| 1 | #include <ATen/Tensor.h> |
|---|---|
| 2 | #include <ATen/core/dispatch/Dispatcher.h> |
| 3 | |
| 4 | // @generated by torchgen/gen.py from Operators.cpp |
| 5 | // NOTE See [Sharded File] comment in VariableType |
| 6 | |
| 7 | #ifndef AT_PER_OPERATOR_HEADERS |
| 8 | #include <ATen/Operators.h> |
| 9 | #else |
| 10 | #include <ATen/ops/_cast_Int.h> |
| 11 | #include <ATen/ops/_cast_Long.h> |
| 12 | #include <ATen/ops/_backward.h> |
| 13 | #include <ATen/ops/data.h> |
| 14 | #include <ATen/ops/retain_grad.h> |
| 15 | #include <ATen/ops/rename.h> |
| 16 | #include <ATen/ops/rename.h> |
| 17 | #include <ATen/ops/_cudnn_rnn_backward.h> |
| 18 | #include <ATen/ops/native_dropout_backward.h> |
| 19 | #include <ATen/ops/feature_dropout.h> |
| 20 | #include <ATen/ops/feature_dropout.h> |
| 21 | #include <ATen/ops/conj.h> |
| 22 | #include <ATen/ops/_add_relu.h> |
| 23 | #include <ATen/ops/_add_relu.h> |
| 24 | #include <ATen/ops/_add_relu.h> |
| 25 | #include <ATen/ops/_add_relu.h> |
| 26 | #include <ATen/ops/_add_relu.h> |
| 27 | #include <ATen/ops/affine_grid_generator.h> |
| 28 | #include <ATen/ops/_is_any_true.h> |
| 29 | #include <ATen/ops/arange.h> |
| 30 | #include <ATen/ops/arange.h> |
| 31 | #include <ATen/ops/arange.h> |
| 32 | #include <ATen/ops/arange.h> |
| 33 | #include <ATen/ops/arange.h> |
| 34 | #include <ATen/ops/_dim_arange.h> |
| 35 | #include <ATen/ops/arcsinh.h> |
| 36 | #include <ATen/ops/arcsinh.h> |
| 37 | #include <ATen/ops/arcsinh.h> |
| 38 | #include <ATen/ops/atanh.h> |
| 39 | #include <ATen/ops/atanh.h> |
| 40 | #include <ATen/ops/atanh.h> |
| 41 | #include <ATen/ops/arcsin.h> |
| 42 | #include <ATen/ops/arcsin.h> |
| 43 | #include <ATen/ops/arcsin.h> |
| 44 | #include <ATen/ops/bartlett_window.h> |
| 45 | #include <ATen/ops/bartlett_window.h> |
| 46 | #include <ATen/ops/binary_cross_entropy.h> |
| 47 | #include <ATen/ops/binary_cross_entropy.h> |
| 48 | #include <ATen/ops/bmm.h> |
| 49 | #include <ATen/ops/bmm.h> |
| 50 | #include <ATen/ops/_sparse_broadcast_to.h> |
| 51 | #include <ATen/ops/concat.h> |
| 52 | #include <ATen/ops/concat.h> |
| 53 | #include <ATen/ops/concat.h> |
| 54 | #include <ATen/ops/concat.h> |
| 55 | #include <ATen/ops/chain_matmul.h> |
| 56 | #include <ATen/ops/chain_matmul.h> |
| 57 | #include <ATen/ops/clamp_min.h> |
| 58 | #include <ATen/ops/clamp_min.h> |
| 59 | #include <ATen/ops/clamp_min.h> |
| 60 | #include <ATen/ops/clamp_min.h> |
| 61 | #include <ATen/ops/clamp_min.h> |
| 62 | #include <ATen/ops/clamp_min.h> |
| 63 | #include <ATen/ops/_convolution_mode.h> |
| 64 | #include <ATen/ops/conv1d.h> |
| 65 | #include <ATen/ops/conv3d.h> |
| 66 | #include <ATen/ops/conv1d.h> |
| 67 | #include <ATen/ops/conv3d.h> |
| 68 | #include <ATen/ops/conv_tbc_backward.h> |
| 69 | #include <ATen/ops/conv_transpose3d.h> |
| 70 | #include <ATen/ops/copy.h> |
| 71 | #include <ATen/ops/copy.h> |
| 72 | #include <ATen/ops/_copy_from_and_resize.h> |
| 73 | #include <ATen/ops/cudnn_convolution.h> |
| 74 | #include <ATen/ops/cudnn_convolution_relu.h> |
| 75 | #include <ATen/ops/cumprod.h> |
| 76 | #include <ATen/ops/cumprod.h> |
| 77 | #include <ATen/ops/cumprod.h> |
| 78 | #include <ATen/ops/cumprod.h> |
| 79 | #include <ATen/ops/cumprod.h> |
| 80 | #include <ATen/ops/cumprod.h> |
| 81 | #include <ATen/ops/cumulative_trapezoid.h> |
| 82 | #include <ATen/ops/cumulative_trapezoid.h> |
| 83 | #include <ATen/ops/ctc_loss.h> |
| 84 | #include <ATen/ops/ctc_loss.h> |
| 85 | #include <ATen/ops/diag_embed.h> |
| 86 | #include <ATen/ops/diagonal.h> |
| 87 | #include <ATen/ops/diagonal.h> |
| 88 | #include <ATen/ops/divide.h> |
| 89 | #include <ATen/ops/divide.h> |
| 90 | #include <ATen/ops/divide.h> |
| 91 | #include <ATen/ops/divide.h> |
| 92 | #include <ATen/ops/divide.h> |
| 93 | #include <ATen/ops/divide.h> |
| 94 | #include <ATen/ops/divide.h> |
| 95 | #include <ATen/ops/divide.h> |
| 96 | #include <ATen/ops/divide.h> |
| 97 | #include <ATen/ops/divide.h> |
| 98 | #include <ATen/ops/_empty_affine_quantized.h> |
| 99 | #include <ATen/ops/_resize_output.h> |
| 100 | #include <ATen/ops/empty_like.h> |
| 101 | #include <ATen/ops/expand.h> |
| 102 | #include <ATen/ops/flatten.h> |
| 103 | #include <ATen/ops/flatten.h> |
| 104 | #include <ATen/ops/flatten.h> |
| 105 | #include <ATen/ops/flatten.h> |
| 106 | #include <ATen/ops/floor.h> |
| 107 | #include <ATen/ops/floor.h> |
| 108 | #include <ATen/ops/floor.h> |
| 109 | #include <ATen/ops/grid_sampler_3d_backward.h> |
| 110 | #include <ATen/ops/hinge_embedding_loss.h> |
| 111 | #include <ATen/ops/native_group_norm.h> |
| 112 | #include <ATen/ops/_fft_r2c.h> |
| 113 | #include <ATen/ops/_fft_r2c.h> |
| 114 | #include <ATen/ops/is_neg.h> |
| 115 | #include <ATen/ops/isreal.h> |
| 116 | #include <ATen/ops/linear_backward.h> |
| 117 | #include <ATen/ops/mkldnn_linear_backward_input.h> |
| 118 | #include <ATen/ops/mkldnn_linear_backward.h> |
| 119 | #include <ATen/ops/_logcumsumexp.h> |
| 120 | #include <ATen/ops/_logcumsumexp.h> |
| 121 | #include <ATen/ops/value_selecting_reduction_backward.h> |
| 122 | #include <ATen/ops/max_pool1d.h> |
| 123 | #include <ATen/ops/max_pool2d.h> |
| 124 | #include <ATen/ops/mean.h> |
| 125 | #include <ATen/ops/mean.h> |
| 126 | #include <ATen/ops/mean.h> |
| 127 | #include <ATen/ops/mean.h> |
| 128 | #include <ATen/ops/mean.h> |
| 129 | #include <ATen/ops/nanmean.h> |
| 130 | #include <ATen/ops/nanmean.h> |
| 131 | #include <ATen/ops/min.h> |
| 132 | #include <ATen/ops/min.h> |
| 133 | #include <ATen/ops/min.h> |
| 134 | #include <ATen/ops/min.h> |
| 135 | #include <ATen/ops/mm.h> |
| 136 | #include <ATen/ops/mm.h> |
| 137 | #include <ATen/ops/mv.h> |
| 138 | #include <ATen/ops/mv.h> |
| 139 | #include <ATen/ops/narrow_copy.h> |
| 140 | #include <ATen/ops/narrow_copy.h> |
| 141 | #include <ATen/ops/batch_norm_gather_stats_with_counts.h> |
| 142 | #include <ATen/ops/pairwise_distance.h> |
| 143 | #include <ATen/ops/_pdist_backward.h> |
| 144 | #include <ATen/ops/permute.h> |
| 145 | #include <ATen/ops/matrix_H.h> |
| 146 | #include <ATen/ops/pixel_shuffle.h> |
| 147 | #include <ATen/ops/pinverse.h> |
| 148 | #include <ATen/ops/reshape.h> |
| 149 | #include <ATen/ops/_reshape_alias.h> |
| 150 | #include <ATen/ops/select.h> |
| 151 | #include <ATen/ops/select.h> |
| 152 | #include <ATen/ops/celu.h> |
| 153 | #include <ATen/ops/celu.h> |
| 154 | #include <ATen/ops/silu.h> |
| 155 | #include <ATen/ops/silu.h> |
| 156 | #include <ATen/ops/silu.h> |
| 157 | #include <ATen/ops/mish_backward.h> |
| 158 | #include <ATen/ops/logit.h> |
| 159 | #include <ATen/ops/logit.h> |
| 160 | #include <ATen/ops/logit.h> |
| 161 | #include <ATen/ops/sinh.h> |
| 162 | #include <ATen/ops/sinh.h> |
| 163 | #include <ATen/ops/sinh.h> |
| 164 | #include <ATen/ops/slice_backward.h> |
| 165 | #include <ATen/ops/softmax.h> |
| 166 | #include <ATen/ops/softmax.h> |
| 167 | #include <ATen/ops/softmax.h> |
| 168 | #include <ATen/ops/_softmax.h> |
| 169 | #include <ATen/ops/_softmax.h> |
| 170 | #include <ATen/ops/unsafe_split.h> |
| 171 | #include <ATen/ops/dsplit.h> |
| 172 | #include <ATen/ops/dsplit.h> |
| 173 | #include <ATen/ops/vstack.h> |
| 174 | #include <ATen/ops/vstack.h> |
| 175 | #include <ATen/ops/stft.h> |
| 176 | #include <ATen/ops/stft.h> |
| 177 | #include <ATen/ops/_nested_sum_backward.h> |
| 178 | #include <ATen/ops/sum_to_size.h> |
| 179 | #include <ATen/ops/sqrt.h> |
| 180 | #include <ATen/ops/sqrt.h> |
| 181 | #include <ATen/ops/sqrt.h> |
| 182 | #include <ATen/ops/std.h> |
| 183 | #include <ATen/ops/std.h> |
| 184 | #include <ATen/ops/std.h> |
| 185 | #include <ATen/ops/std_mean.h> |
| 186 | #include <ATen/ops/std_mean.h> |
| 187 | #include <ATen/ops/std_mean.h> |
| 188 | #include <ATen/ops/std_mean.h> |
| 189 | #include <ATen/ops/std_mean.h> |
| 190 | #include <ATen/ops/std.h> |
| 191 | #include <ATen/ops/std.h> |
| 192 | #include <ATen/ops/std.h> |
| 193 | #include <ATen/ops/std.h> |
| 194 | #include <ATen/ops/std.h> |
| 195 | #include <ATen/ops/std.h> |
| 196 | #include <ATen/ops/t.h> |
| 197 | #include <ATen/ops/t.h> |
| 198 | #include <ATen/ops/threshold.h> |
| 199 | #include <ATen/ops/threshold.h> |
| 200 | #include <ATen/ops/threshold.h> |
| 201 | #include <ATen/ops/transpose.h> |
| 202 | #include <ATen/ops/transpose.h> |
| 203 | #include <ATen/ops/transpose.h> |
| 204 | #include <ATen/ops/flip.h> |
| 205 | #include <ATen/ops/roll.h> |
| 206 | #include <ATen/ops/_nested_from_padded.h> |
| 207 | #include <ATen/ops/_nested_view_from_buffer.h> |
| 208 | #include <ATen/ops/_trilinear.h> |
| 209 | #include <ATen/ops/type_as.h> |
| 210 | #include <ATen/ops/_has_compatible_shallow_copy_type.h> |
| 211 | #include <ATen/ops/_unique2.h> |
| 212 | #include <ATen/ops/_weight_norm_interface_backward.h> |
| 213 | #include <ATen/ops/zeros_like.h> |
| 214 | #include <ATen/ops/_sparse_csr_prod.h> |
| 215 | #include <ATen/ops/_sparse_softmax_backward_data.h> |
| 216 | #include <ATen/ops/_sparse_log_softmax.h> |
| 217 | #include <ATen/ops/_sparse_log_softmax.h> |
| 218 | #include <ATen/ops/_sparse_log_softmax.h> |
| 219 | #include <ATen/ops/_sparse_log_softmax_backward_data.h> |
| 220 | #include <ATen/ops/_spdiags.h> |
| 221 | #include <ATen/ops/frexp.h> |
| 222 | #include <ATen/ops/frexp.h> |
| 223 | #include <ATen/ops/zero.h> |
| 224 | #include <ATen/ops/rsub.h> |
| 225 | #include <ATen/ops/rsub.h> |
| 226 | #include <ATen/ops/_sparse_mm_reduce_impl.h> |
| 227 | #include <ATen/ops/_sparse_bsr_tensor_unsafe.h> |
| 228 | #include <ATen/ops/_validate_sparse_csc_tensor_args.h> |
| 229 | #include <ATen/ops/_sparse_coo_tensor_with_dims.h> |
| 230 | #include <ATen/ops/to_dense_backward.h> |
| 231 | #include <ATen/ops/_coalesce.h> |
| 232 | #include <ATen/ops/_values.h> |
| 233 | #include <ATen/ops/crow_indices.h> |
| 234 | #include <ATen/ops/q_zero_point.h> |
| 235 | #include <ATen/ops/q_per_channel_scales.h> |
| 236 | #include <ATen/ops/_fake_quantize_learnable_per_tensor_affine_backward.h> |
| 237 | #include <ATen/ops/_fake_quantize_learnable_per_channel_affine_backward.h> |
| 238 | #include <ATen/ops/fused_moving_avg_obs_fake_quant.h> |
| 239 | #include <ATen/ops/_choose_qparams_per_tensor.h> |
| 240 | #include <ATen/ops/meshgrid.h> |
| 241 | #include <ATen/ops/meshgrid.h> |
| 242 | #include <ATen/ops/can_cast.h> |
| 243 | #include <ATen/ops/lstm_mps_backward.h> |
| 244 | #include <ATen/ops/_thnn_fused_lstm_cell_backward_impl.h> |
| 245 | #include <ATen/ops/_thnn_fused_gru_cell.h> |
| 246 | #include <ATen/ops/quantized_rnn_tanh_cell.h> |
| 247 | #include <ATen/ops/_pack_padded_sequence.h> |
| 248 | #include <ATen/ops/is_set_to.h> |
| 249 | #include <ATen/ops/_masked_softmax.h> |
| 250 | #include <ATen/ops/view.h> |
| 251 | #include <ATen/ops/view.h> |
| 252 | #include <ATen/ops/xor.h> |
| 253 | #include <ATen/ops/xor.h> |
| 254 | #include <ATen/ops/xor.h> |
| 255 | #include <ATen/ops/xor.h> |
| 256 | #include <ATen/ops/triu.h> |
| 257 | #include <ATen/ops/lerp.h> |
| 258 | #include <ATen/ops/lerp.h> |
| 259 | #include <ATen/ops/addbmm.h> |
| 260 | #include <ATen/ops/addbmm.h> |
| 261 | #include <ATen/ops/addbmm.h> |
| 262 | #include <ATen/ops/triu.h> |
| 263 | #include <ATen/ops/triu.h> |
| 264 | #include <ATen/ops/not_equal.h> |
| 265 | #include <ATen/ops/not_equal.h> |
| 266 | #include <ATen/ops/not_equal.h> |
| 267 | #include <ATen/ops/not_equal.h> |
| 268 | #include <ATen/ops/not_equal.h> |
| 269 | #include <ATen/ops/not_equal.h> |
| 270 | #include <ATen/ops/greater.h> |
| 271 | #include <ATen/ops/greater.h> |
| 272 | #include <ATen/ops/greater.h> |
| 273 | #include <ATen/ops/greater.h> |
| 274 | #include <ATen/ops/greater.h> |
| 275 | #include <ATen/ops/greater.h> |
| 276 | #include <ATen/ops/gather.h> |
| 277 | #include <ATen/ops/gather.h> |
| 278 | #include <ATen/ops/gather_backward.h> |
| 279 | #include <ATen/ops/gather.h> |
| 280 | #include <ATen/ops/gather.h> |
| 281 | #include <ATen/ops/cross_entropy_loss.h> |
| 282 | #include <ATen/ops/triangular_solve.h> |
| 283 | #include <ATen/ops/triangular_solve.h> |
| 284 | #include <ATen/ops/_linalg_check_errors.h> |
| 285 | #include <ATen/ops/linalg_solve_triangular.h> |
| 286 | #include <ATen/ops/linalg_solve_triangular.h> |
| 287 | #include <ATen/ops/ormqr.h> |
| 288 | #include <ATen/ops/ormqr.h> |
| 289 | #include <ATen/ops/i0.h> |
| 290 | #include <ATen/ops/i0.h> |
| 291 | #include <ATen/ops/i0.h> |
| 292 | #include <ATen/ops/sign.h> |
| 293 | #include <ATen/ops/sign.h> |
| 294 | #include <ATen/ops/sign.h> |
| 295 | #include <ATen/ops/lerp.h> |
| 296 | #include <ATen/ops/lerp.h> |
| 297 | #include <ATen/ops/lerp.h> |
| 298 | #include <ATen/ops/lerp.h> |
| 299 | #include <ATen/ops/min.h> |
| 300 | #include <ATen/ops/fmin.h> |
| 301 | #include <ATen/ops/fmin.h> |
| 302 | #include <ATen/ops/min.h> |
| 303 | #include <ATen/ops/min.h> |
| 304 | #include <ATen/ops/equal.h> |
| 305 | #include <ATen/ops/_foreach_mul.h> |
| 306 | #include <ATen/ops/_foreach_mul.h> |
| 307 | #include <ATen/ops/_foreach_div.h> |
| 308 | #include <ATen/ops/_foreach_div.h> |
| 309 | #include <ATen/ops/_foreach_mul.h> |
| 310 | #include <ATen/ops/_foreach_mul.h> |
| 311 | #include <ATen/ops/_foreach_div.h> |
| 312 | #include <ATen/ops/_foreach_div.h> |
| 313 | #include <ATen/ops/_foreach_div.h> |
| 314 | #include <ATen/ops/_foreach_div.h> |
| 315 | #include <ATen/ops/_foreach_mul.h> |
| 316 | #include <ATen/ops/_foreach_mul.h> |
| 317 | #include <ATen/ops/_foreach_zero.h> |
| 318 | #include <ATen/ops/_foreach_asin.h> |
| 319 | #include <ATen/ops/_foreach_asin.h> |
| 320 | #include <ATen/ops/_foreach_cos.h> |
| 321 | #include <ATen/ops/_foreach_cos.h> |
| 322 | #include <ATen/ops/_foreach_floor.h> |
| 323 | #include <ATen/ops/_foreach_floor.h> |
| 324 | #include <ATen/ops/_foreach_tanh.h> |
| 325 | #include <ATen/ops/_foreach_tanh.h> |
| 326 | #include <ATen/ops/_foreach_addcmul.h> |
| 327 | #include <ATen/ops/_foreach_addcmul.h> |
| 328 | #include <ATen/ops/_foreach_addcmul.h> |
| 329 | #include <ATen/ops/_foreach_addcmul.h> |
| 330 | #include <ATen/ops/_foreach_addcmul.h> |
| 331 | #include <ATen/ops/_foreach_addcmul.h> |
| 332 | #include <ATen/ops/_convert_indices_from_csr_to_coo.h> |
| 333 | #include <ATen/ops/_convert_indices_from_csr_to_coo.h> |
| 334 | #include <ATen/ops/nll_loss.h> |
| 335 | #include <ATen/ops/nll_loss.h> |
| 336 | #include <ATen/ops/nll_loss_backward.h> |
| 337 | #include <ATen/ops/nll_loss_backward.h> |
| 338 | #include <ATen/ops/smooth_l1_loss_backward.h> |
| 339 | #include <ATen/ops/smooth_l1_loss_backward.h> |
| 340 | #include <ATen/ops/huber_loss.h> |
| 341 | #include <ATen/ops/huber_loss.h> |
| 342 | #include <ATen/ops/huber_loss_backward.h> |
| 343 | #include <ATen/ops/huber_loss_backward.h> |
| 344 | #include <ATen/ops/hardsigmoid.h> |
| 345 | #include <ATen/ops/hardsigmoid.h> |
| 346 | #include <ATen/ops/hardsigmoid.h> |
| 347 | #include <ATen/ops/log_sigmoid.h> |
| 348 | #include <ATen/ops/log_sigmoid.h> |
| 349 | #include <ATen/ops/adaptive_avg_pool2d.h> |
| 350 | #include <ATen/ops/adaptive_avg_pool2d.h> |
| 351 | #include <ATen/ops/adaptive_avg_pool3d.h> |
| 352 | #include <ATen/ops/adaptive_avg_pool3d.h> |
| 353 | #include <ATen/ops/_adaptive_avg_pool3d.h> |
| 354 | #include <ATen/ops/adaptive_max_pool2d.h> |
| 355 | #include <ATen/ops/adaptive_max_pool2d.h> |
| 356 | #include <ATen/ops/adaptive_max_pool3d.h> |
| 357 | #include <ATen/ops/adaptive_max_pool3d.h> |
| 358 | #include <ATen/ops/avg_pool2d_backward.h> |
| 359 | #include <ATen/ops/avg_pool2d_backward.h> |
| 360 | #include <ATen/ops/fractional_max_pool2d.h> |
| 361 | #include <ATen/ops/fractional_max_pool2d.h> |
| 362 | #include <ATen/ops/max_unpool2d.h> |
| 363 | #include <ATen/ops/max_unpool2d.h> |
| 364 | #include <ATen/ops/max_unpool3d.h> |
| 365 | #include <ATen/ops/max_unpool3d.h> |
| 366 | #include <ATen/ops/reflection_pad3d_backward.h> |
| 367 | #include <ATen/ops/reflection_pad3d_backward.h> |
| 368 | #include <ATen/ops/replication_pad2d_backward.h> |
| 369 | #include <ATen/ops/replication_pad2d_backward.h> |
| 370 | #include <ATen/ops/replication_pad3d.h> |
| 371 | #include <ATen/ops/replication_pad3d.h> |
| 372 | #include <ATen/ops/upsample_linear1d.h> |
| 373 | #include <ATen/ops/upsample_bilinear2d.h> |
| 374 | #include <ATen/ops/upsample_bicubic2d.h> |
| 375 | #include <ATen/ops/upsample_nearest2d.h> |
| 376 | #include <ATen/ops/upsample_linear1d.h> |
| 377 | #include <ATen/ops/upsample_linear1d.h> |
| 378 | #include <ATen/ops/upsample_bilinear2d.h> |
| 379 | #include <ATen/ops/upsample_bilinear2d.h> |
| 380 | #include <ATen/ops/upsample_bicubic2d.h> |
| 381 | #include <ATen/ops/upsample_bicubic2d.h> |
| 382 | #include <ATen/ops/upsample_bicubic2d_backward.h> |
| 383 | #include <ATen/ops/upsample_bicubic2d_backward.h> |
| 384 | #include <ATen/ops/upsample_trilinear3d_backward.h> |
| 385 | #include <ATen/ops/upsample_trilinear3d_backward.h> |
| 386 | #include <ATen/ops/upsample_nearest2d.h> |
| 387 | #include <ATen/ops/upsample_nearest2d.h> |
| 388 | #include <ATen/ops/upsample_nearest3d_backward.h> |
| 389 | #include <ATen/ops/_upsample_nearest_exact3d_backward.h> |
| 390 | #include <ATen/ops/upsample_nearest3d_backward.h> |
| 391 | #include <ATen/ops/_upsample_nearest_exact3d_backward.h> |
| 392 | #include <ATen/ops/logit_backward.h> |
| 393 | #include <ATen/ops/logit_backward.h> |
| 394 | #include <ATen/ops/slow_conv_transpose2d.h> |
| 395 | #include <ATen/ops/slow_conv_transpose2d.h> |
| 396 | #include <ATen/ops/_slow_conv2d_backward.h> |
| 397 | #include <ATen/ops/_slow_conv2d_backward.h> |
| 398 | #include <ATen/ops/conv_depthwise3d.h> |
| 399 | #include <ATen/ops/slow_conv_dilated2d.h> |
| 400 | #include <ATen/ops/col2im.h> |
| 401 | #include <ATen/ops/col2im.h> |
| 402 | #include <ATen/ops/isfinite.h> |
| 403 | #include <ATen/ops/record_stream.h> |
| 404 | #include <ATen/ops/isposinf.h> |
| 405 | #include <ATen/ops/isposinf.h> |
| 406 | #include <ATen/ops/special_expm1.h> |
| 407 | #include <ATen/ops/special_expm1.h> |
| 408 | #include <ATen/ops/special_exp2.h> |
| 409 | #include <ATen/ops/special_exp2.h> |
| 410 | #include <ATen/ops/special_gammaln.h> |
| 411 | #include <ATen/ops/special_gammaln.h> |
| 412 | #include <ATen/ops/special_erfinv.h> |
| 413 | #include <ATen/ops/special_erfinv.h> |
| 414 | #include <ATen/ops/special_xlog1py.h> |
| 415 | #include <ATen/ops/special_xlog1py.h> |
| 416 | #include <ATen/ops/special_xlog1py.h> |
| 417 | #include <ATen/ops/special_xlog1py.h> |
| 418 | #include <ATen/ops/special_xlog1py.h> |
| 419 | #include <ATen/ops/special_xlog1py.h> |
| 420 | #include <ATen/ops/special_i0.h> |
| 421 | #include <ATen/ops/special_i0.h> |
| 422 | #include <ATen/ops/special_polygamma.h> |
| 423 | #include <ATen/ops/special_polygamma.h> |
| 424 | #include <ATen/ops/special_log1p.h> |
| 425 | #include <ATen/ops/special_log1p.h> |
| 426 | #include <ATen/ops/fft_irfft.h> |
| 427 | #include <ATen/ops/fft_irfft.h> |
| 428 | #include <ATen/ops/fft_ifft2.h> |
| 429 | #include <ATen/ops/fft_ifft2.h> |
| 430 | #include <ATen/ops/fft_irfft2.h> |
| 431 | #include <ATen/ops/fft_irfft2.h> |
| 432 | #include <ATen/ops/fft_rfftn.h> |
| 433 | #include <ATen/ops/fft_rfftn.h> |
| 434 | #include <ATen/ops/linalg_cholesky.h> |
| 435 | #include <ATen/ops/linalg_cholesky.h> |
| 436 | #include <ATen/ops/_linalg_det.h> |
| 437 | #include <ATen/ops/_linalg_det.h> |
| 438 | #include <ATen/ops/linalg_ldl_factor.h> |
| 439 | #include <ATen/ops/linalg_ldl_factor.h> |
| 440 | #include <ATen/ops/linalg_matmul.h> |
| 441 | #include <ATen/ops/linalg_matmul.h> |
| 442 | #include <ATen/ops/linalg_slogdet.h> |
| 443 | #include <ATen/ops/linalg_slogdet.h> |
| 444 | #include <ATen/ops/logdet.h> |
| 445 | #include <ATen/ops/linalg_eigvals.h> |
| 446 | #include <ATen/ops/linalg_eigvals.h> |
| 447 | #include <ATen/ops/linalg_inv_ex.h> |
| 448 | #include <ATen/ops/linalg_inv_ex.h> |
| 449 | #include <ATen/ops/inner.h> |
| 450 | #include <ATen/ops/inner.h> |
| 451 | #include <ATen/ops/linalg_vector_norm.h> |
| 452 | #include <ATen/ops/linalg_vector_norm.h> |
| 453 | #include <ATen/ops/linalg_solve.h> |
| 454 | #include <ATen/ops/linalg_solve.h> |
| 455 | #include <ATen/ops/linalg_tensorinv.h> |
| 456 | #include <ATen/ops/linalg_tensorinv.h> |
| 457 | #include <ATen/ops/linalg_matrix_rank.h> |
| 458 | #include <ATen/ops/linalg_matrix_rank.h> |
| 459 | #include <ATen/ops/linalg_matrix_rank.h> |
| 460 | #include <ATen/ops/linalg_matrix_rank.h> |
| 461 | #include <ATen/ops/linalg_matrix_rank.h> |
| 462 | #include <ATen/ops/linalg_matrix_rank.h> |
| 463 | #include <ATen/ops/linalg_matrix_rank.h> |
| 464 | #include <ATen/ops/linalg_matrix_rank.h> |
| 465 | #include <ATen/ops/_test_optional_filled_intlist.h> |
| 466 | #include <ATen/ops/_test_autograd_multiple_dispatch_view_copy.h> |
| 467 | #include <ATen/ops/pad_sequence.h> |
| 468 | #include <ATen/ops/_fw_primal_copy.h> |
| 469 | #include <ATen/ops/view_as_real_copy.h> |
| 470 | #include <ATen/ops/as_strided_copy.h> |
| 471 | #include <ATen/ops/_reshape_alias_copy.h> |
| 472 | #include <ATen/ops/split_copy.h> |
| 473 | #include <ATen/ops/squeeze_copy.h> |
| 474 | #include <ATen/ops/squeeze_copy.h> |
| 475 | #include <ATen/ops/squeeze_copy.h> |
| 476 | #include <ATen/ops/indices_copy.h> |
| 477 | #include <ATen/ops/ccol_indices_copy.h> |
| 478 | #include <ATen/ops/split_copy.h> |
| 479 | #include <ATen/ops/_scaled_dot_product_efficient_attention.h> |
| 480 | #include <ATen/ops/_chunk_grad_outputs_efficient_attention.h> |
| 481 | #include <ATen/ops/_efficient_attention_forward.h> |
| 482 | #include <ATen/ops/_transformer_decoder_only_layer_fwd.h> |
| 483 | #include <ATen/ops/special_bessel_j1.h> |
| 484 | #include <ATen/ops/special_bessel_j1.h> |
| 485 | #include <ATen/ops/special_chebyshev_polynomial_v.h> |
| 486 | #include <ATen/ops/special_chebyshev_polynomial_v.h> |
| 487 | #include <ATen/ops/special_chebyshev_polynomial_v.h> |
| 488 | #include <ATen/ops/special_chebyshev_polynomial_v.h> |
| 489 | #include <ATen/ops/special_chebyshev_polynomial_v.h> |
| 490 | #include <ATen/ops/special_chebyshev_polynomial_v.h> |
| 491 | #include <ATen/ops/_cudnn_rnn_backward.h> |
| 492 | #include <ATen/ops/native_dropout_backward.h> |
| 493 | #include <ATen/ops/_add_relu.h> |
| 494 | #include <ATen/ops/affine_grid_generator.h> |
| 495 | #include <ATen/ops/bartlett_window.h> |
| 496 | #include <ATen/ops/bartlett_window.h> |
| 497 | #include <ATen/ops/copy.h> |
| 498 | #include <ATen/ops/_copy_from_and_resize.h> |
| 499 | #include <ATen/ops/cudnn_convolution.h> |
| 500 | #include <ATen/ops/cudnn_convolution_relu.h> |
| 501 | #include <ATen/ops/diag_embed.h> |
| 502 | #include <ATen/ops/_empty_affine_quantized.h> |
| 503 | #include <ATen/ops/_resize_output.h> |
| 504 | #include <ATen/ops/_resize_output.h> |
| 505 | #include <ATen/ops/empty_like.h> |
| 506 | #include <ATen/ops/grid_sampler_3d_backward.h> |
| 507 | #include <ATen/ops/native_group_norm.h> |
| 508 | #include <ATen/ops/linear_backward.h> |
| 509 | #include <ATen/ops/mkldnn_linear_backward_input.h> |
| 510 | #include <ATen/ops/mkldnn_linear_backward.h> |
| 511 | #include <ATen/ops/batch_norm_gather_stats_with_counts.h> |
| 512 | #include <ATen/ops/_pdist_backward.h> |
| 513 | #include <ATen/ops/pixel_shuffle.h> |
| 514 | #include <ATen/ops/celu.h> |
| 515 | #include <ATen/ops/slice_backward.h> |
| 516 | #include <ATen/ops/unsafe_split.h> |
| 517 | #include <ATen/ops/std_mean.h> |
| 518 | #include <ATen/ops/flip.h> |
| 519 | #include <ATen/ops/roll.h> |
| 520 | #include <ATen/ops/_nested_from_padded.h> |
| 521 | #include <ATen/ops/_trilinear.h> |
| 522 | #include <ATen/ops/_unique2.h> |
| 523 | #include <ATen/ops/_weight_norm_interface_backward.h> |
| 524 | #include <ATen/ops/zeros_like.h> |
| 525 | #include <ATen/ops/_sparse_csr_prod.h> |
| 526 | #include <ATen/ops/_sparse_softmax_backward_data.h> |
| 527 | #include <ATen/ops/_sparse_log_softmax.h> |
| 528 | #include <ATen/ops/_sparse_log_softmax_backward_data.h> |
| 529 | #include <ATen/ops/_spdiags.h> |
| 530 | #include <ATen/ops/zero.h> |
| 531 | #include <ATen/ops/zero.h> |
| 532 | #include <ATen/ops/rsub.h> |
| 533 | #include <ATen/ops/rsub.h> |
| 534 | #include <ATen/ops/_sparse_coo_tensor_with_dims.h> |
| 535 | #include <ATen/ops/_coalesce.h> |
| 536 | #include <ATen/ops/q_per_channel_scales.h> |
| 537 | #include <ATen/ops/lstm_mps_backward.h> |
| 538 | #include <ATen/ops/_thnn_fused_lstm_cell_backward_impl.h> |
| 539 | #include <ATen/ops/_thnn_fused_gru_cell.h> |
| 540 | #include <ATen/ops/_pack_padded_sequence.h> |
| 541 | #include <ATen/ops/_masked_softmax.h> |
| 542 | #include <ATen/ops/_foreach_mul.h> |
| 543 | #include <ATen/ops/_foreach_div.h> |
| 544 | #include <ATen/ops/_foreach_mul.h> |
| 545 | #include <ATen/ops/_foreach_div.h> |
| 546 | #include <ATen/ops/_foreach_div.h> |
| 547 | #include <ATen/ops/_foreach_mul.h> |
| 548 | #include <ATen/ops/_foreach_zero.h> |
| 549 | #include <ATen/ops/_foreach_zero.h> |
| 550 | #include <ATen/ops/_foreach_asin.h> |
| 551 | #include <ATen/ops/_foreach_cos.h> |
| 552 | #include <ATen/ops/_foreach_floor.h> |
| 553 | #include <ATen/ops/_foreach_tanh.h> |
| 554 | #include <ATen/ops/_foreach_addcmul.h> |
| 555 | #include <ATen/ops/_foreach_addcmul.h> |
| 556 | #include <ATen/ops/_foreach_addcmul.h> |
| 557 | #include <ATen/ops/_adaptive_avg_pool3d.h> |
| 558 | #include <ATen/ops/_slow_conv2d_backward.h> |
| 559 | #include <ATen/ops/conv_depthwise3d.h> |
| 560 | #include <ATen/ops/slow_conv_dilated2d.h> |
| 561 | #include <ATen/ops/_test_optional_filled_intlist.h> |
| 562 | #include <ATen/ops/_test_autograd_multiple_dispatch_view_copy.h> |
| 563 | #include <ATen/ops/_fw_primal_copy.h> |
| 564 | #include <ATen/ops/view_as_real_copy.h> |
| 565 | #include <ATen/ops/as_strided_copy.h> |
| 566 | #include <ATen/ops/_reshape_alias_copy.h> |
| 567 | #include <ATen/ops/squeeze_copy.h> |
| 568 | #include <ATen/ops/squeeze_copy.h> |
| 569 | #include <ATen/ops/squeeze_copy.h> |
| 570 | #include <ATen/ops/indices_copy.h> |
| 571 | #include <ATen/ops/ccol_indices_copy.h> |
| 572 | #include <ATen/ops/_transformer_decoder_only_layer_fwd.h> |
| 573 | #endif |
| 574 | |
| 575 | |
| 576 | |
| 577 | namespace at { namespace _ops { |
| 578 | |
| 579 | |
| 580 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cast_Int, name, "aten::_cast_Int") |
| 581 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cast_Int, overload_name, "") |
| 582 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cast_Int, schema_str, "_cast_Int(Tensor self, bool non_blocking=False) -> Tensor") |
| 583 | |
| 584 | // aten::_cast_Int(Tensor self, bool non_blocking=False) -> Tensor |
| 585 | static C10_NOINLINE c10::TypedOperatorHandle<_cast_Int::schema> create__cast_Int_typed_handle() { |
| 586 | return c10::Dispatcher::singleton() |
| 587 | .findSchemaOrThrow(_cast_Int::name, _cast_Int::overload_name) |
| 588 | .typed<_cast_Int::schema>(); |
| 589 | } |
| 590 | |
| 591 | // aten::_cast_Int(Tensor self, bool non_blocking=False) -> Tensor |
| 592 | at::Tensor _cast_Int::call(const at::Tensor & self, bool non_blocking) { |
| 593 | |
| 594 | static auto op = create__cast_Int_typed_handle(); |
| 595 | return op.call(self, non_blocking); |
| 596 | } |
| 597 | |
| 598 | // aten::_cast_Int(Tensor self, bool non_blocking=False) -> Tensor |
| 599 | at::Tensor _cast_Int::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool non_blocking) { |
| 600 | |
| 601 | static auto op = create__cast_Int_typed_handle(); |
| 602 | return op.redispatch(dispatchKeySet, self, non_blocking); |
| 603 | } |
| 604 | |
| 605 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cast_Long, name, "aten::_cast_Long") |
| 606 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cast_Long, overload_name, "") |
| 607 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cast_Long, schema_str, "_cast_Long(Tensor self, bool non_blocking=False) -> Tensor") |
| 608 | |
| 609 | // aten::_cast_Long(Tensor self, bool non_blocking=False) -> Tensor |
| 610 | static C10_NOINLINE c10::TypedOperatorHandle<_cast_Long::schema> create__cast_Long_typed_handle() { |
| 611 | return c10::Dispatcher::singleton() |
| 612 | .findSchemaOrThrow(_cast_Long::name, _cast_Long::overload_name) |
| 613 | .typed<_cast_Long::schema>(); |
| 614 | } |
| 615 | |
| 616 | // aten::_cast_Long(Tensor self, bool non_blocking=False) -> Tensor |
| 617 | at::Tensor _cast_Long::call(const at::Tensor & self, bool non_blocking) { |
| 618 | |
| 619 | static auto op = create__cast_Long_typed_handle(); |
| 620 | return op.call(self, non_blocking); |
| 621 | } |
| 622 | |
| 623 | // aten::_cast_Long(Tensor self, bool non_blocking=False) -> Tensor |
| 624 | at::Tensor _cast_Long::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool non_blocking) { |
| 625 | |
| 626 | static auto op = create__cast_Long_typed_handle(); |
| 627 | return op.redispatch(dispatchKeySet, self, non_blocking); |
| 628 | } |
| 629 | |
| 630 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_backward, name, "aten::_backward") |
| 631 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_backward, overload_name, "") |
| 632 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_backward, schema_str, "_backward(Tensor self, Tensor[] inputs, Tensor? gradient=None, bool? retain_graph=None, bool create_graph=False) -> ()") |
| 633 | |
| 634 | // aten::_backward(Tensor self, Tensor[] inputs, Tensor? gradient=None, bool? retain_graph=None, bool create_graph=False) -> () |
| 635 | static C10_NOINLINE c10::TypedOperatorHandle<_backward::schema> create__backward_typed_handle() { |
| 636 | return c10::Dispatcher::singleton() |
| 637 | .findSchemaOrThrow(_backward::name, _backward::overload_name) |
| 638 | .typed<_backward::schema>(); |
| 639 | } |
| 640 | |
| 641 | // aten::_backward(Tensor self, Tensor[] inputs, Tensor? gradient=None, bool? retain_graph=None, bool create_graph=False) -> () |
| 642 | void _backward::call(const at::Tensor & self, at::TensorList inputs, const c10::optional<at::Tensor> & gradient, c10::optional<bool> retain_graph, bool create_graph) { |
| 643 | |
| 644 | static auto op = create__backward_typed_handle(); |
| 645 | return op.call(self, inputs, gradient, retain_graph, create_graph); |
| 646 | } |
| 647 | |
| 648 | // aten::_backward(Tensor self, Tensor[] inputs, Tensor? gradient=None, bool? retain_graph=None, bool create_graph=False) -> () |
| 649 | void _backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::TensorList inputs, const c10::optional<at::Tensor> & gradient, c10::optional<bool> retain_graph, bool create_graph) { |
| 650 | |
| 651 | static auto op = create__backward_typed_handle(); |
| 652 | return op.redispatch(dispatchKeySet, self, inputs, gradient, retain_graph, create_graph); |
| 653 | } |
| 654 | |
| 655 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(data, name, "aten::data") |
| 656 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(data, overload_name, "") |
| 657 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(data, schema_str, "data(Tensor self) -> Tensor") |
| 658 | |
| 659 | // aten::data(Tensor self) -> Tensor |
| 660 | static C10_NOINLINE c10::TypedOperatorHandle<data::schema> create_data_typed_handle() { |
| 661 | return c10::Dispatcher::singleton() |
| 662 | .findSchemaOrThrow(data::name, data::overload_name) |
| 663 | .typed<data::schema>(); |
| 664 | } |
| 665 | |
| 666 | // aten::data(Tensor self) -> Tensor |
| 667 | at::Tensor data::call(const at::Tensor & self) { |
| 668 | |
| 669 | static auto op = create_data_typed_handle(); |
| 670 | return op.call(self); |
| 671 | } |
| 672 | |
| 673 | // aten::data(Tensor self) -> Tensor |
| 674 | at::Tensor data::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 675 | |
| 676 | static auto op = create_data_typed_handle(); |
| 677 | return op.redispatch(dispatchKeySet, self); |
| 678 | } |
| 679 | |
| 680 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(retain_grad, name, "aten::retain_grad") |
| 681 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(retain_grad, overload_name, "") |
| 682 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(retain_grad, schema_str, "retain_grad(Tensor(a!) self) -> ()") |
| 683 | |
| 684 | // aten::retain_grad(Tensor(a!) self) -> () |
| 685 | static C10_NOINLINE c10::TypedOperatorHandle<retain_grad::schema> create_retain_grad_typed_handle() { |
| 686 | return c10::Dispatcher::singleton() |
| 687 | .findSchemaOrThrow(retain_grad::name, retain_grad::overload_name) |
| 688 | .typed<retain_grad::schema>(); |
| 689 | } |
| 690 | |
| 691 | // aten::retain_grad(Tensor(a!) self) -> () |
| 692 | void retain_grad::call(at::Tensor & self) { |
| 693 | |
| 694 | static auto op = create_retain_grad_typed_handle(); |
| 695 | return op.call(self); |
| 696 | } |
| 697 | |
| 698 | // aten::retain_grad(Tensor(a!) self) -> () |
| 699 | void retain_grad::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 700 | |
| 701 | static auto op = create_retain_grad_typed_handle(); |
| 702 | return op.redispatch(dispatchKeySet, self); |
| 703 | } |
| 704 | |
| 705 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rename_, name, "aten::rename_") |
| 706 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rename_, overload_name, "") |
| 707 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rename_, schema_str, "rename_(Tensor(a!) self, Dimname[]? names) -> Tensor(a!)") |
| 708 | |
| 709 | // aten::rename_(Tensor(a!) self, Dimname[]? names) -> Tensor(a!) |
| 710 | static C10_NOINLINE c10::TypedOperatorHandle<rename_::schema> create_rename__typed_handle() { |
| 711 | return c10::Dispatcher::singleton() |
| 712 | .findSchemaOrThrow(rename_::name, rename_::overload_name) |
| 713 | .typed<rename_::schema>(); |
| 714 | } |
| 715 | |
| 716 | // aten::rename_(Tensor(a!) self, Dimname[]? names) -> Tensor(a!) |
| 717 | at::Tensor & rename_::call(at::Tensor & self, c10::optional<at::DimnameList> names) { |
| 718 | |
| 719 | static auto op = create_rename__typed_handle(); |
| 720 | return op.call(self, names); |
| 721 | } |
| 722 | |
| 723 | // aten::rename_(Tensor(a!) self, Dimname[]? names) -> Tensor(a!) |
| 724 | at::Tensor & rename_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, c10::optional<at::DimnameList> names) { |
| 725 | |
| 726 | static auto op = create_rename__typed_handle(); |
| 727 | return op.redispatch(dispatchKeySet, self, names); |
| 728 | } |
| 729 | |
| 730 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rename, name, "aten::rename") |
| 731 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rename, overload_name, "") |
| 732 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rename, schema_str, "rename(Tensor(a) self, Dimname[]? names) -> Tensor(a)") |
| 733 | |
| 734 | // aten::rename(Tensor(a) self, Dimname[]? names) -> Tensor(a) |
| 735 | static C10_NOINLINE c10::TypedOperatorHandle<rename::schema> create_rename_typed_handle() { |
| 736 | return c10::Dispatcher::singleton() |
| 737 | .findSchemaOrThrow(rename::name, rename::overload_name) |
| 738 | .typed<rename::schema>(); |
| 739 | } |
| 740 | |
| 741 | // aten::rename(Tensor(a) self, Dimname[]? names) -> Tensor(a) |
| 742 | at::Tensor rename::call(const at::Tensor & self, c10::optional<at::DimnameList> names) { |
| 743 | |
| 744 | static auto op = create_rename_typed_handle(); |
| 745 | return op.call(self, names); |
| 746 | } |
| 747 | |
| 748 | // aten::rename(Tensor(a) self, Dimname[]? names) -> Tensor(a) |
| 749 | at::Tensor rename::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::DimnameList> names) { |
| 750 | |
| 751 | static auto op = create_rename_typed_handle(); |
| 752 | return op.redispatch(dispatchKeySet, self, names); |
| 753 | } |
| 754 | |
| 755 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cudnn_rnn_backward, name, "aten::_cudnn_rnn_backward") |
| 756 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cudnn_rnn_backward, overload_name, "") |
| 757 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cudnn_rnn_backward, schema_str, "_cudnn_rnn_backward(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask) -> (Tensor, Tensor, Tensor, Tensor[])") |
| 758 | |
| 759 | // aten::_cudnn_rnn_backward(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask) -> (Tensor, Tensor, Tensor, Tensor[]) |
| 760 | static C10_NOINLINE c10::TypedOperatorHandle<_cudnn_rnn_backward::schema> create__cudnn_rnn_backward_typed_handle() { |
| 761 | return c10::Dispatcher::singleton() |
| 762 | .findSchemaOrThrow(_cudnn_rnn_backward::name, _cudnn_rnn_backward::overload_name) |
| 763 | .typed<_cudnn_rnn_backward::schema>(); |
| 764 | } |
| 765 | |
| 766 | // aten::_cudnn_rnn_backward(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask) -> (Tensor, Tensor, Tensor, Tensor[]) |
| 767 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,::std::vector<at::Tensor>> _cudnn_rnn_backward::call(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) { |
| 768 | |
| 769 | static auto op = create__cudnn_rnn_backward_typed_handle(); |
| 770 | return op.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); |
| 771 | } |
| 772 | |
| 773 | // aten::_cudnn_rnn_backward(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask) -> (Tensor, Tensor, Tensor, Tensor[]) |
| 774 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,::std::vector<at::Tensor>> _cudnn_rnn_backward::redispatch(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) { |
| 775 | |
| 776 | static auto op = create__cudnn_rnn_backward_typed_handle(); |
| 777 | return op.redispatch(dispatchKeySet, 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); |
| 778 | } |
| 779 | |
| 780 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_dropout_backward, name, "aten::native_dropout_backward") |
| 781 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_dropout_backward, overload_name, "") |
| 782 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_dropout_backward, schema_str, "native_dropout_backward(Tensor grad_output, Tensor mask, float scale) -> Tensor") |
| 783 | |
| 784 | // aten::native_dropout_backward(Tensor grad_output, Tensor mask, float scale) -> Tensor |
| 785 | static C10_NOINLINE c10::TypedOperatorHandle<native_dropout_backward::schema> create_native_dropout_backward_typed_handle() { |
| 786 | return c10::Dispatcher::singleton() |
| 787 | .findSchemaOrThrow(native_dropout_backward::name, native_dropout_backward::overload_name) |
| 788 | .typed<native_dropout_backward::schema>(); |
| 789 | } |
| 790 | |
| 791 | // aten::native_dropout_backward(Tensor grad_output, Tensor mask, float scale) -> Tensor |
| 792 | at::Tensor native_dropout_backward::call(const at::Tensor & grad_output, const at::Tensor & mask, double scale) { |
| 793 | |
| 794 | static auto op = create_native_dropout_backward_typed_handle(); |
| 795 | return op.call(grad_output, mask, scale); |
| 796 | } |
| 797 | |
| 798 | // aten::native_dropout_backward(Tensor grad_output, Tensor mask, float scale) -> Tensor |
| 799 | at::Tensor native_dropout_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & mask, double scale) { |
| 800 | |
| 801 | static auto op = create_native_dropout_backward_typed_handle(); |
| 802 | return op.redispatch(dispatchKeySet, grad_output, mask, scale); |
| 803 | } |
| 804 | |
| 805 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(feature_dropout, name, "aten::feature_dropout") |
| 806 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(feature_dropout, overload_name, "") |
| 807 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(feature_dropout, schema_str, "feature_dropout(Tensor input, float p, bool train) -> Tensor") |
| 808 | |
| 809 | // aten::feature_dropout(Tensor input, float p, bool train) -> Tensor |
| 810 | static C10_NOINLINE c10::TypedOperatorHandle<feature_dropout::schema> create_feature_dropout_typed_handle() { |
| 811 | return c10::Dispatcher::singleton() |
| 812 | .findSchemaOrThrow(feature_dropout::name, feature_dropout::overload_name) |
| 813 | .typed<feature_dropout::schema>(); |
| 814 | } |
| 815 | |
| 816 | // aten::feature_dropout(Tensor input, float p, bool train) -> Tensor |
| 817 | at::Tensor feature_dropout::call(const at::Tensor & input, double p, bool train) { |
| 818 | |
| 819 | static auto op = create_feature_dropout_typed_handle(); |
| 820 | return op.call(input, p, train); |
| 821 | } |
| 822 | |
| 823 | // aten::feature_dropout(Tensor input, float p, bool train) -> Tensor |
| 824 | at::Tensor feature_dropout::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, double p, bool train) { |
| 825 | |
| 826 | static auto op = create_feature_dropout_typed_handle(); |
| 827 | return op.redispatch(dispatchKeySet, input, p, train); |
| 828 | } |
| 829 | |
| 830 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(feature_dropout_, name, "aten::feature_dropout_") |
| 831 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(feature_dropout_, overload_name, "") |
| 832 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(feature_dropout_, schema_str, "feature_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)") |
| 833 | |
| 834 | // aten::feature_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!) |
| 835 | static C10_NOINLINE c10::TypedOperatorHandle<feature_dropout_::schema> create_feature_dropout__typed_handle() { |
| 836 | return c10::Dispatcher::singleton() |
| 837 | .findSchemaOrThrow(feature_dropout_::name, feature_dropout_::overload_name) |
| 838 | .typed<feature_dropout_::schema>(); |
| 839 | } |
| 840 | |
| 841 | // aten::feature_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!) |
| 842 | at::Tensor & feature_dropout_::call(at::Tensor & self, double p, bool train) { |
| 843 | |
| 844 | static auto op = create_feature_dropout__typed_handle(); |
| 845 | return op.call(self, p, train); |
| 846 | } |
| 847 | |
| 848 | // aten::feature_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!) |
| 849 | at::Tensor & feature_dropout_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, double p, bool train) { |
| 850 | |
| 851 | static auto op = create_feature_dropout__typed_handle(); |
| 852 | return op.redispatch(dispatchKeySet, self, p, train); |
| 853 | } |
| 854 | |
| 855 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conj, name, "aten::conj") |
| 856 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conj, overload_name, "") |
| 857 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conj, schema_str, "conj(Tensor(a) self) -> Tensor(a)") |
| 858 | |
| 859 | // aten::conj(Tensor(a) self) -> Tensor(a) |
| 860 | static C10_NOINLINE c10::TypedOperatorHandle<conj::schema> create_conj_typed_handle() { |
| 861 | return c10::Dispatcher::singleton() |
| 862 | .findSchemaOrThrow(conj::name, conj::overload_name) |
| 863 | .typed<conj::schema>(); |
| 864 | } |
| 865 | |
| 866 | // aten::conj(Tensor(a) self) -> Tensor(a) |
| 867 | at::Tensor conj::call(const at::Tensor & self) { |
| 868 | |
| 869 | static auto op = create_conj_typed_handle(); |
| 870 | return op.call(self); |
| 871 | } |
| 872 | |
| 873 | // aten::conj(Tensor(a) self) -> Tensor(a) |
| 874 | at::Tensor conj::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 875 | |
| 876 | static auto op = create_conj_typed_handle(); |
| 877 | return op.redispatch(dispatchKeySet, self); |
| 878 | } |
| 879 | |
| 880 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_add_relu_Tensor, name, "aten::_add_relu") |
| 881 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_add_relu_Tensor, overload_name, "Tensor") |
| 882 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_add_relu_Tensor, schema_str, "_add_relu.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor") |
| 883 | |
| 884 | // aten::_add_relu.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor |
| 885 | static C10_NOINLINE c10::TypedOperatorHandle<_add_relu_Tensor::schema> create__add_relu_Tensor_typed_handle() { |
| 886 | return c10::Dispatcher::singleton() |
| 887 | .findSchemaOrThrow(_add_relu_Tensor::name, _add_relu_Tensor::overload_name) |
| 888 | .typed<_add_relu_Tensor::schema>(); |
| 889 | } |
| 890 | |
| 891 | // aten::_add_relu.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor |
| 892 | at::Tensor _add_relu_Tensor::call(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 893 | |
| 894 | static auto op = create__add_relu_Tensor_typed_handle(); |
| 895 | return op.call(self, other, alpha); |
| 896 | } |
| 897 | |
| 898 | // aten::_add_relu.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor |
| 899 | at::Tensor _add_relu_Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 900 | |
| 901 | static auto op = create__add_relu_Tensor_typed_handle(); |
| 902 | return op.redispatch(dispatchKeySet, self, other, alpha); |
| 903 | } |
| 904 | |
| 905 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_add_relu__Tensor, name, "aten::_add_relu_") |
| 906 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_add_relu__Tensor, overload_name, "Tensor") |
| 907 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_add_relu__Tensor, schema_str, "_add_relu_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)") |
| 908 | |
| 909 | // aten::_add_relu_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!) |
| 910 | static C10_NOINLINE c10::TypedOperatorHandle<_add_relu__Tensor::schema> create__add_relu__Tensor_typed_handle() { |
| 911 | return c10::Dispatcher::singleton() |
| 912 | .findSchemaOrThrow(_add_relu__Tensor::name, _add_relu__Tensor::overload_name) |
| 913 | .typed<_add_relu__Tensor::schema>(); |
| 914 | } |
| 915 | |
| 916 | // aten::_add_relu_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!) |
| 917 | at::Tensor & _add_relu__Tensor::call(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 918 | |
| 919 | static auto op = create__add_relu__Tensor_typed_handle(); |
| 920 | return op.call(self, other, alpha); |
| 921 | } |
| 922 | |
| 923 | // aten::_add_relu_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!) |
| 924 | at::Tensor & _add_relu__Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 925 | |
| 926 | static auto op = create__add_relu__Tensor_typed_handle(); |
| 927 | return op.redispatch(dispatchKeySet, self, other, alpha); |
| 928 | } |
| 929 | |
| 930 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_add_relu_out, name, "aten::_add_relu") |
| 931 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_add_relu_out, overload_name, "out") |
| 932 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_add_relu_out, schema_str, "_add_relu.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)") |
| 933 | |
| 934 | // aten::_add_relu.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) |
| 935 | static C10_NOINLINE c10::TypedOperatorHandle<_add_relu_out::schema> create__add_relu_out_typed_handle() { |
| 936 | return c10::Dispatcher::singleton() |
| 937 | .findSchemaOrThrow(_add_relu_out::name, _add_relu_out::overload_name) |
| 938 | .typed<_add_relu_out::schema>(); |
| 939 | } |
| 940 | |
| 941 | // aten::_add_relu.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) |
| 942 | at::Tensor & _add_relu_out::call(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) { |
| 943 | |
| 944 | static auto op = create__add_relu_out_typed_handle(); |
| 945 | return op.call(self, other, alpha, out); |
| 946 | } |
| 947 | |
| 948 | // aten::_add_relu.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) |
| 949 | at::Tensor & _add_relu_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) { |
| 950 | |
| 951 | static auto op = create__add_relu_out_typed_handle(); |
| 952 | return op.redispatch(dispatchKeySet, self, other, alpha, out); |
| 953 | } |
| 954 | |
| 955 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_add_relu_Scalar, name, "aten::_add_relu") |
| 956 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_add_relu_Scalar, overload_name, "Scalar") |
| 957 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_add_relu_Scalar, schema_str, "_add_relu.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor") |
| 958 | |
| 959 | // aten::_add_relu.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor |
| 960 | static C10_NOINLINE c10::TypedOperatorHandle<_add_relu_Scalar::schema> create__add_relu_Scalar_typed_handle() { |
| 961 | return c10::Dispatcher::singleton() |
| 962 | .findSchemaOrThrow(_add_relu_Scalar::name, _add_relu_Scalar::overload_name) |
| 963 | .typed<_add_relu_Scalar::schema>(); |
| 964 | } |
| 965 | |
| 966 | // aten::_add_relu.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor |
| 967 | at::Tensor _add_relu_Scalar::call(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha) { |
| 968 | |
| 969 | static auto op = create__add_relu_Scalar_typed_handle(); |
| 970 | return op.call(self, other, alpha); |
| 971 | } |
| 972 | |
| 973 | // aten::_add_relu.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor |
| 974 | at::Tensor _add_relu_Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha) { |
| 975 | |
| 976 | static auto op = create__add_relu_Scalar_typed_handle(); |
| 977 | return op.redispatch(dispatchKeySet, self, other, alpha); |
| 978 | } |
| 979 | |
| 980 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_add_relu__Scalar, name, "aten::_add_relu_") |
| 981 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_add_relu__Scalar, overload_name, "Scalar") |
| 982 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_add_relu__Scalar, schema_str, "_add_relu_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)") |
| 983 | |
| 984 | // aten::_add_relu_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!) |
| 985 | static C10_NOINLINE c10::TypedOperatorHandle<_add_relu__Scalar::schema> create__add_relu__Scalar_typed_handle() { |
| 986 | return c10::Dispatcher::singleton() |
| 987 | .findSchemaOrThrow(_add_relu__Scalar::name, _add_relu__Scalar::overload_name) |
| 988 | .typed<_add_relu__Scalar::schema>(); |
| 989 | } |
| 990 | |
| 991 | // aten::_add_relu_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!) |
| 992 | at::Tensor & _add_relu__Scalar::call(at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha) { |
| 993 | |
| 994 | static auto op = create__add_relu__Scalar_typed_handle(); |
| 995 | return op.call(self, other, alpha); |
| 996 | } |
| 997 | |
| 998 | // aten::_add_relu_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!) |
| 999 | at::Tensor & _add_relu__Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha) { |
| 1000 | |
| 1001 | static auto op = create__add_relu__Scalar_typed_handle(); |
| 1002 | return op.redispatch(dispatchKeySet, self, other, alpha); |
| 1003 | } |
| 1004 | |
| 1005 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(affine_grid_generator, name, "aten::affine_grid_generator") |
| 1006 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(affine_grid_generator, overload_name, "") |
| 1007 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(affine_grid_generator, schema_str, "affine_grid_generator(Tensor theta, int[] size, bool align_corners) -> Tensor") |
| 1008 | |
| 1009 | // aten::affine_grid_generator(Tensor theta, int[] size, bool align_corners) -> Tensor |
| 1010 | static C10_NOINLINE c10::TypedOperatorHandle<affine_grid_generator::schema> create_affine_grid_generator_typed_handle() { |
| 1011 | return c10::Dispatcher::singleton() |
| 1012 | .findSchemaOrThrow(affine_grid_generator::name, affine_grid_generator::overload_name) |
| 1013 | .typed<affine_grid_generator::schema>(); |
| 1014 | } |
| 1015 | |
| 1016 | // aten::affine_grid_generator(Tensor theta, int[] size, bool align_corners) -> Tensor |
| 1017 | at::Tensor affine_grid_generator::call(const at::Tensor & theta, at::IntArrayRef size, bool align_corners) { |
| 1018 | |
| 1019 | static auto op = create_affine_grid_generator_typed_handle(); |
| 1020 | return op.call(theta, size, align_corners); |
| 1021 | } |
| 1022 | |
| 1023 | // aten::affine_grid_generator(Tensor theta, int[] size, bool align_corners) -> Tensor |
| 1024 | at::Tensor affine_grid_generator::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & theta, at::IntArrayRef size, bool align_corners) { |
| 1025 | |
| 1026 | static auto op = create_affine_grid_generator_typed_handle(); |
| 1027 | return op.redispatch(dispatchKeySet, theta, size, align_corners); |
| 1028 | } |
| 1029 | |
| 1030 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_is_any_true, name, "aten::_is_any_true") |
| 1031 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_is_any_true, overload_name, "") |
| 1032 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_is_any_true, schema_str, "_is_any_true(Tensor self) -> Tensor") |
| 1033 | |
| 1034 | // aten::_is_any_true(Tensor self) -> Tensor |
| 1035 | static C10_NOINLINE c10::TypedOperatorHandle<_is_any_true::schema> create__is_any_true_typed_handle() { |
| 1036 | return c10::Dispatcher::singleton() |
| 1037 | .findSchemaOrThrow(_is_any_true::name, _is_any_true::overload_name) |
| 1038 | .typed<_is_any_true::schema>(); |
| 1039 | } |
| 1040 | |
| 1041 | // aten::_is_any_true(Tensor self) -> Tensor |
| 1042 | at::Tensor _is_any_true::call(const at::Tensor & self) { |
| 1043 | |
| 1044 | static auto op = create__is_any_true_typed_handle(); |
| 1045 | return op.call(self); |
| 1046 | } |
| 1047 | |
| 1048 | // aten::_is_any_true(Tensor self) -> Tensor |
| 1049 | at::Tensor _is_any_true::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 1050 | |
| 1051 | static auto op = create__is_any_true_typed_handle(); |
| 1052 | return op.redispatch(dispatchKeySet, self); |
| 1053 | } |
| 1054 | |
| 1055 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arange, name, "aten::arange") |
| 1056 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arange, overload_name, "") |
| 1057 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arange, schema_str, "arange(Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor") |
| 1058 | |
| 1059 | // aten::arange(Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 1060 | static C10_NOINLINE c10::TypedOperatorHandle<arange::schema> create_arange_typed_handle() { |
| 1061 | return c10::Dispatcher::singleton() |
| 1062 | .findSchemaOrThrow(arange::name, arange::overload_name) |
| 1063 | .typed<arange::schema>(); |
| 1064 | } |
| 1065 | |
| 1066 | // aten::arange(Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 1067 | at::Tensor arange::call(const at::Scalar & end, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 1068 | |
| 1069 | static auto op = create_arange_typed_handle(); |
| 1070 | return op.call(end, dtype, layout, device, pin_memory); |
| 1071 | } |
| 1072 | |
| 1073 | // aten::arange(Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 1074 | at::Tensor arange::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & end, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 1075 | |
| 1076 | static auto op = create_arange_typed_handle(); |
| 1077 | return op.redispatch(dispatchKeySet, end, dtype, layout, device, pin_memory); |
| 1078 | } |
| 1079 | |
| 1080 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arange_start, name, "aten::arange") |
| 1081 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arange_start, overload_name, "start") |
| 1082 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arange_start, schema_str, "arange.start(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor") |
| 1083 | |
| 1084 | // aten::arange.start(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 1085 | static C10_NOINLINE c10::TypedOperatorHandle<arange_start::schema> create_arange_start_typed_handle() { |
| 1086 | return c10::Dispatcher::singleton() |
| 1087 | .findSchemaOrThrow(arange_start::name, arange_start::overload_name) |
| 1088 | .typed<arange_start::schema>(); |
| 1089 | } |
| 1090 | |
| 1091 | // aten::arange.start(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 1092 | at::Tensor arange_start::call(const at::Scalar & start, const at::Scalar & end, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 1093 | |
| 1094 | static auto op = create_arange_start_typed_handle(); |
| 1095 | return op.call(start, end, dtype, layout, device, pin_memory); |
| 1096 | } |
| 1097 | |
| 1098 | // aten::arange.start(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 1099 | at::Tensor arange_start::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Scalar & end, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 1100 | |
| 1101 | static auto op = create_arange_start_typed_handle(); |
| 1102 | return op.redispatch(dispatchKeySet, start, end, dtype, layout, device, pin_memory); |
| 1103 | } |
| 1104 | |
| 1105 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arange_start_step, name, "aten::arange") |
| 1106 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arange_start_step, overload_name, "start_step") |
| 1107 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arange_start_step, schema_str, "arange.start_step(Scalar start, Scalar end, Scalar step=1, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor") |
| 1108 | |
| 1109 | // aten::arange.start_step(Scalar start, Scalar end, Scalar step=1, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 1110 | static C10_NOINLINE c10::TypedOperatorHandle<arange_start_step::schema> create_arange_start_step_typed_handle() { |
| 1111 | return c10::Dispatcher::singleton() |
| 1112 | .findSchemaOrThrow(arange_start_step::name, arange_start_step::overload_name) |
| 1113 | .typed<arange_start_step::schema>(); |
| 1114 | } |
| 1115 | |
| 1116 | // aten::arange.start_step(Scalar start, Scalar end, Scalar step=1, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 1117 | at::Tensor arange_start_step::call(const at::Scalar & start, const at::Scalar & end, const at::Scalar & step, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 1118 | |
| 1119 | static auto op = create_arange_start_step_typed_handle(); |
| 1120 | return op.call(start, end, step, dtype, layout, device, pin_memory); |
| 1121 | } |
| 1122 | |
| 1123 | // aten::arange.start_step(Scalar start, Scalar end, Scalar step=1, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 1124 | at::Tensor arange_start_step::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Scalar & end, const at::Scalar & step, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 1125 | |
| 1126 | static auto op = create_arange_start_step_typed_handle(); |
| 1127 | return op.redispatch(dispatchKeySet, start, end, step, dtype, layout, device, pin_memory); |
| 1128 | } |
| 1129 | |
| 1130 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arange_out, name, "aten::arange") |
| 1131 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arange_out, overload_name, "out") |
| 1132 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arange_out, schema_str, "arange.out(Scalar end, *, Tensor(a!) out) -> Tensor(a!)") |
| 1133 | |
| 1134 | // aten::arange.out(Scalar end, *, Tensor(a!) out) -> Tensor(a!) |
| 1135 | static C10_NOINLINE c10::TypedOperatorHandle<arange_out::schema> create_arange_out_typed_handle() { |
| 1136 | return c10::Dispatcher::singleton() |
| 1137 | .findSchemaOrThrow(arange_out::name, arange_out::overload_name) |
| 1138 | .typed<arange_out::schema>(); |
| 1139 | } |
| 1140 | |
| 1141 | // aten::arange.out(Scalar end, *, Tensor(a!) out) -> Tensor(a!) |
| 1142 | at::Tensor & arange_out::call(const at::Scalar & end, at::Tensor & out) { |
| 1143 | |
| 1144 | static auto op = create_arange_out_typed_handle(); |
| 1145 | return op.call(end, out); |
| 1146 | } |
| 1147 | |
| 1148 | // aten::arange.out(Scalar end, *, Tensor(a!) out) -> Tensor(a!) |
| 1149 | at::Tensor & arange_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & end, at::Tensor & out) { |
| 1150 | |
| 1151 | static auto op = create_arange_out_typed_handle(); |
| 1152 | return op.redispatch(dispatchKeySet, end, out); |
| 1153 | } |
| 1154 | |
| 1155 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arange_start_out, name, "aten::arange") |
| 1156 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arange_start_out, overload_name, "start_out") |
| 1157 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arange_start_out, schema_str, "arange.start_out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!)") |
| 1158 | |
| 1159 | // aten::arange.start_out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!) |
| 1160 | static C10_NOINLINE c10::TypedOperatorHandle<arange_start_out::schema> create_arange_start_out_typed_handle() { |
| 1161 | return c10::Dispatcher::singleton() |
| 1162 | .findSchemaOrThrow(arange_start_out::name, arange_start_out::overload_name) |
| 1163 | .typed<arange_start_out::schema>(); |
| 1164 | } |
| 1165 | |
| 1166 | // aten::arange.start_out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!) |
| 1167 | at::Tensor & arange_start_out::call(const at::Scalar & start, const at::Scalar & end, const at::Scalar & step, at::Tensor & out) { |
| 1168 | |
| 1169 | static auto op = create_arange_start_out_typed_handle(); |
| 1170 | return op.call(start, end, step, out); |
| 1171 | } |
| 1172 | |
| 1173 | // aten::arange.start_out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!) |
| 1174 | at::Tensor & arange_start_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Scalar & end, const at::Scalar & step, at::Tensor & out) { |
| 1175 | |
| 1176 | static auto op = create_arange_start_out_typed_handle(); |
| 1177 | return op.redispatch(dispatchKeySet, start, end, step, out); |
| 1178 | } |
| 1179 | |
| 1180 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_dim_arange, name, "aten::_dim_arange") |
| 1181 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_dim_arange, overload_name, "") |
| 1182 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_dim_arange, schema_str, "_dim_arange(Tensor like, int dim) -> Tensor") |
| 1183 | |
| 1184 | // aten::_dim_arange(Tensor like, int dim) -> Tensor |
| 1185 | static C10_NOINLINE c10::TypedOperatorHandle<_dim_arange::schema> create__dim_arange_typed_handle() { |
| 1186 | return c10::Dispatcher::singleton() |
| 1187 | .findSchemaOrThrow(_dim_arange::name, _dim_arange::overload_name) |
| 1188 | .typed<_dim_arange::schema>(); |
| 1189 | } |
| 1190 | |
| 1191 | // aten::_dim_arange(Tensor like, int dim) -> Tensor |
| 1192 | at::Tensor _dim_arange::call(const at::Tensor & like, int64_t dim) { |
| 1193 | |
| 1194 | static auto op = create__dim_arange_typed_handle(); |
| 1195 | return op.call(like, dim); |
| 1196 | } |
| 1197 | |
| 1198 | // aten::_dim_arange(Tensor like, int dim) -> Tensor |
| 1199 | at::Tensor _dim_arange::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & like, int64_t dim) { |
| 1200 | |
| 1201 | static auto op = create__dim_arange_typed_handle(); |
| 1202 | return op.redispatch(dispatchKeySet, like, dim); |
| 1203 | } |
| 1204 | |
| 1205 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arcsinh, name, "aten::arcsinh") |
| 1206 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arcsinh, overload_name, "") |
| 1207 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arcsinh, schema_str, "arcsinh(Tensor self) -> Tensor") |
| 1208 | |
| 1209 | // aten::arcsinh(Tensor self) -> Tensor |
| 1210 | static C10_NOINLINE c10::TypedOperatorHandle<arcsinh::schema> create_arcsinh_typed_handle() { |
| 1211 | return c10::Dispatcher::singleton() |
| 1212 | .findSchemaOrThrow(arcsinh::name, arcsinh::overload_name) |
| 1213 | .typed<arcsinh::schema>(); |
| 1214 | } |
| 1215 | |
| 1216 | // aten::arcsinh(Tensor self) -> Tensor |
| 1217 | at::Tensor arcsinh::call(const at::Tensor & self) { |
| 1218 | |
| 1219 | static auto op = create_arcsinh_typed_handle(); |
| 1220 | return op.call(self); |
| 1221 | } |
| 1222 | |
| 1223 | // aten::arcsinh(Tensor self) -> Tensor |
| 1224 | at::Tensor arcsinh::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 1225 | |
| 1226 | static auto op = create_arcsinh_typed_handle(); |
| 1227 | return op.redispatch(dispatchKeySet, self); |
| 1228 | } |
| 1229 | |
| 1230 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arcsinh_, name, "aten::arcsinh_") |
| 1231 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arcsinh_, overload_name, "") |
| 1232 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arcsinh_, schema_str, "arcsinh_(Tensor(a!) self) -> Tensor(a!)") |
| 1233 | |
| 1234 | // aten::arcsinh_(Tensor(a!) self) -> Tensor(a!) |
| 1235 | static C10_NOINLINE c10::TypedOperatorHandle<arcsinh_::schema> create_arcsinh__typed_handle() { |
| 1236 | return c10::Dispatcher::singleton() |
| 1237 | .findSchemaOrThrow(arcsinh_::name, arcsinh_::overload_name) |
| 1238 | .typed<arcsinh_::schema>(); |
| 1239 | } |
| 1240 | |
| 1241 | // aten::arcsinh_(Tensor(a!) self) -> Tensor(a!) |
| 1242 | at::Tensor & arcsinh_::call(at::Tensor & self) { |
| 1243 | |
| 1244 | static auto op = create_arcsinh__typed_handle(); |
| 1245 | return op.call(self); |
| 1246 | } |
| 1247 | |
| 1248 | // aten::arcsinh_(Tensor(a!) self) -> Tensor(a!) |
| 1249 | at::Tensor & arcsinh_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 1250 | |
| 1251 | static auto op = create_arcsinh__typed_handle(); |
| 1252 | return op.redispatch(dispatchKeySet, self); |
| 1253 | } |
| 1254 | |
| 1255 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arcsinh_out, name, "aten::arcsinh") |
| 1256 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arcsinh_out, overload_name, "out") |
| 1257 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arcsinh_out, schema_str, "arcsinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 1258 | |
| 1259 | // aten::arcsinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 1260 | static C10_NOINLINE c10::TypedOperatorHandle<arcsinh_out::schema> create_arcsinh_out_typed_handle() { |
| 1261 | return c10::Dispatcher::singleton() |
| 1262 | .findSchemaOrThrow(arcsinh_out::name, arcsinh_out::overload_name) |
| 1263 | .typed<arcsinh_out::schema>(); |
| 1264 | } |
| 1265 | |
| 1266 | // aten::arcsinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 1267 | at::Tensor & arcsinh_out::call(const at::Tensor & self, at::Tensor & out) { |
| 1268 | |
| 1269 | static auto op = create_arcsinh_out_typed_handle(); |
| 1270 | return op.call(self, out); |
| 1271 | } |
| 1272 | |
| 1273 | // aten::arcsinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 1274 | at::Tensor & arcsinh_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 1275 | |
| 1276 | static auto op = create_arcsinh_out_typed_handle(); |
| 1277 | return op.redispatch(dispatchKeySet, self, out); |
| 1278 | } |
| 1279 | |
| 1280 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(atanh, name, "aten::atanh") |
| 1281 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(atanh, overload_name, "") |
| 1282 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(atanh, schema_str, "atanh(Tensor self) -> Tensor") |
| 1283 | |
| 1284 | // aten::atanh(Tensor self) -> Tensor |
| 1285 | static C10_NOINLINE c10::TypedOperatorHandle<atanh::schema> create_atanh_typed_handle() { |
| 1286 | return c10::Dispatcher::singleton() |
| 1287 | .findSchemaOrThrow(atanh::name, atanh::overload_name) |
| 1288 | .typed<atanh::schema>(); |
| 1289 | } |
| 1290 | |
| 1291 | // aten::atanh(Tensor self) -> Tensor |
| 1292 | at::Tensor atanh::call(const at::Tensor & self) { |
| 1293 | |
| 1294 | static auto op = create_atanh_typed_handle(); |
| 1295 | return op.call(self); |
| 1296 | } |
| 1297 | |
| 1298 | // aten::atanh(Tensor self) -> Tensor |
| 1299 | at::Tensor atanh::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 1300 | |
| 1301 | static auto op = create_atanh_typed_handle(); |
| 1302 | return op.redispatch(dispatchKeySet, self); |
| 1303 | } |
| 1304 | |
| 1305 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(atanh_, name, "aten::atanh_") |
| 1306 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(atanh_, overload_name, "") |
| 1307 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(atanh_, schema_str, "atanh_(Tensor(a!) self) -> Tensor(a!)") |
| 1308 | |
| 1309 | // aten::atanh_(Tensor(a!) self) -> Tensor(a!) |
| 1310 | static C10_NOINLINE c10::TypedOperatorHandle<atanh_::schema> create_atanh__typed_handle() { |
| 1311 | return c10::Dispatcher::singleton() |
| 1312 | .findSchemaOrThrow(atanh_::name, atanh_::overload_name) |
| 1313 | .typed<atanh_::schema>(); |
| 1314 | } |
| 1315 | |
| 1316 | // aten::atanh_(Tensor(a!) self) -> Tensor(a!) |
| 1317 | at::Tensor & atanh_::call(at::Tensor & self) { |
| 1318 | |
| 1319 | static auto op = create_atanh__typed_handle(); |
| 1320 | return op.call(self); |
| 1321 | } |
| 1322 | |
| 1323 | // aten::atanh_(Tensor(a!) self) -> Tensor(a!) |
| 1324 | at::Tensor & atanh_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 1325 | |
| 1326 | static auto op = create_atanh__typed_handle(); |
| 1327 | return op.redispatch(dispatchKeySet, self); |
| 1328 | } |
| 1329 | |
| 1330 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(atanh_out, name, "aten::atanh") |
| 1331 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(atanh_out, overload_name, "out") |
| 1332 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(atanh_out, schema_str, "atanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 1333 | |
| 1334 | // aten::atanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 1335 | static C10_NOINLINE c10::TypedOperatorHandle<atanh_out::schema> create_atanh_out_typed_handle() { |
| 1336 | return c10::Dispatcher::singleton() |
| 1337 | .findSchemaOrThrow(atanh_out::name, atanh_out::overload_name) |
| 1338 | .typed<atanh_out::schema>(); |
| 1339 | } |
| 1340 | |
| 1341 | // aten::atanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 1342 | at::Tensor & atanh_out::call(const at::Tensor & self, at::Tensor & out) { |
| 1343 | |
| 1344 | static auto op = create_atanh_out_typed_handle(); |
| 1345 | return op.call(self, out); |
| 1346 | } |
| 1347 | |
| 1348 | // aten::atanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 1349 | at::Tensor & atanh_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 1350 | |
| 1351 | static auto op = create_atanh_out_typed_handle(); |
| 1352 | return op.redispatch(dispatchKeySet, self, out); |
| 1353 | } |
| 1354 | |
| 1355 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arcsin, name, "aten::arcsin") |
| 1356 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arcsin, overload_name, "") |
| 1357 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arcsin, schema_str, "arcsin(Tensor self) -> Tensor") |
| 1358 | |
| 1359 | // aten::arcsin(Tensor self) -> Tensor |
| 1360 | static C10_NOINLINE c10::TypedOperatorHandle<arcsin::schema> create_arcsin_typed_handle() { |
| 1361 | return c10::Dispatcher::singleton() |
| 1362 | .findSchemaOrThrow(arcsin::name, arcsin::overload_name) |
| 1363 | .typed<arcsin::schema>(); |
| 1364 | } |
| 1365 | |
| 1366 | // aten::arcsin(Tensor self) -> Tensor |
| 1367 | at::Tensor arcsin::call(const at::Tensor & self) { |
| 1368 | |
| 1369 | static auto op = create_arcsin_typed_handle(); |
| 1370 | return op.call(self); |
| 1371 | } |
| 1372 | |
| 1373 | // aten::arcsin(Tensor self) -> Tensor |
| 1374 | at::Tensor arcsin::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 1375 | |
| 1376 | static auto op = create_arcsin_typed_handle(); |
| 1377 | return op.redispatch(dispatchKeySet, self); |
| 1378 | } |
| 1379 | |
| 1380 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arcsin_, name, "aten::arcsin_") |
| 1381 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arcsin_, overload_name, "") |
| 1382 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arcsin_, schema_str, "arcsin_(Tensor(a!) self) -> Tensor(a!)") |
| 1383 | |
| 1384 | // aten::arcsin_(Tensor(a!) self) -> Tensor(a!) |
| 1385 | static C10_NOINLINE c10::TypedOperatorHandle<arcsin_::schema> create_arcsin__typed_handle() { |
| 1386 | return c10::Dispatcher::singleton() |
| 1387 | .findSchemaOrThrow(arcsin_::name, arcsin_::overload_name) |
| 1388 | .typed<arcsin_::schema>(); |
| 1389 | } |
| 1390 | |
| 1391 | // aten::arcsin_(Tensor(a!) self) -> Tensor(a!) |
| 1392 | at::Tensor & arcsin_::call(at::Tensor & self) { |
| 1393 | |
| 1394 | static auto op = create_arcsin__typed_handle(); |
| 1395 | return op.call(self); |
| 1396 | } |
| 1397 | |
| 1398 | // aten::arcsin_(Tensor(a!) self) -> Tensor(a!) |
| 1399 | at::Tensor & arcsin_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 1400 | |
| 1401 | static auto op = create_arcsin__typed_handle(); |
| 1402 | return op.redispatch(dispatchKeySet, self); |
| 1403 | } |
| 1404 | |
| 1405 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arcsin_out, name, "aten::arcsin") |
| 1406 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arcsin_out, overload_name, "out") |
| 1407 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arcsin_out, schema_str, "arcsin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 1408 | |
| 1409 | // aten::arcsin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 1410 | static C10_NOINLINE c10::TypedOperatorHandle<arcsin_out::schema> create_arcsin_out_typed_handle() { |
| 1411 | return c10::Dispatcher::singleton() |
| 1412 | .findSchemaOrThrow(arcsin_out::name, arcsin_out::overload_name) |
| 1413 | .typed<arcsin_out::schema>(); |
| 1414 | } |
| 1415 | |
| 1416 | // aten::arcsin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 1417 | at::Tensor & arcsin_out::call(const at::Tensor & self, at::Tensor & out) { |
| 1418 | |
| 1419 | static auto op = create_arcsin_out_typed_handle(); |
| 1420 | return op.call(self, out); |
| 1421 | } |
| 1422 | |
| 1423 | // aten::arcsin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 1424 | at::Tensor & arcsin_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 1425 | |
| 1426 | static auto op = create_arcsin_out_typed_handle(); |
| 1427 | return op.redispatch(dispatchKeySet, self, out); |
| 1428 | } |
| 1429 | |
| 1430 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bartlett_window, name, "aten::bartlett_window") |
| 1431 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bartlett_window, overload_name, "") |
| 1432 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bartlett_window, schema_str, "bartlett_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor") |
| 1433 | |
| 1434 | // aten::bartlett_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 1435 | static C10_NOINLINE c10::TypedOperatorHandle<bartlett_window::schema> create_bartlett_window_typed_handle() { |
| 1436 | return c10::Dispatcher::singleton() |
| 1437 | .findSchemaOrThrow(bartlett_window::name, bartlett_window::overload_name) |
| 1438 | .typed<bartlett_window::schema>(); |
| 1439 | } |
| 1440 | |
| 1441 | // aten::bartlett_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 1442 | at::Tensor bartlett_window::call(int64_t window_length, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 1443 | |
| 1444 | static auto op = create_bartlett_window_typed_handle(); |
| 1445 | return op.call(window_length, dtype, layout, device, pin_memory); |
| 1446 | } |
| 1447 | |
| 1448 | // aten::bartlett_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 1449 | at::Tensor bartlett_window::redispatch(c10::DispatchKeySet dispatchKeySet, int64_t window_length, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 1450 | |
| 1451 | static auto op = create_bartlett_window_typed_handle(); |
| 1452 | return op.redispatch(dispatchKeySet, window_length, dtype, layout, device, pin_memory); |
| 1453 | } |
| 1454 | |
| 1455 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bartlett_window_periodic, name, "aten::bartlett_window") |
| 1456 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bartlett_window_periodic, overload_name, "periodic") |
| 1457 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bartlett_window_periodic, schema_str, "bartlett_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor") |
| 1458 | |
| 1459 | // aten::bartlett_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 1460 | static C10_NOINLINE c10::TypedOperatorHandle<bartlett_window_periodic::schema> create_bartlett_window_periodic_typed_handle() { |
| 1461 | return c10::Dispatcher::singleton() |
| 1462 | .findSchemaOrThrow(bartlett_window_periodic::name, bartlett_window_periodic::overload_name) |
| 1463 | .typed<bartlett_window_periodic::schema>(); |
| 1464 | } |
| 1465 | |
| 1466 | // aten::bartlett_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 1467 | at::Tensor bartlett_window_periodic::call(int64_t window_length, bool periodic, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 1468 | |
| 1469 | static auto op = create_bartlett_window_periodic_typed_handle(); |
| 1470 | return op.call(window_length, periodic, dtype, layout, device, pin_memory); |
| 1471 | } |
| 1472 | |
| 1473 | // aten::bartlett_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 1474 | at::Tensor bartlett_window_periodic::redispatch(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 1475 | |
| 1476 | static auto op = create_bartlett_window_periodic_typed_handle(); |
| 1477 | return op.redispatch(dispatchKeySet, window_length, periodic, dtype, layout, device, pin_memory); |
| 1478 | } |
| 1479 | |
| 1480 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(binary_cross_entropy, name, "aten::binary_cross_entropy") |
| 1481 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(binary_cross_entropy, overload_name, "") |
| 1482 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(binary_cross_entropy, schema_str, "binary_cross_entropy(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean) -> Tensor") |
| 1483 | |
| 1484 | // aten::binary_cross_entropy(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean) -> Tensor |
| 1485 | static C10_NOINLINE c10::TypedOperatorHandle<binary_cross_entropy::schema> create_binary_cross_entropy_typed_handle() { |
| 1486 | return c10::Dispatcher::singleton() |
| 1487 | .findSchemaOrThrow(binary_cross_entropy::name, binary_cross_entropy::overload_name) |
| 1488 | .typed<binary_cross_entropy::schema>(); |
| 1489 | } |
| 1490 | |
| 1491 | // aten::binary_cross_entropy(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean) -> Tensor |
| 1492 | at::Tensor binary_cross_entropy::call(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction) { |
| 1493 | |
| 1494 | static auto op = create_binary_cross_entropy_typed_handle(); |
| 1495 | return op.call(self, target, weight, reduction); |
| 1496 | } |
| 1497 | |
| 1498 | // aten::binary_cross_entropy(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean) -> Tensor |
| 1499 | at::Tensor binary_cross_entropy::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction) { |
| 1500 | |
| 1501 | static auto op = create_binary_cross_entropy_typed_handle(); |
| 1502 | return op.redispatch(dispatchKeySet, self, target, weight, reduction); |
| 1503 | } |
| 1504 | |
| 1505 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(binary_cross_entropy_out, name, "aten::binary_cross_entropy") |
| 1506 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(binary_cross_entropy_out, overload_name, "out") |
| 1507 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(binary_cross_entropy_out, schema_str, "binary_cross_entropy.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)") |
| 1508 | |
| 1509 | // aten::binary_cross_entropy.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) |
| 1510 | static C10_NOINLINE c10::TypedOperatorHandle<binary_cross_entropy_out::schema> create_binary_cross_entropy_out_typed_handle() { |
| 1511 | return c10::Dispatcher::singleton() |
| 1512 | .findSchemaOrThrow(binary_cross_entropy_out::name, binary_cross_entropy_out::overload_name) |
| 1513 | .typed<binary_cross_entropy_out::schema>(); |
| 1514 | } |
| 1515 | |
| 1516 | // aten::binary_cross_entropy.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) |
| 1517 | at::Tensor & binary_cross_entropy_out::call(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, at::Tensor & out) { |
| 1518 | |
| 1519 | static auto op = create_binary_cross_entropy_out_typed_handle(); |
| 1520 | return op.call(self, target, weight, reduction, out); |
| 1521 | } |
| 1522 | |
| 1523 | // aten::binary_cross_entropy.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) |
| 1524 | at::Tensor & binary_cross_entropy_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, at::Tensor & out) { |
| 1525 | |
| 1526 | static auto op = create_binary_cross_entropy_out_typed_handle(); |
| 1527 | return op.redispatch(dispatchKeySet, self, target, weight, reduction, out); |
| 1528 | } |
| 1529 | |
| 1530 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bmm, name, "aten::bmm") |
| 1531 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bmm, overload_name, "") |
| 1532 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bmm, schema_str, "bmm(Tensor self, Tensor mat2) -> Tensor") |
| 1533 | |
| 1534 | // aten::bmm(Tensor self, Tensor mat2) -> Tensor |
| 1535 | static C10_NOINLINE c10::TypedOperatorHandle<bmm::schema> create_bmm_typed_handle() { |
| 1536 | return c10::Dispatcher::singleton() |
| 1537 | .findSchemaOrThrow(bmm::name, bmm::overload_name) |
| 1538 | .typed<bmm::schema>(); |
| 1539 | } |
| 1540 | |
| 1541 | // aten::bmm(Tensor self, Tensor mat2) -> Tensor |
| 1542 | at::Tensor bmm::call(const at::Tensor & self, const at::Tensor & mat2) { |
| 1543 | |
| 1544 | static auto op = create_bmm_typed_handle(); |
| 1545 | return op.call(self, mat2); |
| 1546 | } |
| 1547 | |
| 1548 | // aten::bmm(Tensor self, Tensor mat2) -> Tensor |
| 1549 | at::Tensor bmm::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat2) { |
| 1550 | |
| 1551 | static auto op = create_bmm_typed_handle(); |
| 1552 | return op.redispatch(dispatchKeySet, self, mat2); |
| 1553 | } |
| 1554 | |
| 1555 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bmm_out, name, "aten::bmm") |
| 1556 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bmm_out, overload_name, "out") |
| 1557 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bmm_out, schema_str, "bmm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)") |
| 1558 | |
| 1559 | // aten::bmm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) |
| 1560 | static C10_NOINLINE c10::TypedOperatorHandle<bmm_out::schema> create_bmm_out_typed_handle() { |
| 1561 | return c10::Dispatcher::singleton() |
| 1562 | .findSchemaOrThrow(bmm_out::name, bmm_out::overload_name) |
| 1563 | .typed<bmm_out::schema>(); |
| 1564 | } |
| 1565 | |
| 1566 | // aten::bmm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) |
| 1567 | at::Tensor & bmm_out::call(const at::Tensor & self, const at::Tensor & mat2, at::Tensor & out) { |
| 1568 | |
| 1569 | static auto op = create_bmm_out_typed_handle(); |
| 1570 | return op.call(self, mat2, out); |
| 1571 | } |
| 1572 | |
| 1573 | // aten::bmm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) |
| 1574 | at::Tensor & bmm_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat2, at::Tensor & out) { |
| 1575 | |
| 1576 | static auto op = create_bmm_out_typed_handle(); |
| 1577 | return op.redispatch(dispatchKeySet, self, mat2, out); |
| 1578 | } |
| 1579 | |
| 1580 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_broadcast_to, name, "aten::_sparse_broadcast_to") |
| 1581 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_broadcast_to, overload_name, "") |
| 1582 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_broadcast_to, schema_str, "_sparse_broadcast_to(Tensor(a) self, int[] size) -> Tensor(a)") |
| 1583 | |
| 1584 | // aten::_sparse_broadcast_to(Tensor(a) self, int[] size) -> Tensor(a) |
| 1585 | static C10_NOINLINE c10::TypedOperatorHandle<_sparse_broadcast_to::schema> create__sparse_broadcast_to_typed_handle() { |
| 1586 | return c10::Dispatcher::singleton() |
| 1587 | .findSchemaOrThrow(_sparse_broadcast_to::name, _sparse_broadcast_to::overload_name) |
| 1588 | .typed<_sparse_broadcast_to::schema>(); |
| 1589 | } |
| 1590 | |
| 1591 | // aten::_sparse_broadcast_to(Tensor(a) self, int[] size) -> Tensor(a) |
| 1592 | at::Tensor _sparse_broadcast_to::call(const at::Tensor & self, at::IntArrayRef size) { |
| 1593 | |
| 1594 | static auto op = create__sparse_broadcast_to_typed_handle(); |
| 1595 | return op.call(self, size); |
| 1596 | } |
| 1597 | |
| 1598 | // aten::_sparse_broadcast_to(Tensor(a) self, int[] size) -> Tensor(a) |
| 1599 | at::Tensor _sparse_broadcast_to::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size) { |
| 1600 | |
| 1601 | static auto op = create__sparse_broadcast_to_typed_handle(); |
| 1602 | return op.redispatch(dispatchKeySet, self, size); |
| 1603 | } |
| 1604 | |
| 1605 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(concat, name, "aten::concat") |
| 1606 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(concat, overload_name, "") |
| 1607 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(concat, schema_str, "concat(Tensor[] tensors, int dim=0) -> Tensor") |
| 1608 | |
| 1609 | // aten::concat(Tensor[] tensors, int dim=0) -> Tensor |
| 1610 | static C10_NOINLINE c10::TypedOperatorHandle<concat::schema> create_concat_typed_handle() { |
| 1611 | return c10::Dispatcher::singleton() |
| 1612 | .findSchemaOrThrow(concat::name, concat::overload_name) |
| 1613 | .typed<concat::schema>(); |
| 1614 | } |
| 1615 | |
| 1616 | // aten::concat(Tensor[] tensors, int dim=0) -> Tensor |
| 1617 | at::Tensor concat::call(at::TensorList tensors, int64_t dim) { |
| 1618 | |
| 1619 | static auto op = create_concat_typed_handle(); |
| 1620 | return op.call(tensors, dim); |
| 1621 | } |
| 1622 | |
| 1623 | // aten::concat(Tensor[] tensors, int dim=0) -> Tensor |
| 1624 | at::Tensor concat::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, int64_t dim) { |
| 1625 | |
| 1626 | static auto op = create_concat_typed_handle(); |
| 1627 | return op.redispatch(dispatchKeySet, tensors, dim); |
| 1628 | } |
| 1629 | |
| 1630 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(concat_out, name, "aten::concat") |
| 1631 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(concat_out, overload_name, "out") |
| 1632 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(concat_out, schema_str, "concat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)") |
| 1633 | |
| 1634 | // aten::concat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) |
| 1635 | static C10_NOINLINE c10::TypedOperatorHandle<concat_out::schema> create_concat_out_typed_handle() { |
| 1636 | return c10::Dispatcher::singleton() |
| 1637 | .findSchemaOrThrow(concat_out::name, concat_out::overload_name) |
| 1638 | .typed<concat_out::schema>(); |
| 1639 | } |
| 1640 | |
| 1641 | // aten::concat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) |
| 1642 | at::Tensor & concat_out::call(at::TensorList tensors, int64_t dim, at::Tensor & out) { |
| 1643 | |
| 1644 | static auto op = create_concat_out_typed_handle(); |
| 1645 | return op.call(tensors, dim, out); |
| 1646 | } |
| 1647 | |
| 1648 | // aten::concat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) |
| 1649 | at::Tensor & concat_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, int64_t dim, at::Tensor & out) { |
| 1650 | |
| 1651 | static auto op = create_concat_out_typed_handle(); |
| 1652 | return op.redispatch(dispatchKeySet, tensors, dim, out); |
| 1653 | } |
| 1654 | |
| 1655 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(concat_names, name, "aten::concat") |
| 1656 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(concat_names, overload_name, "names") |
| 1657 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(concat_names, schema_str, "concat.names(Tensor[] tensors, Dimname dim) -> Tensor") |
| 1658 | |
| 1659 | // aten::concat.names(Tensor[] tensors, Dimname dim) -> Tensor |
| 1660 | static C10_NOINLINE c10::TypedOperatorHandle<concat_names::schema> create_concat_names_typed_handle() { |
| 1661 | return c10::Dispatcher::singleton() |
| 1662 | .findSchemaOrThrow(concat_names::name, concat_names::overload_name) |
| 1663 | .typed<concat_names::schema>(); |
| 1664 | } |
| 1665 | |
| 1666 | // aten::concat.names(Tensor[] tensors, Dimname dim) -> Tensor |
| 1667 | at::Tensor concat_names::call(at::TensorList tensors, at::Dimname dim) { |
| 1668 | |
| 1669 | static auto op = create_concat_names_typed_handle(); |
| 1670 | return op.call(tensors, dim); |
| 1671 | } |
| 1672 | |
| 1673 | // aten::concat.names(Tensor[] tensors, Dimname dim) -> Tensor |
| 1674 | at::Tensor concat_names::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Dimname dim) { |
| 1675 | |
| 1676 | static auto op = create_concat_names_typed_handle(); |
| 1677 | return op.redispatch(dispatchKeySet, tensors, dim); |
| 1678 | } |
| 1679 | |
| 1680 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(concat_names_out, name, "aten::concat") |
| 1681 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(concat_names_out, overload_name, "names_out") |
| 1682 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(concat_names_out, schema_str, "concat.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!)") |
| 1683 | |
| 1684 | // aten::concat.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!) |
| 1685 | static C10_NOINLINE c10::TypedOperatorHandle<concat_names_out::schema> create_concat_names_out_typed_handle() { |
| 1686 | return c10::Dispatcher::singleton() |
| 1687 | .findSchemaOrThrow(concat_names_out::name, concat_names_out::overload_name) |
| 1688 | .typed<concat_names_out::schema>(); |
| 1689 | } |
| 1690 | |
| 1691 | // aten::concat.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!) |
| 1692 | at::Tensor & concat_names_out::call(at::TensorList tensors, at::Dimname dim, at::Tensor & out) { |
| 1693 | |
| 1694 | static auto op = create_concat_names_out_typed_handle(); |
| 1695 | return op.call(tensors, dim, out); |
| 1696 | } |
| 1697 | |
| 1698 | // aten::concat.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!) |
| 1699 | at::Tensor & concat_names_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Dimname dim, at::Tensor & out) { |
| 1700 | |
| 1701 | static auto op = create_concat_names_out_typed_handle(); |
| 1702 | return op.redispatch(dispatchKeySet, tensors, dim, out); |
| 1703 | } |
| 1704 | |
| 1705 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(chain_matmul, name, "aten::chain_matmul") |
| 1706 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(chain_matmul, overload_name, "") |
| 1707 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(chain_matmul, schema_str, "chain_matmul(Tensor[] matrices) -> Tensor") |
| 1708 | |
| 1709 | // aten::chain_matmul(Tensor[] matrices) -> Tensor |
| 1710 | static C10_NOINLINE c10::TypedOperatorHandle<chain_matmul::schema> create_chain_matmul_typed_handle() { |
| 1711 | return c10::Dispatcher::singleton() |
| 1712 | .findSchemaOrThrow(chain_matmul::name, chain_matmul::overload_name) |
| 1713 | .typed<chain_matmul::schema>(); |
| 1714 | } |
| 1715 | |
| 1716 | // aten::chain_matmul(Tensor[] matrices) -> Tensor |
| 1717 | at::Tensor chain_matmul::call(at::TensorList matrices) { |
| 1718 | |
| 1719 | static auto op = create_chain_matmul_typed_handle(); |
| 1720 | return op.call(matrices); |
| 1721 | } |
| 1722 | |
| 1723 | // aten::chain_matmul(Tensor[] matrices) -> Tensor |
| 1724 | at::Tensor chain_matmul::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList matrices) { |
| 1725 | |
| 1726 | static auto op = create_chain_matmul_typed_handle(); |
| 1727 | return op.redispatch(dispatchKeySet, matrices); |
| 1728 | } |
| 1729 | |
| 1730 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(chain_matmul_out, name, "aten::chain_matmul") |
| 1731 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(chain_matmul_out, overload_name, "out") |
| 1732 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(chain_matmul_out, schema_str, "chain_matmul.out(Tensor[] matrices, *, Tensor(a!) out) -> Tensor(a!)") |
| 1733 | |
| 1734 | // aten::chain_matmul.out(Tensor[] matrices, *, Tensor(a!) out) -> Tensor(a!) |
| 1735 | static C10_NOINLINE c10::TypedOperatorHandle<chain_matmul_out::schema> create_chain_matmul_out_typed_handle() { |
| 1736 | return c10::Dispatcher::singleton() |
| 1737 | .findSchemaOrThrow(chain_matmul_out::name, chain_matmul_out::overload_name) |
| 1738 | .typed<chain_matmul_out::schema>(); |
| 1739 | } |
| 1740 | |
| 1741 | // aten::chain_matmul.out(Tensor[] matrices, *, Tensor(a!) out) -> Tensor(a!) |
| 1742 | at::Tensor & chain_matmul_out::call(at::TensorList matrices, at::Tensor & out) { |
| 1743 | |
| 1744 | static auto op = create_chain_matmul_out_typed_handle(); |
| 1745 | return op.call(matrices, out); |
| 1746 | } |
| 1747 | |
| 1748 | // aten::chain_matmul.out(Tensor[] matrices, *, Tensor(a!) out) -> Tensor(a!) |
| 1749 | at::Tensor & chain_matmul_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList matrices, at::Tensor & out) { |
| 1750 | |
| 1751 | static auto op = create_chain_matmul_out_typed_handle(); |
| 1752 | return op.redispatch(dispatchKeySet, matrices, out); |
| 1753 | } |
| 1754 | |
| 1755 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(clamp_min, name, "aten::clamp_min") |
| 1756 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(clamp_min, overload_name, "") |
| 1757 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(clamp_min, schema_str, "clamp_min(Tensor self, Scalar min) -> Tensor") |
| 1758 | |
| 1759 | // aten::clamp_min(Tensor self, Scalar min) -> Tensor |
| 1760 | static C10_NOINLINE c10::TypedOperatorHandle<clamp_min::schema> create_clamp_min_typed_handle() { |
| 1761 | return c10::Dispatcher::singleton() |
| 1762 | .findSchemaOrThrow(clamp_min::name, clamp_min::overload_name) |
| 1763 | .typed<clamp_min::schema>(); |
| 1764 | } |
| 1765 | |
| 1766 | // aten::clamp_min(Tensor self, Scalar min) -> Tensor |
| 1767 | at::Tensor clamp_min::call(const at::Tensor & self, const at::Scalar & min) { |
| 1768 | |
| 1769 | static auto op = create_clamp_min_typed_handle(); |
| 1770 | return op.call(self, min); |
| 1771 | } |
| 1772 | |
| 1773 | // aten::clamp_min(Tensor self, Scalar min) -> Tensor |
| 1774 | at::Tensor clamp_min::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & min) { |
| 1775 | |
| 1776 | static auto op = create_clamp_min_typed_handle(); |
| 1777 | return op.redispatch(dispatchKeySet, self, min); |
| 1778 | } |
| 1779 | |
| 1780 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(clamp_min_Tensor, name, "aten::clamp_min") |
| 1781 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(clamp_min_Tensor, overload_name, "Tensor") |
| 1782 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(clamp_min_Tensor, schema_str, "clamp_min.Tensor(Tensor self, Tensor min) -> Tensor") |
| 1783 | |
| 1784 | // aten::clamp_min.Tensor(Tensor self, Tensor min) -> Tensor |
| 1785 | static C10_NOINLINE c10::TypedOperatorHandle<clamp_min_Tensor::schema> create_clamp_min_Tensor_typed_handle() { |
| 1786 | return c10::Dispatcher::singleton() |
| 1787 | .findSchemaOrThrow(clamp_min_Tensor::name, clamp_min_Tensor::overload_name) |
| 1788 | .typed<clamp_min_Tensor::schema>(); |
| 1789 | } |
| 1790 | |
| 1791 | // aten::clamp_min.Tensor(Tensor self, Tensor min) -> Tensor |
| 1792 | at::Tensor clamp_min_Tensor::call(const at::Tensor & self, const at::Tensor & min) { |
| 1793 | |
| 1794 | static auto op = create_clamp_min_Tensor_typed_handle(); |
| 1795 | return op.call(self, min); |
| 1796 | } |
| 1797 | |
| 1798 | // aten::clamp_min.Tensor(Tensor self, Tensor min) -> Tensor |
| 1799 | at::Tensor clamp_min_Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & min) { |
| 1800 | |
| 1801 | static auto op = create_clamp_min_Tensor_typed_handle(); |
| 1802 | return op.redispatch(dispatchKeySet, self, min); |
| 1803 | } |
| 1804 | |
| 1805 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(clamp_min_, name, "aten::clamp_min_") |
| 1806 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(clamp_min_, overload_name, "") |
| 1807 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(clamp_min_, schema_str, "clamp_min_(Tensor(a!) self, Scalar min) -> Tensor(a!)") |
| 1808 | |
| 1809 | // aten::clamp_min_(Tensor(a!) self, Scalar min) -> Tensor(a!) |
| 1810 | static C10_NOINLINE c10::TypedOperatorHandle<clamp_min_::schema> create_clamp_min__typed_handle() { |
| 1811 | return c10::Dispatcher::singleton() |
| 1812 | .findSchemaOrThrow(clamp_min_::name, clamp_min_::overload_name) |
| 1813 | .typed<clamp_min_::schema>(); |
| 1814 | } |
| 1815 | |
| 1816 | // aten::clamp_min_(Tensor(a!) self, Scalar min) -> Tensor(a!) |
| 1817 | at::Tensor & clamp_min_::call(at::Tensor & self, const at::Scalar & min) { |
| 1818 | |
| 1819 | static auto op = create_clamp_min__typed_handle(); |
| 1820 | return op.call(self, min); |
| 1821 | } |
| 1822 | |
| 1823 | // aten::clamp_min_(Tensor(a!) self, Scalar min) -> Tensor(a!) |
| 1824 | at::Tensor & clamp_min_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & min) { |
| 1825 | |
| 1826 | static auto op = create_clamp_min__typed_handle(); |
| 1827 | return op.redispatch(dispatchKeySet, self, min); |
| 1828 | } |
| 1829 | |
| 1830 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(clamp_min__Tensor, name, "aten::clamp_min_") |
| 1831 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(clamp_min__Tensor, overload_name, "Tensor") |
| 1832 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(clamp_min__Tensor, schema_str, "clamp_min_.Tensor(Tensor(a!) self, Tensor min) -> Tensor(a!)") |
| 1833 | |
| 1834 | // aten::clamp_min_.Tensor(Tensor(a!) self, Tensor min) -> Tensor(a!) |
| 1835 | static C10_NOINLINE c10::TypedOperatorHandle<clamp_min__Tensor::schema> create_clamp_min__Tensor_typed_handle() { |
| 1836 | return c10::Dispatcher::singleton() |
| 1837 | .findSchemaOrThrow(clamp_min__Tensor::name, clamp_min__Tensor::overload_name) |
| 1838 | .typed<clamp_min__Tensor::schema>(); |
| 1839 | } |
| 1840 | |
| 1841 | // aten::clamp_min_.Tensor(Tensor(a!) self, Tensor min) -> Tensor(a!) |
| 1842 | at::Tensor & clamp_min__Tensor::call(at::Tensor & self, const at::Tensor & min) { |
| 1843 | |
| 1844 | static auto op = create_clamp_min__Tensor_typed_handle(); |
| 1845 | return op.call(self, min); |
| 1846 | } |
| 1847 | |
| 1848 | // aten::clamp_min_.Tensor(Tensor(a!) self, Tensor min) -> Tensor(a!) |
| 1849 | at::Tensor & clamp_min__Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & min) { |
| 1850 | |
| 1851 | static auto op = create_clamp_min__Tensor_typed_handle(); |
| 1852 | return op.redispatch(dispatchKeySet, self, min); |
| 1853 | } |
| 1854 | |
| 1855 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(clamp_min_out, name, "aten::clamp_min") |
| 1856 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(clamp_min_out, overload_name, "out") |
| 1857 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(clamp_min_out, schema_str, "clamp_min.out(Tensor self, Scalar min, *, Tensor(a!) out) -> Tensor(a!)") |
| 1858 | |
| 1859 | // aten::clamp_min.out(Tensor self, Scalar min, *, Tensor(a!) out) -> Tensor(a!) |
| 1860 | static C10_NOINLINE c10::TypedOperatorHandle<clamp_min_out::schema> create_clamp_min_out_typed_handle() { |
| 1861 | return c10::Dispatcher::singleton() |
| 1862 | .findSchemaOrThrow(clamp_min_out::name, clamp_min_out::overload_name) |
| 1863 | .typed<clamp_min_out::schema>(); |
| 1864 | } |
| 1865 | |
| 1866 | // aten::clamp_min.out(Tensor self, Scalar min, *, Tensor(a!) out) -> Tensor(a!) |
| 1867 | at::Tensor & clamp_min_out::call(const at::Tensor & self, const at::Scalar & min, at::Tensor & out) { |
| 1868 | |
| 1869 | static auto op = create_clamp_min_out_typed_handle(); |
| 1870 | return op.call(self, min, out); |
| 1871 | } |
| 1872 | |
| 1873 | // aten::clamp_min.out(Tensor self, Scalar min, *, Tensor(a!) out) -> Tensor(a!) |
| 1874 | at::Tensor & clamp_min_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & min, at::Tensor & out) { |
| 1875 | |
| 1876 | static auto op = create_clamp_min_out_typed_handle(); |
| 1877 | return op.redispatch(dispatchKeySet, self, min, out); |
| 1878 | } |
| 1879 | |
| 1880 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(clamp_min_Tensor_out, name, "aten::clamp_min") |
| 1881 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(clamp_min_Tensor_out, overload_name, "Tensor_out") |
| 1882 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(clamp_min_Tensor_out, schema_str, "clamp_min.Tensor_out(Tensor self, Tensor min, *, Tensor(a!) out) -> Tensor(a!)") |
| 1883 | |
| 1884 | // aten::clamp_min.Tensor_out(Tensor self, Tensor min, *, Tensor(a!) out) -> Tensor(a!) |
| 1885 | static C10_NOINLINE c10::TypedOperatorHandle<clamp_min_Tensor_out::schema> create_clamp_min_Tensor_out_typed_handle() { |
| 1886 | return c10::Dispatcher::singleton() |
| 1887 | .findSchemaOrThrow(clamp_min_Tensor_out::name, clamp_min_Tensor_out::overload_name) |
| 1888 | .typed<clamp_min_Tensor_out::schema>(); |
| 1889 | } |
| 1890 | |
| 1891 | // aten::clamp_min.Tensor_out(Tensor self, Tensor min, *, Tensor(a!) out) -> Tensor(a!) |
| 1892 | at::Tensor & clamp_min_Tensor_out::call(const at::Tensor & self, const at::Tensor & min, at::Tensor & out) { |
| 1893 | |
| 1894 | static auto op = create_clamp_min_Tensor_out_typed_handle(); |
| 1895 | return op.call(self, min, out); |
| 1896 | } |
| 1897 | |
| 1898 | // aten::clamp_min.Tensor_out(Tensor self, Tensor min, *, Tensor(a!) out) -> Tensor(a!) |
| 1899 | at::Tensor & clamp_min_Tensor_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & min, at::Tensor & out) { |
| 1900 | |
| 1901 | static auto op = create_clamp_min_Tensor_out_typed_handle(); |
| 1902 | return op.redispatch(dispatchKeySet, self, min, out); |
| 1903 | } |
| 1904 | |
| 1905 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_convolution_mode, name, "aten::_convolution_mode") |
| 1906 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_convolution_mode, overload_name, "") |
| 1907 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_convolution_mode, schema_str, "_convolution_mode(Tensor input, Tensor weight, Tensor? bias, int[] stride, str padding, int[] dilation, int groups) -> Tensor") |
| 1908 | |
| 1909 | // aten::_convolution_mode(Tensor input, Tensor weight, Tensor? bias, int[] stride, str padding, int[] dilation, int groups) -> Tensor |
| 1910 | static C10_NOINLINE c10::TypedOperatorHandle<_convolution_mode::schema> create__convolution_mode_typed_handle() { |
| 1911 | return c10::Dispatcher::singleton() |
| 1912 | .findSchemaOrThrow(_convolution_mode::name, _convolution_mode::overload_name) |
| 1913 | .typed<_convolution_mode::schema>(); |
| 1914 | } |
| 1915 | |
| 1916 | // aten::_convolution_mode(Tensor input, Tensor weight, Tensor? bias, int[] stride, str padding, int[] dilation, int groups) -> Tensor |
| 1917 | at::Tensor _convolution_mode::call(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) { |
| 1918 | |
| 1919 | static auto op = create__convolution_mode_typed_handle(); |
| 1920 | return op.call(input, weight, bias, stride, padding, dilation, groups); |
| 1921 | } |
| 1922 | |
| 1923 | // aten::_convolution_mode(Tensor input, Tensor weight, Tensor? bias, int[] stride, str padding, int[] dilation, int groups) -> Tensor |
| 1924 | at::Tensor _convolution_mode::redispatch(c10::DispatchKeySet dispatchKeySet, 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) { |
| 1925 | |
| 1926 | static auto op = create__convolution_mode_typed_handle(); |
| 1927 | return op.redispatch(dispatchKeySet, input, weight, bias, stride, padding, dilation, groups); |
| 1928 | } |
| 1929 | |
| 1930 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv1d, name, "aten::conv1d") |
| 1931 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv1d, overload_name, "") |
| 1932 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv1d, schema_str, "conv1d(Tensor input, Tensor weight, Tensor? bias=None, int[1] stride=1, int[1] padding=0, int[1] dilation=1, int groups=1) -> Tensor") |
| 1933 | |
| 1934 | // aten::conv1d(Tensor input, Tensor weight, Tensor? bias=None, int[1] stride=1, int[1] padding=0, int[1] dilation=1, int groups=1) -> Tensor |
| 1935 | static C10_NOINLINE c10::TypedOperatorHandle<conv1d::schema> create_conv1d_typed_handle() { |
| 1936 | return c10::Dispatcher::singleton() |
| 1937 | .findSchemaOrThrow(conv1d::name, conv1d::overload_name) |
| 1938 | .typed<conv1d::schema>(); |
| 1939 | } |
| 1940 | |
| 1941 | // aten::conv1d(Tensor input, Tensor weight, Tensor? bias=None, int[1] stride=1, int[1] padding=0, int[1] dilation=1, int groups=1) -> Tensor |
| 1942 | at::Tensor conv1d::call(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) { |
| 1943 | |
| 1944 | static auto op = create_conv1d_typed_handle(); |
| 1945 | return op.call(input, weight, bias, stride, padding, dilation, groups); |
| 1946 | } |
| 1947 | |
| 1948 | // aten::conv1d(Tensor input, Tensor weight, Tensor? bias=None, int[1] stride=1, int[1] padding=0, int[1] dilation=1, int groups=1) -> Tensor |
| 1949 | at::Tensor conv1d::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups) { |
| 1950 | |
| 1951 | static auto op = create_conv1d_typed_handle(); |
| 1952 | return op.redispatch(dispatchKeySet, input, weight, bias, stride, padding, dilation, groups); |
| 1953 | } |
| 1954 | |
| 1955 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv3d, name, "aten::conv3d") |
| 1956 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv3d, overload_name, "") |
| 1957 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv3d, schema_str, "conv3d(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] dilation=1, int groups=1) -> Tensor") |
| 1958 | |
| 1959 | // aten::conv3d(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] dilation=1, int groups=1) -> Tensor |
| 1960 | static C10_NOINLINE c10::TypedOperatorHandle<conv3d::schema> create_conv3d_typed_handle() { |
| 1961 | return c10::Dispatcher::singleton() |
| 1962 | .findSchemaOrThrow(conv3d::name, conv3d::overload_name) |
| 1963 | .typed<conv3d::schema>(); |
| 1964 | } |
| 1965 | |
| 1966 | // aten::conv3d(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] dilation=1, int groups=1) -> Tensor |
| 1967 | at::Tensor conv3d::call(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) { |
| 1968 | |
| 1969 | static auto op = create_conv3d_typed_handle(); |
| 1970 | return op.call(input, weight, bias, stride, padding, dilation, groups); |
| 1971 | } |
| 1972 | |
| 1973 | // aten::conv3d(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] dilation=1, int groups=1) -> Tensor |
| 1974 | at::Tensor conv3d::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups) { |
| 1975 | |
| 1976 | static auto op = create_conv3d_typed_handle(); |
| 1977 | return op.redispatch(dispatchKeySet, input, weight, bias, stride, padding, dilation, groups); |
| 1978 | } |
| 1979 | |
| 1980 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv1d_padding, name, "aten::conv1d") |
| 1981 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv1d_padding, overload_name, "padding") |
| 1982 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv1d_padding, schema_str, "conv1d.padding(Tensor input, Tensor weight, Tensor? bias=None, int[1] stride=1, str padding=\"valid\", int[1] dilation=1, int groups=1) -> Tensor") |
| 1983 | |
| 1984 | // aten::conv1d.padding(Tensor input, Tensor weight, Tensor? bias=None, int[1] stride=1, str padding="valid", int[1] dilation=1, int groups=1) -> Tensor |
| 1985 | static C10_NOINLINE c10::TypedOperatorHandle<conv1d_padding::schema> create_conv1d_padding_typed_handle() { |
| 1986 | return c10::Dispatcher::singleton() |
| 1987 | .findSchemaOrThrow(conv1d_padding::name, conv1d_padding::overload_name) |
| 1988 | .typed<conv1d_padding::schema>(); |
| 1989 | } |
| 1990 | |
| 1991 | // aten::conv1d.padding(Tensor input, Tensor weight, Tensor? bias=None, int[1] stride=1, str padding="valid", int[1] dilation=1, int groups=1) -> Tensor |
| 1992 | at::Tensor conv1d_padding::call(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) { |
| 1993 | |
| 1994 | static auto op = create_conv1d_padding_typed_handle(); |
| 1995 | return op.call(input, weight, bias, stride, padding, dilation, groups); |
| 1996 | } |
| 1997 | |
| 1998 | // aten::conv1d.padding(Tensor input, Tensor weight, Tensor? bias=None, int[1] stride=1, str padding="valid", int[1] dilation=1, int groups=1) -> Tensor |
| 1999 | at::Tensor conv1d_padding::redispatch(c10::DispatchKeySet dispatchKeySet, 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) { |
| 2000 | |
| 2001 | static auto op = create_conv1d_padding_typed_handle(); |
| 2002 | return op.redispatch(dispatchKeySet, input, weight, bias, stride, padding, dilation, groups); |
| 2003 | } |
| 2004 | |
| 2005 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv3d_padding, name, "aten::conv3d") |
| 2006 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv3d_padding, overload_name, "padding") |
| 2007 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv3d_padding, schema_str, "conv3d.padding(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, str padding=\"valid\", int[3] dilation=1, int groups=1) -> Tensor") |
| 2008 | |
| 2009 | // aten::conv3d.padding(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, str padding="valid", int[3] dilation=1, int groups=1) -> Tensor |
| 2010 | static C10_NOINLINE c10::TypedOperatorHandle<conv3d_padding::schema> create_conv3d_padding_typed_handle() { |
| 2011 | return c10::Dispatcher::singleton() |
| 2012 | .findSchemaOrThrow(conv3d_padding::name, conv3d_padding::overload_name) |
| 2013 | .typed<conv3d_padding::schema>(); |
| 2014 | } |
| 2015 | |
| 2016 | // aten::conv3d.padding(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, str padding="valid", int[3] dilation=1, int groups=1) -> Tensor |
| 2017 | at::Tensor conv3d_padding::call(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) { |
| 2018 | |
| 2019 | static auto op = create_conv3d_padding_typed_handle(); |
| 2020 | return op.call(input, weight, bias, stride, padding, dilation, groups); |
| 2021 | } |
| 2022 | |
| 2023 | // aten::conv3d.padding(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, str padding="valid", int[3] dilation=1, int groups=1) -> Tensor |
| 2024 | at::Tensor conv3d_padding::redispatch(c10::DispatchKeySet dispatchKeySet, 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) { |
| 2025 | |
| 2026 | static auto op = create_conv3d_padding_typed_handle(); |
| 2027 | return op.redispatch(dispatchKeySet, input, weight, bias, stride, padding, dilation, groups); |
| 2028 | } |
| 2029 | |
| 2030 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv_tbc_backward, name, "aten::conv_tbc_backward") |
| 2031 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv_tbc_backward, overload_name, "") |
| 2032 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv_tbc_backward, schema_str, "conv_tbc_backward(Tensor self, Tensor input, Tensor weight, Tensor bias, int pad) -> (Tensor, Tensor, Tensor)") |
| 2033 | |
| 2034 | // aten::conv_tbc_backward(Tensor self, Tensor input, Tensor weight, Tensor bias, int pad) -> (Tensor, Tensor, Tensor) |
| 2035 | static C10_NOINLINE c10::TypedOperatorHandle<conv_tbc_backward::schema> create_conv_tbc_backward_typed_handle() { |
| 2036 | return c10::Dispatcher::singleton() |
| 2037 | .findSchemaOrThrow(conv_tbc_backward::name, conv_tbc_backward::overload_name) |
| 2038 | .typed<conv_tbc_backward::schema>(); |
| 2039 | } |
| 2040 | |
| 2041 | // aten::conv_tbc_backward(Tensor self, Tensor input, Tensor weight, Tensor bias, int pad) -> (Tensor, Tensor, Tensor) |
| 2042 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> conv_tbc_backward::call(const at::Tensor & self, const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, int64_t pad) { |
| 2043 | |
| 2044 | static auto op = create_conv_tbc_backward_typed_handle(); |
| 2045 | return op.call(self, input, weight, bias, pad); |
| 2046 | } |
| 2047 | |
| 2048 | // aten::conv_tbc_backward(Tensor self, Tensor input, Tensor weight, Tensor bias, int pad) -> (Tensor, Tensor, Tensor) |
| 2049 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> conv_tbc_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, int64_t pad) { |
| 2050 | |
| 2051 | static auto op = create_conv_tbc_backward_typed_handle(); |
| 2052 | return op.redispatch(dispatchKeySet, self, input, weight, bias, pad); |
| 2053 | } |
| 2054 | |
| 2055 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv_transpose3d_input, name, "aten::conv_transpose3d") |
| 2056 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv_transpose3d_input, overload_name, "input") |
| 2057 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv_transpose3d_input, schema_str, "conv_transpose3d.input(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] output_padding=0, int groups=1, int[3] dilation=1) -> Tensor") |
| 2058 | |
| 2059 | // aten::conv_transpose3d.input(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] output_padding=0, int groups=1, int[3] dilation=1) -> Tensor |
| 2060 | static C10_NOINLINE c10::TypedOperatorHandle<conv_transpose3d_input::schema> create_conv_transpose3d_input_typed_handle() { |
| 2061 | return c10::Dispatcher::singleton() |
| 2062 | .findSchemaOrThrow(conv_transpose3d_input::name, conv_transpose3d_input::overload_name) |
| 2063 | .typed<conv_transpose3d_input::schema>(); |
| 2064 | } |
| 2065 | |
| 2066 | // aten::conv_transpose3d.input(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] output_padding=0, int groups=1, int[3] dilation=1) -> Tensor |
| 2067 | at::Tensor conv_transpose3d_input::call(const at::Tensor & input, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef output_padding, int64_t groups, at::IntArrayRef dilation) { |
| 2068 | |
| 2069 | static auto op = create_conv_transpose3d_input_typed_handle(); |
| 2070 | return op.call(input, weight, bias, stride, padding, output_padding, groups, dilation); |
| 2071 | } |
| 2072 | |
| 2073 | // aten::conv_transpose3d.input(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] output_padding=0, int groups=1, int[3] dilation=1) -> Tensor |
| 2074 | at::Tensor conv_transpose3d_input::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef output_padding, int64_t groups, at::IntArrayRef dilation) { |
| 2075 | |
| 2076 | static auto op = create_conv_transpose3d_input_typed_handle(); |
| 2077 | return op.redispatch(dispatchKeySet, input, weight, bias, stride, padding, output_padding, groups, dilation); |
| 2078 | } |
| 2079 | |
| 2080 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(copy, name, "aten::copy") |
| 2081 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(copy, overload_name, "") |
| 2082 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(copy, schema_str, "copy(Tensor self, Tensor src, bool non_blocking=False) -> Tensor") |
| 2083 | |
| 2084 | // aten::copy(Tensor self, Tensor src, bool non_blocking=False) -> Tensor |
| 2085 | static C10_NOINLINE c10::TypedOperatorHandle<copy::schema> create_copy_typed_handle() { |
| 2086 | return c10::Dispatcher::singleton() |
| 2087 | .findSchemaOrThrow(copy::name, copy::overload_name) |
| 2088 | .typed<copy::schema>(); |
| 2089 | } |
| 2090 | |
| 2091 | // aten::copy(Tensor self, Tensor src, bool non_blocking=False) -> Tensor |
| 2092 | at::Tensor copy::call(const at::Tensor & self, const at::Tensor & src, bool non_blocking) { |
| 2093 | |
| 2094 | static auto op = create_copy_typed_handle(); |
| 2095 | return op.call(self, src, non_blocking); |
| 2096 | } |
| 2097 | |
| 2098 | // aten::copy(Tensor self, Tensor src, bool non_blocking=False) -> Tensor |
| 2099 | at::Tensor copy::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, bool non_blocking) { |
| 2100 | |
| 2101 | static auto op = create_copy_typed_handle(); |
| 2102 | return op.redispatch(dispatchKeySet, self, src, non_blocking); |
| 2103 | } |
| 2104 | |
| 2105 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(copy_, name, "aten::copy_") |
| 2106 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(copy_, overload_name, "") |
| 2107 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(copy_, schema_str, "copy_(Tensor(a!) self, Tensor src, bool non_blocking=False) -> Tensor(a!)") |
| 2108 | |
| 2109 | // aten::copy_(Tensor(a!) self, Tensor src, bool non_blocking=False) -> Tensor(a!) |
| 2110 | static C10_NOINLINE c10::TypedOperatorHandle<copy_::schema> create_copy__typed_handle() { |
| 2111 | return c10::Dispatcher::singleton() |
| 2112 | .findSchemaOrThrow(copy_::name, copy_::overload_name) |
| 2113 | .typed<copy_::schema>(); |
| 2114 | } |
| 2115 | |
| 2116 | // aten::copy_(Tensor(a!) self, Tensor src, bool non_blocking=False) -> Tensor(a!) |
| 2117 | at::Tensor & copy_::call(at::Tensor & self, const at::Tensor & src, bool non_blocking) { |
| 2118 | |
| 2119 | static auto op = create_copy__typed_handle(); |
| 2120 | return op.call(self, src, non_blocking); |
| 2121 | } |
| 2122 | |
| 2123 | // aten::copy_(Tensor(a!) self, Tensor src, bool non_blocking=False) -> Tensor(a!) |
| 2124 | at::Tensor & copy_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & src, bool non_blocking) { |
| 2125 | |
| 2126 | static auto op = create_copy__typed_handle(); |
| 2127 | return op.redispatch(dispatchKeySet, self, src, non_blocking); |
| 2128 | } |
| 2129 | |
| 2130 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_copy_from_and_resize, name, "aten::_copy_from_and_resize") |
| 2131 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_copy_from_and_resize, overload_name, "") |
| 2132 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_copy_from_and_resize, schema_str, "_copy_from_and_resize(Tensor self, Tensor dst) -> Tensor") |
| 2133 | |
| 2134 | // aten::_copy_from_and_resize(Tensor self, Tensor dst) -> Tensor |
| 2135 | static C10_NOINLINE c10::TypedOperatorHandle<_copy_from_and_resize::schema> create__copy_from_and_resize_typed_handle() { |
| 2136 | return c10::Dispatcher::singleton() |
| 2137 | .findSchemaOrThrow(_copy_from_and_resize::name, _copy_from_and_resize::overload_name) |
| 2138 | .typed<_copy_from_and_resize::schema>(); |
| 2139 | } |
| 2140 | |
| 2141 | // aten::_copy_from_and_resize(Tensor self, Tensor dst) -> Tensor |
| 2142 | at::Tensor _copy_from_and_resize::call(const at::Tensor & self, const at::Tensor & dst) { |
| 2143 | |
| 2144 | static auto op = create__copy_from_and_resize_typed_handle(); |
| 2145 | return op.call(self, dst); |
| 2146 | } |
| 2147 | |
| 2148 | // aten::_copy_from_and_resize(Tensor self, Tensor dst) -> Tensor |
| 2149 | at::Tensor _copy_from_and_resize::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & dst) { |
| 2150 | |
| 2151 | static auto op = create__copy_from_and_resize_typed_handle(); |
| 2152 | return op.redispatch(dispatchKeySet, self, dst); |
| 2153 | } |
| 2154 | |
| 2155 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cudnn_convolution, name, "aten::cudnn_convolution") |
| 2156 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cudnn_convolution, overload_name, "") |
| 2157 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cudnn_convolution, schema_str, "cudnn_convolution(Tensor self, Tensor weight, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, bool allow_tf32) -> Tensor") |
| 2158 | |
| 2159 | // aten::cudnn_convolution(Tensor self, Tensor weight, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, bool allow_tf32) -> Tensor |
| 2160 | static C10_NOINLINE c10::TypedOperatorHandle<cudnn_convolution::schema> create_cudnn_convolution_typed_handle() { |
| 2161 | return c10::Dispatcher::singleton() |
| 2162 | .findSchemaOrThrow(cudnn_convolution::name, cudnn_convolution::overload_name) |
| 2163 | .typed<cudnn_convolution::schema>(); |
| 2164 | } |
| 2165 | |
| 2166 | // aten::cudnn_convolution(Tensor self, Tensor weight, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, bool allow_tf32) -> Tensor |
| 2167 | at::Tensor cudnn_convolution::call(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) { |
| 2168 | |
| 2169 | static auto op = create_cudnn_convolution_typed_handle(); |
| 2170 | return op.call(self, weight, padding, stride, dilation, groups, benchmark, deterministic, allow_tf32); |
| 2171 | } |
| 2172 | |
| 2173 | // aten::cudnn_convolution(Tensor self, Tensor weight, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, bool allow_tf32) -> Tensor |
| 2174 | at::Tensor cudnn_convolution::redispatch(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) { |
| 2175 | |
| 2176 | static auto op = create_cudnn_convolution_typed_handle(); |
| 2177 | return op.redispatch(dispatchKeySet, self, weight, padding, stride, dilation, groups, benchmark, deterministic, allow_tf32); |
| 2178 | } |
| 2179 | |
| 2180 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cudnn_convolution_relu, name, "aten::cudnn_convolution_relu") |
| 2181 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cudnn_convolution_relu, overload_name, "") |
| 2182 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cudnn_convolution_relu, schema_str, "cudnn_convolution_relu(Tensor self, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> Tensor") |
| 2183 | |
| 2184 | // aten::cudnn_convolution_relu(Tensor self, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> Tensor |
| 2185 | static C10_NOINLINE c10::TypedOperatorHandle<cudnn_convolution_relu::schema> create_cudnn_convolution_relu_typed_handle() { |
| 2186 | return c10::Dispatcher::singleton() |
| 2187 | .findSchemaOrThrow(cudnn_convolution_relu::name, cudnn_convolution_relu::overload_name) |
| 2188 | .typed<cudnn_convolution_relu::schema>(); |
| 2189 | } |
| 2190 | |
| 2191 | // aten::cudnn_convolution_relu(Tensor self, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> Tensor |
| 2192 | at::Tensor cudnn_convolution_relu::call(const at::Tensor & self, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups) { |
| 2193 | |
| 2194 | static auto op = create_cudnn_convolution_relu_typed_handle(); |
| 2195 | return op.call(self, weight, bias, stride, padding, dilation, groups); |
| 2196 | } |
| 2197 | |
| 2198 | // aten::cudnn_convolution_relu(Tensor self, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups) -> Tensor |
| 2199 | at::Tensor cudnn_convolution_relu::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups) { |
| 2200 | |
| 2201 | static auto op = create_cudnn_convolution_relu_typed_handle(); |
| 2202 | return op.redispatch(dispatchKeySet, self, weight, bias, stride, padding, dilation, groups); |
| 2203 | } |
| 2204 | |
| 2205 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cumprod, name, "aten::cumprod") |
| 2206 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cumprod, overload_name, "") |
| 2207 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cumprod, schema_str, "cumprod(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor") |
| 2208 | |
| 2209 | // aten::cumprod(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor |
| 2210 | static C10_NOINLINE c10::TypedOperatorHandle<cumprod::schema> create_cumprod_typed_handle() { |
| 2211 | return c10::Dispatcher::singleton() |
| 2212 | .findSchemaOrThrow(cumprod::name, cumprod::overload_name) |
| 2213 | .typed<cumprod::schema>(); |
| 2214 | } |
| 2215 | |
| 2216 | // aten::cumprod(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor |
| 2217 | at::Tensor cumprod::call(const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 2218 | |
| 2219 | static auto op = create_cumprod_typed_handle(); |
| 2220 | return op.call(self, dim, dtype); |
| 2221 | } |
| 2222 | |
| 2223 | // aten::cumprod(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor |
| 2224 | at::Tensor cumprod::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 2225 | |
| 2226 | static auto op = create_cumprod_typed_handle(); |
| 2227 | return op.redispatch(dispatchKeySet, self, dim, dtype); |
| 2228 | } |
| 2229 | |
| 2230 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cumprod_, name, "aten::cumprod_") |
| 2231 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cumprod_, overload_name, "") |
| 2232 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cumprod_, schema_str, "cumprod_(Tensor(a!) self, int dim, *, ScalarType? dtype=None) -> Tensor(a!)") |
| 2233 | |
| 2234 | // aten::cumprod_(Tensor(a!) self, int dim, *, ScalarType? dtype=None) -> Tensor(a!) |
| 2235 | static C10_NOINLINE c10::TypedOperatorHandle<cumprod_::schema> create_cumprod__typed_handle() { |
| 2236 | return c10::Dispatcher::singleton() |
| 2237 | .findSchemaOrThrow(cumprod_::name, cumprod_::overload_name) |
| 2238 | .typed<cumprod_::schema>(); |
| 2239 | } |
| 2240 | |
| 2241 | // aten::cumprod_(Tensor(a!) self, int dim, *, ScalarType? dtype=None) -> Tensor(a!) |
| 2242 | at::Tensor & cumprod_::call(at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 2243 | |
| 2244 | static auto op = create_cumprod__typed_handle(); |
| 2245 | return op.call(self, dim, dtype); |
| 2246 | } |
| 2247 | |
| 2248 | // aten::cumprod_(Tensor(a!) self, int dim, *, ScalarType? dtype=None) -> Tensor(a!) |
| 2249 | at::Tensor & cumprod_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 2250 | |
| 2251 | static auto op = create_cumprod__typed_handle(); |
| 2252 | return op.redispatch(dispatchKeySet, self, dim, dtype); |
| 2253 | } |
| 2254 | |
| 2255 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cumprod_out, name, "aten::cumprod") |
| 2256 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cumprod_out, overload_name, "out") |
| 2257 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cumprod_out, schema_str, "cumprod.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)") |
| 2258 | |
| 2259 | // aten::cumprod.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) |
| 2260 | static C10_NOINLINE c10::TypedOperatorHandle<cumprod_out::schema> create_cumprod_out_typed_handle() { |
| 2261 | return c10::Dispatcher::singleton() |
| 2262 | .findSchemaOrThrow(cumprod_out::name, cumprod_out::overload_name) |
| 2263 | .typed<cumprod_out::schema>(); |
| 2264 | } |
| 2265 | |
| 2266 | // aten::cumprod.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) |
| 2267 | at::Tensor & cumprod_out::call(const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 2268 | |
| 2269 | static auto op = create_cumprod_out_typed_handle(); |
| 2270 | return op.call(self, dim, dtype, out); |
| 2271 | } |
| 2272 | |
| 2273 | // aten::cumprod.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) |
| 2274 | at::Tensor & cumprod_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 2275 | |
| 2276 | static auto op = create_cumprod_out_typed_handle(); |
| 2277 | return op.redispatch(dispatchKeySet, self, dim, dtype, out); |
| 2278 | } |
| 2279 | |
| 2280 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cumprod_dimname, name, "aten::cumprod") |
| 2281 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cumprod_dimname, overload_name, "dimname") |
| 2282 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cumprod_dimname, schema_str, "cumprod.dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor") |
| 2283 | |
| 2284 | // aten::cumprod.dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor |
| 2285 | static C10_NOINLINE c10::TypedOperatorHandle<cumprod_dimname::schema> create_cumprod_dimname_typed_handle() { |
| 2286 | return c10::Dispatcher::singleton() |
| 2287 | .findSchemaOrThrow(cumprod_dimname::name, cumprod_dimname::overload_name) |
| 2288 | .typed<cumprod_dimname::schema>(); |
| 2289 | } |
| 2290 | |
| 2291 | // aten::cumprod.dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor |
| 2292 | at::Tensor cumprod_dimname::call(const at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype) { |
| 2293 | |
| 2294 | static auto op = create_cumprod_dimname_typed_handle(); |
| 2295 | return op.call(self, dim, dtype); |
| 2296 | } |
| 2297 | |
| 2298 | // aten::cumprod.dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor |
| 2299 | at::Tensor cumprod_dimname::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype) { |
| 2300 | |
| 2301 | static auto op = create_cumprod_dimname_typed_handle(); |
| 2302 | return op.redispatch(dispatchKeySet, self, dim, dtype); |
| 2303 | } |
| 2304 | |
| 2305 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cumprod__dimname, name, "aten::cumprod_") |
| 2306 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cumprod__dimname, overload_name, "dimname") |
| 2307 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cumprod__dimname, schema_str, "cumprod_.dimname(Tensor(a!) self, Dimname dim, *, ScalarType? dtype=None) -> Tensor(a!)") |
| 2308 | |
| 2309 | // aten::cumprod_.dimname(Tensor(a!) self, Dimname dim, *, ScalarType? dtype=None) -> Tensor(a!) |
| 2310 | static C10_NOINLINE c10::TypedOperatorHandle<cumprod__dimname::schema> create_cumprod__dimname_typed_handle() { |
| 2311 | return c10::Dispatcher::singleton() |
| 2312 | .findSchemaOrThrow(cumprod__dimname::name, cumprod__dimname::overload_name) |
| 2313 | .typed<cumprod__dimname::schema>(); |
| 2314 | } |
| 2315 | |
| 2316 | // aten::cumprod_.dimname(Tensor(a!) self, Dimname dim, *, ScalarType? dtype=None) -> Tensor(a!) |
| 2317 | at::Tensor & cumprod__dimname::call(at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype) { |
| 2318 | |
| 2319 | static auto op = create_cumprod__dimname_typed_handle(); |
| 2320 | return op.call(self, dim, dtype); |
| 2321 | } |
| 2322 | |
| 2323 | // aten::cumprod_.dimname(Tensor(a!) self, Dimname dim, *, ScalarType? dtype=None) -> Tensor(a!) |
| 2324 | at::Tensor & cumprod__dimname::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype) { |
| 2325 | |
| 2326 | static auto op = create_cumprod__dimname_typed_handle(); |
| 2327 | return op.redispatch(dispatchKeySet, self, dim, dtype); |
| 2328 | } |
| 2329 | |
| 2330 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cumprod_dimname_out, name, "aten::cumprod") |
| 2331 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cumprod_dimname_out, overload_name, "dimname_out") |
| 2332 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cumprod_dimname_out, schema_str, "cumprod.dimname_out(Tensor self, Dimname dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)") |
| 2333 | |
| 2334 | // aten::cumprod.dimname_out(Tensor self, Dimname dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) |
| 2335 | static C10_NOINLINE c10::TypedOperatorHandle<cumprod_dimname_out::schema> create_cumprod_dimname_out_typed_handle() { |
| 2336 | return c10::Dispatcher::singleton() |
| 2337 | .findSchemaOrThrow(cumprod_dimname_out::name, cumprod_dimname_out::overload_name) |
| 2338 | .typed<cumprod_dimname_out::schema>(); |
| 2339 | } |
| 2340 | |
| 2341 | // aten::cumprod.dimname_out(Tensor self, Dimname dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) |
| 2342 | at::Tensor & cumprod_dimname_out::call(const at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 2343 | |
| 2344 | static auto op = create_cumprod_dimname_out_typed_handle(); |
| 2345 | return op.call(self, dim, dtype, out); |
| 2346 | } |
| 2347 | |
| 2348 | // aten::cumprod.dimname_out(Tensor self, Dimname dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) |
| 2349 | at::Tensor & cumprod_dimname_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 2350 | |
| 2351 | static auto op = create_cumprod_dimname_out_typed_handle(); |
| 2352 | return op.redispatch(dispatchKeySet, self, dim, dtype, out); |
| 2353 | } |
| 2354 | |
| 2355 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cumulative_trapezoid_x, name, "aten::cumulative_trapezoid") |
| 2356 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cumulative_trapezoid_x, overload_name, "x") |
| 2357 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cumulative_trapezoid_x, schema_str, "cumulative_trapezoid.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor") |
| 2358 | |
| 2359 | // aten::cumulative_trapezoid.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor |
| 2360 | static C10_NOINLINE c10::TypedOperatorHandle<cumulative_trapezoid_x::schema> create_cumulative_trapezoid_x_typed_handle() { |
| 2361 | return c10::Dispatcher::singleton() |
| 2362 | .findSchemaOrThrow(cumulative_trapezoid_x::name, cumulative_trapezoid_x::overload_name) |
| 2363 | .typed<cumulative_trapezoid_x::schema>(); |
| 2364 | } |
| 2365 | |
| 2366 | // aten::cumulative_trapezoid.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor |
| 2367 | at::Tensor cumulative_trapezoid_x::call(const at::Tensor & y, const at::Tensor & x, int64_t dim) { |
| 2368 | |
| 2369 | static auto op = create_cumulative_trapezoid_x_typed_handle(); |
| 2370 | return op.call(y, x, dim); |
| 2371 | } |
| 2372 | |
| 2373 | // aten::cumulative_trapezoid.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor |
| 2374 | at::Tensor cumulative_trapezoid_x::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & y, const at::Tensor & x, int64_t dim) { |
| 2375 | |
| 2376 | static auto op = create_cumulative_trapezoid_x_typed_handle(); |
| 2377 | return op.redispatch(dispatchKeySet, y, x, dim); |
| 2378 | } |
| 2379 | |
| 2380 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cumulative_trapezoid_dx, name, "aten::cumulative_trapezoid") |
| 2381 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cumulative_trapezoid_dx, overload_name, "dx") |
| 2382 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cumulative_trapezoid_dx, schema_str, "cumulative_trapezoid.dx(Tensor y, *, Scalar dx=1, int dim=-1) -> Tensor") |
| 2383 | |
| 2384 | // aten::cumulative_trapezoid.dx(Tensor y, *, Scalar dx=1, int dim=-1) -> Tensor |
| 2385 | static C10_NOINLINE c10::TypedOperatorHandle<cumulative_trapezoid_dx::schema> create_cumulative_trapezoid_dx_typed_handle() { |
| 2386 | return c10::Dispatcher::singleton() |
| 2387 | .findSchemaOrThrow(cumulative_trapezoid_dx::name, cumulative_trapezoid_dx::overload_name) |
| 2388 | .typed<cumulative_trapezoid_dx::schema>(); |
| 2389 | } |
| 2390 | |
| 2391 | // aten::cumulative_trapezoid.dx(Tensor y, *, Scalar dx=1, int dim=-1) -> Tensor |
| 2392 | at::Tensor cumulative_trapezoid_dx::call(const at::Tensor & y, const at::Scalar & dx, int64_t dim) { |
| 2393 | |
| 2394 | static auto op = create_cumulative_trapezoid_dx_typed_handle(); |
| 2395 | return op.call(y, dx, dim); |
| 2396 | } |
| 2397 | |
| 2398 | // aten::cumulative_trapezoid.dx(Tensor y, *, Scalar dx=1, int dim=-1) -> Tensor |
| 2399 | at::Tensor cumulative_trapezoid_dx::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & y, const at::Scalar & dx, int64_t dim) { |
| 2400 | |
| 2401 | static auto op = create_cumulative_trapezoid_dx_typed_handle(); |
| 2402 | return op.redispatch(dispatchKeySet, y, dx, dim); |
| 2403 | } |
| 2404 | |
| 2405 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ctc_loss_IntList, name, "aten::ctc_loss") |
| 2406 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ctc_loss_IntList, overload_name, "IntList") |
| 2407 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ctc_loss_IntList, schema_str, "ctc_loss.IntList(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, int reduction=Mean, bool zero_infinity=False) -> Tensor") |
| 2408 | |
| 2409 | // aten::ctc_loss.IntList(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, int reduction=Mean, bool zero_infinity=False) -> Tensor |
| 2410 | static C10_NOINLINE c10::TypedOperatorHandle<ctc_loss_IntList::schema> create_ctc_loss_IntList_typed_handle() { |
| 2411 | return c10::Dispatcher::singleton() |
| 2412 | .findSchemaOrThrow(ctc_loss_IntList::name, ctc_loss_IntList::overload_name) |
| 2413 | .typed<ctc_loss_IntList::schema>(); |
| 2414 | } |
| 2415 | |
| 2416 | // aten::ctc_loss.IntList(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, int reduction=Mean, bool zero_infinity=False) -> Tensor |
| 2417 | at::Tensor ctc_loss_IntList::call(const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank, int64_t reduction, bool zero_infinity) { |
| 2418 | |
| 2419 | static auto op = create_ctc_loss_IntList_typed_handle(); |
| 2420 | return op.call(log_probs, targets, input_lengths, target_lengths, blank, reduction, zero_infinity); |
| 2421 | } |
| 2422 | |
| 2423 | // aten::ctc_loss.IntList(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, int reduction=Mean, bool zero_infinity=False) -> Tensor |
| 2424 | at::Tensor ctc_loss_IntList::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank, int64_t reduction, bool zero_infinity) { |
| 2425 | |
| 2426 | static auto op = create_ctc_loss_IntList_typed_handle(); |
| 2427 | return op.redispatch(dispatchKeySet, log_probs, targets, input_lengths, target_lengths, blank, reduction, zero_infinity); |
| 2428 | } |
| 2429 | |
| 2430 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ctc_loss_Tensor, name, "aten::ctc_loss") |
| 2431 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ctc_loss_Tensor, overload_name, "Tensor") |
| 2432 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ctc_loss_Tensor, schema_str, "ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank=0, int reduction=Mean, bool zero_infinity=False) -> Tensor") |
| 2433 | |
| 2434 | // aten::ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank=0, int reduction=Mean, bool zero_infinity=False) -> Tensor |
| 2435 | static C10_NOINLINE c10::TypedOperatorHandle<ctc_loss_Tensor::schema> create_ctc_loss_Tensor_typed_handle() { |
| 2436 | return c10::Dispatcher::singleton() |
| 2437 | .findSchemaOrThrow(ctc_loss_Tensor::name, ctc_loss_Tensor::overload_name) |
| 2438 | .typed<ctc_loss_Tensor::schema>(); |
| 2439 | } |
| 2440 | |
| 2441 | // aten::ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank=0, int reduction=Mean, bool zero_infinity=False) -> Tensor |
| 2442 | at::Tensor ctc_loss_Tensor::call(const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank, int64_t reduction, bool zero_infinity) { |
| 2443 | |
| 2444 | static auto op = create_ctc_loss_Tensor_typed_handle(); |
| 2445 | return op.call(log_probs, targets, input_lengths, target_lengths, blank, reduction, zero_infinity); |
| 2446 | } |
| 2447 | |
| 2448 | // aten::ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank=0, int reduction=Mean, bool zero_infinity=False) -> Tensor |
| 2449 | at::Tensor ctc_loss_Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank, int64_t reduction, bool zero_infinity) { |
| 2450 | |
| 2451 | static auto op = create_ctc_loss_Tensor_typed_handle(); |
| 2452 | return op.redispatch(dispatchKeySet, log_probs, targets, input_lengths, target_lengths, blank, reduction, zero_infinity); |
| 2453 | } |
| 2454 | |
| 2455 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(diag_embed, name, "aten::diag_embed") |
| 2456 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(diag_embed, overload_name, "") |
| 2457 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(diag_embed, schema_str, "diag_embed(Tensor self, int offset=0, int dim1=-2, int dim2=-1) -> Tensor") |
| 2458 | |
| 2459 | // aten::diag_embed(Tensor self, int offset=0, int dim1=-2, int dim2=-1) -> Tensor |
| 2460 | static C10_NOINLINE c10::TypedOperatorHandle<diag_embed::schema> create_diag_embed_typed_handle() { |
| 2461 | return c10::Dispatcher::singleton() |
| 2462 | .findSchemaOrThrow(diag_embed::name, diag_embed::overload_name) |
| 2463 | .typed<diag_embed::schema>(); |
| 2464 | } |
| 2465 | |
| 2466 | // aten::diag_embed(Tensor self, int offset=0, int dim1=-2, int dim2=-1) -> Tensor |
| 2467 | at::Tensor diag_embed::call(const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2) { |
| 2468 | |
| 2469 | static auto op = create_diag_embed_typed_handle(); |
| 2470 | return op.call(self, offset, dim1, dim2); |
| 2471 | } |
| 2472 | |
| 2473 | // aten::diag_embed(Tensor self, int offset=0, int dim1=-2, int dim2=-1) -> Tensor |
| 2474 | at::Tensor diag_embed::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2) { |
| 2475 | |
| 2476 | static auto op = create_diag_embed_typed_handle(); |
| 2477 | return op.redispatch(dispatchKeySet, self, offset, dim1, dim2); |
| 2478 | } |
| 2479 | |
| 2480 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(diagonal, name, "aten::diagonal") |
| 2481 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(diagonal, overload_name, "") |
| 2482 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(diagonal, schema_str, "diagonal(Tensor(a) self, int offset=0, int dim1=0, int dim2=1) -> Tensor(a)") |
| 2483 | |
| 2484 | // aten::diagonal(Tensor(a) self, int offset=0, int dim1=0, int dim2=1) -> Tensor(a) |
| 2485 | static C10_NOINLINE c10::TypedOperatorHandle<diagonal::schema> create_diagonal_typed_handle() { |
| 2486 | return c10::Dispatcher::singleton() |
| 2487 | .findSchemaOrThrow(diagonal::name, diagonal::overload_name) |
| 2488 | .typed<diagonal::schema>(); |
| 2489 | } |
| 2490 | |
| 2491 | // aten::diagonal(Tensor(a) self, int offset=0, int dim1=0, int dim2=1) -> Tensor(a) |
| 2492 | at::Tensor diagonal::call(const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2) { |
| 2493 | |
| 2494 | static auto op = create_diagonal_typed_handle(); |
| 2495 | return op.call(self, offset, dim1, dim2); |
| 2496 | } |
| 2497 | |
| 2498 | // aten::diagonal(Tensor(a) self, int offset=0, int dim1=0, int dim2=1) -> Tensor(a) |
| 2499 | at::Tensor diagonal::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2) { |
| 2500 | |
| 2501 | static auto op = create_diagonal_typed_handle(); |
| 2502 | return op.redispatch(dispatchKeySet, self, offset, dim1, dim2); |
| 2503 | } |
| 2504 | |
| 2505 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(diagonal_Dimname, name, "aten::diagonal") |
| 2506 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(diagonal_Dimname, overload_name, "Dimname") |
| 2507 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(diagonal_Dimname, schema_str, "diagonal.Dimname(Tensor(a) self, *, Dimname outdim, Dimname dim1, Dimname dim2, int offset=0) -> Tensor(a)") |
| 2508 | |
| 2509 | // aten::diagonal.Dimname(Tensor(a) self, *, Dimname outdim, Dimname dim1, Dimname dim2, int offset=0) -> Tensor(a) |
| 2510 | static C10_NOINLINE c10::TypedOperatorHandle<diagonal_Dimname::schema> create_diagonal_Dimname_typed_handle() { |
| 2511 | return c10::Dispatcher::singleton() |
| 2512 | .findSchemaOrThrow(diagonal_Dimname::name, diagonal_Dimname::overload_name) |
| 2513 | .typed<diagonal_Dimname::schema>(); |
| 2514 | } |
| 2515 | |
| 2516 | // aten::diagonal.Dimname(Tensor(a) self, *, Dimname outdim, Dimname dim1, Dimname dim2, int offset=0) -> Tensor(a) |
| 2517 | at::Tensor diagonal_Dimname::call(const at::Tensor & self, at::Dimname outdim, at::Dimname dim1, at::Dimname dim2, int64_t offset) { |
| 2518 | |
| 2519 | static auto op = create_diagonal_Dimname_typed_handle(); |
| 2520 | return op.call(self, outdim, dim1, dim2, offset); |
| 2521 | } |
| 2522 | |
| 2523 | // aten::diagonal.Dimname(Tensor(a) self, *, Dimname outdim, Dimname dim1, Dimname dim2, int offset=0) -> Tensor(a) |
| 2524 | at::Tensor diagonal_Dimname::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname outdim, at::Dimname dim1, at::Dimname dim2, int64_t offset) { |
| 2525 | |
| 2526 | static auto op = create_diagonal_Dimname_typed_handle(); |
| 2527 | return op.redispatch(dispatchKeySet, self, outdim, dim1, dim2, offset); |
| 2528 | } |
| 2529 | |
| 2530 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide_Tensor, name, "aten::divide") |
| 2531 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide_Tensor, overload_name, "Tensor") |
| 2532 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide_Tensor, schema_str, "divide.Tensor(Tensor self, Tensor other) -> Tensor") |
| 2533 | |
| 2534 | // aten::divide.Tensor(Tensor self, Tensor other) -> Tensor |
| 2535 | static C10_NOINLINE c10::TypedOperatorHandle<divide_Tensor::schema> create_divide_Tensor_typed_handle() { |
| 2536 | return c10::Dispatcher::singleton() |
| 2537 | .findSchemaOrThrow(divide_Tensor::name, divide_Tensor::overload_name) |
| 2538 | .typed<divide_Tensor::schema>(); |
| 2539 | } |
| 2540 | |
| 2541 | // aten::divide.Tensor(Tensor self, Tensor other) -> Tensor |
| 2542 | at::Tensor divide_Tensor::call(const at::Tensor & self, const at::Tensor & other) { |
| 2543 | |
| 2544 | static auto op = create_divide_Tensor_typed_handle(); |
| 2545 | return op.call(self, other); |
| 2546 | } |
| 2547 | |
| 2548 | // aten::divide.Tensor(Tensor self, Tensor other) -> Tensor |
| 2549 | at::Tensor divide_Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { |
| 2550 | |
| 2551 | static auto op = create_divide_Tensor_typed_handle(); |
| 2552 | return op.redispatch(dispatchKeySet, self, other); |
| 2553 | } |
| 2554 | |
| 2555 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide__Tensor, name, "aten::divide_") |
| 2556 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide__Tensor, overload_name, "Tensor") |
| 2557 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide__Tensor, schema_str, "divide_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)") |
| 2558 | |
| 2559 | // aten::divide_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) |
| 2560 | static C10_NOINLINE c10::TypedOperatorHandle<divide__Tensor::schema> create_divide__Tensor_typed_handle() { |
| 2561 | return c10::Dispatcher::singleton() |
| 2562 | .findSchemaOrThrow(divide__Tensor::name, divide__Tensor::overload_name) |
| 2563 | .typed<divide__Tensor::schema>(); |
| 2564 | } |
| 2565 | |
| 2566 | // aten::divide_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) |
| 2567 | at::Tensor & divide__Tensor::call(at::Tensor & self, const at::Tensor & other) { |
| 2568 | |
| 2569 | static auto op = create_divide__Tensor_typed_handle(); |
| 2570 | return op.call(self, other); |
| 2571 | } |
| 2572 | |
| 2573 | // aten::divide_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) |
| 2574 | at::Tensor & divide__Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 2575 | |
| 2576 | static auto op = create_divide__Tensor_typed_handle(); |
| 2577 | return op.redispatch(dispatchKeySet, self, other); |
| 2578 | } |
| 2579 | |
| 2580 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide_out, name, "aten::divide") |
| 2581 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide_out, overload_name, "out") |
| 2582 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide_out, schema_str, "divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)") |
| 2583 | |
| 2584 | // aten::divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 2585 | static C10_NOINLINE c10::TypedOperatorHandle<divide_out::schema> create_divide_out_typed_handle() { |
| 2586 | return c10::Dispatcher::singleton() |
| 2587 | .findSchemaOrThrow(divide_out::name, divide_out::overload_name) |
| 2588 | .typed<divide_out::schema>(); |
| 2589 | } |
| 2590 | |
| 2591 | // aten::divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 2592 | at::Tensor & divide_out::call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 2593 | |
| 2594 | static auto op = create_divide_out_typed_handle(); |
| 2595 | return op.call(self, other, out); |
| 2596 | } |
| 2597 | |
| 2598 | // aten::divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 2599 | at::Tensor & divide_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 2600 | |
| 2601 | static auto op = create_divide_out_typed_handle(); |
| 2602 | return op.redispatch(dispatchKeySet, self, other, out); |
| 2603 | } |
| 2604 | |
| 2605 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide_Scalar, name, "aten::divide") |
| 2606 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide_Scalar, overload_name, "Scalar") |
| 2607 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide_Scalar, schema_str, "divide.Scalar(Tensor self, Scalar other) -> Tensor") |
| 2608 | |
| 2609 | // aten::divide.Scalar(Tensor self, Scalar other) -> Tensor |
| 2610 | static C10_NOINLINE c10::TypedOperatorHandle<divide_Scalar::schema> create_divide_Scalar_typed_handle() { |
| 2611 | return c10::Dispatcher::singleton() |
| 2612 | .findSchemaOrThrow(divide_Scalar::name, divide_Scalar::overload_name) |
| 2613 | .typed<divide_Scalar::schema>(); |
| 2614 | } |
| 2615 | |
| 2616 | // aten::divide.Scalar(Tensor self, Scalar other) -> Tensor |
| 2617 | at::Tensor divide_Scalar::call(const at::Tensor & self, const at::Scalar & other) { |
| 2618 | |
| 2619 | static auto op = create_divide_Scalar_typed_handle(); |
| 2620 | return op.call(self, other); |
| 2621 | } |
| 2622 | |
| 2623 | // aten::divide.Scalar(Tensor self, Scalar other) -> Tensor |
| 2624 | at::Tensor divide_Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { |
| 2625 | |
| 2626 | static auto op = create_divide_Scalar_typed_handle(); |
| 2627 | return op.redispatch(dispatchKeySet, self, other); |
| 2628 | } |
| 2629 | |
| 2630 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide__Scalar, name, "aten::divide_") |
| 2631 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide__Scalar, overload_name, "Scalar") |
| 2632 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide__Scalar, schema_str, "divide_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)") |
| 2633 | |
| 2634 | // aten::divide_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) |
| 2635 | static C10_NOINLINE c10::TypedOperatorHandle<divide__Scalar::schema> create_divide__Scalar_typed_handle() { |
| 2636 | return c10::Dispatcher::singleton() |
| 2637 | .findSchemaOrThrow(divide__Scalar::name, divide__Scalar::overload_name) |
| 2638 | .typed<divide__Scalar::schema>(); |
| 2639 | } |
| 2640 | |
| 2641 | // aten::divide_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) |
| 2642 | at::Tensor & divide__Scalar::call(at::Tensor & self, const at::Scalar & other) { |
| 2643 | |
| 2644 | static auto op = create_divide__Scalar_typed_handle(); |
| 2645 | return op.call(self, other); |
| 2646 | } |
| 2647 | |
| 2648 | // aten::divide_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) |
| 2649 | at::Tensor & divide__Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { |
| 2650 | |
| 2651 | static auto op = create_divide__Scalar_typed_handle(); |
| 2652 | return op.redispatch(dispatchKeySet, self, other); |
| 2653 | } |
| 2654 | |
| 2655 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide_Tensor_mode, name, "aten::divide") |
| 2656 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide_Tensor_mode, overload_name, "Tensor_mode") |
| 2657 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide_Tensor_mode, schema_str, "divide.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> Tensor") |
| 2658 | |
| 2659 | // aten::divide.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> Tensor |
| 2660 | static C10_NOINLINE c10::TypedOperatorHandle<divide_Tensor_mode::schema> create_divide_Tensor_mode_typed_handle() { |
| 2661 | return c10::Dispatcher::singleton() |
| 2662 | .findSchemaOrThrow(divide_Tensor_mode::name, divide_Tensor_mode::overload_name) |
| 2663 | .typed<divide_Tensor_mode::schema>(); |
| 2664 | } |
| 2665 | |
| 2666 | // aten::divide.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> Tensor |
| 2667 | at::Tensor divide_Tensor_mode::call(const at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode) { |
| 2668 | |
| 2669 | static auto op = create_divide_Tensor_mode_typed_handle(); |
| 2670 | return op.call(self, other, rounding_mode); |
| 2671 | } |
| 2672 | |
| 2673 | // aten::divide.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> Tensor |
| 2674 | at::Tensor divide_Tensor_mode::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode) { |
| 2675 | |
| 2676 | static auto op = create_divide_Tensor_mode_typed_handle(); |
| 2677 | return op.redispatch(dispatchKeySet, self, other, rounding_mode); |
| 2678 | } |
| 2679 | |
| 2680 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide__Tensor_mode, name, "aten::divide_") |
| 2681 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide__Tensor_mode, overload_name, "Tensor_mode") |
| 2682 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide__Tensor_mode, schema_str, "divide_.Tensor_mode(Tensor(a!) self, Tensor other, *, str? rounding_mode) -> Tensor(a!)") |
| 2683 | |
| 2684 | // aten::divide_.Tensor_mode(Tensor(a!) self, Tensor other, *, str? rounding_mode) -> Tensor(a!) |
| 2685 | static C10_NOINLINE c10::TypedOperatorHandle<divide__Tensor_mode::schema> create_divide__Tensor_mode_typed_handle() { |
| 2686 | return c10::Dispatcher::singleton() |
| 2687 | .findSchemaOrThrow(divide__Tensor_mode::name, divide__Tensor_mode::overload_name) |
| 2688 | .typed<divide__Tensor_mode::schema>(); |
| 2689 | } |
| 2690 | |
| 2691 | // aten::divide_.Tensor_mode(Tensor(a!) self, Tensor other, *, str? rounding_mode) -> Tensor(a!) |
| 2692 | at::Tensor & divide__Tensor_mode::call(at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode) { |
| 2693 | |
| 2694 | static auto op = create_divide__Tensor_mode_typed_handle(); |
| 2695 | return op.call(self, other, rounding_mode); |
| 2696 | } |
| 2697 | |
| 2698 | // aten::divide_.Tensor_mode(Tensor(a!) self, Tensor other, *, str? rounding_mode) -> Tensor(a!) |
| 2699 | at::Tensor & divide__Tensor_mode::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode) { |
| 2700 | |
| 2701 | static auto op = create_divide__Tensor_mode_typed_handle(); |
| 2702 | return op.redispatch(dispatchKeySet, self, other, rounding_mode); |
| 2703 | } |
| 2704 | |
| 2705 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide_out_mode, name, "aten::divide") |
| 2706 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide_out_mode, overload_name, "out_mode") |
| 2707 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide_out_mode, schema_str, "divide.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!)") |
| 2708 | |
| 2709 | // aten::divide.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!) |
| 2710 | static C10_NOINLINE c10::TypedOperatorHandle<divide_out_mode::schema> create_divide_out_mode_typed_handle() { |
| 2711 | return c10::Dispatcher::singleton() |
| 2712 | .findSchemaOrThrow(divide_out_mode::name, divide_out_mode::overload_name) |
| 2713 | .typed<divide_out_mode::schema>(); |
| 2714 | } |
| 2715 | |
| 2716 | // aten::divide.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!) |
| 2717 | at::Tensor & divide_out_mode::call(const at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode, at::Tensor & out) { |
| 2718 | |
| 2719 | static auto op = create_divide_out_mode_typed_handle(); |
| 2720 | return op.call(self, other, rounding_mode, out); |
| 2721 | } |
| 2722 | |
| 2723 | // aten::divide.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!) |
| 2724 | at::Tensor & divide_out_mode::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode, at::Tensor & out) { |
| 2725 | |
| 2726 | static auto op = create_divide_out_mode_typed_handle(); |
| 2727 | return op.redispatch(dispatchKeySet, self, other, rounding_mode, out); |
| 2728 | } |
| 2729 | |
| 2730 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide_Scalar_mode, name, "aten::divide") |
| 2731 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide_Scalar_mode, overload_name, "Scalar_mode") |
| 2732 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide_Scalar_mode, schema_str, "divide.Scalar_mode(Tensor self, Scalar other, *, str? rounding_mode) -> Tensor") |
| 2733 | |
| 2734 | // aten::divide.Scalar_mode(Tensor self, Scalar other, *, str? rounding_mode) -> Tensor |
| 2735 | static C10_NOINLINE c10::TypedOperatorHandle<divide_Scalar_mode::schema> create_divide_Scalar_mode_typed_handle() { |
| 2736 | return c10::Dispatcher::singleton() |
| 2737 | .findSchemaOrThrow(divide_Scalar_mode::name, divide_Scalar_mode::overload_name) |
| 2738 | .typed<divide_Scalar_mode::schema>(); |
| 2739 | } |
| 2740 | |
| 2741 | // aten::divide.Scalar_mode(Tensor self, Scalar other, *, str? rounding_mode) -> Tensor |
| 2742 | at::Tensor divide_Scalar_mode::call(const at::Tensor & self, const at::Scalar & other, c10::optional<c10::string_view> rounding_mode) { |
| 2743 | |
| 2744 | static auto op = create_divide_Scalar_mode_typed_handle(); |
| 2745 | return op.call(self, other, rounding_mode); |
| 2746 | } |
| 2747 | |
| 2748 | // aten::divide.Scalar_mode(Tensor self, Scalar other, *, str? rounding_mode) -> Tensor |
| 2749 | at::Tensor divide_Scalar_mode::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, c10::optional<c10::string_view> rounding_mode) { |
| 2750 | |
| 2751 | static auto op = create_divide_Scalar_mode_typed_handle(); |
| 2752 | return op.redispatch(dispatchKeySet, self, other, rounding_mode); |
| 2753 | } |
| 2754 | |
| 2755 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide__Scalar_mode, name, "aten::divide_") |
| 2756 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide__Scalar_mode, overload_name, "Scalar_mode") |
| 2757 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(divide__Scalar_mode, schema_str, "divide_.Scalar_mode(Tensor(a!) self, Scalar other, *, str? rounding_mode) -> Tensor(a!)") |
| 2758 | |
| 2759 | // aten::divide_.Scalar_mode(Tensor(a!) self, Scalar other, *, str? rounding_mode) -> Tensor(a!) |
| 2760 | static C10_NOINLINE c10::TypedOperatorHandle<divide__Scalar_mode::schema> create_divide__Scalar_mode_typed_handle() { |
| 2761 | return c10::Dispatcher::singleton() |
| 2762 | .findSchemaOrThrow(divide__Scalar_mode::name, divide__Scalar_mode::overload_name) |
| 2763 | .typed<divide__Scalar_mode::schema>(); |
| 2764 | } |
| 2765 | |
| 2766 | // aten::divide_.Scalar_mode(Tensor(a!) self, Scalar other, *, str? rounding_mode) -> Tensor(a!) |
| 2767 | at::Tensor & divide__Scalar_mode::call(at::Tensor & self, const at::Scalar & other, c10::optional<c10::string_view> rounding_mode) { |
| 2768 | |
| 2769 | static auto op = create_divide__Scalar_mode_typed_handle(); |
| 2770 | return op.call(self, other, rounding_mode); |
| 2771 | } |
| 2772 | |
| 2773 | // aten::divide_.Scalar_mode(Tensor(a!) self, Scalar other, *, str? rounding_mode) -> Tensor(a!) |
| 2774 | at::Tensor & divide__Scalar_mode::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other, c10::optional<c10::string_view> rounding_mode) { |
| 2775 | |
| 2776 | static auto op = create_divide__Scalar_mode_typed_handle(); |
| 2777 | return op.redispatch(dispatchKeySet, self, other, rounding_mode); |
| 2778 | } |
| 2779 | |
| 2780 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_empty_affine_quantized, name, "aten::_empty_affine_quantized") |
| 2781 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_empty_affine_quantized, overload_name, "") |
| 2782 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_empty_affine_quantized, schema_str, "_empty_affine_quantized(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format) -> Tensor") |
| 2783 | |
| 2784 | // aten::_empty_affine_quantized(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format) -> Tensor |
| 2785 | static C10_NOINLINE c10::TypedOperatorHandle<_empty_affine_quantized::schema> create__empty_affine_quantized_typed_handle() { |
| 2786 | return c10::Dispatcher::singleton() |
| 2787 | .findSchemaOrThrow(_empty_affine_quantized::name, _empty_affine_quantized::overload_name) |
| 2788 | .typed<_empty_affine_quantized::schema>(); |
| 2789 | } |
| 2790 | |
| 2791 | // aten::_empty_affine_quantized(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format) -> Tensor |
| 2792 | at::Tensor _empty_affine_quantized::call(at::IntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory, double scale, int64_t zero_point, c10::optional<at::MemoryFormat> memory_format) { |
| 2793 | |
| 2794 | static auto op = create__empty_affine_quantized_typed_handle(); |
| 2795 | return op.call(size, dtype, layout, device, pin_memory, scale, zero_point, memory_format); |
| 2796 | } |
| 2797 | |
| 2798 | // aten::_empty_affine_quantized(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format) -> Tensor |
| 2799 | at::Tensor _empty_affine_quantized::redispatch(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory, double scale, int64_t zero_point, c10::optional<at::MemoryFormat> memory_format) { |
| 2800 | |
| 2801 | static auto op = create__empty_affine_quantized_typed_handle(); |
| 2802 | return op.redispatch(dispatchKeySet, size, dtype, layout, device, pin_memory, scale, zero_point, memory_format); |
| 2803 | } |
| 2804 | |
| 2805 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_resize_output_, name, "aten::_resize_output_") |
| 2806 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_resize_output_, overload_name, "") |
| 2807 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_resize_output_, schema_str, "_resize_output_(Tensor(a!) self, int[] size, Device device) -> Tensor(a!)") |
| 2808 | |
| 2809 | // aten::_resize_output_(Tensor(a!) self, int[] size, Device device) -> Tensor(a!) |
| 2810 | static C10_NOINLINE c10::TypedOperatorHandle<_resize_output_::schema> create__resize_output__typed_handle() { |
| 2811 | return c10::Dispatcher::singleton() |
| 2812 | .findSchemaOrThrow(_resize_output_::name, _resize_output_::overload_name) |
| 2813 | .typed<_resize_output_::schema>(); |
| 2814 | } |
| 2815 | |
| 2816 | // aten::_resize_output_(Tensor(a!) self, int[] size, Device device) -> Tensor(a!) |
| 2817 | const at::Tensor & _resize_output_::call(const at::Tensor & self, at::IntArrayRef size, at::Device device) { |
| 2818 | |
| 2819 | static auto op = create__resize_output__typed_handle(); |
| 2820 | return op.call(self, size, device); |
| 2821 | } |
| 2822 | |
| 2823 | // aten::_resize_output_(Tensor(a!) self, int[] size, Device device) -> Tensor(a!) |
| 2824 | const at::Tensor & _resize_output_::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::Device device) { |
| 2825 | |
| 2826 | static auto op = create__resize_output__typed_handle(); |
| 2827 | return op.redispatch(dispatchKeySet, self, size, device); |
| 2828 | } |
| 2829 | |
| 2830 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(empty_like, name, "aten::empty_like") |
| 2831 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(empty_like, overload_name, "") |
| 2832 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(empty_like, schema_str, "empty_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor") |
| 2833 | |
| 2834 | // aten::empty_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor |
| 2835 | static C10_NOINLINE c10::TypedOperatorHandle<empty_like::schema> create_empty_like_typed_handle() { |
| 2836 | return c10::Dispatcher::singleton() |
| 2837 | .findSchemaOrThrow(empty_like::name, empty_like::overload_name) |
| 2838 | .typed<empty_like::schema>(); |
| 2839 | } |
| 2840 | |
| 2841 | // aten::empty_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor |
| 2842 | at::Tensor empty_like::call(const at::Tensor & self, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory, c10::optional<at::MemoryFormat> memory_format) { |
| 2843 | |
| 2844 | static auto op = create_empty_like_typed_handle(); |
| 2845 | return op.call(self, dtype, layout, device, pin_memory, memory_format); |
| 2846 | } |
| 2847 | |
| 2848 | // aten::empty_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor |
| 2849 | at::Tensor empty_like::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory, c10::optional<at::MemoryFormat> memory_format) { |
| 2850 | |
| 2851 | static auto op = create_empty_like_typed_handle(); |
| 2852 | return op.redispatch(dispatchKeySet, self, dtype, layout, device, pin_memory, memory_format); |
| 2853 | } |
| 2854 | |
| 2855 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(expand, name, "aten::expand") |
| 2856 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(expand, overload_name, "") |
| 2857 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(expand, schema_str, "expand(Tensor(a) self, SymInt[] size, *, bool implicit=False) -> Tensor(a)") |
| 2858 | |
| 2859 | // aten::expand(Tensor(a) self, SymInt[] size, *, bool implicit=False) -> Tensor(a) |
| 2860 | static C10_NOINLINE c10::TypedOperatorHandle<expand::schema> create_expand_typed_handle() { |
| 2861 | return c10::Dispatcher::singleton() |
| 2862 | .findSchemaOrThrow(expand::name, expand::overload_name) |
| 2863 | .typed<expand::schema>(); |
| 2864 | } |
| 2865 | |
| 2866 | // aten::expand(Tensor(a) self, SymInt[] size, *, bool implicit=False) -> Tensor(a) |
| 2867 | at::Tensor expand::call(const at::Tensor & self, c10::SymIntArrayRef size, bool implicit) { |
| 2868 | |
| 2869 | static auto op = create_expand_typed_handle(); |
| 2870 | return op.call(self, size, implicit); |
| 2871 | } |
| 2872 | |
| 2873 | // aten::expand(Tensor(a) self, SymInt[] size, *, bool implicit=False) -> Tensor(a) |
| 2874 | at::Tensor expand::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, bool implicit) { |
| 2875 | |
| 2876 | static auto op = create_expand_typed_handle(); |
| 2877 | return op.redispatch(dispatchKeySet, self, size, implicit); |
| 2878 | } |
| 2879 | |
| 2880 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(flatten_using_ints, name, "aten::flatten") |
| 2881 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(flatten_using_ints, overload_name, "using_ints") |
| 2882 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(flatten_using_ints, schema_str, "flatten.using_ints(Tensor(a) self, int start_dim=0, int end_dim=-1) -> Tensor(a)") |
| 2883 | |
| 2884 | // aten::flatten.using_ints(Tensor(a) self, int start_dim=0, int end_dim=-1) -> Tensor(a) |
| 2885 | static C10_NOINLINE c10::TypedOperatorHandle<flatten_using_ints::schema> create_flatten_using_ints_typed_handle() { |
| 2886 | return c10::Dispatcher::singleton() |
| 2887 | .findSchemaOrThrow(flatten_using_ints::name, flatten_using_ints::overload_name) |
| 2888 | .typed<flatten_using_ints::schema>(); |
| 2889 | } |
| 2890 | |
| 2891 | // aten::flatten.using_ints(Tensor(a) self, int start_dim=0, int end_dim=-1) -> Tensor(a) |
| 2892 | at::Tensor flatten_using_ints::call(const at::Tensor & self, int64_t start_dim, int64_t end_dim) { |
| 2893 | |
| 2894 | static auto op = create_flatten_using_ints_typed_handle(); |
| 2895 | return op.call(self, start_dim, end_dim); |
| 2896 | } |
| 2897 | |
| 2898 | // aten::flatten.using_ints(Tensor(a) self, int start_dim=0, int end_dim=-1) -> Tensor(a) |
| 2899 | at::Tensor flatten_using_ints::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t start_dim, int64_t end_dim) { |
| 2900 | |
| 2901 | static auto op = create_flatten_using_ints_typed_handle(); |
| 2902 | return op.redispatch(dispatchKeySet, self, start_dim, end_dim); |
| 2903 | } |
| 2904 | |
| 2905 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(flatten_named_out_dim, name, "aten::flatten") |
| 2906 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(flatten_named_out_dim, overload_name, "named_out_dim") |
| 2907 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(flatten_named_out_dim, schema_str, "flatten.named_out_dim(Tensor(a) self, int start_dim, int end_dim, Dimname out_dim) -> Tensor(a)") |
| 2908 | |
| 2909 | // aten::flatten.named_out_dim(Tensor(a) self, int start_dim, int end_dim, Dimname out_dim) -> Tensor(a) |
| 2910 | static C10_NOINLINE c10::TypedOperatorHandle<flatten_named_out_dim::schema> create_flatten_named_out_dim_typed_handle() { |
| 2911 | return c10::Dispatcher::singleton() |
| 2912 | .findSchemaOrThrow(flatten_named_out_dim::name, flatten_named_out_dim::overload_name) |
| 2913 | .typed<flatten_named_out_dim::schema>(); |
| 2914 | } |
| 2915 | |
| 2916 | // aten::flatten.named_out_dim(Tensor(a) self, int start_dim, int end_dim, Dimname out_dim) -> Tensor(a) |
| 2917 | at::Tensor flatten_named_out_dim::call(const at::Tensor & self, int64_t start_dim, int64_t end_dim, at::Dimname out_dim) { |
| 2918 | |
| 2919 | static auto op = create_flatten_named_out_dim_typed_handle(); |
| 2920 | return op.call(self, start_dim, end_dim, out_dim); |
| 2921 | } |
| 2922 | |
| 2923 | // aten::flatten.named_out_dim(Tensor(a) self, int start_dim, int end_dim, Dimname out_dim) -> Tensor(a) |
| 2924 | at::Tensor flatten_named_out_dim::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t start_dim, int64_t end_dim, at::Dimname out_dim) { |
| 2925 | |
| 2926 | static auto op = create_flatten_named_out_dim_typed_handle(); |
| 2927 | return op.redispatch(dispatchKeySet, self, start_dim, end_dim, out_dim); |
| 2928 | } |
| 2929 | |
| 2930 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(flatten_using_names, name, "aten::flatten") |
| 2931 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(flatten_using_names, overload_name, "using_names") |
| 2932 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(flatten_using_names, schema_str, "flatten.using_names(Tensor(a) self, Dimname start_dim, Dimname end_dim, Dimname out_dim) -> Tensor(a)") |
| 2933 | |
| 2934 | // aten::flatten.using_names(Tensor(a) self, Dimname start_dim, Dimname end_dim, Dimname out_dim) -> Tensor(a) |
| 2935 | static C10_NOINLINE c10::TypedOperatorHandle<flatten_using_names::schema> create_flatten_using_names_typed_handle() { |
| 2936 | return c10::Dispatcher::singleton() |
| 2937 | .findSchemaOrThrow(flatten_using_names::name, flatten_using_names::overload_name) |
| 2938 | .typed<flatten_using_names::schema>(); |
| 2939 | } |
| 2940 | |
| 2941 | // aten::flatten.using_names(Tensor(a) self, Dimname start_dim, Dimname end_dim, Dimname out_dim) -> Tensor(a) |
| 2942 | at::Tensor flatten_using_names::call(const at::Tensor & self, at::Dimname start_dim, at::Dimname end_dim, at::Dimname out_dim) { |
| 2943 | |
| 2944 | static auto op = create_flatten_using_names_typed_handle(); |
| 2945 | return op.call(self, start_dim, end_dim, out_dim); |
| 2946 | } |
| 2947 | |
| 2948 | // aten::flatten.using_names(Tensor(a) self, Dimname start_dim, Dimname end_dim, Dimname out_dim) -> Tensor(a) |
| 2949 | at::Tensor flatten_using_names::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname start_dim, at::Dimname end_dim, at::Dimname out_dim) { |
| 2950 | |
| 2951 | static auto op = create_flatten_using_names_typed_handle(); |
| 2952 | return op.redispatch(dispatchKeySet, self, start_dim, end_dim, out_dim); |
| 2953 | } |
| 2954 | |
| 2955 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(flatten_DimnameList, name, "aten::flatten") |
| 2956 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(flatten_DimnameList, overload_name, "DimnameList") |
| 2957 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(flatten_DimnameList, schema_str, "flatten.DimnameList(Tensor(a) self, Dimname[] dims, Dimname out_dim) -> Tensor(a)") |
| 2958 | |
| 2959 | // aten::flatten.DimnameList(Tensor(a) self, Dimname[] dims, Dimname out_dim) -> Tensor(a) |
| 2960 | static C10_NOINLINE c10::TypedOperatorHandle<flatten_DimnameList::schema> create_flatten_DimnameList_typed_handle() { |
| 2961 | return c10::Dispatcher::singleton() |
| 2962 | .findSchemaOrThrow(flatten_DimnameList::name, flatten_DimnameList::overload_name) |
| 2963 | .typed<flatten_DimnameList::schema>(); |
| 2964 | } |
| 2965 | |
| 2966 | // aten::flatten.DimnameList(Tensor(a) self, Dimname[] dims, Dimname out_dim) -> Tensor(a) |
| 2967 | at::Tensor flatten_DimnameList::call(const at::Tensor & self, at::DimnameList dims, at::Dimname out_dim) { |
| 2968 | |
| 2969 | static auto op = create_flatten_DimnameList_typed_handle(); |
| 2970 | return op.call(self, dims, out_dim); |
| 2971 | } |
| 2972 | |
| 2973 | // aten::flatten.DimnameList(Tensor(a) self, Dimname[] dims, Dimname out_dim) -> Tensor(a) |
| 2974 | at::Tensor flatten_DimnameList::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dims, at::Dimname out_dim) { |
| 2975 | |
| 2976 | static auto op = create_flatten_DimnameList_typed_handle(); |
| 2977 | return op.redispatch(dispatchKeySet, self, dims, out_dim); |
| 2978 | } |
| 2979 | |
| 2980 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(floor, name, "aten::floor") |
| 2981 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(floor, overload_name, "") |
| 2982 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(floor, schema_str, "floor(Tensor self) -> Tensor") |
| 2983 | |
| 2984 | // aten::floor(Tensor self) -> Tensor |
| 2985 | static C10_NOINLINE c10::TypedOperatorHandle<floor::schema> create_floor_typed_handle() { |
| 2986 | return c10::Dispatcher::singleton() |
| 2987 | .findSchemaOrThrow(floor::name, floor::overload_name) |
| 2988 | .typed<floor::schema>(); |
| 2989 | } |
| 2990 | |
| 2991 | // aten::floor(Tensor self) -> Tensor |
| 2992 | at::Tensor floor::call(const at::Tensor & self) { |
| 2993 | |
| 2994 | static auto op = create_floor_typed_handle(); |
| 2995 | return op.call(self); |
| 2996 | } |
| 2997 | |
| 2998 | // aten::floor(Tensor self) -> Tensor |
| 2999 | at::Tensor floor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 3000 | |
| 3001 | static auto op = create_floor_typed_handle(); |
| 3002 | return op.redispatch(dispatchKeySet, self); |
| 3003 | } |
| 3004 | |
| 3005 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(floor_, name, "aten::floor_") |
| 3006 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(floor_, overload_name, "") |
| 3007 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(floor_, schema_str, "floor_(Tensor(a!) self) -> Tensor(a!)") |
| 3008 | |
| 3009 | // aten::floor_(Tensor(a!) self) -> Tensor(a!) |
| 3010 | static C10_NOINLINE c10::TypedOperatorHandle<floor_::schema> create_floor__typed_handle() { |
| 3011 | return c10::Dispatcher::singleton() |
| 3012 | .findSchemaOrThrow(floor_::name, floor_::overload_name) |
| 3013 | .typed<floor_::schema>(); |
| 3014 | } |
| 3015 | |
| 3016 | // aten::floor_(Tensor(a!) self) -> Tensor(a!) |
| 3017 | at::Tensor & floor_::call(at::Tensor & self) { |
| 3018 | |
| 3019 | static auto op = create_floor__typed_handle(); |
| 3020 | return op.call(self); |
| 3021 | } |
| 3022 | |
| 3023 | // aten::floor_(Tensor(a!) self) -> Tensor(a!) |
| 3024 | at::Tensor & floor_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 3025 | |
| 3026 | static auto op = create_floor__typed_handle(); |
| 3027 | return op.redispatch(dispatchKeySet, self); |
| 3028 | } |
| 3029 | |
| 3030 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(floor_out, name, "aten::floor") |
| 3031 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(floor_out, overload_name, "out") |
| 3032 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(floor_out, schema_str, "floor.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 3033 | |
| 3034 | // aten::floor.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 3035 | static C10_NOINLINE c10::TypedOperatorHandle<floor_out::schema> create_floor_out_typed_handle() { |
| 3036 | return c10::Dispatcher::singleton() |
| 3037 | .findSchemaOrThrow(floor_out::name, floor_out::overload_name) |
| 3038 | .typed<floor_out::schema>(); |
| 3039 | } |
| 3040 | |
| 3041 | // aten::floor.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 3042 | at::Tensor & floor_out::call(const at::Tensor & self, at::Tensor & out) { |
| 3043 | |
| 3044 | static auto op = create_floor_out_typed_handle(); |
| 3045 | return op.call(self, out); |
| 3046 | } |
| 3047 | |
| 3048 | // aten::floor.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 3049 | at::Tensor & floor_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 3050 | |
| 3051 | static auto op = create_floor_out_typed_handle(); |
| 3052 | return op.redispatch(dispatchKeySet, self, out); |
| 3053 | } |
| 3054 | |
| 3055 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(grid_sampler_3d_backward, name, "aten::grid_sampler_3d_backward") |
| 3056 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(grid_sampler_3d_backward, overload_name, "") |
| 3057 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(grid_sampler_3d_backward, schema_str, "grid_sampler_3d_backward(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask) -> (Tensor, Tensor)") |
| 3058 | |
| 3059 | // aten::grid_sampler_3d_backward(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask) -> (Tensor, Tensor) |
| 3060 | static C10_NOINLINE c10::TypedOperatorHandle<grid_sampler_3d_backward::schema> create_grid_sampler_3d_backward_typed_handle() { |
| 3061 | return c10::Dispatcher::singleton() |
| 3062 | .findSchemaOrThrow(grid_sampler_3d_backward::name, grid_sampler_3d_backward::overload_name) |
| 3063 | .typed<grid_sampler_3d_backward::schema>(); |
| 3064 | } |
| 3065 | |
| 3066 | // aten::grid_sampler_3d_backward(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask) -> (Tensor, Tensor) |
| 3067 | ::std::tuple<at::Tensor,at::Tensor> grid_sampler_3d_backward::call(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array<bool,2> output_mask) { |
| 3068 | |
| 3069 | static auto op = create_grid_sampler_3d_backward_typed_handle(); |
| 3070 | return op.call(grad_output, input, grid, interpolation_mode, padding_mode, align_corners, output_mask); |
| 3071 | } |
| 3072 | |
| 3073 | // aten::grid_sampler_3d_backward(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask) -> (Tensor, Tensor) |
| 3074 | ::std::tuple<at::Tensor,at::Tensor> grid_sampler_3d_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array<bool,2> output_mask) { |
| 3075 | |
| 3076 | static auto op = create_grid_sampler_3d_backward_typed_handle(); |
| 3077 | return op.redispatch(dispatchKeySet, grad_output, input, grid, interpolation_mode, padding_mode, align_corners, output_mask); |
| 3078 | } |
| 3079 | |
| 3080 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hinge_embedding_loss, name, "aten::hinge_embedding_loss") |
| 3081 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hinge_embedding_loss, overload_name, "") |
| 3082 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hinge_embedding_loss, schema_str, "hinge_embedding_loss(Tensor self, Tensor target, float margin=1.0, int reduction=Mean) -> Tensor") |
| 3083 | |
| 3084 | // aten::hinge_embedding_loss(Tensor self, Tensor target, float margin=1.0, int reduction=Mean) -> Tensor |
| 3085 | static C10_NOINLINE c10::TypedOperatorHandle<hinge_embedding_loss::schema> create_hinge_embedding_loss_typed_handle() { |
| 3086 | return c10::Dispatcher::singleton() |
| 3087 | .findSchemaOrThrow(hinge_embedding_loss::name, hinge_embedding_loss::overload_name) |
| 3088 | .typed<hinge_embedding_loss::schema>(); |
| 3089 | } |
| 3090 | |
| 3091 | // aten::hinge_embedding_loss(Tensor self, Tensor target, float margin=1.0, int reduction=Mean) -> Tensor |
| 3092 | at::Tensor hinge_embedding_loss::call(const at::Tensor & self, const at::Tensor & target, double margin, int64_t reduction) { |
| 3093 | |
| 3094 | static auto op = create_hinge_embedding_loss_typed_handle(); |
| 3095 | return op.call(self, target, margin, reduction); |
| 3096 | } |
| 3097 | |
| 3098 | // aten::hinge_embedding_loss(Tensor self, Tensor target, float margin=1.0, int reduction=Mean) -> Tensor |
| 3099 | at::Tensor hinge_embedding_loss::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, double margin, int64_t reduction) { |
| 3100 | |
| 3101 | static auto op = create_hinge_embedding_loss_typed_handle(); |
| 3102 | return op.redispatch(dispatchKeySet, self, target, margin, reduction); |
| 3103 | } |
| 3104 | |
| 3105 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_group_norm, name, "aten::native_group_norm") |
| 3106 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_group_norm, overload_name, "") |
| 3107 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_group_norm, schema_str, "native_group_norm(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps) -> (Tensor, Tensor, Tensor)") |
| 3108 | |
| 3109 | // aten::native_group_norm(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps) -> (Tensor, Tensor, Tensor) |
| 3110 | static C10_NOINLINE c10::TypedOperatorHandle<native_group_norm::schema> create_native_group_norm_typed_handle() { |
| 3111 | return c10::Dispatcher::singleton() |
| 3112 | .findSchemaOrThrow(native_group_norm::name, native_group_norm::overload_name) |
| 3113 | .typed<native_group_norm::schema>(); |
| 3114 | } |
| 3115 | |
| 3116 | // aten::native_group_norm(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps) -> (Tensor, Tensor, Tensor) |
| 3117 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_group_norm::call(const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, double eps) { |
| 3118 | |
| 3119 | static auto op = create_native_group_norm_typed_handle(); |
| 3120 | return op.call(input, weight, bias, N, C, HxW, group, eps); |
| 3121 | } |
| 3122 | |
| 3123 | // aten::native_group_norm(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps) -> (Tensor, Tensor, Tensor) |
| 3124 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_group_norm::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, double eps) { |
| 3125 | |
| 3126 | static auto op = create_native_group_norm_typed_handle(); |
| 3127 | return op.redispatch(dispatchKeySet, input, weight, bias, N, C, HxW, group, eps); |
| 3128 | } |
| 3129 | |
| 3130 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fft_r2c, name, "aten::_fft_r2c") |
| 3131 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fft_r2c, overload_name, "") |
| 3132 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fft_r2c, schema_str, "_fft_r2c(Tensor self, int[] dim, int normalization, bool onesided) -> Tensor") |
| 3133 | |
| 3134 | // aten::_fft_r2c(Tensor self, int[] dim, int normalization, bool onesided) -> Tensor |
| 3135 | static C10_NOINLINE c10::TypedOperatorHandle<_fft_r2c::schema> create__fft_r2c_typed_handle() { |
| 3136 | return c10::Dispatcher::singleton() |
| 3137 | .findSchemaOrThrow(_fft_r2c::name, _fft_r2c::overload_name) |
| 3138 | .typed<_fft_r2c::schema>(); |
| 3139 | } |
| 3140 | |
| 3141 | // aten::_fft_r2c(Tensor self, int[] dim, int normalization, bool onesided) -> Tensor |
| 3142 | at::Tensor _fft_r2c::call(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool onesided) { |
| 3143 | |
| 3144 | static auto op = create__fft_r2c_typed_handle(); |
| 3145 | return op.call(self, dim, normalization, onesided); |
| 3146 | } |
| 3147 | |
| 3148 | // aten::_fft_r2c(Tensor self, int[] dim, int normalization, bool onesided) -> Tensor |
| 3149 | at::Tensor _fft_r2c::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool onesided) { |
| 3150 | |
| 3151 | static auto op = create__fft_r2c_typed_handle(); |
| 3152 | return op.redispatch(dispatchKeySet, self, dim, normalization, onesided); |
| 3153 | } |
| 3154 | |
| 3155 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fft_r2c_out, name, "aten::_fft_r2c") |
| 3156 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fft_r2c_out, overload_name, "out") |
| 3157 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fft_r2c_out, schema_str, "_fft_r2c.out(Tensor self, int[] dim, int normalization, bool onesided, *, Tensor(a!) out) -> Tensor(a!)") |
| 3158 | |
| 3159 | // aten::_fft_r2c.out(Tensor self, int[] dim, int normalization, bool onesided, *, Tensor(a!) out) -> Tensor(a!) |
| 3160 | static C10_NOINLINE c10::TypedOperatorHandle<_fft_r2c_out::schema> create__fft_r2c_out_typed_handle() { |
| 3161 | return c10::Dispatcher::singleton() |
| 3162 | .findSchemaOrThrow(_fft_r2c_out::name, _fft_r2c_out::overload_name) |
| 3163 | .typed<_fft_r2c_out::schema>(); |
| 3164 | } |
| 3165 | |
| 3166 | // aten::_fft_r2c.out(Tensor self, int[] dim, int normalization, bool onesided, *, Tensor(a!) out) -> Tensor(a!) |
| 3167 | at::Tensor & _fft_r2c_out::call(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool onesided, at::Tensor & out) { |
| 3168 | |
| 3169 | static auto op = create__fft_r2c_out_typed_handle(); |
| 3170 | return op.call(self, dim, normalization, onesided, out); |
| 3171 | } |
| 3172 | |
| 3173 | // aten::_fft_r2c.out(Tensor self, int[] dim, int normalization, bool onesided, *, Tensor(a!) out) -> Tensor(a!) |
| 3174 | at::Tensor & _fft_r2c_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool onesided, at::Tensor & out) { |
| 3175 | |
| 3176 | static auto op = create__fft_r2c_out_typed_handle(); |
| 3177 | return op.redispatch(dispatchKeySet, self, dim, normalization, onesided, out); |
| 3178 | } |
| 3179 | |
| 3180 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(is_neg, name, "aten::is_neg") |
| 3181 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(is_neg, overload_name, "") |
| 3182 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(is_neg, schema_str, "is_neg(Tensor self) -> bool") |
| 3183 | |
| 3184 | // aten::is_neg(Tensor self) -> bool |
| 3185 | static C10_NOINLINE c10::TypedOperatorHandle<is_neg::schema> create_is_neg_typed_handle() { |
| 3186 | return c10::Dispatcher::singleton() |
| 3187 | .findSchemaOrThrow(is_neg::name, is_neg::overload_name) |
| 3188 | .typed<is_neg::schema>(); |
| 3189 | } |
| 3190 | |
| 3191 | // aten::is_neg(Tensor self) -> bool |
| 3192 | bool is_neg::call(const at::Tensor & self) { |
| 3193 | |
| 3194 | static auto op = create_is_neg_typed_handle(); |
| 3195 | return op.call(self); |
| 3196 | } |
| 3197 | |
| 3198 | // aten::is_neg(Tensor self) -> bool |
| 3199 | bool is_neg::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 3200 | |
| 3201 | static auto op = create_is_neg_typed_handle(); |
| 3202 | return op.redispatch(dispatchKeySet, self); |
| 3203 | } |
| 3204 | |
| 3205 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(isreal, name, "aten::isreal") |
| 3206 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(isreal, overload_name, "") |
| 3207 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(isreal, schema_str, "isreal(Tensor self) -> Tensor") |
| 3208 | |
| 3209 | // aten::isreal(Tensor self) -> Tensor |
| 3210 | static C10_NOINLINE c10::TypedOperatorHandle<isreal::schema> create_isreal_typed_handle() { |
| 3211 | return c10::Dispatcher::singleton() |
| 3212 | .findSchemaOrThrow(isreal::name, isreal::overload_name) |
| 3213 | .typed<isreal::schema>(); |
| 3214 | } |
| 3215 | |
| 3216 | // aten::isreal(Tensor self) -> Tensor |
| 3217 | at::Tensor isreal::call(const at::Tensor & self) { |
| 3218 | |
| 3219 | static auto op = create_isreal_typed_handle(); |
| 3220 | return op.call(self); |
| 3221 | } |
| 3222 | |
| 3223 | // aten::isreal(Tensor self) -> Tensor |
| 3224 | at::Tensor isreal::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 3225 | |
| 3226 | static auto op = create_isreal_typed_handle(); |
| 3227 | return op.redispatch(dispatchKeySet, self); |
| 3228 | } |
| 3229 | |
| 3230 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linear_backward, name, "aten::linear_backward") |
| 3231 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linear_backward, overload_name, "") |
| 3232 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linear_backward, schema_str, "linear_backward(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask) -> (Tensor, Tensor, Tensor)") |
| 3233 | |
| 3234 | // aten::linear_backward(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask) -> (Tensor, Tensor, Tensor) |
| 3235 | static C10_NOINLINE c10::TypedOperatorHandle<linear_backward::schema> create_linear_backward_typed_handle() { |
| 3236 | return c10::Dispatcher::singleton() |
| 3237 | .findSchemaOrThrow(linear_backward::name, linear_backward::overload_name) |
| 3238 | .typed<linear_backward::schema>(); |
| 3239 | } |
| 3240 | |
| 3241 | // aten::linear_backward(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask) -> (Tensor, Tensor, Tensor) |
| 3242 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> linear_backward::call(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, ::std::array<bool,3> output_mask) { |
| 3243 | |
| 3244 | static auto op = create_linear_backward_typed_handle(); |
| 3245 | return op.call(self, grad_output, weight, output_mask); |
| 3246 | } |
| 3247 | |
| 3248 | // aten::linear_backward(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask) -> (Tensor, Tensor, Tensor) |
| 3249 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> linear_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, ::std::array<bool,3> output_mask) { |
| 3250 | |
| 3251 | static auto op = create_linear_backward_typed_handle(); |
| 3252 | return op.redispatch(dispatchKeySet, self, grad_output, weight, output_mask); |
| 3253 | } |
| 3254 | |
| 3255 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_linear_backward_input, name, "aten::mkldnn_linear_backward_input") |
| 3256 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_linear_backward_input, overload_name, "") |
| 3257 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_linear_backward_input, schema_str, "mkldnn_linear_backward_input(int[] input_size, Tensor grad_output, Tensor weight) -> Tensor") |
| 3258 | |
| 3259 | // aten::mkldnn_linear_backward_input(int[] input_size, Tensor grad_output, Tensor weight) -> Tensor |
| 3260 | static C10_NOINLINE c10::TypedOperatorHandle<mkldnn_linear_backward_input::schema> create_mkldnn_linear_backward_input_typed_handle() { |
| 3261 | return c10::Dispatcher::singleton() |
| 3262 | .findSchemaOrThrow(mkldnn_linear_backward_input::name, mkldnn_linear_backward_input::overload_name) |
| 3263 | .typed<mkldnn_linear_backward_input::schema>(); |
| 3264 | } |
| 3265 | |
| 3266 | // aten::mkldnn_linear_backward_input(int[] input_size, Tensor grad_output, Tensor weight) -> Tensor |
| 3267 | at::Tensor mkldnn_linear_backward_input::call(at::IntArrayRef input_size, const at::Tensor & grad_output, const at::Tensor & weight) { |
| 3268 | |
| 3269 | static auto op = create_mkldnn_linear_backward_input_typed_handle(); |
| 3270 | return op.call(input_size, grad_output, weight); |
| 3271 | } |
| 3272 | |
| 3273 | // aten::mkldnn_linear_backward_input(int[] input_size, Tensor grad_output, Tensor weight) -> Tensor |
| 3274 | at::Tensor mkldnn_linear_backward_input::redispatch(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef input_size, const at::Tensor & grad_output, const at::Tensor & weight) { |
| 3275 | |
| 3276 | static auto op = create_mkldnn_linear_backward_input_typed_handle(); |
| 3277 | return op.redispatch(dispatchKeySet, input_size, grad_output, weight); |
| 3278 | } |
| 3279 | |
| 3280 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_linear_backward, name, "aten::mkldnn_linear_backward") |
| 3281 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_linear_backward, overload_name, "") |
| 3282 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_linear_backward, schema_str, "mkldnn_linear_backward(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask) -> (Tensor, Tensor, Tensor)") |
| 3283 | |
| 3284 | // aten::mkldnn_linear_backward(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask) -> (Tensor, Tensor, Tensor) |
| 3285 | static C10_NOINLINE c10::TypedOperatorHandle<mkldnn_linear_backward::schema> create_mkldnn_linear_backward_typed_handle() { |
| 3286 | return c10::Dispatcher::singleton() |
| 3287 | .findSchemaOrThrow(mkldnn_linear_backward::name, mkldnn_linear_backward::overload_name) |
| 3288 | .typed<mkldnn_linear_backward::schema>(); |
| 3289 | } |
| 3290 | |
| 3291 | // aten::mkldnn_linear_backward(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask) -> (Tensor, Tensor, Tensor) |
| 3292 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> mkldnn_linear_backward::call(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, ::std::array<bool,3> output_mask) { |
| 3293 | |
| 3294 | static auto op = create_mkldnn_linear_backward_typed_handle(); |
| 3295 | return op.call(self, grad_output, weight, output_mask); |
| 3296 | } |
| 3297 | |
| 3298 | // aten::mkldnn_linear_backward(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask) -> (Tensor, Tensor, Tensor) |
| 3299 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> mkldnn_linear_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, ::std::array<bool,3> output_mask) { |
| 3300 | |
| 3301 | static auto op = create_mkldnn_linear_backward_typed_handle(); |
| 3302 | return op.redispatch(dispatchKeySet, self, grad_output, weight, output_mask); |
| 3303 | } |
| 3304 | |
| 3305 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_logcumsumexp, name, "aten::_logcumsumexp") |
| 3306 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_logcumsumexp, overload_name, "") |
| 3307 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_logcumsumexp, schema_str, "_logcumsumexp(Tensor self, int dim) -> Tensor") |
| 3308 | |
| 3309 | // aten::_logcumsumexp(Tensor self, int dim) -> Tensor |
| 3310 | static C10_NOINLINE c10::TypedOperatorHandle<_logcumsumexp::schema> create__logcumsumexp_typed_handle() { |
| 3311 | return c10::Dispatcher::singleton() |
| 3312 | .findSchemaOrThrow(_logcumsumexp::name, _logcumsumexp::overload_name) |
| 3313 | .typed<_logcumsumexp::schema>(); |
| 3314 | } |
| 3315 | |
| 3316 | // aten::_logcumsumexp(Tensor self, int dim) -> Tensor |
| 3317 | at::Tensor _logcumsumexp::call(const at::Tensor & self, int64_t dim) { |
| 3318 | |
| 3319 | static auto op = create__logcumsumexp_typed_handle(); |
| 3320 | return op.call(self, dim); |
| 3321 | } |
| 3322 | |
| 3323 | // aten::_logcumsumexp(Tensor self, int dim) -> Tensor |
| 3324 | at::Tensor _logcumsumexp::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim) { |
| 3325 | |
| 3326 | static auto op = create__logcumsumexp_typed_handle(); |
| 3327 | return op.redispatch(dispatchKeySet, self, dim); |
| 3328 | } |
| 3329 | |
| 3330 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_logcumsumexp_out, name, "aten::_logcumsumexp") |
| 3331 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_logcumsumexp_out, overload_name, "out") |
| 3332 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_logcumsumexp_out, schema_str, "_logcumsumexp.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)") |
| 3333 | |
| 3334 | // aten::_logcumsumexp.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!) |
| 3335 | static C10_NOINLINE c10::TypedOperatorHandle<_logcumsumexp_out::schema> create__logcumsumexp_out_typed_handle() { |
| 3336 | return c10::Dispatcher::singleton() |
| 3337 | .findSchemaOrThrow(_logcumsumexp_out::name, _logcumsumexp_out::overload_name) |
| 3338 | .typed<_logcumsumexp_out::schema>(); |
| 3339 | } |
| 3340 | |
| 3341 | // aten::_logcumsumexp.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!) |
| 3342 | at::Tensor & _logcumsumexp_out::call(const at::Tensor & self, int64_t dim, at::Tensor & out) { |
| 3343 | |
| 3344 | static auto op = create__logcumsumexp_out_typed_handle(); |
| 3345 | return op.call(self, dim, out); |
| 3346 | } |
| 3347 | |
| 3348 | // aten::_logcumsumexp.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!) |
| 3349 | at::Tensor & _logcumsumexp_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, at::Tensor & out) { |
| 3350 | |
| 3351 | static auto op = create__logcumsumexp_out_typed_handle(); |
| 3352 | return op.redispatch(dispatchKeySet, self, dim, out); |
| 3353 | } |
| 3354 | |
| 3355 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(value_selecting_reduction_backward, name, "aten::value_selecting_reduction_backward") |
| 3356 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(value_selecting_reduction_backward, overload_name, "") |
| 3357 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(value_selecting_reduction_backward, schema_str, "value_selecting_reduction_backward(Tensor grad, int dim, Tensor indices, SymInt[] sizes, bool keepdim) -> Tensor") |
| 3358 | |
| 3359 | // aten::value_selecting_reduction_backward(Tensor grad, int dim, Tensor indices, SymInt[] sizes, bool keepdim) -> Tensor |
| 3360 | static C10_NOINLINE c10::TypedOperatorHandle<value_selecting_reduction_backward::schema> create_value_selecting_reduction_backward_typed_handle() { |
| 3361 | return c10::Dispatcher::singleton() |
| 3362 | .findSchemaOrThrow(value_selecting_reduction_backward::name, value_selecting_reduction_backward::overload_name) |
| 3363 | .typed<value_selecting_reduction_backward::schema>(); |
| 3364 | } |
| 3365 | |
| 3366 | // aten::value_selecting_reduction_backward(Tensor grad, int dim, Tensor indices, SymInt[] sizes, bool keepdim) -> Tensor |
| 3367 | at::Tensor value_selecting_reduction_backward::call(const at::Tensor & grad, int64_t dim, const at::Tensor & indices, c10::SymIntArrayRef sizes, bool keepdim) { |
| 3368 | |
| 3369 | static auto op = create_value_selecting_reduction_backward_typed_handle(); |
| 3370 | return op.call(grad, dim, indices, sizes, keepdim); |
| 3371 | } |
| 3372 | |
| 3373 | // aten::value_selecting_reduction_backward(Tensor grad, int dim, Tensor indices, SymInt[] sizes, bool keepdim) -> Tensor |
| 3374 | at::Tensor value_selecting_reduction_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, int64_t dim, const at::Tensor & indices, c10::SymIntArrayRef sizes, bool keepdim) { |
| 3375 | |
| 3376 | static auto op = create_value_selecting_reduction_backward_typed_handle(); |
| 3377 | return op.redispatch(dispatchKeySet, grad, dim, indices, sizes, keepdim); |
| 3378 | } |
| 3379 | |
| 3380 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(max_pool1d, name, "aten::max_pool1d") |
| 3381 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(max_pool1d, overload_name, "") |
| 3382 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(max_pool1d, schema_str, "max_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, int[1] dilation=1, bool ceil_mode=False) -> Tensor") |
| 3383 | |
| 3384 | // aten::max_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, int[1] dilation=1, bool ceil_mode=False) -> Tensor |
| 3385 | static C10_NOINLINE c10::TypedOperatorHandle<max_pool1d::schema> create_max_pool1d_typed_handle() { |
| 3386 | return c10::Dispatcher::singleton() |
| 3387 | .findSchemaOrThrow(max_pool1d::name, max_pool1d::overload_name) |
| 3388 | .typed<max_pool1d::schema>(); |
| 3389 | } |
| 3390 | |
| 3391 | // aten::max_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, int[1] dilation=1, bool ceil_mode=False) -> Tensor |
| 3392 | at::Tensor max_pool1d::call(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { |
| 3393 | |
| 3394 | static auto op = create_max_pool1d_typed_handle(); |
| 3395 | return op.call(self, kernel_size, stride, padding, dilation, ceil_mode); |
| 3396 | } |
| 3397 | |
| 3398 | // aten::max_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, int[1] dilation=1, bool ceil_mode=False) -> Tensor |
| 3399 | at::Tensor max_pool1d::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { |
| 3400 | |
| 3401 | static auto op = create_max_pool1d_typed_handle(); |
| 3402 | return op.redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode); |
| 3403 | } |
| 3404 | |
| 3405 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(max_pool2d, name, "aten::max_pool2d") |
| 3406 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(max_pool2d, overload_name, "") |
| 3407 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(max_pool2d, schema_str, "max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor") |
| 3408 | |
| 3409 | // aten::max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor |
| 3410 | static C10_NOINLINE c10::TypedOperatorHandle<max_pool2d::schema> create_max_pool2d_typed_handle() { |
| 3411 | return c10::Dispatcher::singleton() |
| 3412 | .findSchemaOrThrow(max_pool2d::name, max_pool2d::overload_name) |
| 3413 | .typed<max_pool2d::schema>(); |
| 3414 | } |
| 3415 | |
| 3416 | // aten::max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor |
| 3417 | at::Tensor max_pool2d::call(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { |
| 3418 | |
| 3419 | static auto op = create_max_pool2d_typed_handle(); |
| 3420 | return op.call(self, kernel_size, stride, padding, dilation, ceil_mode); |
| 3421 | } |
| 3422 | |
| 3423 | // aten::max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor |
| 3424 | at::Tensor max_pool2d::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { |
| 3425 | |
| 3426 | static auto op = create_max_pool2d_typed_handle(); |
| 3427 | return op.redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode); |
| 3428 | } |
| 3429 | |
| 3430 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mean, name, "aten::mean") |
| 3431 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mean, overload_name, "") |
| 3432 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mean, schema_str, "mean(Tensor self, *, ScalarType? dtype=None) -> Tensor") |
| 3433 | |
| 3434 | // aten::mean(Tensor self, *, ScalarType? dtype=None) -> Tensor |
| 3435 | static C10_NOINLINE c10::TypedOperatorHandle<mean::schema> create_mean_typed_handle() { |
| 3436 | return c10::Dispatcher::singleton() |
| 3437 | .findSchemaOrThrow(mean::name, mean::overload_name) |
| 3438 | .typed<mean::schema>(); |
| 3439 | } |
| 3440 | |
| 3441 | // aten::mean(Tensor self, *, ScalarType? dtype=None) -> Tensor |
| 3442 | at::Tensor mean::call(const at::Tensor & self, c10::optional<at::ScalarType> dtype) { |
| 3443 | |
| 3444 | static auto op = create_mean_typed_handle(); |
| 3445 | return op.call(self, dtype); |
| 3446 | } |
| 3447 | |
| 3448 | // aten::mean(Tensor self, *, ScalarType? dtype=None) -> Tensor |
| 3449 | at::Tensor mean::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::ScalarType> dtype) { |
| 3450 | |
| 3451 | static auto op = create_mean_typed_handle(); |
| 3452 | return op.redispatch(dispatchKeySet, self, dtype); |
| 3453 | } |
| 3454 | |
| 3455 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mean_dim, name, "aten::mean") |
| 3456 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mean_dim, overload_name, "dim") |
| 3457 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mean_dim, schema_str, "mean.dim(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor") |
| 3458 | |
| 3459 | // aten::mean.dim(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor |
| 3460 | static C10_NOINLINE c10::TypedOperatorHandle<mean_dim::schema> create_mean_dim_typed_handle() { |
| 3461 | return c10::Dispatcher::singleton() |
| 3462 | .findSchemaOrThrow(mean_dim::name, mean_dim::overload_name) |
| 3463 | .typed<mean_dim::schema>(); |
| 3464 | } |
| 3465 | |
| 3466 | // aten::mean.dim(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor |
| 3467 | at::Tensor mean_dim::call(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 3468 | |
| 3469 | static auto op = create_mean_dim_typed_handle(); |
| 3470 | return op.call(self, dim, keepdim, dtype); |
| 3471 | } |
| 3472 | |
| 3473 | // aten::mean.dim(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor |
| 3474 | at::Tensor mean_dim::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 3475 | |
| 3476 | static auto op = create_mean_dim_typed_handle(); |
| 3477 | return op.redispatch(dispatchKeySet, self, dim, keepdim, dtype); |
| 3478 | } |
| 3479 | |
| 3480 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mean_out, name, "aten::mean") |
| 3481 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mean_out, overload_name, "out") |
| 3482 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mean_out, schema_str, "mean.out(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)") |
| 3483 | |
| 3484 | // aten::mean.out(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) |
| 3485 | static C10_NOINLINE c10::TypedOperatorHandle<mean_out::schema> create_mean_out_typed_handle() { |
| 3486 | return c10::Dispatcher::singleton() |
| 3487 | .findSchemaOrThrow(mean_out::name, mean_out::overload_name) |
| 3488 | .typed<mean_out::schema>(); |
| 3489 | } |
| 3490 | |
| 3491 | // aten::mean.out(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) |
| 3492 | at::Tensor & mean_out::call(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 3493 | |
| 3494 | static auto op = create_mean_out_typed_handle(); |
| 3495 | return op.call(self, dim, keepdim, dtype, out); |
| 3496 | } |
| 3497 | |
| 3498 | // aten::mean.out(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) |
| 3499 | at::Tensor & mean_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 3500 | |
| 3501 | static auto op = create_mean_out_typed_handle(); |
| 3502 | return op.redispatch(dispatchKeySet, self, dim, keepdim, dtype, out); |
| 3503 | } |
| 3504 | |
| 3505 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mean_names_dim, name, "aten::mean") |
| 3506 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mean_names_dim, overload_name, "names_dim") |
| 3507 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mean_names_dim, schema_str, "mean.names_dim(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor") |
| 3508 | |
| 3509 | // aten::mean.names_dim(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor |
| 3510 | static C10_NOINLINE c10::TypedOperatorHandle<mean_names_dim::schema> create_mean_names_dim_typed_handle() { |
| 3511 | return c10::Dispatcher::singleton() |
| 3512 | .findSchemaOrThrow(mean_names_dim::name, mean_names_dim::overload_name) |
| 3513 | .typed<mean_names_dim::schema>(); |
| 3514 | } |
| 3515 | |
| 3516 | // aten::mean.names_dim(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor |
| 3517 | at::Tensor mean_names_dim::call(const at::Tensor & self, at::DimnameList dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 3518 | |
| 3519 | static auto op = create_mean_names_dim_typed_handle(); |
| 3520 | return op.call(self, dim, keepdim, dtype); |
| 3521 | } |
| 3522 | |
| 3523 | // aten::mean.names_dim(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor |
| 3524 | at::Tensor mean_names_dim::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 3525 | |
| 3526 | static auto op = create_mean_names_dim_typed_handle(); |
| 3527 | return op.redispatch(dispatchKeySet, self, dim, keepdim, dtype); |
| 3528 | } |
| 3529 | |
| 3530 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mean_names_out, name, "aten::mean") |
| 3531 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mean_names_out, overload_name, "names_out") |
| 3532 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mean_names_out, schema_str, "mean.names_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)") |
| 3533 | |
| 3534 | // aten::mean.names_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) |
| 3535 | static C10_NOINLINE c10::TypedOperatorHandle<mean_names_out::schema> create_mean_names_out_typed_handle() { |
| 3536 | return c10::Dispatcher::singleton() |
| 3537 | .findSchemaOrThrow(mean_names_out::name, mean_names_out::overload_name) |
| 3538 | .typed<mean_names_out::schema>(); |
| 3539 | } |
| 3540 | |
| 3541 | // aten::mean.names_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) |
| 3542 | at::Tensor & mean_names_out::call(const at::Tensor & self, at::DimnameList dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 3543 | |
| 3544 | static auto op = create_mean_names_out_typed_handle(); |
| 3545 | return op.call(self, dim, keepdim, dtype, out); |
| 3546 | } |
| 3547 | |
| 3548 | // aten::mean.names_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) |
| 3549 | at::Tensor & mean_names_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 3550 | |
| 3551 | static auto op = create_mean_names_out_typed_handle(); |
| 3552 | return op.redispatch(dispatchKeySet, self, dim, keepdim, dtype, out); |
| 3553 | } |
| 3554 | |
| 3555 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nanmean, name, "aten::nanmean") |
| 3556 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nanmean, overload_name, "") |
| 3557 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nanmean, schema_str, "nanmean(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor") |
| 3558 | |
| 3559 | // aten::nanmean(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor |
| 3560 | static C10_NOINLINE c10::TypedOperatorHandle<nanmean::schema> create_nanmean_typed_handle() { |
| 3561 | return c10::Dispatcher::singleton() |
| 3562 | .findSchemaOrThrow(nanmean::name, nanmean::overload_name) |
| 3563 | .typed<nanmean::schema>(); |
| 3564 | } |
| 3565 | |
| 3566 | // aten::nanmean(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor |
| 3567 | at::Tensor nanmean::call(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 3568 | |
| 3569 | static auto op = create_nanmean_typed_handle(); |
| 3570 | return op.call(self, dim, keepdim, dtype); |
| 3571 | } |
| 3572 | |
| 3573 | // aten::nanmean(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor |
| 3574 | at::Tensor nanmean::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 3575 | |
| 3576 | static auto op = create_nanmean_typed_handle(); |
| 3577 | return op.redispatch(dispatchKeySet, self, dim, keepdim, dtype); |
| 3578 | } |
| 3579 | |
| 3580 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nanmean_out, name, "aten::nanmean") |
| 3581 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nanmean_out, overload_name, "out") |
| 3582 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nanmean_out, schema_str, "nanmean.out(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)") |
| 3583 | |
| 3584 | // aten::nanmean.out(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) |
| 3585 | static C10_NOINLINE c10::TypedOperatorHandle<nanmean_out::schema> create_nanmean_out_typed_handle() { |
| 3586 | return c10::Dispatcher::singleton() |
| 3587 | .findSchemaOrThrow(nanmean_out::name, nanmean_out::overload_name) |
| 3588 | .typed<nanmean_out::schema>(); |
| 3589 | } |
| 3590 | |
| 3591 | // aten::nanmean.out(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) |
| 3592 | at::Tensor & nanmean_out::call(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 3593 | |
| 3594 | static auto op = create_nanmean_out_typed_handle(); |
| 3595 | return op.call(self, dim, keepdim, dtype, out); |
| 3596 | } |
| 3597 | |
| 3598 | // aten::nanmean.out(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) |
| 3599 | at::Tensor & nanmean_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 3600 | |
| 3601 | static auto op = create_nanmean_out_typed_handle(); |
| 3602 | return op.redispatch(dispatchKeySet, self, dim, keepdim, dtype, out); |
| 3603 | } |
| 3604 | |
| 3605 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(min_dim, name, "aten::min") |
| 3606 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(min_dim, overload_name, "dim") |
| 3607 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(min_dim, schema_str, "min.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)") |
| 3608 | |
| 3609 | // aten::min.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices) |
| 3610 | static C10_NOINLINE c10::TypedOperatorHandle<min_dim::schema> create_min_dim_typed_handle() { |
| 3611 | return c10::Dispatcher::singleton() |
| 3612 | .findSchemaOrThrow(min_dim::name, min_dim::overload_name) |
| 3613 | .typed<min_dim::schema>(); |
| 3614 | } |
| 3615 | |
| 3616 | // aten::min.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices) |
| 3617 | ::std::tuple<at::Tensor,at::Tensor> min_dim::call(const at::Tensor & self, int64_t dim, bool keepdim) { |
| 3618 | |
| 3619 | static auto op = create_min_dim_typed_handle(); |
| 3620 | return op.call(self, dim, keepdim); |
| 3621 | } |
| 3622 | |
| 3623 | // aten::min.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices) |
| 3624 | ::std::tuple<at::Tensor,at::Tensor> min_dim::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool keepdim) { |
| 3625 | |
| 3626 | static auto op = create_min_dim_typed_handle(); |
| 3627 | return op.redispatch(dispatchKeySet, self, dim, keepdim); |
| 3628 | } |
| 3629 | |
| 3630 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(min_dim_min, name, "aten::min") |
| 3631 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(min_dim_min, overload_name, "dim_min") |
| 3632 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(min_dim_min, schema_str, "min.dim_min(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices)") |
| 3633 | |
| 3634 | // aten::min.dim_min(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices) |
| 3635 | static C10_NOINLINE c10::TypedOperatorHandle<min_dim_min::schema> create_min_dim_min_typed_handle() { |
| 3636 | return c10::Dispatcher::singleton() |
| 3637 | .findSchemaOrThrow(min_dim_min::name, min_dim_min::overload_name) |
| 3638 | .typed<min_dim_min::schema>(); |
| 3639 | } |
| 3640 | |
| 3641 | // aten::min.dim_min(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices) |
| 3642 | ::std::tuple<at::Tensor &,at::Tensor &> min_dim_min::call(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & min, at::Tensor & min_indices) { |
| 3643 | |
| 3644 | static auto op = create_min_dim_min_typed_handle(); |
| 3645 | return op.call(self, dim, keepdim, min, min_indices); |
| 3646 | } |
| 3647 | |
| 3648 | // aten::min.dim_min(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices) |
| 3649 | ::std::tuple<at::Tensor &,at::Tensor &> min_dim_min::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & min, at::Tensor & min_indices) { |
| 3650 | |
| 3651 | static auto op = create_min_dim_min_typed_handle(); |
| 3652 | return op.redispatch(dispatchKeySet, self, dim, keepdim, min, min_indices); |
| 3653 | } |
| 3654 | |
| 3655 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(min_names_dim, name, "aten::min") |
| 3656 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(min_names_dim, overload_name, "names_dim") |
| 3657 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(min_names_dim, schema_str, "min.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices)") |
| 3658 | |
| 3659 | // aten::min.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices) |
| 3660 | static C10_NOINLINE c10::TypedOperatorHandle<min_names_dim::schema> create_min_names_dim_typed_handle() { |
| 3661 | return c10::Dispatcher::singleton() |
| 3662 | .findSchemaOrThrow(min_names_dim::name, min_names_dim::overload_name) |
| 3663 | .typed<min_names_dim::schema>(); |
| 3664 | } |
| 3665 | |
| 3666 | // aten::min.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices) |
| 3667 | ::std::tuple<at::Tensor,at::Tensor> min_names_dim::call(const at::Tensor & self, at::Dimname dim, bool keepdim) { |
| 3668 | |
| 3669 | static auto op = create_min_names_dim_typed_handle(); |
| 3670 | return op.call(self, dim, keepdim); |
| 3671 | } |
| 3672 | |
| 3673 | // aten::min.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices) |
| 3674 | ::std::tuple<at::Tensor,at::Tensor> min_names_dim::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool keepdim) { |
| 3675 | |
| 3676 | static auto op = create_min_names_dim_typed_handle(); |
| 3677 | return op.redispatch(dispatchKeySet, self, dim, keepdim); |
| 3678 | } |
| 3679 | |
| 3680 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(min_names_dim_min, name, "aten::min") |
| 3681 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(min_names_dim_min, overload_name, "names_dim_min") |
| 3682 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(min_names_dim_min, schema_str, "min.names_dim_min(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices)") |
| 3683 | |
| 3684 | // aten::min.names_dim_min(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices) |
| 3685 | static C10_NOINLINE c10::TypedOperatorHandle<min_names_dim_min::schema> create_min_names_dim_min_typed_handle() { |
| 3686 | return c10::Dispatcher::singleton() |
| 3687 | .findSchemaOrThrow(min_names_dim_min::name, min_names_dim_min::overload_name) |
| 3688 | .typed<min_names_dim_min::schema>(); |
| 3689 | } |
| 3690 | |
| 3691 | // aten::min.names_dim_min(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices) |
| 3692 | ::std::tuple<at::Tensor &,at::Tensor &> min_names_dim_min::call(const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & min, at::Tensor & min_indices) { |
| 3693 | |
| 3694 | static auto op = create_min_names_dim_min_typed_handle(); |
| 3695 | return op.call(self, dim, keepdim, min, min_indices); |
| 3696 | } |
| 3697 | |
| 3698 | // aten::min.names_dim_min(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices) |
| 3699 | ::std::tuple<at::Tensor &,at::Tensor &> min_names_dim_min::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & min, at::Tensor & min_indices) { |
| 3700 | |
| 3701 | static auto op = create_min_names_dim_min_typed_handle(); |
| 3702 | return op.redispatch(dispatchKeySet, self, dim, keepdim, min, min_indices); |
| 3703 | } |
| 3704 | |
| 3705 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mm, name, "aten::mm") |
| 3706 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mm, overload_name, "") |
| 3707 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mm, schema_str, "mm(Tensor self, Tensor mat2) -> Tensor") |
| 3708 | |
| 3709 | // aten::mm(Tensor self, Tensor mat2) -> Tensor |
| 3710 | static C10_NOINLINE c10::TypedOperatorHandle<mm::schema> create_mm_typed_handle() { |
| 3711 | return c10::Dispatcher::singleton() |
| 3712 | .findSchemaOrThrow(mm::name, mm::overload_name) |
| 3713 | .typed<mm::schema>(); |
| 3714 | } |
| 3715 | |
| 3716 | // aten::mm(Tensor self, Tensor mat2) -> Tensor |
| 3717 | at::Tensor mm::call(const at::Tensor & self, const at::Tensor & mat2) { |
| 3718 | |
| 3719 | static auto op = create_mm_typed_handle(); |
| 3720 | return op.call(self, mat2); |
| 3721 | } |
| 3722 | |
| 3723 | // aten::mm(Tensor self, Tensor mat2) -> Tensor |
| 3724 | at::Tensor mm::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat2) { |
| 3725 | |
| 3726 | static auto op = create_mm_typed_handle(); |
| 3727 | return op.redispatch(dispatchKeySet, self, mat2); |
| 3728 | } |
| 3729 | |
| 3730 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mm_out, name, "aten::mm") |
| 3731 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mm_out, overload_name, "out") |
| 3732 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mm_out, schema_str, "mm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)") |
| 3733 | |
| 3734 | // aten::mm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) |
| 3735 | static C10_NOINLINE c10::TypedOperatorHandle<mm_out::schema> create_mm_out_typed_handle() { |
| 3736 | return c10::Dispatcher::singleton() |
| 3737 | .findSchemaOrThrow(mm_out::name, mm_out::overload_name) |
| 3738 | .typed<mm_out::schema>(); |
| 3739 | } |
| 3740 | |
| 3741 | // aten::mm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) |
| 3742 | at::Tensor & mm_out::call(const at::Tensor & self, const at::Tensor & mat2, at::Tensor & out) { |
| 3743 | |
| 3744 | static auto op = create_mm_out_typed_handle(); |
| 3745 | return op.call(self, mat2, out); |
| 3746 | } |
| 3747 | |
| 3748 | // aten::mm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) |
| 3749 | at::Tensor & mm_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat2, at::Tensor & out) { |
| 3750 | |
| 3751 | static auto op = create_mm_out_typed_handle(); |
| 3752 | return op.redispatch(dispatchKeySet, self, mat2, out); |
| 3753 | } |
| 3754 | |
| 3755 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mv, name, "aten::mv") |
| 3756 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mv, overload_name, "") |
| 3757 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mv, schema_str, "mv(Tensor self, Tensor vec) -> Tensor") |
| 3758 | |
| 3759 | // aten::mv(Tensor self, Tensor vec) -> Tensor |
| 3760 | static C10_NOINLINE c10::TypedOperatorHandle<mv::schema> create_mv_typed_handle() { |
| 3761 | return c10::Dispatcher::singleton() |
| 3762 | .findSchemaOrThrow(mv::name, mv::overload_name) |
| 3763 | .typed<mv::schema>(); |
| 3764 | } |
| 3765 | |
| 3766 | // aten::mv(Tensor self, Tensor vec) -> Tensor |
| 3767 | at::Tensor mv::call(const at::Tensor & self, const at::Tensor & vec) { |
| 3768 | |
| 3769 | static auto op = create_mv_typed_handle(); |
| 3770 | return op.call(self, vec); |
| 3771 | } |
| 3772 | |
| 3773 | // aten::mv(Tensor self, Tensor vec) -> Tensor |
| 3774 | at::Tensor mv::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & vec) { |
| 3775 | |
| 3776 | static auto op = create_mv_typed_handle(); |
| 3777 | return op.redispatch(dispatchKeySet, self, vec); |
| 3778 | } |
| 3779 | |
| 3780 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mv_out, name, "aten::mv") |
| 3781 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mv_out, overload_name, "out") |
| 3782 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mv_out, schema_str, "mv.out(Tensor self, Tensor vec, *, Tensor(a!) out) -> Tensor(a!)") |
| 3783 | |
| 3784 | // aten::mv.out(Tensor self, Tensor vec, *, Tensor(a!) out) -> Tensor(a!) |
| 3785 | static C10_NOINLINE c10::TypedOperatorHandle<mv_out::schema> create_mv_out_typed_handle() { |
| 3786 | return c10::Dispatcher::singleton() |
| 3787 | .findSchemaOrThrow(mv_out::name, mv_out::overload_name) |
| 3788 | .typed<mv_out::schema>(); |
| 3789 | } |
| 3790 | |
| 3791 | // aten::mv.out(Tensor self, Tensor vec, *, Tensor(a!) out) -> Tensor(a!) |
| 3792 | at::Tensor & mv_out::call(const at::Tensor & self, const at::Tensor & vec, at::Tensor & out) { |
| 3793 | |
| 3794 | static auto op = create_mv_out_typed_handle(); |
| 3795 | return op.call(self, vec, out); |
| 3796 | } |
| 3797 | |
| 3798 | // aten::mv.out(Tensor self, Tensor vec, *, Tensor(a!) out) -> Tensor(a!) |
| 3799 | at::Tensor & mv_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & vec, at::Tensor & out) { |
| 3800 | |
| 3801 | static auto op = create_mv_out_typed_handle(); |
| 3802 | return op.redispatch(dispatchKeySet, self, vec, out); |
| 3803 | } |
| 3804 | |
| 3805 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(narrow_copy, name, "aten::narrow_copy") |
| 3806 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(narrow_copy, overload_name, "") |
| 3807 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(narrow_copy, schema_str, "narrow_copy(Tensor self, int dim, SymInt start, SymInt length) -> Tensor") |
| 3808 | |
| 3809 | // aten::narrow_copy(Tensor self, int dim, SymInt start, SymInt length) -> Tensor |
| 3810 | static C10_NOINLINE c10::TypedOperatorHandle<narrow_copy::schema> create_narrow_copy_typed_handle() { |
| 3811 | return c10::Dispatcher::singleton() |
| 3812 | .findSchemaOrThrow(narrow_copy::name, narrow_copy::overload_name) |
| 3813 | .typed<narrow_copy::schema>(); |
| 3814 | } |
| 3815 | |
| 3816 | // aten::narrow_copy(Tensor self, int dim, SymInt start, SymInt length) -> Tensor |
| 3817 | at::Tensor narrow_copy::call(const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length) { |
| 3818 | |
| 3819 | static auto op = create_narrow_copy_typed_handle(); |
| 3820 | return op.call(self, dim, start, length); |
| 3821 | } |
| 3822 | |
| 3823 | // aten::narrow_copy(Tensor self, int dim, SymInt start, SymInt length) -> Tensor |
| 3824 | at::Tensor narrow_copy::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length) { |
| 3825 | |
| 3826 | static auto op = create_narrow_copy_typed_handle(); |
| 3827 | return op.redispatch(dispatchKeySet, self, dim, start, length); |
| 3828 | } |
| 3829 | |
| 3830 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(narrow_copy_out, name, "aten::narrow_copy") |
| 3831 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(narrow_copy_out, overload_name, "out") |
| 3832 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(narrow_copy_out, schema_str, "narrow_copy.out(Tensor self, int dim, SymInt start, SymInt length, *, Tensor(a!) out) -> Tensor(a!)") |
| 3833 | |
| 3834 | // aten::narrow_copy.out(Tensor self, int dim, SymInt start, SymInt length, *, Tensor(a!) out) -> Tensor(a!) |
| 3835 | static C10_NOINLINE c10::TypedOperatorHandle<narrow_copy_out::schema> create_narrow_copy_out_typed_handle() { |
| 3836 | return c10::Dispatcher::singleton() |
| 3837 | .findSchemaOrThrow(narrow_copy_out::name, narrow_copy_out::overload_name) |
| 3838 | .typed<narrow_copy_out::schema>(); |
| 3839 | } |
| 3840 | |
| 3841 | // aten::narrow_copy.out(Tensor self, int dim, SymInt start, SymInt length, *, Tensor(a!) out) -> Tensor(a!) |
| 3842 | at::Tensor & narrow_copy_out::call(const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length, at::Tensor & out) { |
| 3843 | |
| 3844 | static auto op = create_narrow_copy_out_typed_handle(); |
| 3845 | return op.call(self, dim, start, length, out); |
| 3846 | } |
| 3847 | |
| 3848 | // aten::narrow_copy.out(Tensor self, int dim, SymInt start, SymInt length, *, Tensor(a!) out) -> Tensor(a!) |
| 3849 | at::Tensor & narrow_copy_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length, at::Tensor & out) { |
| 3850 | |
| 3851 | static auto op = create_narrow_copy_out_typed_handle(); |
| 3852 | return op.redispatch(dispatchKeySet, self, dim, start, length, out); |
| 3853 | } |
| 3854 | |
| 3855 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(batch_norm_gather_stats_with_counts, name, "aten::batch_norm_gather_stats_with_counts") |
| 3856 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(batch_norm_gather_stats_with_counts, overload_name, "") |
| 3857 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(batch_norm_gather_stats_with_counts, schema_str, "batch_norm_gather_stats_with_counts(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, Tensor counts) -> (Tensor, Tensor)") |
| 3858 | |
| 3859 | // aten::batch_norm_gather_stats_with_counts(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, Tensor counts) -> (Tensor, Tensor) |
| 3860 | static C10_NOINLINE c10::TypedOperatorHandle<batch_norm_gather_stats_with_counts::schema> create_batch_norm_gather_stats_with_counts_typed_handle() { |
| 3861 | return c10::Dispatcher::singleton() |
| 3862 | .findSchemaOrThrow(batch_norm_gather_stats_with_counts::name, batch_norm_gather_stats_with_counts::overload_name) |
| 3863 | .typed<batch_norm_gather_stats_with_counts::schema>(); |
| 3864 | } |
| 3865 | |
| 3866 | // aten::batch_norm_gather_stats_with_counts(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, Tensor counts) -> (Tensor, Tensor) |
| 3867 | ::std::tuple<at::Tensor,at::Tensor> batch_norm_gather_stats_with_counts::call(const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, double momentum, double eps, const at::Tensor & counts) { |
| 3868 | |
| 3869 | static auto op = create_batch_norm_gather_stats_with_counts_typed_handle(); |
| 3870 | return op.call(input, mean, invstd, running_mean, running_var, momentum, eps, counts); |
| 3871 | } |
| 3872 | |
| 3873 | // aten::batch_norm_gather_stats_with_counts(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, Tensor counts) -> (Tensor, Tensor) |
| 3874 | ::std::tuple<at::Tensor,at::Tensor> batch_norm_gather_stats_with_counts::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, double momentum, double eps, const at::Tensor & counts) { |
| 3875 | |
| 3876 | static auto op = create_batch_norm_gather_stats_with_counts_typed_handle(); |
| 3877 | return op.redispatch(dispatchKeySet, input, mean, invstd, running_mean, running_var, momentum, eps, counts); |
| 3878 | } |
| 3879 | |
| 3880 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(pairwise_distance, name, "aten::pairwise_distance") |
| 3881 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(pairwise_distance, overload_name, "") |
| 3882 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(pairwise_distance, schema_str, "pairwise_distance(Tensor x1, Tensor x2, float p=2, float eps=1e-06, bool keepdim=False) -> Tensor") |
| 3883 | |
| 3884 | // aten::pairwise_distance(Tensor x1, Tensor x2, float p=2, float eps=1e-06, bool keepdim=False) -> Tensor |
| 3885 | static C10_NOINLINE c10::TypedOperatorHandle<pairwise_distance::schema> create_pairwise_distance_typed_handle() { |
| 3886 | return c10::Dispatcher::singleton() |
| 3887 | .findSchemaOrThrow(pairwise_distance::name, pairwise_distance::overload_name) |
| 3888 | .typed<pairwise_distance::schema>(); |
| 3889 | } |
| 3890 | |
| 3891 | // aten::pairwise_distance(Tensor x1, Tensor x2, float p=2, float eps=1e-06, bool keepdim=False) -> Tensor |
| 3892 | at::Tensor pairwise_distance::call(const at::Tensor & x1, const at::Tensor & x2, double p, double eps, bool keepdim) { |
| 3893 | |
| 3894 | static auto op = create_pairwise_distance_typed_handle(); |
| 3895 | return op.call(x1, x2, p, eps, keepdim); |
| 3896 | } |
| 3897 | |
| 3898 | // aten::pairwise_distance(Tensor x1, Tensor x2, float p=2, float eps=1e-06, bool keepdim=False) -> Tensor |
| 3899 | at::Tensor pairwise_distance::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x1, const at::Tensor & x2, double p, double eps, bool keepdim) { |
| 3900 | |
| 3901 | static auto op = create_pairwise_distance_typed_handle(); |
| 3902 | return op.redispatch(dispatchKeySet, x1, x2, p, eps, keepdim); |
| 3903 | } |
| 3904 | |
| 3905 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_pdist_backward, name, "aten::_pdist_backward") |
| 3906 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_pdist_backward, overload_name, "") |
| 3907 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_pdist_backward, schema_str, "_pdist_backward(Tensor grad, Tensor self, float p, Tensor pdist) -> Tensor") |
| 3908 | |
| 3909 | // aten::_pdist_backward(Tensor grad, Tensor self, float p, Tensor pdist) -> Tensor |
| 3910 | static C10_NOINLINE c10::TypedOperatorHandle<_pdist_backward::schema> create__pdist_backward_typed_handle() { |
| 3911 | return c10::Dispatcher::singleton() |
| 3912 | .findSchemaOrThrow(_pdist_backward::name, _pdist_backward::overload_name) |
| 3913 | .typed<_pdist_backward::schema>(); |
| 3914 | } |
| 3915 | |
| 3916 | // aten::_pdist_backward(Tensor grad, Tensor self, float p, Tensor pdist) -> Tensor |
| 3917 | at::Tensor _pdist_backward::call(const at::Tensor & grad, const at::Tensor & self, double p, const at::Tensor & pdist) { |
| 3918 | |
| 3919 | static auto op = create__pdist_backward_typed_handle(); |
| 3920 | return op.call(grad, self, p, pdist); |
| 3921 | } |
| 3922 | |
| 3923 | // aten::_pdist_backward(Tensor grad, Tensor self, float p, Tensor pdist) -> Tensor |
| 3924 | at::Tensor _pdist_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & self, double p, const at::Tensor & pdist) { |
| 3925 | |
| 3926 | static auto op = create__pdist_backward_typed_handle(); |
| 3927 | return op.redispatch(dispatchKeySet, grad, self, p, pdist); |
| 3928 | } |
| 3929 | |
| 3930 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(permute, name, "aten::permute") |
| 3931 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(permute, overload_name, "") |
| 3932 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(permute, schema_str, "permute(Tensor(a) self, int[] dims) -> Tensor(a)") |
| 3933 | |
| 3934 | // aten::permute(Tensor(a) self, int[] dims) -> Tensor(a) |
| 3935 | static C10_NOINLINE c10::TypedOperatorHandle<permute::schema> create_permute_typed_handle() { |
| 3936 | return c10::Dispatcher::singleton() |
| 3937 | .findSchemaOrThrow(permute::name, permute::overload_name) |
| 3938 | .typed<permute::schema>(); |
| 3939 | } |
| 3940 | |
| 3941 | // aten::permute(Tensor(a) self, int[] dims) -> Tensor(a) |
| 3942 | at::Tensor permute::call(const at::Tensor & self, at::IntArrayRef dims) { |
| 3943 | |
| 3944 | static auto op = create_permute_typed_handle(); |
| 3945 | return op.call(self, dims); |
| 3946 | } |
| 3947 | |
| 3948 | // aten::permute(Tensor(a) self, int[] dims) -> Tensor(a) |
| 3949 | at::Tensor permute::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dims) { |
| 3950 | |
| 3951 | static auto op = create_permute_typed_handle(); |
| 3952 | return op.redispatch(dispatchKeySet, self, dims); |
| 3953 | } |
| 3954 | |
| 3955 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(matrix_H, name, "aten::matrix_H") |
| 3956 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(matrix_H, overload_name, "") |
| 3957 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(matrix_H, schema_str, "matrix_H(Tensor(a) self) -> Tensor(a)") |
| 3958 | |
| 3959 | // aten::matrix_H(Tensor(a) self) -> Tensor(a) |
| 3960 | static C10_NOINLINE c10::TypedOperatorHandle<matrix_H::schema> create_matrix_H_typed_handle() { |
| 3961 | return c10::Dispatcher::singleton() |
| 3962 | .findSchemaOrThrow(matrix_H::name, matrix_H::overload_name) |
| 3963 | .typed<matrix_H::schema>(); |
| 3964 | } |
| 3965 | |
| 3966 | // aten::matrix_H(Tensor(a) self) -> Tensor(a) |
| 3967 | at::Tensor matrix_H::call(const at::Tensor & self) { |
| 3968 | |
| 3969 | static auto op = create_matrix_H_typed_handle(); |
| 3970 | return op.call(self); |
| 3971 | } |
| 3972 | |
| 3973 | // aten::matrix_H(Tensor(a) self) -> Tensor(a) |
| 3974 | at::Tensor matrix_H::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 3975 | |
| 3976 | static auto op = create_matrix_H_typed_handle(); |
| 3977 | return op.redispatch(dispatchKeySet, self); |
| 3978 | } |
| 3979 | |
| 3980 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(pixel_shuffle, name, "aten::pixel_shuffle") |
| 3981 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(pixel_shuffle, overload_name, "") |
| 3982 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(pixel_shuffle, schema_str, "pixel_shuffle(Tensor self, int upscale_factor) -> Tensor") |
| 3983 | |
| 3984 | // aten::pixel_shuffle(Tensor self, int upscale_factor) -> Tensor |
| 3985 | static C10_NOINLINE c10::TypedOperatorHandle<pixel_shuffle::schema> create_pixel_shuffle_typed_handle() { |
| 3986 | return c10::Dispatcher::singleton() |
| 3987 | .findSchemaOrThrow(pixel_shuffle::name, pixel_shuffle::overload_name) |
| 3988 | .typed<pixel_shuffle::schema>(); |
| 3989 | } |
| 3990 | |
| 3991 | // aten::pixel_shuffle(Tensor self, int upscale_factor) -> Tensor |
| 3992 | at::Tensor pixel_shuffle::call(const at::Tensor & self, int64_t upscale_factor) { |
| 3993 | |
| 3994 | static auto op = create_pixel_shuffle_typed_handle(); |
| 3995 | return op.call(self, upscale_factor); |
| 3996 | } |
| 3997 | |
| 3998 | // aten::pixel_shuffle(Tensor self, int upscale_factor) -> Tensor |
| 3999 | at::Tensor pixel_shuffle::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t upscale_factor) { |
| 4000 | |
| 4001 | static auto op = create_pixel_shuffle_typed_handle(); |
| 4002 | return op.redispatch(dispatchKeySet, self, upscale_factor); |
| 4003 | } |
| 4004 | |
| 4005 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(pinverse, name, "aten::pinverse") |
| 4006 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(pinverse, overload_name, "") |
| 4007 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(pinverse, schema_str, "pinverse(Tensor self, float rcond=1e-15) -> Tensor") |
| 4008 | |
| 4009 | // aten::pinverse(Tensor self, float rcond=1e-15) -> Tensor |
| 4010 | static C10_NOINLINE c10::TypedOperatorHandle<pinverse::schema> create_pinverse_typed_handle() { |
| 4011 | return c10::Dispatcher::singleton() |
| 4012 | .findSchemaOrThrow(pinverse::name, pinverse::overload_name) |
| 4013 | .typed<pinverse::schema>(); |
| 4014 | } |
| 4015 | |
| 4016 | // aten::pinverse(Tensor self, float rcond=1e-15) -> Tensor |
| 4017 | at::Tensor pinverse::call(const at::Tensor & self, double rcond) { |
| 4018 | |
| 4019 | static auto op = create_pinverse_typed_handle(); |
| 4020 | return op.call(self, rcond); |
| 4021 | } |
| 4022 | |
| 4023 | // aten::pinverse(Tensor self, float rcond=1e-15) -> Tensor |
| 4024 | at::Tensor pinverse::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double rcond) { |
| 4025 | |
| 4026 | static auto op = create_pinverse_typed_handle(); |
| 4027 | return op.redispatch(dispatchKeySet, self, rcond); |
| 4028 | } |
| 4029 | |
| 4030 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(reshape, name, "aten::reshape") |
| 4031 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(reshape, overload_name, "") |
| 4032 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(reshape, schema_str, "reshape(Tensor(a) self, SymInt[] shape) -> Tensor(a)") |
| 4033 | |
| 4034 | // aten::reshape(Tensor(a) self, SymInt[] shape) -> Tensor(a) |
| 4035 | static C10_NOINLINE c10::TypedOperatorHandle<reshape::schema> create_reshape_typed_handle() { |
| 4036 | return c10::Dispatcher::singleton() |
| 4037 | .findSchemaOrThrow(reshape::name, reshape::overload_name) |
| 4038 | .typed<reshape::schema>(); |
| 4039 | } |
| 4040 | |
| 4041 | // aten::reshape(Tensor(a) self, SymInt[] shape) -> Tensor(a) |
| 4042 | at::Tensor reshape::call(const at::Tensor & self, c10::SymIntArrayRef shape) { |
| 4043 | |
| 4044 | static auto op = create_reshape_typed_handle(); |
| 4045 | return op.call(self, shape); |
| 4046 | } |
| 4047 | |
| 4048 | // aten::reshape(Tensor(a) self, SymInt[] shape) -> Tensor(a) |
| 4049 | at::Tensor reshape::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef shape) { |
| 4050 | |
| 4051 | static auto op = create_reshape_typed_handle(); |
| 4052 | return op.redispatch(dispatchKeySet, self, shape); |
| 4053 | } |
| 4054 | |
| 4055 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_reshape_alias, name, "aten::_reshape_alias") |
| 4056 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_reshape_alias, overload_name, "") |
| 4057 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_reshape_alias, schema_str, "_reshape_alias(Tensor(a) self, SymInt[] size, SymInt[] stride) -> Tensor(a)") |
| 4058 | |
| 4059 | // aten::_reshape_alias(Tensor(a) self, SymInt[] size, SymInt[] stride) -> Tensor(a) |
| 4060 | static C10_NOINLINE c10::TypedOperatorHandle<_reshape_alias::schema> create__reshape_alias_typed_handle() { |
| 4061 | return c10::Dispatcher::singleton() |
| 4062 | .findSchemaOrThrow(_reshape_alias::name, _reshape_alias::overload_name) |
| 4063 | .typed<_reshape_alias::schema>(); |
| 4064 | } |
| 4065 | |
| 4066 | // aten::_reshape_alias(Tensor(a) self, SymInt[] size, SymInt[] stride) -> Tensor(a) |
| 4067 | at::Tensor _reshape_alias::call(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) { |
| 4068 | |
| 4069 | static auto op = create__reshape_alias_typed_handle(); |
| 4070 | return op.call(self, size, stride); |
| 4071 | } |
| 4072 | |
| 4073 | // aten::_reshape_alias(Tensor(a) self, SymInt[] size, SymInt[] stride) -> Tensor(a) |
| 4074 | at::Tensor _reshape_alias::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) { |
| 4075 | |
| 4076 | static auto op = create__reshape_alias_typed_handle(); |
| 4077 | return op.redispatch(dispatchKeySet, self, size, stride); |
| 4078 | } |
| 4079 | |
| 4080 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(select_Dimname, name, "aten::select") |
| 4081 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(select_Dimname, overload_name, "Dimname") |
| 4082 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(select_Dimname, schema_str, "select.Dimname(Tensor(a) self, Dimname dim, int index) -> Tensor(a)") |
| 4083 | |
| 4084 | // aten::select.Dimname(Tensor(a) self, Dimname dim, int index) -> Tensor(a) |
| 4085 | static C10_NOINLINE c10::TypedOperatorHandle<select_Dimname::schema> create_select_Dimname_typed_handle() { |
| 4086 | return c10::Dispatcher::singleton() |
| 4087 | .findSchemaOrThrow(select_Dimname::name, select_Dimname::overload_name) |
| 4088 | .typed<select_Dimname::schema>(); |
| 4089 | } |
| 4090 | |
| 4091 | // aten::select.Dimname(Tensor(a) self, Dimname dim, int index) -> Tensor(a) |
| 4092 | at::Tensor select_Dimname::call(const at::Tensor & self, at::Dimname dim, int64_t index) { |
| 4093 | |
| 4094 | static auto op = create_select_Dimname_typed_handle(); |
| 4095 | return op.call(self, dim, index); |
| 4096 | } |
| 4097 | |
| 4098 | // aten::select.Dimname(Tensor(a) self, Dimname dim, int index) -> Tensor(a) |
| 4099 | at::Tensor select_Dimname::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, int64_t index) { |
| 4100 | |
| 4101 | static auto op = create_select_Dimname_typed_handle(); |
| 4102 | return op.redispatch(dispatchKeySet, self, dim, index); |
| 4103 | } |
| 4104 | |
| 4105 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(select_int, name, "aten::select") |
| 4106 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(select_int, overload_name, "int") |
| 4107 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(select_int, schema_str, "select.int(Tensor(a) self, int dim, SymInt index) -> Tensor(a)") |
| 4108 | |
| 4109 | // aten::select.int(Tensor(a) self, int dim, SymInt index) -> Tensor(a) |
| 4110 | static C10_NOINLINE c10::TypedOperatorHandle<select_int::schema> create_select_int_typed_handle() { |
| 4111 | return c10::Dispatcher::singleton() |
| 4112 | .findSchemaOrThrow(select_int::name, select_int::overload_name) |
| 4113 | .typed<select_int::schema>(); |
| 4114 | } |
| 4115 | |
| 4116 | // aten::select.int(Tensor(a) self, int dim, SymInt index) -> Tensor(a) |
| 4117 | at::Tensor select_int::call(const at::Tensor & self, int64_t dim, c10::SymInt index) { |
| 4118 | |
| 4119 | static auto op = create_select_int_typed_handle(); |
| 4120 | return op.call(self, dim, index); |
| 4121 | } |
| 4122 | |
| 4123 | // aten::select.int(Tensor(a) self, int dim, SymInt index) -> Tensor(a) |
| 4124 | at::Tensor select_int::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::SymInt index) { |
| 4125 | |
| 4126 | static auto op = create_select_int_typed_handle(); |
| 4127 | return op.redispatch(dispatchKeySet, self, dim, index); |
| 4128 | } |
| 4129 | |
| 4130 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(celu, name, "aten::celu") |
| 4131 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(celu, overload_name, "") |
| 4132 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(celu, schema_str, "celu(Tensor self, Scalar alpha=1.0) -> Tensor") |
| 4133 | |
| 4134 | // aten::celu(Tensor self, Scalar alpha=1.0) -> Tensor |
| 4135 | static C10_NOINLINE c10::TypedOperatorHandle<celu::schema> create_celu_typed_handle() { |
| 4136 | return c10::Dispatcher::singleton() |
| 4137 | .findSchemaOrThrow(celu::name, celu::overload_name) |
| 4138 | .typed<celu::schema>(); |
| 4139 | } |
| 4140 | |
| 4141 | // aten::celu(Tensor self, Scalar alpha=1.0) -> Tensor |
| 4142 | at::Tensor celu::call(const at::Tensor & self, const at::Scalar & alpha) { |
| 4143 | |
| 4144 | static auto op = create_celu_typed_handle(); |
| 4145 | return op.call(self, alpha); |
| 4146 | } |
| 4147 | |
| 4148 | // aten::celu(Tensor self, Scalar alpha=1.0) -> Tensor |
| 4149 | at::Tensor celu::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & alpha) { |
| 4150 | |
| 4151 | static auto op = create_celu_typed_handle(); |
| 4152 | return op.redispatch(dispatchKeySet, self, alpha); |
| 4153 | } |
| 4154 | |
| 4155 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(celu_, name, "aten::celu_") |
| 4156 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(celu_, overload_name, "") |
| 4157 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(celu_, schema_str, "celu_(Tensor(a!) self, Scalar alpha=1.0) -> Tensor(a!)") |
| 4158 | |
| 4159 | // aten::celu_(Tensor(a!) self, Scalar alpha=1.0) -> Tensor(a!) |
| 4160 | static C10_NOINLINE c10::TypedOperatorHandle<celu_::schema> create_celu__typed_handle() { |
| 4161 | return c10::Dispatcher::singleton() |
| 4162 | .findSchemaOrThrow(celu_::name, celu_::overload_name) |
| 4163 | .typed<celu_::schema>(); |
| 4164 | } |
| 4165 | |
| 4166 | // aten::celu_(Tensor(a!) self, Scalar alpha=1.0) -> Tensor(a!) |
| 4167 | at::Tensor & celu_::call(at::Tensor & self, const at::Scalar & alpha) { |
| 4168 | |
| 4169 | static auto op = create_celu__typed_handle(); |
| 4170 | return op.call(self, alpha); |
| 4171 | } |
| 4172 | |
| 4173 | // aten::celu_(Tensor(a!) self, Scalar alpha=1.0) -> Tensor(a!) |
| 4174 | at::Tensor & celu_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & alpha) { |
| 4175 | |
| 4176 | static auto op = create_celu__typed_handle(); |
| 4177 | return op.redispatch(dispatchKeySet, self, alpha); |
| 4178 | } |
| 4179 | |
| 4180 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(silu, name, "aten::silu") |
| 4181 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(silu, overload_name, "") |
| 4182 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(silu, schema_str, "silu(Tensor self) -> Tensor") |
| 4183 | |
| 4184 | // aten::silu(Tensor self) -> Tensor |
| 4185 | static C10_NOINLINE c10::TypedOperatorHandle<silu::schema> create_silu_typed_handle() { |
| 4186 | return c10::Dispatcher::singleton() |
| 4187 | .findSchemaOrThrow(silu::name, silu::overload_name) |
| 4188 | .typed<silu::schema>(); |
| 4189 | } |
| 4190 | |
| 4191 | // aten::silu(Tensor self) -> Tensor |
| 4192 | at::Tensor silu::call(const at::Tensor & self) { |
| 4193 | |
| 4194 | static auto op = create_silu_typed_handle(); |
| 4195 | return op.call(self); |
| 4196 | } |
| 4197 | |
| 4198 | // aten::silu(Tensor self) -> Tensor |
| 4199 | at::Tensor silu::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 4200 | |
| 4201 | static auto op = create_silu_typed_handle(); |
| 4202 | return op.redispatch(dispatchKeySet, self); |
| 4203 | } |
| 4204 | |
| 4205 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(silu_, name, "aten::silu_") |
| 4206 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(silu_, overload_name, "") |
| 4207 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(silu_, schema_str, "silu_(Tensor(a!) self) -> Tensor(a!)") |
| 4208 | |
| 4209 | // aten::silu_(Tensor(a!) self) -> Tensor(a!) |
| 4210 | static C10_NOINLINE c10::TypedOperatorHandle<silu_::schema> create_silu__typed_handle() { |
| 4211 | return c10::Dispatcher::singleton() |
| 4212 | .findSchemaOrThrow(silu_::name, silu_::overload_name) |
| 4213 | .typed<silu_::schema>(); |
| 4214 | } |
| 4215 | |
| 4216 | // aten::silu_(Tensor(a!) self) -> Tensor(a!) |
| 4217 | at::Tensor & silu_::call(at::Tensor & self) { |
| 4218 | |
| 4219 | static auto op = create_silu__typed_handle(); |
| 4220 | return op.call(self); |
| 4221 | } |
| 4222 | |
| 4223 | // aten::silu_(Tensor(a!) self) -> Tensor(a!) |
| 4224 | at::Tensor & silu_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 4225 | |
| 4226 | static auto op = create_silu__typed_handle(); |
| 4227 | return op.redispatch(dispatchKeySet, self); |
| 4228 | } |
| 4229 | |
| 4230 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(silu_out, name, "aten::silu") |
| 4231 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(silu_out, overload_name, "out") |
| 4232 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(silu_out, schema_str, "silu.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 4233 | |
| 4234 | // aten::silu.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 4235 | static C10_NOINLINE c10::TypedOperatorHandle<silu_out::schema> create_silu_out_typed_handle() { |
| 4236 | return c10::Dispatcher::singleton() |
| 4237 | .findSchemaOrThrow(silu_out::name, silu_out::overload_name) |
| 4238 | .typed<silu_out::schema>(); |
| 4239 | } |
| 4240 | |
| 4241 | // aten::silu.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 4242 | at::Tensor & silu_out::call(const at::Tensor & self, at::Tensor & out) { |
| 4243 | |
| 4244 | static auto op = create_silu_out_typed_handle(); |
| 4245 | return op.call(self, out); |
| 4246 | } |
| 4247 | |
| 4248 | // aten::silu.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 4249 | at::Tensor & silu_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 4250 | |
| 4251 | static auto op = create_silu_out_typed_handle(); |
| 4252 | return op.redispatch(dispatchKeySet, self, out); |
| 4253 | } |
| 4254 | |
| 4255 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mish_backward, name, "aten::mish_backward") |
| 4256 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mish_backward, overload_name, "") |
| 4257 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mish_backward, schema_str, "mish_backward(Tensor grad_output, Tensor self) -> Tensor") |
| 4258 | |
| 4259 | // aten::mish_backward(Tensor grad_output, Tensor self) -> Tensor |
| 4260 | static C10_NOINLINE c10::TypedOperatorHandle<mish_backward::schema> create_mish_backward_typed_handle() { |
| 4261 | return c10::Dispatcher::singleton() |
| 4262 | .findSchemaOrThrow(mish_backward::name, mish_backward::overload_name) |
| 4263 | .typed<mish_backward::schema>(); |
| 4264 | } |
| 4265 | |
| 4266 | // aten::mish_backward(Tensor grad_output, Tensor self) -> Tensor |
| 4267 | at::Tensor mish_backward::call(const at::Tensor & grad_output, const at::Tensor & self) { |
| 4268 | |
| 4269 | static auto op = create_mish_backward_typed_handle(); |
| 4270 | return op.call(grad_output, self); |
| 4271 | } |
| 4272 | |
| 4273 | // aten::mish_backward(Tensor grad_output, Tensor self) -> Tensor |
| 4274 | at::Tensor mish_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self) { |
| 4275 | |
| 4276 | static auto op = create_mish_backward_typed_handle(); |
| 4277 | return op.redispatch(dispatchKeySet, grad_output, self); |
| 4278 | } |
| 4279 | |
| 4280 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logit, name, "aten::logit") |
| 4281 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logit, overload_name, "") |
| 4282 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logit, schema_str, "logit(Tensor self, float? eps=None) -> Tensor") |
| 4283 | |
| 4284 | // aten::logit(Tensor self, float? eps=None) -> Tensor |
| 4285 | static C10_NOINLINE c10::TypedOperatorHandle<logit::schema> create_logit_typed_handle() { |
| 4286 | return c10::Dispatcher::singleton() |
| 4287 | .findSchemaOrThrow(logit::name, logit::overload_name) |
| 4288 | .typed<logit::schema>(); |
| 4289 | } |
| 4290 | |
| 4291 | // aten::logit(Tensor self, float? eps=None) -> Tensor |
| 4292 | at::Tensor logit::call(const at::Tensor & self, c10::optional<double> eps) { |
| 4293 | |
| 4294 | static auto op = create_logit_typed_handle(); |
| 4295 | return op.call(self, eps); |
| 4296 | } |
| 4297 | |
| 4298 | // aten::logit(Tensor self, float? eps=None) -> Tensor |
| 4299 | at::Tensor logit::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<double> eps) { |
| 4300 | |
| 4301 | static auto op = create_logit_typed_handle(); |
| 4302 | return op.redispatch(dispatchKeySet, self, eps); |
| 4303 | } |
| 4304 | |
| 4305 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logit_, name, "aten::logit_") |
| 4306 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logit_, overload_name, "") |
| 4307 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logit_, schema_str, "logit_(Tensor(a!) self, float? eps=None) -> Tensor(a!)") |
| 4308 | |
| 4309 | // aten::logit_(Tensor(a!) self, float? eps=None) -> Tensor(a!) |
| 4310 | static C10_NOINLINE c10::TypedOperatorHandle<logit_::schema> create_logit__typed_handle() { |
| 4311 | return c10::Dispatcher::singleton() |
| 4312 | .findSchemaOrThrow(logit_::name, logit_::overload_name) |
| 4313 | .typed<logit_::schema>(); |
| 4314 | } |
| 4315 | |
| 4316 | // aten::logit_(Tensor(a!) self, float? eps=None) -> Tensor(a!) |
| 4317 | at::Tensor & logit_::call(at::Tensor & self, c10::optional<double> eps) { |
| 4318 | |
| 4319 | static auto op = create_logit__typed_handle(); |
| 4320 | return op.call(self, eps); |
| 4321 | } |
| 4322 | |
| 4323 | // aten::logit_(Tensor(a!) self, float? eps=None) -> Tensor(a!) |
| 4324 | at::Tensor & logit_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, c10::optional<double> eps) { |
| 4325 | |
| 4326 | static auto op = create_logit__typed_handle(); |
| 4327 | return op.redispatch(dispatchKeySet, self, eps); |
| 4328 | } |
| 4329 | |
| 4330 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logit_out, name, "aten::logit") |
| 4331 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logit_out, overload_name, "out") |
| 4332 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logit_out, schema_str, "logit.out(Tensor self, float? eps=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 4333 | |
| 4334 | // aten::logit.out(Tensor self, float? eps=None, *, Tensor(a!) out) -> Tensor(a!) |
| 4335 | static C10_NOINLINE c10::TypedOperatorHandle<logit_out::schema> create_logit_out_typed_handle() { |
| 4336 | return c10::Dispatcher::singleton() |
| 4337 | .findSchemaOrThrow(logit_out::name, logit_out::overload_name) |
| 4338 | .typed<logit_out::schema>(); |
| 4339 | } |
| 4340 | |
| 4341 | // aten::logit.out(Tensor self, float? eps=None, *, Tensor(a!) out) -> Tensor(a!) |
| 4342 | at::Tensor & logit_out::call(const at::Tensor & self, c10::optional<double> eps, at::Tensor & out) { |
| 4343 | |
| 4344 | static auto op = create_logit_out_typed_handle(); |
| 4345 | return op.call(self, eps, out); |
| 4346 | } |
| 4347 | |
| 4348 | // aten::logit.out(Tensor self, float? eps=None, *, Tensor(a!) out) -> Tensor(a!) |
| 4349 | at::Tensor & logit_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<double> eps, at::Tensor & out) { |
| 4350 | |
| 4351 | static auto op = create_logit_out_typed_handle(); |
| 4352 | return op.redispatch(dispatchKeySet, self, eps, out); |
| 4353 | } |
| 4354 | |
| 4355 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sinh, name, "aten::sinh") |
| 4356 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sinh, overload_name, "") |
| 4357 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sinh, schema_str, "sinh(Tensor self) -> Tensor") |
| 4358 | |
| 4359 | // aten::sinh(Tensor self) -> Tensor |
| 4360 | static C10_NOINLINE c10::TypedOperatorHandle<sinh::schema> create_sinh_typed_handle() { |
| 4361 | return c10::Dispatcher::singleton() |
| 4362 | .findSchemaOrThrow(sinh::name, sinh::overload_name) |
| 4363 | .typed<sinh::schema>(); |
| 4364 | } |
| 4365 | |
| 4366 | // aten::sinh(Tensor self) -> Tensor |
| 4367 | at::Tensor sinh::call(const at::Tensor & self) { |
| 4368 | |
| 4369 | static auto op = create_sinh_typed_handle(); |
| 4370 | return op.call(self); |
| 4371 | } |
| 4372 | |
| 4373 | // aten::sinh(Tensor self) -> Tensor |
| 4374 | at::Tensor sinh::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 4375 | |
| 4376 | static auto op = create_sinh_typed_handle(); |
| 4377 | return op.redispatch(dispatchKeySet, self); |
| 4378 | } |
| 4379 | |
| 4380 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sinh_, name, "aten::sinh_") |
| 4381 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sinh_, overload_name, "") |
| 4382 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sinh_, schema_str, "sinh_(Tensor(a!) self) -> Tensor(a!)") |
| 4383 | |
| 4384 | // aten::sinh_(Tensor(a!) self) -> Tensor(a!) |
| 4385 | static C10_NOINLINE c10::TypedOperatorHandle<sinh_::schema> create_sinh__typed_handle() { |
| 4386 | return c10::Dispatcher::singleton() |
| 4387 | .findSchemaOrThrow(sinh_::name, sinh_::overload_name) |
| 4388 | .typed<sinh_::schema>(); |
| 4389 | } |
| 4390 | |
| 4391 | // aten::sinh_(Tensor(a!) self) -> Tensor(a!) |
| 4392 | at::Tensor & sinh_::call(at::Tensor & self) { |
| 4393 | |
| 4394 | static auto op = create_sinh__typed_handle(); |
| 4395 | return op.call(self); |
| 4396 | } |
| 4397 | |
| 4398 | // aten::sinh_(Tensor(a!) self) -> Tensor(a!) |
| 4399 | at::Tensor & sinh_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 4400 | |
| 4401 | static auto op = create_sinh__typed_handle(); |
| 4402 | return op.redispatch(dispatchKeySet, self); |
| 4403 | } |
| 4404 | |
| 4405 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sinh_out, name, "aten::sinh") |
| 4406 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sinh_out, overload_name, "out") |
| 4407 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sinh_out, schema_str, "sinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 4408 | |
| 4409 | // aten::sinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 4410 | static C10_NOINLINE c10::TypedOperatorHandle<sinh_out::schema> create_sinh_out_typed_handle() { |
| 4411 | return c10::Dispatcher::singleton() |
| 4412 | .findSchemaOrThrow(sinh_out::name, sinh_out::overload_name) |
| 4413 | .typed<sinh_out::schema>(); |
| 4414 | } |
| 4415 | |
| 4416 | // aten::sinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 4417 | at::Tensor & sinh_out::call(const at::Tensor & self, at::Tensor & out) { |
| 4418 | |
| 4419 | static auto op = create_sinh_out_typed_handle(); |
| 4420 | return op.call(self, out); |
| 4421 | } |
| 4422 | |
| 4423 | // aten::sinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 4424 | at::Tensor & sinh_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 4425 | |
| 4426 | static auto op = create_sinh_out_typed_handle(); |
| 4427 | return op.redispatch(dispatchKeySet, self, out); |
| 4428 | } |
| 4429 | |
| 4430 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slice_backward, name, "aten::slice_backward") |
| 4431 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slice_backward, overload_name, "") |
| 4432 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slice_backward, schema_str, "slice_backward(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step) -> Tensor") |
| 4433 | |
| 4434 | // aten::slice_backward(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step) -> Tensor |
| 4435 | static C10_NOINLINE c10::TypedOperatorHandle<slice_backward::schema> create_slice_backward_typed_handle() { |
| 4436 | return c10::Dispatcher::singleton() |
| 4437 | .findSchemaOrThrow(slice_backward::name, slice_backward::overload_name) |
| 4438 | .typed<slice_backward::schema>(); |
| 4439 | } |
| 4440 | |
| 4441 | // aten::slice_backward(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step) -> Tensor |
| 4442 | at::Tensor slice_backward::call(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step) { |
| 4443 | |
| 4444 | static auto op = create_slice_backward_typed_handle(); |
| 4445 | return op.call(grad_output, input_sizes, dim, start, end, step); |
| 4446 | } |
| 4447 | |
| 4448 | // aten::slice_backward(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step) -> Tensor |
| 4449 | at::Tensor slice_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step) { |
| 4450 | |
| 4451 | static auto op = create_slice_backward_typed_handle(); |
| 4452 | return op.redispatch(dispatchKeySet, grad_output, input_sizes, dim, start, end, step); |
| 4453 | } |
| 4454 | |
| 4455 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(softmax_int, name, "aten::softmax") |
| 4456 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(softmax_int, overload_name, "int") |
| 4457 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(softmax_int, schema_str, "softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor") |
| 4458 | |
| 4459 | // aten::softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor |
| 4460 | static C10_NOINLINE c10::TypedOperatorHandle<softmax_int::schema> create_softmax_int_typed_handle() { |
| 4461 | return c10::Dispatcher::singleton() |
| 4462 | .findSchemaOrThrow(softmax_int::name, softmax_int::overload_name) |
| 4463 | .typed<softmax_int::schema>(); |
| 4464 | } |
| 4465 | |
| 4466 | // aten::softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor |
| 4467 | at::Tensor softmax_int::call(const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 4468 | |
| 4469 | static auto op = create_softmax_int_typed_handle(); |
| 4470 | return op.call(self, dim, dtype); |
| 4471 | } |
| 4472 | |
| 4473 | // aten::softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor |
| 4474 | at::Tensor softmax_int::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 4475 | |
| 4476 | static auto op = create_softmax_int_typed_handle(); |
| 4477 | return op.redispatch(dispatchKeySet, self, dim, dtype); |
| 4478 | } |
| 4479 | |
| 4480 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(softmax_int_out, name, "aten::softmax") |
| 4481 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(softmax_int_out, overload_name, "int_out") |
| 4482 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(softmax_int_out, schema_str, "softmax.int_out(Tensor self, int dim, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 4483 | |
| 4484 | // aten::softmax.int_out(Tensor self, int dim, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!) |
| 4485 | static C10_NOINLINE c10::TypedOperatorHandle<softmax_int_out::schema> create_softmax_int_out_typed_handle() { |
| 4486 | return c10::Dispatcher::singleton() |
| 4487 | .findSchemaOrThrow(softmax_int_out::name, softmax_int_out::overload_name) |
| 4488 | .typed<softmax_int_out::schema>(); |
| 4489 | } |
| 4490 | |
| 4491 | // aten::softmax.int_out(Tensor self, int dim, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!) |
| 4492 | at::Tensor & softmax_int_out::call(const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 4493 | |
| 4494 | static auto op = create_softmax_int_out_typed_handle(); |
| 4495 | return op.call(self, dim, dtype, out); |
| 4496 | } |
| 4497 | |
| 4498 | // aten::softmax.int_out(Tensor self, int dim, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!) |
| 4499 | at::Tensor & softmax_int_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 4500 | |
| 4501 | static auto op = create_softmax_int_out_typed_handle(); |
| 4502 | return op.redispatch(dispatchKeySet, self, dim, dtype, out); |
| 4503 | } |
| 4504 | |
| 4505 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(softmax_Dimname, name, "aten::softmax") |
| 4506 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(softmax_Dimname, overload_name, "Dimname") |
| 4507 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(softmax_Dimname, schema_str, "softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor") |
| 4508 | |
| 4509 | // aten::softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor |
| 4510 | static C10_NOINLINE c10::TypedOperatorHandle<softmax_Dimname::schema> create_softmax_Dimname_typed_handle() { |
| 4511 | return c10::Dispatcher::singleton() |
| 4512 | .findSchemaOrThrow(softmax_Dimname::name, softmax_Dimname::overload_name) |
| 4513 | .typed<softmax_Dimname::schema>(); |
| 4514 | } |
| 4515 | |
| 4516 | // aten::softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor |
| 4517 | at::Tensor softmax_Dimname::call(const at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype) { |
| 4518 | |
| 4519 | static auto op = create_softmax_Dimname_typed_handle(); |
| 4520 | return op.call(self, dim, dtype); |
| 4521 | } |
| 4522 | |
| 4523 | // aten::softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor |
| 4524 | at::Tensor softmax_Dimname::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype) { |
| 4525 | |
| 4526 | static auto op = create_softmax_Dimname_typed_handle(); |
| 4527 | return op.redispatch(dispatchKeySet, self, dim, dtype); |
| 4528 | } |
| 4529 | |
| 4530 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_softmax, name, "aten::_softmax") |
| 4531 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_softmax, overload_name, "") |
| 4532 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_softmax, schema_str, "_softmax(Tensor self, int dim, bool half_to_float) -> Tensor") |
| 4533 | |
| 4534 | // aten::_softmax(Tensor self, int dim, bool half_to_float) -> Tensor |
| 4535 | static C10_NOINLINE c10::TypedOperatorHandle<_softmax::schema> create__softmax_typed_handle() { |
| 4536 | return c10::Dispatcher::singleton() |
| 4537 | .findSchemaOrThrow(_softmax::name, _softmax::overload_name) |
| 4538 | .typed<_softmax::schema>(); |
| 4539 | } |
| 4540 | |
| 4541 | // aten::_softmax(Tensor self, int dim, bool half_to_float) -> Tensor |
| 4542 | at::Tensor _softmax::call(const at::Tensor & self, int64_t dim, bool half_to_float) { |
| 4543 | |
| 4544 | static auto op = create__softmax_typed_handle(); |
| 4545 | return op.call(self, dim, half_to_float); |
| 4546 | } |
| 4547 | |
| 4548 | // aten::_softmax(Tensor self, int dim, bool half_to_float) -> Tensor |
| 4549 | at::Tensor _softmax::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool half_to_float) { |
| 4550 | |
| 4551 | static auto op = create__softmax_typed_handle(); |
| 4552 | return op.redispatch(dispatchKeySet, self, dim, half_to_float); |
| 4553 | } |
| 4554 | |
| 4555 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_softmax_out, name, "aten::_softmax") |
| 4556 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_softmax_out, overload_name, "out") |
| 4557 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_softmax_out, schema_str, "_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!)") |
| 4558 | |
| 4559 | // aten::_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!) |
| 4560 | static C10_NOINLINE c10::TypedOperatorHandle<_softmax_out::schema> create__softmax_out_typed_handle() { |
| 4561 | return c10::Dispatcher::singleton() |
| 4562 | .findSchemaOrThrow(_softmax_out::name, _softmax_out::overload_name) |
| 4563 | .typed<_softmax_out::schema>(); |
| 4564 | } |
| 4565 | |
| 4566 | // aten::_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!) |
| 4567 | at::Tensor & _softmax_out::call(const at::Tensor & self, int64_t dim, bool half_to_float, at::Tensor & out) { |
| 4568 | |
| 4569 | static auto op = create__softmax_out_typed_handle(); |
| 4570 | return op.call(self, dim, half_to_float, out); |
| 4571 | } |
| 4572 | |
| 4573 | // aten::_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!) |
| 4574 | at::Tensor & _softmax_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool half_to_float, at::Tensor & out) { |
| 4575 | |
| 4576 | static auto op = create__softmax_out_typed_handle(); |
| 4577 | return op.redispatch(dispatchKeySet, self, dim, half_to_float, out); |
| 4578 | } |
| 4579 | |
| 4580 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(unsafe_split_Tensor, name, "aten::unsafe_split") |
| 4581 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(unsafe_split_Tensor, overload_name, "Tensor") |
| 4582 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(unsafe_split_Tensor, schema_str, "unsafe_split.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[]") |
| 4583 | |
| 4584 | // aten::unsafe_split.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[] |
| 4585 | static C10_NOINLINE c10::TypedOperatorHandle<unsafe_split_Tensor::schema> create_unsafe_split_Tensor_typed_handle() { |
| 4586 | return c10::Dispatcher::singleton() |
| 4587 | .findSchemaOrThrow(unsafe_split_Tensor::name, unsafe_split_Tensor::overload_name) |
| 4588 | .typed<unsafe_split_Tensor::schema>(); |
| 4589 | } |
| 4590 | |
| 4591 | // aten::unsafe_split.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[] |
| 4592 | ::std::vector<at::Tensor> unsafe_split_Tensor::call(const at::Tensor & self, c10::SymInt split_size, int64_t dim) { |
| 4593 | |
| 4594 | static auto op = create_unsafe_split_Tensor_typed_handle(); |
| 4595 | return op.call(self, split_size, dim); |
| 4596 | } |
| 4597 | |
| 4598 | // aten::unsafe_split.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[] |
| 4599 | ::std::vector<at::Tensor> unsafe_split_Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt split_size, int64_t dim) { |
| 4600 | |
| 4601 | static auto op = create_unsafe_split_Tensor_typed_handle(); |
| 4602 | return op.redispatch(dispatchKeySet, self, split_size, dim); |
| 4603 | } |
| 4604 | |
| 4605 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(dsplit_int, name, "aten::dsplit") |
| 4606 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(dsplit_int, overload_name, "int") |
| 4607 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(dsplit_int, schema_str, "dsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[]") |
| 4608 | |
| 4609 | // aten::dsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[] |
| 4610 | static C10_NOINLINE c10::TypedOperatorHandle<dsplit_int::schema> create_dsplit_int_typed_handle() { |
| 4611 | return c10::Dispatcher::singleton() |
| 4612 | .findSchemaOrThrow(dsplit_int::name, dsplit_int::overload_name) |
| 4613 | .typed<dsplit_int::schema>(); |
| 4614 | } |
| 4615 | |
| 4616 | // aten::dsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[] |
| 4617 | ::std::vector<at::Tensor> dsplit_int::call(const at::Tensor & self, int64_t sections) { |
| 4618 | |
| 4619 | static auto op = create_dsplit_int_typed_handle(); |
| 4620 | return op.call(self, sections); |
| 4621 | } |
| 4622 | |
| 4623 | // aten::dsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[] |
| 4624 | ::std::vector<at::Tensor> dsplit_int::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t sections) { |
| 4625 | |
| 4626 | static auto op = create_dsplit_int_typed_handle(); |
| 4627 | return op.redispatch(dispatchKeySet, self, sections); |
| 4628 | } |
| 4629 | |
| 4630 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(dsplit_array, name, "aten::dsplit") |
| 4631 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(dsplit_array, overload_name, "array") |
| 4632 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(dsplit_array, schema_str, "dsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[]") |
| 4633 | |
| 4634 | // aten::dsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[] |
| 4635 | static C10_NOINLINE c10::TypedOperatorHandle<dsplit_array::schema> create_dsplit_array_typed_handle() { |
| 4636 | return c10::Dispatcher::singleton() |
| 4637 | .findSchemaOrThrow(dsplit_array::name, dsplit_array::overload_name) |
| 4638 | .typed<dsplit_array::schema>(); |
| 4639 | } |
| 4640 | |
| 4641 | // aten::dsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[] |
| 4642 | ::std::vector<at::Tensor> dsplit_array::call(const at::Tensor & self, at::IntArrayRef indices) { |
| 4643 | |
| 4644 | static auto op = create_dsplit_array_typed_handle(); |
| 4645 | return op.call(self, indices); |
| 4646 | } |
| 4647 | |
| 4648 | // aten::dsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[] |
| 4649 | ::std::vector<at::Tensor> dsplit_array::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef indices) { |
| 4650 | |
| 4651 | static auto op = create_dsplit_array_typed_handle(); |
| 4652 | return op.redispatch(dispatchKeySet, self, indices); |
| 4653 | } |
| 4654 | |
| 4655 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(vstack, name, "aten::vstack") |
| 4656 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(vstack, overload_name, "") |
| 4657 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(vstack, schema_str, "vstack(Tensor[] tensors) -> Tensor") |
| 4658 | |
| 4659 | // aten::vstack(Tensor[] tensors) -> Tensor |
| 4660 | static C10_NOINLINE c10::TypedOperatorHandle<vstack::schema> create_vstack_typed_handle() { |
| 4661 | return c10::Dispatcher::singleton() |
| 4662 | .findSchemaOrThrow(vstack::name, vstack::overload_name) |
| 4663 | .typed<vstack::schema>(); |
| 4664 | } |
| 4665 | |
| 4666 | // aten::vstack(Tensor[] tensors) -> Tensor |
| 4667 | at::Tensor vstack::call(at::TensorList tensors) { |
| 4668 | |
| 4669 | static auto op = create_vstack_typed_handle(); |
| 4670 | return op.call(tensors); |
| 4671 | } |
| 4672 | |
| 4673 | // aten::vstack(Tensor[] tensors) -> Tensor |
| 4674 | at::Tensor vstack::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors) { |
| 4675 | |
| 4676 | static auto op = create_vstack_typed_handle(); |
| 4677 | return op.redispatch(dispatchKeySet, tensors); |
| 4678 | } |
| 4679 | |
| 4680 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(vstack_out, name, "aten::vstack") |
| 4681 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(vstack_out, overload_name, "out") |
| 4682 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(vstack_out, schema_str, "vstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)") |
| 4683 | |
| 4684 | // aten::vstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) |
| 4685 | static C10_NOINLINE c10::TypedOperatorHandle<vstack_out::schema> create_vstack_out_typed_handle() { |
| 4686 | return c10::Dispatcher::singleton() |
| 4687 | .findSchemaOrThrow(vstack_out::name, vstack_out::overload_name) |
| 4688 | .typed<vstack_out::schema>(); |
| 4689 | } |
| 4690 | |
| 4691 | // aten::vstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) |
| 4692 | at::Tensor & vstack_out::call(at::TensorList tensors, at::Tensor & out) { |
| 4693 | |
| 4694 | static auto op = create_vstack_out_typed_handle(); |
| 4695 | return op.call(tensors, out); |
| 4696 | } |
| 4697 | |
| 4698 | // aten::vstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) |
| 4699 | at::Tensor & vstack_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Tensor & out) { |
| 4700 | |
| 4701 | static auto op = create_vstack_out_typed_handle(); |
| 4702 | return op.redispatch(dispatchKeySet, tensors, out); |
| 4703 | } |
| 4704 | |
| 4705 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(stft, name, "aten::stft") |
| 4706 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(stft, overload_name, "") |
| 4707 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(stft, schema_str, "stft(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool normalized=False, bool? onesided=None, bool? return_complex=None) -> Tensor") |
| 4708 | |
| 4709 | // aten::stft(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool normalized=False, bool? onesided=None, bool? return_complex=None) -> Tensor |
| 4710 | static C10_NOINLINE c10::TypedOperatorHandle<stft::schema> create_stft_typed_handle() { |
| 4711 | return c10::Dispatcher::singleton() |
| 4712 | .findSchemaOrThrow(stft::name, stft::overload_name) |
| 4713 | .typed<stft::schema>(); |
| 4714 | } |
| 4715 | |
| 4716 | // aten::stft(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool normalized=False, bool? onesided=None, bool? return_complex=None) -> Tensor |
| 4717 | at::Tensor stft::call(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 normalized, c10::optional<bool> onesided, c10::optional<bool> return_complex) { |
| 4718 | |
| 4719 | static auto op = create_stft_typed_handle(); |
| 4720 | return op.call(self, n_fft, hop_length, win_length, window, normalized, onesided, return_complex); |
| 4721 | } |
| 4722 | |
| 4723 | // aten::stft(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool normalized=False, bool? onesided=None, bool? return_complex=None) -> Tensor |
| 4724 | at::Tensor stft::redispatch(c10::DispatchKeySet dispatchKeySet, 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 normalized, c10::optional<bool> onesided, c10::optional<bool> return_complex) { |
| 4725 | |
| 4726 | static auto op = create_stft_typed_handle(); |
| 4727 | return op.redispatch(dispatchKeySet, self, n_fft, hop_length, win_length, window, normalized, onesided, return_complex); |
| 4728 | } |
| 4729 | |
| 4730 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(stft_center, name, "aten::stft") |
| 4731 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(stft_center, overload_name, "center") |
| 4732 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(stft_center, schema_str, "stft.center(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool center=True, str pad_mode=\"reflect\", bool normalized=False, bool? onesided=None, bool? return_complex=None) -> Tensor") |
| 4733 | |
| 4734 | // aten::stft.center(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool center=True, str pad_mode="reflect", bool normalized=False, bool? onesided=None, bool? return_complex=None) -> Tensor |
| 4735 | static C10_NOINLINE c10::TypedOperatorHandle<stft_center::schema> create_stft_center_typed_handle() { |
| 4736 | return c10::Dispatcher::singleton() |
| 4737 | .findSchemaOrThrow(stft_center::name, stft_center::overload_name) |
| 4738 | .typed<stft_center::schema>(); |
| 4739 | } |
| 4740 | |
| 4741 | // aten::stft.center(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool center=True, str pad_mode="reflect", bool normalized=False, bool? onesided=None, bool? return_complex=None) -> Tensor |
| 4742 | at::Tensor stft_center::call(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, c10::string_view pad_mode, bool normalized, c10::optional<bool> onesided, c10::optional<bool> return_complex) { |
| 4743 | |
| 4744 | static auto op = create_stft_center_typed_handle(); |
| 4745 | return op.call(self, n_fft, hop_length, win_length, window, center, pad_mode, normalized, onesided, return_complex); |
| 4746 | } |
| 4747 | |
| 4748 | // aten::stft.center(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool center=True, str pad_mode="reflect", bool normalized=False, bool? onesided=None, bool? return_complex=None) -> Tensor |
| 4749 | at::Tensor stft_center::redispatch(c10::DispatchKeySet dispatchKeySet, 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, c10::string_view pad_mode, bool normalized, c10::optional<bool> onesided, c10::optional<bool> return_complex) { |
| 4750 | |
| 4751 | static auto op = create_stft_center_typed_handle(); |
| 4752 | return op.redispatch(dispatchKeySet, self, n_fft, hop_length, win_length, window, center, pad_mode, normalized, onesided, return_complex); |
| 4753 | } |
| 4754 | |
| 4755 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_nested_sum_backward, name, "aten::_nested_sum_backward") |
| 4756 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_nested_sum_backward, overload_name, "") |
| 4757 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_nested_sum_backward, schema_str, "_nested_sum_backward(Tensor grad, Tensor self, int[1]? dim, bool keepdim=False) -> Tensor") |
| 4758 | |
| 4759 | // aten::_nested_sum_backward(Tensor grad, Tensor self, int[1]? dim, bool keepdim=False) -> Tensor |
| 4760 | static C10_NOINLINE c10::TypedOperatorHandle<_nested_sum_backward::schema> create__nested_sum_backward_typed_handle() { |
| 4761 | return c10::Dispatcher::singleton() |
| 4762 | .findSchemaOrThrow(_nested_sum_backward::name, _nested_sum_backward::overload_name) |
| 4763 | .typed<_nested_sum_backward::schema>(); |
| 4764 | } |
| 4765 | |
| 4766 | // aten::_nested_sum_backward(Tensor grad, Tensor self, int[1]? dim, bool keepdim=False) -> Tensor |
| 4767 | at::Tensor _nested_sum_backward::call(const at::Tensor & grad, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim) { |
| 4768 | |
| 4769 | static auto op = create__nested_sum_backward_typed_handle(); |
| 4770 | return op.call(grad, self, dim, keepdim); |
| 4771 | } |
| 4772 | |
| 4773 | // aten::_nested_sum_backward(Tensor grad, Tensor self, int[1]? dim, bool keepdim=False) -> Tensor |
| 4774 | at::Tensor _nested_sum_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim) { |
| 4775 | |
| 4776 | static auto op = create__nested_sum_backward_typed_handle(); |
| 4777 | return op.redispatch(dispatchKeySet, grad, self, dim, keepdim); |
| 4778 | } |
| 4779 | |
| 4780 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sum_to_size, name, "aten::sum_to_size") |
| 4781 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sum_to_size, overload_name, "") |
| 4782 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sum_to_size, schema_str, "sum_to_size(Tensor self, int[] size) -> Tensor") |
| 4783 | |
| 4784 | // aten::sum_to_size(Tensor self, int[] size) -> Tensor |
| 4785 | static C10_NOINLINE c10::TypedOperatorHandle<sum_to_size::schema> create_sum_to_size_typed_handle() { |
| 4786 | return c10::Dispatcher::singleton() |
| 4787 | .findSchemaOrThrow(sum_to_size::name, sum_to_size::overload_name) |
| 4788 | .typed<sum_to_size::schema>(); |
| 4789 | } |
| 4790 | |
| 4791 | // aten::sum_to_size(Tensor self, int[] size) -> Tensor |
| 4792 | at::Tensor sum_to_size::call(const at::Tensor & self, at::IntArrayRef size) { |
| 4793 | |
| 4794 | static auto op = create_sum_to_size_typed_handle(); |
| 4795 | return op.call(self, size); |
| 4796 | } |
| 4797 | |
| 4798 | // aten::sum_to_size(Tensor self, int[] size) -> Tensor |
| 4799 | at::Tensor sum_to_size::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size) { |
| 4800 | |
| 4801 | static auto op = create_sum_to_size_typed_handle(); |
| 4802 | return op.redispatch(dispatchKeySet, self, size); |
| 4803 | } |
| 4804 | |
| 4805 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sqrt, name, "aten::sqrt") |
| 4806 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sqrt, overload_name, "") |
| 4807 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sqrt, schema_str, "sqrt(Tensor self) -> Tensor") |
| 4808 | |
| 4809 | // aten::sqrt(Tensor self) -> Tensor |
| 4810 | static C10_NOINLINE c10::TypedOperatorHandle<sqrt::schema> create_sqrt_typed_handle() { |
| 4811 | return c10::Dispatcher::singleton() |
| 4812 | .findSchemaOrThrow(sqrt::name, sqrt::overload_name) |
| 4813 | .typed<sqrt::schema>(); |
| 4814 | } |
| 4815 | |
| 4816 | // aten::sqrt(Tensor self) -> Tensor |
| 4817 | at::Tensor sqrt::call(const at::Tensor & self) { |
| 4818 | |
| 4819 | static auto op = create_sqrt_typed_handle(); |
| 4820 | return op.call(self); |
| 4821 | } |
| 4822 | |
| 4823 | // aten::sqrt(Tensor self) -> Tensor |
| 4824 | at::Tensor sqrt::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 4825 | |
| 4826 | static auto op = create_sqrt_typed_handle(); |
| 4827 | return op.redispatch(dispatchKeySet, self); |
| 4828 | } |
| 4829 | |
| 4830 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sqrt_, name, "aten::sqrt_") |
| 4831 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sqrt_, overload_name, "") |
| 4832 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sqrt_, schema_str, "sqrt_(Tensor(a!) self) -> Tensor(a!)") |
| 4833 | |
| 4834 | // aten::sqrt_(Tensor(a!) self) -> Tensor(a!) |
| 4835 | static C10_NOINLINE c10::TypedOperatorHandle<sqrt_::schema> create_sqrt__typed_handle() { |
| 4836 | return c10::Dispatcher::singleton() |
| 4837 | .findSchemaOrThrow(sqrt_::name, sqrt_::overload_name) |
| 4838 | .typed<sqrt_::schema>(); |
| 4839 | } |
| 4840 | |
| 4841 | // aten::sqrt_(Tensor(a!) self) -> Tensor(a!) |
| 4842 | at::Tensor & sqrt_::call(at::Tensor & self) { |
| 4843 | |
| 4844 | static auto op = create_sqrt__typed_handle(); |
| 4845 | return op.call(self); |
| 4846 | } |
| 4847 | |
| 4848 | // aten::sqrt_(Tensor(a!) self) -> Tensor(a!) |
| 4849 | at::Tensor & sqrt_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 4850 | |
| 4851 | static auto op = create_sqrt__typed_handle(); |
| 4852 | return op.redispatch(dispatchKeySet, self); |
| 4853 | } |
| 4854 | |
| 4855 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sqrt_out, name, "aten::sqrt") |
| 4856 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sqrt_out, overload_name, "out") |
| 4857 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sqrt_out, schema_str, "sqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 4858 | |
| 4859 | // aten::sqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 4860 | static C10_NOINLINE c10::TypedOperatorHandle<sqrt_out::schema> create_sqrt_out_typed_handle() { |
| 4861 | return c10::Dispatcher::singleton() |
| 4862 | .findSchemaOrThrow(sqrt_out::name, sqrt_out::overload_name) |
| 4863 | .typed<sqrt_out::schema>(); |
| 4864 | } |
| 4865 | |
| 4866 | // aten::sqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 4867 | at::Tensor & sqrt_out::call(const at::Tensor & self, at::Tensor & out) { |
| 4868 | |
| 4869 | static auto op = create_sqrt_out_typed_handle(); |
| 4870 | return op.call(self, out); |
| 4871 | } |
| 4872 | |
| 4873 | // aten::sqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 4874 | at::Tensor & sqrt_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 4875 | |
| 4876 | static auto op = create_sqrt_out_typed_handle(); |
| 4877 | return op.redispatch(dispatchKeySet, self, out); |
| 4878 | } |
| 4879 | |
| 4880 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std, name, "aten::std") |
| 4881 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std, overload_name, "") |
| 4882 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std, schema_str, "std(Tensor self, bool unbiased=True) -> Tensor") |
| 4883 | |
| 4884 | // aten::std(Tensor self, bool unbiased=True) -> Tensor |
| 4885 | static C10_NOINLINE c10::TypedOperatorHandle<std::schema> create_std_typed_handle() { |
| 4886 | return c10::Dispatcher::singleton() |
| 4887 | .findSchemaOrThrow(std::name, std::overload_name) |
| 4888 | .typed<std::schema>(); |
| 4889 | } |
| 4890 | |
| 4891 | // aten::std(Tensor self, bool unbiased=True) -> Tensor |
| 4892 | at::Tensor std::call(const at::Tensor & self, bool unbiased) { |
| 4893 | |
| 4894 | static auto op = create_std_typed_handle(); |
| 4895 | return op.call(self, unbiased); |
| 4896 | } |
| 4897 | |
| 4898 | // aten::std(Tensor self, bool unbiased=True) -> Tensor |
| 4899 | at::Tensor std::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool unbiased) { |
| 4900 | |
| 4901 | static auto op = create_std_typed_handle(); |
| 4902 | return op.redispatch(dispatchKeySet, self, unbiased); |
| 4903 | } |
| 4904 | |
| 4905 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_dim, name, "aten::std") |
| 4906 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_dim, overload_name, "dim") |
| 4907 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_dim, schema_str, "std.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> Tensor") |
| 4908 | |
| 4909 | // aten::std.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> Tensor |
| 4910 | static C10_NOINLINE c10::TypedOperatorHandle<std_dim::schema> create_std_dim_typed_handle() { |
| 4911 | return c10::Dispatcher::singleton() |
| 4912 | .findSchemaOrThrow(std_dim::name, std_dim::overload_name) |
| 4913 | .typed<std_dim::schema>(); |
| 4914 | } |
| 4915 | |
| 4916 | // aten::std.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> Tensor |
| 4917 | at::Tensor std_dim::call(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim) { |
| 4918 | |
| 4919 | static auto op = create_std_dim_typed_handle(); |
| 4920 | return op.call(self, dim, unbiased, keepdim); |
| 4921 | } |
| 4922 | |
| 4923 | // aten::std.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> Tensor |
| 4924 | at::Tensor std_dim::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim) { |
| 4925 | |
| 4926 | static auto op = create_std_dim_typed_handle(); |
| 4927 | return op.redispatch(dispatchKeySet, self, dim, unbiased, keepdim); |
| 4928 | } |
| 4929 | |
| 4930 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_correction, name, "aten::std") |
| 4931 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_correction, overload_name, "correction") |
| 4932 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_correction, schema_str, "std.correction(Tensor self, int[1]? dim=None, *, int? correction=None, bool keepdim=False) -> Tensor") |
| 4933 | |
| 4934 | // aten::std.correction(Tensor self, int[1]? dim=None, *, int? correction=None, bool keepdim=False) -> Tensor |
| 4935 | static C10_NOINLINE c10::TypedOperatorHandle<std_correction::schema> create_std_correction_typed_handle() { |
| 4936 | return c10::Dispatcher::singleton() |
| 4937 | .findSchemaOrThrow(std_correction::name, std_correction::overload_name) |
| 4938 | .typed<std_correction::schema>(); |
| 4939 | } |
| 4940 | |
| 4941 | // aten::std.correction(Tensor self, int[1]? dim=None, *, int? correction=None, bool keepdim=False) -> Tensor |
| 4942 | at::Tensor std_correction::call(const at::Tensor & self, at::OptionalIntArrayRef dim, c10::optional<int64_t> correction, bool keepdim) { |
| 4943 | |
| 4944 | static auto op = create_std_correction_typed_handle(); |
| 4945 | return op.call(self, dim, correction, keepdim); |
| 4946 | } |
| 4947 | |
| 4948 | // aten::std.correction(Tensor self, int[1]? dim=None, *, int? correction=None, bool keepdim=False) -> Tensor |
| 4949 | at::Tensor std_correction::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, c10::optional<int64_t> correction, bool keepdim) { |
| 4950 | |
| 4951 | static auto op = create_std_correction_typed_handle(); |
| 4952 | return op.redispatch(dispatchKeySet, self, dim, correction, keepdim); |
| 4953 | } |
| 4954 | |
| 4955 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_mean, name, "aten::std_mean") |
| 4956 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_mean, overload_name, "") |
| 4957 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_mean, schema_str, "std_mean(Tensor self, bool unbiased=True) -> (Tensor, Tensor)") |
| 4958 | |
| 4959 | // aten::std_mean(Tensor self, bool unbiased=True) -> (Tensor, Tensor) |
| 4960 | static C10_NOINLINE c10::TypedOperatorHandle<std_mean::schema> create_std_mean_typed_handle() { |
| 4961 | return c10::Dispatcher::singleton() |
| 4962 | .findSchemaOrThrow(std_mean::name, std_mean::overload_name) |
| 4963 | .typed<std_mean::schema>(); |
| 4964 | } |
| 4965 | |
| 4966 | // aten::std_mean(Tensor self, bool unbiased=True) -> (Tensor, Tensor) |
| 4967 | ::std::tuple<at::Tensor,at::Tensor> std_mean::call(const at::Tensor & self, bool unbiased) { |
| 4968 | |
| 4969 | static auto op = create_std_mean_typed_handle(); |
| 4970 | return op.call(self, unbiased); |
| 4971 | } |
| 4972 | |
| 4973 | // aten::std_mean(Tensor self, bool unbiased=True) -> (Tensor, Tensor) |
| 4974 | ::std::tuple<at::Tensor,at::Tensor> std_mean::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool unbiased) { |
| 4975 | |
| 4976 | static auto op = create_std_mean_typed_handle(); |
| 4977 | return op.redispatch(dispatchKeySet, self, unbiased); |
| 4978 | } |
| 4979 | |
| 4980 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_mean_dim, name, "aten::std_mean") |
| 4981 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_mean_dim, overload_name, "dim") |
| 4982 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_mean_dim, schema_str, "std_mean.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor)") |
| 4983 | |
| 4984 | // aten::std_mean.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor) |
| 4985 | static C10_NOINLINE c10::TypedOperatorHandle<std_mean_dim::schema> create_std_mean_dim_typed_handle() { |
| 4986 | return c10::Dispatcher::singleton() |
| 4987 | .findSchemaOrThrow(std_mean_dim::name, std_mean_dim::overload_name) |
| 4988 | .typed<std_mean_dim::schema>(); |
| 4989 | } |
| 4990 | |
| 4991 | // aten::std_mean.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor) |
| 4992 | ::std::tuple<at::Tensor,at::Tensor> std_mean_dim::call(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim) { |
| 4993 | |
| 4994 | static auto op = create_std_mean_dim_typed_handle(); |
| 4995 | return op.call(self, dim, unbiased, keepdim); |
| 4996 | } |
| 4997 | |
| 4998 | // aten::std_mean.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor) |
| 4999 | ::std::tuple<at::Tensor,at::Tensor> std_mean_dim::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim) { |
| 5000 | |
| 5001 | static auto op = create_std_mean_dim_typed_handle(); |
| 5002 | return op.redispatch(dispatchKeySet, self, dim, unbiased, keepdim); |
| 5003 | } |
| 5004 | |
| 5005 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_mean_correction, name, "aten::std_mean") |
| 5006 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_mean_correction, overload_name, "correction") |
| 5007 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_mean_correction, schema_str, "std_mean.correction(Tensor self, int[1]? dim=None, *, int? correction=None, bool keepdim=False) -> (Tensor, Tensor)") |
| 5008 | |
| 5009 | // aten::std_mean.correction(Tensor self, int[1]? dim=None, *, int? correction=None, bool keepdim=False) -> (Tensor, Tensor) |
| 5010 | static C10_NOINLINE c10::TypedOperatorHandle<std_mean_correction::schema> create_std_mean_correction_typed_handle() { |
| 5011 | return c10::Dispatcher::singleton() |
| 5012 | .findSchemaOrThrow(std_mean_correction::name, std_mean_correction::overload_name) |
| 5013 | .typed<std_mean_correction::schema>(); |
| 5014 | } |
| 5015 | |
| 5016 | // aten::std_mean.correction(Tensor self, int[1]? dim=None, *, int? correction=None, bool keepdim=False) -> (Tensor, Tensor) |
| 5017 | ::std::tuple<at::Tensor,at::Tensor> std_mean_correction::call(const at::Tensor & self, at::OptionalIntArrayRef dim, c10::optional<int64_t> correction, bool keepdim) { |
| 5018 | |
| 5019 | static auto op = create_std_mean_correction_typed_handle(); |
| 5020 | return op.call(self, dim, correction, keepdim); |
| 5021 | } |
| 5022 | |
| 5023 | // aten::std_mean.correction(Tensor self, int[1]? dim=None, *, int? correction=None, bool keepdim=False) -> (Tensor, Tensor) |
| 5024 | ::std::tuple<at::Tensor,at::Tensor> std_mean_correction::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, c10::optional<int64_t> correction, bool keepdim) { |
| 5025 | |
| 5026 | static auto op = create_std_mean_correction_typed_handle(); |
| 5027 | return op.redispatch(dispatchKeySet, self, dim, correction, keepdim); |
| 5028 | } |
| 5029 | |
| 5030 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_mean_names_dim, name, "aten::std_mean") |
| 5031 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_mean_names_dim, overload_name, "names_dim") |
| 5032 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_mean_names_dim, schema_str, "std_mean.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor)") |
| 5033 | |
| 5034 | // aten::std_mean.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor) |
| 5035 | static C10_NOINLINE c10::TypedOperatorHandle<std_mean_names_dim::schema> create_std_mean_names_dim_typed_handle() { |
| 5036 | return c10::Dispatcher::singleton() |
| 5037 | .findSchemaOrThrow(std_mean_names_dim::name, std_mean_names_dim::overload_name) |
| 5038 | .typed<std_mean_names_dim::schema>(); |
| 5039 | } |
| 5040 | |
| 5041 | // aten::std_mean.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor) |
| 5042 | ::std::tuple<at::Tensor,at::Tensor> std_mean_names_dim::call(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim) { |
| 5043 | |
| 5044 | static auto op = create_std_mean_names_dim_typed_handle(); |
| 5045 | return op.call(self, dim, unbiased, keepdim); |
| 5046 | } |
| 5047 | |
| 5048 | // aten::std_mean.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor) |
| 5049 | ::std::tuple<at::Tensor,at::Tensor> std_mean_names_dim::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim) { |
| 5050 | |
| 5051 | static auto op = create_std_mean_names_dim_typed_handle(); |
| 5052 | return op.redispatch(dispatchKeySet, self, dim, unbiased, keepdim); |
| 5053 | } |
| 5054 | |
| 5055 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_mean_correction_names, name, "aten::std_mean") |
| 5056 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_mean_correction_names, overload_name, "correction_names") |
| 5057 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_mean_correction_names, schema_str, "std_mean.correction_names(Tensor self, Dimname[1] dim, *, int? correction=None, bool keepdim=False) -> (Tensor, Tensor)") |
| 5058 | |
| 5059 | // aten::std_mean.correction_names(Tensor self, Dimname[1] dim, *, int? correction=None, bool keepdim=False) -> (Tensor, Tensor) |
| 5060 | static C10_NOINLINE c10::TypedOperatorHandle<std_mean_correction_names::schema> create_std_mean_correction_names_typed_handle() { |
| 5061 | return c10::Dispatcher::singleton() |
| 5062 | .findSchemaOrThrow(std_mean_correction_names::name, std_mean_correction_names::overload_name) |
| 5063 | .typed<std_mean_correction_names::schema>(); |
| 5064 | } |
| 5065 | |
| 5066 | // aten::std_mean.correction_names(Tensor self, Dimname[1] dim, *, int? correction=None, bool keepdim=False) -> (Tensor, Tensor) |
| 5067 | ::std::tuple<at::Tensor,at::Tensor> std_mean_correction_names::call(const at::Tensor & self, at::DimnameList dim, c10::optional<int64_t> correction, bool keepdim) { |
| 5068 | |
| 5069 | static auto op = create_std_mean_correction_names_typed_handle(); |
| 5070 | return op.call(self, dim, correction, keepdim); |
| 5071 | } |
| 5072 | |
| 5073 | // aten::std_mean.correction_names(Tensor self, Dimname[1] dim, *, int? correction=None, bool keepdim=False) -> (Tensor, Tensor) |
| 5074 | ::std::tuple<at::Tensor,at::Tensor> std_mean_correction_names::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, c10::optional<int64_t> correction, bool keepdim) { |
| 5075 | |
| 5076 | static auto op = create_std_mean_correction_names_typed_handle(); |
| 5077 | return op.redispatch(dispatchKeySet, self, dim, correction, keepdim); |
| 5078 | } |
| 5079 | |
| 5080 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_out, name, "aten::std") |
| 5081 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_out, overload_name, "out") |
| 5082 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_out, schema_str, "std.out(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)") |
| 5083 | |
| 5084 | // aten::std.out(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) |
| 5085 | static C10_NOINLINE c10::TypedOperatorHandle<std_out::schema> create_std_out_typed_handle() { |
| 5086 | return c10::Dispatcher::singleton() |
| 5087 | .findSchemaOrThrow(std_out::name, std_out::overload_name) |
| 5088 | .typed<std_out::schema>(); |
| 5089 | } |
| 5090 | |
| 5091 | // aten::std.out(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) |
| 5092 | at::Tensor & std_out::call(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim, at::Tensor & out) { |
| 5093 | |
| 5094 | static auto op = create_std_out_typed_handle(); |
| 5095 | return op.call(self, dim, unbiased, keepdim, out); |
| 5096 | } |
| 5097 | |
| 5098 | // aten::std.out(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) |
| 5099 | at::Tensor & std_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim, at::Tensor & out) { |
| 5100 | |
| 5101 | static auto op = create_std_out_typed_handle(); |
| 5102 | return op.redispatch(dispatchKeySet, self, dim, unbiased, keepdim, out); |
| 5103 | } |
| 5104 | |
| 5105 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_correction_out, name, "aten::std") |
| 5106 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_correction_out, overload_name, "correction_out") |
| 5107 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_correction_out, schema_str, "std.correction_out(Tensor self, int[1]? dim=None, *, int? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)") |
| 5108 | |
| 5109 | // aten::std.correction_out(Tensor self, int[1]? dim=None, *, int? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!) |
| 5110 | static C10_NOINLINE c10::TypedOperatorHandle<std_correction_out::schema> create_std_correction_out_typed_handle() { |
| 5111 | return c10::Dispatcher::singleton() |
| 5112 | .findSchemaOrThrow(std_correction_out::name, std_correction_out::overload_name) |
| 5113 | .typed<std_correction_out::schema>(); |
| 5114 | } |
| 5115 | |
| 5116 | // aten::std.correction_out(Tensor self, int[1]? dim=None, *, int? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!) |
| 5117 | at::Tensor & std_correction_out::call(const at::Tensor & self, at::OptionalIntArrayRef dim, c10::optional<int64_t> correction, bool keepdim, at::Tensor & out) { |
| 5118 | |
| 5119 | static auto op = create_std_correction_out_typed_handle(); |
| 5120 | return op.call(self, dim, correction, keepdim, out); |
| 5121 | } |
| 5122 | |
| 5123 | // aten::std.correction_out(Tensor self, int[1]? dim=None, *, int? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!) |
| 5124 | at::Tensor & std_correction_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, c10::optional<int64_t> correction, bool keepdim, at::Tensor & out) { |
| 5125 | |
| 5126 | static auto op = create_std_correction_out_typed_handle(); |
| 5127 | return op.redispatch(dispatchKeySet, self, dim, correction, keepdim, out); |
| 5128 | } |
| 5129 | |
| 5130 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_names_dim, name, "aten::std") |
| 5131 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_names_dim, overload_name, "names_dim") |
| 5132 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_names_dim, schema_str, "std.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> Tensor") |
| 5133 | |
| 5134 | // aten::std.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> Tensor |
| 5135 | static C10_NOINLINE c10::TypedOperatorHandle<std_names_dim::schema> create_std_names_dim_typed_handle() { |
| 5136 | return c10::Dispatcher::singleton() |
| 5137 | .findSchemaOrThrow(std_names_dim::name, std_names_dim::overload_name) |
| 5138 | .typed<std_names_dim::schema>(); |
| 5139 | } |
| 5140 | |
| 5141 | // aten::std.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> Tensor |
| 5142 | at::Tensor std_names_dim::call(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim) { |
| 5143 | |
| 5144 | static auto op = create_std_names_dim_typed_handle(); |
| 5145 | return op.call(self, dim, unbiased, keepdim); |
| 5146 | } |
| 5147 | |
| 5148 | // aten::std.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> Tensor |
| 5149 | at::Tensor std_names_dim::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim) { |
| 5150 | |
| 5151 | static auto op = create_std_names_dim_typed_handle(); |
| 5152 | return op.redispatch(dispatchKeySet, self, dim, unbiased, keepdim); |
| 5153 | } |
| 5154 | |
| 5155 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_names_out, name, "aten::std") |
| 5156 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_names_out, overload_name, "names_out") |
| 5157 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_names_out, schema_str, "std.names_out(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)") |
| 5158 | |
| 5159 | // aten::std.names_out(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) |
| 5160 | static C10_NOINLINE c10::TypedOperatorHandle<std_names_out::schema> create_std_names_out_typed_handle() { |
| 5161 | return c10::Dispatcher::singleton() |
| 5162 | .findSchemaOrThrow(std_names_out::name, std_names_out::overload_name) |
| 5163 | .typed<std_names_out::schema>(); |
| 5164 | } |
| 5165 | |
| 5166 | // aten::std.names_out(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) |
| 5167 | at::Tensor & std_names_out::call(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim, at::Tensor & out) { |
| 5168 | |
| 5169 | static auto op = create_std_names_out_typed_handle(); |
| 5170 | return op.call(self, dim, unbiased, keepdim, out); |
| 5171 | } |
| 5172 | |
| 5173 | // aten::std.names_out(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) |
| 5174 | at::Tensor & std_names_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim, at::Tensor & out) { |
| 5175 | |
| 5176 | static auto op = create_std_names_out_typed_handle(); |
| 5177 | return op.redispatch(dispatchKeySet, self, dim, unbiased, keepdim, out); |
| 5178 | } |
| 5179 | |
| 5180 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_correction_names, name, "aten::std") |
| 5181 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_correction_names, overload_name, "correction_names") |
| 5182 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_correction_names, schema_str, "std.correction_names(Tensor self, Dimname[1] dim, *, int? correction=None, bool keepdim=False) -> Tensor") |
| 5183 | |
| 5184 | // aten::std.correction_names(Tensor self, Dimname[1] dim, *, int? correction=None, bool keepdim=False) -> Tensor |
| 5185 | static C10_NOINLINE c10::TypedOperatorHandle<std_correction_names::schema> create_std_correction_names_typed_handle() { |
| 5186 | return c10::Dispatcher::singleton() |
| 5187 | .findSchemaOrThrow(std_correction_names::name, std_correction_names::overload_name) |
| 5188 | .typed<std_correction_names::schema>(); |
| 5189 | } |
| 5190 | |
| 5191 | // aten::std.correction_names(Tensor self, Dimname[1] dim, *, int? correction=None, bool keepdim=False) -> Tensor |
| 5192 | at::Tensor std_correction_names::call(const at::Tensor & self, at::DimnameList dim, c10::optional<int64_t> correction, bool keepdim) { |
| 5193 | |
| 5194 | static auto op = create_std_correction_names_typed_handle(); |
| 5195 | return op.call(self, dim, correction, keepdim); |
| 5196 | } |
| 5197 | |
| 5198 | // aten::std.correction_names(Tensor self, Dimname[1] dim, *, int? correction=None, bool keepdim=False) -> Tensor |
| 5199 | at::Tensor std_correction_names::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, c10::optional<int64_t> correction, bool keepdim) { |
| 5200 | |
| 5201 | static auto op = create_std_correction_names_typed_handle(); |
| 5202 | return op.redispatch(dispatchKeySet, self, dim, correction, keepdim); |
| 5203 | } |
| 5204 | |
| 5205 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_correction_names_out, name, "aten::std") |
| 5206 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_correction_names_out, overload_name, "correction_names_out") |
| 5207 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_correction_names_out, schema_str, "std.correction_names_out(Tensor self, Dimname[1] dim, *, int? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)") |
| 5208 | |
| 5209 | // aten::std.correction_names_out(Tensor self, Dimname[1] dim, *, int? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!) |
| 5210 | static C10_NOINLINE c10::TypedOperatorHandle<std_correction_names_out::schema> create_std_correction_names_out_typed_handle() { |
| 5211 | return c10::Dispatcher::singleton() |
| 5212 | .findSchemaOrThrow(std_correction_names_out::name, std_correction_names_out::overload_name) |
| 5213 | .typed<std_correction_names_out::schema>(); |
| 5214 | } |
| 5215 | |
| 5216 | // aten::std.correction_names_out(Tensor self, Dimname[1] dim, *, int? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!) |
| 5217 | at::Tensor & std_correction_names_out::call(const at::Tensor & self, at::DimnameList dim, c10::optional<int64_t> correction, bool keepdim, at::Tensor & out) { |
| 5218 | |
| 5219 | static auto op = create_std_correction_names_out_typed_handle(); |
| 5220 | return op.call(self, dim, correction, keepdim, out); |
| 5221 | } |
| 5222 | |
| 5223 | // aten::std.correction_names_out(Tensor self, Dimname[1] dim, *, int? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!) |
| 5224 | at::Tensor & std_correction_names_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, c10::optional<int64_t> correction, bool keepdim, at::Tensor & out) { |
| 5225 | |
| 5226 | static auto op = create_std_correction_names_out_typed_handle(); |
| 5227 | return op.redispatch(dispatchKeySet, self, dim, correction, keepdim, out); |
| 5228 | } |
| 5229 | |
| 5230 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(t, name, "aten::t") |
| 5231 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(t, overload_name, "") |
| 5232 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(t, schema_str, "t(Tensor(a) self) -> Tensor(a)") |
| 5233 | |
| 5234 | // aten::t(Tensor(a) self) -> Tensor(a) |
| 5235 | static C10_NOINLINE c10::TypedOperatorHandle<t::schema> create_t_typed_handle() { |
| 5236 | return c10::Dispatcher::singleton() |
| 5237 | .findSchemaOrThrow(t::name, t::overload_name) |
| 5238 | .typed<t::schema>(); |
| 5239 | } |
| 5240 | |
| 5241 | // aten::t(Tensor(a) self) -> Tensor(a) |
| 5242 | at::Tensor t::call(const at::Tensor & self) { |
| 5243 | |
| 5244 | static auto op = create_t_typed_handle(); |
| 5245 | return op.call(self); |
| 5246 | } |
| 5247 | |
| 5248 | // aten::t(Tensor(a) self) -> Tensor(a) |
| 5249 | at::Tensor t::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 5250 | |
| 5251 | static auto op = create_t_typed_handle(); |
| 5252 | return op.redispatch(dispatchKeySet, self); |
| 5253 | } |
| 5254 | |
| 5255 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(t_, name, "aten::t_") |
| 5256 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(t_, overload_name, "") |
| 5257 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(t_, schema_str, "t_(Tensor(a!) self) -> Tensor(a!)") |
| 5258 | |
| 5259 | // aten::t_(Tensor(a!) self) -> Tensor(a!) |
| 5260 | static C10_NOINLINE c10::TypedOperatorHandle<t_::schema> create_t__typed_handle() { |
| 5261 | return c10::Dispatcher::singleton() |
| 5262 | .findSchemaOrThrow(t_::name, t_::overload_name) |
| 5263 | .typed<t_::schema>(); |
| 5264 | } |
| 5265 | |
| 5266 | // aten::t_(Tensor(a!) self) -> Tensor(a!) |
| 5267 | at::Tensor & t_::call(at::Tensor & self) { |
| 5268 | |
| 5269 | static auto op = create_t__typed_handle(); |
| 5270 | return op.call(self); |
| 5271 | } |
| 5272 | |
| 5273 | // aten::t_(Tensor(a!) self) -> Tensor(a!) |
| 5274 | at::Tensor & t_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 5275 | |
| 5276 | static auto op = create_t__typed_handle(); |
| 5277 | return op.redispatch(dispatchKeySet, self); |
| 5278 | } |
| 5279 | |
| 5280 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(threshold, name, "aten::threshold") |
| 5281 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(threshold, overload_name, "") |
| 5282 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(threshold, schema_str, "threshold(Tensor self, Scalar threshold, Scalar value) -> Tensor") |
| 5283 | |
| 5284 | // aten::threshold(Tensor self, Scalar threshold, Scalar value) -> Tensor |
| 5285 | static C10_NOINLINE c10::TypedOperatorHandle<threshold::schema> create_threshold_typed_handle() { |
| 5286 | return c10::Dispatcher::singleton() |
| 5287 | .findSchemaOrThrow(threshold::name, threshold::overload_name) |
| 5288 | .typed<threshold::schema>(); |
| 5289 | } |
| 5290 | |
| 5291 | // aten::threshold(Tensor self, Scalar threshold, Scalar value) -> Tensor |
| 5292 | at::Tensor threshold::call(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value) { |
| 5293 | |
| 5294 | static auto op = create_threshold_typed_handle(); |
| 5295 | return op.call(self, threshold, value); |
| 5296 | } |
| 5297 | |
| 5298 | // aten::threshold(Tensor self, Scalar threshold, Scalar value) -> Tensor |
| 5299 | at::Tensor threshold::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value) { |
| 5300 | |
| 5301 | static auto op = create_threshold_typed_handle(); |
| 5302 | return op.redispatch(dispatchKeySet, self, threshold, value); |
| 5303 | } |
| 5304 | |
| 5305 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(threshold_, name, "aten::threshold_") |
| 5306 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(threshold_, overload_name, "") |
| 5307 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(threshold_, schema_str, "threshold_(Tensor(a!) self, Scalar threshold, Scalar value) -> Tensor(a!)") |
| 5308 | |
| 5309 | // aten::threshold_(Tensor(a!) self, Scalar threshold, Scalar value) -> Tensor(a!) |
| 5310 | static C10_NOINLINE c10::TypedOperatorHandle<threshold_::schema> create_threshold__typed_handle() { |
| 5311 | return c10::Dispatcher::singleton() |
| 5312 | .findSchemaOrThrow(threshold_::name, threshold_::overload_name) |
| 5313 | .typed<threshold_::schema>(); |
| 5314 | } |
| 5315 | |
| 5316 | // aten::threshold_(Tensor(a!) self, Scalar threshold, Scalar value) -> Tensor(a!) |
| 5317 | at::Tensor & threshold_::call(at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value) { |
| 5318 | |
| 5319 | static auto op = create_threshold__typed_handle(); |
| 5320 | return op.call(self, threshold, value); |
| 5321 | } |
| 5322 | |
| 5323 | // aten::threshold_(Tensor(a!) self, Scalar threshold, Scalar value) -> Tensor(a!) |
| 5324 | at::Tensor & threshold_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value) { |
| 5325 | |
| 5326 | static auto op = create_threshold__typed_handle(); |
| 5327 | return op.redispatch(dispatchKeySet, self, threshold, value); |
| 5328 | } |
| 5329 | |
| 5330 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(threshold_out, name, "aten::threshold") |
| 5331 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(threshold_out, overload_name, "out") |
| 5332 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(threshold_out, schema_str, "threshold.out(Tensor self, Scalar threshold, Scalar value, *, Tensor(a!) out) -> Tensor(a!)") |
| 5333 | |
| 5334 | // aten::threshold.out(Tensor self, Scalar threshold, Scalar value, *, Tensor(a!) out) -> Tensor(a!) |
| 5335 | static C10_NOINLINE c10::TypedOperatorHandle<threshold_out::schema> create_threshold_out_typed_handle() { |
| 5336 | return c10::Dispatcher::singleton() |
| 5337 | .findSchemaOrThrow(threshold_out::name, threshold_out::overload_name) |
| 5338 | .typed<threshold_out::schema>(); |
| 5339 | } |
| 5340 | |
| 5341 | // aten::threshold.out(Tensor self, Scalar threshold, Scalar value, *, Tensor(a!) out) -> Tensor(a!) |
| 5342 | at::Tensor & threshold_out::call(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value, at::Tensor & out) { |
| 5343 | |
| 5344 | static auto op = create_threshold_out_typed_handle(); |
| 5345 | return op.call(self, threshold, value, out); |
| 5346 | } |
| 5347 | |
| 5348 | // aten::threshold.out(Tensor self, Scalar threshold, Scalar value, *, Tensor(a!) out) -> Tensor(a!) |
| 5349 | at::Tensor & threshold_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value, at::Tensor & out) { |
| 5350 | |
| 5351 | static auto op = create_threshold_out_typed_handle(); |
| 5352 | return op.redispatch(dispatchKeySet, self, threshold, value, out); |
| 5353 | } |
| 5354 | |
| 5355 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(transpose_int, name, "aten::transpose") |
| 5356 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(transpose_int, overload_name, "int") |
| 5357 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(transpose_int, schema_str, "transpose.int(Tensor(a) self, int dim0, int dim1) -> Tensor(a)") |
| 5358 | |
| 5359 | // aten::transpose.int(Tensor(a) self, int dim0, int dim1) -> Tensor(a) |
| 5360 | static C10_NOINLINE c10::TypedOperatorHandle<transpose_int::schema> create_transpose_int_typed_handle() { |
| 5361 | return c10::Dispatcher::singleton() |
| 5362 | .findSchemaOrThrow(transpose_int::name, transpose_int::overload_name) |
| 5363 | .typed<transpose_int::schema>(); |
| 5364 | } |
| 5365 | |
| 5366 | // aten::transpose.int(Tensor(a) self, int dim0, int dim1) -> Tensor(a) |
| 5367 | at::Tensor transpose_int::call(const at::Tensor & self, int64_t dim0, int64_t dim1) { |
| 5368 | |
| 5369 | static auto op = create_transpose_int_typed_handle(); |
| 5370 | return op.call(self, dim0, dim1); |
| 5371 | } |
| 5372 | |
| 5373 | // aten::transpose.int(Tensor(a) self, int dim0, int dim1) -> Tensor(a) |
| 5374 | at::Tensor transpose_int::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim0, int64_t dim1) { |
| 5375 | |
| 5376 | static auto op = create_transpose_int_typed_handle(); |
| 5377 | return op.redispatch(dispatchKeySet, self, dim0, dim1); |
| 5378 | } |
| 5379 | |
| 5380 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(transpose_Dimname, name, "aten::transpose") |
| 5381 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(transpose_Dimname, overload_name, "Dimname") |
| 5382 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(transpose_Dimname, schema_str, "transpose.Dimname(Tensor(a) self, Dimname dim0, Dimname dim1) -> Tensor(a)") |
| 5383 | |
| 5384 | // aten::transpose.Dimname(Tensor(a) self, Dimname dim0, Dimname dim1) -> Tensor(a) |
| 5385 | static C10_NOINLINE c10::TypedOperatorHandle<transpose_Dimname::schema> create_transpose_Dimname_typed_handle() { |
| 5386 | return c10::Dispatcher::singleton() |
| 5387 | .findSchemaOrThrow(transpose_Dimname::name, transpose_Dimname::overload_name) |
| 5388 | .typed<transpose_Dimname::schema>(); |
| 5389 | } |
| 5390 | |
| 5391 | // aten::transpose.Dimname(Tensor(a) self, Dimname dim0, Dimname dim1) -> Tensor(a) |
| 5392 | at::Tensor transpose_Dimname::call(const at::Tensor & self, at::Dimname dim0, at::Dimname dim1) { |
| 5393 | |
| 5394 | static auto op = create_transpose_Dimname_typed_handle(); |
| 5395 | return op.call(self, dim0, dim1); |
| 5396 | } |
| 5397 | |
| 5398 | // aten::transpose.Dimname(Tensor(a) self, Dimname dim0, Dimname dim1) -> Tensor(a) |
| 5399 | at::Tensor transpose_Dimname::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim0, at::Dimname dim1) { |
| 5400 | |
| 5401 | static auto op = create_transpose_Dimname_typed_handle(); |
| 5402 | return op.redispatch(dispatchKeySet, self, dim0, dim1); |
| 5403 | } |
| 5404 | |
| 5405 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(transpose_, name, "aten::transpose_") |
| 5406 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(transpose_, overload_name, "") |
| 5407 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(transpose_, schema_str, "transpose_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!)") |
| 5408 | |
| 5409 | // aten::transpose_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!) |
| 5410 | static C10_NOINLINE c10::TypedOperatorHandle<transpose_::schema> create_transpose__typed_handle() { |
| 5411 | return c10::Dispatcher::singleton() |
| 5412 | .findSchemaOrThrow(transpose_::name, transpose_::overload_name) |
| 5413 | .typed<transpose_::schema>(); |
| 5414 | } |
| 5415 | |
| 5416 | // aten::transpose_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!) |
| 5417 | at::Tensor & transpose_::call(at::Tensor & self, int64_t dim0, int64_t dim1) { |
| 5418 | |
| 5419 | static auto op = create_transpose__typed_handle(); |
| 5420 | return op.call(self, dim0, dim1); |
| 5421 | } |
| 5422 | |
| 5423 | // aten::transpose_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!) |
| 5424 | at::Tensor & transpose_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim0, int64_t dim1) { |
| 5425 | |
| 5426 | static auto op = create_transpose__typed_handle(); |
| 5427 | return op.redispatch(dispatchKeySet, self, dim0, dim1); |
| 5428 | } |
| 5429 | |
| 5430 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(flip, name, "aten::flip") |
| 5431 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(flip, overload_name, "") |
| 5432 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(flip, schema_str, "flip(Tensor self, int[] dims) -> Tensor") |
| 5433 | |
| 5434 | // aten::flip(Tensor self, int[] dims) -> Tensor |
| 5435 | static C10_NOINLINE c10::TypedOperatorHandle<flip::schema> create_flip_typed_handle() { |
| 5436 | return c10::Dispatcher::singleton() |
| 5437 | .findSchemaOrThrow(flip::name, flip::overload_name) |
| 5438 | .typed<flip::schema>(); |
| 5439 | } |
| 5440 | |
| 5441 | // aten::flip(Tensor self, int[] dims) -> Tensor |
| 5442 | at::Tensor flip::call(const at::Tensor & self, at::IntArrayRef dims) { |
| 5443 | |
| 5444 | static auto op = create_flip_typed_handle(); |
| 5445 | return op.call(self, dims); |
| 5446 | } |
| 5447 | |
| 5448 | // aten::flip(Tensor self, int[] dims) -> Tensor |
| 5449 | at::Tensor flip::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dims) { |
| 5450 | |
| 5451 | static auto op = create_flip_typed_handle(); |
| 5452 | return op.redispatch(dispatchKeySet, self, dims); |
| 5453 | } |
| 5454 | |
| 5455 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(roll, name, "aten::roll") |
| 5456 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(roll, overload_name, "") |
| 5457 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(roll, schema_str, "roll(Tensor self, int[1] shifts, int[1] dims=[]) -> Tensor") |
| 5458 | |
| 5459 | // aten::roll(Tensor self, int[1] shifts, int[1] dims=[]) -> Tensor |
| 5460 | static C10_NOINLINE c10::TypedOperatorHandle<roll::schema> create_roll_typed_handle() { |
| 5461 | return c10::Dispatcher::singleton() |
| 5462 | .findSchemaOrThrow(roll::name, roll::overload_name) |
| 5463 | .typed<roll::schema>(); |
| 5464 | } |
| 5465 | |
| 5466 | // aten::roll(Tensor self, int[1] shifts, int[1] dims=[]) -> Tensor |
| 5467 | at::Tensor roll::call(const at::Tensor & self, at::IntArrayRef shifts, at::IntArrayRef dims) { |
| 5468 | |
| 5469 | static auto op = create_roll_typed_handle(); |
| 5470 | return op.call(self, shifts, dims); |
| 5471 | } |
| 5472 | |
| 5473 | // aten::roll(Tensor self, int[1] shifts, int[1] dims=[]) -> Tensor |
| 5474 | at::Tensor roll::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef shifts, at::IntArrayRef dims) { |
| 5475 | |
| 5476 | static auto op = create_roll_typed_handle(); |
| 5477 | return op.redispatch(dispatchKeySet, self, shifts, dims); |
| 5478 | } |
| 5479 | |
| 5480 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_nested_from_padded, name, "aten::_nested_from_padded") |
| 5481 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_nested_from_padded, overload_name, "") |
| 5482 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_nested_from_padded, schema_str, "_nested_from_padded(Tensor padded, Tensor cpu_nested_shape_example, bool fuse_transform_0213=False) -> Tensor") |
| 5483 | |
| 5484 | // aten::_nested_from_padded(Tensor padded, Tensor cpu_nested_shape_example, bool fuse_transform_0213=False) -> Tensor |
| 5485 | static C10_NOINLINE c10::TypedOperatorHandle<_nested_from_padded::schema> create__nested_from_padded_typed_handle() { |
| 5486 | return c10::Dispatcher::singleton() |
| 5487 | .findSchemaOrThrow(_nested_from_padded::name, _nested_from_padded::overload_name) |
| 5488 | .typed<_nested_from_padded::schema>(); |
| 5489 | } |
| 5490 | |
| 5491 | // aten::_nested_from_padded(Tensor padded, Tensor cpu_nested_shape_example, bool fuse_transform_0213=False) -> Tensor |
| 5492 | at::Tensor _nested_from_padded::call(const at::Tensor & padded, const at::Tensor & cpu_nested_shape_example, bool fuse_transform_0213) { |
| 5493 | |
| 5494 | static auto op = create__nested_from_padded_typed_handle(); |
| 5495 | return op.call(padded, cpu_nested_shape_example, fuse_transform_0213); |
| 5496 | } |
| 5497 | |
| 5498 | // aten::_nested_from_padded(Tensor padded, Tensor cpu_nested_shape_example, bool fuse_transform_0213=False) -> Tensor |
| 5499 | at::Tensor _nested_from_padded::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & padded, const at::Tensor & cpu_nested_shape_example, bool fuse_transform_0213) { |
| 5500 | |
| 5501 | static auto op = create__nested_from_padded_typed_handle(); |
| 5502 | return op.redispatch(dispatchKeySet, padded, cpu_nested_shape_example, fuse_transform_0213); |
| 5503 | } |
| 5504 | |
| 5505 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_nested_view_from_buffer, name, "aten::_nested_view_from_buffer") |
| 5506 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_nested_view_from_buffer, overload_name, "") |
| 5507 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_nested_view_from_buffer, schema_str, "_nested_view_from_buffer(Tensor(a) self, Tensor nested_size, Tensor nested_strides, int[] offsets) -> Tensor(a)") |
| 5508 | |
| 5509 | // aten::_nested_view_from_buffer(Tensor(a) self, Tensor nested_size, Tensor nested_strides, int[] offsets) -> Tensor(a) |
| 5510 | static C10_NOINLINE c10::TypedOperatorHandle<_nested_view_from_buffer::schema> create__nested_view_from_buffer_typed_handle() { |
| 5511 | return c10::Dispatcher::singleton() |
| 5512 | .findSchemaOrThrow(_nested_view_from_buffer::name, _nested_view_from_buffer::overload_name) |
| 5513 | .typed<_nested_view_from_buffer::schema>(); |
| 5514 | } |
| 5515 | |
| 5516 | // aten::_nested_view_from_buffer(Tensor(a) self, Tensor nested_size, Tensor nested_strides, int[] offsets) -> Tensor(a) |
| 5517 | at::Tensor _nested_view_from_buffer::call(const at::Tensor & self, const at::Tensor & nested_size, const at::Tensor & nested_strides, at::IntArrayRef offsets) { |
| 5518 | |
| 5519 | static auto op = create__nested_view_from_buffer_typed_handle(); |
| 5520 | return op.call(self, nested_size, nested_strides, offsets); |
| 5521 | } |
| 5522 | |
| 5523 | // aten::_nested_view_from_buffer(Tensor(a) self, Tensor nested_size, Tensor nested_strides, int[] offsets) -> Tensor(a) |
| 5524 | at::Tensor _nested_view_from_buffer::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & nested_size, const at::Tensor & nested_strides, at::IntArrayRef offsets) { |
| 5525 | |
| 5526 | static auto op = create__nested_view_from_buffer_typed_handle(); |
| 5527 | return op.redispatch(dispatchKeySet, self, nested_size, nested_strides, offsets); |
| 5528 | } |
| 5529 | |
| 5530 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_trilinear, name, "aten::_trilinear") |
| 5531 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_trilinear, overload_name, "") |
| 5532 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_trilinear, schema_str, "_trilinear(Tensor i1, Tensor i2, Tensor i3, int[] expand1, int[] expand2, int[] expand3, int[] sumdim, int unroll_dim=1) -> Tensor") |
| 5533 | |
| 5534 | // aten::_trilinear(Tensor i1, Tensor i2, Tensor i3, int[] expand1, int[] expand2, int[] expand3, int[] sumdim, int unroll_dim=1) -> Tensor |
| 5535 | static C10_NOINLINE c10::TypedOperatorHandle<_trilinear::schema> create__trilinear_typed_handle() { |
| 5536 | return c10::Dispatcher::singleton() |
| 5537 | .findSchemaOrThrow(_trilinear::name, _trilinear::overload_name) |
| 5538 | .typed<_trilinear::schema>(); |
| 5539 | } |
| 5540 | |
| 5541 | // aten::_trilinear(Tensor i1, Tensor i2, Tensor i3, int[] expand1, int[] expand2, int[] expand3, int[] sumdim, int unroll_dim=1) -> Tensor |
| 5542 | at::Tensor _trilinear::call(const at::Tensor & i1, const at::Tensor & i2, const at::Tensor & i3, at::IntArrayRef expand1, at::IntArrayRef expand2, at::IntArrayRef expand3, at::IntArrayRef sumdim, int64_t unroll_dim) { |
| 5543 | |
| 5544 | static auto op = create__trilinear_typed_handle(); |
| 5545 | return op.call(i1, i2, i3, expand1, expand2, expand3, sumdim, unroll_dim); |
| 5546 | } |
| 5547 | |
| 5548 | // aten::_trilinear(Tensor i1, Tensor i2, Tensor i3, int[] expand1, int[] expand2, int[] expand3, int[] sumdim, int unroll_dim=1) -> Tensor |
| 5549 | at::Tensor _trilinear::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & i1, const at::Tensor & i2, const at::Tensor & i3, at::IntArrayRef expand1, at::IntArrayRef expand2, at::IntArrayRef expand3, at::IntArrayRef sumdim, int64_t unroll_dim) { |
| 5550 | |
| 5551 | static auto op = create__trilinear_typed_handle(); |
| 5552 | return op.redispatch(dispatchKeySet, i1, i2, i3, expand1, expand2, expand3, sumdim, unroll_dim); |
| 5553 | } |
| 5554 | |
| 5555 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(type_as, name, "aten::type_as") |
| 5556 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(type_as, overload_name, "") |
| 5557 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(type_as, schema_str, "type_as(Tensor self, Tensor other) -> Tensor") |
| 5558 | |
| 5559 | // aten::type_as(Tensor self, Tensor other) -> Tensor |
| 5560 | static C10_NOINLINE c10::TypedOperatorHandle<type_as::schema> create_type_as_typed_handle() { |
| 5561 | return c10::Dispatcher::singleton() |
| 5562 | .findSchemaOrThrow(type_as::name, type_as::overload_name) |
| 5563 | .typed<type_as::schema>(); |
| 5564 | } |
| 5565 | |
| 5566 | // aten::type_as(Tensor self, Tensor other) -> Tensor |
| 5567 | at::Tensor type_as::call(const at::Tensor & self, const at::Tensor & other) { |
| 5568 | |
| 5569 | static auto op = create_type_as_typed_handle(); |
| 5570 | return op.call(self, other); |
| 5571 | } |
| 5572 | |
| 5573 | // aten::type_as(Tensor self, Tensor other) -> Tensor |
| 5574 | at::Tensor type_as::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { |
| 5575 | |
| 5576 | static auto op = create_type_as_typed_handle(); |
| 5577 | return op.redispatch(dispatchKeySet, self, other); |
| 5578 | } |
| 5579 | |
| 5580 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_has_compatible_shallow_copy_type, name, "aten::_has_compatible_shallow_copy_type") |
| 5581 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_has_compatible_shallow_copy_type, overload_name, "") |
| 5582 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_has_compatible_shallow_copy_type, schema_str, "_has_compatible_shallow_copy_type(Tensor self, Tensor from) -> bool") |
| 5583 | |
| 5584 | // aten::_has_compatible_shallow_copy_type(Tensor self, Tensor from) -> bool |
| 5585 | static C10_NOINLINE c10::TypedOperatorHandle<_has_compatible_shallow_copy_type::schema> create__has_compatible_shallow_copy_type_typed_handle() { |
| 5586 | return c10::Dispatcher::singleton() |
| 5587 | .findSchemaOrThrow(_has_compatible_shallow_copy_type::name, _has_compatible_shallow_copy_type::overload_name) |
| 5588 | .typed<_has_compatible_shallow_copy_type::schema>(); |
| 5589 | } |
| 5590 | |
| 5591 | // aten::_has_compatible_shallow_copy_type(Tensor self, Tensor from) -> bool |
| 5592 | bool _has_compatible_shallow_copy_type::call(const at::Tensor & self, const at::Tensor & from) { |
| 5593 | |
| 5594 | static auto op = create__has_compatible_shallow_copy_type_typed_handle(); |
| 5595 | return op.call(self, from); |
| 5596 | } |
| 5597 | |
| 5598 | // aten::_has_compatible_shallow_copy_type(Tensor self, Tensor from) -> bool |
| 5599 | bool _has_compatible_shallow_copy_type::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & from) { |
| 5600 | |
| 5601 | static auto op = create__has_compatible_shallow_copy_type_typed_handle(); |
| 5602 | return op.redispatch(dispatchKeySet, self, from); |
| 5603 | } |
| 5604 | |
| 5605 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_unique2, name, "aten::_unique2") |
| 5606 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_unique2, overload_name, "") |
| 5607 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_unique2, schema_str, "_unique2(Tensor self, bool sorted=True, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor)") |
| 5608 | |
| 5609 | // aten::_unique2(Tensor self, bool sorted=True, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor) |
| 5610 | static C10_NOINLINE c10::TypedOperatorHandle<_unique2::schema> create__unique2_typed_handle() { |
| 5611 | return c10::Dispatcher::singleton() |
| 5612 | .findSchemaOrThrow(_unique2::name, _unique2::overload_name) |
| 5613 | .typed<_unique2::schema>(); |
| 5614 | } |
| 5615 | |
| 5616 | // aten::_unique2(Tensor self, bool sorted=True, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor) |
| 5617 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _unique2::call(const at::Tensor & self, bool sorted, bool return_inverse, bool return_counts) { |
| 5618 | |
| 5619 | static auto op = create__unique2_typed_handle(); |
| 5620 | return op.call(self, sorted, return_inverse, return_counts); |
| 5621 | } |
| 5622 | |
| 5623 | // aten::_unique2(Tensor self, bool sorted=True, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor) |
| 5624 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _unique2::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool sorted, bool return_inverse, bool return_counts) { |
| 5625 | |
| 5626 | static auto op = create__unique2_typed_handle(); |
| 5627 | return op.redispatch(dispatchKeySet, self, sorted, return_inverse, return_counts); |
| 5628 | } |
| 5629 | |
| 5630 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_weight_norm_interface_backward, name, "aten::_weight_norm_interface_backward") |
| 5631 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_weight_norm_interface_backward, overload_name, "") |
| 5632 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_weight_norm_interface_backward, schema_str, "_weight_norm_interface_backward(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim) -> (Tensor, Tensor)") |
| 5633 | |
| 5634 | // aten::_weight_norm_interface_backward(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim) -> (Tensor, Tensor) |
| 5635 | static C10_NOINLINE c10::TypedOperatorHandle<_weight_norm_interface_backward::schema> create__weight_norm_interface_backward_typed_handle() { |
| 5636 | return c10::Dispatcher::singleton() |
| 5637 | .findSchemaOrThrow(_weight_norm_interface_backward::name, _weight_norm_interface_backward::overload_name) |
| 5638 | .typed<_weight_norm_interface_backward::schema>(); |
| 5639 | } |
| 5640 | |
| 5641 | // aten::_weight_norm_interface_backward(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim) -> (Tensor, Tensor) |
| 5642 | ::std::tuple<at::Tensor,at::Tensor> _weight_norm_interface_backward::call(const at::Tensor & grad_w, const at::Tensor & saved_v, const at::Tensor & saved_g, const at::Tensor & saved_norms, int64_t dim) { |
| 5643 | |
| 5644 | static auto op = create__weight_norm_interface_backward_typed_handle(); |
| 5645 | return op.call(grad_w, saved_v, saved_g, saved_norms, dim); |
| 5646 | } |
| 5647 | |
| 5648 | // aten::_weight_norm_interface_backward(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim) -> (Tensor, Tensor) |
| 5649 | ::std::tuple<at::Tensor,at::Tensor> _weight_norm_interface_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_w, const at::Tensor & saved_v, const at::Tensor & saved_g, const at::Tensor & saved_norms, int64_t dim) { |
| 5650 | |
| 5651 | static auto op = create__weight_norm_interface_backward_typed_handle(); |
| 5652 | return op.redispatch(dispatchKeySet, grad_w, saved_v, saved_g, saved_norms, dim); |
| 5653 | } |
| 5654 | |
| 5655 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(zeros_like, name, "aten::zeros_like") |
| 5656 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(zeros_like, overload_name, "") |
| 5657 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(zeros_like, schema_str, "zeros_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor") |
| 5658 | |
| 5659 | // aten::zeros_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor |
| 5660 | static C10_NOINLINE c10::TypedOperatorHandle<zeros_like::schema> create_zeros_like_typed_handle() { |
| 5661 | return c10::Dispatcher::singleton() |
| 5662 | .findSchemaOrThrow(zeros_like::name, zeros_like::overload_name) |
| 5663 | .typed<zeros_like::schema>(); |
| 5664 | } |
| 5665 | |
| 5666 | // aten::zeros_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor |
| 5667 | at::Tensor zeros_like::call(const at::Tensor & self, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory, c10::optional<at::MemoryFormat> memory_format) { |
| 5668 | |
| 5669 | static auto op = create_zeros_like_typed_handle(); |
| 5670 | return op.call(self, dtype, layout, device, pin_memory, memory_format); |
| 5671 | } |
| 5672 | |
| 5673 | // aten::zeros_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor |
| 5674 | at::Tensor zeros_like::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory, c10::optional<at::MemoryFormat> memory_format) { |
| 5675 | |
| 5676 | static auto op = create_zeros_like_typed_handle(); |
| 5677 | return op.redispatch(dispatchKeySet, self, dtype, layout, device, pin_memory, memory_format); |
| 5678 | } |
| 5679 | |
| 5680 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_csr_prod_dim_dtype, name, "aten::_sparse_csr_prod") |
| 5681 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_csr_prod_dim_dtype, overload_name, "dim_dtype") |
| 5682 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_csr_prod_dim_dtype, schema_str, "_sparse_csr_prod.dim_dtype(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor") |
| 5683 | |
| 5684 | // aten::_sparse_csr_prod.dim_dtype(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor |
| 5685 | static C10_NOINLINE c10::TypedOperatorHandle<_sparse_csr_prod_dim_dtype::schema> create__sparse_csr_prod_dim_dtype_typed_handle() { |
| 5686 | return c10::Dispatcher::singleton() |
| 5687 | .findSchemaOrThrow(_sparse_csr_prod_dim_dtype::name, _sparse_csr_prod_dim_dtype::overload_name) |
| 5688 | .typed<_sparse_csr_prod_dim_dtype::schema>(); |
| 5689 | } |
| 5690 | |
| 5691 | // aten::_sparse_csr_prod.dim_dtype(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor |
| 5692 | at::Tensor _sparse_csr_prod_dim_dtype::call(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 5693 | |
| 5694 | static auto op = create__sparse_csr_prod_dim_dtype_typed_handle(); |
| 5695 | return op.call(self, dim, keepdim, dtype); |
| 5696 | } |
| 5697 | |
| 5698 | // aten::_sparse_csr_prod.dim_dtype(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor |
| 5699 | at::Tensor _sparse_csr_prod_dim_dtype::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 5700 | |
| 5701 | static auto op = create__sparse_csr_prod_dim_dtype_typed_handle(); |
| 5702 | return op.redispatch(dispatchKeySet, self, dim, keepdim, dtype); |
| 5703 | } |
| 5704 | |
| 5705 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_softmax_backward_data, name, "aten::_sparse_softmax_backward_data") |
| 5706 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_softmax_backward_data, overload_name, "") |
| 5707 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_softmax_backward_data, schema_str, "_sparse_softmax_backward_data(Tensor grad_output, Tensor output, int dim, Tensor self) -> Tensor") |
| 5708 | |
| 5709 | // aten::_sparse_softmax_backward_data(Tensor grad_output, Tensor output, int dim, Tensor self) -> Tensor |
| 5710 | static C10_NOINLINE c10::TypedOperatorHandle<_sparse_softmax_backward_data::schema> create__sparse_softmax_backward_data_typed_handle() { |
| 5711 | return c10::Dispatcher::singleton() |
| 5712 | .findSchemaOrThrow(_sparse_softmax_backward_data::name, _sparse_softmax_backward_data::overload_name) |
| 5713 | .typed<_sparse_softmax_backward_data::schema>(); |
| 5714 | } |
| 5715 | |
| 5716 | // aten::_sparse_softmax_backward_data(Tensor grad_output, Tensor output, int dim, Tensor self) -> Tensor |
| 5717 | at::Tensor _sparse_softmax_backward_data::call(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self) { |
| 5718 | |
| 5719 | static auto op = create__sparse_softmax_backward_data_typed_handle(); |
| 5720 | return op.call(grad_output, output, dim, self); |
| 5721 | } |
| 5722 | |
| 5723 | // aten::_sparse_softmax_backward_data(Tensor grad_output, Tensor output, int dim, Tensor self) -> Tensor |
| 5724 | at::Tensor _sparse_softmax_backward_data::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self) { |
| 5725 | |
| 5726 | static auto op = create__sparse_softmax_backward_data_typed_handle(); |
| 5727 | return op.redispatch(dispatchKeySet, grad_output, output, dim, self); |
| 5728 | } |
| 5729 | |
| 5730 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_log_softmax_int, name, "aten::_sparse_log_softmax") |
| 5731 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_log_softmax_int, overload_name, "int") |
| 5732 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_log_softmax_int, schema_str, "_sparse_log_softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor") |
| 5733 | |
| 5734 | // aten::_sparse_log_softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor |
| 5735 | static C10_NOINLINE c10::TypedOperatorHandle<_sparse_log_softmax_int::schema> create__sparse_log_softmax_int_typed_handle() { |
| 5736 | return c10::Dispatcher::singleton() |
| 5737 | .findSchemaOrThrow(_sparse_log_softmax_int::name, _sparse_log_softmax_int::overload_name) |
| 5738 | .typed<_sparse_log_softmax_int::schema>(); |
| 5739 | } |
| 5740 | |
| 5741 | // aten::_sparse_log_softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor |
| 5742 | at::Tensor _sparse_log_softmax_int::call(const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 5743 | |
| 5744 | static auto op = create__sparse_log_softmax_int_typed_handle(); |
| 5745 | return op.call(self, dim, dtype); |
| 5746 | } |
| 5747 | |
| 5748 | // aten::_sparse_log_softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor |
| 5749 | at::Tensor _sparse_log_softmax_int::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 5750 | |
| 5751 | static auto op = create__sparse_log_softmax_int_typed_handle(); |
| 5752 | return op.redispatch(dispatchKeySet, self, dim, dtype); |
| 5753 | } |
| 5754 | |
| 5755 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_log_softmax_Dimname, name, "aten::_sparse_log_softmax") |
| 5756 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_log_softmax_Dimname, overload_name, "Dimname") |
| 5757 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_log_softmax_Dimname, schema_str, "_sparse_log_softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor") |
| 5758 | |
| 5759 | // aten::_sparse_log_softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor |
| 5760 | static C10_NOINLINE c10::TypedOperatorHandle<_sparse_log_softmax_Dimname::schema> create__sparse_log_softmax_Dimname_typed_handle() { |
| 5761 | return c10::Dispatcher::singleton() |
| 5762 | .findSchemaOrThrow(_sparse_log_softmax_Dimname::name, _sparse_log_softmax_Dimname::overload_name) |
| 5763 | .typed<_sparse_log_softmax_Dimname::schema>(); |
| 5764 | } |
| 5765 | |
| 5766 | // aten::_sparse_log_softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor |
| 5767 | at::Tensor _sparse_log_softmax_Dimname::call(const at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype) { |
| 5768 | |
| 5769 | static auto op = create__sparse_log_softmax_Dimname_typed_handle(); |
| 5770 | return op.call(self, dim, dtype); |
| 5771 | } |
| 5772 | |
| 5773 | // aten::_sparse_log_softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor |
| 5774 | at::Tensor _sparse_log_softmax_Dimname::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype) { |
| 5775 | |
| 5776 | static auto op = create__sparse_log_softmax_Dimname_typed_handle(); |
| 5777 | return op.redispatch(dispatchKeySet, self, dim, dtype); |
| 5778 | } |
| 5779 | |
| 5780 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_log_softmax, name, "aten::_sparse_log_softmax") |
| 5781 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_log_softmax, overload_name, "") |
| 5782 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_log_softmax, schema_str, "_sparse_log_softmax(Tensor self, int dim, bool half_to_float) -> Tensor") |
| 5783 | |
| 5784 | // aten::_sparse_log_softmax(Tensor self, int dim, bool half_to_float) -> Tensor |
| 5785 | static C10_NOINLINE c10::TypedOperatorHandle<_sparse_log_softmax::schema> create__sparse_log_softmax_typed_handle() { |
| 5786 | return c10::Dispatcher::singleton() |
| 5787 | .findSchemaOrThrow(_sparse_log_softmax::name, _sparse_log_softmax::overload_name) |
| 5788 | .typed<_sparse_log_softmax::schema>(); |
| 5789 | } |
| 5790 | |
| 5791 | // aten::_sparse_log_softmax(Tensor self, int dim, bool half_to_float) -> Tensor |
| 5792 | at::Tensor _sparse_log_softmax::call(const at::Tensor & self, int64_t dim, bool half_to_float) { |
| 5793 | |
| 5794 | static auto op = create__sparse_log_softmax_typed_handle(); |
| 5795 | return op.call(self, dim, half_to_float); |
| 5796 | } |
| 5797 | |
| 5798 | // aten::_sparse_log_softmax(Tensor self, int dim, bool half_to_float) -> Tensor |
| 5799 | at::Tensor _sparse_log_softmax::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool half_to_float) { |
| 5800 | |
| 5801 | static auto op = create__sparse_log_softmax_typed_handle(); |
| 5802 | return op.redispatch(dispatchKeySet, self, dim, half_to_float); |
| 5803 | } |
| 5804 | |
| 5805 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_log_softmax_backward_data, name, "aten::_sparse_log_softmax_backward_data") |
| 5806 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_log_softmax_backward_data, overload_name, "") |
| 5807 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_log_softmax_backward_data, schema_str, "_sparse_log_softmax_backward_data(Tensor grad_output, Tensor output, int dim, Tensor self) -> Tensor") |
| 5808 | |
| 5809 | // aten::_sparse_log_softmax_backward_data(Tensor grad_output, Tensor output, int dim, Tensor self) -> Tensor |
| 5810 | static C10_NOINLINE c10::TypedOperatorHandle<_sparse_log_softmax_backward_data::schema> create__sparse_log_softmax_backward_data_typed_handle() { |
| 5811 | return c10::Dispatcher::singleton() |
| 5812 | .findSchemaOrThrow(_sparse_log_softmax_backward_data::name, _sparse_log_softmax_backward_data::overload_name) |
| 5813 | .typed<_sparse_log_softmax_backward_data::schema>(); |
| 5814 | } |
| 5815 | |
| 5816 | // aten::_sparse_log_softmax_backward_data(Tensor grad_output, Tensor output, int dim, Tensor self) -> Tensor |
| 5817 | at::Tensor _sparse_log_softmax_backward_data::call(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self) { |
| 5818 | |
| 5819 | static auto op = create__sparse_log_softmax_backward_data_typed_handle(); |
| 5820 | return op.call(grad_output, output, dim, self); |
| 5821 | } |
| 5822 | |
| 5823 | // aten::_sparse_log_softmax_backward_data(Tensor grad_output, Tensor output, int dim, Tensor self) -> Tensor |
| 5824 | at::Tensor _sparse_log_softmax_backward_data::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self) { |
| 5825 | |
| 5826 | static auto op = create__sparse_log_softmax_backward_data_typed_handle(); |
| 5827 | return op.redispatch(dispatchKeySet, grad_output, output, dim, self); |
| 5828 | } |
| 5829 | |
| 5830 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_spdiags, name, "aten::_spdiags") |
| 5831 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_spdiags, overload_name, "") |
| 5832 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_spdiags, schema_str, "_spdiags(Tensor diagonals, Tensor offsets, int[] shape, Layout? layout=None) -> Tensor") |
| 5833 | |
| 5834 | // aten::_spdiags(Tensor diagonals, Tensor offsets, int[] shape, Layout? layout=None) -> Tensor |
| 5835 | static C10_NOINLINE c10::TypedOperatorHandle<_spdiags::schema> create__spdiags_typed_handle() { |
| 5836 | return c10::Dispatcher::singleton() |
| 5837 | .findSchemaOrThrow(_spdiags::name, _spdiags::overload_name) |
| 5838 | .typed<_spdiags::schema>(); |
| 5839 | } |
| 5840 | |
| 5841 | // aten::_spdiags(Tensor diagonals, Tensor offsets, int[] shape, Layout? layout=None) -> Tensor |
| 5842 | at::Tensor _spdiags::call(const at::Tensor & diagonals, const at::Tensor & offsets, at::IntArrayRef shape, c10::optional<at::Layout> layout) { |
| 5843 | |
| 5844 | static auto op = create__spdiags_typed_handle(); |
| 5845 | return op.call(diagonals, offsets, shape, layout); |
| 5846 | } |
| 5847 | |
| 5848 | // aten::_spdiags(Tensor diagonals, Tensor offsets, int[] shape, Layout? layout=None) -> Tensor |
| 5849 | at::Tensor _spdiags::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & diagonals, const at::Tensor & offsets, at::IntArrayRef shape, c10::optional<at::Layout> layout) { |
| 5850 | |
| 5851 | static auto op = create__spdiags_typed_handle(); |
| 5852 | return op.redispatch(dispatchKeySet, diagonals, offsets, shape, layout); |
| 5853 | } |
| 5854 | |
| 5855 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(frexp_Tensor, name, "aten::frexp") |
| 5856 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(frexp_Tensor, overload_name, "Tensor") |
| 5857 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(frexp_Tensor, schema_str, "frexp.Tensor(Tensor self) -> (Tensor mantissa, Tensor exponent)") |
| 5858 | |
| 5859 | // aten::frexp.Tensor(Tensor self) -> (Tensor mantissa, Tensor exponent) |
| 5860 | static C10_NOINLINE c10::TypedOperatorHandle<frexp_Tensor::schema> create_frexp_Tensor_typed_handle() { |
| 5861 | return c10::Dispatcher::singleton() |
| 5862 | .findSchemaOrThrow(frexp_Tensor::name, frexp_Tensor::overload_name) |
| 5863 | .typed<frexp_Tensor::schema>(); |
| 5864 | } |
| 5865 | |
| 5866 | // aten::frexp.Tensor(Tensor self) -> (Tensor mantissa, Tensor exponent) |
| 5867 | ::std::tuple<at::Tensor,at::Tensor> frexp_Tensor::call(const at::Tensor & self) { |
| 5868 | |
| 5869 | static auto op = create_frexp_Tensor_typed_handle(); |
| 5870 | return op.call(self); |
| 5871 | } |
| 5872 | |
| 5873 | // aten::frexp.Tensor(Tensor self) -> (Tensor mantissa, Tensor exponent) |
| 5874 | ::std::tuple<at::Tensor,at::Tensor> frexp_Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 5875 | |
| 5876 | static auto op = create_frexp_Tensor_typed_handle(); |
| 5877 | return op.redispatch(dispatchKeySet, self); |
| 5878 | } |
| 5879 | |
| 5880 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(frexp_Tensor_out, name, "aten::frexp") |
| 5881 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(frexp_Tensor_out, overload_name, "Tensor_out") |
| 5882 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(frexp_Tensor_out, schema_str, "frexp.Tensor_out(Tensor self, *, Tensor(a!) mantissa, Tensor(b!) exponent) -> (Tensor(a!) mantissa, Tensor(b!) exponent)") |
| 5883 | |
| 5884 | // aten::frexp.Tensor_out(Tensor self, *, Tensor(a!) mantissa, Tensor(b!) exponent) -> (Tensor(a!) mantissa, Tensor(b!) exponent) |
| 5885 | static C10_NOINLINE c10::TypedOperatorHandle<frexp_Tensor_out::schema> create_frexp_Tensor_out_typed_handle() { |
| 5886 | return c10::Dispatcher::singleton() |
| 5887 | .findSchemaOrThrow(frexp_Tensor_out::name, frexp_Tensor_out::overload_name) |
| 5888 | .typed<frexp_Tensor_out::schema>(); |
| 5889 | } |
| 5890 | |
| 5891 | // aten::frexp.Tensor_out(Tensor self, *, Tensor(a!) mantissa, Tensor(b!) exponent) -> (Tensor(a!) mantissa, Tensor(b!) exponent) |
| 5892 | ::std::tuple<at::Tensor &,at::Tensor &> frexp_Tensor_out::call(const at::Tensor & self, at::Tensor & mantissa, at::Tensor & exponent) { |
| 5893 | |
| 5894 | static auto op = create_frexp_Tensor_out_typed_handle(); |
| 5895 | return op.call(self, mantissa, exponent); |
| 5896 | } |
| 5897 | |
| 5898 | // aten::frexp.Tensor_out(Tensor self, *, Tensor(a!) mantissa, Tensor(b!) exponent) -> (Tensor(a!) mantissa, Tensor(b!) exponent) |
| 5899 | ::std::tuple<at::Tensor &,at::Tensor &> frexp_Tensor_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & mantissa, at::Tensor & exponent) { |
| 5900 | |
| 5901 | static auto op = create_frexp_Tensor_out_typed_handle(); |
| 5902 | return op.redispatch(dispatchKeySet, self, mantissa, exponent); |
| 5903 | } |
| 5904 | |
| 5905 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(zero_, name, "aten::zero_") |
| 5906 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(zero_, overload_name, "") |
| 5907 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(zero_, schema_str, "zero_(Tensor(a!) self) -> Tensor(a!)") |
| 5908 | |
| 5909 | // aten::zero_(Tensor(a!) self) -> Tensor(a!) |
| 5910 | static C10_NOINLINE c10::TypedOperatorHandle<zero_::schema> create_zero__typed_handle() { |
| 5911 | return c10::Dispatcher::singleton() |
| 5912 | .findSchemaOrThrow(zero_::name, zero_::overload_name) |
| 5913 | .typed<zero_::schema>(); |
| 5914 | } |
| 5915 | |
| 5916 | // aten::zero_(Tensor(a!) self) -> Tensor(a!) |
| 5917 | at::Tensor & zero_::call(at::Tensor & self) { |
| 5918 | |
| 5919 | static auto op = create_zero__typed_handle(); |
| 5920 | return op.call(self); |
| 5921 | } |
| 5922 | |
| 5923 | // aten::zero_(Tensor(a!) self) -> Tensor(a!) |
| 5924 | at::Tensor & zero_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 5925 | |
| 5926 | static auto op = create_zero__typed_handle(); |
| 5927 | return op.redispatch(dispatchKeySet, self); |
| 5928 | } |
| 5929 | |
| 5930 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rsub_Tensor, name, "aten::rsub") |
| 5931 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rsub_Tensor, overload_name, "Tensor") |
| 5932 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rsub_Tensor, schema_str, "rsub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor") |
| 5933 | |
| 5934 | // aten::rsub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor |
| 5935 | static C10_NOINLINE c10::TypedOperatorHandle<rsub_Tensor::schema> create_rsub_Tensor_typed_handle() { |
| 5936 | return c10::Dispatcher::singleton() |
| 5937 | .findSchemaOrThrow(rsub_Tensor::name, rsub_Tensor::overload_name) |
| 5938 | .typed<rsub_Tensor::schema>(); |
| 5939 | } |
| 5940 | |
| 5941 | // aten::rsub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor |
| 5942 | at::Tensor rsub_Tensor::call(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 5943 | |
| 5944 | static auto op = create_rsub_Tensor_typed_handle(); |
| 5945 | return op.call(self, other, alpha); |
| 5946 | } |
| 5947 | |
| 5948 | // aten::rsub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor |
| 5949 | at::Tensor rsub_Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 5950 | |
| 5951 | static auto op = create_rsub_Tensor_typed_handle(); |
| 5952 | return op.redispatch(dispatchKeySet, self, other, alpha); |
| 5953 | } |
| 5954 | |
| 5955 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rsub_Scalar, name, "aten::rsub") |
| 5956 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rsub_Scalar, overload_name, "Scalar") |
| 5957 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rsub_Scalar, schema_str, "rsub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor") |
| 5958 | |
| 5959 | // aten::rsub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor |
| 5960 | static C10_NOINLINE c10::TypedOperatorHandle<rsub_Scalar::schema> create_rsub_Scalar_typed_handle() { |
| 5961 | return c10::Dispatcher::singleton() |
| 5962 | .findSchemaOrThrow(rsub_Scalar::name, rsub_Scalar::overload_name) |
| 5963 | .typed<rsub_Scalar::schema>(); |
| 5964 | } |
| 5965 | |
| 5966 | // aten::rsub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor |
| 5967 | at::Tensor rsub_Scalar::call(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha) { |
| 5968 | |
| 5969 | static auto op = create_rsub_Scalar_typed_handle(); |
| 5970 | return op.call(self, other, alpha); |
| 5971 | } |
| 5972 | |
| 5973 | // aten::rsub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor |
| 5974 | at::Tensor rsub_Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha) { |
| 5975 | |
| 5976 | static auto op = create_rsub_Scalar_typed_handle(); |
| 5977 | return op.redispatch(dispatchKeySet, self, other, alpha); |
| 5978 | } |
| 5979 | |
| 5980 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_mm_reduce_impl, name, "aten::_sparse_mm_reduce_impl") |
| 5981 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_mm_reduce_impl, overload_name, "") |
| 5982 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_mm_reduce_impl, schema_str, "_sparse_mm_reduce_impl(Tensor self, Tensor other, str reduce) -> (Tensor, Tensor)") |
| 5983 | |
| 5984 | // aten::_sparse_mm_reduce_impl(Tensor self, Tensor other, str reduce) -> (Tensor, Tensor) |
| 5985 | static C10_NOINLINE c10::TypedOperatorHandle<_sparse_mm_reduce_impl::schema> create__sparse_mm_reduce_impl_typed_handle() { |
| 5986 | return c10::Dispatcher::singleton() |
| 5987 | .findSchemaOrThrow(_sparse_mm_reduce_impl::name, _sparse_mm_reduce_impl::overload_name) |
| 5988 | .typed<_sparse_mm_reduce_impl::schema>(); |
| 5989 | } |
| 5990 | |
| 5991 | // aten::_sparse_mm_reduce_impl(Tensor self, Tensor other, str reduce) -> (Tensor, Tensor) |
| 5992 | ::std::tuple<at::Tensor,at::Tensor> _sparse_mm_reduce_impl::call(const at::Tensor & self, const at::Tensor & other, c10::string_view reduce) { |
| 5993 | |
| 5994 | static auto op = create__sparse_mm_reduce_impl_typed_handle(); |
| 5995 | return op.call(self, other, reduce); |
| 5996 | } |
| 5997 | |
| 5998 | // aten::_sparse_mm_reduce_impl(Tensor self, Tensor other, str reduce) -> (Tensor, Tensor) |
| 5999 | ::std::tuple<at::Tensor,at::Tensor> _sparse_mm_reduce_impl::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, c10::string_view reduce) { |
| 6000 | |
| 6001 | static auto op = create__sparse_mm_reduce_impl_typed_handle(); |
| 6002 | return op.redispatch(dispatchKeySet, self, other, reduce); |
| 6003 | } |
| 6004 | |
| 6005 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_bsr_tensor_unsafe, name, "aten::_sparse_bsr_tensor_unsafe") |
| 6006 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_bsr_tensor_unsafe, overload_name, "") |
| 6007 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_bsr_tensor_unsafe, schema_str, "_sparse_bsr_tensor_unsafe(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor") |
| 6008 | |
| 6009 | // aten::_sparse_bsr_tensor_unsafe(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 6010 | static C10_NOINLINE c10::TypedOperatorHandle<_sparse_bsr_tensor_unsafe::schema> create__sparse_bsr_tensor_unsafe_typed_handle() { |
| 6011 | return c10::Dispatcher::singleton() |
| 6012 | .findSchemaOrThrow(_sparse_bsr_tensor_unsafe::name, _sparse_bsr_tensor_unsafe::overload_name) |
| 6013 | .typed<_sparse_bsr_tensor_unsafe::schema>(); |
| 6014 | } |
| 6015 | |
| 6016 | // aten::_sparse_bsr_tensor_unsafe(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 6017 | at::Tensor _sparse_bsr_tensor_unsafe::call(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 6018 | |
| 6019 | static auto op = create__sparse_bsr_tensor_unsafe_typed_handle(); |
| 6020 | return op.call(crow_indices, col_indices, values, size, dtype, layout, device, pin_memory); |
| 6021 | } |
| 6022 | |
| 6023 | // aten::_sparse_bsr_tensor_unsafe(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 6024 | at::Tensor _sparse_bsr_tensor_unsafe::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 6025 | |
| 6026 | static auto op = create__sparse_bsr_tensor_unsafe_typed_handle(); |
| 6027 | return op.redispatch(dispatchKeySet, crow_indices, col_indices, values, size, dtype, layout, device, pin_memory); |
| 6028 | } |
| 6029 | |
| 6030 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_validate_sparse_csc_tensor_args, name, "aten::_validate_sparse_csc_tensor_args") |
| 6031 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_validate_sparse_csc_tensor_args, overload_name, "") |
| 6032 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_validate_sparse_csc_tensor_args, schema_str, "_validate_sparse_csc_tensor_args(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size) -> ()") |
| 6033 | |
| 6034 | // aten::_validate_sparse_csc_tensor_args(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size) -> () |
| 6035 | static C10_NOINLINE c10::TypedOperatorHandle<_validate_sparse_csc_tensor_args::schema> create__validate_sparse_csc_tensor_args_typed_handle() { |
| 6036 | return c10::Dispatcher::singleton() |
| 6037 | .findSchemaOrThrow(_validate_sparse_csc_tensor_args::name, _validate_sparse_csc_tensor_args::overload_name) |
| 6038 | .typed<_validate_sparse_csc_tensor_args::schema>(); |
| 6039 | } |
| 6040 | |
| 6041 | // aten::_validate_sparse_csc_tensor_args(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size) -> () |
| 6042 | void _validate_sparse_csc_tensor_args::call(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size) { |
| 6043 | |
| 6044 | static auto op = create__validate_sparse_csc_tensor_args_typed_handle(); |
| 6045 | return op.call(ccol_indices, row_indices, values, size); |
| 6046 | } |
| 6047 | |
| 6048 | // aten::_validate_sparse_csc_tensor_args(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size) -> () |
| 6049 | void _validate_sparse_csc_tensor_args::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size) { |
| 6050 | |
| 6051 | static auto op = create__validate_sparse_csc_tensor_args_typed_handle(); |
| 6052 | return op.redispatch(dispatchKeySet, ccol_indices, row_indices, values, size); |
| 6053 | } |
| 6054 | |
| 6055 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_coo_tensor_with_dims, name, "aten::_sparse_coo_tensor_with_dims") |
| 6056 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_coo_tensor_with_dims, overload_name, "") |
| 6057 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_coo_tensor_with_dims, schema_str, "_sparse_coo_tensor_with_dims(int sparse_dim, int dense_dim, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor") |
| 6058 | |
| 6059 | // aten::_sparse_coo_tensor_with_dims(int sparse_dim, int dense_dim, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor |
| 6060 | static C10_NOINLINE c10::TypedOperatorHandle<_sparse_coo_tensor_with_dims::schema> create__sparse_coo_tensor_with_dims_typed_handle() { |
| 6061 | return c10::Dispatcher::singleton() |
| 6062 | .findSchemaOrThrow(_sparse_coo_tensor_with_dims::name, _sparse_coo_tensor_with_dims::overload_name) |
| 6063 | .typed<_sparse_coo_tensor_with_dims::schema>(); |
| 6064 | } |
| 6065 | |
| 6066 | // aten::_sparse_coo_tensor_with_dims(int sparse_dim, int dense_dim, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor |
| 6067 | at::Tensor _sparse_coo_tensor_with_dims::call(int64_t sparse_dim, int64_t dense_dim, at::IntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 6068 | |
| 6069 | static auto op = create__sparse_coo_tensor_with_dims_typed_handle(); |
| 6070 | return op.call(sparse_dim, dense_dim, size, dtype, layout, device, pin_memory); |
| 6071 | } |
| 6072 | |
| 6073 | // aten::_sparse_coo_tensor_with_dims(int sparse_dim, int dense_dim, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor |
| 6074 | at::Tensor _sparse_coo_tensor_with_dims::redispatch(c10::DispatchKeySet dispatchKeySet, int64_t sparse_dim, int64_t dense_dim, at::IntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 6075 | |
| 6076 | static auto op = create__sparse_coo_tensor_with_dims_typed_handle(); |
| 6077 | return op.redispatch(dispatchKeySet, sparse_dim, dense_dim, size, dtype, layout, device, pin_memory); |
| 6078 | } |
| 6079 | |
| 6080 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(to_dense_backward, name, "aten::to_dense_backward") |
| 6081 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(to_dense_backward, overload_name, "") |
| 6082 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(to_dense_backward, schema_str, "to_dense_backward(Tensor grad, Tensor input) -> Tensor") |
| 6083 | |
| 6084 | // aten::to_dense_backward(Tensor grad, Tensor input) -> Tensor |
| 6085 | static C10_NOINLINE c10::TypedOperatorHandle<to_dense_backward::schema> create_to_dense_backward_typed_handle() { |
| 6086 | return c10::Dispatcher::singleton() |
| 6087 | .findSchemaOrThrow(to_dense_backward::name, to_dense_backward::overload_name) |
| 6088 | .typed<to_dense_backward::schema>(); |
| 6089 | } |
| 6090 | |
| 6091 | // aten::to_dense_backward(Tensor grad, Tensor input) -> Tensor |
| 6092 | at::Tensor to_dense_backward::call(const at::Tensor & grad, const at::Tensor & input) { |
| 6093 | |
| 6094 | static auto op = create_to_dense_backward_typed_handle(); |
| 6095 | return op.call(grad, input); |
| 6096 | } |
| 6097 | |
| 6098 | // aten::to_dense_backward(Tensor grad, Tensor input) -> Tensor |
| 6099 | at::Tensor to_dense_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & input) { |
| 6100 | |
| 6101 | static auto op = create_to_dense_backward_typed_handle(); |
| 6102 | return op.redispatch(dispatchKeySet, grad, input); |
| 6103 | } |
| 6104 | |
| 6105 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_coalesce, name, "aten::_coalesce") |
| 6106 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_coalesce, overload_name, "") |
| 6107 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_coalesce, schema_str, "_coalesce(Tensor self) -> Tensor") |
| 6108 | |
| 6109 | // aten::_coalesce(Tensor self) -> Tensor |
| 6110 | static C10_NOINLINE c10::TypedOperatorHandle<_coalesce::schema> create__coalesce_typed_handle() { |
| 6111 | return c10::Dispatcher::singleton() |
| 6112 | .findSchemaOrThrow(_coalesce::name, _coalesce::overload_name) |
| 6113 | .typed<_coalesce::schema>(); |
| 6114 | } |
| 6115 | |
| 6116 | // aten::_coalesce(Tensor self) -> Tensor |
| 6117 | at::Tensor _coalesce::call(const at::Tensor & self) { |
| 6118 | |
| 6119 | static auto op = create__coalesce_typed_handle(); |
| 6120 | return op.call(self); |
| 6121 | } |
| 6122 | |
| 6123 | // aten::_coalesce(Tensor self) -> Tensor |
| 6124 | at::Tensor _coalesce::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 6125 | |
| 6126 | static auto op = create__coalesce_typed_handle(); |
| 6127 | return op.redispatch(dispatchKeySet, self); |
| 6128 | } |
| 6129 | |
| 6130 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_values, name, "aten::_values") |
| 6131 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_values, overload_name, "") |
| 6132 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_values, schema_str, "_values(Tensor(a) self) -> Tensor(a)") |
| 6133 | |
| 6134 | // aten::_values(Tensor(a) self) -> Tensor(a) |
| 6135 | static C10_NOINLINE c10::TypedOperatorHandle<_values::schema> create__values_typed_handle() { |
| 6136 | return c10::Dispatcher::singleton() |
| 6137 | .findSchemaOrThrow(_values::name, _values::overload_name) |
| 6138 | .typed<_values::schema>(); |
| 6139 | } |
| 6140 | |
| 6141 | // aten::_values(Tensor(a) self) -> Tensor(a) |
| 6142 | at::Tensor _values::call(const at::Tensor & self) { |
| 6143 | |
| 6144 | static auto op = create__values_typed_handle(); |
| 6145 | return op.call(self); |
| 6146 | } |
| 6147 | |
| 6148 | // aten::_values(Tensor(a) self) -> Tensor(a) |
| 6149 | at::Tensor _values::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 6150 | |
| 6151 | static auto op = create__values_typed_handle(); |
| 6152 | return op.redispatch(dispatchKeySet, self); |
| 6153 | } |
| 6154 | |
| 6155 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(crow_indices, name, "aten::crow_indices") |
| 6156 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(crow_indices, overload_name, "") |
| 6157 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(crow_indices, schema_str, "crow_indices(Tensor(a) self) -> Tensor(a)") |
| 6158 | |
| 6159 | // aten::crow_indices(Tensor(a) self) -> Tensor(a) |
| 6160 | static C10_NOINLINE c10::TypedOperatorHandle<crow_indices::schema> create_crow_indices_typed_handle() { |
| 6161 | return c10::Dispatcher::singleton() |
| 6162 | .findSchemaOrThrow(crow_indices::name, crow_indices::overload_name) |
| 6163 | .typed<crow_indices::schema>(); |
| 6164 | } |
| 6165 | |
| 6166 | // aten::crow_indices(Tensor(a) self) -> Tensor(a) |
| 6167 | at::Tensor crow_indices::call(const at::Tensor & self) { |
| 6168 | |
| 6169 | static auto op = create_crow_indices_typed_handle(); |
| 6170 | return op.call(self); |
| 6171 | } |
| 6172 | |
| 6173 | // aten::crow_indices(Tensor(a) self) -> Tensor(a) |
| 6174 | at::Tensor crow_indices::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 6175 | |
| 6176 | static auto op = create_crow_indices_typed_handle(); |
| 6177 | return op.redispatch(dispatchKeySet, self); |
| 6178 | } |
| 6179 | |
| 6180 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(q_zero_point, name, "aten::q_zero_point") |
| 6181 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(q_zero_point, overload_name, "") |
| 6182 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(q_zero_point, schema_str, "q_zero_point(Tensor self) -> int") |
| 6183 | |
| 6184 | // aten::q_zero_point(Tensor self) -> int |
| 6185 | static C10_NOINLINE c10::TypedOperatorHandle<q_zero_point::schema> create_q_zero_point_typed_handle() { |
| 6186 | return c10::Dispatcher::singleton() |
| 6187 | .findSchemaOrThrow(q_zero_point::name, q_zero_point::overload_name) |
| 6188 | .typed<q_zero_point::schema>(); |
| 6189 | } |
| 6190 | |
| 6191 | // aten::q_zero_point(Tensor self) -> int |
| 6192 | int64_t q_zero_point::call(const at::Tensor & self) { |
| 6193 | |
| 6194 | static auto op = create_q_zero_point_typed_handle(); |
| 6195 | return op.call(self); |
| 6196 | } |
| 6197 | |
| 6198 | // aten::q_zero_point(Tensor self) -> int |
| 6199 | int64_t q_zero_point::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 6200 | |
| 6201 | static auto op = create_q_zero_point_typed_handle(); |
| 6202 | return op.redispatch(dispatchKeySet, self); |
| 6203 | } |
| 6204 | |
| 6205 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(q_per_channel_scales, name, "aten::q_per_channel_scales") |
| 6206 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(q_per_channel_scales, overload_name, "") |
| 6207 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(q_per_channel_scales, schema_str, "q_per_channel_scales(Tensor self) -> Tensor") |
| 6208 | |
| 6209 | // aten::q_per_channel_scales(Tensor self) -> Tensor |
| 6210 | static C10_NOINLINE c10::TypedOperatorHandle<q_per_channel_scales::schema> create_q_per_channel_scales_typed_handle() { |
| 6211 | return c10::Dispatcher::singleton() |
| 6212 | .findSchemaOrThrow(q_per_channel_scales::name, q_per_channel_scales::overload_name) |
| 6213 | .typed<q_per_channel_scales::schema>(); |
| 6214 | } |
| 6215 | |
| 6216 | // aten::q_per_channel_scales(Tensor self) -> Tensor |
| 6217 | at::Tensor q_per_channel_scales::call(const at::Tensor & self) { |
| 6218 | |
| 6219 | static auto op = create_q_per_channel_scales_typed_handle(); |
| 6220 | return op.call(self); |
| 6221 | } |
| 6222 | |
| 6223 | // aten::q_per_channel_scales(Tensor self) -> Tensor |
| 6224 | at::Tensor q_per_channel_scales::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 6225 | |
| 6226 | static auto op = create_q_per_channel_scales_typed_handle(); |
| 6227 | return op.redispatch(dispatchKeySet, self); |
| 6228 | } |
| 6229 | |
| 6230 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fake_quantize_learnable_per_tensor_affine_backward, name, "aten::_fake_quantize_learnable_per_tensor_affine_backward") |
| 6231 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fake_quantize_learnable_per_tensor_affine_backward, overload_name, "") |
| 6232 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fake_quantize_learnable_per_tensor_affine_backward, schema_str, "_fake_quantize_learnable_per_tensor_affine_backward(Tensor grad, Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max, float grad_factor=1.0) -> (Tensor, Tensor, Tensor)") |
| 6233 | |
| 6234 | // aten::_fake_quantize_learnable_per_tensor_affine_backward(Tensor grad, Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max, float grad_factor=1.0) -> (Tensor, Tensor, Tensor) |
| 6235 | static C10_NOINLINE c10::TypedOperatorHandle<_fake_quantize_learnable_per_tensor_affine_backward::schema> create__fake_quantize_learnable_per_tensor_affine_backward_typed_handle() { |
| 6236 | return c10::Dispatcher::singleton() |
| 6237 | .findSchemaOrThrow(_fake_quantize_learnable_per_tensor_affine_backward::name, _fake_quantize_learnable_per_tensor_affine_backward::overload_name) |
| 6238 | .typed<_fake_quantize_learnable_per_tensor_affine_backward::schema>(); |
| 6239 | } |
| 6240 | |
| 6241 | // aten::_fake_quantize_learnable_per_tensor_affine_backward(Tensor grad, Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max, float grad_factor=1.0) -> (Tensor, Tensor, Tensor) |
| 6242 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _fake_quantize_learnable_per_tensor_affine_backward::call(const at::Tensor & grad, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t quant_min, int64_t quant_max, double grad_factor) { |
| 6243 | |
| 6244 | static auto op = create__fake_quantize_learnable_per_tensor_affine_backward_typed_handle(); |
| 6245 | return op.call(grad, self, scale, zero_point, quant_min, quant_max, grad_factor); |
| 6246 | } |
| 6247 | |
| 6248 | // aten::_fake_quantize_learnable_per_tensor_affine_backward(Tensor grad, Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max, float grad_factor=1.0) -> (Tensor, Tensor, Tensor) |
| 6249 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _fake_quantize_learnable_per_tensor_affine_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t quant_min, int64_t quant_max, double grad_factor) { |
| 6250 | |
| 6251 | static auto op = create__fake_quantize_learnable_per_tensor_affine_backward_typed_handle(); |
| 6252 | return op.redispatch(dispatchKeySet, grad, self, scale, zero_point, quant_min, quant_max, grad_factor); |
| 6253 | } |
| 6254 | |
| 6255 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fake_quantize_learnable_per_channel_affine_backward, name, "aten::_fake_quantize_learnable_per_channel_affine_backward") |
| 6256 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fake_quantize_learnable_per_channel_affine_backward, overload_name, "") |
| 6257 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fake_quantize_learnable_per_channel_affine_backward, schema_str, "_fake_quantize_learnable_per_channel_affine_backward(Tensor grad, Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0) -> (Tensor, Tensor, Tensor)") |
| 6258 | |
| 6259 | // aten::_fake_quantize_learnable_per_channel_affine_backward(Tensor grad, Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0) -> (Tensor, Tensor, Tensor) |
| 6260 | static C10_NOINLINE c10::TypedOperatorHandle<_fake_quantize_learnable_per_channel_affine_backward::schema> create__fake_quantize_learnable_per_channel_affine_backward_typed_handle() { |
| 6261 | return c10::Dispatcher::singleton() |
| 6262 | .findSchemaOrThrow(_fake_quantize_learnable_per_channel_affine_backward::name, _fake_quantize_learnable_per_channel_affine_backward::overload_name) |
| 6263 | .typed<_fake_quantize_learnable_per_channel_affine_backward::schema>(); |
| 6264 | } |
| 6265 | |
| 6266 | // aten::_fake_quantize_learnable_per_channel_affine_backward(Tensor grad, Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0) -> (Tensor, Tensor, Tensor) |
| 6267 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _fake_quantize_learnable_per_channel_affine_backward::call(const at::Tensor & grad, 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) { |
| 6268 | |
| 6269 | static auto op = create__fake_quantize_learnable_per_channel_affine_backward_typed_handle(); |
| 6270 | return op.call(grad, self, scale, zero_point, axis, quant_min, quant_max, grad_factor); |
| 6271 | } |
| 6272 | |
| 6273 | // aten::_fake_quantize_learnable_per_channel_affine_backward(Tensor grad, Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0) -> (Tensor, Tensor, Tensor) |
| 6274 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _fake_quantize_learnable_per_channel_affine_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, 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) { |
| 6275 | |
| 6276 | static auto op = create__fake_quantize_learnable_per_channel_affine_backward_typed_handle(); |
| 6277 | return op.redispatch(dispatchKeySet, grad, self, scale, zero_point, axis, quant_min, quant_max, grad_factor); |
| 6278 | } |
| 6279 | |
| 6280 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fused_moving_avg_obs_fake_quant, name, "aten::fused_moving_avg_obs_fake_quant") |
| 6281 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fused_moving_avg_obs_fake_quant, overload_name, "") |
| 6282 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fused_moving_avg_obs_fake_quant, schema_str, "fused_moving_avg_obs_fake_quant(Tensor self, Tensor observer_on, Tensor fake_quant_on, Tensor(a!) running_min, Tensor(b!) running_max, Tensor(c!) scale, Tensor(d!) zero_point, float averaging_const, int quant_min, int quant_max, int ch_axis, bool per_row_fake_quant=False, bool symmetric_quant=False) -> Tensor") |
| 6283 | |
| 6284 | // aten::fused_moving_avg_obs_fake_quant(Tensor self, Tensor observer_on, Tensor fake_quant_on, Tensor(a!) running_min, Tensor(b!) running_max, Tensor(c!) scale, Tensor(d!) zero_point, float averaging_const, int quant_min, int quant_max, int ch_axis, bool per_row_fake_quant=False, bool symmetric_quant=False) -> Tensor |
| 6285 | static C10_NOINLINE c10::TypedOperatorHandle<fused_moving_avg_obs_fake_quant::schema> create_fused_moving_avg_obs_fake_quant_typed_handle() { |
| 6286 | return c10::Dispatcher::singleton() |
| 6287 | .findSchemaOrThrow(fused_moving_avg_obs_fake_quant::name, fused_moving_avg_obs_fake_quant::overload_name) |
| 6288 | .typed<fused_moving_avg_obs_fake_quant::schema>(); |
| 6289 | } |
| 6290 | |
| 6291 | // aten::fused_moving_avg_obs_fake_quant(Tensor self, Tensor observer_on, Tensor fake_quant_on, Tensor(a!) running_min, Tensor(b!) running_max, Tensor(c!) scale, Tensor(d!) zero_point, float averaging_const, int quant_min, int quant_max, int ch_axis, bool per_row_fake_quant=False, bool symmetric_quant=False) -> Tensor |
| 6292 | at::Tensor fused_moving_avg_obs_fake_quant::call(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) { |
| 6293 | |
| 6294 | static auto op = create_fused_moving_avg_obs_fake_quant_typed_handle(); |
| 6295 | return op.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); |
| 6296 | } |
| 6297 | |
| 6298 | // aten::fused_moving_avg_obs_fake_quant(Tensor self, Tensor observer_on, Tensor fake_quant_on, Tensor(a!) running_min, Tensor(b!) running_max, Tensor(c!) scale, Tensor(d!) zero_point, float averaging_const, int quant_min, int quant_max, int ch_axis, bool per_row_fake_quant=False, bool symmetric_quant=False) -> Tensor |
| 6299 | at::Tensor fused_moving_avg_obs_fake_quant::redispatch(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) { |
| 6300 | |
| 6301 | static auto op = create_fused_moving_avg_obs_fake_quant_typed_handle(); |
| 6302 | return op.redispatch(dispatchKeySet, 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); |
| 6303 | } |
| 6304 | |
| 6305 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_choose_qparams_per_tensor, name, "aten::_choose_qparams_per_tensor") |
| 6306 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_choose_qparams_per_tensor, overload_name, "") |
| 6307 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_choose_qparams_per_tensor, schema_str, "_choose_qparams_per_tensor(Tensor self, bool reduce_range=False) -> (float, int)") |
| 6308 | |
| 6309 | // aten::_choose_qparams_per_tensor(Tensor self, bool reduce_range=False) -> (float, int) |
| 6310 | static C10_NOINLINE c10::TypedOperatorHandle<_choose_qparams_per_tensor::schema> create__choose_qparams_per_tensor_typed_handle() { |
| 6311 | return c10::Dispatcher::singleton() |
| 6312 | .findSchemaOrThrow(_choose_qparams_per_tensor::name, _choose_qparams_per_tensor::overload_name) |
| 6313 | .typed<_choose_qparams_per_tensor::schema>(); |
| 6314 | } |
| 6315 | |
| 6316 | // aten::_choose_qparams_per_tensor(Tensor self, bool reduce_range=False) -> (float, int) |
| 6317 | ::std::tuple<double,int64_t> _choose_qparams_per_tensor::call(const at::Tensor & self, bool reduce_range) { |
| 6318 | |
| 6319 | static auto op = create__choose_qparams_per_tensor_typed_handle(); |
| 6320 | return op.call(self, reduce_range); |
| 6321 | } |
| 6322 | |
| 6323 | // aten::_choose_qparams_per_tensor(Tensor self, bool reduce_range=False) -> (float, int) |
| 6324 | ::std::tuple<double,int64_t> _choose_qparams_per_tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool reduce_range) { |
| 6325 | |
| 6326 | static auto op = create__choose_qparams_per_tensor_typed_handle(); |
| 6327 | return op.redispatch(dispatchKeySet, self, reduce_range); |
| 6328 | } |
| 6329 | |
| 6330 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(meshgrid, name, "aten::meshgrid") |
| 6331 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(meshgrid, overload_name, "") |
| 6332 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(meshgrid, schema_str, "meshgrid(Tensor[] tensors) -> Tensor[]") |
| 6333 | |
| 6334 | // aten::meshgrid(Tensor[] tensors) -> Tensor[] |
| 6335 | static C10_NOINLINE c10::TypedOperatorHandle<meshgrid::schema> create_meshgrid_typed_handle() { |
| 6336 | return c10::Dispatcher::singleton() |
| 6337 | .findSchemaOrThrow(meshgrid::name, meshgrid::overload_name) |
| 6338 | .typed<meshgrid::schema>(); |
| 6339 | } |
| 6340 | |
| 6341 | // aten::meshgrid(Tensor[] tensors) -> Tensor[] |
| 6342 | ::std::vector<at::Tensor> meshgrid::call(at::TensorList tensors) { |
| 6343 | |
| 6344 | static auto op = create_meshgrid_typed_handle(); |
| 6345 | return op.call(tensors); |
| 6346 | } |
| 6347 | |
| 6348 | // aten::meshgrid(Tensor[] tensors) -> Tensor[] |
| 6349 | ::std::vector<at::Tensor> meshgrid::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors) { |
| 6350 | |
| 6351 | static auto op = create_meshgrid_typed_handle(); |
| 6352 | return op.redispatch(dispatchKeySet, tensors); |
| 6353 | } |
| 6354 | |
| 6355 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(meshgrid_indexing, name, "aten::meshgrid") |
| 6356 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(meshgrid_indexing, overload_name, "indexing") |
| 6357 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(meshgrid_indexing, schema_str, "meshgrid.indexing(Tensor[] tensors, *, str indexing) -> Tensor[]") |
| 6358 | |
| 6359 | // aten::meshgrid.indexing(Tensor[] tensors, *, str indexing) -> Tensor[] |
| 6360 | static C10_NOINLINE c10::TypedOperatorHandle<meshgrid_indexing::schema> create_meshgrid_indexing_typed_handle() { |
| 6361 | return c10::Dispatcher::singleton() |
| 6362 | .findSchemaOrThrow(meshgrid_indexing::name, meshgrid_indexing::overload_name) |
| 6363 | .typed<meshgrid_indexing::schema>(); |
| 6364 | } |
| 6365 | |
| 6366 | // aten::meshgrid.indexing(Tensor[] tensors, *, str indexing) -> Tensor[] |
| 6367 | ::std::vector<at::Tensor> meshgrid_indexing::call(at::TensorList tensors, c10::string_view indexing) { |
| 6368 | |
| 6369 | static auto op = create_meshgrid_indexing_typed_handle(); |
| 6370 | return op.call(tensors, indexing); |
| 6371 | } |
| 6372 | |
| 6373 | // aten::meshgrid.indexing(Tensor[] tensors, *, str indexing) -> Tensor[] |
| 6374 | ::std::vector<at::Tensor> meshgrid_indexing::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, c10::string_view indexing) { |
| 6375 | |
| 6376 | static auto op = create_meshgrid_indexing_typed_handle(); |
| 6377 | return op.redispatch(dispatchKeySet, tensors, indexing); |
| 6378 | } |
| 6379 | |
| 6380 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(can_cast, name, "aten::can_cast") |
| 6381 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(can_cast, overload_name, "") |
| 6382 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(can_cast, schema_str, "can_cast(ScalarType from, ScalarType to) -> bool") |
| 6383 | |
| 6384 | // aten::can_cast(ScalarType from, ScalarType to) -> bool |
| 6385 | static C10_NOINLINE c10::TypedOperatorHandle<can_cast::schema> create_can_cast_typed_handle() { |
| 6386 | return c10::Dispatcher::singleton() |
| 6387 | .findSchemaOrThrow(can_cast::name, can_cast::overload_name) |
| 6388 | .typed<can_cast::schema>(); |
| 6389 | } |
| 6390 | |
| 6391 | // aten::can_cast(ScalarType from, ScalarType to) -> bool |
| 6392 | bool can_cast::call(at::ScalarType from, at::ScalarType to) { |
| 6393 | |
| 6394 | static auto op = create_can_cast_typed_handle(); |
| 6395 | return op.call(from, to); |
| 6396 | } |
| 6397 | |
| 6398 | // aten::can_cast(ScalarType from, ScalarType to) -> bool |
| 6399 | bool can_cast::redispatch(c10::DispatchKeySet dispatchKeySet, at::ScalarType from, at::ScalarType to) { |
| 6400 | |
| 6401 | static auto op = create_can_cast_typed_handle(); |
| 6402 | return op.redispatch(dispatchKeySet, from, to); |
| 6403 | } |
| 6404 | |
| 6405 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lstm_mps_backward, name, "aten::lstm_mps_backward") |
| 6406 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lstm_mps_backward, overload_name, "") |
| 6407 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lstm_mps_backward, schema_str, "lstm_mps_backward(Tensor grad_y, Tensor? grad_hy, Tensor? grad_cy, Tensor z_state, Tensor cell_state_fwd, Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor[], Tensor[])") |
| 6408 | |
| 6409 | // aten::lstm_mps_backward(Tensor grad_y, Tensor? grad_hy, Tensor? grad_cy, Tensor z_state, Tensor cell_state_fwd, Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor[], Tensor[]) |
| 6410 | static C10_NOINLINE c10::TypedOperatorHandle<lstm_mps_backward::schema> create_lstm_mps_backward_typed_handle() { |
| 6411 | return c10::Dispatcher::singleton() |
| 6412 | .findSchemaOrThrow(lstm_mps_backward::name, lstm_mps_backward::overload_name) |
| 6413 | .typed<lstm_mps_backward::schema>(); |
| 6414 | } |
| 6415 | |
| 6416 | // aten::lstm_mps_backward(Tensor grad_y, Tensor? grad_hy, Tensor? grad_cy, Tensor z_state, Tensor cell_state_fwd, Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor[], Tensor[]) |
| 6417 | ::std::tuple<at::Tensor,::std::vector<at::Tensor>,::std::vector<at::Tensor>> lstm_mps_backward::call(const at::Tensor & grad_y, const c10::optional<at::Tensor> & grad_hy, const c10::optional<at::Tensor> & grad_cy, const at::Tensor & z_state, const at::Tensor & cell_state_fwd, const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) { |
| 6418 | |
| 6419 | static auto op = create_lstm_mps_backward_typed_handle(); |
| 6420 | return op.call(grad_y, grad_hy, grad_cy, z_state, cell_state_fwd, input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); |
| 6421 | } |
| 6422 | |
| 6423 | // aten::lstm_mps_backward(Tensor grad_y, Tensor? grad_hy, Tensor? grad_cy, Tensor z_state, Tensor cell_state_fwd, Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor[], Tensor[]) |
| 6424 | ::std::tuple<at::Tensor,::std::vector<at::Tensor>,::std::vector<at::Tensor>> lstm_mps_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_y, const c10::optional<at::Tensor> & grad_hy, const c10::optional<at::Tensor> & grad_cy, const at::Tensor & z_state, const at::Tensor & cell_state_fwd, const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) { |
| 6425 | |
| 6426 | static auto op = create_lstm_mps_backward_typed_handle(); |
| 6427 | return op.redispatch(dispatchKeySet, grad_y, grad_hy, grad_cy, z_state, cell_state_fwd, input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); |
| 6428 | } |
| 6429 | |
| 6430 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_thnn_fused_lstm_cell_backward_impl, name, "aten::_thnn_fused_lstm_cell_backward_impl") |
| 6431 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_thnn_fused_lstm_cell_backward_impl, overload_name, "") |
| 6432 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_thnn_fused_lstm_cell_backward_impl, schema_str, "_thnn_fused_lstm_cell_backward_impl(Tensor? grad_hy, Tensor? grad_cy, Tensor cx, Tensor cy, Tensor workspace, bool has_bias) -> (Tensor, Tensor, Tensor)") |
| 6433 | |
| 6434 | // aten::_thnn_fused_lstm_cell_backward_impl(Tensor? grad_hy, Tensor? grad_cy, Tensor cx, Tensor cy, Tensor workspace, bool has_bias) -> (Tensor, Tensor, Tensor) |
| 6435 | static C10_NOINLINE c10::TypedOperatorHandle<_thnn_fused_lstm_cell_backward_impl::schema> create__thnn_fused_lstm_cell_backward_impl_typed_handle() { |
| 6436 | return c10::Dispatcher::singleton() |
| 6437 | .findSchemaOrThrow(_thnn_fused_lstm_cell_backward_impl::name, _thnn_fused_lstm_cell_backward_impl::overload_name) |
| 6438 | .typed<_thnn_fused_lstm_cell_backward_impl::schema>(); |
| 6439 | } |
| 6440 | |
| 6441 | // aten::_thnn_fused_lstm_cell_backward_impl(Tensor? grad_hy, Tensor? grad_cy, Tensor cx, Tensor cy, Tensor workspace, bool has_bias) -> (Tensor, Tensor, Tensor) |
| 6442 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _thnn_fused_lstm_cell_backward_impl::call(const c10::optional<at::Tensor> & grad_hy, const c10::optional<at::Tensor> & grad_cy, const at::Tensor & cx, const at::Tensor & cy, const at::Tensor & workspace, bool has_bias) { |
| 6443 | |
| 6444 | static auto op = create__thnn_fused_lstm_cell_backward_impl_typed_handle(); |
| 6445 | return op.call(grad_hy, grad_cy, cx, cy, workspace, has_bias); |
| 6446 | } |
| 6447 | |
| 6448 | // aten::_thnn_fused_lstm_cell_backward_impl(Tensor? grad_hy, Tensor? grad_cy, Tensor cx, Tensor cy, Tensor workspace, bool has_bias) -> (Tensor, Tensor, Tensor) |
| 6449 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _thnn_fused_lstm_cell_backward_impl::redispatch(c10::DispatchKeySet dispatchKeySet, const c10::optional<at::Tensor> & grad_hy, const c10::optional<at::Tensor> & grad_cy, const at::Tensor & cx, const at::Tensor & cy, const at::Tensor & workspace, bool has_bias) { |
| 6450 | |
| 6451 | static auto op = create__thnn_fused_lstm_cell_backward_impl_typed_handle(); |
| 6452 | return op.redispatch(dispatchKeySet, grad_hy, grad_cy, cx, cy, workspace, has_bias); |
| 6453 | } |
| 6454 | |
| 6455 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_thnn_fused_gru_cell, name, "aten::_thnn_fused_gru_cell") |
| 6456 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_thnn_fused_gru_cell, overload_name, "") |
| 6457 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_thnn_fused_gru_cell, schema_str, "_thnn_fused_gru_cell(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None) -> (Tensor, Tensor)") |
| 6458 | |
| 6459 | // aten::_thnn_fused_gru_cell(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None) -> (Tensor, Tensor) |
| 6460 | static C10_NOINLINE c10::TypedOperatorHandle<_thnn_fused_gru_cell::schema> create__thnn_fused_gru_cell_typed_handle() { |
| 6461 | return c10::Dispatcher::singleton() |
| 6462 | .findSchemaOrThrow(_thnn_fused_gru_cell::name, _thnn_fused_gru_cell::overload_name) |
| 6463 | .typed<_thnn_fused_gru_cell::schema>(); |
| 6464 | } |
| 6465 | |
| 6466 | // aten::_thnn_fused_gru_cell(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None) -> (Tensor, Tensor) |
| 6467 | ::std::tuple<at::Tensor,at::Tensor> _thnn_fused_gru_cell::call(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) { |
| 6468 | |
| 6469 | static auto op = create__thnn_fused_gru_cell_typed_handle(); |
| 6470 | return op.call(input_gates, hidden_gates, hx, input_bias, hidden_bias); |
| 6471 | } |
| 6472 | |
| 6473 | // aten::_thnn_fused_gru_cell(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None) -> (Tensor, Tensor) |
| 6474 | ::std::tuple<at::Tensor,at::Tensor> _thnn_fused_gru_cell::redispatch(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) { |
| 6475 | |
| 6476 | static auto op = create__thnn_fused_gru_cell_typed_handle(); |
| 6477 | return op.redispatch(dispatchKeySet, input_gates, hidden_gates, hx, input_bias, hidden_bias); |
| 6478 | } |
| 6479 | |
| 6480 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantized_rnn_tanh_cell, name, "aten::quantized_rnn_tanh_cell") |
| 6481 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantized_rnn_tanh_cell, overload_name, "") |
| 6482 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantized_rnn_tanh_cell, schema_str, "quantized_rnn_tanh_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> Tensor") |
| 6483 | |
| 6484 | // aten::quantized_rnn_tanh_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> Tensor |
| 6485 | static C10_NOINLINE c10::TypedOperatorHandle<quantized_rnn_tanh_cell::schema> create_quantized_rnn_tanh_cell_typed_handle() { |
| 6486 | return c10::Dispatcher::singleton() |
| 6487 | .findSchemaOrThrow(quantized_rnn_tanh_cell::name, quantized_rnn_tanh_cell::overload_name) |
| 6488 | .typed<quantized_rnn_tanh_cell::schema>(); |
| 6489 | } |
| 6490 | |
| 6491 | // aten::quantized_rnn_tanh_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> Tensor |
| 6492 | at::Tensor quantized_rnn_tanh_cell::call(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh) { |
| 6493 | |
| 6494 | static auto op = create_quantized_rnn_tanh_cell_typed_handle(); |
| 6495 | return op.call(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh); |
| 6496 | } |
| 6497 | |
| 6498 | // aten::quantized_rnn_tanh_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> Tensor |
| 6499 | at::Tensor quantized_rnn_tanh_cell::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh) { |
| 6500 | |
| 6501 | static auto op = create_quantized_rnn_tanh_cell_typed_handle(); |
| 6502 | return op.redispatch(dispatchKeySet, input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh); |
| 6503 | } |
| 6504 | |
| 6505 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_pack_padded_sequence, name, "aten::_pack_padded_sequence") |
| 6506 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_pack_padded_sequence, overload_name, "") |
| 6507 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_pack_padded_sequence, schema_str, "_pack_padded_sequence(Tensor input, Tensor lengths, bool batch_first) -> (Tensor, Tensor)") |
| 6508 | |
| 6509 | // aten::_pack_padded_sequence(Tensor input, Tensor lengths, bool batch_first) -> (Tensor, Tensor) |
| 6510 | static C10_NOINLINE c10::TypedOperatorHandle<_pack_padded_sequence::schema> create__pack_padded_sequence_typed_handle() { |
| 6511 | return c10::Dispatcher::singleton() |
| 6512 | .findSchemaOrThrow(_pack_padded_sequence::name, _pack_padded_sequence::overload_name) |
| 6513 | .typed<_pack_padded_sequence::schema>(); |
| 6514 | } |
| 6515 | |
| 6516 | // aten::_pack_padded_sequence(Tensor input, Tensor lengths, bool batch_first) -> (Tensor, Tensor) |
| 6517 | ::std::tuple<at::Tensor,at::Tensor> _pack_padded_sequence::call(const at::Tensor & input, const at::Tensor & lengths, bool batch_first) { |
| 6518 | |
| 6519 | static auto op = create__pack_padded_sequence_typed_handle(); |
| 6520 | return op.call(input, lengths, batch_first); |
| 6521 | } |
| 6522 | |
| 6523 | // aten::_pack_padded_sequence(Tensor input, Tensor lengths, bool batch_first) -> (Tensor, Tensor) |
| 6524 | ::std::tuple<at::Tensor,at::Tensor> _pack_padded_sequence::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & lengths, bool batch_first) { |
| 6525 | |
| 6526 | static auto op = create__pack_padded_sequence_typed_handle(); |
| 6527 | return op.redispatch(dispatchKeySet, input, lengths, batch_first); |
| 6528 | } |
| 6529 | |
| 6530 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(is_set_to, name, "aten::is_set_to") |
| 6531 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(is_set_to, overload_name, "") |
| 6532 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(is_set_to, schema_str, "is_set_to(Tensor self, Tensor tensor) -> bool") |
| 6533 | |
| 6534 | // aten::is_set_to(Tensor self, Tensor tensor) -> bool |
| 6535 | static C10_NOINLINE c10::TypedOperatorHandle<is_set_to::schema> create_is_set_to_typed_handle() { |
| 6536 | return c10::Dispatcher::singleton() |
| 6537 | .findSchemaOrThrow(is_set_to::name, is_set_to::overload_name) |
| 6538 | .typed<is_set_to::schema>(); |
| 6539 | } |
| 6540 | |
| 6541 | // aten::is_set_to(Tensor self, Tensor tensor) -> bool |
| 6542 | bool is_set_to::call(const at::Tensor & self, const at::Tensor & tensor) { |
| 6543 | |
| 6544 | static auto op = create_is_set_to_typed_handle(); |
| 6545 | return op.call(self, tensor); |
| 6546 | } |
| 6547 | |
| 6548 | // aten::is_set_to(Tensor self, Tensor tensor) -> bool |
| 6549 | bool is_set_to::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & tensor) { |
| 6550 | |
| 6551 | static auto op = create_is_set_to_typed_handle(); |
| 6552 | return op.redispatch(dispatchKeySet, self, tensor); |
| 6553 | } |
| 6554 | |
| 6555 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_masked_softmax, name, "aten::_masked_softmax") |
| 6556 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_masked_softmax, overload_name, "") |
| 6557 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_masked_softmax, schema_str, "_masked_softmax(Tensor self, Tensor mask, int? dim=None, int? mask_type=None) -> Tensor") |
| 6558 | |
| 6559 | // aten::_masked_softmax(Tensor self, Tensor mask, int? dim=None, int? mask_type=None) -> Tensor |
| 6560 | static C10_NOINLINE c10::TypedOperatorHandle<_masked_softmax::schema> create__masked_softmax_typed_handle() { |
| 6561 | return c10::Dispatcher::singleton() |
| 6562 | .findSchemaOrThrow(_masked_softmax::name, _masked_softmax::overload_name) |
| 6563 | .typed<_masked_softmax::schema>(); |
| 6564 | } |
| 6565 | |
| 6566 | // aten::_masked_softmax(Tensor self, Tensor mask, int? dim=None, int? mask_type=None) -> Tensor |
| 6567 | at::Tensor _masked_softmax::call(const at::Tensor & self, const at::Tensor & mask, c10::optional<int64_t> dim, c10::optional<int64_t> mask_type) { |
| 6568 | |
| 6569 | static auto op = create__masked_softmax_typed_handle(); |
| 6570 | return op.call(self, mask, dim, mask_type); |
| 6571 | } |
| 6572 | |
| 6573 | // aten::_masked_softmax(Tensor self, Tensor mask, int? dim=None, int? mask_type=None) -> Tensor |
| 6574 | at::Tensor _masked_softmax::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mask, c10::optional<int64_t> dim, c10::optional<int64_t> mask_type) { |
| 6575 | |
| 6576 | static auto op = create__masked_softmax_typed_handle(); |
| 6577 | return op.redispatch(dispatchKeySet, self, mask, dim, mask_type); |
| 6578 | } |
| 6579 | |
| 6580 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(view, name, "aten::view") |
| 6581 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(view, overload_name, "") |
| 6582 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(view, schema_str, "view(Tensor(a) self, SymInt[] size) -> Tensor(a)") |
| 6583 | |
| 6584 | // aten::view(Tensor(a) self, SymInt[] size) -> Tensor(a) |
| 6585 | static C10_NOINLINE c10::TypedOperatorHandle<view::schema> create_view_typed_handle() { |
| 6586 | return c10::Dispatcher::singleton() |
| 6587 | .findSchemaOrThrow(view::name, view::overload_name) |
| 6588 | .typed<view::schema>(); |
| 6589 | } |
| 6590 | |
| 6591 | // aten::view(Tensor(a) self, SymInt[] size) -> Tensor(a) |
| 6592 | at::Tensor view::call(const at::Tensor & self, c10::SymIntArrayRef size) { |
| 6593 | |
| 6594 | static auto op = create_view_typed_handle(); |
| 6595 | return op.call(self, size); |
| 6596 | } |
| 6597 | |
| 6598 | // aten::view(Tensor(a) self, SymInt[] size) -> Tensor(a) |
| 6599 | at::Tensor view::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size) { |
| 6600 | |
| 6601 | static auto op = create_view_typed_handle(); |
| 6602 | return op.redispatch(dispatchKeySet, self, size); |
| 6603 | } |
| 6604 | |
| 6605 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(view_dtype, name, "aten::view") |
| 6606 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(view_dtype, overload_name, "dtype") |
| 6607 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(view_dtype, schema_str, "view.dtype(Tensor(a) self, ScalarType dtype) -> Tensor(a)") |
| 6608 | |
| 6609 | // aten::view.dtype(Tensor(a) self, ScalarType dtype) -> Tensor(a) |
| 6610 | static C10_NOINLINE c10::TypedOperatorHandle<view_dtype::schema> create_view_dtype_typed_handle() { |
| 6611 | return c10::Dispatcher::singleton() |
| 6612 | .findSchemaOrThrow(view_dtype::name, view_dtype::overload_name) |
| 6613 | .typed<view_dtype::schema>(); |
| 6614 | } |
| 6615 | |
| 6616 | // aten::view.dtype(Tensor(a) self, ScalarType dtype) -> Tensor(a) |
| 6617 | at::Tensor view_dtype::call(const at::Tensor & self, at::ScalarType dtype) { |
| 6618 | |
| 6619 | static auto op = create_view_dtype_typed_handle(); |
| 6620 | return op.call(self, dtype); |
| 6621 | } |
| 6622 | |
| 6623 | // aten::view.dtype(Tensor(a) self, ScalarType dtype) -> Tensor(a) |
| 6624 | at::Tensor view_dtype::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::ScalarType dtype) { |
| 6625 | |
| 6626 | static auto op = create_view_dtype_typed_handle(); |
| 6627 | return op.redispatch(dispatchKeySet, self, dtype); |
| 6628 | } |
| 6629 | |
| 6630 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(__xor___Scalar, name, "aten::__xor__") |
| 6631 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(__xor___Scalar, overload_name, "Scalar") |
| 6632 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(__xor___Scalar, schema_str, "__xor__.Scalar(Tensor self, Scalar other) -> Tensor") |
| 6633 | |
| 6634 | // aten::__xor__.Scalar(Tensor self, Scalar other) -> Tensor |
| 6635 | static C10_NOINLINE c10::TypedOperatorHandle<__xor___Scalar::schema> create___xor___Scalar_typed_handle() { |
| 6636 | return c10::Dispatcher::singleton() |
| 6637 | .findSchemaOrThrow(__xor___Scalar::name, __xor___Scalar::overload_name) |
| 6638 | .typed<__xor___Scalar::schema>(); |
| 6639 | } |
| 6640 | |
| 6641 | // aten::__xor__.Scalar(Tensor self, Scalar other) -> Tensor |
| 6642 | at::Tensor __xor___Scalar::call(const at::Tensor & self, const at::Scalar & other) { |
| 6643 | |
| 6644 | static auto op = create___xor___Scalar_typed_handle(); |
| 6645 | return op.call(self, other); |
| 6646 | } |
| 6647 | |
| 6648 | // aten::__xor__.Scalar(Tensor self, Scalar other) -> Tensor |
| 6649 | at::Tensor __xor___Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { |
| 6650 | |
| 6651 | static auto op = create___xor___Scalar_typed_handle(); |
| 6652 | return op.redispatch(dispatchKeySet, self, other); |
| 6653 | } |
| 6654 | |
| 6655 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(__xor___Tensor, name, "aten::__xor__") |
| 6656 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(__xor___Tensor, overload_name, "Tensor") |
| 6657 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(__xor___Tensor, schema_str, "__xor__.Tensor(Tensor self, Tensor other) -> Tensor") |
| 6658 | |
| 6659 | // aten::__xor__.Tensor(Tensor self, Tensor other) -> Tensor |
| 6660 | static C10_NOINLINE c10::TypedOperatorHandle<__xor___Tensor::schema> create___xor___Tensor_typed_handle() { |
| 6661 | return c10::Dispatcher::singleton() |
| 6662 | .findSchemaOrThrow(__xor___Tensor::name, __xor___Tensor::overload_name) |
| 6663 | .typed<__xor___Tensor::schema>(); |
| 6664 | } |
| 6665 | |
| 6666 | // aten::__xor__.Tensor(Tensor self, Tensor other) -> Tensor |
| 6667 | at::Tensor __xor___Tensor::call(const at::Tensor & self, const at::Tensor & other) { |
| 6668 | |
| 6669 | static auto op = create___xor___Tensor_typed_handle(); |
| 6670 | return op.call(self, other); |
| 6671 | } |
| 6672 | |
| 6673 | // aten::__xor__.Tensor(Tensor self, Tensor other) -> Tensor |
| 6674 | at::Tensor __xor___Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { |
| 6675 | |
| 6676 | static auto op = create___xor___Tensor_typed_handle(); |
| 6677 | return op.redispatch(dispatchKeySet, self, other); |
| 6678 | } |
| 6679 | |
| 6680 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(__ixor___Scalar, name, "aten::__ixor__") |
| 6681 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(__ixor___Scalar, overload_name, "Scalar") |
| 6682 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(__ixor___Scalar, schema_str, "__ixor__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)") |
| 6683 | |
| 6684 | // aten::__ixor__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) |
| 6685 | static C10_NOINLINE c10::TypedOperatorHandle<__ixor___Scalar::schema> create___ixor___Scalar_typed_handle() { |
| 6686 | return c10::Dispatcher::singleton() |
| 6687 | .findSchemaOrThrow(__ixor___Scalar::name, __ixor___Scalar::overload_name) |
| 6688 | .typed<__ixor___Scalar::schema>(); |
| 6689 | } |
| 6690 | |
| 6691 | // aten::__ixor__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) |
| 6692 | at::Tensor & __ixor___Scalar::call(at::Tensor & self, const at::Scalar & other) { |
| 6693 | |
| 6694 | static auto op = create___ixor___Scalar_typed_handle(); |
| 6695 | return op.call(self, other); |
| 6696 | } |
| 6697 | |
| 6698 | // aten::__ixor__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) |
| 6699 | at::Tensor & __ixor___Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { |
| 6700 | |
| 6701 | static auto op = create___ixor___Scalar_typed_handle(); |
| 6702 | return op.redispatch(dispatchKeySet, self, other); |
| 6703 | } |
| 6704 | |
| 6705 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(__ixor___Tensor, name, "aten::__ixor__") |
| 6706 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(__ixor___Tensor, overload_name, "Tensor") |
| 6707 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(__ixor___Tensor, schema_str, "__ixor__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)") |
| 6708 | |
| 6709 | // aten::__ixor__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) |
| 6710 | static C10_NOINLINE c10::TypedOperatorHandle<__ixor___Tensor::schema> create___ixor___Tensor_typed_handle() { |
| 6711 | return c10::Dispatcher::singleton() |
| 6712 | .findSchemaOrThrow(__ixor___Tensor::name, __ixor___Tensor::overload_name) |
| 6713 | .typed<__ixor___Tensor::schema>(); |
| 6714 | } |
| 6715 | |
| 6716 | // aten::__ixor__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) |
| 6717 | at::Tensor & __ixor___Tensor::call(at::Tensor & self, const at::Tensor & other) { |
| 6718 | |
| 6719 | static auto op = create___ixor___Tensor_typed_handle(); |
| 6720 | return op.call(self, other); |
| 6721 | } |
| 6722 | |
| 6723 | // aten::__ixor__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) |
| 6724 | at::Tensor & __ixor___Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 6725 | |
| 6726 | static auto op = create___ixor___Tensor_typed_handle(); |
| 6727 | return op.redispatch(dispatchKeySet, self, other); |
| 6728 | } |
| 6729 | |
| 6730 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(triu_, name, "aten::triu_") |
| 6731 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(triu_, overload_name, "") |
| 6732 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(triu_, schema_str, "triu_(Tensor(a!) self, int diagonal=0) -> Tensor(a!)") |
| 6733 | |
| 6734 | // aten::triu_(Tensor(a!) self, int diagonal=0) -> Tensor(a!) |
| 6735 | static C10_NOINLINE c10::TypedOperatorHandle<triu_::schema> create_triu__typed_handle() { |
| 6736 | return c10::Dispatcher::singleton() |
| 6737 | .findSchemaOrThrow(triu_::name, triu_::overload_name) |
| 6738 | .typed<triu_::schema>(); |
| 6739 | } |
| 6740 | |
| 6741 | // aten::triu_(Tensor(a!) self, int diagonal=0) -> Tensor(a!) |
| 6742 | at::Tensor & triu_::call(at::Tensor & self, int64_t diagonal) { |
| 6743 | |
| 6744 | static auto op = create_triu__typed_handle(); |
| 6745 | return op.call(self, diagonal); |
| 6746 | } |
| 6747 | |
| 6748 | // aten::triu_(Tensor(a!) self, int diagonal=0) -> Tensor(a!) |
| 6749 | at::Tensor & triu_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t diagonal) { |
| 6750 | |
| 6751 | static auto op = create_triu__typed_handle(); |
| 6752 | return op.redispatch(dispatchKeySet, self, diagonal); |
| 6753 | } |
| 6754 | |
| 6755 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lerp__Scalar, name, "aten::lerp_") |
| 6756 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lerp__Scalar, overload_name, "Scalar") |
| 6757 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lerp__Scalar, schema_str, "lerp_.Scalar(Tensor(a!) self, Tensor end, Scalar weight) -> Tensor(a!)") |
| 6758 | |
| 6759 | // aten::lerp_.Scalar(Tensor(a!) self, Tensor end, Scalar weight) -> Tensor(a!) |
| 6760 | static C10_NOINLINE c10::TypedOperatorHandle<lerp__Scalar::schema> create_lerp__Scalar_typed_handle() { |
| 6761 | return c10::Dispatcher::singleton() |
| 6762 | .findSchemaOrThrow(lerp__Scalar::name, lerp__Scalar::overload_name) |
| 6763 | .typed<lerp__Scalar::schema>(); |
| 6764 | } |
| 6765 | |
| 6766 | // aten::lerp_.Scalar(Tensor(a!) self, Tensor end, Scalar weight) -> Tensor(a!) |
| 6767 | at::Tensor & lerp__Scalar::call(at::Tensor & self, const at::Tensor & end, const at::Scalar & weight) { |
| 6768 | |
| 6769 | static auto op = create_lerp__Scalar_typed_handle(); |
| 6770 | return op.call(self, end, weight); |
| 6771 | } |
| 6772 | |
| 6773 | // aten::lerp_.Scalar(Tensor(a!) self, Tensor end, Scalar weight) -> Tensor(a!) |
| 6774 | at::Tensor & lerp__Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & end, const at::Scalar & weight) { |
| 6775 | |
| 6776 | static auto op = create_lerp__Scalar_typed_handle(); |
| 6777 | return op.redispatch(dispatchKeySet, self, end, weight); |
| 6778 | } |
| 6779 | |
| 6780 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lerp__Tensor, name, "aten::lerp_") |
| 6781 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lerp__Tensor, overload_name, "Tensor") |
| 6782 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lerp__Tensor, schema_str, "lerp_.Tensor(Tensor(a!) self, Tensor end, Tensor weight) -> Tensor(a!)") |
| 6783 | |
| 6784 | // aten::lerp_.Tensor(Tensor(a!) self, Tensor end, Tensor weight) -> Tensor(a!) |
| 6785 | static C10_NOINLINE c10::TypedOperatorHandle<lerp__Tensor::schema> create_lerp__Tensor_typed_handle() { |
| 6786 | return c10::Dispatcher::singleton() |
| 6787 | .findSchemaOrThrow(lerp__Tensor::name, lerp__Tensor::overload_name) |
| 6788 | .typed<lerp__Tensor::schema>(); |
| 6789 | } |
| 6790 | |
| 6791 | // aten::lerp_.Tensor(Tensor(a!) self, Tensor end, Tensor weight) -> Tensor(a!) |
| 6792 | at::Tensor & lerp__Tensor::call(at::Tensor & self, const at::Tensor & end, const at::Tensor & weight) { |
| 6793 | |
| 6794 | static auto op = create_lerp__Tensor_typed_handle(); |
| 6795 | return op.call(self, end, weight); |
| 6796 | } |
| 6797 | |
| 6798 | // aten::lerp_.Tensor(Tensor(a!) self, Tensor end, Tensor weight) -> Tensor(a!) |
| 6799 | at::Tensor & lerp__Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & end, const at::Tensor & weight) { |
| 6800 | |
| 6801 | static auto op = create_lerp__Tensor_typed_handle(); |
| 6802 | return op.redispatch(dispatchKeySet, self, end, weight); |
| 6803 | } |
| 6804 | |
| 6805 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(addbmm_, name, "aten::addbmm_") |
| 6806 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(addbmm_, overload_name, "") |
| 6807 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(addbmm_, schema_str, "addbmm_(Tensor(a!) self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)") |
| 6808 | |
| 6809 | // aten::addbmm_(Tensor(a!) self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!) |
| 6810 | static C10_NOINLINE c10::TypedOperatorHandle<addbmm_::schema> create_addbmm__typed_handle() { |
| 6811 | return c10::Dispatcher::singleton() |
| 6812 | .findSchemaOrThrow(addbmm_::name, addbmm_::overload_name) |
| 6813 | .typed<addbmm_::schema>(); |
| 6814 | } |
| 6815 | |
| 6816 | // aten::addbmm_(Tensor(a!) self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!) |
| 6817 | at::Tensor & addbmm_::call(at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 6818 | |
| 6819 | static auto op = create_addbmm__typed_handle(); |
| 6820 | return op.call(self, batch1, batch2, beta, alpha); |
| 6821 | } |
| 6822 | |
| 6823 | // aten::addbmm_(Tensor(a!) self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!) |
| 6824 | at::Tensor & addbmm_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 6825 | |
| 6826 | static auto op = create_addbmm__typed_handle(); |
| 6827 | return op.redispatch(dispatchKeySet, self, batch1, batch2, beta, alpha); |
| 6828 | } |
| 6829 | |
| 6830 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(addbmm_out, name, "aten::addbmm") |
| 6831 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(addbmm_out, overload_name, "out") |
| 6832 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(addbmm_out, schema_str, "addbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)") |
| 6833 | |
| 6834 | // aten::addbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) |
| 6835 | static C10_NOINLINE c10::TypedOperatorHandle<addbmm_out::schema> create_addbmm_out_typed_handle() { |
| 6836 | return c10::Dispatcher::singleton() |
| 6837 | .findSchemaOrThrow(addbmm_out::name, addbmm_out::overload_name) |
| 6838 | .typed<addbmm_out::schema>(); |
| 6839 | } |
| 6840 | |
| 6841 | // aten::addbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) |
| 6842 | at::Tensor & addbmm_out::call(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { |
| 6843 | |
| 6844 | static auto op = create_addbmm_out_typed_handle(); |
| 6845 | return op.call(self, batch1, batch2, beta, alpha, out); |
| 6846 | } |
| 6847 | |
| 6848 | // aten::addbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) |
| 6849 | at::Tensor & addbmm_out::redispatch(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) { |
| 6850 | |
| 6851 | static auto op = create_addbmm_out_typed_handle(); |
| 6852 | return op.redispatch(dispatchKeySet, self, batch1, batch2, beta, alpha, out); |
| 6853 | } |
| 6854 | |
| 6855 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(addbmm, name, "aten::addbmm") |
| 6856 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(addbmm, overload_name, "") |
| 6857 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(addbmm, schema_str, "addbmm(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor") |
| 6858 | |
| 6859 | // aten::addbmm(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor |
| 6860 | static C10_NOINLINE c10::TypedOperatorHandle<addbmm::schema> create_addbmm_typed_handle() { |
| 6861 | return c10::Dispatcher::singleton() |
| 6862 | .findSchemaOrThrow(addbmm::name, addbmm::overload_name) |
| 6863 | .typed<addbmm::schema>(); |
| 6864 | } |
| 6865 | |
| 6866 | // aten::addbmm(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor |
| 6867 | at::Tensor addbmm::call(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 6868 | |
| 6869 | static auto op = create_addbmm_typed_handle(); |
| 6870 | return op.call(self, batch1, batch2, beta, alpha); |
| 6871 | } |
| 6872 | |
| 6873 | // aten::addbmm(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor |
| 6874 | at::Tensor addbmm::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 6875 | |
| 6876 | static auto op = create_addbmm_typed_handle(); |
| 6877 | return op.redispatch(dispatchKeySet, self, batch1, batch2, beta, alpha); |
| 6878 | } |
| 6879 | |
| 6880 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(triu_out, name, "aten::triu") |
| 6881 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(triu_out, overload_name, "out") |
| 6882 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(triu_out, schema_str, "triu.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)") |
| 6883 | |
| 6884 | // aten::triu.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!) |
| 6885 | static C10_NOINLINE c10::TypedOperatorHandle<triu_out::schema> create_triu_out_typed_handle() { |
| 6886 | return c10::Dispatcher::singleton() |
| 6887 | .findSchemaOrThrow(triu_out::name, triu_out::overload_name) |
| 6888 | .typed<triu_out::schema>(); |
| 6889 | } |
| 6890 | |
| 6891 | // aten::triu.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!) |
| 6892 | at::Tensor & triu_out::call(const at::Tensor & self, int64_t diagonal, at::Tensor & out) { |
| 6893 | |
| 6894 | static auto op = create_triu_out_typed_handle(); |
| 6895 | return op.call(self, diagonal, out); |
| 6896 | } |
| 6897 | |
| 6898 | // aten::triu.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!) |
| 6899 | at::Tensor & triu_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t diagonal, at::Tensor & out) { |
| 6900 | |
| 6901 | static auto op = create_triu_out_typed_handle(); |
| 6902 | return op.redispatch(dispatchKeySet, self, diagonal, out); |
| 6903 | } |
| 6904 | |
| 6905 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(triu, name, "aten::triu") |
| 6906 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(triu, overload_name, "") |
| 6907 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(triu, schema_str, "triu(Tensor self, int diagonal=0) -> Tensor") |
| 6908 | |
| 6909 | // aten::triu(Tensor self, int diagonal=0) -> Tensor |
| 6910 | static C10_NOINLINE c10::TypedOperatorHandle<triu::schema> create_triu_typed_handle() { |
| 6911 | return c10::Dispatcher::singleton() |
| 6912 | .findSchemaOrThrow(triu::name, triu::overload_name) |
| 6913 | .typed<triu::schema>(); |
| 6914 | } |
| 6915 | |
| 6916 | // aten::triu(Tensor self, int diagonal=0) -> Tensor |
| 6917 | at::Tensor triu::call(const at::Tensor & self, int64_t diagonal) { |
| 6918 | |
| 6919 | static auto op = create_triu_typed_handle(); |
| 6920 | return op.call(self, diagonal); |
| 6921 | } |
| 6922 | |
| 6923 | // aten::triu(Tensor self, int diagonal=0) -> Tensor |
| 6924 | at::Tensor triu::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t diagonal) { |
| 6925 | |
| 6926 | static auto op = create_triu_typed_handle(); |
| 6927 | return op.redispatch(dispatchKeySet, self, diagonal); |
| 6928 | } |
| 6929 | |
| 6930 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(not_equal_Scalar_out, name, "aten::not_equal") |
| 6931 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(not_equal_Scalar_out, overload_name, "Scalar_out") |
| 6932 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(not_equal_Scalar_out, schema_str, "not_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)") |
| 6933 | |
| 6934 | // aten::not_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) |
| 6935 | static C10_NOINLINE c10::TypedOperatorHandle<not_equal_Scalar_out::schema> create_not_equal_Scalar_out_typed_handle() { |
| 6936 | return c10::Dispatcher::singleton() |
| 6937 | .findSchemaOrThrow(not_equal_Scalar_out::name, not_equal_Scalar_out::overload_name) |
| 6938 | .typed<not_equal_Scalar_out::schema>(); |
| 6939 | } |
| 6940 | |
| 6941 | // aten::not_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) |
| 6942 | at::Tensor & not_equal_Scalar_out::call(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 6943 | |
| 6944 | static auto op = create_not_equal_Scalar_out_typed_handle(); |
| 6945 | return op.call(self, other, out); |
| 6946 | } |
| 6947 | |
| 6948 | // aten::not_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) |
| 6949 | at::Tensor & not_equal_Scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 6950 | |
| 6951 | static auto op = create_not_equal_Scalar_out_typed_handle(); |
| 6952 | return op.redispatch(dispatchKeySet, self, other, out); |
| 6953 | } |
| 6954 | |
| 6955 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(not_equal_Scalar, name, "aten::not_equal") |
| 6956 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(not_equal_Scalar, overload_name, "Scalar") |
| 6957 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(not_equal_Scalar, schema_str, "not_equal.Scalar(Tensor self, Scalar other) -> Tensor") |
| 6958 | |
| 6959 | // aten::not_equal.Scalar(Tensor self, Scalar other) -> Tensor |
| 6960 | static C10_NOINLINE c10::TypedOperatorHandle<not_equal_Scalar::schema> create_not_equal_Scalar_typed_handle() { |
| 6961 | return c10::Dispatcher::singleton() |
| 6962 | .findSchemaOrThrow(not_equal_Scalar::name, not_equal_Scalar::overload_name) |
| 6963 | .typed<not_equal_Scalar::schema>(); |
| 6964 | } |
| 6965 | |
| 6966 | // aten::not_equal.Scalar(Tensor self, Scalar other) -> Tensor |
| 6967 | at::Tensor not_equal_Scalar::call(const at::Tensor & self, const at::Scalar & other) { |
| 6968 | |
| 6969 | static auto op = create_not_equal_Scalar_typed_handle(); |
| 6970 | return op.call(self, other); |
| 6971 | } |
| 6972 | |
| 6973 | // aten::not_equal.Scalar(Tensor self, Scalar other) -> Tensor |
| 6974 | at::Tensor not_equal_Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { |
| 6975 | |
| 6976 | static auto op = create_not_equal_Scalar_typed_handle(); |
| 6977 | return op.redispatch(dispatchKeySet, self, other); |
| 6978 | } |
| 6979 | |
| 6980 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(not_equal_Tensor_out, name, "aten::not_equal") |
| 6981 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(not_equal_Tensor_out, overload_name, "Tensor_out") |
| 6982 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(not_equal_Tensor_out, schema_str, "not_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)") |
| 6983 | |
| 6984 | // aten::not_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 6985 | static C10_NOINLINE c10::TypedOperatorHandle<not_equal_Tensor_out::schema> create_not_equal_Tensor_out_typed_handle() { |
| 6986 | return c10::Dispatcher::singleton() |
| 6987 | .findSchemaOrThrow(not_equal_Tensor_out::name, not_equal_Tensor_out::overload_name) |
| 6988 | .typed<not_equal_Tensor_out::schema>(); |
| 6989 | } |
| 6990 | |
| 6991 | // aten::not_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 6992 | at::Tensor & not_equal_Tensor_out::call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 6993 | |
| 6994 | static auto op = create_not_equal_Tensor_out_typed_handle(); |
| 6995 | return op.call(self, other, out); |
| 6996 | } |
| 6997 | |
| 6998 | // aten::not_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 6999 | at::Tensor & not_equal_Tensor_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 7000 | |
| 7001 | static auto op = create_not_equal_Tensor_out_typed_handle(); |
| 7002 | return op.redispatch(dispatchKeySet, self, other, out); |
| 7003 | } |
| 7004 | |
| 7005 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(not_equal_Tensor, name, "aten::not_equal") |
| 7006 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(not_equal_Tensor, overload_name, "Tensor") |
| 7007 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(not_equal_Tensor, schema_str, "not_equal.Tensor(Tensor self, Tensor other) -> Tensor") |
| 7008 | |
| 7009 | // aten::not_equal.Tensor(Tensor self, Tensor other) -> Tensor |
| 7010 | static C10_NOINLINE c10::TypedOperatorHandle<not_equal_Tensor::schema> create_not_equal_Tensor_typed_handle() { |
| 7011 | return c10::Dispatcher::singleton() |
| 7012 | .findSchemaOrThrow(not_equal_Tensor::name, not_equal_Tensor::overload_name) |
| 7013 | .typed<not_equal_Tensor::schema>(); |
| 7014 | } |
| 7015 | |
| 7016 | // aten::not_equal.Tensor(Tensor self, Tensor other) -> Tensor |
| 7017 | at::Tensor not_equal_Tensor::call(const at::Tensor & self, const at::Tensor & other) { |
| 7018 | |
| 7019 | static auto op = create_not_equal_Tensor_typed_handle(); |
| 7020 | return op.call(self, other); |
| 7021 | } |
| 7022 | |
| 7023 | // aten::not_equal.Tensor(Tensor self, Tensor other) -> Tensor |
| 7024 | at::Tensor not_equal_Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { |
| 7025 | |
| 7026 | static auto op = create_not_equal_Tensor_typed_handle(); |
| 7027 | return op.redispatch(dispatchKeySet, self, other); |
| 7028 | } |
| 7029 | |
| 7030 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(not_equal__Scalar, name, "aten::not_equal_") |
| 7031 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(not_equal__Scalar, overload_name, "Scalar") |
| 7032 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(not_equal__Scalar, schema_str, "not_equal_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)") |
| 7033 | |
| 7034 | // aten::not_equal_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) |
| 7035 | static C10_NOINLINE c10::TypedOperatorHandle<not_equal__Scalar::schema> create_not_equal__Scalar_typed_handle() { |
| 7036 | return c10::Dispatcher::singleton() |
| 7037 | .findSchemaOrThrow(not_equal__Scalar::name, not_equal__Scalar::overload_name) |
| 7038 | .typed<not_equal__Scalar::schema>(); |
| 7039 | } |
| 7040 | |
| 7041 | // aten::not_equal_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) |
| 7042 | at::Tensor & not_equal__Scalar::call(at::Tensor & self, const at::Scalar & other) { |
| 7043 | |
| 7044 | static auto op = create_not_equal__Scalar_typed_handle(); |
| 7045 | return op.call(self, other); |
| 7046 | } |
| 7047 | |
| 7048 | // aten::not_equal_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) |
| 7049 | at::Tensor & not_equal__Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { |
| 7050 | |
| 7051 | static auto op = create_not_equal__Scalar_typed_handle(); |
| 7052 | return op.redispatch(dispatchKeySet, self, other); |
| 7053 | } |
| 7054 | |
| 7055 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(not_equal__Tensor, name, "aten::not_equal_") |
| 7056 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(not_equal__Tensor, overload_name, "Tensor") |
| 7057 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(not_equal__Tensor, schema_str, "not_equal_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)") |
| 7058 | |
| 7059 | // aten::not_equal_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) |
| 7060 | static C10_NOINLINE c10::TypedOperatorHandle<not_equal__Tensor::schema> create_not_equal__Tensor_typed_handle() { |
| 7061 | return c10::Dispatcher::singleton() |
| 7062 | .findSchemaOrThrow(not_equal__Tensor::name, not_equal__Tensor::overload_name) |
| 7063 | .typed<not_equal__Tensor::schema>(); |
| 7064 | } |
| 7065 | |
| 7066 | // aten::not_equal_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) |
| 7067 | at::Tensor & not_equal__Tensor::call(at::Tensor & self, const at::Tensor & other) { |
| 7068 | |
| 7069 | static auto op = create_not_equal__Tensor_typed_handle(); |
| 7070 | return op.call(self, other); |
| 7071 | } |
| 7072 | |
| 7073 | // aten::not_equal_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) |
| 7074 | at::Tensor & not_equal__Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 7075 | |
| 7076 | static auto op = create_not_equal__Tensor_typed_handle(); |
| 7077 | return op.redispatch(dispatchKeySet, self, other); |
| 7078 | } |
| 7079 | |
| 7080 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(greater_Scalar_out, name, "aten::greater") |
| 7081 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(greater_Scalar_out, overload_name, "Scalar_out") |
| 7082 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(greater_Scalar_out, schema_str, "greater.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)") |
| 7083 | |
| 7084 | // aten::greater.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) |
| 7085 | static C10_NOINLINE c10::TypedOperatorHandle<greater_Scalar_out::schema> create_greater_Scalar_out_typed_handle() { |
| 7086 | return c10::Dispatcher::singleton() |
| 7087 | .findSchemaOrThrow(greater_Scalar_out::name, greater_Scalar_out::overload_name) |
| 7088 | .typed<greater_Scalar_out::schema>(); |
| 7089 | } |
| 7090 | |
| 7091 | // aten::greater.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) |
| 7092 | at::Tensor & greater_Scalar_out::call(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 7093 | |
| 7094 | static auto op = create_greater_Scalar_out_typed_handle(); |
| 7095 | return op.call(self, other, out); |
| 7096 | } |
| 7097 | |
| 7098 | // aten::greater.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) |
| 7099 | at::Tensor & greater_Scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 7100 | |
| 7101 | static auto op = create_greater_Scalar_out_typed_handle(); |
| 7102 | return op.redispatch(dispatchKeySet, self, other, out); |
| 7103 | } |
| 7104 | |
| 7105 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(greater_Scalar, name, "aten::greater") |
| 7106 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(greater_Scalar, overload_name, "Scalar") |
| 7107 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(greater_Scalar, schema_str, "greater.Scalar(Tensor self, Scalar other) -> Tensor") |
| 7108 | |
| 7109 | // aten::greater.Scalar(Tensor self, Scalar other) -> Tensor |
| 7110 | static C10_NOINLINE c10::TypedOperatorHandle<greater_Scalar::schema> create_greater_Scalar_typed_handle() { |
| 7111 | return c10::Dispatcher::singleton() |
| 7112 | .findSchemaOrThrow(greater_Scalar::name, greater_Scalar::overload_name) |
| 7113 | .typed<greater_Scalar::schema>(); |
| 7114 | } |
| 7115 | |
| 7116 | // aten::greater.Scalar(Tensor self, Scalar other) -> Tensor |
| 7117 | at::Tensor greater_Scalar::call(const at::Tensor & self, const at::Scalar & other) { |
| 7118 | |
| 7119 | static auto op = create_greater_Scalar_typed_handle(); |
| 7120 | return op.call(self, other); |
| 7121 | } |
| 7122 | |
| 7123 | // aten::greater.Scalar(Tensor self, Scalar other) -> Tensor |
| 7124 | at::Tensor greater_Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { |
| 7125 | |
| 7126 | static auto op = create_greater_Scalar_typed_handle(); |
| 7127 | return op.redispatch(dispatchKeySet, self, other); |
| 7128 | } |
| 7129 | |
| 7130 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(greater_Tensor_out, name, "aten::greater") |
| 7131 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(greater_Tensor_out, overload_name, "Tensor_out") |
| 7132 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(greater_Tensor_out, schema_str, "greater.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)") |
| 7133 | |
| 7134 | // aten::greater.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 7135 | static C10_NOINLINE c10::TypedOperatorHandle<greater_Tensor_out::schema> create_greater_Tensor_out_typed_handle() { |
| 7136 | return c10::Dispatcher::singleton() |
| 7137 | .findSchemaOrThrow(greater_Tensor_out::name, greater_Tensor_out::overload_name) |
| 7138 | .typed<greater_Tensor_out::schema>(); |
| 7139 | } |
| 7140 | |
| 7141 | // aten::greater.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 7142 | at::Tensor & greater_Tensor_out::call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 7143 | |
| 7144 | static auto op = create_greater_Tensor_out_typed_handle(); |
| 7145 | return op.call(self, other, out); |
| 7146 | } |
| 7147 | |
| 7148 | // aten::greater.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 7149 | at::Tensor & greater_Tensor_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 7150 | |
| 7151 | static auto op = create_greater_Tensor_out_typed_handle(); |
| 7152 | return op.redispatch(dispatchKeySet, self, other, out); |
| 7153 | } |
| 7154 | |
| 7155 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(greater_Tensor, name, "aten::greater") |
| 7156 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(greater_Tensor, overload_name, "Tensor") |
| 7157 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(greater_Tensor, schema_str, "greater.Tensor(Tensor self, Tensor other) -> Tensor") |
| 7158 | |
| 7159 | // aten::greater.Tensor(Tensor self, Tensor other) -> Tensor |
| 7160 | static C10_NOINLINE c10::TypedOperatorHandle<greater_Tensor::schema> create_greater_Tensor_typed_handle() { |
| 7161 | return c10::Dispatcher::singleton() |
| 7162 | .findSchemaOrThrow(greater_Tensor::name, greater_Tensor::overload_name) |
| 7163 | .typed<greater_Tensor::schema>(); |
| 7164 | } |
| 7165 | |
| 7166 | // aten::greater.Tensor(Tensor self, Tensor other) -> Tensor |
| 7167 | at::Tensor greater_Tensor::call(const at::Tensor & self, const at::Tensor & other) { |
| 7168 | |
| 7169 | static auto op = create_greater_Tensor_typed_handle(); |
| 7170 | return op.call(self, other); |
| 7171 | } |
| 7172 | |
| 7173 | // aten::greater.Tensor(Tensor self, Tensor other) -> Tensor |
| 7174 | at::Tensor greater_Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { |
| 7175 | |
| 7176 | static auto op = create_greater_Tensor_typed_handle(); |
| 7177 | return op.redispatch(dispatchKeySet, self, other); |
| 7178 | } |
| 7179 | |
| 7180 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(greater__Scalar, name, "aten::greater_") |
| 7181 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(greater__Scalar, overload_name, "Scalar") |
| 7182 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(greater__Scalar, schema_str, "greater_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)") |
| 7183 | |
| 7184 | // aten::greater_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) |
| 7185 | static C10_NOINLINE c10::TypedOperatorHandle<greater__Scalar::schema> create_greater__Scalar_typed_handle() { |
| 7186 | return c10::Dispatcher::singleton() |
| 7187 | .findSchemaOrThrow(greater__Scalar::name, greater__Scalar::overload_name) |
| 7188 | .typed<greater__Scalar::schema>(); |
| 7189 | } |
| 7190 | |
| 7191 | // aten::greater_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) |
| 7192 | at::Tensor & greater__Scalar::call(at::Tensor & self, const at::Scalar & other) { |
| 7193 | |
| 7194 | static auto op = create_greater__Scalar_typed_handle(); |
| 7195 | return op.call(self, other); |
| 7196 | } |
| 7197 | |
| 7198 | // aten::greater_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) |
| 7199 | at::Tensor & greater__Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { |
| 7200 | |
| 7201 | static auto op = create_greater__Scalar_typed_handle(); |
| 7202 | return op.redispatch(dispatchKeySet, self, other); |
| 7203 | } |
| 7204 | |
| 7205 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(greater__Tensor, name, "aten::greater_") |
| 7206 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(greater__Tensor, overload_name, "Tensor") |
| 7207 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(greater__Tensor, schema_str, "greater_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)") |
| 7208 | |
| 7209 | // aten::greater_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) |
| 7210 | static C10_NOINLINE c10::TypedOperatorHandle<greater__Tensor::schema> create_greater__Tensor_typed_handle() { |
| 7211 | return c10::Dispatcher::singleton() |
| 7212 | .findSchemaOrThrow(greater__Tensor::name, greater__Tensor::overload_name) |
| 7213 | .typed<greater__Tensor::schema>(); |
| 7214 | } |
| 7215 | |
| 7216 | // aten::greater_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) |
| 7217 | at::Tensor & greater__Tensor::call(at::Tensor & self, const at::Tensor & other) { |
| 7218 | |
| 7219 | static auto op = create_greater__Tensor_typed_handle(); |
| 7220 | return op.call(self, other); |
| 7221 | } |
| 7222 | |
| 7223 | // aten::greater_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) |
| 7224 | at::Tensor & greater__Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 7225 | |
| 7226 | static auto op = create_greater__Tensor_typed_handle(); |
| 7227 | return op.redispatch(dispatchKeySet, self, other); |
| 7228 | } |
| 7229 | |
| 7230 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gather_out, name, "aten::gather") |
| 7231 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gather_out, overload_name, "out") |
| 7232 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gather_out, schema_str, "gather.out(Tensor self, int dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!)") |
| 7233 | |
| 7234 | // aten::gather.out(Tensor self, int dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!) |
| 7235 | static C10_NOINLINE c10::TypedOperatorHandle<gather_out::schema> create_gather_out_typed_handle() { |
| 7236 | return c10::Dispatcher::singleton() |
| 7237 | .findSchemaOrThrow(gather_out::name, gather_out::overload_name) |
| 7238 | .typed<gather_out::schema>(); |
| 7239 | } |
| 7240 | |
| 7241 | // aten::gather.out(Tensor self, int dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!) |
| 7242 | at::Tensor & gather_out::call(const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad, at::Tensor & out) { |
| 7243 | |
| 7244 | static auto op = create_gather_out_typed_handle(); |
| 7245 | return op.call(self, dim, index, sparse_grad, out); |
| 7246 | } |
| 7247 | |
| 7248 | // aten::gather.out(Tensor self, int dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!) |
| 7249 | at::Tensor & gather_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad, at::Tensor & out) { |
| 7250 | |
| 7251 | static auto op = create_gather_out_typed_handle(); |
| 7252 | return op.redispatch(dispatchKeySet, self, dim, index, sparse_grad, out); |
| 7253 | } |
| 7254 | |
| 7255 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gather, name, "aten::gather") |
| 7256 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gather, overload_name, "") |
| 7257 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gather, schema_str, "gather(Tensor self, int dim, Tensor index, *, bool sparse_grad=False) -> Tensor") |
| 7258 | |
| 7259 | // aten::gather(Tensor self, int dim, Tensor index, *, bool sparse_grad=False) -> Tensor |
| 7260 | static C10_NOINLINE c10::TypedOperatorHandle<gather::schema> create_gather_typed_handle() { |
| 7261 | return c10::Dispatcher::singleton() |
| 7262 | .findSchemaOrThrow(gather::name, gather::overload_name) |
| 7263 | .typed<gather::schema>(); |
| 7264 | } |
| 7265 | |
| 7266 | // aten::gather(Tensor self, int dim, Tensor index, *, bool sparse_grad=False) -> Tensor |
| 7267 | at::Tensor gather::call(const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad) { |
| 7268 | |
| 7269 | static auto op = create_gather_typed_handle(); |
| 7270 | return op.call(self, dim, index, sparse_grad); |
| 7271 | } |
| 7272 | |
| 7273 | // aten::gather(Tensor self, int dim, Tensor index, *, bool sparse_grad=False) -> Tensor |
| 7274 | at::Tensor gather::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad) { |
| 7275 | |
| 7276 | static auto op = create_gather_typed_handle(); |
| 7277 | return op.redispatch(dispatchKeySet, self, dim, index, sparse_grad); |
| 7278 | } |
| 7279 | |
| 7280 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gather_backward, name, "aten::gather_backward") |
| 7281 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gather_backward, overload_name, "") |
| 7282 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gather_backward, schema_str, "gather_backward(Tensor grad, Tensor self, int dim, Tensor index, bool sparse_grad) -> Tensor") |
| 7283 | |
| 7284 | // aten::gather_backward(Tensor grad, Tensor self, int dim, Tensor index, bool sparse_grad) -> Tensor |
| 7285 | static C10_NOINLINE c10::TypedOperatorHandle<gather_backward::schema> create_gather_backward_typed_handle() { |
| 7286 | return c10::Dispatcher::singleton() |
| 7287 | .findSchemaOrThrow(gather_backward::name, gather_backward::overload_name) |
| 7288 | .typed<gather_backward::schema>(); |
| 7289 | } |
| 7290 | |
| 7291 | // aten::gather_backward(Tensor grad, Tensor self, int dim, Tensor index, bool sparse_grad) -> Tensor |
| 7292 | at::Tensor gather_backward::call(const at::Tensor & grad, const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad) { |
| 7293 | |
| 7294 | static auto op = create_gather_backward_typed_handle(); |
| 7295 | return op.call(grad, self, dim, index, sparse_grad); |
| 7296 | } |
| 7297 | |
| 7298 | // aten::gather_backward(Tensor grad, Tensor self, int dim, Tensor index, bool sparse_grad) -> Tensor |
| 7299 | at::Tensor gather_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad) { |
| 7300 | |
| 7301 | static auto op = create_gather_backward_typed_handle(); |
| 7302 | return op.redispatch(dispatchKeySet, grad, self, dim, index, sparse_grad); |
| 7303 | } |
| 7304 | |
| 7305 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gather_dimname_out, name, "aten::gather") |
| 7306 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gather_dimname_out, overload_name, "dimname_out") |
| 7307 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gather_dimname_out, schema_str, "gather.dimname_out(Tensor self, Dimname dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!)") |
| 7308 | |
| 7309 | // aten::gather.dimname_out(Tensor self, Dimname dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!) |
| 7310 | static C10_NOINLINE c10::TypedOperatorHandle<gather_dimname_out::schema> create_gather_dimname_out_typed_handle() { |
| 7311 | return c10::Dispatcher::singleton() |
| 7312 | .findSchemaOrThrow(gather_dimname_out::name, gather_dimname_out::overload_name) |
| 7313 | .typed<gather_dimname_out::schema>(); |
| 7314 | } |
| 7315 | |
| 7316 | // aten::gather.dimname_out(Tensor self, Dimname dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!) |
| 7317 | at::Tensor & gather_dimname_out::call(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, bool sparse_grad, at::Tensor & out) { |
| 7318 | |
| 7319 | static auto op = create_gather_dimname_out_typed_handle(); |
| 7320 | return op.call(self, dim, index, sparse_grad, out); |
| 7321 | } |
| 7322 | |
| 7323 | // aten::gather.dimname_out(Tensor self, Dimname dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!) |
| 7324 | at::Tensor & gather_dimname_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, const at::Tensor & index, bool sparse_grad, at::Tensor & out) { |
| 7325 | |
| 7326 | static auto op = create_gather_dimname_out_typed_handle(); |
| 7327 | return op.redispatch(dispatchKeySet, self, dim, index, sparse_grad, out); |
| 7328 | } |
| 7329 | |
| 7330 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gather_dimname, name, "aten::gather") |
| 7331 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gather_dimname, overload_name, "dimname") |
| 7332 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gather_dimname, schema_str, "gather.dimname(Tensor self, Dimname dim, Tensor index, *, bool sparse_grad=False) -> Tensor") |
| 7333 | |
| 7334 | // aten::gather.dimname(Tensor self, Dimname dim, Tensor index, *, bool sparse_grad=False) -> Tensor |
| 7335 | static C10_NOINLINE c10::TypedOperatorHandle<gather_dimname::schema> create_gather_dimname_typed_handle() { |
| 7336 | return c10::Dispatcher::singleton() |
| 7337 | .findSchemaOrThrow(gather_dimname::name, gather_dimname::overload_name) |
| 7338 | .typed<gather_dimname::schema>(); |
| 7339 | } |
| 7340 | |
| 7341 | // aten::gather.dimname(Tensor self, Dimname dim, Tensor index, *, bool sparse_grad=False) -> Tensor |
| 7342 | at::Tensor gather_dimname::call(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, bool sparse_grad) { |
| 7343 | |
| 7344 | static auto op = create_gather_dimname_typed_handle(); |
| 7345 | return op.call(self, dim, index, sparse_grad); |
| 7346 | } |
| 7347 | |
| 7348 | // aten::gather.dimname(Tensor self, Dimname dim, Tensor index, *, bool sparse_grad=False) -> Tensor |
| 7349 | at::Tensor gather_dimname::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, const at::Tensor & index, bool sparse_grad) { |
| 7350 | |
| 7351 | static auto op = create_gather_dimname_typed_handle(); |
| 7352 | return op.redispatch(dispatchKeySet, self, dim, index, sparse_grad); |
| 7353 | } |
| 7354 | |
| 7355 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cross_entropy_loss, name, "aten::cross_entropy_loss") |
| 7356 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cross_entropy_loss, overload_name, "") |
| 7357 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cross_entropy_loss, schema_str, "cross_entropy_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, float label_smoothing=0.0) -> Tensor") |
| 7358 | |
| 7359 | // aten::cross_entropy_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, float label_smoothing=0.0) -> Tensor |
| 7360 | static C10_NOINLINE c10::TypedOperatorHandle<cross_entropy_loss::schema> create_cross_entropy_loss_typed_handle() { |
| 7361 | return c10::Dispatcher::singleton() |
| 7362 | .findSchemaOrThrow(cross_entropy_loss::name, cross_entropy_loss::overload_name) |
| 7363 | .typed<cross_entropy_loss::schema>(); |
| 7364 | } |
| 7365 | |
| 7366 | // aten::cross_entropy_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, float label_smoothing=0.0) -> Tensor |
| 7367 | at::Tensor cross_entropy_loss::call(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, double label_smoothing) { |
| 7368 | |
| 7369 | static auto op = create_cross_entropy_loss_typed_handle(); |
| 7370 | return op.call(self, target, weight, reduction, ignore_index, label_smoothing); |
| 7371 | } |
| 7372 | |
| 7373 | // aten::cross_entropy_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, float label_smoothing=0.0) -> Tensor |
| 7374 | at::Tensor cross_entropy_loss::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, double label_smoothing) { |
| 7375 | |
| 7376 | static auto op = create_cross_entropy_loss_typed_handle(); |
| 7377 | return op.redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index, label_smoothing); |
| 7378 | } |
| 7379 | |
| 7380 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(triangular_solve_X, name, "aten::triangular_solve") |
| 7381 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(triangular_solve_X, overload_name, "X") |
| 7382 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(triangular_solve_X, schema_str, "triangular_solve.X(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False, *, Tensor(a!) X, Tensor(b!) M) -> (Tensor(a!) solution, Tensor(b!) cloned_coefficient)") |
| 7383 | |
| 7384 | // aten::triangular_solve.X(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False, *, Tensor(a!) X, Tensor(b!) M) -> (Tensor(a!) solution, Tensor(b!) cloned_coefficient) |
| 7385 | static C10_NOINLINE c10::TypedOperatorHandle<triangular_solve_X::schema> create_triangular_solve_X_typed_handle() { |
| 7386 | return c10::Dispatcher::singleton() |
| 7387 | .findSchemaOrThrow(triangular_solve_X::name, triangular_solve_X::overload_name) |
| 7388 | .typed<triangular_solve_X::schema>(); |
| 7389 | } |
| 7390 | |
| 7391 | // aten::triangular_solve.X(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False, *, Tensor(a!) X, Tensor(b!) M) -> (Tensor(a!) solution, Tensor(b!) cloned_coefficient) |
| 7392 | ::std::tuple<at::Tensor &,at::Tensor &> triangular_solve_X::call(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular, at::Tensor & X, at::Tensor & M) { |
| 7393 | |
| 7394 | static auto op = create_triangular_solve_X_typed_handle(); |
| 7395 | return op.call(self, A, upper, transpose, unitriangular, X, M); |
| 7396 | } |
| 7397 | |
| 7398 | // aten::triangular_solve.X(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False, *, Tensor(a!) X, Tensor(b!) M) -> (Tensor(a!) solution, Tensor(b!) cloned_coefficient) |
| 7399 | ::std::tuple<at::Tensor &,at::Tensor &> triangular_solve_X::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular, at::Tensor & X, at::Tensor & M) { |
| 7400 | |
| 7401 | static auto op = create_triangular_solve_X_typed_handle(); |
| 7402 | return op.redispatch(dispatchKeySet, self, A, upper, transpose, unitriangular, X, M); |
| 7403 | } |
| 7404 | |
| 7405 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(triangular_solve, name, "aten::triangular_solve") |
| 7406 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(triangular_solve, overload_name, "") |
| 7407 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(triangular_solve, schema_str, "triangular_solve(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False) -> (Tensor solution, Tensor cloned_coefficient)") |
| 7408 | |
| 7409 | // aten::triangular_solve(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False) -> (Tensor solution, Tensor cloned_coefficient) |
| 7410 | static C10_NOINLINE c10::TypedOperatorHandle<triangular_solve::schema> create_triangular_solve_typed_handle() { |
| 7411 | return c10::Dispatcher::singleton() |
| 7412 | .findSchemaOrThrow(triangular_solve::name, triangular_solve::overload_name) |
| 7413 | .typed<triangular_solve::schema>(); |
| 7414 | } |
| 7415 | |
| 7416 | // aten::triangular_solve(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False) -> (Tensor solution, Tensor cloned_coefficient) |
| 7417 | ::std::tuple<at::Tensor,at::Tensor> triangular_solve::call(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular) { |
| 7418 | |
| 7419 | static auto op = create_triangular_solve_typed_handle(); |
| 7420 | return op.call(self, A, upper, transpose, unitriangular); |
| 7421 | } |
| 7422 | |
| 7423 | // aten::triangular_solve(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False) -> (Tensor solution, Tensor cloned_coefficient) |
| 7424 | ::std::tuple<at::Tensor,at::Tensor> triangular_solve::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular) { |
| 7425 | |
| 7426 | static auto op = create_triangular_solve_typed_handle(); |
| 7427 | return op.redispatch(dispatchKeySet, self, A, upper, transpose, unitriangular); |
| 7428 | } |
| 7429 | |
| 7430 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_linalg_check_errors, name, "aten::_linalg_check_errors") |
| 7431 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_linalg_check_errors, overload_name, "") |
| 7432 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_linalg_check_errors, schema_str, "_linalg_check_errors(Tensor info, str api_name, *, bool is_matrix) -> ()") |
| 7433 | |
| 7434 | // aten::_linalg_check_errors(Tensor info, str api_name, *, bool is_matrix) -> () |
| 7435 | static C10_NOINLINE c10::TypedOperatorHandle<_linalg_check_errors::schema> create__linalg_check_errors_typed_handle() { |
| 7436 | return c10::Dispatcher::singleton() |
| 7437 | .findSchemaOrThrow(_linalg_check_errors::name, _linalg_check_errors::overload_name) |
| 7438 | .typed<_linalg_check_errors::schema>(); |
| 7439 | } |
| 7440 | |
| 7441 | // aten::_linalg_check_errors(Tensor info, str api_name, *, bool is_matrix) -> () |
| 7442 | void _linalg_check_errors::call(const at::Tensor & info, c10::string_view api_name, bool is_matrix) { |
| 7443 | |
| 7444 | static auto op = create__linalg_check_errors_typed_handle(); |
| 7445 | return op.call(info, api_name, is_matrix); |
| 7446 | } |
| 7447 | |
| 7448 | // aten::_linalg_check_errors(Tensor info, str api_name, *, bool is_matrix) -> () |
| 7449 | void _linalg_check_errors::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & info, c10::string_view api_name, bool is_matrix) { |
| 7450 | |
| 7451 | static auto op = create__linalg_check_errors_typed_handle(); |
| 7452 | return op.redispatch(dispatchKeySet, info, api_name, is_matrix); |
| 7453 | } |
| 7454 | |
| 7455 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_solve_triangular_out, name, "aten::linalg_solve_triangular") |
| 7456 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_solve_triangular_out, overload_name, "out") |
| 7457 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_solve_triangular_out, schema_str, "linalg_solve_triangular.out(Tensor self, Tensor B, *, bool upper, bool left=True, bool unitriangular=False, Tensor(a!) out) -> Tensor(a!)") |
| 7458 | |
| 7459 | // aten::linalg_solve_triangular.out(Tensor self, Tensor B, *, bool upper, bool left=True, bool unitriangular=False, Tensor(a!) out) -> Tensor(a!) |
| 7460 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_solve_triangular_out::schema> create_linalg_solve_triangular_out_typed_handle() { |
| 7461 | return c10::Dispatcher::singleton() |
| 7462 | .findSchemaOrThrow(linalg_solve_triangular_out::name, linalg_solve_triangular_out::overload_name) |
| 7463 | .typed<linalg_solve_triangular_out::schema>(); |
| 7464 | } |
| 7465 | |
| 7466 | // aten::linalg_solve_triangular.out(Tensor self, Tensor B, *, bool upper, bool left=True, bool unitriangular=False, Tensor(a!) out) -> Tensor(a!) |
| 7467 | at::Tensor & linalg_solve_triangular_out::call(const at::Tensor & self, const at::Tensor & B, bool upper, bool left, bool unitriangular, at::Tensor & out) { |
| 7468 | |
| 7469 | static auto op = create_linalg_solve_triangular_out_typed_handle(); |
| 7470 | return op.call(self, B, upper, left, unitriangular, out); |
| 7471 | } |
| 7472 | |
| 7473 | // aten::linalg_solve_triangular.out(Tensor self, Tensor B, *, bool upper, bool left=True, bool unitriangular=False, Tensor(a!) out) -> Tensor(a!) |
| 7474 | at::Tensor & linalg_solve_triangular_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & B, bool upper, bool left, bool unitriangular, at::Tensor & out) { |
| 7475 | |
| 7476 | static auto op = create_linalg_solve_triangular_out_typed_handle(); |
| 7477 | return op.redispatch(dispatchKeySet, self, B, upper, left, unitriangular, out); |
| 7478 | } |
| 7479 | |
| 7480 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_solve_triangular, name, "aten::linalg_solve_triangular") |
| 7481 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_solve_triangular, overload_name, "") |
| 7482 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_solve_triangular, schema_str, "linalg_solve_triangular(Tensor self, Tensor B, *, bool upper, bool left=True, bool unitriangular=False) -> Tensor") |
| 7483 | |
| 7484 | // aten::linalg_solve_triangular(Tensor self, Tensor B, *, bool upper, bool left=True, bool unitriangular=False) -> Tensor |
| 7485 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_solve_triangular::schema> create_linalg_solve_triangular_typed_handle() { |
| 7486 | return c10::Dispatcher::singleton() |
| 7487 | .findSchemaOrThrow(linalg_solve_triangular::name, linalg_solve_triangular::overload_name) |
| 7488 | .typed<linalg_solve_triangular::schema>(); |
| 7489 | } |
| 7490 | |
| 7491 | // aten::linalg_solve_triangular(Tensor self, Tensor B, *, bool upper, bool left=True, bool unitriangular=False) -> Tensor |
| 7492 | at::Tensor linalg_solve_triangular::call(const at::Tensor & self, const at::Tensor & B, bool upper, bool left, bool unitriangular) { |
| 7493 | |
| 7494 | static auto op = create_linalg_solve_triangular_typed_handle(); |
| 7495 | return op.call(self, B, upper, left, unitriangular); |
| 7496 | } |
| 7497 | |
| 7498 | // aten::linalg_solve_triangular(Tensor self, Tensor B, *, bool upper, bool left=True, bool unitriangular=False) -> Tensor |
| 7499 | at::Tensor linalg_solve_triangular::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & B, bool upper, bool left, bool unitriangular) { |
| 7500 | |
| 7501 | static auto op = create_linalg_solve_triangular_typed_handle(); |
| 7502 | return op.redispatch(dispatchKeySet, self, B, upper, left, unitriangular); |
| 7503 | } |
| 7504 | |
| 7505 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ormqr_out, name, "aten::ormqr") |
| 7506 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ormqr_out, overload_name, "out") |
| 7507 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ormqr_out, schema_str, "ormqr.out(Tensor self, Tensor input2, Tensor input3, bool left=True, bool transpose=False, *, Tensor(a!) out) -> Tensor(a!)") |
| 7508 | |
| 7509 | // aten::ormqr.out(Tensor self, Tensor input2, Tensor input3, bool left=True, bool transpose=False, *, Tensor(a!) out) -> Tensor(a!) |
| 7510 | static C10_NOINLINE c10::TypedOperatorHandle<ormqr_out::schema> create_ormqr_out_typed_handle() { |
| 7511 | return c10::Dispatcher::singleton() |
| 7512 | .findSchemaOrThrow(ormqr_out::name, ormqr_out::overload_name) |
| 7513 | .typed<ormqr_out::schema>(); |
| 7514 | } |
| 7515 | |
| 7516 | // aten::ormqr.out(Tensor self, Tensor input2, Tensor input3, bool left=True, bool transpose=False, *, Tensor(a!) out) -> Tensor(a!) |
| 7517 | at::Tensor & ormqr_out::call(const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left, bool transpose, at::Tensor & out) { |
| 7518 | |
| 7519 | static auto op = create_ormqr_out_typed_handle(); |
| 7520 | return op.call(self, input2, input3, left, transpose, out); |
| 7521 | } |
| 7522 | |
| 7523 | // aten::ormqr.out(Tensor self, Tensor input2, Tensor input3, bool left=True, bool transpose=False, *, Tensor(a!) out) -> Tensor(a!) |
| 7524 | at::Tensor & ormqr_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left, bool transpose, at::Tensor & out) { |
| 7525 | |
| 7526 | static auto op = create_ormqr_out_typed_handle(); |
| 7527 | return op.redispatch(dispatchKeySet, self, input2, input3, left, transpose, out); |
| 7528 | } |
| 7529 | |
| 7530 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ormqr, name, "aten::ormqr") |
| 7531 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ormqr, overload_name, "") |
| 7532 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ormqr, schema_str, "ormqr(Tensor self, Tensor input2, Tensor input3, bool left=True, bool transpose=False) -> Tensor") |
| 7533 | |
| 7534 | // aten::ormqr(Tensor self, Tensor input2, Tensor input3, bool left=True, bool transpose=False) -> Tensor |
| 7535 | static C10_NOINLINE c10::TypedOperatorHandle<ormqr::schema> create_ormqr_typed_handle() { |
| 7536 | return c10::Dispatcher::singleton() |
| 7537 | .findSchemaOrThrow(ormqr::name, ormqr::overload_name) |
| 7538 | .typed<ormqr::schema>(); |
| 7539 | } |
| 7540 | |
| 7541 | // aten::ormqr(Tensor self, Tensor input2, Tensor input3, bool left=True, bool transpose=False) -> Tensor |
| 7542 | at::Tensor ormqr::call(const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left, bool transpose) { |
| 7543 | |
| 7544 | static auto op = create_ormqr_typed_handle(); |
| 7545 | return op.call(self, input2, input3, left, transpose); |
| 7546 | } |
| 7547 | |
| 7548 | // aten::ormqr(Tensor self, Tensor input2, Tensor input3, bool left=True, bool transpose=False) -> Tensor |
| 7549 | at::Tensor ormqr::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left, bool transpose) { |
| 7550 | |
| 7551 | static auto op = create_ormqr_typed_handle(); |
| 7552 | return op.redispatch(dispatchKeySet, self, input2, input3, left, transpose); |
| 7553 | } |
| 7554 | |
| 7555 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(i0, name, "aten::i0") |
| 7556 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(i0, overload_name, "") |
| 7557 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(i0, schema_str, "i0(Tensor self) -> Tensor") |
| 7558 | |
| 7559 | // aten::i0(Tensor self) -> Tensor |
| 7560 | static C10_NOINLINE c10::TypedOperatorHandle<i0::schema> create_i0_typed_handle() { |
| 7561 | return c10::Dispatcher::singleton() |
| 7562 | .findSchemaOrThrow(i0::name, i0::overload_name) |
| 7563 | .typed<i0::schema>(); |
| 7564 | } |
| 7565 | |
| 7566 | // aten::i0(Tensor self) -> Tensor |
| 7567 | at::Tensor i0::call(const at::Tensor & self) { |
| 7568 | |
| 7569 | static auto op = create_i0_typed_handle(); |
| 7570 | return op.call(self); |
| 7571 | } |
| 7572 | |
| 7573 | // aten::i0(Tensor self) -> Tensor |
| 7574 | at::Tensor i0::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 7575 | |
| 7576 | static auto op = create_i0_typed_handle(); |
| 7577 | return op.redispatch(dispatchKeySet, self); |
| 7578 | } |
| 7579 | |
| 7580 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(i0_, name, "aten::i0_") |
| 7581 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(i0_, overload_name, "") |
| 7582 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(i0_, schema_str, "i0_(Tensor(a!) self) -> Tensor(a!)") |
| 7583 | |
| 7584 | // aten::i0_(Tensor(a!) self) -> Tensor(a!) |
| 7585 | static C10_NOINLINE c10::TypedOperatorHandle<i0_::schema> create_i0__typed_handle() { |
| 7586 | return c10::Dispatcher::singleton() |
| 7587 | .findSchemaOrThrow(i0_::name, i0_::overload_name) |
| 7588 | .typed<i0_::schema>(); |
| 7589 | } |
| 7590 | |
| 7591 | // aten::i0_(Tensor(a!) self) -> Tensor(a!) |
| 7592 | at::Tensor & i0_::call(at::Tensor & self) { |
| 7593 | |
| 7594 | static auto op = create_i0__typed_handle(); |
| 7595 | return op.call(self); |
| 7596 | } |
| 7597 | |
| 7598 | // aten::i0_(Tensor(a!) self) -> Tensor(a!) |
| 7599 | at::Tensor & i0_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 7600 | |
| 7601 | static auto op = create_i0__typed_handle(); |
| 7602 | return op.redispatch(dispatchKeySet, self); |
| 7603 | } |
| 7604 | |
| 7605 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(i0_out, name, "aten::i0") |
| 7606 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(i0_out, overload_name, "out") |
| 7607 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(i0_out, schema_str, "i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 7608 | |
| 7609 | // aten::i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 7610 | static C10_NOINLINE c10::TypedOperatorHandle<i0_out::schema> create_i0_out_typed_handle() { |
| 7611 | return c10::Dispatcher::singleton() |
| 7612 | .findSchemaOrThrow(i0_out::name, i0_out::overload_name) |
| 7613 | .typed<i0_out::schema>(); |
| 7614 | } |
| 7615 | |
| 7616 | // aten::i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 7617 | at::Tensor & i0_out::call(const at::Tensor & self, at::Tensor & out) { |
| 7618 | |
| 7619 | static auto op = create_i0_out_typed_handle(); |
| 7620 | return op.call(self, out); |
| 7621 | } |
| 7622 | |
| 7623 | // aten::i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 7624 | at::Tensor & i0_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 7625 | |
| 7626 | static auto op = create_i0_out_typed_handle(); |
| 7627 | return op.redispatch(dispatchKeySet, self, out); |
| 7628 | } |
| 7629 | |
| 7630 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sign, name, "aten::sign") |
| 7631 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sign, overload_name, "") |
| 7632 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sign, schema_str, "sign(Tensor self) -> Tensor") |
| 7633 | |
| 7634 | // aten::sign(Tensor self) -> Tensor |
| 7635 | static C10_NOINLINE c10::TypedOperatorHandle<sign::schema> create_sign_typed_handle() { |
| 7636 | return c10::Dispatcher::singleton() |
| 7637 | .findSchemaOrThrow(sign::name, sign::overload_name) |
| 7638 | .typed<sign::schema>(); |
| 7639 | } |
| 7640 | |
| 7641 | // aten::sign(Tensor self) -> Tensor |
| 7642 | at::Tensor sign::call(const at::Tensor & self) { |
| 7643 | |
| 7644 | static auto op = create_sign_typed_handle(); |
| 7645 | return op.call(self); |
| 7646 | } |
| 7647 | |
| 7648 | // aten::sign(Tensor self) -> Tensor |
| 7649 | at::Tensor sign::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 7650 | |
| 7651 | static auto op = create_sign_typed_handle(); |
| 7652 | return op.redispatch(dispatchKeySet, self); |
| 7653 | } |
| 7654 | |
| 7655 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sign_, name, "aten::sign_") |
| 7656 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sign_, overload_name, "") |
| 7657 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sign_, schema_str, "sign_(Tensor(a!) self) -> Tensor(a!)") |
| 7658 | |
| 7659 | // aten::sign_(Tensor(a!) self) -> Tensor(a!) |
| 7660 | static C10_NOINLINE c10::TypedOperatorHandle<sign_::schema> create_sign__typed_handle() { |
| 7661 | return c10::Dispatcher::singleton() |
| 7662 | .findSchemaOrThrow(sign_::name, sign_::overload_name) |
| 7663 | .typed<sign_::schema>(); |
| 7664 | } |
| 7665 | |
| 7666 | // aten::sign_(Tensor(a!) self) -> Tensor(a!) |
| 7667 | at::Tensor & sign_::call(at::Tensor & self) { |
| 7668 | |
| 7669 | static auto op = create_sign__typed_handle(); |
| 7670 | return op.call(self); |
| 7671 | } |
| 7672 | |
| 7673 | // aten::sign_(Tensor(a!) self) -> Tensor(a!) |
| 7674 | at::Tensor & sign_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 7675 | |
| 7676 | static auto op = create_sign__typed_handle(); |
| 7677 | return op.redispatch(dispatchKeySet, self); |
| 7678 | } |
| 7679 | |
| 7680 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sign_out, name, "aten::sign") |
| 7681 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sign_out, overload_name, "out") |
| 7682 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sign_out, schema_str, "sign.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 7683 | |
| 7684 | // aten::sign.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 7685 | static C10_NOINLINE c10::TypedOperatorHandle<sign_out::schema> create_sign_out_typed_handle() { |
| 7686 | return c10::Dispatcher::singleton() |
| 7687 | .findSchemaOrThrow(sign_out::name, sign_out::overload_name) |
| 7688 | .typed<sign_out::schema>(); |
| 7689 | } |
| 7690 | |
| 7691 | // aten::sign.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 7692 | at::Tensor & sign_out::call(const at::Tensor & self, at::Tensor & out) { |
| 7693 | |
| 7694 | static auto op = create_sign_out_typed_handle(); |
| 7695 | return op.call(self, out); |
| 7696 | } |
| 7697 | |
| 7698 | // aten::sign.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 7699 | at::Tensor & sign_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 7700 | |
| 7701 | static auto op = create_sign_out_typed_handle(); |
| 7702 | return op.redispatch(dispatchKeySet, self, out); |
| 7703 | } |
| 7704 | |
| 7705 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lerp_Scalar_out, name, "aten::lerp") |
| 7706 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lerp_Scalar_out, overload_name, "Scalar_out") |
| 7707 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lerp_Scalar_out, schema_str, "lerp.Scalar_out(Tensor self, Tensor end, Scalar weight, *, Tensor(a!) out) -> Tensor(a!)") |
| 7708 | |
| 7709 | // aten::lerp.Scalar_out(Tensor self, Tensor end, Scalar weight, *, Tensor(a!) out) -> Tensor(a!) |
| 7710 | static C10_NOINLINE c10::TypedOperatorHandle<lerp_Scalar_out::schema> create_lerp_Scalar_out_typed_handle() { |
| 7711 | return c10::Dispatcher::singleton() |
| 7712 | .findSchemaOrThrow(lerp_Scalar_out::name, lerp_Scalar_out::overload_name) |
| 7713 | .typed<lerp_Scalar_out::schema>(); |
| 7714 | } |
| 7715 | |
| 7716 | // aten::lerp.Scalar_out(Tensor self, Tensor end, Scalar weight, *, Tensor(a!) out) -> Tensor(a!) |
| 7717 | at::Tensor & lerp_Scalar_out::call(const at::Tensor & self, const at::Tensor & end, const at::Scalar & weight, at::Tensor & out) { |
| 7718 | |
| 7719 | static auto op = create_lerp_Scalar_out_typed_handle(); |
| 7720 | return op.call(self, end, weight, out); |
| 7721 | } |
| 7722 | |
| 7723 | // aten::lerp.Scalar_out(Tensor self, Tensor end, Scalar weight, *, Tensor(a!) out) -> Tensor(a!) |
| 7724 | at::Tensor & lerp_Scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & end, const at::Scalar & weight, at::Tensor & out) { |
| 7725 | |
| 7726 | static auto op = create_lerp_Scalar_out_typed_handle(); |
| 7727 | return op.redispatch(dispatchKeySet, self, end, weight, out); |
| 7728 | } |
| 7729 | |
| 7730 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lerp_Tensor_out, name, "aten::lerp") |
| 7731 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lerp_Tensor_out, overload_name, "Tensor_out") |
| 7732 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lerp_Tensor_out, schema_str, "lerp.Tensor_out(Tensor self, Tensor end, Tensor weight, *, Tensor(a!) out) -> Tensor(a!)") |
| 7733 | |
| 7734 | // aten::lerp.Tensor_out(Tensor self, Tensor end, Tensor weight, *, Tensor(a!) out) -> Tensor(a!) |
| 7735 | static C10_NOINLINE c10::TypedOperatorHandle<lerp_Tensor_out::schema> create_lerp_Tensor_out_typed_handle() { |
| 7736 | return c10::Dispatcher::singleton() |
| 7737 | .findSchemaOrThrow(lerp_Tensor_out::name, lerp_Tensor_out::overload_name) |
| 7738 | .typed<lerp_Tensor_out::schema>(); |
| 7739 | } |
| 7740 | |
| 7741 | // aten::lerp.Tensor_out(Tensor self, Tensor end, Tensor weight, *, Tensor(a!) out) -> Tensor(a!) |
| 7742 | at::Tensor & lerp_Tensor_out::call(const at::Tensor & self, const at::Tensor & end, const at::Tensor & weight, at::Tensor & out) { |
| 7743 | |
| 7744 | static auto op = create_lerp_Tensor_out_typed_handle(); |
| 7745 | return op.call(self, end, weight, out); |
| 7746 | } |
| 7747 | |
| 7748 | // aten::lerp.Tensor_out(Tensor self, Tensor end, Tensor weight, *, Tensor(a!) out) -> Tensor(a!) |
| 7749 | at::Tensor & lerp_Tensor_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & end, const at::Tensor & weight, at::Tensor & out) { |
| 7750 | |
| 7751 | static auto op = create_lerp_Tensor_out_typed_handle(); |
| 7752 | return op.redispatch(dispatchKeySet, self, end, weight, out); |
| 7753 | } |
| 7754 | |
| 7755 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lerp_Scalar, name, "aten::lerp") |
| 7756 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lerp_Scalar, overload_name, "Scalar") |
| 7757 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lerp_Scalar, schema_str, "lerp.Scalar(Tensor self, Tensor end, Scalar weight) -> Tensor") |
| 7758 | |
| 7759 | // aten::lerp.Scalar(Tensor self, Tensor end, Scalar weight) -> Tensor |
| 7760 | static C10_NOINLINE c10::TypedOperatorHandle<lerp_Scalar::schema> create_lerp_Scalar_typed_handle() { |
| 7761 | return c10::Dispatcher::singleton() |
| 7762 | .findSchemaOrThrow(lerp_Scalar::name, lerp_Scalar::overload_name) |
| 7763 | .typed<lerp_Scalar::schema>(); |
| 7764 | } |
| 7765 | |
| 7766 | // aten::lerp.Scalar(Tensor self, Tensor end, Scalar weight) -> Tensor |
| 7767 | at::Tensor lerp_Scalar::call(const at::Tensor & self, const at::Tensor & end, const at::Scalar & weight) { |
| 7768 | |
| 7769 | static auto op = create_lerp_Scalar_typed_handle(); |
| 7770 | return op.call(self, end, weight); |
| 7771 | } |
| 7772 | |
| 7773 | // aten::lerp.Scalar(Tensor self, Tensor end, Scalar weight) -> Tensor |
| 7774 | at::Tensor lerp_Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & end, const at::Scalar & weight) { |
| 7775 | |
| 7776 | static auto op = create_lerp_Scalar_typed_handle(); |
| 7777 | return op.redispatch(dispatchKeySet, self, end, weight); |
| 7778 | } |
| 7779 | |
| 7780 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lerp_Tensor, name, "aten::lerp") |
| 7781 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lerp_Tensor, overload_name, "Tensor") |
| 7782 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lerp_Tensor, schema_str, "lerp.Tensor(Tensor self, Tensor end, Tensor weight) -> Tensor") |
| 7783 | |
| 7784 | // aten::lerp.Tensor(Tensor self, Tensor end, Tensor weight) -> Tensor |
| 7785 | static C10_NOINLINE c10::TypedOperatorHandle<lerp_Tensor::schema> create_lerp_Tensor_typed_handle() { |
| 7786 | return c10::Dispatcher::singleton() |
| 7787 | .findSchemaOrThrow(lerp_Tensor::name, lerp_Tensor::overload_name) |
| 7788 | .typed<lerp_Tensor::schema>(); |
| 7789 | } |
| 7790 | |
| 7791 | // aten::lerp.Tensor(Tensor self, Tensor end, Tensor weight) -> Tensor |
| 7792 | at::Tensor lerp_Tensor::call(const at::Tensor & self, const at::Tensor & end, const at::Tensor & weight) { |
| 7793 | |
| 7794 | static auto op = create_lerp_Tensor_typed_handle(); |
| 7795 | return op.call(self, end, weight); |
| 7796 | } |
| 7797 | |
| 7798 | // aten::lerp.Tensor(Tensor self, Tensor end, Tensor weight) -> Tensor |
| 7799 | at::Tensor lerp_Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & end, const at::Tensor & weight) { |
| 7800 | |
| 7801 | static auto op = create_lerp_Tensor_typed_handle(); |
| 7802 | return op.redispatch(dispatchKeySet, self, end, weight); |
| 7803 | } |
| 7804 | |
| 7805 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(min, name, "aten::min") |
| 7806 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(min, overload_name, "") |
| 7807 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(min, schema_str, "min(Tensor self) -> Tensor") |
| 7808 | |
| 7809 | // aten::min(Tensor self) -> Tensor |
| 7810 | static C10_NOINLINE c10::TypedOperatorHandle<min::schema> create_min_typed_handle() { |
| 7811 | return c10::Dispatcher::singleton() |
| 7812 | .findSchemaOrThrow(min::name, min::overload_name) |
| 7813 | .typed<min::schema>(); |
| 7814 | } |
| 7815 | |
| 7816 | // aten::min(Tensor self) -> Tensor |
| 7817 | at::Tensor min::call(const at::Tensor & self) { |
| 7818 | |
| 7819 | static auto op = create_min_typed_handle(); |
| 7820 | return op.call(self); |
| 7821 | } |
| 7822 | |
| 7823 | // aten::min(Tensor self) -> Tensor |
| 7824 | at::Tensor min::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 7825 | |
| 7826 | static auto op = create_min_typed_handle(); |
| 7827 | return op.redispatch(dispatchKeySet, self); |
| 7828 | } |
| 7829 | |
| 7830 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fmin, name, "aten::fmin") |
| 7831 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fmin, overload_name, "") |
| 7832 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fmin, schema_str, "fmin(Tensor self, Tensor other) -> Tensor") |
| 7833 | |
| 7834 | // aten::fmin(Tensor self, Tensor other) -> Tensor |
| 7835 | static C10_NOINLINE c10::TypedOperatorHandle<fmin::schema> create_fmin_typed_handle() { |
| 7836 | return c10::Dispatcher::singleton() |
| 7837 | .findSchemaOrThrow(fmin::name, fmin::overload_name) |
| 7838 | .typed<fmin::schema>(); |
| 7839 | } |
| 7840 | |
| 7841 | // aten::fmin(Tensor self, Tensor other) -> Tensor |
| 7842 | at::Tensor fmin::call(const at::Tensor & self, const at::Tensor & other) { |
| 7843 | |
| 7844 | static auto op = create_fmin_typed_handle(); |
| 7845 | return op.call(self, other); |
| 7846 | } |
| 7847 | |
| 7848 | // aten::fmin(Tensor self, Tensor other) -> Tensor |
| 7849 | at::Tensor fmin::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { |
| 7850 | |
| 7851 | static auto op = create_fmin_typed_handle(); |
| 7852 | return op.redispatch(dispatchKeySet, self, other); |
| 7853 | } |
| 7854 | |
| 7855 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fmin_out, name, "aten::fmin") |
| 7856 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fmin_out, overload_name, "out") |
| 7857 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fmin_out, schema_str, "fmin.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)") |
| 7858 | |
| 7859 | // aten::fmin.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 7860 | static C10_NOINLINE c10::TypedOperatorHandle<fmin_out::schema> create_fmin_out_typed_handle() { |
| 7861 | return c10::Dispatcher::singleton() |
| 7862 | .findSchemaOrThrow(fmin_out::name, fmin_out::overload_name) |
| 7863 | .typed<fmin_out::schema>(); |
| 7864 | } |
| 7865 | |
| 7866 | // aten::fmin.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 7867 | at::Tensor & fmin_out::call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 7868 | |
| 7869 | static auto op = create_fmin_out_typed_handle(); |
| 7870 | return op.call(self, other, out); |
| 7871 | } |
| 7872 | |
| 7873 | // aten::fmin.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 7874 | at::Tensor & fmin_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 7875 | |
| 7876 | static auto op = create_fmin_out_typed_handle(); |
| 7877 | return op.redispatch(dispatchKeySet, self, other, out); |
| 7878 | } |
| 7879 | |
| 7880 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(min_out, name, "aten::min") |
| 7881 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(min_out, overload_name, "out") |
| 7882 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(min_out, schema_str, "min.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)") |
| 7883 | |
| 7884 | // aten::min.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 7885 | static C10_NOINLINE c10::TypedOperatorHandle<min_out::schema> create_min_out_typed_handle() { |
| 7886 | return c10::Dispatcher::singleton() |
| 7887 | .findSchemaOrThrow(min_out::name, min_out::overload_name) |
| 7888 | .typed<min_out::schema>(); |
| 7889 | } |
| 7890 | |
| 7891 | // aten::min.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 7892 | at::Tensor & min_out::call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 7893 | |
| 7894 | static auto op = create_min_out_typed_handle(); |
| 7895 | return op.call(self, other, out); |
| 7896 | } |
| 7897 | |
| 7898 | // aten::min.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 7899 | at::Tensor & min_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 7900 | |
| 7901 | static auto op = create_min_out_typed_handle(); |
| 7902 | return op.redispatch(dispatchKeySet, self, other, out); |
| 7903 | } |
| 7904 | |
| 7905 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(min_other, name, "aten::min") |
| 7906 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(min_other, overload_name, "other") |
| 7907 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(min_other, schema_str, "min.other(Tensor self, Tensor other) -> Tensor") |
| 7908 | |
| 7909 | // aten::min.other(Tensor self, Tensor other) -> Tensor |
| 7910 | static C10_NOINLINE c10::TypedOperatorHandle<min_other::schema> create_min_other_typed_handle() { |
| 7911 | return c10::Dispatcher::singleton() |
| 7912 | .findSchemaOrThrow(min_other::name, min_other::overload_name) |
| 7913 | .typed<min_other::schema>(); |
| 7914 | } |
| 7915 | |
| 7916 | // aten::min.other(Tensor self, Tensor other) -> Tensor |
| 7917 | at::Tensor min_other::call(const at::Tensor & self, const at::Tensor & other) { |
| 7918 | |
| 7919 | static auto op = create_min_other_typed_handle(); |
| 7920 | return op.call(self, other); |
| 7921 | } |
| 7922 | |
| 7923 | // aten::min.other(Tensor self, Tensor other) -> Tensor |
| 7924 | at::Tensor min_other::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { |
| 7925 | |
| 7926 | static auto op = create_min_other_typed_handle(); |
| 7927 | return op.redispatch(dispatchKeySet, self, other); |
| 7928 | } |
| 7929 | |
| 7930 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(equal, name, "aten::equal") |
| 7931 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(equal, overload_name, "") |
| 7932 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(equal, schema_str, "equal(Tensor self, Tensor other) -> bool") |
| 7933 | |
| 7934 | // aten::equal(Tensor self, Tensor other) -> bool |
| 7935 | static C10_NOINLINE c10::TypedOperatorHandle<equal::schema> create_equal_typed_handle() { |
| 7936 | return c10::Dispatcher::singleton() |
| 7937 | .findSchemaOrThrow(equal::name, equal::overload_name) |
| 7938 | .typed<equal::schema>(); |
| 7939 | } |
| 7940 | |
| 7941 | // aten::equal(Tensor self, Tensor other) -> bool |
| 7942 | bool equal::call(const at::Tensor & self, const at::Tensor & other) { |
| 7943 | |
| 7944 | static auto op = create_equal_typed_handle(); |
| 7945 | return op.call(self, other); |
| 7946 | } |
| 7947 | |
| 7948 | // aten::equal(Tensor self, Tensor other) -> bool |
| 7949 | bool equal::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { |
| 7950 | |
| 7951 | static auto op = create_equal_typed_handle(); |
| 7952 | return op.redispatch(dispatchKeySet, self, other); |
| 7953 | } |
| 7954 | |
| 7955 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_mul_Scalar, name, "aten::_foreach_mul") |
| 7956 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_mul_Scalar, overload_name, "Scalar") |
| 7957 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_mul_Scalar, schema_str, "_foreach_mul.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]") |
| 7958 | |
| 7959 | // aten::_foreach_mul.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] |
| 7960 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_mul_Scalar::schema> create__foreach_mul_Scalar_typed_handle() { |
| 7961 | return c10::Dispatcher::singleton() |
| 7962 | .findSchemaOrThrow(_foreach_mul_Scalar::name, _foreach_mul_Scalar::overload_name) |
| 7963 | .typed<_foreach_mul_Scalar::schema>(); |
| 7964 | } |
| 7965 | |
| 7966 | // aten::_foreach_mul.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] |
| 7967 | ::std::vector<at::Tensor> _foreach_mul_Scalar::call(at::TensorList self, const at::Scalar & scalar) { |
| 7968 | |
| 7969 | static auto op = create__foreach_mul_Scalar_typed_handle(); |
| 7970 | return op.call(self, scalar); |
| 7971 | } |
| 7972 | |
| 7973 | // aten::_foreach_mul.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] |
| 7974 | ::std::vector<at::Tensor> _foreach_mul_Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { |
| 7975 | |
| 7976 | static auto op = create__foreach_mul_Scalar_typed_handle(); |
| 7977 | return op.redispatch(dispatchKeySet, self, scalar); |
| 7978 | } |
| 7979 | |
| 7980 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_mul__Scalar, name, "aten::_foreach_mul_") |
| 7981 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_mul__Scalar, overload_name, "Scalar") |
| 7982 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_mul__Scalar, schema_str, "_foreach_mul_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()") |
| 7983 | |
| 7984 | // aten::_foreach_mul_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () |
| 7985 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_mul__Scalar::schema> create__foreach_mul__Scalar_typed_handle() { |
| 7986 | return c10::Dispatcher::singleton() |
| 7987 | .findSchemaOrThrow(_foreach_mul__Scalar::name, _foreach_mul__Scalar::overload_name) |
| 7988 | .typed<_foreach_mul__Scalar::schema>(); |
| 7989 | } |
| 7990 | |
| 7991 | // aten::_foreach_mul_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () |
| 7992 | void _foreach_mul__Scalar::call(at::TensorList self, const at::Scalar & scalar) { |
| 7993 | |
| 7994 | static auto op = create__foreach_mul__Scalar_typed_handle(); |
| 7995 | return op.call(self, scalar); |
| 7996 | } |
| 7997 | |
| 7998 | // aten::_foreach_mul_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () |
| 7999 | void _foreach_mul__Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { |
| 8000 | |
| 8001 | static auto op = create__foreach_mul__Scalar_typed_handle(); |
| 8002 | return op.redispatch(dispatchKeySet, self, scalar); |
| 8003 | } |
| 8004 | |
| 8005 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_div_Scalar, name, "aten::_foreach_div") |
| 8006 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_div_Scalar, overload_name, "Scalar") |
| 8007 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_div_Scalar, schema_str, "_foreach_div.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]") |
| 8008 | |
| 8009 | // aten::_foreach_div.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] |
| 8010 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_div_Scalar::schema> create__foreach_div_Scalar_typed_handle() { |
| 8011 | return c10::Dispatcher::singleton() |
| 8012 | .findSchemaOrThrow(_foreach_div_Scalar::name, _foreach_div_Scalar::overload_name) |
| 8013 | .typed<_foreach_div_Scalar::schema>(); |
| 8014 | } |
| 8015 | |
| 8016 | // aten::_foreach_div.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] |
| 8017 | ::std::vector<at::Tensor> _foreach_div_Scalar::call(at::TensorList self, const at::Scalar & scalar) { |
| 8018 | |
| 8019 | static auto op = create__foreach_div_Scalar_typed_handle(); |
| 8020 | return op.call(self, scalar); |
| 8021 | } |
| 8022 | |
| 8023 | // aten::_foreach_div.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] |
| 8024 | ::std::vector<at::Tensor> _foreach_div_Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { |
| 8025 | |
| 8026 | static auto op = create__foreach_div_Scalar_typed_handle(); |
| 8027 | return op.redispatch(dispatchKeySet, self, scalar); |
| 8028 | } |
| 8029 | |
| 8030 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_div__Scalar, name, "aten::_foreach_div_") |
| 8031 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_div__Scalar, overload_name, "Scalar") |
| 8032 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_div__Scalar, schema_str, "_foreach_div_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()") |
| 8033 | |
| 8034 | // aten::_foreach_div_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () |
| 8035 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_div__Scalar::schema> create__foreach_div__Scalar_typed_handle() { |
| 8036 | return c10::Dispatcher::singleton() |
| 8037 | .findSchemaOrThrow(_foreach_div__Scalar::name, _foreach_div__Scalar::overload_name) |
| 8038 | .typed<_foreach_div__Scalar::schema>(); |
| 8039 | } |
| 8040 | |
| 8041 | // aten::_foreach_div_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () |
| 8042 | void _foreach_div__Scalar::call(at::TensorList self, const at::Scalar & scalar) { |
| 8043 | |
| 8044 | static auto op = create__foreach_div__Scalar_typed_handle(); |
| 8045 | return op.call(self, scalar); |
| 8046 | } |
| 8047 | |
| 8048 | // aten::_foreach_div_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () |
| 8049 | void _foreach_div__Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { |
| 8050 | |
| 8051 | static auto op = create__foreach_div__Scalar_typed_handle(); |
| 8052 | return op.redispatch(dispatchKeySet, self, scalar); |
| 8053 | } |
| 8054 | |
| 8055 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_mul_List, name, "aten::_foreach_mul") |
| 8056 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_mul_List, overload_name, "List") |
| 8057 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_mul_List, schema_str, "_foreach_mul.List(Tensor[] self, Tensor[] other) -> Tensor[]") |
| 8058 | |
| 8059 | // aten::_foreach_mul.List(Tensor[] self, Tensor[] other) -> Tensor[] |
| 8060 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_mul_List::schema> create__foreach_mul_List_typed_handle() { |
| 8061 | return c10::Dispatcher::singleton() |
| 8062 | .findSchemaOrThrow(_foreach_mul_List::name, _foreach_mul_List::overload_name) |
| 8063 | .typed<_foreach_mul_List::schema>(); |
| 8064 | } |
| 8065 | |
| 8066 | // aten::_foreach_mul.List(Tensor[] self, Tensor[] other) -> Tensor[] |
| 8067 | ::std::vector<at::Tensor> _foreach_mul_List::call(at::TensorList self, at::TensorList other) { |
| 8068 | |
| 8069 | static auto op = create__foreach_mul_List_typed_handle(); |
| 8070 | return op.call(self, other); |
| 8071 | } |
| 8072 | |
| 8073 | // aten::_foreach_mul.List(Tensor[] self, Tensor[] other) -> Tensor[] |
| 8074 | ::std::vector<at::Tensor> _foreach_mul_List::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other) { |
| 8075 | |
| 8076 | static auto op = create__foreach_mul_List_typed_handle(); |
| 8077 | return op.redispatch(dispatchKeySet, self, other); |
| 8078 | } |
| 8079 | |
| 8080 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_mul__List, name, "aten::_foreach_mul_") |
| 8081 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_mul__List, overload_name, "List") |
| 8082 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_mul__List, schema_str, "_foreach_mul_.List(Tensor(a!)[] self, Tensor[] other) -> ()") |
| 8083 | |
| 8084 | // aten::_foreach_mul_.List(Tensor(a!)[] self, Tensor[] other) -> () |
| 8085 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_mul__List::schema> create__foreach_mul__List_typed_handle() { |
| 8086 | return c10::Dispatcher::singleton() |
| 8087 | .findSchemaOrThrow(_foreach_mul__List::name, _foreach_mul__List::overload_name) |
| 8088 | .typed<_foreach_mul__List::schema>(); |
| 8089 | } |
| 8090 | |
| 8091 | // aten::_foreach_mul_.List(Tensor(a!)[] self, Tensor[] other) -> () |
| 8092 | void _foreach_mul__List::call(at::TensorList self, at::TensorList other) { |
| 8093 | |
| 8094 | static auto op = create__foreach_mul__List_typed_handle(); |
| 8095 | return op.call(self, other); |
| 8096 | } |
| 8097 | |
| 8098 | // aten::_foreach_mul_.List(Tensor(a!)[] self, Tensor[] other) -> () |
| 8099 | void _foreach_mul__List::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other) { |
| 8100 | |
| 8101 | static auto op = create__foreach_mul__List_typed_handle(); |
| 8102 | return op.redispatch(dispatchKeySet, self, other); |
| 8103 | } |
| 8104 | |
| 8105 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_div_List, name, "aten::_foreach_div") |
| 8106 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_div_List, overload_name, "List") |
| 8107 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_div_List, schema_str, "_foreach_div.List(Tensor[] self, Tensor[] other) -> Tensor[]") |
| 8108 | |
| 8109 | // aten::_foreach_div.List(Tensor[] self, Tensor[] other) -> Tensor[] |
| 8110 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_div_List::schema> create__foreach_div_List_typed_handle() { |
| 8111 | return c10::Dispatcher::singleton() |
| 8112 | .findSchemaOrThrow(_foreach_div_List::name, _foreach_div_List::overload_name) |
| 8113 | .typed<_foreach_div_List::schema>(); |
| 8114 | } |
| 8115 | |
| 8116 | // aten::_foreach_div.List(Tensor[] self, Tensor[] other) -> Tensor[] |
| 8117 | ::std::vector<at::Tensor> _foreach_div_List::call(at::TensorList self, at::TensorList other) { |
| 8118 | |
| 8119 | static auto op = create__foreach_div_List_typed_handle(); |
| 8120 | return op.call(self, other); |
| 8121 | } |
| 8122 | |
| 8123 | // aten::_foreach_div.List(Tensor[] self, Tensor[] other) -> Tensor[] |
| 8124 | ::std::vector<at::Tensor> _foreach_div_List::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other) { |
| 8125 | |
| 8126 | static auto op = create__foreach_div_List_typed_handle(); |
| 8127 | return op.redispatch(dispatchKeySet, self, other); |
| 8128 | } |
| 8129 | |
| 8130 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_div__List, name, "aten::_foreach_div_") |
| 8131 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_div__List, overload_name, "List") |
| 8132 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_div__List, schema_str, "_foreach_div_.List(Tensor(a!)[] self, Tensor[] other) -> ()") |
| 8133 | |
| 8134 | // aten::_foreach_div_.List(Tensor(a!)[] self, Tensor[] other) -> () |
| 8135 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_div__List::schema> create__foreach_div__List_typed_handle() { |
| 8136 | return c10::Dispatcher::singleton() |
| 8137 | .findSchemaOrThrow(_foreach_div__List::name, _foreach_div__List::overload_name) |
| 8138 | .typed<_foreach_div__List::schema>(); |
| 8139 | } |
| 8140 | |
| 8141 | // aten::_foreach_div_.List(Tensor(a!)[] self, Tensor[] other) -> () |
| 8142 | void _foreach_div__List::call(at::TensorList self, at::TensorList other) { |
| 8143 | |
| 8144 | static auto op = create__foreach_div__List_typed_handle(); |
| 8145 | return op.call(self, other); |
| 8146 | } |
| 8147 | |
| 8148 | // aten::_foreach_div_.List(Tensor(a!)[] self, Tensor[] other) -> () |
| 8149 | void _foreach_div__List::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other) { |
| 8150 | |
| 8151 | static auto op = create__foreach_div__List_typed_handle(); |
| 8152 | return op.redispatch(dispatchKeySet, self, other); |
| 8153 | } |
| 8154 | |
| 8155 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_div_ScalarList, name, "aten::_foreach_div") |
| 8156 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_div_ScalarList, overload_name, "ScalarList") |
| 8157 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_div_ScalarList, schema_str, "_foreach_div.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]") |
| 8158 | |
| 8159 | // aten::_foreach_div.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] |
| 8160 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_div_ScalarList::schema> create__foreach_div_ScalarList_typed_handle() { |
| 8161 | return c10::Dispatcher::singleton() |
| 8162 | .findSchemaOrThrow(_foreach_div_ScalarList::name, _foreach_div_ScalarList::overload_name) |
| 8163 | .typed<_foreach_div_ScalarList::schema>(); |
| 8164 | } |
| 8165 | |
| 8166 | // aten::_foreach_div.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] |
| 8167 | ::std::vector<at::Tensor> _foreach_div_ScalarList::call(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 8168 | |
| 8169 | static auto op = create__foreach_div_ScalarList_typed_handle(); |
| 8170 | return op.call(self, scalars); |
| 8171 | } |
| 8172 | |
| 8173 | // aten::_foreach_div.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] |
| 8174 | ::std::vector<at::Tensor> _foreach_div_ScalarList::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 8175 | |
| 8176 | static auto op = create__foreach_div_ScalarList_typed_handle(); |
| 8177 | return op.redispatch(dispatchKeySet, self, scalars); |
| 8178 | } |
| 8179 | |
| 8180 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_div__ScalarList, name, "aten::_foreach_div_") |
| 8181 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_div__ScalarList, overload_name, "ScalarList") |
| 8182 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_div__ScalarList, schema_str, "_foreach_div_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()") |
| 8183 | |
| 8184 | // aten::_foreach_div_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () |
| 8185 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_div__ScalarList::schema> create__foreach_div__ScalarList_typed_handle() { |
| 8186 | return c10::Dispatcher::singleton() |
| 8187 | .findSchemaOrThrow(_foreach_div__ScalarList::name, _foreach_div__ScalarList::overload_name) |
| 8188 | .typed<_foreach_div__ScalarList::schema>(); |
| 8189 | } |
| 8190 | |
| 8191 | // aten::_foreach_div_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () |
| 8192 | void _foreach_div__ScalarList::call(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 8193 | |
| 8194 | static auto op = create__foreach_div__ScalarList_typed_handle(); |
| 8195 | return op.call(self, scalars); |
| 8196 | } |
| 8197 | |
| 8198 | // aten::_foreach_div_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () |
| 8199 | void _foreach_div__ScalarList::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 8200 | |
| 8201 | static auto op = create__foreach_div__ScalarList_typed_handle(); |
| 8202 | return op.redispatch(dispatchKeySet, self, scalars); |
| 8203 | } |
| 8204 | |
| 8205 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_mul_ScalarList, name, "aten::_foreach_mul") |
| 8206 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_mul_ScalarList, overload_name, "ScalarList") |
| 8207 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_mul_ScalarList, schema_str, "_foreach_mul.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]") |
| 8208 | |
| 8209 | // aten::_foreach_mul.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] |
| 8210 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_mul_ScalarList::schema> create__foreach_mul_ScalarList_typed_handle() { |
| 8211 | return c10::Dispatcher::singleton() |
| 8212 | .findSchemaOrThrow(_foreach_mul_ScalarList::name, _foreach_mul_ScalarList::overload_name) |
| 8213 | .typed<_foreach_mul_ScalarList::schema>(); |
| 8214 | } |
| 8215 | |
| 8216 | // aten::_foreach_mul.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] |
| 8217 | ::std::vector<at::Tensor> _foreach_mul_ScalarList::call(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 8218 | |
| 8219 | static auto op = create__foreach_mul_ScalarList_typed_handle(); |
| 8220 | return op.call(self, scalars); |
| 8221 | } |
| 8222 | |
| 8223 | // aten::_foreach_mul.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] |
| 8224 | ::std::vector<at::Tensor> _foreach_mul_ScalarList::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 8225 | |
| 8226 | static auto op = create__foreach_mul_ScalarList_typed_handle(); |
| 8227 | return op.redispatch(dispatchKeySet, self, scalars); |
| 8228 | } |
| 8229 | |
| 8230 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_mul__ScalarList, name, "aten::_foreach_mul_") |
| 8231 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_mul__ScalarList, overload_name, "ScalarList") |
| 8232 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_mul__ScalarList, schema_str, "_foreach_mul_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()") |
| 8233 | |
| 8234 | // aten::_foreach_mul_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () |
| 8235 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_mul__ScalarList::schema> create__foreach_mul__ScalarList_typed_handle() { |
| 8236 | return c10::Dispatcher::singleton() |
| 8237 | .findSchemaOrThrow(_foreach_mul__ScalarList::name, _foreach_mul__ScalarList::overload_name) |
| 8238 | .typed<_foreach_mul__ScalarList::schema>(); |
| 8239 | } |
| 8240 | |
| 8241 | // aten::_foreach_mul_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () |
| 8242 | void _foreach_mul__ScalarList::call(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 8243 | |
| 8244 | static auto op = create__foreach_mul__ScalarList_typed_handle(); |
| 8245 | return op.call(self, scalars); |
| 8246 | } |
| 8247 | |
| 8248 | // aten::_foreach_mul_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () |
| 8249 | void _foreach_mul__ScalarList::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 8250 | |
| 8251 | static auto op = create__foreach_mul__ScalarList_typed_handle(); |
| 8252 | return op.redispatch(dispatchKeySet, self, scalars); |
| 8253 | } |
| 8254 | |
| 8255 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_zero_, name, "aten::_foreach_zero_") |
| 8256 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_zero_, overload_name, "") |
| 8257 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_zero_, schema_str, "_foreach_zero_(Tensor(a!)[] self) -> ()") |
| 8258 | |
| 8259 | // aten::_foreach_zero_(Tensor(a!)[] self) -> () |
| 8260 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_zero_::schema> create__foreach_zero__typed_handle() { |
| 8261 | return c10::Dispatcher::singleton() |
| 8262 | .findSchemaOrThrow(_foreach_zero_::name, _foreach_zero_::overload_name) |
| 8263 | .typed<_foreach_zero_::schema>(); |
| 8264 | } |
| 8265 | |
| 8266 | // aten::_foreach_zero_(Tensor(a!)[] self) -> () |
| 8267 | void _foreach_zero_::call(at::TensorList self) { |
| 8268 | |
| 8269 | static auto op = create__foreach_zero__typed_handle(); |
| 8270 | return op.call(self); |
| 8271 | } |
| 8272 | |
| 8273 | // aten::_foreach_zero_(Tensor(a!)[] self) -> () |
| 8274 | void _foreach_zero_::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 8275 | |
| 8276 | static auto op = create__foreach_zero__typed_handle(); |
| 8277 | return op.redispatch(dispatchKeySet, self); |
| 8278 | } |
| 8279 | |
| 8280 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_asin, name, "aten::_foreach_asin") |
| 8281 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_asin, overload_name, "") |
| 8282 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_asin, schema_str, "_foreach_asin(Tensor[] self) -> Tensor[]") |
| 8283 | |
| 8284 | // aten::_foreach_asin(Tensor[] self) -> Tensor[] |
| 8285 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_asin::schema> create__foreach_asin_typed_handle() { |
| 8286 | return c10::Dispatcher::singleton() |
| 8287 | .findSchemaOrThrow(_foreach_asin::name, _foreach_asin::overload_name) |
| 8288 | .typed<_foreach_asin::schema>(); |
| 8289 | } |
| 8290 | |
| 8291 | // aten::_foreach_asin(Tensor[] self) -> Tensor[] |
| 8292 | ::std::vector<at::Tensor> _foreach_asin::call(at::TensorList self) { |
| 8293 | |
| 8294 | static auto op = create__foreach_asin_typed_handle(); |
| 8295 | return op.call(self); |
| 8296 | } |
| 8297 | |
| 8298 | // aten::_foreach_asin(Tensor[] self) -> Tensor[] |
| 8299 | ::std::vector<at::Tensor> _foreach_asin::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 8300 | |
| 8301 | static auto op = create__foreach_asin_typed_handle(); |
| 8302 | return op.redispatch(dispatchKeySet, self); |
| 8303 | } |
| 8304 | |
| 8305 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_asin_, name, "aten::_foreach_asin_") |
| 8306 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_asin_, overload_name, "") |
| 8307 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_asin_, schema_str, "_foreach_asin_(Tensor(a!)[] self) -> ()") |
| 8308 | |
| 8309 | // aten::_foreach_asin_(Tensor(a!)[] self) -> () |
| 8310 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_asin_::schema> create__foreach_asin__typed_handle() { |
| 8311 | return c10::Dispatcher::singleton() |
| 8312 | .findSchemaOrThrow(_foreach_asin_::name, _foreach_asin_::overload_name) |
| 8313 | .typed<_foreach_asin_::schema>(); |
| 8314 | } |
| 8315 | |
| 8316 | // aten::_foreach_asin_(Tensor(a!)[] self) -> () |
| 8317 | void _foreach_asin_::call(at::TensorList self) { |
| 8318 | |
| 8319 | static auto op = create__foreach_asin__typed_handle(); |
| 8320 | return op.call(self); |
| 8321 | } |
| 8322 | |
| 8323 | // aten::_foreach_asin_(Tensor(a!)[] self) -> () |
| 8324 | void _foreach_asin_::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 8325 | |
| 8326 | static auto op = create__foreach_asin__typed_handle(); |
| 8327 | return op.redispatch(dispatchKeySet, self); |
| 8328 | } |
| 8329 | |
| 8330 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_cos, name, "aten::_foreach_cos") |
| 8331 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_cos, overload_name, "") |
| 8332 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_cos, schema_str, "_foreach_cos(Tensor[] self) -> Tensor[]") |
| 8333 | |
| 8334 | // aten::_foreach_cos(Tensor[] self) -> Tensor[] |
| 8335 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_cos::schema> create__foreach_cos_typed_handle() { |
| 8336 | return c10::Dispatcher::singleton() |
| 8337 | .findSchemaOrThrow(_foreach_cos::name, _foreach_cos::overload_name) |
| 8338 | .typed<_foreach_cos::schema>(); |
| 8339 | } |
| 8340 | |
| 8341 | // aten::_foreach_cos(Tensor[] self) -> Tensor[] |
| 8342 | ::std::vector<at::Tensor> _foreach_cos::call(at::TensorList self) { |
| 8343 | |
| 8344 | static auto op = create__foreach_cos_typed_handle(); |
| 8345 | return op.call(self); |
| 8346 | } |
| 8347 | |
| 8348 | // aten::_foreach_cos(Tensor[] self) -> Tensor[] |
| 8349 | ::std::vector<at::Tensor> _foreach_cos::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 8350 | |
| 8351 | static auto op = create__foreach_cos_typed_handle(); |
| 8352 | return op.redispatch(dispatchKeySet, self); |
| 8353 | } |
| 8354 | |
| 8355 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_cos_, name, "aten::_foreach_cos_") |
| 8356 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_cos_, overload_name, "") |
| 8357 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_cos_, schema_str, "_foreach_cos_(Tensor(a!)[] self) -> ()") |
| 8358 | |
| 8359 | // aten::_foreach_cos_(Tensor(a!)[] self) -> () |
| 8360 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_cos_::schema> create__foreach_cos__typed_handle() { |
| 8361 | return c10::Dispatcher::singleton() |
| 8362 | .findSchemaOrThrow(_foreach_cos_::name, _foreach_cos_::overload_name) |
| 8363 | .typed<_foreach_cos_::schema>(); |
| 8364 | } |
| 8365 | |
| 8366 | // aten::_foreach_cos_(Tensor(a!)[] self) -> () |
| 8367 | void _foreach_cos_::call(at::TensorList self) { |
| 8368 | |
| 8369 | static auto op = create__foreach_cos__typed_handle(); |
| 8370 | return op.call(self); |
| 8371 | } |
| 8372 | |
| 8373 | // aten::_foreach_cos_(Tensor(a!)[] self) -> () |
| 8374 | void _foreach_cos_::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 8375 | |
| 8376 | static auto op = create__foreach_cos__typed_handle(); |
| 8377 | return op.redispatch(dispatchKeySet, self); |
| 8378 | } |
| 8379 | |
| 8380 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_floor, name, "aten::_foreach_floor") |
| 8381 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_floor, overload_name, "") |
| 8382 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_floor, schema_str, "_foreach_floor(Tensor[] self) -> Tensor[]") |
| 8383 | |
| 8384 | // aten::_foreach_floor(Tensor[] self) -> Tensor[] |
| 8385 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_floor::schema> create__foreach_floor_typed_handle() { |
| 8386 | return c10::Dispatcher::singleton() |
| 8387 | .findSchemaOrThrow(_foreach_floor::name, _foreach_floor::overload_name) |
| 8388 | .typed<_foreach_floor::schema>(); |
| 8389 | } |
| 8390 | |
| 8391 | // aten::_foreach_floor(Tensor[] self) -> Tensor[] |
| 8392 | ::std::vector<at::Tensor> _foreach_floor::call(at::TensorList self) { |
| 8393 | |
| 8394 | static auto op = create__foreach_floor_typed_handle(); |
| 8395 | return op.call(self); |
| 8396 | } |
| 8397 | |
| 8398 | // aten::_foreach_floor(Tensor[] self) -> Tensor[] |
| 8399 | ::std::vector<at::Tensor> _foreach_floor::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 8400 | |
| 8401 | static auto op = create__foreach_floor_typed_handle(); |
| 8402 | return op.redispatch(dispatchKeySet, self); |
| 8403 | } |
| 8404 | |
| 8405 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_floor_, name, "aten::_foreach_floor_") |
| 8406 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_floor_, overload_name, "") |
| 8407 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_floor_, schema_str, "_foreach_floor_(Tensor(a!)[] self) -> ()") |
| 8408 | |
| 8409 | // aten::_foreach_floor_(Tensor(a!)[] self) -> () |
| 8410 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_floor_::schema> create__foreach_floor__typed_handle() { |
| 8411 | return c10::Dispatcher::singleton() |
| 8412 | .findSchemaOrThrow(_foreach_floor_::name, _foreach_floor_::overload_name) |
| 8413 | .typed<_foreach_floor_::schema>(); |
| 8414 | } |
| 8415 | |
| 8416 | // aten::_foreach_floor_(Tensor(a!)[] self) -> () |
| 8417 | void _foreach_floor_::call(at::TensorList self) { |
| 8418 | |
| 8419 | static auto op = create__foreach_floor__typed_handle(); |
| 8420 | return op.call(self); |
| 8421 | } |
| 8422 | |
| 8423 | // aten::_foreach_floor_(Tensor(a!)[] self) -> () |
| 8424 | void _foreach_floor_::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 8425 | |
| 8426 | static auto op = create__foreach_floor__typed_handle(); |
| 8427 | return op.redispatch(dispatchKeySet, self); |
| 8428 | } |
| 8429 | |
| 8430 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_tanh, name, "aten::_foreach_tanh") |
| 8431 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_tanh, overload_name, "") |
| 8432 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_tanh, schema_str, "_foreach_tanh(Tensor[] self) -> Tensor[]") |
| 8433 | |
| 8434 | // aten::_foreach_tanh(Tensor[] self) -> Tensor[] |
| 8435 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_tanh::schema> create__foreach_tanh_typed_handle() { |
| 8436 | return c10::Dispatcher::singleton() |
| 8437 | .findSchemaOrThrow(_foreach_tanh::name, _foreach_tanh::overload_name) |
| 8438 | .typed<_foreach_tanh::schema>(); |
| 8439 | } |
| 8440 | |
| 8441 | // aten::_foreach_tanh(Tensor[] self) -> Tensor[] |
| 8442 | ::std::vector<at::Tensor> _foreach_tanh::call(at::TensorList self) { |
| 8443 | |
| 8444 | static auto op = create__foreach_tanh_typed_handle(); |
| 8445 | return op.call(self); |
| 8446 | } |
| 8447 | |
| 8448 | // aten::_foreach_tanh(Tensor[] self) -> Tensor[] |
| 8449 | ::std::vector<at::Tensor> _foreach_tanh::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 8450 | |
| 8451 | static auto op = create__foreach_tanh_typed_handle(); |
| 8452 | return op.redispatch(dispatchKeySet, self); |
| 8453 | } |
| 8454 | |
| 8455 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_tanh_, name, "aten::_foreach_tanh_") |
| 8456 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_tanh_, overload_name, "") |
| 8457 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_tanh_, schema_str, "_foreach_tanh_(Tensor(a!)[] self) -> ()") |
| 8458 | |
| 8459 | // aten::_foreach_tanh_(Tensor(a!)[] self) -> () |
| 8460 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_tanh_::schema> create__foreach_tanh__typed_handle() { |
| 8461 | return c10::Dispatcher::singleton() |
| 8462 | .findSchemaOrThrow(_foreach_tanh_::name, _foreach_tanh_::overload_name) |
| 8463 | .typed<_foreach_tanh_::schema>(); |
| 8464 | } |
| 8465 | |
| 8466 | // aten::_foreach_tanh_(Tensor(a!)[] self) -> () |
| 8467 | void _foreach_tanh_::call(at::TensorList self) { |
| 8468 | |
| 8469 | static auto op = create__foreach_tanh__typed_handle(); |
| 8470 | return op.call(self); |
| 8471 | } |
| 8472 | |
| 8473 | // aten::_foreach_tanh_(Tensor(a!)[] self) -> () |
| 8474 | void _foreach_tanh_::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 8475 | |
| 8476 | static auto op = create__foreach_tanh__typed_handle(); |
| 8477 | return op.redispatch(dispatchKeySet, self); |
| 8478 | } |
| 8479 | |
| 8480 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_addcmul__Scalar, name, "aten::_foreach_addcmul_") |
| 8481 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_addcmul__Scalar, overload_name, "Scalar") |
| 8482 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_addcmul__Scalar, schema_str, "_foreach_addcmul_.Scalar(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> ()") |
| 8483 | |
| 8484 | // aten::_foreach_addcmul_.Scalar(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> () |
| 8485 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_addcmul__Scalar::schema> create__foreach_addcmul__Scalar_typed_handle() { |
| 8486 | return c10::Dispatcher::singleton() |
| 8487 | .findSchemaOrThrow(_foreach_addcmul__Scalar::name, _foreach_addcmul__Scalar::overload_name) |
| 8488 | .typed<_foreach_addcmul__Scalar::schema>(); |
| 8489 | } |
| 8490 | |
| 8491 | // aten::_foreach_addcmul_.Scalar(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> () |
| 8492 | void _foreach_addcmul__Scalar::call(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value) { |
| 8493 | |
| 8494 | static auto op = create__foreach_addcmul__Scalar_typed_handle(); |
| 8495 | return op.call(self, tensor1, tensor2, value); |
| 8496 | } |
| 8497 | |
| 8498 | // aten::_foreach_addcmul_.Scalar(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> () |
| 8499 | void _foreach_addcmul__Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value) { |
| 8500 | |
| 8501 | static auto op = create__foreach_addcmul__Scalar_typed_handle(); |
| 8502 | return op.redispatch(dispatchKeySet, self, tensor1, tensor2, value); |
| 8503 | } |
| 8504 | |
| 8505 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_addcmul__ScalarList, name, "aten::_foreach_addcmul_") |
| 8506 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_addcmul__ScalarList, overload_name, "ScalarList") |
| 8507 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_addcmul__ScalarList, schema_str, "_foreach_addcmul_.ScalarList(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> ()") |
| 8508 | |
| 8509 | // aten::_foreach_addcmul_.ScalarList(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> () |
| 8510 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_addcmul__ScalarList::schema> create__foreach_addcmul__ScalarList_typed_handle() { |
| 8511 | return c10::Dispatcher::singleton() |
| 8512 | .findSchemaOrThrow(_foreach_addcmul__ScalarList::name, _foreach_addcmul__ScalarList::overload_name) |
| 8513 | .typed<_foreach_addcmul__ScalarList::schema>(); |
| 8514 | } |
| 8515 | |
| 8516 | // aten::_foreach_addcmul_.ScalarList(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> () |
| 8517 | void _foreach_addcmul__ScalarList::call(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars) { |
| 8518 | |
| 8519 | static auto op = create__foreach_addcmul__ScalarList_typed_handle(); |
| 8520 | return op.call(self, tensor1, tensor2, scalars); |
| 8521 | } |
| 8522 | |
| 8523 | // aten::_foreach_addcmul_.ScalarList(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> () |
| 8524 | void _foreach_addcmul__ScalarList::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars) { |
| 8525 | |
| 8526 | static auto op = create__foreach_addcmul__ScalarList_typed_handle(); |
| 8527 | return op.redispatch(dispatchKeySet, self, tensor1, tensor2, scalars); |
| 8528 | } |
| 8529 | |
| 8530 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_addcmul__Tensor, name, "aten::_foreach_addcmul_") |
| 8531 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_addcmul__Tensor, overload_name, "Tensor") |
| 8532 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_addcmul__Tensor, schema_str, "_foreach_addcmul_.Tensor(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> ()") |
| 8533 | |
| 8534 | // aten::_foreach_addcmul_.Tensor(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> () |
| 8535 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_addcmul__Tensor::schema> create__foreach_addcmul__Tensor_typed_handle() { |
| 8536 | return c10::Dispatcher::singleton() |
| 8537 | .findSchemaOrThrow(_foreach_addcmul__Tensor::name, _foreach_addcmul__Tensor::overload_name) |
| 8538 | .typed<_foreach_addcmul__Tensor::schema>(); |
| 8539 | } |
| 8540 | |
| 8541 | // aten::_foreach_addcmul_.Tensor(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> () |
| 8542 | void _foreach_addcmul__Tensor::call(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) { |
| 8543 | |
| 8544 | static auto op = create__foreach_addcmul__Tensor_typed_handle(); |
| 8545 | return op.call(self, tensor1, tensor2, scalars); |
| 8546 | } |
| 8547 | |
| 8548 | // aten::_foreach_addcmul_.Tensor(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> () |
| 8549 | void _foreach_addcmul__Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) { |
| 8550 | |
| 8551 | static auto op = create__foreach_addcmul__Tensor_typed_handle(); |
| 8552 | return op.redispatch(dispatchKeySet, self, tensor1, tensor2, scalars); |
| 8553 | } |
| 8554 | |
| 8555 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_addcmul_Scalar, name, "aten::_foreach_addcmul") |
| 8556 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_addcmul_Scalar, overload_name, "Scalar") |
| 8557 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_addcmul_Scalar, schema_str, "_foreach_addcmul.Scalar(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> Tensor[]") |
| 8558 | |
| 8559 | // aten::_foreach_addcmul.Scalar(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> Tensor[] |
| 8560 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_addcmul_Scalar::schema> create__foreach_addcmul_Scalar_typed_handle() { |
| 8561 | return c10::Dispatcher::singleton() |
| 8562 | .findSchemaOrThrow(_foreach_addcmul_Scalar::name, _foreach_addcmul_Scalar::overload_name) |
| 8563 | .typed<_foreach_addcmul_Scalar::schema>(); |
| 8564 | } |
| 8565 | |
| 8566 | // aten::_foreach_addcmul.Scalar(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> Tensor[] |
| 8567 | ::std::vector<at::Tensor> _foreach_addcmul_Scalar::call(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value) { |
| 8568 | |
| 8569 | static auto op = create__foreach_addcmul_Scalar_typed_handle(); |
| 8570 | return op.call(self, tensor1, tensor2, value); |
| 8571 | } |
| 8572 | |
| 8573 | // aten::_foreach_addcmul.Scalar(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> Tensor[] |
| 8574 | ::std::vector<at::Tensor> _foreach_addcmul_Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value) { |
| 8575 | |
| 8576 | static auto op = create__foreach_addcmul_Scalar_typed_handle(); |
| 8577 | return op.redispatch(dispatchKeySet, self, tensor1, tensor2, value); |
| 8578 | } |
| 8579 | |
| 8580 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_addcmul_ScalarList, name, "aten::_foreach_addcmul") |
| 8581 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_addcmul_ScalarList, overload_name, "ScalarList") |
| 8582 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_addcmul_ScalarList, schema_str, "_foreach_addcmul.ScalarList(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> Tensor[]") |
| 8583 | |
| 8584 | // aten::_foreach_addcmul.ScalarList(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> Tensor[] |
| 8585 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_addcmul_ScalarList::schema> create__foreach_addcmul_ScalarList_typed_handle() { |
| 8586 | return c10::Dispatcher::singleton() |
| 8587 | .findSchemaOrThrow(_foreach_addcmul_ScalarList::name, _foreach_addcmul_ScalarList::overload_name) |
| 8588 | .typed<_foreach_addcmul_ScalarList::schema>(); |
| 8589 | } |
| 8590 | |
| 8591 | // aten::_foreach_addcmul.ScalarList(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> Tensor[] |
| 8592 | ::std::vector<at::Tensor> _foreach_addcmul_ScalarList::call(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars) { |
| 8593 | |
| 8594 | static auto op = create__foreach_addcmul_ScalarList_typed_handle(); |
| 8595 | return op.call(self, tensor1, tensor2, scalars); |
| 8596 | } |
| 8597 | |
| 8598 | // aten::_foreach_addcmul.ScalarList(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> Tensor[] |
| 8599 | ::std::vector<at::Tensor> _foreach_addcmul_ScalarList::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars) { |
| 8600 | |
| 8601 | static auto op = create__foreach_addcmul_ScalarList_typed_handle(); |
| 8602 | return op.redispatch(dispatchKeySet, self, tensor1, tensor2, scalars); |
| 8603 | } |
| 8604 | |
| 8605 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_addcmul_Tensor, name, "aten::_foreach_addcmul") |
| 8606 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_addcmul_Tensor, overload_name, "Tensor") |
| 8607 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_addcmul_Tensor, schema_str, "_foreach_addcmul.Tensor(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> Tensor[]") |
| 8608 | |
| 8609 | // aten::_foreach_addcmul.Tensor(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> Tensor[] |
| 8610 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_addcmul_Tensor::schema> create__foreach_addcmul_Tensor_typed_handle() { |
| 8611 | return c10::Dispatcher::singleton() |
| 8612 | .findSchemaOrThrow(_foreach_addcmul_Tensor::name, _foreach_addcmul_Tensor::overload_name) |
| 8613 | .typed<_foreach_addcmul_Tensor::schema>(); |
| 8614 | } |
| 8615 | |
| 8616 | // aten::_foreach_addcmul.Tensor(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> Tensor[] |
| 8617 | ::std::vector<at::Tensor> _foreach_addcmul_Tensor::call(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) { |
| 8618 | |
| 8619 | static auto op = create__foreach_addcmul_Tensor_typed_handle(); |
| 8620 | return op.call(self, tensor1, tensor2, scalars); |
| 8621 | } |
| 8622 | |
| 8623 | // aten::_foreach_addcmul.Tensor(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> Tensor[] |
| 8624 | ::std::vector<at::Tensor> _foreach_addcmul_Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) { |
| 8625 | |
| 8626 | static auto op = create__foreach_addcmul_Tensor_typed_handle(); |
| 8627 | return op.redispatch(dispatchKeySet, self, tensor1, tensor2, scalars); |
| 8628 | } |
| 8629 | |
| 8630 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_convert_indices_from_csr_to_coo, name, "aten::_convert_indices_from_csr_to_coo") |
| 8631 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_convert_indices_from_csr_to_coo, overload_name, "") |
| 8632 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_convert_indices_from_csr_to_coo, schema_str, "_convert_indices_from_csr_to_coo(Tensor crow_indices, Tensor col_indices, *, bool out_int32=False, bool transpose=False) -> Tensor") |
| 8633 | |
| 8634 | // aten::_convert_indices_from_csr_to_coo(Tensor crow_indices, Tensor col_indices, *, bool out_int32=False, bool transpose=False) -> Tensor |
| 8635 | static C10_NOINLINE c10::TypedOperatorHandle<_convert_indices_from_csr_to_coo::schema> create__convert_indices_from_csr_to_coo_typed_handle() { |
| 8636 | return c10::Dispatcher::singleton() |
| 8637 | .findSchemaOrThrow(_convert_indices_from_csr_to_coo::name, _convert_indices_from_csr_to_coo::overload_name) |
| 8638 | .typed<_convert_indices_from_csr_to_coo::schema>(); |
| 8639 | } |
| 8640 | |
| 8641 | // aten::_convert_indices_from_csr_to_coo(Tensor crow_indices, Tensor col_indices, *, bool out_int32=False, bool transpose=False) -> Tensor |
| 8642 | at::Tensor _convert_indices_from_csr_to_coo::call(const at::Tensor & crow_indices, const at::Tensor & col_indices, bool out_int32, bool transpose) { |
| 8643 | |
| 8644 | static auto op = create__convert_indices_from_csr_to_coo_typed_handle(); |
| 8645 | return op.call(crow_indices, col_indices, out_int32, transpose); |
| 8646 | } |
| 8647 | |
| 8648 | // aten::_convert_indices_from_csr_to_coo(Tensor crow_indices, Tensor col_indices, *, bool out_int32=False, bool transpose=False) -> Tensor |
| 8649 | at::Tensor _convert_indices_from_csr_to_coo::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & crow_indices, const at::Tensor & col_indices, bool out_int32, bool transpose) { |
| 8650 | |
| 8651 | static auto op = create__convert_indices_from_csr_to_coo_typed_handle(); |
| 8652 | return op.redispatch(dispatchKeySet, crow_indices, col_indices, out_int32, transpose); |
| 8653 | } |
| 8654 | |
| 8655 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_convert_indices_from_csr_to_coo_out, name, "aten::_convert_indices_from_csr_to_coo") |
| 8656 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_convert_indices_from_csr_to_coo_out, overload_name, "out") |
| 8657 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_convert_indices_from_csr_to_coo_out, schema_str, "_convert_indices_from_csr_to_coo.out(Tensor crow_indices, Tensor col_indices, *, bool out_int32=False, bool transpose=False, Tensor(a!) out) -> Tensor(a!)") |
| 8658 | |
| 8659 | // aten::_convert_indices_from_csr_to_coo.out(Tensor crow_indices, Tensor col_indices, *, bool out_int32=False, bool transpose=False, Tensor(a!) out) -> Tensor(a!) |
| 8660 | static C10_NOINLINE c10::TypedOperatorHandle<_convert_indices_from_csr_to_coo_out::schema> create__convert_indices_from_csr_to_coo_out_typed_handle() { |
| 8661 | return c10::Dispatcher::singleton() |
| 8662 | .findSchemaOrThrow(_convert_indices_from_csr_to_coo_out::name, _convert_indices_from_csr_to_coo_out::overload_name) |
| 8663 | .typed<_convert_indices_from_csr_to_coo_out::schema>(); |
| 8664 | } |
| 8665 | |
| 8666 | // aten::_convert_indices_from_csr_to_coo.out(Tensor crow_indices, Tensor col_indices, *, bool out_int32=False, bool transpose=False, Tensor(a!) out) -> Tensor(a!) |
| 8667 | at::Tensor & _convert_indices_from_csr_to_coo_out::call(const at::Tensor & crow_indices, const at::Tensor & col_indices, bool out_int32, bool transpose, at::Tensor & out) { |
| 8668 | |
| 8669 | static auto op = create__convert_indices_from_csr_to_coo_out_typed_handle(); |
| 8670 | return op.call(crow_indices, col_indices, out_int32, transpose, out); |
| 8671 | } |
| 8672 | |
| 8673 | // aten::_convert_indices_from_csr_to_coo.out(Tensor crow_indices, Tensor col_indices, *, bool out_int32=False, bool transpose=False, Tensor(a!) out) -> Tensor(a!) |
| 8674 | at::Tensor & _convert_indices_from_csr_to_coo_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & crow_indices, const at::Tensor & col_indices, bool out_int32, bool transpose, at::Tensor & out) { |
| 8675 | |
| 8676 | static auto op = create__convert_indices_from_csr_to_coo_out_typed_handle(); |
| 8677 | return op.redispatch(dispatchKeySet, crow_indices, col_indices, out_int32, transpose, out); |
| 8678 | } |
| 8679 | |
| 8680 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss_out, name, "aten::nll_loss") |
| 8681 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss_out, overload_name, "out") |
| 8682 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss_out, schema_str, "nll_loss.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!)") |
| 8683 | |
| 8684 | // aten::nll_loss.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!) |
| 8685 | static C10_NOINLINE c10::TypedOperatorHandle<nll_loss_out::schema> create_nll_loss_out_typed_handle() { |
| 8686 | return c10::Dispatcher::singleton() |
| 8687 | .findSchemaOrThrow(nll_loss_out::name, nll_loss_out::overload_name) |
| 8688 | .typed<nll_loss_out::schema>(); |
| 8689 | } |
| 8690 | |
| 8691 | // aten::nll_loss.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!) |
| 8692 | at::Tensor & nll_loss_out::call(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, at::Tensor & out) { |
| 8693 | |
| 8694 | static auto op = create_nll_loss_out_typed_handle(); |
| 8695 | return op.call(self, target, weight, reduction, ignore_index, out); |
| 8696 | } |
| 8697 | |
| 8698 | // aten::nll_loss.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!) |
| 8699 | at::Tensor & nll_loss_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, at::Tensor & out) { |
| 8700 | |
| 8701 | static auto op = create_nll_loss_out_typed_handle(); |
| 8702 | return op.redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index, out); |
| 8703 | } |
| 8704 | |
| 8705 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss, name, "aten::nll_loss") |
| 8706 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss, overload_name, "") |
| 8707 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss, schema_str, "nll_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor") |
| 8708 | |
| 8709 | // aten::nll_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor |
| 8710 | static C10_NOINLINE c10::TypedOperatorHandle<nll_loss::schema> create_nll_loss_typed_handle() { |
| 8711 | return c10::Dispatcher::singleton() |
| 8712 | .findSchemaOrThrow(nll_loss::name, nll_loss::overload_name) |
| 8713 | .typed<nll_loss::schema>(); |
| 8714 | } |
| 8715 | |
| 8716 | // aten::nll_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor |
| 8717 | at::Tensor nll_loss::call(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index) { |
| 8718 | |
| 8719 | static auto op = create_nll_loss_typed_handle(); |
| 8720 | return op.call(self, target, weight, reduction, ignore_index); |
| 8721 | } |
| 8722 | |
| 8723 | // aten::nll_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor |
| 8724 | at::Tensor nll_loss::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index) { |
| 8725 | |
| 8726 | static auto op = create_nll_loss_typed_handle(); |
| 8727 | return op.redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index); |
| 8728 | } |
| 8729 | |
| 8730 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss_backward_grad_input, name, "aten::nll_loss_backward") |
| 8731 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss_backward_grad_input, overload_name, "grad_input") |
| 8732 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss_backward_grad_input, schema_str, "nll_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!)") |
| 8733 | |
| 8734 | // aten::nll_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 8735 | static C10_NOINLINE c10::TypedOperatorHandle<nll_loss_backward_grad_input::schema> create_nll_loss_backward_grad_input_typed_handle() { |
| 8736 | return c10::Dispatcher::singleton() |
| 8737 | .findSchemaOrThrow(nll_loss_backward_grad_input::name, nll_loss_backward_grad_input::overload_name) |
| 8738 | .typed<nll_loss_backward_grad_input::schema>(); |
| 8739 | } |
| 8740 | |
| 8741 | // aten::nll_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 8742 | at::Tensor & nll_loss_backward_grad_input::call(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight, at::Tensor & grad_input) { |
| 8743 | |
| 8744 | static auto op = create_nll_loss_backward_grad_input_typed_handle(); |
| 8745 | return op.call(grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input); |
| 8746 | } |
| 8747 | |
| 8748 | // aten::nll_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 8749 | at::Tensor & nll_loss_backward_grad_input::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight, at::Tensor & grad_input) { |
| 8750 | |
| 8751 | static auto op = create_nll_loss_backward_grad_input_typed_handle(); |
| 8752 | return op.redispatch(dispatchKeySet, grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input); |
| 8753 | } |
| 8754 | |
| 8755 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss_backward, name, "aten::nll_loss_backward") |
| 8756 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss_backward, overload_name, "") |
| 8757 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss_backward, schema_str, "nll_loss_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight) -> Tensor") |
| 8758 | |
| 8759 | // aten::nll_loss_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight) -> Tensor |
| 8760 | static C10_NOINLINE c10::TypedOperatorHandle<nll_loss_backward::schema> create_nll_loss_backward_typed_handle() { |
| 8761 | return c10::Dispatcher::singleton() |
| 8762 | .findSchemaOrThrow(nll_loss_backward::name, nll_loss_backward::overload_name) |
| 8763 | .typed<nll_loss_backward::schema>(); |
| 8764 | } |
| 8765 | |
| 8766 | // aten::nll_loss_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight) -> Tensor |
| 8767 | at::Tensor nll_loss_backward::call(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight) { |
| 8768 | |
| 8769 | static auto op = create_nll_loss_backward_typed_handle(); |
| 8770 | return op.call(grad_output, self, target, weight, reduction, ignore_index, total_weight); |
| 8771 | } |
| 8772 | |
| 8773 | // aten::nll_loss_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight) -> Tensor |
| 8774 | at::Tensor nll_loss_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight) { |
| 8775 | |
| 8776 | static auto op = create_nll_loss_backward_typed_handle(); |
| 8777 | return op.redispatch(dispatchKeySet, grad_output, self, target, weight, reduction, ignore_index, total_weight); |
| 8778 | } |
| 8779 | |
| 8780 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(smooth_l1_loss_backward_grad_input, name, "aten::smooth_l1_loss_backward") |
| 8781 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(smooth_l1_loss_backward_grad_input, overload_name, "grad_input") |
| 8782 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(smooth_l1_loss_backward_grad_input, schema_str, "smooth_l1_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, float beta, *, Tensor(a!) grad_input) -> Tensor(a!)") |
| 8783 | |
| 8784 | // aten::smooth_l1_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, float beta, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 8785 | static C10_NOINLINE c10::TypedOperatorHandle<smooth_l1_loss_backward_grad_input::schema> create_smooth_l1_loss_backward_grad_input_typed_handle() { |
| 8786 | return c10::Dispatcher::singleton() |
| 8787 | .findSchemaOrThrow(smooth_l1_loss_backward_grad_input::name, smooth_l1_loss_backward_grad_input::overload_name) |
| 8788 | .typed<smooth_l1_loss_backward_grad_input::schema>(); |
| 8789 | } |
| 8790 | |
| 8791 | // aten::smooth_l1_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, float beta, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 8792 | at::Tensor & smooth_l1_loss_backward_grad_input::call(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta, at::Tensor & grad_input) { |
| 8793 | |
| 8794 | static auto op = create_smooth_l1_loss_backward_grad_input_typed_handle(); |
| 8795 | return op.call(grad_output, self, target, reduction, beta, grad_input); |
| 8796 | } |
| 8797 | |
| 8798 | // aten::smooth_l1_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, float beta, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 8799 | at::Tensor & smooth_l1_loss_backward_grad_input::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta, at::Tensor & grad_input) { |
| 8800 | |
| 8801 | static auto op = create_smooth_l1_loss_backward_grad_input_typed_handle(); |
| 8802 | return op.redispatch(dispatchKeySet, grad_output, self, target, reduction, beta, grad_input); |
| 8803 | } |
| 8804 | |
| 8805 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(smooth_l1_loss_backward, name, "aten::smooth_l1_loss_backward") |
| 8806 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(smooth_l1_loss_backward, overload_name, "") |
| 8807 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(smooth_l1_loss_backward, schema_str, "smooth_l1_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, float beta) -> Tensor") |
| 8808 | |
| 8809 | // aten::smooth_l1_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, float beta) -> Tensor |
| 8810 | static C10_NOINLINE c10::TypedOperatorHandle<smooth_l1_loss_backward::schema> create_smooth_l1_loss_backward_typed_handle() { |
| 8811 | return c10::Dispatcher::singleton() |
| 8812 | .findSchemaOrThrow(smooth_l1_loss_backward::name, smooth_l1_loss_backward::overload_name) |
| 8813 | .typed<smooth_l1_loss_backward::schema>(); |
| 8814 | } |
| 8815 | |
| 8816 | // aten::smooth_l1_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, float beta) -> Tensor |
| 8817 | at::Tensor smooth_l1_loss_backward::call(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta) { |
| 8818 | |
| 8819 | static auto op = create_smooth_l1_loss_backward_typed_handle(); |
| 8820 | return op.call(grad_output, self, target, reduction, beta); |
| 8821 | } |
| 8822 | |
| 8823 | // aten::smooth_l1_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, float beta) -> Tensor |
| 8824 | at::Tensor smooth_l1_loss_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta) { |
| 8825 | |
| 8826 | static auto op = create_smooth_l1_loss_backward_typed_handle(); |
| 8827 | return op.redispatch(dispatchKeySet, grad_output, self, target, reduction, beta); |
| 8828 | } |
| 8829 | |
| 8830 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(huber_loss_out, name, "aten::huber_loss") |
| 8831 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(huber_loss_out, overload_name, "out") |
| 8832 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(huber_loss_out, schema_str, "huber_loss.out(Tensor self, Tensor target, int reduction=Mean, float delta=1.0, *, Tensor(a!) out) -> Tensor(a!)") |
| 8833 | |
| 8834 | // aten::huber_loss.out(Tensor self, Tensor target, int reduction=Mean, float delta=1.0, *, Tensor(a!) out) -> Tensor(a!) |
| 8835 | static C10_NOINLINE c10::TypedOperatorHandle<huber_loss_out::schema> create_huber_loss_out_typed_handle() { |
| 8836 | return c10::Dispatcher::singleton() |
| 8837 | .findSchemaOrThrow(huber_loss_out::name, huber_loss_out::overload_name) |
| 8838 | .typed<huber_loss_out::schema>(); |
| 8839 | } |
| 8840 | |
| 8841 | // aten::huber_loss.out(Tensor self, Tensor target, int reduction=Mean, float delta=1.0, *, Tensor(a!) out) -> Tensor(a!) |
| 8842 | at::Tensor & huber_loss_out::call(const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta, at::Tensor & out) { |
| 8843 | |
| 8844 | static auto op = create_huber_loss_out_typed_handle(); |
| 8845 | return op.call(self, target, reduction, delta, out); |
| 8846 | } |
| 8847 | |
| 8848 | // aten::huber_loss.out(Tensor self, Tensor target, int reduction=Mean, float delta=1.0, *, Tensor(a!) out) -> Tensor(a!) |
| 8849 | at::Tensor & huber_loss_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta, at::Tensor & out) { |
| 8850 | |
| 8851 | static auto op = create_huber_loss_out_typed_handle(); |
| 8852 | return op.redispatch(dispatchKeySet, self, target, reduction, delta, out); |
| 8853 | } |
| 8854 | |
| 8855 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(huber_loss, name, "aten::huber_loss") |
| 8856 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(huber_loss, overload_name, "") |
| 8857 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(huber_loss, schema_str, "huber_loss(Tensor self, Tensor target, int reduction=Mean, float delta=1.0) -> Tensor") |
| 8858 | |
| 8859 | // aten::huber_loss(Tensor self, Tensor target, int reduction=Mean, float delta=1.0) -> Tensor |
| 8860 | static C10_NOINLINE c10::TypedOperatorHandle<huber_loss::schema> create_huber_loss_typed_handle() { |
| 8861 | return c10::Dispatcher::singleton() |
| 8862 | .findSchemaOrThrow(huber_loss::name, huber_loss::overload_name) |
| 8863 | .typed<huber_loss::schema>(); |
| 8864 | } |
| 8865 | |
| 8866 | // aten::huber_loss(Tensor self, Tensor target, int reduction=Mean, float delta=1.0) -> Tensor |
| 8867 | at::Tensor huber_loss::call(const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta) { |
| 8868 | |
| 8869 | static auto op = create_huber_loss_typed_handle(); |
| 8870 | return op.call(self, target, reduction, delta); |
| 8871 | } |
| 8872 | |
| 8873 | // aten::huber_loss(Tensor self, Tensor target, int reduction=Mean, float delta=1.0) -> Tensor |
| 8874 | at::Tensor huber_loss::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta) { |
| 8875 | |
| 8876 | static auto op = create_huber_loss_typed_handle(); |
| 8877 | return op.redispatch(dispatchKeySet, self, target, reduction, delta); |
| 8878 | } |
| 8879 | |
| 8880 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(huber_loss_backward_out, name, "aten::huber_loss_backward") |
| 8881 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(huber_loss_backward_out, overload_name, "out") |
| 8882 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(huber_loss_backward_out, schema_str, "huber_loss_backward.out(Tensor grad_output, Tensor self, Tensor target, int reduction, float delta, *, Tensor(a!) grad_input) -> Tensor(a!)") |
| 8883 | |
| 8884 | // aten::huber_loss_backward.out(Tensor grad_output, Tensor self, Tensor target, int reduction, float delta, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 8885 | static C10_NOINLINE c10::TypedOperatorHandle<huber_loss_backward_out::schema> create_huber_loss_backward_out_typed_handle() { |
| 8886 | return c10::Dispatcher::singleton() |
| 8887 | .findSchemaOrThrow(huber_loss_backward_out::name, huber_loss_backward_out::overload_name) |
| 8888 | .typed<huber_loss_backward_out::schema>(); |
| 8889 | } |
| 8890 | |
| 8891 | // aten::huber_loss_backward.out(Tensor grad_output, Tensor self, Tensor target, int reduction, float delta, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 8892 | at::Tensor & huber_loss_backward_out::call(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta, at::Tensor & grad_input) { |
| 8893 | |
| 8894 | static auto op = create_huber_loss_backward_out_typed_handle(); |
| 8895 | return op.call(grad_output, self, target, reduction, delta, grad_input); |
| 8896 | } |
| 8897 | |
| 8898 | // aten::huber_loss_backward.out(Tensor grad_output, Tensor self, Tensor target, int reduction, float delta, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 8899 | at::Tensor & huber_loss_backward_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta, at::Tensor & grad_input) { |
| 8900 | |
| 8901 | static auto op = create_huber_loss_backward_out_typed_handle(); |
| 8902 | return op.redispatch(dispatchKeySet, grad_output, self, target, reduction, delta, grad_input); |
| 8903 | } |
| 8904 | |
| 8905 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(huber_loss_backward, name, "aten::huber_loss_backward") |
| 8906 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(huber_loss_backward, overload_name, "") |
| 8907 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(huber_loss_backward, schema_str, "huber_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, float delta) -> Tensor") |
| 8908 | |
| 8909 | // aten::huber_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, float delta) -> Tensor |
| 8910 | static C10_NOINLINE c10::TypedOperatorHandle<huber_loss_backward::schema> create_huber_loss_backward_typed_handle() { |
| 8911 | return c10::Dispatcher::singleton() |
| 8912 | .findSchemaOrThrow(huber_loss_backward::name, huber_loss_backward::overload_name) |
| 8913 | .typed<huber_loss_backward::schema>(); |
| 8914 | } |
| 8915 | |
| 8916 | // aten::huber_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, float delta) -> Tensor |
| 8917 | at::Tensor huber_loss_backward::call(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta) { |
| 8918 | |
| 8919 | static auto op = create_huber_loss_backward_typed_handle(); |
| 8920 | return op.call(grad_output, self, target, reduction, delta); |
| 8921 | } |
| 8922 | |
| 8923 | // aten::huber_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, float delta) -> Tensor |
| 8924 | at::Tensor huber_loss_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta) { |
| 8925 | |
| 8926 | static auto op = create_huber_loss_backward_typed_handle(); |
| 8927 | return op.redispatch(dispatchKeySet, grad_output, self, target, reduction, delta); |
| 8928 | } |
| 8929 | |
| 8930 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardsigmoid_out, name, "aten::hardsigmoid") |
| 8931 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardsigmoid_out, overload_name, "out") |
| 8932 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardsigmoid_out, schema_str, "hardsigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 8933 | |
| 8934 | // aten::hardsigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 8935 | static C10_NOINLINE c10::TypedOperatorHandle<hardsigmoid_out::schema> create_hardsigmoid_out_typed_handle() { |
| 8936 | return c10::Dispatcher::singleton() |
| 8937 | .findSchemaOrThrow(hardsigmoid_out::name, hardsigmoid_out::overload_name) |
| 8938 | .typed<hardsigmoid_out::schema>(); |
| 8939 | } |
| 8940 | |
| 8941 | // aten::hardsigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 8942 | at::Tensor & hardsigmoid_out::call(const at::Tensor & self, at::Tensor & out) { |
| 8943 | |
| 8944 | static auto op = create_hardsigmoid_out_typed_handle(); |
| 8945 | return op.call(self, out); |
| 8946 | } |
| 8947 | |
| 8948 | // aten::hardsigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 8949 | at::Tensor & hardsigmoid_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 8950 | |
| 8951 | static auto op = create_hardsigmoid_out_typed_handle(); |
| 8952 | return op.redispatch(dispatchKeySet, self, out); |
| 8953 | } |
| 8954 | |
| 8955 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardsigmoid, name, "aten::hardsigmoid") |
| 8956 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardsigmoid, overload_name, "") |
| 8957 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardsigmoid, schema_str, "hardsigmoid(Tensor self) -> Tensor") |
| 8958 | |
| 8959 | // aten::hardsigmoid(Tensor self) -> Tensor |
| 8960 | static C10_NOINLINE c10::TypedOperatorHandle<hardsigmoid::schema> create_hardsigmoid_typed_handle() { |
| 8961 | return c10::Dispatcher::singleton() |
| 8962 | .findSchemaOrThrow(hardsigmoid::name, hardsigmoid::overload_name) |
| 8963 | .typed<hardsigmoid::schema>(); |
| 8964 | } |
| 8965 | |
| 8966 | // aten::hardsigmoid(Tensor self) -> Tensor |
| 8967 | at::Tensor hardsigmoid::call(const at::Tensor & self) { |
| 8968 | |
| 8969 | static auto op = create_hardsigmoid_typed_handle(); |
| 8970 | return op.call(self); |
| 8971 | } |
| 8972 | |
| 8973 | // aten::hardsigmoid(Tensor self) -> Tensor |
| 8974 | at::Tensor hardsigmoid::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 8975 | |
| 8976 | static auto op = create_hardsigmoid_typed_handle(); |
| 8977 | return op.redispatch(dispatchKeySet, self); |
| 8978 | } |
| 8979 | |
| 8980 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardsigmoid_, name, "aten::hardsigmoid_") |
| 8981 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardsigmoid_, overload_name, "") |
| 8982 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardsigmoid_, schema_str, "hardsigmoid_(Tensor(a!) self) -> Tensor(a!)") |
| 8983 | |
| 8984 | // aten::hardsigmoid_(Tensor(a!) self) -> Tensor(a!) |
| 8985 | static C10_NOINLINE c10::TypedOperatorHandle<hardsigmoid_::schema> create_hardsigmoid__typed_handle() { |
| 8986 | return c10::Dispatcher::singleton() |
| 8987 | .findSchemaOrThrow(hardsigmoid_::name, hardsigmoid_::overload_name) |
| 8988 | .typed<hardsigmoid_::schema>(); |
| 8989 | } |
| 8990 | |
| 8991 | // aten::hardsigmoid_(Tensor(a!) self) -> Tensor(a!) |
| 8992 | at::Tensor & hardsigmoid_::call(at::Tensor & self) { |
| 8993 | |
| 8994 | static auto op = create_hardsigmoid__typed_handle(); |
| 8995 | return op.call(self); |
| 8996 | } |
| 8997 | |
| 8998 | // aten::hardsigmoid_(Tensor(a!) self) -> Tensor(a!) |
| 8999 | at::Tensor & hardsigmoid_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 9000 | |
| 9001 | static auto op = create_hardsigmoid__typed_handle(); |
| 9002 | return op.redispatch(dispatchKeySet, self); |
| 9003 | } |
| 9004 | |
| 9005 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(log_sigmoid_out, name, "aten::log_sigmoid") |
| 9006 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(log_sigmoid_out, overload_name, "out") |
| 9007 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(log_sigmoid_out, schema_str, "log_sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 9008 | |
| 9009 | // aten::log_sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 9010 | static C10_NOINLINE c10::TypedOperatorHandle<log_sigmoid_out::schema> create_log_sigmoid_out_typed_handle() { |
| 9011 | return c10::Dispatcher::singleton() |
| 9012 | .findSchemaOrThrow(log_sigmoid_out::name, log_sigmoid_out::overload_name) |
| 9013 | .typed<log_sigmoid_out::schema>(); |
| 9014 | } |
| 9015 | |
| 9016 | // aten::log_sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 9017 | at::Tensor & log_sigmoid_out::call(const at::Tensor & self, at::Tensor & out) { |
| 9018 | |
| 9019 | static auto op = create_log_sigmoid_out_typed_handle(); |
| 9020 | return op.call(self, out); |
| 9021 | } |
| 9022 | |
| 9023 | // aten::log_sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 9024 | at::Tensor & log_sigmoid_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 9025 | |
| 9026 | static auto op = create_log_sigmoid_out_typed_handle(); |
| 9027 | return op.redispatch(dispatchKeySet, self, out); |
| 9028 | } |
| 9029 | |
| 9030 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(log_sigmoid, name, "aten::log_sigmoid") |
| 9031 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(log_sigmoid, overload_name, "") |
| 9032 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(log_sigmoid, schema_str, "log_sigmoid(Tensor self) -> Tensor") |
| 9033 | |
| 9034 | // aten::log_sigmoid(Tensor self) -> Tensor |
| 9035 | static C10_NOINLINE c10::TypedOperatorHandle<log_sigmoid::schema> create_log_sigmoid_typed_handle() { |
| 9036 | return c10::Dispatcher::singleton() |
| 9037 | .findSchemaOrThrow(log_sigmoid::name, log_sigmoid::overload_name) |
| 9038 | .typed<log_sigmoid::schema>(); |
| 9039 | } |
| 9040 | |
| 9041 | // aten::log_sigmoid(Tensor self) -> Tensor |
| 9042 | at::Tensor log_sigmoid::call(const at::Tensor & self) { |
| 9043 | |
| 9044 | static auto op = create_log_sigmoid_typed_handle(); |
| 9045 | return op.call(self); |
| 9046 | } |
| 9047 | |
| 9048 | // aten::log_sigmoid(Tensor self) -> Tensor |
| 9049 | at::Tensor log_sigmoid::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 9050 | |
| 9051 | static auto op = create_log_sigmoid_typed_handle(); |
| 9052 | return op.redispatch(dispatchKeySet, self); |
| 9053 | } |
| 9054 | |
| 9055 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_avg_pool2d_out, name, "aten::adaptive_avg_pool2d") |
| 9056 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_avg_pool2d_out, overload_name, "out") |
| 9057 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_avg_pool2d_out, schema_str, "adaptive_avg_pool2d.out(Tensor self, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!)") |
| 9058 | |
| 9059 | // aten::adaptive_avg_pool2d.out(Tensor self, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!) |
| 9060 | static C10_NOINLINE c10::TypedOperatorHandle<adaptive_avg_pool2d_out::schema> create_adaptive_avg_pool2d_out_typed_handle() { |
| 9061 | return c10::Dispatcher::singleton() |
| 9062 | .findSchemaOrThrow(adaptive_avg_pool2d_out::name, adaptive_avg_pool2d_out::overload_name) |
| 9063 | .typed<adaptive_avg_pool2d_out::schema>(); |
| 9064 | } |
| 9065 | |
| 9066 | // aten::adaptive_avg_pool2d.out(Tensor self, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!) |
| 9067 | at::Tensor & adaptive_avg_pool2d_out::call(const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out) { |
| 9068 | |
| 9069 | static auto op = create_adaptive_avg_pool2d_out_typed_handle(); |
| 9070 | return op.call(self, output_size, out); |
| 9071 | } |
| 9072 | |
| 9073 | // aten::adaptive_avg_pool2d.out(Tensor self, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!) |
| 9074 | at::Tensor & adaptive_avg_pool2d_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out) { |
| 9075 | |
| 9076 | static auto op = create_adaptive_avg_pool2d_out_typed_handle(); |
| 9077 | return op.redispatch(dispatchKeySet, self, output_size, out); |
| 9078 | } |
| 9079 | |
| 9080 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_avg_pool2d, name, "aten::adaptive_avg_pool2d") |
| 9081 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_avg_pool2d, overload_name, "") |
| 9082 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_avg_pool2d, schema_str, "adaptive_avg_pool2d(Tensor self, SymInt[2] output_size) -> Tensor") |
| 9083 | |
| 9084 | // aten::adaptive_avg_pool2d(Tensor self, SymInt[2] output_size) -> Tensor |
| 9085 | static C10_NOINLINE c10::TypedOperatorHandle<adaptive_avg_pool2d::schema> create_adaptive_avg_pool2d_typed_handle() { |
| 9086 | return c10::Dispatcher::singleton() |
| 9087 | .findSchemaOrThrow(adaptive_avg_pool2d::name, adaptive_avg_pool2d::overload_name) |
| 9088 | .typed<adaptive_avg_pool2d::schema>(); |
| 9089 | } |
| 9090 | |
| 9091 | // aten::adaptive_avg_pool2d(Tensor self, SymInt[2] output_size) -> Tensor |
| 9092 | at::Tensor adaptive_avg_pool2d::call(const at::Tensor & self, c10::SymIntArrayRef output_size) { |
| 9093 | |
| 9094 | static auto op = create_adaptive_avg_pool2d_typed_handle(); |
| 9095 | return op.call(self, output_size); |
| 9096 | } |
| 9097 | |
| 9098 | // aten::adaptive_avg_pool2d(Tensor self, SymInt[2] output_size) -> Tensor |
| 9099 | at::Tensor adaptive_avg_pool2d::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size) { |
| 9100 | |
| 9101 | static auto op = create_adaptive_avg_pool2d_typed_handle(); |
| 9102 | return op.redispatch(dispatchKeySet, self, output_size); |
| 9103 | } |
| 9104 | |
| 9105 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_avg_pool3d_out, name, "aten::adaptive_avg_pool3d") |
| 9106 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_avg_pool3d_out, overload_name, "out") |
| 9107 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_avg_pool3d_out, schema_str, "adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!)") |
| 9108 | |
| 9109 | // aten::adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!) |
| 9110 | static C10_NOINLINE c10::TypedOperatorHandle<adaptive_avg_pool3d_out::schema> create_adaptive_avg_pool3d_out_typed_handle() { |
| 9111 | return c10::Dispatcher::singleton() |
| 9112 | .findSchemaOrThrow(adaptive_avg_pool3d_out::name, adaptive_avg_pool3d_out::overload_name) |
| 9113 | .typed<adaptive_avg_pool3d_out::schema>(); |
| 9114 | } |
| 9115 | |
| 9116 | // aten::adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!) |
| 9117 | at::Tensor & adaptive_avg_pool3d_out::call(const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out) { |
| 9118 | |
| 9119 | static auto op = create_adaptive_avg_pool3d_out_typed_handle(); |
| 9120 | return op.call(self, output_size, out); |
| 9121 | } |
| 9122 | |
| 9123 | // aten::adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!) |
| 9124 | at::Tensor & adaptive_avg_pool3d_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out) { |
| 9125 | |
| 9126 | static auto op = create_adaptive_avg_pool3d_out_typed_handle(); |
| 9127 | return op.redispatch(dispatchKeySet, self, output_size, out); |
| 9128 | } |
| 9129 | |
| 9130 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_avg_pool3d, name, "aten::adaptive_avg_pool3d") |
| 9131 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_avg_pool3d, overload_name, "") |
| 9132 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_avg_pool3d, schema_str, "adaptive_avg_pool3d(Tensor self, SymInt[3] output_size) -> Tensor") |
| 9133 | |
| 9134 | // aten::adaptive_avg_pool3d(Tensor self, SymInt[3] output_size) -> Tensor |
| 9135 | static C10_NOINLINE c10::TypedOperatorHandle<adaptive_avg_pool3d::schema> create_adaptive_avg_pool3d_typed_handle() { |
| 9136 | return c10::Dispatcher::singleton() |
| 9137 | .findSchemaOrThrow(adaptive_avg_pool3d::name, adaptive_avg_pool3d::overload_name) |
| 9138 | .typed<adaptive_avg_pool3d::schema>(); |
| 9139 | } |
| 9140 | |
| 9141 | // aten::adaptive_avg_pool3d(Tensor self, SymInt[3] output_size) -> Tensor |
| 9142 | at::Tensor adaptive_avg_pool3d::call(const at::Tensor & self, c10::SymIntArrayRef output_size) { |
| 9143 | |
| 9144 | static auto op = create_adaptive_avg_pool3d_typed_handle(); |
| 9145 | return op.call(self, output_size); |
| 9146 | } |
| 9147 | |
| 9148 | // aten::adaptive_avg_pool3d(Tensor self, SymInt[3] output_size) -> Tensor |
| 9149 | at::Tensor adaptive_avg_pool3d::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size) { |
| 9150 | |
| 9151 | static auto op = create_adaptive_avg_pool3d_typed_handle(); |
| 9152 | return op.redispatch(dispatchKeySet, self, output_size); |
| 9153 | } |
| 9154 | |
| 9155 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_adaptive_avg_pool3d, name, "aten::_adaptive_avg_pool3d") |
| 9156 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_adaptive_avg_pool3d, overload_name, "") |
| 9157 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_adaptive_avg_pool3d, schema_str, "_adaptive_avg_pool3d(Tensor self, SymInt[3] output_size) -> Tensor") |
| 9158 | |
| 9159 | // aten::_adaptive_avg_pool3d(Tensor self, SymInt[3] output_size) -> Tensor |
| 9160 | static C10_NOINLINE c10::TypedOperatorHandle<_adaptive_avg_pool3d::schema> create__adaptive_avg_pool3d_typed_handle() { |
| 9161 | return c10::Dispatcher::singleton() |
| 9162 | .findSchemaOrThrow(_adaptive_avg_pool3d::name, _adaptive_avg_pool3d::overload_name) |
| 9163 | .typed<_adaptive_avg_pool3d::schema>(); |
| 9164 | } |
| 9165 | |
| 9166 | // aten::_adaptive_avg_pool3d(Tensor self, SymInt[3] output_size) -> Tensor |
| 9167 | at::Tensor _adaptive_avg_pool3d::call(const at::Tensor & self, c10::SymIntArrayRef output_size) { |
| 9168 | |
| 9169 | static auto op = create__adaptive_avg_pool3d_typed_handle(); |
| 9170 | return op.call(self, output_size); |
| 9171 | } |
| 9172 | |
| 9173 | // aten::_adaptive_avg_pool3d(Tensor self, SymInt[3] output_size) -> Tensor |
| 9174 | at::Tensor _adaptive_avg_pool3d::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size) { |
| 9175 | |
| 9176 | static auto op = create__adaptive_avg_pool3d_typed_handle(); |
| 9177 | return op.redispatch(dispatchKeySet, self, output_size); |
| 9178 | } |
| 9179 | |
| 9180 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_max_pool2d_out, name, "aten::adaptive_max_pool2d") |
| 9181 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_max_pool2d_out, overload_name, "out") |
| 9182 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_max_pool2d_out, schema_str, "adaptive_max_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))") |
| 9183 | |
| 9184 | // aten::adaptive_max_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) |
| 9185 | static C10_NOINLINE c10::TypedOperatorHandle<adaptive_max_pool2d_out::schema> create_adaptive_max_pool2d_out_typed_handle() { |
| 9186 | return c10::Dispatcher::singleton() |
| 9187 | .findSchemaOrThrow(adaptive_max_pool2d_out::name, adaptive_max_pool2d_out::overload_name) |
| 9188 | .typed<adaptive_max_pool2d_out::schema>(); |
| 9189 | } |
| 9190 | |
| 9191 | // aten::adaptive_max_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) |
| 9192 | ::std::tuple<at::Tensor &,at::Tensor &> adaptive_max_pool2d_out::call(const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out, at::Tensor & indices) { |
| 9193 | |
| 9194 | static auto op = create_adaptive_max_pool2d_out_typed_handle(); |
| 9195 | return op.call(self, output_size, out, indices); |
| 9196 | } |
| 9197 | |
| 9198 | // aten::adaptive_max_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) |
| 9199 | ::std::tuple<at::Tensor &,at::Tensor &> adaptive_max_pool2d_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out, at::Tensor & indices) { |
| 9200 | |
| 9201 | static auto op = create_adaptive_max_pool2d_out_typed_handle(); |
| 9202 | return op.redispatch(dispatchKeySet, self, output_size, out, indices); |
| 9203 | } |
| 9204 | |
| 9205 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_max_pool2d, name, "aten::adaptive_max_pool2d") |
| 9206 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_max_pool2d, overload_name, "") |
| 9207 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_max_pool2d, schema_str, "adaptive_max_pool2d(Tensor self, int[2] output_size) -> (Tensor, Tensor)") |
| 9208 | |
| 9209 | // aten::adaptive_max_pool2d(Tensor self, int[2] output_size) -> (Tensor, Tensor) |
| 9210 | static C10_NOINLINE c10::TypedOperatorHandle<adaptive_max_pool2d::schema> create_adaptive_max_pool2d_typed_handle() { |
| 9211 | return c10::Dispatcher::singleton() |
| 9212 | .findSchemaOrThrow(adaptive_max_pool2d::name, adaptive_max_pool2d::overload_name) |
| 9213 | .typed<adaptive_max_pool2d::schema>(); |
| 9214 | } |
| 9215 | |
| 9216 | // aten::adaptive_max_pool2d(Tensor self, int[2] output_size) -> (Tensor, Tensor) |
| 9217 | ::std::tuple<at::Tensor,at::Tensor> adaptive_max_pool2d::call(const at::Tensor & self, at::IntArrayRef output_size) { |
| 9218 | |
| 9219 | static auto op = create_adaptive_max_pool2d_typed_handle(); |
| 9220 | return op.call(self, output_size); |
| 9221 | } |
| 9222 | |
| 9223 | // aten::adaptive_max_pool2d(Tensor self, int[2] output_size) -> (Tensor, Tensor) |
| 9224 | ::std::tuple<at::Tensor,at::Tensor> adaptive_max_pool2d::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size) { |
| 9225 | |
| 9226 | static auto op = create_adaptive_max_pool2d_typed_handle(); |
| 9227 | return op.redispatch(dispatchKeySet, self, output_size); |
| 9228 | } |
| 9229 | |
| 9230 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_max_pool3d_out, name, "aten::adaptive_max_pool3d") |
| 9231 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_max_pool3d_out, overload_name, "out") |
| 9232 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_max_pool3d_out, schema_str, "adaptive_max_pool3d.out(Tensor self, int[3] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))") |
| 9233 | |
| 9234 | // aten::adaptive_max_pool3d.out(Tensor self, int[3] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) |
| 9235 | static C10_NOINLINE c10::TypedOperatorHandle<adaptive_max_pool3d_out::schema> create_adaptive_max_pool3d_out_typed_handle() { |
| 9236 | return c10::Dispatcher::singleton() |
| 9237 | .findSchemaOrThrow(adaptive_max_pool3d_out::name, adaptive_max_pool3d_out::overload_name) |
| 9238 | .typed<adaptive_max_pool3d_out::schema>(); |
| 9239 | } |
| 9240 | |
| 9241 | // aten::adaptive_max_pool3d.out(Tensor self, int[3] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) |
| 9242 | ::std::tuple<at::Tensor &,at::Tensor &> adaptive_max_pool3d_out::call(const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out, at::Tensor & indices) { |
| 9243 | |
| 9244 | static auto op = create_adaptive_max_pool3d_out_typed_handle(); |
| 9245 | return op.call(self, output_size, out, indices); |
| 9246 | } |
| 9247 | |
| 9248 | // aten::adaptive_max_pool3d.out(Tensor self, int[3] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) |
| 9249 | ::std::tuple<at::Tensor &,at::Tensor &> adaptive_max_pool3d_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out, at::Tensor & indices) { |
| 9250 | |
| 9251 | static auto op = create_adaptive_max_pool3d_out_typed_handle(); |
| 9252 | return op.redispatch(dispatchKeySet, self, output_size, out, indices); |
| 9253 | } |
| 9254 | |
| 9255 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_max_pool3d, name, "aten::adaptive_max_pool3d") |
| 9256 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_max_pool3d, overload_name, "") |
| 9257 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_max_pool3d, schema_str, "adaptive_max_pool3d(Tensor self, int[3] output_size) -> (Tensor, Tensor)") |
| 9258 | |
| 9259 | // aten::adaptive_max_pool3d(Tensor self, int[3] output_size) -> (Tensor, Tensor) |
| 9260 | static C10_NOINLINE c10::TypedOperatorHandle<adaptive_max_pool3d::schema> create_adaptive_max_pool3d_typed_handle() { |
| 9261 | return c10::Dispatcher::singleton() |
| 9262 | .findSchemaOrThrow(adaptive_max_pool3d::name, adaptive_max_pool3d::overload_name) |
| 9263 | .typed<adaptive_max_pool3d::schema>(); |
| 9264 | } |
| 9265 | |
| 9266 | // aten::adaptive_max_pool3d(Tensor self, int[3] output_size) -> (Tensor, Tensor) |
| 9267 | ::std::tuple<at::Tensor,at::Tensor> adaptive_max_pool3d::call(const at::Tensor & self, at::IntArrayRef output_size) { |
| 9268 | |
| 9269 | static auto op = create_adaptive_max_pool3d_typed_handle(); |
| 9270 | return op.call(self, output_size); |
| 9271 | } |
| 9272 | |
| 9273 | // aten::adaptive_max_pool3d(Tensor self, int[3] output_size) -> (Tensor, Tensor) |
| 9274 | ::std::tuple<at::Tensor,at::Tensor> adaptive_max_pool3d::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size) { |
| 9275 | |
| 9276 | static auto op = create_adaptive_max_pool3d_typed_handle(); |
| 9277 | return op.redispatch(dispatchKeySet, self, output_size); |
| 9278 | } |
| 9279 | |
| 9280 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(avg_pool2d_backward_grad_input, name, "aten::avg_pool2d_backward") |
| 9281 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(avg_pool2d_backward_grad_input, overload_name, "grad_input") |
| 9282 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(avg_pool2d_backward_grad_input, schema_str, "avg_pool2d_backward.grad_input(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, bool ceil_mode, bool count_include_pad, int? divisor_override, *, Tensor(a!) grad_input) -> Tensor(a!)") |
| 9283 | |
| 9284 | // aten::avg_pool2d_backward.grad_input(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, bool ceil_mode, bool count_include_pad, int? divisor_override, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 9285 | static C10_NOINLINE c10::TypedOperatorHandle<avg_pool2d_backward_grad_input::schema> create_avg_pool2d_backward_grad_input_typed_handle() { |
| 9286 | return c10::Dispatcher::singleton() |
| 9287 | .findSchemaOrThrow(avg_pool2d_backward_grad_input::name, avg_pool2d_backward_grad_input::overload_name) |
| 9288 | .typed<avg_pool2d_backward_grad_input::schema>(); |
| 9289 | } |
| 9290 | |
| 9291 | // aten::avg_pool2d_backward.grad_input(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, bool ceil_mode, bool count_include_pad, int? divisor_override, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 9292 | at::Tensor & avg_pool2d_backward_grad_input::call(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) { |
| 9293 | |
| 9294 | static auto op = create_avg_pool2d_backward_grad_input_typed_handle(); |
| 9295 | return op.call(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, grad_input); |
| 9296 | } |
| 9297 | |
| 9298 | // aten::avg_pool2d_backward.grad_input(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, bool ceil_mode, bool count_include_pad, int? divisor_override, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 9299 | at::Tensor & avg_pool2d_backward_grad_input::redispatch(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) { |
| 9300 | |
| 9301 | static auto op = create_avg_pool2d_backward_grad_input_typed_handle(); |
| 9302 | return op.redispatch(dispatchKeySet, grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, grad_input); |
| 9303 | } |
| 9304 | |
| 9305 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(avg_pool2d_backward, name, "aten::avg_pool2d_backward") |
| 9306 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(avg_pool2d_backward, overload_name, "") |
| 9307 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(avg_pool2d_backward, schema_str, "avg_pool2d_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, bool ceil_mode, bool count_include_pad, int? divisor_override) -> Tensor") |
| 9308 | |
| 9309 | // aten::avg_pool2d_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, bool ceil_mode, bool count_include_pad, int? divisor_override) -> Tensor |
| 9310 | static C10_NOINLINE c10::TypedOperatorHandle<avg_pool2d_backward::schema> create_avg_pool2d_backward_typed_handle() { |
| 9311 | return c10::Dispatcher::singleton() |
| 9312 | .findSchemaOrThrow(avg_pool2d_backward::name, avg_pool2d_backward::overload_name) |
| 9313 | .typed<avg_pool2d_backward::schema>(); |
| 9314 | } |
| 9315 | |
| 9316 | // aten::avg_pool2d_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, bool ceil_mode, bool count_include_pad, int? divisor_override) -> Tensor |
| 9317 | at::Tensor avg_pool2d_backward::call(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) { |
| 9318 | |
| 9319 | static auto op = create_avg_pool2d_backward_typed_handle(); |
| 9320 | return op.call(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); |
| 9321 | } |
| 9322 | |
| 9323 | // aten::avg_pool2d_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, bool ceil_mode, bool count_include_pad, int? divisor_override) -> Tensor |
| 9324 | at::Tensor avg_pool2d_backward::redispatch(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) { |
| 9325 | |
| 9326 | static auto op = create_avg_pool2d_backward_typed_handle(); |
| 9327 | return op.redispatch(dispatchKeySet, grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); |
| 9328 | } |
| 9329 | |
| 9330 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fractional_max_pool2d_output, name, "aten::fractional_max_pool2d") |
| 9331 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fractional_max_pool2d_output, overload_name, "output") |
| 9332 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fractional_max_pool2d_output, schema_str, "fractional_max_pool2d.output(Tensor self, int[2] kernel_size, int[2] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))") |
| 9333 | |
| 9334 | // aten::fractional_max_pool2d.output(Tensor self, int[2] kernel_size, int[2] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) |
| 9335 | static C10_NOINLINE c10::TypedOperatorHandle<fractional_max_pool2d_output::schema> create_fractional_max_pool2d_output_typed_handle() { |
| 9336 | return c10::Dispatcher::singleton() |
| 9337 | .findSchemaOrThrow(fractional_max_pool2d_output::name, fractional_max_pool2d_output::overload_name) |
| 9338 | .typed<fractional_max_pool2d_output::schema>(); |
| 9339 | } |
| 9340 | |
| 9341 | // aten::fractional_max_pool2d.output(Tensor self, int[2] kernel_size, int[2] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) |
| 9342 | ::std::tuple<at::Tensor &,at::Tensor &> fractional_max_pool2d_output::call(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples, at::Tensor & output, at::Tensor & indices) { |
| 9343 | |
| 9344 | static auto op = create_fractional_max_pool2d_output_typed_handle(); |
| 9345 | return op.call(self, kernel_size, output_size, random_samples, output, indices); |
| 9346 | } |
| 9347 | |
| 9348 | // aten::fractional_max_pool2d.output(Tensor self, int[2] kernel_size, int[2] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) |
| 9349 | ::std::tuple<at::Tensor &,at::Tensor &> fractional_max_pool2d_output::redispatch(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) { |
| 9350 | |
| 9351 | static auto op = create_fractional_max_pool2d_output_typed_handle(); |
| 9352 | return op.redispatch(dispatchKeySet, self, kernel_size, output_size, random_samples, output, indices); |
| 9353 | } |
| 9354 | |
| 9355 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fractional_max_pool2d, name, "aten::fractional_max_pool2d") |
| 9356 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fractional_max_pool2d, overload_name, "") |
| 9357 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fractional_max_pool2d, schema_str, "fractional_max_pool2d(Tensor self, int[2] kernel_size, int[2] output_size, Tensor random_samples) -> (Tensor, Tensor)") |
| 9358 | |
| 9359 | // aten::fractional_max_pool2d(Tensor self, int[2] kernel_size, int[2] output_size, Tensor random_samples) -> (Tensor, Tensor) |
| 9360 | static C10_NOINLINE c10::TypedOperatorHandle<fractional_max_pool2d::schema> create_fractional_max_pool2d_typed_handle() { |
| 9361 | return c10::Dispatcher::singleton() |
| 9362 | .findSchemaOrThrow(fractional_max_pool2d::name, fractional_max_pool2d::overload_name) |
| 9363 | .typed<fractional_max_pool2d::schema>(); |
| 9364 | } |
| 9365 | |
| 9366 | // aten::fractional_max_pool2d(Tensor self, int[2] kernel_size, int[2] output_size, Tensor random_samples) -> (Tensor, Tensor) |
| 9367 | ::std::tuple<at::Tensor,at::Tensor> fractional_max_pool2d::call(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples) { |
| 9368 | |
| 9369 | static auto op = create_fractional_max_pool2d_typed_handle(); |
| 9370 | return op.call(self, kernel_size, output_size, random_samples); |
| 9371 | } |
| 9372 | |
| 9373 | // aten::fractional_max_pool2d(Tensor self, int[2] kernel_size, int[2] output_size, Tensor random_samples) -> (Tensor, Tensor) |
| 9374 | ::std::tuple<at::Tensor,at::Tensor> fractional_max_pool2d::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples) { |
| 9375 | |
| 9376 | static auto op = create_fractional_max_pool2d_typed_handle(); |
| 9377 | return op.redispatch(dispatchKeySet, self, kernel_size, output_size, random_samples); |
| 9378 | } |
| 9379 | |
| 9380 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(max_unpool2d_out, name, "aten::max_unpool2d") |
| 9381 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(max_unpool2d_out, overload_name, "out") |
| 9382 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(max_unpool2d_out, schema_str, "max_unpool2d.out(Tensor self, Tensor indices, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!)") |
| 9383 | |
| 9384 | // aten::max_unpool2d.out(Tensor self, Tensor indices, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!) |
| 9385 | static C10_NOINLINE c10::TypedOperatorHandle<max_unpool2d_out::schema> create_max_unpool2d_out_typed_handle() { |
| 9386 | return c10::Dispatcher::singleton() |
| 9387 | .findSchemaOrThrow(max_unpool2d_out::name, max_unpool2d_out::overload_name) |
| 9388 | .typed<max_unpool2d_out::schema>(); |
| 9389 | } |
| 9390 | |
| 9391 | // aten::max_unpool2d.out(Tensor self, Tensor indices, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!) |
| 9392 | at::Tensor & max_unpool2d_out::call(const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::Tensor & out) { |
| 9393 | |
| 9394 | static auto op = create_max_unpool2d_out_typed_handle(); |
| 9395 | return op.call(self, indices, output_size, out); |
| 9396 | } |
| 9397 | |
| 9398 | // aten::max_unpool2d.out(Tensor self, Tensor indices, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!) |
| 9399 | at::Tensor & max_unpool2d_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::Tensor & out) { |
| 9400 | |
| 9401 | static auto op = create_max_unpool2d_out_typed_handle(); |
| 9402 | return op.redispatch(dispatchKeySet, self, indices, output_size, out); |
| 9403 | } |
| 9404 | |
| 9405 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(max_unpool2d, name, "aten::max_unpool2d") |
| 9406 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(max_unpool2d, overload_name, "") |
| 9407 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(max_unpool2d, schema_str, "max_unpool2d(Tensor self, Tensor indices, int[2] output_size) -> Tensor") |
| 9408 | |
| 9409 | // aten::max_unpool2d(Tensor self, Tensor indices, int[2] output_size) -> Tensor |
| 9410 | static C10_NOINLINE c10::TypedOperatorHandle<max_unpool2d::schema> create_max_unpool2d_typed_handle() { |
| 9411 | return c10::Dispatcher::singleton() |
| 9412 | .findSchemaOrThrow(max_unpool2d::name, max_unpool2d::overload_name) |
| 9413 | .typed<max_unpool2d::schema>(); |
| 9414 | } |
| 9415 | |
| 9416 | // aten::max_unpool2d(Tensor self, Tensor indices, int[2] output_size) -> Tensor |
| 9417 | at::Tensor max_unpool2d::call(const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size) { |
| 9418 | |
| 9419 | static auto op = create_max_unpool2d_typed_handle(); |
| 9420 | return op.call(self, indices, output_size); |
| 9421 | } |
| 9422 | |
| 9423 | // aten::max_unpool2d(Tensor self, Tensor indices, int[2] output_size) -> Tensor |
| 9424 | at::Tensor max_unpool2d::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size) { |
| 9425 | |
| 9426 | static auto op = create_max_unpool2d_typed_handle(); |
| 9427 | return op.redispatch(dispatchKeySet, self, indices, output_size); |
| 9428 | } |
| 9429 | |
| 9430 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(max_unpool3d_out, name, "aten::max_unpool3d") |
| 9431 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(max_unpool3d_out, overload_name, "out") |
| 9432 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(max_unpool3d_out, schema_str, "max_unpool3d.out(Tensor self, Tensor indices, int[3] output_size, int[3] stride, int[3] padding, *, Tensor(a!) out) -> Tensor(a!)") |
| 9433 | |
| 9434 | // aten::max_unpool3d.out(Tensor self, Tensor indices, int[3] output_size, int[3] stride, int[3] padding, *, Tensor(a!) out) -> Tensor(a!) |
| 9435 | static C10_NOINLINE c10::TypedOperatorHandle<max_unpool3d_out::schema> create_max_unpool3d_out_typed_handle() { |
| 9436 | return c10::Dispatcher::singleton() |
| 9437 | .findSchemaOrThrow(max_unpool3d_out::name, max_unpool3d_out::overload_name) |
| 9438 | .typed<max_unpool3d_out::schema>(); |
| 9439 | } |
| 9440 | |
| 9441 | // aten::max_unpool3d.out(Tensor self, Tensor indices, int[3] output_size, int[3] stride, int[3] padding, *, Tensor(a!) out) -> Tensor(a!) |
| 9442 | at::Tensor & max_unpool3d_out::call(const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & out) { |
| 9443 | |
| 9444 | static auto op = create_max_unpool3d_out_typed_handle(); |
| 9445 | return op.call(self, indices, output_size, stride, padding, out); |
| 9446 | } |
| 9447 | |
| 9448 | // aten::max_unpool3d.out(Tensor self, Tensor indices, int[3] output_size, int[3] stride, int[3] padding, *, Tensor(a!) out) -> Tensor(a!) |
| 9449 | at::Tensor & max_unpool3d_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & out) { |
| 9450 | |
| 9451 | static auto op = create_max_unpool3d_out_typed_handle(); |
| 9452 | return op.redispatch(dispatchKeySet, self, indices, output_size, stride, padding, out); |
| 9453 | } |
| 9454 | |
| 9455 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(max_unpool3d, name, "aten::max_unpool3d") |
| 9456 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(max_unpool3d, overload_name, "") |
| 9457 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(max_unpool3d, schema_str, "max_unpool3d(Tensor self, Tensor indices, int[3] output_size, int[3] stride, int[3] padding) -> Tensor") |
| 9458 | |
| 9459 | // aten::max_unpool3d(Tensor self, Tensor indices, int[3] output_size, int[3] stride, int[3] padding) -> Tensor |
| 9460 | static C10_NOINLINE c10::TypedOperatorHandle<max_unpool3d::schema> create_max_unpool3d_typed_handle() { |
| 9461 | return c10::Dispatcher::singleton() |
| 9462 | .findSchemaOrThrow(max_unpool3d::name, max_unpool3d::overload_name) |
| 9463 | .typed<max_unpool3d::schema>(); |
| 9464 | } |
| 9465 | |
| 9466 | // aten::max_unpool3d(Tensor self, Tensor indices, int[3] output_size, int[3] stride, int[3] padding) -> Tensor |
| 9467 | at::Tensor max_unpool3d::call(const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding) { |
| 9468 | |
| 9469 | static auto op = create_max_unpool3d_typed_handle(); |
| 9470 | return op.call(self, indices, output_size, stride, padding); |
| 9471 | } |
| 9472 | |
| 9473 | // aten::max_unpool3d(Tensor self, Tensor indices, int[3] output_size, int[3] stride, int[3] padding) -> Tensor |
| 9474 | at::Tensor max_unpool3d::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding) { |
| 9475 | |
| 9476 | static auto op = create_max_unpool3d_typed_handle(); |
| 9477 | return op.redispatch(dispatchKeySet, self, indices, output_size, stride, padding); |
| 9478 | } |
| 9479 | |
| 9480 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(reflection_pad3d_backward_grad_input, name, "aten::reflection_pad3d_backward") |
| 9481 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(reflection_pad3d_backward_grad_input, overload_name, "grad_input") |
| 9482 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(reflection_pad3d_backward_grad_input, schema_str, "reflection_pad3d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!)") |
| 9483 | |
| 9484 | // aten::reflection_pad3d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 9485 | static C10_NOINLINE c10::TypedOperatorHandle<reflection_pad3d_backward_grad_input::schema> create_reflection_pad3d_backward_grad_input_typed_handle() { |
| 9486 | return c10::Dispatcher::singleton() |
| 9487 | .findSchemaOrThrow(reflection_pad3d_backward_grad_input::name, reflection_pad3d_backward_grad_input::overload_name) |
| 9488 | .typed<reflection_pad3d_backward_grad_input::schema>(); |
| 9489 | } |
| 9490 | |
| 9491 | // aten::reflection_pad3d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 9492 | at::Tensor & reflection_pad3d_backward_grad_input::call(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) { |
| 9493 | |
| 9494 | static auto op = create_reflection_pad3d_backward_grad_input_typed_handle(); |
| 9495 | return op.call(grad_output, self, padding, grad_input); |
| 9496 | } |
| 9497 | |
| 9498 | // aten::reflection_pad3d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 9499 | at::Tensor & reflection_pad3d_backward_grad_input::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) { |
| 9500 | |
| 9501 | static auto op = create_reflection_pad3d_backward_grad_input_typed_handle(); |
| 9502 | return op.redispatch(dispatchKeySet, grad_output, self, padding, grad_input); |
| 9503 | } |
| 9504 | |
| 9505 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(reflection_pad3d_backward, name, "aten::reflection_pad3d_backward") |
| 9506 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(reflection_pad3d_backward, overload_name, "") |
| 9507 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(reflection_pad3d_backward, schema_str, "reflection_pad3d_backward(Tensor grad_output, Tensor self, SymInt[6] padding) -> Tensor") |
| 9508 | |
| 9509 | // aten::reflection_pad3d_backward(Tensor grad_output, Tensor self, SymInt[6] padding) -> Tensor |
| 9510 | static C10_NOINLINE c10::TypedOperatorHandle<reflection_pad3d_backward::schema> create_reflection_pad3d_backward_typed_handle() { |
| 9511 | return c10::Dispatcher::singleton() |
| 9512 | .findSchemaOrThrow(reflection_pad3d_backward::name, reflection_pad3d_backward::overload_name) |
| 9513 | .typed<reflection_pad3d_backward::schema>(); |
| 9514 | } |
| 9515 | |
| 9516 | // aten::reflection_pad3d_backward(Tensor grad_output, Tensor self, SymInt[6] padding) -> Tensor |
| 9517 | at::Tensor reflection_pad3d_backward::call(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 9518 | |
| 9519 | static auto op = create_reflection_pad3d_backward_typed_handle(); |
| 9520 | return op.call(grad_output, self, padding); |
| 9521 | } |
| 9522 | |
| 9523 | // aten::reflection_pad3d_backward(Tensor grad_output, Tensor self, SymInt[6] padding) -> Tensor |
| 9524 | at::Tensor reflection_pad3d_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 9525 | |
| 9526 | static auto op = create_reflection_pad3d_backward_typed_handle(); |
| 9527 | return op.redispatch(dispatchKeySet, grad_output, self, padding); |
| 9528 | } |
| 9529 | |
| 9530 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(replication_pad2d_backward_grad_input, name, "aten::replication_pad2d_backward") |
| 9531 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(replication_pad2d_backward_grad_input, overload_name, "grad_input") |
| 9532 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(replication_pad2d_backward_grad_input, schema_str, "replication_pad2d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!)") |
| 9533 | |
| 9534 | // aten::replication_pad2d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 9535 | static C10_NOINLINE c10::TypedOperatorHandle<replication_pad2d_backward_grad_input::schema> create_replication_pad2d_backward_grad_input_typed_handle() { |
| 9536 | return c10::Dispatcher::singleton() |
| 9537 | .findSchemaOrThrow(replication_pad2d_backward_grad_input::name, replication_pad2d_backward_grad_input::overload_name) |
| 9538 | .typed<replication_pad2d_backward_grad_input::schema>(); |
| 9539 | } |
| 9540 | |
| 9541 | // aten::replication_pad2d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 9542 | at::Tensor & replication_pad2d_backward_grad_input::call(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) { |
| 9543 | |
| 9544 | static auto op = create_replication_pad2d_backward_grad_input_typed_handle(); |
| 9545 | return op.call(grad_output, self, padding, grad_input); |
| 9546 | } |
| 9547 | |
| 9548 | // aten::replication_pad2d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 9549 | at::Tensor & replication_pad2d_backward_grad_input::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) { |
| 9550 | |
| 9551 | static auto op = create_replication_pad2d_backward_grad_input_typed_handle(); |
| 9552 | return op.redispatch(dispatchKeySet, grad_output, self, padding, grad_input); |
| 9553 | } |
| 9554 | |
| 9555 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(replication_pad2d_backward, name, "aten::replication_pad2d_backward") |
| 9556 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(replication_pad2d_backward, overload_name, "") |
| 9557 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(replication_pad2d_backward, schema_str, "replication_pad2d_backward(Tensor grad_output, Tensor self, SymInt[4] padding) -> Tensor") |
| 9558 | |
| 9559 | // aten::replication_pad2d_backward(Tensor grad_output, Tensor self, SymInt[4] padding) -> Tensor |
| 9560 | static C10_NOINLINE c10::TypedOperatorHandle<replication_pad2d_backward::schema> create_replication_pad2d_backward_typed_handle() { |
| 9561 | return c10::Dispatcher::singleton() |
| 9562 | .findSchemaOrThrow(replication_pad2d_backward::name, replication_pad2d_backward::overload_name) |
| 9563 | .typed<replication_pad2d_backward::schema>(); |
| 9564 | } |
| 9565 | |
| 9566 | // aten::replication_pad2d_backward(Tensor grad_output, Tensor self, SymInt[4] padding) -> Tensor |
| 9567 | at::Tensor replication_pad2d_backward::call(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 9568 | |
| 9569 | static auto op = create_replication_pad2d_backward_typed_handle(); |
| 9570 | return op.call(grad_output, self, padding); |
| 9571 | } |
| 9572 | |
| 9573 | // aten::replication_pad2d_backward(Tensor grad_output, Tensor self, SymInt[4] padding) -> Tensor |
| 9574 | at::Tensor replication_pad2d_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 9575 | |
| 9576 | static auto op = create_replication_pad2d_backward_typed_handle(); |
| 9577 | return op.redispatch(dispatchKeySet, grad_output, self, padding); |
| 9578 | } |
| 9579 | |
| 9580 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(replication_pad3d_out, name, "aten::replication_pad3d") |
| 9581 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(replication_pad3d_out, overload_name, "out") |
| 9582 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(replication_pad3d_out, schema_str, "replication_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!)") |
| 9583 | |
| 9584 | // aten::replication_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!) |
| 9585 | static C10_NOINLINE c10::TypedOperatorHandle<replication_pad3d_out::schema> create_replication_pad3d_out_typed_handle() { |
| 9586 | return c10::Dispatcher::singleton() |
| 9587 | .findSchemaOrThrow(replication_pad3d_out::name, replication_pad3d_out::overload_name) |
| 9588 | .typed<replication_pad3d_out::schema>(); |
| 9589 | } |
| 9590 | |
| 9591 | // aten::replication_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!) |
| 9592 | at::Tensor & replication_pad3d_out::call(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) { |
| 9593 | |
| 9594 | static auto op = create_replication_pad3d_out_typed_handle(); |
| 9595 | return op.call(self, padding, out); |
| 9596 | } |
| 9597 | |
| 9598 | // aten::replication_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!) |
| 9599 | at::Tensor & replication_pad3d_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) { |
| 9600 | |
| 9601 | static auto op = create_replication_pad3d_out_typed_handle(); |
| 9602 | return op.redispatch(dispatchKeySet, self, padding, out); |
| 9603 | } |
| 9604 | |
| 9605 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(replication_pad3d, name, "aten::replication_pad3d") |
| 9606 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(replication_pad3d, overload_name, "") |
| 9607 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(replication_pad3d, schema_str, "replication_pad3d(Tensor self, SymInt[6] padding) -> Tensor") |
| 9608 | |
| 9609 | // aten::replication_pad3d(Tensor self, SymInt[6] padding) -> Tensor |
| 9610 | static C10_NOINLINE c10::TypedOperatorHandle<replication_pad3d::schema> create_replication_pad3d_typed_handle() { |
| 9611 | return c10::Dispatcher::singleton() |
| 9612 | .findSchemaOrThrow(replication_pad3d::name, replication_pad3d::overload_name) |
| 9613 | .typed<replication_pad3d::schema>(); |
| 9614 | } |
| 9615 | |
| 9616 | // aten::replication_pad3d(Tensor self, SymInt[6] padding) -> Tensor |
| 9617 | at::Tensor replication_pad3d::call(const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 9618 | |
| 9619 | static auto op = create_replication_pad3d_typed_handle(); |
| 9620 | return op.call(self, padding); |
| 9621 | } |
| 9622 | |
| 9623 | // aten::replication_pad3d(Tensor self, SymInt[6] padding) -> Tensor |
| 9624 | at::Tensor replication_pad3d::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 9625 | |
| 9626 | static auto op = create_replication_pad3d_typed_handle(); |
| 9627 | return op.redispatch(dispatchKeySet, self, padding); |
| 9628 | } |
| 9629 | |
| 9630 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_linear1d_vec, name, "aten::upsample_linear1d") |
| 9631 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_linear1d_vec, overload_name, "vec") |
| 9632 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_linear1d_vec, schema_str, "upsample_linear1d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor") |
| 9633 | |
| 9634 | // aten::upsample_linear1d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor |
| 9635 | static C10_NOINLINE c10::TypedOperatorHandle<upsample_linear1d_vec::schema> create_upsample_linear1d_vec_typed_handle() { |
| 9636 | return c10::Dispatcher::singleton() |
| 9637 | .findSchemaOrThrow(upsample_linear1d_vec::name, upsample_linear1d_vec::overload_name) |
| 9638 | .typed<upsample_linear1d_vec::schema>(); |
| 9639 | } |
| 9640 | |
| 9641 | // aten::upsample_linear1d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor |
| 9642 | at::Tensor upsample_linear1d_vec::call(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, c10::optional<at::ArrayRef<double>> scale_factors) { |
| 9643 | |
| 9644 | static auto op = create_upsample_linear1d_vec_typed_handle(); |
| 9645 | return op.call(input, output_size, align_corners, scale_factors); |
| 9646 | } |
| 9647 | |
| 9648 | // aten::upsample_linear1d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor |
| 9649 | at::Tensor upsample_linear1d_vec::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, c10::optional<at::ArrayRef<double>> scale_factors) { |
| 9650 | |
| 9651 | static auto op = create_upsample_linear1d_vec_typed_handle(); |
| 9652 | return op.redispatch(dispatchKeySet, input, output_size, align_corners, scale_factors); |
| 9653 | } |
| 9654 | |
| 9655 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_bilinear2d_vec, name, "aten::upsample_bilinear2d") |
| 9656 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_bilinear2d_vec, overload_name, "vec") |
| 9657 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_bilinear2d_vec, schema_str, "upsample_bilinear2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor") |
| 9658 | |
| 9659 | // aten::upsample_bilinear2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor |
| 9660 | static C10_NOINLINE c10::TypedOperatorHandle<upsample_bilinear2d_vec::schema> create_upsample_bilinear2d_vec_typed_handle() { |
| 9661 | return c10::Dispatcher::singleton() |
| 9662 | .findSchemaOrThrow(upsample_bilinear2d_vec::name, upsample_bilinear2d_vec::overload_name) |
| 9663 | .typed<upsample_bilinear2d_vec::schema>(); |
| 9664 | } |
| 9665 | |
| 9666 | // aten::upsample_bilinear2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor |
| 9667 | at::Tensor upsample_bilinear2d_vec::call(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, c10::optional<at::ArrayRef<double>> scale_factors) { |
| 9668 | |
| 9669 | static auto op = create_upsample_bilinear2d_vec_typed_handle(); |
| 9670 | return op.call(input, output_size, align_corners, scale_factors); |
| 9671 | } |
| 9672 | |
| 9673 | // aten::upsample_bilinear2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor |
| 9674 | at::Tensor upsample_bilinear2d_vec::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, c10::optional<at::ArrayRef<double>> scale_factors) { |
| 9675 | |
| 9676 | static auto op = create_upsample_bilinear2d_vec_typed_handle(); |
| 9677 | return op.redispatch(dispatchKeySet, input, output_size, align_corners, scale_factors); |
| 9678 | } |
| 9679 | |
| 9680 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_bicubic2d_vec, name, "aten::upsample_bicubic2d") |
| 9681 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_bicubic2d_vec, overload_name, "vec") |
| 9682 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_bicubic2d_vec, schema_str, "upsample_bicubic2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor") |
| 9683 | |
| 9684 | // aten::upsample_bicubic2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor |
| 9685 | static C10_NOINLINE c10::TypedOperatorHandle<upsample_bicubic2d_vec::schema> create_upsample_bicubic2d_vec_typed_handle() { |
| 9686 | return c10::Dispatcher::singleton() |
| 9687 | .findSchemaOrThrow(upsample_bicubic2d_vec::name, upsample_bicubic2d_vec::overload_name) |
| 9688 | .typed<upsample_bicubic2d_vec::schema>(); |
| 9689 | } |
| 9690 | |
| 9691 | // aten::upsample_bicubic2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor |
| 9692 | at::Tensor upsample_bicubic2d_vec::call(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, c10::optional<at::ArrayRef<double>> scale_factors) { |
| 9693 | |
| 9694 | static auto op = create_upsample_bicubic2d_vec_typed_handle(); |
| 9695 | return op.call(input, output_size, align_corners, scale_factors); |
| 9696 | } |
| 9697 | |
| 9698 | // aten::upsample_bicubic2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor |
| 9699 | at::Tensor upsample_bicubic2d_vec::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, c10::optional<at::ArrayRef<double>> scale_factors) { |
| 9700 | |
| 9701 | static auto op = create_upsample_bicubic2d_vec_typed_handle(); |
| 9702 | return op.redispatch(dispatchKeySet, input, output_size, align_corners, scale_factors); |
| 9703 | } |
| 9704 | |
| 9705 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_nearest2d_vec, name, "aten::upsample_nearest2d") |
| 9706 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_nearest2d_vec, overload_name, "vec") |
| 9707 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_nearest2d_vec, schema_str, "upsample_nearest2d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor") |
| 9708 | |
| 9709 | // aten::upsample_nearest2d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor |
| 9710 | static C10_NOINLINE c10::TypedOperatorHandle<upsample_nearest2d_vec::schema> create_upsample_nearest2d_vec_typed_handle() { |
| 9711 | return c10::Dispatcher::singleton() |
| 9712 | .findSchemaOrThrow(upsample_nearest2d_vec::name, upsample_nearest2d_vec::overload_name) |
| 9713 | .typed<upsample_nearest2d_vec::schema>(); |
| 9714 | } |
| 9715 | |
| 9716 | // aten::upsample_nearest2d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor |
| 9717 | at::Tensor upsample_nearest2d_vec::call(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, c10::optional<at::ArrayRef<double>> scale_factors) { |
| 9718 | |
| 9719 | static auto op = create_upsample_nearest2d_vec_typed_handle(); |
| 9720 | return op.call(input, output_size, scale_factors); |
| 9721 | } |
| 9722 | |
| 9723 | // aten::upsample_nearest2d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor |
| 9724 | at::Tensor upsample_nearest2d_vec::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, c10::optional<at::ArrayRef<double>> scale_factors) { |
| 9725 | |
| 9726 | static auto op = create_upsample_nearest2d_vec_typed_handle(); |
| 9727 | return op.redispatch(dispatchKeySet, input, output_size, scale_factors); |
| 9728 | } |
| 9729 | |
| 9730 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_linear1d_out, name, "aten::upsample_linear1d") |
| 9731 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_linear1d_out, overload_name, "out") |
| 9732 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_linear1d_out, schema_str, "upsample_linear1d.out(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 9733 | |
| 9734 | // aten::upsample_linear1d.out(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None, *, Tensor(a!) out) -> Tensor(a!) |
| 9735 | static C10_NOINLINE c10::TypedOperatorHandle<upsample_linear1d_out::schema> create_upsample_linear1d_out_typed_handle() { |
| 9736 | return c10::Dispatcher::singleton() |
| 9737 | .findSchemaOrThrow(upsample_linear1d_out::name, upsample_linear1d_out::overload_name) |
| 9738 | .typed<upsample_linear1d_out::schema>(); |
| 9739 | } |
| 9740 | |
| 9741 | // aten::upsample_linear1d.out(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None, *, Tensor(a!) out) -> Tensor(a!) |
| 9742 | at::Tensor & upsample_linear1d_out::call(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales, at::Tensor & out) { |
| 9743 | |
| 9744 | static auto op = create_upsample_linear1d_out_typed_handle(); |
| 9745 | return op.call(self, output_size, align_corners, scales, out); |
| 9746 | } |
| 9747 | |
| 9748 | // aten::upsample_linear1d.out(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None, *, Tensor(a!) out) -> Tensor(a!) |
| 9749 | at::Tensor & upsample_linear1d_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales, at::Tensor & out) { |
| 9750 | |
| 9751 | static auto op = create_upsample_linear1d_out_typed_handle(); |
| 9752 | return op.redispatch(dispatchKeySet, self, output_size, align_corners, scales, out); |
| 9753 | } |
| 9754 | |
| 9755 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_linear1d, name, "aten::upsample_linear1d") |
| 9756 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_linear1d, overload_name, "") |
| 9757 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_linear1d, schema_str, "upsample_linear1d(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None) -> Tensor") |
| 9758 | |
| 9759 | // aten::upsample_linear1d(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None) -> Tensor |
| 9760 | static C10_NOINLINE c10::TypedOperatorHandle<upsample_linear1d::schema> create_upsample_linear1d_typed_handle() { |
| 9761 | return c10::Dispatcher::singleton() |
| 9762 | .findSchemaOrThrow(upsample_linear1d::name, upsample_linear1d::overload_name) |
| 9763 | .typed<upsample_linear1d::schema>(); |
| 9764 | } |
| 9765 | |
| 9766 | // aten::upsample_linear1d(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None) -> Tensor |
| 9767 | at::Tensor upsample_linear1d::call(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales) { |
| 9768 | |
| 9769 | static auto op = create_upsample_linear1d_typed_handle(); |
| 9770 | return op.call(self, output_size, align_corners, scales); |
| 9771 | } |
| 9772 | |
| 9773 | // aten::upsample_linear1d(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None) -> Tensor |
| 9774 | at::Tensor upsample_linear1d::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales) { |
| 9775 | |
| 9776 | static auto op = create_upsample_linear1d_typed_handle(); |
| 9777 | return op.redispatch(dispatchKeySet, self, output_size, align_corners, scales); |
| 9778 | } |
| 9779 | |
| 9780 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_bilinear2d_out, name, "aten::upsample_bilinear2d") |
| 9781 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_bilinear2d_out, overload_name, "out") |
| 9782 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_bilinear2d_out, schema_str, "upsample_bilinear2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 9783 | |
| 9784 | // aten::upsample_bilinear2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) |
| 9785 | static C10_NOINLINE c10::TypedOperatorHandle<upsample_bilinear2d_out::schema> create_upsample_bilinear2d_out_typed_handle() { |
| 9786 | return c10::Dispatcher::singleton() |
| 9787 | .findSchemaOrThrow(upsample_bilinear2d_out::name, upsample_bilinear2d_out::overload_name) |
| 9788 | .typed<upsample_bilinear2d_out::schema>(); |
| 9789 | } |
| 9790 | |
| 9791 | // aten::upsample_bilinear2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) |
| 9792 | at::Tensor & upsample_bilinear2d_out::call(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 9793 | |
| 9794 | static auto op = create_upsample_bilinear2d_out_typed_handle(); |
| 9795 | return op.call(self, output_size, align_corners, scales_h, scales_w, out); |
| 9796 | } |
| 9797 | |
| 9798 | // aten::upsample_bilinear2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) |
| 9799 | at::Tensor & upsample_bilinear2d_out::redispatch(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) { |
| 9800 | |
| 9801 | static auto op = create_upsample_bilinear2d_out_typed_handle(); |
| 9802 | return op.redispatch(dispatchKeySet, self, output_size, align_corners, scales_h, scales_w, out); |
| 9803 | } |
| 9804 | |
| 9805 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_bilinear2d, name, "aten::upsample_bilinear2d") |
| 9806 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_bilinear2d, overload_name, "") |
| 9807 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_bilinear2d, schema_str, "upsample_bilinear2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor") |
| 9808 | |
| 9809 | // aten::upsample_bilinear2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor |
| 9810 | static C10_NOINLINE c10::TypedOperatorHandle<upsample_bilinear2d::schema> create_upsample_bilinear2d_typed_handle() { |
| 9811 | return c10::Dispatcher::singleton() |
| 9812 | .findSchemaOrThrow(upsample_bilinear2d::name, upsample_bilinear2d::overload_name) |
| 9813 | .typed<upsample_bilinear2d::schema>(); |
| 9814 | } |
| 9815 | |
| 9816 | // aten::upsample_bilinear2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor |
| 9817 | at::Tensor upsample_bilinear2d::call(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 9818 | |
| 9819 | static auto op = create_upsample_bilinear2d_typed_handle(); |
| 9820 | return op.call(self, output_size, align_corners, scales_h, scales_w); |
| 9821 | } |
| 9822 | |
| 9823 | // aten::upsample_bilinear2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor |
| 9824 | at::Tensor upsample_bilinear2d::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 9825 | |
| 9826 | static auto op = create_upsample_bilinear2d_typed_handle(); |
| 9827 | return op.redispatch(dispatchKeySet, self, output_size, align_corners, scales_h, scales_w); |
| 9828 | } |
| 9829 | |
| 9830 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_bicubic2d_out, name, "aten::upsample_bicubic2d") |
| 9831 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_bicubic2d_out, overload_name, "out") |
| 9832 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_bicubic2d_out, schema_str, "upsample_bicubic2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 9833 | |
| 9834 | // aten::upsample_bicubic2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) |
| 9835 | static C10_NOINLINE c10::TypedOperatorHandle<upsample_bicubic2d_out::schema> create_upsample_bicubic2d_out_typed_handle() { |
| 9836 | return c10::Dispatcher::singleton() |
| 9837 | .findSchemaOrThrow(upsample_bicubic2d_out::name, upsample_bicubic2d_out::overload_name) |
| 9838 | .typed<upsample_bicubic2d_out::schema>(); |
| 9839 | } |
| 9840 | |
| 9841 | // aten::upsample_bicubic2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) |
| 9842 | at::Tensor & upsample_bicubic2d_out::call(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 9843 | |
| 9844 | static auto op = create_upsample_bicubic2d_out_typed_handle(); |
| 9845 | return op.call(self, output_size, align_corners, scales_h, scales_w, out); |
| 9846 | } |
| 9847 | |
| 9848 | // aten::upsample_bicubic2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) |
| 9849 | at::Tensor & upsample_bicubic2d_out::redispatch(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) { |
| 9850 | |
| 9851 | static auto op = create_upsample_bicubic2d_out_typed_handle(); |
| 9852 | return op.redispatch(dispatchKeySet, self, output_size, align_corners, scales_h, scales_w, out); |
| 9853 | } |
| 9854 | |
| 9855 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_bicubic2d, name, "aten::upsample_bicubic2d") |
| 9856 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_bicubic2d, overload_name, "") |
| 9857 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_bicubic2d, schema_str, "upsample_bicubic2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor") |
| 9858 | |
| 9859 | // aten::upsample_bicubic2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor |
| 9860 | static C10_NOINLINE c10::TypedOperatorHandle<upsample_bicubic2d::schema> create_upsample_bicubic2d_typed_handle() { |
| 9861 | return c10::Dispatcher::singleton() |
| 9862 | .findSchemaOrThrow(upsample_bicubic2d::name, upsample_bicubic2d::overload_name) |
| 9863 | .typed<upsample_bicubic2d::schema>(); |
| 9864 | } |
| 9865 | |
| 9866 | // aten::upsample_bicubic2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor |
| 9867 | at::Tensor upsample_bicubic2d::call(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 9868 | |
| 9869 | static auto op = create_upsample_bicubic2d_typed_handle(); |
| 9870 | return op.call(self, output_size, align_corners, scales_h, scales_w); |
| 9871 | } |
| 9872 | |
| 9873 | // aten::upsample_bicubic2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor |
| 9874 | at::Tensor upsample_bicubic2d::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 9875 | |
| 9876 | static auto op = create_upsample_bicubic2d_typed_handle(); |
| 9877 | return op.redispatch(dispatchKeySet, self, output_size, align_corners, scales_h, scales_w); |
| 9878 | } |
| 9879 | |
| 9880 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_bicubic2d_backward_grad_input, name, "aten::upsample_bicubic2d_backward") |
| 9881 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_bicubic2d_backward_grad_input, overload_name, "grad_input") |
| 9882 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_bicubic2d_backward_grad_input, schema_str, "upsample_bicubic2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)") |
| 9883 | |
| 9884 | // aten::upsample_bicubic2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 9885 | static C10_NOINLINE c10::TypedOperatorHandle<upsample_bicubic2d_backward_grad_input::schema> create_upsample_bicubic2d_backward_grad_input_typed_handle() { |
| 9886 | return c10::Dispatcher::singleton() |
| 9887 | .findSchemaOrThrow(upsample_bicubic2d_backward_grad_input::name, upsample_bicubic2d_backward_grad_input::overload_name) |
| 9888 | .typed<upsample_bicubic2d_backward_grad_input::schema>(); |
| 9889 | } |
| 9890 | |
| 9891 | // aten::upsample_bicubic2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 9892 | at::Tensor & upsample_bicubic2d_backward_grad_input::call(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) { |
| 9893 | |
| 9894 | static auto op = create_upsample_bicubic2d_backward_grad_input_typed_handle(); |
| 9895 | return op.call(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input); |
| 9896 | } |
| 9897 | |
| 9898 | // aten::upsample_bicubic2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 9899 | at::Tensor & upsample_bicubic2d_backward_grad_input::redispatch(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) { |
| 9900 | |
| 9901 | static auto op = create_upsample_bicubic2d_backward_grad_input_typed_handle(); |
| 9902 | return op.redispatch(dispatchKeySet, grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input); |
| 9903 | } |
| 9904 | |
| 9905 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_bicubic2d_backward, name, "aten::upsample_bicubic2d_backward") |
| 9906 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_bicubic2d_backward, overload_name, "") |
| 9907 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_bicubic2d_backward, schema_str, "upsample_bicubic2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor") |
| 9908 | |
| 9909 | // aten::upsample_bicubic2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor |
| 9910 | static C10_NOINLINE c10::TypedOperatorHandle<upsample_bicubic2d_backward::schema> create_upsample_bicubic2d_backward_typed_handle() { |
| 9911 | return c10::Dispatcher::singleton() |
| 9912 | .findSchemaOrThrow(upsample_bicubic2d_backward::name, upsample_bicubic2d_backward::overload_name) |
| 9913 | .typed<upsample_bicubic2d_backward::schema>(); |
| 9914 | } |
| 9915 | |
| 9916 | // aten::upsample_bicubic2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor |
| 9917 | at::Tensor upsample_bicubic2d_backward::call(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) { |
| 9918 | |
| 9919 | static auto op = create_upsample_bicubic2d_backward_typed_handle(); |
| 9920 | return op.call(grad_output, output_size, input_size, align_corners, scales_h, scales_w); |
| 9921 | } |
| 9922 | |
| 9923 | // aten::upsample_bicubic2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor |
| 9924 | at::Tensor upsample_bicubic2d_backward::redispatch(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) { |
| 9925 | |
| 9926 | static auto op = create_upsample_bicubic2d_backward_typed_handle(); |
| 9927 | return op.redispatch(dispatchKeySet, grad_output, output_size, input_size, align_corners, scales_h, scales_w); |
| 9928 | } |
| 9929 | |
| 9930 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_trilinear3d_backward_grad_input, name, "aten::upsample_trilinear3d_backward") |
| 9931 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_trilinear3d_backward_grad_input, overload_name, "grad_input") |
| 9932 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_trilinear3d_backward_grad_input, schema_str, "upsample_trilinear3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)") |
| 9933 | |
| 9934 | // aten::upsample_trilinear3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 9935 | static C10_NOINLINE c10::TypedOperatorHandle<upsample_trilinear3d_backward_grad_input::schema> create_upsample_trilinear3d_backward_grad_input_typed_handle() { |
| 9936 | return c10::Dispatcher::singleton() |
| 9937 | .findSchemaOrThrow(upsample_trilinear3d_backward_grad_input::name, upsample_trilinear3d_backward_grad_input::overload_name) |
| 9938 | .typed<upsample_trilinear3d_backward_grad_input::schema>(); |
| 9939 | } |
| 9940 | |
| 9941 | // aten::upsample_trilinear3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 9942 | at::Tensor & upsample_trilinear3d_backward_grad_input::call(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) { |
| 9943 | |
| 9944 | static auto op = create_upsample_trilinear3d_backward_grad_input_typed_handle(); |
| 9945 | return op.call(grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w, grad_input); |
| 9946 | } |
| 9947 | |
| 9948 | // aten::upsample_trilinear3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 9949 | at::Tensor & upsample_trilinear3d_backward_grad_input::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) { |
| 9950 | |
| 9951 | static auto op = create_upsample_trilinear3d_backward_grad_input_typed_handle(); |
| 9952 | return op.redispatch(dispatchKeySet, grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w, grad_input); |
| 9953 | } |
| 9954 | |
| 9955 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_trilinear3d_backward, name, "aten::upsample_trilinear3d_backward") |
| 9956 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_trilinear3d_backward, overload_name, "") |
| 9957 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_trilinear3d_backward, schema_str, "upsample_trilinear3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor") |
| 9958 | |
| 9959 | // aten::upsample_trilinear3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor |
| 9960 | static C10_NOINLINE c10::TypedOperatorHandle<upsample_trilinear3d_backward::schema> create_upsample_trilinear3d_backward_typed_handle() { |
| 9961 | return c10::Dispatcher::singleton() |
| 9962 | .findSchemaOrThrow(upsample_trilinear3d_backward::name, upsample_trilinear3d_backward::overload_name) |
| 9963 | .typed<upsample_trilinear3d_backward::schema>(); |
| 9964 | } |
| 9965 | |
| 9966 | // aten::upsample_trilinear3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor |
| 9967 | at::Tensor upsample_trilinear3d_backward::call(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 9968 | |
| 9969 | static auto op = create_upsample_trilinear3d_backward_typed_handle(); |
| 9970 | return op.call(grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w); |
| 9971 | } |
| 9972 | |
| 9973 | // aten::upsample_trilinear3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor |
| 9974 | at::Tensor upsample_trilinear3d_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 9975 | |
| 9976 | static auto op = create_upsample_trilinear3d_backward_typed_handle(); |
| 9977 | return op.redispatch(dispatchKeySet, grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w); |
| 9978 | } |
| 9979 | |
| 9980 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_nearest2d_out, name, "aten::upsample_nearest2d") |
| 9981 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_nearest2d_out, overload_name, "out") |
| 9982 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_nearest2d_out, schema_str, "upsample_nearest2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 9983 | |
| 9984 | // aten::upsample_nearest2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) |
| 9985 | static C10_NOINLINE c10::TypedOperatorHandle<upsample_nearest2d_out::schema> create_upsample_nearest2d_out_typed_handle() { |
| 9986 | return c10::Dispatcher::singleton() |
| 9987 | .findSchemaOrThrow(upsample_nearest2d_out::name, upsample_nearest2d_out::overload_name) |
| 9988 | .typed<upsample_nearest2d_out::schema>(); |
| 9989 | } |
| 9990 | |
| 9991 | // aten::upsample_nearest2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) |
| 9992 | at::Tensor & upsample_nearest2d_out::call(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 9993 | |
| 9994 | static auto op = create_upsample_nearest2d_out_typed_handle(); |
| 9995 | return op.call(self, output_size, scales_h, scales_w, out); |
| 9996 | } |
| 9997 | |
| 9998 | // aten::upsample_nearest2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) |
| 9999 | at::Tensor & upsample_nearest2d_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & out) { |
| 10000 | |
| 10001 | static auto op = create_upsample_nearest2d_out_typed_handle(); |
| 10002 | return op.redispatch(dispatchKeySet, self, output_size, scales_h, scales_w, out); |
| 10003 | } |
| 10004 | |
| 10005 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_nearest2d, name, "aten::upsample_nearest2d") |
| 10006 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_nearest2d, overload_name, "") |
| 10007 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_nearest2d, schema_str, "upsample_nearest2d(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None) -> Tensor") |
| 10008 | |
| 10009 | // aten::upsample_nearest2d(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None) -> Tensor |
| 10010 | static C10_NOINLINE c10::TypedOperatorHandle<upsample_nearest2d::schema> create_upsample_nearest2d_typed_handle() { |
| 10011 | return c10::Dispatcher::singleton() |
| 10012 | .findSchemaOrThrow(upsample_nearest2d::name, upsample_nearest2d::overload_name) |
| 10013 | .typed<upsample_nearest2d::schema>(); |
| 10014 | } |
| 10015 | |
| 10016 | // aten::upsample_nearest2d(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None) -> Tensor |
| 10017 | at::Tensor upsample_nearest2d::call(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 10018 | |
| 10019 | static auto op = create_upsample_nearest2d_typed_handle(); |
| 10020 | return op.call(self, output_size, scales_h, scales_w); |
| 10021 | } |
| 10022 | |
| 10023 | // aten::upsample_nearest2d(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None) -> Tensor |
| 10024 | at::Tensor upsample_nearest2d::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 10025 | |
| 10026 | static auto op = create_upsample_nearest2d_typed_handle(); |
| 10027 | return op.redispatch(dispatchKeySet, self, output_size, scales_h, scales_w); |
| 10028 | } |
| 10029 | |
| 10030 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_nearest3d_backward_grad_input, name, "aten::upsample_nearest3d_backward") |
| 10031 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_nearest3d_backward_grad_input, overload_name, "grad_input") |
| 10032 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_nearest3d_backward_grad_input, schema_str, "upsample_nearest3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)") |
| 10033 | |
| 10034 | // aten::upsample_nearest3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 10035 | static C10_NOINLINE c10::TypedOperatorHandle<upsample_nearest3d_backward_grad_input::schema> create_upsample_nearest3d_backward_grad_input_typed_handle() { |
| 10036 | return c10::Dispatcher::singleton() |
| 10037 | .findSchemaOrThrow(upsample_nearest3d_backward_grad_input::name, upsample_nearest3d_backward_grad_input::overload_name) |
| 10038 | .typed<upsample_nearest3d_backward_grad_input::schema>(); |
| 10039 | } |
| 10040 | |
| 10041 | // aten::upsample_nearest3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 10042 | at::Tensor & upsample_nearest3d_backward_grad_input::call(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) { |
| 10043 | |
| 10044 | static auto op = create_upsample_nearest3d_backward_grad_input_typed_handle(); |
| 10045 | return op.call(grad_output, output_size, input_size, scales_d, scales_h, scales_w, grad_input); |
| 10046 | } |
| 10047 | |
| 10048 | // aten::upsample_nearest3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 10049 | at::Tensor & upsample_nearest3d_backward_grad_input::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) { |
| 10050 | |
| 10051 | static auto op = create_upsample_nearest3d_backward_grad_input_typed_handle(); |
| 10052 | return op.redispatch(dispatchKeySet, grad_output, output_size, input_size, scales_d, scales_h, scales_w, grad_input); |
| 10053 | } |
| 10054 | |
| 10055 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_upsample_nearest_exact3d_backward_grad_input, name, "aten::_upsample_nearest_exact3d_backward") |
| 10056 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_upsample_nearest_exact3d_backward_grad_input, overload_name, "grad_input") |
| 10057 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_upsample_nearest_exact3d_backward_grad_input, schema_str, "_upsample_nearest_exact3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)") |
| 10058 | |
| 10059 | // aten::_upsample_nearest_exact3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 10060 | static C10_NOINLINE c10::TypedOperatorHandle<_upsample_nearest_exact3d_backward_grad_input::schema> create__upsample_nearest_exact3d_backward_grad_input_typed_handle() { |
| 10061 | return c10::Dispatcher::singleton() |
| 10062 | .findSchemaOrThrow(_upsample_nearest_exact3d_backward_grad_input::name, _upsample_nearest_exact3d_backward_grad_input::overload_name) |
| 10063 | .typed<_upsample_nearest_exact3d_backward_grad_input::schema>(); |
| 10064 | } |
| 10065 | |
| 10066 | // aten::_upsample_nearest_exact3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 10067 | at::Tensor & _upsample_nearest_exact3d_backward_grad_input::call(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) { |
| 10068 | |
| 10069 | static auto op = create__upsample_nearest_exact3d_backward_grad_input_typed_handle(); |
| 10070 | return op.call(grad_output, output_size, input_size, scales_d, scales_h, scales_w, grad_input); |
| 10071 | } |
| 10072 | |
| 10073 | // aten::_upsample_nearest_exact3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 10074 | at::Tensor & _upsample_nearest_exact3d_backward_grad_input::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor & grad_input) { |
| 10075 | |
| 10076 | static auto op = create__upsample_nearest_exact3d_backward_grad_input_typed_handle(); |
| 10077 | return op.redispatch(dispatchKeySet, grad_output, output_size, input_size, scales_d, scales_h, scales_w, grad_input); |
| 10078 | } |
| 10079 | |
| 10080 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_nearest3d_backward, name, "aten::upsample_nearest3d_backward") |
| 10081 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_nearest3d_backward, overload_name, "") |
| 10082 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_nearest3d_backward, schema_str, "upsample_nearest3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor") |
| 10083 | |
| 10084 | // aten::upsample_nearest3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor |
| 10085 | static C10_NOINLINE c10::TypedOperatorHandle<upsample_nearest3d_backward::schema> create_upsample_nearest3d_backward_typed_handle() { |
| 10086 | return c10::Dispatcher::singleton() |
| 10087 | .findSchemaOrThrow(upsample_nearest3d_backward::name, upsample_nearest3d_backward::overload_name) |
| 10088 | .typed<upsample_nearest3d_backward::schema>(); |
| 10089 | } |
| 10090 | |
| 10091 | // aten::upsample_nearest3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor |
| 10092 | at::Tensor upsample_nearest3d_backward::call(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 10093 | |
| 10094 | static auto op = create_upsample_nearest3d_backward_typed_handle(); |
| 10095 | return op.call(grad_output, output_size, input_size, scales_d, scales_h, scales_w); |
| 10096 | } |
| 10097 | |
| 10098 | // aten::upsample_nearest3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor |
| 10099 | at::Tensor upsample_nearest3d_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 10100 | |
| 10101 | static auto op = create_upsample_nearest3d_backward_typed_handle(); |
| 10102 | return op.redispatch(dispatchKeySet, grad_output, output_size, input_size, scales_d, scales_h, scales_w); |
| 10103 | } |
| 10104 | |
| 10105 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_upsample_nearest_exact3d_backward, name, "aten::_upsample_nearest_exact3d_backward") |
| 10106 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_upsample_nearest_exact3d_backward, overload_name, "") |
| 10107 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_upsample_nearest_exact3d_backward, schema_str, "_upsample_nearest_exact3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor") |
| 10108 | |
| 10109 | // aten::_upsample_nearest_exact3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor |
| 10110 | static C10_NOINLINE c10::TypedOperatorHandle<_upsample_nearest_exact3d_backward::schema> create__upsample_nearest_exact3d_backward_typed_handle() { |
| 10111 | return c10::Dispatcher::singleton() |
| 10112 | .findSchemaOrThrow(_upsample_nearest_exact3d_backward::name, _upsample_nearest_exact3d_backward::overload_name) |
| 10113 | .typed<_upsample_nearest_exact3d_backward::schema>(); |
| 10114 | } |
| 10115 | |
| 10116 | // aten::_upsample_nearest_exact3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor |
| 10117 | at::Tensor _upsample_nearest_exact3d_backward::call(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 10118 | |
| 10119 | static auto op = create__upsample_nearest_exact3d_backward_typed_handle(); |
| 10120 | return op.call(grad_output, output_size, input_size, scales_d, scales_h, scales_w); |
| 10121 | } |
| 10122 | |
| 10123 | // aten::_upsample_nearest_exact3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor |
| 10124 | at::Tensor _upsample_nearest_exact3d_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 10125 | |
| 10126 | static auto op = create__upsample_nearest_exact3d_backward_typed_handle(); |
| 10127 | return op.redispatch(dispatchKeySet, grad_output, output_size, input_size, scales_d, scales_h, scales_w); |
| 10128 | } |
| 10129 | |
| 10130 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logit_backward_grad_input, name, "aten::logit_backward") |
| 10131 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logit_backward_grad_input, overload_name, "grad_input") |
| 10132 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logit_backward_grad_input, schema_str, "logit_backward.grad_input(Tensor grad_output, Tensor self, float? eps=None, *, Tensor(a!) grad_input) -> Tensor(a!)") |
| 10133 | |
| 10134 | // aten::logit_backward.grad_input(Tensor grad_output, Tensor self, float? eps=None, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 10135 | static C10_NOINLINE c10::TypedOperatorHandle<logit_backward_grad_input::schema> create_logit_backward_grad_input_typed_handle() { |
| 10136 | return c10::Dispatcher::singleton() |
| 10137 | .findSchemaOrThrow(logit_backward_grad_input::name, logit_backward_grad_input::overload_name) |
| 10138 | .typed<logit_backward_grad_input::schema>(); |
| 10139 | } |
| 10140 | |
| 10141 | // aten::logit_backward.grad_input(Tensor grad_output, Tensor self, float? eps=None, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 10142 | at::Tensor & logit_backward_grad_input::call(const at::Tensor & grad_output, const at::Tensor & self, c10::optional<double> eps, at::Tensor & grad_input) { |
| 10143 | |
| 10144 | static auto op = create_logit_backward_grad_input_typed_handle(); |
| 10145 | return op.call(grad_output, self, eps, grad_input); |
| 10146 | } |
| 10147 | |
| 10148 | // aten::logit_backward.grad_input(Tensor grad_output, Tensor self, float? eps=None, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 10149 | at::Tensor & logit_backward_grad_input::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::optional<double> eps, at::Tensor & grad_input) { |
| 10150 | |
| 10151 | static auto op = create_logit_backward_grad_input_typed_handle(); |
| 10152 | return op.redispatch(dispatchKeySet, grad_output, self, eps, grad_input); |
| 10153 | } |
| 10154 | |
| 10155 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logit_backward, name, "aten::logit_backward") |
| 10156 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logit_backward, overload_name, "") |
| 10157 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logit_backward, schema_str, "logit_backward(Tensor grad_output, Tensor self, float? eps=None) -> Tensor") |
| 10158 | |
| 10159 | // aten::logit_backward(Tensor grad_output, Tensor self, float? eps=None) -> Tensor |
| 10160 | static C10_NOINLINE c10::TypedOperatorHandle<logit_backward::schema> create_logit_backward_typed_handle() { |
| 10161 | return c10::Dispatcher::singleton() |
| 10162 | .findSchemaOrThrow(logit_backward::name, logit_backward::overload_name) |
| 10163 | .typed<logit_backward::schema>(); |
| 10164 | } |
| 10165 | |
| 10166 | // aten::logit_backward(Tensor grad_output, Tensor self, float? eps=None) -> Tensor |
| 10167 | at::Tensor logit_backward::call(const at::Tensor & grad_output, const at::Tensor & self, c10::optional<double> eps) { |
| 10168 | |
| 10169 | static auto op = create_logit_backward_typed_handle(); |
| 10170 | return op.call(grad_output, self, eps); |
| 10171 | } |
| 10172 | |
| 10173 | // aten::logit_backward(Tensor grad_output, Tensor self, float? eps=None) -> Tensor |
| 10174 | at::Tensor logit_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::optional<double> eps) { |
| 10175 | |
| 10176 | static auto op = create_logit_backward_typed_handle(); |
| 10177 | return op.redispatch(dispatchKeySet, grad_output, self, eps); |
| 10178 | } |
| 10179 | |
| 10180 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slow_conv_transpose2d_out, name, "aten::slow_conv_transpose2d") |
| 10181 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slow_conv_transpose2d_out, overload_name, "out") |
| 10182 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slow_conv_transpose2d_out, schema_str, "slow_conv_transpose2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, int[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!)") |
| 10183 | |
| 10184 | // aten::slow_conv_transpose2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, int[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!) |
| 10185 | static C10_NOINLINE c10::TypedOperatorHandle<slow_conv_transpose2d_out::schema> create_slow_conv_transpose2d_out_typed_handle() { |
| 10186 | return c10::Dispatcher::singleton() |
| 10187 | .findSchemaOrThrow(slow_conv_transpose2d_out::name, slow_conv_transpose2d_out::overload_name) |
| 10188 | .typed<slow_conv_transpose2d_out::schema>(); |
| 10189 | } |
| 10190 | |
| 10191 | // aten::slow_conv_transpose2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, int[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!) |
| 10192 | at::Tensor & slow_conv_transpose2d_out::call(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, at::IntArrayRef dilation, at::Tensor & out) { |
| 10193 | |
| 10194 | static auto op = create_slow_conv_transpose2d_out_typed_handle(); |
| 10195 | return op.call(self, weight, kernel_size, bias, stride, padding, output_padding, dilation, out); |
| 10196 | } |
| 10197 | |
| 10198 | // aten::slow_conv_transpose2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, int[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!) |
| 10199 | at::Tensor & slow_conv_transpose2d_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, at::IntArrayRef dilation, at::Tensor & out) { |
| 10200 | |
| 10201 | static auto op = create_slow_conv_transpose2d_out_typed_handle(); |
| 10202 | return op.redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, output_padding, dilation, out); |
| 10203 | } |
| 10204 | |
| 10205 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slow_conv_transpose2d, name, "aten::slow_conv_transpose2d") |
| 10206 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slow_conv_transpose2d, overload_name, "") |
| 10207 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slow_conv_transpose2d, schema_str, "slow_conv_transpose2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, int[2] dilation=1) -> Tensor") |
| 10208 | |
| 10209 | // aten::slow_conv_transpose2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, int[2] dilation=1) -> Tensor |
| 10210 | static C10_NOINLINE c10::TypedOperatorHandle<slow_conv_transpose2d::schema> create_slow_conv_transpose2d_typed_handle() { |
| 10211 | return c10::Dispatcher::singleton() |
| 10212 | .findSchemaOrThrow(slow_conv_transpose2d::name, slow_conv_transpose2d::overload_name) |
| 10213 | .typed<slow_conv_transpose2d::schema>(); |
| 10214 | } |
| 10215 | |
| 10216 | // aten::slow_conv_transpose2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, int[2] dilation=1) -> Tensor |
| 10217 | at::Tensor slow_conv_transpose2d::call(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, at::IntArrayRef dilation) { |
| 10218 | |
| 10219 | static auto op = create_slow_conv_transpose2d_typed_handle(); |
| 10220 | return op.call(self, weight, kernel_size, bias, stride, padding, output_padding, dilation); |
| 10221 | } |
| 10222 | |
| 10223 | // aten::slow_conv_transpose2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, int[2] dilation=1) -> Tensor |
| 10224 | at::Tensor slow_conv_transpose2d::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, at::IntArrayRef dilation) { |
| 10225 | |
| 10226 | static auto op = create_slow_conv_transpose2d_typed_handle(); |
| 10227 | return op.redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, output_padding, dilation); |
| 10228 | } |
| 10229 | |
| 10230 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_slow_conv2d_backward_grad_input, name, "aten::_slow_conv2d_backward") |
| 10231 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_slow_conv2d_backward_grad_input, overload_name, "grad_input") |
| 10232 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_slow_conv2d_backward_grad_input, schema_str, "_slow_conv2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor weight, int[2] kernel_size, int[2] stride, int[2] padding, *, Tensor(a!) grad_input, Tensor(b!) grad_weight, Tensor(c!) grad_bias) -> (Tensor(a!), Tensor(b!), Tensor(c!))") |
| 10233 | |
| 10234 | // aten::_slow_conv2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor weight, int[2] kernel_size, int[2] stride, int[2] padding, *, Tensor(a!) grad_input, Tensor(b!) grad_weight, Tensor(c!) grad_bias) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 10235 | static C10_NOINLINE c10::TypedOperatorHandle<_slow_conv2d_backward_grad_input::schema> create__slow_conv2d_backward_grad_input_typed_handle() { |
| 10236 | return c10::Dispatcher::singleton() |
| 10237 | .findSchemaOrThrow(_slow_conv2d_backward_grad_input::name, _slow_conv2d_backward_grad_input::overload_name) |
| 10238 | .typed<_slow_conv2d_backward_grad_input::schema>(); |
| 10239 | } |
| 10240 | |
| 10241 | // aten::_slow_conv2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor weight, int[2] kernel_size, int[2] stride, int[2] padding, *, Tensor(a!) grad_input, Tensor(b!) grad_weight, Tensor(c!) grad_bias) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 10242 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _slow_conv2d_backward_grad_input::call(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias) { |
| 10243 | |
| 10244 | static auto op = create__slow_conv2d_backward_grad_input_typed_handle(); |
| 10245 | return op.call(grad_output, self, weight, kernel_size, stride, padding, grad_input, grad_weight, grad_bias); |
| 10246 | } |
| 10247 | |
| 10248 | // aten::_slow_conv2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor weight, int[2] kernel_size, int[2] stride, int[2] padding, *, Tensor(a!) grad_input, Tensor(b!) grad_weight, Tensor(c!) grad_bias) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 10249 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _slow_conv2d_backward_grad_input::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias) { |
| 10250 | |
| 10251 | static auto op = create__slow_conv2d_backward_grad_input_typed_handle(); |
| 10252 | return op.redispatch(dispatchKeySet, grad_output, self, weight, kernel_size, stride, padding, grad_input, grad_weight, grad_bias); |
| 10253 | } |
| 10254 | |
| 10255 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_slow_conv2d_backward_output_mask, name, "aten::_slow_conv2d_backward") |
| 10256 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_slow_conv2d_backward_output_mask, overload_name, "output_mask") |
| 10257 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_slow_conv2d_backward_output_mask, schema_str, "_slow_conv2d_backward.output_mask(Tensor grad_output, Tensor self, Tensor weight, int[2] kernel_size, int[2] stride, int[2] padding, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias)") |
| 10258 | |
| 10259 | // aten::_slow_conv2d_backward.output_mask(Tensor grad_output, Tensor self, Tensor weight, int[2] kernel_size, int[2] stride, int[2] padding, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias) |
| 10260 | static C10_NOINLINE c10::TypedOperatorHandle<_slow_conv2d_backward_output_mask::schema> create__slow_conv2d_backward_output_mask_typed_handle() { |
| 10261 | return c10::Dispatcher::singleton() |
| 10262 | .findSchemaOrThrow(_slow_conv2d_backward_output_mask::name, _slow_conv2d_backward_output_mask::overload_name) |
| 10263 | .typed<_slow_conv2d_backward_output_mask::schema>(); |
| 10264 | } |
| 10265 | |
| 10266 | // aten::_slow_conv2d_backward.output_mask(Tensor grad_output, Tensor self, Tensor weight, int[2] kernel_size, int[2] stride, int[2] padding, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias) |
| 10267 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _slow_conv2d_backward_output_mask::call(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, ::std::array<bool,3> output_mask) { |
| 10268 | |
| 10269 | static auto op = create__slow_conv2d_backward_output_mask_typed_handle(); |
| 10270 | return op.call(grad_output, self, weight, kernel_size, stride, padding, output_mask); |
| 10271 | } |
| 10272 | |
| 10273 | // aten::_slow_conv2d_backward.output_mask(Tensor grad_output, Tensor self, Tensor weight, int[2] kernel_size, int[2] stride, int[2] padding, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias) |
| 10274 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _slow_conv2d_backward_output_mask::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, ::std::array<bool,3> output_mask) { |
| 10275 | |
| 10276 | static auto op = create__slow_conv2d_backward_output_mask_typed_handle(); |
| 10277 | return op.redispatch(dispatchKeySet, grad_output, self, weight, kernel_size, stride, padding, output_mask); |
| 10278 | } |
| 10279 | |
| 10280 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv_depthwise3d, name, "aten::conv_depthwise3d") |
| 10281 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv_depthwise3d, overload_name, "") |
| 10282 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv_depthwise3d, schema_str, "conv_depthwise3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias, int[3] stride, SymInt[3] padding, int[3] dilation) -> Tensor") |
| 10283 | |
| 10284 | // aten::conv_depthwise3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias, int[3] stride, SymInt[3] padding, int[3] dilation) -> Tensor |
| 10285 | static C10_NOINLINE c10::TypedOperatorHandle<conv_depthwise3d::schema> create_conv_depthwise3d_typed_handle() { |
| 10286 | return c10::Dispatcher::singleton() |
| 10287 | .findSchemaOrThrow(conv_depthwise3d::name, conv_depthwise3d::overload_name) |
| 10288 | .typed<conv_depthwise3d::schema>(); |
| 10289 | } |
| 10290 | |
| 10291 | // aten::conv_depthwise3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias, int[3] stride, SymInt[3] padding, int[3] dilation) -> Tensor |
| 10292 | at::Tensor conv_depthwise3d::call(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) { |
| 10293 | |
| 10294 | static auto op = create_conv_depthwise3d_typed_handle(); |
| 10295 | return op.call(self, weight, kernel_size, bias, stride, padding, dilation); |
| 10296 | } |
| 10297 | |
| 10298 | // aten::conv_depthwise3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias, int[3] stride, SymInt[3] padding, int[3] dilation) -> Tensor |
| 10299 | at::Tensor conv_depthwise3d::redispatch(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) { |
| 10300 | |
| 10301 | static auto op = create_conv_depthwise3d_typed_handle(); |
| 10302 | return op.redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, dilation); |
| 10303 | } |
| 10304 | |
| 10305 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slow_conv_dilated2d, name, "aten::slow_conv_dilated2d") |
| 10306 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slow_conv_dilated2d, overload_name, "") |
| 10307 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slow_conv_dilated2d, schema_str, "slow_conv_dilated2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, SymInt[2] padding=0, int[2] dilation=1) -> Tensor") |
| 10308 | |
| 10309 | // aten::slow_conv_dilated2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, SymInt[2] padding=0, int[2] dilation=1) -> Tensor |
| 10310 | static C10_NOINLINE c10::TypedOperatorHandle<slow_conv_dilated2d::schema> create_slow_conv_dilated2d_typed_handle() { |
| 10311 | return c10::Dispatcher::singleton() |
| 10312 | .findSchemaOrThrow(slow_conv_dilated2d::name, slow_conv_dilated2d::overload_name) |
| 10313 | .typed<slow_conv_dilated2d::schema>(); |
| 10314 | } |
| 10315 | |
| 10316 | // aten::slow_conv_dilated2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, SymInt[2] padding=0, int[2] dilation=1) -> Tensor |
| 10317 | at::Tensor slow_conv_dilated2d::call(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) { |
| 10318 | |
| 10319 | static auto op = create_slow_conv_dilated2d_typed_handle(); |
| 10320 | return op.call(self, weight, kernel_size, bias, stride, padding, dilation); |
| 10321 | } |
| 10322 | |
| 10323 | // aten::slow_conv_dilated2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, SymInt[2] padding=0, int[2] dilation=1) -> Tensor |
| 10324 | at::Tensor slow_conv_dilated2d::redispatch(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) { |
| 10325 | |
| 10326 | static auto op = create_slow_conv_dilated2d_typed_handle(); |
| 10327 | return op.redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, dilation); |
| 10328 | } |
| 10329 | |
| 10330 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(col2im_out, name, "aten::col2im") |
| 10331 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(col2im_out, overload_name, "out") |
| 10332 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(col2im_out, schema_str, "col2im.out(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!)") |
| 10333 | |
| 10334 | // aten::col2im.out(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!) |
| 10335 | static C10_NOINLINE c10::TypedOperatorHandle<col2im_out::schema> create_col2im_out_typed_handle() { |
| 10336 | return c10::Dispatcher::singleton() |
| 10337 | .findSchemaOrThrow(col2im_out::name, col2im_out::overload_name) |
| 10338 | .typed<col2im_out::schema>(); |
| 10339 | } |
| 10340 | |
| 10341 | // aten::col2im.out(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!) |
| 10342 | at::Tensor & col2im_out::call(const at::Tensor & self, c10::SymIntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride, at::Tensor & out) { |
| 10343 | |
| 10344 | static auto op = create_col2im_out_typed_handle(); |
| 10345 | return op.call(self, output_size, kernel_size, dilation, padding, stride, out); |
| 10346 | } |
| 10347 | |
| 10348 | // aten::col2im.out(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!) |
| 10349 | at::Tensor & col2im_out::redispatch(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) { |
| 10350 | |
| 10351 | static auto op = create_col2im_out_typed_handle(); |
| 10352 | return op.redispatch(dispatchKeySet, self, output_size, kernel_size, dilation, padding, stride, out); |
| 10353 | } |
| 10354 | |
| 10355 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(col2im, name, "aten::col2im") |
| 10356 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(col2im, overload_name, "") |
| 10357 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(col2im, schema_str, "col2im(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride) -> Tensor") |
| 10358 | |
| 10359 | // aten::col2im(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride) -> Tensor |
| 10360 | static C10_NOINLINE c10::TypedOperatorHandle<col2im::schema> create_col2im_typed_handle() { |
| 10361 | return c10::Dispatcher::singleton() |
| 10362 | .findSchemaOrThrow(col2im::name, col2im::overload_name) |
| 10363 | .typed<col2im::schema>(); |
| 10364 | } |
| 10365 | |
| 10366 | // aten::col2im(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride) -> Tensor |
| 10367 | at::Tensor col2im::call(const at::Tensor & self, c10::SymIntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) { |
| 10368 | |
| 10369 | static auto op = create_col2im_typed_handle(); |
| 10370 | return op.call(self, output_size, kernel_size, dilation, padding, stride); |
| 10371 | } |
| 10372 | |
| 10373 | // aten::col2im(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride) -> Tensor |
| 10374 | at::Tensor col2im::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) { |
| 10375 | |
| 10376 | static auto op = create_col2im_typed_handle(); |
| 10377 | return op.redispatch(dispatchKeySet, self, output_size, kernel_size, dilation, padding, stride); |
| 10378 | } |
| 10379 | |
| 10380 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(isfinite, name, "aten::isfinite") |
| 10381 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(isfinite, overload_name, "") |
| 10382 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(isfinite, schema_str, "isfinite(Tensor self) -> Tensor") |
| 10383 | |
| 10384 | // aten::isfinite(Tensor self) -> Tensor |
| 10385 | static C10_NOINLINE c10::TypedOperatorHandle<isfinite::schema> create_isfinite_typed_handle() { |
| 10386 | return c10::Dispatcher::singleton() |
| 10387 | .findSchemaOrThrow(isfinite::name, isfinite::overload_name) |
| 10388 | .typed<isfinite::schema>(); |
| 10389 | } |
| 10390 | |
| 10391 | // aten::isfinite(Tensor self) -> Tensor |
| 10392 | at::Tensor isfinite::call(const at::Tensor & self) { |
| 10393 | |
| 10394 | static auto op = create_isfinite_typed_handle(); |
| 10395 | return op.call(self); |
| 10396 | } |
| 10397 | |
| 10398 | // aten::isfinite(Tensor self) -> Tensor |
| 10399 | at::Tensor isfinite::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 10400 | |
| 10401 | static auto op = create_isfinite_typed_handle(); |
| 10402 | return op.redispatch(dispatchKeySet, self); |
| 10403 | } |
| 10404 | |
| 10405 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(record_stream, name, "aten::record_stream") |
| 10406 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(record_stream, overload_name, "") |
| 10407 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(record_stream, schema_str, "record_stream(Tensor(a!) self, Stream s) -> ()") |
| 10408 | |
| 10409 | // aten::record_stream(Tensor(a!) self, Stream s) -> () |
| 10410 | static C10_NOINLINE c10::TypedOperatorHandle<record_stream::schema> create_record_stream_typed_handle() { |
| 10411 | return c10::Dispatcher::singleton() |
| 10412 | .findSchemaOrThrow(record_stream::name, record_stream::overload_name) |
| 10413 | .typed<record_stream::schema>(); |
| 10414 | } |
| 10415 | |
| 10416 | // aten::record_stream(Tensor(a!) self, Stream s) -> () |
| 10417 | void record_stream::call(at::Tensor & self, at::Stream s) { |
| 10418 | |
| 10419 | static auto op = create_record_stream_typed_handle(); |
| 10420 | return op.call(self, s); |
| 10421 | } |
| 10422 | |
| 10423 | // aten::record_stream(Tensor(a!) self, Stream s) -> () |
| 10424 | void record_stream::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, at::Stream s) { |
| 10425 | |
| 10426 | static auto op = create_record_stream_typed_handle(); |
| 10427 | return op.redispatch(dispatchKeySet, self, s); |
| 10428 | } |
| 10429 | |
| 10430 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(isposinf, name, "aten::isposinf") |
| 10431 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(isposinf, overload_name, "") |
| 10432 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(isposinf, schema_str, "isposinf(Tensor self) -> Tensor") |
| 10433 | |
| 10434 | // aten::isposinf(Tensor self) -> Tensor |
| 10435 | static C10_NOINLINE c10::TypedOperatorHandle<isposinf::schema> create_isposinf_typed_handle() { |
| 10436 | return c10::Dispatcher::singleton() |
| 10437 | .findSchemaOrThrow(isposinf::name, isposinf::overload_name) |
| 10438 | .typed<isposinf::schema>(); |
| 10439 | } |
| 10440 | |
| 10441 | // aten::isposinf(Tensor self) -> Tensor |
| 10442 | at::Tensor isposinf::call(const at::Tensor & self) { |
| 10443 | |
| 10444 | static auto op = create_isposinf_typed_handle(); |
| 10445 | return op.call(self); |
| 10446 | } |
| 10447 | |
| 10448 | // aten::isposinf(Tensor self) -> Tensor |
| 10449 | at::Tensor isposinf::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 10450 | |
| 10451 | static auto op = create_isposinf_typed_handle(); |
| 10452 | return op.redispatch(dispatchKeySet, self); |
| 10453 | } |
| 10454 | |
| 10455 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(isposinf_out, name, "aten::isposinf") |
| 10456 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(isposinf_out, overload_name, "out") |
| 10457 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(isposinf_out, schema_str, "isposinf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 10458 | |
| 10459 | // aten::isposinf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 10460 | static C10_NOINLINE c10::TypedOperatorHandle<isposinf_out::schema> create_isposinf_out_typed_handle() { |
| 10461 | return c10::Dispatcher::singleton() |
| 10462 | .findSchemaOrThrow(isposinf_out::name, isposinf_out::overload_name) |
| 10463 | .typed<isposinf_out::schema>(); |
| 10464 | } |
| 10465 | |
| 10466 | // aten::isposinf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 10467 | at::Tensor & isposinf_out::call(const at::Tensor & self, at::Tensor & out) { |
| 10468 | |
| 10469 | static auto op = create_isposinf_out_typed_handle(); |
| 10470 | return op.call(self, out); |
| 10471 | } |
| 10472 | |
| 10473 | // aten::isposinf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 10474 | at::Tensor & isposinf_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 10475 | |
| 10476 | static auto op = create_isposinf_out_typed_handle(); |
| 10477 | return op.redispatch(dispatchKeySet, self, out); |
| 10478 | } |
| 10479 | |
| 10480 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_expm1, name, "aten::special_expm1") |
| 10481 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_expm1, overload_name, "") |
| 10482 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_expm1, schema_str, "special_expm1(Tensor self) -> Tensor") |
| 10483 | |
| 10484 | // aten::special_expm1(Tensor self) -> Tensor |
| 10485 | static C10_NOINLINE c10::TypedOperatorHandle<special_expm1::schema> create_special_expm1_typed_handle() { |
| 10486 | return c10::Dispatcher::singleton() |
| 10487 | .findSchemaOrThrow(special_expm1::name, special_expm1::overload_name) |
| 10488 | .typed<special_expm1::schema>(); |
| 10489 | } |
| 10490 | |
| 10491 | // aten::special_expm1(Tensor self) -> Tensor |
| 10492 | at::Tensor special_expm1::call(const at::Tensor & self) { |
| 10493 | |
| 10494 | static auto op = create_special_expm1_typed_handle(); |
| 10495 | return op.call(self); |
| 10496 | } |
| 10497 | |
| 10498 | // aten::special_expm1(Tensor self) -> Tensor |
| 10499 | at::Tensor special_expm1::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 10500 | |
| 10501 | static auto op = create_special_expm1_typed_handle(); |
| 10502 | return op.redispatch(dispatchKeySet, self); |
| 10503 | } |
| 10504 | |
| 10505 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_expm1_out, name, "aten::special_expm1") |
| 10506 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_expm1_out, overload_name, "out") |
| 10507 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_expm1_out, schema_str, "special_expm1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 10508 | |
| 10509 | // aten::special_expm1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 10510 | static C10_NOINLINE c10::TypedOperatorHandle<special_expm1_out::schema> create_special_expm1_out_typed_handle() { |
| 10511 | return c10::Dispatcher::singleton() |
| 10512 | .findSchemaOrThrow(special_expm1_out::name, special_expm1_out::overload_name) |
| 10513 | .typed<special_expm1_out::schema>(); |
| 10514 | } |
| 10515 | |
| 10516 | // aten::special_expm1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 10517 | at::Tensor & special_expm1_out::call(const at::Tensor & self, at::Tensor & out) { |
| 10518 | |
| 10519 | static auto op = create_special_expm1_out_typed_handle(); |
| 10520 | return op.call(self, out); |
| 10521 | } |
| 10522 | |
| 10523 | // aten::special_expm1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 10524 | at::Tensor & special_expm1_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 10525 | |
| 10526 | static auto op = create_special_expm1_out_typed_handle(); |
| 10527 | return op.redispatch(dispatchKeySet, self, out); |
| 10528 | } |
| 10529 | |
| 10530 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_exp2, name, "aten::special_exp2") |
| 10531 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_exp2, overload_name, "") |
| 10532 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_exp2, schema_str, "special_exp2(Tensor self) -> Tensor") |
| 10533 | |
| 10534 | // aten::special_exp2(Tensor self) -> Tensor |
| 10535 | static C10_NOINLINE c10::TypedOperatorHandle<special_exp2::schema> create_special_exp2_typed_handle() { |
| 10536 | return c10::Dispatcher::singleton() |
| 10537 | .findSchemaOrThrow(special_exp2::name, special_exp2::overload_name) |
| 10538 | .typed<special_exp2::schema>(); |
| 10539 | } |
| 10540 | |
| 10541 | // aten::special_exp2(Tensor self) -> Tensor |
| 10542 | at::Tensor special_exp2::call(const at::Tensor & self) { |
| 10543 | |
| 10544 | static auto op = create_special_exp2_typed_handle(); |
| 10545 | return op.call(self); |
| 10546 | } |
| 10547 | |
| 10548 | // aten::special_exp2(Tensor self) -> Tensor |
| 10549 | at::Tensor special_exp2::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 10550 | |
| 10551 | static auto op = create_special_exp2_typed_handle(); |
| 10552 | return op.redispatch(dispatchKeySet, self); |
| 10553 | } |
| 10554 | |
| 10555 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_exp2_out, name, "aten::special_exp2") |
| 10556 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_exp2_out, overload_name, "out") |
| 10557 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_exp2_out, schema_str, "special_exp2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 10558 | |
| 10559 | // aten::special_exp2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 10560 | static C10_NOINLINE c10::TypedOperatorHandle<special_exp2_out::schema> create_special_exp2_out_typed_handle() { |
| 10561 | return c10::Dispatcher::singleton() |
| 10562 | .findSchemaOrThrow(special_exp2_out::name, special_exp2_out::overload_name) |
| 10563 | .typed<special_exp2_out::schema>(); |
| 10564 | } |
| 10565 | |
| 10566 | // aten::special_exp2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 10567 | at::Tensor & special_exp2_out::call(const at::Tensor & self, at::Tensor & out) { |
| 10568 | |
| 10569 | static auto op = create_special_exp2_out_typed_handle(); |
| 10570 | return op.call(self, out); |
| 10571 | } |
| 10572 | |
| 10573 | // aten::special_exp2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 10574 | at::Tensor & special_exp2_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 10575 | |
| 10576 | static auto op = create_special_exp2_out_typed_handle(); |
| 10577 | return op.redispatch(dispatchKeySet, self, out); |
| 10578 | } |
| 10579 | |
| 10580 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_gammaln, name, "aten::special_gammaln") |
| 10581 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_gammaln, overload_name, "") |
| 10582 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_gammaln, schema_str, "special_gammaln(Tensor self) -> Tensor") |
| 10583 | |
| 10584 | // aten::special_gammaln(Tensor self) -> Tensor |
| 10585 | static C10_NOINLINE c10::TypedOperatorHandle<special_gammaln::schema> create_special_gammaln_typed_handle() { |
| 10586 | return c10::Dispatcher::singleton() |
| 10587 | .findSchemaOrThrow(special_gammaln::name, special_gammaln::overload_name) |
| 10588 | .typed<special_gammaln::schema>(); |
| 10589 | } |
| 10590 | |
| 10591 | // aten::special_gammaln(Tensor self) -> Tensor |
| 10592 | at::Tensor special_gammaln::call(const at::Tensor & self) { |
| 10593 | |
| 10594 | static auto op = create_special_gammaln_typed_handle(); |
| 10595 | return op.call(self); |
| 10596 | } |
| 10597 | |
| 10598 | // aten::special_gammaln(Tensor self) -> Tensor |
| 10599 | at::Tensor special_gammaln::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 10600 | |
| 10601 | static auto op = create_special_gammaln_typed_handle(); |
| 10602 | return op.redispatch(dispatchKeySet, self); |
| 10603 | } |
| 10604 | |
| 10605 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_gammaln_out, name, "aten::special_gammaln") |
| 10606 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_gammaln_out, overload_name, "out") |
| 10607 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_gammaln_out, schema_str, "special_gammaln.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 10608 | |
| 10609 | // aten::special_gammaln.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 10610 | static C10_NOINLINE c10::TypedOperatorHandle<special_gammaln_out::schema> create_special_gammaln_out_typed_handle() { |
| 10611 | return c10::Dispatcher::singleton() |
| 10612 | .findSchemaOrThrow(special_gammaln_out::name, special_gammaln_out::overload_name) |
| 10613 | .typed<special_gammaln_out::schema>(); |
| 10614 | } |
| 10615 | |
| 10616 | // aten::special_gammaln.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 10617 | at::Tensor & special_gammaln_out::call(const at::Tensor & self, at::Tensor & out) { |
| 10618 | |
| 10619 | static auto op = create_special_gammaln_out_typed_handle(); |
| 10620 | return op.call(self, out); |
| 10621 | } |
| 10622 | |
| 10623 | // aten::special_gammaln.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 10624 | at::Tensor & special_gammaln_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 10625 | |
| 10626 | static auto op = create_special_gammaln_out_typed_handle(); |
| 10627 | return op.redispatch(dispatchKeySet, self, out); |
| 10628 | } |
| 10629 | |
| 10630 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_erfinv, name, "aten::special_erfinv") |
| 10631 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_erfinv, overload_name, "") |
| 10632 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_erfinv, schema_str, "special_erfinv(Tensor self) -> Tensor") |
| 10633 | |
| 10634 | // aten::special_erfinv(Tensor self) -> Tensor |
| 10635 | static C10_NOINLINE c10::TypedOperatorHandle<special_erfinv::schema> create_special_erfinv_typed_handle() { |
| 10636 | return c10::Dispatcher::singleton() |
| 10637 | .findSchemaOrThrow(special_erfinv::name, special_erfinv::overload_name) |
| 10638 | .typed<special_erfinv::schema>(); |
| 10639 | } |
| 10640 | |
| 10641 | // aten::special_erfinv(Tensor self) -> Tensor |
| 10642 | at::Tensor special_erfinv::call(const at::Tensor & self) { |
| 10643 | |
| 10644 | static auto op = create_special_erfinv_typed_handle(); |
| 10645 | return op.call(self); |
| 10646 | } |
| 10647 | |
| 10648 | // aten::special_erfinv(Tensor self) -> Tensor |
| 10649 | at::Tensor special_erfinv::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 10650 | |
| 10651 | static auto op = create_special_erfinv_typed_handle(); |
| 10652 | return op.redispatch(dispatchKeySet, self); |
| 10653 | } |
| 10654 | |
| 10655 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_erfinv_out, name, "aten::special_erfinv") |
| 10656 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_erfinv_out, overload_name, "out") |
| 10657 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_erfinv_out, schema_str, "special_erfinv.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 10658 | |
| 10659 | // aten::special_erfinv.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 10660 | static C10_NOINLINE c10::TypedOperatorHandle<special_erfinv_out::schema> create_special_erfinv_out_typed_handle() { |
| 10661 | return c10::Dispatcher::singleton() |
| 10662 | .findSchemaOrThrow(special_erfinv_out::name, special_erfinv_out::overload_name) |
| 10663 | .typed<special_erfinv_out::schema>(); |
| 10664 | } |
| 10665 | |
| 10666 | // aten::special_erfinv.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 10667 | at::Tensor & special_erfinv_out::call(const at::Tensor & self, at::Tensor & out) { |
| 10668 | |
| 10669 | static auto op = create_special_erfinv_out_typed_handle(); |
| 10670 | return op.call(self, out); |
| 10671 | } |
| 10672 | |
| 10673 | // aten::special_erfinv.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 10674 | at::Tensor & special_erfinv_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 10675 | |
| 10676 | static auto op = create_special_erfinv_out_typed_handle(); |
| 10677 | return op.redispatch(dispatchKeySet, self, out); |
| 10678 | } |
| 10679 | |
| 10680 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlog1py, name, "aten::special_xlog1py") |
| 10681 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlog1py, overload_name, "") |
| 10682 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlog1py, schema_str, "special_xlog1py(Tensor self, Tensor other) -> Tensor") |
| 10683 | |
| 10684 | // aten::special_xlog1py(Tensor self, Tensor other) -> Tensor |
| 10685 | static C10_NOINLINE c10::TypedOperatorHandle<special_xlog1py::schema> create_special_xlog1py_typed_handle() { |
| 10686 | return c10::Dispatcher::singleton() |
| 10687 | .findSchemaOrThrow(special_xlog1py::name, special_xlog1py::overload_name) |
| 10688 | .typed<special_xlog1py::schema>(); |
| 10689 | } |
| 10690 | |
| 10691 | // aten::special_xlog1py(Tensor self, Tensor other) -> Tensor |
| 10692 | at::Tensor special_xlog1py::call(const at::Tensor & self, const at::Tensor & other) { |
| 10693 | |
| 10694 | static auto op = create_special_xlog1py_typed_handle(); |
| 10695 | return op.call(self, other); |
| 10696 | } |
| 10697 | |
| 10698 | // aten::special_xlog1py(Tensor self, Tensor other) -> Tensor |
| 10699 | at::Tensor special_xlog1py::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { |
| 10700 | |
| 10701 | static auto op = create_special_xlog1py_typed_handle(); |
| 10702 | return op.redispatch(dispatchKeySet, self, other); |
| 10703 | } |
| 10704 | |
| 10705 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlog1py_self_scalar, name, "aten::special_xlog1py") |
| 10706 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlog1py_self_scalar, overload_name, "self_scalar") |
| 10707 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlog1py_self_scalar, schema_str, "special_xlog1py.self_scalar(Scalar self, Tensor other) -> Tensor") |
| 10708 | |
| 10709 | // aten::special_xlog1py.self_scalar(Scalar self, Tensor other) -> Tensor |
| 10710 | static C10_NOINLINE c10::TypedOperatorHandle<special_xlog1py_self_scalar::schema> create_special_xlog1py_self_scalar_typed_handle() { |
| 10711 | return c10::Dispatcher::singleton() |
| 10712 | .findSchemaOrThrow(special_xlog1py_self_scalar::name, special_xlog1py_self_scalar::overload_name) |
| 10713 | .typed<special_xlog1py_self_scalar::schema>(); |
| 10714 | } |
| 10715 | |
| 10716 | // aten::special_xlog1py.self_scalar(Scalar self, Tensor other) -> Tensor |
| 10717 | at::Tensor special_xlog1py_self_scalar::call(const at::Scalar & self, const at::Tensor & other) { |
| 10718 | |
| 10719 | static auto op = create_special_xlog1py_self_scalar_typed_handle(); |
| 10720 | return op.call(self, other); |
| 10721 | } |
| 10722 | |
| 10723 | // aten::special_xlog1py.self_scalar(Scalar self, Tensor other) -> Tensor |
| 10724 | at::Tensor special_xlog1py_self_scalar::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other) { |
| 10725 | |
| 10726 | static auto op = create_special_xlog1py_self_scalar_typed_handle(); |
| 10727 | return op.redispatch(dispatchKeySet, self, other); |
| 10728 | } |
| 10729 | |
| 10730 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlog1py_other_scalar, name, "aten::special_xlog1py") |
| 10731 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlog1py_other_scalar, overload_name, "other_scalar") |
| 10732 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlog1py_other_scalar, schema_str, "special_xlog1py.other_scalar(Tensor self, Scalar other) -> Tensor") |
| 10733 | |
| 10734 | // aten::special_xlog1py.other_scalar(Tensor self, Scalar other) -> Tensor |
| 10735 | static C10_NOINLINE c10::TypedOperatorHandle<special_xlog1py_other_scalar::schema> create_special_xlog1py_other_scalar_typed_handle() { |
| 10736 | return c10::Dispatcher::singleton() |
| 10737 | .findSchemaOrThrow(special_xlog1py_other_scalar::name, special_xlog1py_other_scalar::overload_name) |
| 10738 | .typed<special_xlog1py_other_scalar::schema>(); |
| 10739 | } |
| 10740 | |
| 10741 | // aten::special_xlog1py.other_scalar(Tensor self, Scalar other) -> Tensor |
| 10742 | at::Tensor special_xlog1py_other_scalar::call(const at::Tensor & self, const at::Scalar & other) { |
| 10743 | |
| 10744 | static auto op = create_special_xlog1py_other_scalar_typed_handle(); |
| 10745 | return op.call(self, other); |
| 10746 | } |
| 10747 | |
| 10748 | // aten::special_xlog1py.other_scalar(Tensor self, Scalar other) -> Tensor |
| 10749 | at::Tensor special_xlog1py_other_scalar::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { |
| 10750 | |
| 10751 | static auto op = create_special_xlog1py_other_scalar_typed_handle(); |
| 10752 | return op.redispatch(dispatchKeySet, self, other); |
| 10753 | } |
| 10754 | |
| 10755 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlog1py_out, name, "aten::special_xlog1py") |
| 10756 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlog1py_out, overload_name, "out") |
| 10757 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlog1py_out, schema_str, "special_xlog1py.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)") |
| 10758 | |
| 10759 | // aten::special_xlog1py.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 10760 | static C10_NOINLINE c10::TypedOperatorHandle<special_xlog1py_out::schema> create_special_xlog1py_out_typed_handle() { |
| 10761 | return c10::Dispatcher::singleton() |
| 10762 | .findSchemaOrThrow(special_xlog1py_out::name, special_xlog1py_out::overload_name) |
| 10763 | .typed<special_xlog1py_out::schema>(); |
| 10764 | } |
| 10765 | |
| 10766 | // aten::special_xlog1py.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 10767 | at::Tensor & special_xlog1py_out::call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 10768 | |
| 10769 | static auto op = create_special_xlog1py_out_typed_handle(); |
| 10770 | return op.call(self, other, out); |
| 10771 | } |
| 10772 | |
| 10773 | // aten::special_xlog1py.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 10774 | at::Tensor & special_xlog1py_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 10775 | |
| 10776 | static auto op = create_special_xlog1py_out_typed_handle(); |
| 10777 | return op.redispatch(dispatchKeySet, self, other, out); |
| 10778 | } |
| 10779 | |
| 10780 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlog1py_self_scalar_out, name, "aten::special_xlog1py") |
| 10781 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlog1py_self_scalar_out, overload_name, "self_scalar_out") |
| 10782 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlog1py_self_scalar_out, schema_str, "special_xlog1py.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)") |
| 10783 | |
| 10784 | // aten::special_xlog1py.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 10785 | static C10_NOINLINE c10::TypedOperatorHandle<special_xlog1py_self_scalar_out::schema> create_special_xlog1py_self_scalar_out_typed_handle() { |
| 10786 | return c10::Dispatcher::singleton() |
| 10787 | .findSchemaOrThrow(special_xlog1py_self_scalar_out::name, special_xlog1py_self_scalar_out::overload_name) |
| 10788 | .typed<special_xlog1py_self_scalar_out::schema>(); |
| 10789 | } |
| 10790 | |
| 10791 | // aten::special_xlog1py.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 10792 | at::Tensor & special_xlog1py_self_scalar_out::call(const at::Scalar & self, const at::Tensor & other, at::Tensor & out) { |
| 10793 | |
| 10794 | static auto op = create_special_xlog1py_self_scalar_out_typed_handle(); |
| 10795 | return op.call(self, other, out); |
| 10796 | } |
| 10797 | |
| 10798 | // aten::special_xlog1py.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 10799 | at::Tensor & special_xlog1py_self_scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other, at::Tensor & out) { |
| 10800 | |
| 10801 | static auto op = create_special_xlog1py_self_scalar_out_typed_handle(); |
| 10802 | return op.redispatch(dispatchKeySet, self, other, out); |
| 10803 | } |
| 10804 | |
| 10805 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlog1py_other_scalar_out, name, "aten::special_xlog1py") |
| 10806 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlog1py_other_scalar_out, overload_name, "other_scalar_out") |
| 10807 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlog1py_other_scalar_out, schema_str, "special_xlog1py.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)") |
| 10808 | |
| 10809 | // aten::special_xlog1py.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) |
| 10810 | static C10_NOINLINE c10::TypedOperatorHandle<special_xlog1py_other_scalar_out::schema> create_special_xlog1py_other_scalar_out_typed_handle() { |
| 10811 | return c10::Dispatcher::singleton() |
| 10812 | .findSchemaOrThrow(special_xlog1py_other_scalar_out::name, special_xlog1py_other_scalar_out::overload_name) |
| 10813 | .typed<special_xlog1py_other_scalar_out::schema>(); |
| 10814 | } |
| 10815 | |
| 10816 | // aten::special_xlog1py.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) |
| 10817 | at::Tensor & special_xlog1py_other_scalar_out::call(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 10818 | |
| 10819 | static auto op = create_special_xlog1py_other_scalar_out_typed_handle(); |
| 10820 | return op.call(self, other, out); |
| 10821 | } |
| 10822 | |
| 10823 | // aten::special_xlog1py.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) |
| 10824 | at::Tensor & special_xlog1py_other_scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 10825 | |
| 10826 | static auto op = create_special_xlog1py_other_scalar_out_typed_handle(); |
| 10827 | return op.redispatch(dispatchKeySet, self, other, out); |
| 10828 | } |
| 10829 | |
| 10830 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_i0, name, "aten::special_i0") |
| 10831 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_i0, overload_name, "") |
| 10832 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_i0, schema_str, "special_i0(Tensor self) -> Tensor") |
| 10833 | |
| 10834 | // aten::special_i0(Tensor self) -> Tensor |
| 10835 | static C10_NOINLINE c10::TypedOperatorHandle<special_i0::schema> create_special_i0_typed_handle() { |
| 10836 | return c10::Dispatcher::singleton() |
| 10837 | .findSchemaOrThrow(special_i0::name, special_i0::overload_name) |
| 10838 | .typed<special_i0::schema>(); |
| 10839 | } |
| 10840 | |
| 10841 | // aten::special_i0(Tensor self) -> Tensor |
| 10842 | at::Tensor special_i0::call(const at::Tensor & self) { |
| 10843 | |
| 10844 | static auto op = create_special_i0_typed_handle(); |
| 10845 | return op.call(self); |
| 10846 | } |
| 10847 | |
| 10848 | // aten::special_i0(Tensor self) -> Tensor |
| 10849 | at::Tensor special_i0::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 10850 | |
| 10851 | static auto op = create_special_i0_typed_handle(); |
| 10852 | return op.redispatch(dispatchKeySet, self); |
| 10853 | } |
| 10854 | |
| 10855 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_i0_out, name, "aten::special_i0") |
| 10856 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_i0_out, overload_name, "out") |
| 10857 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_i0_out, schema_str, "special_i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 10858 | |
| 10859 | // aten::special_i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 10860 | static C10_NOINLINE c10::TypedOperatorHandle<special_i0_out::schema> create_special_i0_out_typed_handle() { |
| 10861 | return c10::Dispatcher::singleton() |
| 10862 | .findSchemaOrThrow(special_i0_out::name, special_i0_out::overload_name) |
| 10863 | .typed<special_i0_out::schema>(); |
| 10864 | } |
| 10865 | |
| 10866 | // aten::special_i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 10867 | at::Tensor & special_i0_out::call(const at::Tensor & self, at::Tensor & out) { |
| 10868 | |
| 10869 | static auto op = create_special_i0_out_typed_handle(); |
| 10870 | return op.call(self, out); |
| 10871 | } |
| 10872 | |
| 10873 | // aten::special_i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 10874 | at::Tensor & special_i0_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 10875 | |
| 10876 | static auto op = create_special_i0_out_typed_handle(); |
| 10877 | return op.redispatch(dispatchKeySet, self, out); |
| 10878 | } |
| 10879 | |
| 10880 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_polygamma, name, "aten::special_polygamma") |
| 10881 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_polygamma, overload_name, "") |
| 10882 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_polygamma, schema_str, "special_polygamma(int n, Tensor self) -> Tensor") |
| 10883 | |
| 10884 | // aten::special_polygamma(int n, Tensor self) -> Tensor |
| 10885 | static C10_NOINLINE c10::TypedOperatorHandle<special_polygamma::schema> create_special_polygamma_typed_handle() { |
| 10886 | return c10::Dispatcher::singleton() |
| 10887 | .findSchemaOrThrow(special_polygamma::name, special_polygamma::overload_name) |
| 10888 | .typed<special_polygamma::schema>(); |
| 10889 | } |
| 10890 | |
| 10891 | // aten::special_polygamma(int n, Tensor self) -> Tensor |
| 10892 | at::Tensor special_polygamma::call(int64_t n, const at::Tensor & self) { |
| 10893 | |
| 10894 | static auto op = create_special_polygamma_typed_handle(); |
| 10895 | return op.call(n, self); |
| 10896 | } |
| 10897 | |
| 10898 | // aten::special_polygamma(int n, Tensor self) -> Tensor |
| 10899 | at::Tensor special_polygamma::redispatch(c10::DispatchKeySet dispatchKeySet, int64_t n, const at::Tensor & self) { |
| 10900 | |
| 10901 | static auto op = create_special_polygamma_typed_handle(); |
| 10902 | return op.redispatch(dispatchKeySet, n, self); |
| 10903 | } |
| 10904 | |
| 10905 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_polygamma_out, name, "aten::special_polygamma") |
| 10906 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_polygamma_out, overload_name, "out") |
| 10907 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_polygamma_out, schema_str, "special_polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 10908 | |
| 10909 | // aten::special_polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 10910 | static C10_NOINLINE c10::TypedOperatorHandle<special_polygamma_out::schema> create_special_polygamma_out_typed_handle() { |
| 10911 | return c10::Dispatcher::singleton() |
| 10912 | .findSchemaOrThrow(special_polygamma_out::name, special_polygamma_out::overload_name) |
| 10913 | .typed<special_polygamma_out::schema>(); |
| 10914 | } |
| 10915 | |
| 10916 | // aten::special_polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 10917 | at::Tensor & special_polygamma_out::call(int64_t n, const at::Tensor & self, at::Tensor & out) { |
| 10918 | |
| 10919 | static auto op = create_special_polygamma_out_typed_handle(); |
| 10920 | return op.call(n, self, out); |
| 10921 | } |
| 10922 | |
| 10923 | // aten::special_polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 10924 | at::Tensor & special_polygamma_out::redispatch(c10::DispatchKeySet dispatchKeySet, int64_t n, const at::Tensor & self, at::Tensor & out) { |
| 10925 | |
| 10926 | static auto op = create_special_polygamma_out_typed_handle(); |
| 10927 | return op.redispatch(dispatchKeySet, n, self, out); |
| 10928 | } |
| 10929 | |
| 10930 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_log1p, name, "aten::special_log1p") |
| 10931 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_log1p, overload_name, "") |
| 10932 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_log1p, schema_str, "special_log1p(Tensor self) -> Tensor") |
| 10933 | |
| 10934 | // aten::special_log1p(Tensor self) -> Tensor |
| 10935 | static C10_NOINLINE c10::TypedOperatorHandle<special_log1p::schema> create_special_log1p_typed_handle() { |
| 10936 | return c10::Dispatcher::singleton() |
| 10937 | .findSchemaOrThrow(special_log1p::name, special_log1p::overload_name) |
| 10938 | .typed<special_log1p::schema>(); |
| 10939 | } |
| 10940 | |
| 10941 | // aten::special_log1p(Tensor self) -> Tensor |
| 10942 | at::Tensor special_log1p::call(const at::Tensor & self) { |
| 10943 | |
| 10944 | static auto op = create_special_log1p_typed_handle(); |
| 10945 | return op.call(self); |
| 10946 | } |
| 10947 | |
| 10948 | // aten::special_log1p(Tensor self) -> Tensor |
| 10949 | at::Tensor special_log1p::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 10950 | |
| 10951 | static auto op = create_special_log1p_typed_handle(); |
| 10952 | return op.redispatch(dispatchKeySet, self); |
| 10953 | } |
| 10954 | |
| 10955 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_log1p_out, name, "aten::special_log1p") |
| 10956 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_log1p_out, overload_name, "out") |
| 10957 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_log1p_out, schema_str, "special_log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 10958 | |
| 10959 | // aten::special_log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 10960 | static C10_NOINLINE c10::TypedOperatorHandle<special_log1p_out::schema> create_special_log1p_out_typed_handle() { |
| 10961 | return c10::Dispatcher::singleton() |
| 10962 | .findSchemaOrThrow(special_log1p_out::name, special_log1p_out::overload_name) |
| 10963 | .typed<special_log1p_out::schema>(); |
| 10964 | } |
| 10965 | |
| 10966 | // aten::special_log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 10967 | at::Tensor & special_log1p_out::call(const at::Tensor & self, at::Tensor & out) { |
| 10968 | |
| 10969 | static auto op = create_special_log1p_out_typed_handle(); |
| 10970 | return op.call(self, out); |
| 10971 | } |
| 10972 | |
| 10973 | // aten::special_log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 10974 | at::Tensor & special_log1p_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 10975 | |
| 10976 | static auto op = create_special_log1p_out_typed_handle(); |
| 10977 | return op.redispatch(dispatchKeySet, self, out); |
| 10978 | } |
| 10979 | |
| 10980 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_irfft, name, "aten::fft_irfft") |
| 10981 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_irfft, overload_name, "") |
| 10982 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_irfft, schema_str, "fft_irfft(Tensor self, int? n=None, int dim=-1, str? norm=None) -> Tensor") |
| 10983 | |
| 10984 | // aten::fft_irfft(Tensor self, int? n=None, int dim=-1, str? norm=None) -> Tensor |
| 10985 | static C10_NOINLINE c10::TypedOperatorHandle<fft_irfft::schema> create_fft_irfft_typed_handle() { |
| 10986 | return c10::Dispatcher::singleton() |
| 10987 | .findSchemaOrThrow(fft_irfft::name, fft_irfft::overload_name) |
| 10988 | .typed<fft_irfft::schema>(); |
| 10989 | } |
| 10990 | |
| 10991 | // aten::fft_irfft(Tensor self, int? n=None, int dim=-1, str? norm=None) -> Tensor |
| 10992 | at::Tensor fft_irfft::call(const at::Tensor & self, c10::optional<int64_t> n, int64_t dim, c10::optional<c10::string_view> norm) { |
| 10993 | |
| 10994 | static auto op = create_fft_irfft_typed_handle(); |
| 10995 | return op.call(self, n, dim, norm); |
| 10996 | } |
| 10997 | |
| 10998 | // aten::fft_irfft(Tensor self, int? n=None, int dim=-1, str? norm=None) -> Tensor |
| 10999 | at::Tensor fft_irfft::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<int64_t> n, int64_t dim, c10::optional<c10::string_view> norm) { |
| 11000 | |
| 11001 | static auto op = create_fft_irfft_typed_handle(); |
| 11002 | return op.redispatch(dispatchKeySet, self, n, dim, norm); |
| 11003 | } |
| 11004 | |
| 11005 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_irfft_out, name, "aten::fft_irfft") |
| 11006 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_irfft_out, overload_name, "out") |
| 11007 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_irfft_out, schema_str, "fft_irfft.out(Tensor self, int? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 11008 | |
| 11009 | // aten::fft_irfft.out(Tensor self, int? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11010 | static C10_NOINLINE c10::TypedOperatorHandle<fft_irfft_out::schema> create_fft_irfft_out_typed_handle() { |
| 11011 | return c10::Dispatcher::singleton() |
| 11012 | .findSchemaOrThrow(fft_irfft_out::name, fft_irfft_out::overload_name) |
| 11013 | .typed<fft_irfft_out::schema>(); |
| 11014 | } |
| 11015 | |
| 11016 | // aten::fft_irfft.out(Tensor self, int? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11017 | at::Tensor & fft_irfft_out::call(const at::Tensor & self, c10::optional<int64_t> n, int64_t dim, c10::optional<c10::string_view> norm, at::Tensor & out) { |
| 11018 | |
| 11019 | static auto op = create_fft_irfft_out_typed_handle(); |
| 11020 | return op.call(self, n, dim, norm, out); |
| 11021 | } |
| 11022 | |
| 11023 | // aten::fft_irfft.out(Tensor self, int? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11024 | at::Tensor & fft_irfft_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<int64_t> n, int64_t dim, c10::optional<c10::string_view> norm, at::Tensor & out) { |
| 11025 | |
| 11026 | static auto op = create_fft_irfft_out_typed_handle(); |
| 11027 | return op.redispatch(dispatchKeySet, self, n, dim, norm, out); |
| 11028 | } |
| 11029 | |
| 11030 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_ifft2, name, "aten::fft_ifft2") |
| 11031 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_ifft2, overload_name, "") |
| 11032 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_ifft2, schema_str, "fft_ifft2(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor") |
| 11033 | |
| 11034 | // aten::fft_ifft2(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor |
| 11035 | static C10_NOINLINE c10::TypedOperatorHandle<fft_ifft2::schema> create_fft_ifft2_typed_handle() { |
| 11036 | return c10::Dispatcher::singleton() |
| 11037 | .findSchemaOrThrow(fft_ifft2::name, fft_ifft2::overload_name) |
| 11038 | .typed<fft_ifft2::schema>(); |
| 11039 | } |
| 11040 | |
| 11041 | // aten::fft_ifft2(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor |
| 11042 | at::Tensor fft_ifft2::call(const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional<c10::string_view> norm) { |
| 11043 | |
| 11044 | static auto op = create_fft_ifft2_typed_handle(); |
| 11045 | return op.call(self, s, dim, norm); |
| 11046 | } |
| 11047 | |
| 11048 | // aten::fft_ifft2(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor |
| 11049 | at::Tensor fft_ifft2::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional<c10::string_view> norm) { |
| 11050 | |
| 11051 | static auto op = create_fft_ifft2_typed_handle(); |
| 11052 | return op.redispatch(dispatchKeySet, self, s, dim, norm); |
| 11053 | } |
| 11054 | |
| 11055 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_ifft2_out, name, "aten::fft_ifft2") |
| 11056 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_ifft2_out, overload_name, "out") |
| 11057 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_ifft2_out, schema_str, "fft_ifft2.out(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 11058 | |
| 11059 | // aten::fft_ifft2.out(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11060 | static C10_NOINLINE c10::TypedOperatorHandle<fft_ifft2_out::schema> create_fft_ifft2_out_typed_handle() { |
| 11061 | return c10::Dispatcher::singleton() |
| 11062 | .findSchemaOrThrow(fft_ifft2_out::name, fft_ifft2_out::overload_name) |
| 11063 | .typed<fft_ifft2_out::schema>(); |
| 11064 | } |
| 11065 | |
| 11066 | // aten::fft_ifft2.out(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11067 | at::Tensor & fft_ifft2_out::call(const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional<c10::string_view> norm, at::Tensor & out) { |
| 11068 | |
| 11069 | static auto op = create_fft_ifft2_out_typed_handle(); |
| 11070 | return op.call(self, s, dim, norm, out); |
| 11071 | } |
| 11072 | |
| 11073 | // aten::fft_ifft2.out(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11074 | at::Tensor & fft_ifft2_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional<c10::string_view> norm, at::Tensor & out) { |
| 11075 | |
| 11076 | static auto op = create_fft_ifft2_out_typed_handle(); |
| 11077 | return op.redispatch(dispatchKeySet, self, s, dim, norm, out); |
| 11078 | } |
| 11079 | |
| 11080 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_irfft2, name, "aten::fft_irfft2") |
| 11081 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_irfft2, overload_name, "") |
| 11082 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_irfft2, schema_str, "fft_irfft2(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor") |
| 11083 | |
| 11084 | // aten::fft_irfft2(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor |
| 11085 | static C10_NOINLINE c10::TypedOperatorHandle<fft_irfft2::schema> create_fft_irfft2_typed_handle() { |
| 11086 | return c10::Dispatcher::singleton() |
| 11087 | .findSchemaOrThrow(fft_irfft2::name, fft_irfft2::overload_name) |
| 11088 | .typed<fft_irfft2::schema>(); |
| 11089 | } |
| 11090 | |
| 11091 | // aten::fft_irfft2(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor |
| 11092 | at::Tensor fft_irfft2::call(const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional<c10::string_view> norm) { |
| 11093 | |
| 11094 | static auto op = create_fft_irfft2_typed_handle(); |
| 11095 | return op.call(self, s, dim, norm); |
| 11096 | } |
| 11097 | |
| 11098 | // aten::fft_irfft2(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor |
| 11099 | at::Tensor fft_irfft2::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional<c10::string_view> norm) { |
| 11100 | |
| 11101 | static auto op = create_fft_irfft2_typed_handle(); |
| 11102 | return op.redispatch(dispatchKeySet, self, s, dim, norm); |
| 11103 | } |
| 11104 | |
| 11105 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_irfft2_out, name, "aten::fft_irfft2") |
| 11106 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_irfft2_out, overload_name, "out") |
| 11107 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_irfft2_out, schema_str, "fft_irfft2.out(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 11108 | |
| 11109 | // aten::fft_irfft2.out(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11110 | static C10_NOINLINE c10::TypedOperatorHandle<fft_irfft2_out::schema> create_fft_irfft2_out_typed_handle() { |
| 11111 | return c10::Dispatcher::singleton() |
| 11112 | .findSchemaOrThrow(fft_irfft2_out::name, fft_irfft2_out::overload_name) |
| 11113 | .typed<fft_irfft2_out::schema>(); |
| 11114 | } |
| 11115 | |
| 11116 | // aten::fft_irfft2.out(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11117 | at::Tensor & fft_irfft2_out::call(const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional<c10::string_view> norm, at::Tensor & out) { |
| 11118 | |
| 11119 | static auto op = create_fft_irfft2_out_typed_handle(); |
| 11120 | return op.call(self, s, dim, norm, out); |
| 11121 | } |
| 11122 | |
| 11123 | // aten::fft_irfft2.out(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11124 | at::Tensor & fft_irfft2_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional<c10::string_view> norm, at::Tensor & out) { |
| 11125 | |
| 11126 | static auto op = create_fft_irfft2_out_typed_handle(); |
| 11127 | return op.redispatch(dispatchKeySet, self, s, dim, norm, out); |
| 11128 | } |
| 11129 | |
| 11130 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_rfftn, name, "aten::fft_rfftn") |
| 11131 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_rfftn, overload_name, "") |
| 11132 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_rfftn, schema_str, "fft_rfftn(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor") |
| 11133 | |
| 11134 | // aten::fft_rfftn(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor |
| 11135 | static C10_NOINLINE c10::TypedOperatorHandle<fft_rfftn::schema> create_fft_rfftn_typed_handle() { |
| 11136 | return c10::Dispatcher::singleton() |
| 11137 | .findSchemaOrThrow(fft_rfftn::name, fft_rfftn::overload_name) |
| 11138 | .typed<fft_rfftn::schema>(); |
| 11139 | } |
| 11140 | |
| 11141 | // aten::fft_rfftn(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor |
| 11142 | at::Tensor fft_rfftn::call(const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional<c10::string_view> norm) { |
| 11143 | |
| 11144 | static auto op = create_fft_rfftn_typed_handle(); |
| 11145 | return op.call(self, s, dim, norm); |
| 11146 | } |
| 11147 | |
| 11148 | // aten::fft_rfftn(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor |
| 11149 | at::Tensor fft_rfftn::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional<c10::string_view> norm) { |
| 11150 | |
| 11151 | static auto op = create_fft_rfftn_typed_handle(); |
| 11152 | return op.redispatch(dispatchKeySet, self, s, dim, norm); |
| 11153 | } |
| 11154 | |
| 11155 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_rfftn_out, name, "aten::fft_rfftn") |
| 11156 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_rfftn_out, overload_name, "out") |
| 11157 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_rfftn_out, schema_str, "fft_rfftn.out(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 11158 | |
| 11159 | // aten::fft_rfftn.out(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11160 | static C10_NOINLINE c10::TypedOperatorHandle<fft_rfftn_out::schema> create_fft_rfftn_out_typed_handle() { |
| 11161 | return c10::Dispatcher::singleton() |
| 11162 | .findSchemaOrThrow(fft_rfftn_out::name, fft_rfftn_out::overload_name) |
| 11163 | .typed<fft_rfftn_out::schema>(); |
| 11164 | } |
| 11165 | |
| 11166 | // aten::fft_rfftn.out(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11167 | at::Tensor & fft_rfftn_out::call(const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional<c10::string_view> norm, at::Tensor & out) { |
| 11168 | |
| 11169 | static auto op = create_fft_rfftn_out_typed_handle(); |
| 11170 | return op.call(self, s, dim, norm, out); |
| 11171 | } |
| 11172 | |
| 11173 | // aten::fft_rfftn.out(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11174 | at::Tensor & fft_rfftn_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional<c10::string_view> norm, at::Tensor & out) { |
| 11175 | |
| 11176 | static auto op = create_fft_rfftn_out_typed_handle(); |
| 11177 | return op.redispatch(dispatchKeySet, self, s, dim, norm, out); |
| 11178 | } |
| 11179 | |
| 11180 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cholesky, name, "aten::linalg_cholesky") |
| 11181 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cholesky, overload_name, "") |
| 11182 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cholesky, schema_str, "linalg_cholesky(Tensor self, *, bool upper=False) -> Tensor") |
| 11183 | |
| 11184 | // aten::linalg_cholesky(Tensor self, *, bool upper=False) -> Tensor |
| 11185 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_cholesky::schema> create_linalg_cholesky_typed_handle() { |
| 11186 | return c10::Dispatcher::singleton() |
| 11187 | .findSchemaOrThrow(linalg_cholesky::name, linalg_cholesky::overload_name) |
| 11188 | .typed<linalg_cholesky::schema>(); |
| 11189 | } |
| 11190 | |
| 11191 | // aten::linalg_cholesky(Tensor self, *, bool upper=False) -> Tensor |
| 11192 | at::Tensor linalg_cholesky::call(const at::Tensor & self, bool upper) { |
| 11193 | |
| 11194 | static auto op = create_linalg_cholesky_typed_handle(); |
| 11195 | return op.call(self, upper); |
| 11196 | } |
| 11197 | |
| 11198 | // aten::linalg_cholesky(Tensor self, *, bool upper=False) -> Tensor |
| 11199 | at::Tensor linalg_cholesky::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool upper) { |
| 11200 | |
| 11201 | static auto op = create_linalg_cholesky_typed_handle(); |
| 11202 | return op.redispatch(dispatchKeySet, self, upper); |
| 11203 | } |
| 11204 | |
| 11205 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cholesky_out, name, "aten::linalg_cholesky") |
| 11206 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cholesky_out, overload_name, "out") |
| 11207 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cholesky_out, schema_str, "linalg_cholesky.out(Tensor self, *, bool upper=False, Tensor(a!) out) -> Tensor(a!)") |
| 11208 | |
| 11209 | // aten::linalg_cholesky.out(Tensor self, *, bool upper=False, Tensor(a!) out) -> Tensor(a!) |
| 11210 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_cholesky_out::schema> create_linalg_cholesky_out_typed_handle() { |
| 11211 | return c10::Dispatcher::singleton() |
| 11212 | .findSchemaOrThrow(linalg_cholesky_out::name, linalg_cholesky_out::overload_name) |
| 11213 | .typed<linalg_cholesky_out::schema>(); |
| 11214 | } |
| 11215 | |
| 11216 | // aten::linalg_cholesky.out(Tensor self, *, bool upper=False, Tensor(a!) out) -> Tensor(a!) |
| 11217 | at::Tensor & linalg_cholesky_out::call(const at::Tensor & self, bool upper, at::Tensor & out) { |
| 11218 | |
| 11219 | static auto op = create_linalg_cholesky_out_typed_handle(); |
| 11220 | return op.call(self, upper, out); |
| 11221 | } |
| 11222 | |
| 11223 | // aten::linalg_cholesky.out(Tensor self, *, bool upper=False, Tensor(a!) out) -> Tensor(a!) |
| 11224 | at::Tensor & linalg_cholesky_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool upper, at::Tensor & out) { |
| 11225 | |
| 11226 | static auto op = create_linalg_cholesky_out_typed_handle(); |
| 11227 | return op.redispatch(dispatchKeySet, self, upper, out); |
| 11228 | } |
| 11229 | |
| 11230 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_linalg_det, name, "aten::_linalg_det") |
| 11231 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_linalg_det, overload_name, "") |
| 11232 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_linalg_det, schema_str, "_linalg_det(Tensor A) -> (Tensor result, Tensor LU, Tensor pivots)") |
| 11233 | |
| 11234 | // aten::_linalg_det(Tensor A) -> (Tensor result, Tensor LU, Tensor pivots) |
| 11235 | static C10_NOINLINE c10::TypedOperatorHandle<_linalg_det::schema> create__linalg_det_typed_handle() { |
| 11236 | return c10::Dispatcher::singleton() |
| 11237 | .findSchemaOrThrow(_linalg_det::name, _linalg_det::overload_name) |
| 11238 | .typed<_linalg_det::schema>(); |
| 11239 | } |
| 11240 | |
| 11241 | // aten::_linalg_det(Tensor A) -> (Tensor result, Tensor LU, Tensor pivots) |
| 11242 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _linalg_det::call(const at::Tensor & A) { |
| 11243 | |
| 11244 | static auto op = create__linalg_det_typed_handle(); |
| 11245 | return op.call(A); |
| 11246 | } |
| 11247 | |
| 11248 | // aten::_linalg_det(Tensor A) -> (Tensor result, Tensor LU, Tensor pivots) |
| 11249 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _linalg_det::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A) { |
| 11250 | |
| 11251 | static auto op = create__linalg_det_typed_handle(); |
| 11252 | return op.redispatch(dispatchKeySet, A); |
| 11253 | } |
| 11254 | |
| 11255 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_linalg_det_result, name, "aten::_linalg_det") |
| 11256 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_linalg_det_result, overload_name, "result") |
| 11257 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_linalg_det_result, schema_str, "_linalg_det.result(Tensor A, *, Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots) -> (Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots)") |
| 11258 | |
| 11259 | // aten::_linalg_det.result(Tensor A, *, Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots) -> (Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots) |
| 11260 | static C10_NOINLINE c10::TypedOperatorHandle<_linalg_det_result::schema> create__linalg_det_result_typed_handle() { |
| 11261 | return c10::Dispatcher::singleton() |
| 11262 | .findSchemaOrThrow(_linalg_det_result::name, _linalg_det_result::overload_name) |
| 11263 | .typed<_linalg_det_result::schema>(); |
| 11264 | } |
| 11265 | |
| 11266 | // aten::_linalg_det.result(Tensor A, *, Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots) -> (Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots) |
| 11267 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _linalg_det_result::call(const at::Tensor & A, at::Tensor & result, at::Tensor & LU, at::Tensor & pivots) { |
| 11268 | |
| 11269 | static auto op = create__linalg_det_result_typed_handle(); |
| 11270 | return op.call(A, result, LU, pivots); |
| 11271 | } |
| 11272 | |
| 11273 | // aten::_linalg_det.result(Tensor A, *, Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots) -> (Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots) |
| 11274 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _linalg_det_result::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, at::Tensor & result, at::Tensor & LU, at::Tensor & pivots) { |
| 11275 | |
| 11276 | static auto op = create__linalg_det_result_typed_handle(); |
| 11277 | return op.redispatch(dispatchKeySet, A, result, LU, pivots); |
| 11278 | } |
| 11279 | |
| 11280 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_ldl_factor, name, "aten::linalg_ldl_factor") |
| 11281 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_ldl_factor, overload_name, "") |
| 11282 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_ldl_factor, schema_str, "linalg_ldl_factor(Tensor self, *, bool hermitian=False) -> (Tensor LD, Tensor pivots)") |
| 11283 | |
| 11284 | // aten::linalg_ldl_factor(Tensor self, *, bool hermitian=False) -> (Tensor LD, Tensor pivots) |
| 11285 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_ldl_factor::schema> create_linalg_ldl_factor_typed_handle() { |
| 11286 | return c10::Dispatcher::singleton() |
| 11287 | .findSchemaOrThrow(linalg_ldl_factor::name, linalg_ldl_factor::overload_name) |
| 11288 | .typed<linalg_ldl_factor::schema>(); |
| 11289 | } |
| 11290 | |
| 11291 | // aten::linalg_ldl_factor(Tensor self, *, bool hermitian=False) -> (Tensor LD, Tensor pivots) |
| 11292 | ::std::tuple<at::Tensor,at::Tensor> linalg_ldl_factor::call(const at::Tensor & self, bool hermitian) { |
| 11293 | |
| 11294 | static auto op = create_linalg_ldl_factor_typed_handle(); |
| 11295 | return op.call(self, hermitian); |
| 11296 | } |
| 11297 | |
| 11298 | // aten::linalg_ldl_factor(Tensor self, *, bool hermitian=False) -> (Tensor LD, Tensor pivots) |
| 11299 | ::std::tuple<at::Tensor,at::Tensor> linalg_ldl_factor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool hermitian) { |
| 11300 | |
| 11301 | static auto op = create_linalg_ldl_factor_typed_handle(); |
| 11302 | return op.redispatch(dispatchKeySet, self, hermitian); |
| 11303 | } |
| 11304 | |
| 11305 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_ldl_factor_out, name, "aten::linalg_ldl_factor") |
| 11306 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_ldl_factor_out, overload_name, "out") |
| 11307 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_ldl_factor_out, schema_str, "linalg_ldl_factor.out(Tensor self, *, bool hermitian=False, Tensor(a!) LD, Tensor(b!) pivots) -> (Tensor(a!) LD, Tensor(b!) pivots)") |
| 11308 | |
| 11309 | // aten::linalg_ldl_factor.out(Tensor self, *, bool hermitian=False, Tensor(a!) LD, Tensor(b!) pivots) -> (Tensor(a!) LD, Tensor(b!) pivots) |
| 11310 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_ldl_factor_out::schema> create_linalg_ldl_factor_out_typed_handle() { |
| 11311 | return c10::Dispatcher::singleton() |
| 11312 | .findSchemaOrThrow(linalg_ldl_factor_out::name, linalg_ldl_factor_out::overload_name) |
| 11313 | .typed<linalg_ldl_factor_out::schema>(); |
| 11314 | } |
| 11315 | |
| 11316 | // aten::linalg_ldl_factor.out(Tensor self, *, bool hermitian=False, Tensor(a!) LD, Tensor(b!) pivots) -> (Tensor(a!) LD, Tensor(b!) pivots) |
| 11317 | ::std::tuple<at::Tensor &,at::Tensor &> linalg_ldl_factor_out::call(const at::Tensor & self, bool hermitian, at::Tensor & LD, at::Tensor & pivots) { |
| 11318 | |
| 11319 | static auto op = create_linalg_ldl_factor_out_typed_handle(); |
| 11320 | return op.call(self, hermitian, LD, pivots); |
| 11321 | } |
| 11322 | |
| 11323 | // aten::linalg_ldl_factor.out(Tensor self, *, bool hermitian=False, Tensor(a!) LD, Tensor(b!) pivots) -> (Tensor(a!) LD, Tensor(b!) pivots) |
| 11324 | ::std::tuple<at::Tensor &,at::Tensor &> linalg_ldl_factor_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool hermitian, at::Tensor & LD, at::Tensor & pivots) { |
| 11325 | |
| 11326 | static auto op = create_linalg_ldl_factor_out_typed_handle(); |
| 11327 | return op.redispatch(dispatchKeySet, self, hermitian, LD, pivots); |
| 11328 | } |
| 11329 | |
| 11330 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matmul, name, "aten::linalg_matmul") |
| 11331 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matmul, overload_name, "") |
| 11332 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matmul, schema_str, "linalg_matmul(Tensor self, Tensor other) -> Tensor") |
| 11333 | |
| 11334 | // aten::linalg_matmul(Tensor self, Tensor other) -> Tensor |
| 11335 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_matmul::schema> create_linalg_matmul_typed_handle() { |
| 11336 | return c10::Dispatcher::singleton() |
| 11337 | .findSchemaOrThrow(linalg_matmul::name, linalg_matmul::overload_name) |
| 11338 | .typed<linalg_matmul::schema>(); |
| 11339 | } |
| 11340 | |
| 11341 | // aten::linalg_matmul(Tensor self, Tensor other) -> Tensor |
| 11342 | at::Tensor linalg_matmul::call(const at::Tensor & self, const at::Tensor & other) { |
| 11343 | |
| 11344 | static auto op = create_linalg_matmul_typed_handle(); |
| 11345 | return op.call(self, other); |
| 11346 | } |
| 11347 | |
| 11348 | // aten::linalg_matmul(Tensor self, Tensor other) -> Tensor |
| 11349 | at::Tensor linalg_matmul::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { |
| 11350 | |
| 11351 | static auto op = create_linalg_matmul_typed_handle(); |
| 11352 | return op.redispatch(dispatchKeySet, self, other); |
| 11353 | } |
| 11354 | |
| 11355 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matmul_out, name, "aten::linalg_matmul") |
| 11356 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matmul_out, overload_name, "out") |
| 11357 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matmul_out, schema_str, "linalg_matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)") |
| 11358 | |
| 11359 | // aten::linalg_matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 11360 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_matmul_out::schema> create_linalg_matmul_out_typed_handle() { |
| 11361 | return c10::Dispatcher::singleton() |
| 11362 | .findSchemaOrThrow(linalg_matmul_out::name, linalg_matmul_out::overload_name) |
| 11363 | .typed<linalg_matmul_out::schema>(); |
| 11364 | } |
| 11365 | |
| 11366 | // aten::linalg_matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 11367 | at::Tensor & linalg_matmul_out::call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 11368 | |
| 11369 | static auto op = create_linalg_matmul_out_typed_handle(); |
| 11370 | return op.call(self, other, out); |
| 11371 | } |
| 11372 | |
| 11373 | // aten::linalg_matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 11374 | at::Tensor & linalg_matmul_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 11375 | |
| 11376 | static auto op = create_linalg_matmul_out_typed_handle(); |
| 11377 | return op.redispatch(dispatchKeySet, self, other, out); |
| 11378 | } |
| 11379 | |
| 11380 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_slogdet, name, "aten::linalg_slogdet") |
| 11381 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_slogdet, overload_name, "") |
| 11382 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_slogdet, schema_str, "linalg_slogdet(Tensor A) -> (Tensor sign, Tensor logabsdet)") |
| 11383 | |
| 11384 | // aten::linalg_slogdet(Tensor A) -> (Tensor sign, Tensor logabsdet) |
| 11385 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_slogdet::schema> create_linalg_slogdet_typed_handle() { |
| 11386 | return c10::Dispatcher::singleton() |
| 11387 | .findSchemaOrThrow(linalg_slogdet::name, linalg_slogdet::overload_name) |
| 11388 | .typed<linalg_slogdet::schema>(); |
| 11389 | } |
| 11390 | |
| 11391 | // aten::linalg_slogdet(Tensor A) -> (Tensor sign, Tensor logabsdet) |
| 11392 | ::std::tuple<at::Tensor,at::Tensor> linalg_slogdet::call(const at::Tensor & A) { |
| 11393 | |
| 11394 | static auto op = create_linalg_slogdet_typed_handle(); |
| 11395 | return op.call(A); |
| 11396 | } |
| 11397 | |
| 11398 | // aten::linalg_slogdet(Tensor A) -> (Tensor sign, Tensor logabsdet) |
| 11399 | ::std::tuple<at::Tensor,at::Tensor> linalg_slogdet::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A) { |
| 11400 | |
| 11401 | static auto op = create_linalg_slogdet_typed_handle(); |
| 11402 | return op.redispatch(dispatchKeySet, A); |
| 11403 | } |
| 11404 | |
| 11405 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_slogdet_out, name, "aten::linalg_slogdet") |
| 11406 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_slogdet_out, overload_name, "out") |
| 11407 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_slogdet_out, schema_str, "linalg_slogdet.out(Tensor A, *, Tensor(a!) sign, Tensor(b!) logabsdet) -> (Tensor(a!) sign, Tensor(b!) logabsdet)") |
| 11408 | |
| 11409 | // aten::linalg_slogdet.out(Tensor A, *, Tensor(a!) sign, Tensor(b!) logabsdet) -> (Tensor(a!) sign, Tensor(b!) logabsdet) |
| 11410 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_slogdet_out::schema> create_linalg_slogdet_out_typed_handle() { |
| 11411 | return c10::Dispatcher::singleton() |
| 11412 | .findSchemaOrThrow(linalg_slogdet_out::name, linalg_slogdet_out::overload_name) |
| 11413 | .typed<linalg_slogdet_out::schema>(); |
| 11414 | } |
| 11415 | |
| 11416 | // aten::linalg_slogdet.out(Tensor A, *, Tensor(a!) sign, Tensor(b!) logabsdet) -> (Tensor(a!) sign, Tensor(b!) logabsdet) |
| 11417 | ::std::tuple<at::Tensor &,at::Tensor &> linalg_slogdet_out::call(const at::Tensor & A, at::Tensor & sign, at::Tensor & logabsdet) { |
| 11418 | |
| 11419 | static auto op = create_linalg_slogdet_out_typed_handle(); |
| 11420 | return op.call(A, sign, logabsdet); |
| 11421 | } |
| 11422 | |
| 11423 | // aten::linalg_slogdet.out(Tensor A, *, Tensor(a!) sign, Tensor(b!) logabsdet) -> (Tensor(a!) sign, Tensor(b!) logabsdet) |
| 11424 | ::std::tuple<at::Tensor &,at::Tensor &> linalg_slogdet_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, at::Tensor & sign, at::Tensor & logabsdet) { |
| 11425 | |
| 11426 | static auto op = create_linalg_slogdet_out_typed_handle(); |
| 11427 | return op.redispatch(dispatchKeySet, A, sign, logabsdet); |
| 11428 | } |
| 11429 | |
| 11430 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logdet, name, "aten::logdet") |
| 11431 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logdet, overload_name, "") |
| 11432 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logdet, schema_str, "logdet(Tensor self) -> Tensor") |
| 11433 | |
| 11434 | // aten::logdet(Tensor self) -> Tensor |
| 11435 | static C10_NOINLINE c10::TypedOperatorHandle<logdet::schema> create_logdet_typed_handle() { |
| 11436 | return c10::Dispatcher::singleton() |
| 11437 | .findSchemaOrThrow(logdet::name, logdet::overload_name) |
| 11438 | .typed<logdet::schema>(); |
| 11439 | } |
| 11440 | |
| 11441 | // aten::logdet(Tensor self) -> Tensor |
| 11442 | at::Tensor logdet::call(const at::Tensor & self) { |
| 11443 | |
| 11444 | static auto op = create_logdet_typed_handle(); |
| 11445 | return op.call(self); |
| 11446 | } |
| 11447 | |
| 11448 | // aten::logdet(Tensor self) -> Tensor |
| 11449 | at::Tensor logdet::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 11450 | |
| 11451 | static auto op = create_logdet_typed_handle(); |
| 11452 | return op.redispatch(dispatchKeySet, self); |
| 11453 | } |
| 11454 | |
| 11455 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_eigvals, name, "aten::linalg_eigvals") |
| 11456 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_eigvals, overload_name, "") |
| 11457 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_eigvals, schema_str, "linalg_eigvals(Tensor self) -> Tensor") |
| 11458 | |
| 11459 | // aten::linalg_eigvals(Tensor self) -> Tensor |
| 11460 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_eigvals::schema> create_linalg_eigvals_typed_handle() { |
| 11461 | return c10::Dispatcher::singleton() |
| 11462 | .findSchemaOrThrow(linalg_eigvals::name, linalg_eigvals::overload_name) |
| 11463 | .typed<linalg_eigvals::schema>(); |
| 11464 | } |
| 11465 | |
| 11466 | // aten::linalg_eigvals(Tensor self) -> Tensor |
| 11467 | at::Tensor linalg_eigvals::call(const at::Tensor & self) { |
| 11468 | |
| 11469 | static auto op = create_linalg_eigvals_typed_handle(); |
| 11470 | return op.call(self); |
| 11471 | } |
| 11472 | |
| 11473 | // aten::linalg_eigvals(Tensor self) -> Tensor |
| 11474 | at::Tensor linalg_eigvals::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 11475 | |
| 11476 | static auto op = create_linalg_eigvals_typed_handle(); |
| 11477 | return op.redispatch(dispatchKeySet, self); |
| 11478 | } |
| 11479 | |
| 11480 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_eigvals_out, name, "aten::linalg_eigvals") |
| 11481 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_eigvals_out, overload_name, "out") |
| 11482 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_eigvals_out, schema_str, "linalg_eigvals.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 11483 | |
| 11484 | // aten::linalg_eigvals.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 11485 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_eigvals_out::schema> create_linalg_eigvals_out_typed_handle() { |
| 11486 | return c10::Dispatcher::singleton() |
| 11487 | .findSchemaOrThrow(linalg_eigvals_out::name, linalg_eigvals_out::overload_name) |
| 11488 | .typed<linalg_eigvals_out::schema>(); |
| 11489 | } |
| 11490 | |
| 11491 | // aten::linalg_eigvals.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 11492 | at::Tensor & linalg_eigvals_out::call(const at::Tensor & self, at::Tensor & out) { |
| 11493 | |
| 11494 | static auto op = create_linalg_eigvals_out_typed_handle(); |
| 11495 | return op.call(self, out); |
| 11496 | } |
| 11497 | |
| 11498 | // aten::linalg_eigvals.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 11499 | at::Tensor & linalg_eigvals_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 11500 | |
| 11501 | static auto op = create_linalg_eigvals_out_typed_handle(); |
| 11502 | return op.redispatch(dispatchKeySet, self, out); |
| 11503 | } |
| 11504 | |
| 11505 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_inv_ex, name, "aten::linalg_inv_ex") |
| 11506 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_inv_ex, overload_name, "") |
| 11507 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_inv_ex, schema_str, "linalg_inv_ex(Tensor A, *, bool check_errors=False) -> (Tensor inverse, Tensor info)") |
| 11508 | |
| 11509 | // aten::linalg_inv_ex(Tensor A, *, bool check_errors=False) -> (Tensor inverse, Tensor info) |
| 11510 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_inv_ex::schema> create_linalg_inv_ex_typed_handle() { |
| 11511 | return c10::Dispatcher::singleton() |
| 11512 | .findSchemaOrThrow(linalg_inv_ex::name, linalg_inv_ex::overload_name) |
| 11513 | .typed<linalg_inv_ex::schema>(); |
| 11514 | } |
| 11515 | |
| 11516 | // aten::linalg_inv_ex(Tensor A, *, bool check_errors=False) -> (Tensor inverse, Tensor info) |
| 11517 | ::std::tuple<at::Tensor,at::Tensor> linalg_inv_ex::call(const at::Tensor & A, bool check_errors) { |
| 11518 | |
| 11519 | static auto op = create_linalg_inv_ex_typed_handle(); |
| 11520 | return op.call(A, check_errors); |
| 11521 | } |
| 11522 | |
| 11523 | // aten::linalg_inv_ex(Tensor A, *, bool check_errors=False) -> (Tensor inverse, Tensor info) |
| 11524 | ::std::tuple<at::Tensor,at::Tensor> linalg_inv_ex::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, bool check_errors) { |
| 11525 | |
| 11526 | static auto op = create_linalg_inv_ex_typed_handle(); |
| 11527 | return op.redispatch(dispatchKeySet, A, check_errors); |
| 11528 | } |
| 11529 | |
| 11530 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_inv_ex_inverse, name, "aten::linalg_inv_ex") |
| 11531 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_inv_ex_inverse, overload_name, "inverse") |
| 11532 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_inv_ex_inverse, schema_str, "linalg_inv_ex.inverse(Tensor A, *, bool check_errors=False, Tensor(a!) inverse, Tensor(b!) info) -> (Tensor(a!) inverse, Tensor(b!) info)") |
| 11533 | |
| 11534 | // aten::linalg_inv_ex.inverse(Tensor A, *, bool check_errors=False, Tensor(a!) inverse, Tensor(b!) info) -> (Tensor(a!) inverse, Tensor(b!) info) |
| 11535 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_inv_ex_inverse::schema> create_linalg_inv_ex_inverse_typed_handle() { |
| 11536 | return c10::Dispatcher::singleton() |
| 11537 | .findSchemaOrThrow(linalg_inv_ex_inverse::name, linalg_inv_ex_inverse::overload_name) |
| 11538 | .typed<linalg_inv_ex_inverse::schema>(); |
| 11539 | } |
| 11540 | |
| 11541 | // aten::linalg_inv_ex.inverse(Tensor A, *, bool check_errors=False, Tensor(a!) inverse, Tensor(b!) info) -> (Tensor(a!) inverse, Tensor(b!) info) |
| 11542 | ::std::tuple<at::Tensor &,at::Tensor &> linalg_inv_ex_inverse::call(const at::Tensor & A, bool check_errors, at::Tensor & inverse, at::Tensor & info) { |
| 11543 | |
| 11544 | static auto op = create_linalg_inv_ex_inverse_typed_handle(); |
| 11545 | return op.call(A, check_errors, inverse, info); |
| 11546 | } |
| 11547 | |
| 11548 | // aten::linalg_inv_ex.inverse(Tensor A, *, bool check_errors=False, Tensor(a!) inverse, Tensor(b!) info) -> (Tensor(a!) inverse, Tensor(b!) info) |
| 11549 | ::std::tuple<at::Tensor &,at::Tensor &> linalg_inv_ex_inverse::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, bool check_errors, at::Tensor & inverse, at::Tensor & info) { |
| 11550 | |
| 11551 | static auto op = create_linalg_inv_ex_inverse_typed_handle(); |
| 11552 | return op.redispatch(dispatchKeySet, A, check_errors, inverse, info); |
| 11553 | } |
| 11554 | |
| 11555 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(inner, name, "aten::inner") |
| 11556 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(inner, overload_name, "") |
| 11557 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(inner, schema_str, "inner(Tensor self, Tensor other) -> Tensor") |
| 11558 | |
| 11559 | // aten::inner(Tensor self, Tensor other) -> Tensor |
| 11560 | static C10_NOINLINE c10::TypedOperatorHandle<inner::schema> create_inner_typed_handle() { |
| 11561 | return c10::Dispatcher::singleton() |
| 11562 | .findSchemaOrThrow(inner::name, inner::overload_name) |
| 11563 | .typed<inner::schema>(); |
| 11564 | } |
| 11565 | |
| 11566 | // aten::inner(Tensor self, Tensor other) -> Tensor |
| 11567 | at::Tensor inner::call(const at::Tensor & self, const at::Tensor & other) { |
| 11568 | |
| 11569 | static auto op = create_inner_typed_handle(); |
| 11570 | return op.call(self, other); |
| 11571 | } |
| 11572 | |
| 11573 | // aten::inner(Tensor self, Tensor other) -> Tensor |
| 11574 | at::Tensor inner::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { |
| 11575 | |
| 11576 | static auto op = create_inner_typed_handle(); |
| 11577 | return op.redispatch(dispatchKeySet, self, other); |
| 11578 | } |
| 11579 | |
| 11580 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(inner_out, name, "aten::inner") |
| 11581 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(inner_out, overload_name, "out") |
| 11582 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(inner_out, schema_str, "inner.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)") |
| 11583 | |
| 11584 | // aten::inner.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 11585 | static C10_NOINLINE c10::TypedOperatorHandle<inner_out::schema> create_inner_out_typed_handle() { |
| 11586 | return c10::Dispatcher::singleton() |
| 11587 | .findSchemaOrThrow(inner_out::name, inner_out::overload_name) |
| 11588 | .typed<inner_out::schema>(); |
| 11589 | } |
| 11590 | |
| 11591 | // aten::inner.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 11592 | at::Tensor & inner_out::call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 11593 | |
| 11594 | static auto op = create_inner_out_typed_handle(); |
| 11595 | return op.call(self, other, out); |
| 11596 | } |
| 11597 | |
| 11598 | // aten::inner.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 11599 | at::Tensor & inner_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 11600 | |
| 11601 | static auto op = create_inner_out_typed_handle(); |
| 11602 | return op.redispatch(dispatchKeySet, self, other, out); |
| 11603 | } |
| 11604 | |
| 11605 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_vector_norm, name, "aten::linalg_vector_norm") |
| 11606 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_vector_norm, overload_name, "") |
| 11607 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_vector_norm, schema_str, "linalg_vector_norm(Tensor self, Scalar ord=2, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor") |
| 11608 | |
| 11609 | // aten::linalg_vector_norm(Tensor self, Scalar ord=2, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor |
| 11610 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_vector_norm::schema> create_linalg_vector_norm_typed_handle() { |
| 11611 | return c10::Dispatcher::singleton() |
| 11612 | .findSchemaOrThrow(linalg_vector_norm::name, linalg_vector_norm::overload_name) |
| 11613 | .typed<linalg_vector_norm::schema>(); |
| 11614 | } |
| 11615 | |
| 11616 | // aten::linalg_vector_norm(Tensor self, Scalar ord=2, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor |
| 11617 | at::Tensor linalg_vector_norm::call(const at::Tensor & self, const at::Scalar & ord, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 11618 | |
| 11619 | static auto op = create_linalg_vector_norm_typed_handle(); |
| 11620 | return op.call(self, ord, dim, keepdim, dtype); |
| 11621 | } |
| 11622 | |
| 11623 | // aten::linalg_vector_norm(Tensor self, Scalar ord=2, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor |
| 11624 | at::Tensor linalg_vector_norm::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & ord, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 11625 | |
| 11626 | static auto op = create_linalg_vector_norm_typed_handle(); |
| 11627 | return op.redispatch(dispatchKeySet, self, ord, dim, keepdim, dtype); |
| 11628 | } |
| 11629 | |
| 11630 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_vector_norm_out, name, "aten::linalg_vector_norm") |
| 11631 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_vector_norm_out, overload_name, "out") |
| 11632 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_vector_norm_out, schema_str, "linalg_vector_norm.out(Tensor self, Scalar ord=2, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)") |
| 11633 | |
| 11634 | // aten::linalg_vector_norm.out(Tensor self, Scalar ord=2, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) |
| 11635 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_vector_norm_out::schema> create_linalg_vector_norm_out_typed_handle() { |
| 11636 | return c10::Dispatcher::singleton() |
| 11637 | .findSchemaOrThrow(linalg_vector_norm_out::name, linalg_vector_norm_out::overload_name) |
| 11638 | .typed<linalg_vector_norm_out::schema>(); |
| 11639 | } |
| 11640 | |
| 11641 | // aten::linalg_vector_norm.out(Tensor self, Scalar ord=2, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) |
| 11642 | at::Tensor & linalg_vector_norm_out::call(const at::Tensor & self, const at::Scalar & ord, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 11643 | |
| 11644 | static auto op = create_linalg_vector_norm_out_typed_handle(); |
| 11645 | return op.call(self, ord, dim, keepdim, dtype, out); |
| 11646 | } |
| 11647 | |
| 11648 | // aten::linalg_vector_norm.out(Tensor self, Scalar ord=2, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) |
| 11649 | at::Tensor & linalg_vector_norm_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & ord, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 11650 | |
| 11651 | static auto op = create_linalg_vector_norm_out_typed_handle(); |
| 11652 | return op.redispatch(dispatchKeySet, self, ord, dim, keepdim, dtype, out); |
| 11653 | } |
| 11654 | |
| 11655 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_solve, name, "aten::linalg_solve") |
| 11656 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_solve, overload_name, "") |
| 11657 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_solve, schema_str, "linalg_solve(Tensor A, Tensor B, *, bool left=True) -> Tensor") |
| 11658 | |
| 11659 | // aten::linalg_solve(Tensor A, Tensor B, *, bool left=True) -> Tensor |
| 11660 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_solve::schema> create_linalg_solve_typed_handle() { |
| 11661 | return c10::Dispatcher::singleton() |
| 11662 | .findSchemaOrThrow(linalg_solve::name, linalg_solve::overload_name) |
| 11663 | .typed<linalg_solve::schema>(); |
| 11664 | } |
| 11665 | |
| 11666 | // aten::linalg_solve(Tensor A, Tensor B, *, bool left=True) -> Tensor |
| 11667 | at::Tensor linalg_solve::call(const at::Tensor & A, const at::Tensor & B, bool left) { |
| 11668 | |
| 11669 | static auto op = create_linalg_solve_typed_handle(); |
| 11670 | return op.call(A, B, left); |
| 11671 | } |
| 11672 | |
| 11673 | // aten::linalg_solve(Tensor A, Tensor B, *, bool left=True) -> Tensor |
| 11674 | at::Tensor linalg_solve::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, const at::Tensor & B, bool left) { |
| 11675 | |
| 11676 | static auto op = create_linalg_solve_typed_handle(); |
| 11677 | return op.redispatch(dispatchKeySet, A, B, left); |
| 11678 | } |
| 11679 | |
| 11680 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_solve_out, name, "aten::linalg_solve") |
| 11681 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_solve_out, overload_name, "out") |
| 11682 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_solve_out, schema_str, "linalg_solve.out(Tensor A, Tensor B, *, bool left=True, Tensor(a!) out) -> Tensor(a!)") |
| 11683 | |
| 11684 | // aten::linalg_solve.out(Tensor A, Tensor B, *, bool left=True, Tensor(a!) out) -> Tensor(a!) |
| 11685 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_solve_out::schema> create_linalg_solve_out_typed_handle() { |
| 11686 | return c10::Dispatcher::singleton() |
| 11687 | .findSchemaOrThrow(linalg_solve_out::name, linalg_solve_out::overload_name) |
| 11688 | .typed<linalg_solve_out::schema>(); |
| 11689 | } |
| 11690 | |
| 11691 | // aten::linalg_solve.out(Tensor A, Tensor B, *, bool left=True, Tensor(a!) out) -> Tensor(a!) |
| 11692 | at::Tensor & linalg_solve_out::call(const at::Tensor & A, const at::Tensor & B, bool left, at::Tensor & out) { |
| 11693 | |
| 11694 | static auto op = create_linalg_solve_out_typed_handle(); |
| 11695 | return op.call(A, B, left, out); |
| 11696 | } |
| 11697 | |
| 11698 | // aten::linalg_solve.out(Tensor A, Tensor B, *, bool left=True, Tensor(a!) out) -> Tensor(a!) |
| 11699 | at::Tensor & linalg_solve_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, const at::Tensor & B, bool left, at::Tensor & out) { |
| 11700 | |
| 11701 | static auto op = create_linalg_solve_out_typed_handle(); |
| 11702 | return op.redispatch(dispatchKeySet, A, B, left, out); |
| 11703 | } |
| 11704 | |
| 11705 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_tensorinv, name, "aten::linalg_tensorinv") |
| 11706 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_tensorinv, overload_name, "") |
| 11707 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_tensorinv, schema_str, "linalg_tensorinv(Tensor self, int ind=2) -> Tensor") |
| 11708 | |
| 11709 | // aten::linalg_tensorinv(Tensor self, int ind=2) -> Tensor |
| 11710 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_tensorinv::schema> create_linalg_tensorinv_typed_handle() { |
| 11711 | return c10::Dispatcher::singleton() |
| 11712 | .findSchemaOrThrow(linalg_tensorinv::name, linalg_tensorinv::overload_name) |
| 11713 | .typed<linalg_tensorinv::schema>(); |
| 11714 | } |
| 11715 | |
| 11716 | // aten::linalg_tensorinv(Tensor self, int ind=2) -> Tensor |
| 11717 | at::Tensor linalg_tensorinv::call(const at::Tensor & self, int64_t ind) { |
| 11718 | |
| 11719 | static auto op = create_linalg_tensorinv_typed_handle(); |
| 11720 | return op.call(self, ind); |
| 11721 | } |
| 11722 | |
| 11723 | // aten::linalg_tensorinv(Tensor self, int ind=2) -> Tensor |
| 11724 | at::Tensor linalg_tensorinv::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t ind) { |
| 11725 | |
| 11726 | static auto op = create_linalg_tensorinv_typed_handle(); |
| 11727 | return op.redispatch(dispatchKeySet, self, ind); |
| 11728 | } |
| 11729 | |
| 11730 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_tensorinv_out, name, "aten::linalg_tensorinv") |
| 11731 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_tensorinv_out, overload_name, "out") |
| 11732 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_tensorinv_out, schema_str, "linalg_tensorinv.out(Tensor self, int ind=2, *, Tensor(a!) out) -> Tensor(a!)") |
| 11733 | |
| 11734 | // aten::linalg_tensorinv.out(Tensor self, int ind=2, *, Tensor(a!) out) -> Tensor(a!) |
| 11735 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_tensorinv_out::schema> create_linalg_tensorinv_out_typed_handle() { |
| 11736 | return c10::Dispatcher::singleton() |
| 11737 | .findSchemaOrThrow(linalg_tensorinv_out::name, linalg_tensorinv_out::overload_name) |
| 11738 | .typed<linalg_tensorinv_out::schema>(); |
| 11739 | } |
| 11740 | |
| 11741 | // aten::linalg_tensorinv.out(Tensor self, int ind=2, *, Tensor(a!) out) -> Tensor(a!) |
| 11742 | at::Tensor & linalg_tensorinv_out::call(const at::Tensor & self, int64_t ind, at::Tensor & out) { |
| 11743 | |
| 11744 | static auto op = create_linalg_tensorinv_out_typed_handle(); |
| 11745 | return op.call(self, ind, out); |
| 11746 | } |
| 11747 | |
| 11748 | // aten::linalg_tensorinv.out(Tensor self, int ind=2, *, Tensor(a!) out) -> Tensor(a!) |
| 11749 | at::Tensor & linalg_tensorinv_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t ind, at::Tensor & out) { |
| 11750 | |
| 11751 | static auto op = create_linalg_tensorinv_out_typed_handle(); |
| 11752 | return op.redispatch(dispatchKeySet, self, ind, out); |
| 11753 | } |
| 11754 | |
| 11755 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matrix_rank_atol_rtol_tensor, name, "aten::linalg_matrix_rank") |
| 11756 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matrix_rank_atol_rtol_tensor, overload_name, "atol_rtol_tensor") |
| 11757 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matrix_rank_atol_rtol_tensor, schema_str, "linalg_matrix_rank.atol_rtol_tensor(Tensor input, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False) -> Tensor") |
| 11758 | |
| 11759 | // aten::linalg_matrix_rank.atol_rtol_tensor(Tensor input, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False) -> Tensor |
| 11760 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_matrix_rank_atol_rtol_tensor::schema> create_linalg_matrix_rank_atol_rtol_tensor_typed_handle() { |
| 11761 | return c10::Dispatcher::singleton() |
| 11762 | .findSchemaOrThrow(linalg_matrix_rank_atol_rtol_tensor::name, linalg_matrix_rank_atol_rtol_tensor::overload_name) |
| 11763 | .typed<linalg_matrix_rank_atol_rtol_tensor::schema>(); |
| 11764 | } |
| 11765 | |
| 11766 | // aten::linalg_matrix_rank.atol_rtol_tensor(Tensor input, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False) -> Tensor |
| 11767 | at::Tensor linalg_matrix_rank_atol_rtol_tensor::call(const at::Tensor & input, const c10::optional<at::Tensor> & atol, const c10::optional<at::Tensor> & rtol, bool hermitian) { |
| 11768 | |
| 11769 | static auto op = create_linalg_matrix_rank_atol_rtol_tensor_typed_handle(); |
| 11770 | return op.call(input, atol, rtol, hermitian); |
| 11771 | } |
| 11772 | |
| 11773 | // aten::linalg_matrix_rank.atol_rtol_tensor(Tensor input, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False) -> Tensor |
| 11774 | at::Tensor linalg_matrix_rank_atol_rtol_tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional<at::Tensor> & atol, const c10::optional<at::Tensor> & rtol, bool hermitian) { |
| 11775 | |
| 11776 | static auto op = create_linalg_matrix_rank_atol_rtol_tensor_typed_handle(); |
| 11777 | return op.redispatch(dispatchKeySet, input, atol, rtol, hermitian); |
| 11778 | } |
| 11779 | |
| 11780 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matrix_rank_atol_rtol_tensor_out, name, "aten::linalg_matrix_rank") |
| 11781 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matrix_rank_atol_rtol_tensor_out, overload_name, "atol_rtol_tensor_out") |
| 11782 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matrix_rank_atol_rtol_tensor_out, schema_str, "linalg_matrix_rank.atol_rtol_tensor_out(Tensor input, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)") |
| 11783 | |
| 11784 | // aten::linalg_matrix_rank.atol_rtol_tensor_out(Tensor input, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!) |
| 11785 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_matrix_rank_atol_rtol_tensor_out::schema> create_linalg_matrix_rank_atol_rtol_tensor_out_typed_handle() { |
| 11786 | return c10::Dispatcher::singleton() |
| 11787 | .findSchemaOrThrow(linalg_matrix_rank_atol_rtol_tensor_out::name, linalg_matrix_rank_atol_rtol_tensor_out::overload_name) |
| 11788 | .typed<linalg_matrix_rank_atol_rtol_tensor_out::schema>(); |
| 11789 | } |
| 11790 | |
| 11791 | // aten::linalg_matrix_rank.atol_rtol_tensor_out(Tensor input, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!) |
| 11792 | at::Tensor & linalg_matrix_rank_atol_rtol_tensor_out::call(const at::Tensor & input, const c10::optional<at::Tensor> & atol, const c10::optional<at::Tensor> & rtol, bool hermitian, at::Tensor & out) { |
| 11793 | |
| 11794 | static auto op = create_linalg_matrix_rank_atol_rtol_tensor_out_typed_handle(); |
| 11795 | return op.call(input, atol, rtol, hermitian, out); |
| 11796 | } |
| 11797 | |
| 11798 | // aten::linalg_matrix_rank.atol_rtol_tensor_out(Tensor input, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!) |
| 11799 | at::Tensor & linalg_matrix_rank_atol_rtol_tensor_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional<at::Tensor> & atol, const c10::optional<at::Tensor> & rtol, bool hermitian, at::Tensor & out) { |
| 11800 | |
| 11801 | static auto op = create_linalg_matrix_rank_atol_rtol_tensor_out_typed_handle(); |
| 11802 | return op.redispatch(dispatchKeySet, input, atol, rtol, hermitian, out); |
| 11803 | } |
| 11804 | |
| 11805 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matrix_rank_atol_rtol_float, name, "aten::linalg_matrix_rank") |
| 11806 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matrix_rank_atol_rtol_float, overload_name, "atol_rtol_float") |
| 11807 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matrix_rank_atol_rtol_float, schema_str, "linalg_matrix_rank.atol_rtol_float(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False) -> Tensor") |
| 11808 | |
| 11809 | // aten::linalg_matrix_rank.atol_rtol_float(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False) -> Tensor |
| 11810 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_matrix_rank_atol_rtol_float::schema> create_linalg_matrix_rank_atol_rtol_float_typed_handle() { |
| 11811 | return c10::Dispatcher::singleton() |
| 11812 | .findSchemaOrThrow(linalg_matrix_rank_atol_rtol_float::name, linalg_matrix_rank_atol_rtol_float::overload_name) |
| 11813 | .typed<linalg_matrix_rank_atol_rtol_float::schema>(); |
| 11814 | } |
| 11815 | |
| 11816 | // aten::linalg_matrix_rank.atol_rtol_float(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False) -> Tensor |
| 11817 | at::Tensor linalg_matrix_rank_atol_rtol_float::call(const at::Tensor & self, c10::optional<double> atol, c10::optional<double> rtol, bool hermitian) { |
| 11818 | |
| 11819 | static auto op = create_linalg_matrix_rank_atol_rtol_float_typed_handle(); |
| 11820 | return op.call(self, atol, rtol, hermitian); |
| 11821 | } |
| 11822 | |
| 11823 | // aten::linalg_matrix_rank.atol_rtol_float(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False) -> Tensor |
| 11824 | at::Tensor linalg_matrix_rank_atol_rtol_float::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<double> atol, c10::optional<double> rtol, bool hermitian) { |
| 11825 | |
| 11826 | static auto op = create_linalg_matrix_rank_atol_rtol_float_typed_handle(); |
| 11827 | return op.redispatch(dispatchKeySet, self, atol, rtol, hermitian); |
| 11828 | } |
| 11829 | |
| 11830 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matrix_rank_atol_rtol_float_out, name, "aten::linalg_matrix_rank") |
| 11831 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matrix_rank_atol_rtol_float_out, overload_name, "atol_rtol_float_out") |
| 11832 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matrix_rank_atol_rtol_float_out, schema_str, "linalg_matrix_rank.atol_rtol_float_out(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)") |
| 11833 | |
| 11834 | // aten::linalg_matrix_rank.atol_rtol_float_out(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!) |
| 11835 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_matrix_rank_atol_rtol_float_out::schema> create_linalg_matrix_rank_atol_rtol_float_out_typed_handle() { |
| 11836 | return c10::Dispatcher::singleton() |
| 11837 | .findSchemaOrThrow(linalg_matrix_rank_atol_rtol_float_out::name, linalg_matrix_rank_atol_rtol_float_out::overload_name) |
| 11838 | .typed<linalg_matrix_rank_atol_rtol_float_out::schema>(); |
| 11839 | } |
| 11840 | |
| 11841 | // aten::linalg_matrix_rank.atol_rtol_float_out(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!) |
| 11842 | at::Tensor & linalg_matrix_rank_atol_rtol_float_out::call(const at::Tensor & self, c10::optional<double> atol, c10::optional<double> rtol, bool hermitian, at::Tensor & out) { |
| 11843 | |
| 11844 | static auto op = create_linalg_matrix_rank_atol_rtol_float_out_typed_handle(); |
| 11845 | return op.call(self, atol, rtol, hermitian, out); |
| 11846 | } |
| 11847 | |
| 11848 | // aten::linalg_matrix_rank.atol_rtol_float_out(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!) |
| 11849 | at::Tensor & linalg_matrix_rank_atol_rtol_float_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<double> atol, c10::optional<double> rtol, bool hermitian, at::Tensor & out) { |
| 11850 | |
| 11851 | static auto op = create_linalg_matrix_rank_atol_rtol_float_out_typed_handle(); |
| 11852 | return op.redispatch(dispatchKeySet, self, atol, rtol, hermitian, out); |
| 11853 | } |
| 11854 | |
| 11855 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matrix_rank, name, "aten::linalg_matrix_rank") |
| 11856 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matrix_rank, overload_name, "") |
| 11857 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matrix_rank, schema_str, "linalg_matrix_rank(Tensor self, float tol, bool hermitian=False) -> Tensor") |
| 11858 | |
| 11859 | // aten::linalg_matrix_rank(Tensor self, float tol, bool hermitian=False) -> Tensor |
| 11860 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_matrix_rank::schema> create_linalg_matrix_rank_typed_handle() { |
| 11861 | return c10::Dispatcher::singleton() |
| 11862 | .findSchemaOrThrow(linalg_matrix_rank::name, linalg_matrix_rank::overload_name) |
| 11863 | .typed<linalg_matrix_rank::schema>(); |
| 11864 | } |
| 11865 | |
| 11866 | // aten::linalg_matrix_rank(Tensor self, float tol, bool hermitian=False) -> Tensor |
| 11867 | at::Tensor linalg_matrix_rank::call(const at::Tensor & self, double tol, bool hermitian) { |
| 11868 | |
| 11869 | static auto op = create_linalg_matrix_rank_typed_handle(); |
| 11870 | return op.call(self, tol, hermitian); |
| 11871 | } |
| 11872 | |
| 11873 | // aten::linalg_matrix_rank(Tensor self, float tol, bool hermitian=False) -> Tensor |
| 11874 | at::Tensor linalg_matrix_rank::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double tol, bool hermitian) { |
| 11875 | |
| 11876 | static auto op = create_linalg_matrix_rank_typed_handle(); |
| 11877 | return op.redispatch(dispatchKeySet, self, tol, hermitian); |
| 11878 | } |
| 11879 | |
| 11880 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matrix_rank_out, name, "aten::linalg_matrix_rank") |
| 11881 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matrix_rank_out, overload_name, "out") |
| 11882 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matrix_rank_out, schema_str, "linalg_matrix_rank.out(Tensor self, float tol, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!)") |
| 11883 | |
| 11884 | // aten::linalg_matrix_rank.out(Tensor self, float tol, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!) |
| 11885 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_matrix_rank_out::schema> create_linalg_matrix_rank_out_typed_handle() { |
| 11886 | return c10::Dispatcher::singleton() |
| 11887 | .findSchemaOrThrow(linalg_matrix_rank_out::name, linalg_matrix_rank_out::overload_name) |
| 11888 | .typed<linalg_matrix_rank_out::schema>(); |
| 11889 | } |
| 11890 | |
| 11891 | // aten::linalg_matrix_rank.out(Tensor self, float tol, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!) |
| 11892 | at::Tensor & linalg_matrix_rank_out::call(const at::Tensor & self, double tol, bool hermitian, at::Tensor & out) { |
| 11893 | |
| 11894 | static auto op = create_linalg_matrix_rank_out_typed_handle(); |
| 11895 | return op.call(self, tol, hermitian, out); |
| 11896 | } |
| 11897 | |
| 11898 | // aten::linalg_matrix_rank.out(Tensor self, float tol, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!) |
| 11899 | at::Tensor & linalg_matrix_rank_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double tol, bool hermitian, at::Tensor & out) { |
| 11900 | |
| 11901 | static auto op = create_linalg_matrix_rank_out_typed_handle(); |
| 11902 | return op.redispatch(dispatchKeySet, self, tol, hermitian, out); |
| 11903 | } |
| 11904 | |
| 11905 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matrix_rank_tol_tensor, name, "aten::linalg_matrix_rank") |
| 11906 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matrix_rank_tol_tensor, overload_name, "tol_tensor") |
| 11907 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matrix_rank_tol_tensor, schema_str, "linalg_matrix_rank.tol_tensor(Tensor input, Tensor tol, bool hermitian=False) -> Tensor") |
| 11908 | |
| 11909 | // aten::linalg_matrix_rank.tol_tensor(Tensor input, Tensor tol, bool hermitian=False) -> Tensor |
| 11910 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_matrix_rank_tol_tensor::schema> create_linalg_matrix_rank_tol_tensor_typed_handle() { |
| 11911 | return c10::Dispatcher::singleton() |
| 11912 | .findSchemaOrThrow(linalg_matrix_rank_tol_tensor::name, linalg_matrix_rank_tol_tensor::overload_name) |
| 11913 | .typed<linalg_matrix_rank_tol_tensor::schema>(); |
| 11914 | } |
| 11915 | |
| 11916 | // aten::linalg_matrix_rank.tol_tensor(Tensor input, Tensor tol, bool hermitian=False) -> Tensor |
| 11917 | at::Tensor linalg_matrix_rank_tol_tensor::call(const at::Tensor & input, const at::Tensor & tol, bool hermitian) { |
| 11918 | |
| 11919 | static auto op = create_linalg_matrix_rank_tol_tensor_typed_handle(); |
| 11920 | return op.call(input, tol, hermitian); |
| 11921 | } |
| 11922 | |
| 11923 | // aten::linalg_matrix_rank.tol_tensor(Tensor input, Tensor tol, bool hermitian=False) -> Tensor |
| 11924 | at::Tensor linalg_matrix_rank_tol_tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & tol, bool hermitian) { |
| 11925 | |
| 11926 | static auto op = create_linalg_matrix_rank_tol_tensor_typed_handle(); |
| 11927 | return op.redispatch(dispatchKeySet, input, tol, hermitian); |
| 11928 | } |
| 11929 | |
| 11930 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matrix_rank_out_tol_tensor, name, "aten::linalg_matrix_rank") |
| 11931 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matrix_rank_out_tol_tensor, overload_name, "out_tol_tensor") |
| 11932 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_matrix_rank_out_tol_tensor, schema_str, "linalg_matrix_rank.out_tol_tensor(Tensor input, Tensor tol, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!)") |
| 11933 | |
| 11934 | // aten::linalg_matrix_rank.out_tol_tensor(Tensor input, Tensor tol, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!) |
| 11935 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_matrix_rank_out_tol_tensor::schema> create_linalg_matrix_rank_out_tol_tensor_typed_handle() { |
| 11936 | return c10::Dispatcher::singleton() |
| 11937 | .findSchemaOrThrow(linalg_matrix_rank_out_tol_tensor::name, linalg_matrix_rank_out_tol_tensor::overload_name) |
| 11938 | .typed<linalg_matrix_rank_out_tol_tensor::schema>(); |
| 11939 | } |
| 11940 | |
| 11941 | // aten::linalg_matrix_rank.out_tol_tensor(Tensor input, Tensor tol, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!) |
| 11942 | at::Tensor & linalg_matrix_rank_out_tol_tensor::call(const at::Tensor & input, const at::Tensor & tol, bool hermitian, at::Tensor & out) { |
| 11943 | |
| 11944 | static auto op = create_linalg_matrix_rank_out_tol_tensor_typed_handle(); |
| 11945 | return op.call(input, tol, hermitian, out); |
| 11946 | } |
| 11947 | |
| 11948 | // aten::linalg_matrix_rank.out_tol_tensor(Tensor input, Tensor tol, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!) |
| 11949 | at::Tensor & linalg_matrix_rank_out_tol_tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & tol, bool hermitian, at::Tensor & out) { |
| 11950 | |
| 11951 | static auto op = create_linalg_matrix_rank_out_tol_tensor_typed_handle(); |
| 11952 | return op.redispatch(dispatchKeySet, input, tol, hermitian, out); |
| 11953 | } |
| 11954 | |
| 11955 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_test_optional_filled_intlist, name, "aten::_test_optional_filled_intlist") |
| 11956 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_test_optional_filled_intlist, overload_name, "") |
| 11957 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_test_optional_filled_intlist, schema_str, "_test_optional_filled_intlist(Tensor values, int[2]? addends) -> Tensor") |
| 11958 | |
| 11959 | // aten::_test_optional_filled_intlist(Tensor values, int[2]? addends) -> Tensor |
| 11960 | static C10_NOINLINE c10::TypedOperatorHandle<_test_optional_filled_intlist::schema> create__test_optional_filled_intlist_typed_handle() { |
| 11961 | return c10::Dispatcher::singleton() |
| 11962 | .findSchemaOrThrow(_test_optional_filled_intlist::name, _test_optional_filled_intlist::overload_name) |
| 11963 | .typed<_test_optional_filled_intlist::schema>(); |
| 11964 | } |
| 11965 | |
| 11966 | // aten::_test_optional_filled_intlist(Tensor values, int[2]? addends) -> Tensor |
| 11967 | at::Tensor _test_optional_filled_intlist::call(const at::Tensor & values, at::OptionalIntArrayRef addends) { |
| 11968 | |
| 11969 | static auto op = create__test_optional_filled_intlist_typed_handle(); |
| 11970 | return op.call(values, addends); |
| 11971 | } |
| 11972 | |
| 11973 | // aten::_test_optional_filled_intlist(Tensor values, int[2]? addends) -> Tensor |
| 11974 | at::Tensor _test_optional_filled_intlist::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & values, at::OptionalIntArrayRef addends) { |
| 11975 | |
| 11976 | static auto op = create__test_optional_filled_intlist_typed_handle(); |
| 11977 | return op.redispatch(dispatchKeySet, values, addends); |
| 11978 | } |
| 11979 | |
| 11980 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_test_autograd_multiple_dispatch_view_copy, name, "aten::_test_autograd_multiple_dispatch_view_copy") |
| 11981 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_test_autograd_multiple_dispatch_view_copy, overload_name, "") |
| 11982 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_test_autograd_multiple_dispatch_view_copy, schema_str, "_test_autograd_multiple_dispatch_view_copy(Tensor self) -> Tensor") |
| 11983 | |
| 11984 | // aten::_test_autograd_multiple_dispatch_view_copy(Tensor self) -> Tensor |
| 11985 | static C10_NOINLINE c10::TypedOperatorHandle<_test_autograd_multiple_dispatch_view_copy::schema> create__test_autograd_multiple_dispatch_view_copy_typed_handle() { |
| 11986 | return c10::Dispatcher::singleton() |
| 11987 | .findSchemaOrThrow(_test_autograd_multiple_dispatch_view_copy::name, _test_autograd_multiple_dispatch_view_copy::overload_name) |
| 11988 | .typed<_test_autograd_multiple_dispatch_view_copy::schema>(); |
| 11989 | } |
| 11990 | |
| 11991 | // aten::_test_autograd_multiple_dispatch_view_copy(Tensor self) -> Tensor |
| 11992 | at::Tensor _test_autograd_multiple_dispatch_view_copy::call(const at::Tensor & self) { |
| 11993 | |
| 11994 | static auto op = create__test_autograd_multiple_dispatch_view_copy_typed_handle(); |
| 11995 | return op.call(self); |
| 11996 | } |
| 11997 | |
| 11998 | // aten::_test_autograd_multiple_dispatch_view_copy(Tensor self) -> Tensor |
| 11999 | at::Tensor _test_autograd_multiple_dispatch_view_copy::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 12000 | |
| 12001 | static auto op = create__test_autograd_multiple_dispatch_view_copy_typed_handle(); |
| 12002 | return op.redispatch(dispatchKeySet, self); |
| 12003 | } |
| 12004 | |
| 12005 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(pad_sequence, name, "aten::pad_sequence") |
| 12006 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(pad_sequence, overload_name, "") |
| 12007 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(pad_sequence, schema_str, "pad_sequence(Tensor[] sequences, bool batch_first=False, float padding_value=0.0) -> Tensor") |
| 12008 | |
| 12009 | // aten::pad_sequence(Tensor[] sequences, bool batch_first=False, float padding_value=0.0) -> Tensor |
| 12010 | static C10_NOINLINE c10::TypedOperatorHandle<pad_sequence::schema> create_pad_sequence_typed_handle() { |
| 12011 | return c10::Dispatcher::singleton() |
| 12012 | .findSchemaOrThrow(pad_sequence::name, pad_sequence::overload_name) |
| 12013 | .typed<pad_sequence::schema>(); |
| 12014 | } |
| 12015 | |
| 12016 | // aten::pad_sequence(Tensor[] sequences, bool batch_first=False, float padding_value=0.0) -> Tensor |
| 12017 | at::Tensor pad_sequence::call(at::TensorList sequences, bool batch_first, double padding_value) { |
| 12018 | |
| 12019 | static auto op = create_pad_sequence_typed_handle(); |
| 12020 | return op.call(sequences, batch_first, padding_value); |
| 12021 | } |
| 12022 | |
| 12023 | // aten::pad_sequence(Tensor[] sequences, bool batch_first=False, float padding_value=0.0) -> Tensor |
| 12024 | at::Tensor pad_sequence::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList sequences, bool batch_first, double padding_value) { |
| 12025 | |
| 12026 | static auto op = create_pad_sequence_typed_handle(); |
| 12027 | return op.redispatch(dispatchKeySet, sequences, batch_first, padding_value); |
| 12028 | } |
| 12029 | |
| 12030 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fw_primal_copy, name, "aten::_fw_primal_copy") |
| 12031 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fw_primal_copy, overload_name, "") |
| 12032 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fw_primal_copy, schema_str, "_fw_primal_copy(Tensor self, int level) -> Tensor") |
| 12033 | |
| 12034 | // aten::_fw_primal_copy(Tensor self, int level) -> Tensor |
| 12035 | static C10_NOINLINE c10::TypedOperatorHandle<_fw_primal_copy::schema> create__fw_primal_copy_typed_handle() { |
| 12036 | return c10::Dispatcher::singleton() |
| 12037 | .findSchemaOrThrow(_fw_primal_copy::name, _fw_primal_copy::overload_name) |
| 12038 | .typed<_fw_primal_copy::schema>(); |
| 12039 | } |
| 12040 | |
| 12041 | // aten::_fw_primal_copy(Tensor self, int level) -> Tensor |
| 12042 | at::Tensor _fw_primal_copy::call(const at::Tensor & self, int64_t level) { |
| 12043 | |
| 12044 | static auto op = create__fw_primal_copy_typed_handle(); |
| 12045 | return op.call(self, level); |
| 12046 | } |
| 12047 | |
| 12048 | // aten::_fw_primal_copy(Tensor self, int level) -> Tensor |
| 12049 | at::Tensor _fw_primal_copy::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t level) { |
| 12050 | |
| 12051 | static auto op = create__fw_primal_copy_typed_handle(); |
| 12052 | return op.redispatch(dispatchKeySet, self, level); |
| 12053 | } |
| 12054 | |
| 12055 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(view_as_real_copy, name, "aten::view_as_real_copy") |
| 12056 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(view_as_real_copy, overload_name, "") |
| 12057 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(view_as_real_copy, schema_str, "view_as_real_copy(Tensor self) -> Tensor") |
| 12058 | |
| 12059 | // aten::view_as_real_copy(Tensor self) -> Tensor |
| 12060 | static C10_NOINLINE c10::TypedOperatorHandle<view_as_real_copy::schema> create_view_as_real_copy_typed_handle() { |
| 12061 | return c10::Dispatcher::singleton() |
| 12062 | .findSchemaOrThrow(view_as_real_copy::name, view_as_real_copy::overload_name) |
| 12063 | .typed<view_as_real_copy::schema>(); |
| 12064 | } |
| 12065 | |
| 12066 | // aten::view_as_real_copy(Tensor self) -> Tensor |
| 12067 | at::Tensor view_as_real_copy::call(const at::Tensor & self) { |
| 12068 | |
| 12069 | static auto op = create_view_as_real_copy_typed_handle(); |
| 12070 | return op.call(self); |
| 12071 | } |
| 12072 | |
| 12073 | // aten::view_as_real_copy(Tensor self) -> Tensor |
| 12074 | at::Tensor view_as_real_copy::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 12075 | |
| 12076 | static auto op = create_view_as_real_copy_typed_handle(); |
| 12077 | return op.redispatch(dispatchKeySet, self); |
| 12078 | } |
| 12079 | |
| 12080 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(as_strided_copy, name, "aten::as_strided_copy") |
| 12081 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(as_strided_copy, overload_name, "") |
| 12082 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(as_strided_copy, schema_str, "as_strided_copy(Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor") |
| 12083 | |
| 12084 | // aten::as_strided_copy(Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor |
| 12085 | static C10_NOINLINE c10::TypedOperatorHandle<as_strided_copy::schema> create_as_strided_copy_typed_handle() { |
| 12086 | return c10::Dispatcher::singleton() |
| 12087 | .findSchemaOrThrow(as_strided_copy::name, as_strided_copy::overload_name) |
| 12088 | .typed<as_strided_copy::schema>(); |
| 12089 | } |
| 12090 | |
| 12091 | // aten::as_strided_copy(Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor |
| 12092 | at::Tensor as_strided_copy::call(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional<c10::SymInt> storage_offset) { |
| 12093 | |
| 12094 | static auto op = create_as_strided_copy_typed_handle(); |
| 12095 | return op.call(self, size, stride, storage_offset); |
| 12096 | } |
| 12097 | |
| 12098 | // aten::as_strided_copy(Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor |
| 12099 | at::Tensor as_strided_copy::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional<c10::SymInt> storage_offset) { |
| 12100 | |
| 12101 | static auto op = create_as_strided_copy_typed_handle(); |
| 12102 | return op.redispatch(dispatchKeySet, self, size, stride, storage_offset); |
| 12103 | } |
| 12104 | |
| 12105 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_reshape_alias_copy, name, "aten::_reshape_alias_copy") |
| 12106 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_reshape_alias_copy, overload_name, "") |
| 12107 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_reshape_alias_copy, schema_str, "_reshape_alias_copy(Tensor self, SymInt[] size, SymInt[] stride) -> Tensor") |
| 12108 | |
| 12109 | // aten::_reshape_alias_copy(Tensor self, SymInt[] size, SymInt[] stride) -> Tensor |
| 12110 | static C10_NOINLINE c10::TypedOperatorHandle<_reshape_alias_copy::schema> create__reshape_alias_copy_typed_handle() { |
| 12111 | return c10::Dispatcher::singleton() |
| 12112 | .findSchemaOrThrow(_reshape_alias_copy::name, _reshape_alias_copy::overload_name) |
| 12113 | .typed<_reshape_alias_copy::schema>(); |
| 12114 | } |
| 12115 | |
| 12116 | // aten::_reshape_alias_copy(Tensor self, SymInt[] size, SymInt[] stride) -> Tensor |
| 12117 | at::Tensor _reshape_alias_copy::call(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) { |
| 12118 | |
| 12119 | static auto op = create__reshape_alias_copy_typed_handle(); |
| 12120 | return op.call(self, size, stride); |
| 12121 | } |
| 12122 | |
| 12123 | // aten::_reshape_alias_copy(Tensor self, SymInt[] size, SymInt[] stride) -> Tensor |
| 12124 | at::Tensor _reshape_alias_copy::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) { |
| 12125 | |
| 12126 | static auto op = create__reshape_alias_copy_typed_handle(); |
| 12127 | return op.redispatch(dispatchKeySet, self, size, stride); |
| 12128 | } |
| 12129 | |
| 12130 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(split_copy_Tensor, name, "aten::split_copy") |
| 12131 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(split_copy_Tensor, overload_name, "Tensor") |
| 12132 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(split_copy_Tensor, schema_str, "split_copy.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[]") |
| 12133 | |
| 12134 | // aten::split_copy.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[] |
| 12135 | static C10_NOINLINE c10::TypedOperatorHandle<split_copy_Tensor::schema> create_split_copy_Tensor_typed_handle() { |
| 12136 | return c10::Dispatcher::singleton() |
| 12137 | .findSchemaOrThrow(split_copy_Tensor::name, split_copy_Tensor::overload_name) |
| 12138 | .typed<split_copy_Tensor::schema>(); |
| 12139 | } |
| 12140 | |
| 12141 | // aten::split_copy.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[] |
| 12142 | ::std::vector<at::Tensor> split_copy_Tensor::call(const at::Tensor & self, c10::SymInt split_size, int64_t dim) { |
| 12143 | |
| 12144 | static auto op = create_split_copy_Tensor_typed_handle(); |
| 12145 | return op.call(self, split_size, dim); |
| 12146 | } |
| 12147 | |
| 12148 | // aten::split_copy.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[] |
| 12149 | ::std::vector<at::Tensor> split_copy_Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt split_size, int64_t dim) { |
| 12150 | |
| 12151 | static auto op = create_split_copy_Tensor_typed_handle(); |
| 12152 | return op.redispatch(dispatchKeySet, self, split_size, dim); |
| 12153 | } |
| 12154 | |
| 12155 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(squeeze_copy, name, "aten::squeeze_copy") |
| 12156 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(squeeze_copy, overload_name, "") |
| 12157 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(squeeze_copy, schema_str, "squeeze_copy(Tensor self) -> Tensor") |
| 12158 | |
| 12159 | // aten::squeeze_copy(Tensor self) -> Tensor |
| 12160 | static C10_NOINLINE c10::TypedOperatorHandle<squeeze_copy::schema> create_squeeze_copy_typed_handle() { |
| 12161 | return c10::Dispatcher::singleton() |
| 12162 | .findSchemaOrThrow(squeeze_copy::name, squeeze_copy::overload_name) |
| 12163 | .typed<squeeze_copy::schema>(); |
| 12164 | } |
| 12165 | |
| 12166 | // aten::squeeze_copy(Tensor self) -> Tensor |
| 12167 | at::Tensor squeeze_copy::call(const at::Tensor & self) { |
| 12168 | |
| 12169 | static auto op = create_squeeze_copy_typed_handle(); |
| 12170 | return op.call(self); |
| 12171 | } |
| 12172 | |
| 12173 | // aten::squeeze_copy(Tensor self) -> Tensor |
| 12174 | at::Tensor squeeze_copy::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 12175 | |
| 12176 | static auto op = create_squeeze_copy_typed_handle(); |
| 12177 | return op.redispatch(dispatchKeySet, self); |
| 12178 | } |
| 12179 | |
| 12180 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(squeeze_copy_dim, name, "aten::squeeze_copy") |
| 12181 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(squeeze_copy_dim, overload_name, "dim") |
| 12182 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(squeeze_copy_dim, schema_str, "squeeze_copy.dim(Tensor self, int dim) -> Tensor") |
| 12183 | |
| 12184 | // aten::squeeze_copy.dim(Tensor self, int dim) -> Tensor |
| 12185 | static C10_NOINLINE c10::TypedOperatorHandle<squeeze_copy_dim::schema> create_squeeze_copy_dim_typed_handle() { |
| 12186 | return c10::Dispatcher::singleton() |
| 12187 | .findSchemaOrThrow(squeeze_copy_dim::name, squeeze_copy_dim::overload_name) |
| 12188 | .typed<squeeze_copy_dim::schema>(); |
| 12189 | } |
| 12190 | |
| 12191 | // aten::squeeze_copy.dim(Tensor self, int dim) -> Tensor |
| 12192 | at::Tensor squeeze_copy_dim::call(const at::Tensor & self, int64_t dim) { |
| 12193 | |
| 12194 | static auto op = create_squeeze_copy_dim_typed_handle(); |
| 12195 | return op.call(self, dim); |
| 12196 | } |
| 12197 | |
| 12198 | // aten::squeeze_copy.dim(Tensor self, int dim) -> Tensor |
| 12199 | at::Tensor squeeze_copy_dim::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim) { |
| 12200 | |
| 12201 | static auto op = create_squeeze_copy_dim_typed_handle(); |
| 12202 | return op.redispatch(dispatchKeySet, self, dim); |
| 12203 | } |
| 12204 | |
| 12205 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(squeeze_copy_dims, name, "aten::squeeze_copy") |
| 12206 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(squeeze_copy_dims, overload_name, "dims") |
| 12207 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(squeeze_copy_dims, schema_str, "squeeze_copy.dims(Tensor self, int[] dim) -> Tensor") |
| 12208 | |
| 12209 | // aten::squeeze_copy.dims(Tensor self, int[] dim) -> Tensor |
| 12210 | static C10_NOINLINE c10::TypedOperatorHandle<squeeze_copy_dims::schema> create_squeeze_copy_dims_typed_handle() { |
| 12211 | return c10::Dispatcher::singleton() |
| 12212 | .findSchemaOrThrow(squeeze_copy_dims::name, squeeze_copy_dims::overload_name) |
| 12213 | .typed<squeeze_copy_dims::schema>(); |
| 12214 | } |
| 12215 | |
| 12216 | // aten::squeeze_copy.dims(Tensor self, int[] dim) -> Tensor |
| 12217 | at::Tensor squeeze_copy_dims::call(const at::Tensor & self, at::IntArrayRef dim) { |
| 12218 | |
| 12219 | static auto op = create_squeeze_copy_dims_typed_handle(); |
| 12220 | return op.call(self, dim); |
| 12221 | } |
| 12222 | |
| 12223 | // aten::squeeze_copy.dims(Tensor self, int[] dim) -> Tensor |
| 12224 | at::Tensor squeeze_copy_dims::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim) { |
| 12225 | |
| 12226 | static auto op = create_squeeze_copy_dims_typed_handle(); |
| 12227 | return op.redispatch(dispatchKeySet, self, dim); |
| 12228 | } |
| 12229 | |
| 12230 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(indices_copy, name, "aten::indices_copy") |
| 12231 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(indices_copy, overload_name, "") |
| 12232 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(indices_copy, schema_str, "indices_copy(Tensor self) -> Tensor") |
| 12233 | |
| 12234 | // aten::indices_copy(Tensor self) -> Tensor |
| 12235 | static C10_NOINLINE c10::TypedOperatorHandle<indices_copy::schema> create_indices_copy_typed_handle() { |
| 12236 | return c10::Dispatcher::singleton() |
| 12237 | .findSchemaOrThrow(indices_copy::name, indices_copy::overload_name) |
| 12238 | .typed<indices_copy::schema>(); |
| 12239 | } |
| 12240 | |
| 12241 | // aten::indices_copy(Tensor self) -> Tensor |
| 12242 | at::Tensor indices_copy::call(const at::Tensor & self) { |
| 12243 | |
| 12244 | static auto op = create_indices_copy_typed_handle(); |
| 12245 | return op.call(self); |
| 12246 | } |
| 12247 | |
| 12248 | // aten::indices_copy(Tensor self) -> Tensor |
| 12249 | at::Tensor indices_copy::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 12250 | |
| 12251 | static auto op = create_indices_copy_typed_handle(); |
| 12252 | return op.redispatch(dispatchKeySet, self); |
| 12253 | } |
| 12254 | |
| 12255 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ccol_indices_copy, name, "aten::ccol_indices_copy") |
| 12256 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ccol_indices_copy, overload_name, "") |
| 12257 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ccol_indices_copy, schema_str, "ccol_indices_copy(Tensor self) -> Tensor") |
| 12258 | |
| 12259 | // aten::ccol_indices_copy(Tensor self) -> Tensor |
| 12260 | static C10_NOINLINE c10::TypedOperatorHandle<ccol_indices_copy::schema> create_ccol_indices_copy_typed_handle() { |
| 12261 | return c10::Dispatcher::singleton() |
| 12262 | .findSchemaOrThrow(ccol_indices_copy::name, ccol_indices_copy::overload_name) |
| 12263 | .typed<ccol_indices_copy::schema>(); |
| 12264 | } |
| 12265 | |
| 12266 | // aten::ccol_indices_copy(Tensor self) -> Tensor |
| 12267 | at::Tensor ccol_indices_copy::call(const at::Tensor & self) { |
| 12268 | |
| 12269 | static auto op = create_ccol_indices_copy_typed_handle(); |
| 12270 | return op.call(self); |
| 12271 | } |
| 12272 | |
| 12273 | // aten::ccol_indices_copy(Tensor self) -> Tensor |
| 12274 | at::Tensor ccol_indices_copy::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 12275 | |
| 12276 | static auto op = create_ccol_indices_copy_typed_handle(); |
| 12277 | return op.redispatch(dispatchKeySet, self); |
| 12278 | } |
| 12279 | |
| 12280 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(split_copy_Tensor_out, name, "aten::split_copy") |
| 12281 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(split_copy_Tensor_out, overload_name, "Tensor_out") |
| 12282 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(split_copy_Tensor_out, schema_str, "split_copy.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> ()") |
| 12283 | |
| 12284 | // aten::split_copy.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> () |
| 12285 | static C10_NOINLINE c10::TypedOperatorHandle<split_copy_Tensor_out::schema> create_split_copy_Tensor_out_typed_handle() { |
| 12286 | return c10::Dispatcher::singleton() |
| 12287 | .findSchemaOrThrow(split_copy_Tensor_out::name, split_copy_Tensor_out::overload_name) |
| 12288 | .typed<split_copy_Tensor_out::schema>(); |
| 12289 | } |
| 12290 | |
| 12291 | // aten::split_copy.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> () |
| 12292 | void split_copy_Tensor_out::call(const at::Tensor & self, c10::SymInt split_size, int64_t dim, at::TensorList out) { |
| 12293 | |
| 12294 | static auto op = create_split_copy_Tensor_out_typed_handle(); |
| 12295 | return op.call(self, split_size, dim, out); |
| 12296 | } |
| 12297 | |
| 12298 | // aten::split_copy.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> () |
| 12299 | void split_copy_Tensor_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt split_size, int64_t dim, at::TensorList out) { |
| 12300 | |
| 12301 | static auto op = create_split_copy_Tensor_out_typed_handle(); |
| 12302 | return op.redispatch(dispatchKeySet, self, split_size, dim, out); |
| 12303 | } |
| 12304 | |
| 12305 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_scaled_dot_product_efficient_attention, name, "aten::_scaled_dot_product_efficient_attention") |
| 12306 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_scaled_dot_product_efficient_attention, overload_name, "") |
| 12307 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_scaled_dot_product_efficient_attention, schema_str, "_scaled_dot_product_efficient_attention(Tensor query, Tensor key, Tensor value, bool compute_log_sumexp, bool is_causal=False) -> (Tensor, Tensor)") |
| 12308 | |
| 12309 | // aten::_scaled_dot_product_efficient_attention(Tensor query, Tensor key, Tensor value, bool compute_log_sumexp, bool is_causal=False) -> (Tensor, Tensor) |
| 12310 | static C10_NOINLINE c10::TypedOperatorHandle<_scaled_dot_product_efficient_attention::schema> create__scaled_dot_product_efficient_attention_typed_handle() { |
| 12311 | return c10::Dispatcher::singleton() |
| 12312 | .findSchemaOrThrow(_scaled_dot_product_efficient_attention::name, _scaled_dot_product_efficient_attention::overload_name) |
| 12313 | .typed<_scaled_dot_product_efficient_attention::schema>(); |
| 12314 | } |
| 12315 | |
| 12316 | // aten::_scaled_dot_product_efficient_attention(Tensor query, Tensor key, Tensor value, bool compute_log_sumexp, bool is_causal=False) -> (Tensor, Tensor) |
| 12317 | ::std::tuple<at::Tensor,at::Tensor> _scaled_dot_product_efficient_attention::call(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, bool compute_log_sumexp, bool is_causal) { |
| 12318 | |
| 12319 | static auto op = create__scaled_dot_product_efficient_attention_typed_handle(); |
| 12320 | return op.call(query, key, value, compute_log_sumexp, is_causal); |
| 12321 | } |
| 12322 | |
| 12323 | // aten::_scaled_dot_product_efficient_attention(Tensor query, Tensor key, Tensor value, bool compute_log_sumexp, bool is_causal=False) -> (Tensor, Tensor) |
| 12324 | ::std::tuple<at::Tensor,at::Tensor> _scaled_dot_product_efficient_attention::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, bool compute_log_sumexp, bool is_causal) { |
| 12325 | |
| 12326 | static auto op = create__scaled_dot_product_efficient_attention_typed_handle(); |
| 12327 | return op.redispatch(dispatchKeySet, query, key, value, compute_log_sumexp, is_causal); |
| 12328 | } |
| 12329 | |
| 12330 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_chunk_grad_outputs_efficient_attention, name, "aten::_chunk_grad_outputs_efficient_attention") |
| 12331 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_chunk_grad_outputs_efficient_attention, overload_name, "") |
| 12332 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_chunk_grad_outputs_efficient_attention, schema_str, "_chunk_grad_outputs_efficient_attention(Tensor query, Tensor key, Tensor value, bool is_causal=False) -> bool") |
| 12333 | |
| 12334 | // aten::_chunk_grad_outputs_efficient_attention(Tensor query, Tensor key, Tensor value, bool is_causal=False) -> bool |
| 12335 | static C10_NOINLINE c10::TypedOperatorHandle<_chunk_grad_outputs_efficient_attention::schema> create__chunk_grad_outputs_efficient_attention_typed_handle() { |
| 12336 | return c10::Dispatcher::singleton() |
| 12337 | .findSchemaOrThrow(_chunk_grad_outputs_efficient_attention::name, _chunk_grad_outputs_efficient_attention::overload_name) |
| 12338 | .typed<_chunk_grad_outputs_efficient_attention::schema>(); |
| 12339 | } |
| 12340 | |
| 12341 | // aten::_chunk_grad_outputs_efficient_attention(Tensor query, Tensor key, Tensor value, bool is_causal=False) -> bool |
| 12342 | bool _chunk_grad_outputs_efficient_attention::call(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, bool is_causal) { |
| 12343 | |
| 12344 | static auto op = create__chunk_grad_outputs_efficient_attention_typed_handle(); |
| 12345 | return op.call(query, key, value, is_causal); |
| 12346 | } |
| 12347 | |
| 12348 | // aten::_chunk_grad_outputs_efficient_attention(Tensor query, Tensor key, Tensor value, bool is_causal=False) -> bool |
| 12349 | bool _chunk_grad_outputs_efficient_attention::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, bool is_causal) { |
| 12350 | |
| 12351 | static auto op = create__chunk_grad_outputs_efficient_attention_typed_handle(); |
| 12352 | return op.redispatch(dispatchKeySet, query, key, value, is_causal); |
| 12353 | } |
| 12354 | |
| 12355 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_efficient_attention_forward, name, "aten::_efficient_attention_forward") |
| 12356 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_efficient_attention_forward, overload_name, "") |
| 12357 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_efficient_attention_forward, schema_str, "_efficient_attention_forward(Tensor query, Tensor key, Tensor value, Tensor? cu_seqlens_q, Tensor? cu_seqlens_k, int? max_seqlen_q, bool compute_log_sumexp=False, bool causal=False) -> (Tensor, Tensor)") |
| 12358 | |
| 12359 | // aten::_efficient_attention_forward(Tensor query, Tensor key, Tensor value, Tensor? cu_seqlens_q, Tensor? cu_seqlens_k, int? max_seqlen_q, bool compute_log_sumexp=False, bool causal=False) -> (Tensor, Tensor) |
| 12360 | static C10_NOINLINE c10::TypedOperatorHandle<_efficient_attention_forward::schema> create__efficient_attention_forward_typed_handle() { |
| 12361 | return c10::Dispatcher::singleton() |
| 12362 | .findSchemaOrThrow(_efficient_attention_forward::name, _efficient_attention_forward::overload_name) |
| 12363 | .typed<_efficient_attention_forward::schema>(); |
| 12364 | } |
| 12365 | |
| 12366 | // aten::_efficient_attention_forward(Tensor query, Tensor key, Tensor value, Tensor? cu_seqlens_q, Tensor? cu_seqlens_k, int? max_seqlen_q, bool compute_log_sumexp=False, bool causal=False) -> (Tensor, Tensor) |
| 12367 | ::std::tuple<at::Tensor,at::Tensor> _efficient_attention_forward::call(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const c10::optional<at::Tensor> & cu_seqlens_q, const c10::optional<at::Tensor> & cu_seqlens_k, c10::optional<int64_t> max_seqlen_q, bool compute_log_sumexp, bool causal) { |
| 12368 | |
| 12369 | static auto op = create__efficient_attention_forward_typed_handle(); |
| 12370 | return op.call(query, key, value, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, compute_log_sumexp, causal); |
| 12371 | } |
| 12372 | |
| 12373 | // aten::_efficient_attention_forward(Tensor query, Tensor key, Tensor value, Tensor? cu_seqlens_q, Tensor? cu_seqlens_k, int? max_seqlen_q, bool compute_log_sumexp=False, bool causal=False) -> (Tensor, Tensor) |
| 12374 | ::std::tuple<at::Tensor,at::Tensor> _efficient_attention_forward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const c10::optional<at::Tensor> & cu_seqlens_q, const c10::optional<at::Tensor> & cu_seqlens_k, c10::optional<int64_t> max_seqlen_q, bool compute_log_sumexp, bool causal) { |
| 12375 | |
| 12376 | static auto op = create__efficient_attention_forward_typed_handle(); |
| 12377 | return op.redispatch(dispatchKeySet, query, key, value, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, compute_log_sumexp, causal); |
| 12378 | } |
| 12379 | |
| 12380 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_transformer_decoder_only_layer_fwd, name, "aten::_transformer_decoder_only_layer_fwd") |
| 12381 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_transformer_decoder_only_layer_fwd, overload_name, "") |
| 12382 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_transformer_decoder_only_layer_fwd, schema_str, "_transformer_decoder_only_layer_fwd(Tensor src, int embed_dim, int num_heads, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, bool use_gelu, bool norm_first, float eps, Tensor norm_weight_1, Tensor norm_bias_1, Tensor norm_weight_2, Tensor norm_bias_2, Tensor ffn_weight_1, Tensor ffn_bias_1, Tensor ffn_weight_2, Tensor ffn_bias_2, Tensor? mask=None, Tensor? incr_key=None, Tensor? incr_value=None) -> (Tensor, Tensor, Tensor)") |
| 12383 | |
| 12384 | // aten::_transformer_decoder_only_layer_fwd(Tensor src, int embed_dim, int num_heads, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, bool use_gelu, bool norm_first, float eps, Tensor norm_weight_1, Tensor norm_bias_1, Tensor norm_weight_2, Tensor norm_bias_2, Tensor ffn_weight_1, Tensor ffn_bias_1, Tensor ffn_weight_2, Tensor ffn_bias_2, Tensor? mask=None, Tensor? incr_key=None, Tensor? incr_value=None) -> (Tensor, Tensor, Tensor) |
| 12385 | static C10_NOINLINE c10::TypedOperatorHandle<_transformer_decoder_only_layer_fwd::schema> create__transformer_decoder_only_layer_fwd_typed_handle() { |
| 12386 | return c10::Dispatcher::singleton() |
| 12387 | .findSchemaOrThrow(_transformer_decoder_only_layer_fwd::name, _transformer_decoder_only_layer_fwd::overload_name) |
| 12388 | .typed<_transformer_decoder_only_layer_fwd::schema>(); |
| 12389 | } |
| 12390 | |
| 12391 | // aten::_transformer_decoder_only_layer_fwd(Tensor src, int embed_dim, int num_heads, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, bool use_gelu, bool norm_first, float eps, Tensor norm_weight_1, Tensor norm_bias_1, Tensor norm_weight_2, Tensor norm_bias_2, Tensor ffn_weight_1, Tensor ffn_bias_1, Tensor ffn_weight_2, Tensor ffn_bias_2, Tensor? mask=None, Tensor? incr_key=None, Tensor? incr_value=None) -> (Tensor, Tensor, Tensor) |
| 12392 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _transformer_decoder_only_layer_fwd::call(const at::Tensor & src, int64_t embed_dim, int64_t num_heads, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, bool use_gelu, bool norm_first, double eps, const at::Tensor & norm_weight_1, const at::Tensor & norm_bias_1, const at::Tensor & norm_weight_2, const at::Tensor & norm_bias_2, const at::Tensor & ffn_weight_1, const at::Tensor & ffn_bias_1, const at::Tensor & ffn_weight_2, const at::Tensor & ffn_bias_2, const c10::optional<at::Tensor> & mask, const c10::optional<at::Tensor> & incr_key, const c10::optional<at::Tensor> & incr_value) { |
| 12393 | |
| 12394 | static auto op = create__transformer_decoder_only_layer_fwd_typed_handle(); |
| 12395 | return op.call(src, embed_dim, num_heads, qkv_weight, qkv_bias, proj_weight, proj_bias, use_gelu, norm_first, eps, norm_weight_1, norm_bias_1, norm_weight_2, norm_bias_2, ffn_weight_1, ffn_bias_1, ffn_weight_2, ffn_bias_2, mask, incr_key, incr_value); |
| 12396 | } |
| 12397 | |
| 12398 | // aten::_transformer_decoder_only_layer_fwd(Tensor src, int embed_dim, int num_heads, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, bool use_gelu, bool norm_first, float eps, Tensor norm_weight_1, Tensor norm_bias_1, Tensor norm_weight_2, Tensor norm_bias_2, Tensor ffn_weight_1, Tensor ffn_bias_1, Tensor ffn_weight_2, Tensor ffn_bias_2, Tensor? mask=None, Tensor? incr_key=None, Tensor? incr_value=None) -> (Tensor, Tensor, Tensor) |
| 12399 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _transformer_decoder_only_layer_fwd::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & src, int64_t embed_dim, int64_t num_heads, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, bool use_gelu, bool norm_first, double eps, const at::Tensor & norm_weight_1, const at::Tensor & norm_bias_1, const at::Tensor & norm_weight_2, const at::Tensor & norm_bias_2, const at::Tensor & ffn_weight_1, const at::Tensor & ffn_bias_1, const at::Tensor & ffn_weight_2, const at::Tensor & ffn_bias_2, const c10::optional<at::Tensor> & mask, const c10::optional<at::Tensor> & incr_key, const c10::optional<at::Tensor> & incr_value) { |
| 12400 | |
| 12401 | static auto op = create__transformer_decoder_only_layer_fwd_typed_handle(); |
| 12402 | return op.redispatch(dispatchKeySet, src, embed_dim, num_heads, qkv_weight, qkv_bias, proj_weight, proj_bias, use_gelu, norm_first, eps, norm_weight_1, norm_bias_1, norm_weight_2, norm_bias_2, ffn_weight_1, ffn_bias_1, ffn_weight_2, ffn_bias_2, mask, incr_key, incr_value); |
| 12403 | } |
| 12404 | |
| 12405 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_bessel_j1, name, "aten::special_bessel_j1") |
| 12406 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_bessel_j1, overload_name, "") |
| 12407 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_bessel_j1, schema_str, "special_bessel_j1(Tensor self) -> Tensor") |
| 12408 | |
| 12409 | // aten::special_bessel_j1(Tensor self) -> Tensor |
| 12410 | static C10_NOINLINE c10::TypedOperatorHandle<special_bessel_j1::schema> create_special_bessel_j1_typed_handle() { |
| 12411 | return c10::Dispatcher::singleton() |
| 12412 | .findSchemaOrThrow(special_bessel_j1::name, special_bessel_j1::overload_name) |
| 12413 | .typed<special_bessel_j1::schema>(); |
| 12414 | } |
| 12415 | |
| 12416 | // aten::special_bessel_j1(Tensor self) -> Tensor |
| 12417 | at::Tensor special_bessel_j1::call(const at::Tensor & self) { |
| 12418 | |
| 12419 | static auto op = create_special_bessel_j1_typed_handle(); |
| 12420 | return op.call(self); |
| 12421 | } |
| 12422 | |
| 12423 | // aten::special_bessel_j1(Tensor self) -> Tensor |
| 12424 | at::Tensor special_bessel_j1::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 12425 | |
| 12426 | static auto op = create_special_bessel_j1_typed_handle(); |
| 12427 | return op.redispatch(dispatchKeySet, self); |
| 12428 | } |
| 12429 | |
| 12430 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_bessel_j1_out, name, "aten::special_bessel_j1") |
| 12431 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_bessel_j1_out, overload_name, "out") |
| 12432 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_bessel_j1_out, schema_str, "special_bessel_j1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 12433 | |
| 12434 | // aten::special_bessel_j1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 12435 | static C10_NOINLINE c10::TypedOperatorHandle<special_bessel_j1_out::schema> create_special_bessel_j1_out_typed_handle() { |
| 12436 | return c10::Dispatcher::singleton() |
| 12437 | .findSchemaOrThrow(special_bessel_j1_out::name, special_bessel_j1_out::overload_name) |
| 12438 | .typed<special_bessel_j1_out::schema>(); |
| 12439 | } |
| 12440 | |
| 12441 | // aten::special_bessel_j1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 12442 | at::Tensor & special_bessel_j1_out::call(const at::Tensor & self, at::Tensor & out) { |
| 12443 | |
| 12444 | static auto op = create_special_bessel_j1_out_typed_handle(); |
| 12445 | return op.call(self, out); |
| 12446 | } |
| 12447 | |
| 12448 | // aten::special_bessel_j1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 12449 | at::Tensor & special_bessel_j1_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 12450 | |
| 12451 | static auto op = create_special_bessel_j1_out_typed_handle(); |
| 12452 | return op.redispatch(dispatchKeySet, self, out); |
| 12453 | } |
| 12454 | |
| 12455 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_v, name, "aten::special_chebyshev_polynomial_v") |
| 12456 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_v, overload_name, "") |
| 12457 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_v, schema_str, "special_chebyshev_polynomial_v(Tensor x, Tensor n) -> Tensor") |
| 12458 | |
| 12459 | // aten::special_chebyshev_polynomial_v(Tensor x, Tensor n) -> Tensor |
| 12460 | static C10_NOINLINE c10::TypedOperatorHandle<special_chebyshev_polynomial_v::schema> create_special_chebyshev_polynomial_v_typed_handle() { |
| 12461 | return c10::Dispatcher::singleton() |
| 12462 | .findSchemaOrThrow(special_chebyshev_polynomial_v::name, special_chebyshev_polynomial_v::overload_name) |
| 12463 | .typed<special_chebyshev_polynomial_v::schema>(); |
| 12464 | } |
| 12465 | |
| 12466 | // aten::special_chebyshev_polynomial_v(Tensor x, Tensor n) -> Tensor |
| 12467 | at::Tensor special_chebyshev_polynomial_v::call(const at::Tensor & x, const at::Tensor & n) { |
| 12468 | |
| 12469 | static auto op = create_special_chebyshev_polynomial_v_typed_handle(); |
| 12470 | return op.call(x, n); |
| 12471 | } |
| 12472 | |
| 12473 | // aten::special_chebyshev_polynomial_v(Tensor x, Tensor n) -> Tensor |
| 12474 | at::Tensor special_chebyshev_polynomial_v::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n) { |
| 12475 | |
| 12476 | static auto op = create_special_chebyshev_polynomial_v_typed_handle(); |
| 12477 | return op.redispatch(dispatchKeySet, x, n); |
| 12478 | } |
| 12479 | |
| 12480 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_v_x_scalar, name, "aten::special_chebyshev_polynomial_v") |
| 12481 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_v_x_scalar, overload_name, "x_scalar") |
| 12482 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_v_x_scalar, schema_str, "special_chebyshev_polynomial_v.x_scalar(Scalar x, Tensor n) -> Tensor") |
| 12483 | |
| 12484 | // aten::special_chebyshev_polynomial_v.x_scalar(Scalar x, Tensor n) -> Tensor |
| 12485 | static C10_NOINLINE c10::TypedOperatorHandle<special_chebyshev_polynomial_v_x_scalar::schema> create_special_chebyshev_polynomial_v_x_scalar_typed_handle() { |
| 12486 | return c10::Dispatcher::singleton() |
| 12487 | .findSchemaOrThrow(special_chebyshev_polynomial_v_x_scalar::name, special_chebyshev_polynomial_v_x_scalar::overload_name) |
| 12488 | .typed<special_chebyshev_polynomial_v_x_scalar::schema>(); |
| 12489 | } |
| 12490 | |
| 12491 | // aten::special_chebyshev_polynomial_v.x_scalar(Scalar x, Tensor n) -> Tensor |
| 12492 | at::Tensor special_chebyshev_polynomial_v_x_scalar::call(const at::Scalar & x, const at::Tensor & n) { |
| 12493 | |
| 12494 | static auto op = create_special_chebyshev_polynomial_v_x_scalar_typed_handle(); |
| 12495 | return op.call(x, n); |
| 12496 | } |
| 12497 | |
| 12498 | // aten::special_chebyshev_polynomial_v.x_scalar(Scalar x, Tensor n) -> Tensor |
| 12499 | at::Tensor special_chebyshev_polynomial_v_x_scalar::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n) { |
| 12500 | |
| 12501 | static auto op = create_special_chebyshev_polynomial_v_x_scalar_typed_handle(); |
| 12502 | return op.redispatch(dispatchKeySet, x, n); |
| 12503 | } |
| 12504 | |
| 12505 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_v_n_scalar, name, "aten::special_chebyshev_polynomial_v") |
| 12506 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_v_n_scalar, overload_name, "n_scalar") |
| 12507 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_v_n_scalar, schema_str, "special_chebyshev_polynomial_v.n_scalar(Tensor x, Scalar n) -> Tensor") |
| 12508 | |
| 12509 | // aten::special_chebyshev_polynomial_v.n_scalar(Tensor x, Scalar n) -> Tensor |
| 12510 | static C10_NOINLINE c10::TypedOperatorHandle<special_chebyshev_polynomial_v_n_scalar::schema> create_special_chebyshev_polynomial_v_n_scalar_typed_handle() { |
| 12511 | return c10::Dispatcher::singleton() |
| 12512 | .findSchemaOrThrow(special_chebyshev_polynomial_v_n_scalar::name, special_chebyshev_polynomial_v_n_scalar::overload_name) |
| 12513 | .typed<special_chebyshev_polynomial_v_n_scalar::schema>(); |
| 12514 | } |
| 12515 | |
| 12516 | // aten::special_chebyshev_polynomial_v.n_scalar(Tensor x, Scalar n) -> Tensor |
| 12517 | at::Tensor special_chebyshev_polynomial_v_n_scalar::call(const at::Tensor & x, const at::Scalar & n) { |
| 12518 | |
| 12519 | static auto op = create_special_chebyshev_polynomial_v_n_scalar_typed_handle(); |
| 12520 | return op.call(x, n); |
| 12521 | } |
| 12522 | |
| 12523 | // aten::special_chebyshev_polynomial_v.n_scalar(Tensor x, Scalar n) -> Tensor |
| 12524 | at::Tensor special_chebyshev_polynomial_v_n_scalar::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n) { |
| 12525 | |
| 12526 | static auto op = create_special_chebyshev_polynomial_v_n_scalar_typed_handle(); |
| 12527 | return op.redispatch(dispatchKeySet, x, n); |
| 12528 | } |
| 12529 | |
| 12530 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_v_out, name, "aten::special_chebyshev_polynomial_v") |
| 12531 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_v_out, overload_name, "out") |
| 12532 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_v_out, schema_str, "special_chebyshev_polynomial_v.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)") |
| 12533 | |
| 12534 | // aten::special_chebyshev_polynomial_v.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) |
| 12535 | static C10_NOINLINE c10::TypedOperatorHandle<special_chebyshev_polynomial_v_out::schema> create_special_chebyshev_polynomial_v_out_typed_handle() { |
| 12536 | return c10::Dispatcher::singleton() |
| 12537 | .findSchemaOrThrow(special_chebyshev_polynomial_v_out::name, special_chebyshev_polynomial_v_out::overload_name) |
| 12538 | .typed<special_chebyshev_polynomial_v_out::schema>(); |
| 12539 | } |
| 12540 | |
| 12541 | // aten::special_chebyshev_polynomial_v.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) |
| 12542 | at::Tensor & special_chebyshev_polynomial_v_out::call(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 12543 | |
| 12544 | static auto op = create_special_chebyshev_polynomial_v_out_typed_handle(); |
| 12545 | return op.call(x, n, out); |
| 12546 | } |
| 12547 | |
| 12548 | // aten::special_chebyshev_polynomial_v.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) |
| 12549 | at::Tensor & special_chebyshev_polynomial_v_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 12550 | |
| 12551 | static auto op = create_special_chebyshev_polynomial_v_out_typed_handle(); |
| 12552 | return op.redispatch(dispatchKeySet, x, n, out); |
| 12553 | } |
| 12554 | |
| 12555 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_v_x_scalar_out, name, "aten::special_chebyshev_polynomial_v") |
| 12556 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_v_x_scalar_out, overload_name, "x_scalar_out") |
| 12557 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_v_x_scalar_out, schema_str, "special_chebyshev_polynomial_v.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)") |
| 12558 | |
| 12559 | // aten::special_chebyshev_polynomial_v.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) |
| 12560 | static C10_NOINLINE c10::TypedOperatorHandle<special_chebyshev_polynomial_v_x_scalar_out::schema> create_special_chebyshev_polynomial_v_x_scalar_out_typed_handle() { |
| 12561 | return c10::Dispatcher::singleton() |
| 12562 | .findSchemaOrThrow(special_chebyshev_polynomial_v_x_scalar_out::name, special_chebyshev_polynomial_v_x_scalar_out::overload_name) |
| 12563 | .typed<special_chebyshev_polynomial_v_x_scalar_out::schema>(); |
| 12564 | } |
| 12565 | |
| 12566 | // aten::special_chebyshev_polynomial_v.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) |
| 12567 | at::Tensor & special_chebyshev_polynomial_v_x_scalar_out::call(const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { |
| 12568 | |
| 12569 | static auto op = create_special_chebyshev_polynomial_v_x_scalar_out_typed_handle(); |
| 12570 | return op.call(x, n, out); |
| 12571 | } |
| 12572 | |
| 12573 | // aten::special_chebyshev_polynomial_v.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) |
| 12574 | at::Tensor & special_chebyshev_polynomial_v_x_scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { |
| 12575 | |
| 12576 | static auto op = create_special_chebyshev_polynomial_v_x_scalar_out_typed_handle(); |
| 12577 | return op.redispatch(dispatchKeySet, x, n, out); |
| 12578 | } |
| 12579 | |
| 12580 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_v_n_scalar_out, name, "aten::special_chebyshev_polynomial_v") |
| 12581 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_v_n_scalar_out, overload_name, "n_scalar_out") |
| 12582 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_v_n_scalar_out, schema_str, "special_chebyshev_polynomial_v.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)") |
| 12583 | |
| 12584 | // aten::special_chebyshev_polynomial_v.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) |
| 12585 | static C10_NOINLINE c10::TypedOperatorHandle<special_chebyshev_polynomial_v_n_scalar_out::schema> create_special_chebyshev_polynomial_v_n_scalar_out_typed_handle() { |
| 12586 | return c10::Dispatcher::singleton() |
| 12587 | .findSchemaOrThrow(special_chebyshev_polynomial_v_n_scalar_out::name, special_chebyshev_polynomial_v_n_scalar_out::overload_name) |
| 12588 | .typed<special_chebyshev_polynomial_v_n_scalar_out::schema>(); |
| 12589 | } |
| 12590 | |
| 12591 | // aten::special_chebyshev_polynomial_v.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) |
| 12592 | at::Tensor & special_chebyshev_polynomial_v_n_scalar_out::call(const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { |
| 12593 | |
| 12594 | static auto op = create_special_chebyshev_polynomial_v_n_scalar_out_typed_handle(); |
| 12595 | return op.call(x, n, out); |
| 12596 | } |
| 12597 | |
| 12598 | // aten::special_chebyshev_polynomial_v.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) |
| 12599 | at::Tensor & special_chebyshev_polynomial_v_n_scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { |
| 12600 | |
| 12601 | static auto op = create_special_chebyshev_polynomial_v_n_scalar_out_typed_handle(); |
| 12602 | return op.redispatch(dispatchKeySet, x, n, out); |
| 12603 | } |
| 12604 | |
| 12605 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cudnn_rnn_backward_out, name, "aten::_cudnn_rnn_backward") |
| 12606 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cudnn_rnn_backward_out, overload_name, "out") |
| 12607 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cudnn_rnn_backward_out, schema_str, "_cudnn_rnn_backward.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!)[] out3) -> ()") |
| 12608 | |
| 12609 | // aten::_cudnn_rnn_backward.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!)[] out3) -> () |
| 12610 | static C10_NOINLINE c10::TypedOperatorHandle<_cudnn_rnn_backward_out::schema> create__cudnn_rnn_backward_out_typed_handle() { |
| 12611 | return c10::Dispatcher::singleton() |
| 12612 | .findSchemaOrThrow(_cudnn_rnn_backward_out::name, _cudnn_rnn_backward_out::overload_name) |
| 12613 | .typed<_cudnn_rnn_backward_out::schema>(); |
| 12614 | } |
| 12615 | |
| 12616 | // aten::_cudnn_rnn_backward.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!)[] out3) -> () |
| 12617 | void _cudnn_rnn_backward_out::call(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) { |
| 12618 | |
| 12619 | static auto op = create__cudnn_rnn_backward_out_typed_handle(); |
| 12620 | return op.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); |
| 12621 | } |
| 12622 | |
| 12623 | // aten::_cudnn_rnn_backward.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!)[] out3) -> () |
| 12624 | void _cudnn_rnn_backward_out::redispatch(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) { |
| 12625 | |
| 12626 | static auto op = create__cudnn_rnn_backward_out_typed_handle(); |
| 12627 | return op.redispatch(dispatchKeySet, 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); |
| 12628 | } |
| 12629 | |
| 12630 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_dropout_backward_out, name, "aten::native_dropout_backward") |
| 12631 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_dropout_backward_out, overload_name, "out") |
| 12632 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_dropout_backward_out, schema_str, "native_dropout_backward.out(Tensor grad_output, Tensor mask, float scale, *, Tensor(a!) out) -> Tensor(a!)") |
| 12633 | |
| 12634 | // aten::native_dropout_backward.out(Tensor grad_output, Tensor mask, float scale, *, Tensor(a!) out) -> Tensor(a!) |
| 12635 | static C10_NOINLINE c10::TypedOperatorHandle<native_dropout_backward_out::schema> create_native_dropout_backward_out_typed_handle() { |
| 12636 | return c10::Dispatcher::singleton() |
| 12637 | .findSchemaOrThrow(native_dropout_backward_out::name, native_dropout_backward_out::overload_name) |
| 12638 | .typed<native_dropout_backward_out::schema>(); |
| 12639 | } |
| 12640 | |
| 12641 | // aten::native_dropout_backward.out(Tensor grad_output, Tensor mask, float scale, *, Tensor(a!) out) -> Tensor(a!) |
| 12642 | at::Tensor & native_dropout_backward_out::call(const at::Tensor & grad_output, const at::Tensor & mask, double scale, at::Tensor & out) { |
| 12643 | |
| 12644 | static auto op = create_native_dropout_backward_out_typed_handle(); |
| 12645 | return op.call(grad_output, mask, scale, out); |
| 12646 | } |
| 12647 | |
| 12648 | // aten::native_dropout_backward.out(Tensor grad_output, Tensor mask, float scale, *, Tensor(a!) out) -> Tensor(a!) |
| 12649 | at::Tensor & native_dropout_backward_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & mask, double scale, at::Tensor & out) { |
| 12650 | |
| 12651 | static auto op = create_native_dropout_backward_out_typed_handle(); |
| 12652 | return op.redispatch(dispatchKeySet, grad_output, mask, scale, out); |
| 12653 | } |
| 12654 | |
| 12655 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_add_relu_Scalar_out, name, "aten::_add_relu") |
| 12656 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_add_relu_Scalar_out, overload_name, "Scalar_out") |
| 12657 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_add_relu_Scalar_out, schema_str, "_add_relu.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!)") |
| 12658 | |
| 12659 | // aten::_add_relu.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!) |
| 12660 | static C10_NOINLINE c10::TypedOperatorHandle<_add_relu_Scalar_out::schema> create__add_relu_Scalar_out_typed_handle() { |
| 12661 | return c10::Dispatcher::singleton() |
| 12662 | .findSchemaOrThrow(_add_relu_Scalar_out::name, _add_relu_Scalar_out::overload_name) |
| 12663 | .typed<_add_relu_Scalar_out::schema>(); |
| 12664 | } |
| 12665 | |
| 12666 | // aten::_add_relu.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!) |
| 12667 | at::Tensor & _add_relu_Scalar_out::call(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha, at::Tensor & out) { |
| 12668 | |
| 12669 | static auto op = create__add_relu_Scalar_out_typed_handle(); |
| 12670 | return op.call(self, other, alpha, out); |
| 12671 | } |
| 12672 | |
| 12673 | // aten::_add_relu.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!) |
| 12674 | at::Tensor & _add_relu_Scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha, at::Tensor & out) { |
| 12675 | |
| 12676 | static auto op = create__add_relu_Scalar_out_typed_handle(); |
| 12677 | return op.redispatch(dispatchKeySet, self, other, alpha, out); |
| 12678 | } |
| 12679 | |
| 12680 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(affine_grid_generator_out, name, "aten::affine_grid_generator") |
| 12681 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(affine_grid_generator_out, overload_name, "out") |
| 12682 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(affine_grid_generator_out, schema_str, "affine_grid_generator.out(Tensor theta, int[] size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)") |
| 12683 | |
| 12684 | // aten::affine_grid_generator.out(Tensor theta, int[] size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!) |
| 12685 | static C10_NOINLINE c10::TypedOperatorHandle<affine_grid_generator_out::schema> create_affine_grid_generator_out_typed_handle() { |
| 12686 | return c10::Dispatcher::singleton() |
| 12687 | .findSchemaOrThrow(affine_grid_generator_out::name, affine_grid_generator_out::overload_name) |
| 12688 | .typed<affine_grid_generator_out::schema>(); |
| 12689 | } |
| 12690 | |
| 12691 | // aten::affine_grid_generator.out(Tensor theta, int[] size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!) |
| 12692 | at::Tensor & affine_grid_generator_out::call(const at::Tensor & theta, at::IntArrayRef size, bool align_corners, at::Tensor & out) { |
| 12693 | |
| 12694 | static auto op = create_affine_grid_generator_out_typed_handle(); |
| 12695 | return op.call(theta, size, align_corners, out); |
| 12696 | } |
| 12697 | |
| 12698 | // aten::affine_grid_generator.out(Tensor theta, int[] size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!) |
| 12699 | at::Tensor & affine_grid_generator_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & theta, at::IntArrayRef size, bool align_corners, at::Tensor & out) { |
| 12700 | |
| 12701 | static auto op = create_affine_grid_generator_out_typed_handle(); |
| 12702 | return op.redispatch(dispatchKeySet, theta, size, align_corners, out); |
| 12703 | } |
| 12704 | |
| 12705 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bartlett_window_out, name, "aten::bartlett_window") |
| 12706 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bartlett_window_out, overload_name, "out") |
| 12707 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bartlett_window_out, schema_str, "bartlett_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)") |
| 12708 | |
| 12709 | // aten::bartlett_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!) |
| 12710 | static C10_NOINLINE c10::TypedOperatorHandle<bartlett_window_out::schema> create_bartlett_window_out_typed_handle() { |
| 12711 | return c10::Dispatcher::singleton() |
| 12712 | .findSchemaOrThrow(bartlett_window_out::name, bartlett_window_out::overload_name) |
| 12713 | .typed<bartlett_window_out::schema>(); |
| 12714 | } |
| 12715 | |
| 12716 | // aten::bartlett_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!) |
| 12717 | at::Tensor & bartlett_window_out::call(int64_t window_length, at::Tensor & out) { |
| 12718 | |
| 12719 | static auto op = create_bartlett_window_out_typed_handle(); |
| 12720 | return op.call(window_length, out); |
| 12721 | } |
| 12722 | |
| 12723 | // aten::bartlett_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!) |
| 12724 | at::Tensor & bartlett_window_out::redispatch(c10::DispatchKeySet dispatchKeySet, int64_t window_length, at::Tensor & out) { |
| 12725 | |
| 12726 | static auto op = create_bartlett_window_out_typed_handle(); |
| 12727 | return op.redispatch(dispatchKeySet, window_length, out); |
| 12728 | } |
| 12729 | |
| 12730 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bartlett_window_periodic_out, name, "aten::bartlett_window") |
| 12731 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bartlett_window_periodic_out, overload_name, "periodic_out") |
| 12732 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bartlett_window_periodic_out, schema_str, "bartlett_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)") |
| 12733 | |
| 12734 | // aten::bartlett_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!) |
| 12735 | static C10_NOINLINE c10::TypedOperatorHandle<bartlett_window_periodic_out::schema> create_bartlett_window_periodic_out_typed_handle() { |
| 12736 | return c10::Dispatcher::singleton() |
| 12737 | .findSchemaOrThrow(bartlett_window_periodic_out::name, bartlett_window_periodic_out::overload_name) |
| 12738 | .typed<bartlett_window_periodic_out::schema>(); |
| 12739 | } |
| 12740 | |
| 12741 | // aten::bartlett_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!) |
| 12742 | at::Tensor & bartlett_window_periodic_out::call(int64_t window_length, bool periodic, at::Tensor & out) { |
| 12743 | |
| 12744 | static auto op = create_bartlett_window_periodic_out_typed_handle(); |
| 12745 | return op.call(window_length, periodic, out); |
| 12746 | } |
| 12747 | |
| 12748 | // aten::bartlett_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!) |
| 12749 | at::Tensor & bartlett_window_periodic_out::redispatch(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, at::Tensor & out) { |
| 12750 | |
| 12751 | static auto op = create_bartlett_window_periodic_out_typed_handle(); |
| 12752 | return op.redispatch(dispatchKeySet, window_length, periodic, out); |
| 12753 | } |
| 12754 | |
| 12755 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(copy_out, name, "aten::copy") |
| 12756 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(copy_out, overload_name, "out") |
| 12757 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(copy_out, schema_str, "copy.out(Tensor self, Tensor src, bool non_blocking=False, *, Tensor(a!) out) -> Tensor(a!)") |
| 12758 | |
| 12759 | // aten::copy.out(Tensor self, Tensor src, bool non_blocking=False, *, Tensor(a!) out) -> Tensor(a!) |
| 12760 | static C10_NOINLINE c10::TypedOperatorHandle<copy_out::schema> create_copy_out_typed_handle() { |
| 12761 | return c10::Dispatcher::singleton() |
| 12762 | .findSchemaOrThrow(copy_out::name, copy_out::overload_name) |
| 12763 | .typed<copy_out::schema>(); |
| 12764 | } |
| 12765 | |
| 12766 | // aten::copy.out(Tensor self, Tensor src, bool non_blocking=False, *, Tensor(a!) out) -> Tensor(a!) |
| 12767 | at::Tensor & copy_out::call(const at::Tensor & self, const at::Tensor & src, bool non_blocking, at::Tensor & out) { |
| 12768 | |
| 12769 | static auto op = create_copy_out_typed_handle(); |
| 12770 | return op.call(self, src, non_blocking, out); |
| 12771 | } |
| 12772 | |
| 12773 | // aten::copy.out(Tensor self, Tensor src, bool non_blocking=False, *, Tensor(a!) out) -> Tensor(a!) |
| 12774 | at::Tensor & copy_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, bool non_blocking, at::Tensor & out) { |
| 12775 | |
| 12776 | static auto op = create_copy_out_typed_handle(); |
| 12777 | return op.redispatch(dispatchKeySet, self, src, non_blocking, out); |
| 12778 | } |
| 12779 | |
| 12780 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_copy_from_and_resize_out, name, "aten::_copy_from_and_resize") |
| 12781 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_copy_from_and_resize_out, overload_name, "out") |
| 12782 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_copy_from_and_resize_out, schema_str, "_copy_from_and_resize.out(Tensor self, Tensor dst, *, Tensor(a!) out) -> Tensor(a!)") |
| 12783 | |
| 12784 | // aten::_copy_from_and_resize.out(Tensor self, Tensor dst, *, Tensor(a!) out) -> Tensor(a!) |
| 12785 | static C10_NOINLINE c10::TypedOperatorHandle<_copy_from_and_resize_out::schema> create__copy_from_and_resize_out_typed_handle() { |
| 12786 | return c10::Dispatcher::singleton() |
| 12787 | .findSchemaOrThrow(_copy_from_and_resize_out::name, _copy_from_and_resize_out::overload_name) |
| 12788 | .typed<_copy_from_and_resize_out::schema>(); |
| 12789 | } |
| 12790 | |
| 12791 | // aten::_copy_from_and_resize.out(Tensor self, Tensor dst, *, Tensor(a!) out) -> Tensor(a!) |
| 12792 | at::Tensor & _copy_from_and_resize_out::call(const at::Tensor & self, const at::Tensor & dst, at::Tensor & out) { |
| 12793 | |
| 12794 | static auto op = create__copy_from_and_resize_out_typed_handle(); |
| 12795 | return op.call(self, dst, out); |
| 12796 | } |
| 12797 | |
| 12798 | // aten::_copy_from_and_resize.out(Tensor self, Tensor dst, *, Tensor(a!) out) -> Tensor(a!) |
| 12799 | at::Tensor & _copy_from_and_resize_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & dst, at::Tensor & out) { |
| 12800 | |
| 12801 | static auto op = create__copy_from_and_resize_out_typed_handle(); |
| 12802 | return op.redispatch(dispatchKeySet, self, dst, out); |
| 12803 | } |
| 12804 | |
| 12805 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cudnn_convolution_out, name, "aten::cudnn_convolution") |
| 12806 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cudnn_convolution_out, overload_name, "out") |
| 12807 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cudnn_convolution_out, schema_str, "cudnn_convolution.out(Tensor self, Tensor weight, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!)") |
| 12808 | |
| 12809 | // aten::cudnn_convolution.out(Tensor self, Tensor weight, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!) |
| 12810 | static C10_NOINLINE c10::TypedOperatorHandle<cudnn_convolution_out::schema> create_cudnn_convolution_out_typed_handle() { |
| 12811 | return c10::Dispatcher::singleton() |
| 12812 | .findSchemaOrThrow(cudnn_convolution_out::name, cudnn_convolution_out::overload_name) |
| 12813 | .typed<cudnn_convolution_out::schema>(); |
| 12814 | } |
| 12815 | |
| 12816 | // aten::cudnn_convolution.out(Tensor self, Tensor weight, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!) |
| 12817 | at::Tensor & cudnn_convolution_out::call(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) { |
| 12818 | |
| 12819 | static auto op = create_cudnn_convolution_out_typed_handle(); |
| 12820 | return op.call(self, weight, padding, stride, dilation, groups, benchmark, deterministic, allow_tf32, out); |
| 12821 | } |
| 12822 | |
| 12823 | // aten::cudnn_convolution.out(Tensor self, Tensor weight, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!) |
| 12824 | at::Tensor & cudnn_convolution_out::redispatch(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) { |
| 12825 | |
| 12826 | static auto op = create_cudnn_convolution_out_typed_handle(); |
| 12827 | return op.redispatch(dispatchKeySet, self, weight, padding, stride, dilation, groups, benchmark, deterministic, allow_tf32, out); |
| 12828 | } |
| 12829 | |
| 12830 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cudnn_convolution_relu_out, name, "aten::cudnn_convolution_relu") |
| 12831 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cudnn_convolution_relu_out, overload_name, "out") |
| 12832 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cudnn_convolution_relu_out, schema_str, "cudnn_convolution_relu.out(Tensor self, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups, *, Tensor(a!) out) -> Tensor(a!)") |
| 12833 | |
| 12834 | // aten::cudnn_convolution_relu.out(Tensor self, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups, *, Tensor(a!) out) -> Tensor(a!) |
| 12835 | static C10_NOINLINE c10::TypedOperatorHandle<cudnn_convolution_relu_out::schema> create_cudnn_convolution_relu_out_typed_handle() { |
| 12836 | return c10::Dispatcher::singleton() |
| 12837 | .findSchemaOrThrow(cudnn_convolution_relu_out::name, cudnn_convolution_relu_out::overload_name) |
| 12838 | .typed<cudnn_convolution_relu_out::schema>(); |
| 12839 | } |
| 12840 | |
| 12841 | // aten::cudnn_convolution_relu.out(Tensor self, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups, *, Tensor(a!) out) -> Tensor(a!) |
| 12842 | at::Tensor & cudnn_convolution_relu_out::call(const at::Tensor & self, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups, at::Tensor & out) { |
| 12843 | |
| 12844 | static auto op = create_cudnn_convolution_relu_out_typed_handle(); |
| 12845 | return op.call(self, weight, bias, stride, padding, dilation, groups, out); |
| 12846 | } |
| 12847 | |
| 12848 | // aten::cudnn_convolution_relu.out(Tensor self, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups, *, Tensor(a!) out) -> Tensor(a!) |
| 12849 | at::Tensor & cudnn_convolution_relu_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups, at::Tensor & out) { |
| 12850 | |
| 12851 | static auto op = create_cudnn_convolution_relu_out_typed_handle(); |
| 12852 | return op.redispatch(dispatchKeySet, self, weight, bias, stride, padding, dilation, groups, out); |
| 12853 | } |
| 12854 | |
| 12855 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(diag_embed_out, name, "aten::diag_embed") |
| 12856 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(diag_embed_out, overload_name, "out") |
| 12857 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(diag_embed_out, schema_str, "diag_embed.out(Tensor self, int offset=0, int dim1=-2, int dim2=-1, *, Tensor(a!) out) -> Tensor(a!)") |
| 12858 | |
| 12859 | // aten::diag_embed.out(Tensor self, int offset=0, int dim1=-2, int dim2=-1, *, Tensor(a!) out) -> Tensor(a!) |
| 12860 | static C10_NOINLINE c10::TypedOperatorHandle<diag_embed_out::schema> create_diag_embed_out_typed_handle() { |
| 12861 | return c10::Dispatcher::singleton() |
| 12862 | .findSchemaOrThrow(diag_embed_out::name, diag_embed_out::overload_name) |
| 12863 | .typed<diag_embed_out::schema>(); |
| 12864 | } |
| 12865 | |
| 12866 | // aten::diag_embed.out(Tensor self, int offset=0, int dim1=-2, int dim2=-1, *, Tensor(a!) out) -> Tensor(a!) |
| 12867 | at::Tensor & diag_embed_out::call(const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2, at::Tensor & out) { |
| 12868 | |
| 12869 | static auto op = create_diag_embed_out_typed_handle(); |
| 12870 | return op.call(self, offset, dim1, dim2, out); |
| 12871 | } |
| 12872 | |
| 12873 | // aten::diag_embed.out(Tensor self, int offset=0, int dim1=-2, int dim2=-1, *, Tensor(a!) out) -> Tensor(a!) |
| 12874 | at::Tensor & diag_embed_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2, at::Tensor & out) { |
| 12875 | |
| 12876 | static auto op = create_diag_embed_out_typed_handle(); |
| 12877 | return op.redispatch(dispatchKeySet, self, offset, dim1, dim2, out); |
| 12878 | } |
| 12879 | |
| 12880 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_empty_affine_quantized_out, name, "aten::_empty_affine_quantized") |
| 12881 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_empty_affine_quantized_out, overload_name, "out") |
| 12882 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_empty_affine_quantized_out, schema_str, "_empty_affine_quantized.out(int[] size, *, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!)") |
| 12883 | |
| 12884 | // aten::_empty_affine_quantized.out(int[] size, *, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!) |
| 12885 | static C10_NOINLINE c10::TypedOperatorHandle<_empty_affine_quantized_out::schema> create__empty_affine_quantized_out_typed_handle() { |
| 12886 | return c10::Dispatcher::singleton() |
| 12887 | .findSchemaOrThrow(_empty_affine_quantized_out::name, _empty_affine_quantized_out::overload_name) |
| 12888 | .typed<_empty_affine_quantized_out::schema>(); |
| 12889 | } |
| 12890 | |
| 12891 | // aten::_empty_affine_quantized.out(int[] size, *, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!) |
| 12892 | at::Tensor & _empty_affine_quantized_out::call(at::IntArrayRef size, double scale, int64_t zero_point, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out) { |
| 12893 | |
| 12894 | static auto op = create__empty_affine_quantized_out_typed_handle(); |
| 12895 | return op.call(size, scale, zero_point, memory_format, out); |
| 12896 | } |
| 12897 | |
| 12898 | // aten::_empty_affine_quantized.out(int[] size, *, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!) |
| 12899 | at::Tensor & _empty_affine_quantized_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, double scale, int64_t zero_point, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out) { |
| 12900 | |
| 12901 | static auto op = create__empty_affine_quantized_out_typed_handle(); |
| 12902 | return op.redispatch(dispatchKeySet, size, scale, zero_point, memory_format, out); |
| 12903 | } |
| 12904 | |
| 12905 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_resize_output_out, name, "aten::_resize_output") |
| 12906 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_resize_output_out, overload_name, "out") |
| 12907 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_resize_output_out, schema_str, "_resize_output.out(Tensor self, int[] size, Device device, *, Tensor(a!) out) -> Tensor(a!)") |
| 12908 | |
| 12909 | // aten::_resize_output.out(Tensor self, int[] size, Device device, *, Tensor(a!) out) -> Tensor(a!) |
| 12910 | static C10_NOINLINE c10::TypedOperatorHandle<_resize_output_out::schema> create__resize_output_out_typed_handle() { |
| 12911 | return c10::Dispatcher::singleton() |
| 12912 | .findSchemaOrThrow(_resize_output_out::name, _resize_output_out::overload_name) |
| 12913 | .typed<_resize_output_out::schema>(); |
| 12914 | } |
| 12915 | |
| 12916 | // aten::_resize_output.out(Tensor self, int[] size, Device device, *, Tensor(a!) out) -> Tensor(a!) |
| 12917 | const at::Tensor & _resize_output_out::call(const at::Tensor & self, at::IntArrayRef size, at::Device device, const at::Tensor & out) { |
| 12918 | |
| 12919 | static auto op = create__resize_output_out_typed_handle(); |
| 12920 | return op.call(self, size, device, out); |
| 12921 | } |
| 12922 | |
| 12923 | // aten::_resize_output.out(Tensor self, int[] size, Device device, *, Tensor(a!) out) -> Tensor(a!) |
| 12924 | const at::Tensor & _resize_output_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::Device device, const at::Tensor & out) { |
| 12925 | |
| 12926 | static auto op = create__resize_output_out_typed_handle(); |
| 12927 | return op.redispatch(dispatchKeySet, self, size, device, out); |
| 12928 | } |
| 12929 | |
| 12930 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_resize_output, name, "aten::_resize_output") |
| 12931 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_resize_output, overload_name, "") |
| 12932 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_resize_output, schema_str, "_resize_output(Tensor self, int[] size, Device device) -> Tensor") |
| 12933 | |
| 12934 | // aten::_resize_output(Tensor self, int[] size, Device device) -> Tensor |
| 12935 | static C10_NOINLINE c10::TypedOperatorHandle<_resize_output::schema> create__resize_output_typed_handle() { |
| 12936 | return c10::Dispatcher::singleton() |
| 12937 | .findSchemaOrThrow(_resize_output::name, _resize_output::overload_name) |
| 12938 | .typed<_resize_output::schema>(); |
| 12939 | } |
| 12940 | |
| 12941 | // aten::_resize_output(Tensor self, int[] size, Device device) -> Tensor |
| 12942 | at::Tensor _resize_output::call(const at::Tensor & self, at::IntArrayRef size, at::Device device) { |
| 12943 | |
| 12944 | static auto op = create__resize_output_typed_handle(); |
| 12945 | return op.call(self, size, device); |
| 12946 | } |
| 12947 | |
| 12948 | // aten::_resize_output(Tensor self, int[] size, Device device) -> Tensor |
| 12949 | at::Tensor _resize_output::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::Device device) { |
| 12950 | |
| 12951 | static auto op = create__resize_output_typed_handle(); |
| 12952 | return op.redispatch(dispatchKeySet, self, size, device); |
| 12953 | } |
| 12954 | |
| 12955 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(empty_like_out, name, "aten::empty_like") |
| 12956 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(empty_like_out, overload_name, "out") |
| 12957 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(empty_like_out, schema_str, "empty_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)") |
| 12958 | |
| 12959 | // aten::empty_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) |
| 12960 | static C10_NOINLINE c10::TypedOperatorHandle<empty_like_out::schema> create_empty_like_out_typed_handle() { |
| 12961 | return c10::Dispatcher::singleton() |
| 12962 | .findSchemaOrThrow(empty_like_out::name, empty_like_out::overload_name) |
| 12963 | .typed<empty_like_out::schema>(); |
| 12964 | } |
| 12965 | |
| 12966 | // aten::empty_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) |
| 12967 | at::Tensor & empty_like_out::call(const at::Tensor & self, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out) { |
| 12968 | |
| 12969 | static auto op = create_empty_like_out_typed_handle(); |
| 12970 | return op.call(self, memory_format, out); |
| 12971 | } |
| 12972 | |
| 12973 | // aten::empty_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) |
| 12974 | at::Tensor & empty_like_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out) { |
| 12975 | |
| 12976 | static auto op = create_empty_like_out_typed_handle(); |
| 12977 | return op.redispatch(dispatchKeySet, self, memory_format, out); |
| 12978 | } |
| 12979 | |
| 12980 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(grid_sampler_3d_backward_out, name, "aten::grid_sampler_3d_backward") |
| 12981 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(grid_sampler_3d_backward_out, overload_name, "out") |
| 12982 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(grid_sampler_3d_backward_out, schema_str, "grid_sampler_3d_backward.out(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))") |
| 12983 | |
| 12984 | // aten::grid_sampler_3d_backward.out(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) |
| 12985 | static C10_NOINLINE c10::TypedOperatorHandle<grid_sampler_3d_backward_out::schema> create_grid_sampler_3d_backward_out_typed_handle() { |
| 12986 | return c10::Dispatcher::singleton() |
| 12987 | .findSchemaOrThrow(grid_sampler_3d_backward_out::name, grid_sampler_3d_backward_out::overload_name) |
| 12988 | .typed<grid_sampler_3d_backward_out::schema>(); |
| 12989 | } |
| 12990 | |
| 12991 | // aten::grid_sampler_3d_backward.out(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) |
| 12992 | ::std::tuple<at::Tensor &,at::Tensor &> grid_sampler_3d_backward_out::call(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array<bool,2> output_mask, at::Tensor & out0, at::Tensor & out1) { |
| 12993 | |
| 12994 | static auto op = create_grid_sampler_3d_backward_out_typed_handle(); |
| 12995 | return op.call(grad_output, input, grid, interpolation_mode, padding_mode, align_corners, output_mask, out0, out1); |
| 12996 | } |
| 12997 | |
| 12998 | // aten::grid_sampler_3d_backward.out(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) |
| 12999 | ::std::tuple<at::Tensor &,at::Tensor &> grid_sampler_3d_backward_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array<bool,2> output_mask, at::Tensor & out0, at::Tensor & out1) { |
| 13000 | |
| 13001 | static auto op = create_grid_sampler_3d_backward_out_typed_handle(); |
| 13002 | return op.redispatch(dispatchKeySet, grad_output, input, grid, interpolation_mode, padding_mode, align_corners, output_mask, out0, out1); |
| 13003 | } |
| 13004 | |
| 13005 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_group_norm_out, name, "aten::native_group_norm") |
| 13006 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_group_norm_out, overload_name, "out") |
| 13007 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_group_norm_out, schema_str, "native_group_norm.out(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))") |
| 13008 | |
| 13009 | // aten::native_group_norm.out(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 13010 | static C10_NOINLINE c10::TypedOperatorHandle<native_group_norm_out::schema> create_native_group_norm_out_typed_handle() { |
| 13011 | return c10::Dispatcher::singleton() |
| 13012 | .findSchemaOrThrow(native_group_norm_out::name, native_group_norm_out::overload_name) |
| 13013 | .typed<native_group_norm_out::schema>(); |
| 13014 | } |
| 13015 | |
| 13016 | // aten::native_group_norm.out(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 13017 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_group_norm_out::call(const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, double eps, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 13018 | |
| 13019 | static auto op = create_native_group_norm_out_typed_handle(); |
| 13020 | return op.call(input, weight, bias, N, C, HxW, group, eps, out0, out1, out2); |
| 13021 | } |
| 13022 | |
| 13023 | // aten::native_group_norm.out(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 13024 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_group_norm_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, double eps, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 13025 | |
| 13026 | static auto op = create_native_group_norm_out_typed_handle(); |
| 13027 | return op.redispatch(dispatchKeySet, input, weight, bias, N, C, HxW, group, eps, out0, out1, out2); |
| 13028 | } |
| 13029 | |
| 13030 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linear_backward_out, name, "aten::linear_backward") |
| 13031 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linear_backward_out, overload_name, "out") |
| 13032 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linear_backward_out, schema_str, "linear_backward.out(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))") |
| 13033 | |
| 13034 | // aten::linear_backward.out(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 13035 | static C10_NOINLINE c10::TypedOperatorHandle<linear_backward_out::schema> create_linear_backward_out_typed_handle() { |
| 13036 | return c10::Dispatcher::singleton() |
| 13037 | .findSchemaOrThrow(linear_backward_out::name, linear_backward_out::overload_name) |
| 13038 | .typed<linear_backward_out::schema>(); |
| 13039 | } |
| 13040 | |
| 13041 | // aten::linear_backward.out(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 13042 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linear_backward_out::call(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) { |
| 13043 | |
| 13044 | static auto op = create_linear_backward_out_typed_handle(); |
| 13045 | return op.call(self, grad_output, weight, output_mask, out0, out1, out2); |
| 13046 | } |
| 13047 | |
| 13048 | // aten::linear_backward.out(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 13049 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linear_backward_out::redispatch(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) { |
| 13050 | |
| 13051 | static auto op = create_linear_backward_out_typed_handle(); |
| 13052 | return op.redispatch(dispatchKeySet, self, grad_output, weight, output_mask, out0, out1, out2); |
| 13053 | } |
| 13054 | |
| 13055 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_linear_backward_input_out, name, "aten::mkldnn_linear_backward_input") |
| 13056 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_linear_backward_input_out, overload_name, "out") |
| 13057 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_linear_backward_input_out, schema_str, "mkldnn_linear_backward_input.out(int[] input_size, Tensor grad_output, Tensor weight, *, Tensor(a!) out) -> Tensor(a!)") |
| 13058 | |
| 13059 | // aten::mkldnn_linear_backward_input.out(int[] input_size, Tensor grad_output, Tensor weight, *, Tensor(a!) out) -> Tensor(a!) |
| 13060 | static C10_NOINLINE c10::TypedOperatorHandle<mkldnn_linear_backward_input_out::schema> create_mkldnn_linear_backward_input_out_typed_handle() { |
| 13061 | return c10::Dispatcher::singleton() |
| 13062 | .findSchemaOrThrow(mkldnn_linear_backward_input_out::name, mkldnn_linear_backward_input_out::overload_name) |
| 13063 | .typed<mkldnn_linear_backward_input_out::schema>(); |
| 13064 | } |
| 13065 | |
| 13066 | // aten::mkldnn_linear_backward_input.out(int[] input_size, Tensor grad_output, Tensor weight, *, Tensor(a!) out) -> Tensor(a!) |
| 13067 | at::Tensor & mkldnn_linear_backward_input_out::call(at::IntArrayRef input_size, const at::Tensor & grad_output, const at::Tensor & weight, at::Tensor & out) { |
| 13068 | |
| 13069 | static auto op = create_mkldnn_linear_backward_input_out_typed_handle(); |
| 13070 | return op.call(input_size, grad_output, weight, out); |
| 13071 | } |
| 13072 | |
| 13073 | // aten::mkldnn_linear_backward_input.out(int[] input_size, Tensor grad_output, Tensor weight, *, Tensor(a!) out) -> Tensor(a!) |
| 13074 | at::Tensor & mkldnn_linear_backward_input_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef input_size, const at::Tensor & grad_output, const at::Tensor & weight, at::Tensor & out) { |
| 13075 | |
| 13076 | static auto op = create_mkldnn_linear_backward_input_out_typed_handle(); |
| 13077 | return op.redispatch(dispatchKeySet, input_size, grad_output, weight, out); |
| 13078 | } |
| 13079 | |
| 13080 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_linear_backward_out, name, "aten::mkldnn_linear_backward") |
| 13081 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_linear_backward_out, overload_name, "out") |
| 13082 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_linear_backward_out, schema_str, "mkldnn_linear_backward.out(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))") |
| 13083 | |
| 13084 | // aten::mkldnn_linear_backward.out(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 13085 | static C10_NOINLINE c10::TypedOperatorHandle<mkldnn_linear_backward_out::schema> create_mkldnn_linear_backward_out_typed_handle() { |
| 13086 | return c10::Dispatcher::singleton() |
| 13087 | .findSchemaOrThrow(mkldnn_linear_backward_out::name, mkldnn_linear_backward_out::overload_name) |
| 13088 | .typed<mkldnn_linear_backward_out::schema>(); |
| 13089 | } |
| 13090 | |
| 13091 | // aten::mkldnn_linear_backward.out(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 13092 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> mkldnn_linear_backward_out::call(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) { |
| 13093 | |
| 13094 | static auto op = create_mkldnn_linear_backward_out_typed_handle(); |
| 13095 | return op.call(self, grad_output, weight, output_mask, out0, out1, out2); |
| 13096 | } |
| 13097 | |
| 13098 | // aten::mkldnn_linear_backward.out(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 13099 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> mkldnn_linear_backward_out::redispatch(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) { |
| 13100 | |
| 13101 | static auto op = create_mkldnn_linear_backward_out_typed_handle(); |
| 13102 | return op.redispatch(dispatchKeySet, self, grad_output, weight, output_mask, out0, out1, out2); |
| 13103 | } |
| 13104 | |
| 13105 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(batch_norm_gather_stats_with_counts_out, name, "aten::batch_norm_gather_stats_with_counts") |
| 13106 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(batch_norm_gather_stats_with_counts_out, overload_name, "out") |
| 13107 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(batch_norm_gather_stats_with_counts_out, schema_str, "batch_norm_gather_stats_with_counts.out(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, Tensor counts, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))") |
| 13108 | |
| 13109 | // aten::batch_norm_gather_stats_with_counts.out(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, Tensor counts, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) |
| 13110 | static C10_NOINLINE c10::TypedOperatorHandle<batch_norm_gather_stats_with_counts_out::schema> create_batch_norm_gather_stats_with_counts_out_typed_handle() { |
| 13111 | return c10::Dispatcher::singleton() |
| 13112 | .findSchemaOrThrow(batch_norm_gather_stats_with_counts_out::name, batch_norm_gather_stats_with_counts_out::overload_name) |
| 13113 | .typed<batch_norm_gather_stats_with_counts_out::schema>(); |
| 13114 | } |
| 13115 | |
| 13116 | // aten::batch_norm_gather_stats_with_counts.out(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, Tensor counts, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) |
| 13117 | ::std::tuple<at::Tensor &,at::Tensor &> batch_norm_gather_stats_with_counts_out::call(const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, double momentum, double eps, const at::Tensor & counts, at::Tensor & out0, at::Tensor & out1) { |
| 13118 | |
| 13119 | static auto op = create_batch_norm_gather_stats_with_counts_out_typed_handle(); |
| 13120 | return op.call(input, mean, invstd, running_mean, running_var, momentum, eps, counts, out0, out1); |
| 13121 | } |
| 13122 | |
| 13123 | // aten::batch_norm_gather_stats_with_counts.out(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, Tensor counts, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) |
| 13124 | ::std::tuple<at::Tensor &,at::Tensor &> batch_norm_gather_stats_with_counts_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, double momentum, double eps, const at::Tensor & counts, at::Tensor & out0, at::Tensor & out1) { |
| 13125 | |
| 13126 | static auto op = create_batch_norm_gather_stats_with_counts_out_typed_handle(); |
| 13127 | return op.redispatch(dispatchKeySet, input, mean, invstd, running_mean, running_var, momentum, eps, counts, out0, out1); |
| 13128 | } |
| 13129 | |
| 13130 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_pdist_backward_out, name, "aten::_pdist_backward") |
| 13131 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_pdist_backward_out, overload_name, "out") |
| 13132 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_pdist_backward_out, schema_str, "_pdist_backward.out(Tensor grad, Tensor self, float p, Tensor pdist, *, Tensor(a!) out) -> Tensor(a!)") |
| 13133 | |
| 13134 | // aten::_pdist_backward.out(Tensor grad, Tensor self, float p, Tensor pdist, *, Tensor(a!) out) -> Tensor(a!) |
| 13135 | static C10_NOINLINE c10::TypedOperatorHandle<_pdist_backward_out::schema> create__pdist_backward_out_typed_handle() { |
| 13136 | return c10::Dispatcher::singleton() |
| 13137 | .findSchemaOrThrow(_pdist_backward_out::name, _pdist_backward_out::overload_name) |
| 13138 | .typed<_pdist_backward_out::schema>(); |
| 13139 | } |
| 13140 | |
| 13141 | // aten::_pdist_backward.out(Tensor grad, Tensor self, float p, Tensor pdist, *, Tensor(a!) out) -> Tensor(a!) |
| 13142 | at::Tensor & _pdist_backward_out::call(const at::Tensor & grad, const at::Tensor & self, double p, const at::Tensor & pdist, at::Tensor & out) { |
| 13143 | |
| 13144 | static auto op = create__pdist_backward_out_typed_handle(); |
| 13145 | return op.call(grad, self, p, pdist, out); |
| 13146 | } |
| 13147 | |
| 13148 | // aten::_pdist_backward.out(Tensor grad, Tensor self, float p, Tensor pdist, *, Tensor(a!) out) -> Tensor(a!) |
| 13149 | at::Tensor & _pdist_backward_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & self, double p, const at::Tensor & pdist, at::Tensor & out) { |
| 13150 | |
| 13151 | static auto op = create__pdist_backward_out_typed_handle(); |
| 13152 | return op.redispatch(dispatchKeySet, grad, self, p, pdist, out); |
| 13153 | } |
| 13154 | |
| 13155 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(pixel_shuffle_out, name, "aten::pixel_shuffle") |
| 13156 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(pixel_shuffle_out, overload_name, "out") |
| 13157 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(pixel_shuffle_out, schema_str, "pixel_shuffle.out(Tensor self, int upscale_factor, *, Tensor(a!) out) -> Tensor(a!)") |
| 13158 | |
| 13159 | // aten::pixel_shuffle.out(Tensor self, int upscale_factor, *, Tensor(a!) out) -> Tensor(a!) |
| 13160 | static C10_NOINLINE c10::TypedOperatorHandle<pixel_shuffle_out::schema> create_pixel_shuffle_out_typed_handle() { |
| 13161 | return c10::Dispatcher::singleton() |
| 13162 | .findSchemaOrThrow(pixel_shuffle_out::name, pixel_shuffle_out::overload_name) |
| 13163 | .typed<pixel_shuffle_out::schema>(); |
| 13164 | } |
| 13165 | |
| 13166 | // aten::pixel_shuffle.out(Tensor self, int upscale_factor, *, Tensor(a!) out) -> Tensor(a!) |
| 13167 | at::Tensor & pixel_shuffle_out::call(const at::Tensor & self, int64_t upscale_factor, at::Tensor & out) { |
| 13168 | |
| 13169 | static auto op = create_pixel_shuffle_out_typed_handle(); |
| 13170 | return op.call(self, upscale_factor, out); |
| 13171 | } |
| 13172 | |
| 13173 | // aten::pixel_shuffle.out(Tensor self, int upscale_factor, *, Tensor(a!) out) -> Tensor(a!) |
| 13174 | at::Tensor & pixel_shuffle_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t upscale_factor, at::Tensor & out) { |
| 13175 | |
| 13176 | static auto op = create_pixel_shuffle_out_typed_handle(); |
| 13177 | return op.redispatch(dispatchKeySet, self, upscale_factor, out); |
| 13178 | } |
| 13179 | |
| 13180 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(celu_out, name, "aten::celu") |
| 13181 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(celu_out, overload_name, "out") |
| 13182 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(celu_out, schema_str, "celu.out(Tensor self, Scalar alpha=1.0, *, Tensor(a!) out) -> Tensor(a!)") |
| 13183 | |
| 13184 | // aten::celu.out(Tensor self, Scalar alpha=1.0, *, Tensor(a!) out) -> Tensor(a!) |
| 13185 | static C10_NOINLINE c10::TypedOperatorHandle<celu_out::schema> create_celu_out_typed_handle() { |
| 13186 | return c10::Dispatcher::singleton() |
| 13187 | .findSchemaOrThrow(celu_out::name, celu_out::overload_name) |
| 13188 | .typed<celu_out::schema>(); |
| 13189 | } |
| 13190 | |
| 13191 | // aten::celu.out(Tensor self, Scalar alpha=1.0, *, Tensor(a!) out) -> Tensor(a!) |
| 13192 | at::Tensor & celu_out::call(const at::Tensor & self, const at::Scalar & alpha, at::Tensor & out) { |
| 13193 | |
| 13194 | static auto op = create_celu_out_typed_handle(); |
| 13195 | return op.call(self, alpha, out); |
| 13196 | } |
| 13197 | |
| 13198 | // aten::celu.out(Tensor self, Scalar alpha=1.0, *, Tensor(a!) out) -> Tensor(a!) |
| 13199 | at::Tensor & celu_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & alpha, at::Tensor & out) { |
| 13200 | |
| 13201 | static auto op = create_celu_out_typed_handle(); |
| 13202 | return op.redispatch(dispatchKeySet, self, alpha, out); |
| 13203 | } |
| 13204 | |
| 13205 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slice_backward_out, name, "aten::slice_backward") |
| 13206 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slice_backward_out, overload_name, "out") |
| 13207 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slice_backward_out, schema_str, "slice_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step, *, Tensor(a!) out) -> Tensor(a!)") |
| 13208 | |
| 13209 | // aten::slice_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step, *, Tensor(a!) out) -> Tensor(a!) |
| 13210 | static C10_NOINLINE c10::TypedOperatorHandle<slice_backward_out::schema> create_slice_backward_out_typed_handle() { |
| 13211 | return c10::Dispatcher::singleton() |
| 13212 | .findSchemaOrThrow(slice_backward_out::name, slice_backward_out::overload_name) |
| 13213 | .typed<slice_backward_out::schema>(); |
| 13214 | } |
| 13215 | |
| 13216 | // aten::slice_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step, *, Tensor(a!) out) -> Tensor(a!) |
| 13217 | at::Tensor & slice_backward_out::call(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step, at::Tensor & out) { |
| 13218 | |
| 13219 | static auto op = create_slice_backward_out_typed_handle(); |
| 13220 | return op.call(grad_output, input_sizes, dim, start, end, step, out); |
| 13221 | } |
| 13222 | |
| 13223 | // aten::slice_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step, *, Tensor(a!) out) -> Tensor(a!) |
| 13224 | at::Tensor & slice_backward_out::redispatch(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) { |
| 13225 | |
| 13226 | static auto op = create_slice_backward_out_typed_handle(); |
| 13227 | return op.redispatch(dispatchKeySet, grad_output, input_sizes, dim, start, end, step, out); |
| 13228 | } |
| 13229 | |
| 13230 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(unsafe_split_Tensor_out, name, "aten::unsafe_split") |
| 13231 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(unsafe_split_Tensor_out, overload_name, "Tensor_out") |
| 13232 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(unsafe_split_Tensor_out, schema_str, "unsafe_split.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> ()") |
| 13233 | |
| 13234 | // aten::unsafe_split.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> () |
| 13235 | static C10_NOINLINE c10::TypedOperatorHandle<unsafe_split_Tensor_out::schema> create_unsafe_split_Tensor_out_typed_handle() { |
| 13236 | return c10::Dispatcher::singleton() |
| 13237 | .findSchemaOrThrow(unsafe_split_Tensor_out::name, unsafe_split_Tensor_out::overload_name) |
| 13238 | .typed<unsafe_split_Tensor_out::schema>(); |
| 13239 | } |
| 13240 | |
| 13241 | // aten::unsafe_split.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> () |
| 13242 | void unsafe_split_Tensor_out::call(const at::Tensor & self, c10::SymInt split_size, int64_t dim, at::TensorList out) { |
| 13243 | |
| 13244 | static auto op = create_unsafe_split_Tensor_out_typed_handle(); |
| 13245 | return op.call(self, split_size, dim, out); |
| 13246 | } |
| 13247 | |
| 13248 | // aten::unsafe_split.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> () |
| 13249 | void unsafe_split_Tensor_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt split_size, int64_t dim, at::TensorList out) { |
| 13250 | |
| 13251 | static auto op = create_unsafe_split_Tensor_out_typed_handle(); |
| 13252 | return op.redispatch(dispatchKeySet, self, split_size, dim, out); |
| 13253 | } |
| 13254 | |
| 13255 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_mean_correction_out, name, "aten::std_mean") |
| 13256 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_mean_correction_out, overload_name, "correction_out") |
| 13257 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(std_mean_correction_out, schema_str, "std_mean.correction_out(Tensor self, int[1]? dim=None, *, int? correction=None, bool keepdim=False, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))") |
| 13258 | |
| 13259 | // aten::std_mean.correction_out(Tensor self, int[1]? dim=None, *, int? correction=None, bool keepdim=False, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) |
| 13260 | static C10_NOINLINE c10::TypedOperatorHandle<std_mean_correction_out::schema> create_std_mean_correction_out_typed_handle() { |
| 13261 | return c10::Dispatcher::singleton() |
| 13262 | .findSchemaOrThrow(std_mean_correction_out::name, std_mean_correction_out::overload_name) |
| 13263 | .typed<std_mean_correction_out::schema>(); |
| 13264 | } |
| 13265 | |
| 13266 | // aten::std_mean.correction_out(Tensor self, int[1]? dim=None, *, int? correction=None, bool keepdim=False, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) |
| 13267 | ::std::tuple<at::Tensor &,at::Tensor &> std_mean_correction_out::call(const at::Tensor & self, at::OptionalIntArrayRef dim, c10::optional<int64_t> correction, bool keepdim, at::Tensor & out0, at::Tensor & out1) { |
| 13268 | |
| 13269 | static auto op = create_std_mean_correction_out_typed_handle(); |
| 13270 | return op.call(self, dim, correction, keepdim, out0, out1); |
| 13271 | } |
| 13272 | |
| 13273 | // aten::std_mean.correction_out(Tensor self, int[1]? dim=None, *, int? correction=None, bool keepdim=False, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) |
| 13274 | ::std::tuple<at::Tensor &,at::Tensor &> std_mean_correction_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, c10::optional<int64_t> correction, bool keepdim, at::Tensor & out0, at::Tensor & out1) { |
| 13275 | |
| 13276 | static auto op = create_std_mean_correction_out_typed_handle(); |
| 13277 | return op.redispatch(dispatchKeySet, self, dim, correction, keepdim, out0, out1); |
| 13278 | } |
| 13279 | |
| 13280 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(flip_out, name, "aten::flip") |
| 13281 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(flip_out, overload_name, "out") |
| 13282 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(flip_out, schema_str, "flip.out(Tensor self, int[] dims, *, Tensor(a!) out) -> Tensor(a!)") |
| 13283 | |
| 13284 | // aten::flip.out(Tensor self, int[] dims, *, Tensor(a!) out) -> Tensor(a!) |
| 13285 | static C10_NOINLINE c10::TypedOperatorHandle<flip_out::schema> create_flip_out_typed_handle() { |
| 13286 | return c10::Dispatcher::singleton() |
| 13287 | .findSchemaOrThrow(flip_out::name, flip_out::overload_name) |
| 13288 | .typed<flip_out::schema>(); |
| 13289 | } |
| 13290 | |
| 13291 | // aten::flip.out(Tensor self, int[] dims, *, Tensor(a!) out) -> Tensor(a!) |
| 13292 | at::Tensor & flip_out::call(const at::Tensor & self, at::IntArrayRef dims, at::Tensor & out) { |
| 13293 | |
| 13294 | static auto op = create_flip_out_typed_handle(); |
| 13295 | return op.call(self, dims, out); |
| 13296 | } |
| 13297 | |
| 13298 | // aten::flip.out(Tensor self, int[] dims, *, Tensor(a!) out) -> Tensor(a!) |
| 13299 | at::Tensor & flip_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dims, at::Tensor & out) { |
| 13300 | |
| 13301 | static auto op = create_flip_out_typed_handle(); |
| 13302 | return op.redispatch(dispatchKeySet, self, dims, out); |
| 13303 | } |
| 13304 | |
| 13305 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(roll_out, name, "aten::roll") |
| 13306 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(roll_out, overload_name, "out") |
| 13307 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(roll_out, schema_str, "roll.out(Tensor self, int[1] shifts, int[1] dims=[], *, Tensor(a!) out) -> Tensor(a!)") |
| 13308 | |
| 13309 | // aten::roll.out(Tensor self, int[1] shifts, int[1] dims=[], *, Tensor(a!) out) -> Tensor(a!) |
| 13310 | static C10_NOINLINE c10::TypedOperatorHandle<roll_out::schema> create_roll_out_typed_handle() { |
| 13311 | return c10::Dispatcher::singleton() |
| 13312 | .findSchemaOrThrow(roll_out::name, roll_out::overload_name) |
| 13313 | .typed<roll_out::schema>(); |
| 13314 | } |
| 13315 | |
| 13316 | // aten::roll.out(Tensor self, int[1] shifts, int[1] dims=[], *, Tensor(a!) out) -> Tensor(a!) |
| 13317 | at::Tensor & roll_out::call(const at::Tensor & self, at::IntArrayRef shifts, at::IntArrayRef dims, at::Tensor & out) { |
| 13318 | |
| 13319 | static auto op = create_roll_out_typed_handle(); |
| 13320 | return op.call(self, shifts, dims, out); |
| 13321 | } |
| 13322 | |
| 13323 | // aten::roll.out(Tensor self, int[1] shifts, int[1] dims=[], *, Tensor(a!) out) -> Tensor(a!) |
| 13324 | at::Tensor & roll_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef shifts, at::IntArrayRef dims, at::Tensor & out) { |
| 13325 | |
| 13326 | static auto op = create_roll_out_typed_handle(); |
| 13327 | return op.redispatch(dispatchKeySet, self, shifts, dims, out); |
| 13328 | } |
| 13329 | |
| 13330 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_nested_from_padded_out, name, "aten::_nested_from_padded") |
| 13331 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_nested_from_padded_out, overload_name, "out") |
| 13332 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_nested_from_padded_out, schema_str, "_nested_from_padded.out(Tensor padded, Tensor cpu_nested_shape_example, bool fuse_transform_0213=False, *, Tensor(a!) out) -> Tensor(a!)") |
| 13333 | |
| 13334 | // aten::_nested_from_padded.out(Tensor padded, Tensor cpu_nested_shape_example, bool fuse_transform_0213=False, *, Tensor(a!) out) -> Tensor(a!) |
| 13335 | static C10_NOINLINE c10::TypedOperatorHandle<_nested_from_padded_out::schema> create__nested_from_padded_out_typed_handle() { |
| 13336 | return c10::Dispatcher::singleton() |
| 13337 | .findSchemaOrThrow(_nested_from_padded_out::name, _nested_from_padded_out::overload_name) |
| 13338 | .typed<_nested_from_padded_out::schema>(); |
| 13339 | } |
| 13340 | |
| 13341 | // aten::_nested_from_padded.out(Tensor padded, Tensor cpu_nested_shape_example, bool fuse_transform_0213=False, *, Tensor(a!) out) -> Tensor(a!) |
| 13342 | at::Tensor & _nested_from_padded_out::call(const at::Tensor & padded, const at::Tensor & cpu_nested_shape_example, bool fuse_transform_0213, at::Tensor & out) { |
| 13343 | |
| 13344 | static auto op = create__nested_from_padded_out_typed_handle(); |
| 13345 | return op.call(padded, cpu_nested_shape_example, fuse_transform_0213, out); |
| 13346 | } |
| 13347 | |
| 13348 | // aten::_nested_from_padded.out(Tensor padded, Tensor cpu_nested_shape_example, bool fuse_transform_0213=False, *, Tensor(a!) out) -> Tensor(a!) |
| 13349 | at::Tensor & _nested_from_padded_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & padded, const at::Tensor & cpu_nested_shape_example, bool fuse_transform_0213, at::Tensor & out) { |
| 13350 | |
| 13351 | static auto op = create__nested_from_padded_out_typed_handle(); |
| 13352 | return op.redispatch(dispatchKeySet, padded, cpu_nested_shape_example, fuse_transform_0213, out); |
| 13353 | } |
| 13354 | |
| 13355 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_trilinear_out, name, "aten::_trilinear") |
| 13356 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_trilinear_out, overload_name, "out") |
| 13357 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_trilinear_out, schema_str, "_trilinear.out(Tensor i1, Tensor i2, Tensor i3, int[] expand1, int[] expand2, int[] expand3, int[] sumdim, int unroll_dim=1, *, Tensor(a!) out) -> Tensor(a!)") |
| 13358 | |
| 13359 | // aten::_trilinear.out(Tensor i1, Tensor i2, Tensor i3, int[] expand1, int[] expand2, int[] expand3, int[] sumdim, int unroll_dim=1, *, Tensor(a!) out) -> Tensor(a!) |
| 13360 | static C10_NOINLINE c10::TypedOperatorHandle<_trilinear_out::schema> create__trilinear_out_typed_handle() { |
| 13361 | return c10::Dispatcher::singleton() |
| 13362 | .findSchemaOrThrow(_trilinear_out::name, _trilinear_out::overload_name) |
| 13363 | .typed<_trilinear_out::schema>(); |
| 13364 | } |
| 13365 | |
| 13366 | // aten::_trilinear.out(Tensor i1, Tensor i2, Tensor i3, int[] expand1, int[] expand2, int[] expand3, int[] sumdim, int unroll_dim=1, *, Tensor(a!) out) -> Tensor(a!) |
| 13367 | at::Tensor & _trilinear_out::call(const at::Tensor & i1, const at::Tensor & i2, const at::Tensor & i3, at::IntArrayRef expand1, at::IntArrayRef expand2, at::IntArrayRef expand3, at::IntArrayRef sumdim, int64_t unroll_dim, at::Tensor & out) { |
| 13368 | |
| 13369 | static auto op = create__trilinear_out_typed_handle(); |
| 13370 | return op.call(i1, i2, i3, expand1, expand2, expand3, sumdim, unroll_dim, out); |
| 13371 | } |
| 13372 | |
| 13373 | // aten::_trilinear.out(Tensor i1, Tensor i2, Tensor i3, int[] expand1, int[] expand2, int[] expand3, int[] sumdim, int unroll_dim=1, *, Tensor(a!) out) -> Tensor(a!) |
| 13374 | at::Tensor & _trilinear_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & i1, const at::Tensor & i2, const at::Tensor & i3, at::IntArrayRef expand1, at::IntArrayRef expand2, at::IntArrayRef expand3, at::IntArrayRef sumdim, int64_t unroll_dim, at::Tensor & out) { |
| 13375 | |
| 13376 | static auto op = create__trilinear_out_typed_handle(); |
| 13377 | return op.redispatch(dispatchKeySet, i1, i2, i3, expand1, expand2, expand3, sumdim, unroll_dim, out); |
| 13378 | } |
| 13379 | |
| 13380 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_unique2_out, name, "aten::_unique2") |
| 13381 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_unique2_out, overload_name, "out") |
| 13382 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_unique2_out, schema_str, "_unique2.out(Tensor self, bool sorted=True, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))") |
| 13383 | |
| 13384 | // aten::_unique2.out(Tensor self, bool sorted=True, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 13385 | static C10_NOINLINE c10::TypedOperatorHandle<_unique2_out::schema> create__unique2_out_typed_handle() { |
| 13386 | return c10::Dispatcher::singleton() |
| 13387 | .findSchemaOrThrow(_unique2_out::name, _unique2_out::overload_name) |
| 13388 | .typed<_unique2_out::schema>(); |
| 13389 | } |
| 13390 | |
| 13391 | // aten::_unique2.out(Tensor self, bool sorted=True, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 13392 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _unique2_out::call(const at::Tensor & self, bool sorted, bool return_inverse, bool return_counts, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 13393 | |
| 13394 | static auto op = create__unique2_out_typed_handle(); |
| 13395 | return op.call(self, sorted, return_inverse, return_counts, out0, out1, out2); |
| 13396 | } |
| 13397 | |
| 13398 | // aten::_unique2.out(Tensor self, bool sorted=True, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 13399 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _unique2_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool sorted, bool return_inverse, bool return_counts, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 13400 | |
| 13401 | static auto op = create__unique2_out_typed_handle(); |
| 13402 | return op.redispatch(dispatchKeySet, self, sorted, return_inverse, return_counts, out0, out1, out2); |
| 13403 | } |
| 13404 | |
| 13405 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_weight_norm_interface_backward_out, name, "aten::_weight_norm_interface_backward") |
| 13406 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_weight_norm_interface_backward_out, overload_name, "out") |
| 13407 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_weight_norm_interface_backward_out, schema_str, "_weight_norm_interface_backward.out(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))") |
| 13408 | |
| 13409 | // aten::_weight_norm_interface_backward.out(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) |
| 13410 | static C10_NOINLINE c10::TypedOperatorHandle<_weight_norm_interface_backward_out::schema> create__weight_norm_interface_backward_out_typed_handle() { |
| 13411 | return c10::Dispatcher::singleton() |
| 13412 | .findSchemaOrThrow(_weight_norm_interface_backward_out::name, _weight_norm_interface_backward_out::overload_name) |
| 13413 | .typed<_weight_norm_interface_backward_out::schema>(); |
| 13414 | } |
| 13415 | |
| 13416 | // aten::_weight_norm_interface_backward.out(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) |
| 13417 | ::std::tuple<at::Tensor &,at::Tensor &> _weight_norm_interface_backward_out::call(const at::Tensor & grad_w, const at::Tensor & saved_v, const at::Tensor & saved_g, const at::Tensor & saved_norms, int64_t dim, at::Tensor & out0, at::Tensor & out1) { |
| 13418 | |
| 13419 | static auto op = create__weight_norm_interface_backward_out_typed_handle(); |
| 13420 | return op.call(grad_w, saved_v, saved_g, saved_norms, dim, out0, out1); |
| 13421 | } |
| 13422 | |
| 13423 | // aten::_weight_norm_interface_backward.out(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) |
| 13424 | ::std::tuple<at::Tensor &,at::Tensor &> _weight_norm_interface_backward_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_w, const at::Tensor & saved_v, const at::Tensor & saved_g, const at::Tensor & saved_norms, int64_t dim, at::Tensor & out0, at::Tensor & out1) { |
| 13425 | |
| 13426 | static auto op = create__weight_norm_interface_backward_out_typed_handle(); |
| 13427 | return op.redispatch(dispatchKeySet, grad_w, saved_v, saved_g, saved_norms, dim, out0, out1); |
| 13428 | } |
| 13429 | |
| 13430 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(zeros_like_out, name, "aten::zeros_like") |
| 13431 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(zeros_like_out, overload_name, "out") |
| 13432 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(zeros_like_out, schema_str, "zeros_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)") |
| 13433 | |
| 13434 | // aten::zeros_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) |
| 13435 | static C10_NOINLINE c10::TypedOperatorHandle<zeros_like_out::schema> create_zeros_like_out_typed_handle() { |
| 13436 | return c10::Dispatcher::singleton() |
| 13437 | .findSchemaOrThrow(zeros_like_out::name, zeros_like_out::overload_name) |
| 13438 | .typed<zeros_like_out::schema>(); |
| 13439 | } |
| 13440 | |
| 13441 | // aten::zeros_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) |
| 13442 | at::Tensor & zeros_like_out::call(const at::Tensor & self, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out) { |
| 13443 | |
| 13444 | static auto op = create_zeros_like_out_typed_handle(); |
| 13445 | return op.call(self, memory_format, out); |
| 13446 | } |
| 13447 | |
| 13448 | // aten::zeros_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) |
| 13449 | at::Tensor & zeros_like_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out) { |
| 13450 | |
| 13451 | static auto op = create_zeros_like_out_typed_handle(); |
| 13452 | return op.redispatch(dispatchKeySet, self, memory_format, out); |
| 13453 | } |
| 13454 | |
| 13455 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_csr_prod_dim_dtype_out, name, "aten::_sparse_csr_prod") |
| 13456 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_csr_prod_dim_dtype_out, overload_name, "dim_dtype_out") |
| 13457 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_csr_prod_dim_dtype_out, schema_str, "_sparse_csr_prod.dim_dtype_out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)") |
| 13458 | |
| 13459 | // aten::_sparse_csr_prod.dim_dtype_out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) |
| 13460 | static C10_NOINLINE c10::TypedOperatorHandle<_sparse_csr_prod_dim_dtype_out::schema> create__sparse_csr_prod_dim_dtype_out_typed_handle() { |
| 13461 | return c10::Dispatcher::singleton() |
| 13462 | .findSchemaOrThrow(_sparse_csr_prod_dim_dtype_out::name, _sparse_csr_prod_dim_dtype_out::overload_name) |
| 13463 | .typed<_sparse_csr_prod_dim_dtype_out::schema>(); |
| 13464 | } |
| 13465 | |
| 13466 | // aten::_sparse_csr_prod.dim_dtype_out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) |
| 13467 | at::Tensor & _sparse_csr_prod_dim_dtype_out::call(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 13468 | |
| 13469 | static auto op = create__sparse_csr_prod_dim_dtype_out_typed_handle(); |
| 13470 | return op.call(self, dim, keepdim, dtype, out); |
| 13471 | } |
| 13472 | |
| 13473 | // aten::_sparse_csr_prod.dim_dtype_out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) |
| 13474 | at::Tensor & _sparse_csr_prod_dim_dtype_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) { |
| 13475 | |
| 13476 | static auto op = create__sparse_csr_prod_dim_dtype_out_typed_handle(); |
| 13477 | return op.redispatch(dispatchKeySet, self, dim, keepdim, dtype, out); |
| 13478 | } |
| 13479 | |
| 13480 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_softmax_backward_data_out, name, "aten::_sparse_softmax_backward_data") |
| 13481 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_softmax_backward_data_out, overload_name, "out") |
| 13482 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_softmax_backward_data_out, schema_str, "_sparse_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 13483 | |
| 13484 | // aten::_sparse_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 13485 | static C10_NOINLINE c10::TypedOperatorHandle<_sparse_softmax_backward_data_out::schema> create__sparse_softmax_backward_data_out_typed_handle() { |
| 13486 | return c10::Dispatcher::singleton() |
| 13487 | .findSchemaOrThrow(_sparse_softmax_backward_data_out::name, _sparse_softmax_backward_data_out::overload_name) |
| 13488 | .typed<_sparse_softmax_backward_data_out::schema>(); |
| 13489 | } |
| 13490 | |
| 13491 | // aten::_sparse_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 13492 | at::Tensor & _sparse_softmax_backward_data_out::call(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self, at::Tensor & out) { |
| 13493 | |
| 13494 | static auto op = create__sparse_softmax_backward_data_out_typed_handle(); |
| 13495 | return op.call(grad_output, output, dim, self, out); |
| 13496 | } |
| 13497 | |
| 13498 | // aten::_sparse_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 13499 | at::Tensor & _sparse_softmax_backward_data_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self, at::Tensor & out) { |
| 13500 | |
| 13501 | static auto op = create__sparse_softmax_backward_data_out_typed_handle(); |
| 13502 | return op.redispatch(dispatchKeySet, grad_output, output, dim, self, out); |
| 13503 | } |
| 13504 | |
| 13505 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_log_softmax_out, name, "aten::_sparse_log_softmax") |
| 13506 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_log_softmax_out, overload_name, "out") |
| 13507 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_log_softmax_out, schema_str, "_sparse_log_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!)") |
| 13508 | |
| 13509 | // aten::_sparse_log_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!) |
| 13510 | static C10_NOINLINE c10::TypedOperatorHandle<_sparse_log_softmax_out::schema> create__sparse_log_softmax_out_typed_handle() { |
| 13511 | return c10::Dispatcher::singleton() |
| 13512 | .findSchemaOrThrow(_sparse_log_softmax_out::name, _sparse_log_softmax_out::overload_name) |
| 13513 | .typed<_sparse_log_softmax_out::schema>(); |
| 13514 | } |
| 13515 | |
| 13516 | // aten::_sparse_log_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!) |
| 13517 | at::Tensor & _sparse_log_softmax_out::call(const at::Tensor & self, int64_t dim, bool half_to_float, at::Tensor & out) { |
| 13518 | |
| 13519 | static auto op = create__sparse_log_softmax_out_typed_handle(); |
| 13520 | return op.call(self, dim, half_to_float, out); |
| 13521 | } |
| 13522 | |
| 13523 | // aten::_sparse_log_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!) |
| 13524 | at::Tensor & _sparse_log_softmax_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool half_to_float, at::Tensor & out) { |
| 13525 | |
| 13526 | static auto op = create__sparse_log_softmax_out_typed_handle(); |
| 13527 | return op.redispatch(dispatchKeySet, self, dim, half_to_float, out); |
| 13528 | } |
| 13529 | |
| 13530 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_log_softmax_backward_data_out, name, "aten::_sparse_log_softmax_backward_data") |
| 13531 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_log_softmax_backward_data_out, overload_name, "out") |
| 13532 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_log_softmax_backward_data_out, schema_str, "_sparse_log_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 13533 | |
| 13534 | // aten::_sparse_log_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 13535 | static C10_NOINLINE c10::TypedOperatorHandle<_sparse_log_softmax_backward_data_out::schema> create__sparse_log_softmax_backward_data_out_typed_handle() { |
| 13536 | return c10::Dispatcher::singleton() |
| 13537 | .findSchemaOrThrow(_sparse_log_softmax_backward_data_out::name, _sparse_log_softmax_backward_data_out::overload_name) |
| 13538 | .typed<_sparse_log_softmax_backward_data_out::schema>(); |
| 13539 | } |
| 13540 | |
| 13541 | // aten::_sparse_log_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 13542 | at::Tensor & _sparse_log_softmax_backward_data_out::call(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self, at::Tensor & out) { |
| 13543 | |
| 13544 | static auto op = create__sparse_log_softmax_backward_data_out_typed_handle(); |
| 13545 | return op.call(grad_output, output, dim, self, out); |
| 13546 | } |
| 13547 | |
| 13548 | // aten::_sparse_log_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 13549 | at::Tensor & _sparse_log_softmax_backward_data_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self, at::Tensor & out) { |
| 13550 | |
| 13551 | static auto op = create__sparse_log_softmax_backward_data_out_typed_handle(); |
| 13552 | return op.redispatch(dispatchKeySet, grad_output, output, dim, self, out); |
| 13553 | } |
| 13554 | |
| 13555 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_spdiags_out, name, "aten::_spdiags") |
| 13556 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_spdiags_out, overload_name, "out") |
| 13557 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_spdiags_out, schema_str, "_spdiags.out(Tensor diagonals, Tensor offsets, int[] shape, Layout? layout=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 13558 | |
| 13559 | // aten::_spdiags.out(Tensor diagonals, Tensor offsets, int[] shape, Layout? layout=None, *, Tensor(a!) out) -> Tensor(a!) |
| 13560 | static C10_NOINLINE c10::TypedOperatorHandle<_spdiags_out::schema> create__spdiags_out_typed_handle() { |
| 13561 | return c10::Dispatcher::singleton() |
| 13562 | .findSchemaOrThrow(_spdiags_out::name, _spdiags_out::overload_name) |
| 13563 | .typed<_spdiags_out::schema>(); |
| 13564 | } |
| 13565 | |
| 13566 | // aten::_spdiags.out(Tensor diagonals, Tensor offsets, int[] shape, Layout? layout=None, *, Tensor(a!) out) -> Tensor(a!) |
| 13567 | at::Tensor & _spdiags_out::call(const at::Tensor & diagonals, const at::Tensor & offsets, at::IntArrayRef shape, c10::optional<at::Layout> layout, at::Tensor & out) { |
| 13568 | |
| 13569 | static auto op = create__spdiags_out_typed_handle(); |
| 13570 | return op.call(diagonals, offsets, shape, layout, out); |
| 13571 | } |
| 13572 | |
| 13573 | // aten::_spdiags.out(Tensor diagonals, Tensor offsets, int[] shape, Layout? layout=None, *, Tensor(a!) out) -> Tensor(a!) |
| 13574 | at::Tensor & _spdiags_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & diagonals, const at::Tensor & offsets, at::IntArrayRef shape, c10::optional<at::Layout> layout, at::Tensor & out) { |
| 13575 | |
| 13576 | static auto op = create__spdiags_out_typed_handle(); |
| 13577 | return op.redispatch(dispatchKeySet, diagonals, offsets, shape, layout, out); |
| 13578 | } |
| 13579 | |
| 13580 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(zero_out, name, "aten::zero") |
| 13581 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(zero_out, overload_name, "out") |
| 13582 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(zero_out, schema_str, "zero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 13583 | |
| 13584 | // aten::zero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 13585 | static C10_NOINLINE c10::TypedOperatorHandle<zero_out::schema> create_zero_out_typed_handle() { |
| 13586 | return c10::Dispatcher::singleton() |
| 13587 | .findSchemaOrThrow(zero_out::name, zero_out::overload_name) |
| 13588 | .typed<zero_out::schema>(); |
| 13589 | } |
| 13590 | |
| 13591 | // aten::zero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 13592 | at::Tensor & zero_out::call(const at::Tensor & self, at::Tensor & out) { |
| 13593 | |
| 13594 | static auto op = create_zero_out_typed_handle(); |
| 13595 | return op.call(self, out); |
| 13596 | } |
| 13597 | |
| 13598 | // aten::zero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 13599 | at::Tensor & zero_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 13600 | |
| 13601 | static auto op = create_zero_out_typed_handle(); |
| 13602 | return op.redispatch(dispatchKeySet, self, out); |
| 13603 | } |
| 13604 | |
| 13605 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(zero, name, "aten::zero") |
| 13606 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(zero, overload_name, "") |
| 13607 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(zero, schema_str, "zero(Tensor self) -> Tensor") |
| 13608 | |
| 13609 | // aten::zero(Tensor self) -> Tensor |
| 13610 | static C10_NOINLINE c10::TypedOperatorHandle<zero::schema> create_zero_typed_handle() { |
| 13611 | return c10::Dispatcher::singleton() |
| 13612 | .findSchemaOrThrow(zero::name, zero::overload_name) |
| 13613 | .typed<zero::schema>(); |
| 13614 | } |
| 13615 | |
| 13616 | // aten::zero(Tensor self) -> Tensor |
| 13617 | at::Tensor zero::call(const at::Tensor & self) { |
| 13618 | |
| 13619 | static auto op = create_zero_typed_handle(); |
| 13620 | return op.call(self); |
| 13621 | } |
| 13622 | |
| 13623 | // aten::zero(Tensor self) -> Tensor |
| 13624 | at::Tensor zero::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 13625 | |
| 13626 | static auto op = create_zero_typed_handle(); |
| 13627 | return op.redispatch(dispatchKeySet, self); |
| 13628 | } |
| 13629 | |
| 13630 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rsub_Tensor_out, name, "aten::rsub") |
| 13631 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rsub_Tensor_out, overload_name, "Tensor_out") |
| 13632 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rsub_Tensor_out, schema_str, "rsub.Tensor_out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)") |
| 13633 | |
| 13634 | // aten::rsub.Tensor_out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) |
| 13635 | static C10_NOINLINE c10::TypedOperatorHandle<rsub_Tensor_out::schema> create_rsub_Tensor_out_typed_handle() { |
| 13636 | return c10::Dispatcher::singleton() |
| 13637 | .findSchemaOrThrow(rsub_Tensor_out::name, rsub_Tensor_out::overload_name) |
| 13638 | .typed<rsub_Tensor_out::schema>(); |
| 13639 | } |
| 13640 | |
| 13641 | // aten::rsub.Tensor_out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) |
| 13642 | at::Tensor & rsub_Tensor_out::call(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) { |
| 13643 | |
| 13644 | static auto op = create_rsub_Tensor_out_typed_handle(); |
| 13645 | return op.call(self, other, alpha, out); |
| 13646 | } |
| 13647 | |
| 13648 | // aten::rsub.Tensor_out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) |
| 13649 | at::Tensor & rsub_Tensor_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) { |
| 13650 | |
| 13651 | static auto op = create_rsub_Tensor_out_typed_handle(); |
| 13652 | return op.redispatch(dispatchKeySet, self, other, alpha, out); |
| 13653 | } |
| 13654 | |
| 13655 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rsub_Scalar_out, name, "aten::rsub") |
| 13656 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rsub_Scalar_out, overload_name, "Scalar_out") |
| 13657 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rsub_Scalar_out, schema_str, "rsub.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!)") |
| 13658 | |
| 13659 | // aten::rsub.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!) |
| 13660 | static C10_NOINLINE c10::TypedOperatorHandle<rsub_Scalar_out::schema> create_rsub_Scalar_out_typed_handle() { |
| 13661 | return c10::Dispatcher::singleton() |
| 13662 | .findSchemaOrThrow(rsub_Scalar_out::name, rsub_Scalar_out::overload_name) |
| 13663 | .typed<rsub_Scalar_out::schema>(); |
| 13664 | } |
| 13665 | |
| 13666 | // aten::rsub.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!) |
| 13667 | at::Tensor & rsub_Scalar_out::call(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha, at::Tensor & out) { |
| 13668 | |
| 13669 | static auto op = create_rsub_Scalar_out_typed_handle(); |
| 13670 | return op.call(self, other, alpha, out); |
| 13671 | } |
| 13672 | |
| 13673 | // aten::rsub.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!) |
| 13674 | at::Tensor & rsub_Scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha, at::Tensor & out) { |
| 13675 | |
| 13676 | static auto op = create_rsub_Scalar_out_typed_handle(); |
| 13677 | return op.redispatch(dispatchKeySet, self, other, alpha, out); |
| 13678 | } |
| 13679 | |
| 13680 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_coo_tensor_with_dims_out, name, "aten::_sparse_coo_tensor_with_dims") |
| 13681 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_coo_tensor_with_dims_out, overload_name, "out") |
| 13682 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_coo_tensor_with_dims_out, schema_str, "_sparse_coo_tensor_with_dims.out(int sparse_dim, int dense_dim, int[] size, *, Tensor(a!) out) -> Tensor(a!)") |
| 13683 | |
| 13684 | // aten::_sparse_coo_tensor_with_dims.out(int sparse_dim, int dense_dim, int[] size, *, Tensor(a!) out) -> Tensor(a!) |
| 13685 | static C10_NOINLINE c10::TypedOperatorHandle<_sparse_coo_tensor_with_dims_out::schema> create__sparse_coo_tensor_with_dims_out_typed_handle() { |
| 13686 | return c10::Dispatcher::singleton() |
| 13687 | .findSchemaOrThrow(_sparse_coo_tensor_with_dims_out::name, _sparse_coo_tensor_with_dims_out::overload_name) |
| 13688 | .typed<_sparse_coo_tensor_with_dims_out::schema>(); |
| 13689 | } |
| 13690 | |
| 13691 | // aten::_sparse_coo_tensor_with_dims.out(int sparse_dim, int dense_dim, int[] size, *, Tensor(a!) out) -> Tensor(a!) |
| 13692 | at::Tensor & _sparse_coo_tensor_with_dims_out::call(int64_t sparse_dim, int64_t dense_dim, at::IntArrayRef size, at::Tensor & out) { |
| 13693 | |
| 13694 | static auto op = create__sparse_coo_tensor_with_dims_out_typed_handle(); |
| 13695 | return op.call(sparse_dim, dense_dim, size, out); |
| 13696 | } |
| 13697 | |
| 13698 | // aten::_sparse_coo_tensor_with_dims.out(int sparse_dim, int dense_dim, int[] size, *, Tensor(a!) out) -> Tensor(a!) |
| 13699 | at::Tensor & _sparse_coo_tensor_with_dims_out::redispatch(c10::DispatchKeySet dispatchKeySet, int64_t sparse_dim, int64_t dense_dim, at::IntArrayRef size, at::Tensor & out) { |
| 13700 | |
| 13701 | static auto op = create__sparse_coo_tensor_with_dims_out_typed_handle(); |
| 13702 | return op.redispatch(dispatchKeySet, sparse_dim, dense_dim, size, out); |
| 13703 | } |
| 13704 | |
| 13705 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_coalesce_out, name, "aten::_coalesce") |
| 13706 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_coalesce_out, overload_name, "out") |
| 13707 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_coalesce_out, schema_str, "_coalesce.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 13708 | |
| 13709 | // aten::_coalesce.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 13710 | static C10_NOINLINE c10::TypedOperatorHandle<_coalesce_out::schema> create__coalesce_out_typed_handle() { |
| 13711 | return c10::Dispatcher::singleton() |
| 13712 | .findSchemaOrThrow(_coalesce_out::name, _coalesce_out::overload_name) |
| 13713 | .typed<_coalesce_out::schema>(); |
| 13714 | } |
| 13715 | |
| 13716 | // aten::_coalesce.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 13717 | at::Tensor & _coalesce_out::call(const at::Tensor & self, at::Tensor & out) { |
| 13718 | |
| 13719 | static auto op = create__coalesce_out_typed_handle(); |
| 13720 | return op.call(self, out); |
| 13721 | } |
| 13722 | |
| 13723 | // aten::_coalesce.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 13724 | at::Tensor & _coalesce_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 13725 | |
| 13726 | static auto op = create__coalesce_out_typed_handle(); |
| 13727 | return op.redispatch(dispatchKeySet, self, out); |
| 13728 | } |
| 13729 | |
| 13730 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(q_per_channel_scales_out, name, "aten::q_per_channel_scales") |
| 13731 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(q_per_channel_scales_out, overload_name, "out") |
| 13732 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(q_per_channel_scales_out, schema_str, "q_per_channel_scales.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 13733 | |
| 13734 | // aten::q_per_channel_scales.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 13735 | static C10_NOINLINE c10::TypedOperatorHandle<q_per_channel_scales_out::schema> create_q_per_channel_scales_out_typed_handle() { |
| 13736 | return c10::Dispatcher::singleton() |
| 13737 | .findSchemaOrThrow(q_per_channel_scales_out::name, q_per_channel_scales_out::overload_name) |
| 13738 | .typed<q_per_channel_scales_out::schema>(); |
| 13739 | } |
| 13740 | |
| 13741 | // aten::q_per_channel_scales.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 13742 | at::Tensor & q_per_channel_scales_out::call(const at::Tensor & self, at::Tensor & out) { |
| 13743 | |
| 13744 | static auto op = create_q_per_channel_scales_out_typed_handle(); |
| 13745 | return op.call(self, out); |
| 13746 | } |
| 13747 | |
| 13748 | // aten::q_per_channel_scales.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 13749 | at::Tensor & q_per_channel_scales_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 13750 | |
| 13751 | static auto op = create_q_per_channel_scales_out_typed_handle(); |
| 13752 | return op.redispatch(dispatchKeySet, self, out); |
| 13753 | } |
| 13754 | |
| 13755 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lstm_mps_backward_out, name, "aten::lstm_mps_backward") |
| 13756 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lstm_mps_backward_out, overload_name, "out") |
| 13757 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lstm_mps_backward_out, schema_str, "lstm_mps_backward.out(Tensor grad_y, Tensor? grad_hy, Tensor? grad_cy, Tensor z_state, Tensor cell_state_fwd, Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first, *, Tensor(a!) out0, Tensor(b!)[] out1, Tensor(c!)[] out2) -> ()") |
| 13758 | |
| 13759 | // aten::lstm_mps_backward.out(Tensor grad_y, Tensor? grad_hy, Tensor? grad_cy, Tensor z_state, Tensor cell_state_fwd, Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first, *, Tensor(a!) out0, Tensor(b!)[] out1, Tensor(c!)[] out2) -> () |
| 13760 | static C10_NOINLINE c10::TypedOperatorHandle<lstm_mps_backward_out::schema> create_lstm_mps_backward_out_typed_handle() { |
| 13761 | return c10::Dispatcher::singleton() |
| 13762 | .findSchemaOrThrow(lstm_mps_backward_out::name, lstm_mps_backward_out::overload_name) |
| 13763 | .typed<lstm_mps_backward_out::schema>(); |
| 13764 | } |
| 13765 | |
| 13766 | // aten::lstm_mps_backward.out(Tensor grad_y, Tensor? grad_hy, Tensor? grad_cy, Tensor z_state, Tensor cell_state_fwd, Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first, *, Tensor(a!) out0, Tensor(b!)[] out1, Tensor(c!)[] out2) -> () |
| 13767 | void lstm_mps_backward_out::call(const at::Tensor & grad_y, const c10::optional<at::Tensor> & grad_hy, const c10::optional<at::Tensor> & grad_cy, const at::Tensor & z_state, const at::Tensor & cell_state_fwd, const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first, at::Tensor & out0, at::TensorList out1, at::TensorList out2) { |
| 13768 | |
| 13769 | static auto op = create_lstm_mps_backward_out_typed_handle(); |
| 13770 | return op.call(grad_y, grad_hy, grad_cy, z_state, cell_state_fwd, input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first, out0, out1, out2); |
| 13771 | } |
| 13772 | |
| 13773 | // aten::lstm_mps_backward.out(Tensor grad_y, Tensor? grad_hy, Tensor? grad_cy, Tensor z_state, Tensor cell_state_fwd, Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first, *, Tensor(a!) out0, Tensor(b!)[] out1, Tensor(c!)[] out2) -> () |
| 13774 | void lstm_mps_backward_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_y, const c10::optional<at::Tensor> & grad_hy, const c10::optional<at::Tensor> & grad_cy, const at::Tensor & z_state, const at::Tensor & cell_state_fwd, const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first, at::Tensor & out0, at::TensorList out1, at::TensorList out2) { |
| 13775 | |
| 13776 | static auto op = create_lstm_mps_backward_out_typed_handle(); |
| 13777 | return op.redispatch(dispatchKeySet, grad_y, grad_hy, grad_cy, z_state, cell_state_fwd, input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first, out0, out1, out2); |
| 13778 | } |
| 13779 | |
| 13780 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_thnn_fused_lstm_cell_backward_impl_out, name, "aten::_thnn_fused_lstm_cell_backward_impl") |
| 13781 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_thnn_fused_lstm_cell_backward_impl_out, overload_name, "out") |
| 13782 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_thnn_fused_lstm_cell_backward_impl_out, schema_str, "_thnn_fused_lstm_cell_backward_impl.out(Tensor? grad_hy, Tensor? grad_cy, Tensor cx, Tensor cy, Tensor workspace, bool has_bias, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))") |
| 13783 | |
| 13784 | // aten::_thnn_fused_lstm_cell_backward_impl.out(Tensor? grad_hy, Tensor? grad_cy, Tensor cx, Tensor cy, Tensor workspace, bool has_bias, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 13785 | static C10_NOINLINE c10::TypedOperatorHandle<_thnn_fused_lstm_cell_backward_impl_out::schema> create__thnn_fused_lstm_cell_backward_impl_out_typed_handle() { |
| 13786 | return c10::Dispatcher::singleton() |
| 13787 | .findSchemaOrThrow(_thnn_fused_lstm_cell_backward_impl_out::name, _thnn_fused_lstm_cell_backward_impl_out::overload_name) |
| 13788 | .typed<_thnn_fused_lstm_cell_backward_impl_out::schema>(); |
| 13789 | } |
| 13790 | |
| 13791 | // aten::_thnn_fused_lstm_cell_backward_impl.out(Tensor? grad_hy, Tensor? grad_cy, Tensor cx, Tensor cy, Tensor workspace, bool has_bias, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 13792 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _thnn_fused_lstm_cell_backward_impl_out::call(const c10::optional<at::Tensor> & grad_hy, const c10::optional<at::Tensor> & grad_cy, const at::Tensor & cx, const at::Tensor & cy, const at::Tensor & workspace, bool has_bias, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 13793 | |
| 13794 | static auto op = create__thnn_fused_lstm_cell_backward_impl_out_typed_handle(); |
| 13795 | return op.call(grad_hy, grad_cy, cx, cy, workspace, has_bias, out0, out1, out2); |
| 13796 | } |
| 13797 | |
| 13798 | // aten::_thnn_fused_lstm_cell_backward_impl.out(Tensor? grad_hy, Tensor? grad_cy, Tensor cx, Tensor cy, Tensor workspace, bool has_bias, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 13799 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _thnn_fused_lstm_cell_backward_impl_out::redispatch(c10::DispatchKeySet dispatchKeySet, const c10::optional<at::Tensor> & grad_hy, const c10::optional<at::Tensor> & grad_cy, const at::Tensor & cx, const at::Tensor & cy, const at::Tensor & workspace, bool has_bias, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 13800 | |
| 13801 | static auto op = create__thnn_fused_lstm_cell_backward_impl_out_typed_handle(); |
| 13802 | return op.redispatch(dispatchKeySet, grad_hy, grad_cy, cx, cy, workspace, has_bias, out0, out1, out2); |
| 13803 | } |
| 13804 | |
| 13805 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_thnn_fused_gru_cell_out, name, "aten::_thnn_fused_gru_cell") |
| 13806 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_thnn_fused_gru_cell_out, overload_name, "out") |
| 13807 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_thnn_fused_gru_cell_out, schema_str, "_thnn_fused_gru_cell.out(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))") |
| 13808 | |
| 13809 | // aten::_thnn_fused_gru_cell.out(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) |
| 13810 | static C10_NOINLINE c10::TypedOperatorHandle<_thnn_fused_gru_cell_out::schema> create__thnn_fused_gru_cell_out_typed_handle() { |
| 13811 | return c10::Dispatcher::singleton() |
| 13812 | .findSchemaOrThrow(_thnn_fused_gru_cell_out::name, _thnn_fused_gru_cell_out::overload_name) |
| 13813 | .typed<_thnn_fused_gru_cell_out::schema>(); |
| 13814 | } |
| 13815 | |
| 13816 | // aten::_thnn_fused_gru_cell.out(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) |
| 13817 | ::std::tuple<at::Tensor &,at::Tensor &> _thnn_fused_gru_cell_out::call(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) { |
| 13818 | |
| 13819 | static auto op = create__thnn_fused_gru_cell_out_typed_handle(); |
| 13820 | return op.call(input_gates, hidden_gates, hx, input_bias, hidden_bias, out0, out1); |
| 13821 | } |
| 13822 | |
| 13823 | // aten::_thnn_fused_gru_cell.out(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) |
| 13824 | ::std::tuple<at::Tensor &,at::Tensor &> _thnn_fused_gru_cell_out::redispatch(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) { |
| 13825 | |
| 13826 | static auto op = create__thnn_fused_gru_cell_out_typed_handle(); |
| 13827 | return op.redispatch(dispatchKeySet, input_gates, hidden_gates, hx, input_bias, hidden_bias, out0, out1); |
| 13828 | } |
| 13829 | |
| 13830 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_pack_padded_sequence_out, name, "aten::_pack_padded_sequence") |
| 13831 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_pack_padded_sequence_out, overload_name, "out") |
| 13832 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_pack_padded_sequence_out, schema_str, "_pack_padded_sequence.out(Tensor input, Tensor lengths, bool batch_first, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))") |
| 13833 | |
| 13834 | // aten::_pack_padded_sequence.out(Tensor input, Tensor lengths, bool batch_first, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) |
| 13835 | static C10_NOINLINE c10::TypedOperatorHandle<_pack_padded_sequence_out::schema> create__pack_padded_sequence_out_typed_handle() { |
| 13836 | return c10::Dispatcher::singleton() |
| 13837 | .findSchemaOrThrow(_pack_padded_sequence_out::name, _pack_padded_sequence_out::overload_name) |
| 13838 | .typed<_pack_padded_sequence_out::schema>(); |
| 13839 | } |
| 13840 | |
| 13841 | // aten::_pack_padded_sequence.out(Tensor input, Tensor lengths, bool batch_first, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) |
| 13842 | ::std::tuple<at::Tensor &,at::Tensor &> _pack_padded_sequence_out::call(const at::Tensor & input, const at::Tensor & lengths, bool batch_first, at::Tensor & out0, at::Tensor & out1) { |
| 13843 | |
| 13844 | static auto op = create__pack_padded_sequence_out_typed_handle(); |
| 13845 | return op.call(input, lengths, batch_first, out0, out1); |
| 13846 | } |
| 13847 | |
| 13848 | // aten::_pack_padded_sequence.out(Tensor input, Tensor lengths, bool batch_first, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) |
| 13849 | ::std::tuple<at::Tensor &,at::Tensor &> _pack_padded_sequence_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & lengths, bool batch_first, at::Tensor & out0, at::Tensor & out1) { |
| 13850 | |
| 13851 | static auto op = create__pack_padded_sequence_out_typed_handle(); |
| 13852 | return op.redispatch(dispatchKeySet, input, lengths, batch_first, out0, out1); |
| 13853 | } |
| 13854 | |
| 13855 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_masked_softmax_out, name, "aten::_masked_softmax") |
| 13856 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_masked_softmax_out, overload_name, "out") |
| 13857 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_masked_softmax_out, schema_str, "_masked_softmax.out(Tensor self, Tensor mask, int? dim=None, int? mask_type=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 13858 | |
| 13859 | // aten::_masked_softmax.out(Tensor self, Tensor mask, int? dim=None, int? mask_type=None, *, Tensor(a!) out) -> Tensor(a!) |
| 13860 | static C10_NOINLINE c10::TypedOperatorHandle<_masked_softmax_out::schema> create__masked_softmax_out_typed_handle() { |
| 13861 | return c10::Dispatcher::singleton() |
| 13862 | .findSchemaOrThrow(_masked_softmax_out::name, _masked_softmax_out::overload_name) |
| 13863 | .typed<_masked_softmax_out::schema>(); |
| 13864 | } |
| 13865 | |
| 13866 | // aten::_masked_softmax.out(Tensor self, Tensor mask, int? dim=None, int? mask_type=None, *, Tensor(a!) out) -> Tensor(a!) |
| 13867 | at::Tensor & _masked_softmax_out::call(const at::Tensor & self, const at::Tensor & mask, c10::optional<int64_t> dim, c10::optional<int64_t> mask_type, at::Tensor & out) { |
| 13868 | |
| 13869 | static auto op = create__masked_softmax_out_typed_handle(); |
| 13870 | return op.call(self, mask, dim, mask_type, out); |
| 13871 | } |
| 13872 | |
| 13873 | // aten::_masked_softmax.out(Tensor self, Tensor mask, int? dim=None, int? mask_type=None, *, Tensor(a!) out) -> Tensor(a!) |
| 13874 | at::Tensor & _masked_softmax_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mask, c10::optional<int64_t> dim, c10::optional<int64_t> mask_type, at::Tensor & out) { |
| 13875 | |
| 13876 | static auto op = create__masked_softmax_out_typed_handle(); |
| 13877 | return op.redispatch(dispatchKeySet, self, mask, dim, mask_type, out); |
| 13878 | } |
| 13879 | |
| 13880 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_mul_Scalar_out, name, "aten::_foreach_mul") |
| 13881 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_mul_Scalar_out, overload_name, "Scalar_out") |
| 13882 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_mul_Scalar_out, schema_str, "_foreach_mul.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()") |
| 13883 | |
| 13884 | // aten::_foreach_mul.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () |
| 13885 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_mul_Scalar_out::schema> create__foreach_mul_Scalar_out_typed_handle() { |
| 13886 | return c10::Dispatcher::singleton() |
| 13887 | .findSchemaOrThrow(_foreach_mul_Scalar_out::name, _foreach_mul_Scalar_out::overload_name) |
| 13888 | .typed<_foreach_mul_Scalar_out::schema>(); |
| 13889 | } |
| 13890 | |
| 13891 | // aten::_foreach_mul.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () |
| 13892 | void _foreach_mul_Scalar_out::call(at::TensorList self, const at::Scalar & scalar, at::TensorList out) { |
| 13893 | |
| 13894 | static auto op = create__foreach_mul_Scalar_out_typed_handle(); |
| 13895 | return op.call(self, scalar, out); |
| 13896 | } |
| 13897 | |
| 13898 | // aten::_foreach_mul.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () |
| 13899 | void _foreach_mul_Scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar, at::TensorList out) { |
| 13900 | |
| 13901 | static auto op = create__foreach_mul_Scalar_out_typed_handle(); |
| 13902 | return op.redispatch(dispatchKeySet, self, scalar, out); |
| 13903 | } |
| 13904 | |
| 13905 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_div_Scalar_out, name, "aten::_foreach_div") |
| 13906 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_div_Scalar_out, overload_name, "Scalar_out") |
| 13907 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_div_Scalar_out, schema_str, "_foreach_div.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()") |
| 13908 | |
| 13909 | // aten::_foreach_div.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () |
| 13910 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_div_Scalar_out::schema> create__foreach_div_Scalar_out_typed_handle() { |
| 13911 | return c10::Dispatcher::singleton() |
| 13912 | .findSchemaOrThrow(_foreach_div_Scalar_out::name, _foreach_div_Scalar_out::overload_name) |
| 13913 | .typed<_foreach_div_Scalar_out::schema>(); |
| 13914 | } |
| 13915 | |
| 13916 | // aten::_foreach_div.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () |
| 13917 | void _foreach_div_Scalar_out::call(at::TensorList self, const at::Scalar & scalar, at::TensorList out) { |
| 13918 | |
| 13919 | static auto op = create__foreach_div_Scalar_out_typed_handle(); |
| 13920 | return op.call(self, scalar, out); |
| 13921 | } |
| 13922 | |
| 13923 | // aten::_foreach_div.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () |
| 13924 | void _foreach_div_Scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar, at::TensorList out) { |
| 13925 | |
| 13926 | static auto op = create__foreach_div_Scalar_out_typed_handle(); |
| 13927 | return op.redispatch(dispatchKeySet, self, scalar, out); |
| 13928 | } |
| 13929 | |
| 13930 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_mul_List_out, name, "aten::_foreach_mul") |
| 13931 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_mul_List_out, overload_name, "List_out") |
| 13932 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_mul_List_out, schema_str, "_foreach_mul.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()") |
| 13933 | |
| 13934 | // aten::_foreach_mul.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> () |
| 13935 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_mul_List_out::schema> create__foreach_mul_List_out_typed_handle() { |
| 13936 | return c10::Dispatcher::singleton() |
| 13937 | .findSchemaOrThrow(_foreach_mul_List_out::name, _foreach_mul_List_out::overload_name) |
| 13938 | .typed<_foreach_mul_List_out::schema>(); |
| 13939 | } |
| 13940 | |
| 13941 | // aten::_foreach_mul.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> () |
| 13942 | void _foreach_mul_List_out::call(at::TensorList self, at::TensorList other, at::TensorList out) { |
| 13943 | |
| 13944 | static auto op = create__foreach_mul_List_out_typed_handle(); |
| 13945 | return op.call(self, other, out); |
| 13946 | } |
| 13947 | |
| 13948 | // aten::_foreach_mul.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> () |
| 13949 | void _foreach_mul_List_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other, at::TensorList out) { |
| 13950 | |
| 13951 | static auto op = create__foreach_mul_List_out_typed_handle(); |
| 13952 | return op.redispatch(dispatchKeySet, self, other, out); |
| 13953 | } |
| 13954 | |
| 13955 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_div_List_out, name, "aten::_foreach_div") |
| 13956 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_div_List_out, overload_name, "List_out") |
| 13957 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_div_List_out, schema_str, "_foreach_div.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()") |
| 13958 | |
| 13959 | // aten::_foreach_div.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> () |
| 13960 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_div_List_out::schema> create__foreach_div_List_out_typed_handle() { |
| 13961 | return c10::Dispatcher::singleton() |
| 13962 | .findSchemaOrThrow(_foreach_div_List_out::name, _foreach_div_List_out::overload_name) |
| 13963 | .typed<_foreach_div_List_out::schema>(); |
| 13964 | } |
| 13965 | |
| 13966 | // aten::_foreach_div.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> () |
| 13967 | void _foreach_div_List_out::call(at::TensorList self, at::TensorList other, at::TensorList out) { |
| 13968 | |
| 13969 | static auto op = create__foreach_div_List_out_typed_handle(); |
| 13970 | return op.call(self, other, out); |
| 13971 | } |
| 13972 | |
| 13973 | // aten::_foreach_div.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> () |
| 13974 | void _foreach_div_List_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other, at::TensorList out) { |
| 13975 | |
| 13976 | static auto op = create__foreach_div_List_out_typed_handle(); |
| 13977 | return op.redispatch(dispatchKeySet, self, other, out); |
| 13978 | } |
| 13979 | |
| 13980 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_div_ScalarList_out, name, "aten::_foreach_div") |
| 13981 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_div_ScalarList_out, overload_name, "ScalarList_out") |
| 13982 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_div_ScalarList_out, schema_str, "_foreach_div.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()") |
| 13983 | |
| 13984 | // aten::_foreach_div.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () |
| 13985 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_div_ScalarList_out::schema> create__foreach_div_ScalarList_out_typed_handle() { |
| 13986 | return c10::Dispatcher::singleton() |
| 13987 | .findSchemaOrThrow(_foreach_div_ScalarList_out::name, _foreach_div_ScalarList_out::overload_name) |
| 13988 | .typed<_foreach_div_ScalarList_out::schema>(); |
| 13989 | } |
| 13990 | |
| 13991 | // aten::_foreach_div.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () |
| 13992 | void _foreach_div_ScalarList_out::call(at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out) { |
| 13993 | |
| 13994 | static auto op = create__foreach_div_ScalarList_out_typed_handle(); |
| 13995 | return op.call(self, scalars, out); |
| 13996 | } |
| 13997 | |
| 13998 | // aten::_foreach_div.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () |
| 13999 | void _foreach_div_ScalarList_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out) { |
| 14000 | |
| 14001 | static auto op = create__foreach_div_ScalarList_out_typed_handle(); |
| 14002 | return op.redispatch(dispatchKeySet, self, scalars, out); |
| 14003 | } |
| 14004 | |
| 14005 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_mul_ScalarList_out, name, "aten::_foreach_mul") |
| 14006 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_mul_ScalarList_out, overload_name, "ScalarList_out") |
| 14007 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_mul_ScalarList_out, schema_str, "_foreach_mul.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()") |
| 14008 | |
| 14009 | // aten::_foreach_mul.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () |
| 14010 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_mul_ScalarList_out::schema> create__foreach_mul_ScalarList_out_typed_handle() { |
| 14011 | return c10::Dispatcher::singleton() |
| 14012 | .findSchemaOrThrow(_foreach_mul_ScalarList_out::name, _foreach_mul_ScalarList_out::overload_name) |
| 14013 | .typed<_foreach_mul_ScalarList_out::schema>(); |
| 14014 | } |
| 14015 | |
| 14016 | // aten::_foreach_mul.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () |
| 14017 | void _foreach_mul_ScalarList_out::call(at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out) { |
| 14018 | |
| 14019 | static auto op = create__foreach_mul_ScalarList_out_typed_handle(); |
| 14020 | return op.call(self, scalars, out); |
| 14021 | } |
| 14022 | |
| 14023 | // aten::_foreach_mul.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () |
| 14024 | void _foreach_mul_ScalarList_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out) { |
| 14025 | |
| 14026 | static auto op = create__foreach_mul_ScalarList_out_typed_handle(); |
| 14027 | return op.redispatch(dispatchKeySet, self, scalars, out); |
| 14028 | } |
| 14029 | |
| 14030 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_zero_out, name, "aten::_foreach_zero") |
| 14031 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_zero_out, overload_name, "out") |
| 14032 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_zero_out, schema_str, "_foreach_zero.out(Tensor[] self, *, Tensor(a!)[] out) -> ()") |
| 14033 | |
| 14034 | // aten::_foreach_zero.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 14035 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_zero_out::schema> create__foreach_zero_out_typed_handle() { |
| 14036 | return c10::Dispatcher::singleton() |
| 14037 | .findSchemaOrThrow(_foreach_zero_out::name, _foreach_zero_out::overload_name) |
| 14038 | .typed<_foreach_zero_out::schema>(); |
| 14039 | } |
| 14040 | |
| 14041 | // aten::_foreach_zero.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 14042 | void _foreach_zero_out::call(at::TensorList self, at::TensorList out) { |
| 14043 | |
| 14044 | static auto op = create__foreach_zero_out_typed_handle(); |
| 14045 | return op.call(self, out); |
| 14046 | } |
| 14047 | |
| 14048 | // aten::_foreach_zero.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 14049 | void _foreach_zero_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 14050 | |
| 14051 | static auto op = create__foreach_zero_out_typed_handle(); |
| 14052 | return op.redispatch(dispatchKeySet, self, out); |
| 14053 | } |
| 14054 | |
| 14055 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_zero, name, "aten::_foreach_zero") |
| 14056 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_zero, overload_name, "") |
| 14057 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_zero, schema_str, "_foreach_zero(Tensor[] self) -> Tensor[] self_out") |
| 14058 | |
| 14059 | // aten::_foreach_zero(Tensor[] self) -> Tensor[] self_out |
| 14060 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_zero::schema> create__foreach_zero_typed_handle() { |
| 14061 | return c10::Dispatcher::singleton() |
| 14062 | .findSchemaOrThrow(_foreach_zero::name, _foreach_zero::overload_name) |
| 14063 | .typed<_foreach_zero::schema>(); |
| 14064 | } |
| 14065 | |
| 14066 | // aten::_foreach_zero(Tensor[] self) -> Tensor[] self_out |
| 14067 | ::std::vector<at::Tensor> _foreach_zero::call(at::TensorList self) { |
| 14068 | |
| 14069 | static auto op = create__foreach_zero_typed_handle(); |
| 14070 | return op.call(self); |
| 14071 | } |
| 14072 | |
| 14073 | // aten::_foreach_zero(Tensor[] self) -> Tensor[] self_out |
| 14074 | ::std::vector<at::Tensor> _foreach_zero::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 14075 | |
| 14076 | static auto op = create__foreach_zero_typed_handle(); |
| 14077 | return op.redispatch(dispatchKeySet, self); |
| 14078 | } |
| 14079 | |
| 14080 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_asin_out, name, "aten::_foreach_asin") |
| 14081 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_asin_out, overload_name, "out") |
| 14082 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_asin_out, schema_str, "_foreach_asin.out(Tensor[] self, *, Tensor(a!)[] out) -> ()") |
| 14083 | |
| 14084 | // aten::_foreach_asin.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 14085 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_asin_out::schema> create__foreach_asin_out_typed_handle() { |
| 14086 | return c10::Dispatcher::singleton() |
| 14087 | .findSchemaOrThrow(_foreach_asin_out::name, _foreach_asin_out::overload_name) |
| 14088 | .typed<_foreach_asin_out::schema>(); |
| 14089 | } |
| 14090 | |
| 14091 | // aten::_foreach_asin.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 14092 | void _foreach_asin_out::call(at::TensorList self, at::TensorList out) { |
| 14093 | |
| 14094 | static auto op = create__foreach_asin_out_typed_handle(); |
| 14095 | return op.call(self, out); |
| 14096 | } |
| 14097 | |
| 14098 | // aten::_foreach_asin.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 14099 | void _foreach_asin_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 14100 | |
| 14101 | static auto op = create__foreach_asin_out_typed_handle(); |
| 14102 | return op.redispatch(dispatchKeySet, self, out); |
| 14103 | } |
| 14104 | |
| 14105 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_cos_out, name, "aten::_foreach_cos") |
| 14106 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_cos_out, overload_name, "out") |
| 14107 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_cos_out, schema_str, "_foreach_cos.out(Tensor[] self, *, Tensor(a!)[] out) -> ()") |
| 14108 | |
| 14109 | // aten::_foreach_cos.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 14110 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_cos_out::schema> create__foreach_cos_out_typed_handle() { |
| 14111 | return c10::Dispatcher::singleton() |
| 14112 | .findSchemaOrThrow(_foreach_cos_out::name, _foreach_cos_out::overload_name) |
| 14113 | .typed<_foreach_cos_out::schema>(); |
| 14114 | } |
| 14115 | |
| 14116 | // aten::_foreach_cos.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 14117 | void _foreach_cos_out::call(at::TensorList self, at::TensorList out) { |
| 14118 | |
| 14119 | static auto op = create__foreach_cos_out_typed_handle(); |
| 14120 | return op.call(self, out); |
| 14121 | } |
| 14122 | |
| 14123 | // aten::_foreach_cos.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 14124 | void _foreach_cos_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 14125 | |
| 14126 | static auto op = create__foreach_cos_out_typed_handle(); |
| 14127 | return op.redispatch(dispatchKeySet, self, out); |
| 14128 | } |
| 14129 | |
| 14130 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_floor_out, name, "aten::_foreach_floor") |
| 14131 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_floor_out, overload_name, "out") |
| 14132 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_floor_out, schema_str, "_foreach_floor.out(Tensor[] self, *, Tensor(a!)[] out) -> ()") |
| 14133 | |
| 14134 | // aten::_foreach_floor.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 14135 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_floor_out::schema> create__foreach_floor_out_typed_handle() { |
| 14136 | return c10::Dispatcher::singleton() |
| 14137 | .findSchemaOrThrow(_foreach_floor_out::name, _foreach_floor_out::overload_name) |
| 14138 | .typed<_foreach_floor_out::schema>(); |
| 14139 | } |
| 14140 | |
| 14141 | // aten::_foreach_floor.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 14142 | void _foreach_floor_out::call(at::TensorList self, at::TensorList out) { |
| 14143 | |
| 14144 | static auto op = create__foreach_floor_out_typed_handle(); |
| 14145 | return op.call(self, out); |
| 14146 | } |
| 14147 | |
| 14148 | // aten::_foreach_floor.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 14149 | void _foreach_floor_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 14150 | |
| 14151 | static auto op = create__foreach_floor_out_typed_handle(); |
| 14152 | return op.redispatch(dispatchKeySet, self, out); |
| 14153 | } |
| 14154 | |
| 14155 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_tanh_out, name, "aten::_foreach_tanh") |
| 14156 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_tanh_out, overload_name, "out") |
| 14157 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_tanh_out, schema_str, "_foreach_tanh.out(Tensor[] self, *, Tensor(a!)[] out) -> ()") |
| 14158 | |
| 14159 | // aten::_foreach_tanh.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 14160 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_tanh_out::schema> create__foreach_tanh_out_typed_handle() { |
| 14161 | return c10::Dispatcher::singleton() |
| 14162 | .findSchemaOrThrow(_foreach_tanh_out::name, _foreach_tanh_out::overload_name) |
| 14163 | .typed<_foreach_tanh_out::schema>(); |
| 14164 | } |
| 14165 | |
| 14166 | // aten::_foreach_tanh.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 14167 | void _foreach_tanh_out::call(at::TensorList self, at::TensorList out) { |
| 14168 | |
| 14169 | static auto op = create__foreach_tanh_out_typed_handle(); |
| 14170 | return op.call(self, out); |
| 14171 | } |
| 14172 | |
| 14173 | // aten::_foreach_tanh.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 14174 | void _foreach_tanh_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 14175 | |
| 14176 | static auto op = create__foreach_tanh_out_typed_handle(); |
| 14177 | return op.redispatch(dispatchKeySet, self, out); |
| 14178 | } |
| 14179 | |
| 14180 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_addcmul_Scalar_out, name, "aten::_foreach_addcmul") |
| 14181 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_addcmul_Scalar_out, overload_name, "Scalar_out") |
| 14182 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_addcmul_Scalar_out, schema_str, "_foreach_addcmul.Scalar_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1, *, Tensor(a!)[] out) -> ()") |
| 14183 | |
| 14184 | // aten::_foreach_addcmul.Scalar_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1, *, Tensor(a!)[] out) -> () |
| 14185 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_addcmul_Scalar_out::schema> create__foreach_addcmul_Scalar_out_typed_handle() { |
| 14186 | return c10::Dispatcher::singleton() |
| 14187 | .findSchemaOrThrow(_foreach_addcmul_Scalar_out::name, _foreach_addcmul_Scalar_out::overload_name) |
| 14188 | .typed<_foreach_addcmul_Scalar_out::schema>(); |
| 14189 | } |
| 14190 | |
| 14191 | // aten::_foreach_addcmul.Scalar_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1, *, Tensor(a!)[] out) -> () |
| 14192 | void _foreach_addcmul_Scalar_out::call(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value, at::TensorList out) { |
| 14193 | |
| 14194 | static auto op = create__foreach_addcmul_Scalar_out_typed_handle(); |
| 14195 | return op.call(self, tensor1, tensor2, value, out); |
| 14196 | } |
| 14197 | |
| 14198 | // aten::_foreach_addcmul.Scalar_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1, *, Tensor(a!)[] out) -> () |
| 14199 | void _foreach_addcmul_Scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value, at::TensorList out) { |
| 14200 | |
| 14201 | static auto op = create__foreach_addcmul_Scalar_out_typed_handle(); |
| 14202 | return op.redispatch(dispatchKeySet, self, tensor1, tensor2, value, out); |
| 14203 | } |
| 14204 | |
| 14205 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_addcmul_ScalarList_out, name, "aten::_foreach_addcmul") |
| 14206 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_addcmul_ScalarList_out, overload_name, "ScalarList_out") |
| 14207 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_addcmul_ScalarList_out, schema_str, "_foreach_addcmul.ScalarList_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars, *, Tensor(a!)[] out) -> ()") |
| 14208 | |
| 14209 | // aten::_foreach_addcmul.ScalarList_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars, *, Tensor(a!)[] out) -> () |
| 14210 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_addcmul_ScalarList_out::schema> create__foreach_addcmul_ScalarList_out_typed_handle() { |
| 14211 | return c10::Dispatcher::singleton() |
| 14212 | .findSchemaOrThrow(_foreach_addcmul_ScalarList_out::name, _foreach_addcmul_ScalarList_out::overload_name) |
| 14213 | .typed<_foreach_addcmul_ScalarList_out::schema>(); |
| 14214 | } |
| 14215 | |
| 14216 | // aten::_foreach_addcmul.ScalarList_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars, *, Tensor(a!)[] out) -> () |
| 14217 | void _foreach_addcmul_ScalarList_out::call(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars, at::TensorList out) { |
| 14218 | |
| 14219 | static auto op = create__foreach_addcmul_ScalarList_out_typed_handle(); |
| 14220 | return op.call(self, tensor1, tensor2, scalars, out); |
| 14221 | } |
| 14222 | |
| 14223 | // aten::_foreach_addcmul.ScalarList_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars, *, Tensor(a!)[] out) -> () |
| 14224 | void _foreach_addcmul_ScalarList_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars, at::TensorList out) { |
| 14225 | |
| 14226 | static auto op = create__foreach_addcmul_ScalarList_out_typed_handle(); |
| 14227 | return op.redispatch(dispatchKeySet, self, tensor1, tensor2, scalars, out); |
| 14228 | } |
| 14229 | |
| 14230 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_addcmul_Tensor_out, name, "aten::_foreach_addcmul") |
| 14231 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_addcmul_Tensor_out, overload_name, "Tensor_out") |
| 14232 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_addcmul_Tensor_out, schema_str, "_foreach_addcmul.Tensor_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars, *, Tensor(a!)[] out) -> ()") |
| 14233 | |
| 14234 | // aten::_foreach_addcmul.Tensor_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars, *, Tensor(a!)[] out) -> () |
| 14235 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_addcmul_Tensor_out::schema> create__foreach_addcmul_Tensor_out_typed_handle() { |
| 14236 | return c10::Dispatcher::singleton() |
| 14237 | .findSchemaOrThrow(_foreach_addcmul_Tensor_out::name, _foreach_addcmul_Tensor_out::overload_name) |
| 14238 | .typed<_foreach_addcmul_Tensor_out::schema>(); |
| 14239 | } |
| 14240 | |
| 14241 | // aten::_foreach_addcmul.Tensor_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars, *, Tensor(a!)[] out) -> () |
| 14242 | void _foreach_addcmul_Tensor_out::call(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars, at::TensorList out) { |
| 14243 | |
| 14244 | static auto op = create__foreach_addcmul_Tensor_out_typed_handle(); |
| 14245 | return op.call(self, tensor1, tensor2, scalars, out); |
| 14246 | } |
| 14247 | |
| 14248 | // aten::_foreach_addcmul.Tensor_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars, *, Tensor(a!)[] out) -> () |
| 14249 | void _foreach_addcmul_Tensor_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars, at::TensorList out) { |
| 14250 | |
| 14251 | static auto op = create__foreach_addcmul_Tensor_out_typed_handle(); |
| 14252 | return op.redispatch(dispatchKeySet, self, tensor1, tensor2, scalars, out); |
| 14253 | } |
| 14254 | |
| 14255 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_adaptive_avg_pool3d_out, name, "aten::_adaptive_avg_pool3d") |
| 14256 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_adaptive_avg_pool3d_out, overload_name, "out") |
| 14257 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_adaptive_avg_pool3d_out, schema_str, "_adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!)") |
| 14258 | |
| 14259 | // aten::_adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!) |
| 14260 | static C10_NOINLINE c10::TypedOperatorHandle<_adaptive_avg_pool3d_out::schema> create__adaptive_avg_pool3d_out_typed_handle() { |
| 14261 | return c10::Dispatcher::singleton() |
| 14262 | .findSchemaOrThrow(_adaptive_avg_pool3d_out::name, _adaptive_avg_pool3d_out::overload_name) |
| 14263 | .typed<_adaptive_avg_pool3d_out::schema>(); |
| 14264 | } |
| 14265 | |
| 14266 | // aten::_adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!) |
| 14267 | at::Tensor & _adaptive_avg_pool3d_out::call(const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out) { |
| 14268 | |
| 14269 | static auto op = create__adaptive_avg_pool3d_out_typed_handle(); |
| 14270 | return op.call(self, output_size, out); |
| 14271 | } |
| 14272 | |
| 14273 | // aten::_adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!) |
| 14274 | at::Tensor & _adaptive_avg_pool3d_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out) { |
| 14275 | |
| 14276 | static auto op = create__adaptive_avg_pool3d_out_typed_handle(); |
| 14277 | return op.redispatch(dispatchKeySet, self, output_size, out); |
| 14278 | } |
| 14279 | |
| 14280 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_slow_conv2d_backward_output_mask_out, name, "aten::_slow_conv2d_backward") |
| 14281 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_slow_conv2d_backward_output_mask_out, overload_name, "output_mask_out") |
| 14282 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_slow_conv2d_backward_output_mask_out, schema_str, "_slow_conv2d_backward.output_mask_out(Tensor grad_output, Tensor self, Tensor weight, int[2] kernel_size, int[2] stride, int[2] padding, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))") |
| 14283 | |
| 14284 | // aten::_slow_conv2d_backward.output_mask_out(Tensor grad_output, Tensor self, Tensor weight, int[2] kernel_size, int[2] stride, int[2] padding, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 14285 | static C10_NOINLINE c10::TypedOperatorHandle<_slow_conv2d_backward_output_mask_out::schema> create__slow_conv2d_backward_output_mask_out_typed_handle() { |
| 14286 | return c10::Dispatcher::singleton() |
| 14287 | .findSchemaOrThrow(_slow_conv2d_backward_output_mask_out::name, _slow_conv2d_backward_output_mask_out::overload_name) |
| 14288 | .typed<_slow_conv2d_backward_output_mask_out::schema>(); |
| 14289 | } |
| 14290 | |
| 14291 | // aten::_slow_conv2d_backward.output_mask_out(Tensor grad_output, Tensor self, Tensor weight, int[2] kernel_size, int[2] stride, int[2] padding, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 14292 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _slow_conv2d_backward_output_mask_out::call(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 14293 | |
| 14294 | static auto op = create__slow_conv2d_backward_output_mask_out_typed_handle(); |
| 14295 | return op.call(grad_output, self, weight, kernel_size, stride, padding, output_mask, out0, out1, out2); |
| 14296 | } |
| 14297 | |
| 14298 | // aten::_slow_conv2d_backward.output_mask_out(Tensor grad_output, Tensor self, Tensor weight, int[2] kernel_size, int[2] stride, int[2] padding, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 14299 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _slow_conv2d_backward_output_mask_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 14300 | |
| 14301 | static auto op = create__slow_conv2d_backward_output_mask_out_typed_handle(); |
| 14302 | return op.redispatch(dispatchKeySet, grad_output, self, weight, kernel_size, stride, padding, output_mask, out0, out1, out2); |
| 14303 | } |
| 14304 | |
| 14305 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv_depthwise3d_out, name, "aten::conv_depthwise3d") |
| 14306 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv_depthwise3d_out, overload_name, "out") |
| 14307 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv_depthwise3d_out, schema_str, "conv_depthwise3d.out(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias, int[3] stride, SymInt[3] padding, int[3] dilation, *, Tensor(a!) out) -> Tensor(a!)") |
| 14308 | |
| 14309 | // aten::conv_depthwise3d.out(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias, int[3] stride, SymInt[3] padding, int[3] dilation, *, Tensor(a!) out) -> Tensor(a!) |
| 14310 | static C10_NOINLINE c10::TypedOperatorHandle<conv_depthwise3d_out::schema> create_conv_depthwise3d_out_typed_handle() { |
| 14311 | return c10::Dispatcher::singleton() |
| 14312 | .findSchemaOrThrow(conv_depthwise3d_out::name, conv_depthwise3d_out::overload_name) |
| 14313 | .typed<conv_depthwise3d_out::schema>(); |
| 14314 | } |
| 14315 | |
| 14316 | // aten::conv_depthwise3d.out(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias, int[3] stride, SymInt[3] padding, int[3] dilation, *, Tensor(a!) out) -> Tensor(a!) |
| 14317 | at::Tensor & conv_depthwise3d_out::call(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::SymIntArrayRef padding, at::IntArrayRef dilation, at::Tensor & out) { |
| 14318 | |
| 14319 | static auto op = create_conv_depthwise3d_out_typed_handle(); |
| 14320 | return op.call(self, weight, kernel_size, bias, stride, padding, dilation, out); |
| 14321 | } |
| 14322 | |
| 14323 | // aten::conv_depthwise3d.out(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias, int[3] stride, SymInt[3] padding, int[3] dilation, *, Tensor(a!) out) -> Tensor(a!) |
| 14324 | at::Tensor & conv_depthwise3d_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::SymIntArrayRef padding, at::IntArrayRef dilation, at::Tensor & out) { |
| 14325 | |
| 14326 | static auto op = create_conv_depthwise3d_out_typed_handle(); |
| 14327 | return op.redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, dilation, out); |
| 14328 | } |
| 14329 | |
| 14330 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slow_conv_dilated2d_out, name, "aten::slow_conv_dilated2d") |
| 14331 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slow_conv_dilated2d_out, overload_name, "out") |
| 14332 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slow_conv_dilated2d_out, schema_str, "slow_conv_dilated2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, SymInt[2] padding=0, int[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!)") |
| 14333 | |
| 14334 | // aten::slow_conv_dilated2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, SymInt[2] padding=0, int[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!) |
| 14335 | static C10_NOINLINE c10::TypedOperatorHandle<slow_conv_dilated2d_out::schema> create_slow_conv_dilated2d_out_typed_handle() { |
| 14336 | return c10::Dispatcher::singleton() |
| 14337 | .findSchemaOrThrow(slow_conv_dilated2d_out::name, slow_conv_dilated2d_out::overload_name) |
| 14338 | .typed<slow_conv_dilated2d_out::schema>(); |
| 14339 | } |
| 14340 | |
| 14341 | // aten::slow_conv_dilated2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, SymInt[2] padding=0, int[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!) |
| 14342 | at::Tensor & slow_conv_dilated2d_out::call(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::SymIntArrayRef padding, at::IntArrayRef dilation, at::Tensor & out) { |
| 14343 | |
| 14344 | static auto op = create_slow_conv_dilated2d_out_typed_handle(); |
| 14345 | return op.call(self, weight, kernel_size, bias, stride, padding, dilation, out); |
| 14346 | } |
| 14347 | |
| 14348 | // aten::slow_conv_dilated2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, SymInt[2] padding=0, int[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!) |
| 14349 | at::Tensor & slow_conv_dilated2d_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::SymIntArrayRef padding, at::IntArrayRef dilation, at::Tensor & out) { |
| 14350 | |
| 14351 | static auto op = create_slow_conv_dilated2d_out_typed_handle(); |
| 14352 | return op.redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, dilation, out); |
| 14353 | } |
| 14354 | |
| 14355 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_test_optional_filled_intlist_out, name, "aten::_test_optional_filled_intlist") |
| 14356 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_test_optional_filled_intlist_out, overload_name, "out") |
| 14357 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_test_optional_filled_intlist_out, schema_str, "_test_optional_filled_intlist.out(Tensor values, int[2]? addends, *, Tensor(a!) out) -> Tensor(a!)") |
| 14358 | |
| 14359 | // aten::_test_optional_filled_intlist.out(Tensor values, int[2]? addends, *, Tensor(a!) out) -> Tensor(a!) |
| 14360 | static C10_NOINLINE c10::TypedOperatorHandle<_test_optional_filled_intlist_out::schema> create__test_optional_filled_intlist_out_typed_handle() { |
| 14361 | return c10::Dispatcher::singleton() |
| 14362 | .findSchemaOrThrow(_test_optional_filled_intlist_out::name, _test_optional_filled_intlist_out::overload_name) |
| 14363 | .typed<_test_optional_filled_intlist_out::schema>(); |
| 14364 | } |
| 14365 | |
| 14366 | // aten::_test_optional_filled_intlist.out(Tensor values, int[2]? addends, *, Tensor(a!) out) -> Tensor(a!) |
| 14367 | at::Tensor & _test_optional_filled_intlist_out::call(const at::Tensor & values, at::OptionalIntArrayRef addends, at::Tensor & out) { |
| 14368 | |
| 14369 | static auto op = create__test_optional_filled_intlist_out_typed_handle(); |
| 14370 | return op.call(values, addends, out); |
| 14371 | } |
| 14372 | |
| 14373 | // aten::_test_optional_filled_intlist.out(Tensor values, int[2]? addends, *, Tensor(a!) out) -> Tensor(a!) |
| 14374 | at::Tensor & _test_optional_filled_intlist_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & values, at::OptionalIntArrayRef addends, at::Tensor & out) { |
| 14375 | |
| 14376 | static auto op = create__test_optional_filled_intlist_out_typed_handle(); |
| 14377 | return op.redispatch(dispatchKeySet, values, addends, out); |
| 14378 | } |
| 14379 | |
| 14380 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_test_autograd_multiple_dispatch_view_copy_out, name, "aten::_test_autograd_multiple_dispatch_view_copy") |
| 14381 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_test_autograd_multiple_dispatch_view_copy_out, overload_name, "out") |
| 14382 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_test_autograd_multiple_dispatch_view_copy_out, schema_str, "_test_autograd_multiple_dispatch_view_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 14383 | |
| 14384 | // aten::_test_autograd_multiple_dispatch_view_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 14385 | static C10_NOINLINE c10::TypedOperatorHandle<_test_autograd_multiple_dispatch_view_copy_out::schema> create__test_autograd_multiple_dispatch_view_copy_out_typed_handle() { |
| 14386 | return c10::Dispatcher::singleton() |
| 14387 | .findSchemaOrThrow(_test_autograd_multiple_dispatch_view_copy_out::name, _test_autograd_multiple_dispatch_view_copy_out::overload_name) |
| 14388 | .typed<_test_autograd_multiple_dispatch_view_copy_out::schema>(); |
| 14389 | } |
| 14390 | |
| 14391 | // aten::_test_autograd_multiple_dispatch_view_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 14392 | at::Tensor & _test_autograd_multiple_dispatch_view_copy_out::call(const at::Tensor & self, at::Tensor & out) { |
| 14393 | |
| 14394 | static auto op = create__test_autograd_multiple_dispatch_view_copy_out_typed_handle(); |
| 14395 | return op.call(self, out); |
| 14396 | } |
| 14397 | |
| 14398 | // aten::_test_autograd_multiple_dispatch_view_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 14399 | at::Tensor & _test_autograd_multiple_dispatch_view_copy_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 14400 | |
| 14401 | static auto op = create__test_autograd_multiple_dispatch_view_copy_out_typed_handle(); |
| 14402 | return op.redispatch(dispatchKeySet, self, out); |
| 14403 | } |
| 14404 | |
| 14405 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fw_primal_copy_out, name, "aten::_fw_primal_copy") |
| 14406 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fw_primal_copy_out, overload_name, "out") |
| 14407 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fw_primal_copy_out, schema_str, "_fw_primal_copy.out(Tensor self, int level, *, Tensor(a!) out) -> Tensor(a!)") |
| 14408 | |
| 14409 | // aten::_fw_primal_copy.out(Tensor self, int level, *, Tensor(a!) out) -> Tensor(a!) |
| 14410 | static C10_NOINLINE c10::TypedOperatorHandle<_fw_primal_copy_out::schema> create__fw_primal_copy_out_typed_handle() { |
| 14411 | return c10::Dispatcher::singleton() |
| 14412 | .findSchemaOrThrow(_fw_primal_copy_out::name, _fw_primal_copy_out::overload_name) |
| 14413 | .typed<_fw_primal_copy_out::schema>(); |
| 14414 | } |
| 14415 | |
| 14416 | // aten::_fw_primal_copy.out(Tensor self, int level, *, Tensor(a!) out) -> Tensor(a!) |
| 14417 | at::Tensor & _fw_primal_copy_out::call(const at::Tensor & self, int64_t level, at::Tensor & out) { |
| 14418 | |
| 14419 | static auto op = create__fw_primal_copy_out_typed_handle(); |
| 14420 | return op.call(self, level, out); |
| 14421 | } |
| 14422 | |
| 14423 | // aten::_fw_primal_copy.out(Tensor self, int level, *, Tensor(a!) out) -> Tensor(a!) |
| 14424 | at::Tensor & _fw_primal_copy_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t level, at::Tensor & out) { |
| 14425 | |
| 14426 | static auto op = create__fw_primal_copy_out_typed_handle(); |
| 14427 | return op.redispatch(dispatchKeySet, self, level, out); |
| 14428 | } |
| 14429 | |
| 14430 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(view_as_real_copy_out, name, "aten::view_as_real_copy") |
| 14431 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(view_as_real_copy_out, overload_name, "out") |
| 14432 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(view_as_real_copy_out, schema_str, "view_as_real_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 14433 | |
| 14434 | // aten::view_as_real_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 14435 | static C10_NOINLINE c10::TypedOperatorHandle<view_as_real_copy_out::schema> create_view_as_real_copy_out_typed_handle() { |
| 14436 | return c10::Dispatcher::singleton() |
| 14437 | .findSchemaOrThrow(view_as_real_copy_out::name, view_as_real_copy_out::overload_name) |
| 14438 | .typed<view_as_real_copy_out::schema>(); |
| 14439 | } |
| 14440 | |
| 14441 | // aten::view_as_real_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 14442 | at::Tensor & view_as_real_copy_out::call(const at::Tensor & self, at::Tensor & out) { |
| 14443 | |
| 14444 | static auto op = create_view_as_real_copy_out_typed_handle(); |
| 14445 | return op.call(self, out); |
| 14446 | } |
| 14447 | |
| 14448 | // aten::view_as_real_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 14449 | at::Tensor & view_as_real_copy_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 14450 | |
| 14451 | static auto op = create_view_as_real_copy_out_typed_handle(); |
| 14452 | return op.redispatch(dispatchKeySet, self, out); |
| 14453 | } |
| 14454 | |
| 14455 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(as_strided_copy_out, name, "aten::as_strided_copy") |
| 14456 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(as_strided_copy_out, overload_name, "out") |
| 14457 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(as_strided_copy_out, schema_str, "as_strided_copy.out(Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 14458 | |
| 14459 | // aten::as_strided_copy.out(Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None, *, Tensor(a!) out) -> Tensor(a!) |
| 14460 | static C10_NOINLINE c10::TypedOperatorHandle<as_strided_copy_out::schema> create_as_strided_copy_out_typed_handle() { |
| 14461 | return c10::Dispatcher::singleton() |
| 14462 | .findSchemaOrThrow(as_strided_copy_out::name, as_strided_copy_out::overload_name) |
| 14463 | .typed<as_strided_copy_out::schema>(); |
| 14464 | } |
| 14465 | |
| 14466 | // aten::as_strided_copy.out(Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None, *, Tensor(a!) out) -> Tensor(a!) |
| 14467 | at::Tensor & as_strided_copy_out::call(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional<c10::SymInt> storage_offset, at::Tensor & out) { |
| 14468 | |
| 14469 | static auto op = create_as_strided_copy_out_typed_handle(); |
| 14470 | return op.call(self, size, stride, storage_offset, out); |
| 14471 | } |
| 14472 | |
| 14473 | // aten::as_strided_copy.out(Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None, *, Tensor(a!) out) -> Tensor(a!) |
| 14474 | at::Tensor & as_strided_copy_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional<c10::SymInt> storage_offset, at::Tensor & out) { |
| 14475 | |
| 14476 | static auto op = create_as_strided_copy_out_typed_handle(); |
| 14477 | return op.redispatch(dispatchKeySet, self, size, stride, storage_offset, out); |
| 14478 | } |
| 14479 | |
| 14480 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_reshape_alias_copy_out, name, "aten::_reshape_alias_copy") |
| 14481 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_reshape_alias_copy_out, overload_name, "out") |
| 14482 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_reshape_alias_copy_out, schema_str, "_reshape_alias_copy.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!)") |
| 14483 | |
| 14484 | // aten::_reshape_alias_copy.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!) |
| 14485 | static C10_NOINLINE c10::TypedOperatorHandle<_reshape_alias_copy_out::schema> create__reshape_alias_copy_out_typed_handle() { |
| 14486 | return c10::Dispatcher::singleton() |
| 14487 | .findSchemaOrThrow(_reshape_alias_copy_out::name, _reshape_alias_copy_out::overload_name) |
| 14488 | .typed<_reshape_alias_copy_out::schema>(); |
| 14489 | } |
| 14490 | |
| 14491 | // aten::_reshape_alias_copy.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!) |
| 14492 | at::Tensor & _reshape_alias_copy_out::call(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::Tensor & out) { |
| 14493 | |
| 14494 | static auto op = create__reshape_alias_copy_out_typed_handle(); |
| 14495 | return op.call(self, size, stride, out); |
| 14496 | } |
| 14497 | |
| 14498 | // aten::_reshape_alias_copy.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!) |
| 14499 | at::Tensor & _reshape_alias_copy_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::Tensor & out) { |
| 14500 | |
| 14501 | static auto op = create__reshape_alias_copy_out_typed_handle(); |
| 14502 | return op.redispatch(dispatchKeySet, self, size, stride, out); |
| 14503 | } |
| 14504 | |
| 14505 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(squeeze_copy_out, name, "aten::squeeze_copy") |
| 14506 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(squeeze_copy_out, overload_name, "out") |
| 14507 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(squeeze_copy_out, schema_str, "squeeze_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 14508 | |
| 14509 | // aten::squeeze_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 14510 | static C10_NOINLINE c10::TypedOperatorHandle<squeeze_copy_out::schema> create_squeeze_copy_out_typed_handle() { |
| 14511 | return c10::Dispatcher::singleton() |
| 14512 | .findSchemaOrThrow(squeeze_copy_out::name, squeeze_copy_out::overload_name) |
| 14513 | .typed<squeeze_copy_out::schema>(); |
| 14514 | } |
| 14515 | |
| 14516 | // aten::squeeze_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 14517 | at::Tensor & squeeze_copy_out::call(const at::Tensor & self, at::Tensor & out) { |
| 14518 | |
| 14519 | static auto op = create_squeeze_copy_out_typed_handle(); |
| 14520 | return op.call(self, out); |
| 14521 | } |
| 14522 | |
| 14523 | // aten::squeeze_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 14524 | at::Tensor & squeeze_copy_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 14525 | |
| 14526 | static auto op = create_squeeze_copy_out_typed_handle(); |
| 14527 | return op.redispatch(dispatchKeySet, self, out); |
| 14528 | } |
| 14529 | |
| 14530 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(squeeze_copy_dim_out, name, "aten::squeeze_copy") |
| 14531 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(squeeze_copy_dim_out, overload_name, "dim_out") |
| 14532 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(squeeze_copy_dim_out, schema_str, "squeeze_copy.dim_out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)") |
| 14533 | |
| 14534 | // aten::squeeze_copy.dim_out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!) |
| 14535 | static C10_NOINLINE c10::TypedOperatorHandle<squeeze_copy_dim_out::schema> create_squeeze_copy_dim_out_typed_handle() { |
| 14536 | return c10::Dispatcher::singleton() |
| 14537 | .findSchemaOrThrow(squeeze_copy_dim_out::name, squeeze_copy_dim_out::overload_name) |
| 14538 | .typed<squeeze_copy_dim_out::schema>(); |
| 14539 | } |
| 14540 | |
| 14541 | // aten::squeeze_copy.dim_out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!) |
| 14542 | at::Tensor & squeeze_copy_dim_out::call(const at::Tensor & self, int64_t dim, at::Tensor & out) { |
| 14543 | |
| 14544 | static auto op = create_squeeze_copy_dim_out_typed_handle(); |
| 14545 | return op.call(self, dim, out); |
| 14546 | } |
| 14547 | |
| 14548 | // aten::squeeze_copy.dim_out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!) |
| 14549 | at::Tensor & squeeze_copy_dim_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, at::Tensor & out) { |
| 14550 | |
| 14551 | static auto op = create_squeeze_copy_dim_out_typed_handle(); |
| 14552 | return op.redispatch(dispatchKeySet, self, dim, out); |
| 14553 | } |
| 14554 | |
| 14555 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(squeeze_copy_dims_out, name, "aten::squeeze_copy") |
| 14556 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(squeeze_copy_dims_out, overload_name, "dims_out") |
| 14557 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(squeeze_copy_dims_out, schema_str, "squeeze_copy.dims_out(Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!)") |
| 14558 | |
| 14559 | // aten::squeeze_copy.dims_out(Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!) |
| 14560 | static C10_NOINLINE c10::TypedOperatorHandle<squeeze_copy_dims_out::schema> create_squeeze_copy_dims_out_typed_handle() { |
| 14561 | return c10::Dispatcher::singleton() |
| 14562 | .findSchemaOrThrow(squeeze_copy_dims_out::name, squeeze_copy_dims_out::overload_name) |
| 14563 | .typed<squeeze_copy_dims_out::schema>(); |
| 14564 | } |
| 14565 | |
| 14566 | // aten::squeeze_copy.dims_out(Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!) |
| 14567 | at::Tensor & squeeze_copy_dims_out::call(const at::Tensor & self, at::IntArrayRef dim, at::Tensor & out) { |
| 14568 | |
| 14569 | static auto op = create_squeeze_copy_dims_out_typed_handle(); |
| 14570 | return op.call(self, dim, out); |
| 14571 | } |
| 14572 | |
| 14573 | // aten::squeeze_copy.dims_out(Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!) |
| 14574 | at::Tensor & squeeze_copy_dims_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, at::Tensor & out) { |
| 14575 | |
| 14576 | static auto op = create_squeeze_copy_dims_out_typed_handle(); |
| 14577 | return op.redispatch(dispatchKeySet, self, dim, out); |
| 14578 | } |
| 14579 | |
| 14580 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(indices_copy_out, name, "aten::indices_copy") |
| 14581 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(indices_copy_out, overload_name, "out") |
| 14582 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(indices_copy_out, schema_str, "indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 14583 | |
| 14584 | // aten::indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 14585 | static C10_NOINLINE c10::TypedOperatorHandle<indices_copy_out::schema> create_indices_copy_out_typed_handle() { |
| 14586 | return c10::Dispatcher::singleton() |
| 14587 | .findSchemaOrThrow(indices_copy_out::name, indices_copy_out::overload_name) |
| 14588 | .typed<indices_copy_out::schema>(); |
| 14589 | } |
| 14590 | |
| 14591 | // aten::indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 14592 | at::Tensor & indices_copy_out::call(const at::Tensor & self, at::Tensor & out) { |
| 14593 | |
| 14594 | static auto op = create_indices_copy_out_typed_handle(); |
| 14595 | return op.call(self, out); |
| 14596 | } |
| 14597 | |
| 14598 | // aten::indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 14599 | at::Tensor & indices_copy_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 14600 | |
| 14601 | static auto op = create_indices_copy_out_typed_handle(); |
| 14602 | return op.redispatch(dispatchKeySet, self, out); |
| 14603 | } |
| 14604 | |
| 14605 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ccol_indices_copy_out, name, "aten::ccol_indices_copy") |
| 14606 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ccol_indices_copy_out, overload_name, "out") |
| 14607 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ccol_indices_copy_out, schema_str, "ccol_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 14608 | |
| 14609 | // aten::ccol_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 14610 | static C10_NOINLINE c10::TypedOperatorHandle<ccol_indices_copy_out::schema> create_ccol_indices_copy_out_typed_handle() { |
| 14611 | return c10::Dispatcher::singleton() |
| 14612 | .findSchemaOrThrow(ccol_indices_copy_out::name, ccol_indices_copy_out::overload_name) |
| 14613 | .typed<ccol_indices_copy_out::schema>(); |
| 14614 | } |
| 14615 | |
| 14616 | // aten::ccol_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 14617 | at::Tensor & ccol_indices_copy_out::call(const at::Tensor & self, at::Tensor & out) { |
| 14618 | |
| 14619 | static auto op = create_ccol_indices_copy_out_typed_handle(); |
| 14620 | return op.call(self, out); |
| 14621 | } |
| 14622 | |
| 14623 | // aten::ccol_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 14624 | at::Tensor & ccol_indices_copy_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 14625 | |
| 14626 | static auto op = create_ccol_indices_copy_out_typed_handle(); |
| 14627 | return op.redispatch(dispatchKeySet, self, out); |
| 14628 | } |
| 14629 | |
| 14630 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_transformer_decoder_only_layer_fwd_out, name, "aten::_transformer_decoder_only_layer_fwd") |
| 14631 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_transformer_decoder_only_layer_fwd_out, overload_name, "out") |
| 14632 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_transformer_decoder_only_layer_fwd_out, schema_str, "_transformer_decoder_only_layer_fwd.out(Tensor src, int embed_dim, int num_heads, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, bool use_gelu, bool norm_first, float eps, Tensor norm_weight_1, Tensor norm_bias_1, Tensor norm_weight_2, Tensor norm_bias_2, Tensor ffn_weight_1, Tensor ffn_bias_1, Tensor ffn_weight_2, Tensor ffn_bias_2, Tensor? mask=None, Tensor? incr_key=None, Tensor? incr_value=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))") |
| 14633 | |
| 14634 | // aten::_transformer_decoder_only_layer_fwd.out(Tensor src, int embed_dim, int num_heads, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, bool use_gelu, bool norm_first, float eps, Tensor norm_weight_1, Tensor norm_bias_1, Tensor norm_weight_2, Tensor norm_bias_2, Tensor ffn_weight_1, Tensor ffn_bias_1, Tensor ffn_weight_2, Tensor ffn_bias_2, Tensor? mask=None, Tensor? incr_key=None, Tensor? incr_value=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 14635 | static C10_NOINLINE c10::TypedOperatorHandle<_transformer_decoder_only_layer_fwd_out::schema> create__transformer_decoder_only_layer_fwd_out_typed_handle() { |
| 14636 | return c10::Dispatcher::singleton() |
| 14637 | .findSchemaOrThrow(_transformer_decoder_only_layer_fwd_out::name, _transformer_decoder_only_layer_fwd_out::overload_name) |
| 14638 | .typed<_transformer_decoder_only_layer_fwd_out::schema>(); |
| 14639 | } |
| 14640 | |
| 14641 | // aten::_transformer_decoder_only_layer_fwd.out(Tensor src, int embed_dim, int num_heads, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, bool use_gelu, bool norm_first, float eps, Tensor norm_weight_1, Tensor norm_bias_1, Tensor norm_weight_2, Tensor norm_bias_2, Tensor ffn_weight_1, Tensor ffn_bias_1, Tensor ffn_weight_2, Tensor ffn_bias_2, Tensor? mask=None, Tensor? incr_key=None, Tensor? incr_value=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 14642 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _transformer_decoder_only_layer_fwd_out::call(const at::Tensor & src, int64_t embed_dim, int64_t num_heads, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, bool use_gelu, bool norm_first, double eps, const at::Tensor & norm_weight_1, const at::Tensor & norm_bias_1, const at::Tensor & norm_weight_2, const at::Tensor & norm_bias_2, const at::Tensor & ffn_weight_1, const at::Tensor & ffn_bias_1, const at::Tensor & ffn_weight_2, const at::Tensor & ffn_bias_2, const c10::optional<at::Tensor> & mask, const c10::optional<at::Tensor> & incr_key, const c10::optional<at::Tensor> & incr_value, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 14643 | |
| 14644 | static auto op = create__transformer_decoder_only_layer_fwd_out_typed_handle(); |
| 14645 | return op.call(src, embed_dim, num_heads, qkv_weight, qkv_bias, proj_weight, proj_bias, use_gelu, norm_first, eps, norm_weight_1, norm_bias_1, norm_weight_2, norm_bias_2, ffn_weight_1, ffn_bias_1, ffn_weight_2, ffn_bias_2, mask, incr_key, incr_value, out0, out1, out2); |
| 14646 | } |
| 14647 | |
| 14648 | // aten::_transformer_decoder_only_layer_fwd.out(Tensor src, int embed_dim, int num_heads, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, bool use_gelu, bool norm_first, float eps, Tensor norm_weight_1, Tensor norm_bias_1, Tensor norm_weight_2, Tensor norm_bias_2, Tensor ffn_weight_1, Tensor ffn_bias_1, Tensor ffn_weight_2, Tensor ffn_bias_2, Tensor? mask=None, Tensor? incr_key=None, Tensor? incr_value=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 14649 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _transformer_decoder_only_layer_fwd_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & src, int64_t embed_dim, int64_t num_heads, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, bool use_gelu, bool norm_first, double eps, const at::Tensor & norm_weight_1, const at::Tensor & norm_bias_1, const at::Tensor & norm_weight_2, const at::Tensor & norm_bias_2, const at::Tensor & ffn_weight_1, const at::Tensor & ffn_bias_1, const at::Tensor & ffn_weight_2, const at::Tensor & ffn_bias_2, const c10::optional<at::Tensor> & mask, const c10::optional<at::Tensor> & incr_key, const c10::optional<at::Tensor> & incr_value, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 14650 | |
| 14651 | static auto op = create__transformer_decoder_only_layer_fwd_out_typed_handle(); |
| 14652 | return op.redispatch(dispatchKeySet, src, embed_dim, num_heads, qkv_weight, qkv_bias, proj_weight, proj_bias, use_gelu, norm_first, eps, norm_weight_1, norm_bias_1, norm_weight_2, norm_bias_2, ffn_weight_1, ffn_bias_1, ffn_weight_2, ffn_bias_2, mask, incr_key, incr_value, out0, out1, out2); |
| 14653 | } |
| 14654 | |
| 14655 | }} // namespace at::_ops |
| 14656 |
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