tensor> & per_sample_weights, bool include_last_offset, c10::optional<int64_t> padding_idx)>(at::native::embedding_bag)); |
| 20835 | m.impl("_embedding_bag_backward", static_cast<at::Tensor (*)(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional<at::Tensor> & per_sample_weights, int64_t padding_idx)>(at::native::_embedding_bag_backward_symint)); |
| 20836 | m.impl("_cufft_get_plan_cache_size", static_cast<int64_t (*)(int64_t device_index)>(at::native::_cufft_get_plan_cache_size)); |
| 20837 | m.impl("_cufft_get_plan_cache_max_size", static_cast<int64_t (*)(int64_t device_index)>(at::native::_cufft_get_plan_cache_max_size)); |
| 20838 | m.impl("_is_zerotensor", static_cast<bool (*)(const at::Tensor & self)>(at::native::_is_zerotensor)); |
| 20839 | m.impl("is_inference", static_cast<bool (*)(const at::Tensor & self)>(at::native::is_inference)); |
| 20840 | m.impl("kl_div", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & target, int64_t reduction, bool log_target)>(at::native::kl_div)); |
| 20841 | m.impl("margin_ranking_loss", static_cast<at::Tensor (*)(const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & target, double margin, int64_t reduction)>(at::native::margin_ranking_loss)); |
| 20842 | m.impl("matrix_exp", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::matrix_exp)); |
| 20843 | m.impl("_sparse_mm", static_cast<at::Tensor (*)(const at::Tensor & sparse, const at::Tensor & dense)>(at::native::_sparse_mm)); |
| 20844 | m.impl("_sparse_mm.reduce", static_cast<at::Tensor (*)(const at::Tensor & sparse, const at::Tensor & dense, c10::string_view reduce)>(at::native::_sparse_mm)); |
| 20845 | m.impl("_nnpack_available", static_cast<bool (*)()>(at::native::_nnpack_available)); |
| 20846 | m.impl("relu6", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::relu6)); |
| 20847 | m.impl("relu6_", static_cast<at::Tensor & (*)(at::Tensor & self)>(at::native::relu6_)); |
| 20848 | m.impl("selu", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::selu)); |
| 20849 | m.impl("selu_", static_cast<at::Tensor & (*)(at::Tensor & self)>(at::native::selu_)); |
| 20850 | m.impl("istft", static_cast<at::Tensor (*)(const at::Tensor & self, int64_t n_fft, c10::optional<int64_t> hop_length, c10::optional<int64_t> win_length, const c10::optional<at::Tensor> & window, bool center, bool normalized, c10::optional<bool> onesided, c10::optional<int64_t> length, bool return_complex)>(at::native::istft)); |
| 20851 | m.impl("std_mean", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & self, bool unbiased)>(at::native::std_mean)); |
| 20852 | m.impl("std_mean.dim", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim)>(at::native::std_mean)); |
| 20853 | m.impl("std_mean.names_dim", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim)>(at::native::std_mean)); |
| 20854 | m.impl("std_mean.correction_names", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & self, at::DimnameList dim, c10::optional<int64_t> correction, bool keepdim)>(at::native::std_mean)); |
| 20855 | m.impl("flipud", static_cast<at::Tensor (*)(const at::Tensor & self)>(at::native::flipud)); |
| 20856 | m.impl("var_mean", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & self, bool unbiased)>(at::native::var_mean)); |
| 20857 | m.impl("var_mean.dim", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim)>(at::native::var_mean)); |
| 20858 | m.impl("var_mean.names_dim", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim)>(at::native::var_mean)); |
| 20859 | m.impl("var_mean.correction_names", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & self, at::DimnameList dim, c10::optional<int64_t> correction, bool keepdim)>(at::native::var_mean)); |
| 20860 | m.impl("norm_except_dim", static_cast<at::Tensor (*)(const at::Tensor & v, int64_t pow, int64_t dim)>(at::native::norm_except_dim)); |
| 20861 | m.impl("_sparse_compressed_tensor_unsafe", 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_unsafe)); |
| 20862 | m.impl("sparse_coo_tensor.indices", static_cast<at::Tensor (*)(const at::Tensor & 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_coo_tensor)); |
| 20863 | m.impl("sparse_coo_tensor.indices_size", static_cast<at::Tensor (*)(const at::Tensor & 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_coo_tensor)); |
| 20864 | m.impl("_validate_sparse_bsc_tensor_args", static_cast<void (*)(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size)>(at::native::_validate_sparse_bsc_tensor_args)); |
| 20865 | m.impl("to_mkldnn_backward", static_cast<at::Tensor (*)(const at::Tensor & grad, const at::Tensor & input)>(at::native::to_mkldnn_backward)); |
| 20866 | m.impl("fake_quantize_per_tensor_affine", static_cast<at::Tensor (*)(const at::Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max)>(at::native::fake_quantize_per_tensor_affine)); |
| 20867 | m.impl("fake_quantize_per_tensor_affine.tensor_qparams", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t quant_min, int64_t quant_max)>(at::native::fake_quantize_per_tensor_affine)); |
| 20868 | m.impl("fused_moving_avg_obs_fake_quant", static_cast<at::Tensor (*)(const at::Tensor & self, const at::Tensor & observer_on, const at::Tensor & fake_quant_on, at::Tensor & running_min, at::Tensor & running_max, at::Tensor & scale, at::Tensor & zero_point, double averaging_const, int64_t quant_min, int64_t quant_max, int64_t ch_axis, bool per_row_fake_quant, bool symmetric_quant)>(at::native::fused_moving_avg_obs_fake_quant)); |
| 20869 | m.impl("_choose_qparams_per_tensor", static_cast<::std::tuple<double,int64_t> (*)(const at::Tensor & self, bool reduce_range)>(at::native::_choose_qparams_per_tensor)); |
| 20870 | m.impl("meshgrid", static_cast<::std::vector<at::Tensor> (*)(at::TensorList tensors)>(at::native::meshgrid)); |
| 20871 | m.impl("meshgrid.indexing", static_cast<::std::vector<at::Tensor> (*)(at::TensorList tensors, c10::string_view indexing)>(at::native::meshgrid)); |
| 20872 | m.impl("cartesian_prod", static_cast<at::Tensor (*)(at::TensorList tensors)>(at::native::cartesian_prod)); |
| 20873 | m.impl("can_cast", static_cast<bool (*)(at::ScalarType from, at::ScalarType to)>(at::native::can_cast)); |
| 20874 | m.impl("promote_types", static_cast<at::ScalarType (*)(at::ScalarType type1, at::ScalarType type2)>(at::native::promote_types)); |
| 20875 | m.impl("rnn_tanh.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::rnn_tanh)); |
| 20876 | m.impl("rnn_tanh.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::rnn_tanh)); |
| 20877 | m.impl("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 c10::optional<at::Tensor> & b_ih, const c10::optional<at::Tensor> & b_hh)>(at::native::gru_cell)); |
| 20878 | m.impl("rnn_relu_cell", static_cast<at::Tensor (*)(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const c10::optional<at::Tensor> & b_ih, const c10::optional<at::Tensor> & b_hh)>(at::native::rnn_relu_cell)); |
| 20879 | m.impl("_pad_packed_sequence", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & data, const at::Tensor & batch_sizes, bool batch_first, const at::Scalar & padding_value, int64_t total_length)>(at::native::_pad_packed_sequence)); |
| 20880 | m.impl("gather_backward", static_cast<at::Tensor (*)(const at::Tensor & grad, const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad)>(at::native::gather_backward)); |
| 20881 | m.impl("_gather_sparse_backward", static_cast<at::Tensor (*)(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & grad)>(at::native::_gather_sparse_backward)); |
| 20882 | m.impl("linalg_vander", static_cast<at::Tensor (*)(const at::Tensor & x, c10::optional<int64_t> N)>(at::native::linalg_vander)); |
| 20883 | m.impl("argsort", static_cast<at::Tensor (*)(const at::Tensor & self, int64_t dim, bool descending)>(at::native::argsort)); |
| 20884 | m.impl("argsort.dimname", static_cast<at::Tensor (*)(const at::Tensor & self, at::Dimname dim, bool descending)>(at::native::argsort)); |
| 20885 | m.impl("_pad_circular", static_cast<at::Tensor (*)(const at::Tensor & self, c10::SymIntArrayRef pad)>(at::native::_pad_circular_symint)); |
| 20886 | m.impl("upsample_trilinear3d.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_trilinear3d)); |
| 20887 | m.impl("_upsample_bicubic2d_aa.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_aa)); |
| 20888 | m.impl("special_softmax", static_cast<at::Tensor (*)(const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype)>(at::native::special_softmax)); |
| 20889 | m.impl("fft_fftshift", static_cast<at::Tensor (*)(const at::Tensor & self, at::OptionalIntArrayRef dim)>(at::native::fft_fftshift)); |
| 20890 | m.impl("nested_to_padded_tensor", static_cast<at::Tensor (*)(const at::Tensor & self, double padding, at::OptionalIntArrayRef output_size)>(at::native::nested_to_padded_tensor)); |
| 20891 | m.impl("_test_ambiguous_defaults.a", static_cast<at::Tensor (*)(const at::Tensor & dummy, int64_t a, int64_t b)>(at::native::_test_ambiguous_defaults)); |
| 20892 | m.impl("_test_ambiguous_defaults.b", static_cast<at::Tensor (*)(const at::Tensor & dummy, int64_t a, c10::string_view b)>(at::native::_test_ambiguous_defaults)); |
| 20893 | m.impl("_test_autograd_multiple_dispatch.ntonly", static_cast<at::Tensor (*)(const at::Tensor & self, bool b)>(at::native::_test_autograd_multiple_dispatch_ntonly)); |
| 20894 | m.impl("pad_sequence", static_cast<at::Tensor (*)(at::TensorList sequences, bool batch_first, double padding_value)>(at::native::pad_sequence)); |
| 20895 | m.impl("flatten_dense_tensors", static_cast<at::Tensor (*)(at::TensorList tensors)>(at::native::flatten_dense_tensors)); |
| 20896 | m.impl("_scaled_dot_product_attention", static_cast<::std::tuple<at::Tensor,at::Tensor> (*)(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const c10::optional<at::Tensor> & attn_mask, double dropout_p, bool need_attn_weights, bool is_causal)>(at::native::_scaled_dot_product_attention));; |
| 20897 | } |
| 20898 | |
| 20899 | } // namespace |
| 20900 | |
| 20901 | } // namespace at |
| 20902 |
Generated on 2023-Feb-12 from project pytorch revision 2c76838d7ff
Powered by 
Code Browser 2.1
Generator usage only permitted with license.