| 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_Double.h> |
| 11 | #include <ATen/ops/_cast_Float.h> |
| 12 | #include <ATen/ops/_cast_Half.h> |
| 13 | #include <ATen/ops/_version.h> |
| 14 | #include <ATen/ops/_make_dual.h> |
| 15 | #include <ATen/ops/align_as.h> |
| 16 | #include <ATen/ops/_assert_tensor_metadata.h> |
| 17 | #include <ATen/ops/refine_names.h> |
| 18 | #include <ATen/ops/_use_cudnn_rnn_flatten_weight.h> |
| 19 | #include <ATen/ops/_cudnn_rnn_flatten_weight.h> |
| 20 | #include <ATen/ops/_sobol_engine_ff.h> |
| 21 | #include <ATen/ops/_sobol_engine_scramble.h> |
| 22 | #include <ATen/ops/feature_alpha_dropout.h> |
| 23 | #include <ATen/ops/feature_alpha_dropout.h> |
| 24 | #include <ATen/ops/abs.h> |
| 25 | #include <ATen/ops/abs.h> |
| 26 | #include <ATen/ops/abs.h> |
| 27 | #include <ATen/ops/imag.h> |
| 28 | #include <ATen/ops/resolve_conj.h> |
| 29 | #include <ATen/ops/resolve_neg.h> |
| 30 | #include <ATen/ops/adaptive_max_pool1d.h> |
| 31 | #include <ATen/ops/addmv.h> |
| 32 | #include <ATen/ops/addmv.h> |
| 33 | #include <ATen/ops/addmv.h> |
| 34 | #include <ATen/ops/addr.h> |
| 35 | #include <ATen/ops/addr.h> |
| 36 | #include <ATen/ops/addr.h> |
| 37 | #include <ATen/ops/affine_grid_generator_backward.h> |
| 38 | #include <ATen/ops/argmin.h> |
| 39 | #include <ATen/ops/argmin.h> |
| 40 | #include <ATen/ops/atan.h> |
| 41 | #include <ATen/ops/atan.h> |
| 42 | #include <ATen/ops/atan.h> |
| 43 | #include <ATen/ops/arctan.h> |
| 44 | #include <ATen/ops/arctan.h> |
| 45 | #include <ATen/ops/arctan.h> |
| 46 | #include <ATen/ops/quantized_batch_norm.h> |
| 47 | #include <ATen/ops/binary_cross_entropy_backward.h> |
| 48 | #include <ATen/ops/binary_cross_entropy_backward.h> |
| 49 | #include <ATen/ops/bitwise_not.h> |
| 50 | #include <ATen/ops/bitwise_not.h> |
| 51 | #include <ATen/ops/bitwise_not.h> |
| 52 | #include <ATen/ops/logical_not.h> |
| 53 | #include <ATen/ops/logical_not.h> |
| 54 | #include <ATen/ops/logical_not.h> |
| 55 | #include <ATen/ops/concatenate.h> |
| 56 | #include <ATen/ops/concatenate.h> |
| 57 | #include <ATen/ops/concatenate.h> |
| 58 | #include <ATen/ops/concatenate.h> |
| 59 | #include <ATen/ops/ceil.h> |
| 60 | #include <ATen/ops/ceil.h> |
| 61 | #include <ATen/ops/ceil.h> |
| 62 | #include <ATen/ops/conv_tbc.h> |
| 63 | #include <ATen/ops/cosh.h> |
| 64 | #include <ATen/ops/cosh.h> |
| 65 | #include <ATen/ops/cosh.h> |
| 66 | #include <ATen/ops/cosine_embedding_loss.h> |
| 67 | #include <ATen/ops/cudnn_affine_grid_generator_backward.h> |
| 68 | #include <ATen/ops/cudnn_grid_sampler.h> |
| 69 | #include <ATen/ops/cummin.h> |
| 70 | #include <ATen/ops/cummin.h> |
| 71 | #include <ATen/ops/cummin.h> |
| 72 | #include <ATen/ops/cummin.h> |
| 73 | #include <ATen/ops/_cummin_helper.h> |
| 74 | #include <ATen/ops/div.h> |
| 75 | #include <ATen/ops/div.h> |
| 76 | #include <ATen/ops/div.h> |
| 77 | #include <ATen/ops/div.h> |
| 78 | #include <ATen/ops/div.h> |
| 79 | #include <ATen/ops/div.h> |
| 80 | #include <ATen/ops/div.h> |
| 81 | #include <ATen/ops/div.h> |
| 82 | #include <ATen/ops/div.h> |
| 83 | #include <ATen/ops/div.h> |
| 84 | #include <ATen/ops/_embedding_bag_forward_only.h> |
| 85 | #include <ATen/ops/embedding_bag.h> |
| 86 | #include <ATen/ops/embedding_bag.h> |
| 87 | #include <ATen/ops/new_zeros.h> |
| 88 | #include <ATen/ops/erf.h> |
| 89 | #include <ATen/ops/erf.h> |
| 90 | #include <ATen/ops/erf.h> |
| 91 | #include <ATen/ops/grid_sampler.h> |
| 92 | #include <ATen/ops/_grid_sampler_2d_cpu_fallback.h> |
| 93 | #include <ATen/ops/grid_sampler_3d.h> |
| 94 | #include <ATen/ops/hann_window.h> |
| 95 | #include <ATen/ops/hann_window.h> |
| 96 | #include <ATen/ops/hamming_window.h> |
| 97 | #include <ATen/ops/hamming_window.h> |
| 98 | #include <ATen/ops/hamming_window.h> |
| 99 | #include <ATen/ops/hamming_window.h> |
| 100 | #include <ATen/ops/native_group_norm_backward.h> |
| 101 | #include <ATen/ops/_fft_c2c.h> |
| 102 | #include <ATen/ops/_fft_c2c.h> |
| 103 | #include <ATen/ops/_validate_compressed_sparse_indices.h> |
| 104 | #include <ATen/ops/_cufft_get_plan_cache_size.h> |
| 105 | #include <ATen/ops/_cufft_get_plan_cache_max_size.h> |
| 106 | #include <ATen/ops/index.h> |
| 107 | #include <ATen/ops/index.h> |
| 108 | #include <ATen/ops/isnan.h> |
| 109 | #include <ATen/ops/kthvalue.h> |
| 110 | #include <ATen/ops/kthvalue.h> |
| 111 | #include <ATen/ops/kthvalue.h> |
| 112 | #include <ATen/ops/kthvalue.h> |
| 113 | #include <ATen/ops/native_layer_norm.h> |
| 114 | #include <ATen/ops/nan_to_num.h> |
| 115 | #include <ATen/ops/nan_to_num.h> |
| 116 | #include <ATen/ops/nan_to_num.h> |
| 117 | #include <ATen/ops/fbgemm_linear_int8_weight_fp32_activation.h> |
| 118 | #include <ATen/ops/fbgemm_linear_int8_weight.h> |
| 119 | #include <ATen/ops/fbgemm_linear_fp16_weight_fp32_activation.h> |
| 120 | #include <ATen/ops/xlogy.h> |
| 121 | #include <ATen/ops/xlogy.h> |
| 122 | #include <ATen/ops/xlogy.h> |
| 123 | #include <ATen/ops/xlogy.h> |
| 124 | #include <ATen/ops/xlogy.h> |
| 125 | #include <ATen/ops/xlogy.h> |
| 126 | #include <ATen/ops/xlogy.h> |
| 127 | #include <ATen/ops/xlogy.h> |
| 128 | #include <ATen/ops/_log_softmax_backward_data.h> |
| 129 | #include <ATen/ops/_log_softmax_backward_data.h> |
| 130 | #include <ATen/ops/logcumsumexp.h> |
| 131 | #include <ATen/ops/logcumsumexp.h> |
| 132 | #include <ATen/ops/logcumsumexp.h> |
| 133 | #include <ATen/ops/logcumsumexp.h> |
| 134 | #include <ATen/ops/matrix_exp_backward.h> |
| 135 | #include <ATen/ops/amax.h> |
| 136 | #include <ATen/ops/amax.h> |
| 137 | #include <ATen/ops/_mps_max_pool2d.h> |
| 138 | #include <ATen/ops/mkldnn_max_pool2d.h> |
| 139 | #include <ATen/ops/quantized_max_pool2d.h> |
| 140 | #include <ATen/ops/amin.h> |
| 141 | #include <ATen/ops/amin.h> |
| 142 | #include <ATen/ops/_mps_convolution.h> |
| 143 | #include <ATen/ops/mkldnn_rnn_layer_backward.h> |
| 144 | #include <ATen/ops/miopen_depthwise_convolution.h> |
| 145 | #include <ATen/ops/native_batch_norm.h> |
| 146 | #include <ATen/ops/native_batch_norm.h> |
| 147 | #include <ATen/ops/batch_norm_stats.h> |
| 148 | #include <ATen/ops/batch_norm_gather_stats.h> |
| 149 | #include <ATen/ops/native_batch_norm_backward.h> |
| 150 | #include <ATen/ops/batch_norm_backward_reduce.h> |
| 151 | #include <ATen/ops/is_vulkan_available.h> |
| 152 | #include <ATen/ops/_nnpack_spatial_convolution.h> |
| 153 | #include <ATen/ops/ones.h> |
| 154 | #include <ATen/ops/ones.h> |
| 155 | #include <ATen/ops/ones.h> |
| 156 | #include <ATen/ops/_cdist_forward.h> |
| 157 | #include <ATen/ops/cosine_similarity.h> |
| 158 | #include <ATen/ops/movedim.h> |
| 159 | #include <ATen/ops/movedim.h> |
| 160 | #include <ATen/ops/numpy_T.h> |
| 161 | #include <ATen/ops/mH.h> |
| 162 | #include <ATen/ops/rand_like.h> |
| 163 | #include <ATen/ops/randint_like.h> |
| 164 | #include <ATen/ops/randint_like.h> |
| 165 | #include <ATen/ops/round.h> |
| 166 | #include <ATen/ops/round.h> |
| 167 | #include <ATen/ops/round.h> |
| 168 | #include <ATen/ops/round.h> |
| 169 | #include <ATen/ops/round.h> |
| 170 | #include <ATen/ops/round.h> |
| 171 | #include <ATen/ops/gelu.h> |
| 172 | #include <ATen/ops/gelu.h> |
| 173 | #include <ATen/ops/gelu.h> |
| 174 | #include <ATen/ops/hardshrink.h> |
| 175 | #include <ATen/ops/hardshrink.h> |
| 176 | #include <ATen/ops/select_backward.h> |
| 177 | #include <ATen/ops/mish.h> |
| 178 | #include <ATen/ops/mish.h> |
| 179 | #include <ATen/ops/mish.h> |
| 180 | #include <ATen/ops/sigmoid.h> |
| 181 | #include <ATen/ops/sigmoid.h> |
| 182 | #include <ATen/ops/sigmoid.h> |
| 183 | #include <ATen/ops/detach.h> |
| 184 | #include <ATen/ops/detach.h> |
| 185 | #include <ATen/ops/size.h> |
| 186 | #include <ATen/ops/size.h> |
| 187 | #include <ATen/ops/slice_scatter.h> |
| 188 | #include <ATen/ops/_softmax_backward_data.h> |
| 189 | #include <ATen/ops/_softmax_backward_data.h> |
| 190 | #include <ATen/ops/split_with_sizes.h> |
| 191 | #include <ATen/ops/hsplit.h> |
| 192 | #include <ATen/ops/hsplit.h> |
| 193 | #include <ATen/ops/stack.h> |
| 194 | #include <ATen/ops/stack.h> |
| 195 | #include <ATen/ops/_stack.h> |
| 196 | #include <ATen/ops/_stack.h> |
| 197 | #include <ATen/ops/square.h> |
| 198 | #include <ATen/ops/square.h> |
| 199 | #include <ATen/ops/square.h> |
| 200 | #include <ATen/ops/tanh.h> |
| 201 | #include <ATen/ops/tanh.h> |
| 202 | #include <ATen/ops/tanh.h> |
| 203 | #include <ATen/ops/tensordot.h> |
| 204 | #include <ATen/ops/tensordot.h> |
| 205 | #include <ATen/ops/tile.h> |
| 206 | #include <ATen/ops/_mkldnn_transpose.h> |
| 207 | #include <ATen/ops/_mkldnn_transpose.h> |
| 208 | #include <ATen/ops/fliplr.h> |
| 209 | #include <ATen/ops/_nested_from_padded_and_nested_example.h> |
| 210 | #include <ATen/ops/fix.h> |
| 211 | #include <ATen/ops/fix.h> |
| 212 | #include <ATen/ops/fix.h> |
| 213 | #include <ATen/ops/unique_dim.h> |
| 214 | #include <ATen/ops/unique_consecutive.h> |
| 215 | #include <ATen/ops/vander.h> |
| 216 | #include <ATen/ops/view_as.h> |
| 217 | #include <ATen/ops/_dirichlet_grad.h> |
| 218 | #include <ATen/ops/frobenius_norm.h> |
| 219 | #include <ATen/ops/frobenius_norm.h> |
| 220 | #include <ATen/ops/clone.h> |
| 221 | #include <ATen/ops/positive.h> |
| 222 | #include <ATen/ops/resize_as.h> |
| 223 | #include <ATen/ops/resize_as_sparse.h> |
| 224 | #include <ATen/ops/sparse_sampled_addmm.h> |
| 225 | #include <ATen/ops/sparse_sampled_addmm.h> |
| 226 | #include <ATen/ops/sparse_csr_tensor.h> |
| 227 | #include <ATen/ops/sparse_csr_tensor.h> |
| 228 | #include <ATen/ops/_sparse_bsc_tensor_unsafe.h> |
| 229 | #include <ATen/ops/dense_dim.h> |
| 230 | #include <ATen/ops/_dimV.h> |
| 231 | #include <ATen/ops/coalesce.h> |
| 232 | #include <ATen/ops/_indices.h> |
| 233 | #include <ATen/ops/to_sparse_csc.h> |
| 234 | #include <ATen/ops/mkldnn_reorder_conv2d_weight.h> |
| 235 | #include <ATen/ops/quantize_per_channel.h> |
| 236 | #include <ATen/ops/dequantize.h> |
| 237 | #include <ATen/ops/dequantize.h> |
| 238 | #include <ATen/ops/q_per_channel_zero_points.h> |
| 239 | #include <ATen/ops/fake_quantize_per_tensor_affine.h> |
| 240 | #include <ATen/ops/fake_quantize_per_tensor_affine.h> |
| 241 | #include <ATen/ops/_fake_quantize_learnable_per_channel_affine.h> |
| 242 | #include <ATen/ops/_autocast_to_full_precision.h> |
| 243 | #include <ATen/ops/to.h> |
| 244 | #include <ATen/ops/to.h> |
| 245 | #include <ATen/ops/to.h> |
| 246 | #include <ATen/ops/to.h> |
| 247 | #include <ATen/ops/combinations.h> |
| 248 | #include <ATen/ops/item.h> |
| 249 | #include <ATen/ops/_lstm_mps.h> |
| 250 | #include <ATen/ops/_thnn_fused_lstm_cell.h> |
| 251 | #include <ATen/ops/lstm.h> |
| 252 | #include <ATen/ops/lstm.h> |
| 253 | #include <ATen/ops/gru.h> |
| 254 | #include <ATen/ops/gru.h> |
| 255 | #include <ATen/ops/rnn_tanh.h> |
| 256 | #include <ATen/ops/rnn_tanh.h> |
| 257 | #include <ATen/ops/rnn_relu_cell.h> |
| 258 | #include <ATen/ops/_pad_packed_sequence.h> |
| 259 | #include <ATen/ops/lift_fresh_copy.h> |
| 260 | #include <ATen/ops/index_reduce.h> |
| 261 | #include <ATen/ops/index_reduce.h> |
| 262 | #include <ATen/ops/index_reduce.h> |
| 263 | #include <ATen/ops/index_fill.h> |
| 264 | #include <ATen/ops/index_fill.h> |
| 265 | #include <ATen/ops/index_fill.h> |
| 266 | #include <ATen/ops/index_fill.h> |
| 267 | #include <ATen/ops/index_fill.h> |
| 268 | #include <ATen/ops/index_fill.h> |
| 269 | #include <ATen/ops/index_fill.h> |
| 270 | #include <ATen/ops/index_fill.h> |
| 271 | #include <ATen/ops/scatter_add.h> |
| 272 | #include <ATen/ops/scatter_add.h> |
| 273 | #include <ATen/ops/scatter_add.h> |
| 274 | #include <ATen/ops/scatter_add.h> |
| 275 | #include <ATen/ops/digamma.h> |
| 276 | #include <ATen/ops/random.h> |
| 277 | #include <ATen/ops/random.h> |
| 278 | #include <ATen/ops/random.h> |
| 279 | #include <ATen/ops/cauchy.h> |
| 280 | #include <ATen/ops/log_normal.h> |
| 281 | #include <ATen/ops/cross.h> |
| 282 | #include <ATen/ops/cross.h> |
| 283 | #include <ATen/ops/ne.h> |
| 284 | #include <ATen/ops/ne.h> |
| 285 | #include <ATen/ops/ne.h> |
| 286 | #include <ATen/ops/ne.h> |
| 287 | #include <ATen/ops/ne.h> |
| 288 | #include <ATen/ops/ne.h> |
| 289 | #include <ATen/ops/ge.h> |
| 290 | #include <ATen/ops/ge.h> |
| 291 | #include <ATen/ops/ge.h> |
| 292 | #include <ATen/ops/ge.h> |
| 293 | #include <ATen/ops/ge.h> |
| 294 | #include <ATen/ops/ge.h> |
| 295 | #include <ATen/ops/_gather_sparse_backward.h> |
| 296 | #include <ATen/ops/linalg_vander.h> |
| 297 | #include <ATen/ops/swapaxes.h> |
| 298 | #include <ATen/ops/swapaxes.h> |
| 299 | #include <ATen/ops/cholesky_solve.h> |
| 300 | #include <ATen/ops/cholesky_solve.h> |
| 301 | #include <ATen/ops/qr.h> |
| 302 | #include <ATen/ops/qr.h> |
| 303 | #include <ATen/ops/digamma.h> |
| 304 | #include <ATen/ops/digamma.h> |
| 305 | #include <ATen/ops/polygamma.h> |
| 306 | #include <ATen/ops/polygamma.h> |
| 307 | #include <ATen/ops/polygamma.h> |
| 308 | #include <ATen/ops/histc.h> |
| 309 | #include <ATen/ops/histc.h> |
| 310 | #include <ATen/ops/_histogramdd_bin_edges.h> |
| 311 | #include <ATen/ops/_histogramdd_from_bin_tensors.h> |
| 312 | #include <ATen/ops/nextafter.h> |
| 313 | #include <ATen/ops/nextafter.h> |
| 314 | #include <ATen/ops/nextafter.h> |
| 315 | #include <ATen/ops/maximum.h> |
| 316 | #include <ATen/ops/maximum.h> |
| 317 | #include <ATen/ops/minimum.h> |
| 318 | #include <ATen/ops/minimum.h> |
| 319 | #include <ATen/ops/quantile.h> |
| 320 | #include <ATen/ops/quantile.h> |
| 321 | #include <ATen/ops/quantile.h> |
| 322 | #include <ATen/ops/quantile.h> |
| 323 | #include <ATen/ops/msort.h> |
| 324 | #include <ATen/ops/msort.h> |
| 325 | #include <ATen/ops/argsort.h> |
| 326 | #include <ATen/ops/argsort.h> |
| 327 | #include <ATen/ops/argsort.h> |
| 328 | #include <ATen/ops/topk.h> |
| 329 | #include <ATen/ops/topk.h> |
| 330 | #include <ATen/ops/unfold_backward.h> |
| 331 | #include <ATen/ops/normal.h> |
| 332 | #include <ATen/ops/normal.h> |
| 333 | #include <ATen/ops/normal.h> |
| 334 | #include <ATen/ops/normal.h> |
| 335 | #include <ATen/ops/normal.h> |
| 336 | #include <ATen/ops/normal.h> |
| 337 | #include <ATen/ops/normal.h> |
| 338 | #include <ATen/ops/normal.h> |
| 339 | #include <ATen/ops/normal.h> |
| 340 | #include <ATen/ops/normal.h> |
| 341 | #include <ATen/ops/alias.h> |
| 342 | #include <ATen/ops/_foreach_sub.h> |
| 343 | #include <ATen/ops/_foreach_sub.h> |
| 344 | #include <ATen/ops/_foreach_maximum.h> |
| 345 | #include <ATen/ops/_foreach_maximum.h> |
| 346 | #include <ATen/ops/_foreach_sub.h> |
| 347 | #include <ATen/ops/_foreach_sub.h> |
| 348 | #include <ATen/ops/_foreach_maximum.h> |
| 349 | #include <ATen/ops/_foreach_maximum.h> |
| 350 | #include <ATen/ops/_foreach_sub.h> |
| 351 | #include <ATen/ops/_foreach_sub.h> |
| 352 | #include <ATen/ops/_foreach_maximum.h> |
| 353 | #include <ATen/ops/_foreach_maximum.h> |
| 354 | #include <ATen/ops/_foreach_acos.h> |
| 355 | #include <ATen/ops/_foreach_acos.h> |
| 356 | #include <ATen/ops/_foreach_atan.h> |
| 357 | #include <ATen/ops/_foreach_atan.h> |
| 358 | #include <ATen/ops/_foreach_ceil.h> |
| 359 | #include <ATen/ops/_foreach_ceil.h> |
| 360 | #include <ATen/ops/_foreach_erf.h> |
| 361 | #include <ATen/ops/_foreach_erf.h> |
| 362 | #include <ATen/ops/_foreach_log2.h> |
| 363 | #include <ATen/ops/_foreach_log2.h> |
| 364 | #include <ATen/ops/bucketize.h> |
| 365 | #include <ATen/ops/bucketize.h> |
| 366 | #include <ATen/ops/bucketize.h> |
| 367 | #include <ATen/ops/mse_loss.h> |
| 368 | #include <ATen/ops/mse_loss.h> |
| 369 | #include <ATen/ops/l1_loss.h> |
| 370 | #include <ATen/ops/nll_loss_nd.h> |
| 371 | #include <ATen/ops/nll_loss2d.h> |
| 372 | #include <ATen/ops/nll_loss2d.h> |
| 373 | #include <ATen/ops/nll_loss2d_forward.h> |
| 374 | #include <ATen/ops/nll_loss2d_forward.h> |
| 375 | #include <ATen/ops/nll_loss2d_backward.h> |
| 376 | #include <ATen/ops/nll_loss2d_backward.h> |
| 377 | #include <ATen/ops/soft_margin_loss.h> |
| 378 | #include <ATen/ops/soft_margin_loss.h> |
| 379 | #include <ATen/ops/glu.h> |
| 380 | #include <ATen/ops/glu.h> |
| 381 | #include <ATen/ops/glu_backward_jvp.h> |
| 382 | #include <ATen/ops/hardtanh.h> |
| 383 | #include <ATen/ops/hardtanh.h> |
| 384 | #include <ATen/ops/hardtanh.h> |
| 385 | #include <ATen/ops/hardswish_backward.h> |
| 386 | #include <ATen/ops/leaky_relu.h> |
| 387 | #include <ATen/ops/leaky_relu.h> |
| 388 | #include <ATen/ops/leaky_relu.h> |
| 389 | #include <ATen/ops/log_sigmoid_forward.h> |
| 390 | #include <ATen/ops/log_sigmoid_forward.h> |
| 391 | #include <ATen/ops/log_sigmoid_backward.h> |
| 392 | #include <ATen/ops/log_sigmoid_backward.h> |
| 393 | #include <ATen/ops/softshrink.h> |
| 394 | #include <ATen/ops/softshrink.h> |
| 395 | #include <ATen/ops/adaptive_avg_pool3d_backward.h> |
| 396 | #include <ATen/ops/_adaptive_avg_pool3d_backward.h> |
| 397 | #include <ATen/ops/adaptive_max_pool2d_backward.h> |
| 398 | #include <ATen/ops/adaptive_max_pool2d_backward.h> |
| 399 | #include <ATen/ops/adaptive_max_pool3d_backward.h> |
| 400 | #include <ATen/ops/adaptive_max_pool3d_backward.h> |
| 401 | #include <ATen/ops/fractional_max_pool3d.h> |
| 402 | #include <ATen/ops/fractional_max_pool3d.h> |
| 403 | #include <ATen/ops/reflection_pad3d.h> |
| 404 | #include <ATen/ops/reflection_pad3d.h> |
| 405 | #include <ATen/ops/replication_pad1d.h> |
| 406 | #include <ATen/ops/replication_pad1d.h> |
| 407 | #include <ATen/ops/replication_pad2d.h> |
| 408 | #include <ATen/ops/replication_pad2d.h> |
| 409 | #include <ATen/ops/_pad_circular.h> |
| 410 | #include <ATen/ops/pad.h> |
| 411 | #include <ATen/ops/upsample_nearest1d.h> |
| 412 | #include <ATen/ops/_upsample_nearest_exact1d.h> |
| 413 | #include <ATen/ops/upsample_nearest1d.h> |
| 414 | #include <ATen/ops/_upsample_nearest_exact1d.h> |
| 415 | #include <ATen/ops/upsample_nearest1d.h> |
| 416 | #include <ATen/ops/_upsample_nearest_exact1d.h> |
| 417 | #include <ATen/ops/_conv_depthwise2d.h> |
| 418 | #include <ATen/ops/_conv_depthwise2d.h> |
| 419 | #include <ATen/ops/slow_conv3d.h> |
| 420 | #include <ATen/ops/slow_conv3d.h> |
| 421 | #include <ATen/ops/_remove_batch_dim.h> |
| 422 | #include <ATen/ops/special_log_ndtr.h> |
| 423 | #include <ATen/ops/special_log_ndtr.h> |
| 424 | #include <ATen/ops/special_erf.h> |
| 425 | #include <ATen/ops/special_erf.h> |
| 426 | #include <ATen/ops/special_xlogy.h> |
| 427 | #include <ATen/ops/special_xlogy.h> |
| 428 | #include <ATen/ops/special_xlogy.h> |
| 429 | #include <ATen/ops/special_xlogy.h> |
| 430 | #include <ATen/ops/special_xlogy.h> |
| 431 | #include <ATen/ops/special_xlogy.h> |
| 432 | #include <ATen/ops/special_expit.h> |
| 433 | #include <ATen/ops/special_expit.h> |
| 434 | #include <ATen/ops/special_sinc.h> |
| 435 | #include <ATen/ops/special_sinc.h> |
| 436 | #include <ATen/ops/special_softmax.h> |
| 437 | #include <ATen/ops/fft_fft.h> |
| 438 | #include <ATen/ops/fft_fft.h> |
| 439 | #include <ATen/ops/fft_rfft.h> |
| 440 | #include <ATen/ops/fft_rfft.h> |
| 441 | #include <ATen/ops/fft_hfft2.h> |
| 442 | #include <ATen/ops/fft_hfft2.h> |
| 443 | #include <ATen/ops/fft_ifftn.h> |
| 444 | #include <ATen/ops/fft_ifftn.h> |
| 445 | #include <ATen/ops/fft_ihfftn.h> |
| 446 | #include <ATen/ops/fft_ihfftn.h> |
| 447 | #include <ATen/ops/fft_fftfreq.h> |
| 448 | #include <ATen/ops/fft_fftfreq.h> |
| 449 | #include <ATen/ops/fft_rfftfreq.h> |
| 450 | #include <ATen/ops/fft_rfftfreq.h> |
| 451 | #include <ATen/ops/linalg_cholesky_ex.h> |
| 452 | #include <ATen/ops/linalg_cholesky_ex.h> |
| 453 | #include <ATen/ops/linalg_cross.h> |
| 454 | #include <ATen/ops/linalg_cross.h> |
| 455 | #include <ATen/ops/linalg_lu_factor_ex.h> |
| 456 | #include <ATen/ops/linalg_lu_factor_ex.h> |
| 457 | #include <ATen/ops/det.h> |
| 458 | #include <ATen/ops/inverse.h> |
| 459 | #include <ATen/ops/inverse.h> |
| 460 | #include <ATen/ops/linalg_cond.h> |
| 461 | #include <ATen/ops/linalg_cond.h> |
| 462 | #include <ATen/ops/linalg_cond.h> |
| 463 | #include <ATen/ops/linalg_cond.h> |
| 464 | #include <ATen/ops/linalg_pinv.h> |
| 465 | #include <ATen/ops/linalg_pinv.h> |
| 466 | #include <ATen/ops/linalg_pinv.h> |
| 467 | #include <ATen/ops/linalg_pinv.h> |
| 468 | #include <ATen/ops/linalg_pinv.h> |
| 469 | #include <ATen/ops/linalg_pinv.h> |
| 470 | #include <ATen/ops/linalg_pinv.h> |
| 471 | #include <ATen/ops/linalg_pinv.h> |
| 472 | #include <ATen/ops/linalg_solve_ex.h> |
| 473 | #include <ATen/ops/linalg_solve_ex.h> |
| 474 | #include <ATen/ops/linalg_tensorsolve.h> |
| 475 | #include <ATen/ops/linalg_tensorsolve.h> |
| 476 | #include <ATen/ops/linalg_multi_dot.h> |
| 477 | #include <ATen/ops/linalg_multi_dot.h> |
| 478 | #include <ATen/ops/_test_string_default.h> |
| 479 | #include <ATen/ops/flatten_dense_tensors.h> |
| 480 | #include <ATen/ops/_conj_copy.h> |
| 481 | #include <ATen/ops/detach_copy.h> |
| 482 | #include <ATen/ops/row_indices_copy.h> |
| 483 | #include <ATen/ops/_transformer_encoder_layer_fwd.h> |
| 484 | #include <ATen/ops/_native_multi_head_attention.h> |
| 485 | #include <ATen/ops/_scaled_dot_product_attention.h> |
| 486 | #include <ATen/ops/_fused_sdp_choice.h> |
| 487 | #include <ATen/ops/_scaled_dot_product_flash_attention.h> |
| 488 | #include <ATen/ops/_scaled_dot_product_efficient_attention_backward.h> |
| 489 | #include <ATen/ops/_flash_attention_backward.h> |
| 490 | #include <ATen/ops/_efficient_attention_backward.h> |
| 491 | #include <ATen/ops/_triton_multi_head_attention.h> |
| 492 | #include <ATen/ops/special_airy_ai.h> |
| 493 | #include <ATen/ops/special_airy_ai.h> |
| 494 | #include <ATen/ops/special_chebyshev_polynomial_w.h> |
| 495 | #include <ATen/ops/special_chebyshev_polynomial_w.h> |
| 496 | #include <ATen/ops/special_chebyshev_polynomial_w.h> |
| 497 | #include <ATen/ops/special_chebyshev_polynomial_w.h> |
| 498 | #include <ATen/ops/special_chebyshev_polynomial_w.h> |
| 499 | #include <ATen/ops/special_chebyshev_polynomial_w.h> |
| 500 | #include <ATen/ops/special_hermite_polynomial_h.h> |
| 501 | #include <ATen/ops/special_hermite_polynomial_h.h> |
| 502 | #include <ATen/ops/special_hermite_polynomial_h.h> |
| 503 | #include <ATen/ops/special_hermite_polynomial_h.h> |
| 504 | #include <ATen/ops/special_hermite_polynomial_h.h> |
| 505 | #include <ATen/ops/special_hermite_polynomial_h.h> |
| 506 | #include <ATen/ops/special_modified_bessel_i0.h> |
| 507 | #include <ATen/ops/special_modified_bessel_i0.h> |
| 508 | #include <ATen/ops/special_modified_bessel_k0.h> |
| 509 | #include <ATen/ops/special_modified_bessel_k0.h> |
| 510 | #include <ATen/ops/special_modified_bessel_k1.h> |
| 511 | #include <ATen/ops/special_modified_bessel_k1.h> |
| 512 | #include <ATen/ops/special_shifted_chebyshev_polynomial_t.h> |
| 513 | #include <ATen/ops/special_shifted_chebyshev_polynomial_t.h> |
| 514 | #include <ATen/ops/special_shifted_chebyshev_polynomial_t.h> |
| 515 | #include <ATen/ops/special_shifted_chebyshev_polynomial_t.h> |
| 516 | #include <ATen/ops/special_shifted_chebyshev_polynomial_t.h> |
| 517 | #include <ATen/ops/special_shifted_chebyshev_polynomial_t.h> |
| 518 | #include <ATen/ops/_fused_adamw.h> |
| 519 | #include <ATen/ops/_cudnn_rnn_flatten_weight.h> |
| 520 | #include <ATen/ops/quantized_batch_norm.h> |
| 521 | #include <ATen/ops/conv_tbc.h> |
| 522 | #include <ATen/ops/cudnn_affine_grid_generator_backward.h> |
| 523 | #include <ATen/ops/cudnn_grid_sampler.h> |
| 524 | #include <ATen/ops/div.h> |
| 525 | #include <ATen/ops/div.h> |
| 526 | #include <ATen/ops/_embedding_bag_forward_only.h> |
| 527 | #include <ATen/ops/new_zeros.h> |
| 528 | #include <ATen/ops/_grid_sampler_2d_cpu_fallback.h> |
| 529 | #include <ATen/ops/grid_sampler_3d.h> |
| 530 | #include <ATen/ops/hann_window.h> |
| 531 | #include <ATen/ops/hann_window.h> |
| 532 | #include <ATen/ops/hamming_window.h> |
| 533 | #include <ATen/ops/hamming_window.h> |
| 534 | #include <ATen/ops/hamming_window.h> |
| 535 | #include <ATen/ops/hamming_window.h> |
| 536 | #include <ATen/ops/native_group_norm_backward.h> |
| 537 | #include <ATen/ops/isnan.h> |
| 538 | #include <ATen/ops/native_layer_norm.h> |
| 539 | #include <ATen/ops/_mps_max_pool2d.h> |
| 540 | #include <ATen/ops/mkldnn_max_pool2d.h> |
| 541 | #include <ATen/ops/quantized_max_pool2d.h> |
| 542 | #include <ATen/ops/_mps_convolution.h> |
| 543 | #include <ATen/ops/mkldnn_rnn_layer_backward.h> |
| 544 | #include <ATen/ops/miopen_depthwise_convolution.h> |
| 545 | #include <ATen/ops/batch_norm_stats.h> |
| 546 | #include <ATen/ops/batch_norm_gather_stats.h> |
| 547 | #include <ATen/ops/native_batch_norm_backward.h> |
| 548 | #include <ATen/ops/batch_norm_backward_reduce.h> |
| 549 | #include <ATen/ops/_nnpack_spatial_convolution.h> |
| 550 | #include <ATen/ops/ones.h> |
| 551 | #include <ATen/ops/_cdist_forward.h> |
| 552 | #include <ATen/ops/rand_like.h> |
| 553 | #include <ATen/ops/randint_like.h> |
| 554 | #include <ATen/ops/randint_like.h> |
| 555 | #include <ATen/ops/select_backward.h> |
| 556 | #include <ATen/ops/slice_scatter.h> |
| 557 | #include <ATen/ops/_mkldnn_transpose.h> |
| 558 | #include <ATen/ops/_nested_from_padded_and_nested_example.h> |
| 559 | #include <ATen/ops/unique_dim.h> |
| 560 | #include <ATen/ops/unique_consecutive.h> |
| 561 | #include <ATen/ops/_dirichlet_grad.h> |
| 562 | #include <ATen/ops/clone.h> |
| 563 | #include <ATen/ops/resize_as.h> |
| 564 | #include <ATen/ops/resize_as.h> |
| 565 | #include <ATen/ops/resize_as_sparse.h> |
| 566 | #include <ATen/ops/resize_as_sparse.h> |
| 567 | #include <ATen/ops/to_sparse_csc.h> |
| 568 | #include <ATen/ops/mkldnn_reorder_conv2d_weight.h> |
| 569 | #include <ATen/ops/quantize_per_channel.h> |
| 570 | #include <ATen/ops/dequantize.h> |
| 571 | #include <ATen/ops/dequantize.h> |
| 572 | #include <ATen/ops/q_per_channel_zero_points.h> |
| 573 | #include <ATen/ops/_fake_quantize_learnable_per_channel_affine.h> |
| 574 | #include <ATen/ops/_lstm_mps.h> |
| 575 | #include <ATen/ops/_thnn_fused_lstm_cell.h> |
| 576 | #include <ATen/ops/lift_fresh_copy.h> |
| 577 | #include <ATen/ops/index_fill.h> |
| 578 | #include <ATen/ops/index_fill.h> |
| 579 | #include <ATen/ops/random.h> |
| 580 | #include <ATen/ops/random.h> |
| 581 | #include <ATen/ops/random.h> |
| 582 | #include <ATen/ops/random.h> |
| 583 | #include <ATen/ops/random.h> |
| 584 | #include <ATen/ops/random.h> |
| 585 | #include <ATen/ops/cauchy.h> |
| 586 | #include <ATen/ops/cauchy.h> |
| 587 | #include <ATen/ops/log_normal.h> |
| 588 | #include <ATen/ops/log_normal.h> |
| 589 | #include <ATen/ops/_histogramdd_bin_edges.h> |
| 590 | #include <ATen/ops/_histogramdd_from_bin_tensors.h> |
| 591 | #include <ATen/ops/argsort.h> |
| 592 | #include <ATen/ops/unfold_backward.h> |
| 593 | #include <ATen/ops/normal.h> |
| 594 | #include <ATen/ops/_foreach_sub.h> |
| 595 | #include <ATen/ops/_foreach_maximum.h> |
| 596 | #include <ATen/ops/_foreach_sub.h> |
| 597 | #include <ATen/ops/_foreach_maximum.h> |
| 598 | #include <ATen/ops/_foreach_sub.h> |
| 599 | #include <ATen/ops/_foreach_maximum.h> |
| 600 | #include <ATen/ops/_foreach_acos.h> |
| 601 | #include <ATen/ops/_foreach_atan.h> |
| 602 | #include <ATen/ops/_foreach_ceil.h> |
| 603 | #include <ATen/ops/_foreach_erf.h> |
| 604 | #include <ATen/ops/_foreach_log2.h> |
| 605 | #include <ATen/ops/bucketize.h> |
| 606 | #include <ATen/ops/glu_backward_jvp.h> |
| 607 | #include <ATen/ops/hardswish_backward.h> |
| 608 | #include <ATen/ops/_adaptive_avg_pool3d_backward.h> |
| 609 | #include <ATen/ops/_conj_copy.h> |
| 610 | #include <ATen/ops/detach_copy.h> |
| 611 | #include <ATen/ops/row_indices_copy.h> |
| 612 | #include <ATen/ops/_transformer_encoder_layer_fwd.h> |
| 613 | #include <ATen/ops/_native_multi_head_attention.h> |
| 614 | #include <ATen/ops/_triton_multi_head_attention.h> |
| 615 | #include <ATen/ops/_fused_adamw.h> |
| 616 | #include <ATen/ops/_fused_adamw.h> |
| 617 | #endif |
| 618 | |
| 619 | |
| 620 | |
| 621 | namespace at { namespace _ops { |
| 622 | |
| 623 | |
| 624 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cast_Double, name, "aten::_cast_Double") |
| 625 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cast_Double, overload_name, "") |
| 626 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cast_Double, schema_str, "_cast_Double(Tensor self, bool non_blocking=False) -> Tensor") |
| 627 | |
| 628 | // aten::_cast_Double(Tensor self, bool non_blocking=False) -> Tensor |
| 629 | static C10_NOINLINE c10::TypedOperatorHandle<_cast_Double::schema> create__cast_Double_typed_handle() { |
| 630 | return c10::Dispatcher::singleton() |
| 631 | .findSchemaOrThrow(_cast_Double::name, _cast_Double::overload_name) |
| 632 | .typed<_cast_Double::schema>(); |
| 633 | } |
| 634 | |
| 635 | // aten::_cast_Double(Tensor self, bool non_blocking=False) -> Tensor |
| 636 | at::Tensor _cast_Double::call(const at::Tensor & self, bool non_blocking) { |
| 637 | |
| 638 | static auto op = create__cast_Double_typed_handle(); |
| 639 | return op.call(self, non_blocking); |
| 640 | } |
| 641 | |
| 642 | // aten::_cast_Double(Tensor self, bool non_blocking=False) -> Tensor |
| 643 | at::Tensor _cast_Double::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool non_blocking) { |
| 644 | |
| 645 | static auto op = create__cast_Double_typed_handle(); |
| 646 | return op.redispatch(dispatchKeySet, self, non_blocking); |
| 647 | } |
| 648 | |
| 649 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cast_Float, name, "aten::_cast_Float") |
| 650 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cast_Float, overload_name, "") |
| 651 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cast_Float, schema_str, "_cast_Float(Tensor self, bool non_blocking=False) -> Tensor") |
| 652 | |
| 653 | // aten::_cast_Float(Tensor self, bool non_blocking=False) -> Tensor |
| 654 | static C10_NOINLINE c10::TypedOperatorHandle<_cast_Float::schema> create__cast_Float_typed_handle() { |
| 655 | return c10::Dispatcher::singleton() |
| 656 | .findSchemaOrThrow(_cast_Float::name, _cast_Float::overload_name) |
| 657 | .typed<_cast_Float::schema>(); |
| 658 | } |
| 659 | |
| 660 | // aten::_cast_Float(Tensor self, bool non_blocking=False) -> Tensor |
| 661 | at::Tensor _cast_Float::call(const at::Tensor & self, bool non_blocking) { |
| 662 | |
| 663 | static auto op = create__cast_Float_typed_handle(); |
| 664 | return op.call(self, non_blocking); |
| 665 | } |
| 666 | |
| 667 | // aten::_cast_Float(Tensor self, bool non_blocking=False) -> Tensor |
| 668 | at::Tensor _cast_Float::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool non_blocking) { |
| 669 | |
| 670 | static auto op = create__cast_Float_typed_handle(); |
| 671 | return op.redispatch(dispatchKeySet, self, non_blocking); |
| 672 | } |
| 673 | |
| 674 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cast_Half, name, "aten::_cast_Half") |
| 675 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cast_Half, overload_name, "") |
| 676 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cast_Half, schema_str, "_cast_Half(Tensor self, bool non_blocking=False) -> Tensor") |
| 677 | |
| 678 | // aten::_cast_Half(Tensor self, bool non_blocking=False) -> Tensor |
| 679 | static C10_NOINLINE c10::TypedOperatorHandle<_cast_Half::schema> create__cast_Half_typed_handle() { |
| 680 | return c10::Dispatcher::singleton() |
| 681 | .findSchemaOrThrow(_cast_Half::name, _cast_Half::overload_name) |
| 682 | .typed<_cast_Half::schema>(); |
| 683 | } |
| 684 | |
| 685 | // aten::_cast_Half(Tensor self, bool non_blocking=False) -> Tensor |
| 686 | at::Tensor _cast_Half::call(const at::Tensor & self, bool non_blocking) { |
| 687 | |
| 688 | static auto op = create__cast_Half_typed_handle(); |
| 689 | return op.call(self, non_blocking); |
| 690 | } |
| 691 | |
| 692 | // aten::_cast_Half(Tensor self, bool non_blocking=False) -> Tensor |
| 693 | at::Tensor _cast_Half::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool non_blocking) { |
| 694 | |
| 695 | static auto op = create__cast_Half_typed_handle(); |
| 696 | return op.redispatch(dispatchKeySet, self, non_blocking); |
| 697 | } |
| 698 | |
| 699 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_version, name, "aten::_version") |
| 700 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_version, overload_name, "") |
| 701 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_version, schema_str, "_version(Tensor self) -> int") |
| 702 | |
| 703 | // aten::_version(Tensor self) -> int |
| 704 | static C10_NOINLINE c10::TypedOperatorHandle<_version::schema> create__version_typed_handle() { |
| 705 | return c10::Dispatcher::singleton() |
| 706 | .findSchemaOrThrow(_version::name, _version::overload_name) |
| 707 | .typed<_version::schema>(); |
| 708 | } |
| 709 | |
| 710 | // aten::_version(Tensor self) -> int |
| 711 | int64_t _version::call(const at::Tensor & self) { |
| 712 | |
| 713 | static auto op = create__version_typed_handle(); |
| 714 | return op.call(self); |
| 715 | } |
| 716 | |
| 717 | // aten::_version(Tensor self) -> int |
| 718 | int64_t _version::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 719 | |
| 720 | static auto op = create__version_typed_handle(); |
| 721 | return op.redispatch(dispatchKeySet, self); |
| 722 | } |
| 723 | |
| 724 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_make_dual, name, "aten::_make_dual") |
| 725 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_make_dual, overload_name, "") |
| 726 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_make_dual, schema_str, "_make_dual(Tensor(a) primal, Tensor tangent, int level) -> Tensor(a)") |
| 727 | |
| 728 | // aten::_make_dual(Tensor(a) primal, Tensor tangent, int level) -> Tensor(a) |
| 729 | static C10_NOINLINE c10::TypedOperatorHandle<_make_dual::schema> create__make_dual_typed_handle() { |
| 730 | return c10::Dispatcher::singleton() |
| 731 | .findSchemaOrThrow(_make_dual::name, _make_dual::overload_name) |
| 732 | .typed<_make_dual::schema>(); |
| 733 | } |
| 734 | |
| 735 | // aten::_make_dual(Tensor(a) primal, Tensor tangent, int level) -> Tensor(a) |
| 736 | at::Tensor _make_dual::call(const at::Tensor & primal, const at::Tensor & tangent, int64_t level) { |
| 737 | |
| 738 | static auto op = create__make_dual_typed_handle(); |
| 739 | return op.call(primal, tangent, level); |
| 740 | } |
| 741 | |
| 742 | // aten::_make_dual(Tensor(a) primal, Tensor tangent, int level) -> Tensor(a) |
| 743 | at::Tensor _make_dual::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & primal, const at::Tensor & tangent, int64_t level) { |
| 744 | |
| 745 | static auto op = create__make_dual_typed_handle(); |
| 746 | return op.redispatch(dispatchKeySet, primal, tangent, level); |
| 747 | } |
| 748 | |
| 749 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(align_as, name, "aten::align_as") |
| 750 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(align_as, overload_name, "") |
| 751 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(align_as, schema_str, "align_as(Tensor self, Tensor other) -> Tensor") |
| 752 | |
| 753 | // aten::align_as(Tensor self, Tensor other) -> Tensor |
| 754 | static C10_NOINLINE c10::TypedOperatorHandle<align_as::schema> create_align_as_typed_handle() { |
| 755 | return c10::Dispatcher::singleton() |
| 756 | .findSchemaOrThrow(align_as::name, align_as::overload_name) |
| 757 | .typed<align_as::schema>(); |
| 758 | } |
| 759 | |
| 760 | // aten::align_as(Tensor self, Tensor other) -> Tensor |
| 761 | at::Tensor align_as::call(const at::Tensor & self, const at::Tensor & other) { |
| 762 | |
| 763 | static auto op = create_align_as_typed_handle(); |
| 764 | return op.call(self, other); |
| 765 | } |
| 766 | |
| 767 | // aten::align_as(Tensor self, Tensor other) -> Tensor |
| 768 | at::Tensor align_as::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { |
| 769 | |
| 770 | static auto op = create_align_as_typed_handle(); |
| 771 | return op.redispatch(dispatchKeySet, self, other); |
| 772 | } |
| 773 | |
| 774 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_assert_tensor_metadata, name, "aten::_assert_tensor_metadata") |
| 775 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_assert_tensor_metadata, overload_name, "") |
| 776 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_assert_tensor_metadata, schema_str, "_assert_tensor_metadata(Tensor a, int[]? size=None, int[]? stride=None, ScalarType? dtype=None) -> ()") |
| 777 | |
| 778 | // aten::_assert_tensor_metadata(Tensor a, int[]? size=None, int[]? stride=None, ScalarType? dtype=None) -> () |
| 779 | static C10_NOINLINE c10::TypedOperatorHandle<_assert_tensor_metadata::schema> create__assert_tensor_metadata_typed_handle() { |
| 780 | return c10::Dispatcher::singleton() |
| 781 | .findSchemaOrThrow(_assert_tensor_metadata::name, _assert_tensor_metadata::overload_name) |
| 782 | .typed<_assert_tensor_metadata::schema>(); |
| 783 | } |
| 784 | |
| 785 | // aten::_assert_tensor_metadata(Tensor a, int[]? size=None, int[]? stride=None, ScalarType? dtype=None) -> () |
| 786 | void _assert_tensor_metadata::call(const at::Tensor & a, at::OptionalIntArrayRef size, at::OptionalIntArrayRef stride, c10::optional<at::ScalarType> dtype) { |
| 787 | |
| 788 | static auto op = create__assert_tensor_metadata_typed_handle(); |
| 789 | return op.call(a, size, stride, dtype); |
| 790 | } |
| 791 | |
| 792 | // aten::_assert_tensor_metadata(Tensor a, int[]? size=None, int[]? stride=None, ScalarType? dtype=None) -> () |
| 793 | void _assert_tensor_metadata::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & a, at::OptionalIntArrayRef size, at::OptionalIntArrayRef stride, c10::optional<at::ScalarType> dtype) { |
| 794 | |
| 795 | static auto op = create__assert_tensor_metadata_typed_handle(); |
| 796 | return op.redispatch(dispatchKeySet, a, size, stride, dtype); |
| 797 | } |
| 798 | |
| 799 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(refine_names, name, "aten::refine_names") |
| 800 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(refine_names, overload_name, "") |
| 801 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(refine_names, schema_str, "refine_names(Tensor(a) self, Dimname[] names) -> Tensor(a)") |
| 802 | |
| 803 | // aten::refine_names(Tensor(a) self, Dimname[] names) -> Tensor(a) |
| 804 | static C10_NOINLINE c10::TypedOperatorHandle<refine_names::schema> create_refine_names_typed_handle() { |
| 805 | return c10::Dispatcher::singleton() |
| 806 | .findSchemaOrThrow(refine_names::name, refine_names::overload_name) |
| 807 | .typed<refine_names::schema>(); |
| 808 | } |
| 809 | |
| 810 | // aten::refine_names(Tensor(a) self, Dimname[] names) -> Tensor(a) |
| 811 | at::Tensor refine_names::call(const at::Tensor & self, at::DimnameList names) { |
| 812 | |
| 813 | static auto op = create_refine_names_typed_handle(); |
| 814 | return op.call(self, names); |
| 815 | } |
| 816 | |
| 817 | // aten::refine_names(Tensor(a) self, Dimname[] names) -> Tensor(a) |
| 818 | at::Tensor refine_names::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList names) { |
| 819 | |
| 820 | static auto op = create_refine_names_typed_handle(); |
| 821 | return op.redispatch(dispatchKeySet, self, names); |
| 822 | } |
| 823 | |
| 824 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_use_cudnn_rnn_flatten_weight, name, "aten::_use_cudnn_rnn_flatten_weight") |
| 825 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_use_cudnn_rnn_flatten_weight, overload_name, "") |
| 826 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_use_cudnn_rnn_flatten_weight, schema_str, "_use_cudnn_rnn_flatten_weight() -> bool") |
| 827 | |
| 828 | // aten::_use_cudnn_rnn_flatten_weight() -> bool |
| 829 | static C10_NOINLINE c10::TypedOperatorHandle<_use_cudnn_rnn_flatten_weight::schema> create__use_cudnn_rnn_flatten_weight_typed_handle() { |
| 830 | return c10::Dispatcher::singleton() |
| 831 | .findSchemaOrThrow(_use_cudnn_rnn_flatten_weight::name, _use_cudnn_rnn_flatten_weight::overload_name) |
| 832 | .typed<_use_cudnn_rnn_flatten_weight::schema>(); |
| 833 | } |
| 834 | |
| 835 | // aten::_use_cudnn_rnn_flatten_weight() -> bool |
| 836 | bool _use_cudnn_rnn_flatten_weight::call() { |
| 837 | |
| 838 | static auto op = create__use_cudnn_rnn_flatten_weight_typed_handle(); |
| 839 | return op.call(); |
| 840 | } |
| 841 | |
| 842 | // aten::_use_cudnn_rnn_flatten_weight() -> bool |
| 843 | bool _use_cudnn_rnn_flatten_weight::redispatch(c10::DispatchKeySet dispatchKeySet) { |
| 844 | |
| 845 | static auto op = create__use_cudnn_rnn_flatten_weight_typed_handle(); |
| 846 | return op.redispatch(dispatchKeySet); |
| 847 | } |
| 848 | |
| 849 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cudnn_rnn_flatten_weight, name, "aten::_cudnn_rnn_flatten_weight") |
| 850 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cudnn_rnn_flatten_weight, overload_name, "") |
| 851 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cudnn_rnn_flatten_weight, schema_str, "_cudnn_rnn_flatten_weight(Tensor[] weight_arr, int weight_stride0, SymInt input_size, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, bool bidirectional) -> Tensor") |
| 852 | |
| 853 | // aten::_cudnn_rnn_flatten_weight(Tensor[] weight_arr, int weight_stride0, SymInt input_size, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, bool bidirectional) -> Tensor |
| 854 | static C10_NOINLINE c10::TypedOperatorHandle<_cudnn_rnn_flatten_weight::schema> create__cudnn_rnn_flatten_weight_typed_handle() { |
| 855 | return c10::Dispatcher::singleton() |
| 856 | .findSchemaOrThrow(_cudnn_rnn_flatten_weight::name, _cudnn_rnn_flatten_weight::overload_name) |
| 857 | .typed<_cudnn_rnn_flatten_weight::schema>(); |
| 858 | } |
| 859 | |
| 860 | // aten::_cudnn_rnn_flatten_weight(Tensor[] weight_arr, int weight_stride0, SymInt input_size, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, bool bidirectional) -> Tensor |
| 861 | at::Tensor _cudnn_rnn_flatten_weight::call(at::TensorList weight_arr, int64_t weight_stride0, c10::SymInt input_size, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, bool bidirectional) { |
| 862 | |
| 863 | static auto op = create__cudnn_rnn_flatten_weight_typed_handle(); |
| 864 | return op.call(weight_arr, weight_stride0, input_size, mode, hidden_size, proj_size, num_layers, batch_first, bidirectional); |
| 865 | } |
| 866 | |
| 867 | // aten::_cudnn_rnn_flatten_weight(Tensor[] weight_arr, int weight_stride0, SymInt input_size, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, bool bidirectional) -> Tensor |
| 868 | at::Tensor _cudnn_rnn_flatten_weight::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList weight_arr, int64_t weight_stride0, c10::SymInt input_size, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, bool bidirectional) { |
| 869 | |
| 870 | static auto op = create__cudnn_rnn_flatten_weight_typed_handle(); |
| 871 | return op.redispatch(dispatchKeySet, weight_arr, weight_stride0, input_size, mode, hidden_size, proj_size, num_layers, batch_first, bidirectional); |
| 872 | } |
| 873 | |
| 874 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sobol_engine_ff_, name, "aten::_sobol_engine_ff_") |
| 875 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sobol_engine_ff_, overload_name, "") |
| 876 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sobol_engine_ff_, schema_str, "_sobol_engine_ff_(Tensor(a!) self, int n, Tensor sobolstate, int dimension, int num_generated) -> Tensor(a!)") |
| 877 | |
| 878 | // aten::_sobol_engine_ff_(Tensor(a!) self, int n, Tensor sobolstate, int dimension, int num_generated) -> Tensor(a!) |
| 879 | static C10_NOINLINE c10::TypedOperatorHandle<_sobol_engine_ff_::schema> create__sobol_engine_ff__typed_handle() { |
| 880 | return c10::Dispatcher::singleton() |
| 881 | .findSchemaOrThrow(_sobol_engine_ff_::name, _sobol_engine_ff_::overload_name) |
| 882 | .typed<_sobol_engine_ff_::schema>(); |
| 883 | } |
| 884 | |
| 885 | // aten::_sobol_engine_ff_(Tensor(a!) self, int n, Tensor sobolstate, int dimension, int num_generated) -> Tensor(a!) |
| 886 | at::Tensor & _sobol_engine_ff_::call(at::Tensor & self, int64_t n, const at::Tensor & sobolstate, int64_t dimension, int64_t num_generated) { |
| 887 | |
| 888 | static auto op = create__sobol_engine_ff__typed_handle(); |
| 889 | return op.call(self, n, sobolstate, dimension, num_generated); |
| 890 | } |
| 891 | |
| 892 | // aten::_sobol_engine_ff_(Tensor(a!) self, int n, Tensor sobolstate, int dimension, int num_generated) -> Tensor(a!) |
| 893 | at::Tensor & _sobol_engine_ff_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t n, const at::Tensor & sobolstate, int64_t dimension, int64_t num_generated) { |
| 894 | |
| 895 | static auto op = create__sobol_engine_ff__typed_handle(); |
| 896 | return op.redispatch(dispatchKeySet, self, n, sobolstate, dimension, num_generated); |
| 897 | } |
| 898 | |
| 899 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sobol_engine_scramble_, name, "aten::_sobol_engine_scramble_") |
| 900 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sobol_engine_scramble_, overload_name, "") |
| 901 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sobol_engine_scramble_, schema_str, "_sobol_engine_scramble_(Tensor(a!) self, Tensor ltm, int dimension) -> Tensor(a!)") |
| 902 | |
| 903 | // aten::_sobol_engine_scramble_(Tensor(a!) self, Tensor ltm, int dimension) -> Tensor(a!) |
| 904 | static C10_NOINLINE c10::TypedOperatorHandle<_sobol_engine_scramble_::schema> create__sobol_engine_scramble__typed_handle() { |
| 905 | return c10::Dispatcher::singleton() |
| 906 | .findSchemaOrThrow(_sobol_engine_scramble_::name, _sobol_engine_scramble_::overload_name) |
| 907 | .typed<_sobol_engine_scramble_::schema>(); |
| 908 | } |
| 909 | |
| 910 | // aten::_sobol_engine_scramble_(Tensor(a!) self, Tensor ltm, int dimension) -> Tensor(a!) |
| 911 | at::Tensor & _sobol_engine_scramble_::call(at::Tensor & self, const at::Tensor & ltm, int64_t dimension) { |
| 912 | |
| 913 | static auto op = create__sobol_engine_scramble__typed_handle(); |
| 914 | return op.call(self, ltm, dimension); |
| 915 | } |
| 916 | |
| 917 | // aten::_sobol_engine_scramble_(Tensor(a!) self, Tensor ltm, int dimension) -> Tensor(a!) |
| 918 | at::Tensor & _sobol_engine_scramble_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & ltm, int64_t dimension) { |
| 919 | |
| 920 | static auto op = create__sobol_engine_scramble__typed_handle(); |
| 921 | return op.redispatch(dispatchKeySet, self, ltm, dimension); |
| 922 | } |
| 923 | |
| 924 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(feature_alpha_dropout, name, "aten::feature_alpha_dropout") |
| 925 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(feature_alpha_dropout, overload_name, "") |
| 926 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(feature_alpha_dropout, schema_str, "feature_alpha_dropout(Tensor input, float p, bool train) -> Tensor") |
| 927 | |
| 928 | // aten::feature_alpha_dropout(Tensor input, float p, bool train) -> Tensor |
| 929 | static C10_NOINLINE c10::TypedOperatorHandle<feature_alpha_dropout::schema> create_feature_alpha_dropout_typed_handle() { |
| 930 | return c10::Dispatcher::singleton() |
| 931 | .findSchemaOrThrow(feature_alpha_dropout::name, feature_alpha_dropout::overload_name) |
| 932 | .typed<feature_alpha_dropout::schema>(); |
| 933 | } |
| 934 | |
| 935 | // aten::feature_alpha_dropout(Tensor input, float p, bool train) -> Tensor |
| 936 | at::Tensor feature_alpha_dropout::call(const at::Tensor & input, double p, bool train) { |
| 937 | |
| 938 | static auto op = create_feature_alpha_dropout_typed_handle(); |
| 939 | return op.call(input, p, train); |
| 940 | } |
| 941 | |
| 942 | // aten::feature_alpha_dropout(Tensor input, float p, bool train) -> Tensor |
| 943 | at::Tensor feature_alpha_dropout::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, double p, bool train) { |
| 944 | |
| 945 | static auto op = create_feature_alpha_dropout_typed_handle(); |
| 946 | return op.redispatch(dispatchKeySet, input, p, train); |
| 947 | } |
| 948 | |
| 949 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(feature_alpha_dropout_, name, "aten::feature_alpha_dropout_") |
| 950 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(feature_alpha_dropout_, overload_name, "") |
| 951 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(feature_alpha_dropout_, schema_str, "feature_alpha_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)") |
| 952 | |
| 953 | // aten::feature_alpha_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!) |
| 954 | static C10_NOINLINE c10::TypedOperatorHandle<feature_alpha_dropout_::schema> create_feature_alpha_dropout__typed_handle() { |
| 955 | return c10::Dispatcher::singleton() |
| 956 | .findSchemaOrThrow(feature_alpha_dropout_::name, feature_alpha_dropout_::overload_name) |
| 957 | .typed<feature_alpha_dropout_::schema>(); |
| 958 | } |
| 959 | |
| 960 | // aten::feature_alpha_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!) |
| 961 | at::Tensor & feature_alpha_dropout_::call(at::Tensor & self, double p, bool train) { |
| 962 | |
| 963 | static auto op = create_feature_alpha_dropout__typed_handle(); |
| 964 | return op.call(self, p, train); |
| 965 | } |
| 966 | |
| 967 | // aten::feature_alpha_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!) |
| 968 | at::Tensor & feature_alpha_dropout_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, double p, bool train) { |
| 969 | |
| 970 | static auto op = create_feature_alpha_dropout__typed_handle(); |
| 971 | return op.redispatch(dispatchKeySet, self, p, train); |
| 972 | } |
| 973 | |
| 974 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(abs, name, "aten::abs") |
| 975 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(abs, overload_name, "") |
| 976 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(abs, schema_str, "abs(Tensor self) -> Tensor") |
| 977 | |
| 978 | // aten::abs(Tensor self) -> Tensor |
| 979 | static C10_NOINLINE c10::TypedOperatorHandle<abs::schema> create_abs_typed_handle() { |
| 980 | return c10::Dispatcher::singleton() |
| 981 | .findSchemaOrThrow(abs::name, abs::overload_name) |
| 982 | .typed<abs::schema>(); |
| 983 | } |
| 984 | |
| 985 | // aten::abs(Tensor self) -> Tensor |
| 986 | at::Tensor abs::call(const at::Tensor & self) { |
| 987 | |
| 988 | static auto op = create_abs_typed_handle(); |
| 989 | return op.call(self); |
| 990 | } |
| 991 | |
| 992 | // aten::abs(Tensor self) -> Tensor |
| 993 | at::Tensor abs::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 994 | |
| 995 | static auto op = create_abs_typed_handle(); |
| 996 | return op.redispatch(dispatchKeySet, self); |
| 997 | } |
| 998 | |
| 999 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(abs_, name, "aten::abs_") |
| 1000 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(abs_, overload_name, "") |
| 1001 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(abs_, schema_str, "abs_(Tensor(a!) self) -> Tensor(a!)") |
| 1002 | |
| 1003 | // aten::abs_(Tensor(a!) self) -> Tensor(a!) |
| 1004 | static C10_NOINLINE c10::TypedOperatorHandle<abs_::schema> create_abs__typed_handle() { |
| 1005 | return c10::Dispatcher::singleton() |
| 1006 | .findSchemaOrThrow(abs_::name, abs_::overload_name) |
| 1007 | .typed<abs_::schema>(); |
| 1008 | } |
| 1009 | |
| 1010 | // aten::abs_(Tensor(a!) self) -> Tensor(a!) |
| 1011 | at::Tensor & abs_::call(at::Tensor & self) { |
| 1012 | |
| 1013 | static auto op = create_abs__typed_handle(); |
| 1014 | return op.call(self); |
| 1015 | } |
| 1016 | |
| 1017 | // aten::abs_(Tensor(a!) self) -> Tensor(a!) |
| 1018 | at::Tensor & abs_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 1019 | |
| 1020 | static auto op = create_abs__typed_handle(); |
| 1021 | return op.redispatch(dispatchKeySet, self); |
| 1022 | } |
| 1023 | |
| 1024 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(abs_out, name, "aten::abs") |
| 1025 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(abs_out, overload_name, "out") |
| 1026 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(abs_out, schema_str, "abs.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 1027 | |
| 1028 | // aten::abs.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 1029 | static C10_NOINLINE c10::TypedOperatorHandle<abs_out::schema> create_abs_out_typed_handle() { |
| 1030 | return c10::Dispatcher::singleton() |
| 1031 | .findSchemaOrThrow(abs_out::name, abs_out::overload_name) |
| 1032 | .typed<abs_out::schema>(); |
| 1033 | } |
| 1034 | |
| 1035 | // aten::abs.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 1036 | at::Tensor & abs_out::call(const at::Tensor & self, at::Tensor & out) { |
| 1037 | |
| 1038 | static auto op = create_abs_out_typed_handle(); |
| 1039 | return op.call(self, out); |
| 1040 | } |
| 1041 | |
| 1042 | // aten::abs.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 1043 | at::Tensor & abs_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 1044 | |
| 1045 | static auto op = create_abs_out_typed_handle(); |
| 1046 | return op.redispatch(dispatchKeySet, self, out); |
| 1047 | } |
| 1048 | |
| 1049 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(imag, name, "aten::imag") |
| 1050 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(imag, overload_name, "") |
| 1051 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(imag, schema_str, "imag(Tensor(a) self) -> Tensor(a)") |
| 1052 | |
| 1053 | // aten::imag(Tensor(a) self) -> Tensor(a) |
| 1054 | static C10_NOINLINE c10::TypedOperatorHandle<imag::schema> create_imag_typed_handle() { |
| 1055 | return c10::Dispatcher::singleton() |
| 1056 | .findSchemaOrThrow(imag::name, imag::overload_name) |
| 1057 | .typed<imag::schema>(); |
| 1058 | } |
| 1059 | |
| 1060 | // aten::imag(Tensor(a) self) -> Tensor(a) |
| 1061 | at::Tensor imag::call(const at::Tensor & self) { |
| 1062 | |
| 1063 | static auto op = create_imag_typed_handle(); |
| 1064 | return op.call(self); |
| 1065 | } |
| 1066 | |
| 1067 | // aten::imag(Tensor(a) self) -> Tensor(a) |
| 1068 | at::Tensor imag::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 1069 | |
| 1070 | static auto op = create_imag_typed_handle(); |
| 1071 | return op.redispatch(dispatchKeySet, self); |
| 1072 | } |
| 1073 | |
| 1074 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(resolve_conj, name, "aten::resolve_conj") |
| 1075 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(resolve_conj, overload_name, "") |
| 1076 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(resolve_conj, schema_str, "resolve_conj(Tensor(a) self) -> Tensor(a)") |
| 1077 | |
| 1078 | // aten::resolve_conj(Tensor(a) self) -> Tensor(a) |
| 1079 | static C10_NOINLINE c10::TypedOperatorHandle<resolve_conj::schema> create_resolve_conj_typed_handle() { |
| 1080 | return c10::Dispatcher::singleton() |
| 1081 | .findSchemaOrThrow(resolve_conj::name, resolve_conj::overload_name) |
| 1082 | .typed<resolve_conj::schema>(); |
| 1083 | } |
| 1084 | |
| 1085 | // aten::resolve_conj(Tensor(a) self) -> Tensor(a) |
| 1086 | at::Tensor resolve_conj::call(const at::Tensor & self) { |
| 1087 | |
| 1088 | static auto op = create_resolve_conj_typed_handle(); |
| 1089 | return op.call(self); |
| 1090 | } |
| 1091 | |
| 1092 | // aten::resolve_conj(Tensor(a) self) -> Tensor(a) |
| 1093 | at::Tensor resolve_conj::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 1094 | |
| 1095 | static auto op = create_resolve_conj_typed_handle(); |
| 1096 | return op.redispatch(dispatchKeySet, self); |
| 1097 | } |
| 1098 | |
| 1099 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(resolve_neg, name, "aten::resolve_neg") |
| 1100 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(resolve_neg, overload_name, "") |
| 1101 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(resolve_neg, schema_str, "resolve_neg(Tensor(a) self) -> Tensor(a)") |
| 1102 | |
| 1103 | // aten::resolve_neg(Tensor(a) self) -> Tensor(a) |
| 1104 | static C10_NOINLINE c10::TypedOperatorHandle<resolve_neg::schema> create_resolve_neg_typed_handle() { |
| 1105 | return c10::Dispatcher::singleton() |
| 1106 | .findSchemaOrThrow(resolve_neg::name, resolve_neg::overload_name) |
| 1107 | .typed<resolve_neg::schema>(); |
| 1108 | } |
| 1109 | |
| 1110 | // aten::resolve_neg(Tensor(a) self) -> Tensor(a) |
| 1111 | at::Tensor resolve_neg::call(const at::Tensor & self) { |
| 1112 | |
| 1113 | static auto op = create_resolve_neg_typed_handle(); |
| 1114 | return op.call(self); |
| 1115 | } |
| 1116 | |
| 1117 | // aten::resolve_neg(Tensor(a) self) -> Tensor(a) |
| 1118 | at::Tensor resolve_neg::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 1119 | |
| 1120 | static auto op = create_resolve_neg_typed_handle(); |
| 1121 | return op.redispatch(dispatchKeySet, self); |
| 1122 | } |
| 1123 | |
| 1124 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_max_pool1d, name, "aten::adaptive_max_pool1d") |
| 1125 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_max_pool1d, overload_name, "") |
| 1126 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_max_pool1d, schema_str, "adaptive_max_pool1d(Tensor self, int[1] output_size) -> (Tensor, Tensor)") |
| 1127 | |
| 1128 | // aten::adaptive_max_pool1d(Tensor self, int[1] output_size) -> (Tensor, Tensor) |
| 1129 | static C10_NOINLINE c10::TypedOperatorHandle<adaptive_max_pool1d::schema> create_adaptive_max_pool1d_typed_handle() { |
| 1130 | return c10::Dispatcher::singleton() |
| 1131 | .findSchemaOrThrow(adaptive_max_pool1d::name, adaptive_max_pool1d::overload_name) |
| 1132 | .typed<adaptive_max_pool1d::schema>(); |
| 1133 | } |
| 1134 | |
| 1135 | // aten::adaptive_max_pool1d(Tensor self, int[1] output_size) -> (Tensor, Tensor) |
| 1136 | ::std::tuple<at::Tensor,at::Tensor> adaptive_max_pool1d::call(const at::Tensor & self, at::IntArrayRef output_size) { |
| 1137 | |
| 1138 | static auto op = create_adaptive_max_pool1d_typed_handle(); |
| 1139 | return op.call(self, output_size); |
| 1140 | } |
| 1141 | |
| 1142 | // aten::adaptive_max_pool1d(Tensor self, int[1] output_size) -> (Tensor, Tensor) |
| 1143 | ::std::tuple<at::Tensor,at::Tensor> adaptive_max_pool1d::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size) { |
| 1144 | |
| 1145 | static auto op = create_adaptive_max_pool1d_typed_handle(); |
| 1146 | return op.redispatch(dispatchKeySet, self, output_size); |
| 1147 | } |
| 1148 | |
| 1149 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(addmv, name, "aten::addmv") |
| 1150 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(addmv, overload_name, "") |
| 1151 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(addmv, schema_str, "addmv(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor") |
| 1152 | |
| 1153 | // aten::addmv(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor |
| 1154 | static C10_NOINLINE c10::TypedOperatorHandle<addmv::schema> create_addmv_typed_handle() { |
| 1155 | return c10::Dispatcher::singleton() |
| 1156 | .findSchemaOrThrow(addmv::name, addmv::overload_name) |
| 1157 | .typed<addmv::schema>(); |
| 1158 | } |
| 1159 | |
| 1160 | // aten::addmv(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor |
| 1161 | at::Tensor addmv::call(const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha) { |
| 1162 | |
| 1163 | static auto op = create_addmv_typed_handle(); |
| 1164 | return op.call(self, mat, vec, beta, alpha); |
| 1165 | } |
| 1166 | |
| 1167 | // aten::addmv(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor |
| 1168 | at::Tensor addmv::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha) { |
| 1169 | |
| 1170 | static auto op = create_addmv_typed_handle(); |
| 1171 | return op.redispatch(dispatchKeySet, self, mat, vec, beta, alpha); |
| 1172 | } |
| 1173 | |
| 1174 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(addmv_, name, "aten::addmv_") |
| 1175 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(addmv_, overload_name, "") |
| 1176 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(addmv_, schema_str, "addmv_(Tensor(a!) self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)") |
| 1177 | |
| 1178 | // aten::addmv_(Tensor(a!) self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!) |
| 1179 | static C10_NOINLINE c10::TypedOperatorHandle<addmv_::schema> create_addmv__typed_handle() { |
| 1180 | return c10::Dispatcher::singleton() |
| 1181 | .findSchemaOrThrow(addmv_::name, addmv_::overload_name) |
| 1182 | .typed<addmv_::schema>(); |
| 1183 | } |
| 1184 | |
| 1185 | // aten::addmv_(Tensor(a!) self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!) |
| 1186 | at::Tensor & addmv_::call(at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha) { |
| 1187 | |
| 1188 | static auto op = create_addmv__typed_handle(); |
| 1189 | return op.call(self, mat, vec, beta, alpha); |
| 1190 | } |
| 1191 | |
| 1192 | // aten::addmv_(Tensor(a!) self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!) |
| 1193 | at::Tensor & addmv_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha) { |
| 1194 | |
| 1195 | static auto op = create_addmv__typed_handle(); |
| 1196 | return op.redispatch(dispatchKeySet, self, mat, vec, beta, alpha); |
| 1197 | } |
| 1198 | |
| 1199 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(addmv_out, name, "aten::addmv") |
| 1200 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(addmv_out, overload_name, "out") |
| 1201 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(addmv_out, schema_str, "addmv.out(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)") |
| 1202 | |
| 1203 | // aten::addmv.out(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) |
| 1204 | static C10_NOINLINE c10::TypedOperatorHandle<addmv_out::schema> create_addmv_out_typed_handle() { |
| 1205 | return c10::Dispatcher::singleton() |
| 1206 | .findSchemaOrThrow(addmv_out::name, addmv_out::overload_name) |
| 1207 | .typed<addmv_out::schema>(); |
| 1208 | } |
| 1209 | |
| 1210 | // aten::addmv.out(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) |
| 1211 | at::Tensor & addmv_out::call(const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { |
| 1212 | |
| 1213 | static auto op = create_addmv_out_typed_handle(); |
| 1214 | return op.call(self, mat, vec, beta, alpha, out); |
| 1215 | } |
| 1216 | |
| 1217 | // aten::addmv.out(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) |
| 1218 | at::Tensor & addmv_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { |
| 1219 | |
| 1220 | static auto op = create_addmv_out_typed_handle(); |
| 1221 | return op.redispatch(dispatchKeySet, self, mat, vec, beta, alpha, out); |
| 1222 | } |
| 1223 | |
| 1224 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(addr, name, "aten::addr") |
| 1225 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(addr, overload_name, "") |
| 1226 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(addr, schema_str, "addr(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor") |
| 1227 | |
| 1228 | // aten::addr(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor |
| 1229 | static C10_NOINLINE c10::TypedOperatorHandle<addr::schema> create_addr_typed_handle() { |
| 1230 | return c10::Dispatcher::singleton() |
| 1231 | .findSchemaOrThrow(addr::name, addr::overload_name) |
| 1232 | .typed<addr::schema>(); |
| 1233 | } |
| 1234 | |
| 1235 | // aten::addr(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor |
| 1236 | at::Tensor addr::call(const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 1237 | |
| 1238 | static auto op = create_addr_typed_handle(); |
| 1239 | return op.call(self, vec1, vec2, beta, alpha); |
| 1240 | } |
| 1241 | |
| 1242 | // aten::addr(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor |
| 1243 | at::Tensor addr::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 1244 | |
| 1245 | static auto op = create_addr_typed_handle(); |
| 1246 | return op.redispatch(dispatchKeySet, self, vec1, vec2, beta, alpha); |
| 1247 | } |
| 1248 | |
| 1249 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(addr_, name, "aten::addr_") |
| 1250 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(addr_, overload_name, "") |
| 1251 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(addr_, schema_str, "addr_(Tensor(a!) self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)") |
| 1252 | |
| 1253 | // aten::addr_(Tensor(a!) self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!) |
| 1254 | static C10_NOINLINE c10::TypedOperatorHandle<addr_::schema> create_addr__typed_handle() { |
| 1255 | return c10::Dispatcher::singleton() |
| 1256 | .findSchemaOrThrow(addr_::name, addr_::overload_name) |
| 1257 | .typed<addr_::schema>(); |
| 1258 | } |
| 1259 | |
| 1260 | // aten::addr_(Tensor(a!) self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!) |
| 1261 | at::Tensor & addr_::call(at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 1262 | |
| 1263 | static auto op = create_addr__typed_handle(); |
| 1264 | return op.call(self, vec1, vec2, beta, alpha); |
| 1265 | } |
| 1266 | |
| 1267 | // aten::addr_(Tensor(a!) self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!) |
| 1268 | at::Tensor & addr_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 1269 | |
| 1270 | static auto op = create_addr__typed_handle(); |
| 1271 | return op.redispatch(dispatchKeySet, self, vec1, vec2, beta, alpha); |
| 1272 | } |
| 1273 | |
| 1274 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(addr_out, name, "aten::addr") |
| 1275 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(addr_out, overload_name, "out") |
| 1276 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(addr_out, schema_str, "addr.out(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)") |
| 1277 | |
| 1278 | // aten::addr.out(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) |
| 1279 | static C10_NOINLINE c10::TypedOperatorHandle<addr_out::schema> create_addr_out_typed_handle() { |
| 1280 | return c10::Dispatcher::singleton() |
| 1281 | .findSchemaOrThrow(addr_out::name, addr_out::overload_name) |
| 1282 | .typed<addr_out::schema>(); |
| 1283 | } |
| 1284 | |
| 1285 | // aten::addr.out(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) |
| 1286 | at::Tensor & addr_out::call(const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { |
| 1287 | |
| 1288 | static auto op = create_addr_out_typed_handle(); |
| 1289 | return op.call(self, vec1, vec2, beta, alpha, out); |
| 1290 | } |
| 1291 | |
| 1292 | // aten::addr.out(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) |
| 1293 | at::Tensor & addr_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { |
| 1294 | |
| 1295 | static auto op = create_addr_out_typed_handle(); |
| 1296 | return op.redispatch(dispatchKeySet, self, vec1, vec2, beta, alpha, out); |
| 1297 | } |
| 1298 | |
| 1299 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(affine_grid_generator_backward, name, "aten::affine_grid_generator_backward") |
| 1300 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(affine_grid_generator_backward, overload_name, "") |
| 1301 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(affine_grid_generator_backward, schema_str, "affine_grid_generator_backward(Tensor grad, int[] size, bool align_corners) -> Tensor") |
| 1302 | |
| 1303 | // aten::affine_grid_generator_backward(Tensor grad, int[] size, bool align_corners) -> Tensor |
| 1304 | static C10_NOINLINE c10::TypedOperatorHandle<affine_grid_generator_backward::schema> create_affine_grid_generator_backward_typed_handle() { |
| 1305 | return c10::Dispatcher::singleton() |
| 1306 | .findSchemaOrThrow(affine_grid_generator_backward::name, affine_grid_generator_backward::overload_name) |
| 1307 | .typed<affine_grid_generator_backward::schema>(); |
| 1308 | } |
| 1309 | |
| 1310 | // aten::affine_grid_generator_backward(Tensor grad, int[] size, bool align_corners) -> Tensor |
| 1311 | at::Tensor affine_grid_generator_backward::call(const at::Tensor & grad, at::IntArrayRef size, bool align_corners) { |
| 1312 | |
| 1313 | static auto op = create_affine_grid_generator_backward_typed_handle(); |
| 1314 | return op.call(grad, size, align_corners); |
| 1315 | } |
| 1316 | |
| 1317 | // aten::affine_grid_generator_backward(Tensor grad, int[] size, bool align_corners) -> Tensor |
| 1318 | at::Tensor affine_grid_generator_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, at::IntArrayRef size, bool align_corners) { |
| 1319 | |
| 1320 | static auto op = create_affine_grid_generator_backward_typed_handle(); |
| 1321 | return op.redispatch(dispatchKeySet, grad, size, align_corners); |
| 1322 | } |
| 1323 | |
| 1324 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(argmin, name, "aten::argmin") |
| 1325 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(argmin, overload_name, "") |
| 1326 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(argmin, schema_str, "argmin(Tensor self, int? dim=None, bool keepdim=False) -> Tensor") |
| 1327 | |
| 1328 | // aten::argmin(Tensor self, int? dim=None, bool keepdim=False) -> Tensor |
| 1329 | static C10_NOINLINE c10::TypedOperatorHandle<argmin::schema> create_argmin_typed_handle() { |
| 1330 | return c10::Dispatcher::singleton() |
| 1331 | .findSchemaOrThrow(argmin::name, argmin::overload_name) |
| 1332 | .typed<argmin::schema>(); |
| 1333 | } |
| 1334 | |
| 1335 | // aten::argmin(Tensor self, int? dim=None, bool keepdim=False) -> Tensor |
| 1336 | at::Tensor argmin::call(const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim) { |
| 1337 | |
| 1338 | static auto op = create_argmin_typed_handle(); |
| 1339 | return op.call(self, dim, keepdim); |
| 1340 | } |
| 1341 | |
| 1342 | // aten::argmin(Tensor self, int? dim=None, bool keepdim=False) -> Tensor |
| 1343 | at::Tensor argmin::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim) { |
| 1344 | |
| 1345 | static auto op = create_argmin_typed_handle(); |
| 1346 | return op.redispatch(dispatchKeySet, self, dim, keepdim); |
| 1347 | } |
| 1348 | |
| 1349 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(argmin_out, name, "aten::argmin") |
| 1350 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(argmin_out, overload_name, "out") |
| 1351 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(argmin_out, schema_str, "argmin.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)") |
| 1352 | |
| 1353 | // aten::argmin.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) |
| 1354 | static C10_NOINLINE c10::TypedOperatorHandle<argmin_out::schema> create_argmin_out_typed_handle() { |
| 1355 | return c10::Dispatcher::singleton() |
| 1356 | .findSchemaOrThrow(argmin_out::name, argmin_out::overload_name) |
| 1357 | .typed<argmin_out::schema>(); |
| 1358 | } |
| 1359 | |
| 1360 | // aten::argmin.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) |
| 1361 | at::Tensor & argmin_out::call(const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim, at::Tensor & out) { |
| 1362 | |
| 1363 | static auto op = create_argmin_out_typed_handle(); |
| 1364 | return op.call(self, dim, keepdim, out); |
| 1365 | } |
| 1366 | |
| 1367 | // aten::argmin.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) |
| 1368 | at::Tensor & argmin_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim, at::Tensor & out) { |
| 1369 | |
| 1370 | static auto op = create_argmin_out_typed_handle(); |
| 1371 | return op.redispatch(dispatchKeySet, self, dim, keepdim, out); |
| 1372 | } |
| 1373 | |
| 1374 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(atan, name, "aten::atan") |
| 1375 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(atan, overload_name, "") |
| 1376 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(atan, schema_str, "atan(Tensor self) -> Tensor") |
| 1377 | |
| 1378 | // aten::atan(Tensor self) -> Tensor |
| 1379 | static C10_NOINLINE c10::TypedOperatorHandle<atan::schema> create_atan_typed_handle() { |
| 1380 | return c10::Dispatcher::singleton() |
| 1381 | .findSchemaOrThrow(atan::name, atan::overload_name) |
| 1382 | .typed<atan::schema>(); |
| 1383 | } |
| 1384 | |
| 1385 | // aten::atan(Tensor self) -> Tensor |
| 1386 | at::Tensor atan::call(const at::Tensor & self) { |
| 1387 | |
| 1388 | static auto op = create_atan_typed_handle(); |
| 1389 | return op.call(self); |
| 1390 | } |
| 1391 | |
| 1392 | // aten::atan(Tensor self) -> Tensor |
| 1393 | at::Tensor atan::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 1394 | |
| 1395 | static auto op = create_atan_typed_handle(); |
| 1396 | return op.redispatch(dispatchKeySet, self); |
| 1397 | } |
| 1398 | |
| 1399 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(atan_, name, "aten::atan_") |
| 1400 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(atan_, overload_name, "") |
| 1401 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(atan_, schema_str, "atan_(Tensor(a!) self) -> Tensor(a!)") |
| 1402 | |
| 1403 | // aten::atan_(Tensor(a!) self) -> Tensor(a!) |
| 1404 | static C10_NOINLINE c10::TypedOperatorHandle<atan_::schema> create_atan__typed_handle() { |
| 1405 | return c10::Dispatcher::singleton() |
| 1406 | .findSchemaOrThrow(atan_::name, atan_::overload_name) |
| 1407 | .typed<atan_::schema>(); |
| 1408 | } |
| 1409 | |
| 1410 | // aten::atan_(Tensor(a!) self) -> Tensor(a!) |
| 1411 | at::Tensor & atan_::call(at::Tensor & self) { |
| 1412 | |
| 1413 | static auto op = create_atan__typed_handle(); |
| 1414 | return op.call(self); |
| 1415 | } |
| 1416 | |
| 1417 | // aten::atan_(Tensor(a!) self) -> Tensor(a!) |
| 1418 | at::Tensor & atan_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 1419 | |
| 1420 | static auto op = create_atan__typed_handle(); |
| 1421 | return op.redispatch(dispatchKeySet, self); |
| 1422 | } |
| 1423 | |
| 1424 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(atan_out, name, "aten::atan") |
| 1425 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(atan_out, overload_name, "out") |
| 1426 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(atan_out, schema_str, "atan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 1427 | |
| 1428 | // aten::atan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 1429 | static C10_NOINLINE c10::TypedOperatorHandle<atan_out::schema> create_atan_out_typed_handle() { |
| 1430 | return c10::Dispatcher::singleton() |
| 1431 | .findSchemaOrThrow(atan_out::name, atan_out::overload_name) |
| 1432 | .typed<atan_out::schema>(); |
| 1433 | } |
| 1434 | |
| 1435 | // aten::atan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 1436 | at::Tensor & atan_out::call(const at::Tensor & self, at::Tensor & out) { |
| 1437 | |
| 1438 | static auto op = create_atan_out_typed_handle(); |
| 1439 | return op.call(self, out); |
| 1440 | } |
| 1441 | |
| 1442 | // aten::atan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 1443 | at::Tensor & atan_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 1444 | |
| 1445 | static auto op = create_atan_out_typed_handle(); |
| 1446 | return op.redispatch(dispatchKeySet, self, out); |
| 1447 | } |
| 1448 | |
| 1449 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arctan, name, "aten::arctan") |
| 1450 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arctan, overload_name, "") |
| 1451 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arctan, schema_str, "arctan(Tensor self) -> Tensor") |
| 1452 | |
| 1453 | // aten::arctan(Tensor self) -> Tensor |
| 1454 | static C10_NOINLINE c10::TypedOperatorHandle<arctan::schema> create_arctan_typed_handle() { |
| 1455 | return c10::Dispatcher::singleton() |
| 1456 | .findSchemaOrThrow(arctan::name, arctan::overload_name) |
| 1457 | .typed<arctan::schema>(); |
| 1458 | } |
| 1459 | |
| 1460 | // aten::arctan(Tensor self) -> Tensor |
| 1461 | at::Tensor arctan::call(const at::Tensor & self) { |
| 1462 | |
| 1463 | static auto op = create_arctan_typed_handle(); |
| 1464 | return op.call(self); |
| 1465 | } |
| 1466 | |
| 1467 | // aten::arctan(Tensor self) -> Tensor |
| 1468 | at::Tensor arctan::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 1469 | |
| 1470 | static auto op = create_arctan_typed_handle(); |
| 1471 | return op.redispatch(dispatchKeySet, self); |
| 1472 | } |
| 1473 | |
| 1474 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arctan_, name, "aten::arctan_") |
| 1475 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arctan_, overload_name, "") |
| 1476 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arctan_, schema_str, "arctan_(Tensor(a!) self) -> Tensor(a!)") |
| 1477 | |
| 1478 | // aten::arctan_(Tensor(a!) self) -> Tensor(a!) |
| 1479 | static C10_NOINLINE c10::TypedOperatorHandle<arctan_::schema> create_arctan__typed_handle() { |
| 1480 | return c10::Dispatcher::singleton() |
| 1481 | .findSchemaOrThrow(arctan_::name, arctan_::overload_name) |
| 1482 | .typed<arctan_::schema>(); |
| 1483 | } |
| 1484 | |
| 1485 | // aten::arctan_(Tensor(a!) self) -> Tensor(a!) |
| 1486 | at::Tensor & arctan_::call(at::Tensor & self) { |
| 1487 | |
| 1488 | static auto op = create_arctan__typed_handle(); |
| 1489 | return op.call(self); |
| 1490 | } |
| 1491 | |
| 1492 | // aten::arctan_(Tensor(a!) self) -> Tensor(a!) |
| 1493 | at::Tensor & arctan_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 1494 | |
| 1495 | static auto op = create_arctan__typed_handle(); |
| 1496 | return op.redispatch(dispatchKeySet, self); |
| 1497 | } |
| 1498 | |
| 1499 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arctan_out, name, "aten::arctan") |
| 1500 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arctan_out, overload_name, "out") |
| 1501 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(arctan_out, schema_str, "arctan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 1502 | |
| 1503 | // aten::arctan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 1504 | static C10_NOINLINE c10::TypedOperatorHandle<arctan_out::schema> create_arctan_out_typed_handle() { |
| 1505 | return c10::Dispatcher::singleton() |
| 1506 | .findSchemaOrThrow(arctan_out::name, arctan_out::overload_name) |
| 1507 | .typed<arctan_out::schema>(); |
| 1508 | } |
| 1509 | |
| 1510 | // aten::arctan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 1511 | at::Tensor & arctan_out::call(const at::Tensor & self, at::Tensor & out) { |
| 1512 | |
| 1513 | static auto op = create_arctan_out_typed_handle(); |
| 1514 | return op.call(self, out); |
| 1515 | } |
| 1516 | |
| 1517 | // aten::arctan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 1518 | at::Tensor & arctan_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 1519 | |
| 1520 | static auto op = create_arctan_out_typed_handle(); |
| 1521 | return op.redispatch(dispatchKeySet, self, out); |
| 1522 | } |
| 1523 | |
| 1524 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantized_batch_norm, name, "aten::quantized_batch_norm") |
| 1525 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantized_batch_norm, overload_name, "") |
| 1526 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantized_batch_norm, schema_str, "quantized_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor") |
| 1527 | |
| 1528 | // aten::quantized_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor |
| 1529 | static C10_NOINLINE c10::TypedOperatorHandle<quantized_batch_norm::schema> create_quantized_batch_norm_typed_handle() { |
| 1530 | return c10::Dispatcher::singleton() |
| 1531 | .findSchemaOrThrow(quantized_batch_norm::name, quantized_batch_norm::overload_name) |
| 1532 | .typed<quantized_batch_norm::schema>(); |
| 1533 | } |
| 1534 | |
| 1535 | // aten::quantized_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor |
| 1536 | at::Tensor quantized_batch_norm::call(const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, const at::Tensor & mean, const at::Tensor & var, double eps, double output_scale, int64_t output_zero_point) { |
| 1537 | |
| 1538 | static auto op = create_quantized_batch_norm_typed_handle(); |
| 1539 | return op.call(input, weight, bias, mean, var, eps, output_scale, output_zero_point); |
| 1540 | } |
| 1541 | |
| 1542 | // aten::quantized_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor |
| 1543 | at::Tensor quantized_batch_norm::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, const at::Tensor & mean, const at::Tensor & var, double eps, double output_scale, int64_t output_zero_point) { |
| 1544 | |
| 1545 | static auto op = create_quantized_batch_norm_typed_handle(); |
| 1546 | return op.redispatch(dispatchKeySet, input, weight, bias, mean, var, eps, output_scale, output_zero_point); |
| 1547 | } |
| 1548 | |
| 1549 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(binary_cross_entropy_backward, name, "aten::binary_cross_entropy_backward") |
| 1550 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(binary_cross_entropy_backward, overload_name, "") |
| 1551 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(binary_cross_entropy_backward, schema_str, "binary_cross_entropy_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean) -> Tensor") |
| 1552 | |
| 1553 | // aten::binary_cross_entropy_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean) -> Tensor |
| 1554 | static C10_NOINLINE c10::TypedOperatorHandle<binary_cross_entropy_backward::schema> create_binary_cross_entropy_backward_typed_handle() { |
| 1555 | return c10::Dispatcher::singleton() |
| 1556 | .findSchemaOrThrow(binary_cross_entropy_backward::name, binary_cross_entropy_backward::overload_name) |
| 1557 | .typed<binary_cross_entropy_backward::schema>(); |
| 1558 | } |
| 1559 | |
| 1560 | // aten::binary_cross_entropy_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean) -> Tensor |
| 1561 | at::Tensor binary_cross_entropy_backward::call(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction) { |
| 1562 | |
| 1563 | static auto op = create_binary_cross_entropy_backward_typed_handle(); |
| 1564 | return op.call(grad_output, self, target, weight, reduction); |
| 1565 | } |
| 1566 | |
| 1567 | // aten::binary_cross_entropy_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean) -> Tensor |
| 1568 | at::Tensor binary_cross_entropy_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) { |
| 1569 | |
| 1570 | static auto op = create_binary_cross_entropy_backward_typed_handle(); |
| 1571 | return op.redispatch(dispatchKeySet, grad_output, self, target, weight, reduction); |
| 1572 | } |
| 1573 | |
| 1574 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(binary_cross_entropy_backward_grad_input, name, "aten::binary_cross_entropy_backward") |
| 1575 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(binary_cross_entropy_backward_grad_input, overload_name, "grad_input") |
| 1576 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(binary_cross_entropy_backward_grad_input, schema_str, "binary_cross_entropy_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) grad_input) -> Tensor(a!)") |
| 1577 | |
| 1578 | // aten::binary_cross_entropy_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 1579 | static C10_NOINLINE c10::TypedOperatorHandle<binary_cross_entropy_backward_grad_input::schema> create_binary_cross_entropy_backward_grad_input_typed_handle() { |
| 1580 | return c10::Dispatcher::singleton() |
| 1581 | .findSchemaOrThrow(binary_cross_entropy_backward_grad_input::name, binary_cross_entropy_backward_grad_input::overload_name) |
| 1582 | .typed<binary_cross_entropy_backward_grad_input::schema>(); |
| 1583 | } |
| 1584 | |
| 1585 | // aten::binary_cross_entropy_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 1586 | at::Tensor & binary_cross_entropy_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, at::Tensor & grad_input) { |
| 1587 | |
| 1588 | static auto op = create_binary_cross_entropy_backward_grad_input_typed_handle(); |
| 1589 | return op.call(grad_output, self, target, weight, reduction, grad_input); |
| 1590 | } |
| 1591 | |
| 1592 | // aten::binary_cross_entropy_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 1593 | at::Tensor & binary_cross_entropy_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, at::Tensor & grad_input) { |
| 1594 | |
| 1595 | static auto op = create_binary_cross_entropy_backward_grad_input_typed_handle(); |
| 1596 | return op.redispatch(dispatchKeySet, grad_output, self, target, weight, reduction, grad_input); |
| 1597 | } |
| 1598 | |
| 1599 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bitwise_not, name, "aten::bitwise_not") |
| 1600 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bitwise_not, overload_name, "") |
| 1601 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bitwise_not, schema_str, "bitwise_not(Tensor self) -> Tensor") |
| 1602 | |
| 1603 | // aten::bitwise_not(Tensor self) -> Tensor |
| 1604 | static C10_NOINLINE c10::TypedOperatorHandle<bitwise_not::schema> create_bitwise_not_typed_handle() { |
| 1605 | return c10::Dispatcher::singleton() |
| 1606 | .findSchemaOrThrow(bitwise_not::name, bitwise_not::overload_name) |
| 1607 | .typed<bitwise_not::schema>(); |
| 1608 | } |
| 1609 | |
| 1610 | // aten::bitwise_not(Tensor self) -> Tensor |
| 1611 | at::Tensor bitwise_not::call(const at::Tensor & self) { |
| 1612 | |
| 1613 | static auto op = create_bitwise_not_typed_handle(); |
| 1614 | return op.call(self); |
| 1615 | } |
| 1616 | |
| 1617 | // aten::bitwise_not(Tensor self) -> Tensor |
| 1618 | at::Tensor bitwise_not::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 1619 | |
| 1620 | static auto op = create_bitwise_not_typed_handle(); |
| 1621 | return op.redispatch(dispatchKeySet, self); |
| 1622 | } |
| 1623 | |
| 1624 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bitwise_not_, name, "aten::bitwise_not_") |
| 1625 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bitwise_not_, overload_name, "") |
| 1626 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bitwise_not_, schema_str, "bitwise_not_(Tensor(a!) self) -> Tensor(a!)") |
| 1627 | |
| 1628 | // aten::bitwise_not_(Tensor(a!) self) -> Tensor(a!) |
| 1629 | static C10_NOINLINE c10::TypedOperatorHandle<bitwise_not_::schema> create_bitwise_not__typed_handle() { |
| 1630 | return c10::Dispatcher::singleton() |
| 1631 | .findSchemaOrThrow(bitwise_not_::name, bitwise_not_::overload_name) |
| 1632 | .typed<bitwise_not_::schema>(); |
| 1633 | } |
| 1634 | |
| 1635 | // aten::bitwise_not_(Tensor(a!) self) -> Tensor(a!) |
| 1636 | at::Tensor & bitwise_not_::call(at::Tensor & self) { |
| 1637 | |
| 1638 | static auto op = create_bitwise_not__typed_handle(); |
| 1639 | return op.call(self); |
| 1640 | } |
| 1641 | |
| 1642 | // aten::bitwise_not_(Tensor(a!) self) -> Tensor(a!) |
| 1643 | at::Tensor & bitwise_not_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 1644 | |
| 1645 | static auto op = create_bitwise_not__typed_handle(); |
| 1646 | return op.redispatch(dispatchKeySet, self); |
| 1647 | } |
| 1648 | |
| 1649 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bitwise_not_out, name, "aten::bitwise_not") |
| 1650 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bitwise_not_out, overload_name, "out") |
| 1651 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bitwise_not_out, schema_str, "bitwise_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 1652 | |
| 1653 | // aten::bitwise_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 1654 | static C10_NOINLINE c10::TypedOperatorHandle<bitwise_not_out::schema> create_bitwise_not_out_typed_handle() { |
| 1655 | return c10::Dispatcher::singleton() |
| 1656 | .findSchemaOrThrow(bitwise_not_out::name, bitwise_not_out::overload_name) |
| 1657 | .typed<bitwise_not_out::schema>(); |
| 1658 | } |
| 1659 | |
| 1660 | // aten::bitwise_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 1661 | at::Tensor & bitwise_not_out::call(const at::Tensor & self, at::Tensor & out) { |
| 1662 | |
| 1663 | static auto op = create_bitwise_not_out_typed_handle(); |
| 1664 | return op.call(self, out); |
| 1665 | } |
| 1666 | |
| 1667 | // aten::bitwise_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 1668 | at::Tensor & bitwise_not_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 1669 | |
| 1670 | static auto op = create_bitwise_not_out_typed_handle(); |
| 1671 | return op.redispatch(dispatchKeySet, self, out); |
| 1672 | } |
| 1673 | |
| 1674 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logical_not, name, "aten::logical_not") |
| 1675 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logical_not, overload_name, "") |
| 1676 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logical_not, schema_str, "logical_not(Tensor self) -> Tensor") |
| 1677 | |
| 1678 | // aten::logical_not(Tensor self) -> Tensor |
| 1679 | static C10_NOINLINE c10::TypedOperatorHandle<logical_not::schema> create_logical_not_typed_handle() { |
| 1680 | return c10::Dispatcher::singleton() |
| 1681 | .findSchemaOrThrow(logical_not::name, logical_not::overload_name) |
| 1682 | .typed<logical_not::schema>(); |
| 1683 | } |
| 1684 | |
| 1685 | // aten::logical_not(Tensor self) -> Tensor |
| 1686 | at::Tensor logical_not::call(const at::Tensor & self) { |
| 1687 | |
| 1688 | static auto op = create_logical_not_typed_handle(); |
| 1689 | return op.call(self); |
| 1690 | } |
| 1691 | |
| 1692 | // aten::logical_not(Tensor self) -> Tensor |
| 1693 | at::Tensor logical_not::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 1694 | |
| 1695 | static auto op = create_logical_not_typed_handle(); |
| 1696 | return op.redispatch(dispatchKeySet, self); |
| 1697 | } |
| 1698 | |
| 1699 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logical_not_, name, "aten::logical_not_") |
| 1700 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logical_not_, overload_name, "") |
| 1701 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logical_not_, schema_str, "logical_not_(Tensor(a!) self) -> Tensor(a!)") |
| 1702 | |
| 1703 | // aten::logical_not_(Tensor(a!) self) -> Tensor(a!) |
| 1704 | static C10_NOINLINE c10::TypedOperatorHandle<logical_not_::schema> create_logical_not__typed_handle() { |
| 1705 | return c10::Dispatcher::singleton() |
| 1706 | .findSchemaOrThrow(logical_not_::name, logical_not_::overload_name) |
| 1707 | .typed<logical_not_::schema>(); |
| 1708 | } |
| 1709 | |
| 1710 | // aten::logical_not_(Tensor(a!) self) -> Tensor(a!) |
| 1711 | at::Tensor & logical_not_::call(at::Tensor & self) { |
| 1712 | |
| 1713 | static auto op = create_logical_not__typed_handle(); |
| 1714 | return op.call(self); |
| 1715 | } |
| 1716 | |
| 1717 | // aten::logical_not_(Tensor(a!) self) -> Tensor(a!) |
| 1718 | at::Tensor & logical_not_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 1719 | |
| 1720 | static auto op = create_logical_not__typed_handle(); |
| 1721 | return op.redispatch(dispatchKeySet, self); |
| 1722 | } |
| 1723 | |
| 1724 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logical_not_out, name, "aten::logical_not") |
| 1725 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logical_not_out, overload_name, "out") |
| 1726 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logical_not_out, schema_str, "logical_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 1727 | |
| 1728 | // aten::logical_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 1729 | static C10_NOINLINE c10::TypedOperatorHandle<logical_not_out::schema> create_logical_not_out_typed_handle() { |
| 1730 | return c10::Dispatcher::singleton() |
| 1731 | .findSchemaOrThrow(logical_not_out::name, logical_not_out::overload_name) |
| 1732 | .typed<logical_not_out::schema>(); |
| 1733 | } |
| 1734 | |
| 1735 | // aten::logical_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 1736 | at::Tensor & logical_not_out::call(const at::Tensor & self, at::Tensor & out) { |
| 1737 | |
| 1738 | static auto op = create_logical_not_out_typed_handle(); |
| 1739 | return op.call(self, out); |
| 1740 | } |
| 1741 | |
| 1742 | // aten::logical_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 1743 | at::Tensor & logical_not_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 1744 | |
| 1745 | static auto op = create_logical_not_out_typed_handle(); |
| 1746 | return op.redispatch(dispatchKeySet, self, out); |
| 1747 | } |
| 1748 | |
| 1749 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(concatenate, name, "aten::concatenate") |
| 1750 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(concatenate, overload_name, "") |
| 1751 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(concatenate, schema_str, "concatenate(Tensor[] tensors, int dim=0) -> Tensor") |
| 1752 | |
| 1753 | // aten::concatenate(Tensor[] tensors, int dim=0) -> Tensor |
| 1754 | static C10_NOINLINE c10::TypedOperatorHandle<concatenate::schema> create_concatenate_typed_handle() { |
| 1755 | return c10::Dispatcher::singleton() |
| 1756 | .findSchemaOrThrow(concatenate::name, concatenate::overload_name) |
| 1757 | .typed<concatenate::schema>(); |
| 1758 | } |
| 1759 | |
| 1760 | // aten::concatenate(Tensor[] tensors, int dim=0) -> Tensor |
| 1761 | at::Tensor concatenate::call(at::TensorList tensors, int64_t dim) { |
| 1762 | |
| 1763 | static auto op = create_concatenate_typed_handle(); |
| 1764 | return op.call(tensors, dim); |
| 1765 | } |
| 1766 | |
| 1767 | // aten::concatenate(Tensor[] tensors, int dim=0) -> Tensor |
| 1768 | at::Tensor concatenate::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, int64_t dim) { |
| 1769 | |
| 1770 | static auto op = create_concatenate_typed_handle(); |
| 1771 | return op.redispatch(dispatchKeySet, tensors, dim); |
| 1772 | } |
| 1773 | |
| 1774 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(concatenate_out, name, "aten::concatenate") |
| 1775 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(concatenate_out, overload_name, "out") |
| 1776 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(concatenate_out, schema_str, "concatenate.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)") |
| 1777 | |
| 1778 | // aten::concatenate.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) |
| 1779 | static C10_NOINLINE c10::TypedOperatorHandle<concatenate_out::schema> create_concatenate_out_typed_handle() { |
| 1780 | return c10::Dispatcher::singleton() |
| 1781 | .findSchemaOrThrow(concatenate_out::name, concatenate_out::overload_name) |
| 1782 | .typed<concatenate_out::schema>(); |
| 1783 | } |
| 1784 | |
| 1785 | // aten::concatenate.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) |
| 1786 | at::Tensor & concatenate_out::call(at::TensorList tensors, int64_t dim, at::Tensor & out) { |
| 1787 | |
| 1788 | static auto op = create_concatenate_out_typed_handle(); |
| 1789 | return op.call(tensors, dim, out); |
| 1790 | } |
| 1791 | |
| 1792 | // aten::concatenate.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) |
| 1793 | at::Tensor & concatenate_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, int64_t dim, at::Tensor & out) { |
| 1794 | |
| 1795 | static auto op = create_concatenate_out_typed_handle(); |
| 1796 | return op.redispatch(dispatchKeySet, tensors, dim, out); |
| 1797 | } |
| 1798 | |
| 1799 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(concatenate_names, name, "aten::concatenate") |
| 1800 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(concatenate_names, overload_name, "names") |
| 1801 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(concatenate_names, schema_str, "concatenate.names(Tensor[] tensors, Dimname dim) -> Tensor") |
| 1802 | |
| 1803 | // aten::concatenate.names(Tensor[] tensors, Dimname dim) -> Tensor |
| 1804 | static C10_NOINLINE c10::TypedOperatorHandle<concatenate_names::schema> create_concatenate_names_typed_handle() { |
| 1805 | return c10::Dispatcher::singleton() |
| 1806 | .findSchemaOrThrow(concatenate_names::name, concatenate_names::overload_name) |
| 1807 | .typed<concatenate_names::schema>(); |
| 1808 | } |
| 1809 | |
| 1810 | // aten::concatenate.names(Tensor[] tensors, Dimname dim) -> Tensor |
| 1811 | at::Tensor concatenate_names::call(at::TensorList tensors, at::Dimname dim) { |
| 1812 | |
| 1813 | static auto op = create_concatenate_names_typed_handle(); |
| 1814 | return op.call(tensors, dim); |
| 1815 | } |
| 1816 | |
| 1817 | // aten::concatenate.names(Tensor[] tensors, Dimname dim) -> Tensor |
| 1818 | at::Tensor concatenate_names::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Dimname dim) { |
| 1819 | |
| 1820 | static auto op = create_concatenate_names_typed_handle(); |
| 1821 | return op.redispatch(dispatchKeySet, tensors, dim); |
| 1822 | } |
| 1823 | |
| 1824 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(concatenate_names_out, name, "aten::concatenate") |
| 1825 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(concatenate_names_out, overload_name, "names_out") |
| 1826 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(concatenate_names_out, schema_str, "concatenate.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!)") |
| 1827 | |
| 1828 | // aten::concatenate.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!) |
| 1829 | static C10_NOINLINE c10::TypedOperatorHandle<concatenate_names_out::schema> create_concatenate_names_out_typed_handle() { |
| 1830 | return c10::Dispatcher::singleton() |
| 1831 | .findSchemaOrThrow(concatenate_names_out::name, concatenate_names_out::overload_name) |
| 1832 | .typed<concatenate_names_out::schema>(); |
| 1833 | } |
| 1834 | |
| 1835 | // aten::concatenate.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!) |
| 1836 | at::Tensor & concatenate_names_out::call(at::TensorList tensors, at::Dimname dim, at::Tensor & out) { |
| 1837 | |
| 1838 | static auto op = create_concatenate_names_out_typed_handle(); |
| 1839 | return op.call(tensors, dim, out); |
| 1840 | } |
| 1841 | |
| 1842 | // aten::concatenate.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!) |
| 1843 | at::Tensor & concatenate_names_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Dimname dim, at::Tensor & out) { |
| 1844 | |
| 1845 | static auto op = create_concatenate_names_out_typed_handle(); |
| 1846 | return op.redispatch(dispatchKeySet, tensors, dim, out); |
| 1847 | } |
| 1848 | |
| 1849 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ceil, name, "aten::ceil") |
| 1850 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ceil, overload_name, "") |
| 1851 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ceil, schema_str, "ceil(Tensor self) -> Tensor") |
| 1852 | |
| 1853 | // aten::ceil(Tensor self) -> Tensor |
| 1854 | static C10_NOINLINE c10::TypedOperatorHandle<ceil::schema> create_ceil_typed_handle() { |
| 1855 | return c10::Dispatcher::singleton() |
| 1856 | .findSchemaOrThrow(ceil::name, ceil::overload_name) |
| 1857 | .typed<ceil::schema>(); |
| 1858 | } |
| 1859 | |
| 1860 | // aten::ceil(Tensor self) -> Tensor |
| 1861 | at::Tensor ceil::call(const at::Tensor & self) { |
| 1862 | |
| 1863 | static auto op = create_ceil_typed_handle(); |
| 1864 | return op.call(self); |
| 1865 | } |
| 1866 | |
| 1867 | // aten::ceil(Tensor self) -> Tensor |
| 1868 | at::Tensor ceil::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 1869 | |
| 1870 | static auto op = create_ceil_typed_handle(); |
| 1871 | return op.redispatch(dispatchKeySet, self); |
| 1872 | } |
| 1873 | |
| 1874 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ceil_, name, "aten::ceil_") |
| 1875 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ceil_, overload_name, "") |
| 1876 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ceil_, schema_str, "ceil_(Tensor(a!) self) -> Tensor(a!)") |
| 1877 | |
| 1878 | // aten::ceil_(Tensor(a!) self) -> Tensor(a!) |
| 1879 | static C10_NOINLINE c10::TypedOperatorHandle<ceil_::schema> create_ceil__typed_handle() { |
| 1880 | return c10::Dispatcher::singleton() |
| 1881 | .findSchemaOrThrow(ceil_::name, ceil_::overload_name) |
| 1882 | .typed<ceil_::schema>(); |
| 1883 | } |
| 1884 | |
| 1885 | // aten::ceil_(Tensor(a!) self) -> Tensor(a!) |
| 1886 | at::Tensor & ceil_::call(at::Tensor & self) { |
| 1887 | |
| 1888 | static auto op = create_ceil__typed_handle(); |
| 1889 | return op.call(self); |
| 1890 | } |
| 1891 | |
| 1892 | // aten::ceil_(Tensor(a!) self) -> Tensor(a!) |
| 1893 | at::Tensor & ceil_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 1894 | |
| 1895 | static auto op = create_ceil__typed_handle(); |
| 1896 | return op.redispatch(dispatchKeySet, self); |
| 1897 | } |
| 1898 | |
| 1899 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ceil_out, name, "aten::ceil") |
| 1900 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ceil_out, overload_name, "out") |
| 1901 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ceil_out, schema_str, "ceil.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 1902 | |
| 1903 | // aten::ceil.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 1904 | static C10_NOINLINE c10::TypedOperatorHandle<ceil_out::schema> create_ceil_out_typed_handle() { |
| 1905 | return c10::Dispatcher::singleton() |
| 1906 | .findSchemaOrThrow(ceil_out::name, ceil_out::overload_name) |
| 1907 | .typed<ceil_out::schema>(); |
| 1908 | } |
| 1909 | |
| 1910 | // aten::ceil.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 1911 | at::Tensor & ceil_out::call(const at::Tensor & self, at::Tensor & out) { |
| 1912 | |
| 1913 | static auto op = create_ceil_out_typed_handle(); |
| 1914 | return op.call(self, out); |
| 1915 | } |
| 1916 | |
| 1917 | // aten::ceil.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 1918 | at::Tensor & ceil_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 1919 | |
| 1920 | static auto op = create_ceil_out_typed_handle(); |
| 1921 | return op.redispatch(dispatchKeySet, self, out); |
| 1922 | } |
| 1923 | |
| 1924 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv_tbc, name, "aten::conv_tbc") |
| 1925 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv_tbc, overload_name, "") |
| 1926 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv_tbc, schema_str, "conv_tbc(Tensor self, Tensor weight, Tensor bias, int pad=0) -> Tensor") |
| 1927 | |
| 1928 | // aten::conv_tbc(Tensor self, Tensor weight, Tensor bias, int pad=0) -> Tensor |
| 1929 | static C10_NOINLINE c10::TypedOperatorHandle<conv_tbc::schema> create_conv_tbc_typed_handle() { |
| 1930 | return c10::Dispatcher::singleton() |
| 1931 | .findSchemaOrThrow(conv_tbc::name, conv_tbc::overload_name) |
| 1932 | .typed<conv_tbc::schema>(); |
| 1933 | } |
| 1934 | |
| 1935 | // aten::conv_tbc(Tensor self, Tensor weight, Tensor bias, int pad=0) -> Tensor |
| 1936 | at::Tensor conv_tbc::call(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & bias, int64_t pad) { |
| 1937 | |
| 1938 | static auto op = create_conv_tbc_typed_handle(); |
| 1939 | return op.call(self, weight, bias, pad); |
| 1940 | } |
| 1941 | |
| 1942 | // aten::conv_tbc(Tensor self, Tensor weight, Tensor bias, int pad=0) -> Tensor |
| 1943 | at::Tensor conv_tbc::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const at::Tensor & bias, int64_t pad) { |
| 1944 | |
| 1945 | static auto op = create_conv_tbc_typed_handle(); |
| 1946 | return op.redispatch(dispatchKeySet, self, weight, bias, pad); |
| 1947 | } |
| 1948 | |
| 1949 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cosh, name, "aten::cosh") |
| 1950 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cosh, overload_name, "") |
| 1951 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cosh, schema_str, "cosh(Tensor self) -> Tensor") |
| 1952 | |
| 1953 | // aten::cosh(Tensor self) -> Tensor |
| 1954 | static C10_NOINLINE c10::TypedOperatorHandle<cosh::schema> create_cosh_typed_handle() { |
| 1955 | return c10::Dispatcher::singleton() |
| 1956 | .findSchemaOrThrow(cosh::name, cosh::overload_name) |
| 1957 | .typed<cosh::schema>(); |
| 1958 | } |
| 1959 | |
| 1960 | // aten::cosh(Tensor self) -> Tensor |
| 1961 | at::Tensor cosh::call(const at::Tensor & self) { |
| 1962 | |
| 1963 | static auto op = create_cosh_typed_handle(); |
| 1964 | return op.call(self); |
| 1965 | } |
| 1966 | |
| 1967 | // aten::cosh(Tensor self) -> Tensor |
| 1968 | at::Tensor cosh::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 1969 | |
| 1970 | static auto op = create_cosh_typed_handle(); |
| 1971 | return op.redispatch(dispatchKeySet, self); |
| 1972 | } |
| 1973 | |
| 1974 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cosh_, name, "aten::cosh_") |
| 1975 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cosh_, overload_name, "") |
| 1976 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cosh_, schema_str, "cosh_(Tensor(a!) self) -> Tensor(a!)") |
| 1977 | |
| 1978 | // aten::cosh_(Tensor(a!) self) -> Tensor(a!) |
| 1979 | static C10_NOINLINE c10::TypedOperatorHandle<cosh_::schema> create_cosh__typed_handle() { |
| 1980 | return c10::Dispatcher::singleton() |
| 1981 | .findSchemaOrThrow(cosh_::name, cosh_::overload_name) |
| 1982 | .typed<cosh_::schema>(); |
| 1983 | } |
| 1984 | |
| 1985 | // aten::cosh_(Tensor(a!) self) -> Tensor(a!) |
| 1986 | at::Tensor & cosh_::call(at::Tensor & self) { |
| 1987 | |
| 1988 | static auto op = create_cosh__typed_handle(); |
| 1989 | return op.call(self); |
| 1990 | } |
| 1991 | |
| 1992 | // aten::cosh_(Tensor(a!) self) -> Tensor(a!) |
| 1993 | at::Tensor & cosh_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 1994 | |
| 1995 | static auto op = create_cosh__typed_handle(); |
| 1996 | return op.redispatch(dispatchKeySet, self); |
| 1997 | } |
| 1998 | |
| 1999 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cosh_out, name, "aten::cosh") |
| 2000 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cosh_out, overload_name, "out") |
| 2001 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cosh_out, schema_str, "cosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 2002 | |
| 2003 | // aten::cosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 2004 | static C10_NOINLINE c10::TypedOperatorHandle<cosh_out::schema> create_cosh_out_typed_handle() { |
| 2005 | return c10::Dispatcher::singleton() |
| 2006 | .findSchemaOrThrow(cosh_out::name, cosh_out::overload_name) |
| 2007 | .typed<cosh_out::schema>(); |
| 2008 | } |
| 2009 | |
| 2010 | // aten::cosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 2011 | at::Tensor & cosh_out::call(const at::Tensor & self, at::Tensor & out) { |
| 2012 | |
| 2013 | static auto op = create_cosh_out_typed_handle(); |
| 2014 | return op.call(self, out); |
| 2015 | } |
| 2016 | |
| 2017 | // aten::cosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 2018 | at::Tensor & cosh_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 2019 | |
| 2020 | static auto op = create_cosh_out_typed_handle(); |
| 2021 | return op.redispatch(dispatchKeySet, self, out); |
| 2022 | } |
| 2023 | |
| 2024 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cosine_embedding_loss, name, "aten::cosine_embedding_loss") |
| 2025 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cosine_embedding_loss, overload_name, "") |
| 2026 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cosine_embedding_loss, schema_str, "cosine_embedding_loss(Tensor input1, Tensor input2, Tensor target, float margin=0.0, int reduction=Mean) -> Tensor") |
| 2027 | |
| 2028 | // aten::cosine_embedding_loss(Tensor input1, Tensor input2, Tensor target, float margin=0.0, int reduction=Mean) -> Tensor |
| 2029 | static C10_NOINLINE c10::TypedOperatorHandle<cosine_embedding_loss::schema> create_cosine_embedding_loss_typed_handle() { |
| 2030 | return c10::Dispatcher::singleton() |
| 2031 | .findSchemaOrThrow(cosine_embedding_loss::name, cosine_embedding_loss::overload_name) |
| 2032 | .typed<cosine_embedding_loss::schema>(); |
| 2033 | } |
| 2034 | |
| 2035 | // aten::cosine_embedding_loss(Tensor input1, Tensor input2, Tensor target, float margin=0.0, int reduction=Mean) -> Tensor |
| 2036 | at::Tensor cosine_embedding_loss::call(const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & target, double margin, int64_t reduction) { |
| 2037 | |
| 2038 | static auto op = create_cosine_embedding_loss_typed_handle(); |
| 2039 | return op.call(input1, input2, target, margin, reduction); |
| 2040 | } |
| 2041 | |
| 2042 | // aten::cosine_embedding_loss(Tensor input1, Tensor input2, Tensor target, float margin=0.0, int reduction=Mean) -> Tensor |
| 2043 | at::Tensor cosine_embedding_loss::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & target, double margin, int64_t reduction) { |
| 2044 | |
| 2045 | static auto op = create_cosine_embedding_loss_typed_handle(); |
| 2046 | return op.redispatch(dispatchKeySet, input1, input2, target, margin, reduction); |
| 2047 | } |
| 2048 | |
| 2049 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cudnn_affine_grid_generator_backward, name, "aten::cudnn_affine_grid_generator_backward") |
| 2050 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cudnn_affine_grid_generator_backward, overload_name, "") |
| 2051 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cudnn_affine_grid_generator_backward, schema_str, "cudnn_affine_grid_generator_backward(Tensor grad, int N, int C, int H, int W) -> Tensor grad_theta") |
| 2052 | |
| 2053 | // aten::cudnn_affine_grid_generator_backward(Tensor grad, int N, int C, int H, int W) -> Tensor grad_theta |
| 2054 | static C10_NOINLINE c10::TypedOperatorHandle<cudnn_affine_grid_generator_backward::schema> create_cudnn_affine_grid_generator_backward_typed_handle() { |
| 2055 | return c10::Dispatcher::singleton() |
| 2056 | .findSchemaOrThrow(cudnn_affine_grid_generator_backward::name, cudnn_affine_grid_generator_backward::overload_name) |
| 2057 | .typed<cudnn_affine_grid_generator_backward::schema>(); |
| 2058 | } |
| 2059 | |
| 2060 | // aten::cudnn_affine_grid_generator_backward(Tensor grad, int N, int C, int H, int W) -> Tensor grad_theta |
| 2061 | at::Tensor cudnn_affine_grid_generator_backward::call(const at::Tensor & grad, int64_t N, int64_t C, int64_t H, int64_t W) { |
| 2062 | |
| 2063 | static auto op = create_cudnn_affine_grid_generator_backward_typed_handle(); |
| 2064 | return op.call(grad, N, C, H, W); |
| 2065 | } |
| 2066 | |
| 2067 | // aten::cudnn_affine_grid_generator_backward(Tensor grad, int N, int C, int H, int W) -> Tensor grad_theta |
| 2068 | at::Tensor cudnn_affine_grid_generator_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, int64_t N, int64_t C, int64_t H, int64_t W) { |
| 2069 | |
| 2070 | static auto op = create_cudnn_affine_grid_generator_backward_typed_handle(); |
| 2071 | return op.redispatch(dispatchKeySet, grad, N, C, H, W); |
| 2072 | } |
| 2073 | |
| 2074 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cudnn_grid_sampler, name, "aten::cudnn_grid_sampler") |
| 2075 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cudnn_grid_sampler, overload_name, "") |
| 2076 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cudnn_grid_sampler, schema_str, "cudnn_grid_sampler(Tensor self, Tensor grid) -> Tensor output") |
| 2077 | |
| 2078 | // aten::cudnn_grid_sampler(Tensor self, Tensor grid) -> Tensor output |
| 2079 | static C10_NOINLINE c10::TypedOperatorHandle<cudnn_grid_sampler::schema> create_cudnn_grid_sampler_typed_handle() { |
| 2080 | return c10::Dispatcher::singleton() |
| 2081 | .findSchemaOrThrow(cudnn_grid_sampler::name, cudnn_grid_sampler::overload_name) |
| 2082 | .typed<cudnn_grid_sampler::schema>(); |
| 2083 | } |
| 2084 | |
| 2085 | // aten::cudnn_grid_sampler(Tensor self, Tensor grid) -> Tensor output |
| 2086 | at::Tensor cudnn_grid_sampler::call(const at::Tensor & self, const at::Tensor & grid) { |
| 2087 | |
| 2088 | static auto op = create_cudnn_grid_sampler_typed_handle(); |
| 2089 | return op.call(self, grid); |
| 2090 | } |
| 2091 | |
| 2092 | // aten::cudnn_grid_sampler(Tensor self, Tensor grid) -> Tensor output |
| 2093 | at::Tensor cudnn_grid_sampler::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & grid) { |
| 2094 | |
| 2095 | static auto op = create_cudnn_grid_sampler_typed_handle(); |
| 2096 | return op.redispatch(dispatchKeySet, self, grid); |
| 2097 | } |
| 2098 | |
| 2099 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cummin, name, "aten::cummin") |
| 2100 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cummin, overload_name, "") |
| 2101 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cummin, schema_str, "cummin(Tensor self, int dim) -> (Tensor values, Tensor indices)") |
| 2102 | |
| 2103 | // aten::cummin(Tensor self, int dim) -> (Tensor values, Tensor indices) |
| 2104 | static C10_NOINLINE c10::TypedOperatorHandle<cummin::schema> create_cummin_typed_handle() { |
| 2105 | return c10::Dispatcher::singleton() |
| 2106 | .findSchemaOrThrow(cummin::name, cummin::overload_name) |
| 2107 | .typed<cummin::schema>(); |
| 2108 | } |
| 2109 | |
| 2110 | // aten::cummin(Tensor self, int dim) -> (Tensor values, Tensor indices) |
| 2111 | ::std::tuple<at::Tensor,at::Tensor> cummin::call(const at::Tensor & self, int64_t dim) { |
| 2112 | |
| 2113 | static auto op = create_cummin_typed_handle(); |
| 2114 | return op.call(self, dim); |
| 2115 | } |
| 2116 | |
| 2117 | // aten::cummin(Tensor self, int dim) -> (Tensor values, Tensor indices) |
| 2118 | ::std::tuple<at::Tensor,at::Tensor> cummin::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim) { |
| 2119 | |
| 2120 | static auto op = create_cummin_typed_handle(); |
| 2121 | return op.redispatch(dispatchKeySet, self, dim); |
| 2122 | } |
| 2123 | |
| 2124 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cummin_out, name, "aten::cummin") |
| 2125 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cummin_out, overload_name, "out") |
| 2126 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cummin_out, schema_str, "cummin.out(Tensor self, int dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)") |
| 2127 | |
| 2128 | // aten::cummin.out(Tensor self, int dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) |
| 2129 | static C10_NOINLINE c10::TypedOperatorHandle<cummin_out::schema> create_cummin_out_typed_handle() { |
| 2130 | return c10::Dispatcher::singleton() |
| 2131 | .findSchemaOrThrow(cummin_out::name, cummin_out::overload_name) |
| 2132 | .typed<cummin_out::schema>(); |
| 2133 | } |
| 2134 | |
| 2135 | // aten::cummin.out(Tensor self, int dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) |
| 2136 | ::std::tuple<at::Tensor &,at::Tensor &> cummin_out::call(const at::Tensor & self, int64_t dim, at::Tensor & values, at::Tensor & indices) { |
| 2137 | |
| 2138 | static auto op = create_cummin_out_typed_handle(); |
| 2139 | return op.call(self, dim, values, indices); |
| 2140 | } |
| 2141 | |
| 2142 | // aten::cummin.out(Tensor self, int dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) |
| 2143 | ::std::tuple<at::Tensor &,at::Tensor &> cummin_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, at::Tensor & values, at::Tensor & indices) { |
| 2144 | |
| 2145 | static auto op = create_cummin_out_typed_handle(); |
| 2146 | return op.redispatch(dispatchKeySet, self, dim, values, indices); |
| 2147 | } |
| 2148 | |
| 2149 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cummin_dimname, name, "aten::cummin") |
| 2150 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cummin_dimname, overload_name, "dimname") |
| 2151 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cummin_dimname, schema_str, "cummin.dimname(Tensor self, Dimname dim) -> (Tensor values, Tensor indices)") |
| 2152 | |
| 2153 | // aten::cummin.dimname(Tensor self, Dimname dim) -> (Tensor values, Tensor indices) |
| 2154 | static C10_NOINLINE c10::TypedOperatorHandle<cummin_dimname::schema> create_cummin_dimname_typed_handle() { |
| 2155 | return c10::Dispatcher::singleton() |
| 2156 | .findSchemaOrThrow(cummin_dimname::name, cummin_dimname::overload_name) |
| 2157 | .typed<cummin_dimname::schema>(); |
| 2158 | } |
| 2159 | |
| 2160 | // aten::cummin.dimname(Tensor self, Dimname dim) -> (Tensor values, Tensor indices) |
| 2161 | ::std::tuple<at::Tensor,at::Tensor> cummin_dimname::call(const at::Tensor & self, at::Dimname dim) { |
| 2162 | |
| 2163 | static auto op = create_cummin_dimname_typed_handle(); |
| 2164 | return op.call(self, dim); |
| 2165 | } |
| 2166 | |
| 2167 | // aten::cummin.dimname(Tensor self, Dimname dim) -> (Tensor values, Tensor indices) |
| 2168 | ::std::tuple<at::Tensor,at::Tensor> cummin_dimname::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim) { |
| 2169 | |
| 2170 | static auto op = create_cummin_dimname_typed_handle(); |
| 2171 | return op.redispatch(dispatchKeySet, self, dim); |
| 2172 | } |
| 2173 | |
| 2174 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cummin_dimname_out, name, "aten::cummin") |
| 2175 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cummin_dimname_out, overload_name, "dimname_out") |
| 2176 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cummin_dimname_out, schema_str, "cummin.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)") |
| 2177 | |
| 2178 | // aten::cummin.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) |
| 2179 | static C10_NOINLINE c10::TypedOperatorHandle<cummin_dimname_out::schema> create_cummin_dimname_out_typed_handle() { |
| 2180 | return c10::Dispatcher::singleton() |
| 2181 | .findSchemaOrThrow(cummin_dimname_out::name, cummin_dimname_out::overload_name) |
| 2182 | .typed<cummin_dimname_out::schema>(); |
| 2183 | } |
| 2184 | |
| 2185 | // aten::cummin.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) |
| 2186 | ::std::tuple<at::Tensor &,at::Tensor &> cummin_dimname_out::call(const at::Tensor & self, at::Dimname dim, at::Tensor & values, at::Tensor & indices) { |
| 2187 | |
| 2188 | static auto op = create_cummin_dimname_out_typed_handle(); |
| 2189 | return op.call(self, dim, values, indices); |
| 2190 | } |
| 2191 | |
| 2192 | // aten::cummin.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) |
| 2193 | ::std::tuple<at::Tensor &,at::Tensor &> cummin_dimname_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, at::Tensor & values, at::Tensor & indices) { |
| 2194 | |
| 2195 | static auto op = create_cummin_dimname_out_typed_handle(); |
| 2196 | return op.redispatch(dispatchKeySet, self, dim, values, indices); |
| 2197 | } |
| 2198 | |
| 2199 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cummin_helper, name, "aten::_cummin_helper") |
| 2200 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cummin_helper, overload_name, "") |
| 2201 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cummin_helper, schema_str, "_cummin_helper(Tensor self, Tensor(a!) values, Tensor(b!) indices, int dim) -> ()") |
| 2202 | |
| 2203 | // aten::_cummin_helper(Tensor self, Tensor(a!) values, Tensor(b!) indices, int dim) -> () |
| 2204 | static C10_NOINLINE c10::TypedOperatorHandle<_cummin_helper::schema> create__cummin_helper_typed_handle() { |
| 2205 | return c10::Dispatcher::singleton() |
| 2206 | .findSchemaOrThrow(_cummin_helper::name, _cummin_helper::overload_name) |
| 2207 | .typed<_cummin_helper::schema>(); |
| 2208 | } |
| 2209 | |
| 2210 | // aten::_cummin_helper(Tensor self, Tensor(a!) values, Tensor(b!) indices, int dim) -> () |
| 2211 | void _cummin_helper::call(const at::Tensor & self, at::Tensor & values, at::Tensor & indices, int64_t dim) { |
| 2212 | |
| 2213 | static auto op = create__cummin_helper_typed_handle(); |
| 2214 | return op.call(self, values, indices, dim); |
| 2215 | } |
| 2216 | |
| 2217 | // aten::_cummin_helper(Tensor self, Tensor(a!) values, Tensor(b!) indices, int dim) -> () |
| 2218 | void _cummin_helper::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & values, at::Tensor & indices, int64_t dim) { |
| 2219 | |
| 2220 | static auto op = create__cummin_helper_typed_handle(); |
| 2221 | return op.redispatch(dispatchKeySet, self, values, indices, dim); |
| 2222 | } |
| 2223 | |
| 2224 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div_Tensor, name, "aten::div") |
| 2225 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div_Tensor, overload_name, "Tensor") |
| 2226 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div_Tensor, schema_str, "div.Tensor(Tensor self, Tensor other) -> Tensor") |
| 2227 | |
| 2228 | // aten::div.Tensor(Tensor self, Tensor other) -> Tensor |
| 2229 | static C10_NOINLINE c10::TypedOperatorHandle<div_Tensor::schema> create_div_Tensor_typed_handle() { |
| 2230 | return c10::Dispatcher::singleton() |
| 2231 | .findSchemaOrThrow(div_Tensor::name, div_Tensor::overload_name) |
| 2232 | .typed<div_Tensor::schema>(); |
| 2233 | } |
| 2234 | |
| 2235 | // aten::div.Tensor(Tensor self, Tensor other) -> Tensor |
| 2236 | at::Tensor div_Tensor::call(const at::Tensor & self, const at::Tensor & other) { |
| 2237 | |
| 2238 | static auto op = create_div_Tensor_typed_handle(); |
| 2239 | return op.call(self, other); |
| 2240 | } |
| 2241 | |
| 2242 | // aten::div.Tensor(Tensor self, Tensor other) -> Tensor |
| 2243 | at::Tensor div_Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { |
| 2244 | |
| 2245 | static auto op = create_div_Tensor_typed_handle(); |
| 2246 | return op.redispatch(dispatchKeySet, self, other); |
| 2247 | } |
| 2248 | |
| 2249 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div__Tensor, name, "aten::div_") |
| 2250 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div__Tensor, overload_name, "Tensor") |
| 2251 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div__Tensor, schema_str, "div_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)") |
| 2252 | |
| 2253 | // aten::div_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) |
| 2254 | static C10_NOINLINE c10::TypedOperatorHandle<div__Tensor::schema> create_div__Tensor_typed_handle() { |
| 2255 | return c10::Dispatcher::singleton() |
| 2256 | .findSchemaOrThrow(div__Tensor::name, div__Tensor::overload_name) |
| 2257 | .typed<div__Tensor::schema>(); |
| 2258 | } |
| 2259 | |
| 2260 | // aten::div_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) |
| 2261 | at::Tensor & div__Tensor::call(at::Tensor & self, const at::Tensor & other) { |
| 2262 | |
| 2263 | static auto op = create_div__Tensor_typed_handle(); |
| 2264 | return op.call(self, other); |
| 2265 | } |
| 2266 | |
| 2267 | // aten::div_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) |
| 2268 | at::Tensor & div__Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 2269 | |
| 2270 | static auto op = create_div__Tensor_typed_handle(); |
| 2271 | return op.redispatch(dispatchKeySet, self, other); |
| 2272 | } |
| 2273 | |
| 2274 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div_out, name, "aten::div") |
| 2275 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div_out, overload_name, "out") |
| 2276 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div_out, schema_str, "div.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)") |
| 2277 | |
| 2278 | // aten::div.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 2279 | static C10_NOINLINE c10::TypedOperatorHandle<div_out::schema> create_div_out_typed_handle() { |
| 2280 | return c10::Dispatcher::singleton() |
| 2281 | .findSchemaOrThrow(div_out::name, div_out::overload_name) |
| 2282 | .typed<div_out::schema>(); |
| 2283 | } |
| 2284 | |
| 2285 | // aten::div.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 2286 | at::Tensor & div_out::call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 2287 | |
| 2288 | static auto op = create_div_out_typed_handle(); |
| 2289 | return op.call(self, other, out); |
| 2290 | } |
| 2291 | |
| 2292 | // aten::div.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 2293 | at::Tensor & div_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 2294 | |
| 2295 | static auto op = create_div_out_typed_handle(); |
| 2296 | return op.redispatch(dispatchKeySet, self, other, out); |
| 2297 | } |
| 2298 | |
| 2299 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div_Tensor_mode, name, "aten::div") |
| 2300 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div_Tensor_mode, overload_name, "Tensor_mode") |
| 2301 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div_Tensor_mode, schema_str, "div.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> Tensor") |
| 2302 | |
| 2303 | // aten::div.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> Tensor |
| 2304 | static C10_NOINLINE c10::TypedOperatorHandle<div_Tensor_mode::schema> create_div_Tensor_mode_typed_handle() { |
| 2305 | return c10::Dispatcher::singleton() |
| 2306 | .findSchemaOrThrow(div_Tensor_mode::name, div_Tensor_mode::overload_name) |
| 2307 | .typed<div_Tensor_mode::schema>(); |
| 2308 | } |
| 2309 | |
| 2310 | // aten::div.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> Tensor |
| 2311 | at::Tensor div_Tensor_mode::call(const at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode) { |
| 2312 | |
| 2313 | static auto op = create_div_Tensor_mode_typed_handle(); |
| 2314 | return op.call(self, other, rounding_mode); |
| 2315 | } |
| 2316 | |
| 2317 | // aten::div.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> Tensor |
| 2318 | at::Tensor div_Tensor_mode::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode) { |
| 2319 | |
| 2320 | static auto op = create_div_Tensor_mode_typed_handle(); |
| 2321 | return op.redispatch(dispatchKeySet, self, other, rounding_mode); |
| 2322 | } |
| 2323 | |
| 2324 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div__Tensor_mode, name, "aten::div_") |
| 2325 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div__Tensor_mode, overload_name, "Tensor_mode") |
| 2326 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div__Tensor_mode, schema_str, "div_.Tensor_mode(Tensor(a!) self, Tensor other, *, str? rounding_mode) -> Tensor(a!)") |
| 2327 | |
| 2328 | // aten::div_.Tensor_mode(Tensor(a!) self, Tensor other, *, str? rounding_mode) -> Tensor(a!) |
| 2329 | static C10_NOINLINE c10::TypedOperatorHandle<div__Tensor_mode::schema> create_div__Tensor_mode_typed_handle() { |
| 2330 | return c10::Dispatcher::singleton() |
| 2331 | .findSchemaOrThrow(div__Tensor_mode::name, div__Tensor_mode::overload_name) |
| 2332 | .typed<div__Tensor_mode::schema>(); |
| 2333 | } |
| 2334 | |
| 2335 | // aten::div_.Tensor_mode(Tensor(a!) self, Tensor other, *, str? rounding_mode) -> Tensor(a!) |
| 2336 | at::Tensor & div__Tensor_mode::call(at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode) { |
| 2337 | |
| 2338 | static auto op = create_div__Tensor_mode_typed_handle(); |
| 2339 | return op.call(self, other, rounding_mode); |
| 2340 | } |
| 2341 | |
| 2342 | // aten::div_.Tensor_mode(Tensor(a!) self, Tensor other, *, str? rounding_mode) -> Tensor(a!) |
| 2343 | at::Tensor & div__Tensor_mode::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode) { |
| 2344 | |
| 2345 | static auto op = create_div__Tensor_mode_typed_handle(); |
| 2346 | return op.redispatch(dispatchKeySet, self, other, rounding_mode); |
| 2347 | } |
| 2348 | |
| 2349 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div_out_mode, name, "aten::div") |
| 2350 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div_out_mode, overload_name, "out_mode") |
| 2351 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div_out_mode, schema_str, "div.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!)") |
| 2352 | |
| 2353 | // aten::div.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!) |
| 2354 | static C10_NOINLINE c10::TypedOperatorHandle<div_out_mode::schema> create_div_out_mode_typed_handle() { |
| 2355 | return c10::Dispatcher::singleton() |
| 2356 | .findSchemaOrThrow(div_out_mode::name, div_out_mode::overload_name) |
| 2357 | .typed<div_out_mode::schema>(); |
| 2358 | } |
| 2359 | |
| 2360 | // aten::div.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!) |
| 2361 | at::Tensor & div_out_mode::call(const at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode, at::Tensor & out) { |
| 2362 | |
| 2363 | static auto op = create_div_out_mode_typed_handle(); |
| 2364 | return op.call(self, other, rounding_mode, out); |
| 2365 | } |
| 2366 | |
| 2367 | // aten::div.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!) |
| 2368 | at::Tensor & div_out_mode::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode, at::Tensor & out) { |
| 2369 | |
| 2370 | static auto op = create_div_out_mode_typed_handle(); |
| 2371 | return op.redispatch(dispatchKeySet, self, other, rounding_mode, out); |
| 2372 | } |
| 2373 | |
| 2374 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div_Scalar, name, "aten::div") |
| 2375 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div_Scalar, overload_name, "Scalar") |
| 2376 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div_Scalar, schema_str, "div.Scalar(Tensor self, Scalar other) -> Tensor") |
| 2377 | |
| 2378 | // aten::div.Scalar(Tensor self, Scalar other) -> Tensor |
| 2379 | static C10_NOINLINE c10::TypedOperatorHandle<div_Scalar::schema> create_div_Scalar_typed_handle() { |
| 2380 | return c10::Dispatcher::singleton() |
| 2381 | .findSchemaOrThrow(div_Scalar::name, div_Scalar::overload_name) |
| 2382 | .typed<div_Scalar::schema>(); |
| 2383 | } |
| 2384 | |
| 2385 | // aten::div.Scalar(Tensor self, Scalar other) -> Tensor |
| 2386 | at::Tensor div_Scalar::call(const at::Tensor & self, const at::Scalar & other) { |
| 2387 | |
| 2388 | static auto op = create_div_Scalar_typed_handle(); |
| 2389 | return op.call(self, other); |
| 2390 | } |
| 2391 | |
| 2392 | // aten::div.Scalar(Tensor self, Scalar other) -> Tensor |
| 2393 | at::Tensor div_Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { |
| 2394 | |
| 2395 | static auto op = create_div_Scalar_typed_handle(); |
| 2396 | return op.redispatch(dispatchKeySet, self, other); |
| 2397 | } |
| 2398 | |
| 2399 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div__Scalar, name, "aten::div_") |
| 2400 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div__Scalar, overload_name, "Scalar") |
| 2401 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div__Scalar, schema_str, "div_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)") |
| 2402 | |
| 2403 | // aten::div_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) |
| 2404 | static C10_NOINLINE c10::TypedOperatorHandle<div__Scalar::schema> create_div__Scalar_typed_handle() { |
| 2405 | return c10::Dispatcher::singleton() |
| 2406 | .findSchemaOrThrow(div__Scalar::name, div__Scalar::overload_name) |
| 2407 | .typed<div__Scalar::schema>(); |
| 2408 | } |
| 2409 | |
| 2410 | // aten::div_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) |
| 2411 | at::Tensor & div__Scalar::call(at::Tensor & self, const at::Scalar & other) { |
| 2412 | |
| 2413 | static auto op = create_div__Scalar_typed_handle(); |
| 2414 | return op.call(self, other); |
| 2415 | } |
| 2416 | |
| 2417 | // aten::div_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) |
| 2418 | at::Tensor & div__Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { |
| 2419 | |
| 2420 | static auto op = create_div__Scalar_typed_handle(); |
| 2421 | return op.redispatch(dispatchKeySet, self, other); |
| 2422 | } |
| 2423 | |
| 2424 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div_Scalar_mode, name, "aten::div") |
| 2425 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div_Scalar_mode, overload_name, "Scalar_mode") |
| 2426 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div_Scalar_mode, schema_str, "div.Scalar_mode(Tensor self, Scalar other, *, str? rounding_mode) -> Tensor") |
| 2427 | |
| 2428 | // aten::div.Scalar_mode(Tensor self, Scalar other, *, str? rounding_mode) -> Tensor |
| 2429 | static C10_NOINLINE c10::TypedOperatorHandle<div_Scalar_mode::schema> create_div_Scalar_mode_typed_handle() { |
| 2430 | return c10::Dispatcher::singleton() |
| 2431 | .findSchemaOrThrow(div_Scalar_mode::name, div_Scalar_mode::overload_name) |
| 2432 | .typed<div_Scalar_mode::schema>(); |
| 2433 | } |
| 2434 | |
| 2435 | // aten::div.Scalar_mode(Tensor self, Scalar other, *, str? rounding_mode) -> Tensor |
| 2436 | at::Tensor div_Scalar_mode::call(const at::Tensor & self, const at::Scalar & other, c10::optional<c10::string_view> rounding_mode) { |
| 2437 | |
| 2438 | static auto op = create_div_Scalar_mode_typed_handle(); |
| 2439 | return op.call(self, other, rounding_mode); |
| 2440 | } |
| 2441 | |
| 2442 | // aten::div.Scalar_mode(Tensor self, Scalar other, *, str? rounding_mode) -> Tensor |
| 2443 | at::Tensor div_Scalar_mode::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, c10::optional<c10::string_view> rounding_mode) { |
| 2444 | |
| 2445 | static auto op = create_div_Scalar_mode_typed_handle(); |
| 2446 | return op.redispatch(dispatchKeySet, self, other, rounding_mode); |
| 2447 | } |
| 2448 | |
| 2449 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div__Scalar_mode, name, "aten::div_") |
| 2450 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div__Scalar_mode, overload_name, "Scalar_mode") |
| 2451 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div__Scalar_mode, schema_str, "div_.Scalar_mode(Tensor(a!) self, Scalar other, *, str? rounding_mode) -> Tensor(a!)") |
| 2452 | |
| 2453 | // aten::div_.Scalar_mode(Tensor(a!) self, Scalar other, *, str? rounding_mode) -> Tensor(a!) |
| 2454 | static C10_NOINLINE c10::TypedOperatorHandle<div__Scalar_mode::schema> create_div__Scalar_mode_typed_handle() { |
| 2455 | return c10::Dispatcher::singleton() |
| 2456 | .findSchemaOrThrow(div__Scalar_mode::name, div__Scalar_mode::overload_name) |
| 2457 | .typed<div__Scalar_mode::schema>(); |
| 2458 | } |
| 2459 | |
| 2460 | // aten::div_.Scalar_mode(Tensor(a!) self, Scalar other, *, str? rounding_mode) -> Tensor(a!) |
| 2461 | at::Tensor & div__Scalar_mode::call(at::Tensor & self, const at::Scalar & other, c10::optional<c10::string_view> rounding_mode) { |
| 2462 | |
| 2463 | static auto op = create_div__Scalar_mode_typed_handle(); |
| 2464 | return op.call(self, other, rounding_mode); |
| 2465 | } |
| 2466 | |
| 2467 | // aten::div_.Scalar_mode(Tensor(a!) self, Scalar other, *, str? rounding_mode) -> Tensor(a!) |
| 2468 | at::Tensor & div__Scalar_mode::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other, c10::optional<c10::string_view> rounding_mode) { |
| 2469 | |
| 2470 | static auto op = create_div__Scalar_mode_typed_handle(); |
| 2471 | return op.redispatch(dispatchKeySet, self, other, rounding_mode); |
| 2472 | } |
| 2473 | |
| 2474 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_embedding_bag_forward_only, name, "aten::_embedding_bag_forward_only") |
| 2475 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_embedding_bag_forward_only, overload_name, "") |
| 2476 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_embedding_bag_forward_only, schema_str, "_embedding_bag_forward_only(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1) -> (Tensor, Tensor, Tensor, Tensor)") |
| 2477 | |
| 2478 | // aten::_embedding_bag_forward_only(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1) -> (Tensor, Tensor, Tensor, Tensor) |
| 2479 | static C10_NOINLINE c10::TypedOperatorHandle<_embedding_bag_forward_only::schema> create__embedding_bag_forward_only_typed_handle() { |
| 2480 | return c10::Dispatcher::singleton() |
| 2481 | .findSchemaOrThrow(_embedding_bag_forward_only::name, _embedding_bag_forward_only::overload_name) |
| 2482 | .typed<_embedding_bag_forward_only::schema>(); |
| 2483 | } |
| 2484 | |
| 2485 | // aten::_embedding_bag_forward_only(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1) -> (Tensor, Tensor, Tensor, Tensor) |
| 2486 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _embedding_bag_forward_only::call(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional<at::Tensor> & per_sample_weights, bool include_last_offset, int64_t padding_idx) { |
| 2487 | |
| 2488 | static auto op = create__embedding_bag_forward_only_typed_handle(); |
| 2489 | return op.call(weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx); |
| 2490 | } |
| 2491 | |
| 2492 | // aten::_embedding_bag_forward_only(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1) -> (Tensor, Tensor, Tensor, Tensor) |
| 2493 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _embedding_bag_forward_only::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional<at::Tensor> & per_sample_weights, bool include_last_offset, int64_t padding_idx) { |
| 2494 | |
| 2495 | static auto op = create__embedding_bag_forward_only_typed_handle(); |
| 2496 | return op.redispatch(dispatchKeySet, weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx); |
| 2497 | } |
| 2498 | |
| 2499 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(embedding_bag, name, "aten::embedding_bag") |
| 2500 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(embedding_bag, overload_name, "") |
| 2501 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(embedding_bag, schema_str, "embedding_bag(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False) -> (Tensor, Tensor, Tensor, Tensor)") |
| 2502 | |
| 2503 | // aten::embedding_bag(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False) -> (Tensor, Tensor, Tensor, Tensor) |
| 2504 | static C10_NOINLINE c10::TypedOperatorHandle<embedding_bag::schema> create_embedding_bag_typed_handle() { |
| 2505 | return c10::Dispatcher::singleton() |
| 2506 | .findSchemaOrThrow(embedding_bag::name, embedding_bag::overload_name) |
| 2507 | .typed<embedding_bag::schema>(); |
| 2508 | } |
| 2509 | |
| 2510 | // aten::embedding_bag(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False) -> (Tensor, Tensor, Tensor, Tensor) |
| 2511 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> embedding_bag::call(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional<at::Tensor> & per_sample_weights, bool include_last_offset) { |
| 2512 | |
| 2513 | static auto op = create_embedding_bag_typed_handle(); |
| 2514 | return op.call(weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset); |
| 2515 | } |
| 2516 | |
| 2517 | // aten::embedding_bag(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False) -> (Tensor, Tensor, Tensor, Tensor) |
| 2518 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> embedding_bag::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional<at::Tensor> & per_sample_weights, bool include_last_offset) { |
| 2519 | |
| 2520 | static auto op = create_embedding_bag_typed_handle(); |
| 2521 | return op.redispatch(dispatchKeySet, weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset); |
| 2522 | } |
| 2523 | |
| 2524 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(embedding_bag_padding_idx, name, "aten::embedding_bag") |
| 2525 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(embedding_bag_padding_idx, overload_name, "padding_idx") |
| 2526 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(embedding_bag_padding_idx, schema_str, "embedding_bag.padding_idx(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq, int mode, bool sparse, Tensor? per_sample_weights, bool include_last_offset, int? padding_idx) -> (Tensor, Tensor, Tensor, Tensor)") |
| 2527 | |
| 2528 | // aten::embedding_bag.padding_idx(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq, int mode, bool sparse, Tensor? per_sample_weights, bool include_last_offset, int? padding_idx) -> (Tensor, Tensor, Tensor, Tensor) |
| 2529 | static C10_NOINLINE c10::TypedOperatorHandle<embedding_bag_padding_idx::schema> create_embedding_bag_padding_idx_typed_handle() { |
| 2530 | return c10::Dispatcher::singleton() |
| 2531 | .findSchemaOrThrow(embedding_bag_padding_idx::name, embedding_bag_padding_idx::overload_name) |
| 2532 | .typed<embedding_bag_padding_idx::schema>(); |
| 2533 | } |
| 2534 | |
| 2535 | // aten::embedding_bag.padding_idx(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq, int mode, bool sparse, Tensor? per_sample_weights, bool include_last_offset, int? padding_idx) -> (Tensor, Tensor, Tensor, Tensor) |
| 2536 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> embedding_bag_padding_idx::call(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional<at::Tensor> & per_sample_weights, bool include_last_offset, c10::optional<int64_t> padding_idx) { |
| 2537 | |
| 2538 | static auto op = create_embedding_bag_padding_idx_typed_handle(); |
| 2539 | return op.call(weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx); |
| 2540 | } |
| 2541 | |
| 2542 | // aten::embedding_bag.padding_idx(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq, int mode, bool sparse, Tensor? per_sample_weights, bool include_last_offset, int? padding_idx) -> (Tensor, Tensor, Tensor, Tensor) |
| 2543 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> embedding_bag_padding_idx::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional<at::Tensor> & per_sample_weights, bool include_last_offset, c10::optional<int64_t> padding_idx) { |
| 2544 | |
| 2545 | static auto op = create_embedding_bag_padding_idx_typed_handle(); |
| 2546 | return op.redispatch(dispatchKeySet, weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx); |
| 2547 | } |
| 2548 | |
| 2549 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(new_zeros, name, "aten::new_zeros") |
| 2550 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(new_zeros, overload_name, "") |
| 2551 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(new_zeros, schema_str, "new_zeros(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor") |
| 2552 | |
| 2553 | // aten::new_zeros(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 2554 | static C10_NOINLINE c10::TypedOperatorHandle<new_zeros::schema> create_new_zeros_typed_handle() { |
| 2555 | return c10::Dispatcher::singleton() |
| 2556 | .findSchemaOrThrow(new_zeros::name, new_zeros::overload_name) |
| 2557 | .typed<new_zeros::schema>(); |
| 2558 | } |
| 2559 | |
| 2560 | // aten::new_zeros(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 2561 | at::Tensor new_zeros::call(const at::Tensor & self, c10::SymIntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 2562 | |
| 2563 | static auto op = create_new_zeros_typed_handle(); |
| 2564 | return op.call(self, size, dtype, layout, device, pin_memory); |
| 2565 | } |
| 2566 | |
| 2567 | // aten::new_zeros(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 2568 | at::Tensor new_zeros::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 2569 | |
| 2570 | static auto op = create_new_zeros_typed_handle(); |
| 2571 | return op.redispatch(dispatchKeySet, self, size, dtype, layout, device, pin_memory); |
| 2572 | } |
| 2573 | |
| 2574 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(erf, name, "aten::erf") |
| 2575 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(erf, overload_name, "") |
| 2576 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(erf, schema_str, "erf(Tensor self) -> Tensor") |
| 2577 | |
| 2578 | // aten::erf(Tensor self) -> Tensor |
| 2579 | static C10_NOINLINE c10::TypedOperatorHandle<erf::schema> create_erf_typed_handle() { |
| 2580 | return c10::Dispatcher::singleton() |
| 2581 | .findSchemaOrThrow(erf::name, erf::overload_name) |
| 2582 | .typed<erf::schema>(); |
| 2583 | } |
| 2584 | |
| 2585 | // aten::erf(Tensor self) -> Tensor |
| 2586 | at::Tensor erf::call(const at::Tensor & self) { |
| 2587 | |
| 2588 | static auto op = create_erf_typed_handle(); |
| 2589 | return op.call(self); |
| 2590 | } |
| 2591 | |
| 2592 | // aten::erf(Tensor self) -> Tensor |
| 2593 | at::Tensor erf::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 2594 | |
| 2595 | static auto op = create_erf_typed_handle(); |
| 2596 | return op.redispatch(dispatchKeySet, self); |
| 2597 | } |
| 2598 | |
| 2599 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(erf_, name, "aten::erf_") |
| 2600 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(erf_, overload_name, "") |
| 2601 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(erf_, schema_str, "erf_(Tensor(a!) self) -> Tensor(a!)") |
| 2602 | |
| 2603 | // aten::erf_(Tensor(a!) self) -> Tensor(a!) |
| 2604 | static C10_NOINLINE c10::TypedOperatorHandle<erf_::schema> create_erf__typed_handle() { |
| 2605 | return c10::Dispatcher::singleton() |
| 2606 | .findSchemaOrThrow(erf_::name, erf_::overload_name) |
| 2607 | .typed<erf_::schema>(); |
| 2608 | } |
| 2609 | |
| 2610 | // aten::erf_(Tensor(a!) self) -> Tensor(a!) |
| 2611 | at::Tensor & erf_::call(at::Tensor & self) { |
| 2612 | |
| 2613 | static auto op = create_erf__typed_handle(); |
| 2614 | return op.call(self); |
| 2615 | } |
| 2616 | |
| 2617 | // aten::erf_(Tensor(a!) self) -> Tensor(a!) |
| 2618 | at::Tensor & erf_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 2619 | |
| 2620 | static auto op = create_erf__typed_handle(); |
| 2621 | return op.redispatch(dispatchKeySet, self); |
| 2622 | } |
| 2623 | |
| 2624 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(erf_out, name, "aten::erf") |
| 2625 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(erf_out, overload_name, "out") |
| 2626 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(erf_out, schema_str, "erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 2627 | |
| 2628 | // aten::erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 2629 | static C10_NOINLINE c10::TypedOperatorHandle<erf_out::schema> create_erf_out_typed_handle() { |
| 2630 | return c10::Dispatcher::singleton() |
| 2631 | .findSchemaOrThrow(erf_out::name, erf_out::overload_name) |
| 2632 | .typed<erf_out::schema>(); |
| 2633 | } |
| 2634 | |
| 2635 | // aten::erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 2636 | at::Tensor & erf_out::call(const at::Tensor & self, at::Tensor & out) { |
| 2637 | |
| 2638 | static auto op = create_erf_out_typed_handle(); |
| 2639 | return op.call(self, out); |
| 2640 | } |
| 2641 | |
| 2642 | // aten::erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 2643 | at::Tensor & erf_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 2644 | |
| 2645 | static auto op = create_erf_out_typed_handle(); |
| 2646 | return op.redispatch(dispatchKeySet, self, out); |
| 2647 | } |
| 2648 | |
| 2649 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(grid_sampler, name, "aten::grid_sampler") |
| 2650 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(grid_sampler, overload_name, "") |
| 2651 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(grid_sampler, schema_str, "grid_sampler(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor") |
| 2652 | |
| 2653 | // aten::grid_sampler(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor |
| 2654 | static C10_NOINLINE c10::TypedOperatorHandle<grid_sampler::schema> create_grid_sampler_typed_handle() { |
| 2655 | return c10::Dispatcher::singleton() |
| 2656 | .findSchemaOrThrow(grid_sampler::name, grid_sampler::overload_name) |
| 2657 | .typed<grid_sampler::schema>(); |
| 2658 | } |
| 2659 | |
| 2660 | // aten::grid_sampler(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor |
| 2661 | at::Tensor grid_sampler::call(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { |
| 2662 | |
| 2663 | static auto op = create_grid_sampler_typed_handle(); |
| 2664 | return op.call(input, grid, interpolation_mode, padding_mode, align_corners); |
| 2665 | } |
| 2666 | |
| 2667 | // aten::grid_sampler(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor |
| 2668 | at::Tensor grid_sampler::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { |
| 2669 | |
| 2670 | static auto op = create_grid_sampler_typed_handle(); |
| 2671 | return op.redispatch(dispatchKeySet, input, grid, interpolation_mode, padding_mode, align_corners); |
| 2672 | } |
| 2673 | |
| 2674 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_grid_sampler_2d_cpu_fallback, name, "aten::_grid_sampler_2d_cpu_fallback") |
| 2675 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_grid_sampler_2d_cpu_fallback, overload_name, "") |
| 2676 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_grid_sampler_2d_cpu_fallback, schema_str, "_grid_sampler_2d_cpu_fallback(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor") |
| 2677 | |
| 2678 | // aten::_grid_sampler_2d_cpu_fallback(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor |
| 2679 | static C10_NOINLINE c10::TypedOperatorHandle<_grid_sampler_2d_cpu_fallback::schema> create__grid_sampler_2d_cpu_fallback_typed_handle() { |
| 2680 | return c10::Dispatcher::singleton() |
| 2681 | .findSchemaOrThrow(_grid_sampler_2d_cpu_fallback::name, _grid_sampler_2d_cpu_fallback::overload_name) |
| 2682 | .typed<_grid_sampler_2d_cpu_fallback::schema>(); |
| 2683 | } |
| 2684 | |
| 2685 | // aten::_grid_sampler_2d_cpu_fallback(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor |
| 2686 | at::Tensor _grid_sampler_2d_cpu_fallback::call(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { |
| 2687 | |
| 2688 | static auto op = create__grid_sampler_2d_cpu_fallback_typed_handle(); |
| 2689 | return op.call(input, grid, interpolation_mode, padding_mode, align_corners); |
| 2690 | } |
| 2691 | |
| 2692 | // aten::_grid_sampler_2d_cpu_fallback(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor |
| 2693 | at::Tensor _grid_sampler_2d_cpu_fallback::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { |
| 2694 | |
| 2695 | static auto op = create__grid_sampler_2d_cpu_fallback_typed_handle(); |
| 2696 | return op.redispatch(dispatchKeySet, input, grid, interpolation_mode, padding_mode, align_corners); |
| 2697 | } |
| 2698 | |
| 2699 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(grid_sampler_3d, name, "aten::grid_sampler_3d") |
| 2700 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(grid_sampler_3d, overload_name, "") |
| 2701 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(grid_sampler_3d, schema_str, "grid_sampler_3d(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor") |
| 2702 | |
| 2703 | // aten::grid_sampler_3d(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor |
| 2704 | static C10_NOINLINE c10::TypedOperatorHandle<grid_sampler_3d::schema> create_grid_sampler_3d_typed_handle() { |
| 2705 | return c10::Dispatcher::singleton() |
| 2706 | .findSchemaOrThrow(grid_sampler_3d::name, grid_sampler_3d::overload_name) |
| 2707 | .typed<grid_sampler_3d::schema>(); |
| 2708 | } |
| 2709 | |
| 2710 | // aten::grid_sampler_3d(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor |
| 2711 | at::Tensor grid_sampler_3d::call(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { |
| 2712 | |
| 2713 | static auto op = create_grid_sampler_3d_typed_handle(); |
| 2714 | return op.call(input, grid, interpolation_mode, padding_mode, align_corners); |
| 2715 | } |
| 2716 | |
| 2717 | // aten::grid_sampler_3d(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor |
| 2718 | at::Tensor grid_sampler_3d::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { |
| 2719 | |
| 2720 | static auto op = create_grid_sampler_3d_typed_handle(); |
| 2721 | return op.redispatch(dispatchKeySet, input, grid, interpolation_mode, padding_mode, align_corners); |
| 2722 | } |
| 2723 | |
| 2724 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hann_window, name, "aten::hann_window") |
| 2725 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hann_window, overload_name, "") |
| 2726 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hann_window, schema_str, "hann_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor") |
| 2727 | |
| 2728 | // aten::hann_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 2729 | static C10_NOINLINE c10::TypedOperatorHandle<hann_window::schema> create_hann_window_typed_handle() { |
| 2730 | return c10::Dispatcher::singleton() |
| 2731 | .findSchemaOrThrow(hann_window::name, hann_window::overload_name) |
| 2732 | .typed<hann_window::schema>(); |
| 2733 | } |
| 2734 | |
| 2735 | // aten::hann_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 2736 | at::Tensor hann_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) { |
| 2737 | |
| 2738 | static auto op = create_hann_window_typed_handle(); |
| 2739 | return op.call(window_length, dtype, layout, device, pin_memory); |
| 2740 | } |
| 2741 | |
| 2742 | // aten::hann_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 2743 | at::Tensor hann_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) { |
| 2744 | |
| 2745 | static auto op = create_hann_window_typed_handle(); |
| 2746 | return op.redispatch(dispatchKeySet, window_length, dtype, layout, device, pin_memory); |
| 2747 | } |
| 2748 | |
| 2749 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hann_window_periodic, name, "aten::hann_window") |
| 2750 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hann_window_periodic, overload_name, "periodic") |
| 2751 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hann_window_periodic, schema_str, "hann_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor") |
| 2752 | |
| 2753 | // aten::hann_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 2754 | static C10_NOINLINE c10::TypedOperatorHandle<hann_window_periodic::schema> create_hann_window_periodic_typed_handle() { |
| 2755 | return c10::Dispatcher::singleton() |
| 2756 | .findSchemaOrThrow(hann_window_periodic::name, hann_window_periodic::overload_name) |
| 2757 | .typed<hann_window_periodic::schema>(); |
| 2758 | } |
| 2759 | |
| 2760 | // aten::hann_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 2761 | at::Tensor hann_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) { |
| 2762 | |
| 2763 | static auto op = create_hann_window_periodic_typed_handle(); |
| 2764 | return op.call(window_length, periodic, dtype, layout, device, pin_memory); |
| 2765 | } |
| 2766 | |
| 2767 | // aten::hann_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 2768 | at::Tensor hann_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) { |
| 2769 | |
| 2770 | static auto op = create_hann_window_periodic_typed_handle(); |
| 2771 | return op.redispatch(dispatchKeySet, window_length, periodic, dtype, layout, device, pin_memory); |
| 2772 | } |
| 2773 | |
| 2774 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hamming_window, name, "aten::hamming_window") |
| 2775 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hamming_window, overload_name, "") |
| 2776 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hamming_window, schema_str, "hamming_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor") |
| 2777 | |
| 2778 | // aten::hamming_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 2779 | static C10_NOINLINE c10::TypedOperatorHandle<hamming_window::schema> create_hamming_window_typed_handle() { |
| 2780 | return c10::Dispatcher::singleton() |
| 2781 | .findSchemaOrThrow(hamming_window::name, hamming_window::overload_name) |
| 2782 | .typed<hamming_window::schema>(); |
| 2783 | } |
| 2784 | |
| 2785 | // aten::hamming_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 2786 | at::Tensor hamming_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) { |
| 2787 | |
| 2788 | static auto op = create_hamming_window_typed_handle(); |
| 2789 | return op.call(window_length, dtype, layout, device, pin_memory); |
| 2790 | } |
| 2791 | |
| 2792 | // aten::hamming_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 2793 | at::Tensor hamming_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) { |
| 2794 | |
| 2795 | static auto op = create_hamming_window_typed_handle(); |
| 2796 | return op.redispatch(dispatchKeySet, window_length, dtype, layout, device, pin_memory); |
| 2797 | } |
| 2798 | |
| 2799 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hamming_window_periodic, name, "aten::hamming_window") |
| 2800 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hamming_window_periodic, overload_name, "periodic") |
| 2801 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hamming_window_periodic, schema_str, "hamming_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor") |
| 2802 | |
| 2803 | // aten::hamming_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 2804 | static C10_NOINLINE c10::TypedOperatorHandle<hamming_window_periodic::schema> create_hamming_window_periodic_typed_handle() { |
| 2805 | return c10::Dispatcher::singleton() |
| 2806 | .findSchemaOrThrow(hamming_window_periodic::name, hamming_window_periodic::overload_name) |
| 2807 | .typed<hamming_window_periodic::schema>(); |
| 2808 | } |
| 2809 | |
| 2810 | // aten::hamming_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 2811 | at::Tensor hamming_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) { |
| 2812 | |
| 2813 | static auto op = create_hamming_window_periodic_typed_handle(); |
| 2814 | return op.call(window_length, periodic, dtype, layout, device, pin_memory); |
| 2815 | } |
| 2816 | |
| 2817 | // aten::hamming_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 2818 | at::Tensor hamming_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) { |
| 2819 | |
| 2820 | static auto op = create_hamming_window_periodic_typed_handle(); |
| 2821 | return op.redispatch(dispatchKeySet, window_length, periodic, dtype, layout, device, pin_memory); |
| 2822 | } |
| 2823 | |
| 2824 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hamming_window_periodic_alpha, name, "aten::hamming_window") |
| 2825 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hamming_window_periodic_alpha, overload_name, "periodic_alpha") |
| 2826 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hamming_window_periodic_alpha, schema_str, "hamming_window.periodic_alpha(int window_length, bool periodic, float alpha, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor") |
| 2827 | |
| 2828 | // aten::hamming_window.periodic_alpha(int window_length, bool periodic, float alpha, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 2829 | static C10_NOINLINE c10::TypedOperatorHandle<hamming_window_periodic_alpha::schema> create_hamming_window_periodic_alpha_typed_handle() { |
| 2830 | return c10::Dispatcher::singleton() |
| 2831 | .findSchemaOrThrow(hamming_window_periodic_alpha::name, hamming_window_periodic_alpha::overload_name) |
| 2832 | .typed<hamming_window_periodic_alpha::schema>(); |
| 2833 | } |
| 2834 | |
| 2835 | // aten::hamming_window.periodic_alpha(int window_length, bool periodic, float alpha, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 2836 | at::Tensor hamming_window_periodic_alpha::call(int64_t window_length, bool periodic, double alpha, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 2837 | |
| 2838 | static auto op = create_hamming_window_periodic_alpha_typed_handle(); |
| 2839 | return op.call(window_length, periodic, alpha, dtype, layout, device, pin_memory); |
| 2840 | } |
| 2841 | |
| 2842 | // aten::hamming_window.periodic_alpha(int window_length, bool periodic, float alpha, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 2843 | at::Tensor hamming_window_periodic_alpha::redispatch(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, double alpha, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 2844 | |
| 2845 | static auto op = create_hamming_window_periodic_alpha_typed_handle(); |
| 2846 | return op.redispatch(dispatchKeySet, window_length, periodic, alpha, dtype, layout, device, pin_memory); |
| 2847 | } |
| 2848 | |
| 2849 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hamming_window_periodic_alpha_beta, name, "aten::hamming_window") |
| 2850 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hamming_window_periodic_alpha_beta, overload_name, "periodic_alpha_beta") |
| 2851 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hamming_window_periodic_alpha_beta, schema_str, "hamming_window.periodic_alpha_beta(int window_length, bool periodic, float alpha, float beta, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor") |
| 2852 | |
| 2853 | // aten::hamming_window.periodic_alpha_beta(int window_length, bool periodic, float alpha, float beta, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 2854 | static C10_NOINLINE c10::TypedOperatorHandle<hamming_window_periodic_alpha_beta::schema> create_hamming_window_periodic_alpha_beta_typed_handle() { |
| 2855 | return c10::Dispatcher::singleton() |
| 2856 | .findSchemaOrThrow(hamming_window_periodic_alpha_beta::name, hamming_window_periodic_alpha_beta::overload_name) |
| 2857 | .typed<hamming_window_periodic_alpha_beta::schema>(); |
| 2858 | } |
| 2859 | |
| 2860 | // aten::hamming_window.periodic_alpha_beta(int window_length, bool periodic, float alpha, float beta, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 2861 | at::Tensor hamming_window_periodic_alpha_beta::call(int64_t window_length, bool periodic, double alpha, double beta, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 2862 | |
| 2863 | static auto op = create_hamming_window_periodic_alpha_beta_typed_handle(); |
| 2864 | return op.call(window_length, periodic, alpha, beta, dtype, layout, device, pin_memory); |
| 2865 | } |
| 2866 | |
| 2867 | // aten::hamming_window.periodic_alpha_beta(int window_length, bool periodic, float alpha, float beta, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 2868 | at::Tensor hamming_window_periodic_alpha_beta::redispatch(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, double alpha, double beta, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 2869 | |
| 2870 | static auto op = create_hamming_window_periodic_alpha_beta_typed_handle(); |
| 2871 | return op.redispatch(dispatchKeySet, window_length, periodic, alpha, beta, dtype, layout, device, pin_memory); |
| 2872 | } |
| 2873 | |
| 2874 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_group_norm_backward, name, "aten::native_group_norm_backward") |
| 2875 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_group_norm_backward, overload_name, "") |
| 2876 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_group_norm_backward, schema_str, "native_group_norm_backward(Tensor grad_out, Tensor input, Tensor mean, Tensor rstd, Tensor? weight, SymInt N, SymInt C, SymInt HxW, int group, bool[3] output_mask) -> (Tensor, Tensor, Tensor)") |
| 2877 | |
| 2878 | // aten::native_group_norm_backward(Tensor grad_out, Tensor input, Tensor mean, Tensor rstd, Tensor? weight, SymInt N, SymInt C, SymInt HxW, int group, bool[3] output_mask) -> (Tensor, Tensor, Tensor) |
| 2879 | static C10_NOINLINE c10::TypedOperatorHandle<native_group_norm_backward::schema> create_native_group_norm_backward_typed_handle() { |
| 2880 | return c10::Dispatcher::singleton() |
| 2881 | .findSchemaOrThrow(native_group_norm_backward::name, native_group_norm_backward::overload_name) |
| 2882 | .typed<native_group_norm_backward::schema>(); |
| 2883 | } |
| 2884 | |
| 2885 | // aten::native_group_norm_backward(Tensor grad_out, Tensor input, Tensor mean, Tensor rstd, Tensor? weight, SymInt N, SymInt C, SymInt HxW, int group, bool[3] output_mask) -> (Tensor, Tensor, Tensor) |
| 2886 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_group_norm_backward::call(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const c10::optional<at::Tensor> & weight, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, ::std::array<bool,3> output_mask) { |
| 2887 | |
| 2888 | static auto op = create_native_group_norm_backward_typed_handle(); |
| 2889 | return op.call(grad_out, input, mean, rstd, weight, N, C, HxW, group, output_mask); |
| 2890 | } |
| 2891 | |
| 2892 | // aten::native_group_norm_backward(Tensor grad_out, Tensor input, Tensor mean, Tensor rstd, Tensor? weight, SymInt N, SymInt C, SymInt HxW, int group, bool[3] output_mask) -> (Tensor, Tensor, Tensor) |
| 2893 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_group_norm_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const c10::optional<at::Tensor> & weight, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, ::std::array<bool,3> output_mask) { |
| 2894 | |
| 2895 | static auto op = create_native_group_norm_backward_typed_handle(); |
| 2896 | return op.redispatch(dispatchKeySet, grad_out, input, mean, rstd, weight, N, C, HxW, group, output_mask); |
| 2897 | } |
| 2898 | |
| 2899 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fft_c2c, name, "aten::_fft_c2c") |
| 2900 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fft_c2c, overload_name, "") |
| 2901 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fft_c2c, schema_str, "_fft_c2c(Tensor self, SymInt[] dim, int normalization, bool forward) -> Tensor") |
| 2902 | |
| 2903 | // aten::_fft_c2c(Tensor self, SymInt[] dim, int normalization, bool forward) -> Tensor |
| 2904 | static C10_NOINLINE c10::TypedOperatorHandle<_fft_c2c::schema> create__fft_c2c_typed_handle() { |
| 2905 | return c10::Dispatcher::singleton() |
| 2906 | .findSchemaOrThrow(_fft_c2c::name, _fft_c2c::overload_name) |
| 2907 | .typed<_fft_c2c::schema>(); |
| 2908 | } |
| 2909 | |
| 2910 | // aten::_fft_c2c(Tensor self, SymInt[] dim, int normalization, bool forward) -> Tensor |
| 2911 | at::Tensor _fft_c2c::call(const at::Tensor & self, c10::SymIntArrayRef dim, int64_t normalization, bool forward) { |
| 2912 | |
| 2913 | static auto op = create__fft_c2c_typed_handle(); |
| 2914 | return op.call(self, dim, normalization, forward); |
| 2915 | } |
| 2916 | |
| 2917 | // aten::_fft_c2c(Tensor self, SymInt[] dim, int normalization, bool forward) -> Tensor |
| 2918 | at::Tensor _fft_c2c::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef dim, int64_t normalization, bool forward) { |
| 2919 | |
| 2920 | static auto op = create__fft_c2c_typed_handle(); |
| 2921 | return op.redispatch(dispatchKeySet, self, dim, normalization, forward); |
| 2922 | } |
| 2923 | |
| 2924 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fft_c2c_out, name, "aten::_fft_c2c") |
| 2925 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fft_c2c_out, overload_name, "out") |
| 2926 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fft_c2c_out, schema_str, "_fft_c2c.out(Tensor self, SymInt[] dim, int normalization, bool forward, *, Tensor(a!) out) -> Tensor(a!)") |
| 2927 | |
| 2928 | // aten::_fft_c2c.out(Tensor self, SymInt[] dim, int normalization, bool forward, *, Tensor(a!) out) -> Tensor(a!) |
| 2929 | static C10_NOINLINE c10::TypedOperatorHandle<_fft_c2c_out::schema> create__fft_c2c_out_typed_handle() { |
| 2930 | return c10::Dispatcher::singleton() |
| 2931 | .findSchemaOrThrow(_fft_c2c_out::name, _fft_c2c_out::overload_name) |
| 2932 | .typed<_fft_c2c_out::schema>(); |
| 2933 | } |
| 2934 | |
| 2935 | // aten::_fft_c2c.out(Tensor self, SymInt[] dim, int normalization, bool forward, *, Tensor(a!) out) -> Tensor(a!) |
| 2936 | at::Tensor & _fft_c2c_out::call(const at::Tensor & self, c10::SymIntArrayRef dim, int64_t normalization, bool forward, at::Tensor & out) { |
| 2937 | |
| 2938 | static auto op = create__fft_c2c_out_typed_handle(); |
| 2939 | return op.call(self, dim, normalization, forward, out); |
| 2940 | } |
| 2941 | |
| 2942 | // aten::_fft_c2c.out(Tensor self, SymInt[] dim, int normalization, bool forward, *, Tensor(a!) out) -> Tensor(a!) |
| 2943 | at::Tensor & _fft_c2c_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef dim, int64_t normalization, bool forward, at::Tensor & out) { |
| 2944 | |
| 2945 | static auto op = create__fft_c2c_out_typed_handle(); |
| 2946 | return op.redispatch(dispatchKeySet, self, dim, normalization, forward, out); |
| 2947 | } |
| 2948 | |
| 2949 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_validate_compressed_sparse_indices, name, "aten::_validate_compressed_sparse_indices") |
| 2950 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_validate_compressed_sparse_indices, overload_name, "") |
| 2951 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_validate_compressed_sparse_indices, schema_str, "_validate_compressed_sparse_indices(bool is_crow, Tensor compressed_idx, Tensor plain_idx, int cdim, int dim, int nnz) -> ()") |
| 2952 | |
| 2953 | // aten::_validate_compressed_sparse_indices(bool is_crow, Tensor compressed_idx, Tensor plain_idx, int cdim, int dim, int nnz) -> () |
| 2954 | static C10_NOINLINE c10::TypedOperatorHandle<_validate_compressed_sparse_indices::schema> create__validate_compressed_sparse_indices_typed_handle() { |
| 2955 | return c10::Dispatcher::singleton() |
| 2956 | .findSchemaOrThrow(_validate_compressed_sparse_indices::name, _validate_compressed_sparse_indices::overload_name) |
| 2957 | .typed<_validate_compressed_sparse_indices::schema>(); |
| 2958 | } |
| 2959 | |
| 2960 | // aten::_validate_compressed_sparse_indices(bool is_crow, Tensor compressed_idx, Tensor plain_idx, int cdim, int dim, int nnz) -> () |
| 2961 | void _validate_compressed_sparse_indices::call(bool is_crow, const at::Tensor & compressed_idx, const at::Tensor & plain_idx, int64_t cdim, int64_t dim, int64_t nnz) { |
| 2962 | |
| 2963 | static auto op = create__validate_compressed_sparse_indices_typed_handle(); |
| 2964 | return op.call(is_crow, compressed_idx, plain_idx, cdim, dim, nnz); |
| 2965 | } |
| 2966 | |
| 2967 | // aten::_validate_compressed_sparse_indices(bool is_crow, Tensor compressed_idx, Tensor plain_idx, int cdim, int dim, int nnz) -> () |
| 2968 | void _validate_compressed_sparse_indices::redispatch(c10::DispatchKeySet dispatchKeySet, bool is_crow, const at::Tensor & compressed_idx, const at::Tensor & plain_idx, int64_t cdim, int64_t dim, int64_t nnz) { |
| 2969 | |
| 2970 | static auto op = create__validate_compressed_sparse_indices_typed_handle(); |
| 2971 | return op.redispatch(dispatchKeySet, is_crow, compressed_idx, plain_idx, cdim, dim, nnz); |
| 2972 | } |
| 2973 | |
| 2974 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cufft_get_plan_cache_size, name, "aten::_cufft_get_plan_cache_size") |
| 2975 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cufft_get_plan_cache_size, overload_name, "") |
| 2976 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cufft_get_plan_cache_size, schema_str, "_cufft_get_plan_cache_size(int device_index) -> int") |
| 2977 | |
| 2978 | // aten::_cufft_get_plan_cache_size(int device_index) -> int |
| 2979 | static C10_NOINLINE c10::TypedOperatorHandle<_cufft_get_plan_cache_size::schema> create__cufft_get_plan_cache_size_typed_handle() { |
| 2980 | return c10::Dispatcher::singleton() |
| 2981 | .findSchemaOrThrow(_cufft_get_plan_cache_size::name, _cufft_get_plan_cache_size::overload_name) |
| 2982 | .typed<_cufft_get_plan_cache_size::schema>(); |
| 2983 | } |
| 2984 | |
| 2985 | // aten::_cufft_get_plan_cache_size(int device_index) -> int |
| 2986 | int64_t _cufft_get_plan_cache_size::call(int64_t device_index) { |
| 2987 | |
| 2988 | static auto op = create__cufft_get_plan_cache_size_typed_handle(); |
| 2989 | return op.call(device_index); |
| 2990 | } |
| 2991 | |
| 2992 | // aten::_cufft_get_plan_cache_size(int device_index) -> int |
| 2993 | int64_t _cufft_get_plan_cache_size::redispatch(c10::DispatchKeySet dispatchKeySet, int64_t device_index) { |
| 2994 | |
| 2995 | static auto op = create__cufft_get_plan_cache_size_typed_handle(); |
| 2996 | return op.redispatch(dispatchKeySet, device_index); |
| 2997 | } |
| 2998 | |
| 2999 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cufft_get_plan_cache_max_size, name, "aten::_cufft_get_plan_cache_max_size") |
| 3000 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cufft_get_plan_cache_max_size, overload_name, "") |
| 3001 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cufft_get_plan_cache_max_size, schema_str, "_cufft_get_plan_cache_max_size(int device_index) -> int") |
| 3002 | |
| 3003 | // aten::_cufft_get_plan_cache_max_size(int device_index) -> int |
| 3004 | static C10_NOINLINE c10::TypedOperatorHandle<_cufft_get_plan_cache_max_size::schema> create__cufft_get_plan_cache_max_size_typed_handle() { |
| 3005 | return c10::Dispatcher::singleton() |
| 3006 | .findSchemaOrThrow(_cufft_get_plan_cache_max_size::name, _cufft_get_plan_cache_max_size::overload_name) |
| 3007 | .typed<_cufft_get_plan_cache_max_size::schema>(); |
| 3008 | } |
| 3009 | |
| 3010 | // aten::_cufft_get_plan_cache_max_size(int device_index) -> int |
| 3011 | int64_t _cufft_get_plan_cache_max_size::call(int64_t device_index) { |
| 3012 | |
| 3013 | static auto op = create__cufft_get_plan_cache_max_size_typed_handle(); |
| 3014 | return op.call(device_index); |
| 3015 | } |
| 3016 | |
| 3017 | // aten::_cufft_get_plan_cache_max_size(int device_index) -> int |
| 3018 | int64_t _cufft_get_plan_cache_max_size::redispatch(c10::DispatchKeySet dispatchKeySet, int64_t device_index) { |
| 3019 | |
| 3020 | static auto op = create__cufft_get_plan_cache_max_size_typed_handle(); |
| 3021 | return op.redispatch(dispatchKeySet, device_index); |
| 3022 | } |
| 3023 | |
| 3024 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_Tensor, name, "aten::index") |
| 3025 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_Tensor, overload_name, "Tensor") |
| 3026 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_Tensor, schema_str, "index.Tensor(Tensor self, Tensor?[] indices) -> Tensor") |
| 3027 | |
| 3028 | // aten::index.Tensor(Tensor self, Tensor?[] indices) -> Tensor |
| 3029 | static C10_NOINLINE c10::TypedOperatorHandle<index_Tensor::schema> create_index_Tensor_typed_handle() { |
| 3030 | return c10::Dispatcher::singleton() |
| 3031 | .findSchemaOrThrow(index_Tensor::name, index_Tensor::overload_name) |
| 3032 | .typed<index_Tensor::schema>(); |
| 3033 | } |
| 3034 | |
| 3035 | // aten::index.Tensor(Tensor self, Tensor?[] indices) -> Tensor |
| 3036 | at::Tensor index_Tensor::call(const at::Tensor & self, const c10::List<c10::optional<at::Tensor>> & indices) { |
| 3037 | |
| 3038 | static auto op = create_index_Tensor_typed_handle(); |
| 3039 | return op.call(self, indices); |
| 3040 | } |
| 3041 | |
| 3042 | // aten::index.Tensor(Tensor self, Tensor?[] indices) -> Tensor |
| 3043 | at::Tensor index_Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::List<c10::optional<at::Tensor>> & indices) { |
| 3044 | |
| 3045 | static auto op = create_index_Tensor_typed_handle(); |
| 3046 | return op.redispatch(dispatchKeySet, self, indices); |
| 3047 | } |
| 3048 | |
| 3049 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_Tensor_out, name, "aten::index") |
| 3050 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_Tensor_out, overload_name, "Tensor_out") |
| 3051 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_Tensor_out, schema_str, "index.Tensor_out(Tensor self, Tensor?[] indices, *, Tensor(a!) out) -> Tensor(a!)") |
| 3052 | |
| 3053 | // aten::index.Tensor_out(Tensor self, Tensor?[] indices, *, Tensor(a!) out) -> Tensor(a!) |
| 3054 | static C10_NOINLINE c10::TypedOperatorHandle<index_Tensor_out::schema> create_index_Tensor_out_typed_handle() { |
| 3055 | return c10::Dispatcher::singleton() |
| 3056 | .findSchemaOrThrow(index_Tensor_out::name, index_Tensor_out::overload_name) |
| 3057 | .typed<index_Tensor_out::schema>(); |
| 3058 | } |
| 3059 | |
| 3060 | // aten::index.Tensor_out(Tensor self, Tensor?[] indices, *, Tensor(a!) out) -> Tensor(a!) |
| 3061 | at::Tensor & index_Tensor_out::call(const at::Tensor & self, const c10::List<c10::optional<at::Tensor>> & indices, at::Tensor & out) { |
| 3062 | |
| 3063 | static auto op = create_index_Tensor_out_typed_handle(); |
| 3064 | return op.call(self, indices, out); |
| 3065 | } |
| 3066 | |
| 3067 | // aten::index.Tensor_out(Tensor self, Tensor?[] indices, *, Tensor(a!) out) -> Tensor(a!) |
| 3068 | at::Tensor & index_Tensor_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::List<c10::optional<at::Tensor>> & indices, at::Tensor & out) { |
| 3069 | |
| 3070 | static auto op = create_index_Tensor_out_typed_handle(); |
| 3071 | return op.redispatch(dispatchKeySet, self, indices, out); |
| 3072 | } |
| 3073 | |
| 3074 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(isnan, name, "aten::isnan") |
| 3075 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(isnan, overload_name, "") |
| 3076 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(isnan, schema_str, "isnan(Tensor self) -> Tensor") |
| 3077 | |
| 3078 | // aten::isnan(Tensor self) -> Tensor |
| 3079 | static C10_NOINLINE c10::TypedOperatorHandle<isnan::schema> create_isnan_typed_handle() { |
| 3080 | return c10::Dispatcher::singleton() |
| 3081 | .findSchemaOrThrow(isnan::name, isnan::overload_name) |
| 3082 | .typed<isnan::schema>(); |
| 3083 | } |
| 3084 | |
| 3085 | // aten::isnan(Tensor self) -> Tensor |
| 3086 | at::Tensor isnan::call(const at::Tensor & self) { |
| 3087 | |
| 3088 | static auto op = create_isnan_typed_handle(); |
| 3089 | return op.call(self); |
| 3090 | } |
| 3091 | |
| 3092 | // aten::isnan(Tensor self) -> Tensor |
| 3093 | at::Tensor isnan::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 3094 | |
| 3095 | static auto op = create_isnan_typed_handle(); |
| 3096 | return op.redispatch(dispatchKeySet, self); |
| 3097 | } |
| 3098 | |
| 3099 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(kthvalue, name, "aten::kthvalue") |
| 3100 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(kthvalue, overload_name, "") |
| 3101 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(kthvalue, schema_str, "kthvalue(Tensor self, int k, int dim=-1, bool keepdim=False) -> (Tensor values, Tensor indices)") |
| 3102 | |
| 3103 | // aten::kthvalue(Tensor self, int k, int dim=-1, bool keepdim=False) -> (Tensor values, Tensor indices) |
| 3104 | static C10_NOINLINE c10::TypedOperatorHandle<kthvalue::schema> create_kthvalue_typed_handle() { |
| 3105 | return c10::Dispatcher::singleton() |
| 3106 | .findSchemaOrThrow(kthvalue::name, kthvalue::overload_name) |
| 3107 | .typed<kthvalue::schema>(); |
| 3108 | } |
| 3109 | |
| 3110 | // aten::kthvalue(Tensor self, int k, int dim=-1, bool keepdim=False) -> (Tensor values, Tensor indices) |
| 3111 | ::std::tuple<at::Tensor,at::Tensor> kthvalue::call(const at::Tensor & self, int64_t k, int64_t dim, bool keepdim) { |
| 3112 | |
| 3113 | static auto op = create_kthvalue_typed_handle(); |
| 3114 | return op.call(self, k, dim, keepdim); |
| 3115 | } |
| 3116 | |
| 3117 | // aten::kthvalue(Tensor self, int k, int dim=-1, bool keepdim=False) -> (Tensor values, Tensor indices) |
| 3118 | ::std::tuple<at::Tensor,at::Tensor> kthvalue::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t k, int64_t dim, bool keepdim) { |
| 3119 | |
| 3120 | static auto op = create_kthvalue_typed_handle(); |
| 3121 | return op.redispatch(dispatchKeySet, self, k, dim, keepdim); |
| 3122 | } |
| 3123 | |
| 3124 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(kthvalue_values, name, "aten::kthvalue") |
| 3125 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(kthvalue_values, overload_name, "values") |
| 3126 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(kthvalue_values, schema_str, "kthvalue.values(Tensor self, int k, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)") |
| 3127 | |
| 3128 | // aten::kthvalue.values(Tensor self, int k, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) |
| 3129 | static C10_NOINLINE c10::TypedOperatorHandle<kthvalue_values::schema> create_kthvalue_values_typed_handle() { |
| 3130 | return c10::Dispatcher::singleton() |
| 3131 | .findSchemaOrThrow(kthvalue_values::name, kthvalue_values::overload_name) |
| 3132 | .typed<kthvalue_values::schema>(); |
| 3133 | } |
| 3134 | |
| 3135 | // aten::kthvalue.values(Tensor self, int k, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) |
| 3136 | ::std::tuple<at::Tensor &,at::Tensor &> kthvalue_values::call(const at::Tensor & self, int64_t k, int64_t dim, bool keepdim, at::Tensor & values, at::Tensor & indices) { |
| 3137 | |
| 3138 | static auto op = create_kthvalue_values_typed_handle(); |
| 3139 | return op.call(self, k, dim, keepdim, values, indices); |
| 3140 | } |
| 3141 | |
| 3142 | // aten::kthvalue.values(Tensor self, int k, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) |
| 3143 | ::std::tuple<at::Tensor &,at::Tensor &> kthvalue_values::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t k, int64_t dim, bool keepdim, at::Tensor & values, at::Tensor & indices) { |
| 3144 | |
| 3145 | static auto op = create_kthvalue_values_typed_handle(); |
| 3146 | return op.redispatch(dispatchKeySet, self, k, dim, keepdim, values, indices); |
| 3147 | } |
| 3148 | |
| 3149 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(kthvalue_dimname, name, "aten::kthvalue") |
| 3150 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(kthvalue_dimname, overload_name, "dimname") |
| 3151 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(kthvalue_dimname, schema_str, "kthvalue.dimname(Tensor self, int k, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices)") |
| 3152 | |
| 3153 | // aten::kthvalue.dimname(Tensor self, int k, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices) |
| 3154 | static C10_NOINLINE c10::TypedOperatorHandle<kthvalue_dimname::schema> create_kthvalue_dimname_typed_handle() { |
| 3155 | return c10::Dispatcher::singleton() |
| 3156 | .findSchemaOrThrow(kthvalue_dimname::name, kthvalue_dimname::overload_name) |
| 3157 | .typed<kthvalue_dimname::schema>(); |
| 3158 | } |
| 3159 | |
| 3160 | // aten::kthvalue.dimname(Tensor self, int k, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices) |
| 3161 | ::std::tuple<at::Tensor,at::Tensor> kthvalue_dimname::call(const at::Tensor & self, int64_t k, at::Dimname dim, bool keepdim) { |
| 3162 | |
| 3163 | static auto op = create_kthvalue_dimname_typed_handle(); |
| 3164 | return op.call(self, k, dim, keepdim); |
| 3165 | } |
| 3166 | |
| 3167 | // aten::kthvalue.dimname(Tensor self, int k, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices) |
| 3168 | ::std::tuple<at::Tensor,at::Tensor> kthvalue_dimname::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t k, at::Dimname dim, bool keepdim) { |
| 3169 | |
| 3170 | static auto op = create_kthvalue_dimname_typed_handle(); |
| 3171 | return op.redispatch(dispatchKeySet, self, k, dim, keepdim); |
| 3172 | } |
| 3173 | |
| 3174 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(kthvalue_dimname_out, name, "aten::kthvalue") |
| 3175 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(kthvalue_dimname_out, overload_name, "dimname_out") |
| 3176 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(kthvalue_dimname_out, schema_str, "kthvalue.dimname_out(Tensor self, int k, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)") |
| 3177 | |
| 3178 | // aten::kthvalue.dimname_out(Tensor self, int k, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) |
| 3179 | static C10_NOINLINE c10::TypedOperatorHandle<kthvalue_dimname_out::schema> create_kthvalue_dimname_out_typed_handle() { |
| 3180 | return c10::Dispatcher::singleton() |
| 3181 | .findSchemaOrThrow(kthvalue_dimname_out::name, kthvalue_dimname_out::overload_name) |
| 3182 | .typed<kthvalue_dimname_out::schema>(); |
| 3183 | } |
| 3184 | |
| 3185 | // aten::kthvalue.dimname_out(Tensor self, int k, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) |
| 3186 | ::std::tuple<at::Tensor &,at::Tensor &> kthvalue_dimname_out::call(const at::Tensor & self, int64_t k, at::Dimname dim, bool keepdim, at::Tensor & values, at::Tensor & indices) { |
| 3187 | |
| 3188 | static auto op = create_kthvalue_dimname_out_typed_handle(); |
| 3189 | return op.call(self, k, dim, keepdim, values, indices); |
| 3190 | } |
| 3191 | |
| 3192 | // aten::kthvalue.dimname_out(Tensor self, int k, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) |
| 3193 | ::std::tuple<at::Tensor &,at::Tensor &> kthvalue_dimname_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t k, at::Dimname dim, bool keepdim, at::Tensor & values, at::Tensor & indices) { |
| 3194 | |
| 3195 | static auto op = create_kthvalue_dimname_out_typed_handle(); |
| 3196 | return op.redispatch(dispatchKeySet, self, k, dim, keepdim, values, indices); |
| 3197 | } |
| 3198 | |
| 3199 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_layer_norm, name, "aten::native_layer_norm") |
| 3200 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_layer_norm, overload_name, "") |
| 3201 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_layer_norm, schema_str, "native_layer_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps) -> (Tensor, Tensor, Tensor)") |
| 3202 | |
| 3203 | // aten::native_layer_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps) -> (Tensor, Tensor, Tensor) |
| 3204 | static C10_NOINLINE c10::TypedOperatorHandle<native_layer_norm::schema> create_native_layer_norm_typed_handle() { |
| 3205 | return c10::Dispatcher::singleton() |
| 3206 | .findSchemaOrThrow(native_layer_norm::name, native_layer_norm::overload_name) |
| 3207 | .typed<native_layer_norm::schema>(); |
| 3208 | } |
| 3209 | |
| 3210 | // aten::native_layer_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps) -> (Tensor, Tensor, Tensor) |
| 3211 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_layer_norm::call(const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, double eps) { |
| 3212 | |
| 3213 | static auto op = create_native_layer_norm_typed_handle(); |
| 3214 | return op.call(input, normalized_shape, weight, bias, eps); |
| 3215 | } |
| 3216 | |
| 3217 | // aten::native_layer_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps) -> (Tensor, Tensor, Tensor) |
| 3218 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_layer_norm::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, double eps) { |
| 3219 | |
| 3220 | static auto op = create_native_layer_norm_typed_handle(); |
| 3221 | return op.redispatch(dispatchKeySet, input, normalized_shape, weight, bias, eps); |
| 3222 | } |
| 3223 | |
| 3224 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nan_to_num, name, "aten::nan_to_num") |
| 3225 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nan_to_num, overload_name, "") |
| 3226 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nan_to_num, schema_str, "nan_to_num(Tensor self, float? nan=None, float? posinf=None, float? neginf=None) -> Tensor") |
| 3227 | |
| 3228 | // aten::nan_to_num(Tensor self, float? nan=None, float? posinf=None, float? neginf=None) -> Tensor |
| 3229 | static C10_NOINLINE c10::TypedOperatorHandle<nan_to_num::schema> create_nan_to_num_typed_handle() { |
| 3230 | return c10::Dispatcher::singleton() |
| 3231 | .findSchemaOrThrow(nan_to_num::name, nan_to_num::overload_name) |
| 3232 | .typed<nan_to_num::schema>(); |
| 3233 | } |
| 3234 | |
| 3235 | // aten::nan_to_num(Tensor self, float? nan=None, float? posinf=None, float? neginf=None) -> Tensor |
| 3236 | at::Tensor nan_to_num::call(const at::Tensor & self, c10::optional<double> nan, c10::optional<double> posinf, c10::optional<double> neginf) { |
| 3237 | |
| 3238 | static auto op = create_nan_to_num_typed_handle(); |
| 3239 | return op.call(self, nan, posinf, neginf); |
| 3240 | } |
| 3241 | |
| 3242 | // aten::nan_to_num(Tensor self, float? nan=None, float? posinf=None, float? neginf=None) -> Tensor |
| 3243 | at::Tensor nan_to_num::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<double> nan, c10::optional<double> posinf, c10::optional<double> neginf) { |
| 3244 | |
| 3245 | static auto op = create_nan_to_num_typed_handle(); |
| 3246 | return op.redispatch(dispatchKeySet, self, nan, posinf, neginf); |
| 3247 | } |
| 3248 | |
| 3249 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nan_to_num_, name, "aten::nan_to_num_") |
| 3250 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nan_to_num_, overload_name, "") |
| 3251 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nan_to_num_, schema_str, "nan_to_num_(Tensor(a!) self, float? nan=None, float? posinf=None, float? neginf=None) -> Tensor(a!)") |
| 3252 | |
| 3253 | // aten::nan_to_num_(Tensor(a!) self, float? nan=None, float? posinf=None, float? neginf=None) -> Tensor(a!) |
| 3254 | static C10_NOINLINE c10::TypedOperatorHandle<nan_to_num_::schema> create_nan_to_num__typed_handle() { |
| 3255 | return c10::Dispatcher::singleton() |
| 3256 | .findSchemaOrThrow(nan_to_num_::name, nan_to_num_::overload_name) |
| 3257 | .typed<nan_to_num_::schema>(); |
| 3258 | } |
| 3259 | |
| 3260 | // aten::nan_to_num_(Tensor(a!) self, float? nan=None, float? posinf=None, float? neginf=None) -> Tensor(a!) |
| 3261 | at::Tensor & nan_to_num_::call(at::Tensor & self, c10::optional<double> nan, c10::optional<double> posinf, c10::optional<double> neginf) { |
| 3262 | |
| 3263 | static auto op = create_nan_to_num__typed_handle(); |
| 3264 | return op.call(self, nan, posinf, neginf); |
| 3265 | } |
| 3266 | |
| 3267 | // aten::nan_to_num_(Tensor(a!) self, float? nan=None, float? posinf=None, float? neginf=None) -> Tensor(a!) |
| 3268 | at::Tensor & nan_to_num_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, c10::optional<double> nan, c10::optional<double> posinf, c10::optional<double> neginf) { |
| 3269 | |
| 3270 | static auto op = create_nan_to_num__typed_handle(); |
| 3271 | return op.redispatch(dispatchKeySet, self, nan, posinf, neginf); |
| 3272 | } |
| 3273 | |
| 3274 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nan_to_num_out, name, "aten::nan_to_num") |
| 3275 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nan_to_num_out, overload_name, "out") |
| 3276 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nan_to_num_out, schema_str, "nan_to_num.out(Tensor self, float? nan=None, float? posinf=None, float? neginf=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 3277 | |
| 3278 | // aten::nan_to_num.out(Tensor self, float? nan=None, float? posinf=None, float? neginf=None, *, Tensor(a!) out) -> Tensor(a!) |
| 3279 | static C10_NOINLINE c10::TypedOperatorHandle<nan_to_num_out::schema> create_nan_to_num_out_typed_handle() { |
| 3280 | return c10::Dispatcher::singleton() |
| 3281 | .findSchemaOrThrow(nan_to_num_out::name, nan_to_num_out::overload_name) |
| 3282 | .typed<nan_to_num_out::schema>(); |
| 3283 | } |
| 3284 | |
| 3285 | // aten::nan_to_num.out(Tensor self, float? nan=None, float? posinf=None, float? neginf=None, *, Tensor(a!) out) -> Tensor(a!) |
| 3286 | at::Tensor & nan_to_num_out::call(const at::Tensor & self, c10::optional<double> nan, c10::optional<double> posinf, c10::optional<double> neginf, at::Tensor & out) { |
| 3287 | |
| 3288 | static auto op = create_nan_to_num_out_typed_handle(); |
| 3289 | return op.call(self, nan, posinf, neginf, out); |
| 3290 | } |
| 3291 | |
| 3292 | // aten::nan_to_num.out(Tensor self, float? nan=None, float? posinf=None, float? neginf=None, *, Tensor(a!) out) -> Tensor(a!) |
| 3293 | at::Tensor & nan_to_num_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<double> nan, c10::optional<double> posinf, c10::optional<double> neginf, at::Tensor & out) { |
| 3294 | |
| 3295 | static auto op = create_nan_to_num_out_typed_handle(); |
| 3296 | return op.redispatch(dispatchKeySet, self, nan, posinf, neginf, out); |
| 3297 | } |
| 3298 | |
| 3299 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fbgemm_linear_int8_weight_fp32_activation, name, "aten::fbgemm_linear_int8_weight_fp32_activation") |
| 3300 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fbgemm_linear_int8_weight_fp32_activation, overload_name, "") |
| 3301 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fbgemm_linear_int8_weight_fp32_activation, schema_str, "fbgemm_linear_int8_weight_fp32_activation(Tensor input, Tensor weight, Tensor packed, Tensor col_offsets, Scalar weight_scale, Scalar weight_zero_point, Tensor bias) -> Tensor") |
| 3302 | |
| 3303 | // aten::fbgemm_linear_int8_weight_fp32_activation(Tensor input, Tensor weight, Tensor packed, Tensor col_offsets, Scalar weight_scale, Scalar weight_zero_point, Tensor bias) -> Tensor |
| 3304 | static C10_NOINLINE c10::TypedOperatorHandle<fbgemm_linear_int8_weight_fp32_activation::schema> create_fbgemm_linear_int8_weight_fp32_activation_typed_handle() { |
| 3305 | return c10::Dispatcher::singleton() |
| 3306 | .findSchemaOrThrow(fbgemm_linear_int8_weight_fp32_activation::name, fbgemm_linear_int8_weight_fp32_activation::overload_name) |
| 3307 | .typed<fbgemm_linear_int8_weight_fp32_activation::schema>(); |
| 3308 | } |
| 3309 | |
| 3310 | // aten::fbgemm_linear_int8_weight_fp32_activation(Tensor input, Tensor weight, Tensor packed, Tensor col_offsets, Scalar weight_scale, Scalar weight_zero_point, Tensor bias) -> Tensor |
| 3311 | at::Tensor fbgemm_linear_int8_weight_fp32_activation::call(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & packed, const at::Tensor & col_offsets, const at::Scalar & weight_scale, const at::Scalar & weight_zero_point, const at::Tensor & bias) { |
| 3312 | |
| 3313 | static auto op = create_fbgemm_linear_int8_weight_fp32_activation_typed_handle(); |
| 3314 | return op.call(input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias); |
| 3315 | } |
| 3316 | |
| 3317 | // aten::fbgemm_linear_int8_weight_fp32_activation(Tensor input, Tensor weight, Tensor packed, Tensor col_offsets, Scalar weight_scale, Scalar weight_zero_point, Tensor bias) -> Tensor |
| 3318 | at::Tensor fbgemm_linear_int8_weight_fp32_activation::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const at::Tensor & packed, const at::Tensor & col_offsets, const at::Scalar & weight_scale, const at::Scalar & weight_zero_point, const at::Tensor & bias) { |
| 3319 | |
| 3320 | static auto op = create_fbgemm_linear_int8_weight_fp32_activation_typed_handle(); |
| 3321 | return op.redispatch(dispatchKeySet, input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias); |
| 3322 | } |
| 3323 | |
| 3324 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fbgemm_linear_int8_weight, name, "aten::fbgemm_linear_int8_weight") |
| 3325 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fbgemm_linear_int8_weight, overload_name, "") |
| 3326 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fbgemm_linear_int8_weight, schema_str, "fbgemm_linear_int8_weight(Tensor input, Tensor weight, Tensor packed, Tensor col_offsets, Scalar weight_scale, Scalar weight_zero_point, Tensor bias) -> Tensor") |
| 3327 | |
| 3328 | // aten::fbgemm_linear_int8_weight(Tensor input, Tensor weight, Tensor packed, Tensor col_offsets, Scalar weight_scale, Scalar weight_zero_point, Tensor bias) -> Tensor |
| 3329 | static C10_NOINLINE c10::TypedOperatorHandle<fbgemm_linear_int8_weight::schema> create_fbgemm_linear_int8_weight_typed_handle() { |
| 3330 | return c10::Dispatcher::singleton() |
| 3331 | .findSchemaOrThrow(fbgemm_linear_int8_weight::name, fbgemm_linear_int8_weight::overload_name) |
| 3332 | .typed<fbgemm_linear_int8_weight::schema>(); |
| 3333 | } |
| 3334 | |
| 3335 | // aten::fbgemm_linear_int8_weight(Tensor input, Tensor weight, Tensor packed, Tensor col_offsets, Scalar weight_scale, Scalar weight_zero_point, Tensor bias) -> Tensor |
| 3336 | at::Tensor fbgemm_linear_int8_weight::call(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & packed, const at::Tensor & col_offsets, const at::Scalar & weight_scale, const at::Scalar & weight_zero_point, const at::Tensor & bias) { |
| 3337 | |
| 3338 | static auto op = create_fbgemm_linear_int8_weight_typed_handle(); |
| 3339 | return op.call(input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias); |
| 3340 | } |
| 3341 | |
| 3342 | // aten::fbgemm_linear_int8_weight(Tensor input, Tensor weight, Tensor packed, Tensor col_offsets, Scalar weight_scale, Scalar weight_zero_point, Tensor bias) -> Tensor |
| 3343 | at::Tensor fbgemm_linear_int8_weight::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const at::Tensor & packed, const at::Tensor & col_offsets, const at::Scalar & weight_scale, const at::Scalar & weight_zero_point, const at::Tensor & bias) { |
| 3344 | |
| 3345 | static auto op = create_fbgemm_linear_int8_weight_typed_handle(); |
| 3346 | return op.redispatch(dispatchKeySet, input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias); |
| 3347 | } |
| 3348 | |
| 3349 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fbgemm_linear_fp16_weight_fp32_activation, name, "aten::fbgemm_linear_fp16_weight_fp32_activation") |
| 3350 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fbgemm_linear_fp16_weight_fp32_activation, overload_name, "") |
| 3351 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fbgemm_linear_fp16_weight_fp32_activation, schema_str, "fbgemm_linear_fp16_weight_fp32_activation(Tensor input, Tensor packed_weight, Tensor bias) -> Tensor") |
| 3352 | |
| 3353 | // aten::fbgemm_linear_fp16_weight_fp32_activation(Tensor input, Tensor packed_weight, Tensor bias) -> Tensor |
| 3354 | static C10_NOINLINE c10::TypedOperatorHandle<fbgemm_linear_fp16_weight_fp32_activation::schema> create_fbgemm_linear_fp16_weight_fp32_activation_typed_handle() { |
| 3355 | return c10::Dispatcher::singleton() |
| 3356 | .findSchemaOrThrow(fbgemm_linear_fp16_weight_fp32_activation::name, fbgemm_linear_fp16_weight_fp32_activation::overload_name) |
| 3357 | .typed<fbgemm_linear_fp16_weight_fp32_activation::schema>(); |
| 3358 | } |
| 3359 | |
| 3360 | // aten::fbgemm_linear_fp16_weight_fp32_activation(Tensor input, Tensor packed_weight, Tensor bias) -> Tensor |
| 3361 | at::Tensor fbgemm_linear_fp16_weight_fp32_activation::call(const at::Tensor & input, const at::Tensor & packed_weight, const at::Tensor & bias) { |
| 3362 | |
| 3363 | static auto op = create_fbgemm_linear_fp16_weight_fp32_activation_typed_handle(); |
| 3364 | return op.call(input, packed_weight, bias); |
| 3365 | } |
| 3366 | |
| 3367 | // aten::fbgemm_linear_fp16_weight_fp32_activation(Tensor input, Tensor packed_weight, Tensor bias) -> Tensor |
| 3368 | at::Tensor fbgemm_linear_fp16_weight_fp32_activation::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & packed_weight, const at::Tensor & bias) { |
| 3369 | |
| 3370 | static auto op = create_fbgemm_linear_fp16_weight_fp32_activation_typed_handle(); |
| 3371 | return op.redispatch(dispatchKeySet, input, packed_weight, bias); |
| 3372 | } |
| 3373 | |
| 3374 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(xlogy_Tensor, name, "aten::xlogy") |
| 3375 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(xlogy_Tensor, overload_name, "Tensor") |
| 3376 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(xlogy_Tensor, schema_str, "xlogy.Tensor(Tensor self, Tensor other) -> Tensor") |
| 3377 | |
| 3378 | // aten::xlogy.Tensor(Tensor self, Tensor other) -> Tensor |
| 3379 | static C10_NOINLINE c10::TypedOperatorHandle<xlogy_Tensor::schema> create_xlogy_Tensor_typed_handle() { |
| 3380 | return c10::Dispatcher::singleton() |
| 3381 | .findSchemaOrThrow(xlogy_Tensor::name, xlogy_Tensor::overload_name) |
| 3382 | .typed<xlogy_Tensor::schema>(); |
| 3383 | } |
| 3384 | |
| 3385 | // aten::xlogy.Tensor(Tensor self, Tensor other) -> Tensor |
| 3386 | at::Tensor xlogy_Tensor::call(const at::Tensor & self, const at::Tensor & other) { |
| 3387 | |
| 3388 | static auto op = create_xlogy_Tensor_typed_handle(); |
| 3389 | return op.call(self, other); |
| 3390 | } |
| 3391 | |
| 3392 | // aten::xlogy.Tensor(Tensor self, Tensor other) -> Tensor |
| 3393 | at::Tensor xlogy_Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { |
| 3394 | |
| 3395 | static auto op = create_xlogy_Tensor_typed_handle(); |
| 3396 | return op.redispatch(dispatchKeySet, self, other); |
| 3397 | } |
| 3398 | |
| 3399 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(xlogy_Scalar_Self, name, "aten::xlogy") |
| 3400 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(xlogy_Scalar_Self, overload_name, "Scalar_Self") |
| 3401 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(xlogy_Scalar_Self, schema_str, "xlogy.Scalar_Self(Scalar self, Tensor other) -> Tensor") |
| 3402 | |
| 3403 | // aten::xlogy.Scalar_Self(Scalar self, Tensor other) -> Tensor |
| 3404 | static C10_NOINLINE c10::TypedOperatorHandle<xlogy_Scalar_Self::schema> create_xlogy_Scalar_Self_typed_handle() { |
| 3405 | return c10::Dispatcher::singleton() |
| 3406 | .findSchemaOrThrow(xlogy_Scalar_Self::name, xlogy_Scalar_Self::overload_name) |
| 3407 | .typed<xlogy_Scalar_Self::schema>(); |
| 3408 | } |
| 3409 | |
| 3410 | // aten::xlogy.Scalar_Self(Scalar self, Tensor other) -> Tensor |
| 3411 | at::Tensor xlogy_Scalar_Self::call(const at::Scalar & self, const at::Tensor & other) { |
| 3412 | |
| 3413 | static auto op = create_xlogy_Scalar_Self_typed_handle(); |
| 3414 | return op.call(self, other); |
| 3415 | } |
| 3416 | |
| 3417 | // aten::xlogy.Scalar_Self(Scalar self, Tensor other) -> Tensor |
| 3418 | at::Tensor xlogy_Scalar_Self::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other) { |
| 3419 | |
| 3420 | static auto op = create_xlogy_Scalar_Self_typed_handle(); |
| 3421 | return op.redispatch(dispatchKeySet, self, other); |
| 3422 | } |
| 3423 | |
| 3424 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(xlogy_Scalar_Other, name, "aten::xlogy") |
| 3425 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(xlogy_Scalar_Other, overload_name, "Scalar_Other") |
| 3426 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(xlogy_Scalar_Other, schema_str, "xlogy.Scalar_Other(Tensor self, Scalar other) -> Tensor") |
| 3427 | |
| 3428 | // aten::xlogy.Scalar_Other(Tensor self, Scalar other) -> Tensor |
| 3429 | static C10_NOINLINE c10::TypedOperatorHandle<xlogy_Scalar_Other::schema> create_xlogy_Scalar_Other_typed_handle() { |
| 3430 | return c10::Dispatcher::singleton() |
| 3431 | .findSchemaOrThrow(xlogy_Scalar_Other::name, xlogy_Scalar_Other::overload_name) |
| 3432 | .typed<xlogy_Scalar_Other::schema>(); |
| 3433 | } |
| 3434 | |
| 3435 | // aten::xlogy.Scalar_Other(Tensor self, Scalar other) -> Tensor |
| 3436 | at::Tensor xlogy_Scalar_Other::call(const at::Tensor & self, const at::Scalar & other) { |
| 3437 | |
| 3438 | static auto op = create_xlogy_Scalar_Other_typed_handle(); |
| 3439 | return op.call(self, other); |
| 3440 | } |
| 3441 | |
| 3442 | // aten::xlogy.Scalar_Other(Tensor self, Scalar other) -> Tensor |
| 3443 | at::Tensor xlogy_Scalar_Other::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { |
| 3444 | |
| 3445 | static auto op = create_xlogy_Scalar_Other_typed_handle(); |
| 3446 | return op.redispatch(dispatchKeySet, self, other); |
| 3447 | } |
| 3448 | |
| 3449 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(xlogy__Tensor, name, "aten::xlogy_") |
| 3450 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(xlogy__Tensor, overload_name, "Tensor") |
| 3451 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(xlogy__Tensor, schema_str, "xlogy_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)") |
| 3452 | |
| 3453 | // aten::xlogy_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) |
| 3454 | static C10_NOINLINE c10::TypedOperatorHandle<xlogy__Tensor::schema> create_xlogy__Tensor_typed_handle() { |
| 3455 | return c10::Dispatcher::singleton() |
| 3456 | .findSchemaOrThrow(xlogy__Tensor::name, xlogy__Tensor::overload_name) |
| 3457 | .typed<xlogy__Tensor::schema>(); |
| 3458 | } |
| 3459 | |
| 3460 | // aten::xlogy_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) |
| 3461 | at::Tensor & xlogy__Tensor::call(at::Tensor & self, const at::Tensor & other) { |
| 3462 | |
| 3463 | static auto op = create_xlogy__Tensor_typed_handle(); |
| 3464 | return op.call(self, other); |
| 3465 | } |
| 3466 | |
| 3467 | // aten::xlogy_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) |
| 3468 | at::Tensor & xlogy__Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 3469 | |
| 3470 | static auto op = create_xlogy__Tensor_typed_handle(); |
| 3471 | return op.redispatch(dispatchKeySet, self, other); |
| 3472 | } |
| 3473 | |
| 3474 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(xlogy__Scalar_Other, name, "aten::xlogy_") |
| 3475 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(xlogy__Scalar_Other, overload_name, "Scalar_Other") |
| 3476 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(xlogy__Scalar_Other, schema_str, "xlogy_.Scalar_Other(Tensor(a!) self, Scalar other) -> Tensor(a!)") |
| 3477 | |
| 3478 | // aten::xlogy_.Scalar_Other(Tensor(a!) self, Scalar other) -> Tensor(a!) |
| 3479 | static C10_NOINLINE c10::TypedOperatorHandle<xlogy__Scalar_Other::schema> create_xlogy__Scalar_Other_typed_handle() { |
| 3480 | return c10::Dispatcher::singleton() |
| 3481 | .findSchemaOrThrow(xlogy__Scalar_Other::name, xlogy__Scalar_Other::overload_name) |
| 3482 | .typed<xlogy__Scalar_Other::schema>(); |
| 3483 | } |
| 3484 | |
| 3485 | // aten::xlogy_.Scalar_Other(Tensor(a!) self, Scalar other) -> Tensor(a!) |
| 3486 | at::Tensor & xlogy__Scalar_Other::call(at::Tensor & self, const at::Scalar & other) { |
| 3487 | |
| 3488 | static auto op = create_xlogy__Scalar_Other_typed_handle(); |
| 3489 | return op.call(self, other); |
| 3490 | } |
| 3491 | |
| 3492 | // aten::xlogy_.Scalar_Other(Tensor(a!) self, Scalar other) -> Tensor(a!) |
| 3493 | at::Tensor & xlogy__Scalar_Other::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { |
| 3494 | |
| 3495 | static auto op = create_xlogy__Scalar_Other_typed_handle(); |
| 3496 | return op.redispatch(dispatchKeySet, self, other); |
| 3497 | } |
| 3498 | |
| 3499 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(xlogy_OutTensor, name, "aten::xlogy") |
| 3500 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(xlogy_OutTensor, overload_name, "OutTensor") |
| 3501 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(xlogy_OutTensor, schema_str, "xlogy.OutTensor(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)") |
| 3502 | |
| 3503 | // aten::xlogy.OutTensor(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 3504 | static C10_NOINLINE c10::TypedOperatorHandle<xlogy_OutTensor::schema> create_xlogy_OutTensor_typed_handle() { |
| 3505 | return c10::Dispatcher::singleton() |
| 3506 | .findSchemaOrThrow(xlogy_OutTensor::name, xlogy_OutTensor::overload_name) |
| 3507 | .typed<xlogy_OutTensor::schema>(); |
| 3508 | } |
| 3509 | |
| 3510 | // aten::xlogy.OutTensor(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 3511 | at::Tensor & xlogy_OutTensor::call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 3512 | |
| 3513 | static auto op = create_xlogy_OutTensor_typed_handle(); |
| 3514 | return op.call(self, other, out); |
| 3515 | } |
| 3516 | |
| 3517 | // aten::xlogy.OutTensor(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 3518 | at::Tensor & xlogy_OutTensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 3519 | |
| 3520 | static auto op = create_xlogy_OutTensor_typed_handle(); |
| 3521 | return op.redispatch(dispatchKeySet, self, other, out); |
| 3522 | } |
| 3523 | |
| 3524 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(xlogy_OutScalar_Self, name, "aten::xlogy") |
| 3525 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(xlogy_OutScalar_Self, overload_name, "OutScalar_Self") |
| 3526 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(xlogy_OutScalar_Self, schema_str, "xlogy.OutScalar_Self(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)") |
| 3527 | |
| 3528 | // aten::xlogy.OutScalar_Self(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 3529 | static C10_NOINLINE c10::TypedOperatorHandle<xlogy_OutScalar_Self::schema> create_xlogy_OutScalar_Self_typed_handle() { |
| 3530 | return c10::Dispatcher::singleton() |
| 3531 | .findSchemaOrThrow(xlogy_OutScalar_Self::name, xlogy_OutScalar_Self::overload_name) |
| 3532 | .typed<xlogy_OutScalar_Self::schema>(); |
| 3533 | } |
| 3534 | |
| 3535 | // aten::xlogy.OutScalar_Self(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 3536 | at::Tensor & xlogy_OutScalar_Self::call(const at::Scalar & self, const at::Tensor & other, at::Tensor & out) { |
| 3537 | |
| 3538 | static auto op = create_xlogy_OutScalar_Self_typed_handle(); |
| 3539 | return op.call(self, other, out); |
| 3540 | } |
| 3541 | |
| 3542 | // aten::xlogy.OutScalar_Self(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 3543 | at::Tensor & xlogy_OutScalar_Self::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other, at::Tensor & out) { |
| 3544 | |
| 3545 | static auto op = create_xlogy_OutScalar_Self_typed_handle(); |
| 3546 | return op.redispatch(dispatchKeySet, self, other, out); |
| 3547 | } |
| 3548 | |
| 3549 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(xlogy_OutScalar_Other, name, "aten::xlogy") |
| 3550 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(xlogy_OutScalar_Other, overload_name, "OutScalar_Other") |
| 3551 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(xlogy_OutScalar_Other, schema_str, "xlogy.OutScalar_Other(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)") |
| 3552 | |
| 3553 | // aten::xlogy.OutScalar_Other(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) |
| 3554 | static C10_NOINLINE c10::TypedOperatorHandle<xlogy_OutScalar_Other::schema> create_xlogy_OutScalar_Other_typed_handle() { |
| 3555 | return c10::Dispatcher::singleton() |
| 3556 | .findSchemaOrThrow(xlogy_OutScalar_Other::name, xlogy_OutScalar_Other::overload_name) |
| 3557 | .typed<xlogy_OutScalar_Other::schema>(); |
| 3558 | } |
| 3559 | |
| 3560 | // aten::xlogy.OutScalar_Other(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) |
| 3561 | at::Tensor & xlogy_OutScalar_Other::call(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 3562 | |
| 3563 | static auto op = create_xlogy_OutScalar_Other_typed_handle(); |
| 3564 | return op.call(self, other, out); |
| 3565 | } |
| 3566 | |
| 3567 | // aten::xlogy.OutScalar_Other(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) |
| 3568 | at::Tensor & xlogy_OutScalar_Other::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 3569 | |
| 3570 | static auto op = create_xlogy_OutScalar_Other_typed_handle(); |
| 3571 | return op.redispatch(dispatchKeySet, self, other, out); |
| 3572 | } |
| 3573 | |
| 3574 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_log_softmax_backward_data, name, "aten::_log_softmax_backward_data") |
| 3575 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_log_softmax_backward_data, overload_name, "") |
| 3576 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_log_softmax_backward_data, schema_str, "_log_softmax_backward_data(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype) -> Tensor") |
| 3577 | |
| 3578 | // aten::_log_softmax_backward_data(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype) -> Tensor |
| 3579 | static C10_NOINLINE c10::TypedOperatorHandle<_log_softmax_backward_data::schema> create__log_softmax_backward_data_typed_handle() { |
| 3580 | return c10::Dispatcher::singleton() |
| 3581 | .findSchemaOrThrow(_log_softmax_backward_data::name, _log_softmax_backward_data::overload_name) |
| 3582 | .typed<_log_softmax_backward_data::schema>(); |
| 3583 | } |
| 3584 | |
| 3585 | // aten::_log_softmax_backward_data(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype) -> Tensor |
| 3586 | at::Tensor _log_softmax_backward_data::call(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) { |
| 3587 | |
| 3588 | static auto op = create__log_softmax_backward_data_typed_handle(); |
| 3589 | return op.call(grad_output, output, dim, input_dtype); |
| 3590 | } |
| 3591 | |
| 3592 | // aten::_log_softmax_backward_data(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype) -> Tensor |
| 3593 | at::Tensor _log_softmax_backward_data::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) { |
| 3594 | |
| 3595 | static auto op = create__log_softmax_backward_data_typed_handle(); |
| 3596 | return op.redispatch(dispatchKeySet, grad_output, output, dim, input_dtype); |
| 3597 | } |
| 3598 | |
| 3599 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_log_softmax_backward_data_out, name, "aten::_log_softmax_backward_data") |
| 3600 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_log_softmax_backward_data_out, overload_name, "out") |
| 3601 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_log_softmax_backward_data_out, schema_str, "_log_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype, *, Tensor(a!) out) -> Tensor(a!)") |
| 3602 | |
| 3603 | // aten::_log_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype, *, Tensor(a!) out) -> Tensor(a!) |
| 3604 | static C10_NOINLINE c10::TypedOperatorHandle<_log_softmax_backward_data_out::schema> create__log_softmax_backward_data_out_typed_handle() { |
| 3605 | return c10::Dispatcher::singleton() |
| 3606 | .findSchemaOrThrow(_log_softmax_backward_data_out::name, _log_softmax_backward_data_out::overload_name) |
| 3607 | .typed<_log_softmax_backward_data_out::schema>(); |
| 3608 | } |
| 3609 | |
| 3610 | // aten::_log_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype, *, Tensor(a!) out) -> Tensor(a!) |
| 3611 | at::Tensor & _log_softmax_backward_data_out::call(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype, at::Tensor & out) { |
| 3612 | |
| 3613 | static auto op = create__log_softmax_backward_data_out_typed_handle(); |
| 3614 | return op.call(grad_output, output, dim, input_dtype, out); |
| 3615 | } |
| 3616 | |
| 3617 | // aten::_log_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype, *, Tensor(a!) out) -> Tensor(a!) |
| 3618 | at::Tensor & _log_softmax_backward_data_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype, at::Tensor & out) { |
| 3619 | |
| 3620 | static auto op = create__log_softmax_backward_data_out_typed_handle(); |
| 3621 | return op.redispatch(dispatchKeySet, grad_output, output, dim, input_dtype, out); |
| 3622 | } |
| 3623 | |
| 3624 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logcumsumexp, name, "aten::logcumsumexp") |
| 3625 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logcumsumexp, overload_name, "") |
| 3626 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logcumsumexp, schema_str, "logcumsumexp(Tensor self, int dim) -> Tensor") |
| 3627 | |
| 3628 | // aten::logcumsumexp(Tensor self, int dim) -> Tensor |
| 3629 | static C10_NOINLINE c10::TypedOperatorHandle<logcumsumexp::schema> create_logcumsumexp_typed_handle() { |
| 3630 | return c10::Dispatcher::singleton() |
| 3631 | .findSchemaOrThrow(logcumsumexp::name, logcumsumexp::overload_name) |
| 3632 | .typed<logcumsumexp::schema>(); |
| 3633 | } |
| 3634 | |
| 3635 | // aten::logcumsumexp(Tensor self, int dim) -> Tensor |
| 3636 | at::Tensor logcumsumexp::call(const at::Tensor & self, int64_t dim) { |
| 3637 | |
| 3638 | static auto op = create_logcumsumexp_typed_handle(); |
| 3639 | return op.call(self, dim); |
| 3640 | } |
| 3641 | |
| 3642 | // aten::logcumsumexp(Tensor self, int dim) -> Tensor |
| 3643 | at::Tensor logcumsumexp::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim) { |
| 3644 | |
| 3645 | static auto op = create_logcumsumexp_typed_handle(); |
| 3646 | return op.redispatch(dispatchKeySet, self, dim); |
| 3647 | } |
| 3648 | |
| 3649 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logcumsumexp_out, name, "aten::logcumsumexp") |
| 3650 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logcumsumexp_out, overload_name, "out") |
| 3651 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logcumsumexp_out, schema_str, "logcumsumexp.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)") |
| 3652 | |
| 3653 | // aten::logcumsumexp.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!) |
| 3654 | static C10_NOINLINE c10::TypedOperatorHandle<logcumsumexp_out::schema> create_logcumsumexp_out_typed_handle() { |
| 3655 | return c10::Dispatcher::singleton() |
| 3656 | .findSchemaOrThrow(logcumsumexp_out::name, logcumsumexp_out::overload_name) |
| 3657 | .typed<logcumsumexp_out::schema>(); |
| 3658 | } |
| 3659 | |
| 3660 | // aten::logcumsumexp.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!) |
| 3661 | at::Tensor & logcumsumexp_out::call(const at::Tensor & self, int64_t dim, at::Tensor & out) { |
| 3662 | |
| 3663 | static auto op = create_logcumsumexp_out_typed_handle(); |
| 3664 | return op.call(self, dim, out); |
| 3665 | } |
| 3666 | |
| 3667 | // aten::logcumsumexp.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!) |
| 3668 | at::Tensor & logcumsumexp_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, at::Tensor & out) { |
| 3669 | |
| 3670 | static auto op = create_logcumsumexp_out_typed_handle(); |
| 3671 | return op.redispatch(dispatchKeySet, self, dim, out); |
| 3672 | } |
| 3673 | |
| 3674 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logcumsumexp_dimname, name, "aten::logcumsumexp") |
| 3675 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logcumsumexp_dimname, overload_name, "dimname") |
| 3676 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logcumsumexp_dimname, schema_str, "logcumsumexp.dimname(Tensor self, Dimname dim) -> Tensor") |
| 3677 | |
| 3678 | // aten::logcumsumexp.dimname(Tensor self, Dimname dim) -> Tensor |
| 3679 | static C10_NOINLINE c10::TypedOperatorHandle<logcumsumexp_dimname::schema> create_logcumsumexp_dimname_typed_handle() { |
| 3680 | return c10::Dispatcher::singleton() |
| 3681 | .findSchemaOrThrow(logcumsumexp_dimname::name, logcumsumexp_dimname::overload_name) |
| 3682 | .typed<logcumsumexp_dimname::schema>(); |
| 3683 | } |
| 3684 | |
| 3685 | // aten::logcumsumexp.dimname(Tensor self, Dimname dim) -> Tensor |
| 3686 | at::Tensor logcumsumexp_dimname::call(const at::Tensor & self, at::Dimname dim) { |
| 3687 | |
| 3688 | static auto op = create_logcumsumexp_dimname_typed_handle(); |
| 3689 | return op.call(self, dim); |
| 3690 | } |
| 3691 | |
| 3692 | // aten::logcumsumexp.dimname(Tensor self, Dimname dim) -> Tensor |
| 3693 | at::Tensor logcumsumexp_dimname::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim) { |
| 3694 | |
| 3695 | static auto op = create_logcumsumexp_dimname_typed_handle(); |
| 3696 | return op.redispatch(dispatchKeySet, self, dim); |
| 3697 | } |
| 3698 | |
| 3699 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logcumsumexp_dimname_out, name, "aten::logcumsumexp") |
| 3700 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logcumsumexp_dimname_out, overload_name, "dimname_out") |
| 3701 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(logcumsumexp_dimname_out, schema_str, "logcumsumexp.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) out) -> Tensor(a!)") |
| 3702 | |
| 3703 | // aten::logcumsumexp.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) out) -> Tensor(a!) |
| 3704 | static C10_NOINLINE c10::TypedOperatorHandle<logcumsumexp_dimname_out::schema> create_logcumsumexp_dimname_out_typed_handle() { |
| 3705 | return c10::Dispatcher::singleton() |
| 3706 | .findSchemaOrThrow(logcumsumexp_dimname_out::name, logcumsumexp_dimname_out::overload_name) |
| 3707 | .typed<logcumsumexp_dimname_out::schema>(); |
| 3708 | } |
| 3709 | |
| 3710 | // aten::logcumsumexp.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) out) -> Tensor(a!) |
| 3711 | at::Tensor & logcumsumexp_dimname_out::call(const at::Tensor & self, at::Dimname dim, at::Tensor & out) { |
| 3712 | |
| 3713 | static auto op = create_logcumsumexp_dimname_out_typed_handle(); |
| 3714 | return op.call(self, dim, out); |
| 3715 | } |
| 3716 | |
| 3717 | // aten::logcumsumexp.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) out) -> Tensor(a!) |
| 3718 | at::Tensor & logcumsumexp_dimname_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, at::Tensor & out) { |
| 3719 | |
| 3720 | static auto op = create_logcumsumexp_dimname_out_typed_handle(); |
| 3721 | return op.redispatch(dispatchKeySet, self, dim, out); |
| 3722 | } |
| 3723 | |
| 3724 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(matrix_exp_backward, name, "aten::matrix_exp_backward") |
| 3725 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(matrix_exp_backward, overload_name, "") |
| 3726 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(matrix_exp_backward, schema_str, "matrix_exp_backward(Tensor self, Tensor grad) -> Tensor") |
| 3727 | |
| 3728 | // aten::matrix_exp_backward(Tensor self, Tensor grad) -> Tensor |
| 3729 | static C10_NOINLINE c10::TypedOperatorHandle<matrix_exp_backward::schema> create_matrix_exp_backward_typed_handle() { |
| 3730 | return c10::Dispatcher::singleton() |
| 3731 | .findSchemaOrThrow(matrix_exp_backward::name, matrix_exp_backward::overload_name) |
| 3732 | .typed<matrix_exp_backward::schema>(); |
| 3733 | } |
| 3734 | |
| 3735 | // aten::matrix_exp_backward(Tensor self, Tensor grad) -> Tensor |
| 3736 | at::Tensor matrix_exp_backward::call(const at::Tensor & self, const at::Tensor & grad) { |
| 3737 | |
| 3738 | static auto op = create_matrix_exp_backward_typed_handle(); |
| 3739 | return op.call(self, grad); |
| 3740 | } |
| 3741 | |
| 3742 | // aten::matrix_exp_backward(Tensor self, Tensor grad) -> Tensor |
| 3743 | at::Tensor matrix_exp_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & grad) { |
| 3744 | |
| 3745 | static auto op = create_matrix_exp_backward_typed_handle(); |
| 3746 | return op.redispatch(dispatchKeySet, self, grad); |
| 3747 | } |
| 3748 | |
| 3749 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(amax, name, "aten::amax") |
| 3750 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(amax, overload_name, "") |
| 3751 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(amax, schema_str, "amax(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor") |
| 3752 | |
| 3753 | // aten::amax(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor |
| 3754 | static C10_NOINLINE c10::TypedOperatorHandle<amax::schema> create_amax_typed_handle() { |
| 3755 | return c10::Dispatcher::singleton() |
| 3756 | .findSchemaOrThrow(amax::name, amax::overload_name) |
| 3757 | .typed<amax::schema>(); |
| 3758 | } |
| 3759 | |
| 3760 | // aten::amax(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor |
| 3761 | at::Tensor amax::call(const at::Tensor & self, at::IntArrayRef dim, bool keepdim) { |
| 3762 | |
| 3763 | static auto op = create_amax_typed_handle(); |
| 3764 | return op.call(self, dim, keepdim); |
| 3765 | } |
| 3766 | |
| 3767 | // aten::amax(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor |
| 3768 | at::Tensor amax::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim) { |
| 3769 | |
| 3770 | static auto op = create_amax_typed_handle(); |
| 3771 | return op.redispatch(dispatchKeySet, self, dim, keepdim); |
| 3772 | } |
| 3773 | |
| 3774 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(amax_out, name, "aten::amax") |
| 3775 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(amax_out, overload_name, "out") |
| 3776 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(amax_out, schema_str, "amax.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)") |
| 3777 | |
| 3778 | // aten::amax.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) |
| 3779 | static C10_NOINLINE c10::TypedOperatorHandle<amax_out::schema> create_amax_out_typed_handle() { |
| 3780 | return c10::Dispatcher::singleton() |
| 3781 | .findSchemaOrThrow(amax_out::name, amax_out::overload_name) |
| 3782 | .typed<amax_out::schema>(); |
| 3783 | } |
| 3784 | |
| 3785 | // aten::amax.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) |
| 3786 | at::Tensor & amax_out::call(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { |
| 3787 | |
| 3788 | static auto op = create_amax_out_typed_handle(); |
| 3789 | return op.call(self, dim, keepdim, out); |
| 3790 | } |
| 3791 | |
| 3792 | // aten::amax.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) |
| 3793 | at::Tensor & amax_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { |
| 3794 | |
| 3795 | static auto op = create_amax_out_typed_handle(); |
| 3796 | return op.redispatch(dispatchKeySet, self, dim, keepdim, out); |
| 3797 | } |
| 3798 | |
| 3799 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_mps_max_pool2d, name, "aten::_mps_max_pool2d") |
| 3800 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_mps_max_pool2d, overload_name, "") |
| 3801 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_mps_max_pool2d, schema_str, "_mps_max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor") |
| 3802 | |
| 3803 | // aten::_mps_max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor |
| 3804 | static C10_NOINLINE c10::TypedOperatorHandle<_mps_max_pool2d::schema> create__mps_max_pool2d_typed_handle() { |
| 3805 | return c10::Dispatcher::singleton() |
| 3806 | .findSchemaOrThrow(_mps_max_pool2d::name, _mps_max_pool2d::overload_name) |
| 3807 | .typed<_mps_max_pool2d::schema>(); |
| 3808 | } |
| 3809 | |
| 3810 | // aten::_mps_max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor |
| 3811 | at::Tensor _mps_max_pool2d::call(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { |
| 3812 | |
| 3813 | static auto op = create__mps_max_pool2d_typed_handle(); |
| 3814 | return op.call(self, kernel_size, stride, padding, dilation, ceil_mode); |
| 3815 | } |
| 3816 | |
| 3817 | // aten::_mps_max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor |
| 3818 | at::Tensor _mps_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) { |
| 3819 | |
| 3820 | static auto op = create__mps_max_pool2d_typed_handle(); |
| 3821 | return op.redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode); |
| 3822 | } |
| 3823 | |
| 3824 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_max_pool2d, name, "aten::mkldnn_max_pool2d") |
| 3825 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_max_pool2d, overload_name, "") |
| 3826 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_max_pool2d, schema_str, "mkldnn_max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor") |
| 3827 | |
| 3828 | // aten::mkldnn_max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor |
| 3829 | static C10_NOINLINE c10::TypedOperatorHandle<mkldnn_max_pool2d::schema> create_mkldnn_max_pool2d_typed_handle() { |
| 3830 | return c10::Dispatcher::singleton() |
| 3831 | .findSchemaOrThrow(mkldnn_max_pool2d::name, mkldnn_max_pool2d::overload_name) |
| 3832 | .typed<mkldnn_max_pool2d::schema>(); |
| 3833 | } |
| 3834 | |
| 3835 | // aten::mkldnn_max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor |
| 3836 | at::Tensor mkldnn_max_pool2d::call(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { |
| 3837 | |
| 3838 | static auto op = create_mkldnn_max_pool2d_typed_handle(); |
| 3839 | return op.call(self, kernel_size, stride, padding, dilation, ceil_mode); |
| 3840 | } |
| 3841 | |
| 3842 | // aten::mkldnn_max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor |
| 3843 | at::Tensor mkldnn_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) { |
| 3844 | |
| 3845 | static auto op = create_mkldnn_max_pool2d_typed_handle(); |
| 3846 | return op.redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode); |
| 3847 | } |
| 3848 | |
| 3849 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantized_max_pool2d, name, "aten::quantized_max_pool2d") |
| 3850 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantized_max_pool2d, overload_name, "") |
| 3851 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantized_max_pool2d, schema_str, "quantized_max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor") |
| 3852 | |
| 3853 | // aten::quantized_max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor |
| 3854 | static C10_NOINLINE c10::TypedOperatorHandle<quantized_max_pool2d::schema> create_quantized_max_pool2d_typed_handle() { |
| 3855 | return c10::Dispatcher::singleton() |
| 3856 | .findSchemaOrThrow(quantized_max_pool2d::name, quantized_max_pool2d::overload_name) |
| 3857 | .typed<quantized_max_pool2d::schema>(); |
| 3858 | } |
| 3859 | |
| 3860 | // aten::quantized_max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor |
| 3861 | at::Tensor quantized_max_pool2d::call(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { |
| 3862 | |
| 3863 | static auto op = create_quantized_max_pool2d_typed_handle(); |
| 3864 | return op.call(self, kernel_size, stride, padding, dilation, ceil_mode); |
| 3865 | } |
| 3866 | |
| 3867 | // aten::quantized_max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor |
| 3868 | at::Tensor quantized_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) { |
| 3869 | |
| 3870 | static auto op = create_quantized_max_pool2d_typed_handle(); |
| 3871 | return op.redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode); |
| 3872 | } |
| 3873 | |
| 3874 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(amin, name, "aten::amin") |
| 3875 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(amin, overload_name, "") |
| 3876 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(amin, schema_str, "amin(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor") |
| 3877 | |
| 3878 | // aten::amin(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor |
| 3879 | static C10_NOINLINE c10::TypedOperatorHandle<amin::schema> create_amin_typed_handle() { |
| 3880 | return c10::Dispatcher::singleton() |
| 3881 | .findSchemaOrThrow(amin::name, amin::overload_name) |
| 3882 | .typed<amin::schema>(); |
| 3883 | } |
| 3884 | |
| 3885 | // aten::amin(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor |
| 3886 | at::Tensor amin::call(const at::Tensor & self, at::IntArrayRef dim, bool keepdim) { |
| 3887 | |
| 3888 | static auto op = create_amin_typed_handle(); |
| 3889 | return op.call(self, dim, keepdim); |
| 3890 | } |
| 3891 | |
| 3892 | // aten::amin(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor |
| 3893 | at::Tensor amin::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim) { |
| 3894 | |
| 3895 | static auto op = create_amin_typed_handle(); |
| 3896 | return op.redispatch(dispatchKeySet, self, dim, keepdim); |
| 3897 | } |
| 3898 | |
| 3899 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(amin_out, name, "aten::amin") |
| 3900 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(amin_out, overload_name, "out") |
| 3901 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(amin_out, schema_str, "amin.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)") |
| 3902 | |
| 3903 | // aten::amin.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) |
| 3904 | static C10_NOINLINE c10::TypedOperatorHandle<amin_out::schema> create_amin_out_typed_handle() { |
| 3905 | return c10::Dispatcher::singleton() |
| 3906 | .findSchemaOrThrow(amin_out::name, amin_out::overload_name) |
| 3907 | .typed<amin_out::schema>(); |
| 3908 | } |
| 3909 | |
| 3910 | // aten::amin.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) |
| 3911 | at::Tensor & amin_out::call(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { |
| 3912 | |
| 3913 | static auto op = create_amin_out_typed_handle(); |
| 3914 | return op.call(self, dim, keepdim, out); |
| 3915 | } |
| 3916 | |
| 3917 | // aten::amin.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) |
| 3918 | at::Tensor & amin_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { |
| 3919 | |
| 3920 | static auto op = create_amin_out_typed_handle(); |
| 3921 | return op.redispatch(dispatchKeySet, self, dim, keepdim, out); |
| 3922 | } |
| 3923 | |
| 3924 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_mps_convolution, name, "aten::_mps_convolution") |
| 3925 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_mps_convolution, overload_name, "") |
| 3926 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_mps_convolution, schema_str, "_mps_convolution(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups) -> Tensor") |
| 3927 | |
| 3928 | // aten::_mps_convolution(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups) -> Tensor |
| 3929 | static C10_NOINLINE c10::TypedOperatorHandle<_mps_convolution::schema> create__mps_convolution_typed_handle() { |
| 3930 | return c10::Dispatcher::singleton() |
| 3931 | .findSchemaOrThrow(_mps_convolution::name, _mps_convolution::overload_name) |
| 3932 | .typed<_mps_convolution::schema>(); |
| 3933 | } |
| 3934 | |
| 3935 | // aten::_mps_convolution(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups) -> Tensor |
| 3936 | at::Tensor _mps_convolution::call(const at::Tensor & self, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups) { |
| 3937 | |
| 3938 | static auto op = create__mps_convolution_typed_handle(); |
| 3939 | return op.call(self, weight, bias, padding, stride, dilation, groups); |
| 3940 | } |
| 3941 | |
| 3942 | // aten::_mps_convolution(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups) -> Tensor |
| 3943 | at::Tensor _mps_convolution::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups) { |
| 3944 | |
| 3945 | static auto op = create__mps_convolution_typed_handle(); |
| 3946 | return op.redispatch(dispatchKeySet, self, weight, bias, padding, stride, dilation, groups); |
| 3947 | } |
| 3948 | |
| 3949 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_rnn_layer_backward, name, "aten::mkldnn_rnn_layer_backward") |
| 3950 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_rnn_layer_backward, overload_name, "") |
| 3951 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_rnn_layer_backward, schema_str, "mkldnn_rnn_layer_backward(Tensor input, Tensor weight1, Tensor weight2, Tensor weight3, Tensor weight4, Tensor hx_, Tensor cx_tmp, Tensor output, Tensor hy_, Tensor cy_, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, bool reverse, int mode, int hidden_size, int num_layers, bool has_biases, bool train, bool bidirectional, int[] batch_sizes, bool batch_first, Tensor workspace) -> (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor)") |
| 3952 | |
| 3953 | // aten::mkldnn_rnn_layer_backward(Tensor input, Tensor weight1, Tensor weight2, Tensor weight3, Tensor weight4, Tensor hx_, Tensor cx_tmp, Tensor output, Tensor hy_, Tensor cy_, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, bool reverse, int mode, int hidden_size, int num_layers, bool has_biases, bool train, bool bidirectional, int[] batch_sizes, bool batch_first, Tensor workspace) -> (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor) |
| 3954 | static C10_NOINLINE c10::TypedOperatorHandle<mkldnn_rnn_layer_backward::schema> create_mkldnn_rnn_layer_backward_typed_handle() { |
| 3955 | return c10::Dispatcher::singleton() |
| 3956 | .findSchemaOrThrow(mkldnn_rnn_layer_backward::name, mkldnn_rnn_layer_backward::overload_name) |
| 3957 | .typed<mkldnn_rnn_layer_backward::schema>(); |
| 3958 | } |
| 3959 | |
| 3960 | // aten::mkldnn_rnn_layer_backward(Tensor input, Tensor weight1, Tensor weight2, Tensor weight3, Tensor weight4, Tensor hx_, Tensor cx_tmp, Tensor output, Tensor hy_, Tensor cy_, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, bool reverse, int mode, int hidden_size, int num_layers, bool has_biases, bool train, bool bidirectional, int[] batch_sizes, bool batch_first, Tensor workspace) -> (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor) |
| 3961 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> mkldnn_rnn_layer_backward::call(const at::Tensor & input, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & weight4, const at::Tensor & hx_, const at::Tensor & cx_tmp, const at::Tensor & output, const at::Tensor & hy_, const at::Tensor & cy_, const c10::optional<at::Tensor> & grad_output, const c10::optional<at::Tensor> & grad_hy, const c10::optional<at::Tensor> & grad_cy, bool reverse, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool train, bool bidirectional, at::IntArrayRef batch_sizes, bool batch_first, const at::Tensor & workspace) { |
| 3962 | |
| 3963 | static auto op = create_mkldnn_rnn_layer_backward_typed_handle(); |
| 3964 | return op.call(input, weight1, weight2, weight3, weight4, hx_, cx_tmp, output, hy_, cy_, grad_output, grad_hy, grad_cy, reverse, mode, hidden_size, num_layers, has_biases, train, bidirectional, batch_sizes, batch_first, workspace); |
| 3965 | } |
| 3966 | |
| 3967 | // aten::mkldnn_rnn_layer_backward(Tensor input, Tensor weight1, Tensor weight2, Tensor weight3, Tensor weight4, Tensor hx_, Tensor cx_tmp, Tensor output, Tensor hy_, Tensor cy_, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, bool reverse, int mode, int hidden_size, int num_layers, bool has_biases, bool train, bool bidirectional, int[] batch_sizes, bool batch_first, Tensor workspace) -> (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor) |
| 3968 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> mkldnn_rnn_layer_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & weight4, const at::Tensor & hx_, const at::Tensor & cx_tmp, const at::Tensor & output, const at::Tensor & hy_, const at::Tensor & cy_, const c10::optional<at::Tensor> & grad_output, const c10::optional<at::Tensor> & grad_hy, const c10::optional<at::Tensor> & grad_cy, bool reverse, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool train, bool bidirectional, at::IntArrayRef batch_sizes, bool batch_first, const at::Tensor & workspace) { |
| 3969 | |
| 3970 | static auto op = create_mkldnn_rnn_layer_backward_typed_handle(); |
| 3971 | return op.redispatch(dispatchKeySet, input, weight1, weight2, weight3, weight4, hx_, cx_tmp, output, hy_, cy_, grad_output, grad_hy, grad_cy, reverse, mode, hidden_size, num_layers, has_biases, train, bidirectional, batch_sizes, batch_first, workspace); |
| 3972 | } |
| 3973 | |
| 3974 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(miopen_depthwise_convolution, name, "aten::miopen_depthwise_convolution") |
| 3975 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(miopen_depthwise_convolution, overload_name, "") |
| 3976 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(miopen_depthwise_convolution, schema_str, "miopen_depthwise_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensor") |
| 3977 | |
| 3978 | // aten::miopen_depthwise_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensor |
| 3979 | static C10_NOINLINE c10::TypedOperatorHandle<miopen_depthwise_convolution::schema> create_miopen_depthwise_convolution_typed_handle() { |
| 3980 | return c10::Dispatcher::singleton() |
| 3981 | .findSchemaOrThrow(miopen_depthwise_convolution::name, miopen_depthwise_convolution::overload_name) |
| 3982 | .typed<miopen_depthwise_convolution::schema>(); |
| 3983 | } |
| 3984 | |
| 3985 | // aten::miopen_depthwise_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensor |
| 3986 | at::Tensor miopen_depthwise_convolution::call(const at::Tensor & self, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) { |
| 3987 | |
| 3988 | static auto op = create_miopen_depthwise_convolution_typed_handle(); |
| 3989 | return op.call(self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic); |
| 3990 | } |
| 3991 | |
| 3992 | // aten::miopen_depthwise_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensor |
| 3993 | at::Tensor miopen_depthwise_convolution::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) { |
| 3994 | |
| 3995 | static auto op = create_miopen_depthwise_convolution_typed_handle(); |
| 3996 | return op.redispatch(dispatchKeySet, self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic); |
| 3997 | } |
| 3998 | |
| 3999 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_batch_norm, name, "aten::native_batch_norm") |
| 4000 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_batch_norm, overload_name, "") |
| 4001 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_batch_norm, schema_str, "native_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)") |
| 4002 | |
| 4003 | // aten::native_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor) |
| 4004 | static C10_NOINLINE c10::TypedOperatorHandle<native_batch_norm::schema> create_native_batch_norm_typed_handle() { |
| 4005 | return c10::Dispatcher::singleton() |
| 4006 | .findSchemaOrThrow(native_batch_norm::name, native_batch_norm::overload_name) |
| 4007 | .typed<native_batch_norm::schema>(); |
| 4008 | } |
| 4009 | |
| 4010 | // aten::native_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor) |
| 4011 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_batch_norm::call(const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, bool training, double momentum, double eps) { |
| 4012 | |
| 4013 | static auto op = create_native_batch_norm_typed_handle(); |
| 4014 | return op.call(input, weight, bias, running_mean, running_var, training, momentum, eps); |
| 4015 | } |
| 4016 | |
| 4017 | // aten::native_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor) |
| 4018 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_batch_norm::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, bool training, double momentum, double eps) { |
| 4019 | |
| 4020 | static auto op = create_native_batch_norm_typed_handle(); |
| 4021 | return op.redispatch(dispatchKeySet, input, weight, bias, running_mean, running_var, training, momentum, eps); |
| 4022 | } |
| 4023 | |
| 4024 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_batch_norm_out, name, "aten::native_batch_norm") |
| 4025 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_batch_norm_out, overload_name, "out") |
| 4026 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_batch_norm_out, schema_str, "native_batch_norm.out(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, *, Tensor(a!) out, Tensor(b!) save_mean, Tensor(c!) save_invstd) -> (Tensor(a!), Tensor(b!), Tensor(c!))") |
| 4027 | |
| 4028 | // aten::native_batch_norm.out(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, *, Tensor(a!) out, Tensor(b!) save_mean, Tensor(c!) save_invstd) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 4029 | static C10_NOINLINE c10::TypedOperatorHandle<native_batch_norm_out::schema> create_native_batch_norm_out_typed_handle() { |
| 4030 | return c10::Dispatcher::singleton() |
| 4031 | .findSchemaOrThrow(native_batch_norm_out::name, native_batch_norm_out::overload_name) |
| 4032 | .typed<native_batch_norm_out::schema>(); |
| 4033 | } |
| 4034 | |
| 4035 | // aten::native_batch_norm.out(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, *, Tensor(a!) out, Tensor(b!) save_mean, Tensor(c!) save_invstd) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 4036 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_batch_norm_out::call(const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, bool training, double momentum, double eps, at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd) { |
| 4037 | |
| 4038 | static auto op = create_native_batch_norm_out_typed_handle(); |
| 4039 | return op.call(input, weight, bias, running_mean, running_var, training, momentum, eps, out, save_mean, save_invstd); |
| 4040 | } |
| 4041 | |
| 4042 | // aten::native_batch_norm.out(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, *, Tensor(a!) out, Tensor(b!) save_mean, Tensor(c!) save_invstd) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 4043 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_batch_norm_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, bool training, double momentum, double eps, at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd) { |
| 4044 | |
| 4045 | static auto op = create_native_batch_norm_out_typed_handle(); |
| 4046 | return op.redispatch(dispatchKeySet, input, weight, bias, running_mean, running_var, training, momentum, eps, out, save_mean, save_invstd); |
| 4047 | } |
| 4048 | |
| 4049 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(batch_norm_stats, name, "aten::batch_norm_stats") |
| 4050 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(batch_norm_stats, overload_name, "") |
| 4051 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(batch_norm_stats, schema_str, "batch_norm_stats(Tensor input, float eps) -> (Tensor, Tensor)") |
| 4052 | |
| 4053 | // aten::batch_norm_stats(Tensor input, float eps) -> (Tensor, Tensor) |
| 4054 | static C10_NOINLINE c10::TypedOperatorHandle<batch_norm_stats::schema> create_batch_norm_stats_typed_handle() { |
| 4055 | return c10::Dispatcher::singleton() |
| 4056 | .findSchemaOrThrow(batch_norm_stats::name, batch_norm_stats::overload_name) |
| 4057 | .typed<batch_norm_stats::schema>(); |
| 4058 | } |
| 4059 | |
| 4060 | // aten::batch_norm_stats(Tensor input, float eps) -> (Tensor, Tensor) |
| 4061 | ::std::tuple<at::Tensor,at::Tensor> batch_norm_stats::call(const at::Tensor & input, double eps) { |
| 4062 | |
| 4063 | static auto op = create_batch_norm_stats_typed_handle(); |
| 4064 | return op.call(input, eps); |
| 4065 | } |
| 4066 | |
| 4067 | // aten::batch_norm_stats(Tensor input, float eps) -> (Tensor, Tensor) |
| 4068 | ::std::tuple<at::Tensor,at::Tensor> batch_norm_stats::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, double eps) { |
| 4069 | |
| 4070 | static auto op = create_batch_norm_stats_typed_handle(); |
| 4071 | return op.redispatch(dispatchKeySet, input, eps); |
| 4072 | } |
| 4073 | |
| 4074 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(batch_norm_gather_stats, name, "aten::batch_norm_gather_stats") |
| 4075 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(batch_norm_gather_stats, overload_name, "") |
| 4076 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(batch_norm_gather_stats, schema_str, "batch_norm_gather_stats(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int count) -> (Tensor, Tensor)") |
| 4077 | |
| 4078 | // aten::batch_norm_gather_stats(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int count) -> (Tensor, Tensor) |
| 4079 | static C10_NOINLINE c10::TypedOperatorHandle<batch_norm_gather_stats::schema> create_batch_norm_gather_stats_typed_handle() { |
| 4080 | return c10::Dispatcher::singleton() |
| 4081 | .findSchemaOrThrow(batch_norm_gather_stats::name, batch_norm_gather_stats::overload_name) |
| 4082 | .typed<batch_norm_gather_stats::schema>(); |
| 4083 | } |
| 4084 | |
| 4085 | // aten::batch_norm_gather_stats(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int count) -> (Tensor, Tensor) |
| 4086 | ::std::tuple<at::Tensor,at::Tensor> batch_norm_gather_stats::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, int64_t count) { |
| 4087 | |
| 4088 | static auto op = create_batch_norm_gather_stats_typed_handle(); |
| 4089 | return op.call(input, mean, invstd, running_mean, running_var, momentum, eps, count); |
| 4090 | } |
| 4091 | |
| 4092 | // aten::batch_norm_gather_stats(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int count) -> (Tensor, Tensor) |
| 4093 | ::std::tuple<at::Tensor,at::Tensor> batch_norm_gather_stats::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, int64_t count) { |
| 4094 | |
| 4095 | static auto op = create_batch_norm_gather_stats_typed_handle(); |
| 4096 | return op.redispatch(dispatchKeySet, input, mean, invstd, running_mean, running_var, momentum, eps, count); |
| 4097 | } |
| 4098 | |
| 4099 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_batch_norm_backward, name, "aten::native_batch_norm_backward") |
| 4100 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_batch_norm_backward, overload_name, "") |
| 4101 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_batch_norm_backward, schema_str, "native_batch_norm_backward(Tensor grad_out, Tensor input, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_invstd, bool train, float eps, bool[3] output_mask) -> (Tensor, Tensor, Tensor)") |
| 4102 | |
| 4103 | // aten::native_batch_norm_backward(Tensor grad_out, Tensor input, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_invstd, bool train, float eps, bool[3] output_mask) -> (Tensor, Tensor, Tensor) |
| 4104 | static C10_NOINLINE c10::TypedOperatorHandle<native_batch_norm_backward::schema> create_native_batch_norm_backward_typed_handle() { |
| 4105 | return c10::Dispatcher::singleton() |
| 4106 | .findSchemaOrThrow(native_batch_norm_backward::name, native_batch_norm_backward::overload_name) |
| 4107 | .typed<native_batch_norm_backward::schema>(); |
| 4108 | } |
| 4109 | |
| 4110 | // aten::native_batch_norm_backward(Tensor grad_out, Tensor input, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_invstd, bool train, float eps, bool[3] output_mask) -> (Tensor, Tensor, Tensor) |
| 4111 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_batch_norm_backward::call(const at::Tensor & grad_out, const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, const c10::optional<at::Tensor> & save_mean, const c10::optional<at::Tensor> & save_invstd, bool train, double eps, ::std::array<bool,3> output_mask) { |
| 4112 | |
| 4113 | static auto op = create_native_batch_norm_backward_typed_handle(); |
| 4114 | return op.call(grad_out, input, weight, running_mean, running_var, save_mean, save_invstd, train, eps, output_mask); |
| 4115 | } |
| 4116 | |
| 4117 | // aten::native_batch_norm_backward(Tensor grad_out, Tensor input, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_invstd, bool train, float eps, bool[3] output_mask) -> (Tensor, Tensor, Tensor) |
| 4118 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_batch_norm_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, const c10::optional<at::Tensor> & save_mean, const c10::optional<at::Tensor> & save_invstd, bool train, double eps, ::std::array<bool,3> output_mask) { |
| 4119 | |
| 4120 | static auto op = create_native_batch_norm_backward_typed_handle(); |
| 4121 | return op.redispatch(dispatchKeySet, grad_out, input, weight, running_mean, running_var, save_mean, save_invstd, train, eps, output_mask); |
| 4122 | } |
| 4123 | |
| 4124 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(batch_norm_backward_reduce, name, "aten::batch_norm_backward_reduce") |
| 4125 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(batch_norm_backward_reduce, overload_name, "") |
| 4126 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(batch_norm_backward_reduce, schema_str, "batch_norm_backward_reduce(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, bool input_g, bool weight_g, bool bias_g) -> (Tensor, Tensor, Tensor, Tensor)") |
| 4127 | |
| 4128 | // aten::batch_norm_backward_reduce(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, bool input_g, bool weight_g, bool bias_g) -> (Tensor, Tensor, Tensor, Tensor) |
| 4129 | static C10_NOINLINE c10::TypedOperatorHandle<batch_norm_backward_reduce::schema> create_batch_norm_backward_reduce_typed_handle() { |
| 4130 | return c10::Dispatcher::singleton() |
| 4131 | .findSchemaOrThrow(batch_norm_backward_reduce::name, batch_norm_backward_reduce::overload_name) |
| 4132 | .typed<batch_norm_backward_reduce::schema>(); |
| 4133 | } |
| 4134 | |
| 4135 | // aten::batch_norm_backward_reduce(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, bool input_g, bool weight_g, bool bias_g) -> (Tensor, Tensor, Tensor, Tensor) |
| 4136 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> batch_norm_backward_reduce::call(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional<at::Tensor> & weight, bool input_g, bool weight_g, bool bias_g) { |
| 4137 | |
| 4138 | static auto op = create_batch_norm_backward_reduce_typed_handle(); |
| 4139 | return op.call(grad_out, input, mean, invstd, weight, input_g, weight_g, bias_g); |
| 4140 | } |
| 4141 | |
| 4142 | // aten::batch_norm_backward_reduce(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, bool input_g, bool weight_g, bool bias_g) -> (Tensor, Tensor, Tensor, Tensor) |
| 4143 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> batch_norm_backward_reduce::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional<at::Tensor> & weight, bool input_g, bool weight_g, bool bias_g) { |
| 4144 | |
| 4145 | static auto op = create_batch_norm_backward_reduce_typed_handle(); |
| 4146 | return op.redispatch(dispatchKeySet, grad_out, input, mean, invstd, weight, input_g, weight_g, bias_g); |
| 4147 | } |
| 4148 | |
| 4149 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(is_vulkan_available, name, "aten::is_vulkan_available") |
| 4150 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(is_vulkan_available, overload_name, "") |
| 4151 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(is_vulkan_available, schema_str, "is_vulkan_available() -> bool") |
| 4152 | |
| 4153 | // aten::is_vulkan_available() -> bool |
| 4154 | static C10_NOINLINE c10::TypedOperatorHandle<is_vulkan_available::schema> create_is_vulkan_available_typed_handle() { |
| 4155 | return c10::Dispatcher::singleton() |
| 4156 | .findSchemaOrThrow(is_vulkan_available::name, is_vulkan_available::overload_name) |
| 4157 | .typed<is_vulkan_available::schema>(); |
| 4158 | } |
| 4159 | |
| 4160 | // aten::is_vulkan_available() -> bool |
| 4161 | bool is_vulkan_available::call() { |
| 4162 | |
| 4163 | static auto op = create_is_vulkan_available_typed_handle(); |
| 4164 | return op.call(); |
| 4165 | } |
| 4166 | |
| 4167 | // aten::is_vulkan_available() -> bool |
| 4168 | bool is_vulkan_available::redispatch(c10::DispatchKeySet dispatchKeySet) { |
| 4169 | |
| 4170 | static auto op = create_is_vulkan_available_typed_handle(); |
| 4171 | return op.redispatch(dispatchKeySet); |
| 4172 | } |
| 4173 | |
| 4174 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_nnpack_spatial_convolution, name, "aten::_nnpack_spatial_convolution") |
| 4175 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_nnpack_spatial_convolution, overload_name, "") |
| 4176 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_nnpack_spatial_convolution, schema_str, "_nnpack_spatial_convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[2] padding, int[2] stride=1) -> Tensor") |
| 4177 | |
| 4178 | // aten::_nnpack_spatial_convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[2] padding, int[2] stride=1) -> Tensor |
| 4179 | static C10_NOINLINE c10::TypedOperatorHandle<_nnpack_spatial_convolution::schema> create__nnpack_spatial_convolution_typed_handle() { |
| 4180 | return c10::Dispatcher::singleton() |
| 4181 | .findSchemaOrThrow(_nnpack_spatial_convolution::name, _nnpack_spatial_convolution::overload_name) |
| 4182 | .typed<_nnpack_spatial_convolution::schema>(); |
| 4183 | } |
| 4184 | |
| 4185 | // aten::_nnpack_spatial_convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[2] padding, int[2] stride=1) -> Tensor |
| 4186 | at::Tensor _nnpack_spatial_convolution::call(const at::Tensor & input, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, at::IntArrayRef stride) { |
| 4187 | |
| 4188 | static auto op = create__nnpack_spatial_convolution_typed_handle(); |
| 4189 | return op.call(input, weight, bias, padding, stride); |
| 4190 | } |
| 4191 | |
| 4192 | // aten::_nnpack_spatial_convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[2] padding, int[2] stride=1) -> Tensor |
| 4193 | at::Tensor _nnpack_spatial_convolution::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, at::IntArrayRef stride) { |
| 4194 | |
| 4195 | static auto op = create__nnpack_spatial_convolution_typed_handle(); |
| 4196 | return op.redispatch(dispatchKeySet, input, weight, bias, padding, stride); |
| 4197 | } |
| 4198 | |
| 4199 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ones_names, name, "aten::ones") |
| 4200 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ones_names, overload_name, "names") |
| 4201 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ones_names, schema_str, "ones.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor") |
| 4202 | |
| 4203 | // aten::ones.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 4204 | static C10_NOINLINE c10::TypedOperatorHandle<ones_names::schema> create_ones_names_typed_handle() { |
| 4205 | return c10::Dispatcher::singleton() |
| 4206 | .findSchemaOrThrow(ones_names::name, ones_names::overload_name) |
| 4207 | .typed<ones_names::schema>(); |
| 4208 | } |
| 4209 | |
| 4210 | // aten::ones.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 4211 | at::Tensor ones_names::call(at::IntArrayRef size, c10::optional<at::DimnameList> names, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 4212 | |
| 4213 | static auto op = create_ones_names_typed_handle(); |
| 4214 | return op.call(size, names, dtype, layout, device, pin_memory); |
| 4215 | } |
| 4216 | |
| 4217 | // aten::ones.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 4218 | at::Tensor ones_names::redispatch(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional<at::DimnameList> names, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 4219 | |
| 4220 | static auto op = create_ones_names_typed_handle(); |
| 4221 | return op.redispatch(dispatchKeySet, size, names, dtype, layout, device, pin_memory); |
| 4222 | } |
| 4223 | |
| 4224 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ones, name, "aten::ones") |
| 4225 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ones, overload_name, "") |
| 4226 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ones, schema_str, "ones(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor") |
| 4227 | |
| 4228 | // aten::ones(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 4229 | static C10_NOINLINE c10::TypedOperatorHandle<ones::schema> create_ones_typed_handle() { |
| 4230 | return c10::Dispatcher::singleton() |
| 4231 | .findSchemaOrThrow(ones::name, ones::overload_name) |
| 4232 | .typed<ones::schema>(); |
| 4233 | } |
| 4234 | |
| 4235 | // aten::ones(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 4236 | at::Tensor ones::call(c10::SymIntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 4237 | |
| 4238 | static auto op = create_ones_typed_handle(); |
| 4239 | return op.call(size, dtype, layout, device, pin_memory); |
| 4240 | } |
| 4241 | |
| 4242 | // aten::ones(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 4243 | at::Tensor ones::redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 4244 | |
| 4245 | static auto op = create_ones_typed_handle(); |
| 4246 | return op.redispatch(dispatchKeySet, size, dtype, layout, device, pin_memory); |
| 4247 | } |
| 4248 | |
| 4249 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ones_out, name, "aten::ones") |
| 4250 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ones_out, overload_name, "out") |
| 4251 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ones_out, schema_str, "ones.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)") |
| 4252 | |
| 4253 | // aten::ones.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) |
| 4254 | static C10_NOINLINE c10::TypedOperatorHandle<ones_out::schema> create_ones_out_typed_handle() { |
| 4255 | return c10::Dispatcher::singleton() |
| 4256 | .findSchemaOrThrow(ones_out::name, ones_out::overload_name) |
| 4257 | .typed<ones_out::schema>(); |
| 4258 | } |
| 4259 | |
| 4260 | // aten::ones.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) |
| 4261 | at::Tensor & ones_out::call(c10::SymIntArrayRef size, at::Tensor & out) { |
| 4262 | |
| 4263 | static auto op = create_ones_out_typed_handle(); |
| 4264 | return op.call(size, out); |
| 4265 | } |
| 4266 | |
| 4267 | // aten::ones.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) |
| 4268 | at::Tensor & ones_out::redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, at::Tensor & out) { |
| 4269 | |
| 4270 | static auto op = create_ones_out_typed_handle(); |
| 4271 | return op.redispatch(dispatchKeySet, size, out); |
| 4272 | } |
| 4273 | |
| 4274 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cdist_forward, name, "aten::_cdist_forward") |
| 4275 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cdist_forward, overload_name, "") |
| 4276 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cdist_forward, schema_str, "_cdist_forward(Tensor x1, Tensor x2, float p, int? compute_mode) -> Tensor") |
| 4277 | |
| 4278 | // aten::_cdist_forward(Tensor x1, Tensor x2, float p, int? compute_mode) -> Tensor |
| 4279 | static C10_NOINLINE c10::TypedOperatorHandle<_cdist_forward::schema> create__cdist_forward_typed_handle() { |
| 4280 | return c10::Dispatcher::singleton() |
| 4281 | .findSchemaOrThrow(_cdist_forward::name, _cdist_forward::overload_name) |
| 4282 | .typed<_cdist_forward::schema>(); |
| 4283 | } |
| 4284 | |
| 4285 | // aten::_cdist_forward(Tensor x1, Tensor x2, float p, int? compute_mode) -> Tensor |
| 4286 | at::Tensor _cdist_forward::call(const at::Tensor & x1, const at::Tensor & x2, double p, c10::optional<int64_t> compute_mode) { |
| 4287 | |
| 4288 | static auto op = create__cdist_forward_typed_handle(); |
| 4289 | return op.call(x1, x2, p, compute_mode); |
| 4290 | } |
| 4291 | |
| 4292 | // aten::_cdist_forward(Tensor x1, Tensor x2, float p, int? compute_mode) -> Tensor |
| 4293 | at::Tensor _cdist_forward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x1, const at::Tensor & x2, double p, c10::optional<int64_t> compute_mode) { |
| 4294 | |
| 4295 | static auto op = create__cdist_forward_typed_handle(); |
| 4296 | return op.redispatch(dispatchKeySet, x1, x2, p, compute_mode); |
| 4297 | } |
| 4298 | |
| 4299 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cosine_similarity, name, "aten::cosine_similarity") |
| 4300 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cosine_similarity, overload_name, "") |
| 4301 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cosine_similarity, schema_str, "cosine_similarity(Tensor x1, Tensor x2, int dim=1, float eps=1e-08) -> Tensor") |
| 4302 | |
| 4303 | // aten::cosine_similarity(Tensor x1, Tensor x2, int dim=1, float eps=1e-08) -> Tensor |
| 4304 | static C10_NOINLINE c10::TypedOperatorHandle<cosine_similarity::schema> create_cosine_similarity_typed_handle() { |
| 4305 | return c10::Dispatcher::singleton() |
| 4306 | .findSchemaOrThrow(cosine_similarity::name, cosine_similarity::overload_name) |
| 4307 | .typed<cosine_similarity::schema>(); |
| 4308 | } |
| 4309 | |
| 4310 | // aten::cosine_similarity(Tensor x1, Tensor x2, int dim=1, float eps=1e-08) -> Tensor |
| 4311 | at::Tensor cosine_similarity::call(const at::Tensor & x1, const at::Tensor & x2, int64_t dim, double eps) { |
| 4312 | |
| 4313 | static auto op = create_cosine_similarity_typed_handle(); |
| 4314 | return op.call(x1, x2, dim, eps); |
| 4315 | } |
| 4316 | |
| 4317 | // aten::cosine_similarity(Tensor x1, Tensor x2, int dim=1, float eps=1e-08) -> Tensor |
| 4318 | at::Tensor cosine_similarity::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x1, const at::Tensor & x2, int64_t dim, double eps) { |
| 4319 | |
| 4320 | static auto op = create_cosine_similarity_typed_handle(); |
| 4321 | return op.redispatch(dispatchKeySet, x1, x2, dim, eps); |
| 4322 | } |
| 4323 | |
| 4324 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(movedim_intlist, name, "aten::movedim") |
| 4325 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(movedim_intlist, overload_name, "intlist") |
| 4326 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(movedim_intlist, schema_str, "movedim.intlist(Tensor(a) self, int[] source, int[] destination) -> Tensor(a)") |
| 4327 | |
| 4328 | // aten::movedim.intlist(Tensor(a) self, int[] source, int[] destination) -> Tensor(a) |
| 4329 | static C10_NOINLINE c10::TypedOperatorHandle<movedim_intlist::schema> create_movedim_intlist_typed_handle() { |
| 4330 | return c10::Dispatcher::singleton() |
| 4331 | .findSchemaOrThrow(movedim_intlist::name, movedim_intlist::overload_name) |
| 4332 | .typed<movedim_intlist::schema>(); |
| 4333 | } |
| 4334 | |
| 4335 | // aten::movedim.intlist(Tensor(a) self, int[] source, int[] destination) -> Tensor(a) |
| 4336 | at::Tensor movedim_intlist::call(const at::Tensor & self, at::IntArrayRef source, at::IntArrayRef destination) { |
| 4337 | |
| 4338 | static auto op = create_movedim_intlist_typed_handle(); |
| 4339 | return op.call(self, source, destination); |
| 4340 | } |
| 4341 | |
| 4342 | // aten::movedim.intlist(Tensor(a) self, int[] source, int[] destination) -> Tensor(a) |
| 4343 | at::Tensor movedim_intlist::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef source, at::IntArrayRef destination) { |
| 4344 | |
| 4345 | static auto op = create_movedim_intlist_typed_handle(); |
| 4346 | return op.redispatch(dispatchKeySet, self, source, destination); |
| 4347 | } |
| 4348 | |
| 4349 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(movedim_int, name, "aten::movedim") |
| 4350 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(movedim_int, overload_name, "int") |
| 4351 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(movedim_int, schema_str, "movedim.int(Tensor(a) self, int source, int destination) -> Tensor(a)") |
| 4352 | |
| 4353 | // aten::movedim.int(Tensor(a) self, int source, int destination) -> Tensor(a) |
| 4354 | static C10_NOINLINE c10::TypedOperatorHandle<movedim_int::schema> create_movedim_int_typed_handle() { |
| 4355 | return c10::Dispatcher::singleton() |
| 4356 | .findSchemaOrThrow(movedim_int::name, movedim_int::overload_name) |
| 4357 | .typed<movedim_int::schema>(); |
| 4358 | } |
| 4359 | |
| 4360 | // aten::movedim.int(Tensor(a) self, int source, int destination) -> Tensor(a) |
| 4361 | at::Tensor movedim_int::call(const at::Tensor & self, int64_t source, int64_t destination) { |
| 4362 | |
| 4363 | static auto op = create_movedim_int_typed_handle(); |
| 4364 | return op.call(self, source, destination); |
| 4365 | } |
| 4366 | |
| 4367 | // aten::movedim.int(Tensor(a) self, int source, int destination) -> Tensor(a) |
| 4368 | at::Tensor movedim_int::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t source, int64_t destination) { |
| 4369 | |
| 4370 | static auto op = create_movedim_int_typed_handle(); |
| 4371 | return op.redispatch(dispatchKeySet, self, source, destination); |
| 4372 | } |
| 4373 | |
| 4374 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(numpy_T, name, "aten::numpy_T") |
| 4375 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(numpy_T, overload_name, "") |
| 4376 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(numpy_T, schema_str, "numpy_T(Tensor(a) self) -> Tensor(a)") |
| 4377 | |
| 4378 | // aten::numpy_T(Tensor(a) self) -> Tensor(a) |
| 4379 | static C10_NOINLINE c10::TypedOperatorHandle<numpy_T::schema> create_numpy_T_typed_handle() { |
| 4380 | return c10::Dispatcher::singleton() |
| 4381 | .findSchemaOrThrow(numpy_T::name, numpy_T::overload_name) |
| 4382 | .typed<numpy_T::schema>(); |
| 4383 | } |
| 4384 | |
| 4385 | // aten::numpy_T(Tensor(a) self) -> Tensor(a) |
| 4386 | at::Tensor numpy_T::call(const at::Tensor & self) { |
| 4387 | |
| 4388 | static auto op = create_numpy_T_typed_handle(); |
| 4389 | return op.call(self); |
| 4390 | } |
| 4391 | |
| 4392 | // aten::numpy_T(Tensor(a) self) -> Tensor(a) |
| 4393 | at::Tensor numpy_T::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 4394 | |
| 4395 | static auto op = create_numpy_T_typed_handle(); |
| 4396 | return op.redispatch(dispatchKeySet, self); |
| 4397 | } |
| 4398 | |
| 4399 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mH, name, "aten::mH") |
| 4400 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mH, overload_name, "") |
| 4401 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mH, schema_str, "mH(Tensor(a) self) -> Tensor(a)") |
| 4402 | |
| 4403 | // aten::mH(Tensor(a) self) -> Tensor(a) |
| 4404 | static C10_NOINLINE c10::TypedOperatorHandle<mH::schema> create_mH_typed_handle() { |
| 4405 | return c10::Dispatcher::singleton() |
| 4406 | .findSchemaOrThrow(mH::name, mH::overload_name) |
| 4407 | .typed<mH::schema>(); |
| 4408 | } |
| 4409 | |
| 4410 | // aten::mH(Tensor(a) self) -> Tensor(a) |
| 4411 | at::Tensor mH::call(const at::Tensor & self) { |
| 4412 | |
| 4413 | static auto op = create_mH_typed_handle(); |
| 4414 | return op.call(self); |
| 4415 | } |
| 4416 | |
| 4417 | // aten::mH(Tensor(a) self) -> Tensor(a) |
| 4418 | at::Tensor mH::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 4419 | |
| 4420 | static auto op = create_mH_typed_handle(); |
| 4421 | return op.redispatch(dispatchKeySet, self); |
| 4422 | } |
| 4423 | |
| 4424 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rand_like, name, "aten::rand_like") |
| 4425 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rand_like, overload_name, "") |
| 4426 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rand_like, schema_str, "rand_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor") |
| 4427 | |
| 4428 | // aten::rand_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor |
| 4429 | static C10_NOINLINE c10::TypedOperatorHandle<rand_like::schema> create_rand_like_typed_handle() { |
| 4430 | return c10::Dispatcher::singleton() |
| 4431 | .findSchemaOrThrow(rand_like::name, rand_like::overload_name) |
| 4432 | .typed<rand_like::schema>(); |
| 4433 | } |
| 4434 | |
| 4435 | // aten::rand_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor |
| 4436 | at::Tensor rand_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) { |
| 4437 | |
| 4438 | static auto op = create_rand_like_typed_handle(); |
| 4439 | return op.call(self, dtype, layout, device, pin_memory, memory_format); |
| 4440 | } |
| 4441 | |
| 4442 | // aten::rand_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor |
| 4443 | at::Tensor rand_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) { |
| 4444 | |
| 4445 | static auto op = create_rand_like_typed_handle(); |
| 4446 | return op.redispatch(dispatchKeySet, self, dtype, layout, device, pin_memory, memory_format); |
| 4447 | } |
| 4448 | |
| 4449 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(randint_like, name, "aten::randint_like") |
| 4450 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(randint_like, overload_name, "") |
| 4451 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(randint_like, schema_str, "randint_like(Tensor self, int high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor") |
| 4452 | |
| 4453 | // aten::randint_like(Tensor self, int high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor |
| 4454 | static C10_NOINLINE c10::TypedOperatorHandle<randint_like::schema> create_randint_like_typed_handle() { |
| 4455 | return c10::Dispatcher::singleton() |
| 4456 | .findSchemaOrThrow(randint_like::name, randint_like::overload_name) |
| 4457 | .typed<randint_like::schema>(); |
| 4458 | } |
| 4459 | |
| 4460 | // aten::randint_like(Tensor self, int high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor |
| 4461 | at::Tensor randint_like::call(const at::Tensor & self, int64_t high, 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) { |
| 4462 | |
| 4463 | static auto op = create_randint_like_typed_handle(); |
| 4464 | return op.call(self, high, dtype, layout, device, pin_memory, memory_format); |
| 4465 | } |
| 4466 | |
| 4467 | // aten::randint_like(Tensor self, int high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor |
| 4468 | at::Tensor randint_like::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t high, 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) { |
| 4469 | |
| 4470 | static auto op = create_randint_like_typed_handle(); |
| 4471 | return op.redispatch(dispatchKeySet, self, high, dtype, layout, device, pin_memory, memory_format); |
| 4472 | } |
| 4473 | |
| 4474 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(randint_like_low_dtype, name, "aten::randint_like") |
| 4475 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(randint_like_low_dtype, overload_name, "low_dtype") |
| 4476 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(randint_like_low_dtype, schema_str, "randint_like.low_dtype(Tensor self, int low, int high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor") |
| 4477 | |
| 4478 | // aten::randint_like.low_dtype(Tensor self, int low, int high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor |
| 4479 | static C10_NOINLINE c10::TypedOperatorHandle<randint_like_low_dtype::schema> create_randint_like_low_dtype_typed_handle() { |
| 4480 | return c10::Dispatcher::singleton() |
| 4481 | .findSchemaOrThrow(randint_like_low_dtype::name, randint_like_low_dtype::overload_name) |
| 4482 | .typed<randint_like_low_dtype::schema>(); |
| 4483 | } |
| 4484 | |
| 4485 | // aten::randint_like.low_dtype(Tensor self, int low, int high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor |
| 4486 | at::Tensor randint_like_low_dtype::call(const at::Tensor & self, int64_t low, int64_t high, 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) { |
| 4487 | |
| 4488 | static auto op = create_randint_like_low_dtype_typed_handle(); |
| 4489 | return op.call(self, low, high, dtype, layout, device, pin_memory, memory_format); |
| 4490 | } |
| 4491 | |
| 4492 | // aten::randint_like.low_dtype(Tensor self, int low, int high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor |
| 4493 | at::Tensor randint_like_low_dtype::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t low, int64_t high, 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) { |
| 4494 | |
| 4495 | static auto op = create_randint_like_low_dtype_typed_handle(); |
| 4496 | return op.redispatch(dispatchKeySet, self, low, high, dtype, layout, device, pin_memory, memory_format); |
| 4497 | } |
| 4498 | |
| 4499 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(round, name, "aten::round") |
| 4500 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(round, overload_name, "") |
| 4501 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(round, schema_str, "round(Tensor self) -> Tensor") |
| 4502 | |
| 4503 | // aten::round(Tensor self) -> Tensor |
| 4504 | static C10_NOINLINE c10::TypedOperatorHandle<round::schema> create_round_typed_handle() { |
| 4505 | return c10::Dispatcher::singleton() |
| 4506 | .findSchemaOrThrow(round::name, round::overload_name) |
| 4507 | .typed<round::schema>(); |
| 4508 | } |
| 4509 | |
| 4510 | // aten::round(Tensor self) -> Tensor |
| 4511 | at::Tensor round::call(const at::Tensor & self) { |
| 4512 | |
| 4513 | static auto op = create_round_typed_handle(); |
| 4514 | return op.call(self); |
| 4515 | } |
| 4516 | |
| 4517 | // aten::round(Tensor self) -> Tensor |
| 4518 | at::Tensor round::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 4519 | |
| 4520 | static auto op = create_round_typed_handle(); |
| 4521 | return op.redispatch(dispatchKeySet, self); |
| 4522 | } |
| 4523 | |
| 4524 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(round_, name, "aten::round_") |
| 4525 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(round_, overload_name, "") |
| 4526 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(round_, schema_str, "round_(Tensor(a!) self) -> Tensor(a!)") |
| 4527 | |
| 4528 | // aten::round_(Tensor(a!) self) -> Tensor(a!) |
| 4529 | static C10_NOINLINE c10::TypedOperatorHandle<round_::schema> create_round__typed_handle() { |
| 4530 | return c10::Dispatcher::singleton() |
| 4531 | .findSchemaOrThrow(round_::name, round_::overload_name) |
| 4532 | .typed<round_::schema>(); |
| 4533 | } |
| 4534 | |
| 4535 | // aten::round_(Tensor(a!) self) -> Tensor(a!) |
| 4536 | at::Tensor & round_::call(at::Tensor & self) { |
| 4537 | |
| 4538 | static auto op = create_round__typed_handle(); |
| 4539 | return op.call(self); |
| 4540 | } |
| 4541 | |
| 4542 | // aten::round_(Tensor(a!) self) -> Tensor(a!) |
| 4543 | at::Tensor & round_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 4544 | |
| 4545 | static auto op = create_round__typed_handle(); |
| 4546 | return op.redispatch(dispatchKeySet, self); |
| 4547 | } |
| 4548 | |
| 4549 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(round_out, name, "aten::round") |
| 4550 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(round_out, overload_name, "out") |
| 4551 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(round_out, schema_str, "round.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 4552 | |
| 4553 | // aten::round.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 4554 | static C10_NOINLINE c10::TypedOperatorHandle<round_out::schema> create_round_out_typed_handle() { |
| 4555 | return c10::Dispatcher::singleton() |
| 4556 | .findSchemaOrThrow(round_out::name, round_out::overload_name) |
| 4557 | .typed<round_out::schema>(); |
| 4558 | } |
| 4559 | |
| 4560 | // aten::round.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 4561 | at::Tensor & round_out::call(const at::Tensor & self, at::Tensor & out) { |
| 4562 | |
| 4563 | static auto op = create_round_out_typed_handle(); |
| 4564 | return op.call(self, out); |
| 4565 | } |
| 4566 | |
| 4567 | // aten::round.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 4568 | at::Tensor & round_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 4569 | |
| 4570 | static auto op = create_round_out_typed_handle(); |
| 4571 | return op.redispatch(dispatchKeySet, self, out); |
| 4572 | } |
| 4573 | |
| 4574 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(round_decimals, name, "aten::round") |
| 4575 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(round_decimals, overload_name, "decimals") |
| 4576 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(round_decimals, schema_str, "round.decimals(Tensor self, *, int decimals) -> Tensor") |
| 4577 | |
| 4578 | // aten::round.decimals(Tensor self, *, int decimals) -> Tensor |
| 4579 | static C10_NOINLINE c10::TypedOperatorHandle<round_decimals::schema> create_round_decimals_typed_handle() { |
| 4580 | return c10::Dispatcher::singleton() |
| 4581 | .findSchemaOrThrow(round_decimals::name, round_decimals::overload_name) |
| 4582 | .typed<round_decimals::schema>(); |
| 4583 | } |
| 4584 | |
| 4585 | // aten::round.decimals(Tensor self, *, int decimals) -> Tensor |
| 4586 | at::Tensor round_decimals::call(const at::Tensor & self, int64_t decimals) { |
| 4587 | |
| 4588 | static auto op = create_round_decimals_typed_handle(); |
| 4589 | return op.call(self, decimals); |
| 4590 | } |
| 4591 | |
| 4592 | // aten::round.decimals(Tensor self, *, int decimals) -> Tensor |
| 4593 | at::Tensor round_decimals::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t decimals) { |
| 4594 | |
| 4595 | static auto op = create_round_decimals_typed_handle(); |
| 4596 | return op.redispatch(dispatchKeySet, self, decimals); |
| 4597 | } |
| 4598 | |
| 4599 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(round__decimals, name, "aten::round_") |
| 4600 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(round__decimals, overload_name, "decimals") |
| 4601 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(round__decimals, schema_str, "round_.decimals(Tensor(a!) self, *, int decimals) -> Tensor(a!)") |
| 4602 | |
| 4603 | // aten::round_.decimals(Tensor(a!) self, *, int decimals) -> Tensor(a!) |
| 4604 | static C10_NOINLINE c10::TypedOperatorHandle<round__decimals::schema> create_round__decimals_typed_handle() { |
| 4605 | return c10::Dispatcher::singleton() |
| 4606 | .findSchemaOrThrow(round__decimals::name, round__decimals::overload_name) |
| 4607 | .typed<round__decimals::schema>(); |
| 4608 | } |
| 4609 | |
| 4610 | // aten::round_.decimals(Tensor(a!) self, *, int decimals) -> Tensor(a!) |
| 4611 | at::Tensor & round__decimals::call(at::Tensor & self, int64_t decimals) { |
| 4612 | |
| 4613 | static auto op = create_round__decimals_typed_handle(); |
| 4614 | return op.call(self, decimals); |
| 4615 | } |
| 4616 | |
| 4617 | // aten::round_.decimals(Tensor(a!) self, *, int decimals) -> Tensor(a!) |
| 4618 | at::Tensor & round__decimals::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t decimals) { |
| 4619 | |
| 4620 | static auto op = create_round__decimals_typed_handle(); |
| 4621 | return op.redispatch(dispatchKeySet, self, decimals); |
| 4622 | } |
| 4623 | |
| 4624 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(round_decimals_out, name, "aten::round") |
| 4625 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(round_decimals_out, overload_name, "decimals_out") |
| 4626 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(round_decimals_out, schema_str, "round.decimals_out(Tensor self, *, int decimals, Tensor(a!) out) -> Tensor(a!)") |
| 4627 | |
| 4628 | // aten::round.decimals_out(Tensor self, *, int decimals, Tensor(a!) out) -> Tensor(a!) |
| 4629 | static C10_NOINLINE c10::TypedOperatorHandle<round_decimals_out::schema> create_round_decimals_out_typed_handle() { |
| 4630 | return c10::Dispatcher::singleton() |
| 4631 | .findSchemaOrThrow(round_decimals_out::name, round_decimals_out::overload_name) |
| 4632 | .typed<round_decimals_out::schema>(); |
| 4633 | } |
| 4634 | |
| 4635 | // aten::round.decimals_out(Tensor self, *, int decimals, Tensor(a!) out) -> Tensor(a!) |
| 4636 | at::Tensor & round_decimals_out::call(const at::Tensor & self, int64_t decimals, at::Tensor & out) { |
| 4637 | |
| 4638 | static auto op = create_round_decimals_out_typed_handle(); |
| 4639 | return op.call(self, decimals, out); |
| 4640 | } |
| 4641 | |
| 4642 | // aten::round.decimals_out(Tensor self, *, int decimals, Tensor(a!) out) -> Tensor(a!) |
| 4643 | at::Tensor & round_decimals_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t decimals, at::Tensor & out) { |
| 4644 | |
| 4645 | static auto op = create_round_decimals_out_typed_handle(); |
| 4646 | return op.redispatch(dispatchKeySet, self, decimals, out); |
| 4647 | } |
| 4648 | |
| 4649 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gelu_out, name, "aten::gelu") |
| 4650 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gelu_out, overload_name, "out") |
| 4651 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gelu_out, schema_str, "gelu.out(Tensor self, *, str approximate='none', Tensor(a!) out) -> Tensor(a!)") |
| 4652 | |
| 4653 | // aten::gelu.out(Tensor self, *, str approximate='none', Tensor(a!) out) -> Tensor(a!) |
| 4654 | static C10_NOINLINE c10::TypedOperatorHandle<gelu_out::schema> create_gelu_out_typed_handle() { |
| 4655 | return c10::Dispatcher::singleton() |
| 4656 | .findSchemaOrThrow(gelu_out::name, gelu_out::overload_name) |
| 4657 | .typed<gelu_out::schema>(); |
| 4658 | } |
| 4659 | |
| 4660 | // aten::gelu.out(Tensor self, *, str approximate='none', Tensor(a!) out) -> Tensor(a!) |
| 4661 | at::Tensor & gelu_out::call(const at::Tensor & self, c10::string_view approximate, at::Tensor & out) { |
| 4662 | |
| 4663 | static auto op = create_gelu_out_typed_handle(); |
| 4664 | return op.call(self, approximate, out); |
| 4665 | } |
| 4666 | |
| 4667 | // aten::gelu.out(Tensor self, *, str approximate='none', Tensor(a!) out) -> Tensor(a!) |
| 4668 | at::Tensor & gelu_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::string_view approximate, at::Tensor & out) { |
| 4669 | |
| 4670 | static auto op = create_gelu_out_typed_handle(); |
| 4671 | return op.redispatch(dispatchKeySet, self, approximate, out); |
| 4672 | } |
| 4673 | |
| 4674 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gelu_, name, "aten::gelu_") |
| 4675 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gelu_, overload_name, "") |
| 4676 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gelu_, schema_str, "gelu_(Tensor(a!) self, *, str approximate='none') -> Tensor(a!)") |
| 4677 | |
| 4678 | // aten::gelu_(Tensor(a!) self, *, str approximate='none') -> Tensor(a!) |
| 4679 | static C10_NOINLINE c10::TypedOperatorHandle<gelu_::schema> create_gelu__typed_handle() { |
| 4680 | return c10::Dispatcher::singleton() |
| 4681 | .findSchemaOrThrow(gelu_::name, gelu_::overload_name) |
| 4682 | .typed<gelu_::schema>(); |
| 4683 | } |
| 4684 | |
| 4685 | // aten::gelu_(Tensor(a!) self, *, str approximate='none') -> Tensor(a!) |
| 4686 | at::Tensor & gelu_::call(at::Tensor & self, c10::string_view approximate) { |
| 4687 | |
| 4688 | static auto op = create_gelu__typed_handle(); |
| 4689 | return op.call(self, approximate); |
| 4690 | } |
| 4691 | |
| 4692 | // aten::gelu_(Tensor(a!) self, *, str approximate='none') -> Tensor(a!) |
| 4693 | at::Tensor & gelu_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, c10::string_view approximate) { |
| 4694 | |
| 4695 | static auto op = create_gelu__typed_handle(); |
| 4696 | return op.redispatch(dispatchKeySet, self, approximate); |
| 4697 | } |
| 4698 | |
| 4699 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gelu, name, "aten::gelu") |
| 4700 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gelu, overload_name, "") |
| 4701 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gelu, schema_str, "gelu(Tensor self, *, str approximate='none') -> Tensor") |
| 4702 | |
| 4703 | // aten::gelu(Tensor self, *, str approximate='none') -> Tensor |
| 4704 | static C10_NOINLINE c10::TypedOperatorHandle<gelu::schema> create_gelu_typed_handle() { |
| 4705 | return c10::Dispatcher::singleton() |
| 4706 | .findSchemaOrThrow(gelu::name, gelu::overload_name) |
| 4707 | .typed<gelu::schema>(); |
| 4708 | } |
| 4709 | |
| 4710 | // aten::gelu(Tensor self, *, str approximate='none') -> Tensor |
| 4711 | at::Tensor gelu::call(const at::Tensor & self, c10::string_view approximate) { |
| 4712 | |
| 4713 | static auto op = create_gelu_typed_handle(); |
| 4714 | return op.call(self, approximate); |
| 4715 | } |
| 4716 | |
| 4717 | // aten::gelu(Tensor self, *, str approximate='none') -> Tensor |
| 4718 | at::Tensor gelu::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::string_view approximate) { |
| 4719 | |
| 4720 | static auto op = create_gelu_typed_handle(); |
| 4721 | return op.redispatch(dispatchKeySet, self, approximate); |
| 4722 | } |
| 4723 | |
| 4724 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardshrink_out, name, "aten::hardshrink") |
| 4725 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardshrink_out, overload_name, "out") |
| 4726 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardshrink_out, schema_str, "hardshrink.out(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!)") |
| 4727 | |
| 4728 | // aten::hardshrink.out(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!) |
| 4729 | static C10_NOINLINE c10::TypedOperatorHandle<hardshrink_out::schema> create_hardshrink_out_typed_handle() { |
| 4730 | return c10::Dispatcher::singleton() |
| 4731 | .findSchemaOrThrow(hardshrink_out::name, hardshrink_out::overload_name) |
| 4732 | .typed<hardshrink_out::schema>(); |
| 4733 | } |
| 4734 | |
| 4735 | // aten::hardshrink.out(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!) |
| 4736 | at::Tensor & hardshrink_out::call(const at::Tensor & self, const at::Scalar & lambd, at::Tensor & out) { |
| 4737 | |
| 4738 | static auto op = create_hardshrink_out_typed_handle(); |
| 4739 | return op.call(self, lambd, out); |
| 4740 | } |
| 4741 | |
| 4742 | // aten::hardshrink.out(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!) |
| 4743 | at::Tensor & hardshrink_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & lambd, at::Tensor & out) { |
| 4744 | |
| 4745 | static auto op = create_hardshrink_out_typed_handle(); |
| 4746 | return op.redispatch(dispatchKeySet, self, lambd, out); |
| 4747 | } |
| 4748 | |
| 4749 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardshrink, name, "aten::hardshrink") |
| 4750 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardshrink, overload_name, "") |
| 4751 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardshrink, schema_str, "hardshrink(Tensor self, Scalar lambd=0.5) -> Tensor") |
| 4752 | |
| 4753 | // aten::hardshrink(Tensor self, Scalar lambd=0.5) -> Tensor |
| 4754 | static C10_NOINLINE c10::TypedOperatorHandle<hardshrink::schema> create_hardshrink_typed_handle() { |
| 4755 | return c10::Dispatcher::singleton() |
| 4756 | .findSchemaOrThrow(hardshrink::name, hardshrink::overload_name) |
| 4757 | .typed<hardshrink::schema>(); |
| 4758 | } |
| 4759 | |
| 4760 | // aten::hardshrink(Tensor self, Scalar lambd=0.5) -> Tensor |
| 4761 | at::Tensor hardshrink::call(const at::Tensor & self, const at::Scalar & lambd) { |
| 4762 | |
| 4763 | static auto op = create_hardshrink_typed_handle(); |
| 4764 | return op.call(self, lambd); |
| 4765 | } |
| 4766 | |
| 4767 | // aten::hardshrink(Tensor self, Scalar lambd=0.5) -> Tensor |
| 4768 | at::Tensor hardshrink::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & lambd) { |
| 4769 | |
| 4770 | static auto op = create_hardshrink_typed_handle(); |
| 4771 | return op.redispatch(dispatchKeySet, self, lambd); |
| 4772 | } |
| 4773 | |
| 4774 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(select_backward, name, "aten::select_backward") |
| 4775 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(select_backward, overload_name, "") |
| 4776 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(select_backward, schema_str, "select_backward(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt index) -> Tensor") |
| 4777 | |
| 4778 | // aten::select_backward(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt index) -> Tensor |
| 4779 | static C10_NOINLINE c10::TypedOperatorHandle<select_backward::schema> create_select_backward_typed_handle() { |
| 4780 | return c10::Dispatcher::singleton() |
| 4781 | .findSchemaOrThrow(select_backward::name, select_backward::overload_name) |
| 4782 | .typed<select_backward::schema>(); |
| 4783 | } |
| 4784 | |
| 4785 | // aten::select_backward(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt index) -> Tensor |
| 4786 | at::Tensor select_backward::call(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt index) { |
| 4787 | |
| 4788 | static auto op = create_select_backward_typed_handle(); |
| 4789 | return op.call(grad_output, input_sizes, dim, index); |
| 4790 | } |
| 4791 | |
| 4792 | // aten::select_backward(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt index) -> Tensor |
| 4793 | at::Tensor select_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt index) { |
| 4794 | |
| 4795 | static auto op = create_select_backward_typed_handle(); |
| 4796 | return op.redispatch(dispatchKeySet, grad_output, input_sizes, dim, index); |
| 4797 | } |
| 4798 | |
| 4799 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mish, name, "aten::mish") |
| 4800 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mish, overload_name, "") |
| 4801 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mish, schema_str, "mish(Tensor self) -> Tensor") |
| 4802 | |
| 4803 | // aten::mish(Tensor self) -> Tensor |
| 4804 | static C10_NOINLINE c10::TypedOperatorHandle<mish::schema> create_mish_typed_handle() { |
| 4805 | return c10::Dispatcher::singleton() |
| 4806 | .findSchemaOrThrow(mish::name, mish::overload_name) |
| 4807 | .typed<mish::schema>(); |
| 4808 | } |
| 4809 | |
| 4810 | // aten::mish(Tensor self) -> Tensor |
| 4811 | at::Tensor mish::call(const at::Tensor & self) { |
| 4812 | |
| 4813 | static auto op = create_mish_typed_handle(); |
| 4814 | return op.call(self); |
| 4815 | } |
| 4816 | |
| 4817 | // aten::mish(Tensor self) -> Tensor |
| 4818 | at::Tensor mish::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 4819 | |
| 4820 | static auto op = create_mish_typed_handle(); |
| 4821 | return op.redispatch(dispatchKeySet, self); |
| 4822 | } |
| 4823 | |
| 4824 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mish_, name, "aten::mish_") |
| 4825 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mish_, overload_name, "") |
| 4826 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mish_, schema_str, "mish_(Tensor(a!) self) -> Tensor(a!)") |
| 4827 | |
| 4828 | // aten::mish_(Tensor(a!) self) -> Tensor(a!) |
| 4829 | static C10_NOINLINE c10::TypedOperatorHandle<mish_::schema> create_mish__typed_handle() { |
| 4830 | return c10::Dispatcher::singleton() |
| 4831 | .findSchemaOrThrow(mish_::name, mish_::overload_name) |
| 4832 | .typed<mish_::schema>(); |
| 4833 | } |
| 4834 | |
| 4835 | // aten::mish_(Tensor(a!) self) -> Tensor(a!) |
| 4836 | at::Tensor & mish_::call(at::Tensor & self) { |
| 4837 | |
| 4838 | static auto op = create_mish__typed_handle(); |
| 4839 | return op.call(self); |
| 4840 | } |
| 4841 | |
| 4842 | // aten::mish_(Tensor(a!) self) -> Tensor(a!) |
| 4843 | at::Tensor & mish_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 4844 | |
| 4845 | static auto op = create_mish__typed_handle(); |
| 4846 | return op.redispatch(dispatchKeySet, self); |
| 4847 | } |
| 4848 | |
| 4849 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mish_out, name, "aten::mish") |
| 4850 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mish_out, overload_name, "out") |
| 4851 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mish_out, schema_str, "mish.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 4852 | |
| 4853 | // aten::mish.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 4854 | static C10_NOINLINE c10::TypedOperatorHandle<mish_out::schema> create_mish_out_typed_handle() { |
| 4855 | return c10::Dispatcher::singleton() |
| 4856 | .findSchemaOrThrow(mish_out::name, mish_out::overload_name) |
| 4857 | .typed<mish_out::schema>(); |
| 4858 | } |
| 4859 | |
| 4860 | // aten::mish.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 4861 | at::Tensor & mish_out::call(const at::Tensor & self, at::Tensor & out) { |
| 4862 | |
| 4863 | static auto op = create_mish_out_typed_handle(); |
| 4864 | return op.call(self, out); |
| 4865 | } |
| 4866 | |
| 4867 | // aten::mish.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 4868 | at::Tensor & mish_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 4869 | |
| 4870 | static auto op = create_mish_out_typed_handle(); |
| 4871 | return op.redispatch(dispatchKeySet, self, out); |
| 4872 | } |
| 4873 | |
| 4874 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sigmoid, name, "aten::sigmoid") |
| 4875 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sigmoid, overload_name, "") |
| 4876 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sigmoid, schema_str, "sigmoid(Tensor self) -> Tensor") |
| 4877 | |
| 4878 | // aten::sigmoid(Tensor self) -> Tensor |
| 4879 | static C10_NOINLINE c10::TypedOperatorHandle<sigmoid::schema> create_sigmoid_typed_handle() { |
| 4880 | return c10::Dispatcher::singleton() |
| 4881 | .findSchemaOrThrow(sigmoid::name, sigmoid::overload_name) |
| 4882 | .typed<sigmoid::schema>(); |
| 4883 | } |
| 4884 | |
| 4885 | // aten::sigmoid(Tensor self) -> Tensor |
| 4886 | at::Tensor sigmoid::call(const at::Tensor & self) { |
| 4887 | |
| 4888 | static auto op = create_sigmoid_typed_handle(); |
| 4889 | return op.call(self); |
| 4890 | } |
| 4891 | |
| 4892 | // aten::sigmoid(Tensor self) -> Tensor |
| 4893 | at::Tensor sigmoid::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 4894 | |
| 4895 | static auto op = create_sigmoid_typed_handle(); |
| 4896 | return op.redispatch(dispatchKeySet, self); |
| 4897 | } |
| 4898 | |
| 4899 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sigmoid_, name, "aten::sigmoid_") |
| 4900 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sigmoid_, overload_name, "") |
| 4901 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sigmoid_, schema_str, "sigmoid_(Tensor(a!) self) -> Tensor(a!)") |
| 4902 | |
| 4903 | // aten::sigmoid_(Tensor(a!) self) -> Tensor(a!) |
| 4904 | static C10_NOINLINE c10::TypedOperatorHandle<sigmoid_::schema> create_sigmoid__typed_handle() { |
| 4905 | return c10::Dispatcher::singleton() |
| 4906 | .findSchemaOrThrow(sigmoid_::name, sigmoid_::overload_name) |
| 4907 | .typed<sigmoid_::schema>(); |
| 4908 | } |
| 4909 | |
| 4910 | // aten::sigmoid_(Tensor(a!) self) -> Tensor(a!) |
| 4911 | at::Tensor & sigmoid_::call(at::Tensor & self) { |
| 4912 | |
| 4913 | static auto op = create_sigmoid__typed_handle(); |
| 4914 | return op.call(self); |
| 4915 | } |
| 4916 | |
| 4917 | // aten::sigmoid_(Tensor(a!) self) -> Tensor(a!) |
| 4918 | at::Tensor & sigmoid_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 4919 | |
| 4920 | static auto op = create_sigmoid__typed_handle(); |
| 4921 | return op.redispatch(dispatchKeySet, self); |
| 4922 | } |
| 4923 | |
| 4924 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sigmoid_out, name, "aten::sigmoid") |
| 4925 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sigmoid_out, overload_name, "out") |
| 4926 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sigmoid_out, schema_str, "sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 4927 | |
| 4928 | // aten::sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 4929 | static C10_NOINLINE c10::TypedOperatorHandle<sigmoid_out::schema> create_sigmoid_out_typed_handle() { |
| 4930 | return c10::Dispatcher::singleton() |
| 4931 | .findSchemaOrThrow(sigmoid_out::name, sigmoid_out::overload_name) |
| 4932 | .typed<sigmoid_out::schema>(); |
| 4933 | } |
| 4934 | |
| 4935 | // aten::sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 4936 | at::Tensor & sigmoid_out::call(const at::Tensor & self, at::Tensor & out) { |
| 4937 | |
| 4938 | static auto op = create_sigmoid_out_typed_handle(); |
| 4939 | return op.call(self, out); |
| 4940 | } |
| 4941 | |
| 4942 | // aten::sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 4943 | at::Tensor & sigmoid_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 4944 | |
| 4945 | static auto op = create_sigmoid_out_typed_handle(); |
| 4946 | return op.redispatch(dispatchKeySet, self, out); |
| 4947 | } |
| 4948 | |
| 4949 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(detach, name, "aten::detach") |
| 4950 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(detach, overload_name, "") |
| 4951 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(detach, schema_str, "detach(Tensor(a) self) -> Tensor(a)") |
| 4952 | |
| 4953 | // aten::detach(Tensor(a) self) -> Tensor(a) |
| 4954 | static C10_NOINLINE c10::TypedOperatorHandle<detach::schema> create_detach_typed_handle() { |
| 4955 | return c10::Dispatcher::singleton() |
| 4956 | .findSchemaOrThrow(detach::name, detach::overload_name) |
| 4957 | .typed<detach::schema>(); |
| 4958 | } |
| 4959 | |
| 4960 | // aten::detach(Tensor(a) self) -> Tensor(a) |
| 4961 | at::Tensor detach::call(const at::Tensor & self) { |
| 4962 | |
| 4963 | static auto op = create_detach_typed_handle(); |
| 4964 | return op.call(self); |
| 4965 | } |
| 4966 | |
| 4967 | // aten::detach(Tensor(a) self) -> Tensor(a) |
| 4968 | at::Tensor detach::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 4969 | |
| 4970 | static auto op = create_detach_typed_handle(); |
| 4971 | return op.redispatch(dispatchKeySet, self); |
| 4972 | } |
| 4973 | |
| 4974 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(detach_, name, "aten::detach_") |
| 4975 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(detach_, overload_name, "") |
| 4976 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(detach_, schema_str, "detach_(Tensor(a!) self) -> Tensor(a!)") |
| 4977 | |
| 4978 | // aten::detach_(Tensor(a!) self) -> Tensor(a!) |
| 4979 | static C10_NOINLINE c10::TypedOperatorHandle<detach_::schema> create_detach__typed_handle() { |
| 4980 | return c10::Dispatcher::singleton() |
| 4981 | .findSchemaOrThrow(detach_::name, detach_::overload_name) |
| 4982 | .typed<detach_::schema>(); |
| 4983 | } |
| 4984 | |
| 4985 | // aten::detach_(Tensor(a!) self) -> Tensor(a!) |
| 4986 | at::Tensor & detach_::call(at::Tensor & self) { |
| 4987 | |
| 4988 | static auto op = create_detach__typed_handle(); |
| 4989 | return op.call(self); |
| 4990 | } |
| 4991 | |
| 4992 | // aten::detach_(Tensor(a!) self) -> Tensor(a!) |
| 4993 | at::Tensor & detach_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 4994 | |
| 4995 | static auto op = create_detach__typed_handle(); |
| 4996 | return op.redispatch(dispatchKeySet, self); |
| 4997 | } |
| 4998 | |
| 4999 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(size_int, name, "aten::size") |
| 5000 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(size_int, overload_name, "int") |
| 5001 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(size_int, schema_str, "size.int(Tensor self, int dim) -> int") |
| 5002 | |
| 5003 | // aten::size.int(Tensor self, int dim) -> int |
| 5004 | static C10_NOINLINE c10::TypedOperatorHandle<size_int::schema> create_size_int_typed_handle() { |
| 5005 | return c10::Dispatcher::singleton() |
| 5006 | .findSchemaOrThrow(size_int::name, size_int::overload_name) |
| 5007 | .typed<size_int::schema>(); |
| 5008 | } |
| 5009 | |
| 5010 | // aten::size.int(Tensor self, int dim) -> int |
| 5011 | int64_t size_int::call(const at::Tensor & self, int64_t dim) { |
| 5012 | |
| 5013 | static auto op = create_size_int_typed_handle(); |
| 5014 | return op.call(self, dim); |
| 5015 | } |
| 5016 | |
| 5017 | // aten::size.int(Tensor self, int dim) -> int |
| 5018 | int64_t size_int::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim) { |
| 5019 | |
| 5020 | static auto op = create_size_int_typed_handle(); |
| 5021 | return op.redispatch(dispatchKeySet, self, dim); |
| 5022 | } |
| 5023 | |
| 5024 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(size_Dimname, name, "aten::size") |
| 5025 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(size_Dimname, overload_name, "Dimname") |
| 5026 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(size_Dimname, schema_str, "size.Dimname(Tensor self, Dimname dim) -> int") |
| 5027 | |
| 5028 | // aten::size.Dimname(Tensor self, Dimname dim) -> int |
| 5029 | static C10_NOINLINE c10::TypedOperatorHandle<size_Dimname::schema> create_size_Dimname_typed_handle() { |
| 5030 | return c10::Dispatcher::singleton() |
| 5031 | .findSchemaOrThrow(size_Dimname::name, size_Dimname::overload_name) |
| 5032 | .typed<size_Dimname::schema>(); |
| 5033 | } |
| 5034 | |
| 5035 | // aten::size.Dimname(Tensor self, Dimname dim) -> int |
| 5036 | int64_t size_Dimname::call(const at::Tensor & self, at::Dimname dim) { |
| 5037 | |
| 5038 | static auto op = create_size_Dimname_typed_handle(); |
| 5039 | return op.call(self, dim); |
| 5040 | } |
| 5041 | |
| 5042 | // aten::size.Dimname(Tensor self, Dimname dim) -> int |
| 5043 | int64_t size_Dimname::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim) { |
| 5044 | |
| 5045 | static auto op = create_size_Dimname_typed_handle(); |
| 5046 | return op.redispatch(dispatchKeySet, self, dim); |
| 5047 | } |
| 5048 | |
| 5049 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slice_scatter, name, "aten::slice_scatter") |
| 5050 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slice_scatter, overload_name, "") |
| 5051 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slice_scatter, schema_str, "slice_scatter(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor") |
| 5052 | |
| 5053 | // aten::slice_scatter(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor |
| 5054 | static C10_NOINLINE c10::TypedOperatorHandle<slice_scatter::schema> create_slice_scatter_typed_handle() { |
| 5055 | return c10::Dispatcher::singleton() |
| 5056 | .findSchemaOrThrow(slice_scatter::name, slice_scatter::overload_name) |
| 5057 | .typed<slice_scatter::schema>(); |
| 5058 | } |
| 5059 | |
| 5060 | // aten::slice_scatter(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor |
| 5061 | at::Tensor slice_scatter::call(const at::Tensor & self, const at::Tensor & src, int64_t dim, c10::optional<c10::SymInt> start, c10::optional<c10::SymInt> end, c10::SymInt step) { |
| 5062 | |
| 5063 | static auto op = create_slice_scatter_typed_handle(); |
| 5064 | return op.call(self, src, dim, start, end, step); |
| 5065 | } |
| 5066 | |
| 5067 | // aten::slice_scatter(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor |
| 5068 | at::Tensor slice_scatter::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, int64_t dim, c10::optional<c10::SymInt> start, c10::optional<c10::SymInt> end, c10::SymInt step) { |
| 5069 | |
| 5070 | static auto op = create_slice_scatter_typed_handle(); |
| 5071 | return op.redispatch(dispatchKeySet, self, src, dim, start, end, step); |
| 5072 | } |
| 5073 | |
| 5074 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_softmax_backward_data, name, "aten::_softmax_backward_data") |
| 5075 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_softmax_backward_data, overload_name, "") |
| 5076 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_softmax_backward_data, schema_str, "_softmax_backward_data(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype) -> Tensor") |
| 5077 | |
| 5078 | // aten::_softmax_backward_data(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype) -> Tensor |
| 5079 | static C10_NOINLINE c10::TypedOperatorHandle<_softmax_backward_data::schema> create__softmax_backward_data_typed_handle() { |
| 5080 | return c10::Dispatcher::singleton() |
| 5081 | .findSchemaOrThrow(_softmax_backward_data::name, _softmax_backward_data::overload_name) |
| 5082 | .typed<_softmax_backward_data::schema>(); |
| 5083 | } |
| 5084 | |
| 5085 | // aten::_softmax_backward_data(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype) -> Tensor |
| 5086 | at::Tensor _softmax_backward_data::call(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) { |
| 5087 | |
| 5088 | static auto op = create__softmax_backward_data_typed_handle(); |
| 5089 | return op.call(grad_output, output, dim, input_dtype); |
| 5090 | } |
| 5091 | |
| 5092 | // aten::_softmax_backward_data(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype) -> Tensor |
| 5093 | at::Tensor _softmax_backward_data::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) { |
| 5094 | |
| 5095 | static auto op = create__softmax_backward_data_typed_handle(); |
| 5096 | return op.redispatch(dispatchKeySet, grad_output, output, dim, input_dtype); |
| 5097 | } |
| 5098 | |
| 5099 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_softmax_backward_data_out, name, "aten::_softmax_backward_data") |
| 5100 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_softmax_backward_data_out, overload_name, "out") |
| 5101 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_softmax_backward_data_out, schema_str, "_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype, *, Tensor(a!) grad_input) -> Tensor(a!)") |
| 5102 | |
| 5103 | // aten::_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 5104 | static C10_NOINLINE c10::TypedOperatorHandle<_softmax_backward_data_out::schema> create__softmax_backward_data_out_typed_handle() { |
| 5105 | return c10::Dispatcher::singleton() |
| 5106 | .findSchemaOrThrow(_softmax_backward_data_out::name, _softmax_backward_data_out::overload_name) |
| 5107 | .typed<_softmax_backward_data_out::schema>(); |
| 5108 | } |
| 5109 | |
| 5110 | // aten::_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 5111 | at::Tensor & _softmax_backward_data_out::call(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype, at::Tensor & grad_input) { |
| 5112 | |
| 5113 | static auto op = create__softmax_backward_data_out_typed_handle(); |
| 5114 | return op.call(grad_output, output, dim, input_dtype, grad_input); |
| 5115 | } |
| 5116 | |
| 5117 | // aten::_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 5118 | at::Tensor & _softmax_backward_data_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype, at::Tensor & grad_input) { |
| 5119 | |
| 5120 | static auto op = create__softmax_backward_data_out_typed_handle(); |
| 5121 | return op.redispatch(dispatchKeySet, grad_output, output, dim, input_dtype, grad_input); |
| 5122 | } |
| 5123 | |
| 5124 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(split_with_sizes, name, "aten::split_with_sizes") |
| 5125 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(split_with_sizes, overload_name, "") |
| 5126 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(split_with_sizes, schema_str, "split_with_sizes(Tensor(a -> *) self, SymInt[] split_sizes, int dim=0) -> Tensor(a)[]") |
| 5127 | |
| 5128 | // aten::split_with_sizes(Tensor(a -> *) self, SymInt[] split_sizes, int dim=0) -> Tensor(a)[] |
| 5129 | static C10_NOINLINE c10::TypedOperatorHandle<split_with_sizes::schema> create_split_with_sizes_typed_handle() { |
| 5130 | return c10::Dispatcher::singleton() |
| 5131 | .findSchemaOrThrow(split_with_sizes::name, split_with_sizes::overload_name) |
| 5132 | .typed<split_with_sizes::schema>(); |
| 5133 | } |
| 5134 | |
| 5135 | // aten::split_with_sizes(Tensor(a -> *) self, SymInt[] split_sizes, int dim=0) -> Tensor(a)[] |
| 5136 | ::std::vector<at::Tensor> split_with_sizes::call(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim) { |
| 5137 | |
| 5138 | static auto op = create_split_with_sizes_typed_handle(); |
| 5139 | return op.call(self, split_sizes, dim); |
| 5140 | } |
| 5141 | |
| 5142 | // aten::split_with_sizes(Tensor(a -> *) self, SymInt[] split_sizes, int dim=0) -> Tensor(a)[] |
| 5143 | ::std::vector<at::Tensor> split_with_sizes::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim) { |
| 5144 | |
| 5145 | static auto op = create_split_with_sizes_typed_handle(); |
| 5146 | return op.redispatch(dispatchKeySet, self, split_sizes, dim); |
| 5147 | } |
| 5148 | |
| 5149 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hsplit_int, name, "aten::hsplit") |
| 5150 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hsplit_int, overload_name, "int") |
| 5151 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hsplit_int, schema_str, "hsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[]") |
| 5152 | |
| 5153 | // aten::hsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[] |
| 5154 | static C10_NOINLINE c10::TypedOperatorHandle<hsplit_int::schema> create_hsplit_int_typed_handle() { |
| 5155 | return c10::Dispatcher::singleton() |
| 5156 | .findSchemaOrThrow(hsplit_int::name, hsplit_int::overload_name) |
| 5157 | .typed<hsplit_int::schema>(); |
| 5158 | } |
| 5159 | |
| 5160 | // aten::hsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[] |
| 5161 | ::std::vector<at::Tensor> hsplit_int::call(const at::Tensor & self, int64_t sections) { |
| 5162 | |
| 5163 | static auto op = create_hsplit_int_typed_handle(); |
| 5164 | return op.call(self, sections); |
| 5165 | } |
| 5166 | |
| 5167 | // aten::hsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[] |
| 5168 | ::std::vector<at::Tensor> hsplit_int::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t sections) { |
| 5169 | |
| 5170 | static auto op = create_hsplit_int_typed_handle(); |
| 5171 | return op.redispatch(dispatchKeySet, self, sections); |
| 5172 | } |
| 5173 | |
| 5174 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hsplit_array, name, "aten::hsplit") |
| 5175 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hsplit_array, overload_name, "array") |
| 5176 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hsplit_array, schema_str, "hsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[]") |
| 5177 | |
| 5178 | // aten::hsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[] |
| 5179 | static C10_NOINLINE c10::TypedOperatorHandle<hsplit_array::schema> create_hsplit_array_typed_handle() { |
| 5180 | return c10::Dispatcher::singleton() |
| 5181 | .findSchemaOrThrow(hsplit_array::name, hsplit_array::overload_name) |
| 5182 | .typed<hsplit_array::schema>(); |
| 5183 | } |
| 5184 | |
| 5185 | // aten::hsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[] |
| 5186 | ::std::vector<at::Tensor> hsplit_array::call(const at::Tensor & self, at::IntArrayRef indices) { |
| 5187 | |
| 5188 | static auto op = create_hsplit_array_typed_handle(); |
| 5189 | return op.call(self, indices); |
| 5190 | } |
| 5191 | |
| 5192 | // aten::hsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[] |
| 5193 | ::std::vector<at::Tensor> hsplit_array::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef indices) { |
| 5194 | |
| 5195 | static auto op = create_hsplit_array_typed_handle(); |
| 5196 | return op.redispatch(dispatchKeySet, self, indices); |
| 5197 | } |
| 5198 | |
| 5199 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(stack, name, "aten::stack") |
| 5200 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(stack, overload_name, "") |
| 5201 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(stack, schema_str, "stack(Tensor[] tensors, int dim=0) -> Tensor") |
| 5202 | |
| 5203 | // aten::stack(Tensor[] tensors, int dim=0) -> Tensor |
| 5204 | static C10_NOINLINE c10::TypedOperatorHandle<stack::schema> create_stack_typed_handle() { |
| 5205 | return c10::Dispatcher::singleton() |
| 5206 | .findSchemaOrThrow(stack::name, stack::overload_name) |
| 5207 | .typed<stack::schema>(); |
| 5208 | } |
| 5209 | |
| 5210 | // aten::stack(Tensor[] tensors, int dim=0) -> Tensor |
| 5211 | at::Tensor stack::call(at::TensorList tensors, int64_t dim) { |
| 5212 | |
| 5213 | static auto op = create_stack_typed_handle(); |
| 5214 | return op.call(tensors, dim); |
| 5215 | } |
| 5216 | |
| 5217 | // aten::stack(Tensor[] tensors, int dim=0) -> Tensor |
| 5218 | at::Tensor stack::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, int64_t dim) { |
| 5219 | |
| 5220 | static auto op = create_stack_typed_handle(); |
| 5221 | return op.redispatch(dispatchKeySet, tensors, dim); |
| 5222 | } |
| 5223 | |
| 5224 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(stack_out, name, "aten::stack") |
| 5225 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(stack_out, overload_name, "out") |
| 5226 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(stack_out, schema_str, "stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)") |
| 5227 | |
| 5228 | // aten::stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) |
| 5229 | static C10_NOINLINE c10::TypedOperatorHandle<stack_out::schema> create_stack_out_typed_handle() { |
| 5230 | return c10::Dispatcher::singleton() |
| 5231 | .findSchemaOrThrow(stack_out::name, stack_out::overload_name) |
| 5232 | .typed<stack_out::schema>(); |
| 5233 | } |
| 5234 | |
| 5235 | // aten::stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) |
| 5236 | at::Tensor & stack_out::call(at::TensorList tensors, int64_t dim, at::Tensor & out) { |
| 5237 | |
| 5238 | static auto op = create_stack_out_typed_handle(); |
| 5239 | return op.call(tensors, dim, out); |
| 5240 | } |
| 5241 | |
| 5242 | // aten::stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) |
| 5243 | at::Tensor & stack_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, int64_t dim, at::Tensor & out) { |
| 5244 | |
| 5245 | static auto op = create_stack_out_typed_handle(); |
| 5246 | return op.redispatch(dispatchKeySet, tensors, dim, out); |
| 5247 | } |
| 5248 | |
| 5249 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_stack, name, "aten::_stack") |
| 5250 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_stack, overload_name, "") |
| 5251 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_stack, schema_str, "_stack(Tensor[] tensors, int dim=0) -> Tensor") |
| 5252 | |
| 5253 | // aten::_stack(Tensor[] tensors, int dim=0) -> Tensor |
| 5254 | static C10_NOINLINE c10::TypedOperatorHandle<_stack::schema> create__stack_typed_handle() { |
| 5255 | return c10::Dispatcher::singleton() |
| 5256 | .findSchemaOrThrow(_stack::name, _stack::overload_name) |
| 5257 | .typed<_stack::schema>(); |
| 5258 | } |
| 5259 | |
| 5260 | // aten::_stack(Tensor[] tensors, int dim=0) -> Tensor |
| 5261 | at::Tensor _stack::call(at::TensorList tensors, int64_t dim) { |
| 5262 | |
| 5263 | static auto op = create__stack_typed_handle(); |
| 5264 | return op.call(tensors, dim); |
| 5265 | } |
| 5266 | |
| 5267 | // aten::_stack(Tensor[] tensors, int dim=0) -> Tensor |
| 5268 | at::Tensor _stack::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, int64_t dim) { |
| 5269 | |
| 5270 | static auto op = create__stack_typed_handle(); |
| 5271 | return op.redispatch(dispatchKeySet, tensors, dim); |
| 5272 | } |
| 5273 | |
| 5274 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_stack_out, name, "aten::_stack") |
| 5275 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_stack_out, overload_name, "out") |
| 5276 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_stack_out, schema_str, "_stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)") |
| 5277 | |
| 5278 | // aten::_stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) |
| 5279 | static C10_NOINLINE c10::TypedOperatorHandle<_stack_out::schema> create__stack_out_typed_handle() { |
| 5280 | return c10::Dispatcher::singleton() |
| 5281 | .findSchemaOrThrow(_stack_out::name, _stack_out::overload_name) |
| 5282 | .typed<_stack_out::schema>(); |
| 5283 | } |
| 5284 | |
| 5285 | // aten::_stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) |
| 5286 | at::Tensor & _stack_out::call(at::TensorList tensors, int64_t dim, at::Tensor & out) { |
| 5287 | |
| 5288 | static auto op = create__stack_out_typed_handle(); |
| 5289 | return op.call(tensors, dim, out); |
| 5290 | } |
| 5291 | |
| 5292 | // aten::_stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) |
| 5293 | at::Tensor & _stack_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, int64_t dim, at::Tensor & out) { |
| 5294 | |
| 5295 | static auto op = create__stack_out_typed_handle(); |
| 5296 | return op.redispatch(dispatchKeySet, tensors, dim, out); |
| 5297 | } |
| 5298 | |
| 5299 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(square, name, "aten::square") |
| 5300 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(square, overload_name, "") |
| 5301 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(square, schema_str, "square(Tensor self) -> Tensor") |
| 5302 | |
| 5303 | // aten::square(Tensor self) -> Tensor |
| 5304 | static C10_NOINLINE c10::TypedOperatorHandle<square::schema> create_square_typed_handle() { |
| 5305 | return c10::Dispatcher::singleton() |
| 5306 | .findSchemaOrThrow(square::name, square::overload_name) |
| 5307 | .typed<square::schema>(); |
| 5308 | } |
| 5309 | |
| 5310 | // aten::square(Tensor self) -> Tensor |
| 5311 | at::Tensor square::call(const at::Tensor & self) { |
| 5312 | |
| 5313 | static auto op = create_square_typed_handle(); |
| 5314 | return op.call(self); |
| 5315 | } |
| 5316 | |
| 5317 | // aten::square(Tensor self) -> Tensor |
| 5318 | at::Tensor square::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 5319 | |
| 5320 | static auto op = create_square_typed_handle(); |
| 5321 | return op.redispatch(dispatchKeySet, self); |
| 5322 | } |
| 5323 | |
| 5324 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(square_, name, "aten::square_") |
| 5325 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(square_, overload_name, "") |
| 5326 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(square_, schema_str, "square_(Tensor(a!) self) -> Tensor(a!)") |
| 5327 | |
| 5328 | // aten::square_(Tensor(a!) self) -> Tensor(a!) |
| 5329 | static C10_NOINLINE c10::TypedOperatorHandle<square_::schema> create_square__typed_handle() { |
| 5330 | return c10::Dispatcher::singleton() |
| 5331 | .findSchemaOrThrow(square_::name, square_::overload_name) |
| 5332 | .typed<square_::schema>(); |
| 5333 | } |
| 5334 | |
| 5335 | // aten::square_(Tensor(a!) self) -> Tensor(a!) |
| 5336 | at::Tensor & square_::call(at::Tensor & self) { |
| 5337 | |
| 5338 | static auto op = create_square__typed_handle(); |
| 5339 | return op.call(self); |
| 5340 | } |
| 5341 | |
| 5342 | // aten::square_(Tensor(a!) self) -> Tensor(a!) |
| 5343 | at::Tensor & square_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 5344 | |
| 5345 | static auto op = create_square__typed_handle(); |
| 5346 | return op.redispatch(dispatchKeySet, self); |
| 5347 | } |
| 5348 | |
| 5349 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(square_out, name, "aten::square") |
| 5350 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(square_out, overload_name, "out") |
| 5351 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(square_out, schema_str, "square.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 5352 | |
| 5353 | // aten::square.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 5354 | static C10_NOINLINE c10::TypedOperatorHandle<square_out::schema> create_square_out_typed_handle() { |
| 5355 | return c10::Dispatcher::singleton() |
| 5356 | .findSchemaOrThrow(square_out::name, square_out::overload_name) |
| 5357 | .typed<square_out::schema>(); |
| 5358 | } |
| 5359 | |
| 5360 | // aten::square.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 5361 | at::Tensor & square_out::call(const at::Tensor & self, at::Tensor & out) { |
| 5362 | |
| 5363 | static auto op = create_square_out_typed_handle(); |
| 5364 | return op.call(self, out); |
| 5365 | } |
| 5366 | |
| 5367 | // aten::square.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 5368 | at::Tensor & square_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 5369 | |
| 5370 | static auto op = create_square_out_typed_handle(); |
| 5371 | return op.redispatch(dispatchKeySet, self, out); |
| 5372 | } |
| 5373 | |
| 5374 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(tanh, name, "aten::tanh") |
| 5375 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(tanh, overload_name, "") |
| 5376 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(tanh, schema_str, "tanh(Tensor self) -> Tensor") |
| 5377 | |
| 5378 | // aten::tanh(Tensor self) -> Tensor |
| 5379 | static C10_NOINLINE c10::TypedOperatorHandle<tanh::schema> create_tanh_typed_handle() { |
| 5380 | return c10::Dispatcher::singleton() |
| 5381 | .findSchemaOrThrow(tanh::name, tanh::overload_name) |
| 5382 | .typed<tanh::schema>(); |
| 5383 | } |
| 5384 | |
| 5385 | // aten::tanh(Tensor self) -> Tensor |
| 5386 | at::Tensor tanh::call(const at::Tensor & self) { |
| 5387 | |
| 5388 | static auto op = create_tanh_typed_handle(); |
| 5389 | return op.call(self); |
| 5390 | } |
| 5391 | |
| 5392 | // aten::tanh(Tensor self) -> Tensor |
| 5393 | at::Tensor tanh::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 5394 | |
| 5395 | static auto op = create_tanh_typed_handle(); |
| 5396 | return op.redispatch(dispatchKeySet, self); |
| 5397 | } |
| 5398 | |
| 5399 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(tanh_, name, "aten::tanh_") |
| 5400 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(tanh_, overload_name, "") |
| 5401 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(tanh_, schema_str, "tanh_(Tensor(a!) self) -> Tensor(a!)") |
| 5402 | |
| 5403 | // aten::tanh_(Tensor(a!) self) -> Tensor(a!) |
| 5404 | static C10_NOINLINE c10::TypedOperatorHandle<tanh_::schema> create_tanh__typed_handle() { |
| 5405 | return c10::Dispatcher::singleton() |
| 5406 | .findSchemaOrThrow(tanh_::name, tanh_::overload_name) |
| 5407 | .typed<tanh_::schema>(); |
| 5408 | } |
| 5409 | |
| 5410 | // aten::tanh_(Tensor(a!) self) -> Tensor(a!) |
| 5411 | at::Tensor & tanh_::call(at::Tensor & self) { |
| 5412 | |
| 5413 | static auto op = create_tanh__typed_handle(); |
| 5414 | return op.call(self); |
| 5415 | } |
| 5416 | |
| 5417 | // aten::tanh_(Tensor(a!) self) -> Tensor(a!) |
| 5418 | at::Tensor & tanh_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 5419 | |
| 5420 | static auto op = create_tanh__typed_handle(); |
| 5421 | return op.redispatch(dispatchKeySet, self); |
| 5422 | } |
| 5423 | |
| 5424 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(tanh_out, name, "aten::tanh") |
| 5425 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(tanh_out, overload_name, "out") |
| 5426 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(tanh_out, schema_str, "tanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 5427 | |
| 5428 | // aten::tanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 5429 | static C10_NOINLINE c10::TypedOperatorHandle<tanh_out::schema> create_tanh_out_typed_handle() { |
| 5430 | return c10::Dispatcher::singleton() |
| 5431 | .findSchemaOrThrow(tanh_out::name, tanh_out::overload_name) |
| 5432 | .typed<tanh_out::schema>(); |
| 5433 | } |
| 5434 | |
| 5435 | // aten::tanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 5436 | at::Tensor & tanh_out::call(const at::Tensor & self, at::Tensor & out) { |
| 5437 | |
| 5438 | static auto op = create_tanh_out_typed_handle(); |
| 5439 | return op.call(self, out); |
| 5440 | } |
| 5441 | |
| 5442 | // aten::tanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 5443 | at::Tensor & tanh_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 5444 | |
| 5445 | static auto op = create_tanh_out_typed_handle(); |
| 5446 | return op.redispatch(dispatchKeySet, self, out); |
| 5447 | } |
| 5448 | |
| 5449 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(tensordot, name, "aten::tensordot") |
| 5450 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(tensordot, overload_name, "") |
| 5451 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(tensordot, schema_str, "tensordot(Tensor self, Tensor other, int[] dims_self, int[] dims_other) -> Tensor") |
| 5452 | |
| 5453 | // aten::tensordot(Tensor self, Tensor other, int[] dims_self, int[] dims_other) -> Tensor |
| 5454 | static C10_NOINLINE c10::TypedOperatorHandle<tensordot::schema> create_tensordot_typed_handle() { |
| 5455 | return c10::Dispatcher::singleton() |
| 5456 | .findSchemaOrThrow(tensordot::name, tensordot::overload_name) |
| 5457 | .typed<tensordot::schema>(); |
| 5458 | } |
| 5459 | |
| 5460 | // aten::tensordot(Tensor self, Tensor other, int[] dims_self, int[] dims_other) -> Tensor |
| 5461 | at::Tensor tensordot::call(const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other) { |
| 5462 | |
| 5463 | static auto op = create_tensordot_typed_handle(); |
| 5464 | return op.call(self, other, dims_self, dims_other); |
| 5465 | } |
| 5466 | |
| 5467 | // aten::tensordot(Tensor self, Tensor other, int[] dims_self, int[] dims_other) -> Tensor |
| 5468 | at::Tensor tensordot::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other) { |
| 5469 | |
| 5470 | static auto op = create_tensordot_typed_handle(); |
| 5471 | return op.redispatch(dispatchKeySet, self, other, dims_self, dims_other); |
| 5472 | } |
| 5473 | |
| 5474 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(tensordot_out, name, "aten::tensordot") |
| 5475 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(tensordot_out, overload_name, "out") |
| 5476 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(tensordot_out, schema_str, "tensordot.out(Tensor self, Tensor other, int[] dims_self, int[] dims_other, *, Tensor(a!) out) -> Tensor(a!)") |
| 5477 | |
| 5478 | // aten::tensordot.out(Tensor self, Tensor other, int[] dims_self, int[] dims_other, *, Tensor(a!) out) -> Tensor(a!) |
| 5479 | static C10_NOINLINE c10::TypedOperatorHandle<tensordot_out::schema> create_tensordot_out_typed_handle() { |
| 5480 | return c10::Dispatcher::singleton() |
| 5481 | .findSchemaOrThrow(tensordot_out::name, tensordot_out::overload_name) |
| 5482 | .typed<tensordot_out::schema>(); |
| 5483 | } |
| 5484 | |
| 5485 | // aten::tensordot.out(Tensor self, Tensor other, int[] dims_self, int[] dims_other, *, Tensor(a!) out) -> Tensor(a!) |
| 5486 | at::Tensor & tensordot_out::call(const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other, at::Tensor & out) { |
| 5487 | |
| 5488 | static auto op = create_tensordot_out_typed_handle(); |
| 5489 | return op.call(self, other, dims_self, dims_other, out); |
| 5490 | } |
| 5491 | |
| 5492 | // aten::tensordot.out(Tensor self, Tensor other, int[] dims_self, int[] dims_other, *, Tensor(a!) out) -> Tensor(a!) |
| 5493 | at::Tensor & tensordot_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other, at::Tensor & out) { |
| 5494 | |
| 5495 | static auto op = create_tensordot_out_typed_handle(); |
| 5496 | return op.redispatch(dispatchKeySet, self, other, dims_self, dims_other, out); |
| 5497 | } |
| 5498 | |
| 5499 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(tile, name, "aten::tile") |
| 5500 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(tile, overload_name, "") |
| 5501 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(tile, schema_str, "tile(Tensor self, int[] dims) -> Tensor") |
| 5502 | |
| 5503 | // aten::tile(Tensor self, int[] dims) -> Tensor |
| 5504 | static C10_NOINLINE c10::TypedOperatorHandle<tile::schema> create_tile_typed_handle() { |
| 5505 | return c10::Dispatcher::singleton() |
| 5506 | .findSchemaOrThrow(tile::name, tile::overload_name) |
| 5507 | .typed<tile::schema>(); |
| 5508 | } |
| 5509 | |
| 5510 | // aten::tile(Tensor self, int[] dims) -> Tensor |
| 5511 | at::Tensor tile::call(const at::Tensor & self, at::IntArrayRef dims) { |
| 5512 | |
| 5513 | static auto op = create_tile_typed_handle(); |
| 5514 | return op.call(self, dims); |
| 5515 | } |
| 5516 | |
| 5517 | // aten::tile(Tensor self, int[] dims) -> Tensor |
| 5518 | at::Tensor tile::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dims) { |
| 5519 | |
| 5520 | static auto op = create_tile_typed_handle(); |
| 5521 | return op.redispatch(dispatchKeySet, self, dims); |
| 5522 | } |
| 5523 | |
| 5524 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_mkldnn_transpose, name, "aten::_mkldnn_transpose") |
| 5525 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_mkldnn_transpose, overload_name, "") |
| 5526 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_mkldnn_transpose, schema_str, "_mkldnn_transpose(Tensor self, int dim0, int dim1) -> Tensor") |
| 5527 | |
| 5528 | // aten::_mkldnn_transpose(Tensor self, int dim0, int dim1) -> Tensor |
| 5529 | static C10_NOINLINE c10::TypedOperatorHandle<_mkldnn_transpose::schema> create__mkldnn_transpose_typed_handle() { |
| 5530 | return c10::Dispatcher::singleton() |
| 5531 | .findSchemaOrThrow(_mkldnn_transpose::name, _mkldnn_transpose::overload_name) |
| 5532 | .typed<_mkldnn_transpose::schema>(); |
| 5533 | } |
| 5534 | |
| 5535 | // aten::_mkldnn_transpose(Tensor self, int dim0, int dim1) -> Tensor |
| 5536 | at::Tensor _mkldnn_transpose::call(const at::Tensor & self, int64_t dim0, int64_t dim1) { |
| 5537 | |
| 5538 | static auto op = create__mkldnn_transpose_typed_handle(); |
| 5539 | return op.call(self, dim0, dim1); |
| 5540 | } |
| 5541 | |
| 5542 | // aten::_mkldnn_transpose(Tensor self, int dim0, int dim1) -> Tensor |
| 5543 | at::Tensor _mkldnn_transpose::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim0, int64_t dim1) { |
| 5544 | |
| 5545 | static auto op = create__mkldnn_transpose_typed_handle(); |
| 5546 | return op.redispatch(dispatchKeySet, self, dim0, dim1); |
| 5547 | } |
| 5548 | |
| 5549 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_mkldnn_transpose_, name, "aten::_mkldnn_transpose_") |
| 5550 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_mkldnn_transpose_, overload_name, "") |
| 5551 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_mkldnn_transpose_, schema_str, "_mkldnn_transpose_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!)") |
| 5552 | |
| 5553 | // aten::_mkldnn_transpose_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!) |
| 5554 | static C10_NOINLINE c10::TypedOperatorHandle<_mkldnn_transpose_::schema> create__mkldnn_transpose__typed_handle() { |
| 5555 | return c10::Dispatcher::singleton() |
| 5556 | .findSchemaOrThrow(_mkldnn_transpose_::name, _mkldnn_transpose_::overload_name) |
| 5557 | .typed<_mkldnn_transpose_::schema>(); |
| 5558 | } |
| 5559 | |
| 5560 | // aten::_mkldnn_transpose_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!) |
| 5561 | at::Tensor & _mkldnn_transpose_::call(at::Tensor & self, int64_t dim0, int64_t dim1) { |
| 5562 | |
| 5563 | static auto op = create__mkldnn_transpose__typed_handle(); |
| 5564 | return op.call(self, dim0, dim1); |
| 5565 | } |
| 5566 | |
| 5567 | // aten::_mkldnn_transpose_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!) |
| 5568 | at::Tensor & _mkldnn_transpose_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim0, int64_t dim1) { |
| 5569 | |
| 5570 | static auto op = create__mkldnn_transpose__typed_handle(); |
| 5571 | return op.redispatch(dispatchKeySet, self, dim0, dim1); |
| 5572 | } |
| 5573 | |
| 5574 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fliplr, name, "aten::fliplr") |
| 5575 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fliplr, overload_name, "") |
| 5576 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fliplr, schema_str, "fliplr(Tensor self) -> Tensor") |
| 5577 | |
| 5578 | // aten::fliplr(Tensor self) -> Tensor |
| 5579 | static C10_NOINLINE c10::TypedOperatorHandle<fliplr::schema> create_fliplr_typed_handle() { |
| 5580 | return c10::Dispatcher::singleton() |
| 5581 | .findSchemaOrThrow(fliplr::name, fliplr::overload_name) |
| 5582 | .typed<fliplr::schema>(); |
| 5583 | } |
| 5584 | |
| 5585 | // aten::fliplr(Tensor self) -> Tensor |
| 5586 | at::Tensor fliplr::call(const at::Tensor & self) { |
| 5587 | |
| 5588 | static auto op = create_fliplr_typed_handle(); |
| 5589 | return op.call(self); |
| 5590 | } |
| 5591 | |
| 5592 | // aten::fliplr(Tensor self) -> Tensor |
| 5593 | at::Tensor fliplr::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 5594 | |
| 5595 | static auto op = create_fliplr_typed_handle(); |
| 5596 | return op.redispatch(dispatchKeySet, self); |
| 5597 | } |
| 5598 | |
| 5599 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_nested_from_padded_and_nested_example, name, "aten::_nested_from_padded_and_nested_example") |
| 5600 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_nested_from_padded_and_nested_example, overload_name, "") |
| 5601 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_nested_from_padded_and_nested_example, schema_str, "_nested_from_padded_and_nested_example(Tensor padded, Tensor nt_example) -> Tensor") |
| 5602 | |
| 5603 | // aten::_nested_from_padded_and_nested_example(Tensor padded, Tensor nt_example) -> Tensor |
| 5604 | static C10_NOINLINE c10::TypedOperatorHandle<_nested_from_padded_and_nested_example::schema> create__nested_from_padded_and_nested_example_typed_handle() { |
| 5605 | return c10::Dispatcher::singleton() |
| 5606 | .findSchemaOrThrow(_nested_from_padded_and_nested_example::name, _nested_from_padded_and_nested_example::overload_name) |
| 5607 | .typed<_nested_from_padded_and_nested_example::schema>(); |
| 5608 | } |
| 5609 | |
| 5610 | // aten::_nested_from_padded_and_nested_example(Tensor padded, Tensor nt_example) -> Tensor |
| 5611 | at::Tensor _nested_from_padded_and_nested_example::call(const at::Tensor & padded, const at::Tensor & nt_example) { |
| 5612 | |
| 5613 | static auto op = create__nested_from_padded_and_nested_example_typed_handle(); |
| 5614 | return op.call(padded, nt_example); |
| 5615 | } |
| 5616 | |
| 5617 | // aten::_nested_from_padded_and_nested_example(Tensor padded, Tensor nt_example) -> Tensor |
| 5618 | at::Tensor _nested_from_padded_and_nested_example::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & padded, const at::Tensor & nt_example) { |
| 5619 | |
| 5620 | static auto op = create__nested_from_padded_and_nested_example_typed_handle(); |
| 5621 | return op.redispatch(dispatchKeySet, padded, nt_example); |
| 5622 | } |
| 5623 | |
| 5624 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fix, name, "aten::fix") |
| 5625 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fix, overload_name, "") |
| 5626 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fix, schema_str, "fix(Tensor self) -> Tensor") |
| 5627 | |
| 5628 | // aten::fix(Tensor self) -> Tensor |
| 5629 | static C10_NOINLINE c10::TypedOperatorHandle<fix::schema> create_fix_typed_handle() { |
| 5630 | return c10::Dispatcher::singleton() |
| 5631 | .findSchemaOrThrow(fix::name, fix::overload_name) |
| 5632 | .typed<fix::schema>(); |
| 5633 | } |
| 5634 | |
| 5635 | // aten::fix(Tensor self) -> Tensor |
| 5636 | at::Tensor fix::call(const at::Tensor & self) { |
| 5637 | |
| 5638 | static auto op = create_fix_typed_handle(); |
| 5639 | return op.call(self); |
| 5640 | } |
| 5641 | |
| 5642 | // aten::fix(Tensor self) -> Tensor |
| 5643 | at::Tensor fix::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 5644 | |
| 5645 | static auto op = create_fix_typed_handle(); |
| 5646 | return op.redispatch(dispatchKeySet, self); |
| 5647 | } |
| 5648 | |
| 5649 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fix_, name, "aten::fix_") |
| 5650 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fix_, overload_name, "") |
| 5651 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fix_, schema_str, "fix_(Tensor(a!) self) -> Tensor(a!)") |
| 5652 | |
| 5653 | // aten::fix_(Tensor(a!) self) -> Tensor(a!) |
| 5654 | static C10_NOINLINE c10::TypedOperatorHandle<fix_::schema> create_fix__typed_handle() { |
| 5655 | return c10::Dispatcher::singleton() |
| 5656 | .findSchemaOrThrow(fix_::name, fix_::overload_name) |
| 5657 | .typed<fix_::schema>(); |
| 5658 | } |
| 5659 | |
| 5660 | // aten::fix_(Tensor(a!) self) -> Tensor(a!) |
| 5661 | at::Tensor & fix_::call(at::Tensor & self) { |
| 5662 | |
| 5663 | static auto op = create_fix__typed_handle(); |
| 5664 | return op.call(self); |
| 5665 | } |
| 5666 | |
| 5667 | // aten::fix_(Tensor(a!) self) -> Tensor(a!) |
| 5668 | at::Tensor & fix_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 5669 | |
| 5670 | static auto op = create_fix__typed_handle(); |
| 5671 | return op.redispatch(dispatchKeySet, self); |
| 5672 | } |
| 5673 | |
| 5674 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fix_out, name, "aten::fix") |
| 5675 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fix_out, overload_name, "out") |
| 5676 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fix_out, schema_str, "fix.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 5677 | |
| 5678 | // aten::fix.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 5679 | static C10_NOINLINE c10::TypedOperatorHandle<fix_out::schema> create_fix_out_typed_handle() { |
| 5680 | return c10::Dispatcher::singleton() |
| 5681 | .findSchemaOrThrow(fix_out::name, fix_out::overload_name) |
| 5682 | .typed<fix_out::schema>(); |
| 5683 | } |
| 5684 | |
| 5685 | // aten::fix.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 5686 | at::Tensor & fix_out::call(const at::Tensor & self, at::Tensor & out) { |
| 5687 | |
| 5688 | static auto op = create_fix_out_typed_handle(); |
| 5689 | return op.call(self, out); |
| 5690 | } |
| 5691 | |
| 5692 | // aten::fix.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 5693 | at::Tensor & fix_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 5694 | |
| 5695 | static auto op = create_fix_out_typed_handle(); |
| 5696 | return op.redispatch(dispatchKeySet, self, out); |
| 5697 | } |
| 5698 | |
| 5699 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(unique_dim, name, "aten::unique_dim") |
| 5700 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(unique_dim, overload_name, "") |
| 5701 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(unique_dim, schema_str, "unique_dim(Tensor self, int dim, bool sorted=True, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor)") |
| 5702 | |
| 5703 | // aten::unique_dim(Tensor self, int dim, bool sorted=True, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor) |
| 5704 | static C10_NOINLINE c10::TypedOperatorHandle<unique_dim::schema> create_unique_dim_typed_handle() { |
| 5705 | return c10::Dispatcher::singleton() |
| 5706 | .findSchemaOrThrow(unique_dim::name, unique_dim::overload_name) |
| 5707 | .typed<unique_dim::schema>(); |
| 5708 | } |
| 5709 | |
| 5710 | // aten::unique_dim(Tensor self, int dim, bool sorted=True, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor) |
| 5711 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_dim::call(const at::Tensor & self, int64_t dim, bool sorted, bool return_inverse, bool return_counts) { |
| 5712 | |
| 5713 | static auto op = create_unique_dim_typed_handle(); |
| 5714 | return op.call(self, dim, sorted, return_inverse, return_counts); |
| 5715 | } |
| 5716 | |
| 5717 | // aten::unique_dim(Tensor self, int dim, bool sorted=True, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor) |
| 5718 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_dim::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool sorted, bool return_inverse, bool return_counts) { |
| 5719 | |
| 5720 | static auto op = create_unique_dim_typed_handle(); |
| 5721 | return op.redispatch(dispatchKeySet, self, dim, sorted, return_inverse, return_counts); |
| 5722 | } |
| 5723 | |
| 5724 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(unique_consecutive, name, "aten::unique_consecutive") |
| 5725 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(unique_consecutive, overload_name, "") |
| 5726 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(unique_consecutive, schema_str, "unique_consecutive(Tensor self, bool return_inverse=False, bool return_counts=False, int? dim=None) -> (Tensor, Tensor, Tensor)") |
| 5727 | |
| 5728 | // aten::unique_consecutive(Tensor self, bool return_inverse=False, bool return_counts=False, int? dim=None) -> (Tensor, Tensor, Tensor) |
| 5729 | static C10_NOINLINE c10::TypedOperatorHandle<unique_consecutive::schema> create_unique_consecutive_typed_handle() { |
| 5730 | return c10::Dispatcher::singleton() |
| 5731 | .findSchemaOrThrow(unique_consecutive::name, unique_consecutive::overload_name) |
| 5732 | .typed<unique_consecutive::schema>(); |
| 5733 | } |
| 5734 | |
| 5735 | // aten::unique_consecutive(Tensor self, bool return_inverse=False, bool return_counts=False, int? dim=None) -> (Tensor, Tensor, Tensor) |
| 5736 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_consecutive::call(const at::Tensor & self, bool return_inverse, bool return_counts, c10::optional<int64_t> dim) { |
| 5737 | |
| 5738 | static auto op = create_unique_consecutive_typed_handle(); |
| 5739 | return op.call(self, return_inverse, return_counts, dim); |
| 5740 | } |
| 5741 | |
| 5742 | // aten::unique_consecutive(Tensor self, bool return_inverse=False, bool return_counts=False, int? dim=None) -> (Tensor, Tensor, Tensor) |
| 5743 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_consecutive::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool return_inverse, bool return_counts, c10::optional<int64_t> dim) { |
| 5744 | |
| 5745 | static auto op = create_unique_consecutive_typed_handle(); |
| 5746 | return op.redispatch(dispatchKeySet, self, return_inverse, return_counts, dim); |
| 5747 | } |
| 5748 | |
| 5749 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(vander, name, "aten::vander") |
| 5750 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(vander, overload_name, "") |
| 5751 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(vander, schema_str, "vander(Tensor x, int? N=None, bool increasing=False) -> Tensor") |
| 5752 | |
| 5753 | // aten::vander(Tensor x, int? N=None, bool increasing=False) -> Tensor |
| 5754 | static C10_NOINLINE c10::TypedOperatorHandle<vander::schema> create_vander_typed_handle() { |
| 5755 | return c10::Dispatcher::singleton() |
| 5756 | .findSchemaOrThrow(vander::name, vander::overload_name) |
| 5757 | .typed<vander::schema>(); |
| 5758 | } |
| 5759 | |
| 5760 | // aten::vander(Tensor x, int? N=None, bool increasing=False) -> Tensor |
| 5761 | at::Tensor vander::call(const at::Tensor & x, c10::optional<int64_t> N, bool increasing) { |
| 5762 | |
| 5763 | static auto op = create_vander_typed_handle(); |
| 5764 | return op.call(x, N, increasing); |
| 5765 | } |
| 5766 | |
| 5767 | // aten::vander(Tensor x, int? N=None, bool increasing=False) -> Tensor |
| 5768 | at::Tensor vander::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, c10::optional<int64_t> N, bool increasing) { |
| 5769 | |
| 5770 | static auto op = create_vander_typed_handle(); |
| 5771 | return op.redispatch(dispatchKeySet, x, N, increasing); |
| 5772 | } |
| 5773 | |
| 5774 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(view_as, name, "aten::view_as") |
| 5775 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(view_as, overload_name, "") |
| 5776 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(view_as, schema_str, "view_as(Tensor(a) self, Tensor other) -> Tensor(a)") |
| 5777 | |
| 5778 | // aten::view_as(Tensor(a) self, Tensor other) -> Tensor(a) |
| 5779 | static C10_NOINLINE c10::TypedOperatorHandle<view_as::schema> create_view_as_typed_handle() { |
| 5780 | return c10::Dispatcher::singleton() |
| 5781 | .findSchemaOrThrow(view_as::name, view_as::overload_name) |
| 5782 | .typed<view_as::schema>(); |
| 5783 | } |
| 5784 | |
| 5785 | // aten::view_as(Tensor(a) self, Tensor other) -> Tensor(a) |
| 5786 | at::Tensor view_as::call(const at::Tensor & self, const at::Tensor & other) { |
| 5787 | |
| 5788 | static auto op = create_view_as_typed_handle(); |
| 5789 | return op.call(self, other); |
| 5790 | } |
| 5791 | |
| 5792 | // aten::view_as(Tensor(a) self, Tensor other) -> Tensor(a) |
| 5793 | at::Tensor view_as::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { |
| 5794 | |
| 5795 | static auto op = create_view_as_typed_handle(); |
| 5796 | return op.redispatch(dispatchKeySet, self, other); |
| 5797 | } |
| 5798 | |
| 5799 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_dirichlet_grad, name, "aten::_dirichlet_grad") |
| 5800 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_dirichlet_grad, overload_name, "") |
| 5801 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_dirichlet_grad, schema_str, "_dirichlet_grad(Tensor x, Tensor alpha, Tensor total) -> Tensor") |
| 5802 | |
| 5803 | // aten::_dirichlet_grad(Tensor x, Tensor alpha, Tensor total) -> Tensor |
| 5804 | static C10_NOINLINE c10::TypedOperatorHandle<_dirichlet_grad::schema> create__dirichlet_grad_typed_handle() { |
| 5805 | return c10::Dispatcher::singleton() |
| 5806 | .findSchemaOrThrow(_dirichlet_grad::name, _dirichlet_grad::overload_name) |
| 5807 | .typed<_dirichlet_grad::schema>(); |
| 5808 | } |
| 5809 | |
| 5810 | // aten::_dirichlet_grad(Tensor x, Tensor alpha, Tensor total) -> Tensor |
| 5811 | at::Tensor _dirichlet_grad::call(const at::Tensor & x, const at::Tensor & alpha, const at::Tensor & total) { |
| 5812 | |
| 5813 | static auto op = create__dirichlet_grad_typed_handle(); |
| 5814 | return op.call(x, alpha, total); |
| 5815 | } |
| 5816 | |
| 5817 | // aten::_dirichlet_grad(Tensor x, Tensor alpha, Tensor total) -> Tensor |
| 5818 | at::Tensor _dirichlet_grad::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & alpha, const at::Tensor & total) { |
| 5819 | |
| 5820 | static auto op = create__dirichlet_grad_typed_handle(); |
| 5821 | return op.redispatch(dispatchKeySet, x, alpha, total); |
| 5822 | } |
| 5823 | |
| 5824 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(frobenius_norm_dim, name, "aten::frobenius_norm") |
| 5825 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(frobenius_norm_dim, overload_name, "dim") |
| 5826 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(frobenius_norm_dim, schema_str, "frobenius_norm.dim(Tensor self, int[1] dim, bool keepdim=False) -> Tensor") |
| 5827 | |
| 5828 | // aten::frobenius_norm.dim(Tensor self, int[1] dim, bool keepdim=False) -> Tensor |
| 5829 | static C10_NOINLINE c10::TypedOperatorHandle<frobenius_norm_dim::schema> create_frobenius_norm_dim_typed_handle() { |
| 5830 | return c10::Dispatcher::singleton() |
| 5831 | .findSchemaOrThrow(frobenius_norm_dim::name, frobenius_norm_dim::overload_name) |
| 5832 | .typed<frobenius_norm_dim::schema>(); |
| 5833 | } |
| 5834 | |
| 5835 | // aten::frobenius_norm.dim(Tensor self, int[1] dim, bool keepdim=False) -> Tensor |
| 5836 | at::Tensor frobenius_norm_dim::call(const at::Tensor & self, at::IntArrayRef dim, bool keepdim) { |
| 5837 | |
| 5838 | static auto op = create_frobenius_norm_dim_typed_handle(); |
| 5839 | return op.call(self, dim, keepdim); |
| 5840 | } |
| 5841 | |
| 5842 | // aten::frobenius_norm.dim(Tensor self, int[1] dim, bool keepdim=False) -> Tensor |
| 5843 | at::Tensor frobenius_norm_dim::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim) { |
| 5844 | |
| 5845 | static auto op = create_frobenius_norm_dim_typed_handle(); |
| 5846 | return op.redispatch(dispatchKeySet, self, dim, keepdim); |
| 5847 | } |
| 5848 | |
| 5849 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(frobenius_norm_out, name, "aten::frobenius_norm") |
| 5850 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(frobenius_norm_out, overload_name, "out") |
| 5851 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(frobenius_norm_out, schema_str, "frobenius_norm.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)") |
| 5852 | |
| 5853 | // aten::frobenius_norm.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) |
| 5854 | static C10_NOINLINE c10::TypedOperatorHandle<frobenius_norm_out::schema> create_frobenius_norm_out_typed_handle() { |
| 5855 | return c10::Dispatcher::singleton() |
| 5856 | .findSchemaOrThrow(frobenius_norm_out::name, frobenius_norm_out::overload_name) |
| 5857 | .typed<frobenius_norm_out::schema>(); |
| 5858 | } |
| 5859 | |
| 5860 | // aten::frobenius_norm.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) |
| 5861 | at::Tensor & frobenius_norm_out::call(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { |
| 5862 | |
| 5863 | static auto op = create_frobenius_norm_out_typed_handle(); |
| 5864 | return op.call(self, dim, keepdim, out); |
| 5865 | } |
| 5866 | |
| 5867 | // aten::frobenius_norm.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) |
| 5868 | at::Tensor & frobenius_norm_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { |
| 5869 | |
| 5870 | static auto op = create_frobenius_norm_out_typed_handle(); |
| 5871 | return op.redispatch(dispatchKeySet, self, dim, keepdim, out); |
| 5872 | } |
| 5873 | |
| 5874 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(clone, name, "aten::clone") |
| 5875 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(clone, overload_name, "") |
| 5876 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(clone, schema_str, "clone(Tensor self, *, MemoryFormat? memory_format=None) -> Tensor") |
| 5877 | |
| 5878 | // aten::clone(Tensor self, *, MemoryFormat? memory_format=None) -> Tensor |
| 5879 | static C10_NOINLINE c10::TypedOperatorHandle<clone::schema> create_clone_typed_handle() { |
| 5880 | return c10::Dispatcher::singleton() |
| 5881 | .findSchemaOrThrow(clone::name, clone::overload_name) |
| 5882 | .typed<clone::schema>(); |
| 5883 | } |
| 5884 | |
| 5885 | // aten::clone(Tensor self, *, MemoryFormat? memory_format=None) -> Tensor |
| 5886 | at::Tensor clone::call(const at::Tensor & self, c10::optional<at::MemoryFormat> memory_format) { |
| 5887 | |
| 5888 | static auto op = create_clone_typed_handle(); |
| 5889 | return op.call(self, memory_format); |
| 5890 | } |
| 5891 | |
| 5892 | // aten::clone(Tensor self, *, MemoryFormat? memory_format=None) -> Tensor |
| 5893 | at::Tensor clone::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::MemoryFormat> memory_format) { |
| 5894 | |
| 5895 | static auto op = create_clone_typed_handle(); |
| 5896 | return op.redispatch(dispatchKeySet, self, memory_format); |
| 5897 | } |
| 5898 | |
| 5899 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(positive, name, "aten::positive") |
| 5900 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(positive, overload_name, "") |
| 5901 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(positive, schema_str, "positive(Tensor(a) self) -> Tensor(a)") |
| 5902 | |
| 5903 | // aten::positive(Tensor(a) self) -> Tensor(a) |
| 5904 | static C10_NOINLINE c10::TypedOperatorHandle<positive::schema> create_positive_typed_handle() { |
| 5905 | return c10::Dispatcher::singleton() |
| 5906 | .findSchemaOrThrow(positive::name, positive::overload_name) |
| 5907 | .typed<positive::schema>(); |
| 5908 | } |
| 5909 | |
| 5910 | // aten::positive(Tensor(a) self) -> Tensor(a) |
| 5911 | at::Tensor positive::call(const at::Tensor & self) { |
| 5912 | |
| 5913 | static auto op = create_positive_typed_handle(); |
| 5914 | return op.call(self); |
| 5915 | } |
| 5916 | |
| 5917 | // aten::positive(Tensor(a) self) -> Tensor(a) |
| 5918 | at::Tensor positive::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 5919 | |
| 5920 | static auto op = create_positive_typed_handle(); |
| 5921 | return op.redispatch(dispatchKeySet, self); |
| 5922 | } |
| 5923 | |
| 5924 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(resize_as_, name, "aten::resize_as_") |
| 5925 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(resize_as_, overload_name, "") |
| 5926 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(resize_as_, schema_str, "resize_as_(Tensor(a!) self, Tensor the_template, *, MemoryFormat? memory_format=None) -> Tensor(a!)") |
| 5927 | |
| 5928 | // aten::resize_as_(Tensor(a!) self, Tensor the_template, *, MemoryFormat? memory_format=None) -> Tensor(a!) |
| 5929 | static C10_NOINLINE c10::TypedOperatorHandle<resize_as_::schema> create_resize_as__typed_handle() { |
| 5930 | return c10::Dispatcher::singleton() |
| 5931 | .findSchemaOrThrow(resize_as_::name, resize_as_::overload_name) |
| 5932 | .typed<resize_as_::schema>(); |
| 5933 | } |
| 5934 | |
| 5935 | // aten::resize_as_(Tensor(a!) self, Tensor the_template, *, MemoryFormat? memory_format=None) -> Tensor(a!) |
| 5936 | const at::Tensor & resize_as_::call(const at::Tensor & self, const at::Tensor & the_template, c10::optional<at::MemoryFormat> memory_format) { |
| 5937 | |
| 5938 | static auto op = create_resize_as__typed_handle(); |
| 5939 | return op.call(self, the_template, memory_format); |
| 5940 | } |
| 5941 | |
| 5942 | // aten::resize_as_(Tensor(a!) self, Tensor the_template, *, MemoryFormat? memory_format=None) -> Tensor(a!) |
| 5943 | const at::Tensor & resize_as_::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & the_template, c10::optional<at::MemoryFormat> memory_format) { |
| 5944 | |
| 5945 | static auto op = create_resize_as__typed_handle(); |
| 5946 | return op.redispatch(dispatchKeySet, self, the_template, memory_format); |
| 5947 | } |
| 5948 | |
| 5949 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(resize_as_sparse_, name, "aten::resize_as_sparse_") |
| 5950 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(resize_as_sparse_, overload_name, "") |
| 5951 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(resize_as_sparse_, schema_str, "resize_as_sparse_(Tensor(a!) self, Tensor the_template) -> Tensor(a!)") |
| 5952 | |
| 5953 | // aten::resize_as_sparse_(Tensor(a!) self, Tensor the_template) -> Tensor(a!) |
| 5954 | static C10_NOINLINE c10::TypedOperatorHandle<resize_as_sparse_::schema> create_resize_as_sparse__typed_handle() { |
| 5955 | return c10::Dispatcher::singleton() |
| 5956 | .findSchemaOrThrow(resize_as_sparse_::name, resize_as_sparse_::overload_name) |
| 5957 | .typed<resize_as_sparse_::schema>(); |
| 5958 | } |
| 5959 | |
| 5960 | // aten::resize_as_sparse_(Tensor(a!) self, Tensor the_template) -> Tensor(a!) |
| 5961 | const at::Tensor & resize_as_sparse_::call(const at::Tensor & self, const at::Tensor & the_template) { |
| 5962 | |
| 5963 | static auto op = create_resize_as_sparse__typed_handle(); |
| 5964 | return op.call(self, the_template); |
| 5965 | } |
| 5966 | |
| 5967 | // aten::resize_as_sparse_(Tensor(a!) self, Tensor the_template) -> Tensor(a!) |
| 5968 | const at::Tensor & resize_as_sparse_::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & the_template) { |
| 5969 | |
| 5970 | static auto op = create_resize_as_sparse__typed_handle(); |
| 5971 | return op.redispatch(dispatchKeySet, self, the_template); |
| 5972 | } |
| 5973 | |
| 5974 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sparse_sampled_addmm_out, name, "aten::sparse_sampled_addmm") |
| 5975 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sparse_sampled_addmm_out, overload_name, "out") |
| 5976 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sparse_sampled_addmm_out, schema_str, "sparse_sampled_addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)") |
| 5977 | |
| 5978 | // aten::sparse_sampled_addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) |
| 5979 | static C10_NOINLINE c10::TypedOperatorHandle<sparse_sampled_addmm_out::schema> create_sparse_sampled_addmm_out_typed_handle() { |
| 5980 | return c10::Dispatcher::singleton() |
| 5981 | .findSchemaOrThrow(sparse_sampled_addmm_out::name, sparse_sampled_addmm_out::overload_name) |
| 5982 | .typed<sparse_sampled_addmm_out::schema>(); |
| 5983 | } |
| 5984 | |
| 5985 | // aten::sparse_sampled_addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) |
| 5986 | at::Tensor & sparse_sampled_addmm_out::call(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { |
| 5987 | |
| 5988 | static auto op = create_sparse_sampled_addmm_out_typed_handle(); |
| 5989 | return op.call(self, mat1, mat2, beta, alpha, out); |
| 5990 | } |
| 5991 | |
| 5992 | // aten::sparse_sampled_addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) |
| 5993 | at::Tensor & sparse_sampled_addmm_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { |
| 5994 | |
| 5995 | static auto op = create_sparse_sampled_addmm_out_typed_handle(); |
| 5996 | return op.redispatch(dispatchKeySet, self, mat1, mat2, beta, alpha, out); |
| 5997 | } |
| 5998 | |
| 5999 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sparse_sampled_addmm, name, "aten::sparse_sampled_addmm") |
| 6000 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sparse_sampled_addmm, overload_name, "") |
| 6001 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sparse_sampled_addmm, schema_str, "sparse_sampled_addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor") |
| 6002 | |
| 6003 | // aten::sparse_sampled_addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor |
| 6004 | static C10_NOINLINE c10::TypedOperatorHandle<sparse_sampled_addmm::schema> create_sparse_sampled_addmm_typed_handle() { |
| 6005 | return c10::Dispatcher::singleton() |
| 6006 | .findSchemaOrThrow(sparse_sampled_addmm::name, sparse_sampled_addmm::overload_name) |
| 6007 | .typed<sparse_sampled_addmm::schema>(); |
| 6008 | } |
| 6009 | |
| 6010 | // aten::sparse_sampled_addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor |
| 6011 | at::Tensor sparse_sampled_addmm::call(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 6012 | |
| 6013 | static auto op = create_sparse_sampled_addmm_typed_handle(); |
| 6014 | return op.call(self, mat1, mat2, beta, alpha); |
| 6015 | } |
| 6016 | |
| 6017 | // aten::sparse_sampled_addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor |
| 6018 | at::Tensor sparse_sampled_addmm::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 6019 | |
| 6020 | static auto op = create_sparse_sampled_addmm_typed_handle(); |
| 6021 | return op.redispatch(dispatchKeySet, self, mat1, mat2, beta, alpha); |
| 6022 | } |
| 6023 | |
| 6024 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sparse_csr_tensor_crow_col_value_size, name, "aten::sparse_csr_tensor") |
| 6025 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sparse_csr_tensor_crow_col_value_size, overload_name, "crow_col_value_size") |
| 6026 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sparse_csr_tensor_crow_col_value_size, schema_str, "sparse_csr_tensor.crow_col_value_size(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor") |
| 6027 | |
| 6028 | // aten::sparse_csr_tensor.crow_col_value_size(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor |
| 6029 | static C10_NOINLINE c10::TypedOperatorHandle<sparse_csr_tensor_crow_col_value_size::schema> create_sparse_csr_tensor_crow_col_value_size_typed_handle() { |
| 6030 | return c10::Dispatcher::singleton() |
| 6031 | .findSchemaOrThrow(sparse_csr_tensor_crow_col_value_size::name, sparse_csr_tensor_crow_col_value_size::overload_name) |
| 6032 | .typed<sparse_csr_tensor_crow_col_value_size::schema>(); |
| 6033 | } |
| 6034 | |
| 6035 | // aten::sparse_csr_tensor.crow_col_value_size(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor |
| 6036 | at::Tensor sparse_csr_tensor_crow_col_value_size::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) { |
| 6037 | |
| 6038 | static auto op = create_sparse_csr_tensor_crow_col_value_size_typed_handle(); |
| 6039 | return op.call(crow_indices, col_indices, values, size, dtype, layout, device, pin_memory); |
| 6040 | } |
| 6041 | |
| 6042 | // aten::sparse_csr_tensor.crow_col_value_size(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor |
| 6043 | at::Tensor sparse_csr_tensor_crow_col_value_size::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) { |
| 6044 | |
| 6045 | static auto op = create_sparse_csr_tensor_crow_col_value_size_typed_handle(); |
| 6046 | return op.redispatch(dispatchKeySet, crow_indices, col_indices, values, size, dtype, layout, device, pin_memory); |
| 6047 | } |
| 6048 | |
| 6049 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sparse_csr_tensor_crow_col_value, name, "aten::sparse_csr_tensor") |
| 6050 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sparse_csr_tensor_crow_col_value, overload_name, "crow_col_value") |
| 6051 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(sparse_csr_tensor_crow_col_value, schema_str, "sparse_csr_tensor.crow_col_value(Tensor crow_indices, Tensor col_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor") |
| 6052 | |
| 6053 | // aten::sparse_csr_tensor.crow_col_value(Tensor crow_indices, Tensor col_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor |
| 6054 | static C10_NOINLINE c10::TypedOperatorHandle<sparse_csr_tensor_crow_col_value::schema> create_sparse_csr_tensor_crow_col_value_typed_handle() { |
| 6055 | return c10::Dispatcher::singleton() |
| 6056 | .findSchemaOrThrow(sparse_csr_tensor_crow_col_value::name, sparse_csr_tensor_crow_col_value::overload_name) |
| 6057 | .typed<sparse_csr_tensor_crow_col_value::schema>(); |
| 6058 | } |
| 6059 | |
| 6060 | // aten::sparse_csr_tensor.crow_col_value(Tensor crow_indices, Tensor col_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor |
| 6061 | at::Tensor sparse_csr_tensor_crow_col_value::call(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 6062 | |
| 6063 | static auto op = create_sparse_csr_tensor_crow_col_value_typed_handle(); |
| 6064 | return op.call(crow_indices, col_indices, values, dtype, layout, device, pin_memory); |
| 6065 | } |
| 6066 | |
| 6067 | // aten::sparse_csr_tensor.crow_col_value(Tensor crow_indices, Tensor col_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor |
| 6068 | at::Tensor sparse_csr_tensor_crow_col_value::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 6069 | |
| 6070 | static auto op = create_sparse_csr_tensor_crow_col_value_typed_handle(); |
| 6071 | return op.redispatch(dispatchKeySet, crow_indices, col_indices, values, dtype, layout, device, pin_memory); |
| 6072 | } |
| 6073 | |
| 6074 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_bsc_tensor_unsafe, name, "aten::_sparse_bsc_tensor_unsafe") |
| 6075 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_bsc_tensor_unsafe, overload_name, "") |
| 6076 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_sparse_bsc_tensor_unsafe, schema_str, "_sparse_bsc_tensor_unsafe(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor") |
| 6077 | |
| 6078 | // aten::_sparse_bsc_tensor_unsafe(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 6079 | static C10_NOINLINE c10::TypedOperatorHandle<_sparse_bsc_tensor_unsafe::schema> create__sparse_bsc_tensor_unsafe_typed_handle() { |
| 6080 | return c10::Dispatcher::singleton() |
| 6081 | .findSchemaOrThrow(_sparse_bsc_tensor_unsafe::name, _sparse_bsc_tensor_unsafe::overload_name) |
| 6082 | .typed<_sparse_bsc_tensor_unsafe::schema>(); |
| 6083 | } |
| 6084 | |
| 6085 | // aten::_sparse_bsc_tensor_unsafe(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 6086 | at::Tensor _sparse_bsc_tensor_unsafe::call(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 6087 | |
| 6088 | static auto op = create__sparse_bsc_tensor_unsafe_typed_handle(); |
| 6089 | return op.call(ccol_indices, row_indices, values, size, dtype, layout, device, pin_memory); |
| 6090 | } |
| 6091 | |
| 6092 | // aten::_sparse_bsc_tensor_unsafe(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 6093 | at::Tensor _sparse_bsc_tensor_unsafe::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 6094 | |
| 6095 | static auto op = create__sparse_bsc_tensor_unsafe_typed_handle(); |
| 6096 | return op.redispatch(dispatchKeySet, ccol_indices, row_indices, values, size, dtype, layout, device, pin_memory); |
| 6097 | } |
| 6098 | |
| 6099 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(dense_dim, name, "aten::dense_dim") |
| 6100 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(dense_dim, overload_name, "") |
| 6101 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(dense_dim, schema_str, "dense_dim(Tensor self) -> int") |
| 6102 | |
| 6103 | // aten::dense_dim(Tensor self) -> int |
| 6104 | static C10_NOINLINE c10::TypedOperatorHandle<dense_dim::schema> create_dense_dim_typed_handle() { |
| 6105 | return c10::Dispatcher::singleton() |
| 6106 | .findSchemaOrThrow(dense_dim::name, dense_dim::overload_name) |
| 6107 | .typed<dense_dim::schema>(); |
| 6108 | } |
| 6109 | |
| 6110 | // aten::dense_dim(Tensor self) -> int |
| 6111 | int64_t dense_dim::call(const at::Tensor & self) { |
| 6112 | |
| 6113 | static auto op = create_dense_dim_typed_handle(); |
| 6114 | return op.call(self); |
| 6115 | } |
| 6116 | |
| 6117 | // aten::dense_dim(Tensor self) -> int |
| 6118 | int64_t dense_dim::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 6119 | |
| 6120 | static auto op = create_dense_dim_typed_handle(); |
| 6121 | return op.redispatch(dispatchKeySet, self); |
| 6122 | } |
| 6123 | |
| 6124 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_dimV, name, "aten::_dimV") |
| 6125 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_dimV, overload_name, "") |
| 6126 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_dimV, schema_str, "_dimV(Tensor self) -> int") |
| 6127 | |
| 6128 | // aten::_dimV(Tensor self) -> int |
| 6129 | static C10_NOINLINE c10::TypedOperatorHandle<_dimV::schema> create__dimV_typed_handle() { |
| 6130 | return c10::Dispatcher::singleton() |
| 6131 | .findSchemaOrThrow(_dimV::name, _dimV::overload_name) |
| 6132 | .typed<_dimV::schema>(); |
| 6133 | } |
| 6134 | |
| 6135 | // aten::_dimV(Tensor self) -> int |
| 6136 | int64_t _dimV::call(const at::Tensor & self) { |
| 6137 | |
| 6138 | static auto op = create__dimV_typed_handle(); |
| 6139 | return op.call(self); |
| 6140 | } |
| 6141 | |
| 6142 | // aten::_dimV(Tensor self) -> int |
| 6143 | int64_t _dimV::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 6144 | |
| 6145 | static auto op = create__dimV_typed_handle(); |
| 6146 | return op.redispatch(dispatchKeySet, self); |
| 6147 | } |
| 6148 | |
| 6149 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(coalesce, name, "aten::coalesce") |
| 6150 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(coalesce, overload_name, "") |
| 6151 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(coalesce, schema_str, "coalesce(Tensor(a) self) -> Tensor(a)") |
| 6152 | |
| 6153 | // aten::coalesce(Tensor(a) self) -> Tensor(a) |
| 6154 | static C10_NOINLINE c10::TypedOperatorHandle<coalesce::schema> create_coalesce_typed_handle() { |
| 6155 | return c10::Dispatcher::singleton() |
| 6156 | .findSchemaOrThrow(coalesce::name, coalesce::overload_name) |
| 6157 | .typed<coalesce::schema>(); |
| 6158 | } |
| 6159 | |
| 6160 | // aten::coalesce(Tensor(a) self) -> Tensor(a) |
| 6161 | at::Tensor coalesce::call(const at::Tensor & self) { |
| 6162 | |
| 6163 | static auto op = create_coalesce_typed_handle(); |
| 6164 | return op.call(self); |
| 6165 | } |
| 6166 | |
| 6167 | // aten::coalesce(Tensor(a) self) -> Tensor(a) |
| 6168 | at::Tensor coalesce::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 6169 | |
| 6170 | static auto op = create_coalesce_typed_handle(); |
| 6171 | return op.redispatch(dispatchKeySet, self); |
| 6172 | } |
| 6173 | |
| 6174 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_indices, name, "aten::_indices") |
| 6175 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_indices, overload_name, "") |
| 6176 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_indices, schema_str, "_indices(Tensor(a) self) -> Tensor(a)") |
| 6177 | |
| 6178 | // aten::_indices(Tensor(a) self) -> Tensor(a) |
| 6179 | static C10_NOINLINE c10::TypedOperatorHandle<_indices::schema> create__indices_typed_handle() { |
| 6180 | return c10::Dispatcher::singleton() |
| 6181 | .findSchemaOrThrow(_indices::name, _indices::overload_name) |
| 6182 | .typed<_indices::schema>(); |
| 6183 | } |
| 6184 | |
| 6185 | // aten::_indices(Tensor(a) self) -> Tensor(a) |
| 6186 | at::Tensor _indices::call(const at::Tensor & self) { |
| 6187 | |
| 6188 | static auto op = create__indices_typed_handle(); |
| 6189 | return op.call(self); |
| 6190 | } |
| 6191 | |
| 6192 | // aten::_indices(Tensor(a) self) -> Tensor(a) |
| 6193 | at::Tensor _indices::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 6194 | |
| 6195 | static auto op = create__indices_typed_handle(); |
| 6196 | return op.redispatch(dispatchKeySet, self); |
| 6197 | } |
| 6198 | |
| 6199 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(to_sparse_csc, name, "aten::to_sparse_csc") |
| 6200 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(to_sparse_csc, overload_name, "") |
| 6201 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(to_sparse_csc, schema_str, "to_sparse_csc(Tensor self, int? dense_dim=None) -> Tensor") |
| 6202 | |
| 6203 | // aten::to_sparse_csc(Tensor self, int? dense_dim=None) -> Tensor |
| 6204 | static C10_NOINLINE c10::TypedOperatorHandle<to_sparse_csc::schema> create_to_sparse_csc_typed_handle() { |
| 6205 | return c10::Dispatcher::singleton() |
| 6206 | .findSchemaOrThrow(to_sparse_csc::name, to_sparse_csc::overload_name) |
| 6207 | .typed<to_sparse_csc::schema>(); |
| 6208 | } |
| 6209 | |
| 6210 | // aten::to_sparse_csc(Tensor self, int? dense_dim=None) -> Tensor |
| 6211 | at::Tensor to_sparse_csc::call(const at::Tensor & self, c10::optional<int64_t> dense_dim) { |
| 6212 | |
| 6213 | static auto op = create_to_sparse_csc_typed_handle(); |
| 6214 | return op.call(self, dense_dim); |
| 6215 | } |
| 6216 | |
| 6217 | // aten::to_sparse_csc(Tensor self, int? dense_dim=None) -> Tensor |
| 6218 | at::Tensor to_sparse_csc::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<int64_t> dense_dim) { |
| 6219 | |
| 6220 | static auto op = create_to_sparse_csc_typed_handle(); |
| 6221 | return op.redispatch(dispatchKeySet, self, dense_dim); |
| 6222 | } |
| 6223 | |
| 6224 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_reorder_conv2d_weight, name, "aten::mkldnn_reorder_conv2d_weight") |
| 6225 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_reorder_conv2d_weight, overload_name, "") |
| 6226 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_reorder_conv2d_weight, schema_str, "mkldnn_reorder_conv2d_weight(Tensor self, int[2] padding=0, int[2] stride=1, int[2] dilation=1, int groups=1, int[]? input_size=None) -> Tensor") |
| 6227 | |
| 6228 | // aten::mkldnn_reorder_conv2d_weight(Tensor self, int[2] padding=0, int[2] stride=1, int[2] dilation=1, int groups=1, int[]? input_size=None) -> Tensor |
| 6229 | static C10_NOINLINE c10::TypedOperatorHandle<mkldnn_reorder_conv2d_weight::schema> create_mkldnn_reorder_conv2d_weight_typed_handle() { |
| 6230 | return c10::Dispatcher::singleton() |
| 6231 | .findSchemaOrThrow(mkldnn_reorder_conv2d_weight::name, mkldnn_reorder_conv2d_weight::overload_name) |
| 6232 | .typed<mkldnn_reorder_conv2d_weight::schema>(); |
| 6233 | } |
| 6234 | |
| 6235 | // aten::mkldnn_reorder_conv2d_weight(Tensor self, int[2] padding=0, int[2] stride=1, int[2] dilation=1, int groups=1, int[]? input_size=None) -> Tensor |
| 6236 | at::Tensor mkldnn_reorder_conv2d_weight::call(const at::Tensor & self, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, at::OptionalIntArrayRef input_size) { |
| 6237 | |
| 6238 | static auto op = create_mkldnn_reorder_conv2d_weight_typed_handle(); |
| 6239 | return op.call(self, padding, stride, dilation, groups, input_size); |
| 6240 | } |
| 6241 | |
| 6242 | // aten::mkldnn_reorder_conv2d_weight(Tensor self, int[2] padding=0, int[2] stride=1, int[2] dilation=1, int groups=1, int[]? input_size=None) -> Tensor |
| 6243 | at::Tensor mkldnn_reorder_conv2d_weight::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, at::OptionalIntArrayRef input_size) { |
| 6244 | |
| 6245 | static auto op = create_mkldnn_reorder_conv2d_weight_typed_handle(); |
| 6246 | return op.redispatch(dispatchKeySet, self, padding, stride, dilation, groups, input_size); |
| 6247 | } |
| 6248 | |
| 6249 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantize_per_channel, name, "aten::quantize_per_channel") |
| 6250 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantize_per_channel, overload_name, "") |
| 6251 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantize_per_channel, schema_str, "quantize_per_channel(Tensor self, Tensor scales, Tensor zero_points, int axis, ScalarType dtype) -> Tensor") |
| 6252 | |
| 6253 | // aten::quantize_per_channel(Tensor self, Tensor scales, Tensor zero_points, int axis, ScalarType dtype) -> Tensor |
| 6254 | static C10_NOINLINE c10::TypedOperatorHandle<quantize_per_channel::schema> create_quantize_per_channel_typed_handle() { |
| 6255 | return c10::Dispatcher::singleton() |
| 6256 | .findSchemaOrThrow(quantize_per_channel::name, quantize_per_channel::overload_name) |
| 6257 | .typed<quantize_per_channel::schema>(); |
| 6258 | } |
| 6259 | |
| 6260 | // aten::quantize_per_channel(Tensor self, Tensor scales, Tensor zero_points, int axis, ScalarType dtype) -> Tensor |
| 6261 | at::Tensor quantize_per_channel::call(const at::Tensor & self, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::ScalarType dtype) { |
| 6262 | |
| 6263 | static auto op = create_quantize_per_channel_typed_handle(); |
| 6264 | return op.call(self, scales, zero_points, axis, dtype); |
| 6265 | } |
| 6266 | |
| 6267 | // aten::quantize_per_channel(Tensor self, Tensor scales, Tensor zero_points, int axis, ScalarType dtype) -> Tensor |
| 6268 | at::Tensor quantize_per_channel::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::ScalarType dtype) { |
| 6269 | |
| 6270 | static auto op = create_quantize_per_channel_typed_handle(); |
| 6271 | return op.redispatch(dispatchKeySet, self, scales, zero_points, axis, dtype); |
| 6272 | } |
| 6273 | |
| 6274 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(dequantize_self, name, "aten::dequantize") |
| 6275 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(dequantize_self, overload_name, "self") |
| 6276 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(dequantize_self, schema_str, "dequantize.self(Tensor self) -> Tensor") |
| 6277 | |
| 6278 | // aten::dequantize.self(Tensor self) -> Tensor |
| 6279 | static C10_NOINLINE c10::TypedOperatorHandle<dequantize_self::schema> create_dequantize_self_typed_handle() { |
| 6280 | return c10::Dispatcher::singleton() |
| 6281 | .findSchemaOrThrow(dequantize_self::name, dequantize_self::overload_name) |
| 6282 | .typed<dequantize_self::schema>(); |
| 6283 | } |
| 6284 | |
| 6285 | // aten::dequantize.self(Tensor self) -> Tensor |
| 6286 | at::Tensor dequantize_self::call(const at::Tensor & self) { |
| 6287 | |
| 6288 | static auto op = create_dequantize_self_typed_handle(); |
| 6289 | return op.call(self); |
| 6290 | } |
| 6291 | |
| 6292 | // aten::dequantize.self(Tensor self) -> Tensor |
| 6293 | at::Tensor dequantize_self::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 6294 | |
| 6295 | static auto op = create_dequantize_self_typed_handle(); |
| 6296 | return op.redispatch(dispatchKeySet, self); |
| 6297 | } |
| 6298 | |
| 6299 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(dequantize_tensors, name, "aten::dequantize") |
| 6300 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(dequantize_tensors, overload_name, "tensors") |
| 6301 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(dequantize_tensors, schema_str, "dequantize.tensors(Tensor[] tensors) -> Tensor[]") |
| 6302 | |
| 6303 | // aten::dequantize.tensors(Tensor[] tensors) -> Tensor[] |
| 6304 | static C10_NOINLINE c10::TypedOperatorHandle<dequantize_tensors::schema> create_dequantize_tensors_typed_handle() { |
| 6305 | return c10::Dispatcher::singleton() |
| 6306 | .findSchemaOrThrow(dequantize_tensors::name, dequantize_tensors::overload_name) |
| 6307 | .typed<dequantize_tensors::schema>(); |
| 6308 | } |
| 6309 | |
| 6310 | // aten::dequantize.tensors(Tensor[] tensors) -> Tensor[] |
| 6311 | ::std::vector<at::Tensor> dequantize_tensors::call(at::TensorList tensors) { |
| 6312 | |
| 6313 | static auto op = create_dequantize_tensors_typed_handle(); |
| 6314 | return op.call(tensors); |
| 6315 | } |
| 6316 | |
| 6317 | // aten::dequantize.tensors(Tensor[] tensors) -> Tensor[] |
| 6318 | ::std::vector<at::Tensor> dequantize_tensors::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors) { |
| 6319 | |
| 6320 | static auto op = create_dequantize_tensors_typed_handle(); |
| 6321 | return op.redispatch(dispatchKeySet, tensors); |
| 6322 | } |
| 6323 | |
| 6324 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(q_per_channel_zero_points, name, "aten::q_per_channel_zero_points") |
| 6325 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(q_per_channel_zero_points, overload_name, "") |
| 6326 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(q_per_channel_zero_points, schema_str, "q_per_channel_zero_points(Tensor self) -> Tensor") |
| 6327 | |
| 6328 | // aten::q_per_channel_zero_points(Tensor self) -> Tensor |
| 6329 | static C10_NOINLINE c10::TypedOperatorHandle<q_per_channel_zero_points::schema> create_q_per_channel_zero_points_typed_handle() { |
| 6330 | return c10::Dispatcher::singleton() |
| 6331 | .findSchemaOrThrow(q_per_channel_zero_points::name, q_per_channel_zero_points::overload_name) |
| 6332 | .typed<q_per_channel_zero_points::schema>(); |
| 6333 | } |
| 6334 | |
| 6335 | // aten::q_per_channel_zero_points(Tensor self) -> Tensor |
| 6336 | at::Tensor q_per_channel_zero_points::call(const at::Tensor & self) { |
| 6337 | |
| 6338 | static auto op = create_q_per_channel_zero_points_typed_handle(); |
| 6339 | return op.call(self); |
| 6340 | } |
| 6341 | |
| 6342 | // aten::q_per_channel_zero_points(Tensor self) -> Tensor |
| 6343 | at::Tensor q_per_channel_zero_points::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 6344 | |
| 6345 | static auto op = create_q_per_channel_zero_points_typed_handle(); |
| 6346 | return op.redispatch(dispatchKeySet, self); |
| 6347 | } |
| 6348 | |
| 6349 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fake_quantize_per_tensor_affine, name, "aten::fake_quantize_per_tensor_affine") |
| 6350 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fake_quantize_per_tensor_affine, overload_name, "") |
| 6351 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fake_quantize_per_tensor_affine, schema_str, "fake_quantize_per_tensor_affine(Tensor self, float scale, int zero_point, int quant_min, int quant_max) -> Tensor") |
| 6352 | |
| 6353 | // aten::fake_quantize_per_tensor_affine(Tensor self, float scale, int zero_point, int quant_min, int quant_max) -> Tensor |
| 6354 | static C10_NOINLINE c10::TypedOperatorHandle<fake_quantize_per_tensor_affine::schema> create_fake_quantize_per_tensor_affine_typed_handle() { |
| 6355 | return c10::Dispatcher::singleton() |
| 6356 | .findSchemaOrThrow(fake_quantize_per_tensor_affine::name, fake_quantize_per_tensor_affine::overload_name) |
| 6357 | .typed<fake_quantize_per_tensor_affine::schema>(); |
| 6358 | } |
| 6359 | |
| 6360 | // aten::fake_quantize_per_tensor_affine(Tensor self, float scale, int zero_point, int quant_min, int quant_max) -> Tensor |
| 6361 | at::Tensor fake_quantize_per_tensor_affine::call(const at::Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max) { |
| 6362 | |
| 6363 | static auto op = create_fake_quantize_per_tensor_affine_typed_handle(); |
| 6364 | return op.call(self, scale, zero_point, quant_min, quant_max); |
| 6365 | } |
| 6366 | |
| 6367 | // aten::fake_quantize_per_tensor_affine(Tensor self, float scale, int zero_point, int quant_min, int quant_max) -> Tensor |
| 6368 | at::Tensor fake_quantize_per_tensor_affine::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max) { |
| 6369 | |
| 6370 | static auto op = create_fake_quantize_per_tensor_affine_typed_handle(); |
| 6371 | return op.redispatch(dispatchKeySet, self, scale, zero_point, quant_min, quant_max); |
| 6372 | } |
| 6373 | |
| 6374 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fake_quantize_per_tensor_affine_tensor_qparams, name, "aten::fake_quantize_per_tensor_affine") |
| 6375 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fake_quantize_per_tensor_affine_tensor_qparams, overload_name, "tensor_qparams") |
| 6376 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fake_quantize_per_tensor_affine_tensor_qparams, schema_str, "fake_quantize_per_tensor_affine.tensor_qparams(Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max) -> Tensor") |
| 6377 | |
| 6378 | // aten::fake_quantize_per_tensor_affine.tensor_qparams(Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max) -> Tensor |
| 6379 | static C10_NOINLINE c10::TypedOperatorHandle<fake_quantize_per_tensor_affine_tensor_qparams::schema> create_fake_quantize_per_tensor_affine_tensor_qparams_typed_handle() { |
| 6380 | return c10::Dispatcher::singleton() |
| 6381 | .findSchemaOrThrow(fake_quantize_per_tensor_affine_tensor_qparams::name, fake_quantize_per_tensor_affine_tensor_qparams::overload_name) |
| 6382 | .typed<fake_quantize_per_tensor_affine_tensor_qparams::schema>(); |
| 6383 | } |
| 6384 | |
| 6385 | // aten::fake_quantize_per_tensor_affine.tensor_qparams(Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max) -> Tensor |
| 6386 | at::Tensor fake_quantize_per_tensor_affine_tensor_qparams::call(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t quant_min, int64_t quant_max) { |
| 6387 | |
| 6388 | static auto op = create_fake_quantize_per_tensor_affine_tensor_qparams_typed_handle(); |
| 6389 | return op.call(self, scale, zero_point, quant_min, quant_max); |
| 6390 | } |
| 6391 | |
| 6392 | // aten::fake_quantize_per_tensor_affine.tensor_qparams(Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max) -> Tensor |
| 6393 | at::Tensor fake_quantize_per_tensor_affine_tensor_qparams::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t quant_min, int64_t quant_max) { |
| 6394 | |
| 6395 | static auto op = create_fake_quantize_per_tensor_affine_tensor_qparams_typed_handle(); |
| 6396 | return op.redispatch(dispatchKeySet, self, scale, zero_point, quant_min, quant_max); |
| 6397 | } |
| 6398 | |
| 6399 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fake_quantize_learnable_per_channel_affine, name, "aten::_fake_quantize_learnable_per_channel_affine") |
| 6400 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fake_quantize_learnable_per_channel_affine, overload_name, "") |
| 6401 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fake_quantize_learnable_per_channel_affine, schema_str, "_fake_quantize_learnable_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0) -> Tensor") |
| 6402 | |
| 6403 | // aten::_fake_quantize_learnable_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0) -> Tensor |
| 6404 | static C10_NOINLINE c10::TypedOperatorHandle<_fake_quantize_learnable_per_channel_affine::schema> create__fake_quantize_learnable_per_channel_affine_typed_handle() { |
| 6405 | return c10::Dispatcher::singleton() |
| 6406 | .findSchemaOrThrow(_fake_quantize_learnable_per_channel_affine::name, _fake_quantize_learnable_per_channel_affine::overload_name) |
| 6407 | .typed<_fake_quantize_learnable_per_channel_affine::schema>(); |
| 6408 | } |
| 6409 | |
| 6410 | // aten::_fake_quantize_learnable_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0) -> Tensor |
| 6411 | at::Tensor _fake_quantize_learnable_per_channel_affine::call(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) { |
| 6412 | |
| 6413 | static auto op = create__fake_quantize_learnable_per_channel_affine_typed_handle(); |
| 6414 | return op.call(self, scale, zero_point, axis, quant_min, quant_max, grad_factor); |
| 6415 | } |
| 6416 | |
| 6417 | // aten::_fake_quantize_learnable_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0) -> Tensor |
| 6418 | at::Tensor _fake_quantize_learnable_per_channel_affine::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max, double grad_factor) { |
| 6419 | |
| 6420 | static auto op = create__fake_quantize_learnable_per_channel_affine_typed_handle(); |
| 6421 | return op.redispatch(dispatchKeySet, self, scale, zero_point, axis, quant_min, quant_max, grad_factor); |
| 6422 | } |
| 6423 | |
| 6424 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_autocast_to_full_precision, name, "aten::_autocast_to_full_precision") |
| 6425 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_autocast_to_full_precision, overload_name, "") |
| 6426 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_autocast_to_full_precision, schema_str, "_autocast_to_full_precision(Tensor(a) self, bool cuda_enabled, bool cpu_enabled) -> Tensor(a)") |
| 6427 | |
| 6428 | // aten::_autocast_to_full_precision(Tensor(a) self, bool cuda_enabled, bool cpu_enabled) -> Tensor(a) |
| 6429 | static C10_NOINLINE c10::TypedOperatorHandle<_autocast_to_full_precision::schema> create__autocast_to_full_precision_typed_handle() { |
| 6430 | return c10::Dispatcher::singleton() |
| 6431 | .findSchemaOrThrow(_autocast_to_full_precision::name, _autocast_to_full_precision::overload_name) |
| 6432 | .typed<_autocast_to_full_precision::schema>(); |
| 6433 | } |
| 6434 | |
| 6435 | // aten::_autocast_to_full_precision(Tensor(a) self, bool cuda_enabled, bool cpu_enabled) -> Tensor(a) |
| 6436 | at::Tensor _autocast_to_full_precision::call(const at::Tensor & self, bool cuda_enabled, bool cpu_enabled) { |
| 6437 | |
| 6438 | static auto op = create__autocast_to_full_precision_typed_handle(); |
| 6439 | return op.call(self, cuda_enabled, cpu_enabled); |
| 6440 | } |
| 6441 | |
| 6442 | // aten::_autocast_to_full_precision(Tensor(a) self, bool cuda_enabled, bool cpu_enabled) -> Tensor(a) |
| 6443 | at::Tensor _autocast_to_full_precision::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool cuda_enabled, bool cpu_enabled) { |
| 6444 | |
| 6445 | static auto op = create__autocast_to_full_precision_typed_handle(); |
| 6446 | return op.redispatch(dispatchKeySet, self, cuda_enabled, cpu_enabled); |
| 6447 | } |
| 6448 | |
| 6449 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(to_dtype_layout, name, "aten::to") |
| 6450 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(to_dtype_layout, overload_name, "dtype_layout") |
| 6451 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(to_dtype_layout, schema_str, "to.dtype_layout(Tensor(a) self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a)") |
| 6452 | |
| 6453 | // aten::to.dtype_layout(Tensor(a) self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a) |
| 6454 | static C10_NOINLINE c10::TypedOperatorHandle<to_dtype_layout::schema> create_to_dtype_layout_typed_handle() { |
| 6455 | return c10::Dispatcher::singleton() |
| 6456 | .findSchemaOrThrow(to_dtype_layout::name, to_dtype_layout::overload_name) |
| 6457 | .typed<to_dtype_layout::schema>(); |
| 6458 | } |
| 6459 | |
| 6460 | // aten::to.dtype_layout(Tensor(a) self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a) |
| 6461 | at::Tensor to_dtype_layout::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, bool non_blocking, bool copy, c10::optional<at::MemoryFormat> memory_format) { |
| 6462 | |
| 6463 | static auto op = create_to_dtype_layout_typed_handle(); |
| 6464 | return op.call(self, dtype, layout, device, pin_memory, non_blocking, copy, memory_format); |
| 6465 | } |
| 6466 | |
| 6467 | // aten::to.dtype_layout(Tensor(a) self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a) |
| 6468 | at::Tensor to_dtype_layout::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, bool non_blocking, bool copy, c10::optional<at::MemoryFormat> memory_format) { |
| 6469 | |
| 6470 | static auto op = create_to_dtype_layout_typed_handle(); |
| 6471 | return op.redispatch(dispatchKeySet, self, dtype, layout, device, pin_memory, non_blocking, copy, memory_format); |
| 6472 | } |
| 6473 | |
| 6474 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(to_device, name, "aten::to") |
| 6475 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(to_device, overload_name, "device") |
| 6476 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(to_device, schema_str, "to.device(Tensor(a) self, Device device, ScalarType dtype, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a)") |
| 6477 | |
| 6478 | // aten::to.device(Tensor(a) self, Device device, ScalarType dtype, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a) |
| 6479 | static C10_NOINLINE c10::TypedOperatorHandle<to_device::schema> create_to_device_typed_handle() { |
| 6480 | return c10::Dispatcher::singleton() |
| 6481 | .findSchemaOrThrow(to_device::name, to_device::overload_name) |
| 6482 | .typed<to_device::schema>(); |
| 6483 | } |
| 6484 | |
| 6485 | // aten::to.device(Tensor(a) self, Device device, ScalarType dtype, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a) |
| 6486 | at::Tensor to_device::call(const at::Tensor & self, at::Device device, at::ScalarType dtype, bool non_blocking, bool copy, c10::optional<at::MemoryFormat> memory_format) { |
| 6487 | |
| 6488 | static auto op = create_to_device_typed_handle(); |
| 6489 | return op.call(self, device, dtype, non_blocking, copy, memory_format); |
| 6490 | } |
| 6491 | |
| 6492 | // aten::to.device(Tensor(a) self, Device device, ScalarType dtype, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a) |
| 6493 | at::Tensor to_device::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Device device, at::ScalarType dtype, bool non_blocking, bool copy, c10::optional<at::MemoryFormat> memory_format) { |
| 6494 | |
| 6495 | static auto op = create_to_device_typed_handle(); |
| 6496 | return op.redispatch(dispatchKeySet, self, device, dtype, non_blocking, copy, memory_format); |
| 6497 | } |
| 6498 | |
| 6499 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(to_dtype, name, "aten::to") |
| 6500 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(to_dtype, overload_name, "dtype") |
| 6501 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(to_dtype, schema_str, "to.dtype(Tensor(a) self, ScalarType dtype, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a)") |
| 6502 | |
| 6503 | // aten::to.dtype(Tensor(a) self, ScalarType dtype, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a) |
| 6504 | static C10_NOINLINE c10::TypedOperatorHandle<to_dtype::schema> create_to_dtype_typed_handle() { |
| 6505 | return c10::Dispatcher::singleton() |
| 6506 | .findSchemaOrThrow(to_dtype::name, to_dtype::overload_name) |
| 6507 | .typed<to_dtype::schema>(); |
| 6508 | } |
| 6509 | |
| 6510 | // aten::to.dtype(Tensor(a) self, ScalarType dtype, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a) |
| 6511 | at::Tensor to_dtype::call(const at::Tensor & self, at::ScalarType dtype, bool non_blocking, bool copy, c10::optional<at::MemoryFormat> memory_format) { |
| 6512 | |
| 6513 | static auto op = create_to_dtype_typed_handle(); |
| 6514 | return op.call(self, dtype, non_blocking, copy, memory_format); |
| 6515 | } |
| 6516 | |
| 6517 | // aten::to.dtype(Tensor(a) self, ScalarType dtype, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a) |
| 6518 | at::Tensor to_dtype::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::ScalarType dtype, bool non_blocking, bool copy, c10::optional<at::MemoryFormat> memory_format) { |
| 6519 | |
| 6520 | static auto op = create_to_dtype_typed_handle(); |
| 6521 | return op.redispatch(dispatchKeySet, self, dtype, non_blocking, copy, memory_format); |
| 6522 | } |
| 6523 | |
| 6524 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(to_other, name, "aten::to") |
| 6525 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(to_other, overload_name, "other") |
| 6526 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(to_other, schema_str, "to.other(Tensor(a) self, Tensor other, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a)") |
| 6527 | |
| 6528 | // aten::to.other(Tensor(a) self, Tensor other, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a) |
| 6529 | static C10_NOINLINE c10::TypedOperatorHandle<to_other::schema> create_to_other_typed_handle() { |
| 6530 | return c10::Dispatcher::singleton() |
| 6531 | .findSchemaOrThrow(to_other::name, to_other::overload_name) |
| 6532 | .typed<to_other::schema>(); |
| 6533 | } |
| 6534 | |
| 6535 | // aten::to.other(Tensor(a) self, Tensor other, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a) |
| 6536 | at::Tensor to_other::call(const at::Tensor & self, const at::Tensor & other, bool non_blocking, bool copy, c10::optional<at::MemoryFormat> memory_format) { |
| 6537 | |
| 6538 | static auto op = create_to_other_typed_handle(); |
| 6539 | return op.call(self, other, non_blocking, copy, memory_format); |
| 6540 | } |
| 6541 | |
| 6542 | // aten::to.other(Tensor(a) self, Tensor other, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a) |
| 6543 | at::Tensor to_other::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, bool non_blocking, bool copy, c10::optional<at::MemoryFormat> memory_format) { |
| 6544 | |
| 6545 | static auto op = create_to_other_typed_handle(); |
| 6546 | return op.redispatch(dispatchKeySet, self, other, non_blocking, copy, memory_format); |
| 6547 | } |
| 6548 | |
| 6549 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(combinations, name, "aten::combinations") |
| 6550 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(combinations, overload_name, "") |
| 6551 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(combinations, schema_str, "combinations(Tensor self, int r=2, bool with_replacement=False) -> Tensor") |
| 6552 | |
| 6553 | // aten::combinations(Tensor self, int r=2, bool with_replacement=False) -> Tensor |
| 6554 | static C10_NOINLINE c10::TypedOperatorHandle<combinations::schema> create_combinations_typed_handle() { |
| 6555 | return c10::Dispatcher::singleton() |
| 6556 | .findSchemaOrThrow(combinations::name, combinations::overload_name) |
| 6557 | .typed<combinations::schema>(); |
| 6558 | } |
| 6559 | |
| 6560 | // aten::combinations(Tensor self, int r=2, bool with_replacement=False) -> Tensor |
| 6561 | at::Tensor combinations::call(const at::Tensor & self, int64_t r, bool with_replacement) { |
| 6562 | |
| 6563 | static auto op = create_combinations_typed_handle(); |
| 6564 | return op.call(self, r, with_replacement); |
| 6565 | } |
| 6566 | |
| 6567 | // aten::combinations(Tensor self, int r=2, bool with_replacement=False) -> Tensor |
| 6568 | at::Tensor combinations::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t r, bool with_replacement) { |
| 6569 | |
| 6570 | static auto op = create_combinations_typed_handle(); |
| 6571 | return op.redispatch(dispatchKeySet, self, r, with_replacement); |
| 6572 | } |
| 6573 | |
| 6574 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(item, name, "aten::item") |
| 6575 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(item, overload_name, "") |
| 6576 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(item, schema_str, "item(Tensor self) -> Scalar") |
| 6577 | |
| 6578 | // aten::item(Tensor self) -> Scalar |
| 6579 | static C10_NOINLINE c10::TypedOperatorHandle<item::schema> create_item_typed_handle() { |
| 6580 | return c10::Dispatcher::singleton() |
| 6581 | .findSchemaOrThrow(item::name, item::overload_name) |
| 6582 | .typed<item::schema>(); |
| 6583 | } |
| 6584 | |
| 6585 | // aten::item(Tensor self) -> Scalar |
| 6586 | at::Scalar item::call(const at::Tensor & self) { |
| 6587 | |
| 6588 | static auto op = create_item_typed_handle(); |
| 6589 | return op.call(self); |
| 6590 | } |
| 6591 | |
| 6592 | // aten::item(Tensor self) -> Scalar |
| 6593 | at::Scalar item::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 6594 | |
| 6595 | static auto op = create_item_typed_handle(); |
| 6596 | return op.redispatch(dispatchKeySet, self); |
| 6597 | } |
| 6598 | |
| 6599 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_lstm_mps, name, "aten::_lstm_mps") |
| 6600 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_lstm_mps, overload_name, "") |
| 6601 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_lstm_mps, schema_str, "_lstm_mps(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor, Tensor, Tensor, Tensor)") |
| 6602 | |
| 6603 | // aten::_lstm_mps(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor, Tensor, Tensor, Tensor) |
| 6604 | static C10_NOINLINE c10::TypedOperatorHandle<_lstm_mps::schema> create__lstm_mps_typed_handle() { |
| 6605 | return c10::Dispatcher::singleton() |
| 6606 | .findSchemaOrThrow(_lstm_mps::name, _lstm_mps::overload_name) |
| 6607 | .typed<_lstm_mps::schema>(); |
| 6608 | } |
| 6609 | |
| 6610 | // aten::_lstm_mps(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor, Tensor, Tensor, Tensor) |
| 6611 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _lstm_mps::call(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) { |
| 6612 | |
| 6613 | static auto op = create__lstm_mps_typed_handle(); |
| 6614 | return op.call(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); |
| 6615 | } |
| 6616 | |
| 6617 | // aten::_lstm_mps(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor, Tensor, Tensor, Tensor) |
| 6618 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _lstm_mps::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) { |
| 6619 | |
| 6620 | static auto op = create__lstm_mps_typed_handle(); |
| 6621 | return op.redispatch(dispatchKeySet, input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); |
| 6622 | } |
| 6623 | |
| 6624 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_thnn_fused_lstm_cell, name, "aten::_thnn_fused_lstm_cell") |
| 6625 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_thnn_fused_lstm_cell, overload_name, "") |
| 6626 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_thnn_fused_lstm_cell, schema_str, "_thnn_fused_lstm_cell(Tensor input_gates, Tensor hidden_gates, Tensor cx, Tensor? input_bias=None, Tensor? hidden_bias=None) -> (Tensor, Tensor, Tensor)") |
| 6627 | |
| 6628 | // aten::_thnn_fused_lstm_cell(Tensor input_gates, Tensor hidden_gates, Tensor cx, Tensor? input_bias=None, Tensor? hidden_bias=None) -> (Tensor, Tensor, Tensor) |
| 6629 | static C10_NOINLINE c10::TypedOperatorHandle<_thnn_fused_lstm_cell::schema> create__thnn_fused_lstm_cell_typed_handle() { |
| 6630 | return c10::Dispatcher::singleton() |
| 6631 | .findSchemaOrThrow(_thnn_fused_lstm_cell::name, _thnn_fused_lstm_cell::overload_name) |
| 6632 | .typed<_thnn_fused_lstm_cell::schema>(); |
| 6633 | } |
| 6634 | |
| 6635 | // aten::_thnn_fused_lstm_cell(Tensor input_gates, Tensor hidden_gates, Tensor cx, Tensor? input_bias=None, Tensor? hidden_bias=None) -> (Tensor, Tensor, Tensor) |
| 6636 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _thnn_fused_lstm_cell::call(const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & cx, const c10::optional<at::Tensor> & input_bias, const c10::optional<at::Tensor> & hidden_bias) { |
| 6637 | |
| 6638 | static auto op = create__thnn_fused_lstm_cell_typed_handle(); |
| 6639 | return op.call(input_gates, hidden_gates, cx, input_bias, hidden_bias); |
| 6640 | } |
| 6641 | |
| 6642 | // aten::_thnn_fused_lstm_cell(Tensor input_gates, Tensor hidden_gates, Tensor cx, Tensor? input_bias=None, Tensor? hidden_bias=None) -> (Tensor, Tensor, Tensor) |
| 6643 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _thnn_fused_lstm_cell::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & cx, const c10::optional<at::Tensor> & input_bias, const c10::optional<at::Tensor> & hidden_bias) { |
| 6644 | |
| 6645 | static auto op = create__thnn_fused_lstm_cell_typed_handle(); |
| 6646 | return op.redispatch(dispatchKeySet, input_gates, hidden_gates, cx, input_bias, hidden_bias); |
| 6647 | } |
| 6648 | |
| 6649 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lstm_input, name, "aten::lstm") |
| 6650 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lstm_input, overload_name, "input") |
| 6651 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lstm_input, schema_str, "lstm.input(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor, Tensor)") |
| 6652 | |
| 6653 | // aten::lstm.input(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor, Tensor) |
| 6654 | static C10_NOINLINE c10::TypedOperatorHandle<lstm_input::schema> create_lstm_input_typed_handle() { |
| 6655 | return c10::Dispatcher::singleton() |
| 6656 | .findSchemaOrThrow(lstm_input::name, lstm_input::overload_name) |
| 6657 | .typed<lstm_input::schema>(); |
| 6658 | } |
| 6659 | |
| 6660 | // aten::lstm.input(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor, Tensor) |
| 6661 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> lstm_input::call(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) { |
| 6662 | |
| 6663 | static auto op = create_lstm_input_typed_handle(); |
| 6664 | return op.call(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); |
| 6665 | } |
| 6666 | |
| 6667 | // aten::lstm.input(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor, Tensor) |
| 6668 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> lstm_input::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) { |
| 6669 | |
| 6670 | static auto op = create_lstm_input_typed_handle(); |
| 6671 | return op.redispatch(dispatchKeySet, input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); |
| 6672 | } |
| 6673 | |
| 6674 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lstm_data, name, "aten::lstm") |
| 6675 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lstm_data, overload_name, "data") |
| 6676 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lstm_data, schema_str, "lstm.data(Tensor data, Tensor batch_sizes, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor, Tensor)") |
| 6677 | |
| 6678 | // aten::lstm.data(Tensor data, Tensor batch_sizes, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor, Tensor) |
| 6679 | static C10_NOINLINE c10::TypedOperatorHandle<lstm_data::schema> create_lstm_data_typed_handle() { |
| 6680 | return c10::Dispatcher::singleton() |
| 6681 | .findSchemaOrThrow(lstm_data::name, lstm_data::overload_name) |
| 6682 | .typed<lstm_data::schema>(); |
| 6683 | } |
| 6684 | |
| 6685 | // aten::lstm.data(Tensor data, Tensor batch_sizes, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor, Tensor) |
| 6686 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> lstm_data::call(const at::Tensor & data, const at::Tensor & batch_sizes, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) { |
| 6687 | |
| 6688 | static auto op = create_lstm_data_typed_handle(); |
| 6689 | return op.call(data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); |
| 6690 | } |
| 6691 | |
| 6692 | // aten::lstm.data(Tensor data, Tensor batch_sizes, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor, Tensor) |
| 6693 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> lstm_data::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & data, const at::Tensor & batch_sizes, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) { |
| 6694 | |
| 6695 | static auto op = create_lstm_data_typed_handle(); |
| 6696 | return op.redispatch(dispatchKeySet, data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); |
| 6697 | } |
| 6698 | |
| 6699 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gru_input, name, "aten::gru") |
| 6700 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gru_input, overload_name, "input") |
| 6701 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gru_input, schema_str, "gru.input(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor)") |
| 6702 | |
| 6703 | // aten::gru.input(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor) |
| 6704 | static C10_NOINLINE c10::TypedOperatorHandle<gru_input::schema> create_gru_input_typed_handle() { |
| 6705 | return c10::Dispatcher::singleton() |
| 6706 | .findSchemaOrThrow(gru_input::name, gru_input::overload_name) |
| 6707 | .typed<gru_input::schema>(); |
| 6708 | } |
| 6709 | |
| 6710 | // aten::gru.input(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor) |
| 6711 | ::std::tuple<at::Tensor,at::Tensor> gru_input::call(const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) { |
| 6712 | |
| 6713 | static auto op = create_gru_input_typed_handle(); |
| 6714 | return op.call(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); |
| 6715 | } |
| 6716 | |
| 6717 | // aten::gru.input(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor) |
| 6718 | ::std::tuple<at::Tensor,at::Tensor> gru_input::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) { |
| 6719 | |
| 6720 | static auto op = create_gru_input_typed_handle(); |
| 6721 | return op.redispatch(dispatchKeySet, input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); |
| 6722 | } |
| 6723 | |
| 6724 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gru_data, name, "aten::gru") |
| 6725 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gru_data, overload_name, "data") |
| 6726 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(gru_data, schema_str, "gru.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor)") |
| 6727 | |
| 6728 | // aten::gru.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor) |
| 6729 | static C10_NOINLINE c10::TypedOperatorHandle<gru_data::schema> create_gru_data_typed_handle() { |
| 6730 | return c10::Dispatcher::singleton() |
| 6731 | .findSchemaOrThrow(gru_data::name, gru_data::overload_name) |
| 6732 | .typed<gru_data::schema>(); |
| 6733 | } |
| 6734 | |
| 6735 | // aten::gru.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor) |
| 6736 | ::std::tuple<at::Tensor,at::Tensor> gru_data::call(const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) { |
| 6737 | |
| 6738 | static auto op = create_gru_data_typed_handle(); |
| 6739 | return op.call(data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); |
| 6740 | } |
| 6741 | |
| 6742 | // aten::gru.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor) |
| 6743 | ::std::tuple<at::Tensor,at::Tensor> gru_data::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) { |
| 6744 | |
| 6745 | static auto op = create_gru_data_typed_handle(); |
| 6746 | return op.redispatch(dispatchKeySet, data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); |
| 6747 | } |
| 6748 | |
| 6749 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rnn_tanh_input, name, "aten::rnn_tanh") |
| 6750 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rnn_tanh_input, overload_name, "input") |
| 6751 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rnn_tanh_input, schema_str, "rnn_tanh.input(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor)") |
| 6752 | |
| 6753 | // aten::rnn_tanh.input(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor) |
| 6754 | static C10_NOINLINE c10::TypedOperatorHandle<rnn_tanh_input::schema> create_rnn_tanh_input_typed_handle() { |
| 6755 | return c10::Dispatcher::singleton() |
| 6756 | .findSchemaOrThrow(rnn_tanh_input::name, rnn_tanh_input::overload_name) |
| 6757 | .typed<rnn_tanh_input::schema>(); |
| 6758 | } |
| 6759 | |
| 6760 | // aten::rnn_tanh.input(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor) |
| 6761 | ::std::tuple<at::Tensor,at::Tensor> rnn_tanh_input::call(const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) { |
| 6762 | |
| 6763 | static auto op = create_rnn_tanh_input_typed_handle(); |
| 6764 | return op.call(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); |
| 6765 | } |
| 6766 | |
| 6767 | // aten::rnn_tanh.input(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor) |
| 6768 | ::std::tuple<at::Tensor,at::Tensor> rnn_tanh_input::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) { |
| 6769 | |
| 6770 | static auto op = create_rnn_tanh_input_typed_handle(); |
| 6771 | return op.redispatch(dispatchKeySet, input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); |
| 6772 | } |
| 6773 | |
| 6774 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rnn_tanh_data, name, "aten::rnn_tanh") |
| 6775 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rnn_tanh_data, overload_name, "data") |
| 6776 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rnn_tanh_data, schema_str, "rnn_tanh.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor)") |
| 6777 | |
| 6778 | // aten::rnn_tanh.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor) |
| 6779 | static C10_NOINLINE c10::TypedOperatorHandle<rnn_tanh_data::schema> create_rnn_tanh_data_typed_handle() { |
| 6780 | return c10::Dispatcher::singleton() |
| 6781 | .findSchemaOrThrow(rnn_tanh_data::name, rnn_tanh_data::overload_name) |
| 6782 | .typed<rnn_tanh_data::schema>(); |
| 6783 | } |
| 6784 | |
| 6785 | // aten::rnn_tanh.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor) |
| 6786 | ::std::tuple<at::Tensor,at::Tensor> rnn_tanh_data::call(const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) { |
| 6787 | |
| 6788 | static auto op = create_rnn_tanh_data_typed_handle(); |
| 6789 | return op.call(data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); |
| 6790 | } |
| 6791 | |
| 6792 | // aten::rnn_tanh.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor) |
| 6793 | ::std::tuple<at::Tensor,at::Tensor> rnn_tanh_data::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) { |
| 6794 | |
| 6795 | static auto op = create_rnn_tanh_data_typed_handle(); |
| 6796 | return op.redispatch(dispatchKeySet, data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); |
| 6797 | } |
| 6798 | |
| 6799 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rnn_relu_cell, name, "aten::rnn_relu_cell") |
| 6800 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rnn_relu_cell, overload_name, "") |
| 6801 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rnn_relu_cell, schema_str, "rnn_relu_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensor") |
| 6802 | |
| 6803 | // aten::rnn_relu_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensor |
| 6804 | static C10_NOINLINE c10::TypedOperatorHandle<rnn_relu_cell::schema> create_rnn_relu_cell_typed_handle() { |
| 6805 | return c10::Dispatcher::singleton() |
| 6806 | .findSchemaOrThrow(rnn_relu_cell::name, rnn_relu_cell::overload_name) |
| 6807 | .typed<rnn_relu_cell::schema>(); |
| 6808 | } |
| 6809 | |
| 6810 | // aten::rnn_relu_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensor |
| 6811 | at::Tensor rnn_relu_cell::call(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const c10::optional<at::Tensor> & b_ih, const c10::optional<at::Tensor> & b_hh) { |
| 6812 | |
| 6813 | static auto op = create_rnn_relu_cell_typed_handle(); |
| 6814 | return op.call(input, hx, w_ih, w_hh, b_ih, b_hh); |
| 6815 | } |
| 6816 | |
| 6817 | // aten::rnn_relu_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensor |
| 6818 | at::Tensor rnn_relu_cell::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const c10::optional<at::Tensor> & b_ih, const c10::optional<at::Tensor> & b_hh) { |
| 6819 | |
| 6820 | static auto op = create_rnn_relu_cell_typed_handle(); |
| 6821 | return op.redispatch(dispatchKeySet, input, hx, w_ih, w_hh, b_ih, b_hh); |
| 6822 | } |
| 6823 | |
| 6824 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_pad_packed_sequence, name, "aten::_pad_packed_sequence") |
| 6825 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_pad_packed_sequence, overload_name, "") |
| 6826 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_pad_packed_sequence, schema_str, "_pad_packed_sequence(Tensor data, Tensor batch_sizes, bool batch_first, Scalar padding_value, int total_length) -> (Tensor, Tensor)") |
| 6827 | |
| 6828 | // aten::_pad_packed_sequence(Tensor data, Tensor batch_sizes, bool batch_first, Scalar padding_value, int total_length) -> (Tensor, Tensor) |
| 6829 | static C10_NOINLINE c10::TypedOperatorHandle<_pad_packed_sequence::schema> create__pad_packed_sequence_typed_handle() { |
| 6830 | return c10::Dispatcher::singleton() |
| 6831 | .findSchemaOrThrow(_pad_packed_sequence::name, _pad_packed_sequence::overload_name) |
| 6832 | .typed<_pad_packed_sequence::schema>(); |
| 6833 | } |
| 6834 | |
| 6835 | // aten::_pad_packed_sequence(Tensor data, Tensor batch_sizes, bool batch_first, Scalar padding_value, int total_length) -> (Tensor, Tensor) |
| 6836 | ::std::tuple<at::Tensor,at::Tensor> _pad_packed_sequence::call(const at::Tensor & data, const at::Tensor & batch_sizes, bool batch_first, const at::Scalar & padding_value, int64_t total_length) { |
| 6837 | |
| 6838 | static auto op = create__pad_packed_sequence_typed_handle(); |
| 6839 | return op.call(data, batch_sizes, batch_first, padding_value, total_length); |
| 6840 | } |
| 6841 | |
| 6842 | // aten::_pad_packed_sequence(Tensor data, Tensor batch_sizes, bool batch_first, Scalar padding_value, int total_length) -> (Tensor, Tensor) |
| 6843 | ::std::tuple<at::Tensor,at::Tensor> _pad_packed_sequence::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & data, const at::Tensor & batch_sizes, bool batch_first, const at::Scalar & padding_value, int64_t total_length) { |
| 6844 | |
| 6845 | static auto op = create__pad_packed_sequence_typed_handle(); |
| 6846 | return op.redispatch(dispatchKeySet, data, batch_sizes, batch_first, padding_value, total_length); |
| 6847 | } |
| 6848 | |
| 6849 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lift_fresh_copy, name, "aten::lift_fresh_copy") |
| 6850 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lift_fresh_copy, overload_name, "") |
| 6851 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lift_fresh_copy, schema_str, "lift_fresh_copy(Tensor self) -> Tensor") |
| 6852 | |
| 6853 | // aten::lift_fresh_copy(Tensor self) -> Tensor |
| 6854 | static C10_NOINLINE c10::TypedOperatorHandle<lift_fresh_copy::schema> create_lift_fresh_copy_typed_handle() { |
| 6855 | return c10::Dispatcher::singleton() |
| 6856 | .findSchemaOrThrow(lift_fresh_copy::name, lift_fresh_copy::overload_name) |
| 6857 | .typed<lift_fresh_copy::schema>(); |
| 6858 | } |
| 6859 | |
| 6860 | // aten::lift_fresh_copy(Tensor self) -> Tensor |
| 6861 | at::Tensor lift_fresh_copy::call(const at::Tensor & self) { |
| 6862 | |
| 6863 | static auto op = create_lift_fresh_copy_typed_handle(); |
| 6864 | return op.call(self); |
| 6865 | } |
| 6866 | |
| 6867 | // aten::lift_fresh_copy(Tensor self) -> Tensor |
| 6868 | at::Tensor lift_fresh_copy::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 6869 | |
| 6870 | static auto op = create_lift_fresh_copy_typed_handle(); |
| 6871 | return op.redispatch(dispatchKeySet, self); |
| 6872 | } |
| 6873 | |
| 6874 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_reduce_out, name, "aten::index_reduce") |
| 6875 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_reduce_out, overload_name, "out") |
| 6876 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_reduce_out, schema_str, "index_reduce.out(Tensor self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True, Tensor(a!) out) -> Tensor(a!)") |
| 6877 | |
| 6878 | // aten::index_reduce.out(Tensor self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True, Tensor(a!) out) -> Tensor(a!) |
| 6879 | static C10_NOINLINE c10::TypedOperatorHandle<index_reduce_out::schema> create_index_reduce_out_typed_handle() { |
| 6880 | return c10::Dispatcher::singleton() |
| 6881 | .findSchemaOrThrow(index_reduce_out::name, index_reduce_out::overload_name) |
| 6882 | .typed<index_reduce_out::schema>(); |
| 6883 | } |
| 6884 | |
| 6885 | // aten::index_reduce.out(Tensor self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True, Tensor(a!) out) -> Tensor(a!) |
| 6886 | at::Tensor & index_reduce_out::call(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self, at::Tensor & out) { |
| 6887 | |
| 6888 | static auto op = create_index_reduce_out_typed_handle(); |
| 6889 | return op.call(self, dim, index, source, reduce, include_self, out); |
| 6890 | } |
| 6891 | |
| 6892 | // aten::index_reduce.out(Tensor self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True, Tensor(a!) out) -> Tensor(a!) |
| 6893 | at::Tensor & index_reduce_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self, at::Tensor & out) { |
| 6894 | |
| 6895 | static auto op = create_index_reduce_out_typed_handle(); |
| 6896 | return op.redispatch(dispatchKeySet, self, dim, index, source, reduce, include_self, out); |
| 6897 | } |
| 6898 | |
| 6899 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_reduce_, name, "aten::index_reduce_") |
| 6900 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_reduce_, overload_name, "") |
| 6901 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_reduce_, schema_str, "index_reduce_(Tensor(a!) self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True) -> Tensor(a!)") |
| 6902 | |
| 6903 | // aten::index_reduce_(Tensor(a!) self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True) -> Tensor(a!) |
| 6904 | static C10_NOINLINE c10::TypedOperatorHandle<index_reduce_::schema> create_index_reduce__typed_handle() { |
| 6905 | return c10::Dispatcher::singleton() |
| 6906 | .findSchemaOrThrow(index_reduce_::name, index_reduce_::overload_name) |
| 6907 | .typed<index_reduce_::schema>(); |
| 6908 | } |
| 6909 | |
| 6910 | // aten::index_reduce_(Tensor(a!) self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True) -> Tensor(a!) |
| 6911 | at::Tensor & index_reduce_::call(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self) { |
| 6912 | |
| 6913 | static auto op = create_index_reduce__typed_handle(); |
| 6914 | return op.call(self, dim, index, source, reduce, include_self); |
| 6915 | } |
| 6916 | |
| 6917 | // aten::index_reduce_(Tensor(a!) self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True) -> Tensor(a!) |
| 6918 | at::Tensor & index_reduce_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self) { |
| 6919 | |
| 6920 | static auto op = create_index_reduce__typed_handle(); |
| 6921 | return op.redispatch(dispatchKeySet, self, dim, index, source, reduce, include_self); |
| 6922 | } |
| 6923 | |
| 6924 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_reduce, name, "aten::index_reduce") |
| 6925 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_reduce, overload_name, "") |
| 6926 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_reduce, schema_str, "index_reduce(Tensor self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True) -> Tensor") |
| 6927 | |
| 6928 | // aten::index_reduce(Tensor self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True) -> Tensor |
| 6929 | static C10_NOINLINE c10::TypedOperatorHandle<index_reduce::schema> create_index_reduce_typed_handle() { |
| 6930 | return c10::Dispatcher::singleton() |
| 6931 | .findSchemaOrThrow(index_reduce::name, index_reduce::overload_name) |
| 6932 | .typed<index_reduce::schema>(); |
| 6933 | } |
| 6934 | |
| 6935 | // aten::index_reduce(Tensor self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True) -> Tensor |
| 6936 | at::Tensor index_reduce::call(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self) { |
| 6937 | |
| 6938 | static auto op = create_index_reduce_typed_handle(); |
| 6939 | return op.call(self, dim, index, source, reduce, include_self); |
| 6940 | } |
| 6941 | |
| 6942 | // aten::index_reduce(Tensor self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True) -> Tensor |
| 6943 | at::Tensor index_reduce::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self) { |
| 6944 | |
| 6945 | static auto op = create_index_reduce_typed_handle(); |
| 6946 | return op.redispatch(dispatchKeySet, self, dim, index, source, reduce, include_self); |
| 6947 | } |
| 6948 | |
| 6949 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill__int_Scalar, name, "aten::index_fill_") |
| 6950 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill__int_Scalar, overload_name, "int_Scalar") |
| 6951 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill__int_Scalar, schema_str, "index_fill_.int_Scalar(Tensor(a!) self, int dim, Tensor index, Scalar value) -> Tensor(a!)") |
| 6952 | |
| 6953 | // aten::index_fill_.int_Scalar(Tensor(a!) self, int dim, Tensor index, Scalar value) -> Tensor(a!) |
| 6954 | static C10_NOINLINE c10::TypedOperatorHandle<index_fill__int_Scalar::schema> create_index_fill__int_Scalar_typed_handle() { |
| 6955 | return c10::Dispatcher::singleton() |
| 6956 | .findSchemaOrThrow(index_fill__int_Scalar::name, index_fill__int_Scalar::overload_name) |
| 6957 | .typed<index_fill__int_Scalar::schema>(); |
| 6958 | } |
| 6959 | |
| 6960 | // aten::index_fill_.int_Scalar(Tensor(a!) self, int dim, Tensor index, Scalar value) -> Tensor(a!) |
| 6961 | at::Tensor & index_fill__int_Scalar::call(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) { |
| 6962 | |
| 6963 | static auto op = create_index_fill__int_Scalar_typed_handle(); |
| 6964 | return op.call(self, dim, index, value); |
| 6965 | } |
| 6966 | |
| 6967 | // aten::index_fill_.int_Scalar(Tensor(a!) self, int dim, Tensor index, Scalar value) -> Tensor(a!) |
| 6968 | at::Tensor & index_fill__int_Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) { |
| 6969 | |
| 6970 | static auto op = create_index_fill__int_Scalar_typed_handle(); |
| 6971 | return op.redispatch(dispatchKeySet, self, dim, index, value); |
| 6972 | } |
| 6973 | |
| 6974 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill_int_Scalar, name, "aten::index_fill") |
| 6975 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill_int_Scalar, overload_name, "int_Scalar") |
| 6976 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill_int_Scalar, schema_str, "index_fill.int_Scalar(Tensor self, int dim, Tensor index, Scalar value) -> Tensor") |
| 6977 | |
| 6978 | // aten::index_fill.int_Scalar(Tensor self, int dim, Tensor index, Scalar value) -> Tensor |
| 6979 | static C10_NOINLINE c10::TypedOperatorHandle<index_fill_int_Scalar::schema> create_index_fill_int_Scalar_typed_handle() { |
| 6980 | return c10::Dispatcher::singleton() |
| 6981 | .findSchemaOrThrow(index_fill_int_Scalar::name, index_fill_int_Scalar::overload_name) |
| 6982 | .typed<index_fill_int_Scalar::schema>(); |
| 6983 | } |
| 6984 | |
| 6985 | // aten::index_fill.int_Scalar(Tensor self, int dim, Tensor index, Scalar value) -> Tensor |
| 6986 | at::Tensor index_fill_int_Scalar::call(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) { |
| 6987 | |
| 6988 | static auto op = create_index_fill_int_Scalar_typed_handle(); |
| 6989 | return op.call(self, dim, index, value); |
| 6990 | } |
| 6991 | |
| 6992 | // aten::index_fill.int_Scalar(Tensor self, int dim, Tensor index, Scalar value) -> Tensor |
| 6993 | at::Tensor index_fill_int_Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) { |
| 6994 | |
| 6995 | static auto op = create_index_fill_int_Scalar_typed_handle(); |
| 6996 | return op.redispatch(dispatchKeySet, self, dim, index, value); |
| 6997 | } |
| 6998 | |
| 6999 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill__int_Tensor, name, "aten::index_fill_") |
| 7000 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill__int_Tensor, overload_name, "int_Tensor") |
| 7001 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill__int_Tensor, schema_str, "index_fill_.int_Tensor(Tensor(a!) self, int dim, Tensor index, Tensor value) -> Tensor(a!)") |
| 7002 | |
| 7003 | // aten::index_fill_.int_Tensor(Tensor(a!) self, int dim, Tensor index, Tensor value) -> Tensor(a!) |
| 7004 | static C10_NOINLINE c10::TypedOperatorHandle<index_fill__int_Tensor::schema> create_index_fill__int_Tensor_typed_handle() { |
| 7005 | return c10::Dispatcher::singleton() |
| 7006 | .findSchemaOrThrow(index_fill__int_Tensor::name, index_fill__int_Tensor::overload_name) |
| 7007 | .typed<index_fill__int_Tensor::schema>(); |
| 7008 | } |
| 7009 | |
| 7010 | // aten::index_fill_.int_Tensor(Tensor(a!) self, int dim, Tensor index, Tensor value) -> Tensor(a!) |
| 7011 | at::Tensor & index_fill__int_Tensor::call(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & value) { |
| 7012 | |
| 7013 | static auto op = create_index_fill__int_Tensor_typed_handle(); |
| 7014 | return op.call(self, dim, index, value); |
| 7015 | } |
| 7016 | |
| 7017 | // aten::index_fill_.int_Tensor(Tensor(a!) self, int dim, Tensor index, Tensor value) -> Tensor(a!) |
| 7018 | at::Tensor & index_fill__int_Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & value) { |
| 7019 | |
| 7020 | static auto op = create_index_fill__int_Tensor_typed_handle(); |
| 7021 | return op.redispatch(dispatchKeySet, self, dim, index, value); |
| 7022 | } |
| 7023 | |
| 7024 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill_int_Tensor, name, "aten::index_fill") |
| 7025 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill_int_Tensor, overload_name, "int_Tensor") |
| 7026 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill_int_Tensor, schema_str, "index_fill.int_Tensor(Tensor self, int dim, Tensor index, Tensor value) -> Tensor") |
| 7027 | |
| 7028 | // aten::index_fill.int_Tensor(Tensor self, int dim, Tensor index, Tensor value) -> Tensor |
| 7029 | static C10_NOINLINE c10::TypedOperatorHandle<index_fill_int_Tensor::schema> create_index_fill_int_Tensor_typed_handle() { |
| 7030 | return c10::Dispatcher::singleton() |
| 7031 | .findSchemaOrThrow(index_fill_int_Tensor::name, index_fill_int_Tensor::overload_name) |
| 7032 | .typed<index_fill_int_Tensor::schema>(); |
| 7033 | } |
| 7034 | |
| 7035 | // aten::index_fill.int_Tensor(Tensor self, int dim, Tensor index, Tensor value) -> Tensor |
| 7036 | at::Tensor index_fill_int_Tensor::call(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & value) { |
| 7037 | |
| 7038 | static auto op = create_index_fill_int_Tensor_typed_handle(); |
| 7039 | return op.call(self, dim, index, value); |
| 7040 | } |
| 7041 | |
| 7042 | // aten::index_fill.int_Tensor(Tensor self, int dim, Tensor index, Tensor value) -> Tensor |
| 7043 | at::Tensor index_fill_int_Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & value) { |
| 7044 | |
| 7045 | static auto op = create_index_fill_int_Tensor_typed_handle(); |
| 7046 | return op.redispatch(dispatchKeySet, self, dim, index, value); |
| 7047 | } |
| 7048 | |
| 7049 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill__Dimname_Scalar, name, "aten::index_fill_") |
| 7050 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill__Dimname_Scalar, overload_name, "Dimname_Scalar") |
| 7051 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill__Dimname_Scalar, schema_str, "index_fill_.Dimname_Scalar(Tensor(a!) self, Dimname dim, Tensor index, Scalar value) -> Tensor(a!)") |
| 7052 | |
| 7053 | // aten::index_fill_.Dimname_Scalar(Tensor(a!) self, Dimname dim, Tensor index, Scalar value) -> Tensor(a!) |
| 7054 | static C10_NOINLINE c10::TypedOperatorHandle<index_fill__Dimname_Scalar::schema> create_index_fill__Dimname_Scalar_typed_handle() { |
| 7055 | return c10::Dispatcher::singleton() |
| 7056 | .findSchemaOrThrow(index_fill__Dimname_Scalar::name, index_fill__Dimname_Scalar::overload_name) |
| 7057 | .typed<index_fill__Dimname_Scalar::schema>(); |
| 7058 | } |
| 7059 | |
| 7060 | // aten::index_fill_.Dimname_Scalar(Tensor(a!) self, Dimname dim, Tensor index, Scalar value) -> Tensor(a!) |
| 7061 | at::Tensor & index_fill__Dimname_Scalar::call(at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Scalar & value) { |
| 7062 | |
| 7063 | static auto op = create_index_fill__Dimname_Scalar_typed_handle(); |
| 7064 | return op.call(self, dim, index, value); |
| 7065 | } |
| 7066 | |
| 7067 | // aten::index_fill_.Dimname_Scalar(Tensor(a!) self, Dimname dim, Tensor index, Scalar value) -> Tensor(a!) |
| 7068 | at::Tensor & index_fill__Dimname_Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Scalar & value) { |
| 7069 | |
| 7070 | static auto op = create_index_fill__Dimname_Scalar_typed_handle(); |
| 7071 | return op.redispatch(dispatchKeySet, self, dim, index, value); |
| 7072 | } |
| 7073 | |
| 7074 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill__Dimname_Tensor, name, "aten::index_fill_") |
| 7075 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill__Dimname_Tensor, overload_name, "Dimname_Tensor") |
| 7076 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill__Dimname_Tensor, schema_str, "index_fill_.Dimname_Tensor(Tensor(a!) self, Dimname dim, Tensor index, Tensor value) -> Tensor(a!)") |
| 7077 | |
| 7078 | // aten::index_fill_.Dimname_Tensor(Tensor(a!) self, Dimname dim, Tensor index, Tensor value) -> Tensor(a!) |
| 7079 | static C10_NOINLINE c10::TypedOperatorHandle<index_fill__Dimname_Tensor::schema> create_index_fill__Dimname_Tensor_typed_handle() { |
| 7080 | return c10::Dispatcher::singleton() |
| 7081 | .findSchemaOrThrow(index_fill__Dimname_Tensor::name, index_fill__Dimname_Tensor::overload_name) |
| 7082 | .typed<index_fill__Dimname_Tensor::schema>(); |
| 7083 | } |
| 7084 | |
| 7085 | // aten::index_fill_.Dimname_Tensor(Tensor(a!) self, Dimname dim, Tensor index, Tensor value) -> Tensor(a!) |
| 7086 | at::Tensor & index_fill__Dimname_Tensor::call(at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & value) { |
| 7087 | |
| 7088 | static auto op = create_index_fill__Dimname_Tensor_typed_handle(); |
| 7089 | return op.call(self, dim, index, value); |
| 7090 | } |
| 7091 | |
| 7092 | // aten::index_fill_.Dimname_Tensor(Tensor(a!) self, Dimname dim, Tensor index, Tensor value) -> Tensor(a!) |
| 7093 | at::Tensor & index_fill__Dimname_Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & value) { |
| 7094 | |
| 7095 | static auto op = create_index_fill__Dimname_Tensor_typed_handle(); |
| 7096 | return op.redispatch(dispatchKeySet, self, dim, index, value); |
| 7097 | } |
| 7098 | |
| 7099 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill_Dimname_Scalar, name, "aten::index_fill") |
| 7100 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill_Dimname_Scalar, overload_name, "Dimname_Scalar") |
| 7101 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill_Dimname_Scalar, schema_str, "index_fill.Dimname_Scalar(Tensor self, Dimname dim, Tensor index, Scalar value) -> Tensor") |
| 7102 | |
| 7103 | // aten::index_fill.Dimname_Scalar(Tensor self, Dimname dim, Tensor index, Scalar value) -> Tensor |
| 7104 | static C10_NOINLINE c10::TypedOperatorHandle<index_fill_Dimname_Scalar::schema> create_index_fill_Dimname_Scalar_typed_handle() { |
| 7105 | return c10::Dispatcher::singleton() |
| 7106 | .findSchemaOrThrow(index_fill_Dimname_Scalar::name, index_fill_Dimname_Scalar::overload_name) |
| 7107 | .typed<index_fill_Dimname_Scalar::schema>(); |
| 7108 | } |
| 7109 | |
| 7110 | // aten::index_fill.Dimname_Scalar(Tensor self, Dimname dim, Tensor index, Scalar value) -> Tensor |
| 7111 | at::Tensor index_fill_Dimname_Scalar::call(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Scalar & value) { |
| 7112 | |
| 7113 | static auto op = create_index_fill_Dimname_Scalar_typed_handle(); |
| 7114 | return op.call(self, dim, index, value); |
| 7115 | } |
| 7116 | |
| 7117 | // aten::index_fill.Dimname_Scalar(Tensor self, Dimname dim, Tensor index, Scalar value) -> Tensor |
| 7118 | at::Tensor index_fill_Dimname_Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Scalar & value) { |
| 7119 | |
| 7120 | static auto op = create_index_fill_Dimname_Scalar_typed_handle(); |
| 7121 | return op.redispatch(dispatchKeySet, self, dim, index, value); |
| 7122 | } |
| 7123 | |
| 7124 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill_Dimname_Tensor, name, "aten::index_fill") |
| 7125 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill_Dimname_Tensor, overload_name, "Dimname_Tensor") |
| 7126 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill_Dimname_Tensor, schema_str, "index_fill.Dimname_Tensor(Tensor self, Dimname dim, Tensor index, Tensor value) -> Tensor") |
| 7127 | |
| 7128 | // aten::index_fill.Dimname_Tensor(Tensor self, Dimname dim, Tensor index, Tensor value) -> Tensor |
| 7129 | static C10_NOINLINE c10::TypedOperatorHandle<index_fill_Dimname_Tensor::schema> create_index_fill_Dimname_Tensor_typed_handle() { |
| 7130 | return c10::Dispatcher::singleton() |
| 7131 | .findSchemaOrThrow(index_fill_Dimname_Tensor::name, index_fill_Dimname_Tensor::overload_name) |
| 7132 | .typed<index_fill_Dimname_Tensor::schema>(); |
| 7133 | } |
| 7134 | |
| 7135 | // aten::index_fill.Dimname_Tensor(Tensor self, Dimname dim, Tensor index, Tensor value) -> Tensor |
| 7136 | at::Tensor index_fill_Dimname_Tensor::call(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & value) { |
| 7137 | |
| 7138 | static auto op = create_index_fill_Dimname_Tensor_typed_handle(); |
| 7139 | return op.call(self, dim, index, value); |
| 7140 | } |
| 7141 | |
| 7142 | // aten::index_fill.Dimname_Tensor(Tensor self, Dimname dim, Tensor index, Tensor value) -> Tensor |
| 7143 | at::Tensor index_fill_Dimname_Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & value) { |
| 7144 | |
| 7145 | static auto op = create_index_fill_Dimname_Tensor_typed_handle(); |
| 7146 | return op.redispatch(dispatchKeySet, self, dim, index, value); |
| 7147 | } |
| 7148 | |
| 7149 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(scatter_add, name, "aten::scatter_add") |
| 7150 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(scatter_add, overload_name, "") |
| 7151 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(scatter_add, schema_str, "scatter_add(Tensor self, int dim, Tensor index, Tensor src) -> Tensor") |
| 7152 | |
| 7153 | // aten::scatter_add(Tensor self, int dim, Tensor index, Tensor src) -> Tensor |
| 7154 | static C10_NOINLINE c10::TypedOperatorHandle<scatter_add::schema> create_scatter_add_typed_handle() { |
| 7155 | return c10::Dispatcher::singleton() |
| 7156 | .findSchemaOrThrow(scatter_add::name, scatter_add::overload_name) |
| 7157 | .typed<scatter_add::schema>(); |
| 7158 | } |
| 7159 | |
| 7160 | // aten::scatter_add(Tensor self, int dim, Tensor index, Tensor src) -> Tensor |
| 7161 | at::Tensor scatter_add::call(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { |
| 7162 | |
| 7163 | static auto op = create_scatter_add_typed_handle(); |
| 7164 | return op.call(self, dim, index, src); |
| 7165 | } |
| 7166 | |
| 7167 | // aten::scatter_add(Tensor self, int dim, Tensor index, Tensor src) -> Tensor |
| 7168 | at::Tensor scatter_add::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { |
| 7169 | |
| 7170 | static auto op = create_scatter_add_typed_handle(); |
| 7171 | return op.redispatch(dispatchKeySet, self, dim, index, src); |
| 7172 | } |
| 7173 | |
| 7174 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(scatter_add_, name, "aten::scatter_add_") |
| 7175 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(scatter_add_, overload_name, "") |
| 7176 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(scatter_add_, schema_str, "scatter_add_(Tensor(a!) self, int dim, Tensor index, Tensor src) -> Tensor(a!)") |
| 7177 | |
| 7178 | // aten::scatter_add_(Tensor(a!) self, int dim, Tensor index, Tensor src) -> Tensor(a!) |
| 7179 | static C10_NOINLINE c10::TypedOperatorHandle<scatter_add_::schema> create_scatter_add__typed_handle() { |
| 7180 | return c10::Dispatcher::singleton() |
| 7181 | .findSchemaOrThrow(scatter_add_::name, scatter_add_::overload_name) |
| 7182 | .typed<scatter_add_::schema>(); |
| 7183 | } |
| 7184 | |
| 7185 | // aten::scatter_add_(Tensor(a!) self, int dim, Tensor index, Tensor src) -> Tensor(a!) |
| 7186 | at::Tensor & scatter_add_::call(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { |
| 7187 | |
| 7188 | static auto op = create_scatter_add__typed_handle(); |
| 7189 | return op.call(self, dim, index, src); |
| 7190 | } |
| 7191 | |
| 7192 | // aten::scatter_add_(Tensor(a!) self, int dim, Tensor index, Tensor src) -> Tensor(a!) |
| 7193 | at::Tensor & scatter_add_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { |
| 7194 | |
| 7195 | static auto op = create_scatter_add__typed_handle(); |
| 7196 | return op.redispatch(dispatchKeySet, self, dim, index, src); |
| 7197 | } |
| 7198 | |
| 7199 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(scatter_add_out, name, "aten::scatter_add") |
| 7200 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(scatter_add_out, overload_name, "out") |
| 7201 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(scatter_add_out, schema_str, "scatter_add.out(Tensor self, int dim, Tensor index, Tensor src, *, Tensor(a!) out) -> Tensor(a!)") |
| 7202 | |
| 7203 | // aten::scatter_add.out(Tensor self, int dim, Tensor index, Tensor src, *, Tensor(a!) out) -> Tensor(a!) |
| 7204 | static C10_NOINLINE c10::TypedOperatorHandle<scatter_add_out::schema> create_scatter_add_out_typed_handle() { |
| 7205 | return c10::Dispatcher::singleton() |
| 7206 | .findSchemaOrThrow(scatter_add_out::name, scatter_add_out::overload_name) |
| 7207 | .typed<scatter_add_out::schema>(); |
| 7208 | } |
| 7209 | |
| 7210 | // aten::scatter_add.out(Tensor self, int dim, Tensor index, Tensor src, *, Tensor(a!) out) -> Tensor(a!) |
| 7211 | at::Tensor & scatter_add_out::call(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, at::Tensor & out) { |
| 7212 | |
| 7213 | static auto op = create_scatter_add_out_typed_handle(); |
| 7214 | return op.call(self, dim, index, src, out); |
| 7215 | } |
| 7216 | |
| 7217 | // aten::scatter_add.out(Tensor self, int dim, Tensor index, Tensor src, *, Tensor(a!) out) -> Tensor(a!) |
| 7218 | at::Tensor & scatter_add_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, at::Tensor & out) { |
| 7219 | |
| 7220 | static auto op = create_scatter_add_out_typed_handle(); |
| 7221 | return op.redispatch(dispatchKeySet, self, dim, index, src, out); |
| 7222 | } |
| 7223 | |
| 7224 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(scatter_add_dimname, name, "aten::scatter_add") |
| 7225 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(scatter_add_dimname, overload_name, "dimname") |
| 7226 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(scatter_add_dimname, schema_str, "scatter_add.dimname(Tensor self, Dimname dim, Tensor index, Tensor src) -> Tensor") |
| 7227 | |
| 7228 | // aten::scatter_add.dimname(Tensor self, Dimname dim, Tensor index, Tensor src) -> Tensor |
| 7229 | static C10_NOINLINE c10::TypedOperatorHandle<scatter_add_dimname::schema> create_scatter_add_dimname_typed_handle() { |
| 7230 | return c10::Dispatcher::singleton() |
| 7231 | .findSchemaOrThrow(scatter_add_dimname::name, scatter_add_dimname::overload_name) |
| 7232 | .typed<scatter_add_dimname::schema>(); |
| 7233 | } |
| 7234 | |
| 7235 | // aten::scatter_add.dimname(Tensor self, Dimname dim, Tensor index, Tensor src) -> Tensor |
| 7236 | at::Tensor scatter_add_dimname::call(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & src) { |
| 7237 | |
| 7238 | static auto op = create_scatter_add_dimname_typed_handle(); |
| 7239 | return op.call(self, dim, index, src); |
| 7240 | } |
| 7241 | |
| 7242 | // aten::scatter_add.dimname(Tensor self, Dimname dim, Tensor index, Tensor src) -> Tensor |
| 7243 | at::Tensor scatter_add_dimname::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & src) { |
| 7244 | |
| 7245 | static auto op = create_scatter_add_dimname_typed_handle(); |
| 7246 | return op.redispatch(dispatchKeySet, self, dim, index, src); |
| 7247 | } |
| 7248 | |
| 7249 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(digamma_, name, "aten::digamma_") |
| 7250 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(digamma_, overload_name, "") |
| 7251 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(digamma_, schema_str, "digamma_(Tensor(a!) self) -> Tensor(a!)") |
| 7252 | |
| 7253 | // aten::digamma_(Tensor(a!) self) -> Tensor(a!) |
| 7254 | static C10_NOINLINE c10::TypedOperatorHandle<digamma_::schema> create_digamma__typed_handle() { |
| 7255 | return c10::Dispatcher::singleton() |
| 7256 | .findSchemaOrThrow(digamma_::name, digamma_::overload_name) |
| 7257 | .typed<digamma_::schema>(); |
| 7258 | } |
| 7259 | |
| 7260 | // aten::digamma_(Tensor(a!) self) -> Tensor(a!) |
| 7261 | at::Tensor & digamma_::call(at::Tensor & self) { |
| 7262 | |
| 7263 | static auto op = create_digamma__typed_handle(); |
| 7264 | return op.call(self); |
| 7265 | } |
| 7266 | |
| 7267 | // aten::digamma_(Tensor(a!) self) -> Tensor(a!) |
| 7268 | at::Tensor & digamma_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { |
| 7269 | |
| 7270 | static auto op = create_digamma__typed_handle(); |
| 7271 | return op.redispatch(dispatchKeySet, self); |
| 7272 | } |
| 7273 | |
| 7274 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(random__from, name, "aten::random_") |
| 7275 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(random__from, overload_name, "from") |
| 7276 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(random__from, schema_str, "random_.from(Tensor(a!) self, int from, int? to, *, Generator? generator=None) -> Tensor(a!)") |
| 7277 | |
| 7278 | // aten::random_.from(Tensor(a!) self, int from, int? to, *, Generator? generator=None) -> Tensor(a!) |
| 7279 | static C10_NOINLINE c10::TypedOperatorHandle<random__from::schema> create_random__from_typed_handle() { |
| 7280 | return c10::Dispatcher::singleton() |
| 7281 | .findSchemaOrThrow(random__from::name, random__from::overload_name) |
| 7282 | .typed<random__from::schema>(); |
| 7283 | } |
| 7284 | |
| 7285 | // aten::random_.from(Tensor(a!) self, int from, int? to, *, Generator? generator=None) -> Tensor(a!) |
| 7286 | at::Tensor & random__from::call(at::Tensor & self, int64_t from, c10::optional<int64_t> to, c10::optional<at::Generator> generator) { |
| 7287 | |
| 7288 | static auto op = create_random__from_typed_handle(); |
| 7289 | return op.call(self, from, to, generator); |
| 7290 | } |
| 7291 | |
| 7292 | // aten::random_.from(Tensor(a!) self, int from, int? to, *, Generator? generator=None) -> Tensor(a!) |
| 7293 | at::Tensor & random__from::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t from, c10::optional<int64_t> to, c10::optional<at::Generator> generator) { |
| 7294 | |
| 7295 | static auto op = create_random__from_typed_handle(); |
| 7296 | return op.redispatch(dispatchKeySet, self, from, to, generator); |
| 7297 | } |
| 7298 | |
| 7299 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(random__to, name, "aten::random_") |
| 7300 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(random__to, overload_name, "to") |
| 7301 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(random__to, schema_str, "random_.to(Tensor(a!) self, int to, *, Generator? generator=None) -> Tensor(a!)") |
| 7302 | |
| 7303 | // aten::random_.to(Tensor(a!) self, int to, *, Generator? generator=None) -> Tensor(a!) |
| 7304 | static C10_NOINLINE c10::TypedOperatorHandle<random__to::schema> create_random__to_typed_handle() { |
| 7305 | return c10::Dispatcher::singleton() |
| 7306 | .findSchemaOrThrow(random__to::name, random__to::overload_name) |
| 7307 | .typed<random__to::schema>(); |
| 7308 | } |
| 7309 | |
| 7310 | // aten::random_.to(Tensor(a!) self, int to, *, Generator? generator=None) -> Tensor(a!) |
| 7311 | at::Tensor & random__to::call(at::Tensor & self, int64_t to, c10::optional<at::Generator> generator) { |
| 7312 | |
| 7313 | static auto op = create_random__to_typed_handle(); |
| 7314 | return op.call(self, to, generator); |
| 7315 | } |
| 7316 | |
| 7317 | // aten::random_.to(Tensor(a!) self, int to, *, Generator? generator=None) -> Tensor(a!) |
| 7318 | at::Tensor & random__to::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t to, c10::optional<at::Generator> generator) { |
| 7319 | |
| 7320 | static auto op = create_random__to_typed_handle(); |
| 7321 | return op.redispatch(dispatchKeySet, self, to, generator); |
| 7322 | } |
| 7323 | |
| 7324 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(random_, name, "aten::random_") |
| 7325 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(random_, overload_name, "") |
| 7326 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(random_, schema_str, "random_(Tensor(a!) self, *, Generator? generator=None) -> Tensor(a!)") |
| 7327 | |
| 7328 | // aten::random_(Tensor(a!) self, *, Generator? generator=None) -> Tensor(a!) |
| 7329 | static C10_NOINLINE c10::TypedOperatorHandle<random_::schema> create_random__typed_handle() { |
| 7330 | return c10::Dispatcher::singleton() |
| 7331 | .findSchemaOrThrow(random_::name, random_::overload_name) |
| 7332 | .typed<random_::schema>(); |
| 7333 | } |
| 7334 | |
| 7335 | // aten::random_(Tensor(a!) self, *, Generator? generator=None) -> Tensor(a!) |
| 7336 | at::Tensor & random_::call(at::Tensor & self, c10::optional<at::Generator> generator) { |
| 7337 | |
| 7338 | static auto op = create_random__typed_handle(); |
| 7339 | return op.call(self, generator); |
| 7340 | } |
| 7341 | |
| 7342 | // aten::random_(Tensor(a!) self, *, Generator? generator=None) -> Tensor(a!) |
| 7343 | at::Tensor & random_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, c10::optional<at::Generator> generator) { |
| 7344 | |
| 7345 | static auto op = create_random__typed_handle(); |
| 7346 | return op.redispatch(dispatchKeySet, self, generator); |
| 7347 | } |
| 7348 | |
| 7349 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cauchy_, name, "aten::cauchy_") |
| 7350 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cauchy_, overload_name, "") |
| 7351 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cauchy_, schema_str, "cauchy_(Tensor(a!) self, float median=0, float sigma=1, *, Generator? generator=None) -> Tensor(a!)") |
| 7352 | |
| 7353 | // aten::cauchy_(Tensor(a!) self, float median=0, float sigma=1, *, Generator? generator=None) -> Tensor(a!) |
| 7354 | static C10_NOINLINE c10::TypedOperatorHandle<cauchy_::schema> create_cauchy__typed_handle() { |
| 7355 | return c10::Dispatcher::singleton() |
| 7356 | .findSchemaOrThrow(cauchy_::name, cauchy_::overload_name) |
| 7357 | .typed<cauchy_::schema>(); |
| 7358 | } |
| 7359 | |
| 7360 | // aten::cauchy_(Tensor(a!) self, float median=0, float sigma=1, *, Generator? generator=None) -> Tensor(a!) |
| 7361 | at::Tensor & cauchy_::call(at::Tensor & self, double median, double sigma, c10::optional<at::Generator> generator) { |
| 7362 | |
| 7363 | static auto op = create_cauchy__typed_handle(); |
| 7364 | return op.call(self, median, sigma, generator); |
| 7365 | } |
| 7366 | |
| 7367 | // aten::cauchy_(Tensor(a!) self, float median=0, float sigma=1, *, Generator? generator=None) -> Tensor(a!) |
| 7368 | at::Tensor & cauchy_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, double median, double sigma, c10::optional<at::Generator> generator) { |
| 7369 | |
| 7370 | static auto op = create_cauchy__typed_handle(); |
| 7371 | return op.redispatch(dispatchKeySet, self, median, sigma, generator); |
| 7372 | } |
| 7373 | |
| 7374 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(log_normal_, name, "aten::log_normal_") |
| 7375 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(log_normal_, overload_name, "") |
| 7376 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(log_normal_, schema_str, "log_normal_(Tensor(a!) self, float mean=1, float std=2, *, Generator? generator=None) -> Tensor(a!)") |
| 7377 | |
| 7378 | // aten::log_normal_(Tensor(a!) self, float mean=1, float std=2, *, Generator? generator=None) -> Tensor(a!) |
| 7379 | static C10_NOINLINE c10::TypedOperatorHandle<log_normal_::schema> create_log_normal__typed_handle() { |
| 7380 | return c10::Dispatcher::singleton() |
| 7381 | .findSchemaOrThrow(log_normal_::name, log_normal_::overload_name) |
| 7382 | .typed<log_normal_::schema>(); |
| 7383 | } |
| 7384 | |
| 7385 | // aten::log_normal_(Tensor(a!) self, float mean=1, float std=2, *, Generator? generator=None) -> Tensor(a!) |
| 7386 | at::Tensor & log_normal_::call(at::Tensor & self, double mean, double std, c10::optional<at::Generator> generator) { |
| 7387 | |
| 7388 | static auto op = create_log_normal__typed_handle(); |
| 7389 | return op.call(self, mean, std, generator); |
| 7390 | } |
| 7391 | |
| 7392 | // aten::log_normal_(Tensor(a!) self, float mean=1, float std=2, *, Generator? generator=None) -> Tensor(a!) |
| 7393 | at::Tensor & log_normal_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, double mean, double std, c10::optional<at::Generator> generator) { |
| 7394 | |
| 7395 | static auto op = create_log_normal__typed_handle(); |
| 7396 | return op.redispatch(dispatchKeySet, self, mean, std, generator); |
| 7397 | } |
| 7398 | |
| 7399 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cross_out, name, "aten::cross") |
| 7400 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cross_out, overload_name, "out") |
| 7401 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cross_out, schema_str, "cross.out(Tensor self, Tensor other, int? dim=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 7402 | |
| 7403 | // aten::cross.out(Tensor self, Tensor other, int? dim=None, *, Tensor(a!) out) -> Tensor(a!) |
| 7404 | static C10_NOINLINE c10::TypedOperatorHandle<cross_out::schema> create_cross_out_typed_handle() { |
| 7405 | return c10::Dispatcher::singleton() |
| 7406 | .findSchemaOrThrow(cross_out::name, cross_out::overload_name) |
| 7407 | .typed<cross_out::schema>(); |
| 7408 | } |
| 7409 | |
| 7410 | // aten::cross.out(Tensor self, Tensor other, int? dim=None, *, Tensor(a!) out) -> Tensor(a!) |
| 7411 | at::Tensor & cross_out::call(const at::Tensor & self, const at::Tensor & other, c10::optional<int64_t> dim, at::Tensor & out) { |
| 7412 | |
| 7413 | static auto op = create_cross_out_typed_handle(); |
| 7414 | return op.call(self, other, dim, out); |
| 7415 | } |
| 7416 | |
| 7417 | // aten::cross.out(Tensor self, Tensor other, int? dim=None, *, Tensor(a!) out) -> Tensor(a!) |
| 7418 | at::Tensor & cross_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, c10::optional<int64_t> dim, at::Tensor & out) { |
| 7419 | |
| 7420 | static auto op = create_cross_out_typed_handle(); |
| 7421 | return op.redispatch(dispatchKeySet, self, other, dim, out); |
| 7422 | } |
| 7423 | |
| 7424 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cross, name, "aten::cross") |
| 7425 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cross, overload_name, "") |
| 7426 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cross, schema_str, "cross(Tensor self, Tensor other, int? dim=None) -> Tensor") |
| 7427 | |
| 7428 | // aten::cross(Tensor self, Tensor other, int? dim=None) -> Tensor |
| 7429 | static C10_NOINLINE c10::TypedOperatorHandle<cross::schema> create_cross_typed_handle() { |
| 7430 | return c10::Dispatcher::singleton() |
| 7431 | .findSchemaOrThrow(cross::name, cross::overload_name) |
| 7432 | .typed<cross::schema>(); |
| 7433 | } |
| 7434 | |
| 7435 | // aten::cross(Tensor self, Tensor other, int? dim=None) -> Tensor |
| 7436 | at::Tensor cross::call(const at::Tensor & self, const at::Tensor & other, c10::optional<int64_t> dim) { |
| 7437 | |
| 7438 | static auto op = create_cross_typed_handle(); |
| 7439 | return op.call(self, other, dim); |
| 7440 | } |
| 7441 | |
| 7442 | // aten::cross(Tensor self, Tensor other, int? dim=None) -> Tensor |
| 7443 | at::Tensor cross::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, c10::optional<int64_t> dim) { |
| 7444 | |
| 7445 | static auto op = create_cross_typed_handle(); |
| 7446 | return op.redispatch(dispatchKeySet, self, other, dim); |
| 7447 | } |
| 7448 | |
| 7449 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ne_Scalar_out, name, "aten::ne") |
| 7450 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ne_Scalar_out, overload_name, "Scalar_out") |
| 7451 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ne_Scalar_out, schema_str, "ne.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)") |
| 7452 | |
| 7453 | // aten::ne.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) |
| 7454 | static C10_NOINLINE c10::TypedOperatorHandle<ne_Scalar_out::schema> create_ne_Scalar_out_typed_handle() { |
| 7455 | return c10::Dispatcher::singleton() |
| 7456 | .findSchemaOrThrow(ne_Scalar_out::name, ne_Scalar_out::overload_name) |
| 7457 | .typed<ne_Scalar_out::schema>(); |
| 7458 | } |
| 7459 | |
| 7460 | // aten::ne.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) |
| 7461 | at::Tensor & ne_Scalar_out::call(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 7462 | |
| 7463 | static auto op = create_ne_Scalar_out_typed_handle(); |
| 7464 | return op.call(self, other, out); |
| 7465 | } |
| 7466 | |
| 7467 | // aten::ne.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) |
| 7468 | at::Tensor & ne_Scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 7469 | |
| 7470 | static auto op = create_ne_Scalar_out_typed_handle(); |
| 7471 | return op.redispatch(dispatchKeySet, self, other, out); |
| 7472 | } |
| 7473 | |
| 7474 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ne_Scalar, name, "aten::ne") |
| 7475 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ne_Scalar, overload_name, "Scalar") |
| 7476 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ne_Scalar, schema_str, "ne.Scalar(Tensor self, Scalar other) -> Tensor") |
| 7477 | |
| 7478 | // aten::ne.Scalar(Tensor self, Scalar other) -> Tensor |
| 7479 | static C10_NOINLINE c10::TypedOperatorHandle<ne_Scalar::schema> create_ne_Scalar_typed_handle() { |
| 7480 | return c10::Dispatcher::singleton() |
| 7481 | .findSchemaOrThrow(ne_Scalar::name, ne_Scalar::overload_name) |
| 7482 | .typed<ne_Scalar::schema>(); |
| 7483 | } |
| 7484 | |
| 7485 | // aten::ne.Scalar(Tensor self, Scalar other) -> Tensor |
| 7486 | at::Tensor ne_Scalar::call(const at::Tensor & self, const at::Scalar & other) { |
| 7487 | |
| 7488 | static auto op = create_ne_Scalar_typed_handle(); |
| 7489 | return op.call(self, other); |
| 7490 | } |
| 7491 | |
| 7492 | // aten::ne.Scalar(Tensor self, Scalar other) -> Tensor |
| 7493 | at::Tensor ne_Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { |
| 7494 | |
| 7495 | static auto op = create_ne_Scalar_typed_handle(); |
| 7496 | return op.redispatch(dispatchKeySet, self, other); |
| 7497 | } |
| 7498 | |
| 7499 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ne_Tensor_out, name, "aten::ne") |
| 7500 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ne_Tensor_out, overload_name, "Tensor_out") |
| 7501 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ne_Tensor_out, schema_str, "ne.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)") |
| 7502 | |
| 7503 | // aten::ne.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 7504 | static C10_NOINLINE c10::TypedOperatorHandle<ne_Tensor_out::schema> create_ne_Tensor_out_typed_handle() { |
| 7505 | return c10::Dispatcher::singleton() |
| 7506 | .findSchemaOrThrow(ne_Tensor_out::name, ne_Tensor_out::overload_name) |
| 7507 | .typed<ne_Tensor_out::schema>(); |
| 7508 | } |
| 7509 | |
| 7510 | // aten::ne.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 7511 | at::Tensor & ne_Tensor_out::call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 7512 | |
| 7513 | static auto op = create_ne_Tensor_out_typed_handle(); |
| 7514 | return op.call(self, other, out); |
| 7515 | } |
| 7516 | |
| 7517 | // aten::ne.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 7518 | at::Tensor & ne_Tensor_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 7519 | |
| 7520 | static auto op = create_ne_Tensor_out_typed_handle(); |
| 7521 | return op.redispatch(dispatchKeySet, self, other, out); |
| 7522 | } |
| 7523 | |
| 7524 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ne_Tensor, name, "aten::ne") |
| 7525 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ne_Tensor, overload_name, "Tensor") |
| 7526 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ne_Tensor, schema_str, "ne.Tensor(Tensor self, Tensor other) -> Tensor") |
| 7527 | |
| 7528 | // aten::ne.Tensor(Tensor self, Tensor other) -> Tensor |
| 7529 | static C10_NOINLINE c10::TypedOperatorHandle<ne_Tensor::schema> create_ne_Tensor_typed_handle() { |
| 7530 | return c10::Dispatcher::singleton() |
| 7531 | .findSchemaOrThrow(ne_Tensor::name, ne_Tensor::overload_name) |
| 7532 | .typed<ne_Tensor::schema>(); |
| 7533 | } |
| 7534 | |
| 7535 | // aten::ne.Tensor(Tensor self, Tensor other) -> Tensor |
| 7536 | at::Tensor ne_Tensor::call(const at::Tensor & self, const at::Tensor & other) { |
| 7537 | |
| 7538 | static auto op = create_ne_Tensor_typed_handle(); |
| 7539 | return op.call(self, other); |
| 7540 | } |
| 7541 | |
| 7542 | // aten::ne.Tensor(Tensor self, Tensor other) -> Tensor |
| 7543 | at::Tensor ne_Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { |
| 7544 | |
| 7545 | static auto op = create_ne_Tensor_typed_handle(); |
| 7546 | return op.redispatch(dispatchKeySet, self, other); |
| 7547 | } |
| 7548 | |
| 7549 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ne__Scalar, name, "aten::ne_") |
| 7550 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ne__Scalar, overload_name, "Scalar") |
| 7551 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ne__Scalar, schema_str, "ne_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)") |
| 7552 | |
| 7553 | // aten::ne_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) |
| 7554 | static C10_NOINLINE c10::TypedOperatorHandle<ne__Scalar::schema> create_ne__Scalar_typed_handle() { |
| 7555 | return c10::Dispatcher::singleton() |
| 7556 | .findSchemaOrThrow(ne__Scalar::name, ne__Scalar::overload_name) |
| 7557 | .typed<ne__Scalar::schema>(); |
| 7558 | } |
| 7559 | |
| 7560 | // aten::ne_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) |
| 7561 | at::Tensor & ne__Scalar::call(at::Tensor & self, const at::Scalar & other) { |
| 7562 | |
| 7563 | static auto op = create_ne__Scalar_typed_handle(); |
| 7564 | return op.call(self, other); |
| 7565 | } |
| 7566 | |
| 7567 | // aten::ne_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) |
| 7568 | at::Tensor & ne__Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { |
| 7569 | |
| 7570 | static auto op = create_ne__Scalar_typed_handle(); |
| 7571 | return op.redispatch(dispatchKeySet, self, other); |
| 7572 | } |
| 7573 | |
| 7574 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ne__Tensor, name, "aten::ne_") |
| 7575 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ne__Tensor, overload_name, "Tensor") |
| 7576 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ne__Tensor, schema_str, "ne_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)") |
| 7577 | |
| 7578 | // aten::ne_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) |
| 7579 | static C10_NOINLINE c10::TypedOperatorHandle<ne__Tensor::schema> create_ne__Tensor_typed_handle() { |
| 7580 | return c10::Dispatcher::singleton() |
| 7581 | .findSchemaOrThrow(ne__Tensor::name, ne__Tensor::overload_name) |
| 7582 | .typed<ne__Tensor::schema>(); |
| 7583 | } |
| 7584 | |
| 7585 | // aten::ne_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) |
| 7586 | at::Tensor & ne__Tensor::call(at::Tensor & self, const at::Tensor & other) { |
| 7587 | |
| 7588 | static auto op = create_ne__Tensor_typed_handle(); |
| 7589 | return op.call(self, other); |
| 7590 | } |
| 7591 | |
| 7592 | // aten::ne_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) |
| 7593 | at::Tensor & ne__Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 7594 | |
| 7595 | static auto op = create_ne__Tensor_typed_handle(); |
| 7596 | return op.redispatch(dispatchKeySet, self, other); |
| 7597 | } |
| 7598 | |
| 7599 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ge_Scalar_out, name, "aten::ge") |
| 7600 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ge_Scalar_out, overload_name, "Scalar_out") |
| 7601 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ge_Scalar_out, schema_str, "ge.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)") |
| 7602 | |
| 7603 | // aten::ge.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) |
| 7604 | static C10_NOINLINE c10::TypedOperatorHandle<ge_Scalar_out::schema> create_ge_Scalar_out_typed_handle() { |
| 7605 | return c10::Dispatcher::singleton() |
| 7606 | .findSchemaOrThrow(ge_Scalar_out::name, ge_Scalar_out::overload_name) |
| 7607 | .typed<ge_Scalar_out::schema>(); |
| 7608 | } |
| 7609 | |
| 7610 | // aten::ge.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) |
| 7611 | at::Tensor & ge_Scalar_out::call(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 7612 | |
| 7613 | static auto op = create_ge_Scalar_out_typed_handle(); |
| 7614 | return op.call(self, other, out); |
| 7615 | } |
| 7616 | |
| 7617 | // aten::ge.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) |
| 7618 | at::Tensor & ge_Scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 7619 | |
| 7620 | static auto op = create_ge_Scalar_out_typed_handle(); |
| 7621 | return op.redispatch(dispatchKeySet, self, other, out); |
| 7622 | } |
| 7623 | |
| 7624 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ge_Scalar, name, "aten::ge") |
| 7625 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ge_Scalar, overload_name, "Scalar") |
| 7626 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ge_Scalar, schema_str, "ge.Scalar(Tensor self, Scalar other) -> Tensor") |
| 7627 | |
| 7628 | // aten::ge.Scalar(Tensor self, Scalar other) -> Tensor |
| 7629 | static C10_NOINLINE c10::TypedOperatorHandle<ge_Scalar::schema> create_ge_Scalar_typed_handle() { |
| 7630 | return c10::Dispatcher::singleton() |
| 7631 | .findSchemaOrThrow(ge_Scalar::name, ge_Scalar::overload_name) |
| 7632 | .typed<ge_Scalar::schema>(); |
| 7633 | } |
| 7634 | |
| 7635 | // aten::ge.Scalar(Tensor self, Scalar other) -> Tensor |
| 7636 | at::Tensor ge_Scalar::call(const at::Tensor & self, const at::Scalar & other) { |
| 7637 | |
| 7638 | static auto op = create_ge_Scalar_typed_handle(); |
| 7639 | return op.call(self, other); |
| 7640 | } |
| 7641 | |
| 7642 | // aten::ge.Scalar(Tensor self, Scalar other) -> Tensor |
| 7643 | at::Tensor ge_Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { |
| 7644 | |
| 7645 | static auto op = create_ge_Scalar_typed_handle(); |
| 7646 | return op.redispatch(dispatchKeySet, self, other); |
| 7647 | } |
| 7648 | |
| 7649 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ge_Tensor_out, name, "aten::ge") |
| 7650 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ge_Tensor_out, overload_name, "Tensor_out") |
| 7651 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ge_Tensor_out, schema_str, "ge.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)") |
| 7652 | |
| 7653 | // aten::ge.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 7654 | static C10_NOINLINE c10::TypedOperatorHandle<ge_Tensor_out::schema> create_ge_Tensor_out_typed_handle() { |
| 7655 | return c10::Dispatcher::singleton() |
| 7656 | .findSchemaOrThrow(ge_Tensor_out::name, ge_Tensor_out::overload_name) |
| 7657 | .typed<ge_Tensor_out::schema>(); |
| 7658 | } |
| 7659 | |
| 7660 | // aten::ge.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 7661 | at::Tensor & ge_Tensor_out::call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 7662 | |
| 7663 | static auto op = create_ge_Tensor_out_typed_handle(); |
| 7664 | return op.call(self, other, out); |
| 7665 | } |
| 7666 | |
| 7667 | // aten::ge.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 7668 | at::Tensor & ge_Tensor_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 7669 | |
| 7670 | static auto op = create_ge_Tensor_out_typed_handle(); |
| 7671 | return op.redispatch(dispatchKeySet, self, other, out); |
| 7672 | } |
| 7673 | |
| 7674 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ge_Tensor, name, "aten::ge") |
| 7675 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ge_Tensor, overload_name, "Tensor") |
| 7676 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ge_Tensor, schema_str, "ge.Tensor(Tensor self, Tensor other) -> Tensor") |
| 7677 | |
| 7678 | // aten::ge.Tensor(Tensor self, Tensor other) -> Tensor |
| 7679 | static C10_NOINLINE c10::TypedOperatorHandle<ge_Tensor::schema> create_ge_Tensor_typed_handle() { |
| 7680 | return c10::Dispatcher::singleton() |
| 7681 | .findSchemaOrThrow(ge_Tensor::name, ge_Tensor::overload_name) |
| 7682 | .typed<ge_Tensor::schema>(); |
| 7683 | } |
| 7684 | |
| 7685 | // aten::ge.Tensor(Tensor self, Tensor other) -> Tensor |
| 7686 | at::Tensor ge_Tensor::call(const at::Tensor & self, const at::Tensor & other) { |
| 7687 | |
| 7688 | static auto op = create_ge_Tensor_typed_handle(); |
| 7689 | return op.call(self, other); |
| 7690 | } |
| 7691 | |
| 7692 | // aten::ge.Tensor(Tensor self, Tensor other) -> Tensor |
| 7693 | at::Tensor ge_Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { |
| 7694 | |
| 7695 | static auto op = create_ge_Tensor_typed_handle(); |
| 7696 | return op.redispatch(dispatchKeySet, self, other); |
| 7697 | } |
| 7698 | |
| 7699 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ge__Scalar, name, "aten::ge_") |
| 7700 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ge__Scalar, overload_name, "Scalar") |
| 7701 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ge__Scalar, schema_str, "ge_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)") |
| 7702 | |
| 7703 | // aten::ge_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) |
| 7704 | static C10_NOINLINE c10::TypedOperatorHandle<ge__Scalar::schema> create_ge__Scalar_typed_handle() { |
| 7705 | return c10::Dispatcher::singleton() |
| 7706 | .findSchemaOrThrow(ge__Scalar::name, ge__Scalar::overload_name) |
| 7707 | .typed<ge__Scalar::schema>(); |
| 7708 | } |
| 7709 | |
| 7710 | // aten::ge_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) |
| 7711 | at::Tensor & ge__Scalar::call(at::Tensor & self, const at::Scalar & other) { |
| 7712 | |
| 7713 | static auto op = create_ge__Scalar_typed_handle(); |
| 7714 | return op.call(self, other); |
| 7715 | } |
| 7716 | |
| 7717 | // aten::ge_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) |
| 7718 | at::Tensor & ge__Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { |
| 7719 | |
| 7720 | static auto op = create_ge__Scalar_typed_handle(); |
| 7721 | return op.redispatch(dispatchKeySet, self, other); |
| 7722 | } |
| 7723 | |
| 7724 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ge__Tensor, name, "aten::ge_") |
| 7725 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ge__Tensor, overload_name, "Tensor") |
| 7726 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ge__Tensor, schema_str, "ge_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)") |
| 7727 | |
| 7728 | // aten::ge_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) |
| 7729 | static C10_NOINLINE c10::TypedOperatorHandle<ge__Tensor::schema> create_ge__Tensor_typed_handle() { |
| 7730 | return c10::Dispatcher::singleton() |
| 7731 | .findSchemaOrThrow(ge__Tensor::name, ge__Tensor::overload_name) |
| 7732 | .typed<ge__Tensor::schema>(); |
| 7733 | } |
| 7734 | |
| 7735 | // aten::ge_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) |
| 7736 | at::Tensor & ge__Tensor::call(at::Tensor & self, const at::Tensor & other) { |
| 7737 | |
| 7738 | static auto op = create_ge__Tensor_typed_handle(); |
| 7739 | return op.call(self, other); |
| 7740 | } |
| 7741 | |
| 7742 | // aten::ge_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) |
| 7743 | at::Tensor & ge__Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 7744 | |
| 7745 | static auto op = create_ge__Tensor_typed_handle(); |
| 7746 | return op.redispatch(dispatchKeySet, self, other); |
| 7747 | } |
| 7748 | |
| 7749 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_gather_sparse_backward, name, "aten::_gather_sparse_backward") |
| 7750 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_gather_sparse_backward, overload_name, "") |
| 7751 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_gather_sparse_backward, schema_str, "_gather_sparse_backward(Tensor self, int dim, Tensor index, Tensor grad) -> Tensor") |
| 7752 | |
| 7753 | // aten::_gather_sparse_backward(Tensor self, int dim, Tensor index, Tensor grad) -> Tensor |
| 7754 | static C10_NOINLINE c10::TypedOperatorHandle<_gather_sparse_backward::schema> create__gather_sparse_backward_typed_handle() { |
| 7755 | return c10::Dispatcher::singleton() |
| 7756 | .findSchemaOrThrow(_gather_sparse_backward::name, _gather_sparse_backward::overload_name) |
| 7757 | .typed<_gather_sparse_backward::schema>(); |
| 7758 | } |
| 7759 | |
| 7760 | // aten::_gather_sparse_backward(Tensor self, int dim, Tensor index, Tensor grad) -> Tensor |
| 7761 | at::Tensor _gather_sparse_backward::call(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & grad) { |
| 7762 | |
| 7763 | static auto op = create__gather_sparse_backward_typed_handle(); |
| 7764 | return op.call(self, dim, index, grad); |
| 7765 | } |
| 7766 | |
| 7767 | // aten::_gather_sparse_backward(Tensor self, int dim, Tensor index, Tensor grad) -> Tensor |
| 7768 | at::Tensor _gather_sparse_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & grad) { |
| 7769 | |
| 7770 | static auto op = create__gather_sparse_backward_typed_handle(); |
| 7771 | return op.redispatch(dispatchKeySet, self, dim, index, grad); |
| 7772 | } |
| 7773 | |
| 7774 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_vander, name, "aten::linalg_vander") |
| 7775 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_vander, overload_name, "") |
| 7776 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_vander, schema_str, "linalg_vander(Tensor x, *, int? N=None) -> Tensor") |
| 7777 | |
| 7778 | // aten::linalg_vander(Tensor x, *, int? N=None) -> Tensor |
| 7779 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_vander::schema> create_linalg_vander_typed_handle() { |
| 7780 | return c10::Dispatcher::singleton() |
| 7781 | .findSchemaOrThrow(linalg_vander::name, linalg_vander::overload_name) |
| 7782 | .typed<linalg_vander::schema>(); |
| 7783 | } |
| 7784 | |
| 7785 | // aten::linalg_vander(Tensor x, *, int? N=None) -> Tensor |
| 7786 | at::Tensor linalg_vander::call(const at::Tensor & x, c10::optional<int64_t> N) { |
| 7787 | |
| 7788 | static auto op = create_linalg_vander_typed_handle(); |
| 7789 | return op.call(x, N); |
| 7790 | } |
| 7791 | |
| 7792 | // aten::linalg_vander(Tensor x, *, int? N=None) -> Tensor |
| 7793 | at::Tensor linalg_vander::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, c10::optional<int64_t> N) { |
| 7794 | |
| 7795 | static auto op = create_linalg_vander_typed_handle(); |
| 7796 | return op.redispatch(dispatchKeySet, x, N); |
| 7797 | } |
| 7798 | |
| 7799 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(swapaxes, name, "aten::swapaxes") |
| 7800 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(swapaxes, overload_name, "") |
| 7801 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(swapaxes, schema_str, "swapaxes(Tensor(a) self, int axis0, int axis1) -> Tensor(a)") |
| 7802 | |
| 7803 | // aten::swapaxes(Tensor(a) self, int axis0, int axis1) -> Tensor(a) |
| 7804 | static C10_NOINLINE c10::TypedOperatorHandle<swapaxes::schema> create_swapaxes_typed_handle() { |
| 7805 | return c10::Dispatcher::singleton() |
| 7806 | .findSchemaOrThrow(swapaxes::name, swapaxes::overload_name) |
| 7807 | .typed<swapaxes::schema>(); |
| 7808 | } |
| 7809 | |
| 7810 | // aten::swapaxes(Tensor(a) self, int axis0, int axis1) -> Tensor(a) |
| 7811 | at::Tensor swapaxes::call(const at::Tensor & self, int64_t axis0, int64_t axis1) { |
| 7812 | |
| 7813 | static auto op = create_swapaxes_typed_handle(); |
| 7814 | return op.call(self, axis0, axis1); |
| 7815 | } |
| 7816 | |
| 7817 | // aten::swapaxes(Tensor(a) self, int axis0, int axis1) -> Tensor(a) |
| 7818 | at::Tensor swapaxes::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t axis0, int64_t axis1) { |
| 7819 | |
| 7820 | static auto op = create_swapaxes_typed_handle(); |
| 7821 | return op.redispatch(dispatchKeySet, self, axis0, axis1); |
| 7822 | } |
| 7823 | |
| 7824 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(swapaxes_, name, "aten::swapaxes_") |
| 7825 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(swapaxes_, overload_name, "") |
| 7826 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(swapaxes_, schema_str, "swapaxes_(Tensor(a!) self, int axis0, int axis1) -> Tensor(a!)") |
| 7827 | |
| 7828 | // aten::swapaxes_(Tensor(a!) self, int axis0, int axis1) -> Tensor(a!) |
| 7829 | static C10_NOINLINE c10::TypedOperatorHandle<swapaxes_::schema> create_swapaxes__typed_handle() { |
| 7830 | return c10::Dispatcher::singleton() |
| 7831 | .findSchemaOrThrow(swapaxes_::name, swapaxes_::overload_name) |
| 7832 | .typed<swapaxes_::schema>(); |
| 7833 | } |
| 7834 | |
| 7835 | // aten::swapaxes_(Tensor(a!) self, int axis0, int axis1) -> Tensor(a!) |
| 7836 | at::Tensor & swapaxes_::call(at::Tensor & self, int64_t axis0, int64_t axis1) { |
| 7837 | |
| 7838 | static auto op = create_swapaxes__typed_handle(); |
| 7839 | return op.call(self, axis0, axis1); |
| 7840 | } |
| 7841 | |
| 7842 | // aten::swapaxes_(Tensor(a!) self, int axis0, int axis1) -> Tensor(a!) |
| 7843 | at::Tensor & swapaxes_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t axis0, int64_t axis1) { |
| 7844 | |
| 7845 | static auto op = create_swapaxes__typed_handle(); |
| 7846 | return op.redispatch(dispatchKeySet, self, axis0, axis1); |
| 7847 | } |
| 7848 | |
| 7849 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cholesky_solve_out, name, "aten::cholesky_solve") |
| 7850 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cholesky_solve_out, overload_name, "out") |
| 7851 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cholesky_solve_out, schema_str, "cholesky_solve.out(Tensor self, Tensor input2, bool upper=False, *, Tensor(a!) out) -> Tensor(a!)") |
| 7852 | |
| 7853 | // aten::cholesky_solve.out(Tensor self, Tensor input2, bool upper=False, *, Tensor(a!) out) -> Tensor(a!) |
| 7854 | static C10_NOINLINE c10::TypedOperatorHandle<cholesky_solve_out::schema> create_cholesky_solve_out_typed_handle() { |
| 7855 | return c10::Dispatcher::singleton() |
| 7856 | .findSchemaOrThrow(cholesky_solve_out::name, cholesky_solve_out::overload_name) |
| 7857 | .typed<cholesky_solve_out::schema>(); |
| 7858 | } |
| 7859 | |
| 7860 | // aten::cholesky_solve.out(Tensor self, Tensor input2, bool upper=False, *, Tensor(a!) out) -> Tensor(a!) |
| 7861 | at::Tensor & cholesky_solve_out::call(const at::Tensor & self, const at::Tensor & input2, bool upper, at::Tensor & out) { |
| 7862 | |
| 7863 | static auto op = create_cholesky_solve_out_typed_handle(); |
| 7864 | return op.call(self, input2, upper, out); |
| 7865 | } |
| 7866 | |
| 7867 | // aten::cholesky_solve.out(Tensor self, Tensor input2, bool upper=False, *, Tensor(a!) out) -> Tensor(a!) |
| 7868 | at::Tensor & cholesky_solve_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & input2, bool upper, at::Tensor & out) { |
| 7869 | |
| 7870 | static auto op = create_cholesky_solve_out_typed_handle(); |
| 7871 | return op.redispatch(dispatchKeySet, self, input2, upper, out); |
| 7872 | } |
| 7873 | |
| 7874 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cholesky_solve, name, "aten::cholesky_solve") |
| 7875 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cholesky_solve, overload_name, "") |
| 7876 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cholesky_solve, schema_str, "cholesky_solve(Tensor self, Tensor input2, bool upper=False) -> Tensor") |
| 7877 | |
| 7878 | // aten::cholesky_solve(Tensor self, Tensor input2, bool upper=False) -> Tensor |
| 7879 | static C10_NOINLINE c10::TypedOperatorHandle<cholesky_solve::schema> create_cholesky_solve_typed_handle() { |
| 7880 | return c10::Dispatcher::singleton() |
| 7881 | .findSchemaOrThrow(cholesky_solve::name, cholesky_solve::overload_name) |
| 7882 | .typed<cholesky_solve::schema>(); |
| 7883 | } |
| 7884 | |
| 7885 | // aten::cholesky_solve(Tensor self, Tensor input2, bool upper=False) -> Tensor |
| 7886 | at::Tensor cholesky_solve::call(const at::Tensor & self, const at::Tensor & input2, bool upper) { |
| 7887 | |
| 7888 | static auto op = create_cholesky_solve_typed_handle(); |
| 7889 | return op.call(self, input2, upper); |
| 7890 | } |
| 7891 | |
| 7892 | // aten::cholesky_solve(Tensor self, Tensor input2, bool upper=False) -> Tensor |
| 7893 | at::Tensor cholesky_solve::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & input2, bool upper) { |
| 7894 | |
| 7895 | static auto op = create_cholesky_solve_typed_handle(); |
| 7896 | return op.redispatch(dispatchKeySet, self, input2, upper); |
| 7897 | } |
| 7898 | |
| 7899 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(qr_Q, name, "aten::qr") |
| 7900 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(qr_Q, overload_name, "Q") |
| 7901 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(qr_Q, schema_str, "qr.Q(Tensor self, bool some=True, *, Tensor(a!) Q, Tensor(b!) R) -> (Tensor(a!) Q, Tensor(b!) R)") |
| 7902 | |
| 7903 | // aten::qr.Q(Tensor self, bool some=True, *, Tensor(a!) Q, Tensor(b!) R) -> (Tensor(a!) Q, Tensor(b!) R) |
| 7904 | static C10_NOINLINE c10::TypedOperatorHandle<qr_Q::schema> create_qr_Q_typed_handle() { |
| 7905 | return c10::Dispatcher::singleton() |
| 7906 | .findSchemaOrThrow(qr_Q::name, qr_Q::overload_name) |
| 7907 | .typed<qr_Q::schema>(); |
| 7908 | } |
| 7909 | |
| 7910 | // aten::qr.Q(Tensor self, bool some=True, *, Tensor(a!) Q, Tensor(b!) R) -> (Tensor(a!) Q, Tensor(b!) R) |
| 7911 | ::std::tuple<at::Tensor &,at::Tensor &> qr_Q::call(const at::Tensor & self, bool some, at::Tensor & Q, at::Tensor & R) { |
| 7912 | |
| 7913 | static auto op = create_qr_Q_typed_handle(); |
| 7914 | return op.call(self, some, Q, R); |
| 7915 | } |
| 7916 | |
| 7917 | // aten::qr.Q(Tensor self, bool some=True, *, Tensor(a!) Q, Tensor(b!) R) -> (Tensor(a!) Q, Tensor(b!) R) |
| 7918 | ::std::tuple<at::Tensor &,at::Tensor &> qr_Q::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool some, at::Tensor & Q, at::Tensor & R) { |
| 7919 | |
| 7920 | static auto op = create_qr_Q_typed_handle(); |
| 7921 | return op.redispatch(dispatchKeySet, self, some, Q, R); |
| 7922 | } |
| 7923 | |
| 7924 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(qr, name, "aten::qr") |
| 7925 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(qr, overload_name, "") |
| 7926 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(qr, schema_str, "qr(Tensor self, bool some=True) -> (Tensor Q, Tensor R)") |
| 7927 | |
| 7928 | // aten::qr(Tensor self, bool some=True) -> (Tensor Q, Tensor R) |
| 7929 | static C10_NOINLINE c10::TypedOperatorHandle<qr::schema> create_qr_typed_handle() { |
| 7930 | return c10::Dispatcher::singleton() |
| 7931 | .findSchemaOrThrow(qr::name, qr::overload_name) |
| 7932 | .typed<qr::schema>(); |
| 7933 | } |
| 7934 | |
| 7935 | // aten::qr(Tensor self, bool some=True) -> (Tensor Q, Tensor R) |
| 7936 | ::std::tuple<at::Tensor,at::Tensor> qr::call(const at::Tensor & self, bool some) { |
| 7937 | |
| 7938 | static auto op = create_qr_typed_handle(); |
| 7939 | return op.call(self, some); |
| 7940 | } |
| 7941 | |
| 7942 | // aten::qr(Tensor self, bool some=True) -> (Tensor Q, Tensor R) |
| 7943 | ::std::tuple<at::Tensor,at::Tensor> qr::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool some) { |
| 7944 | |
| 7945 | static auto op = create_qr_typed_handle(); |
| 7946 | return op.redispatch(dispatchKeySet, self, some); |
| 7947 | } |
| 7948 | |
| 7949 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(digamma_out, name, "aten::digamma") |
| 7950 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(digamma_out, overload_name, "out") |
| 7951 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(digamma_out, schema_str, "digamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 7952 | |
| 7953 | // aten::digamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 7954 | static C10_NOINLINE c10::TypedOperatorHandle<digamma_out::schema> create_digamma_out_typed_handle() { |
| 7955 | return c10::Dispatcher::singleton() |
| 7956 | .findSchemaOrThrow(digamma_out::name, digamma_out::overload_name) |
| 7957 | .typed<digamma_out::schema>(); |
| 7958 | } |
| 7959 | |
| 7960 | // aten::digamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 7961 | at::Tensor & digamma_out::call(const at::Tensor & self, at::Tensor & out) { |
| 7962 | |
| 7963 | static auto op = create_digamma_out_typed_handle(); |
| 7964 | return op.call(self, out); |
| 7965 | } |
| 7966 | |
| 7967 | // aten::digamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 7968 | at::Tensor & digamma_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 7969 | |
| 7970 | static auto op = create_digamma_out_typed_handle(); |
| 7971 | return op.redispatch(dispatchKeySet, self, out); |
| 7972 | } |
| 7973 | |
| 7974 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(digamma, name, "aten::digamma") |
| 7975 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(digamma, overload_name, "") |
| 7976 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(digamma, schema_str, "digamma(Tensor self) -> Tensor") |
| 7977 | |
| 7978 | // aten::digamma(Tensor self) -> Tensor |
| 7979 | static C10_NOINLINE c10::TypedOperatorHandle<digamma::schema> create_digamma_typed_handle() { |
| 7980 | return c10::Dispatcher::singleton() |
| 7981 | .findSchemaOrThrow(digamma::name, digamma::overload_name) |
| 7982 | .typed<digamma::schema>(); |
| 7983 | } |
| 7984 | |
| 7985 | // aten::digamma(Tensor self) -> Tensor |
| 7986 | at::Tensor digamma::call(const at::Tensor & self) { |
| 7987 | |
| 7988 | static auto op = create_digamma_typed_handle(); |
| 7989 | return op.call(self); |
| 7990 | } |
| 7991 | |
| 7992 | // aten::digamma(Tensor self) -> Tensor |
| 7993 | at::Tensor digamma::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 7994 | |
| 7995 | static auto op = create_digamma_typed_handle(); |
| 7996 | return op.redispatch(dispatchKeySet, self); |
| 7997 | } |
| 7998 | |
| 7999 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(polygamma_out, name, "aten::polygamma") |
| 8000 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(polygamma_out, overload_name, "out") |
| 8001 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(polygamma_out, schema_str, "polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 8002 | |
| 8003 | // aten::polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 8004 | static C10_NOINLINE c10::TypedOperatorHandle<polygamma_out::schema> create_polygamma_out_typed_handle() { |
| 8005 | return c10::Dispatcher::singleton() |
| 8006 | .findSchemaOrThrow(polygamma_out::name, polygamma_out::overload_name) |
| 8007 | .typed<polygamma_out::schema>(); |
| 8008 | } |
| 8009 | |
| 8010 | // aten::polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 8011 | at::Tensor & polygamma_out::call(int64_t n, const at::Tensor & self, at::Tensor & out) { |
| 8012 | |
| 8013 | static auto op = create_polygamma_out_typed_handle(); |
| 8014 | return op.call(n, self, out); |
| 8015 | } |
| 8016 | |
| 8017 | // aten::polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 8018 | at::Tensor & polygamma_out::redispatch(c10::DispatchKeySet dispatchKeySet, int64_t n, const at::Tensor & self, at::Tensor & out) { |
| 8019 | |
| 8020 | static auto op = create_polygamma_out_typed_handle(); |
| 8021 | return op.redispatch(dispatchKeySet, n, self, out); |
| 8022 | } |
| 8023 | |
| 8024 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(polygamma, name, "aten::polygamma") |
| 8025 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(polygamma, overload_name, "") |
| 8026 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(polygamma, schema_str, "polygamma(int n, Tensor self) -> Tensor") |
| 8027 | |
| 8028 | // aten::polygamma(int n, Tensor self) -> Tensor |
| 8029 | static C10_NOINLINE c10::TypedOperatorHandle<polygamma::schema> create_polygamma_typed_handle() { |
| 8030 | return c10::Dispatcher::singleton() |
| 8031 | .findSchemaOrThrow(polygamma::name, polygamma::overload_name) |
| 8032 | .typed<polygamma::schema>(); |
| 8033 | } |
| 8034 | |
| 8035 | // aten::polygamma(int n, Tensor self) -> Tensor |
| 8036 | at::Tensor polygamma::call(int64_t n, const at::Tensor & self) { |
| 8037 | |
| 8038 | static auto op = create_polygamma_typed_handle(); |
| 8039 | return op.call(n, self); |
| 8040 | } |
| 8041 | |
| 8042 | // aten::polygamma(int n, Tensor self) -> Tensor |
| 8043 | at::Tensor polygamma::redispatch(c10::DispatchKeySet dispatchKeySet, int64_t n, const at::Tensor & self) { |
| 8044 | |
| 8045 | static auto op = create_polygamma_typed_handle(); |
| 8046 | return op.redispatch(dispatchKeySet, n, self); |
| 8047 | } |
| 8048 | |
| 8049 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(polygamma_, name, "aten::polygamma_") |
| 8050 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(polygamma_, overload_name, "") |
| 8051 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(polygamma_, schema_str, "polygamma_(Tensor(a!) self, int n) -> Tensor(a!)") |
| 8052 | |
| 8053 | // aten::polygamma_(Tensor(a!) self, int n) -> Tensor(a!) |
| 8054 | static C10_NOINLINE c10::TypedOperatorHandle<polygamma_::schema> create_polygamma__typed_handle() { |
| 8055 | return c10::Dispatcher::singleton() |
| 8056 | .findSchemaOrThrow(polygamma_::name, polygamma_::overload_name) |
| 8057 | .typed<polygamma_::schema>(); |
| 8058 | } |
| 8059 | |
| 8060 | // aten::polygamma_(Tensor(a!) self, int n) -> Tensor(a!) |
| 8061 | at::Tensor & polygamma_::call(at::Tensor & self, int64_t n) { |
| 8062 | |
| 8063 | static auto op = create_polygamma__typed_handle(); |
| 8064 | return op.call(self, n); |
| 8065 | } |
| 8066 | |
| 8067 | // aten::polygamma_(Tensor(a!) self, int n) -> Tensor(a!) |
| 8068 | at::Tensor & polygamma_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t n) { |
| 8069 | |
| 8070 | static auto op = create_polygamma__typed_handle(); |
| 8071 | return op.redispatch(dispatchKeySet, self, n); |
| 8072 | } |
| 8073 | |
| 8074 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(histc_out, name, "aten::histc") |
| 8075 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(histc_out, overload_name, "out") |
| 8076 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(histc_out, schema_str, "histc.out(Tensor self, int bins=100, Scalar min=0, Scalar max=0, *, Tensor(a!) out) -> Tensor(a!)") |
| 8077 | |
| 8078 | // aten::histc.out(Tensor self, int bins=100, Scalar min=0, Scalar max=0, *, Tensor(a!) out) -> Tensor(a!) |
| 8079 | static C10_NOINLINE c10::TypedOperatorHandle<histc_out::schema> create_histc_out_typed_handle() { |
| 8080 | return c10::Dispatcher::singleton() |
| 8081 | .findSchemaOrThrow(histc_out::name, histc_out::overload_name) |
| 8082 | .typed<histc_out::schema>(); |
| 8083 | } |
| 8084 | |
| 8085 | // aten::histc.out(Tensor self, int bins=100, Scalar min=0, Scalar max=0, *, Tensor(a!) out) -> Tensor(a!) |
| 8086 | at::Tensor & histc_out::call(const at::Tensor & self, int64_t bins, const at::Scalar & min, const at::Scalar & max, at::Tensor & out) { |
| 8087 | |
| 8088 | static auto op = create_histc_out_typed_handle(); |
| 8089 | return op.call(self, bins, min, max, out); |
| 8090 | } |
| 8091 | |
| 8092 | // aten::histc.out(Tensor self, int bins=100, Scalar min=0, Scalar max=0, *, Tensor(a!) out) -> Tensor(a!) |
| 8093 | at::Tensor & histc_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t bins, const at::Scalar & min, const at::Scalar & max, at::Tensor & out) { |
| 8094 | |
| 8095 | static auto op = create_histc_out_typed_handle(); |
| 8096 | return op.redispatch(dispatchKeySet, self, bins, min, max, out); |
| 8097 | } |
| 8098 | |
| 8099 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(histc, name, "aten::histc") |
| 8100 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(histc, overload_name, "") |
| 8101 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(histc, schema_str, "histc(Tensor self, int bins=100, Scalar min=0, Scalar max=0) -> Tensor") |
| 8102 | |
| 8103 | // aten::histc(Tensor self, int bins=100, Scalar min=0, Scalar max=0) -> Tensor |
| 8104 | static C10_NOINLINE c10::TypedOperatorHandle<histc::schema> create_histc_typed_handle() { |
| 8105 | return c10::Dispatcher::singleton() |
| 8106 | .findSchemaOrThrow(histc::name, histc::overload_name) |
| 8107 | .typed<histc::schema>(); |
| 8108 | } |
| 8109 | |
| 8110 | // aten::histc(Tensor self, int bins=100, Scalar min=0, Scalar max=0) -> Tensor |
| 8111 | at::Tensor histc::call(const at::Tensor & self, int64_t bins, const at::Scalar & min, const at::Scalar & max) { |
| 8112 | |
| 8113 | static auto op = create_histc_typed_handle(); |
| 8114 | return op.call(self, bins, min, max); |
| 8115 | } |
| 8116 | |
| 8117 | // aten::histc(Tensor self, int bins=100, Scalar min=0, Scalar max=0) -> Tensor |
| 8118 | at::Tensor histc::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t bins, const at::Scalar & min, const at::Scalar & max) { |
| 8119 | |
| 8120 | static auto op = create_histc_typed_handle(); |
| 8121 | return op.redispatch(dispatchKeySet, self, bins, min, max); |
| 8122 | } |
| 8123 | |
| 8124 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_histogramdd_bin_edges, name, "aten::_histogramdd_bin_edges") |
| 8125 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_histogramdd_bin_edges, overload_name, "") |
| 8126 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_histogramdd_bin_edges, schema_str, "_histogramdd_bin_edges(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False) -> Tensor[]") |
| 8127 | |
| 8128 | // aten::_histogramdd_bin_edges(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False) -> Tensor[] |
| 8129 | static C10_NOINLINE c10::TypedOperatorHandle<_histogramdd_bin_edges::schema> create__histogramdd_bin_edges_typed_handle() { |
| 8130 | return c10::Dispatcher::singleton() |
| 8131 | .findSchemaOrThrow(_histogramdd_bin_edges::name, _histogramdd_bin_edges::overload_name) |
| 8132 | .typed<_histogramdd_bin_edges::schema>(); |
| 8133 | } |
| 8134 | |
| 8135 | // aten::_histogramdd_bin_edges(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False) -> Tensor[] |
| 8136 | ::std::vector<at::Tensor> _histogramdd_bin_edges::call(const at::Tensor & self, at::IntArrayRef bins, c10::optional<at::ArrayRef<double>> range, const c10::optional<at::Tensor> & weight, bool density) { |
| 8137 | |
| 8138 | static auto op = create__histogramdd_bin_edges_typed_handle(); |
| 8139 | return op.call(self, bins, range, weight, density); |
| 8140 | } |
| 8141 | |
| 8142 | // aten::_histogramdd_bin_edges(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False) -> Tensor[] |
| 8143 | ::std::vector<at::Tensor> _histogramdd_bin_edges::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef bins, c10::optional<at::ArrayRef<double>> range, const c10::optional<at::Tensor> & weight, bool density) { |
| 8144 | |
| 8145 | static auto op = create__histogramdd_bin_edges_typed_handle(); |
| 8146 | return op.redispatch(dispatchKeySet, self, bins, range, weight, density); |
| 8147 | } |
| 8148 | |
| 8149 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_histogramdd_from_bin_tensors, name, "aten::_histogramdd_from_bin_tensors") |
| 8150 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_histogramdd_from_bin_tensors, overload_name, "") |
| 8151 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_histogramdd_from_bin_tensors, schema_str, "_histogramdd_from_bin_tensors(Tensor self, Tensor[] bins, *, Tensor? weight=None, bool density=False) -> Tensor") |
| 8152 | |
| 8153 | // aten::_histogramdd_from_bin_tensors(Tensor self, Tensor[] bins, *, Tensor? weight=None, bool density=False) -> Tensor |
| 8154 | static C10_NOINLINE c10::TypedOperatorHandle<_histogramdd_from_bin_tensors::schema> create__histogramdd_from_bin_tensors_typed_handle() { |
| 8155 | return c10::Dispatcher::singleton() |
| 8156 | .findSchemaOrThrow(_histogramdd_from_bin_tensors::name, _histogramdd_from_bin_tensors::overload_name) |
| 8157 | .typed<_histogramdd_from_bin_tensors::schema>(); |
| 8158 | } |
| 8159 | |
| 8160 | // aten::_histogramdd_from_bin_tensors(Tensor self, Tensor[] bins, *, Tensor? weight=None, bool density=False) -> Tensor |
| 8161 | at::Tensor _histogramdd_from_bin_tensors::call(const at::Tensor & self, at::TensorList bins, const c10::optional<at::Tensor> & weight, bool density) { |
| 8162 | |
| 8163 | static auto op = create__histogramdd_from_bin_tensors_typed_handle(); |
| 8164 | return op.call(self, bins, weight, density); |
| 8165 | } |
| 8166 | |
| 8167 | // aten::_histogramdd_from_bin_tensors(Tensor self, Tensor[] bins, *, Tensor? weight=None, bool density=False) -> Tensor |
| 8168 | at::Tensor _histogramdd_from_bin_tensors::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::TensorList bins, const c10::optional<at::Tensor> & weight, bool density) { |
| 8169 | |
| 8170 | static auto op = create__histogramdd_from_bin_tensors_typed_handle(); |
| 8171 | return op.redispatch(dispatchKeySet, self, bins, weight, density); |
| 8172 | } |
| 8173 | |
| 8174 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nextafter_out, name, "aten::nextafter") |
| 8175 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nextafter_out, overload_name, "out") |
| 8176 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nextafter_out, schema_str, "nextafter.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)") |
| 8177 | |
| 8178 | // aten::nextafter.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 8179 | static C10_NOINLINE c10::TypedOperatorHandle<nextafter_out::schema> create_nextafter_out_typed_handle() { |
| 8180 | return c10::Dispatcher::singleton() |
| 8181 | .findSchemaOrThrow(nextafter_out::name, nextafter_out::overload_name) |
| 8182 | .typed<nextafter_out::schema>(); |
| 8183 | } |
| 8184 | |
| 8185 | // aten::nextafter.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 8186 | at::Tensor & nextafter_out::call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 8187 | |
| 8188 | static auto op = create_nextafter_out_typed_handle(); |
| 8189 | return op.call(self, other, out); |
| 8190 | } |
| 8191 | |
| 8192 | // aten::nextafter.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 8193 | at::Tensor & nextafter_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 8194 | |
| 8195 | static auto op = create_nextafter_out_typed_handle(); |
| 8196 | return op.redispatch(dispatchKeySet, self, other, out); |
| 8197 | } |
| 8198 | |
| 8199 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nextafter, name, "aten::nextafter") |
| 8200 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nextafter, overload_name, "") |
| 8201 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nextafter, schema_str, "nextafter(Tensor self, Tensor other) -> Tensor") |
| 8202 | |
| 8203 | // aten::nextafter(Tensor self, Tensor other) -> Tensor |
| 8204 | static C10_NOINLINE c10::TypedOperatorHandle<nextafter::schema> create_nextafter_typed_handle() { |
| 8205 | return c10::Dispatcher::singleton() |
| 8206 | .findSchemaOrThrow(nextafter::name, nextafter::overload_name) |
| 8207 | .typed<nextafter::schema>(); |
| 8208 | } |
| 8209 | |
| 8210 | // aten::nextafter(Tensor self, Tensor other) -> Tensor |
| 8211 | at::Tensor nextafter::call(const at::Tensor & self, const at::Tensor & other) { |
| 8212 | |
| 8213 | static auto op = create_nextafter_typed_handle(); |
| 8214 | return op.call(self, other); |
| 8215 | } |
| 8216 | |
| 8217 | // aten::nextafter(Tensor self, Tensor other) -> Tensor |
| 8218 | at::Tensor nextafter::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { |
| 8219 | |
| 8220 | static auto op = create_nextafter_typed_handle(); |
| 8221 | return op.redispatch(dispatchKeySet, self, other); |
| 8222 | } |
| 8223 | |
| 8224 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nextafter_, name, "aten::nextafter_") |
| 8225 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nextafter_, overload_name, "") |
| 8226 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nextafter_, schema_str, "nextafter_(Tensor(a!) self, Tensor other) -> Tensor(a!)") |
| 8227 | |
| 8228 | // aten::nextafter_(Tensor(a!) self, Tensor other) -> Tensor(a!) |
| 8229 | static C10_NOINLINE c10::TypedOperatorHandle<nextafter_::schema> create_nextafter__typed_handle() { |
| 8230 | return c10::Dispatcher::singleton() |
| 8231 | .findSchemaOrThrow(nextafter_::name, nextafter_::overload_name) |
| 8232 | .typed<nextafter_::schema>(); |
| 8233 | } |
| 8234 | |
| 8235 | // aten::nextafter_(Tensor(a!) self, Tensor other) -> Tensor(a!) |
| 8236 | at::Tensor & nextafter_::call(at::Tensor & self, const at::Tensor & other) { |
| 8237 | |
| 8238 | static auto op = create_nextafter__typed_handle(); |
| 8239 | return op.call(self, other); |
| 8240 | } |
| 8241 | |
| 8242 | // aten::nextafter_(Tensor(a!) self, Tensor other) -> Tensor(a!) |
| 8243 | at::Tensor & nextafter_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { |
| 8244 | |
| 8245 | static auto op = create_nextafter__typed_handle(); |
| 8246 | return op.redispatch(dispatchKeySet, self, other); |
| 8247 | } |
| 8248 | |
| 8249 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(maximum, name, "aten::maximum") |
| 8250 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(maximum, overload_name, "") |
| 8251 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(maximum, schema_str, "maximum(Tensor self, Tensor other) -> Tensor") |
| 8252 | |
| 8253 | // aten::maximum(Tensor self, Tensor other) -> Tensor |
| 8254 | static C10_NOINLINE c10::TypedOperatorHandle<maximum::schema> create_maximum_typed_handle() { |
| 8255 | return c10::Dispatcher::singleton() |
| 8256 | .findSchemaOrThrow(maximum::name, maximum::overload_name) |
| 8257 | .typed<maximum::schema>(); |
| 8258 | } |
| 8259 | |
| 8260 | // aten::maximum(Tensor self, Tensor other) -> Tensor |
| 8261 | at::Tensor maximum::call(const at::Tensor & self, const at::Tensor & other) { |
| 8262 | |
| 8263 | static auto op = create_maximum_typed_handle(); |
| 8264 | return op.call(self, other); |
| 8265 | } |
| 8266 | |
| 8267 | // aten::maximum(Tensor self, Tensor other) -> Tensor |
| 8268 | at::Tensor maximum::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { |
| 8269 | |
| 8270 | static auto op = create_maximum_typed_handle(); |
| 8271 | return op.redispatch(dispatchKeySet, self, other); |
| 8272 | } |
| 8273 | |
| 8274 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(maximum_out, name, "aten::maximum") |
| 8275 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(maximum_out, overload_name, "out") |
| 8276 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(maximum_out, schema_str, "maximum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)") |
| 8277 | |
| 8278 | // aten::maximum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 8279 | static C10_NOINLINE c10::TypedOperatorHandle<maximum_out::schema> create_maximum_out_typed_handle() { |
| 8280 | return c10::Dispatcher::singleton() |
| 8281 | .findSchemaOrThrow(maximum_out::name, maximum_out::overload_name) |
| 8282 | .typed<maximum_out::schema>(); |
| 8283 | } |
| 8284 | |
| 8285 | // aten::maximum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 8286 | at::Tensor & maximum_out::call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 8287 | |
| 8288 | static auto op = create_maximum_out_typed_handle(); |
| 8289 | return op.call(self, other, out); |
| 8290 | } |
| 8291 | |
| 8292 | // aten::maximum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 8293 | at::Tensor & maximum_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 8294 | |
| 8295 | static auto op = create_maximum_out_typed_handle(); |
| 8296 | return op.redispatch(dispatchKeySet, self, other, out); |
| 8297 | } |
| 8298 | |
| 8299 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(minimum, name, "aten::minimum") |
| 8300 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(minimum, overload_name, "") |
| 8301 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(minimum, schema_str, "minimum(Tensor self, Tensor other) -> Tensor") |
| 8302 | |
| 8303 | // aten::minimum(Tensor self, Tensor other) -> Tensor |
| 8304 | static C10_NOINLINE c10::TypedOperatorHandle<minimum::schema> create_minimum_typed_handle() { |
| 8305 | return c10::Dispatcher::singleton() |
| 8306 | .findSchemaOrThrow(minimum::name, minimum::overload_name) |
| 8307 | .typed<minimum::schema>(); |
| 8308 | } |
| 8309 | |
| 8310 | // aten::minimum(Tensor self, Tensor other) -> Tensor |
| 8311 | at::Tensor minimum::call(const at::Tensor & self, const at::Tensor & other) { |
| 8312 | |
| 8313 | static auto op = create_minimum_typed_handle(); |
| 8314 | return op.call(self, other); |
| 8315 | } |
| 8316 | |
| 8317 | // aten::minimum(Tensor self, Tensor other) -> Tensor |
| 8318 | at::Tensor minimum::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { |
| 8319 | |
| 8320 | static auto op = create_minimum_typed_handle(); |
| 8321 | return op.redispatch(dispatchKeySet, self, other); |
| 8322 | } |
| 8323 | |
| 8324 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(minimum_out, name, "aten::minimum") |
| 8325 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(minimum_out, overload_name, "out") |
| 8326 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(minimum_out, schema_str, "minimum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)") |
| 8327 | |
| 8328 | // aten::minimum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 8329 | static C10_NOINLINE c10::TypedOperatorHandle<minimum_out::schema> create_minimum_out_typed_handle() { |
| 8330 | return c10::Dispatcher::singleton() |
| 8331 | .findSchemaOrThrow(minimum_out::name, minimum_out::overload_name) |
| 8332 | .typed<minimum_out::schema>(); |
| 8333 | } |
| 8334 | |
| 8335 | // aten::minimum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 8336 | at::Tensor & minimum_out::call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 8337 | |
| 8338 | static auto op = create_minimum_out_typed_handle(); |
| 8339 | return op.call(self, other, out); |
| 8340 | } |
| 8341 | |
| 8342 | // aten::minimum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 8343 | at::Tensor & minimum_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 8344 | |
| 8345 | static auto op = create_minimum_out_typed_handle(); |
| 8346 | return op.redispatch(dispatchKeySet, self, other, out); |
| 8347 | } |
| 8348 | |
| 8349 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantile, name, "aten::quantile") |
| 8350 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantile, overload_name, "") |
| 8351 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantile, schema_str, "quantile(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor") |
| 8352 | |
| 8353 | // aten::quantile(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor |
| 8354 | static C10_NOINLINE c10::TypedOperatorHandle<quantile::schema> create_quantile_typed_handle() { |
| 8355 | return c10::Dispatcher::singleton() |
| 8356 | .findSchemaOrThrow(quantile::name, quantile::overload_name) |
| 8357 | .typed<quantile::schema>(); |
| 8358 | } |
| 8359 | |
| 8360 | // aten::quantile(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor |
| 8361 | at::Tensor quantile::call(const at::Tensor & self, const at::Tensor & q, c10::optional<int64_t> dim, bool keepdim, c10::string_view interpolation) { |
| 8362 | |
| 8363 | static auto op = create_quantile_typed_handle(); |
| 8364 | return op.call(self, q, dim, keepdim, interpolation); |
| 8365 | } |
| 8366 | |
| 8367 | // aten::quantile(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor |
| 8368 | at::Tensor quantile::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & q, c10::optional<int64_t> dim, bool keepdim, c10::string_view interpolation) { |
| 8369 | |
| 8370 | static auto op = create_quantile_typed_handle(); |
| 8371 | return op.redispatch(dispatchKeySet, self, q, dim, keepdim, interpolation); |
| 8372 | } |
| 8373 | |
| 8374 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantile_out, name, "aten::quantile") |
| 8375 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantile_out, overload_name, "out") |
| 8376 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantile_out, schema_str, "quantile.out(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!)") |
| 8377 | |
| 8378 | // aten::quantile.out(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!) |
| 8379 | static C10_NOINLINE c10::TypedOperatorHandle<quantile_out::schema> create_quantile_out_typed_handle() { |
| 8380 | return c10::Dispatcher::singleton() |
| 8381 | .findSchemaOrThrow(quantile_out::name, quantile_out::overload_name) |
| 8382 | .typed<quantile_out::schema>(); |
| 8383 | } |
| 8384 | |
| 8385 | // aten::quantile.out(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!) |
| 8386 | at::Tensor & quantile_out::call(const at::Tensor & self, const at::Tensor & q, c10::optional<int64_t> dim, bool keepdim, c10::string_view interpolation, at::Tensor & out) { |
| 8387 | |
| 8388 | static auto op = create_quantile_out_typed_handle(); |
| 8389 | return op.call(self, q, dim, keepdim, interpolation, out); |
| 8390 | } |
| 8391 | |
| 8392 | // aten::quantile.out(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!) |
| 8393 | at::Tensor & quantile_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & q, c10::optional<int64_t> dim, bool keepdim, c10::string_view interpolation, at::Tensor & out) { |
| 8394 | |
| 8395 | static auto op = create_quantile_out_typed_handle(); |
| 8396 | return op.redispatch(dispatchKeySet, self, q, dim, keepdim, interpolation, out); |
| 8397 | } |
| 8398 | |
| 8399 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantile_scalar, name, "aten::quantile") |
| 8400 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantile_scalar, overload_name, "scalar") |
| 8401 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantile_scalar, schema_str, "quantile.scalar(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor") |
| 8402 | |
| 8403 | // aten::quantile.scalar(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor |
| 8404 | static C10_NOINLINE c10::TypedOperatorHandle<quantile_scalar::schema> create_quantile_scalar_typed_handle() { |
| 8405 | return c10::Dispatcher::singleton() |
| 8406 | .findSchemaOrThrow(quantile_scalar::name, quantile_scalar::overload_name) |
| 8407 | .typed<quantile_scalar::schema>(); |
| 8408 | } |
| 8409 | |
| 8410 | // aten::quantile.scalar(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor |
| 8411 | at::Tensor quantile_scalar::call(const at::Tensor & self, double q, c10::optional<int64_t> dim, bool keepdim, c10::string_view interpolation) { |
| 8412 | |
| 8413 | static auto op = create_quantile_scalar_typed_handle(); |
| 8414 | return op.call(self, q, dim, keepdim, interpolation); |
| 8415 | } |
| 8416 | |
| 8417 | // aten::quantile.scalar(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor |
| 8418 | at::Tensor quantile_scalar::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double q, c10::optional<int64_t> dim, bool keepdim, c10::string_view interpolation) { |
| 8419 | |
| 8420 | static auto op = create_quantile_scalar_typed_handle(); |
| 8421 | return op.redispatch(dispatchKeySet, self, q, dim, keepdim, interpolation); |
| 8422 | } |
| 8423 | |
| 8424 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantile_scalar_out, name, "aten::quantile") |
| 8425 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantile_scalar_out, overload_name, "scalar_out") |
| 8426 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantile_scalar_out, schema_str, "quantile.scalar_out(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!)") |
| 8427 | |
| 8428 | // aten::quantile.scalar_out(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!) |
| 8429 | static C10_NOINLINE c10::TypedOperatorHandle<quantile_scalar_out::schema> create_quantile_scalar_out_typed_handle() { |
| 8430 | return c10::Dispatcher::singleton() |
| 8431 | .findSchemaOrThrow(quantile_scalar_out::name, quantile_scalar_out::overload_name) |
| 8432 | .typed<quantile_scalar_out::schema>(); |
| 8433 | } |
| 8434 | |
| 8435 | // aten::quantile.scalar_out(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!) |
| 8436 | at::Tensor & quantile_scalar_out::call(const at::Tensor & self, double q, c10::optional<int64_t> dim, bool keepdim, c10::string_view interpolation, at::Tensor & out) { |
| 8437 | |
| 8438 | static auto op = create_quantile_scalar_out_typed_handle(); |
| 8439 | return op.call(self, q, dim, keepdim, interpolation, out); |
| 8440 | } |
| 8441 | |
| 8442 | // aten::quantile.scalar_out(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!) |
| 8443 | at::Tensor & quantile_scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double q, c10::optional<int64_t> dim, bool keepdim, c10::string_view interpolation, at::Tensor & out) { |
| 8444 | |
| 8445 | static auto op = create_quantile_scalar_out_typed_handle(); |
| 8446 | return op.redispatch(dispatchKeySet, self, q, dim, keepdim, interpolation, out); |
| 8447 | } |
| 8448 | |
| 8449 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(msort_out, name, "aten::msort") |
| 8450 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(msort_out, overload_name, "out") |
| 8451 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(msort_out, schema_str, "msort.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 8452 | |
| 8453 | // aten::msort.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 8454 | static C10_NOINLINE c10::TypedOperatorHandle<msort_out::schema> create_msort_out_typed_handle() { |
| 8455 | return c10::Dispatcher::singleton() |
| 8456 | .findSchemaOrThrow(msort_out::name, msort_out::overload_name) |
| 8457 | .typed<msort_out::schema>(); |
| 8458 | } |
| 8459 | |
| 8460 | // aten::msort.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 8461 | at::Tensor & msort_out::call(const at::Tensor & self, at::Tensor & out) { |
| 8462 | |
| 8463 | static auto op = create_msort_out_typed_handle(); |
| 8464 | return op.call(self, out); |
| 8465 | } |
| 8466 | |
| 8467 | // aten::msort.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 8468 | at::Tensor & msort_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 8469 | |
| 8470 | static auto op = create_msort_out_typed_handle(); |
| 8471 | return op.redispatch(dispatchKeySet, self, out); |
| 8472 | } |
| 8473 | |
| 8474 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(msort, name, "aten::msort") |
| 8475 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(msort, overload_name, "") |
| 8476 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(msort, schema_str, "msort(Tensor self) -> Tensor") |
| 8477 | |
| 8478 | // aten::msort(Tensor self) -> Tensor |
| 8479 | static C10_NOINLINE c10::TypedOperatorHandle<msort::schema> create_msort_typed_handle() { |
| 8480 | return c10::Dispatcher::singleton() |
| 8481 | .findSchemaOrThrow(msort::name, msort::overload_name) |
| 8482 | .typed<msort::schema>(); |
| 8483 | } |
| 8484 | |
| 8485 | // aten::msort(Tensor self) -> Tensor |
| 8486 | at::Tensor msort::call(const at::Tensor & self) { |
| 8487 | |
| 8488 | static auto op = create_msort_typed_handle(); |
| 8489 | return op.call(self); |
| 8490 | } |
| 8491 | |
| 8492 | // aten::msort(Tensor self) -> Tensor |
| 8493 | at::Tensor msort::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 8494 | |
| 8495 | static auto op = create_msort_typed_handle(); |
| 8496 | return op.redispatch(dispatchKeySet, self); |
| 8497 | } |
| 8498 | |
| 8499 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(argsort, name, "aten::argsort") |
| 8500 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(argsort, overload_name, "") |
| 8501 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(argsort, schema_str, "argsort(Tensor self, int dim=-1, bool descending=False) -> Tensor") |
| 8502 | |
| 8503 | // aten::argsort(Tensor self, int dim=-1, bool descending=False) -> Tensor |
| 8504 | static C10_NOINLINE c10::TypedOperatorHandle<argsort::schema> create_argsort_typed_handle() { |
| 8505 | return c10::Dispatcher::singleton() |
| 8506 | .findSchemaOrThrow(argsort::name, argsort::overload_name) |
| 8507 | .typed<argsort::schema>(); |
| 8508 | } |
| 8509 | |
| 8510 | // aten::argsort(Tensor self, int dim=-1, bool descending=False) -> Tensor |
| 8511 | at::Tensor argsort::call(const at::Tensor & self, int64_t dim, bool descending) { |
| 8512 | |
| 8513 | static auto op = create_argsort_typed_handle(); |
| 8514 | return op.call(self, dim, descending); |
| 8515 | } |
| 8516 | |
| 8517 | // aten::argsort(Tensor self, int dim=-1, bool descending=False) -> Tensor |
| 8518 | at::Tensor argsort::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool descending) { |
| 8519 | |
| 8520 | static auto op = create_argsort_typed_handle(); |
| 8521 | return op.redispatch(dispatchKeySet, self, dim, descending); |
| 8522 | } |
| 8523 | |
| 8524 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(argsort_stable, name, "aten::argsort") |
| 8525 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(argsort_stable, overload_name, "stable") |
| 8526 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(argsort_stable, schema_str, "argsort.stable(Tensor self, *, bool stable, int dim=-1, bool descending=False) -> Tensor") |
| 8527 | |
| 8528 | // aten::argsort.stable(Tensor self, *, bool stable, int dim=-1, bool descending=False) -> Tensor |
| 8529 | static C10_NOINLINE c10::TypedOperatorHandle<argsort_stable::schema> create_argsort_stable_typed_handle() { |
| 8530 | return c10::Dispatcher::singleton() |
| 8531 | .findSchemaOrThrow(argsort_stable::name, argsort_stable::overload_name) |
| 8532 | .typed<argsort_stable::schema>(); |
| 8533 | } |
| 8534 | |
| 8535 | // aten::argsort.stable(Tensor self, *, bool stable, int dim=-1, bool descending=False) -> Tensor |
| 8536 | at::Tensor argsort_stable::call(const at::Tensor & self, bool stable, int64_t dim, bool descending) { |
| 8537 | |
| 8538 | static auto op = create_argsort_stable_typed_handle(); |
| 8539 | return op.call(self, stable, dim, descending); |
| 8540 | } |
| 8541 | |
| 8542 | // aten::argsort.stable(Tensor self, *, bool stable, int dim=-1, bool descending=False) -> Tensor |
| 8543 | at::Tensor argsort_stable::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool stable, int64_t dim, bool descending) { |
| 8544 | |
| 8545 | static auto op = create_argsort_stable_typed_handle(); |
| 8546 | return op.redispatch(dispatchKeySet, self, stable, dim, descending); |
| 8547 | } |
| 8548 | |
| 8549 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(argsort_dimname, name, "aten::argsort") |
| 8550 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(argsort_dimname, overload_name, "dimname") |
| 8551 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(argsort_dimname, schema_str, "argsort.dimname(Tensor self, Dimname dim, bool descending=False) -> Tensor") |
| 8552 | |
| 8553 | // aten::argsort.dimname(Tensor self, Dimname dim, bool descending=False) -> Tensor |
| 8554 | static C10_NOINLINE c10::TypedOperatorHandle<argsort_dimname::schema> create_argsort_dimname_typed_handle() { |
| 8555 | return c10::Dispatcher::singleton() |
| 8556 | .findSchemaOrThrow(argsort_dimname::name, argsort_dimname::overload_name) |
| 8557 | .typed<argsort_dimname::schema>(); |
| 8558 | } |
| 8559 | |
| 8560 | // aten::argsort.dimname(Tensor self, Dimname dim, bool descending=False) -> Tensor |
| 8561 | at::Tensor argsort_dimname::call(const at::Tensor & self, at::Dimname dim, bool descending) { |
| 8562 | |
| 8563 | static auto op = create_argsort_dimname_typed_handle(); |
| 8564 | return op.call(self, dim, descending); |
| 8565 | } |
| 8566 | |
| 8567 | // aten::argsort.dimname(Tensor self, Dimname dim, bool descending=False) -> Tensor |
| 8568 | at::Tensor argsort_dimname::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool descending) { |
| 8569 | |
| 8570 | static auto op = create_argsort_dimname_typed_handle(); |
| 8571 | return op.redispatch(dispatchKeySet, self, dim, descending); |
| 8572 | } |
| 8573 | |
| 8574 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(topk_values, name, "aten::topk") |
| 8575 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(topk_values, overload_name, "values") |
| 8576 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(topk_values, schema_str, "topk.values(Tensor self, int k, int dim=-1, bool largest=True, bool sorted=True, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)") |
| 8577 | |
| 8578 | // aten::topk.values(Tensor self, int k, int dim=-1, bool largest=True, bool sorted=True, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) |
| 8579 | static C10_NOINLINE c10::TypedOperatorHandle<topk_values::schema> create_topk_values_typed_handle() { |
| 8580 | return c10::Dispatcher::singleton() |
| 8581 | .findSchemaOrThrow(topk_values::name, topk_values::overload_name) |
| 8582 | .typed<topk_values::schema>(); |
| 8583 | } |
| 8584 | |
| 8585 | // aten::topk.values(Tensor self, int k, int dim=-1, bool largest=True, bool sorted=True, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) |
| 8586 | ::std::tuple<at::Tensor &,at::Tensor &> topk_values::call(const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted, at::Tensor & values, at::Tensor & indices) { |
| 8587 | |
| 8588 | static auto op = create_topk_values_typed_handle(); |
| 8589 | return op.call(self, k, dim, largest, sorted, values, indices); |
| 8590 | } |
| 8591 | |
| 8592 | // aten::topk.values(Tensor self, int k, int dim=-1, bool largest=True, bool sorted=True, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) |
| 8593 | ::std::tuple<at::Tensor &,at::Tensor &> topk_values::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted, at::Tensor & values, at::Tensor & indices) { |
| 8594 | |
| 8595 | static auto op = create_topk_values_typed_handle(); |
| 8596 | return op.redispatch(dispatchKeySet, self, k, dim, largest, sorted, values, indices); |
| 8597 | } |
| 8598 | |
| 8599 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(topk, name, "aten::topk") |
| 8600 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(topk, overload_name, "") |
| 8601 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(topk, schema_str, "topk(Tensor self, int k, int dim=-1, bool largest=True, bool sorted=True) -> (Tensor values, Tensor indices)") |
| 8602 | |
| 8603 | // aten::topk(Tensor self, int k, int dim=-1, bool largest=True, bool sorted=True) -> (Tensor values, Tensor indices) |
| 8604 | static C10_NOINLINE c10::TypedOperatorHandle<topk::schema> create_topk_typed_handle() { |
| 8605 | return c10::Dispatcher::singleton() |
| 8606 | .findSchemaOrThrow(topk::name, topk::overload_name) |
| 8607 | .typed<topk::schema>(); |
| 8608 | } |
| 8609 | |
| 8610 | // aten::topk(Tensor self, int k, int dim=-1, bool largest=True, bool sorted=True) -> (Tensor values, Tensor indices) |
| 8611 | ::std::tuple<at::Tensor,at::Tensor> topk::call(const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted) { |
| 8612 | |
| 8613 | static auto op = create_topk_typed_handle(); |
| 8614 | return op.call(self, k, dim, largest, sorted); |
| 8615 | } |
| 8616 | |
| 8617 | // aten::topk(Tensor self, int k, int dim=-1, bool largest=True, bool sorted=True) -> (Tensor values, Tensor indices) |
| 8618 | ::std::tuple<at::Tensor,at::Tensor> topk::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted) { |
| 8619 | |
| 8620 | static auto op = create_topk_typed_handle(); |
| 8621 | return op.redispatch(dispatchKeySet, self, k, dim, largest, sorted); |
| 8622 | } |
| 8623 | |
| 8624 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(unfold_backward, name, "aten::unfold_backward") |
| 8625 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(unfold_backward, overload_name, "") |
| 8626 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(unfold_backward, schema_str, "unfold_backward(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step) -> Tensor") |
| 8627 | |
| 8628 | // aten::unfold_backward(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step) -> Tensor |
| 8629 | static C10_NOINLINE c10::TypedOperatorHandle<unfold_backward::schema> create_unfold_backward_typed_handle() { |
| 8630 | return c10::Dispatcher::singleton() |
| 8631 | .findSchemaOrThrow(unfold_backward::name, unfold_backward::overload_name) |
| 8632 | .typed<unfold_backward::schema>(); |
| 8633 | } |
| 8634 | |
| 8635 | // aten::unfold_backward(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step) -> Tensor |
| 8636 | at::Tensor unfold_backward::call(const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step) { |
| 8637 | |
| 8638 | static auto op = create_unfold_backward_typed_handle(); |
| 8639 | return op.call(grad_in, input_sizes, dim, size, step); |
| 8640 | } |
| 8641 | |
| 8642 | // aten::unfold_backward(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step) -> Tensor |
| 8643 | at::Tensor unfold_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step) { |
| 8644 | |
| 8645 | static auto op = create_unfold_backward_typed_handle(); |
| 8646 | return op.redispatch(dispatchKeySet, grad_in, input_sizes, dim, size, step); |
| 8647 | } |
| 8648 | |
| 8649 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_, name, "aten::normal_") |
| 8650 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_, overload_name, "") |
| 8651 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_, schema_str, "normal_(Tensor(a!) self, float mean=0, float std=1, *, Generator? generator=None) -> Tensor(a!)") |
| 8652 | |
| 8653 | // aten::normal_(Tensor(a!) self, float mean=0, float std=1, *, Generator? generator=None) -> Tensor(a!) |
| 8654 | static C10_NOINLINE c10::TypedOperatorHandle<normal_::schema> create_normal__typed_handle() { |
| 8655 | return c10::Dispatcher::singleton() |
| 8656 | .findSchemaOrThrow(normal_::name, normal_::overload_name) |
| 8657 | .typed<normal_::schema>(); |
| 8658 | } |
| 8659 | |
| 8660 | // aten::normal_(Tensor(a!) self, float mean=0, float std=1, *, Generator? generator=None) -> Tensor(a!) |
| 8661 | at::Tensor & normal_::call(at::Tensor & self, double mean, double std, c10::optional<at::Generator> generator) { |
| 8662 | |
| 8663 | static auto op = create_normal__typed_handle(); |
| 8664 | return op.call(self, mean, std, generator); |
| 8665 | } |
| 8666 | |
| 8667 | // aten::normal_(Tensor(a!) self, float mean=0, float std=1, *, Generator? generator=None) -> Tensor(a!) |
| 8668 | at::Tensor & normal_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, double mean, double std, c10::optional<at::Generator> generator) { |
| 8669 | |
| 8670 | static auto op = create_normal__typed_handle(); |
| 8671 | return op.redispatch(dispatchKeySet, self, mean, std, generator); |
| 8672 | } |
| 8673 | |
| 8674 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_functional, name, "aten::normal_functional") |
| 8675 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_functional, overload_name, "") |
| 8676 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_functional, schema_str, "normal_functional(Tensor self, float mean=0, float std=1, *, Generator? generator=None) -> Tensor") |
| 8677 | |
| 8678 | // aten::normal_functional(Tensor self, float mean=0, float std=1, *, Generator? generator=None) -> Tensor |
| 8679 | static C10_NOINLINE c10::TypedOperatorHandle<normal_functional::schema> create_normal_functional_typed_handle() { |
| 8680 | return c10::Dispatcher::singleton() |
| 8681 | .findSchemaOrThrow(normal_functional::name, normal_functional::overload_name) |
| 8682 | .typed<normal_functional::schema>(); |
| 8683 | } |
| 8684 | |
| 8685 | // aten::normal_functional(Tensor self, float mean=0, float std=1, *, Generator? generator=None) -> Tensor |
| 8686 | at::Tensor normal_functional::call(const at::Tensor & self, double mean, double std, c10::optional<at::Generator> generator) { |
| 8687 | |
| 8688 | static auto op = create_normal_functional_typed_handle(); |
| 8689 | return op.call(self, mean, std, generator); |
| 8690 | } |
| 8691 | |
| 8692 | // aten::normal_functional(Tensor self, float mean=0, float std=1, *, Generator? generator=None) -> Tensor |
| 8693 | at::Tensor normal_functional::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double mean, double std, c10::optional<at::Generator> generator) { |
| 8694 | |
| 8695 | static auto op = create_normal_functional_typed_handle(); |
| 8696 | return op.redispatch(dispatchKeySet, self, mean, std, generator); |
| 8697 | } |
| 8698 | |
| 8699 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_Tensor_float_out, name, "aten::normal") |
| 8700 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_Tensor_float_out, overload_name, "Tensor_float_out") |
| 8701 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_Tensor_float_out, schema_str, "normal.Tensor_float_out(Tensor mean, float std=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)") |
| 8702 | |
| 8703 | // aten::normal.Tensor_float_out(Tensor mean, float std=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 8704 | static C10_NOINLINE c10::TypedOperatorHandle<normal_Tensor_float_out::schema> create_normal_Tensor_float_out_typed_handle() { |
| 8705 | return c10::Dispatcher::singleton() |
| 8706 | .findSchemaOrThrow(normal_Tensor_float_out::name, normal_Tensor_float_out::overload_name) |
| 8707 | .typed<normal_Tensor_float_out::schema>(); |
| 8708 | } |
| 8709 | |
| 8710 | // aten::normal.Tensor_float_out(Tensor mean, float std=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 8711 | at::Tensor & normal_Tensor_float_out::call(const at::Tensor & mean, double std, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 8712 | |
| 8713 | static auto op = create_normal_Tensor_float_out_typed_handle(); |
| 8714 | return op.call(mean, std, generator, out); |
| 8715 | } |
| 8716 | |
| 8717 | // aten::normal.Tensor_float_out(Tensor mean, float std=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 8718 | at::Tensor & normal_Tensor_float_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & mean, double std, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 8719 | |
| 8720 | static auto op = create_normal_Tensor_float_out_typed_handle(); |
| 8721 | return op.redispatch(dispatchKeySet, mean, std, generator, out); |
| 8722 | } |
| 8723 | |
| 8724 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_Tensor_float, name, "aten::normal") |
| 8725 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_Tensor_float, overload_name, "Tensor_float") |
| 8726 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_Tensor_float, schema_str, "normal.Tensor_float(Tensor mean, float std=1, *, Generator? generator=None) -> Tensor") |
| 8727 | |
| 8728 | // aten::normal.Tensor_float(Tensor mean, float std=1, *, Generator? generator=None) -> Tensor |
| 8729 | static C10_NOINLINE c10::TypedOperatorHandle<normal_Tensor_float::schema> create_normal_Tensor_float_typed_handle() { |
| 8730 | return c10::Dispatcher::singleton() |
| 8731 | .findSchemaOrThrow(normal_Tensor_float::name, normal_Tensor_float::overload_name) |
| 8732 | .typed<normal_Tensor_float::schema>(); |
| 8733 | } |
| 8734 | |
| 8735 | // aten::normal.Tensor_float(Tensor mean, float std=1, *, Generator? generator=None) -> Tensor |
| 8736 | at::Tensor normal_Tensor_float::call(const at::Tensor & mean, double std, c10::optional<at::Generator> generator) { |
| 8737 | |
| 8738 | static auto op = create_normal_Tensor_float_typed_handle(); |
| 8739 | return op.call(mean, std, generator); |
| 8740 | } |
| 8741 | |
| 8742 | // aten::normal.Tensor_float(Tensor mean, float std=1, *, Generator? generator=None) -> Tensor |
| 8743 | at::Tensor normal_Tensor_float::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & mean, double std, c10::optional<at::Generator> generator) { |
| 8744 | |
| 8745 | static auto op = create_normal_Tensor_float_typed_handle(); |
| 8746 | return op.redispatch(dispatchKeySet, mean, std, generator); |
| 8747 | } |
| 8748 | |
| 8749 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_float_Tensor_out, name, "aten::normal") |
| 8750 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_float_Tensor_out, overload_name, "float_Tensor_out") |
| 8751 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_float_Tensor_out, schema_str, "normal.float_Tensor_out(float mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)") |
| 8752 | |
| 8753 | // aten::normal.float_Tensor_out(float mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 8754 | static C10_NOINLINE c10::TypedOperatorHandle<normal_float_Tensor_out::schema> create_normal_float_Tensor_out_typed_handle() { |
| 8755 | return c10::Dispatcher::singleton() |
| 8756 | .findSchemaOrThrow(normal_float_Tensor_out::name, normal_float_Tensor_out::overload_name) |
| 8757 | .typed<normal_float_Tensor_out::schema>(); |
| 8758 | } |
| 8759 | |
| 8760 | // aten::normal.float_Tensor_out(float mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 8761 | at::Tensor & normal_float_Tensor_out::call(double mean, const at::Tensor & std, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 8762 | |
| 8763 | static auto op = create_normal_float_Tensor_out_typed_handle(); |
| 8764 | return op.call(mean, std, generator, out); |
| 8765 | } |
| 8766 | |
| 8767 | // aten::normal.float_Tensor_out(float mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 8768 | at::Tensor & normal_float_Tensor_out::redispatch(c10::DispatchKeySet dispatchKeySet, double mean, const at::Tensor & std, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 8769 | |
| 8770 | static auto op = create_normal_float_Tensor_out_typed_handle(); |
| 8771 | return op.redispatch(dispatchKeySet, mean, std, generator, out); |
| 8772 | } |
| 8773 | |
| 8774 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_float_Tensor, name, "aten::normal") |
| 8775 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_float_Tensor, overload_name, "float_Tensor") |
| 8776 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_float_Tensor, schema_str, "normal.float_Tensor(float mean, Tensor std, *, Generator? generator=None) -> Tensor") |
| 8777 | |
| 8778 | // aten::normal.float_Tensor(float mean, Tensor std, *, Generator? generator=None) -> Tensor |
| 8779 | static C10_NOINLINE c10::TypedOperatorHandle<normal_float_Tensor::schema> create_normal_float_Tensor_typed_handle() { |
| 8780 | return c10::Dispatcher::singleton() |
| 8781 | .findSchemaOrThrow(normal_float_Tensor::name, normal_float_Tensor::overload_name) |
| 8782 | .typed<normal_float_Tensor::schema>(); |
| 8783 | } |
| 8784 | |
| 8785 | // aten::normal.float_Tensor(float mean, Tensor std, *, Generator? generator=None) -> Tensor |
| 8786 | at::Tensor normal_float_Tensor::call(double mean, const at::Tensor & std, c10::optional<at::Generator> generator) { |
| 8787 | |
| 8788 | static auto op = create_normal_float_Tensor_typed_handle(); |
| 8789 | return op.call(mean, std, generator); |
| 8790 | } |
| 8791 | |
| 8792 | // aten::normal.float_Tensor(float mean, Tensor std, *, Generator? generator=None) -> Tensor |
| 8793 | at::Tensor normal_float_Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, double mean, const at::Tensor & std, c10::optional<at::Generator> generator) { |
| 8794 | |
| 8795 | static auto op = create_normal_float_Tensor_typed_handle(); |
| 8796 | return op.redispatch(dispatchKeySet, mean, std, generator); |
| 8797 | } |
| 8798 | |
| 8799 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_Tensor_Tensor_out, name, "aten::normal") |
| 8800 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_Tensor_Tensor_out, overload_name, "Tensor_Tensor_out") |
| 8801 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_Tensor_Tensor_out, schema_str, "normal.Tensor_Tensor_out(Tensor mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)") |
| 8802 | |
| 8803 | // aten::normal.Tensor_Tensor_out(Tensor mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 8804 | static C10_NOINLINE c10::TypedOperatorHandle<normal_Tensor_Tensor_out::schema> create_normal_Tensor_Tensor_out_typed_handle() { |
| 8805 | return c10::Dispatcher::singleton() |
| 8806 | .findSchemaOrThrow(normal_Tensor_Tensor_out::name, normal_Tensor_Tensor_out::overload_name) |
| 8807 | .typed<normal_Tensor_Tensor_out::schema>(); |
| 8808 | } |
| 8809 | |
| 8810 | // aten::normal.Tensor_Tensor_out(Tensor mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 8811 | at::Tensor & normal_Tensor_Tensor_out::call(const at::Tensor & mean, const at::Tensor & std, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 8812 | |
| 8813 | static auto op = create_normal_Tensor_Tensor_out_typed_handle(); |
| 8814 | return op.call(mean, std, generator, out); |
| 8815 | } |
| 8816 | |
| 8817 | // aten::normal.Tensor_Tensor_out(Tensor mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 8818 | at::Tensor & normal_Tensor_Tensor_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & mean, const at::Tensor & std, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 8819 | |
| 8820 | static auto op = create_normal_Tensor_Tensor_out_typed_handle(); |
| 8821 | return op.redispatch(dispatchKeySet, mean, std, generator, out); |
| 8822 | } |
| 8823 | |
| 8824 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_Tensor_Tensor, name, "aten::normal") |
| 8825 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_Tensor_Tensor, overload_name, "Tensor_Tensor") |
| 8826 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_Tensor_Tensor, schema_str, "normal.Tensor_Tensor(Tensor mean, Tensor std, *, Generator? generator=None) -> Tensor") |
| 8827 | |
| 8828 | // aten::normal.Tensor_Tensor(Tensor mean, Tensor std, *, Generator? generator=None) -> Tensor |
| 8829 | static C10_NOINLINE c10::TypedOperatorHandle<normal_Tensor_Tensor::schema> create_normal_Tensor_Tensor_typed_handle() { |
| 8830 | return c10::Dispatcher::singleton() |
| 8831 | .findSchemaOrThrow(normal_Tensor_Tensor::name, normal_Tensor_Tensor::overload_name) |
| 8832 | .typed<normal_Tensor_Tensor::schema>(); |
| 8833 | } |
| 8834 | |
| 8835 | // aten::normal.Tensor_Tensor(Tensor mean, Tensor std, *, Generator? generator=None) -> Tensor |
| 8836 | at::Tensor normal_Tensor_Tensor::call(const at::Tensor & mean, const at::Tensor & std, c10::optional<at::Generator> generator) { |
| 8837 | |
| 8838 | static auto op = create_normal_Tensor_Tensor_typed_handle(); |
| 8839 | return op.call(mean, std, generator); |
| 8840 | } |
| 8841 | |
| 8842 | // aten::normal.Tensor_Tensor(Tensor mean, Tensor std, *, Generator? generator=None) -> Tensor |
| 8843 | at::Tensor normal_Tensor_Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & mean, const at::Tensor & std, c10::optional<at::Generator> generator) { |
| 8844 | |
| 8845 | static auto op = create_normal_Tensor_Tensor_typed_handle(); |
| 8846 | return op.redispatch(dispatchKeySet, mean, std, generator); |
| 8847 | } |
| 8848 | |
| 8849 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_float_float, name, "aten::normal") |
| 8850 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_float_float, overload_name, "float_float") |
| 8851 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_float_float, schema_str, "normal.float_float(float mean, float std, SymInt[] size, *, Generator? generator=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor") |
| 8852 | |
| 8853 | // aten::normal.float_float(float mean, float std, SymInt[] size, *, Generator? generator=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 8854 | static C10_NOINLINE c10::TypedOperatorHandle<normal_float_float::schema> create_normal_float_float_typed_handle() { |
| 8855 | return c10::Dispatcher::singleton() |
| 8856 | .findSchemaOrThrow(normal_float_float::name, normal_float_float::overload_name) |
| 8857 | .typed<normal_float_float::schema>(); |
| 8858 | } |
| 8859 | |
| 8860 | // aten::normal.float_float(float mean, float std, SymInt[] size, *, Generator? generator=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 8861 | at::Tensor normal_float_float::call(double mean, double std, c10::SymIntArrayRef size, c10::optional<at::Generator> generator, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 8862 | |
| 8863 | static auto op = create_normal_float_float_typed_handle(); |
| 8864 | return op.call(mean, std, size, generator, dtype, layout, device, pin_memory); |
| 8865 | } |
| 8866 | |
| 8867 | // aten::normal.float_float(float mean, float std, SymInt[] size, *, Generator? generator=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 8868 | at::Tensor normal_float_float::redispatch(c10::DispatchKeySet dispatchKeySet, double mean, double std, c10::SymIntArrayRef size, c10::optional<at::Generator> generator, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 8869 | |
| 8870 | static auto op = create_normal_float_float_typed_handle(); |
| 8871 | return op.redispatch(dispatchKeySet, mean, std, size, generator, dtype, layout, device, pin_memory); |
| 8872 | } |
| 8873 | |
| 8874 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_float_float_out, name, "aten::normal") |
| 8875 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_float_float_out, overload_name, "float_float_out") |
| 8876 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_float_float_out, schema_str, "normal.float_float_out(float mean, float std, SymInt[] size, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)") |
| 8877 | |
| 8878 | // aten::normal.float_float_out(float mean, float std, SymInt[] size, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 8879 | static C10_NOINLINE c10::TypedOperatorHandle<normal_float_float_out::schema> create_normal_float_float_out_typed_handle() { |
| 8880 | return c10::Dispatcher::singleton() |
| 8881 | .findSchemaOrThrow(normal_float_float_out::name, normal_float_float_out::overload_name) |
| 8882 | .typed<normal_float_float_out::schema>(); |
| 8883 | } |
| 8884 | |
| 8885 | // aten::normal.float_float_out(float mean, float std, SymInt[] size, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 8886 | at::Tensor & normal_float_float_out::call(double mean, double std, c10::SymIntArrayRef size, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 8887 | |
| 8888 | static auto op = create_normal_float_float_out_typed_handle(); |
| 8889 | return op.call(mean, std, size, generator, out); |
| 8890 | } |
| 8891 | |
| 8892 | // aten::normal.float_float_out(float mean, float std, SymInt[] size, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 8893 | at::Tensor & normal_float_float_out::redispatch(c10::DispatchKeySet dispatchKeySet, double mean, double std, c10::SymIntArrayRef size, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 8894 | |
| 8895 | static auto op = create_normal_float_float_out_typed_handle(); |
| 8896 | return op.redispatch(dispatchKeySet, mean, std, size, generator, out); |
| 8897 | } |
| 8898 | |
| 8899 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(alias, name, "aten::alias") |
| 8900 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(alias, overload_name, "") |
| 8901 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(alias, schema_str, "alias(Tensor(a) self) -> Tensor(a)") |
| 8902 | |
| 8903 | // aten::alias(Tensor(a) self) -> Tensor(a) |
| 8904 | static C10_NOINLINE c10::TypedOperatorHandle<alias::schema> create_alias_typed_handle() { |
| 8905 | return c10::Dispatcher::singleton() |
| 8906 | .findSchemaOrThrow(alias::name, alias::overload_name) |
| 8907 | .typed<alias::schema>(); |
| 8908 | } |
| 8909 | |
| 8910 | // aten::alias(Tensor(a) self) -> Tensor(a) |
| 8911 | at::Tensor alias::call(const at::Tensor & self) { |
| 8912 | |
| 8913 | static auto op = create_alias_typed_handle(); |
| 8914 | return op.call(self); |
| 8915 | } |
| 8916 | |
| 8917 | // aten::alias(Tensor(a) self) -> Tensor(a) |
| 8918 | at::Tensor alias::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 8919 | |
| 8920 | static auto op = create_alias_typed_handle(); |
| 8921 | return op.redispatch(dispatchKeySet, self); |
| 8922 | } |
| 8923 | |
| 8924 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_sub_Scalar, name, "aten::_foreach_sub") |
| 8925 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_sub_Scalar, overload_name, "Scalar") |
| 8926 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_sub_Scalar, schema_str, "_foreach_sub.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]") |
| 8927 | |
| 8928 | // aten::_foreach_sub.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] |
| 8929 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_sub_Scalar::schema> create__foreach_sub_Scalar_typed_handle() { |
| 8930 | return c10::Dispatcher::singleton() |
| 8931 | .findSchemaOrThrow(_foreach_sub_Scalar::name, _foreach_sub_Scalar::overload_name) |
| 8932 | .typed<_foreach_sub_Scalar::schema>(); |
| 8933 | } |
| 8934 | |
| 8935 | // aten::_foreach_sub.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] |
| 8936 | ::std::vector<at::Tensor> _foreach_sub_Scalar::call(at::TensorList self, const at::Scalar & scalar) { |
| 8937 | |
| 8938 | static auto op = create__foreach_sub_Scalar_typed_handle(); |
| 8939 | return op.call(self, scalar); |
| 8940 | } |
| 8941 | |
| 8942 | // aten::_foreach_sub.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] |
| 8943 | ::std::vector<at::Tensor> _foreach_sub_Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { |
| 8944 | |
| 8945 | static auto op = create__foreach_sub_Scalar_typed_handle(); |
| 8946 | return op.redispatch(dispatchKeySet, self, scalar); |
| 8947 | } |
| 8948 | |
| 8949 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_sub__Scalar, name, "aten::_foreach_sub_") |
| 8950 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_sub__Scalar, overload_name, "Scalar") |
| 8951 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_sub__Scalar, schema_str, "_foreach_sub_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()") |
| 8952 | |
| 8953 | // aten::_foreach_sub_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () |
| 8954 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_sub__Scalar::schema> create__foreach_sub__Scalar_typed_handle() { |
| 8955 | return c10::Dispatcher::singleton() |
| 8956 | .findSchemaOrThrow(_foreach_sub__Scalar::name, _foreach_sub__Scalar::overload_name) |
| 8957 | .typed<_foreach_sub__Scalar::schema>(); |
| 8958 | } |
| 8959 | |
| 8960 | // aten::_foreach_sub_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () |
| 8961 | void _foreach_sub__Scalar::call(at::TensorList self, const at::Scalar & scalar) { |
| 8962 | |
| 8963 | static auto op = create__foreach_sub__Scalar_typed_handle(); |
| 8964 | return op.call(self, scalar); |
| 8965 | } |
| 8966 | |
| 8967 | // aten::_foreach_sub_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () |
| 8968 | void _foreach_sub__Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { |
| 8969 | |
| 8970 | static auto op = create__foreach_sub__Scalar_typed_handle(); |
| 8971 | return op.redispatch(dispatchKeySet, self, scalar); |
| 8972 | } |
| 8973 | |
| 8974 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_maximum_Scalar, name, "aten::_foreach_maximum") |
| 8975 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_maximum_Scalar, overload_name, "Scalar") |
| 8976 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_maximum_Scalar, schema_str, "_foreach_maximum.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]") |
| 8977 | |
| 8978 | // aten::_foreach_maximum.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] |
| 8979 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_maximum_Scalar::schema> create__foreach_maximum_Scalar_typed_handle() { |
| 8980 | return c10::Dispatcher::singleton() |
| 8981 | .findSchemaOrThrow(_foreach_maximum_Scalar::name, _foreach_maximum_Scalar::overload_name) |
| 8982 | .typed<_foreach_maximum_Scalar::schema>(); |
| 8983 | } |
| 8984 | |
| 8985 | // aten::_foreach_maximum.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] |
| 8986 | ::std::vector<at::Tensor> _foreach_maximum_Scalar::call(at::TensorList self, const at::Scalar & scalar) { |
| 8987 | |
| 8988 | static auto op = create__foreach_maximum_Scalar_typed_handle(); |
| 8989 | return op.call(self, scalar); |
| 8990 | } |
| 8991 | |
| 8992 | // aten::_foreach_maximum.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] |
| 8993 | ::std::vector<at::Tensor> _foreach_maximum_Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { |
| 8994 | |
| 8995 | static auto op = create__foreach_maximum_Scalar_typed_handle(); |
| 8996 | return op.redispatch(dispatchKeySet, self, scalar); |
| 8997 | } |
| 8998 | |
| 8999 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_maximum__Scalar, name, "aten::_foreach_maximum_") |
| 9000 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_maximum__Scalar, overload_name, "Scalar") |
| 9001 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_maximum__Scalar, schema_str, "_foreach_maximum_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()") |
| 9002 | |
| 9003 | // aten::_foreach_maximum_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () |
| 9004 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_maximum__Scalar::schema> create__foreach_maximum__Scalar_typed_handle() { |
| 9005 | return c10::Dispatcher::singleton() |
| 9006 | .findSchemaOrThrow(_foreach_maximum__Scalar::name, _foreach_maximum__Scalar::overload_name) |
| 9007 | .typed<_foreach_maximum__Scalar::schema>(); |
| 9008 | } |
| 9009 | |
| 9010 | // aten::_foreach_maximum_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () |
| 9011 | void _foreach_maximum__Scalar::call(at::TensorList self, const at::Scalar & scalar) { |
| 9012 | |
| 9013 | static auto op = create__foreach_maximum__Scalar_typed_handle(); |
| 9014 | return op.call(self, scalar); |
| 9015 | } |
| 9016 | |
| 9017 | // aten::_foreach_maximum_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () |
| 9018 | void _foreach_maximum__Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { |
| 9019 | |
| 9020 | static auto op = create__foreach_maximum__Scalar_typed_handle(); |
| 9021 | return op.redispatch(dispatchKeySet, self, scalar); |
| 9022 | } |
| 9023 | |
| 9024 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_sub_List, name, "aten::_foreach_sub") |
| 9025 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_sub_List, overload_name, "List") |
| 9026 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_sub_List, schema_str, "_foreach_sub.List(Tensor[] self, Tensor[] other, *, Scalar alpha=1) -> Tensor[]") |
| 9027 | |
| 9028 | // aten::_foreach_sub.List(Tensor[] self, Tensor[] other, *, Scalar alpha=1) -> Tensor[] |
| 9029 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_sub_List::schema> create__foreach_sub_List_typed_handle() { |
| 9030 | return c10::Dispatcher::singleton() |
| 9031 | .findSchemaOrThrow(_foreach_sub_List::name, _foreach_sub_List::overload_name) |
| 9032 | .typed<_foreach_sub_List::schema>(); |
| 9033 | } |
| 9034 | |
| 9035 | // aten::_foreach_sub.List(Tensor[] self, Tensor[] other, *, Scalar alpha=1) -> Tensor[] |
| 9036 | ::std::vector<at::Tensor> _foreach_sub_List::call(at::TensorList self, at::TensorList other, const at::Scalar & alpha) { |
| 9037 | |
| 9038 | static auto op = create__foreach_sub_List_typed_handle(); |
| 9039 | return op.call(self, other, alpha); |
| 9040 | } |
| 9041 | |
| 9042 | // aten::_foreach_sub.List(Tensor[] self, Tensor[] other, *, Scalar alpha=1) -> Tensor[] |
| 9043 | ::std::vector<at::Tensor> _foreach_sub_List::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other, const at::Scalar & alpha) { |
| 9044 | |
| 9045 | static auto op = create__foreach_sub_List_typed_handle(); |
| 9046 | return op.redispatch(dispatchKeySet, self, other, alpha); |
| 9047 | } |
| 9048 | |
| 9049 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_sub__List, name, "aten::_foreach_sub_") |
| 9050 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_sub__List, overload_name, "List") |
| 9051 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_sub__List, schema_str, "_foreach_sub_.List(Tensor(a!)[] self, Tensor[] other, *, Scalar alpha=1) -> ()") |
| 9052 | |
| 9053 | // aten::_foreach_sub_.List(Tensor(a!)[] self, Tensor[] other, *, Scalar alpha=1) -> () |
| 9054 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_sub__List::schema> create__foreach_sub__List_typed_handle() { |
| 9055 | return c10::Dispatcher::singleton() |
| 9056 | .findSchemaOrThrow(_foreach_sub__List::name, _foreach_sub__List::overload_name) |
| 9057 | .typed<_foreach_sub__List::schema>(); |
| 9058 | } |
| 9059 | |
| 9060 | // aten::_foreach_sub_.List(Tensor(a!)[] self, Tensor[] other, *, Scalar alpha=1) -> () |
| 9061 | void _foreach_sub__List::call(at::TensorList self, at::TensorList other, const at::Scalar & alpha) { |
| 9062 | |
| 9063 | static auto op = create__foreach_sub__List_typed_handle(); |
| 9064 | return op.call(self, other, alpha); |
| 9065 | } |
| 9066 | |
| 9067 | // aten::_foreach_sub_.List(Tensor(a!)[] self, Tensor[] other, *, Scalar alpha=1) -> () |
| 9068 | void _foreach_sub__List::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other, const at::Scalar & alpha) { |
| 9069 | |
| 9070 | static auto op = create__foreach_sub__List_typed_handle(); |
| 9071 | return op.redispatch(dispatchKeySet, self, other, alpha); |
| 9072 | } |
| 9073 | |
| 9074 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_maximum_List, name, "aten::_foreach_maximum") |
| 9075 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_maximum_List, overload_name, "List") |
| 9076 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_maximum_List, schema_str, "_foreach_maximum.List(Tensor[] self, Tensor[] other) -> Tensor[]") |
| 9077 | |
| 9078 | // aten::_foreach_maximum.List(Tensor[] self, Tensor[] other) -> Tensor[] |
| 9079 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_maximum_List::schema> create__foreach_maximum_List_typed_handle() { |
| 9080 | return c10::Dispatcher::singleton() |
| 9081 | .findSchemaOrThrow(_foreach_maximum_List::name, _foreach_maximum_List::overload_name) |
| 9082 | .typed<_foreach_maximum_List::schema>(); |
| 9083 | } |
| 9084 | |
| 9085 | // aten::_foreach_maximum.List(Tensor[] self, Tensor[] other) -> Tensor[] |
| 9086 | ::std::vector<at::Tensor> _foreach_maximum_List::call(at::TensorList self, at::TensorList other) { |
| 9087 | |
| 9088 | static auto op = create__foreach_maximum_List_typed_handle(); |
| 9089 | return op.call(self, other); |
| 9090 | } |
| 9091 | |
| 9092 | // aten::_foreach_maximum.List(Tensor[] self, Tensor[] other) -> Tensor[] |
| 9093 | ::std::vector<at::Tensor> _foreach_maximum_List::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other) { |
| 9094 | |
| 9095 | static auto op = create__foreach_maximum_List_typed_handle(); |
| 9096 | return op.redispatch(dispatchKeySet, self, other); |
| 9097 | } |
| 9098 | |
| 9099 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_maximum__List, name, "aten::_foreach_maximum_") |
| 9100 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_maximum__List, overload_name, "List") |
| 9101 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_maximum__List, schema_str, "_foreach_maximum_.List(Tensor(a!)[] self, Tensor[] other) -> ()") |
| 9102 | |
| 9103 | // aten::_foreach_maximum_.List(Tensor(a!)[] self, Tensor[] other) -> () |
| 9104 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_maximum__List::schema> create__foreach_maximum__List_typed_handle() { |
| 9105 | return c10::Dispatcher::singleton() |
| 9106 | .findSchemaOrThrow(_foreach_maximum__List::name, _foreach_maximum__List::overload_name) |
| 9107 | .typed<_foreach_maximum__List::schema>(); |
| 9108 | } |
| 9109 | |
| 9110 | // aten::_foreach_maximum_.List(Tensor(a!)[] self, Tensor[] other) -> () |
| 9111 | void _foreach_maximum__List::call(at::TensorList self, at::TensorList other) { |
| 9112 | |
| 9113 | static auto op = create__foreach_maximum__List_typed_handle(); |
| 9114 | return op.call(self, other); |
| 9115 | } |
| 9116 | |
| 9117 | // aten::_foreach_maximum_.List(Tensor(a!)[] self, Tensor[] other) -> () |
| 9118 | void _foreach_maximum__List::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other) { |
| 9119 | |
| 9120 | static auto op = create__foreach_maximum__List_typed_handle(); |
| 9121 | return op.redispatch(dispatchKeySet, self, other); |
| 9122 | } |
| 9123 | |
| 9124 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_sub_ScalarList, name, "aten::_foreach_sub") |
| 9125 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_sub_ScalarList, overload_name, "ScalarList") |
| 9126 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_sub_ScalarList, schema_str, "_foreach_sub.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]") |
| 9127 | |
| 9128 | // aten::_foreach_sub.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] |
| 9129 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_sub_ScalarList::schema> create__foreach_sub_ScalarList_typed_handle() { |
| 9130 | return c10::Dispatcher::singleton() |
| 9131 | .findSchemaOrThrow(_foreach_sub_ScalarList::name, _foreach_sub_ScalarList::overload_name) |
| 9132 | .typed<_foreach_sub_ScalarList::schema>(); |
| 9133 | } |
| 9134 | |
| 9135 | // aten::_foreach_sub.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] |
| 9136 | ::std::vector<at::Tensor> _foreach_sub_ScalarList::call(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 9137 | |
| 9138 | static auto op = create__foreach_sub_ScalarList_typed_handle(); |
| 9139 | return op.call(self, scalars); |
| 9140 | } |
| 9141 | |
| 9142 | // aten::_foreach_sub.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] |
| 9143 | ::std::vector<at::Tensor> _foreach_sub_ScalarList::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 9144 | |
| 9145 | static auto op = create__foreach_sub_ScalarList_typed_handle(); |
| 9146 | return op.redispatch(dispatchKeySet, self, scalars); |
| 9147 | } |
| 9148 | |
| 9149 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_sub__ScalarList, name, "aten::_foreach_sub_") |
| 9150 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_sub__ScalarList, overload_name, "ScalarList") |
| 9151 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_sub__ScalarList, schema_str, "_foreach_sub_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()") |
| 9152 | |
| 9153 | // aten::_foreach_sub_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () |
| 9154 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_sub__ScalarList::schema> create__foreach_sub__ScalarList_typed_handle() { |
| 9155 | return c10::Dispatcher::singleton() |
| 9156 | .findSchemaOrThrow(_foreach_sub__ScalarList::name, _foreach_sub__ScalarList::overload_name) |
| 9157 | .typed<_foreach_sub__ScalarList::schema>(); |
| 9158 | } |
| 9159 | |
| 9160 | // aten::_foreach_sub_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () |
| 9161 | void _foreach_sub__ScalarList::call(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 9162 | |
| 9163 | static auto op = create__foreach_sub__ScalarList_typed_handle(); |
| 9164 | return op.call(self, scalars); |
| 9165 | } |
| 9166 | |
| 9167 | // aten::_foreach_sub_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () |
| 9168 | void _foreach_sub__ScalarList::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 9169 | |
| 9170 | static auto op = create__foreach_sub__ScalarList_typed_handle(); |
| 9171 | return op.redispatch(dispatchKeySet, self, scalars); |
| 9172 | } |
| 9173 | |
| 9174 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_maximum_ScalarList, name, "aten::_foreach_maximum") |
| 9175 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_maximum_ScalarList, overload_name, "ScalarList") |
| 9176 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_maximum_ScalarList, schema_str, "_foreach_maximum.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]") |
| 9177 | |
| 9178 | // aten::_foreach_maximum.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] |
| 9179 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_maximum_ScalarList::schema> create__foreach_maximum_ScalarList_typed_handle() { |
| 9180 | return c10::Dispatcher::singleton() |
| 9181 | .findSchemaOrThrow(_foreach_maximum_ScalarList::name, _foreach_maximum_ScalarList::overload_name) |
| 9182 | .typed<_foreach_maximum_ScalarList::schema>(); |
| 9183 | } |
| 9184 | |
| 9185 | // aten::_foreach_maximum.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] |
| 9186 | ::std::vector<at::Tensor> _foreach_maximum_ScalarList::call(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 9187 | |
| 9188 | static auto op = create__foreach_maximum_ScalarList_typed_handle(); |
| 9189 | return op.call(self, scalars); |
| 9190 | } |
| 9191 | |
| 9192 | // aten::_foreach_maximum.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] |
| 9193 | ::std::vector<at::Tensor> _foreach_maximum_ScalarList::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 9194 | |
| 9195 | static auto op = create__foreach_maximum_ScalarList_typed_handle(); |
| 9196 | return op.redispatch(dispatchKeySet, self, scalars); |
| 9197 | } |
| 9198 | |
| 9199 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_maximum__ScalarList, name, "aten::_foreach_maximum_") |
| 9200 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_maximum__ScalarList, overload_name, "ScalarList") |
| 9201 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_maximum__ScalarList, schema_str, "_foreach_maximum_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()") |
| 9202 | |
| 9203 | // aten::_foreach_maximum_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () |
| 9204 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_maximum__ScalarList::schema> create__foreach_maximum__ScalarList_typed_handle() { |
| 9205 | return c10::Dispatcher::singleton() |
| 9206 | .findSchemaOrThrow(_foreach_maximum__ScalarList::name, _foreach_maximum__ScalarList::overload_name) |
| 9207 | .typed<_foreach_maximum__ScalarList::schema>(); |
| 9208 | } |
| 9209 | |
| 9210 | // aten::_foreach_maximum_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () |
| 9211 | void _foreach_maximum__ScalarList::call(at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 9212 | |
| 9213 | static auto op = create__foreach_maximum__ScalarList_typed_handle(); |
| 9214 | return op.call(self, scalars); |
| 9215 | } |
| 9216 | |
| 9217 | // aten::_foreach_maximum_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () |
| 9218 | void _foreach_maximum__ScalarList::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef<at::Scalar> scalars) { |
| 9219 | |
| 9220 | static auto op = create__foreach_maximum__ScalarList_typed_handle(); |
| 9221 | return op.redispatch(dispatchKeySet, self, scalars); |
| 9222 | } |
| 9223 | |
| 9224 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_acos, name, "aten::_foreach_acos") |
| 9225 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_acos, overload_name, "") |
| 9226 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_acos, schema_str, "_foreach_acos(Tensor[] self) -> Tensor[]") |
| 9227 | |
| 9228 | // aten::_foreach_acos(Tensor[] self) -> Tensor[] |
| 9229 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_acos::schema> create__foreach_acos_typed_handle() { |
| 9230 | return c10::Dispatcher::singleton() |
| 9231 | .findSchemaOrThrow(_foreach_acos::name, _foreach_acos::overload_name) |
| 9232 | .typed<_foreach_acos::schema>(); |
| 9233 | } |
| 9234 | |
| 9235 | // aten::_foreach_acos(Tensor[] self) -> Tensor[] |
| 9236 | ::std::vector<at::Tensor> _foreach_acos::call(at::TensorList self) { |
| 9237 | |
| 9238 | static auto op = create__foreach_acos_typed_handle(); |
| 9239 | return op.call(self); |
| 9240 | } |
| 9241 | |
| 9242 | // aten::_foreach_acos(Tensor[] self) -> Tensor[] |
| 9243 | ::std::vector<at::Tensor> _foreach_acos::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 9244 | |
| 9245 | static auto op = create__foreach_acos_typed_handle(); |
| 9246 | return op.redispatch(dispatchKeySet, self); |
| 9247 | } |
| 9248 | |
| 9249 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_acos_, name, "aten::_foreach_acos_") |
| 9250 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_acos_, overload_name, "") |
| 9251 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_acos_, schema_str, "_foreach_acos_(Tensor(a!)[] self) -> ()") |
| 9252 | |
| 9253 | // aten::_foreach_acos_(Tensor(a!)[] self) -> () |
| 9254 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_acos_::schema> create__foreach_acos__typed_handle() { |
| 9255 | return c10::Dispatcher::singleton() |
| 9256 | .findSchemaOrThrow(_foreach_acos_::name, _foreach_acos_::overload_name) |
| 9257 | .typed<_foreach_acos_::schema>(); |
| 9258 | } |
| 9259 | |
| 9260 | // aten::_foreach_acos_(Tensor(a!)[] self) -> () |
| 9261 | void _foreach_acos_::call(at::TensorList self) { |
| 9262 | |
| 9263 | static auto op = create__foreach_acos__typed_handle(); |
| 9264 | return op.call(self); |
| 9265 | } |
| 9266 | |
| 9267 | // aten::_foreach_acos_(Tensor(a!)[] self) -> () |
| 9268 | void _foreach_acos_::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 9269 | |
| 9270 | static auto op = create__foreach_acos__typed_handle(); |
| 9271 | return op.redispatch(dispatchKeySet, self); |
| 9272 | } |
| 9273 | |
| 9274 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_atan, name, "aten::_foreach_atan") |
| 9275 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_atan, overload_name, "") |
| 9276 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_atan, schema_str, "_foreach_atan(Tensor[] self) -> Tensor[]") |
| 9277 | |
| 9278 | // aten::_foreach_atan(Tensor[] self) -> Tensor[] |
| 9279 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_atan::schema> create__foreach_atan_typed_handle() { |
| 9280 | return c10::Dispatcher::singleton() |
| 9281 | .findSchemaOrThrow(_foreach_atan::name, _foreach_atan::overload_name) |
| 9282 | .typed<_foreach_atan::schema>(); |
| 9283 | } |
| 9284 | |
| 9285 | // aten::_foreach_atan(Tensor[] self) -> Tensor[] |
| 9286 | ::std::vector<at::Tensor> _foreach_atan::call(at::TensorList self) { |
| 9287 | |
| 9288 | static auto op = create__foreach_atan_typed_handle(); |
| 9289 | return op.call(self); |
| 9290 | } |
| 9291 | |
| 9292 | // aten::_foreach_atan(Tensor[] self) -> Tensor[] |
| 9293 | ::std::vector<at::Tensor> _foreach_atan::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 9294 | |
| 9295 | static auto op = create__foreach_atan_typed_handle(); |
| 9296 | return op.redispatch(dispatchKeySet, self); |
| 9297 | } |
| 9298 | |
| 9299 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_atan_, name, "aten::_foreach_atan_") |
| 9300 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_atan_, overload_name, "") |
| 9301 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_atan_, schema_str, "_foreach_atan_(Tensor(a!)[] self) -> ()") |
| 9302 | |
| 9303 | // aten::_foreach_atan_(Tensor(a!)[] self) -> () |
| 9304 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_atan_::schema> create__foreach_atan__typed_handle() { |
| 9305 | return c10::Dispatcher::singleton() |
| 9306 | .findSchemaOrThrow(_foreach_atan_::name, _foreach_atan_::overload_name) |
| 9307 | .typed<_foreach_atan_::schema>(); |
| 9308 | } |
| 9309 | |
| 9310 | // aten::_foreach_atan_(Tensor(a!)[] self) -> () |
| 9311 | void _foreach_atan_::call(at::TensorList self) { |
| 9312 | |
| 9313 | static auto op = create__foreach_atan__typed_handle(); |
| 9314 | return op.call(self); |
| 9315 | } |
| 9316 | |
| 9317 | // aten::_foreach_atan_(Tensor(a!)[] self) -> () |
| 9318 | void _foreach_atan_::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 9319 | |
| 9320 | static auto op = create__foreach_atan__typed_handle(); |
| 9321 | return op.redispatch(dispatchKeySet, self); |
| 9322 | } |
| 9323 | |
| 9324 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_ceil, name, "aten::_foreach_ceil") |
| 9325 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_ceil, overload_name, "") |
| 9326 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_ceil, schema_str, "_foreach_ceil(Tensor[] self) -> Tensor[]") |
| 9327 | |
| 9328 | // aten::_foreach_ceil(Tensor[] self) -> Tensor[] |
| 9329 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_ceil::schema> create__foreach_ceil_typed_handle() { |
| 9330 | return c10::Dispatcher::singleton() |
| 9331 | .findSchemaOrThrow(_foreach_ceil::name, _foreach_ceil::overload_name) |
| 9332 | .typed<_foreach_ceil::schema>(); |
| 9333 | } |
| 9334 | |
| 9335 | // aten::_foreach_ceil(Tensor[] self) -> Tensor[] |
| 9336 | ::std::vector<at::Tensor> _foreach_ceil::call(at::TensorList self) { |
| 9337 | |
| 9338 | static auto op = create__foreach_ceil_typed_handle(); |
| 9339 | return op.call(self); |
| 9340 | } |
| 9341 | |
| 9342 | // aten::_foreach_ceil(Tensor[] self) -> Tensor[] |
| 9343 | ::std::vector<at::Tensor> _foreach_ceil::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 9344 | |
| 9345 | static auto op = create__foreach_ceil_typed_handle(); |
| 9346 | return op.redispatch(dispatchKeySet, self); |
| 9347 | } |
| 9348 | |
| 9349 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_ceil_, name, "aten::_foreach_ceil_") |
| 9350 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_ceil_, overload_name, "") |
| 9351 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_ceil_, schema_str, "_foreach_ceil_(Tensor(a!)[] self) -> ()") |
| 9352 | |
| 9353 | // aten::_foreach_ceil_(Tensor(a!)[] self) -> () |
| 9354 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_ceil_::schema> create__foreach_ceil__typed_handle() { |
| 9355 | return c10::Dispatcher::singleton() |
| 9356 | .findSchemaOrThrow(_foreach_ceil_::name, _foreach_ceil_::overload_name) |
| 9357 | .typed<_foreach_ceil_::schema>(); |
| 9358 | } |
| 9359 | |
| 9360 | // aten::_foreach_ceil_(Tensor(a!)[] self) -> () |
| 9361 | void _foreach_ceil_::call(at::TensorList self) { |
| 9362 | |
| 9363 | static auto op = create__foreach_ceil__typed_handle(); |
| 9364 | return op.call(self); |
| 9365 | } |
| 9366 | |
| 9367 | // aten::_foreach_ceil_(Tensor(a!)[] self) -> () |
| 9368 | void _foreach_ceil_::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 9369 | |
| 9370 | static auto op = create__foreach_ceil__typed_handle(); |
| 9371 | return op.redispatch(dispatchKeySet, self); |
| 9372 | } |
| 9373 | |
| 9374 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_erf, name, "aten::_foreach_erf") |
| 9375 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_erf, overload_name, "") |
| 9376 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_erf, schema_str, "_foreach_erf(Tensor[] self) -> Tensor[]") |
| 9377 | |
| 9378 | // aten::_foreach_erf(Tensor[] self) -> Tensor[] |
| 9379 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_erf::schema> create__foreach_erf_typed_handle() { |
| 9380 | return c10::Dispatcher::singleton() |
| 9381 | .findSchemaOrThrow(_foreach_erf::name, _foreach_erf::overload_name) |
| 9382 | .typed<_foreach_erf::schema>(); |
| 9383 | } |
| 9384 | |
| 9385 | // aten::_foreach_erf(Tensor[] self) -> Tensor[] |
| 9386 | ::std::vector<at::Tensor> _foreach_erf::call(at::TensorList self) { |
| 9387 | |
| 9388 | static auto op = create__foreach_erf_typed_handle(); |
| 9389 | return op.call(self); |
| 9390 | } |
| 9391 | |
| 9392 | // aten::_foreach_erf(Tensor[] self) -> Tensor[] |
| 9393 | ::std::vector<at::Tensor> _foreach_erf::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 9394 | |
| 9395 | static auto op = create__foreach_erf_typed_handle(); |
| 9396 | return op.redispatch(dispatchKeySet, self); |
| 9397 | } |
| 9398 | |
| 9399 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_erf_, name, "aten::_foreach_erf_") |
| 9400 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_erf_, overload_name, "") |
| 9401 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_erf_, schema_str, "_foreach_erf_(Tensor(a!)[] self) -> ()") |
| 9402 | |
| 9403 | // aten::_foreach_erf_(Tensor(a!)[] self) -> () |
| 9404 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_erf_::schema> create__foreach_erf__typed_handle() { |
| 9405 | return c10::Dispatcher::singleton() |
| 9406 | .findSchemaOrThrow(_foreach_erf_::name, _foreach_erf_::overload_name) |
| 9407 | .typed<_foreach_erf_::schema>(); |
| 9408 | } |
| 9409 | |
| 9410 | // aten::_foreach_erf_(Tensor(a!)[] self) -> () |
| 9411 | void _foreach_erf_::call(at::TensorList self) { |
| 9412 | |
| 9413 | static auto op = create__foreach_erf__typed_handle(); |
| 9414 | return op.call(self); |
| 9415 | } |
| 9416 | |
| 9417 | // aten::_foreach_erf_(Tensor(a!)[] self) -> () |
| 9418 | void _foreach_erf_::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 9419 | |
| 9420 | static auto op = create__foreach_erf__typed_handle(); |
| 9421 | return op.redispatch(dispatchKeySet, self); |
| 9422 | } |
| 9423 | |
| 9424 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_log2, name, "aten::_foreach_log2") |
| 9425 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_log2, overload_name, "") |
| 9426 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_log2, schema_str, "_foreach_log2(Tensor[] self) -> Tensor[]") |
| 9427 | |
| 9428 | // aten::_foreach_log2(Tensor[] self) -> Tensor[] |
| 9429 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_log2::schema> create__foreach_log2_typed_handle() { |
| 9430 | return c10::Dispatcher::singleton() |
| 9431 | .findSchemaOrThrow(_foreach_log2::name, _foreach_log2::overload_name) |
| 9432 | .typed<_foreach_log2::schema>(); |
| 9433 | } |
| 9434 | |
| 9435 | // aten::_foreach_log2(Tensor[] self) -> Tensor[] |
| 9436 | ::std::vector<at::Tensor> _foreach_log2::call(at::TensorList self) { |
| 9437 | |
| 9438 | static auto op = create__foreach_log2_typed_handle(); |
| 9439 | return op.call(self); |
| 9440 | } |
| 9441 | |
| 9442 | // aten::_foreach_log2(Tensor[] self) -> Tensor[] |
| 9443 | ::std::vector<at::Tensor> _foreach_log2::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 9444 | |
| 9445 | static auto op = create__foreach_log2_typed_handle(); |
| 9446 | return op.redispatch(dispatchKeySet, self); |
| 9447 | } |
| 9448 | |
| 9449 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_log2_, name, "aten::_foreach_log2_") |
| 9450 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_log2_, overload_name, "") |
| 9451 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_log2_, schema_str, "_foreach_log2_(Tensor(a!)[] self) -> ()") |
| 9452 | |
| 9453 | // aten::_foreach_log2_(Tensor(a!)[] self) -> () |
| 9454 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_log2_::schema> create__foreach_log2__typed_handle() { |
| 9455 | return c10::Dispatcher::singleton() |
| 9456 | .findSchemaOrThrow(_foreach_log2_::name, _foreach_log2_::overload_name) |
| 9457 | .typed<_foreach_log2_::schema>(); |
| 9458 | } |
| 9459 | |
| 9460 | // aten::_foreach_log2_(Tensor(a!)[] self) -> () |
| 9461 | void _foreach_log2_::call(at::TensorList self) { |
| 9462 | |
| 9463 | static auto op = create__foreach_log2__typed_handle(); |
| 9464 | return op.call(self); |
| 9465 | } |
| 9466 | |
| 9467 | // aten::_foreach_log2_(Tensor(a!)[] self) -> () |
| 9468 | void _foreach_log2_::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { |
| 9469 | |
| 9470 | static auto op = create__foreach_log2__typed_handle(); |
| 9471 | return op.redispatch(dispatchKeySet, self); |
| 9472 | } |
| 9473 | |
| 9474 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bucketize_Tensor, name, "aten::bucketize") |
| 9475 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bucketize_Tensor, overload_name, "Tensor") |
| 9476 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bucketize_Tensor, schema_str, "bucketize.Tensor(Tensor self, Tensor boundaries, *, bool out_int32=False, bool right=False) -> Tensor") |
| 9477 | |
| 9478 | // aten::bucketize.Tensor(Tensor self, Tensor boundaries, *, bool out_int32=False, bool right=False) -> Tensor |
| 9479 | static C10_NOINLINE c10::TypedOperatorHandle<bucketize_Tensor::schema> create_bucketize_Tensor_typed_handle() { |
| 9480 | return c10::Dispatcher::singleton() |
| 9481 | .findSchemaOrThrow(bucketize_Tensor::name, bucketize_Tensor::overload_name) |
| 9482 | .typed<bucketize_Tensor::schema>(); |
| 9483 | } |
| 9484 | |
| 9485 | // aten::bucketize.Tensor(Tensor self, Tensor boundaries, *, bool out_int32=False, bool right=False) -> Tensor |
| 9486 | at::Tensor bucketize_Tensor::call(const at::Tensor & self, const at::Tensor & boundaries, bool out_int32, bool right) { |
| 9487 | |
| 9488 | static auto op = create_bucketize_Tensor_typed_handle(); |
| 9489 | return op.call(self, boundaries, out_int32, right); |
| 9490 | } |
| 9491 | |
| 9492 | // aten::bucketize.Tensor(Tensor self, Tensor boundaries, *, bool out_int32=False, bool right=False) -> Tensor |
| 9493 | at::Tensor bucketize_Tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & boundaries, bool out_int32, bool right) { |
| 9494 | |
| 9495 | static auto op = create_bucketize_Tensor_typed_handle(); |
| 9496 | return op.redispatch(dispatchKeySet, self, boundaries, out_int32, right); |
| 9497 | } |
| 9498 | |
| 9499 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bucketize_Tensor_out, name, "aten::bucketize") |
| 9500 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bucketize_Tensor_out, overload_name, "Tensor_out") |
| 9501 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bucketize_Tensor_out, schema_str, "bucketize.Tensor_out(Tensor self, Tensor boundaries, *, bool out_int32=False, bool right=False, Tensor(a!) out) -> Tensor(a!)") |
| 9502 | |
| 9503 | // aten::bucketize.Tensor_out(Tensor self, Tensor boundaries, *, bool out_int32=False, bool right=False, Tensor(a!) out) -> Tensor(a!) |
| 9504 | static C10_NOINLINE c10::TypedOperatorHandle<bucketize_Tensor_out::schema> create_bucketize_Tensor_out_typed_handle() { |
| 9505 | return c10::Dispatcher::singleton() |
| 9506 | .findSchemaOrThrow(bucketize_Tensor_out::name, bucketize_Tensor_out::overload_name) |
| 9507 | .typed<bucketize_Tensor_out::schema>(); |
| 9508 | } |
| 9509 | |
| 9510 | // aten::bucketize.Tensor_out(Tensor self, Tensor boundaries, *, bool out_int32=False, bool right=False, Tensor(a!) out) -> Tensor(a!) |
| 9511 | at::Tensor & bucketize_Tensor_out::call(const at::Tensor & self, const at::Tensor & boundaries, bool out_int32, bool right, at::Tensor & out) { |
| 9512 | |
| 9513 | static auto op = create_bucketize_Tensor_out_typed_handle(); |
| 9514 | return op.call(self, boundaries, out_int32, right, out); |
| 9515 | } |
| 9516 | |
| 9517 | // aten::bucketize.Tensor_out(Tensor self, Tensor boundaries, *, bool out_int32=False, bool right=False, Tensor(a!) out) -> Tensor(a!) |
| 9518 | at::Tensor & bucketize_Tensor_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & boundaries, bool out_int32, bool right, at::Tensor & out) { |
| 9519 | |
| 9520 | static auto op = create_bucketize_Tensor_out_typed_handle(); |
| 9521 | return op.redispatch(dispatchKeySet, self, boundaries, out_int32, right, out); |
| 9522 | } |
| 9523 | |
| 9524 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bucketize_Scalar, name, "aten::bucketize") |
| 9525 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bucketize_Scalar, overload_name, "Scalar") |
| 9526 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bucketize_Scalar, schema_str, "bucketize.Scalar(Scalar self, Tensor boundaries, *, bool out_int32=False, bool right=False) -> Tensor") |
| 9527 | |
| 9528 | // aten::bucketize.Scalar(Scalar self, Tensor boundaries, *, bool out_int32=False, bool right=False) -> Tensor |
| 9529 | static C10_NOINLINE c10::TypedOperatorHandle<bucketize_Scalar::schema> create_bucketize_Scalar_typed_handle() { |
| 9530 | return c10::Dispatcher::singleton() |
| 9531 | .findSchemaOrThrow(bucketize_Scalar::name, bucketize_Scalar::overload_name) |
| 9532 | .typed<bucketize_Scalar::schema>(); |
| 9533 | } |
| 9534 | |
| 9535 | // aten::bucketize.Scalar(Scalar self, Tensor boundaries, *, bool out_int32=False, bool right=False) -> Tensor |
| 9536 | at::Tensor bucketize_Scalar::call(const at::Scalar & self, const at::Tensor & boundaries, bool out_int32, bool right) { |
| 9537 | |
| 9538 | static auto op = create_bucketize_Scalar_typed_handle(); |
| 9539 | return op.call(self, boundaries, out_int32, right); |
| 9540 | } |
| 9541 | |
| 9542 | // aten::bucketize.Scalar(Scalar self, Tensor boundaries, *, bool out_int32=False, bool right=False) -> Tensor |
| 9543 | at::Tensor bucketize_Scalar::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & boundaries, bool out_int32, bool right) { |
| 9544 | |
| 9545 | static auto op = create_bucketize_Scalar_typed_handle(); |
| 9546 | return op.redispatch(dispatchKeySet, self, boundaries, out_int32, right); |
| 9547 | } |
| 9548 | |
| 9549 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mse_loss_out, name, "aten::mse_loss") |
| 9550 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mse_loss_out, overload_name, "out") |
| 9551 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mse_loss_out, schema_str, "mse_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)") |
| 9552 | |
| 9553 | // aten::mse_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) |
| 9554 | static C10_NOINLINE c10::TypedOperatorHandle<mse_loss_out::schema> create_mse_loss_out_typed_handle() { |
| 9555 | return c10::Dispatcher::singleton() |
| 9556 | .findSchemaOrThrow(mse_loss_out::name, mse_loss_out::overload_name) |
| 9557 | .typed<mse_loss_out::schema>(); |
| 9558 | } |
| 9559 | |
| 9560 | // aten::mse_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) |
| 9561 | at::Tensor & mse_loss_out::call(const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & out) { |
| 9562 | |
| 9563 | static auto op = create_mse_loss_out_typed_handle(); |
| 9564 | return op.call(self, target, reduction, out); |
| 9565 | } |
| 9566 | |
| 9567 | // aten::mse_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) |
| 9568 | at::Tensor & mse_loss_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & out) { |
| 9569 | |
| 9570 | static auto op = create_mse_loss_out_typed_handle(); |
| 9571 | return op.redispatch(dispatchKeySet, self, target, reduction, out); |
| 9572 | } |
| 9573 | |
| 9574 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mse_loss, name, "aten::mse_loss") |
| 9575 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mse_loss, overload_name, "") |
| 9576 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mse_loss, schema_str, "mse_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor") |
| 9577 | |
| 9578 | // aten::mse_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor |
| 9579 | static C10_NOINLINE c10::TypedOperatorHandle<mse_loss::schema> create_mse_loss_typed_handle() { |
| 9580 | return c10::Dispatcher::singleton() |
| 9581 | .findSchemaOrThrow(mse_loss::name, mse_loss::overload_name) |
| 9582 | .typed<mse_loss::schema>(); |
| 9583 | } |
| 9584 | |
| 9585 | // aten::mse_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor |
| 9586 | at::Tensor mse_loss::call(const at::Tensor & self, const at::Tensor & target, int64_t reduction) { |
| 9587 | |
| 9588 | static auto op = create_mse_loss_typed_handle(); |
| 9589 | return op.call(self, target, reduction); |
| 9590 | } |
| 9591 | |
| 9592 | // aten::mse_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor |
| 9593 | at::Tensor mse_loss::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, int64_t reduction) { |
| 9594 | |
| 9595 | static auto op = create_mse_loss_typed_handle(); |
| 9596 | return op.redispatch(dispatchKeySet, self, target, reduction); |
| 9597 | } |
| 9598 | |
| 9599 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(l1_loss, name, "aten::l1_loss") |
| 9600 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(l1_loss, overload_name, "") |
| 9601 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(l1_loss, schema_str, "l1_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor") |
| 9602 | |
| 9603 | // aten::l1_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor |
| 9604 | static C10_NOINLINE c10::TypedOperatorHandle<l1_loss::schema> create_l1_loss_typed_handle() { |
| 9605 | return c10::Dispatcher::singleton() |
| 9606 | .findSchemaOrThrow(l1_loss::name, l1_loss::overload_name) |
| 9607 | .typed<l1_loss::schema>(); |
| 9608 | } |
| 9609 | |
| 9610 | // aten::l1_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor |
| 9611 | at::Tensor l1_loss::call(const at::Tensor & self, const at::Tensor & target, int64_t reduction) { |
| 9612 | |
| 9613 | static auto op = create_l1_loss_typed_handle(); |
| 9614 | return op.call(self, target, reduction); |
| 9615 | } |
| 9616 | |
| 9617 | // aten::l1_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor |
| 9618 | at::Tensor l1_loss::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, int64_t reduction) { |
| 9619 | |
| 9620 | static auto op = create_l1_loss_typed_handle(); |
| 9621 | return op.redispatch(dispatchKeySet, self, target, reduction); |
| 9622 | } |
| 9623 | |
| 9624 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss_nd, name, "aten::nll_loss_nd") |
| 9625 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss_nd, overload_name, "") |
| 9626 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss_nd, schema_str, "nll_loss_nd(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor") |
| 9627 | |
| 9628 | // aten::nll_loss_nd(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor |
| 9629 | static C10_NOINLINE c10::TypedOperatorHandle<nll_loss_nd::schema> create_nll_loss_nd_typed_handle() { |
| 9630 | return c10::Dispatcher::singleton() |
| 9631 | .findSchemaOrThrow(nll_loss_nd::name, nll_loss_nd::overload_name) |
| 9632 | .typed<nll_loss_nd::schema>(); |
| 9633 | } |
| 9634 | |
| 9635 | // aten::nll_loss_nd(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor |
| 9636 | at::Tensor nll_loss_nd::call(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index) { |
| 9637 | |
| 9638 | static auto op = create_nll_loss_nd_typed_handle(); |
| 9639 | return op.call(self, target, weight, reduction, ignore_index); |
| 9640 | } |
| 9641 | |
| 9642 | // aten::nll_loss_nd(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor |
| 9643 | at::Tensor nll_loss_nd::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) { |
| 9644 | |
| 9645 | static auto op = create_nll_loss_nd_typed_handle(); |
| 9646 | return op.redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index); |
| 9647 | } |
| 9648 | |
| 9649 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss2d_out, name, "aten::nll_loss2d") |
| 9650 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss2d_out, overload_name, "out") |
| 9651 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss2d_out, schema_str, "nll_loss2d.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!)") |
| 9652 | |
| 9653 | // aten::nll_loss2d.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!) |
| 9654 | static C10_NOINLINE c10::TypedOperatorHandle<nll_loss2d_out::schema> create_nll_loss2d_out_typed_handle() { |
| 9655 | return c10::Dispatcher::singleton() |
| 9656 | .findSchemaOrThrow(nll_loss2d_out::name, nll_loss2d_out::overload_name) |
| 9657 | .typed<nll_loss2d_out::schema>(); |
| 9658 | } |
| 9659 | |
| 9660 | // aten::nll_loss2d.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!) |
| 9661 | at::Tensor & nll_loss2d_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) { |
| 9662 | |
| 9663 | static auto op = create_nll_loss2d_out_typed_handle(); |
| 9664 | return op.call(self, target, weight, reduction, ignore_index, out); |
| 9665 | } |
| 9666 | |
| 9667 | // aten::nll_loss2d.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!) |
| 9668 | at::Tensor & nll_loss2d_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) { |
| 9669 | |
| 9670 | static auto op = create_nll_loss2d_out_typed_handle(); |
| 9671 | return op.redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index, out); |
| 9672 | } |
| 9673 | |
| 9674 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss2d, name, "aten::nll_loss2d") |
| 9675 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss2d, overload_name, "") |
| 9676 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss2d, schema_str, "nll_loss2d(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor") |
| 9677 | |
| 9678 | // aten::nll_loss2d(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor |
| 9679 | static C10_NOINLINE c10::TypedOperatorHandle<nll_loss2d::schema> create_nll_loss2d_typed_handle() { |
| 9680 | return c10::Dispatcher::singleton() |
| 9681 | .findSchemaOrThrow(nll_loss2d::name, nll_loss2d::overload_name) |
| 9682 | .typed<nll_loss2d::schema>(); |
| 9683 | } |
| 9684 | |
| 9685 | // aten::nll_loss2d(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor |
| 9686 | at::Tensor nll_loss2d::call(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index) { |
| 9687 | |
| 9688 | static auto op = create_nll_loss2d_typed_handle(); |
| 9689 | return op.call(self, target, weight, reduction, ignore_index); |
| 9690 | } |
| 9691 | |
| 9692 | // aten::nll_loss2d(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor |
| 9693 | at::Tensor nll_loss2d::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) { |
| 9694 | |
| 9695 | static auto op = create_nll_loss2d_typed_handle(); |
| 9696 | return op.redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index); |
| 9697 | } |
| 9698 | |
| 9699 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss2d_forward_output, name, "aten::nll_loss2d_forward") |
| 9700 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss2d_forward_output, overload_name, "output") |
| 9701 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss2d_forward_output, schema_str, "nll_loss2d_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!))") |
| 9702 | |
| 9703 | // aten::nll_loss2d_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!)) |
| 9704 | static C10_NOINLINE c10::TypedOperatorHandle<nll_loss2d_forward_output::schema> create_nll_loss2d_forward_output_typed_handle() { |
| 9705 | return c10::Dispatcher::singleton() |
| 9706 | .findSchemaOrThrow(nll_loss2d_forward_output::name, nll_loss2d_forward_output::overload_name) |
| 9707 | .typed<nll_loss2d_forward_output::schema>(); |
| 9708 | } |
| 9709 | |
| 9710 | // aten::nll_loss2d_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!)) |
| 9711 | ::std::tuple<at::Tensor &,at::Tensor &> nll_loss2d_forward_output::call(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, at::Tensor & output, at::Tensor & total_weight) { |
| 9712 | |
| 9713 | static auto op = create_nll_loss2d_forward_output_typed_handle(); |
| 9714 | return op.call(self, target, weight, reduction, ignore_index, output, total_weight); |
| 9715 | } |
| 9716 | |
| 9717 | // aten::nll_loss2d_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!)) |
| 9718 | ::std::tuple<at::Tensor &,at::Tensor &> nll_loss2d_forward_output::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 & output, at::Tensor & total_weight) { |
| 9719 | |
| 9720 | static auto op = create_nll_loss2d_forward_output_typed_handle(); |
| 9721 | return op.redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index, output, total_weight); |
| 9722 | } |
| 9723 | |
| 9724 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss2d_forward, name, "aten::nll_loss2d_forward") |
| 9725 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss2d_forward, overload_name, "") |
| 9726 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss2d_forward, schema_str, "nll_loss2d_forward(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index) -> (Tensor output, Tensor total_weight)") |
| 9727 | |
| 9728 | // aten::nll_loss2d_forward(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index) -> (Tensor output, Tensor total_weight) |
| 9729 | static C10_NOINLINE c10::TypedOperatorHandle<nll_loss2d_forward::schema> create_nll_loss2d_forward_typed_handle() { |
| 9730 | return c10::Dispatcher::singleton() |
| 9731 | .findSchemaOrThrow(nll_loss2d_forward::name, nll_loss2d_forward::overload_name) |
| 9732 | .typed<nll_loss2d_forward::schema>(); |
| 9733 | } |
| 9734 | |
| 9735 | // aten::nll_loss2d_forward(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index) -> (Tensor output, Tensor total_weight) |
| 9736 | ::std::tuple<at::Tensor,at::Tensor> nll_loss2d_forward::call(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index) { |
| 9737 | |
| 9738 | static auto op = create_nll_loss2d_forward_typed_handle(); |
| 9739 | return op.call(self, target, weight, reduction, ignore_index); |
| 9740 | } |
| 9741 | |
| 9742 | // aten::nll_loss2d_forward(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index) -> (Tensor output, Tensor total_weight) |
| 9743 | ::std::tuple<at::Tensor,at::Tensor> nll_loss2d_forward::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) { |
| 9744 | |
| 9745 | static auto op = create_nll_loss2d_forward_typed_handle(); |
| 9746 | return op.redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index); |
| 9747 | } |
| 9748 | |
| 9749 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss2d_backward_grad_input, name, "aten::nll_loss2d_backward") |
| 9750 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss2d_backward_grad_input, overload_name, "grad_input") |
| 9751 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss2d_backward_grad_input, schema_str, "nll_loss2d_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!)") |
| 9752 | |
| 9753 | // aten::nll_loss2d_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!) |
| 9754 | static C10_NOINLINE c10::TypedOperatorHandle<nll_loss2d_backward_grad_input::schema> create_nll_loss2d_backward_grad_input_typed_handle() { |
| 9755 | return c10::Dispatcher::singleton() |
| 9756 | .findSchemaOrThrow(nll_loss2d_backward_grad_input::name, nll_loss2d_backward_grad_input::overload_name) |
| 9757 | .typed<nll_loss2d_backward_grad_input::schema>(); |
| 9758 | } |
| 9759 | |
| 9760 | // aten::nll_loss2d_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!) |
| 9761 | at::Tensor & nll_loss2d_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) { |
| 9762 | |
| 9763 | static auto op = create_nll_loss2d_backward_grad_input_typed_handle(); |
| 9764 | return op.call(grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input); |
| 9765 | } |
| 9766 | |
| 9767 | // aten::nll_loss2d_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!) |
| 9768 | at::Tensor & nll_loss2d_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) { |
| 9769 | |
| 9770 | static auto op = create_nll_loss2d_backward_grad_input_typed_handle(); |
| 9771 | return op.redispatch(dispatchKeySet, grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input); |
| 9772 | } |
| 9773 | |
| 9774 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss2d_backward, name, "aten::nll_loss2d_backward") |
| 9775 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss2d_backward, overload_name, "") |
| 9776 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(nll_loss2d_backward, schema_str, "nll_loss2d_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight) -> Tensor") |
| 9777 | |
| 9778 | // aten::nll_loss2d_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight) -> Tensor |
| 9779 | static C10_NOINLINE c10::TypedOperatorHandle<nll_loss2d_backward::schema> create_nll_loss2d_backward_typed_handle() { |
| 9780 | return c10::Dispatcher::singleton() |
| 9781 | .findSchemaOrThrow(nll_loss2d_backward::name, nll_loss2d_backward::overload_name) |
| 9782 | .typed<nll_loss2d_backward::schema>(); |
| 9783 | } |
| 9784 | |
| 9785 | // aten::nll_loss2d_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight) -> Tensor |
| 9786 | at::Tensor nll_loss2d_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) { |
| 9787 | |
| 9788 | static auto op = create_nll_loss2d_backward_typed_handle(); |
| 9789 | return op.call(grad_output, self, target, weight, reduction, ignore_index, total_weight); |
| 9790 | } |
| 9791 | |
| 9792 | // aten::nll_loss2d_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight) -> Tensor |
| 9793 | at::Tensor nll_loss2d_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) { |
| 9794 | |
| 9795 | static auto op = create_nll_loss2d_backward_typed_handle(); |
| 9796 | return op.redispatch(dispatchKeySet, grad_output, self, target, weight, reduction, ignore_index, total_weight); |
| 9797 | } |
| 9798 | |
| 9799 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(soft_margin_loss_out, name, "aten::soft_margin_loss") |
| 9800 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(soft_margin_loss_out, overload_name, "out") |
| 9801 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(soft_margin_loss_out, schema_str, "soft_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)") |
| 9802 | |
| 9803 | // aten::soft_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) |
| 9804 | static C10_NOINLINE c10::TypedOperatorHandle<soft_margin_loss_out::schema> create_soft_margin_loss_out_typed_handle() { |
| 9805 | return c10::Dispatcher::singleton() |
| 9806 | .findSchemaOrThrow(soft_margin_loss_out::name, soft_margin_loss_out::overload_name) |
| 9807 | .typed<soft_margin_loss_out::schema>(); |
| 9808 | } |
| 9809 | |
| 9810 | // aten::soft_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) |
| 9811 | at::Tensor & soft_margin_loss_out::call(const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & out) { |
| 9812 | |
| 9813 | static auto op = create_soft_margin_loss_out_typed_handle(); |
| 9814 | return op.call(self, target, reduction, out); |
| 9815 | } |
| 9816 | |
| 9817 | // aten::soft_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) |
| 9818 | at::Tensor & soft_margin_loss_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & out) { |
| 9819 | |
| 9820 | static auto op = create_soft_margin_loss_out_typed_handle(); |
| 9821 | return op.redispatch(dispatchKeySet, self, target, reduction, out); |
| 9822 | } |
| 9823 | |
| 9824 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(soft_margin_loss, name, "aten::soft_margin_loss") |
| 9825 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(soft_margin_loss, overload_name, "") |
| 9826 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(soft_margin_loss, schema_str, "soft_margin_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor") |
| 9827 | |
| 9828 | // aten::soft_margin_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor |
| 9829 | static C10_NOINLINE c10::TypedOperatorHandle<soft_margin_loss::schema> create_soft_margin_loss_typed_handle() { |
| 9830 | return c10::Dispatcher::singleton() |
| 9831 | .findSchemaOrThrow(soft_margin_loss::name, soft_margin_loss::overload_name) |
| 9832 | .typed<soft_margin_loss::schema>(); |
| 9833 | } |
| 9834 | |
| 9835 | // aten::soft_margin_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor |
| 9836 | at::Tensor soft_margin_loss::call(const at::Tensor & self, const at::Tensor & target, int64_t reduction) { |
| 9837 | |
| 9838 | static auto op = create_soft_margin_loss_typed_handle(); |
| 9839 | return op.call(self, target, reduction); |
| 9840 | } |
| 9841 | |
| 9842 | // aten::soft_margin_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor |
| 9843 | at::Tensor soft_margin_loss::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, int64_t reduction) { |
| 9844 | |
| 9845 | static auto op = create_soft_margin_loss_typed_handle(); |
| 9846 | return op.redispatch(dispatchKeySet, self, target, reduction); |
| 9847 | } |
| 9848 | |
| 9849 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(glu_out, name, "aten::glu") |
| 9850 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(glu_out, overload_name, "out") |
| 9851 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(glu_out, schema_str, "glu.out(Tensor self, int dim=-1, *, Tensor(a!) out) -> Tensor(a!)") |
| 9852 | |
| 9853 | // aten::glu.out(Tensor self, int dim=-1, *, Tensor(a!) out) -> Tensor(a!) |
| 9854 | static C10_NOINLINE c10::TypedOperatorHandle<glu_out::schema> create_glu_out_typed_handle() { |
| 9855 | return c10::Dispatcher::singleton() |
| 9856 | .findSchemaOrThrow(glu_out::name, glu_out::overload_name) |
| 9857 | .typed<glu_out::schema>(); |
| 9858 | } |
| 9859 | |
| 9860 | // aten::glu.out(Tensor self, int dim=-1, *, Tensor(a!) out) -> Tensor(a!) |
| 9861 | at::Tensor & glu_out::call(const at::Tensor & self, int64_t dim, at::Tensor & out) { |
| 9862 | |
| 9863 | static auto op = create_glu_out_typed_handle(); |
| 9864 | return op.call(self, dim, out); |
| 9865 | } |
| 9866 | |
| 9867 | // aten::glu.out(Tensor self, int dim=-1, *, Tensor(a!) out) -> Tensor(a!) |
| 9868 | at::Tensor & glu_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, at::Tensor & out) { |
| 9869 | |
| 9870 | static auto op = create_glu_out_typed_handle(); |
| 9871 | return op.redispatch(dispatchKeySet, self, dim, out); |
| 9872 | } |
| 9873 | |
| 9874 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(glu, name, "aten::glu") |
| 9875 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(glu, overload_name, "") |
| 9876 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(glu, schema_str, "glu(Tensor self, int dim=-1) -> Tensor") |
| 9877 | |
| 9878 | // aten::glu(Tensor self, int dim=-1) -> Tensor |
| 9879 | static C10_NOINLINE c10::TypedOperatorHandle<glu::schema> create_glu_typed_handle() { |
| 9880 | return c10::Dispatcher::singleton() |
| 9881 | .findSchemaOrThrow(glu::name, glu::overload_name) |
| 9882 | .typed<glu::schema>(); |
| 9883 | } |
| 9884 | |
| 9885 | // aten::glu(Tensor self, int dim=-1) -> Tensor |
| 9886 | at::Tensor glu::call(const at::Tensor & self, int64_t dim) { |
| 9887 | |
| 9888 | static auto op = create_glu_typed_handle(); |
| 9889 | return op.call(self, dim); |
| 9890 | } |
| 9891 | |
| 9892 | // aten::glu(Tensor self, int dim=-1) -> Tensor |
| 9893 | at::Tensor glu::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim) { |
| 9894 | |
| 9895 | static auto op = create_glu_typed_handle(); |
| 9896 | return op.redispatch(dispatchKeySet, self, dim); |
| 9897 | } |
| 9898 | |
| 9899 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(glu_backward_jvp, name, "aten::glu_backward_jvp") |
| 9900 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(glu_backward_jvp, overload_name, "") |
| 9901 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(glu_backward_jvp, schema_str, "glu_backward_jvp(Tensor grad_x, Tensor grad_glu, Tensor x, Tensor dgrad_glu, Tensor dx, int dim) -> Tensor") |
| 9902 | |
| 9903 | // aten::glu_backward_jvp(Tensor grad_x, Tensor grad_glu, Tensor x, Tensor dgrad_glu, Tensor dx, int dim) -> Tensor |
| 9904 | static C10_NOINLINE c10::TypedOperatorHandle<glu_backward_jvp::schema> create_glu_backward_jvp_typed_handle() { |
| 9905 | return c10::Dispatcher::singleton() |
| 9906 | .findSchemaOrThrow(glu_backward_jvp::name, glu_backward_jvp::overload_name) |
| 9907 | .typed<glu_backward_jvp::schema>(); |
| 9908 | } |
| 9909 | |
| 9910 | // aten::glu_backward_jvp(Tensor grad_x, Tensor grad_glu, Tensor x, Tensor dgrad_glu, Tensor dx, int dim) -> Tensor |
| 9911 | at::Tensor glu_backward_jvp::call(const at::Tensor & grad_x, const at::Tensor & grad_glu, const at::Tensor & x, const at::Tensor & dgrad_glu, const at::Tensor & dx, int64_t dim) { |
| 9912 | |
| 9913 | static auto op = create_glu_backward_jvp_typed_handle(); |
| 9914 | return op.call(grad_x, grad_glu, x, dgrad_glu, dx, dim); |
| 9915 | } |
| 9916 | |
| 9917 | // aten::glu_backward_jvp(Tensor grad_x, Tensor grad_glu, Tensor x, Tensor dgrad_glu, Tensor dx, int dim) -> Tensor |
| 9918 | at::Tensor glu_backward_jvp::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_x, const at::Tensor & grad_glu, const at::Tensor & x, const at::Tensor & dgrad_glu, const at::Tensor & dx, int64_t dim) { |
| 9919 | |
| 9920 | static auto op = create_glu_backward_jvp_typed_handle(); |
| 9921 | return op.redispatch(dispatchKeySet, grad_x, grad_glu, x, dgrad_glu, dx, dim); |
| 9922 | } |
| 9923 | |
| 9924 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardtanh_out, name, "aten::hardtanh") |
| 9925 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardtanh_out, overload_name, "out") |
| 9926 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardtanh_out, schema_str, "hardtanh.out(Tensor self, Scalar min_val=-1, Scalar max_val=1, *, Tensor(a!) out) -> Tensor(a!)") |
| 9927 | |
| 9928 | // aten::hardtanh.out(Tensor self, Scalar min_val=-1, Scalar max_val=1, *, Tensor(a!) out) -> Tensor(a!) |
| 9929 | static C10_NOINLINE c10::TypedOperatorHandle<hardtanh_out::schema> create_hardtanh_out_typed_handle() { |
| 9930 | return c10::Dispatcher::singleton() |
| 9931 | .findSchemaOrThrow(hardtanh_out::name, hardtanh_out::overload_name) |
| 9932 | .typed<hardtanh_out::schema>(); |
| 9933 | } |
| 9934 | |
| 9935 | // aten::hardtanh.out(Tensor self, Scalar min_val=-1, Scalar max_val=1, *, Tensor(a!) out) -> Tensor(a!) |
| 9936 | at::Tensor & hardtanh_out::call(const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val, at::Tensor & out) { |
| 9937 | |
| 9938 | static auto op = create_hardtanh_out_typed_handle(); |
| 9939 | return op.call(self, min_val, max_val, out); |
| 9940 | } |
| 9941 | |
| 9942 | // aten::hardtanh.out(Tensor self, Scalar min_val=-1, Scalar max_val=1, *, Tensor(a!) out) -> Tensor(a!) |
| 9943 | at::Tensor & hardtanh_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val, at::Tensor & out) { |
| 9944 | |
| 9945 | static auto op = create_hardtanh_out_typed_handle(); |
| 9946 | return op.redispatch(dispatchKeySet, self, min_val, max_val, out); |
| 9947 | } |
| 9948 | |
| 9949 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardtanh, name, "aten::hardtanh") |
| 9950 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardtanh, overload_name, "") |
| 9951 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardtanh, schema_str, "hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -> Tensor") |
| 9952 | |
| 9953 | // aten::hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -> Tensor |
| 9954 | static C10_NOINLINE c10::TypedOperatorHandle<hardtanh::schema> create_hardtanh_typed_handle() { |
| 9955 | return c10::Dispatcher::singleton() |
| 9956 | .findSchemaOrThrow(hardtanh::name, hardtanh::overload_name) |
| 9957 | .typed<hardtanh::schema>(); |
| 9958 | } |
| 9959 | |
| 9960 | // aten::hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -> Tensor |
| 9961 | at::Tensor hardtanh::call(const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val) { |
| 9962 | |
| 9963 | static auto op = create_hardtanh_typed_handle(); |
| 9964 | return op.call(self, min_val, max_val); |
| 9965 | } |
| 9966 | |
| 9967 | // aten::hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -> Tensor |
| 9968 | at::Tensor hardtanh::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val) { |
| 9969 | |
| 9970 | static auto op = create_hardtanh_typed_handle(); |
| 9971 | return op.redispatch(dispatchKeySet, self, min_val, max_val); |
| 9972 | } |
| 9973 | |
| 9974 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardtanh_, name, "aten::hardtanh_") |
| 9975 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardtanh_, overload_name, "") |
| 9976 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardtanh_, schema_str, "hardtanh_(Tensor(a!) self, Scalar min_val=-1, Scalar max_val=1) -> Tensor(a!)") |
| 9977 | |
| 9978 | // aten::hardtanh_(Tensor(a!) self, Scalar min_val=-1, Scalar max_val=1) -> Tensor(a!) |
| 9979 | static C10_NOINLINE c10::TypedOperatorHandle<hardtanh_::schema> create_hardtanh__typed_handle() { |
| 9980 | return c10::Dispatcher::singleton() |
| 9981 | .findSchemaOrThrow(hardtanh_::name, hardtanh_::overload_name) |
| 9982 | .typed<hardtanh_::schema>(); |
| 9983 | } |
| 9984 | |
| 9985 | // aten::hardtanh_(Tensor(a!) self, Scalar min_val=-1, Scalar max_val=1) -> Tensor(a!) |
| 9986 | at::Tensor & hardtanh_::call(at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val) { |
| 9987 | |
| 9988 | static auto op = create_hardtanh__typed_handle(); |
| 9989 | return op.call(self, min_val, max_val); |
| 9990 | } |
| 9991 | |
| 9992 | // aten::hardtanh_(Tensor(a!) self, Scalar min_val=-1, Scalar max_val=1) -> Tensor(a!) |
| 9993 | at::Tensor & hardtanh_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val) { |
| 9994 | |
| 9995 | static auto op = create_hardtanh__typed_handle(); |
| 9996 | return op.redispatch(dispatchKeySet, self, min_val, max_val); |
| 9997 | } |
| 9998 | |
| 9999 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardswish_backward, name, "aten::hardswish_backward") |
| 10000 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardswish_backward, overload_name, "") |
| 10001 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardswish_backward, schema_str, "hardswish_backward(Tensor grad_output, Tensor self) -> Tensor") |
| 10002 | |
| 10003 | // aten::hardswish_backward(Tensor grad_output, Tensor self) -> Tensor |
| 10004 | static C10_NOINLINE c10::TypedOperatorHandle<hardswish_backward::schema> create_hardswish_backward_typed_handle() { |
| 10005 | return c10::Dispatcher::singleton() |
| 10006 | .findSchemaOrThrow(hardswish_backward::name, hardswish_backward::overload_name) |
| 10007 | .typed<hardswish_backward::schema>(); |
| 10008 | } |
| 10009 | |
| 10010 | // aten::hardswish_backward(Tensor grad_output, Tensor self) -> Tensor |
| 10011 | at::Tensor hardswish_backward::call(const at::Tensor & grad_output, const at::Tensor & self) { |
| 10012 | |
| 10013 | static auto op = create_hardswish_backward_typed_handle(); |
| 10014 | return op.call(grad_output, self); |
| 10015 | } |
| 10016 | |
| 10017 | // aten::hardswish_backward(Tensor grad_output, Tensor self) -> Tensor |
| 10018 | at::Tensor hardswish_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self) { |
| 10019 | |
| 10020 | static auto op = create_hardswish_backward_typed_handle(); |
| 10021 | return op.redispatch(dispatchKeySet, grad_output, self); |
| 10022 | } |
| 10023 | |
| 10024 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(leaky_relu_out, name, "aten::leaky_relu") |
| 10025 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(leaky_relu_out, overload_name, "out") |
| 10026 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(leaky_relu_out, schema_str, "leaky_relu.out(Tensor self, Scalar negative_slope=0.01, *, Tensor(a!) out) -> Tensor(a!)") |
| 10027 | |
| 10028 | // aten::leaky_relu.out(Tensor self, Scalar negative_slope=0.01, *, Tensor(a!) out) -> Tensor(a!) |
| 10029 | static C10_NOINLINE c10::TypedOperatorHandle<leaky_relu_out::schema> create_leaky_relu_out_typed_handle() { |
| 10030 | return c10::Dispatcher::singleton() |
| 10031 | .findSchemaOrThrow(leaky_relu_out::name, leaky_relu_out::overload_name) |
| 10032 | .typed<leaky_relu_out::schema>(); |
| 10033 | } |
| 10034 | |
| 10035 | // aten::leaky_relu.out(Tensor self, Scalar negative_slope=0.01, *, Tensor(a!) out) -> Tensor(a!) |
| 10036 | at::Tensor & leaky_relu_out::call(const at::Tensor & self, const at::Scalar & negative_slope, at::Tensor & out) { |
| 10037 | |
| 10038 | static auto op = create_leaky_relu_out_typed_handle(); |
| 10039 | return op.call(self, negative_slope, out); |
| 10040 | } |
| 10041 | |
| 10042 | // aten::leaky_relu.out(Tensor self, Scalar negative_slope=0.01, *, Tensor(a!) out) -> Tensor(a!) |
| 10043 | at::Tensor & leaky_relu_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & negative_slope, at::Tensor & out) { |
| 10044 | |
| 10045 | static auto op = create_leaky_relu_out_typed_handle(); |
| 10046 | return op.redispatch(dispatchKeySet, self, negative_slope, out); |
| 10047 | } |
| 10048 | |
| 10049 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(leaky_relu, name, "aten::leaky_relu") |
| 10050 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(leaky_relu, overload_name, "") |
| 10051 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(leaky_relu, schema_str, "leaky_relu(Tensor self, Scalar negative_slope=0.01) -> Tensor") |
| 10052 | |
| 10053 | // aten::leaky_relu(Tensor self, Scalar negative_slope=0.01) -> Tensor |
| 10054 | static C10_NOINLINE c10::TypedOperatorHandle<leaky_relu::schema> create_leaky_relu_typed_handle() { |
| 10055 | return c10::Dispatcher::singleton() |
| 10056 | .findSchemaOrThrow(leaky_relu::name, leaky_relu::overload_name) |
| 10057 | .typed<leaky_relu::schema>(); |
| 10058 | } |
| 10059 | |
| 10060 | // aten::leaky_relu(Tensor self, Scalar negative_slope=0.01) -> Tensor |
| 10061 | at::Tensor leaky_relu::call(const at::Tensor & self, const at::Scalar & negative_slope) { |
| 10062 | |
| 10063 | static auto op = create_leaky_relu_typed_handle(); |
| 10064 | return op.call(self, negative_slope); |
| 10065 | } |
| 10066 | |
| 10067 | // aten::leaky_relu(Tensor self, Scalar negative_slope=0.01) -> Tensor |
| 10068 | at::Tensor leaky_relu::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & negative_slope) { |
| 10069 | |
| 10070 | static auto op = create_leaky_relu_typed_handle(); |
| 10071 | return op.redispatch(dispatchKeySet, self, negative_slope); |
| 10072 | } |
| 10073 | |
| 10074 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(leaky_relu_, name, "aten::leaky_relu_") |
| 10075 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(leaky_relu_, overload_name, "") |
| 10076 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(leaky_relu_, schema_str, "leaky_relu_(Tensor(a!) self, Scalar negative_slope=0.01) -> Tensor(a!)") |
| 10077 | |
| 10078 | // aten::leaky_relu_(Tensor(a!) self, Scalar negative_slope=0.01) -> Tensor(a!) |
| 10079 | static C10_NOINLINE c10::TypedOperatorHandle<leaky_relu_::schema> create_leaky_relu__typed_handle() { |
| 10080 | return c10::Dispatcher::singleton() |
| 10081 | .findSchemaOrThrow(leaky_relu_::name, leaky_relu_::overload_name) |
| 10082 | .typed<leaky_relu_::schema>(); |
| 10083 | } |
| 10084 | |
| 10085 | // aten::leaky_relu_(Tensor(a!) self, Scalar negative_slope=0.01) -> Tensor(a!) |
| 10086 | at::Tensor & leaky_relu_::call(at::Tensor & self, const at::Scalar & negative_slope) { |
| 10087 | |
| 10088 | static auto op = create_leaky_relu__typed_handle(); |
| 10089 | return op.call(self, negative_slope); |
| 10090 | } |
| 10091 | |
| 10092 | // aten::leaky_relu_(Tensor(a!) self, Scalar negative_slope=0.01) -> Tensor(a!) |
| 10093 | at::Tensor & leaky_relu_::redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & negative_slope) { |
| 10094 | |
| 10095 | static auto op = create_leaky_relu__typed_handle(); |
| 10096 | return op.redispatch(dispatchKeySet, self, negative_slope); |
| 10097 | } |
| 10098 | |
| 10099 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(log_sigmoid_forward_output, name, "aten::log_sigmoid_forward") |
| 10100 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(log_sigmoid_forward_output, overload_name, "output") |
| 10101 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(log_sigmoid_forward_output, schema_str, "log_sigmoid_forward.output(Tensor self, *, Tensor(a!) output, Tensor(b!) buffer) -> (Tensor(a!), Tensor(b!))") |
| 10102 | |
| 10103 | // aten::log_sigmoid_forward.output(Tensor self, *, Tensor(a!) output, Tensor(b!) buffer) -> (Tensor(a!), Tensor(b!)) |
| 10104 | static C10_NOINLINE c10::TypedOperatorHandle<log_sigmoid_forward_output::schema> create_log_sigmoid_forward_output_typed_handle() { |
| 10105 | return c10::Dispatcher::singleton() |
| 10106 | .findSchemaOrThrow(log_sigmoid_forward_output::name, log_sigmoid_forward_output::overload_name) |
| 10107 | .typed<log_sigmoid_forward_output::schema>(); |
| 10108 | } |
| 10109 | |
| 10110 | // aten::log_sigmoid_forward.output(Tensor self, *, Tensor(a!) output, Tensor(b!) buffer) -> (Tensor(a!), Tensor(b!)) |
| 10111 | ::std::tuple<at::Tensor &,at::Tensor &> log_sigmoid_forward_output::call(const at::Tensor & self, at::Tensor & output, at::Tensor & buffer) { |
| 10112 | |
| 10113 | static auto op = create_log_sigmoid_forward_output_typed_handle(); |
| 10114 | return op.call(self, output, buffer); |
| 10115 | } |
| 10116 | |
| 10117 | // aten::log_sigmoid_forward.output(Tensor self, *, Tensor(a!) output, Tensor(b!) buffer) -> (Tensor(a!), Tensor(b!)) |
| 10118 | ::std::tuple<at::Tensor &,at::Tensor &> log_sigmoid_forward_output::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & output, at::Tensor & buffer) { |
| 10119 | |
| 10120 | static auto op = create_log_sigmoid_forward_output_typed_handle(); |
| 10121 | return op.redispatch(dispatchKeySet, self, output, buffer); |
| 10122 | } |
| 10123 | |
| 10124 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(log_sigmoid_forward, name, "aten::log_sigmoid_forward") |
| 10125 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(log_sigmoid_forward, overload_name, "") |
| 10126 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(log_sigmoid_forward, schema_str, "log_sigmoid_forward(Tensor self) -> (Tensor output, Tensor buffer)") |
| 10127 | |
| 10128 | // aten::log_sigmoid_forward(Tensor self) -> (Tensor output, Tensor buffer) |
| 10129 | static C10_NOINLINE c10::TypedOperatorHandle<log_sigmoid_forward::schema> create_log_sigmoid_forward_typed_handle() { |
| 10130 | return c10::Dispatcher::singleton() |
| 10131 | .findSchemaOrThrow(log_sigmoid_forward::name, log_sigmoid_forward::overload_name) |
| 10132 | .typed<log_sigmoid_forward::schema>(); |
| 10133 | } |
| 10134 | |
| 10135 | // aten::log_sigmoid_forward(Tensor self) -> (Tensor output, Tensor buffer) |
| 10136 | ::std::tuple<at::Tensor,at::Tensor> log_sigmoid_forward::call(const at::Tensor & self) { |
| 10137 | |
| 10138 | static auto op = create_log_sigmoid_forward_typed_handle(); |
| 10139 | return op.call(self); |
| 10140 | } |
| 10141 | |
| 10142 | // aten::log_sigmoid_forward(Tensor self) -> (Tensor output, Tensor buffer) |
| 10143 | ::std::tuple<at::Tensor,at::Tensor> log_sigmoid_forward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 10144 | |
| 10145 | static auto op = create_log_sigmoid_forward_typed_handle(); |
| 10146 | return op.redispatch(dispatchKeySet, self); |
| 10147 | } |
| 10148 | |
| 10149 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(log_sigmoid_backward_grad_input, name, "aten::log_sigmoid_backward") |
| 10150 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(log_sigmoid_backward_grad_input, overload_name, "grad_input") |
| 10151 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(log_sigmoid_backward_grad_input, schema_str, "log_sigmoid_backward.grad_input(Tensor grad_output, Tensor self, Tensor buffer, *, Tensor(a!) grad_input) -> Tensor(a!)") |
| 10152 | |
| 10153 | // aten::log_sigmoid_backward.grad_input(Tensor grad_output, Tensor self, Tensor buffer, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 10154 | static C10_NOINLINE c10::TypedOperatorHandle<log_sigmoid_backward_grad_input::schema> create_log_sigmoid_backward_grad_input_typed_handle() { |
| 10155 | return c10::Dispatcher::singleton() |
| 10156 | .findSchemaOrThrow(log_sigmoid_backward_grad_input::name, log_sigmoid_backward_grad_input::overload_name) |
| 10157 | .typed<log_sigmoid_backward_grad_input::schema>(); |
| 10158 | } |
| 10159 | |
| 10160 | // aten::log_sigmoid_backward.grad_input(Tensor grad_output, Tensor self, Tensor buffer, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 10161 | at::Tensor & log_sigmoid_backward_grad_input::call(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer, at::Tensor & grad_input) { |
| 10162 | |
| 10163 | static auto op = create_log_sigmoid_backward_grad_input_typed_handle(); |
| 10164 | return op.call(grad_output, self, buffer, grad_input); |
| 10165 | } |
| 10166 | |
| 10167 | // aten::log_sigmoid_backward.grad_input(Tensor grad_output, Tensor self, Tensor buffer, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 10168 | at::Tensor & log_sigmoid_backward_grad_input::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer, at::Tensor & grad_input) { |
| 10169 | |
| 10170 | static auto op = create_log_sigmoid_backward_grad_input_typed_handle(); |
| 10171 | return op.redispatch(dispatchKeySet, grad_output, self, buffer, grad_input); |
| 10172 | } |
| 10173 | |
| 10174 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(log_sigmoid_backward, name, "aten::log_sigmoid_backward") |
| 10175 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(log_sigmoid_backward, overload_name, "") |
| 10176 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(log_sigmoid_backward, schema_str, "log_sigmoid_backward(Tensor grad_output, Tensor self, Tensor buffer) -> Tensor") |
| 10177 | |
| 10178 | // aten::log_sigmoid_backward(Tensor grad_output, Tensor self, Tensor buffer) -> Tensor |
| 10179 | static C10_NOINLINE c10::TypedOperatorHandle<log_sigmoid_backward::schema> create_log_sigmoid_backward_typed_handle() { |
| 10180 | return c10::Dispatcher::singleton() |
| 10181 | .findSchemaOrThrow(log_sigmoid_backward::name, log_sigmoid_backward::overload_name) |
| 10182 | .typed<log_sigmoid_backward::schema>(); |
| 10183 | } |
| 10184 | |
| 10185 | // aten::log_sigmoid_backward(Tensor grad_output, Tensor self, Tensor buffer) -> Tensor |
| 10186 | at::Tensor log_sigmoid_backward::call(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer) { |
| 10187 | |
| 10188 | static auto op = create_log_sigmoid_backward_typed_handle(); |
| 10189 | return op.call(grad_output, self, buffer); |
| 10190 | } |
| 10191 | |
| 10192 | // aten::log_sigmoid_backward(Tensor grad_output, Tensor self, Tensor buffer) -> Tensor |
| 10193 | at::Tensor log_sigmoid_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer) { |
| 10194 | |
| 10195 | static auto op = create_log_sigmoid_backward_typed_handle(); |
| 10196 | return op.redispatch(dispatchKeySet, grad_output, self, buffer); |
| 10197 | } |
| 10198 | |
| 10199 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(softshrink_out, name, "aten::softshrink") |
| 10200 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(softshrink_out, overload_name, "out") |
| 10201 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(softshrink_out, schema_str, "softshrink.out(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!)") |
| 10202 | |
| 10203 | // aten::softshrink.out(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!) |
| 10204 | static C10_NOINLINE c10::TypedOperatorHandle<softshrink_out::schema> create_softshrink_out_typed_handle() { |
| 10205 | return c10::Dispatcher::singleton() |
| 10206 | .findSchemaOrThrow(softshrink_out::name, softshrink_out::overload_name) |
| 10207 | .typed<softshrink_out::schema>(); |
| 10208 | } |
| 10209 | |
| 10210 | // aten::softshrink.out(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!) |
| 10211 | at::Tensor & softshrink_out::call(const at::Tensor & self, const at::Scalar & lambd, at::Tensor & out) { |
| 10212 | |
| 10213 | static auto op = create_softshrink_out_typed_handle(); |
| 10214 | return op.call(self, lambd, out); |
| 10215 | } |
| 10216 | |
| 10217 | // aten::softshrink.out(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!) |
| 10218 | at::Tensor & softshrink_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & lambd, at::Tensor & out) { |
| 10219 | |
| 10220 | static auto op = create_softshrink_out_typed_handle(); |
| 10221 | return op.redispatch(dispatchKeySet, self, lambd, out); |
| 10222 | } |
| 10223 | |
| 10224 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(softshrink, name, "aten::softshrink") |
| 10225 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(softshrink, overload_name, "") |
| 10226 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(softshrink, schema_str, "softshrink(Tensor self, Scalar lambd=0.5) -> Tensor") |
| 10227 | |
| 10228 | // aten::softshrink(Tensor self, Scalar lambd=0.5) -> Tensor |
| 10229 | static C10_NOINLINE c10::TypedOperatorHandle<softshrink::schema> create_softshrink_typed_handle() { |
| 10230 | return c10::Dispatcher::singleton() |
| 10231 | .findSchemaOrThrow(softshrink::name, softshrink::overload_name) |
| 10232 | .typed<softshrink::schema>(); |
| 10233 | } |
| 10234 | |
| 10235 | // aten::softshrink(Tensor self, Scalar lambd=0.5) -> Tensor |
| 10236 | at::Tensor softshrink::call(const at::Tensor & self, const at::Scalar & lambd) { |
| 10237 | |
| 10238 | static auto op = create_softshrink_typed_handle(); |
| 10239 | return op.call(self, lambd); |
| 10240 | } |
| 10241 | |
| 10242 | // aten::softshrink(Tensor self, Scalar lambd=0.5) -> Tensor |
| 10243 | at::Tensor softshrink::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & lambd) { |
| 10244 | |
| 10245 | static auto op = create_softshrink_typed_handle(); |
| 10246 | return op.redispatch(dispatchKeySet, self, lambd); |
| 10247 | } |
| 10248 | |
| 10249 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_avg_pool3d_backward_grad_input, name, "aten::adaptive_avg_pool3d_backward") |
| 10250 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_avg_pool3d_backward_grad_input, overload_name, "grad_input") |
| 10251 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_avg_pool3d_backward_grad_input, schema_str, "adaptive_avg_pool3d_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!)") |
| 10252 | |
| 10253 | // aten::adaptive_avg_pool3d_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 10254 | static C10_NOINLINE c10::TypedOperatorHandle<adaptive_avg_pool3d_backward_grad_input::schema> create_adaptive_avg_pool3d_backward_grad_input_typed_handle() { |
| 10255 | return c10::Dispatcher::singleton() |
| 10256 | .findSchemaOrThrow(adaptive_avg_pool3d_backward_grad_input::name, adaptive_avg_pool3d_backward_grad_input::overload_name) |
| 10257 | .typed<adaptive_avg_pool3d_backward_grad_input::schema>(); |
| 10258 | } |
| 10259 | |
| 10260 | // aten::adaptive_avg_pool3d_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 10261 | at::Tensor & adaptive_avg_pool3d_backward_grad_input::call(const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & grad_input) { |
| 10262 | |
| 10263 | static auto op = create_adaptive_avg_pool3d_backward_grad_input_typed_handle(); |
| 10264 | return op.call(grad_output, self, grad_input); |
| 10265 | } |
| 10266 | |
| 10267 | // aten::adaptive_avg_pool3d_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 10268 | at::Tensor & adaptive_avg_pool3d_backward_grad_input::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & grad_input) { |
| 10269 | |
| 10270 | static auto op = create_adaptive_avg_pool3d_backward_grad_input_typed_handle(); |
| 10271 | return op.redispatch(dispatchKeySet, grad_output, self, grad_input); |
| 10272 | } |
| 10273 | |
| 10274 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_adaptive_avg_pool3d_backward, name, "aten::_adaptive_avg_pool3d_backward") |
| 10275 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_adaptive_avg_pool3d_backward, overload_name, "") |
| 10276 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_adaptive_avg_pool3d_backward, schema_str, "_adaptive_avg_pool3d_backward(Tensor grad_output, Tensor self) -> Tensor") |
| 10277 | |
| 10278 | // aten::_adaptive_avg_pool3d_backward(Tensor grad_output, Tensor self) -> Tensor |
| 10279 | static C10_NOINLINE c10::TypedOperatorHandle<_adaptive_avg_pool3d_backward::schema> create__adaptive_avg_pool3d_backward_typed_handle() { |
| 10280 | return c10::Dispatcher::singleton() |
| 10281 | .findSchemaOrThrow(_adaptive_avg_pool3d_backward::name, _adaptive_avg_pool3d_backward::overload_name) |
| 10282 | .typed<_adaptive_avg_pool3d_backward::schema>(); |
| 10283 | } |
| 10284 | |
| 10285 | // aten::_adaptive_avg_pool3d_backward(Tensor grad_output, Tensor self) -> Tensor |
| 10286 | at::Tensor _adaptive_avg_pool3d_backward::call(const at::Tensor & grad_output, const at::Tensor & self) { |
| 10287 | |
| 10288 | static auto op = create__adaptive_avg_pool3d_backward_typed_handle(); |
| 10289 | return op.call(grad_output, self); |
| 10290 | } |
| 10291 | |
| 10292 | // aten::_adaptive_avg_pool3d_backward(Tensor grad_output, Tensor self) -> Tensor |
| 10293 | at::Tensor _adaptive_avg_pool3d_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self) { |
| 10294 | |
| 10295 | static auto op = create__adaptive_avg_pool3d_backward_typed_handle(); |
| 10296 | return op.redispatch(dispatchKeySet, grad_output, self); |
| 10297 | } |
| 10298 | |
| 10299 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_max_pool2d_backward_grad_input, name, "aten::adaptive_max_pool2d_backward") |
| 10300 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_max_pool2d_backward_grad_input, overload_name, "grad_input") |
| 10301 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_max_pool2d_backward_grad_input, schema_str, "adaptive_max_pool2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)") |
| 10302 | |
| 10303 | // aten::adaptive_max_pool2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 10304 | static C10_NOINLINE c10::TypedOperatorHandle<adaptive_max_pool2d_backward_grad_input::schema> create_adaptive_max_pool2d_backward_grad_input_typed_handle() { |
| 10305 | return c10::Dispatcher::singleton() |
| 10306 | .findSchemaOrThrow(adaptive_max_pool2d_backward_grad_input::name, adaptive_max_pool2d_backward_grad_input::overload_name) |
| 10307 | .typed<adaptive_max_pool2d_backward_grad_input::schema>(); |
| 10308 | } |
| 10309 | |
| 10310 | // aten::adaptive_max_pool2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 10311 | at::Tensor & adaptive_max_pool2d_backward_grad_input::call(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::Tensor & grad_input) { |
| 10312 | |
| 10313 | static auto op = create_adaptive_max_pool2d_backward_grad_input_typed_handle(); |
| 10314 | return op.call(grad_output, self, indices, grad_input); |
| 10315 | } |
| 10316 | |
| 10317 | // aten::adaptive_max_pool2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 10318 | at::Tensor & adaptive_max_pool2d_backward_grad_input::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::Tensor & grad_input) { |
| 10319 | |
| 10320 | static auto op = create_adaptive_max_pool2d_backward_grad_input_typed_handle(); |
| 10321 | return op.redispatch(dispatchKeySet, grad_output, self, indices, grad_input); |
| 10322 | } |
| 10323 | |
| 10324 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_max_pool2d_backward, name, "aten::adaptive_max_pool2d_backward") |
| 10325 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_max_pool2d_backward, overload_name, "") |
| 10326 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_max_pool2d_backward, schema_str, "adaptive_max_pool2d_backward(Tensor grad_output, Tensor self, Tensor indices) -> Tensor") |
| 10327 | |
| 10328 | // aten::adaptive_max_pool2d_backward(Tensor grad_output, Tensor self, Tensor indices) -> Tensor |
| 10329 | static C10_NOINLINE c10::TypedOperatorHandle<adaptive_max_pool2d_backward::schema> create_adaptive_max_pool2d_backward_typed_handle() { |
| 10330 | return c10::Dispatcher::singleton() |
| 10331 | .findSchemaOrThrow(adaptive_max_pool2d_backward::name, adaptive_max_pool2d_backward::overload_name) |
| 10332 | .typed<adaptive_max_pool2d_backward::schema>(); |
| 10333 | } |
| 10334 | |
| 10335 | // aten::adaptive_max_pool2d_backward(Tensor grad_output, Tensor self, Tensor indices) -> Tensor |
| 10336 | at::Tensor adaptive_max_pool2d_backward::call(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) { |
| 10337 | |
| 10338 | static auto op = create_adaptive_max_pool2d_backward_typed_handle(); |
| 10339 | return op.call(grad_output, self, indices); |
| 10340 | } |
| 10341 | |
| 10342 | // aten::adaptive_max_pool2d_backward(Tensor grad_output, Tensor self, Tensor indices) -> Tensor |
| 10343 | at::Tensor adaptive_max_pool2d_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) { |
| 10344 | |
| 10345 | static auto op = create_adaptive_max_pool2d_backward_typed_handle(); |
| 10346 | return op.redispatch(dispatchKeySet, grad_output, self, indices); |
| 10347 | } |
| 10348 | |
| 10349 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_max_pool3d_backward_grad_input, name, "aten::adaptive_max_pool3d_backward") |
| 10350 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_max_pool3d_backward_grad_input, overload_name, "grad_input") |
| 10351 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_max_pool3d_backward_grad_input, schema_str, "adaptive_max_pool3d_backward.grad_input(Tensor grad_output, Tensor self, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)") |
| 10352 | |
| 10353 | // aten::adaptive_max_pool3d_backward.grad_input(Tensor grad_output, Tensor self, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 10354 | static C10_NOINLINE c10::TypedOperatorHandle<adaptive_max_pool3d_backward_grad_input::schema> create_adaptive_max_pool3d_backward_grad_input_typed_handle() { |
| 10355 | return c10::Dispatcher::singleton() |
| 10356 | .findSchemaOrThrow(adaptive_max_pool3d_backward_grad_input::name, adaptive_max_pool3d_backward_grad_input::overload_name) |
| 10357 | .typed<adaptive_max_pool3d_backward_grad_input::schema>(); |
| 10358 | } |
| 10359 | |
| 10360 | // aten::adaptive_max_pool3d_backward.grad_input(Tensor grad_output, Tensor self, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 10361 | at::Tensor & adaptive_max_pool3d_backward_grad_input::call(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::Tensor & grad_input) { |
| 10362 | |
| 10363 | static auto op = create_adaptive_max_pool3d_backward_grad_input_typed_handle(); |
| 10364 | return op.call(grad_output, self, indices, grad_input); |
| 10365 | } |
| 10366 | |
| 10367 | // aten::adaptive_max_pool3d_backward.grad_input(Tensor grad_output, Tensor self, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) |
| 10368 | at::Tensor & adaptive_max_pool3d_backward_grad_input::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::Tensor & grad_input) { |
| 10369 | |
| 10370 | static auto op = create_adaptive_max_pool3d_backward_grad_input_typed_handle(); |
| 10371 | return op.redispatch(dispatchKeySet, grad_output, self, indices, grad_input); |
| 10372 | } |
| 10373 | |
| 10374 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_max_pool3d_backward, name, "aten::adaptive_max_pool3d_backward") |
| 10375 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_max_pool3d_backward, overload_name, "") |
| 10376 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(adaptive_max_pool3d_backward, schema_str, "adaptive_max_pool3d_backward(Tensor grad_output, Tensor self, Tensor indices) -> Tensor") |
| 10377 | |
| 10378 | // aten::adaptive_max_pool3d_backward(Tensor grad_output, Tensor self, Tensor indices) -> Tensor |
| 10379 | static C10_NOINLINE c10::TypedOperatorHandle<adaptive_max_pool3d_backward::schema> create_adaptive_max_pool3d_backward_typed_handle() { |
| 10380 | return c10::Dispatcher::singleton() |
| 10381 | .findSchemaOrThrow(adaptive_max_pool3d_backward::name, adaptive_max_pool3d_backward::overload_name) |
| 10382 | .typed<adaptive_max_pool3d_backward::schema>(); |
| 10383 | } |
| 10384 | |
| 10385 | // aten::adaptive_max_pool3d_backward(Tensor grad_output, Tensor self, Tensor indices) -> Tensor |
| 10386 | at::Tensor adaptive_max_pool3d_backward::call(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) { |
| 10387 | |
| 10388 | static auto op = create_adaptive_max_pool3d_backward_typed_handle(); |
| 10389 | return op.call(grad_output, self, indices); |
| 10390 | } |
| 10391 | |
| 10392 | // aten::adaptive_max_pool3d_backward(Tensor grad_output, Tensor self, Tensor indices) -> Tensor |
| 10393 | at::Tensor adaptive_max_pool3d_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) { |
| 10394 | |
| 10395 | static auto op = create_adaptive_max_pool3d_backward_typed_handle(); |
| 10396 | return op.redispatch(dispatchKeySet, grad_output, self, indices); |
| 10397 | } |
| 10398 | |
| 10399 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fractional_max_pool3d_output, name, "aten::fractional_max_pool3d") |
| 10400 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fractional_max_pool3d_output, overload_name, "output") |
| 10401 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fractional_max_pool3d_output, schema_str, "fractional_max_pool3d.output(Tensor self, int[3] kernel_size, int[3] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))") |
| 10402 | |
| 10403 | // aten::fractional_max_pool3d.output(Tensor self, int[3] kernel_size, int[3] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) |
| 10404 | static C10_NOINLINE c10::TypedOperatorHandle<fractional_max_pool3d_output::schema> create_fractional_max_pool3d_output_typed_handle() { |
| 10405 | return c10::Dispatcher::singleton() |
| 10406 | .findSchemaOrThrow(fractional_max_pool3d_output::name, fractional_max_pool3d_output::overload_name) |
| 10407 | .typed<fractional_max_pool3d_output::schema>(); |
| 10408 | } |
| 10409 | |
| 10410 | // aten::fractional_max_pool3d.output(Tensor self, int[3] kernel_size, int[3] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) |
| 10411 | ::std::tuple<at::Tensor &,at::Tensor &> fractional_max_pool3d_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) { |
| 10412 | |
| 10413 | static auto op = create_fractional_max_pool3d_output_typed_handle(); |
| 10414 | return op.call(self, kernel_size, output_size, random_samples, output, indices); |
| 10415 | } |
| 10416 | |
| 10417 | // aten::fractional_max_pool3d.output(Tensor self, int[3] kernel_size, int[3] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) |
| 10418 | ::std::tuple<at::Tensor &,at::Tensor &> fractional_max_pool3d_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) { |
| 10419 | |
| 10420 | static auto op = create_fractional_max_pool3d_output_typed_handle(); |
| 10421 | return op.redispatch(dispatchKeySet, self, kernel_size, output_size, random_samples, output, indices); |
| 10422 | } |
| 10423 | |
| 10424 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fractional_max_pool3d, name, "aten::fractional_max_pool3d") |
| 10425 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fractional_max_pool3d, overload_name, "") |
| 10426 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fractional_max_pool3d, schema_str, "fractional_max_pool3d(Tensor self, int[3] kernel_size, int[3] output_size, Tensor random_samples) -> (Tensor, Tensor)") |
| 10427 | |
| 10428 | // aten::fractional_max_pool3d(Tensor self, int[3] kernel_size, int[3] output_size, Tensor random_samples) -> (Tensor, Tensor) |
| 10429 | static C10_NOINLINE c10::TypedOperatorHandle<fractional_max_pool3d::schema> create_fractional_max_pool3d_typed_handle() { |
| 10430 | return c10::Dispatcher::singleton() |
| 10431 | .findSchemaOrThrow(fractional_max_pool3d::name, fractional_max_pool3d::overload_name) |
| 10432 | .typed<fractional_max_pool3d::schema>(); |
| 10433 | } |
| 10434 | |
| 10435 | // aten::fractional_max_pool3d(Tensor self, int[3] kernel_size, int[3] output_size, Tensor random_samples) -> (Tensor, Tensor) |
| 10436 | ::std::tuple<at::Tensor,at::Tensor> fractional_max_pool3d::call(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples) { |
| 10437 | |
| 10438 | static auto op = create_fractional_max_pool3d_typed_handle(); |
| 10439 | return op.call(self, kernel_size, output_size, random_samples); |
| 10440 | } |
| 10441 | |
| 10442 | // aten::fractional_max_pool3d(Tensor self, int[3] kernel_size, int[3] output_size, Tensor random_samples) -> (Tensor, Tensor) |
| 10443 | ::std::tuple<at::Tensor,at::Tensor> fractional_max_pool3d::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples) { |
| 10444 | |
| 10445 | static auto op = create_fractional_max_pool3d_typed_handle(); |
| 10446 | return op.redispatch(dispatchKeySet, self, kernel_size, output_size, random_samples); |
| 10447 | } |
| 10448 | |
| 10449 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(reflection_pad3d_out, name, "aten::reflection_pad3d") |
| 10450 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(reflection_pad3d_out, overload_name, "out") |
| 10451 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(reflection_pad3d_out, schema_str, "reflection_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!)") |
| 10452 | |
| 10453 | // aten::reflection_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!) |
| 10454 | static C10_NOINLINE c10::TypedOperatorHandle<reflection_pad3d_out::schema> create_reflection_pad3d_out_typed_handle() { |
| 10455 | return c10::Dispatcher::singleton() |
| 10456 | .findSchemaOrThrow(reflection_pad3d_out::name, reflection_pad3d_out::overload_name) |
| 10457 | .typed<reflection_pad3d_out::schema>(); |
| 10458 | } |
| 10459 | |
| 10460 | // aten::reflection_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!) |
| 10461 | at::Tensor & reflection_pad3d_out::call(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) { |
| 10462 | |
| 10463 | static auto op = create_reflection_pad3d_out_typed_handle(); |
| 10464 | return op.call(self, padding, out); |
| 10465 | } |
| 10466 | |
| 10467 | // aten::reflection_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!) |
| 10468 | at::Tensor & reflection_pad3d_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) { |
| 10469 | |
| 10470 | static auto op = create_reflection_pad3d_out_typed_handle(); |
| 10471 | return op.redispatch(dispatchKeySet, self, padding, out); |
| 10472 | } |
| 10473 | |
| 10474 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(reflection_pad3d, name, "aten::reflection_pad3d") |
| 10475 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(reflection_pad3d, overload_name, "") |
| 10476 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(reflection_pad3d, schema_str, "reflection_pad3d(Tensor self, SymInt[6] padding) -> Tensor") |
| 10477 | |
| 10478 | // aten::reflection_pad3d(Tensor self, SymInt[6] padding) -> Tensor |
| 10479 | static C10_NOINLINE c10::TypedOperatorHandle<reflection_pad3d::schema> create_reflection_pad3d_typed_handle() { |
| 10480 | return c10::Dispatcher::singleton() |
| 10481 | .findSchemaOrThrow(reflection_pad3d::name, reflection_pad3d::overload_name) |
| 10482 | .typed<reflection_pad3d::schema>(); |
| 10483 | } |
| 10484 | |
| 10485 | // aten::reflection_pad3d(Tensor self, SymInt[6] padding) -> Tensor |
| 10486 | at::Tensor reflection_pad3d::call(const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 10487 | |
| 10488 | static auto op = create_reflection_pad3d_typed_handle(); |
| 10489 | return op.call(self, padding); |
| 10490 | } |
| 10491 | |
| 10492 | // aten::reflection_pad3d(Tensor self, SymInt[6] padding) -> Tensor |
| 10493 | at::Tensor reflection_pad3d::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 10494 | |
| 10495 | static auto op = create_reflection_pad3d_typed_handle(); |
| 10496 | return op.redispatch(dispatchKeySet, self, padding); |
| 10497 | } |
| 10498 | |
| 10499 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(replication_pad1d_out, name, "aten::replication_pad1d") |
| 10500 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(replication_pad1d_out, overload_name, "out") |
| 10501 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(replication_pad1d_out, schema_str, "replication_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!)") |
| 10502 | |
| 10503 | // aten::replication_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!) |
| 10504 | static C10_NOINLINE c10::TypedOperatorHandle<replication_pad1d_out::schema> create_replication_pad1d_out_typed_handle() { |
| 10505 | return c10::Dispatcher::singleton() |
| 10506 | .findSchemaOrThrow(replication_pad1d_out::name, replication_pad1d_out::overload_name) |
| 10507 | .typed<replication_pad1d_out::schema>(); |
| 10508 | } |
| 10509 | |
| 10510 | // aten::replication_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!) |
| 10511 | at::Tensor & replication_pad1d_out::call(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) { |
| 10512 | |
| 10513 | static auto op = create_replication_pad1d_out_typed_handle(); |
| 10514 | return op.call(self, padding, out); |
| 10515 | } |
| 10516 | |
| 10517 | // aten::replication_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!) |
| 10518 | at::Tensor & replication_pad1d_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) { |
| 10519 | |
| 10520 | static auto op = create_replication_pad1d_out_typed_handle(); |
| 10521 | return op.redispatch(dispatchKeySet, self, padding, out); |
| 10522 | } |
| 10523 | |
| 10524 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(replication_pad1d, name, "aten::replication_pad1d") |
| 10525 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(replication_pad1d, overload_name, "") |
| 10526 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(replication_pad1d, schema_str, "replication_pad1d(Tensor self, SymInt[2] padding) -> Tensor") |
| 10527 | |
| 10528 | // aten::replication_pad1d(Tensor self, SymInt[2] padding) -> Tensor |
| 10529 | static C10_NOINLINE c10::TypedOperatorHandle<replication_pad1d::schema> create_replication_pad1d_typed_handle() { |
| 10530 | return c10::Dispatcher::singleton() |
| 10531 | .findSchemaOrThrow(replication_pad1d::name, replication_pad1d::overload_name) |
| 10532 | .typed<replication_pad1d::schema>(); |
| 10533 | } |
| 10534 | |
| 10535 | // aten::replication_pad1d(Tensor self, SymInt[2] padding) -> Tensor |
| 10536 | at::Tensor replication_pad1d::call(const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 10537 | |
| 10538 | static auto op = create_replication_pad1d_typed_handle(); |
| 10539 | return op.call(self, padding); |
| 10540 | } |
| 10541 | |
| 10542 | // aten::replication_pad1d(Tensor self, SymInt[2] padding) -> Tensor |
| 10543 | at::Tensor replication_pad1d::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 10544 | |
| 10545 | static auto op = create_replication_pad1d_typed_handle(); |
| 10546 | return op.redispatch(dispatchKeySet, self, padding); |
| 10547 | } |
| 10548 | |
| 10549 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(replication_pad2d_out, name, "aten::replication_pad2d") |
| 10550 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(replication_pad2d_out, overload_name, "out") |
| 10551 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(replication_pad2d_out, schema_str, "replication_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!)") |
| 10552 | |
| 10553 | // aten::replication_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!) |
| 10554 | static C10_NOINLINE c10::TypedOperatorHandle<replication_pad2d_out::schema> create_replication_pad2d_out_typed_handle() { |
| 10555 | return c10::Dispatcher::singleton() |
| 10556 | .findSchemaOrThrow(replication_pad2d_out::name, replication_pad2d_out::overload_name) |
| 10557 | .typed<replication_pad2d_out::schema>(); |
| 10558 | } |
| 10559 | |
| 10560 | // aten::replication_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!) |
| 10561 | at::Tensor & replication_pad2d_out::call(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) { |
| 10562 | |
| 10563 | static auto op = create_replication_pad2d_out_typed_handle(); |
| 10564 | return op.call(self, padding, out); |
| 10565 | } |
| 10566 | |
| 10567 | // aten::replication_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!) |
| 10568 | at::Tensor & replication_pad2d_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) { |
| 10569 | |
| 10570 | static auto op = create_replication_pad2d_out_typed_handle(); |
| 10571 | return op.redispatch(dispatchKeySet, self, padding, out); |
| 10572 | } |
| 10573 | |
| 10574 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(replication_pad2d, name, "aten::replication_pad2d") |
| 10575 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(replication_pad2d, overload_name, "") |
| 10576 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(replication_pad2d, schema_str, "replication_pad2d(Tensor self, SymInt[4] padding) -> Tensor") |
| 10577 | |
| 10578 | // aten::replication_pad2d(Tensor self, SymInt[4] padding) -> Tensor |
| 10579 | static C10_NOINLINE c10::TypedOperatorHandle<replication_pad2d::schema> create_replication_pad2d_typed_handle() { |
| 10580 | return c10::Dispatcher::singleton() |
| 10581 | .findSchemaOrThrow(replication_pad2d::name, replication_pad2d::overload_name) |
| 10582 | .typed<replication_pad2d::schema>(); |
| 10583 | } |
| 10584 | |
| 10585 | // aten::replication_pad2d(Tensor self, SymInt[4] padding) -> Tensor |
| 10586 | at::Tensor replication_pad2d::call(const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 10587 | |
| 10588 | static auto op = create_replication_pad2d_typed_handle(); |
| 10589 | return op.call(self, padding); |
| 10590 | } |
| 10591 | |
| 10592 | // aten::replication_pad2d(Tensor self, SymInt[4] padding) -> Tensor |
| 10593 | at::Tensor replication_pad2d::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 10594 | |
| 10595 | static auto op = create_replication_pad2d_typed_handle(); |
| 10596 | return op.redispatch(dispatchKeySet, self, padding); |
| 10597 | } |
| 10598 | |
| 10599 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_pad_circular, name, "aten::_pad_circular") |
| 10600 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_pad_circular, overload_name, "") |
| 10601 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_pad_circular, schema_str, "_pad_circular(Tensor self, SymInt[] pad) -> Tensor") |
| 10602 | |
| 10603 | // aten::_pad_circular(Tensor self, SymInt[] pad) -> Tensor |
| 10604 | static C10_NOINLINE c10::TypedOperatorHandle<_pad_circular::schema> create__pad_circular_typed_handle() { |
| 10605 | return c10::Dispatcher::singleton() |
| 10606 | .findSchemaOrThrow(_pad_circular::name, _pad_circular::overload_name) |
| 10607 | .typed<_pad_circular::schema>(); |
| 10608 | } |
| 10609 | |
| 10610 | // aten::_pad_circular(Tensor self, SymInt[] pad) -> Tensor |
| 10611 | at::Tensor _pad_circular::call(const at::Tensor & self, c10::SymIntArrayRef pad) { |
| 10612 | |
| 10613 | static auto op = create__pad_circular_typed_handle(); |
| 10614 | return op.call(self, pad); |
| 10615 | } |
| 10616 | |
| 10617 | // aten::_pad_circular(Tensor self, SymInt[] pad) -> Tensor |
| 10618 | at::Tensor _pad_circular::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef pad) { |
| 10619 | |
| 10620 | static auto op = create__pad_circular_typed_handle(); |
| 10621 | return op.redispatch(dispatchKeySet, self, pad); |
| 10622 | } |
| 10623 | |
| 10624 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(pad, name, "aten::pad") |
| 10625 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(pad, overload_name, "") |
| 10626 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(pad, schema_str, "pad(Tensor self, SymInt[] pad, str mode=\"constant\", float? value=None) -> Tensor") |
| 10627 | |
| 10628 | // aten::pad(Tensor self, SymInt[] pad, str mode="constant", float? value=None) -> Tensor |
| 10629 | static C10_NOINLINE c10::TypedOperatorHandle<pad::schema> create_pad_typed_handle() { |
| 10630 | return c10::Dispatcher::singleton() |
| 10631 | .findSchemaOrThrow(pad::name, pad::overload_name) |
| 10632 | .typed<pad::schema>(); |
| 10633 | } |
| 10634 | |
| 10635 | // aten::pad(Tensor self, SymInt[] pad, str mode="constant", float? value=None) -> Tensor |
| 10636 | at::Tensor pad::call(const at::Tensor & self, c10::SymIntArrayRef pad, c10::string_view mode, c10::optional<double> value) { |
| 10637 | |
| 10638 | static auto op = create_pad_typed_handle(); |
| 10639 | return op.call(self, pad, mode, value); |
| 10640 | } |
| 10641 | |
| 10642 | // aten::pad(Tensor self, SymInt[] pad, str mode="constant", float? value=None) -> Tensor |
| 10643 | at::Tensor pad::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef pad, c10::string_view mode, c10::optional<double> value) { |
| 10644 | |
| 10645 | static auto op = create_pad_typed_handle(); |
| 10646 | return op.redispatch(dispatchKeySet, self, pad, mode, value); |
| 10647 | } |
| 10648 | |
| 10649 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_nearest1d_vec, name, "aten::upsample_nearest1d") |
| 10650 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_nearest1d_vec, overload_name, "vec") |
| 10651 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_nearest1d_vec, schema_str, "upsample_nearest1d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor") |
| 10652 | |
| 10653 | // aten::upsample_nearest1d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor |
| 10654 | static C10_NOINLINE c10::TypedOperatorHandle<upsample_nearest1d_vec::schema> create_upsample_nearest1d_vec_typed_handle() { |
| 10655 | return c10::Dispatcher::singleton() |
| 10656 | .findSchemaOrThrow(upsample_nearest1d_vec::name, upsample_nearest1d_vec::overload_name) |
| 10657 | .typed<upsample_nearest1d_vec::schema>(); |
| 10658 | } |
| 10659 | |
| 10660 | // aten::upsample_nearest1d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor |
| 10661 | at::Tensor upsample_nearest1d_vec::call(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, c10::optional<at::ArrayRef<double>> scale_factors) { |
| 10662 | |
| 10663 | static auto op = create_upsample_nearest1d_vec_typed_handle(); |
| 10664 | return op.call(input, output_size, scale_factors); |
| 10665 | } |
| 10666 | |
| 10667 | // aten::upsample_nearest1d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor |
| 10668 | at::Tensor upsample_nearest1d_vec::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, c10::optional<at::ArrayRef<double>> scale_factors) { |
| 10669 | |
| 10670 | static auto op = create_upsample_nearest1d_vec_typed_handle(); |
| 10671 | return op.redispatch(dispatchKeySet, input, output_size, scale_factors); |
| 10672 | } |
| 10673 | |
| 10674 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_upsample_nearest_exact1d_vec, name, "aten::_upsample_nearest_exact1d") |
| 10675 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_upsample_nearest_exact1d_vec, overload_name, "vec") |
| 10676 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_upsample_nearest_exact1d_vec, schema_str, "_upsample_nearest_exact1d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor") |
| 10677 | |
| 10678 | // aten::_upsample_nearest_exact1d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor |
| 10679 | static C10_NOINLINE c10::TypedOperatorHandle<_upsample_nearest_exact1d_vec::schema> create__upsample_nearest_exact1d_vec_typed_handle() { |
| 10680 | return c10::Dispatcher::singleton() |
| 10681 | .findSchemaOrThrow(_upsample_nearest_exact1d_vec::name, _upsample_nearest_exact1d_vec::overload_name) |
| 10682 | .typed<_upsample_nearest_exact1d_vec::schema>(); |
| 10683 | } |
| 10684 | |
| 10685 | // aten::_upsample_nearest_exact1d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor |
| 10686 | at::Tensor _upsample_nearest_exact1d_vec::call(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, c10::optional<at::ArrayRef<double>> scale_factors) { |
| 10687 | |
| 10688 | static auto op = create__upsample_nearest_exact1d_vec_typed_handle(); |
| 10689 | return op.call(input, output_size, scale_factors); |
| 10690 | } |
| 10691 | |
| 10692 | // aten::_upsample_nearest_exact1d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor |
| 10693 | at::Tensor _upsample_nearest_exact1d_vec::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, c10::optional<at::ArrayRef<double>> scale_factors) { |
| 10694 | |
| 10695 | static auto op = create__upsample_nearest_exact1d_vec_typed_handle(); |
| 10696 | return op.redispatch(dispatchKeySet, input, output_size, scale_factors); |
| 10697 | } |
| 10698 | |
| 10699 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_nearest1d_out, name, "aten::upsample_nearest1d") |
| 10700 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_nearest1d_out, overload_name, "out") |
| 10701 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_nearest1d_out, schema_str, "upsample_nearest1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 10702 | |
| 10703 | // aten::upsample_nearest1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!) |
| 10704 | static C10_NOINLINE c10::TypedOperatorHandle<upsample_nearest1d_out::schema> create_upsample_nearest1d_out_typed_handle() { |
| 10705 | return c10::Dispatcher::singleton() |
| 10706 | .findSchemaOrThrow(upsample_nearest1d_out::name, upsample_nearest1d_out::overload_name) |
| 10707 | .typed<upsample_nearest1d_out::schema>(); |
| 10708 | } |
| 10709 | |
| 10710 | // aten::upsample_nearest1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!) |
| 10711 | at::Tensor & upsample_nearest1d_out::call(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales, at::Tensor & out) { |
| 10712 | |
| 10713 | static auto op = create_upsample_nearest1d_out_typed_handle(); |
| 10714 | return op.call(self, output_size, scales, out); |
| 10715 | } |
| 10716 | |
| 10717 | // aten::upsample_nearest1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!) |
| 10718 | at::Tensor & upsample_nearest1d_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales, at::Tensor & out) { |
| 10719 | |
| 10720 | static auto op = create_upsample_nearest1d_out_typed_handle(); |
| 10721 | return op.redispatch(dispatchKeySet, self, output_size, scales, out); |
| 10722 | } |
| 10723 | |
| 10724 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_upsample_nearest_exact1d_out, name, "aten::_upsample_nearest_exact1d") |
| 10725 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_upsample_nearest_exact1d_out, overload_name, "out") |
| 10726 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_upsample_nearest_exact1d_out, schema_str, "_upsample_nearest_exact1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 10727 | |
| 10728 | // aten::_upsample_nearest_exact1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!) |
| 10729 | static C10_NOINLINE c10::TypedOperatorHandle<_upsample_nearest_exact1d_out::schema> create__upsample_nearest_exact1d_out_typed_handle() { |
| 10730 | return c10::Dispatcher::singleton() |
| 10731 | .findSchemaOrThrow(_upsample_nearest_exact1d_out::name, _upsample_nearest_exact1d_out::overload_name) |
| 10732 | .typed<_upsample_nearest_exact1d_out::schema>(); |
| 10733 | } |
| 10734 | |
| 10735 | // aten::_upsample_nearest_exact1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!) |
| 10736 | at::Tensor & _upsample_nearest_exact1d_out::call(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales, at::Tensor & out) { |
| 10737 | |
| 10738 | static auto op = create__upsample_nearest_exact1d_out_typed_handle(); |
| 10739 | return op.call(self, output_size, scales, out); |
| 10740 | } |
| 10741 | |
| 10742 | // aten::_upsample_nearest_exact1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!) |
| 10743 | at::Tensor & _upsample_nearest_exact1d_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales, at::Tensor & out) { |
| 10744 | |
| 10745 | static auto op = create__upsample_nearest_exact1d_out_typed_handle(); |
| 10746 | return op.redispatch(dispatchKeySet, self, output_size, scales, out); |
| 10747 | } |
| 10748 | |
| 10749 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_nearest1d, name, "aten::upsample_nearest1d") |
| 10750 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_nearest1d, overload_name, "") |
| 10751 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(upsample_nearest1d, schema_str, "upsample_nearest1d(Tensor self, SymInt[1] output_size, float? scales=None) -> Tensor") |
| 10752 | |
| 10753 | // aten::upsample_nearest1d(Tensor self, SymInt[1] output_size, float? scales=None) -> Tensor |
| 10754 | static C10_NOINLINE c10::TypedOperatorHandle<upsample_nearest1d::schema> create_upsample_nearest1d_typed_handle() { |
| 10755 | return c10::Dispatcher::singleton() |
| 10756 | .findSchemaOrThrow(upsample_nearest1d::name, upsample_nearest1d::overload_name) |
| 10757 | .typed<upsample_nearest1d::schema>(); |
| 10758 | } |
| 10759 | |
| 10760 | // aten::upsample_nearest1d(Tensor self, SymInt[1] output_size, float? scales=None) -> Tensor |
| 10761 | at::Tensor upsample_nearest1d::call(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales) { |
| 10762 | |
| 10763 | static auto op = create_upsample_nearest1d_typed_handle(); |
| 10764 | return op.call(self, output_size, scales); |
| 10765 | } |
| 10766 | |
| 10767 | // aten::upsample_nearest1d(Tensor self, SymInt[1] output_size, float? scales=None) -> Tensor |
| 10768 | at::Tensor upsample_nearest1d::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales) { |
| 10769 | |
| 10770 | static auto op = create_upsample_nearest1d_typed_handle(); |
| 10771 | return op.redispatch(dispatchKeySet, self, output_size, scales); |
| 10772 | } |
| 10773 | |
| 10774 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_upsample_nearest_exact1d, name, "aten::_upsample_nearest_exact1d") |
| 10775 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_upsample_nearest_exact1d, overload_name, "") |
| 10776 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_upsample_nearest_exact1d, schema_str, "_upsample_nearest_exact1d(Tensor self, SymInt[1] output_size, float? scales=None) -> Tensor") |
| 10777 | |
| 10778 | // aten::_upsample_nearest_exact1d(Tensor self, SymInt[1] output_size, float? scales=None) -> Tensor |
| 10779 | static C10_NOINLINE c10::TypedOperatorHandle<_upsample_nearest_exact1d::schema> create__upsample_nearest_exact1d_typed_handle() { |
| 10780 | return c10::Dispatcher::singleton() |
| 10781 | .findSchemaOrThrow(_upsample_nearest_exact1d::name, _upsample_nearest_exact1d::overload_name) |
| 10782 | .typed<_upsample_nearest_exact1d::schema>(); |
| 10783 | } |
| 10784 | |
| 10785 | // aten::_upsample_nearest_exact1d(Tensor self, SymInt[1] output_size, float? scales=None) -> Tensor |
| 10786 | at::Tensor _upsample_nearest_exact1d::call(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales) { |
| 10787 | |
| 10788 | static auto op = create__upsample_nearest_exact1d_typed_handle(); |
| 10789 | return op.call(self, output_size, scales); |
| 10790 | } |
| 10791 | |
| 10792 | // aten::_upsample_nearest_exact1d(Tensor self, SymInt[1] output_size, float? scales=None) -> Tensor |
| 10793 | at::Tensor _upsample_nearest_exact1d::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales) { |
| 10794 | |
| 10795 | static auto op = create__upsample_nearest_exact1d_typed_handle(); |
| 10796 | return op.redispatch(dispatchKeySet, self, output_size, scales); |
| 10797 | } |
| 10798 | |
| 10799 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_conv_depthwise2d_out, name, "aten::_conv_depthwise2d") |
| 10800 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_conv_depthwise2d_out, overload_name, "out") |
| 10801 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_conv_depthwise2d_out, schema_str, "_conv_depthwise2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, SymInt[2] padding, int[2] dilation, *, Tensor(a!) out) -> Tensor(a!)") |
| 10802 | |
| 10803 | // aten::_conv_depthwise2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, SymInt[2] padding, int[2] dilation, *, Tensor(a!) out) -> Tensor(a!) |
| 10804 | static C10_NOINLINE c10::TypedOperatorHandle<_conv_depthwise2d_out::schema> create__conv_depthwise2d_out_typed_handle() { |
| 10805 | return c10::Dispatcher::singleton() |
| 10806 | .findSchemaOrThrow(_conv_depthwise2d_out::name, _conv_depthwise2d_out::overload_name) |
| 10807 | .typed<_conv_depthwise2d_out::schema>(); |
| 10808 | } |
| 10809 | |
| 10810 | // aten::_conv_depthwise2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, SymInt[2] padding, int[2] dilation, *, Tensor(a!) out) -> Tensor(a!) |
| 10811 | const at::Tensor & _conv_depthwise2d_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, const at::Tensor & out) { |
| 10812 | |
| 10813 | static auto op = create__conv_depthwise2d_out_typed_handle(); |
| 10814 | return op.call(self, weight, kernel_size, bias, stride, padding, dilation, out); |
| 10815 | } |
| 10816 | |
| 10817 | // aten::_conv_depthwise2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, SymInt[2] padding, int[2] dilation, *, Tensor(a!) out) -> Tensor(a!) |
| 10818 | const at::Tensor & _conv_depthwise2d_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, const at::Tensor & out) { |
| 10819 | |
| 10820 | static auto op = create__conv_depthwise2d_out_typed_handle(); |
| 10821 | return op.redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, dilation, out); |
| 10822 | } |
| 10823 | |
| 10824 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_conv_depthwise2d, name, "aten::_conv_depthwise2d") |
| 10825 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_conv_depthwise2d, overload_name, "") |
| 10826 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_conv_depthwise2d, schema_str, "_conv_depthwise2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, SymInt[2] padding, int[2] dilation) -> Tensor") |
| 10827 | |
| 10828 | // aten::_conv_depthwise2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, SymInt[2] padding, int[2] dilation) -> Tensor |
| 10829 | static C10_NOINLINE c10::TypedOperatorHandle<_conv_depthwise2d::schema> create__conv_depthwise2d_typed_handle() { |
| 10830 | return c10::Dispatcher::singleton() |
| 10831 | .findSchemaOrThrow(_conv_depthwise2d::name, _conv_depthwise2d::overload_name) |
| 10832 | .typed<_conv_depthwise2d::schema>(); |
| 10833 | } |
| 10834 | |
| 10835 | // aten::_conv_depthwise2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, SymInt[2] padding, int[2] dilation) -> Tensor |
| 10836 | at::Tensor _conv_depthwise2d::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) { |
| 10837 | |
| 10838 | static auto op = create__conv_depthwise2d_typed_handle(); |
| 10839 | return op.call(self, weight, kernel_size, bias, stride, padding, dilation); |
| 10840 | } |
| 10841 | |
| 10842 | // aten::_conv_depthwise2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, SymInt[2] padding, int[2] dilation) -> Tensor |
| 10843 | at::Tensor _conv_depthwise2d::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) { |
| 10844 | |
| 10845 | static auto op = create__conv_depthwise2d_typed_handle(); |
| 10846 | return op.redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, dilation); |
| 10847 | } |
| 10848 | |
| 10849 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slow_conv3d_out, name, "aten::slow_conv3d") |
| 10850 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slow_conv3d_out, overload_name, "out") |
| 10851 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slow_conv3d_out, schema_str, "slow_conv3d.out(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, SymInt[3] padding=0, *, Tensor(a!) out) -> Tensor(a!)") |
| 10852 | |
| 10853 | // aten::slow_conv3d.out(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, SymInt[3] padding=0, *, Tensor(a!) out) -> Tensor(a!) |
| 10854 | static C10_NOINLINE c10::TypedOperatorHandle<slow_conv3d_out::schema> create_slow_conv3d_out_typed_handle() { |
| 10855 | return c10::Dispatcher::singleton() |
| 10856 | .findSchemaOrThrow(slow_conv3d_out::name, slow_conv3d_out::overload_name) |
| 10857 | .typed<slow_conv3d_out::schema>(); |
| 10858 | } |
| 10859 | |
| 10860 | // aten::slow_conv3d.out(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, SymInt[3] padding=0, *, Tensor(a!) out) -> Tensor(a!) |
| 10861 | at::Tensor & slow_conv3d_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::Tensor & out) { |
| 10862 | |
| 10863 | static auto op = create_slow_conv3d_out_typed_handle(); |
| 10864 | return op.call(self, weight, kernel_size, bias, stride, padding, out); |
| 10865 | } |
| 10866 | |
| 10867 | // aten::slow_conv3d.out(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, SymInt[3] padding=0, *, Tensor(a!) out) -> Tensor(a!) |
| 10868 | at::Tensor & slow_conv3d_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::Tensor & out) { |
| 10869 | |
| 10870 | static auto op = create_slow_conv3d_out_typed_handle(); |
| 10871 | return op.redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, out); |
| 10872 | } |
| 10873 | |
| 10874 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slow_conv3d, name, "aten::slow_conv3d") |
| 10875 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slow_conv3d, overload_name, "") |
| 10876 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slow_conv3d, schema_str, "slow_conv3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, SymInt[3] padding=0) -> Tensor") |
| 10877 | |
| 10878 | // aten::slow_conv3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, SymInt[3] padding=0) -> Tensor |
| 10879 | static C10_NOINLINE c10::TypedOperatorHandle<slow_conv3d::schema> create_slow_conv3d_typed_handle() { |
| 10880 | return c10::Dispatcher::singleton() |
| 10881 | .findSchemaOrThrow(slow_conv3d::name, slow_conv3d::overload_name) |
| 10882 | .typed<slow_conv3d::schema>(); |
| 10883 | } |
| 10884 | |
| 10885 | // aten::slow_conv3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, SymInt[3] padding=0) -> Tensor |
| 10886 | at::Tensor slow_conv3d::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) { |
| 10887 | |
| 10888 | static auto op = create_slow_conv3d_typed_handle(); |
| 10889 | return op.call(self, weight, kernel_size, bias, stride, padding); |
| 10890 | } |
| 10891 | |
| 10892 | // aten::slow_conv3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, SymInt[3] padding=0) -> Tensor |
| 10893 | at::Tensor slow_conv3d::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) { |
| 10894 | |
| 10895 | static auto op = create_slow_conv3d_typed_handle(); |
| 10896 | return op.redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding); |
| 10897 | } |
| 10898 | |
| 10899 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_remove_batch_dim, name, "aten::_remove_batch_dim") |
| 10900 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_remove_batch_dim, overload_name, "") |
| 10901 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_remove_batch_dim, schema_str, "_remove_batch_dim(Tensor self, int level, int batch_size, int out_dim) -> Tensor") |
| 10902 | |
| 10903 | // aten::_remove_batch_dim(Tensor self, int level, int batch_size, int out_dim) -> Tensor |
| 10904 | static C10_NOINLINE c10::TypedOperatorHandle<_remove_batch_dim::schema> create__remove_batch_dim_typed_handle() { |
| 10905 | return c10::Dispatcher::singleton() |
| 10906 | .findSchemaOrThrow(_remove_batch_dim::name, _remove_batch_dim::overload_name) |
| 10907 | .typed<_remove_batch_dim::schema>(); |
| 10908 | } |
| 10909 | |
| 10910 | // aten::_remove_batch_dim(Tensor self, int level, int batch_size, int out_dim) -> Tensor |
| 10911 | at::Tensor _remove_batch_dim::call(const at::Tensor & self, int64_t level, int64_t batch_size, int64_t out_dim) { |
| 10912 | |
| 10913 | static auto op = create__remove_batch_dim_typed_handle(); |
| 10914 | return op.call(self, level, batch_size, out_dim); |
| 10915 | } |
| 10916 | |
| 10917 | // aten::_remove_batch_dim(Tensor self, int level, int batch_size, int out_dim) -> Tensor |
| 10918 | at::Tensor _remove_batch_dim::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t level, int64_t batch_size, int64_t out_dim) { |
| 10919 | |
| 10920 | static auto op = create__remove_batch_dim_typed_handle(); |
| 10921 | return op.redispatch(dispatchKeySet, self, level, batch_size, out_dim); |
| 10922 | } |
| 10923 | |
| 10924 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_log_ndtr, name, "aten::special_log_ndtr") |
| 10925 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_log_ndtr, overload_name, "") |
| 10926 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_log_ndtr, schema_str, "special_log_ndtr(Tensor self) -> Tensor") |
| 10927 | |
| 10928 | // aten::special_log_ndtr(Tensor self) -> Tensor |
| 10929 | static C10_NOINLINE c10::TypedOperatorHandle<special_log_ndtr::schema> create_special_log_ndtr_typed_handle() { |
| 10930 | return c10::Dispatcher::singleton() |
| 10931 | .findSchemaOrThrow(special_log_ndtr::name, special_log_ndtr::overload_name) |
| 10932 | .typed<special_log_ndtr::schema>(); |
| 10933 | } |
| 10934 | |
| 10935 | // aten::special_log_ndtr(Tensor self) -> Tensor |
| 10936 | at::Tensor special_log_ndtr::call(const at::Tensor & self) { |
| 10937 | |
| 10938 | static auto op = create_special_log_ndtr_typed_handle(); |
| 10939 | return op.call(self); |
| 10940 | } |
| 10941 | |
| 10942 | // aten::special_log_ndtr(Tensor self) -> Tensor |
| 10943 | at::Tensor special_log_ndtr::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 10944 | |
| 10945 | static auto op = create_special_log_ndtr_typed_handle(); |
| 10946 | return op.redispatch(dispatchKeySet, self); |
| 10947 | } |
| 10948 | |
| 10949 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_log_ndtr_out, name, "aten::special_log_ndtr") |
| 10950 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_log_ndtr_out, overload_name, "out") |
| 10951 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_log_ndtr_out, schema_str, "special_log_ndtr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 10952 | |
| 10953 | // aten::special_log_ndtr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 10954 | static C10_NOINLINE c10::TypedOperatorHandle<special_log_ndtr_out::schema> create_special_log_ndtr_out_typed_handle() { |
| 10955 | return c10::Dispatcher::singleton() |
| 10956 | .findSchemaOrThrow(special_log_ndtr_out::name, special_log_ndtr_out::overload_name) |
| 10957 | .typed<special_log_ndtr_out::schema>(); |
| 10958 | } |
| 10959 | |
| 10960 | // aten::special_log_ndtr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 10961 | at::Tensor & special_log_ndtr_out::call(const at::Tensor & self, at::Tensor & out) { |
| 10962 | |
| 10963 | static auto op = create_special_log_ndtr_out_typed_handle(); |
| 10964 | return op.call(self, out); |
| 10965 | } |
| 10966 | |
| 10967 | // aten::special_log_ndtr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 10968 | at::Tensor & special_log_ndtr_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 10969 | |
| 10970 | static auto op = create_special_log_ndtr_out_typed_handle(); |
| 10971 | return op.redispatch(dispatchKeySet, self, out); |
| 10972 | } |
| 10973 | |
| 10974 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_erf, name, "aten::special_erf") |
| 10975 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_erf, overload_name, "") |
| 10976 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_erf, schema_str, "special_erf(Tensor self) -> Tensor") |
| 10977 | |
| 10978 | // aten::special_erf(Tensor self) -> Tensor |
| 10979 | static C10_NOINLINE c10::TypedOperatorHandle<special_erf::schema> create_special_erf_typed_handle() { |
| 10980 | return c10::Dispatcher::singleton() |
| 10981 | .findSchemaOrThrow(special_erf::name, special_erf::overload_name) |
| 10982 | .typed<special_erf::schema>(); |
| 10983 | } |
| 10984 | |
| 10985 | // aten::special_erf(Tensor self) -> Tensor |
| 10986 | at::Tensor special_erf::call(const at::Tensor & self) { |
| 10987 | |
| 10988 | static auto op = create_special_erf_typed_handle(); |
| 10989 | return op.call(self); |
| 10990 | } |
| 10991 | |
| 10992 | // aten::special_erf(Tensor self) -> Tensor |
| 10993 | at::Tensor special_erf::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 10994 | |
| 10995 | static auto op = create_special_erf_typed_handle(); |
| 10996 | return op.redispatch(dispatchKeySet, self); |
| 10997 | } |
| 10998 | |
| 10999 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_erf_out, name, "aten::special_erf") |
| 11000 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_erf_out, overload_name, "out") |
| 11001 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_erf_out, schema_str, "special_erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 11002 | |
| 11003 | // aten::special_erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 11004 | static C10_NOINLINE c10::TypedOperatorHandle<special_erf_out::schema> create_special_erf_out_typed_handle() { |
| 11005 | return c10::Dispatcher::singleton() |
| 11006 | .findSchemaOrThrow(special_erf_out::name, special_erf_out::overload_name) |
| 11007 | .typed<special_erf_out::schema>(); |
| 11008 | } |
| 11009 | |
| 11010 | // aten::special_erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 11011 | at::Tensor & special_erf_out::call(const at::Tensor & self, at::Tensor & out) { |
| 11012 | |
| 11013 | static auto op = create_special_erf_out_typed_handle(); |
| 11014 | return op.call(self, out); |
| 11015 | } |
| 11016 | |
| 11017 | // aten::special_erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 11018 | at::Tensor & special_erf_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 11019 | |
| 11020 | static auto op = create_special_erf_out_typed_handle(); |
| 11021 | return op.redispatch(dispatchKeySet, self, out); |
| 11022 | } |
| 11023 | |
| 11024 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlogy, name, "aten::special_xlogy") |
| 11025 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlogy, overload_name, "") |
| 11026 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlogy, schema_str, "special_xlogy(Tensor self, Tensor other) -> Tensor") |
| 11027 | |
| 11028 | // aten::special_xlogy(Tensor self, Tensor other) -> Tensor |
| 11029 | static C10_NOINLINE c10::TypedOperatorHandle<special_xlogy::schema> create_special_xlogy_typed_handle() { |
| 11030 | return c10::Dispatcher::singleton() |
| 11031 | .findSchemaOrThrow(special_xlogy::name, special_xlogy::overload_name) |
| 11032 | .typed<special_xlogy::schema>(); |
| 11033 | } |
| 11034 | |
| 11035 | // aten::special_xlogy(Tensor self, Tensor other) -> Tensor |
| 11036 | at::Tensor special_xlogy::call(const at::Tensor & self, const at::Tensor & other) { |
| 11037 | |
| 11038 | static auto op = create_special_xlogy_typed_handle(); |
| 11039 | return op.call(self, other); |
| 11040 | } |
| 11041 | |
| 11042 | // aten::special_xlogy(Tensor self, Tensor other) -> Tensor |
| 11043 | at::Tensor special_xlogy::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { |
| 11044 | |
| 11045 | static auto op = create_special_xlogy_typed_handle(); |
| 11046 | return op.redispatch(dispatchKeySet, self, other); |
| 11047 | } |
| 11048 | |
| 11049 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlogy_self_scalar, name, "aten::special_xlogy") |
| 11050 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlogy_self_scalar, overload_name, "self_scalar") |
| 11051 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlogy_self_scalar, schema_str, "special_xlogy.self_scalar(Scalar self, Tensor other) -> Tensor") |
| 11052 | |
| 11053 | // aten::special_xlogy.self_scalar(Scalar self, Tensor other) -> Tensor |
| 11054 | static C10_NOINLINE c10::TypedOperatorHandle<special_xlogy_self_scalar::schema> create_special_xlogy_self_scalar_typed_handle() { |
| 11055 | return c10::Dispatcher::singleton() |
| 11056 | .findSchemaOrThrow(special_xlogy_self_scalar::name, special_xlogy_self_scalar::overload_name) |
| 11057 | .typed<special_xlogy_self_scalar::schema>(); |
| 11058 | } |
| 11059 | |
| 11060 | // aten::special_xlogy.self_scalar(Scalar self, Tensor other) -> Tensor |
| 11061 | at::Tensor special_xlogy_self_scalar::call(const at::Scalar & self, const at::Tensor & other) { |
| 11062 | |
| 11063 | static auto op = create_special_xlogy_self_scalar_typed_handle(); |
| 11064 | return op.call(self, other); |
| 11065 | } |
| 11066 | |
| 11067 | // aten::special_xlogy.self_scalar(Scalar self, Tensor other) -> Tensor |
| 11068 | at::Tensor special_xlogy_self_scalar::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other) { |
| 11069 | |
| 11070 | static auto op = create_special_xlogy_self_scalar_typed_handle(); |
| 11071 | return op.redispatch(dispatchKeySet, self, other); |
| 11072 | } |
| 11073 | |
| 11074 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlogy_other_scalar, name, "aten::special_xlogy") |
| 11075 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlogy_other_scalar, overload_name, "other_scalar") |
| 11076 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlogy_other_scalar, schema_str, "special_xlogy.other_scalar(Tensor self, Scalar other) -> Tensor") |
| 11077 | |
| 11078 | // aten::special_xlogy.other_scalar(Tensor self, Scalar other) -> Tensor |
| 11079 | static C10_NOINLINE c10::TypedOperatorHandle<special_xlogy_other_scalar::schema> create_special_xlogy_other_scalar_typed_handle() { |
| 11080 | return c10::Dispatcher::singleton() |
| 11081 | .findSchemaOrThrow(special_xlogy_other_scalar::name, special_xlogy_other_scalar::overload_name) |
| 11082 | .typed<special_xlogy_other_scalar::schema>(); |
| 11083 | } |
| 11084 | |
| 11085 | // aten::special_xlogy.other_scalar(Tensor self, Scalar other) -> Tensor |
| 11086 | at::Tensor special_xlogy_other_scalar::call(const at::Tensor & self, const at::Scalar & other) { |
| 11087 | |
| 11088 | static auto op = create_special_xlogy_other_scalar_typed_handle(); |
| 11089 | return op.call(self, other); |
| 11090 | } |
| 11091 | |
| 11092 | // aten::special_xlogy.other_scalar(Tensor self, Scalar other) -> Tensor |
| 11093 | at::Tensor special_xlogy_other_scalar::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { |
| 11094 | |
| 11095 | static auto op = create_special_xlogy_other_scalar_typed_handle(); |
| 11096 | return op.redispatch(dispatchKeySet, self, other); |
| 11097 | } |
| 11098 | |
| 11099 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlogy_out, name, "aten::special_xlogy") |
| 11100 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlogy_out, overload_name, "out") |
| 11101 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlogy_out, schema_str, "special_xlogy.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)") |
| 11102 | |
| 11103 | // aten::special_xlogy.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 11104 | static C10_NOINLINE c10::TypedOperatorHandle<special_xlogy_out::schema> create_special_xlogy_out_typed_handle() { |
| 11105 | return c10::Dispatcher::singleton() |
| 11106 | .findSchemaOrThrow(special_xlogy_out::name, special_xlogy_out::overload_name) |
| 11107 | .typed<special_xlogy_out::schema>(); |
| 11108 | } |
| 11109 | |
| 11110 | // aten::special_xlogy.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 11111 | at::Tensor & special_xlogy_out::call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 11112 | |
| 11113 | static auto op = create_special_xlogy_out_typed_handle(); |
| 11114 | return op.call(self, other, out); |
| 11115 | } |
| 11116 | |
| 11117 | // aten::special_xlogy.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 11118 | at::Tensor & special_xlogy_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { |
| 11119 | |
| 11120 | static auto op = create_special_xlogy_out_typed_handle(); |
| 11121 | return op.redispatch(dispatchKeySet, self, other, out); |
| 11122 | } |
| 11123 | |
| 11124 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlogy_self_scalar_out, name, "aten::special_xlogy") |
| 11125 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlogy_self_scalar_out, overload_name, "self_scalar_out") |
| 11126 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlogy_self_scalar_out, schema_str, "special_xlogy.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)") |
| 11127 | |
| 11128 | // aten::special_xlogy.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 11129 | static C10_NOINLINE c10::TypedOperatorHandle<special_xlogy_self_scalar_out::schema> create_special_xlogy_self_scalar_out_typed_handle() { |
| 11130 | return c10::Dispatcher::singleton() |
| 11131 | .findSchemaOrThrow(special_xlogy_self_scalar_out::name, special_xlogy_self_scalar_out::overload_name) |
| 11132 | .typed<special_xlogy_self_scalar_out::schema>(); |
| 11133 | } |
| 11134 | |
| 11135 | // aten::special_xlogy.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 11136 | at::Tensor & special_xlogy_self_scalar_out::call(const at::Scalar & self, const at::Tensor & other, at::Tensor & out) { |
| 11137 | |
| 11138 | static auto op = create_special_xlogy_self_scalar_out_typed_handle(); |
| 11139 | return op.call(self, other, out); |
| 11140 | } |
| 11141 | |
| 11142 | // aten::special_xlogy.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) |
| 11143 | at::Tensor & special_xlogy_self_scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other, at::Tensor & out) { |
| 11144 | |
| 11145 | static auto op = create_special_xlogy_self_scalar_out_typed_handle(); |
| 11146 | return op.redispatch(dispatchKeySet, self, other, out); |
| 11147 | } |
| 11148 | |
| 11149 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlogy_other_scalar_out, name, "aten::special_xlogy") |
| 11150 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlogy_other_scalar_out, overload_name, "other_scalar_out") |
| 11151 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_xlogy_other_scalar_out, schema_str, "special_xlogy.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)") |
| 11152 | |
| 11153 | // aten::special_xlogy.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) |
| 11154 | static C10_NOINLINE c10::TypedOperatorHandle<special_xlogy_other_scalar_out::schema> create_special_xlogy_other_scalar_out_typed_handle() { |
| 11155 | return c10::Dispatcher::singleton() |
| 11156 | .findSchemaOrThrow(special_xlogy_other_scalar_out::name, special_xlogy_other_scalar_out::overload_name) |
| 11157 | .typed<special_xlogy_other_scalar_out::schema>(); |
| 11158 | } |
| 11159 | |
| 11160 | // aten::special_xlogy.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) |
| 11161 | at::Tensor & special_xlogy_other_scalar_out::call(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 11162 | |
| 11163 | static auto op = create_special_xlogy_other_scalar_out_typed_handle(); |
| 11164 | return op.call(self, other, out); |
| 11165 | } |
| 11166 | |
| 11167 | // aten::special_xlogy.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) |
| 11168 | at::Tensor & special_xlogy_other_scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 11169 | |
| 11170 | static auto op = create_special_xlogy_other_scalar_out_typed_handle(); |
| 11171 | return op.redispatch(dispatchKeySet, self, other, out); |
| 11172 | } |
| 11173 | |
| 11174 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_expit, name, "aten::special_expit") |
| 11175 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_expit, overload_name, "") |
| 11176 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_expit, schema_str, "special_expit(Tensor self) -> Tensor") |
| 11177 | |
| 11178 | // aten::special_expit(Tensor self) -> Tensor |
| 11179 | static C10_NOINLINE c10::TypedOperatorHandle<special_expit::schema> create_special_expit_typed_handle() { |
| 11180 | return c10::Dispatcher::singleton() |
| 11181 | .findSchemaOrThrow(special_expit::name, special_expit::overload_name) |
| 11182 | .typed<special_expit::schema>(); |
| 11183 | } |
| 11184 | |
| 11185 | // aten::special_expit(Tensor self) -> Tensor |
| 11186 | at::Tensor special_expit::call(const at::Tensor & self) { |
| 11187 | |
| 11188 | static auto op = create_special_expit_typed_handle(); |
| 11189 | return op.call(self); |
| 11190 | } |
| 11191 | |
| 11192 | // aten::special_expit(Tensor self) -> Tensor |
| 11193 | at::Tensor special_expit::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 11194 | |
| 11195 | static auto op = create_special_expit_typed_handle(); |
| 11196 | return op.redispatch(dispatchKeySet, self); |
| 11197 | } |
| 11198 | |
| 11199 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_expit_out, name, "aten::special_expit") |
| 11200 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_expit_out, overload_name, "out") |
| 11201 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_expit_out, schema_str, "special_expit.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 11202 | |
| 11203 | // aten::special_expit.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 11204 | static C10_NOINLINE c10::TypedOperatorHandle<special_expit_out::schema> create_special_expit_out_typed_handle() { |
| 11205 | return c10::Dispatcher::singleton() |
| 11206 | .findSchemaOrThrow(special_expit_out::name, special_expit_out::overload_name) |
| 11207 | .typed<special_expit_out::schema>(); |
| 11208 | } |
| 11209 | |
| 11210 | // aten::special_expit.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 11211 | at::Tensor & special_expit_out::call(const at::Tensor & self, at::Tensor & out) { |
| 11212 | |
| 11213 | static auto op = create_special_expit_out_typed_handle(); |
| 11214 | return op.call(self, out); |
| 11215 | } |
| 11216 | |
| 11217 | // aten::special_expit.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 11218 | at::Tensor & special_expit_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 11219 | |
| 11220 | static auto op = create_special_expit_out_typed_handle(); |
| 11221 | return op.redispatch(dispatchKeySet, self, out); |
| 11222 | } |
| 11223 | |
| 11224 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_sinc, name, "aten::special_sinc") |
| 11225 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_sinc, overload_name, "") |
| 11226 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_sinc, schema_str, "special_sinc(Tensor self) -> Tensor") |
| 11227 | |
| 11228 | // aten::special_sinc(Tensor self) -> Tensor |
| 11229 | static C10_NOINLINE c10::TypedOperatorHandle<special_sinc::schema> create_special_sinc_typed_handle() { |
| 11230 | return c10::Dispatcher::singleton() |
| 11231 | .findSchemaOrThrow(special_sinc::name, special_sinc::overload_name) |
| 11232 | .typed<special_sinc::schema>(); |
| 11233 | } |
| 11234 | |
| 11235 | // aten::special_sinc(Tensor self) -> Tensor |
| 11236 | at::Tensor special_sinc::call(const at::Tensor & self) { |
| 11237 | |
| 11238 | static auto op = create_special_sinc_typed_handle(); |
| 11239 | return op.call(self); |
| 11240 | } |
| 11241 | |
| 11242 | // aten::special_sinc(Tensor self) -> Tensor |
| 11243 | at::Tensor special_sinc::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 11244 | |
| 11245 | static auto op = create_special_sinc_typed_handle(); |
| 11246 | return op.redispatch(dispatchKeySet, self); |
| 11247 | } |
| 11248 | |
| 11249 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_sinc_out, name, "aten::special_sinc") |
| 11250 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_sinc_out, overload_name, "out") |
| 11251 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_sinc_out, schema_str, "special_sinc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 11252 | |
| 11253 | // aten::special_sinc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 11254 | static C10_NOINLINE c10::TypedOperatorHandle<special_sinc_out::schema> create_special_sinc_out_typed_handle() { |
| 11255 | return c10::Dispatcher::singleton() |
| 11256 | .findSchemaOrThrow(special_sinc_out::name, special_sinc_out::overload_name) |
| 11257 | .typed<special_sinc_out::schema>(); |
| 11258 | } |
| 11259 | |
| 11260 | // aten::special_sinc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 11261 | at::Tensor & special_sinc_out::call(const at::Tensor & self, at::Tensor & out) { |
| 11262 | |
| 11263 | static auto op = create_special_sinc_out_typed_handle(); |
| 11264 | return op.call(self, out); |
| 11265 | } |
| 11266 | |
| 11267 | // aten::special_sinc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 11268 | at::Tensor & special_sinc_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 11269 | |
| 11270 | static auto op = create_special_sinc_out_typed_handle(); |
| 11271 | return op.redispatch(dispatchKeySet, self, out); |
| 11272 | } |
| 11273 | |
| 11274 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_softmax, name, "aten::special_softmax") |
| 11275 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_softmax, overload_name, "") |
| 11276 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_softmax, schema_str, "special_softmax(Tensor self, int dim, ScalarType? dtype=None) -> Tensor") |
| 11277 | |
| 11278 | // aten::special_softmax(Tensor self, int dim, ScalarType? dtype=None) -> Tensor |
| 11279 | static C10_NOINLINE c10::TypedOperatorHandle<special_softmax::schema> create_special_softmax_typed_handle() { |
| 11280 | return c10::Dispatcher::singleton() |
| 11281 | .findSchemaOrThrow(special_softmax::name, special_softmax::overload_name) |
| 11282 | .typed<special_softmax::schema>(); |
| 11283 | } |
| 11284 | |
| 11285 | // aten::special_softmax(Tensor self, int dim, ScalarType? dtype=None) -> Tensor |
| 11286 | at::Tensor special_softmax::call(const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 11287 | |
| 11288 | static auto op = create_special_softmax_typed_handle(); |
| 11289 | return op.call(self, dim, dtype); |
| 11290 | } |
| 11291 | |
| 11292 | // aten::special_softmax(Tensor self, int dim, ScalarType? dtype=None) -> Tensor |
| 11293 | at::Tensor special_softmax::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 11294 | |
| 11295 | static auto op = create_special_softmax_typed_handle(); |
| 11296 | return op.redispatch(dispatchKeySet, self, dim, dtype); |
| 11297 | } |
| 11298 | |
| 11299 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_fft, name, "aten::fft_fft") |
| 11300 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_fft, overload_name, "") |
| 11301 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_fft, schema_str, "fft_fft(Tensor self, int? n=None, int dim=-1, str? norm=None) -> Tensor") |
| 11302 | |
| 11303 | // aten::fft_fft(Tensor self, int? n=None, int dim=-1, str? norm=None) -> Tensor |
| 11304 | static C10_NOINLINE c10::TypedOperatorHandle<fft_fft::schema> create_fft_fft_typed_handle() { |
| 11305 | return c10::Dispatcher::singleton() |
| 11306 | .findSchemaOrThrow(fft_fft::name, fft_fft::overload_name) |
| 11307 | .typed<fft_fft::schema>(); |
| 11308 | } |
| 11309 | |
| 11310 | // aten::fft_fft(Tensor self, int? n=None, int dim=-1, str? norm=None) -> Tensor |
| 11311 | at::Tensor fft_fft::call(const at::Tensor & self, c10::optional<int64_t> n, int64_t dim, c10::optional<c10::string_view> norm) { |
| 11312 | |
| 11313 | static auto op = create_fft_fft_typed_handle(); |
| 11314 | return op.call(self, n, dim, norm); |
| 11315 | } |
| 11316 | |
| 11317 | // aten::fft_fft(Tensor self, int? n=None, int dim=-1, str? norm=None) -> Tensor |
| 11318 | at::Tensor fft_fft::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<int64_t> n, int64_t dim, c10::optional<c10::string_view> norm) { |
| 11319 | |
| 11320 | static auto op = create_fft_fft_typed_handle(); |
| 11321 | return op.redispatch(dispatchKeySet, self, n, dim, norm); |
| 11322 | } |
| 11323 | |
| 11324 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_fft_out, name, "aten::fft_fft") |
| 11325 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_fft_out, overload_name, "out") |
| 11326 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_fft_out, schema_str, "fft_fft.out(Tensor self, int? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 11327 | |
| 11328 | // aten::fft_fft.out(Tensor self, int? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11329 | static C10_NOINLINE c10::TypedOperatorHandle<fft_fft_out::schema> create_fft_fft_out_typed_handle() { |
| 11330 | return c10::Dispatcher::singleton() |
| 11331 | .findSchemaOrThrow(fft_fft_out::name, fft_fft_out::overload_name) |
| 11332 | .typed<fft_fft_out::schema>(); |
| 11333 | } |
| 11334 | |
| 11335 | // aten::fft_fft.out(Tensor self, int? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11336 | at::Tensor & fft_fft_out::call(const at::Tensor & self, c10::optional<int64_t> n, int64_t dim, c10::optional<c10::string_view> norm, at::Tensor & out) { |
| 11337 | |
| 11338 | static auto op = create_fft_fft_out_typed_handle(); |
| 11339 | return op.call(self, n, dim, norm, out); |
| 11340 | } |
| 11341 | |
| 11342 | // aten::fft_fft.out(Tensor self, int? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11343 | at::Tensor & fft_fft_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) { |
| 11344 | |
| 11345 | static auto op = create_fft_fft_out_typed_handle(); |
| 11346 | return op.redispatch(dispatchKeySet, self, n, dim, norm, out); |
| 11347 | } |
| 11348 | |
| 11349 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_rfft, name, "aten::fft_rfft") |
| 11350 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_rfft, overload_name, "") |
| 11351 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_rfft, schema_str, "fft_rfft(Tensor self, int? n=None, int dim=-1, str? norm=None) -> Tensor") |
| 11352 | |
| 11353 | // aten::fft_rfft(Tensor self, int? n=None, int dim=-1, str? norm=None) -> Tensor |
| 11354 | static C10_NOINLINE c10::TypedOperatorHandle<fft_rfft::schema> create_fft_rfft_typed_handle() { |
| 11355 | return c10::Dispatcher::singleton() |
| 11356 | .findSchemaOrThrow(fft_rfft::name, fft_rfft::overload_name) |
| 11357 | .typed<fft_rfft::schema>(); |
| 11358 | } |
| 11359 | |
| 11360 | // aten::fft_rfft(Tensor self, int? n=None, int dim=-1, str? norm=None) -> Tensor |
| 11361 | at::Tensor fft_rfft::call(const at::Tensor & self, c10::optional<int64_t> n, int64_t dim, c10::optional<c10::string_view> norm) { |
| 11362 | |
| 11363 | static auto op = create_fft_rfft_typed_handle(); |
| 11364 | return op.call(self, n, dim, norm); |
| 11365 | } |
| 11366 | |
| 11367 | // aten::fft_rfft(Tensor self, int? n=None, int dim=-1, str? norm=None) -> Tensor |
| 11368 | at::Tensor fft_rfft::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<int64_t> n, int64_t dim, c10::optional<c10::string_view> norm) { |
| 11369 | |
| 11370 | static auto op = create_fft_rfft_typed_handle(); |
| 11371 | return op.redispatch(dispatchKeySet, self, n, dim, norm); |
| 11372 | } |
| 11373 | |
| 11374 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_rfft_out, name, "aten::fft_rfft") |
| 11375 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_rfft_out, overload_name, "out") |
| 11376 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_rfft_out, schema_str, "fft_rfft.out(Tensor self, int? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 11377 | |
| 11378 | // aten::fft_rfft.out(Tensor self, int? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11379 | static C10_NOINLINE c10::TypedOperatorHandle<fft_rfft_out::schema> create_fft_rfft_out_typed_handle() { |
| 11380 | return c10::Dispatcher::singleton() |
| 11381 | .findSchemaOrThrow(fft_rfft_out::name, fft_rfft_out::overload_name) |
| 11382 | .typed<fft_rfft_out::schema>(); |
| 11383 | } |
| 11384 | |
| 11385 | // aten::fft_rfft.out(Tensor self, int? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11386 | at::Tensor & fft_rfft_out::call(const at::Tensor & self, c10::optional<int64_t> n, int64_t dim, c10::optional<c10::string_view> norm, at::Tensor & out) { |
| 11387 | |
| 11388 | static auto op = create_fft_rfft_out_typed_handle(); |
| 11389 | return op.call(self, n, dim, norm, out); |
| 11390 | } |
| 11391 | |
| 11392 | // aten::fft_rfft.out(Tensor self, int? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11393 | at::Tensor & fft_rfft_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) { |
| 11394 | |
| 11395 | static auto op = create_fft_rfft_out_typed_handle(); |
| 11396 | return op.redispatch(dispatchKeySet, self, n, dim, norm, out); |
| 11397 | } |
| 11398 | |
| 11399 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_hfft2, name, "aten::fft_hfft2") |
| 11400 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_hfft2, overload_name, "") |
| 11401 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_hfft2, schema_str, "fft_hfft2(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor") |
| 11402 | |
| 11403 | // aten::fft_hfft2(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor |
| 11404 | static C10_NOINLINE c10::TypedOperatorHandle<fft_hfft2::schema> create_fft_hfft2_typed_handle() { |
| 11405 | return c10::Dispatcher::singleton() |
| 11406 | .findSchemaOrThrow(fft_hfft2::name, fft_hfft2::overload_name) |
| 11407 | .typed<fft_hfft2::schema>(); |
| 11408 | } |
| 11409 | |
| 11410 | // aten::fft_hfft2(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor |
| 11411 | at::Tensor fft_hfft2::call(const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional<c10::string_view> norm) { |
| 11412 | |
| 11413 | static auto op = create_fft_hfft2_typed_handle(); |
| 11414 | return op.call(self, s, dim, norm); |
| 11415 | } |
| 11416 | |
| 11417 | // aten::fft_hfft2(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor |
| 11418 | at::Tensor fft_hfft2::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional<c10::string_view> norm) { |
| 11419 | |
| 11420 | static auto op = create_fft_hfft2_typed_handle(); |
| 11421 | return op.redispatch(dispatchKeySet, self, s, dim, norm); |
| 11422 | } |
| 11423 | |
| 11424 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_hfft2_out, name, "aten::fft_hfft2") |
| 11425 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_hfft2_out, overload_name, "out") |
| 11426 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_hfft2_out, schema_str, "fft_hfft2.out(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 11427 | |
| 11428 | // aten::fft_hfft2.out(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11429 | static C10_NOINLINE c10::TypedOperatorHandle<fft_hfft2_out::schema> create_fft_hfft2_out_typed_handle() { |
| 11430 | return c10::Dispatcher::singleton() |
| 11431 | .findSchemaOrThrow(fft_hfft2_out::name, fft_hfft2_out::overload_name) |
| 11432 | .typed<fft_hfft2_out::schema>(); |
| 11433 | } |
| 11434 | |
| 11435 | // aten::fft_hfft2.out(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11436 | const at::Tensor & fft_hfft2_out::call(const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional<c10::string_view> norm, const at::Tensor & out) { |
| 11437 | |
| 11438 | static auto op = create_fft_hfft2_out_typed_handle(); |
| 11439 | return op.call(self, s, dim, norm, out); |
| 11440 | } |
| 11441 | |
| 11442 | // aten::fft_hfft2.out(Tensor self, int[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11443 | const at::Tensor & fft_hfft2_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional<c10::string_view> norm, const at::Tensor & out) { |
| 11444 | |
| 11445 | static auto op = create_fft_hfft2_out_typed_handle(); |
| 11446 | return op.redispatch(dispatchKeySet, self, s, dim, norm, out); |
| 11447 | } |
| 11448 | |
| 11449 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_ifftn, name, "aten::fft_ifftn") |
| 11450 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_ifftn, overload_name, "") |
| 11451 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_ifftn, schema_str, "fft_ifftn(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor") |
| 11452 | |
| 11453 | // aten::fft_ifftn(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor |
| 11454 | static C10_NOINLINE c10::TypedOperatorHandle<fft_ifftn::schema> create_fft_ifftn_typed_handle() { |
| 11455 | return c10::Dispatcher::singleton() |
| 11456 | .findSchemaOrThrow(fft_ifftn::name, fft_ifftn::overload_name) |
| 11457 | .typed<fft_ifftn::schema>(); |
| 11458 | } |
| 11459 | |
| 11460 | // aten::fft_ifftn(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor |
| 11461 | at::Tensor fft_ifftn::call(const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional<c10::string_view> norm) { |
| 11462 | |
| 11463 | static auto op = create_fft_ifftn_typed_handle(); |
| 11464 | return op.call(self, s, dim, norm); |
| 11465 | } |
| 11466 | |
| 11467 | // aten::fft_ifftn(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor |
| 11468 | at::Tensor fft_ifftn::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional<c10::string_view> norm) { |
| 11469 | |
| 11470 | static auto op = create_fft_ifftn_typed_handle(); |
| 11471 | return op.redispatch(dispatchKeySet, self, s, dim, norm); |
| 11472 | } |
| 11473 | |
| 11474 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_ifftn_out, name, "aten::fft_ifftn") |
| 11475 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_ifftn_out, overload_name, "out") |
| 11476 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_ifftn_out, schema_str, "fft_ifftn.out(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 11477 | |
| 11478 | // aten::fft_ifftn.out(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11479 | static C10_NOINLINE c10::TypedOperatorHandle<fft_ifftn_out::schema> create_fft_ifftn_out_typed_handle() { |
| 11480 | return c10::Dispatcher::singleton() |
| 11481 | .findSchemaOrThrow(fft_ifftn_out::name, fft_ifftn_out::overload_name) |
| 11482 | .typed<fft_ifftn_out::schema>(); |
| 11483 | } |
| 11484 | |
| 11485 | // aten::fft_ifftn.out(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11486 | at::Tensor & fft_ifftn_out::call(const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional<c10::string_view> norm, at::Tensor & out) { |
| 11487 | |
| 11488 | static auto op = create_fft_ifftn_out_typed_handle(); |
| 11489 | return op.call(self, s, dim, norm, out); |
| 11490 | } |
| 11491 | |
| 11492 | // aten::fft_ifftn.out(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11493 | at::Tensor & fft_ifftn_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional<c10::string_view> norm, at::Tensor & out) { |
| 11494 | |
| 11495 | static auto op = create_fft_ifftn_out_typed_handle(); |
| 11496 | return op.redispatch(dispatchKeySet, self, s, dim, norm, out); |
| 11497 | } |
| 11498 | |
| 11499 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_ihfftn, name, "aten::fft_ihfftn") |
| 11500 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_ihfftn, overload_name, "") |
| 11501 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_ihfftn, schema_str, "fft_ihfftn(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor") |
| 11502 | |
| 11503 | // aten::fft_ihfftn(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor |
| 11504 | static C10_NOINLINE c10::TypedOperatorHandle<fft_ihfftn::schema> create_fft_ihfftn_typed_handle() { |
| 11505 | return c10::Dispatcher::singleton() |
| 11506 | .findSchemaOrThrow(fft_ihfftn::name, fft_ihfftn::overload_name) |
| 11507 | .typed<fft_ihfftn::schema>(); |
| 11508 | } |
| 11509 | |
| 11510 | // aten::fft_ihfftn(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor |
| 11511 | at::Tensor fft_ihfftn::call(const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional<c10::string_view> norm) { |
| 11512 | |
| 11513 | static auto op = create_fft_ihfftn_typed_handle(); |
| 11514 | return op.call(self, s, dim, norm); |
| 11515 | } |
| 11516 | |
| 11517 | // aten::fft_ihfftn(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor |
| 11518 | at::Tensor fft_ihfftn::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional<c10::string_view> norm) { |
| 11519 | |
| 11520 | static auto op = create_fft_ihfftn_typed_handle(); |
| 11521 | return op.redispatch(dispatchKeySet, self, s, dim, norm); |
| 11522 | } |
| 11523 | |
| 11524 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_ihfftn_out, name, "aten::fft_ihfftn") |
| 11525 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_ihfftn_out, overload_name, "out") |
| 11526 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_ihfftn_out, schema_str, "fft_ihfftn.out(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 11527 | |
| 11528 | // aten::fft_ihfftn.out(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11529 | static C10_NOINLINE c10::TypedOperatorHandle<fft_ihfftn_out::schema> create_fft_ihfftn_out_typed_handle() { |
| 11530 | return c10::Dispatcher::singleton() |
| 11531 | .findSchemaOrThrow(fft_ihfftn_out::name, fft_ihfftn_out::overload_name) |
| 11532 | .typed<fft_ihfftn_out::schema>(); |
| 11533 | } |
| 11534 | |
| 11535 | // aten::fft_ihfftn.out(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11536 | const at::Tensor & fft_ihfftn_out::call(const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional<c10::string_view> norm, const at::Tensor & out) { |
| 11537 | |
| 11538 | static auto op = create_fft_ihfftn_out_typed_handle(); |
| 11539 | return op.call(self, s, dim, norm, out); |
| 11540 | } |
| 11541 | |
| 11542 | // aten::fft_ihfftn.out(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11543 | const at::Tensor & fft_ihfftn_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional<c10::string_view> norm, const at::Tensor & out) { |
| 11544 | |
| 11545 | static auto op = create_fft_ihfftn_out_typed_handle(); |
| 11546 | return op.redispatch(dispatchKeySet, self, s, dim, norm, out); |
| 11547 | } |
| 11548 | |
| 11549 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_fftfreq, name, "aten::fft_fftfreq") |
| 11550 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_fftfreq, overload_name, "") |
| 11551 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_fftfreq, schema_str, "fft_fftfreq(int n, float d=1.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor") |
| 11552 | |
| 11553 | // aten::fft_fftfreq(int n, float d=1.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 11554 | static C10_NOINLINE c10::TypedOperatorHandle<fft_fftfreq::schema> create_fft_fftfreq_typed_handle() { |
| 11555 | return c10::Dispatcher::singleton() |
| 11556 | .findSchemaOrThrow(fft_fftfreq::name, fft_fftfreq::overload_name) |
| 11557 | .typed<fft_fftfreq::schema>(); |
| 11558 | } |
| 11559 | |
| 11560 | // aten::fft_fftfreq(int n, float d=1.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 11561 | at::Tensor fft_fftfreq::call(int64_t n, double d, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 11562 | |
| 11563 | static auto op = create_fft_fftfreq_typed_handle(); |
| 11564 | return op.call(n, d, dtype, layout, device, pin_memory); |
| 11565 | } |
| 11566 | |
| 11567 | // aten::fft_fftfreq(int n, float d=1.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 11568 | at::Tensor fft_fftfreq::redispatch(c10::DispatchKeySet dispatchKeySet, int64_t n, double d, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 11569 | |
| 11570 | static auto op = create_fft_fftfreq_typed_handle(); |
| 11571 | return op.redispatch(dispatchKeySet, n, d, dtype, layout, device, pin_memory); |
| 11572 | } |
| 11573 | |
| 11574 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_fftfreq_out, name, "aten::fft_fftfreq") |
| 11575 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_fftfreq_out, overload_name, "out") |
| 11576 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_fftfreq_out, schema_str, "fft_fftfreq.out(int n, float d=1.0, *, Tensor(a!) out) -> Tensor(a!)") |
| 11577 | |
| 11578 | // aten::fft_fftfreq.out(int n, float d=1.0, *, Tensor(a!) out) -> Tensor(a!) |
| 11579 | static C10_NOINLINE c10::TypedOperatorHandle<fft_fftfreq_out::schema> create_fft_fftfreq_out_typed_handle() { |
| 11580 | return c10::Dispatcher::singleton() |
| 11581 | .findSchemaOrThrow(fft_fftfreq_out::name, fft_fftfreq_out::overload_name) |
| 11582 | .typed<fft_fftfreq_out::schema>(); |
| 11583 | } |
| 11584 | |
| 11585 | // aten::fft_fftfreq.out(int n, float d=1.0, *, Tensor(a!) out) -> Tensor(a!) |
| 11586 | at::Tensor & fft_fftfreq_out::call(int64_t n, double d, at::Tensor & out) { |
| 11587 | |
| 11588 | static auto op = create_fft_fftfreq_out_typed_handle(); |
| 11589 | return op.call(n, d, out); |
| 11590 | } |
| 11591 | |
| 11592 | // aten::fft_fftfreq.out(int n, float d=1.0, *, Tensor(a!) out) -> Tensor(a!) |
| 11593 | at::Tensor & fft_fftfreq_out::redispatch(c10::DispatchKeySet dispatchKeySet, int64_t n, double d, at::Tensor & out) { |
| 11594 | |
| 11595 | static auto op = create_fft_fftfreq_out_typed_handle(); |
| 11596 | return op.redispatch(dispatchKeySet, n, d, out); |
| 11597 | } |
| 11598 | |
| 11599 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_rfftfreq, name, "aten::fft_rfftfreq") |
| 11600 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_rfftfreq, overload_name, "") |
| 11601 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_rfftfreq, schema_str, "fft_rfftfreq(int n, float d=1.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor") |
| 11602 | |
| 11603 | // aten::fft_rfftfreq(int n, float d=1.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 11604 | static C10_NOINLINE c10::TypedOperatorHandle<fft_rfftfreq::schema> create_fft_rfftfreq_typed_handle() { |
| 11605 | return c10::Dispatcher::singleton() |
| 11606 | .findSchemaOrThrow(fft_rfftfreq::name, fft_rfftfreq::overload_name) |
| 11607 | .typed<fft_rfftfreq::schema>(); |
| 11608 | } |
| 11609 | |
| 11610 | // aten::fft_rfftfreq(int n, float d=1.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 11611 | at::Tensor fft_rfftfreq::call(int64_t n, double d, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 11612 | |
| 11613 | static auto op = create_fft_rfftfreq_typed_handle(); |
| 11614 | return op.call(n, d, dtype, layout, device, pin_memory); |
| 11615 | } |
| 11616 | |
| 11617 | // aten::fft_rfftfreq(int n, float d=1.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor |
| 11618 | at::Tensor fft_rfftfreq::redispatch(c10::DispatchKeySet dispatchKeySet, int64_t n, double d, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 11619 | |
| 11620 | static auto op = create_fft_rfftfreq_typed_handle(); |
| 11621 | return op.redispatch(dispatchKeySet, n, d, dtype, layout, device, pin_memory); |
| 11622 | } |
| 11623 | |
| 11624 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_rfftfreq_out, name, "aten::fft_rfftfreq") |
| 11625 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_rfftfreq_out, overload_name, "out") |
| 11626 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(fft_rfftfreq_out, schema_str, "fft_rfftfreq.out(int n, float d=1.0, *, Tensor(a!) out) -> Tensor(a!)") |
| 11627 | |
| 11628 | // aten::fft_rfftfreq.out(int n, float d=1.0, *, Tensor(a!) out) -> Tensor(a!) |
| 11629 | static C10_NOINLINE c10::TypedOperatorHandle<fft_rfftfreq_out::schema> create_fft_rfftfreq_out_typed_handle() { |
| 11630 | return c10::Dispatcher::singleton() |
| 11631 | .findSchemaOrThrow(fft_rfftfreq_out::name, fft_rfftfreq_out::overload_name) |
| 11632 | .typed<fft_rfftfreq_out::schema>(); |
| 11633 | } |
| 11634 | |
| 11635 | // aten::fft_rfftfreq.out(int n, float d=1.0, *, Tensor(a!) out) -> Tensor(a!) |
| 11636 | at::Tensor & fft_rfftfreq_out::call(int64_t n, double d, at::Tensor & out) { |
| 11637 | |
| 11638 | static auto op = create_fft_rfftfreq_out_typed_handle(); |
| 11639 | return op.call(n, d, out); |
| 11640 | } |
| 11641 | |
| 11642 | // aten::fft_rfftfreq.out(int n, float d=1.0, *, Tensor(a!) out) -> Tensor(a!) |
| 11643 | at::Tensor & fft_rfftfreq_out::redispatch(c10::DispatchKeySet dispatchKeySet, int64_t n, double d, at::Tensor & out) { |
| 11644 | |
| 11645 | static auto op = create_fft_rfftfreq_out_typed_handle(); |
| 11646 | return op.redispatch(dispatchKeySet, n, d, out); |
| 11647 | } |
| 11648 | |
| 11649 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cholesky_ex, name, "aten::linalg_cholesky_ex") |
| 11650 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cholesky_ex, overload_name, "") |
| 11651 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cholesky_ex, schema_str, "linalg_cholesky_ex(Tensor self, *, bool upper=False, bool check_errors=False) -> (Tensor L, Tensor info)") |
| 11652 | |
| 11653 | // aten::linalg_cholesky_ex(Tensor self, *, bool upper=False, bool check_errors=False) -> (Tensor L, Tensor info) |
| 11654 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_cholesky_ex::schema> create_linalg_cholesky_ex_typed_handle() { |
| 11655 | return c10::Dispatcher::singleton() |
| 11656 | .findSchemaOrThrow(linalg_cholesky_ex::name, linalg_cholesky_ex::overload_name) |
| 11657 | .typed<linalg_cholesky_ex::schema>(); |
| 11658 | } |
| 11659 | |
| 11660 | // aten::linalg_cholesky_ex(Tensor self, *, bool upper=False, bool check_errors=False) -> (Tensor L, Tensor info) |
| 11661 | ::std::tuple<at::Tensor,at::Tensor> linalg_cholesky_ex::call(const at::Tensor & self, bool upper, bool check_errors) { |
| 11662 | |
| 11663 | static auto op = create_linalg_cholesky_ex_typed_handle(); |
| 11664 | return op.call(self, upper, check_errors); |
| 11665 | } |
| 11666 | |
| 11667 | // aten::linalg_cholesky_ex(Tensor self, *, bool upper=False, bool check_errors=False) -> (Tensor L, Tensor info) |
| 11668 | ::std::tuple<at::Tensor,at::Tensor> linalg_cholesky_ex::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool upper, bool check_errors) { |
| 11669 | |
| 11670 | static auto op = create_linalg_cholesky_ex_typed_handle(); |
| 11671 | return op.redispatch(dispatchKeySet, self, upper, check_errors); |
| 11672 | } |
| 11673 | |
| 11674 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cholesky_ex_L, name, "aten::linalg_cholesky_ex") |
| 11675 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cholesky_ex_L, overload_name, "L") |
| 11676 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cholesky_ex_L, schema_str, "linalg_cholesky_ex.L(Tensor self, *, bool upper=False, bool check_errors=False, Tensor(a!) L, Tensor(b!) info) -> (Tensor(a!) L, Tensor(b!) info)") |
| 11677 | |
| 11678 | // aten::linalg_cholesky_ex.L(Tensor self, *, bool upper=False, bool check_errors=False, Tensor(a!) L, Tensor(b!) info) -> (Tensor(a!) L, Tensor(b!) info) |
| 11679 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_cholesky_ex_L::schema> create_linalg_cholesky_ex_L_typed_handle() { |
| 11680 | return c10::Dispatcher::singleton() |
| 11681 | .findSchemaOrThrow(linalg_cholesky_ex_L::name, linalg_cholesky_ex_L::overload_name) |
| 11682 | .typed<linalg_cholesky_ex_L::schema>(); |
| 11683 | } |
| 11684 | |
| 11685 | // aten::linalg_cholesky_ex.L(Tensor self, *, bool upper=False, bool check_errors=False, Tensor(a!) L, Tensor(b!) info) -> (Tensor(a!) L, Tensor(b!) info) |
| 11686 | ::std::tuple<at::Tensor &,at::Tensor &> linalg_cholesky_ex_L::call(const at::Tensor & self, bool upper, bool check_errors, at::Tensor & L, at::Tensor & info) { |
| 11687 | |
| 11688 | static auto op = create_linalg_cholesky_ex_L_typed_handle(); |
| 11689 | return op.call(self, upper, check_errors, L, info); |
| 11690 | } |
| 11691 | |
| 11692 | // aten::linalg_cholesky_ex.L(Tensor self, *, bool upper=False, bool check_errors=False, Tensor(a!) L, Tensor(b!) info) -> (Tensor(a!) L, Tensor(b!) info) |
| 11693 | ::std::tuple<at::Tensor &,at::Tensor &> linalg_cholesky_ex_L::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool upper, bool check_errors, at::Tensor & L, at::Tensor & info) { |
| 11694 | |
| 11695 | static auto op = create_linalg_cholesky_ex_L_typed_handle(); |
| 11696 | return op.redispatch(dispatchKeySet, self, upper, check_errors, L, info); |
| 11697 | } |
| 11698 | |
| 11699 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cross, name, "aten::linalg_cross") |
| 11700 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cross, overload_name, "") |
| 11701 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cross, schema_str, "linalg_cross(Tensor self, Tensor other, *, int dim=-1) -> Tensor") |
| 11702 | |
| 11703 | // aten::linalg_cross(Tensor self, Tensor other, *, int dim=-1) -> Tensor |
| 11704 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_cross::schema> create_linalg_cross_typed_handle() { |
| 11705 | return c10::Dispatcher::singleton() |
| 11706 | .findSchemaOrThrow(linalg_cross::name, linalg_cross::overload_name) |
| 11707 | .typed<linalg_cross::schema>(); |
| 11708 | } |
| 11709 | |
| 11710 | // aten::linalg_cross(Tensor self, Tensor other, *, int dim=-1) -> Tensor |
| 11711 | at::Tensor linalg_cross::call(const at::Tensor & self, const at::Tensor & other, int64_t dim) { |
| 11712 | |
| 11713 | static auto op = create_linalg_cross_typed_handle(); |
| 11714 | return op.call(self, other, dim); |
| 11715 | } |
| 11716 | |
| 11717 | // aten::linalg_cross(Tensor self, Tensor other, *, int dim=-1) -> Tensor |
| 11718 | at::Tensor linalg_cross::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, int64_t dim) { |
| 11719 | |
| 11720 | static auto op = create_linalg_cross_typed_handle(); |
| 11721 | return op.redispatch(dispatchKeySet, self, other, dim); |
| 11722 | } |
| 11723 | |
| 11724 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cross_out, name, "aten::linalg_cross") |
| 11725 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cross_out, overload_name, "out") |
| 11726 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cross_out, schema_str, "linalg_cross.out(Tensor self, Tensor other, *, int dim=-1, Tensor(a!) out) -> Tensor(a!)") |
| 11727 | |
| 11728 | // aten::linalg_cross.out(Tensor self, Tensor other, *, int dim=-1, Tensor(a!) out) -> Tensor(a!) |
| 11729 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_cross_out::schema> create_linalg_cross_out_typed_handle() { |
| 11730 | return c10::Dispatcher::singleton() |
| 11731 | .findSchemaOrThrow(linalg_cross_out::name, linalg_cross_out::overload_name) |
| 11732 | .typed<linalg_cross_out::schema>(); |
| 11733 | } |
| 11734 | |
| 11735 | // aten::linalg_cross.out(Tensor self, Tensor other, *, int dim=-1, Tensor(a!) out) -> Tensor(a!) |
| 11736 | at::Tensor & linalg_cross_out::call(const at::Tensor & self, const at::Tensor & other, int64_t dim, at::Tensor & out) { |
| 11737 | |
| 11738 | static auto op = create_linalg_cross_out_typed_handle(); |
| 11739 | return op.call(self, other, dim, out); |
| 11740 | } |
| 11741 | |
| 11742 | // aten::linalg_cross.out(Tensor self, Tensor other, *, int dim=-1, Tensor(a!) out) -> Tensor(a!) |
| 11743 | at::Tensor & linalg_cross_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, int64_t dim, at::Tensor & out) { |
| 11744 | |
| 11745 | static auto op = create_linalg_cross_out_typed_handle(); |
| 11746 | return op.redispatch(dispatchKeySet, self, other, dim, out); |
| 11747 | } |
| 11748 | |
| 11749 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_lu_factor_ex, name, "aten::linalg_lu_factor_ex") |
| 11750 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_lu_factor_ex, overload_name, "") |
| 11751 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_lu_factor_ex, schema_str, "linalg_lu_factor_ex(Tensor A, *, bool pivot=True, bool check_errors=False) -> (Tensor LU, Tensor pivots, Tensor info)") |
| 11752 | |
| 11753 | // aten::linalg_lu_factor_ex(Tensor A, *, bool pivot=True, bool check_errors=False) -> (Tensor LU, Tensor pivots, Tensor info) |
| 11754 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_lu_factor_ex::schema> create_linalg_lu_factor_ex_typed_handle() { |
| 11755 | return c10::Dispatcher::singleton() |
| 11756 | .findSchemaOrThrow(linalg_lu_factor_ex::name, linalg_lu_factor_ex::overload_name) |
| 11757 | .typed<linalg_lu_factor_ex::schema>(); |
| 11758 | } |
| 11759 | |
| 11760 | // aten::linalg_lu_factor_ex(Tensor A, *, bool pivot=True, bool check_errors=False) -> (Tensor LU, Tensor pivots, Tensor info) |
| 11761 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> linalg_lu_factor_ex::call(const at::Tensor & A, bool pivot, bool check_errors) { |
| 11762 | |
| 11763 | static auto op = create_linalg_lu_factor_ex_typed_handle(); |
| 11764 | return op.call(A, pivot, check_errors); |
| 11765 | } |
| 11766 | |
| 11767 | // aten::linalg_lu_factor_ex(Tensor A, *, bool pivot=True, bool check_errors=False) -> (Tensor LU, Tensor pivots, Tensor info) |
| 11768 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> linalg_lu_factor_ex::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, bool pivot, bool check_errors) { |
| 11769 | |
| 11770 | static auto op = create_linalg_lu_factor_ex_typed_handle(); |
| 11771 | return op.redispatch(dispatchKeySet, A, pivot, check_errors); |
| 11772 | } |
| 11773 | |
| 11774 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_lu_factor_ex_out, name, "aten::linalg_lu_factor_ex") |
| 11775 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_lu_factor_ex_out, overload_name, "out") |
| 11776 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_lu_factor_ex_out, schema_str, "linalg_lu_factor_ex.out(Tensor A, *, bool pivot=True, bool check_errors=False, Tensor(a!) LU, Tensor(b!) pivots, Tensor(c!) info) -> (Tensor(a!) LU, Tensor(b!) pivots, Tensor(c!) info)") |
| 11777 | |
| 11778 | // aten::linalg_lu_factor_ex.out(Tensor A, *, bool pivot=True, bool check_errors=False, Tensor(a!) LU, Tensor(b!) pivots, Tensor(c!) info) -> (Tensor(a!) LU, Tensor(b!) pivots, Tensor(c!) info) |
| 11779 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_lu_factor_ex_out::schema> create_linalg_lu_factor_ex_out_typed_handle() { |
| 11780 | return c10::Dispatcher::singleton() |
| 11781 | .findSchemaOrThrow(linalg_lu_factor_ex_out::name, linalg_lu_factor_ex_out::overload_name) |
| 11782 | .typed<linalg_lu_factor_ex_out::schema>(); |
| 11783 | } |
| 11784 | |
| 11785 | // aten::linalg_lu_factor_ex.out(Tensor A, *, bool pivot=True, bool check_errors=False, Tensor(a!) LU, Tensor(b!) pivots, Tensor(c!) info) -> (Tensor(a!) LU, Tensor(b!) pivots, Tensor(c!) info) |
| 11786 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linalg_lu_factor_ex_out::call(const at::Tensor & A, bool pivot, bool check_errors, at::Tensor & LU, at::Tensor & pivots, at::Tensor & info) { |
| 11787 | |
| 11788 | static auto op = create_linalg_lu_factor_ex_out_typed_handle(); |
| 11789 | return op.call(A, pivot, check_errors, LU, pivots, info); |
| 11790 | } |
| 11791 | |
| 11792 | // aten::linalg_lu_factor_ex.out(Tensor A, *, bool pivot=True, bool check_errors=False, Tensor(a!) LU, Tensor(b!) pivots, Tensor(c!) info) -> (Tensor(a!) LU, Tensor(b!) pivots, Tensor(c!) info) |
| 11793 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linalg_lu_factor_ex_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, bool pivot, bool check_errors, at::Tensor & LU, at::Tensor & pivots, at::Tensor & info) { |
| 11794 | |
| 11795 | static auto op = create_linalg_lu_factor_ex_out_typed_handle(); |
| 11796 | return op.redispatch(dispatchKeySet, A, pivot, check_errors, LU, pivots, info); |
| 11797 | } |
| 11798 | |
| 11799 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(det, name, "aten::det") |
| 11800 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(det, overload_name, "") |
| 11801 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(det, schema_str, "det(Tensor self) -> Tensor") |
| 11802 | |
| 11803 | // aten::det(Tensor self) -> Tensor |
| 11804 | static C10_NOINLINE c10::TypedOperatorHandle<det::schema> create_det_typed_handle() { |
| 11805 | return c10::Dispatcher::singleton() |
| 11806 | .findSchemaOrThrow(det::name, det::overload_name) |
| 11807 | .typed<det::schema>(); |
| 11808 | } |
| 11809 | |
| 11810 | // aten::det(Tensor self) -> Tensor |
| 11811 | at::Tensor det::call(const at::Tensor & self) { |
| 11812 | |
| 11813 | static auto op = create_det_typed_handle(); |
| 11814 | return op.call(self); |
| 11815 | } |
| 11816 | |
| 11817 | // aten::det(Tensor self) -> Tensor |
| 11818 | at::Tensor det::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 11819 | |
| 11820 | static auto op = create_det_typed_handle(); |
| 11821 | return op.redispatch(dispatchKeySet, self); |
| 11822 | } |
| 11823 | |
| 11824 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(inverse, name, "aten::inverse") |
| 11825 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(inverse, overload_name, "") |
| 11826 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(inverse, schema_str, "inverse(Tensor self) -> Tensor") |
| 11827 | |
| 11828 | // aten::inverse(Tensor self) -> Tensor |
| 11829 | static C10_NOINLINE c10::TypedOperatorHandle<inverse::schema> create_inverse_typed_handle() { |
| 11830 | return c10::Dispatcher::singleton() |
| 11831 | .findSchemaOrThrow(inverse::name, inverse::overload_name) |
| 11832 | .typed<inverse::schema>(); |
| 11833 | } |
| 11834 | |
| 11835 | // aten::inverse(Tensor self) -> Tensor |
| 11836 | at::Tensor inverse::call(const at::Tensor & self) { |
| 11837 | |
| 11838 | static auto op = create_inverse_typed_handle(); |
| 11839 | return op.call(self); |
| 11840 | } |
| 11841 | |
| 11842 | // aten::inverse(Tensor self) -> Tensor |
| 11843 | at::Tensor inverse::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 11844 | |
| 11845 | static auto op = create_inverse_typed_handle(); |
| 11846 | return op.redispatch(dispatchKeySet, self); |
| 11847 | } |
| 11848 | |
| 11849 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(inverse_out, name, "aten::inverse") |
| 11850 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(inverse_out, overload_name, "out") |
| 11851 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(inverse_out, schema_str, "inverse.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 11852 | |
| 11853 | // aten::inverse.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 11854 | static C10_NOINLINE c10::TypedOperatorHandle<inverse_out::schema> create_inverse_out_typed_handle() { |
| 11855 | return c10::Dispatcher::singleton() |
| 11856 | .findSchemaOrThrow(inverse_out::name, inverse_out::overload_name) |
| 11857 | .typed<inverse_out::schema>(); |
| 11858 | } |
| 11859 | |
| 11860 | // aten::inverse.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 11861 | at::Tensor & inverse_out::call(const at::Tensor & self, at::Tensor & out) { |
| 11862 | |
| 11863 | static auto op = create_inverse_out_typed_handle(); |
| 11864 | return op.call(self, out); |
| 11865 | } |
| 11866 | |
| 11867 | // aten::inverse.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 11868 | at::Tensor & inverse_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 11869 | |
| 11870 | static auto op = create_inverse_out_typed_handle(); |
| 11871 | return op.redispatch(dispatchKeySet, self, out); |
| 11872 | } |
| 11873 | |
| 11874 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cond, name, "aten::linalg_cond") |
| 11875 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cond, overload_name, "") |
| 11876 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cond, schema_str, "linalg_cond(Tensor self, Scalar? p=None) -> Tensor") |
| 11877 | |
| 11878 | // aten::linalg_cond(Tensor self, Scalar? p=None) -> Tensor |
| 11879 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_cond::schema> create_linalg_cond_typed_handle() { |
| 11880 | return c10::Dispatcher::singleton() |
| 11881 | .findSchemaOrThrow(linalg_cond::name, linalg_cond::overload_name) |
| 11882 | .typed<linalg_cond::schema>(); |
| 11883 | } |
| 11884 | |
| 11885 | // aten::linalg_cond(Tensor self, Scalar? p=None) -> Tensor |
| 11886 | at::Tensor linalg_cond::call(const at::Tensor & self, const c10::optional<at::Scalar> & p) { |
| 11887 | |
| 11888 | static auto op = create_linalg_cond_typed_handle(); |
| 11889 | return op.call(self, p); |
| 11890 | } |
| 11891 | |
| 11892 | // aten::linalg_cond(Tensor self, Scalar? p=None) -> Tensor |
| 11893 | at::Tensor linalg_cond::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional<at::Scalar> & p) { |
| 11894 | |
| 11895 | static auto op = create_linalg_cond_typed_handle(); |
| 11896 | return op.redispatch(dispatchKeySet, self, p); |
| 11897 | } |
| 11898 | |
| 11899 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cond_out, name, "aten::linalg_cond") |
| 11900 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cond_out, overload_name, "out") |
| 11901 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cond_out, schema_str, "linalg_cond.out(Tensor self, Scalar? p=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 11902 | |
| 11903 | // aten::linalg_cond.out(Tensor self, Scalar? p=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11904 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_cond_out::schema> create_linalg_cond_out_typed_handle() { |
| 11905 | return c10::Dispatcher::singleton() |
| 11906 | .findSchemaOrThrow(linalg_cond_out::name, linalg_cond_out::overload_name) |
| 11907 | .typed<linalg_cond_out::schema>(); |
| 11908 | } |
| 11909 | |
| 11910 | // aten::linalg_cond.out(Tensor self, Scalar? p=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11911 | at::Tensor & linalg_cond_out::call(const at::Tensor & self, const c10::optional<at::Scalar> & p, at::Tensor & out) { |
| 11912 | |
| 11913 | static auto op = create_linalg_cond_out_typed_handle(); |
| 11914 | return op.call(self, p, out); |
| 11915 | } |
| 11916 | |
| 11917 | // aten::linalg_cond.out(Tensor self, Scalar? p=None, *, Tensor(a!) out) -> Tensor(a!) |
| 11918 | at::Tensor & linalg_cond_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional<at::Scalar> & p, at::Tensor & out) { |
| 11919 | |
| 11920 | static auto op = create_linalg_cond_out_typed_handle(); |
| 11921 | return op.redispatch(dispatchKeySet, self, p, out); |
| 11922 | } |
| 11923 | |
| 11924 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cond_p_str, name, "aten::linalg_cond") |
| 11925 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cond_p_str, overload_name, "p_str") |
| 11926 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cond_p_str, schema_str, "linalg_cond.p_str(Tensor self, str p) -> Tensor") |
| 11927 | |
| 11928 | // aten::linalg_cond.p_str(Tensor self, str p) -> Tensor |
| 11929 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_cond_p_str::schema> create_linalg_cond_p_str_typed_handle() { |
| 11930 | return c10::Dispatcher::singleton() |
| 11931 | .findSchemaOrThrow(linalg_cond_p_str::name, linalg_cond_p_str::overload_name) |
| 11932 | .typed<linalg_cond_p_str::schema>(); |
| 11933 | } |
| 11934 | |
| 11935 | // aten::linalg_cond.p_str(Tensor self, str p) -> Tensor |
| 11936 | at::Tensor linalg_cond_p_str::call(const at::Tensor & self, c10::string_view p) { |
| 11937 | |
| 11938 | static auto op = create_linalg_cond_p_str_typed_handle(); |
| 11939 | return op.call(self, p); |
| 11940 | } |
| 11941 | |
| 11942 | // aten::linalg_cond.p_str(Tensor self, str p) -> Tensor |
| 11943 | at::Tensor linalg_cond_p_str::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::string_view p) { |
| 11944 | |
| 11945 | static auto op = create_linalg_cond_p_str_typed_handle(); |
| 11946 | return op.redispatch(dispatchKeySet, self, p); |
| 11947 | } |
| 11948 | |
| 11949 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cond_p_str_out, name, "aten::linalg_cond") |
| 11950 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cond_p_str_out, overload_name, "p_str_out") |
| 11951 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_cond_p_str_out, schema_str, "linalg_cond.p_str_out(Tensor self, str p, *, Tensor(a!) out) -> Tensor(a!)") |
| 11952 | |
| 11953 | // aten::linalg_cond.p_str_out(Tensor self, str p, *, Tensor(a!) out) -> Tensor(a!) |
| 11954 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_cond_p_str_out::schema> create_linalg_cond_p_str_out_typed_handle() { |
| 11955 | return c10::Dispatcher::singleton() |
| 11956 | .findSchemaOrThrow(linalg_cond_p_str_out::name, linalg_cond_p_str_out::overload_name) |
| 11957 | .typed<linalg_cond_p_str_out::schema>(); |
| 11958 | } |
| 11959 | |
| 11960 | // aten::linalg_cond.p_str_out(Tensor self, str p, *, Tensor(a!) out) -> Tensor(a!) |
| 11961 | at::Tensor & linalg_cond_p_str_out::call(const at::Tensor & self, c10::string_view p, at::Tensor & out) { |
| 11962 | |
| 11963 | static auto op = create_linalg_cond_p_str_out_typed_handle(); |
| 11964 | return op.call(self, p, out); |
| 11965 | } |
| 11966 | |
| 11967 | // aten::linalg_cond.p_str_out(Tensor self, str p, *, Tensor(a!) out) -> Tensor(a!) |
| 11968 | at::Tensor & linalg_cond_p_str_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::string_view p, at::Tensor & out) { |
| 11969 | |
| 11970 | static auto op = create_linalg_cond_p_str_out_typed_handle(); |
| 11971 | return op.redispatch(dispatchKeySet, self, p, out); |
| 11972 | } |
| 11973 | |
| 11974 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_pinv_atol_rtol_tensor, name, "aten::linalg_pinv") |
| 11975 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_pinv_atol_rtol_tensor, overload_name, "atol_rtol_tensor") |
| 11976 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_pinv_atol_rtol_tensor, schema_str, "linalg_pinv.atol_rtol_tensor(Tensor self, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False) -> Tensor") |
| 11977 | |
| 11978 | // aten::linalg_pinv.atol_rtol_tensor(Tensor self, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False) -> Tensor |
| 11979 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_pinv_atol_rtol_tensor::schema> create_linalg_pinv_atol_rtol_tensor_typed_handle() { |
| 11980 | return c10::Dispatcher::singleton() |
| 11981 | .findSchemaOrThrow(linalg_pinv_atol_rtol_tensor::name, linalg_pinv_atol_rtol_tensor::overload_name) |
| 11982 | .typed<linalg_pinv_atol_rtol_tensor::schema>(); |
| 11983 | } |
| 11984 | |
| 11985 | // aten::linalg_pinv.atol_rtol_tensor(Tensor self, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False) -> Tensor |
| 11986 | at::Tensor linalg_pinv_atol_rtol_tensor::call(const at::Tensor & self, const c10::optional<at::Tensor> & atol, const c10::optional<at::Tensor> & rtol, bool hermitian) { |
| 11987 | |
| 11988 | static auto op = create_linalg_pinv_atol_rtol_tensor_typed_handle(); |
| 11989 | return op.call(self, atol, rtol, hermitian); |
| 11990 | } |
| 11991 | |
| 11992 | // aten::linalg_pinv.atol_rtol_tensor(Tensor self, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False) -> Tensor |
| 11993 | at::Tensor linalg_pinv_atol_rtol_tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional<at::Tensor> & atol, const c10::optional<at::Tensor> & rtol, bool hermitian) { |
| 11994 | |
| 11995 | static auto op = create_linalg_pinv_atol_rtol_tensor_typed_handle(); |
| 11996 | return op.redispatch(dispatchKeySet, self, atol, rtol, hermitian); |
| 11997 | } |
| 11998 | |
| 11999 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_pinv_atol_rtol_tensor_out, name, "aten::linalg_pinv") |
| 12000 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_pinv_atol_rtol_tensor_out, overload_name, "atol_rtol_tensor_out") |
| 12001 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_pinv_atol_rtol_tensor_out, schema_str, "linalg_pinv.atol_rtol_tensor_out(Tensor self, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)") |
| 12002 | |
| 12003 | // aten::linalg_pinv.atol_rtol_tensor_out(Tensor self, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!) |
| 12004 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_pinv_atol_rtol_tensor_out::schema> create_linalg_pinv_atol_rtol_tensor_out_typed_handle() { |
| 12005 | return c10::Dispatcher::singleton() |
| 12006 | .findSchemaOrThrow(linalg_pinv_atol_rtol_tensor_out::name, linalg_pinv_atol_rtol_tensor_out::overload_name) |
| 12007 | .typed<linalg_pinv_atol_rtol_tensor_out::schema>(); |
| 12008 | } |
| 12009 | |
| 12010 | // aten::linalg_pinv.atol_rtol_tensor_out(Tensor self, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!) |
| 12011 | at::Tensor & linalg_pinv_atol_rtol_tensor_out::call(const at::Tensor & self, const c10::optional<at::Tensor> & atol, const c10::optional<at::Tensor> & rtol, bool hermitian, at::Tensor & out) { |
| 12012 | |
| 12013 | static auto op = create_linalg_pinv_atol_rtol_tensor_out_typed_handle(); |
| 12014 | return op.call(self, atol, rtol, hermitian, out); |
| 12015 | } |
| 12016 | |
| 12017 | // aten::linalg_pinv.atol_rtol_tensor_out(Tensor self, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!) |
| 12018 | at::Tensor & linalg_pinv_atol_rtol_tensor_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional<at::Tensor> & atol, const c10::optional<at::Tensor> & rtol, bool hermitian, at::Tensor & out) { |
| 12019 | |
| 12020 | static auto op = create_linalg_pinv_atol_rtol_tensor_out_typed_handle(); |
| 12021 | return op.redispatch(dispatchKeySet, self, atol, rtol, hermitian, out); |
| 12022 | } |
| 12023 | |
| 12024 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_pinv_atol_rtol_float, name, "aten::linalg_pinv") |
| 12025 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_pinv_atol_rtol_float, overload_name, "atol_rtol_float") |
| 12026 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_pinv_atol_rtol_float, schema_str, "linalg_pinv.atol_rtol_float(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False) -> Tensor") |
| 12027 | |
| 12028 | // aten::linalg_pinv.atol_rtol_float(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False) -> Tensor |
| 12029 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_pinv_atol_rtol_float::schema> create_linalg_pinv_atol_rtol_float_typed_handle() { |
| 12030 | return c10::Dispatcher::singleton() |
| 12031 | .findSchemaOrThrow(linalg_pinv_atol_rtol_float::name, linalg_pinv_atol_rtol_float::overload_name) |
| 12032 | .typed<linalg_pinv_atol_rtol_float::schema>(); |
| 12033 | } |
| 12034 | |
| 12035 | // aten::linalg_pinv.atol_rtol_float(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False) -> Tensor |
| 12036 | at::Tensor linalg_pinv_atol_rtol_float::call(const at::Tensor & self, c10::optional<double> atol, c10::optional<double> rtol, bool hermitian) { |
| 12037 | |
| 12038 | static auto op = create_linalg_pinv_atol_rtol_float_typed_handle(); |
| 12039 | return op.call(self, atol, rtol, hermitian); |
| 12040 | } |
| 12041 | |
| 12042 | // aten::linalg_pinv.atol_rtol_float(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False) -> Tensor |
| 12043 | at::Tensor linalg_pinv_atol_rtol_float::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<double> atol, c10::optional<double> rtol, bool hermitian) { |
| 12044 | |
| 12045 | static auto op = create_linalg_pinv_atol_rtol_float_typed_handle(); |
| 12046 | return op.redispatch(dispatchKeySet, self, atol, rtol, hermitian); |
| 12047 | } |
| 12048 | |
| 12049 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_pinv_atol_rtol_float_out, name, "aten::linalg_pinv") |
| 12050 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_pinv_atol_rtol_float_out, overload_name, "atol_rtol_float_out") |
| 12051 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_pinv_atol_rtol_float_out, schema_str, "linalg_pinv.atol_rtol_float_out(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)") |
| 12052 | |
| 12053 | // aten::linalg_pinv.atol_rtol_float_out(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!) |
| 12054 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_pinv_atol_rtol_float_out::schema> create_linalg_pinv_atol_rtol_float_out_typed_handle() { |
| 12055 | return c10::Dispatcher::singleton() |
| 12056 | .findSchemaOrThrow(linalg_pinv_atol_rtol_float_out::name, linalg_pinv_atol_rtol_float_out::overload_name) |
| 12057 | .typed<linalg_pinv_atol_rtol_float_out::schema>(); |
| 12058 | } |
| 12059 | |
| 12060 | // aten::linalg_pinv.atol_rtol_float_out(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!) |
| 12061 | at::Tensor & linalg_pinv_atol_rtol_float_out::call(const at::Tensor & self, c10::optional<double> atol, c10::optional<double> rtol, bool hermitian, at::Tensor & out) { |
| 12062 | |
| 12063 | static auto op = create_linalg_pinv_atol_rtol_float_out_typed_handle(); |
| 12064 | return op.call(self, atol, rtol, hermitian, out); |
| 12065 | } |
| 12066 | |
| 12067 | // aten::linalg_pinv.atol_rtol_float_out(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!) |
| 12068 | at::Tensor & linalg_pinv_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) { |
| 12069 | |
| 12070 | static auto op = create_linalg_pinv_atol_rtol_float_out_typed_handle(); |
| 12071 | return op.redispatch(dispatchKeySet, self, atol, rtol, hermitian, out); |
| 12072 | } |
| 12073 | |
| 12074 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_pinv, name, "aten::linalg_pinv") |
| 12075 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_pinv, overload_name, "") |
| 12076 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_pinv, schema_str, "linalg_pinv(Tensor self, float rcond, bool hermitian=False) -> Tensor") |
| 12077 | |
| 12078 | // aten::linalg_pinv(Tensor self, float rcond, bool hermitian=False) -> Tensor |
| 12079 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_pinv::schema> create_linalg_pinv_typed_handle() { |
| 12080 | return c10::Dispatcher::singleton() |
| 12081 | .findSchemaOrThrow(linalg_pinv::name, linalg_pinv::overload_name) |
| 12082 | .typed<linalg_pinv::schema>(); |
| 12083 | } |
| 12084 | |
| 12085 | // aten::linalg_pinv(Tensor self, float rcond, bool hermitian=False) -> Tensor |
| 12086 | at::Tensor linalg_pinv::call(const at::Tensor & self, double rcond, bool hermitian) { |
| 12087 | |
| 12088 | static auto op = create_linalg_pinv_typed_handle(); |
| 12089 | return op.call(self, rcond, hermitian); |
| 12090 | } |
| 12091 | |
| 12092 | // aten::linalg_pinv(Tensor self, float rcond, bool hermitian=False) -> Tensor |
| 12093 | at::Tensor linalg_pinv::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double rcond, bool hermitian) { |
| 12094 | |
| 12095 | static auto op = create_linalg_pinv_typed_handle(); |
| 12096 | return op.redispatch(dispatchKeySet, self, rcond, hermitian); |
| 12097 | } |
| 12098 | |
| 12099 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_pinv_rcond_tensor, name, "aten::linalg_pinv") |
| 12100 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_pinv_rcond_tensor, overload_name, "rcond_tensor") |
| 12101 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_pinv_rcond_tensor, schema_str, "linalg_pinv.rcond_tensor(Tensor self, Tensor rcond, bool hermitian=False) -> Tensor") |
| 12102 | |
| 12103 | // aten::linalg_pinv.rcond_tensor(Tensor self, Tensor rcond, bool hermitian=False) -> Tensor |
| 12104 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_pinv_rcond_tensor::schema> create_linalg_pinv_rcond_tensor_typed_handle() { |
| 12105 | return c10::Dispatcher::singleton() |
| 12106 | .findSchemaOrThrow(linalg_pinv_rcond_tensor::name, linalg_pinv_rcond_tensor::overload_name) |
| 12107 | .typed<linalg_pinv_rcond_tensor::schema>(); |
| 12108 | } |
| 12109 | |
| 12110 | // aten::linalg_pinv.rcond_tensor(Tensor self, Tensor rcond, bool hermitian=False) -> Tensor |
| 12111 | at::Tensor linalg_pinv_rcond_tensor::call(const at::Tensor & self, const at::Tensor & rcond, bool hermitian) { |
| 12112 | |
| 12113 | static auto op = create_linalg_pinv_rcond_tensor_typed_handle(); |
| 12114 | return op.call(self, rcond, hermitian); |
| 12115 | } |
| 12116 | |
| 12117 | // aten::linalg_pinv.rcond_tensor(Tensor self, Tensor rcond, bool hermitian=False) -> Tensor |
| 12118 | at::Tensor linalg_pinv_rcond_tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & rcond, bool hermitian) { |
| 12119 | |
| 12120 | static auto op = create_linalg_pinv_rcond_tensor_typed_handle(); |
| 12121 | return op.redispatch(dispatchKeySet, self, rcond, hermitian); |
| 12122 | } |
| 12123 | |
| 12124 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_pinv_out, name, "aten::linalg_pinv") |
| 12125 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_pinv_out, overload_name, "out") |
| 12126 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_pinv_out, schema_str, "linalg_pinv.out(Tensor self, float rcond, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!)") |
| 12127 | |
| 12128 | // aten::linalg_pinv.out(Tensor self, float rcond, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!) |
| 12129 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_pinv_out::schema> create_linalg_pinv_out_typed_handle() { |
| 12130 | return c10::Dispatcher::singleton() |
| 12131 | .findSchemaOrThrow(linalg_pinv_out::name, linalg_pinv_out::overload_name) |
| 12132 | .typed<linalg_pinv_out::schema>(); |
| 12133 | } |
| 12134 | |
| 12135 | // aten::linalg_pinv.out(Tensor self, float rcond, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!) |
| 12136 | at::Tensor & linalg_pinv_out::call(const at::Tensor & self, double rcond, bool hermitian, at::Tensor & out) { |
| 12137 | |
| 12138 | static auto op = create_linalg_pinv_out_typed_handle(); |
| 12139 | return op.call(self, rcond, hermitian, out); |
| 12140 | } |
| 12141 | |
| 12142 | // aten::linalg_pinv.out(Tensor self, float rcond, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!) |
| 12143 | at::Tensor & linalg_pinv_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double rcond, bool hermitian, at::Tensor & out) { |
| 12144 | |
| 12145 | static auto op = create_linalg_pinv_out_typed_handle(); |
| 12146 | return op.redispatch(dispatchKeySet, self, rcond, hermitian, out); |
| 12147 | } |
| 12148 | |
| 12149 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_pinv_out_rcond_tensor, name, "aten::linalg_pinv") |
| 12150 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_pinv_out_rcond_tensor, overload_name, "out_rcond_tensor") |
| 12151 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_pinv_out_rcond_tensor, schema_str, "linalg_pinv.out_rcond_tensor(Tensor self, Tensor rcond, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!)") |
| 12152 | |
| 12153 | // aten::linalg_pinv.out_rcond_tensor(Tensor self, Tensor rcond, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!) |
| 12154 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_pinv_out_rcond_tensor::schema> create_linalg_pinv_out_rcond_tensor_typed_handle() { |
| 12155 | return c10::Dispatcher::singleton() |
| 12156 | .findSchemaOrThrow(linalg_pinv_out_rcond_tensor::name, linalg_pinv_out_rcond_tensor::overload_name) |
| 12157 | .typed<linalg_pinv_out_rcond_tensor::schema>(); |
| 12158 | } |
| 12159 | |
| 12160 | // aten::linalg_pinv.out_rcond_tensor(Tensor self, Tensor rcond, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!) |
| 12161 | at::Tensor & linalg_pinv_out_rcond_tensor::call(const at::Tensor & self, const at::Tensor & rcond, bool hermitian, at::Tensor & out) { |
| 12162 | |
| 12163 | static auto op = create_linalg_pinv_out_rcond_tensor_typed_handle(); |
| 12164 | return op.call(self, rcond, hermitian, out); |
| 12165 | } |
| 12166 | |
| 12167 | // aten::linalg_pinv.out_rcond_tensor(Tensor self, Tensor rcond, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!) |
| 12168 | at::Tensor & linalg_pinv_out_rcond_tensor::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & rcond, bool hermitian, at::Tensor & out) { |
| 12169 | |
| 12170 | static auto op = create_linalg_pinv_out_rcond_tensor_typed_handle(); |
| 12171 | return op.redispatch(dispatchKeySet, self, rcond, hermitian, out); |
| 12172 | } |
| 12173 | |
| 12174 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_solve_ex, name, "aten::linalg_solve_ex") |
| 12175 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_solve_ex, overload_name, "") |
| 12176 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_solve_ex, schema_str, "linalg_solve_ex(Tensor A, Tensor B, *, bool left=True, bool check_errors=False) -> (Tensor result, Tensor info)") |
| 12177 | |
| 12178 | // aten::linalg_solve_ex(Tensor A, Tensor B, *, bool left=True, bool check_errors=False) -> (Tensor result, Tensor info) |
| 12179 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_solve_ex::schema> create_linalg_solve_ex_typed_handle() { |
| 12180 | return c10::Dispatcher::singleton() |
| 12181 | .findSchemaOrThrow(linalg_solve_ex::name, linalg_solve_ex::overload_name) |
| 12182 | .typed<linalg_solve_ex::schema>(); |
| 12183 | } |
| 12184 | |
| 12185 | // aten::linalg_solve_ex(Tensor A, Tensor B, *, bool left=True, bool check_errors=False) -> (Tensor result, Tensor info) |
| 12186 | ::std::tuple<at::Tensor,at::Tensor> linalg_solve_ex::call(const at::Tensor & A, const at::Tensor & B, bool left, bool check_errors) { |
| 12187 | |
| 12188 | static auto op = create_linalg_solve_ex_typed_handle(); |
| 12189 | return op.call(A, B, left, check_errors); |
| 12190 | } |
| 12191 | |
| 12192 | // aten::linalg_solve_ex(Tensor A, Tensor B, *, bool left=True, bool check_errors=False) -> (Tensor result, Tensor info) |
| 12193 | ::std::tuple<at::Tensor,at::Tensor> linalg_solve_ex::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, const at::Tensor & B, bool left, bool check_errors) { |
| 12194 | |
| 12195 | static auto op = create_linalg_solve_ex_typed_handle(); |
| 12196 | return op.redispatch(dispatchKeySet, A, B, left, check_errors); |
| 12197 | } |
| 12198 | |
| 12199 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_solve_ex_out, name, "aten::linalg_solve_ex") |
| 12200 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_solve_ex_out, overload_name, "out") |
| 12201 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_solve_ex_out, schema_str, "linalg_solve_ex.out(Tensor A, Tensor B, *, bool left=True, bool check_errors=False, Tensor(a!) result, Tensor(b!) info) -> (Tensor(a!) result, Tensor(b!) info)") |
| 12202 | |
| 12203 | // aten::linalg_solve_ex.out(Tensor A, Tensor B, *, bool left=True, bool check_errors=False, Tensor(a!) result, Tensor(b!) info) -> (Tensor(a!) result, Tensor(b!) info) |
| 12204 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_solve_ex_out::schema> create_linalg_solve_ex_out_typed_handle() { |
| 12205 | return c10::Dispatcher::singleton() |
| 12206 | .findSchemaOrThrow(linalg_solve_ex_out::name, linalg_solve_ex_out::overload_name) |
| 12207 | .typed<linalg_solve_ex_out::schema>(); |
| 12208 | } |
| 12209 | |
| 12210 | // aten::linalg_solve_ex.out(Tensor A, Tensor B, *, bool left=True, bool check_errors=False, Tensor(a!) result, Tensor(b!) info) -> (Tensor(a!) result, Tensor(b!) info) |
| 12211 | ::std::tuple<at::Tensor &,at::Tensor &> linalg_solve_ex_out::call(const at::Tensor & A, const at::Tensor & B, bool left, bool check_errors, at::Tensor & result, at::Tensor & info) { |
| 12212 | |
| 12213 | static auto op = create_linalg_solve_ex_out_typed_handle(); |
| 12214 | return op.call(A, B, left, check_errors, result, info); |
| 12215 | } |
| 12216 | |
| 12217 | // aten::linalg_solve_ex.out(Tensor A, Tensor B, *, bool left=True, bool check_errors=False, Tensor(a!) result, Tensor(b!) info) -> (Tensor(a!) result, Tensor(b!) info) |
| 12218 | ::std::tuple<at::Tensor &,at::Tensor &> linalg_solve_ex_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, const at::Tensor & B, bool left, bool check_errors, at::Tensor & result, at::Tensor & info) { |
| 12219 | |
| 12220 | static auto op = create_linalg_solve_ex_out_typed_handle(); |
| 12221 | return op.redispatch(dispatchKeySet, A, B, left, check_errors, result, info); |
| 12222 | } |
| 12223 | |
| 12224 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_tensorsolve, name, "aten::linalg_tensorsolve") |
| 12225 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_tensorsolve, overload_name, "") |
| 12226 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_tensorsolve, schema_str, "linalg_tensorsolve(Tensor self, Tensor other, int[]? dims=None) -> Tensor") |
| 12227 | |
| 12228 | // aten::linalg_tensorsolve(Tensor self, Tensor other, int[]? dims=None) -> Tensor |
| 12229 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_tensorsolve::schema> create_linalg_tensorsolve_typed_handle() { |
| 12230 | return c10::Dispatcher::singleton() |
| 12231 | .findSchemaOrThrow(linalg_tensorsolve::name, linalg_tensorsolve::overload_name) |
| 12232 | .typed<linalg_tensorsolve::schema>(); |
| 12233 | } |
| 12234 | |
| 12235 | // aten::linalg_tensorsolve(Tensor self, Tensor other, int[]? dims=None) -> Tensor |
| 12236 | at::Tensor linalg_tensorsolve::call(const at::Tensor & self, const at::Tensor & other, at::OptionalIntArrayRef dims) { |
| 12237 | |
| 12238 | static auto op = create_linalg_tensorsolve_typed_handle(); |
| 12239 | return op.call(self, other, dims); |
| 12240 | } |
| 12241 | |
| 12242 | // aten::linalg_tensorsolve(Tensor self, Tensor other, int[]? dims=None) -> Tensor |
| 12243 | at::Tensor linalg_tensorsolve::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::OptionalIntArrayRef dims) { |
| 12244 | |
| 12245 | static auto op = create_linalg_tensorsolve_typed_handle(); |
| 12246 | return op.redispatch(dispatchKeySet, self, other, dims); |
| 12247 | } |
| 12248 | |
| 12249 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_tensorsolve_out, name, "aten::linalg_tensorsolve") |
| 12250 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_tensorsolve_out, overload_name, "out") |
| 12251 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_tensorsolve_out, schema_str, "linalg_tensorsolve.out(Tensor self, Tensor other, int[]? dims=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 12252 | |
| 12253 | // aten::linalg_tensorsolve.out(Tensor self, Tensor other, int[]? dims=None, *, Tensor(a!) out) -> Tensor(a!) |
| 12254 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_tensorsolve_out::schema> create_linalg_tensorsolve_out_typed_handle() { |
| 12255 | return c10::Dispatcher::singleton() |
| 12256 | .findSchemaOrThrow(linalg_tensorsolve_out::name, linalg_tensorsolve_out::overload_name) |
| 12257 | .typed<linalg_tensorsolve_out::schema>(); |
| 12258 | } |
| 12259 | |
| 12260 | // aten::linalg_tensorsolve.out(Tensor self, Tensor other, int[]? dims=None, *, Tensor(a!) out) -> Tensor(a!) |
| 12261 | at::Tensor & linalg_tensorsolve_out::call(const at::Tensor & self, const at::Tensor & other, at::OptionalIntArrayRef dims, at::Tensor & out) { |
| 12262 | |
| 12263 | static auto op = create_linalg_tensorsolve_out_typed_handle(); |
| 12264 | return op.call(self, other, dims, out); |
| 12265 | } |
| 12266 | |
| 12267 | // aten::linalg_tensorsolve.out(Tensor self, Tensor other, int[]? dims=None, *, Tensor(a!) out) -> Tensor(a!) |
| 12268 | at::Tensor & linalg_tensorsolve_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::OptionalIntArrayRef dims, at::Tensor & out) { |
| 12269 | |
| 12270 | static auto op = create_linalg_tensorsolve_out_typed_handle(); |
| 12271 | return op.redispatch(dispatchKeySet, self, other, dims, out); |
| 12272 | } |
| 12273 | |
| 12274 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_multi_dot, name, "aten::linalg_multi_dot") |
| 12275 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_multi_dot, overload_name, "") |
| 12276 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_multi_dot, schema_str, "linalg_multi_dot(Tensor[] tensors) -> Tensor") |
| 12277 | |
| 12278 | // aten::linalg_multi_dot(Tensor[] tensors) -> Tensor |
| 12279 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_multi_dot::schema> create_linalg_multi_dot_typed_handle() { |
| 12280 | return c10::Dispatcher::singleton() |
| 12281 | .findSchemaOrThrow(linalg_multi_dot::name, linalg_multi_dot::overload_name) |
| 12282 | .typed<linalg_multi_dot::schema>(); |
| 12283 | } |
| 12284 | |
| 12285 | // aten::linalg_multi_dot(Tensor[] tensors) -> Tensor |
| 12286 | at::Tensor linalg_multi_dot::call(at::TensorList tensors) { |
| 12287 | |
| 12288 | static auto op = create_linalg_multi_dot_typed_handle(); |
| 12289 | return op.call(tensors); |
| 12290 | } |
| 12291 | |
| 12292 | // aten::linalg_multi_dot(Tensor[] tensors) -> Tensor |
| 12293 | at::Tensor linalg_multi_dot::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors) { |
| 12294 | |
| 12295 | static auto op = create_linalg_multi_dot_typed_handle(); |
| 12296 | return op.redispatch(dispatchKeySet, tensors); |
| 12297 | } |
| 12298 | |
| 12299 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_multi_dot_out, name, "aten::linalg_multi_dot") |
| 12300 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_multi_dot_out, overload_name, "out") |
| 12301 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(linalg_multi_dot_out, schema_str, "linalg_multi_dot.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)") |
| 12302 | |
| 12303 | // aten::linalg_multi_dot.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) |
| 12304 | static C10_NOINLINE c10::TypedOperatorHandle<linalg_multi_dot_out::schema> create_linalg_multi_dot_out_typed_handle() { |
| 12305 | return c10::Dispatcher::singleton() |
| 12306 | .findSchemaOrThrow(linalg_multi_dot_out::name, linalg_multi_dot_out::overload_name) |
| 12307 | .typed<linalg_multi_dot_out::schema>(); |
| 12308 | } |
| 12309 | |
| 12310 | // aten::linalg_multi_dot.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) |
| 12311 | at::Tensor & linalg_multi_dot_out::call(at::TensorList tensors, at::Tensor & out) { |
| 12312 | |
| 12313 | static auto op = create_linalg_multi_dot_out_typed_handle(); |
| 12314 | return op.call(tensors, out); |
| 12315 | } |
| 12316 | |
| 12317 | // aten::linalg_multi_dot.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) |
| 12318 | at::Tensor & linalg_multi_dot_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Tensor & out) { |
| 12319 | |
| 12320 | static auto op = create_linalg_multi_dot_out_typed_handle(); |
| 12321 | return op.redispatch(dispatchKeySet, tensors, out); |
| 12322 | } |
| 12323 | |
| 12324 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_test_string_default, name, "aten::_test_string_default") |
| 12325 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_test_string_default, overload_name, "") |
| 12326 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_test_string_default, schema_str, "_test_string_default(Tensor dummy, str a=\"\\\"'\\\\\", str b='\"\\'\\\\') -> Tensor") |
| 12327 | |
| 12328 | // aten::_test_string_default(Tensor dummy, str a="\"'\\", str b='"\'\\') -> Tensor |
| 12329 | static C10_NOINLINE c10::TypedOperatorHandle<_test_string_default::schema> create__test_string_default_typed_handle() { |
| 12330 | return c10::Dispatcher::singleton() |
| 12331 | .findSchemaOrThrow(_test_string_default::name, _test_string_default::overload_name) |
| 12332 | .typed<_test_string_default::schema>(); |
| 12333 | } |
| 12334 | |
| 12335 | // aten::_test_string_default(Tensor dummy, str a="\"'\\", str b='"\'\\') -> Tensor |
| 12336 | at::Tensor _test_string_default::call(const at::Tensor & dummy, c10::string_view a, c10::string_view b) { |
| 12337 | |
| 12338 | static auto op = create__test_string_default_typed_handle(); |
| 12339 | return op.call(dummy, a, b); |
| 12340 | } |
| 12341 | |
| 12342 | // aten::_test_string_default(Tensor dummy, str a="\"'\\", str b='"\'\\') -> Tensor |
| 12343 | at::Tensor _test_string_default::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & dummy, c10::string_view a, c10::string_view b) { |
| 12344 | |
| 12345 | static auto op = create__test_string_default_typed_handle(); |
| 12346 | return op.redispatch(dispatchKeySet, dummy, a, b); |
| 12347 | } |
| 12348 | |
| 12349 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(flatten_dense_tensors, name, "aten::flatten_dense_tensors") |
| 12350 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(flatten_dense_tensors, overload_name, "") |
| 12351 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(flatten_dense_tensors, schema_str, "flatten_dense_tensors(Tensor[] tensors) -> Tensor") |
| 12352 | |
| 12353 | // aten::flatten_dense_tensors(Tensor[] tensors) -> Tensor |
| 12354 | static C10_NOINLINE c10::TypedOperatorHandle<flatten_dense_tensors::schema> create_flatten_dense_tensors_typed_handle() { |
| 12355 | return c10::Dispatcher::singleton() |
| 12356 | .findSchemaOrThrow(flatten_dense_tensors::name, flatten_dense_tensors::overload_name) |
| 12357 | .typed<flatten_dense_tensors::schema>(); |
| 12358 | } |
| 12359 | |
| 12360 | // aten::flatten_dense_tensors(Tensor[] tensors) -> Tensor |
| 12361 | at::Tensor flatten_dense_tensors::call(at::TensorList tensors) { |
| 12362 | |
| 12363 | static auto op = create_flatten_dense_tensors_typed_handle(); |
| 12364 | return op.call(tensors); |
| 12365 | } |
| 12366 | |
| 12367 | // aten::flatten_dense_tensors(Tensor[] tensors) -> Tensor |
| 12368 | at::Tensor flatten_dense_tensors::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors) { |
| 12369 | |
| 12370 | static auto op = create_flatten_dense_tensors_typed_handle(); |
| 12371 | return op.redispatch(dispatchKeySet, tensors); |
| 12372 | } |
| 12373 | |
| 12374 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_conj_copy, name, "aten::_conj_copy") |
| 12375 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_conj_copy, overload_name, "") |
| 12376 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_conj_copy, schema_str, "_conj_copy(Tensor self) -> Tensor") |
| 12377 | |
| 12378 | // aten::_conj_copy(Tensor self) -> Tensor |
| 12379 | static C10_NOINLINE c10::TypedOperatorHandle<_conj_copy::schema> create__conj_copy_typed_handle() { |
| 12380 | return c10::Dispatcher::singleton() |
| 12381 | .findSchemaOrThrow(_conj_copy::name, _conj_copy::overload_name) |
| 12382 | .typed<_conj_copy::schema>(); |
| 12383 | } |
| 12384 | |
| 12385 | // aten::_conj_copy(Tensor self) -> Tensor |
| 12386 | at::Tensor _conj_copy::call(const at::Tensor & self) { |
| 12387 | |
| 12388 | static auto op = create__conj_copy_typed_handle(); |
| 12389 | return op.call(self); |
| 12390 | } |
| 12391 | |
| 12392 | // aten::_conj_copy(Tensor self) -> Tensor |
| 12393 | at::Tensor _conj_copy::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 12394 | |
| 12395 | static auto op = create__conj_copy_typed_handle(); |
| 12396 | return op.redispatch(dispatchKeySet, self); |
| 12397 | } |
| 12398 | |
| 12399 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(detach_copy, name, "aten::detach_copy") |
| 12400 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(detach_copy, overload_name, "") |
| 12401 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(detach_copy, schema_str, "detach_copy(Tensor self) -> Tensor") |
| 12402 | |
| 12403 | // aten::detach_copy(Tensor self) -> Tensor |
| 12404 | static C10_NOINLINE c10::TypedOperatorHandle<detach_copy::schema> create_detach_copy_typed_handle() { |
| 12405 | return c10::Dispatcher::singleton() |
| 12406 | .findSchemaOrThrow(detach_copy::name, detach_copy::overload_name) |
| 12407 | .typed<detach_copy::schema>(); |
| 12408 | } |
| 12409 | |
| 12410 | // aten::detach_copy(Tensor self) -> Tensor |
| 12411 | at::Tensor detach_copy::call(const at::Tensor & self) { |
| 12412 | |
| 12413 | static auto op = create_detach_copy_typed_handle(); |
| 12414 | return op.call(self); |
| 12415 | } |
| 12416 | |
| 12417 | // aten::detach_copy(Tensor self) -> Tensor |
| 12418 | at::Tensor detach_copy::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 12419 | |
| 12420 | static auto op = create_detach_copy_typed_handle(); |
| 12421 | return op.redispatch(dispatchKeySet, self); |
| 12422 | } |
| 12423 | |
| 12424 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(row_indices_copy, name, "aten::row_indices_copy") |
| 12425 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(row_indices_copy, overload_name, "") |
| 12426 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(row_indices_copy, schema_str, "row_indices_copy(Tensor self) -> Tensor") |
| 12427 | |
| 12428 | // aten::row_indices_copy(Tensor self) -> Tensor |
| 12429 | static C10_NOINLINE c10::TypedOperatorHandle<row_indices_copy::schema> create_row_indices_copy_typed_handle() { |
| 12430 | return c10::Dispatcher::singleton() |
| 12431 | .findSchemaOrThrow(row_indices_copy::name, row_indices_copy::overload_name) |
| 12432 | .typed<row_indices_copy::schema>(); |
| 12433 | } |
| 12434 | |
| 12435 | // aten::row_indices_copy(Tensor self) -> Tensor |
| 12436 | at::Tensor row_indices_copy::call(const at::Tensor & self) { |
| 12437 | |
| 12438 | static auto op = create_row_indices_copy_typed_handle(); |
| 12439 | return op.call(self); |
| 12440 | } |
| 12441 | |
| 12442 | // aten::row_indices_copy(Tensor self) -> Tensor |
| 12443 | at::Tensor row_indices_copy::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 12444 | |
| 12445 | static auto op = create_row_indices_copy_typed_handle(); |
| 12446 | return op.redispatch(dispatchKeySet, self); |
| 12447 | } |
| 12448 | |
| 12449 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_transformer_encoder_layer_fwd, name, "aten::_transformer_encoder_layer_fwd") |
| 12450 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_transformer_encoder_layer_fwd, overload_name, "") |
| 12451 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_transformer_encoder_layer_fwd, schema_str, "_transformer_encoder_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, int? mask_type=None) -> Tensor") |
| 12452 | |
| 12453 | // aten::_transformer_encoder_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, int? mask_type=None) -> Tensor |
| 12454 | static C10_NOINLINE c10::TypedOperatorHandle<_transformer_encoder_layer_fwd::schema> create__transformer_encoder_layer_fwd_typed_handle() { |
| 12455 | return c10::Dispatcher::singleton() |
| 12456 | .findSchemaOrThrow(_transformer_encoder_layer_fwd::name, _transformer_encoder_layer_fwd::overload_name) |
| 12457 | .typed<_transformer_encoder_layer_fwd::schema>(); |
| 12458 | } |
| 12459 | |
| 12460 | // aten::_transformer_encoder_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, int? mask_type=None) -> Tensor |
| 12461 | at::Tensor _transformer_encoder_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, c10::optional<int64_t> mask_type) { |
| 12462 | |
| 12463 | static auto op = create__transformer_encoder_layer_fwd_typed_handle(); |
| 12464 | 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, mask_type); |
| 12465 | } |
| 12466 | |
| 12467 | // aten::_transformer_encoder_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, int? mask_type=None) -> Tensor |
| 12468 | at::Tensor _transformer_encoder_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, c10::optional<int64_t> mask_type) { |
| 12469 | |
| 12470 | static auto op = create__transformer_encoder_layer_fwd_typed_handle(); |
| 12471 | 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, mask_type); |
| 12472 | } |
| 12473 | |
| 12474 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_native_multi_head_attention, name, "aten::_native_multi_head_attention") |
| 12475 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_native_multi_head_attention, overload_name, "") |
| 12476 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_native_multi_head_attention, schema_str, "_native_multi_head_attention(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, bool need_weights=True, bool average_attn_weights=True, int? mask_type=None) -> (Tensor, Tensor)") |
| 12477 | |
| 12478 | // aten::_native_multi_head_attention(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, bool need_weights=True, bool average_attn_weights=True, int? mask_type=None) -> (Tensor, Tensor) |
| 12479 | static C10_NOINLINE c10::TypedOperatorHandle<_native_multi_head_attention::schema> create__native_multi_head_attention_typed_handle() { |
| 12480 | return c10::Dispatcher::singleton() |
| 12481 | .findSchemaOrThrow(_native_multi_head_attention::name, _native_multi_head_attention::overload_name) |
| 12482 | .typed<_native_multi_head_attention::schema>(); |
| 12483 | } |
| 12484 | |
| 12485 | // aten::_native_multi_head_attention(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, bool need_weights=True, bool average_attn_weights=True, int? mask_type=None) -> (Tensor, Tensor) |
| 12486 | ::std::tuple<at::Tensor,at::Tensor> _native_multi_head_attention::call(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const c10::optional<at::Tensor> & mask, bool need_weights, bool average_attn_weights, c10::optional<int64_t> mask_type) { |
| 12487 | |
| 12488 | static auto op = create__native_multi_head_attention_typed_handle(); |
| 12489 | return op.call(query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask, need_weights, average_attn_weights, mask_type); |
| 12490 | } |
| 12491 | |
| 12492 | // aten::_native_multi_head_attention(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, bool need_weights=True, bool average_attn_weights=True, int? mask_type=None) -> (Tensor, Tensor) |
| 12493 | ::std::tuple<at::Tensor,at::Tensor> _native_multi_head_attention::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const c10::optional<at::Tensor> & mask, bool need_weights, bool average_attn_weights, c10::optional<int64_t> mask_type) { |
| 12494 | |
| 12495 | static auto op = create__native_multi_head_attention_typed_handle(); |
| 12496 | return op.redispatch(dispatchKeySet, query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask, need_weights, average_attn_weights, mask_type); |
| 12497 | } |
| 12498 | |
| 12499 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_scaled_dot_product_attention, name, "aten::_scaled_dot_product_attention") |
| 12500 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_scaled_dot_product_attention, overload_name, "") |
| 12501 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_scaled_dot_product_attention, schema_str, "_scaled_dot_product_attention(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool need_attn_weights=False, bool is_causal=False) -> (Tensor, Tensor)") |
| 12502 | |
| 12503 | // aten::_scaled_dot_product_attention(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool need_attn_weights=False, bool is_causal=False) -> (Tensor, Tensor) |
| 12504 | static C10_NOINLINE c10::TypedOperatorHandle<_scaled_dot_product_attention::schema> create__scaled_dot_product_attention_typed_handle() { |
| 12505 | return c10::Dispatcher::singleton() |
| 12506 | .findSchemaOrThrow(_scaled_dot_product_attention::name, _scaled_dot_product_attention::overload_name) |
| 12507 | .typed<_scaled_dot_product_attention::schema>(); |
| 12508 | } |
| 12509 | |
| 12510 | // aten::_scaled_dot_product_attention(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool need_attn_weights=False, bool is_causal=False) -> (Tensor, Tensor) |
| 12511 | ::std::tuple<at::Tensor,at::Tensor> _scaled_dot_product_attention::call(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const c10::optional<at::Tensor> & attn_mask, double dropout_p, bool need_attn_weights, bool is_causal) { |
| 12512 | |
| 12513 | static auto op = create__scaled_dot_product_attention_typed_handle(); |
| 12514 | return op.call(query, key, value, attn_mask, dropout_p, need_attn_weights, is_causal); |
| 12515 | } |
| 12516 | |
| 12517 | // aten::_scaled_dot_product_attention(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool need_attn_weights=False, bool is_causal=False) -> (Tensor, Tensor) |
| 12518 | ::std::tuple<at::Tensor,at::Tensor> _scaled_dot_product_attention::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const c10::optional<at::Tensor> & attn_mask, double dropout_p, bool need_attn_weights, bool is_causal) { |
| 12519 | |
| 12520 | static auto op = create__scaled_dot_product_attention_typed_handle(); |
| 12521 | return op.redispatch(dispatchKeySet, query, key, value, attn_mask, dropout_p, need_attn_weights, is_causal); |
| 12522 | } |
| 12523 | |
| 12524 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fused_sdp_choice, name, "aten::_fused_sdp_choice") |
| 12525 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fused_sdp_choice, overload_name, "") |
| 12526 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fused_sdp_choice, schema_str, "_fused_sdp_choice(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool is_causal=False) -> int") |
| 12527 | |
| 12528 | // aten::_fused_sdp_choice(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool is_causal=False) -> int |
| 12529 | static C10_NOINLINE c10::TypedOperatorHandle<_fused_sdp_choice::schema> create__fused_sdp_choice_typed_handle() { |
| 12530 | return c10::Dispatcher::singleton() |
| 12531 | .findSchemaOrThrow(_fused_sdp_choice::name, _fused_sdp_choice::overload_name) |
| 12532 | .typed<_fused_sdp_choice::schema>(); |
| 12533 | } |
| 12534 | |
| 12535 | // aten::_fused_sdp_choice(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool is_causal=False) -> int |
| 12536 | int64_t _fused_sdp_choice::call(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const c10::optional<at::Tensor> & attn_mask, double dropout_p, bool is_causal) { |
| 12537 | |
| 12538 | static auto op = create__fused_sdp_choice_typed_handle(); |
| 12539 | return op.call(query, key, value, attn_mask, dropout_p, is_causal); |
| 12540 | } |
| 12541 | |
| 12542 | // aten::_fused_sdp_choice(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool is_causal=False) -> int |
| 12543 | int64_t _fused_sdp_choice::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const c10::optional<at::Tensor> & attn_mask, double dropout_p, bool is_causal) { |
| 12544 | |
| 12545 | static auto op = create__fused_sdp_choice_typed_handle(); |
| 12546 | return op.redispatch(dispatchKeySet, query, key, value, attn_mask, dropout_p, is_causal); |
| 12547 | } |
| 12548 | |
| 12549 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_scaled_dot_product_flash_attention, name, "aten::_scaled_dot_product_flash_attention") |
| 12550 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_scaled_dot_product_flash_attention, overload_name, "") |
| 12551 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_scaled_dot_product_flash_attention, schema_str, "_scaled_dot_product_flash_attention(Tensor query, Tensor key, Tensor value, float dropout_p=0.0, bool is_causal=False, bool return_debug_mask=False) -> (Tensor ouput, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, int max_q, int max_k, int philox_seed, int philox_offset, Tensor debug_attn_mask)") |
| 12552 | |
| 12553 | // aten::_scaled_dot_product_flash_attention(Tensor query, Tensor key, Tensor value, float dropout_p=0.0, bool is_causal=False, bool return_debug_mask=False) -> (Tensor ouput, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, int max_q, int max_k, int philox_seed, int philox_offset, Tensor debug_attn_mask) |
| 12554 | static C10_NOINLINE c10::TypedOperatorHandle<_scaled_dot_product_flash_attention::schema> create__scaled_dot_product_flash_attention_typed_handle() { |
| 12555 | return c10::Dispatcher::singleton() |
| 12556 | .findSchemaOrThrow(_scaled_dot_product_flash_attention::name, _scaled_dot_product_flash_attention::overload_name) |
| 12557 | .typed<_scaled_dot_product_flash_attention::schema>(); |
| 12558 | } |
| 12559 | |
| 12560 | // aten::_scaled_dot_product_flash_attention(Tensor query, Tensor key, Tensor value, float dropout_p=0.0, bool is_causal=False, bool return_debug_mask=False) -> (Tensor ouput, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, int max_q, int max_k, int philox_seed, int philox_offset, Tensor debug_attn_mask) |
| 12561 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,int64_t,int64_t,int64_t,int64_t,at::Tensor> _scaled_dot_product_flash_attention::call(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, double dropout_p, bool is_causal, bool return_debug_mask) { |
| 12562 | |
| 12563 | static auto op = create__scaled_dot_product_flash_attention_typed_handle(); |
| 12564 | return op.call(query, key, value, dropout_p, is_causal, return_debug_mask); |
| 12565 | } |
| 12566 | |
| 12567 | // aten::_scaled_dot_product_flash_attention(Tensor query, Tensor key, Tensor value, float dropout_p=0.0, bool is_causal=False, bool return_debug_mask=False) -> (Tensor ouput, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, int max_q, int max_k, int philox_seed, int philox_offset, Tensor debug_attn_mask) |
| 12568 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,int64_t,int64_t,int64_t,int64_t,at::Tensor> _scaled_dot_product_flash_attention::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, double dropout_p, bool is_causal, bool return_debug_mask) { |
| 12569 | |
| 12570 | static auto op = create__scaled_dot_product_flash_attention_typed_handle(); |
| 12571 | return op.redispatch(dispatchKeySet, query, key, value, dropout_p, is_causal, return_debug_mask); |
| 12572 | } |
| 12573 | |
| 12574 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_scaled_dot_product_efficient_attention_backward, name, "aten::_scaled_dot_product_efficient_attention_backward") |
| 12575 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_scaled_dot_product_efficient_attention_backward, overload_name, "") |
| 12576 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_scaled_dot_product_efficient_attention_backward, schema_str, "_scaled_dot_product_efficient_attention_backward(Tensor grad_out_, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, bool is_causal=False, bool chunk_grad_outputs=False) -> (Tensor, Tensor, Tensor)") |
| 12577 | |
| 12578 | // aten::_scaled_dot_product_efficient_attention_backward(Tensor grad_out_, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, bool is_causal=False, bool chunk_grad_outputs=False) -> (Tensor, Tensor, Tensor) |
| 12579 | static C10_NOINLINE c10::TypedOperatorHandle<_scaled_dot_product_efficient_attention_backward::schema> create__scaled_dot_product_efficient_attention_backward_typed_handle() { |
| 12580 | return c10::Dispatcher::singleton() |
| 12581 | .findSchemaOrThrow(_scaled_dot_product_efficient_attention_backward::name, _scaled_dot_product_efficient_attention_backward::overload_name) |
| 12582 | .typed<_scaled_dot_product_efficient_attention_backward::schema>(); |
| 12583 | } |
| 12584 | |
| 12585 | // aten::_scaled_dot_product_efficient_attention_backward(Tensor grad_out_, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, bool is_causal=False, bool chunk_grad_outputs=False) -> (Tensor, Tensor, Tensor) |
| 12586 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_efficient_attention_backward::call(const at::Tensor & grad_out_, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, bool is_causal, bool chunk_grad_outputs) { |
| 12587 | |
| 12588 | static auto op = create__scaled_dot_product_efficient_attention_backward_typed_handle(); |
| 12589 | return op.call(grad_out_, query, key, value, out, logsumexp, is_causal, chunk_grad_outputs); |
| 12590 | } |
| 12591 | |
| 12592 | // aten::_scaled_dot_product_efficient_attention_backward(Tensor grad_out_, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, bool is_causal=False, bool chunk_grad_outputs=False) -> (Tensor, Tensor, Tensor) |
| 12593 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_efficient_attention_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out_, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, bool is_causal, bool chunk_grad_outputs) { |
| 12594 | |
| 12595 | static auto op = create__scaled_dot_product_efficient_attention_backward_typed_handle(); |
| 12596 | return op.redispatch(dispatchKeySet, grad_out_, query, key, value, out, logsumexp, is_causal, chunk_grad_outputs); |
| 12597 | } |
| 12598 | |
| 12599 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_flash_attention_backward, name, "aten::_flash_attention_backward") |
| 12600 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_flash_attention_backward, overload_name, "") |
| 12601 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_flash_attention_backward, schema_str, "_flash_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, int max_q, int max_k, float dropout_p, bool is_causal, int philox_seed, int philox_offset) -> (Tensor, Tensor, Tensor)") |
| 12602 | |
| 12603 | // aten::_flash_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, int max_q, int max_k, float dropout_p, bool is_causal, int philox_seed, int philox_offset) -> (Tensor, Tensor, Tensor) |
| 12604 | static C10_NOINLINE c10::TypedOperatorHandle<_flash_attention_backward::schema> create__flash_attention_backward_typed_handle() { |
| 12605 | return c10::Dispatcher::singleton() |
| 12606 | .findSchemaOrThrow(_flash_attention_backward::name, _flash_attention_backward::overload_name) |
| 12607 | .typed<_flash_attention_backward::schema>(); |
| 12608 | } |
| 12609 | |
| 12610 | // aten::_flash_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, int max_q, int max_k, float dropout_p, bool is_causal, int philox_seed, int philox_offset) -> (Tensor, Tensor, Tensor) |
| 12611 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _flash_attention_backward::call(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, int64_t max_q, int64_t max_k, double dropout_p, bool is_causal, int64_t philox_seed, int64_t philox_offset) { |
| 12612 | |
| 12613 | static auto op = create__flash_attention_backward_typed_handle(); |
| 12614 | return op.call(grad_out, query, key, value, out, logsumexp, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, philox_seed, philox_offset); |
| 12615 | } |
| 12616 | |
| 12617 | // aten::_flash_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, int max_q, int max_k, float dropout_p, bool is_causal, int philox_seed, int philox_offset) -> (Tensor, Tensor, Tensor) |
| 12618 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _flash_attention_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, int64_t max_q, int64_t max_k, double dropout_p, bool is_causal, int64_t philox_seed, int64_t philox_offset) { |
| 12619 | |
| 12620 | static auto op = create__flash_attention_backward_typed_handle(); |
| 12621 | return op.redispatch(dispatchKeySet, grad_out, query, key, value, out, logsumexp, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, philox_seed, philox_offset); |
| 12622 | } |
| 12623 | |
| 12624 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_efficient_attention_backward, name, "aten::_efficient_attention_backward") |
| 12625 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_efficient_attention_backward, overload_name, "") |
| 12626 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_efficient_attention_backward, schema_str, "_efficient_attention_backward(Tensor grad_out_, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, bool is_causal=False, bool chunk_grad_outputs=False) -> (Tensor, Tensor, Tensor)") |
| 12627 | |
| 12628 | // aten::_efficient_attention_backward(Tensor grad_out_, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, bool is_causal=False, bool chunk_grad_outputs=False) -> (Tensor, Tensor, Tensor) |
| 12629 | static C10_NOINLINE c10::TypedOperatorHandle<_efficient_attention_backward::schema> create__efficient_attention_backward_typed_handle() { |
| 12630 | return c10::Dispatcher::singleton() |
| 12631 | .findSchemaOrThrow(_efficient_attention_backward::name, _efficient_attention_backward::overload_name) |
| 12632 | .typed<_efficient_attention_backward::schema>(); |
| 12633 | } |
| 12634 | |
| 12635 | // aten::_efficient_attention_backward(Tensor grad_out_, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, bool is_causal=False, bool chunk_grad_outputs=False) -> (Tensor, Tensor, Tensor) |
| 12636 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _efficient_attention_backward::call(const at::Tensor & grad_out_, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, bool is_causal, bool chunk_grad_outputs) { |
| 12637 | |
| 12638 | static auto op = create__efficient_attention_backward_typed_handle(); |
| 12639 | return op.call(grad_out_, query, key, value, out, logsumexp, is_causal, chunk_grad_outputs); |
| 12640 | } |
| 12641 | |
| 12642 | // aten::_efficient_attention_backward(Tensor grad_out_, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, bool is_causal=False, bool chunk_grad_outputs=False) -> (Tensor, Tensor, Tensor) |
| 12643 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _efficient_attention_backward::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out_, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, bool is_causal, bool chunk_grad_outputs) { |
| 12644 | |
| 12645 | static auto op = create__efficient_attention_backward_typed_handle(); |
| 12646 | return op.redispatch(dispatchKeySet, grad_out_, query, key, value, out, logsumexp, is_causal, chunk_grad_outputs); |
| 12647 | } |
| 12648 | |
| 12649 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_triton_multi_head_attention, name, "aten::_triton_multi_head_attention") |
| 12650 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_triton_multi_head_attention, overload_name, "") |
| 12651 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_triton_multi_head_attention, schema_str, "_triton_multi_head_attention(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None) -> Tensor") |
| 12652 | |
| 12653 | // aten::_triton_multi_head_attention(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None) -> Tensor |
| 12654 | static C10_NOINLINE c10::TypedOperatorHandle<_triton_multi_head_attention::schema> create__triton_multi_head_attention_typed_handle() { |
| 12655 | return c10::Dispatcher::singleton() |
| 12656 | .findSchemaOrThrow(_triton_multi_head_attention::name, _triton_multi_head_attention::overload_name) |
| 12657 | .typed<_triton_multi_head_attention::schema>(); |
| 12658 | } |
| 12659 | |
| 12660 | // aten::_triton_multi_head_attention(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None) -> Tensor |
| 12661 | at::Tensor _triton_multi_head_attention::call(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const c10::optional<at::Tensor> & mask) { |
| 12662 | |
| 12663 | static auto op = create__triton_multi_head_attention_typed_handle(); |
| 12664 | return op.call(query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask); |
| 12665 | } |
| 12666 | |
| 12667 | // aten::_triton_multi_head_attention(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None) -> Tensor |
| 12668 | at::Tensor _triton_multi_head_attention::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const c10::optional<at::Tensor> & mask) { |
| 12669 | |
| 12670 | static auto op = create__triton_multi_head_attention_typed_handle(); |
| 12671 | return op.redispatch(dispatchKeySet, query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask); |
| 12672 | } |
| 12673 | |
| 12674 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_airy_ai, name, "aten::special_airy_ai") |
| 12675 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_airy_ai, overload_name, "") |
| 12676 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_airy_ai, schema_str, "special_airy_ai(Tensor x) -> Tensor") |
| 12677 | |
| 12678 | // aten::special_airy_ai(Tensor x) -> Tensor |
| 12679 | static C10_NOINLINE c10::TypedOperatorHandle<special_airy_ai::schema> create_special_airy_ai_typed_handle() { |
| 12680 | return c10::Dispatcher::singleton() |
| 12681 | .findSchemaOrThrow(special_airy_ai::name, special_airy_ai::overload_name) |
| 12682 | .typed<special_airy_ai::schema>(); |
| 12683 | } |
| 12684 | |
| 12685 | // aten::special_airy_ai(Tensor x) -> Tensor |
| 12686 | at::Tensor special_airy_ai::call(const at::Tensor & x) { |
| 12687 | |
| 12688 | static auto op = create_special_airy_ai_typed_handle(); |
| 12689 | return op.call(x); |
| 12690 | } |
| 12691 | |
| 12692 | // aten::special_airy_ai(Tensor x) -> Tensor |
| 12693 | at::Tensor special_airy_ai::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x) { |
| 12694 | |
| 12695 | static auto op = create_special_airy_ai_typed_handle(); |
| 12696 | return op.redispatch(dispatchKeySet, x); |
| 12697 | } |
| 12698 | |
| 12699 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_airy_ai_out, name, "aten::special_airy_ai") |
| 12700 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_airy_ai_out, overload_name, "out") |
| 12701 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_airy_ai_out, schema_str, "special_airy_ai.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!)") |
| 12702 | |
| 12703 | // aten::special_airy_ai.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!) |
| 12704 | static C10_NOINLINE c10::TypedOperatorHandle<special_airy_ai_out::schema> create_special_airy_ai_out_typed_handle() { |
| 12705 | return c10::Dispatcher::singleton() |
| 12706 | .findSchemaOrThrow(special_airy_ai_out::name, special_airy_ai_out::overload_name) |
| 12707 | .typed<special_airy_ai_out::schema>(); |
| 12708 | } |
| 12709 | |
| 12710 | // aten::special_airy_ai.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!) |
| 12711 | at::Tensor & special_airy_ai_out::call(const at::Tensor & x, at::Tensor & out) { |
| 12712 | |
| 12713 | static auto op = create_special_airy_ai_out_typed_handle(); |
| 12714 | return op.call(x, out); |
| 12715 | } |
| 12716 | |
| 12717 | // aten::special_airy_ai.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!) |
| 12718 | at::Tensor & special_airy_ai_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, at::Tensor & out) { |
| 12719 | |
| 12720 | static auto op = create_special_airy_ai_out_typed_handle(); |
| 12721 | return op.redispatch(dispatchKeySet, x, out); |
| 12722 | } |
| 12723 | |
| 12724 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_w, name, "aten::special_chebyshev_polynomial_w") |
| 12725 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_w, overload_name, "") |
| 12726 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_w, schema_str, "special_chebyshev_polynomial_w(Tensor x, Tensor n) -> Tensor") |
| 12727 | |
| 12728 | // aten::special_chebyshev_polynomial_w(Tensor x, Tensor n) -> Tensor |
| 12729 | static C10_NOINLINE c10::TypedOperatorHandle<special_chebyshev_polynomial_w::schema> create_special_chebyshev_polynomial_w_typed_handle() { |
| 12730 | return c10::Dispatcher::singleton() |
| 12731 | .findSchemaOrThrow(special_chebyshev_polynomial_w::name, special_chebyshev_polynomial_w::overload_name) |
| 12732 | .typed<special_chebyshev_polynomial_w::schema>(); |
| 12733 | } |
| 12734 | |
| 12735 | // aten::special_chebyshev_polynomial_w(Tensor x, Tensor n) -> Tensor |
| 12736 | at::Tensor special_chebyshev_polynomial_w::call(const at::Tensor & x, const at::Tensor & n) { |
| 12737 | |
| 12738 | static auto op = create_special_chebyshev_polynomial_w_typed_handle(); |
| 12739 | return op.call(x, n); |
| 12740 | } |
| 12741 | |
| 12742 | // aten::special_chebyshev_polynomial_w(Tensor x, Tensor n) -> Tensor |
| 12743 | at::Tensor special_chebyshev_polynomial_w::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n) { |
| 12744 | |
| 12745 | static auto op = create_special_chebyshev_polynomial_w_typed_handle(); |
| 12746 | return op.redispatch(dispatchKeySet, x, n); |
| 12747 | } |
| 12748 | |
| 12749 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_w_x_scalar, name, "aten::special_chebyshev_polynomial_w") |
| 12750 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_w_x_scalar, overload_name, "x_scalar") |
| 12751 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_w_x_scalar, schema_str, "special_chebyshev_polynomial_w.x_scalar(Scalar x, Tensor n) -> Tensor") |
| 12752 | |
| 12753 | // aten::special_chebyshev_polynomial_w.x_scalar(Scalar x, Tensor n) -> Tensor |
| 12754 | static C10_NOINLINE c10::TypedOperatorHandle<special_chebyshev_polynomial_w_x_scalar::schema> create_special_chebyshev_polynomial_w_x_scalar_typed_handle() { |
| 12755 | return c10::Dispatcher::singleton() |
| 12756 | .findSchemaOrThrow(special_chebyshev_polynomial_w_x_scalar::name, special_chebyshev_polynomial_w_x_scalar::overload_name) |
| 12757 | .typed<special_chebyshev_polynomial_w_x_scalar::schema>(); |
| 12758 | } |
| 12759 | |
| 12760 | // aten::special_chebyshev_polynomial_w.x_scalar(Scalar x, Tensor n) -> Tensor |
| 12761 | at::Tensor special_chebyshev_polynomial_w_x_scalar::call(const at::Scalar & x, const at::Tensor & n) { |
| 12762 | |
| 12763 | static auto op = create_special_chebyshev_polynomial_w_x_scalar_typed_handle(); |
| 12764 | return op.call(x, n); |
| 12765 | } |
| 12766 | |
| 12767 | // aten::special_chebyshev_polynomial_w.x_scalar(Scalar x, Tensor n) -> Tensor |
| 12768 | at::Tensor special_chebyshev_polynomial_w_x_scalar::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n) { |
| 12769 | |
| 12770 | static auto op = create_special_chebyshev_polynomial_w_x_scalar_typed_handle(); |
| 12771 | return op.redispatch(dispatchKeySet, x, n); |
| 12772 | } |
| 12773 | |
| 12774 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_w_n_scalar, name, "aten::special_chebyshev_polynomial_w") |
| 12775 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_w_n_scalar, overload_name, "n_scalar") |
| 12776 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_w_n_scalar, schema_str, "special_chebyshev_polynomial_w.n_scalar(Tensor x, Scalar n) -> Tensor") |
| 12777 | |
| 12778 | // aten::special_chebyshev_polynomial_w.n_scalar(Tensor x, Scalar n) -> Tensor |
| 12779 | static C10_NOINLINE c10::TypedOperatorHandle<special_chebyshev_polynomial_w_n_scalar::schema> create_special_chebyshev_polynomial_w_n_scalar_typed_handle() { |
| 12780 | return c10::Dispatcher::singleton() |
| 12781 | .findSchemaOrThrow(special_chebyshev_polynomial_w_n_scalar::name, special_chebyshev_polynomial_w_n_scalar::overload_name) |
| 12782 | .typed<special_chebyshev_polynomial_w_n_scalar::schema>(); |
| 12783 | } |
| 12784 | |
| 12785 | // aten::special_chebyshev_polynomial_w.n_scalar(Tensor x, Scalar n) -> Tensor |
| 12786 | at::Tensor special_chebyshev_polynomial_w_n_scalar::call(const at::Tensor & x, const at::Scalar & n) { |
| 12787 | |
| 12788 | static auto op = create_special_chebyshev_polynomial_w_n_scalar_typed_handle(); |
| 12789 | return op.call(x, n); |
| 12790 | } |
| 12791 | |
| 12792 | // aten::special_chebyshev_polynomial_w.n_scalar(Tensor x, Scalar n) -> Tensor |
| 12793 | at::Tensor special_chebyshev_polynomial_w_n_scalar::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n) { |
| 12794 | |
| 12795 | static auto op = create_special_chebyshev_polynomial_w_n_scalar_typed_handle(); |
| 12796 | return op.redispatch(dispatchKeySet, x, n); |
| 12797 | } |
| 12798 | |
| 12799 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_w_out, name, "aten::special_chebyshev_polynomial_w") |
| 12800 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_w_out, overload_name, "out") |
| 12801 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_w_out, schema_str, "special_chebyshev_polynomial_w.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)") |
| 12802 | |
| 12803 | // aten::special_chebyshev_polynomial_w.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) |
| 12804 | static C10_NOINLINE c10::TypedOperatorHandle<special_chebyshev_polynomial_w_out::schema> create_special_chebyshev_polynomial_w_out_typed_handle() { |
| 12805 | return c10::Dispatcher::singleton() |
| 12806 | .findSchemaOrThrow(special_chebyshev_polynomial_w_out::name, special_chebyshev_polynomial_w_out::overload_name) |
| 12807 | .typed<special_chebyshev_polynomial_w_out::schema>(); |
| 12808 | } |
| 12809 | |
| 12810 | // aten::special_chebyshev_polynomial_w.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) |
| 12811 | at::Tensor & special_chebyshev_polynomial_w_out::call(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 12812 | |
| 12813 | static auto op = create_special_chebyshev_polynomial_w_out_typed_handle(); |
| 12814 | return op.call(x, n, out); |
| 12815 | } |
| 12816 | |
| 12817 | // aten::special_chebyshev_polynomial_w.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) |
| 12818 | at::Tensor & special_chebyshev_polynomial_w_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 12819 | |
| 12820 | static auto op = create_special_chebyshev_polynomial_w_out_typed_handle(); |
| 12821 | return op.redispatch(dispatchKeySet, x, n, out); |
| 12822 | } |
| 12823 | |
| 12824 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_w_x_scalar_out, name, "aten::special_chebyshev_polynomial_w") |
| 12825 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_w_x_scalar_out, overload_name, "x_scalar_out") |
| 12826 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_w_x_scalar_out, schema_str, "special_chebyshev_polynomial_w.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)") |
| 12827 | |
| 12828 | // aten::special_chebyshev_polynomial_w.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) |
| 12829 | static C10_NOINLINE c10::TypedOperatorHandle<special_chebyshev_polynomial_w_x_scalar_out::schema> create_special_chebyshev_polynomial_w_x_scalar_out_typed_handle() { |
| 12830 | return c10::Dispatcher::singleton() |
| 12831 | .findSchemaOrThrow(special_chebyshev_polynomial_w_x_scalar_out::name, special_chebyshev_polynomial_w_x_scalar_out::overload_name) |
| 12832 | .typed<special_chebyshev_polynomial_w_x_scalar_out::schema>(); |
| 12833 | } |
| 12834 | |
| 12835 | // aten::special_chebyshev_polynomial_w.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) |
| 12836 | at::Tensor & special_chebyshev_polynomial_w_x_scalar_out::call(const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { |
| 12837 | |
| 12838 | static auto op = create_special_chebyshev_polynomial_w_x_scalar_out_typed_handle(); |
| 12839 | return op.call(x, n, out); |
| 12840 | } |
| 12841 | |
| 12842 | // aten::special_chebyshev_polynomial_w.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) |
| 12843 | at::Tensor & special_chebyshev_polynomial_w_x_scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { |
| 12844 | |
| 12845 | static auto op = create_special_chebyshev_polynomial_w_x_scalar_out_typed_handle(); |
| 12846 | return op.redispatch(dispatchKeySet, x, n, out); |
| 12847 | } |
| 12848 | |
| 12849 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_w_n_scalar_out, name, "aten::special_chebyshev_polynomial_w") |
| 12850 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_w_n_scalar_out, overload_name, "n_scalar_out") |
| 12851 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_chebyshev_polynomial_w_n_scalar_out, schema_str, "special_chebyshev_polynomial_w.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)") |
| 12852 | |
| 12853 | // aten::special_chebyshev_polynomial_w.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) |
| 12854 | static C10_NOINLINE c10::TypedOperatorHandle<special_chebyshev_polynomial_w_n_scalar_out::schema> create_special_chebyshev_polynomial_w_n_scalar_out_typed_handle() { |
| 12855 | return c10::Dispatcher::singleton() |
| 12856 | .findSchemaOrThrow(special_chebyshev_polynomial_w_n_scalar_out::name, special_chebyshev_polynomial_w_n_scalar_out::overload_name) |
| 12857 | .typed<special_chebyshev_polynomial_w_n_scalar_out::schema>(); |
| 12858 | } |
| 12859 | |
| 12860 | // aten::special_chebyshev_polynomial_w.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) |
| 12861 | at::Tensor & special_chebyshev_polynomial_w_n_scalar_out::call(const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { |
| 12862 | |
| 12863 | static auto op = create_special_chebyshev_polynomial_w_n_scalar_out_typed_handle(); |
| 12864 | return op.call(x, n, out); |
| 12865 | } |
| 12866 | |
| 12867 | // aten::special_chebyshev_polynomial_w.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) |
| 12868 | at::Tensor & special_chebyshev_polynomial_w_n_scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { |
| 12869 | |
| 12870 | static auto op = create_special_chebyshev_polynomial_w_n_scalar_out_typed_handle(); |
| 12871 | return op.redispatch(dispatchKeySet, x, n, out); |
| 12872 | } |
| 12873 | |
| 12874 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_hermite_polynomial_h, name, "aten::special_hermite_polynomial_h") |
| 12875 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_hermite_polynomial_h, overload_name, "") |
| 12876 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_hermite_polynomial_h, schema_str, "special_hermite_polynomial_h(Tensor x, Tensor n) -> Tensor") |
| 12877 | |
| 12878 | // aten::special_hermite_polynomial_h(Tensor x, Tensor n) -> Tensor |
| 12879 | static C10_NOINLINE c10::TypedOperatorHandle<special_hermite_polynomial_h::schema> create_special_hermite_polynomial_h_typed_handle() { |
| 12880 | return c10::Dispatcher::singleton() |
| 12881 | .findSchemaOrThrow(special_hermite_polynomial_h::name, special_hermite_polynomial_h::overload_name) |
| 12882 | .typed<special_hermite_polynomial_h::schema>(); |
| 12883 | } |
| 12884 | |
| 12885 | // aten::special_hermite_polynomial_h(Tensor x, Tensor n) -> Tensor |
| 12886 | at::Tensor special_hermite_polynomial_h::call(const at::Tensor & x, const at::Tensor & n) { |
| 12887 | |
| 12888 | static auto op = create_special_hermite_polynomial_h_typed_handle(); |
| 12889 | return op.call(x, n); |
| 12890 | } |
| 12891 | |
| 12892 | // aten::special_hermite_polynomial_h(Tensor x, Tensor n) -> Tensor |
| 12893 | at::Tensor special_hermite_polynomial_h::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n) { |
| 12894 | |
| 12895 | static auto op = create_special_hermite_polynomial_h_typed_handle(); |
| 12896 | return op.redispatch(dispatchKeySet, x, n); |
| 12897 | } |
| 12898 | |
| 12899 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_hermite_polynomial_h_x_scalar, name, "aten::special_hermite_polynomial_h") |
| 12900 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_hermite_polynomial_h_x_scalar, overload_name, "x_scalar") |
| 12901 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_hermite_polynomial_h_x_scalar, schema_str, "special_hermite_polynomial_h.x_scalar(Scalar x, Tensor n) -> Tensor") |
| 12902 | |
| 12903 | // aten::special_hermite_polynomial_h.x_scalar(Scalar x, Tensor n) -> Tensor |
| 12904 | static C10_NOINLINE c10::TypedOperatorHandle<special_hermite_polynomial_h_x_scalar::schema> create_special_hermite_polynomial_h_x_scalar_typed_handle() { |
| 12905 | return c10::Dispatcher::singleton() |
| 12906 | .findSchemaOrThrow(special_hermite_polynomial_h_x_scalar::name, special_hermite_polynomial_h_x_scalar::overload_name) |
| 12907 | .typed<special_hermite_polynomial_h_x_scalar::schema>(); |
| 12908 | } |
| 12909 | |
| 12910 | // aten::special_hermite_polynomial_h.x_scalar(Scalar x, Tensor n) -> Tensor |
| 12911 | at::Tensor special_hermite_polynomial_h_x_scalar::call(const at::Scalar & x, const at::Tensor & n) { |
| 12912 | |
| 12913 | static auto op = create_special_hermite_polynomial_h_x_scalar_typed_handle(); |
| 12914 | return op.call(x, n); |
| 12915 | } |
| 12916 | |
| 12917 | // aten::special_hermite_polynomial_h.x_scalar(Scalar x, Tensor n) -> Tensor |
| 12918 | at::Tensor special_hermite_polynomial_h_x_scalar::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n) { |
| 12919 | |
| 12920 | static auto op = create_special_hermite_polynomial_h_x_scalar_typed_handle(); |
| 12921 | return op.redispatch(dispatchKeySet, x, n); |
| 12922 | } |
| 12923 | |
| 12924 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_hermite_polynomial_h_n_scalar, name, "aten::special_hermite_polynomial_h") |
| 12925 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_hermite_polynomial_h_n_scalar, overload_name, "n_scalar") |
| 12926 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_hermite_polynomial_h_n_scalar, schema_str, "special_hermite_polynomial_h.n_scalar(Tensor x, Scalar n) -> Tensor") |
| 12927 | |
| 12928 | // aten::special_hermite_polynomial_h.n_scalar(Tensor x, Scalar n) -> Tensor |
| 12929 | static C10_NOINLINE c10::TypedOperatorHandle<special_hermite_polynomial_h_n_scalar::schema> create_special_hermite_polynomial_h_n_scalar_typed_handle() { |
| 12930 | return c10::Dispatcher::singleton() |
| 12931 | .findSchemaOrThrow(special_hermite_polynomial_h_n_scalar::name, special_hermite_polynomial_h_n_scalar::overload_name) |
| 12932 | .typed<special_hermite_polynomial_h_n_scalar::schema>(); |
| 12933 | } |
| 12934 | |
| 12935 | // aten::special_hermite_polynomial_h.n_scalar(Tensor x, Scalar n) -> Tensor |
| 12936 | at::Tensor special_hermite_polynomial_h_n_scalar::call(const at::Tensor & x, const at::Scalar & n) { |
| 12937 | |
| 12938 | static auto op = create_special_hermite_polynomial_h_n_scalar_typed_handle(); |
| 12939 | return op.call(x, n); |
| 12940 | } |
| 12941 | |
| 12942 | // aten::special_hermite_polynomial_h.n_scalar(Tensor x, Scalar n) -> Tensor |
| 12943 | at::Tensor special_hermite_polynomial_h_n_scalar::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n) { |
| 12944 | |
| 12945 | static auto op = create_special_hermite_polynomial_h_n_scalar_typed_handle(); |
| 12946 | return op.redispatch(dispatchKeySet, x, n); |
| 12947 | } |
| 12948 | |
| 12949 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_hermite_polynomial_h_out, name, "aten::special_hermite_polynomial_h") |
| 12950 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_hermite_polynomial_h_out, overload_name, "out") |
| 12951 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_hermite_polynomial_h_out, schema_str, "special_hermite_polynomial_h.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)") |
| 12952 | |
| 12953 | // aten::special_hermite_polynomial_h.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) |
| 12954 | static C10_NOINLINE c10::TypedOperatorHandle<special_hermite_polynomial_h_out::schema> create_special_hermite_polynomial_h_out_typed_handle() { |
| 12955 | return c10::Dispatcher::singleton() |
| 12956 | .findSchemaOrThrow(special_hermite_polynomial_h_out::name, special_hermite_polynomial_h_out::overload_name) |
| 12957 | .typed<special_hermite_polynomial_h_out::schema>(); |
| 12958 | } |
| 12959 | |
| 12960 | // aten::special_hermite_polynomial_h.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) |
| 12961 | at::Tensor & special_hermite_polynomial_h_out::call(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 12962 | |
| 12963 | static auto op = create_special_hermite_polynomial_h_out_typed_handle(); |
| 12964 | return op.call(x, n, out); |
| 12965 | } |
| 12966 | |
| 12967 | // aten::special_hermite_polynomial_h.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) |
| 12968 | at::Tensor & special_hermite_polynomial_h_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 12969 | |
| 12970 | static auto op = create_special_hermite_polynomial_h_out_typed_handle(); |
| 12971 | return op.redispatch(dispatchKeySet, x, n, out); |
| 12972 | } |
| 12973 | |
| 12974 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_hermite_polynomial_h_x_scalar_out, name, "aten::special_hermite_polynomial_h") |
| 12975 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_hermite_polynomial_h_x_scalar_out, overload_name, "x_scalar_out") |
| 12976 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_hermite_polynomial_h_x_scalar_out, schema_str, "special_hermite_polynomial_h.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)") |
| 12977 | |
| 12978 | // aten::special_hermite_polynomial_h.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) |
| 12979 | static C10_NOINLINE c10::TypedOperatorHandle<special_hermite_polynomial_h_x_scalar_out::schema> create_special_hermite_polynomial_h_x_scalar_out_typed_handle() { |
| 12980 | return c10::Dispatcher::singleton() |
| 12981 | .findSchemaOrThrow(special_hermite_polynomial_h_x_scalar_out::name, special_hermite_polynomial_h_x_scalar_out::overload_name) |
| 12982 | .typed<special_hermite_polynomial_h_x_scalar_out::schema>(); |
| 12983 | } |
| 12984 | |
| 12985 | // aten::special_hermite_polynomial_h.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) |
| 12986 | at::Tensor & special_hermite_polynomial_h_x_scalar_out::call(const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { |
| 12987 | |
| 12988 | static auto op = create_special_hermite_polynomial_h_x_scalar_out_typed_handle(); |
| 12989 | return op.call(x, n, out); |
| 12990 | } |
| 12991 | |
| 12992 | // aten::special_hermite_polynomial_h.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) |
| 12993 | at::Tensor & special_hermite_polynomial_h_x_scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { |
| 12994 | |
| 12995 | static auto op = create_special_hermite_polynomial_h_x_scalar_out_typed_handle(); |
| 12996 | return op.redispatch(dispatchKeySet, x, n, out); |
| 12997 | } |
| 12998 | |
| 12999 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_hermite_polynomial_h_n_scalar_out, name, "aten::special_hermite_polynomial_h") |
| 13000 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_hermite_polynomial_h_n_scalar_out, overload_name, "n_scalar_out") |
| 13001 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_hermite_polynomial_h_n_scalar_out, schema_str, "special_hermite_polynomial_h.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)") |
| 13002 | |
| 13003 | // aten::special_hermite_polynomial_h.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) |
| 13004 | static C10_NOINLINE c10::TypedOperatorHandle<special_hermite_polynomial_h_n_scalar_out::schema> create_special_hermite_polynomial_h_n_scalar_out_typed_handle() { |
| 13005 | return c10::Dispatcher::singleton() |
| 13006 | .findSchemaOrThrow(special_hermite_polynomial_h_n_scalar_out::name, special_hermite_polynomial_h_n_scalar_out::overload_name) |
| 13007 | .typed<special_hermite_polynomial_h_n_scalar_out::schema>(); |
| 13008 | } |
| 13009 | |
| 13010 | // aten::special_hermite_polynomial_h.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) |
| 13011 | at::Tensor & special_hermite_polynomial_h_n_scalar_out::call(const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { |
| 13012 | |
| 13013 | static auto op = create_special_hermite_polynomial_h_n_scalar_out_typed_handle(); |
| 13014 | return op.call(x, n, out); |
| 13015 | } |
| 13016 | |
| 13017 | // aten::special_hermite_polynomial_h.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) |
| 13018 | at::Tensor & special_hermite_polynomial_h_n_scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { |
| 13019 | |
| 13020 | static auto op = create_special_hermite_polynomial_h_n_scalar_out_typed_handle(); |
| 13021 | return op.redispatch(dispatchKeySet, x, n, out); |
| 13022 | } |
| 13023 | |
| 13024 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_modified_bessel_i0, name, "aten::special_modified_bessel_i0") |
| 13025 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_modified_bessel_i0, overload_name, "") |
| 13026 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_modified_bessel_i0, schema_str, "special_modified_bessel_i0(Tensor self) -> Tensor") |
| 13027 | |
| 13028 | // aten::special_modified_bessel_i0(Tensor self) -> Tensor |
| 13029 | static C10_NOINLINE c10::TypedOperatorHandle<special_modified_bessel_i0::schema> create_special_modified_bessel_i0_typed_handle() { |
| 13030 | return c10::Dispatcher::singleton() |
| 13031 | .findSchemaOrThrow(special_modified_bessel_i0::name, special_modified_bessel_i0::overload_name) |
| 13032 | .typed<special_modified_bessel_i0::schema>(); |
| 13033 | } |
| 13034 | |
| 13035 | // aten::special_modified_bessel_i0(Tensor self) -> Tensor |
| 13036 | at::Tensor special_modified_bessel_i0::call(const at::Tensor & self) { |
| 13037 | |
| 13038 | static auto op = create_special_modified_bessel_i0_typed_handle(); |
| 13039 | return op.call(self); |
| 13040 | } |
| 13041 | |
| 13042 | // aten::special_modified_bessel_i0(Tensor self) -> Tensor |
| 13043 | at::Tensor special_modified_bessel_i0::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 13044 | |
| 13045 | static auto op = create_special_modified_bessel_i0_typed_handle(); |
| 13046 | return op.redispatch(dispatchKeySet, self); |
| 13047 | } |
| 13048 | |
| 13049 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_modified_bessel_i0_out, name, "aten::special_modified_bessel_i0") |
| 13050 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_modified_bessel_i0_out, overload_name, "out") |
| 13051 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_modified_bessel_i0_out, schema_str, "special_modified_bessel_i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 13052 | |
| 13053 | // aten::special_modified_bessel_i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 13054 | static C10_NOINLINE c10::TypedOperatorHandle<special_modified_bessel_i0_out::schema> create_special_modified_bessel_i0_out_typed_handle() { |
| 13055 | return c10::Dispatcher::singleton() |
| 13056 | .findSchemaOrThrow(special_modified_bessel_i0_out::name, special_modified_bessel_i0_out::overload_name) |
| 13057 | .typed<special_modified_bessel_i0_out::schema>(); |
| 13058 | } |
| 13059 | |
| 13060 | // aten::special_modified_bessel_i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 13061 | at::Tensor & special_modified_bessel_i0_out::call(const at::Tensor & self, at::Tensor & out) { |
| 13062 | |
| 13063 | static auto op = create_special_modified_bessel_i0_out_typed_handle(); |
| 13064 | return op.call(self, out); |
| 13065 | } |
| 13066 | |
| 13067 | // aten::special_modified_bessel_i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 13068 | at::Tensor & special_modified_bessel_i0_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 13069 | |
| 13070 | static auto op = create_special_modified_bessel_i0_out_typed_handle(); |
| 13071 | return op.redispatch(dispatchKeySet, self, out); |
| 13072 | } |
| 13073 | |
| 13074 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_modified_bessel_k0, name, "aten::special_modified_bessel_k0") |
| 13075 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_modified_bessel_k0, overload_name, "") |
| 13076 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_modified_bessel_k0, schema_str, "special_modified_bessel_k0(Tensor self) -> Tensor") |
| 13077 | |
| 13078 | // aten::special_modified_bessel_k0(Tensor self) -> Tensor |
| 13079 | static C10_NOINLINE c10::TypedOperatorHandle<special_modified_bessel_k0::schema> create_special_modified_bessel_k0_typed_handle() { |
| 13080 | return c10::Dispatcher::singleton() |
| 13081 | .findSchemaOrThrow(special_modified_bessel_k0::name, special_modified_bessel_k0::overload_name) |
| 13082 | .typed<special_modified_bessel_k0::schema>(); |
| 13083 | } |
| 13084 | |
| 13085 | // aten::special_modified_bessel_k0(Tensor self) -> Tensor |
| 13086 | at::Tensor special_modified_bessel_k0::call(const at::Tensor & self) { |
| 13087 | |
| 13088 | static auto op = create_special_modified_bessel_k0_typed_handle(); |
| 13089 | return op.call(self); |
| 13090 | } |
| 13091 | |
| 13092 | // aten::special_modified_bessel_k0(Tensor self) -> Tensor |
| 13093 | at::Tensor special_modified_bessel_k0::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 13094 | |
| 13095 | static auto op = create_special_modified_bessel_k0_typed_handle(); |
| 13096 | return op.redispatch(dispatchKeySet, self); |
| 13097 | } |
| 13098 | |
| 13099 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_modified_bessel_k0_out, name, "aten::special_modified_bessel_k0") |
| 13100 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_modified_bessel_k0_out, overload_name, "out") |
| 13101 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_modified_bessel_k0_out, schema_str, "special_modified_bessel_k0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 13102 | |
| 13103 | // aten::special_modified_bessel_k0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 13104 | static C10_NOINLINE c10::TypedOperatorHandle<special_modified_bessel_k0_out::schema> create_special_modified_bessel_k0_out_typed_handle() { |
| 13105 | return c10::Dispatcher::singleton() |
| 13106 | .findSchemaOrThrow(special_modified_bessel_k0_out::name, special_modified_bessel_k0_out::overload_name) |
| 13107 | .typed<special_modified_bessel_k0_out::schema>(); |
| 13108 | } |
| 13109 | |
| 13110 | // aten::special_modified_bessel_k0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 13111 | at::Tensor & special_modified_bessel_k0_out::call(const at::Tensor & self, at::Tensor & out) { |
| 13112 | |
| 13113 | static auto op = create_special_modified_bessel_k0_out_typed_handle(); |
| 13114 | return op.call(self, out); |
| 13115 | } |
| 13116 | |
| 13117 | // aten::special_modified_bessel_k0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 13118 | at::Tensor & special_modified_bessel_k0_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 13119 | |
| 13120 | static auto op = create_special_modified_bessel_k0_out_typed_handle(); |
| 13121 | return op.redispatch(dispatchKeySet, self, out); |
| 13122 | } |
| 13123 | |
| 13124 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_modified_bessel_k1, name, "aten::special_modified_bessel_k1") |
| 13125 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_modified_bessel_k1, overload_name, "") |
| 13126 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_modified_bessel_k1, schema_str, "special_modified_bessel_k1(Tensor self) -> Tensor") |
| 13127 | |
| 13128 | // aten::special_modified_bessel_k1(Tensor self) -> Tensor |
| 13129 | static C10_NOINLINE c10::TypedOperatorHandle<special_modified_bessel_k1::schema> create_special_modified_bessel_k1_typed_handle() { |
| 13130 | return c10::Dispatcher::singleton() |
| 13131 | .findSchemaOrThrow(special_modified_bessel_k1::name, special_modified_bessel_k1::overload_name) |
| 13132 | .typed<special_modified_bessel_k1::schema>(); |
| 13133 | } |
| 13134 | |
| 13135 | // aten::special_modified_bessel_k1(Tensor self) -> Tensor |
| 13136 | at::Tensor special_modified_bessel_k1::call(const at::Tensor & self) { |
| 13137 | |
| 13138 | static auto op = create_special_modified_bessel_k1_typed_handle(); |
| 13139 | return op.call(self); |
| 13140 | } |
| 13141 | |
| 13142 | // aten::special_modified_bessel_k1(Tensor self) -> Tensor |
| 13143 | at::Tensor special_modified_bessel_k1::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { |
| 13144 | |
| 13145 | static auto op = create_special_modified_bessel_k1_typed_handle(); |
| 13146 | return op.redispatch(dispatchKeySet, self); |
| 13147 | } |
| 13148 | |
| 13149 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_modified_bessel_k1_out, name, "aten::special_modified_bessel_k1") |
| 13150 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_modified_bessel_k1_out, overload_name, "out") |
| 13151 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_modified_bessel_k1_out, schema_str, "special_modified_bessel_k1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 13152 | |
| 13153 | // aten::special_modified_bessel_k1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 13154 | static C10_NOINLINE c10::TypedOperatorHandle<special_modified_bessel_k1_out::schema> create_special_modified_bessel_k1_out_typed_handle() { |
| 13155 | return c10::Dispatcher::singleton() |
| 13156 | .findSchemaOrThrow(special_modified_bessel_k1_out::name, special_modified_bessel_k1_out::overload_name) |
| 13157 | .typed<special_modified_bessel_k1_out::schema>(); |
| 13158 | } |
| 13159 | |
| 13160 | // aten::special_modified_bessel_k1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 13161 | at::Tensor & special_modified_bessel_k1_out::call(const at::Tensor & self, at::Tensor & out) { |
| 13162 | |
| 13163 | static auto op = create_special_modified_bessel_k1_out_typed_handle(); |
| 13164 | return op.call(self, out); |
| 13165 | } |
| 13166 | |
| 13167 | // aten::special_modified_bessel_k1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 13168 | at::Tensor & special_modified_bessel_k1_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 13169 | |
| 13170 | static auto op = create_special_modified_bessel_k1_out_typed_handle(); |
| 13171 | return op.redispatch(dispatchKeySet, self, out); |
| 13172 | } |
| 13173 | |
| 13174 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_shifted_chebyshev_polynomial_t, name, "aten::special_shifted_chebyshev_polynomial_t") |
| 13175 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_shifted_chebyshev_polynomial_t, overload_name, "") |
| 13176 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_shifted_chebyshev_polynomial_t, schema_str, "special_shifted_chebyshev_polynomial_t(Tensor x, Tensor n) -> Tensor") |
| 13177 | |
| 13178 | // aten::special_shifted_chebyshev_polynomial_t(Tensor x, Tensor n) -> Tensor |
| 13179 | static C10_NOINLINE c10::TypedOperatorHandle<special_shifted_chebyshev_polynomial_t::schema> create_special_shifted_chebyshev_polynomial_t_typed_handle() { |
| 13180 | return c10::Dispatcher::singleton() |
| 13181 | .findSchemaOrThrow(special_shifted_chebyshev_polynomial_t::name, special_shifted_chebyshev_polynomial_t::overload_name) |
| 13182 | .typed<special_shifted_chebyshev_polynomial_t::schema>(); |
| 13183 | } |
| 13184 | |
| 13185 | // aten::special_shifted_chebyshev_polynomial_t(Tensor x, Tensor n) -> Tensor |
| 13186 | at::Tensor special_shifted_chebyshev_polynomial_t::call(const at::Tensor & x, const at::Tensor & n) { |
| 13187 | |
| 13188 | static auto op = create_special_shifted_chebyshev_polynomial_t_typed_handle(); |
| 13189 | return op.call(x, n); |
| 13190 | } |
| 13191 | |
| 13192 | // aten::special_shifted_chebyshev_polynomial_t(Tensor x, Tensor n) -> Tensor |
| 13193 | at::Tensor special_shifted_chebyshev_polynomial_t::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n) { |
| 13194 | |
| 13195 | static auto op = create_special_shifted_chebyshev_polynomial_t_typed_handle(); |
| 13196 | return op.redispatch(dispatchKeySet, x, n); |
| 13197 | } |
| 13198 | |
| 13199 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_shifted_chebyshev_polynomial_t_x_scalar, name, "aten::special_shifted_chebyshev_polynomial_t") |
| 13200 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_shifted_chebyshev_polynomial_t_x_scalar, overload_name, "x_scalar") |
| 13201 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_shifted_chebyshev_polynomial_t_x_scalar, schema_str, "special_shifted_chebyshev_polynomial_t.x_scalar(Scalar x, Tensor n) -> Tensor") |
| 13202 | |
| 13203 | // aten::special_shifted_chebyshev_polynomial_t.x_scalar(Scalar x, Tensor n) -> Tensor |
| 13204 | static C10_NOINLINE c10::TypedOperatorHandle<special_shifted_chebyshev_polynomial_t_x_scalar::schema> create_special_shifted_chebyshev_polynomial_t_x_scalar_typed_handle() { |
| 13205 | return c10::Dispatcher::singleton() |
| 13206 | .findSchemaOrThrow(special_shifted_chebyshev_polynomial_t_x_scalar::name, special_shifted_chebyshev_polynomial_t_x_scalar::overload_name) |
| 13207 | .typed<special_shifted_chebyshev_polynomial_t_x_scalar::schema>(); |
| 13208 | } |
| 13209 | |
| 13210 | // aten::special_shifted_chebyshev_polynomial_t.x_scalar(Scalar x, Tensor n) -> Tensor |
| 13211 | at::Tensor special_shifted_chebyshev_polynomial_t_x_scalar::call(const at::Scalar & x, const at::Tensor & n) { |
| 13212 | |
| 13213 | static auto op = create_special_shifted_chebyshev_polynomial_t_x_scalar_typed_handle(); |
| 13214 | return op.call(x, n); |
| 13215 | } |
| 13216 | |
| 13217 | // aten::special_shifted_chebyshev_polynomial_t.x_scalar(Scalar x, Tensor n) -> Tensor |
| 13218 | at::Tensor special_shifted_chebyshev_polynomial_t_x_scalar::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n) { |
| 13219 | |
| 13220 | static auto op = create_special_shifted_chebyshev_polynomial_t_x_scalar_typed_handle(); |
| 13221 | return op.redispatch(dispatchKeySet, x, n); |
| 13222 | } |
| 13223 | |
| 13224 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_shifted_chebyshev_polynomial_t_n_scalar, name, "aten::special_shifted_chebyshev_polynomial_t") |
| 13225 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_shifted_chebyshev_polynomial_t_n_scalar, overload_name, "n_scalar") |
| 13226 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_shifted_chebyshev_polynomial_t_n_scalar, schema_str, "special_shifted_chebyshev_polynomial_t.n_scalar(Tensor x, Scalar n) -> Tensor") |
| 13227 | |
| 13228 | // aten::special_shifted_chebyshev_polynomial_t.n_scalar(Tensor x, Scalar n) -> Tensor |
| 13229 | static C10_NOINLINE c10::TypedOperatorHandle<special_shifted_chebyshev_polynomial_t_n_scalar::schema> create_special_shifted_chebyshev_polynomial_t_n_scalar_typed_handle() { |
| 13230 | return c10::Dispatcher::singleton() |
| 13231 | .findSchemaOrThrow(special_shifted_chebyshev_polynomial_t_n_scalar::name, special_shifted_chebyshev_polynomial_t_n_scalar::overload_name) |
| 13232 | .typed<special_shifted_chebyshev_polynomial_t_n_scalar::schema>(); |
| 13233 | } |
| 13234 | |
| 13235 | // aten::special_shifted_chebyshev_polynomial_t.n_scalar(Tensor x, Scalar n) -> Tensor |
| 13236 | at::Tensor special_shifted_chebyshev_polynomial_t_n_scalar::call(const at::Tensor & x, const at::Scalar & n) { |
| 13237 | |
| 13238 | static auto op = create_special_shifted_chebyshev_polynomial_t_n_scalar_typed_handle(); |
| 13239 | return op.call(x, n); |
| 13240 | } |
| 13241 | |
| 13242 | // aten::special_shifted_chebyshev_polynomial_t.n_scalar(Tensor x, Scalar n) -> Tensor |
| 13243 | at::Tensor special_shifted_chebyshev_polynomial_t_n_scalar::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n) { |
| 13244 | |
| 13245 | static auto op = create_special_shifted_chebyshev_polynomial_t_n_scalar_typed_handle(); |
| 13246 | return op.redispatch(dispatchKeySet, x, n); |
| 13247 | } |
| 13248 | |
| 13249 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_shifted_chebyshev_polynomial_t_out, name, "aten::special_shifted_chebyshev_polynomial_t") |
| 13250 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_shifted_chebyshev_polynomial_t_out, overload_name, "out") |
| 13251 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_shifted_chebyshev_polynomial_t_out, schema_str, "special_shifted_chebyshev_polynomial_t.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)") |
| 13252 | |
| 13253 | // aten::special_shifted_chebyshev_polynomial_t.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) |
| 13254 | static C10_NOINLINE c10::TypedOperatorHandle<special_shifted_chebyshev_polynomial_t_out::schema> create_special_shifted_chebyshev_polynomial_t_out_typed_handle() { |
| 13255 | return c10::Dispatcher::singleton() |
| 13256 | .findSchemaOrThrow(special_shifted_chebyshev_polynomial_t_out::name, special_shifted_chebyshev_polynomial_t_out::overload_name) |
| 13257 | .typed<special_shifted_chebyshev_polynomial_t_out::schema>(); |
| 13258 | } |
| 13259 | |
| 13260 | // aten::special_shifted_chebyshev_polynomial_t.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) |
| 13261 | at::Tensor & special_shifted_chebyshev_polynomial_t_out::call(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 13262 | |
| 13263 | static auto op = create_special_shifted_chebyshev_polynomial_t_out_typed_handle(); |
| 13264 | return op.call(x, n, out); |
| 13265 | } |
| 13266 | |
| 13267 | // aten::special_shifted_chebyshev_polynomial_t.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) |
| 13268 | at::Tensor & special_shifted_chebyshev_polynomial_t_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { |
| 13269 | |
| 13270 | static auto op = create_special_shifted_chebyshev_polynomial_t_out_typed_handle(); |
| 13271 | return op.redispatch(dispatchKeySet, x, n, out); |
| 13272 | } |
| 13273 | |
| 13274 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_shifted_chebyshev_polynomial_t_x_scalar_out, name, "aten::special_shifted_chebyshev_polynomial_t") |
| 13275 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_shifted_chebyshev_polynomial_t_x_scalar_out, overload_name, "x_scalar_out") |
| 13276 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_shifted_chebyshev_polynomial_t_x_scalar_out, schema_str, "special_shifted_chebyshev_polynomial_t.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)") |
| 13277 | |
| 13278 | // aten::special_shifted_chebyshev_polynomial_t.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) |
| 13279 | static C10_NOINLINE c10::TypedOperatorHandle<special_shifted_chebyshev_polynomial_t_x_scalar_out::schema> create_special_shifted_chebyshev_polynomial_t_x_scalar_out_typed_handle() { |
| 13280 | return c10::Dispatcher::singleton() |
| 13281 | .findSchemaOrThrow(special_shifted_chebyshev_polynomial_t_x_scalar_out::name, special_shifted_chebyshev_polynomial_t_x_scalar_out::overload_name) |
| 13282 | .typed<special_shifted_chebyshev_polynomial_t_x_scalar_out::schema>(); |
| 13283 | } |
| 13284 | |
| 13285 | // aten::special_shifted_chebyshev_polynomial_t.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) |
| 13286 | at::Tensor & special_shifted_chebyshev_polynomial_t_x_scalar_out::call(const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { |
| 13287 | |
| 13288 | static auto op = create_special_shifted_chebyshev_polynomial_t_x_scalar_out_typed_handle(); |
| 13289 | return op.call(x, n, out); |
| 13290 | } |
| 13291 | |
| 13292 | // aten::special_shifted_chebyshev_polynomial_t.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) |
| 13293 | at::Tensor & special_shifted_chebyshev_polynomial_t_x_scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { |
| 13294 | |
| 13295 | static auto op = create_special_shifted_chebyshev_polynomial_t_x_scalar_out_typed_handle(); |
| 13296 | return op.redispatch(dispatchKeySet, x, n, out); |
| 13297 | } |
| 13298 | |
| 13299 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_shifted_chebyshev_polynomial_t_n_scalar_out, name, "aten::special_shifted_chebyshev_polynomial_t") |
| 13300 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_shifted_chebyshev_polynomial_t_n_scalar_out, overload_name, "n_scalar_out") |
| 13301 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(special_shifted_chebyshev_polynomial_t_n_scalar_out, schema_str, "special_shifted_chebyshev_polynomial_t.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)") |
| 13302 | |
| 13303 | // aten::special_shifted_chebyshev_polynomial_t.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) |
| 13304 | static C10_NOINLINE c10::TypedOperatorHandle<special_shifted_chebyshev_polynomial_t_n_scalar_out::schema> create_special_shifted_chebyshev_polynomial_t_n_scalar_out_typed_handle() { |
| 13305 | return c10::Dispatcher::singleton() |
| 13306 | .findSchemaOrThrow(special_shifted_chebyshev_polynomial_t_n_scalar_out::name, special_shifted_chebyshev_polynomial_t_n_scalar_out::overload_name) |
| 13307 | .typed<special_shifted_chebyshev_polynomial_t_n_scalar_out::schema>(); |
| 13308 | } |
| 13309 | |
| 13310 | // aten::special_shifted_chebyshev_polynomial_t.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) |
| 13311 | at::Tensor & special_shifted_chebyshev_polynomial_t_n_scalar_out::call(const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { |
| 13312 | |
| 13313 | static auto op = create_special_shifted_chebyshev_polynomial_t_n_scalar_out_typed_handle(); |
| 13314 | return op.call(x, n, out); |
| 13315 | } |
| 13316 | |
| 13317 | // aten::special_shifted_chebyshev_polynomial_t.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) |
| 13318 | at::Tensor & special_shifted_chebyshev_polynomial_t_n_scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { |
| 13319 | |
| 13320 | static auto op = create_special_shifted_chebyshev_polynomial_t_n_scalar_out_typed_handle(); |
| 13321 | return op.redispatch(dispatchKeySet, x, n, out); |
| 13322 | } |
| 13323 | |
| 13324 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fused_adamw_, name, "aten::_fused_adamw_") |
| 13325 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fused_adamw_, overload_name, "") |
| 13326 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fused_adamw_, schema_str, "_fused_adamw_(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> ()") |
| 13327 | |
| 13328 | // aten::_fused_adamw_(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> () |
| 13329 | static C10_NOINLINE c10::TypedOperatorHandle<_fused_adamw_::schema> create__fused_adamw__typed_handle() { |
| 13330 | return c10::Dispatcher::singleton() |
| 13331 | .findSchemaOrThrow(_fused_adamw_::name, _fused_adamw_::overload_name) |
| 13332 | .typed<_fused_adamw_::schema>(); |
| 13333 | } |
| 13334 | |
| 13335 | // aten::_fused_adamw_(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> () |
| 13336 | void _fused_adamw_::call(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional<at::Tensor> & grad_scale, const c10::optional<at::Tensor> & found_inf) { |
| 13337 | |
| 13338 | static auto op = create__fused_adamw__typed_handle(); |
| 13339 | return op.call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf); |
| 13340 | } |
| 13341 | |
| 13342 | // aten::_fused_adamw_(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> () |
| 13343 | void _fused_adamw_::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional<at::Tensor> & grad_scale, const c10::optional<at::Tensor> & found_inf) { |
| 13344 | |
| 13345 | static auto op = create__fused_adamw__typed_handle(); |
| 13346 | return op.redispatch(dispatchKeySet, self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf); |
| 13347 | } |
| 13348 | |
| 13349 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cudnn_rnn_flatten_weight_out, name, "aten::_cudnn_rnn_flatten_weight") |
| 13350 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cudnn_rnn_flatten_weight_out, overload_name, "out") |
| 13351 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cudnn_rnn_flatten_weight_out, schema_str, "_cudnn_rnn_flatten_weight.out(Tensor[] weight_arr, int weight_stride0, SymInt input_size, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, bool bidirectional, *, Tensor(a!) out) -> Tensor(a!)") |
| 13352 | |
| 13353 | // aten::_cudnn_rnn_flatten_weight.out(Tensor[] weight_arr, int weight_stride0, SymInt input_size, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, bool bidirectional, *, Tensor(a!) out) -> Tensor(a!) |
| 13354 | static C10_NOINLINE c10::TypedOperatorHandle<_cudnn_rnn_flatten_weight_out::schema> create__cudnn_rnn_flatten_weight_out_typed_handle() { |
| 13355 | return c10::Dispatcher::singleton() |
| 13356 | .findSchemaOrThrow(_cudnn_rnn_flatten_weight_out::name, _cudnn_rnn_flatten_weight_out::overload_name) |
| 13357 | .typed<_cudnn_rnn_flatten_weight_out::schema>(); |
| 13358 | } |
| 13359 | |
| 13360 | // aten::_cudnn_rnn_flatten_weight.out(Tensor[] weight_arr, int weight_stride0, SymInt input_size, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, bool bidirectional, *, Tensor(a!) out) -> Tensor(a!) |
| 13361 | at::Tensor & _cudnn_rnn_flatten_weight_out::call(at::TensorList weight_arr, int64_t weight_stride0, c10::SymInt input_size, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, bool bidirectional, at::Tensor & out) { |
| 13362 | |
| 13363 | static auto op = create__cudnn_rnn_flatten_weight_out_typed_handle(); |
| 13364 | return op.call(weight_arr, weight_stride0, input_size, mode, hidden_size, proj_size, num_layers, batch_first, bidirectional, out); |
| 13365 | } |
| 13366 | |
| 13367 | // aten::_cudnn_rnn_flatten_weight.out(Tensor[] weight_arr, int weight_stride0, SymInt input_size, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, bool bidirectional, *, Tensor(a!) out) -> Tensor(a!) |
| 13368 | at::Tensor & _cudnn_rnn_flatten_weight_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList weight_arr, int64_t weight_stride0, c10::SymInt input_size, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, bool bidirectional, at::Tensor & out) { |
| 13369 | |
| 13370 | static auto op = create__cudnn_rnn_flatten_weight_out_typed_handle(); |
| 13371 | return op.redispatch(dispatchKeySet, weight_arr, weight_stride0, input_size, mode, hidden_size, proj_size, num_layers, batch_first, bidirectional, out); |
| 13372 | } |
| 13373 | |
| 13374 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantized_batch_norm_out, name, "aten::quantized_batch_norm") |
| 13375 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantized_batch_norm_out, overload_name, "out") |
| 13376 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantized_batch_norm_out, schema_str, "quantized_batch_norm.out(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point, *, Tensor(a!) out) -> Tensor(a!)") |
| 13377 | |
| 13378 | // aten::quantized_batch_norm.out(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point, *, Tensor(a!) out) -> Tensor(a!) |
| 13379 | static C10_NOINLINE c10::TypedOperatorHandle<quantized_batch_norm_out::schema> create_quantized_batch_norm_out_typed_handle() { |
| 13380 | return c10::Dispatcher::singleton() |
| 13381 | .findSchemaOrThrow(quantized_batch_norm_out::name, quantized_batch_norm_out::overload_name) |
| 13382 | .typed<quantized_batch_norm_out::schema>(); |
| 13383 | } |
| 13384 | |
| 13385 | // aten::quantized_batch_norm.out(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point, *, Tensor(a!) out) -> Tensor(a!) |
| 13386 | at::Tensor & quantized_batch_norm_out::call(const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, const at::Tensor & mean, const at::Tensor & var, double eps, double output_scale, int64_t output_zero_point, at::Tensor & out) { |
| 13387 | |
| 13388 | static auto op = create_quantized_batch_norm_out_typed_handle(); |
| 13389 | return op.call(input, weight, bias, mean, var, eps, output_scale, output_zero_point, out); |
| 13390 | } |
| 13391 | |
| 13392 | // aten::quantized_batch_norm.out(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point, *, Tensor(a!) out) -> Tensor(a!) |
| 13393 | at::Tensor & quantized_batch_norm_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, const at::Tensor & mean, const at::Tensor & var, double eps, double output_scale, int64_t output_zero_point, at::Tensor & out) { |
| 13394 | |
| 13395 | static auto op = create_quantized_batch_norm_out_typed_handle(); |
| 13396 | return op.redispatch(dispatchKeySet, input, weight, bias, mean, var, eps, output_scale, output_zero_point, out); |
| 13397 | } |
| 13398 | |
| 13399 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv_tbc_out, name, "aten::conv_tbc") |
| 13400 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv_tbc_out, overload_name, "out") |
| 13401 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(conv_tbc_out, schema_str, "conv_tbc.out(Tensor self, Tensor weight, Tensor bias, int pad=0, *, Tensor(a!) out) -> Tensor(a!)") |
| 13402 | |
| 13403 | // aten::conv_tbc.out(Tensor self, Tensor weight, Tensor bias, int pad=0, *, Tensor(a!) out) -> Tensor(a!) |
| 13404 | static C10_NOINLINE c10::TypedOperatorHandle<conv_tbc_out::schema> create_conv_tbc_out_typed_handle() { |
| 13405 | return c10::Dispatcher::singleton() |
| 13406 | .findSchemaOrThrow(conv_tbc_out::name, conv_tbc_out::overload_name) |
| 13407 | .typed<conv_tbc_out::schema>(); |
| 13408 | } |
| 13409 | |
| 13410 | // aten::conv_tbc.out(Tensor self, Tensor weight, Tensor bias, int pad=0, *, Tensor(a!) out) -> Tensor(a!) |
| 13411 | at::Tensor & conv_tbc_out::call(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & bias, int64_t pad, at::Tensor & out) { |
| 13412 | |
| 13413 | static auto op = create_conv_tbc_out_typed_handle(); |
| 13414 | return op.call(self, weight, bias, pad, out); |
| 13415 | } |
| 13416 | |
| 13417 | // aten::conv_tbc.out(Tensor self, Tensor weight, Tensor bias, int pad=0, *, Tensor(a!) out) -> Tensor(a!) |
| 13418 | at::Tensor & conv_tbc_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const at::Tensor & bias, int64_t pad, at::Tensor & out) { |
| 13419 | |
| 13420 | static auto op = create_conv_tbc_out_typed_handle(); |
| 13421 | return op.redispatch(dispatchKeySet, self, weight, bias, pad, out); |
| 13422 | } |
| 13423 | |
| 13424 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cudnn_affine_grid_generator_backward_out, name, "aten::cudnn_affine_grid_generator_backward") |
| 13425 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cudnn_affine_grid_generator_backward_out, overload_name, "out") |
| 13426 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cudnn_affine_grid_generator_backward_out, schema_str, "cudnn_affine_grid_generator_backward.out(Tensor grad, int N, int C, int H, int W, *, Tensor(a!) out) -> Tensor(a!)") |
| 13427 | |
| 13428 | // aten::cudnn_affine_grid_generator_backward.out(Tensor grad, int N, int C, int H, int W, *, Tensor(a!) out) -> Tensor(a!) |
| 13429 | static C10_NOINLINE c10::TypedOperatorHandle<cudnn_affine_grid_generator_backward_out::schema> create_cudnn_affine_grid_generator_backward_out_typed_handle() { |
| 13430 | return c10::Dispatcher::singleton() |
| 13431 | .findSchemaOrThrow(cudnn_affine_grid_generator_backward_out::name, cudnn_affine_grid_generator_backward_out::overload_name) |
| 13432 | .typed<cudnn_affine_grid_generator_backward_out::schema>(); |
| 13433 | } |
| 13434 | |
| 13435 | // aten::cudnn_affine_grid_generator_backward.out(Tensor grad, int N, int C, int H, int W, *, Tensor(a!) out) -> Tensor(a!) |
| 13436 | at::Tensor & cudnn_affine_grid_generator_backward_out::call(const at::Tensor & grad, int64_t N, int64_t C, int64_t H, int64_t W, at::Tensor & out) { |
| 13437 | |
| 13438 | static auto op = create_cudnn_affine_grid_generator_backward_out_typed_handle(); |
| 13439 | return op.call(grad, N, C, H, W, out); |
| 13440 | } |
| 13441 | |
| 13442 | // aten::cudnn_affine_grid_generator_backward.out(Tensor grad, int N, int C, int H, int W, *, Tensor(a!) out) -> Tensor(a!) |
| 13443 | at::Tensor & cudnn_affine_grid_generator_backward_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, int64_t N, int64_t C, int64_t H, int64_t W, at::Tensor & out) { |
| 13444 | |
| 13445 | static auto op = create_cudnn_affine_grid_generator_backward_out_typed_handle(); |
| 13446 | return op.redispatch(dispatchKeySet, grad, N, C, H, W, out); |
| 13447 | } |
| 13448 | |
| 13449 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cudnn_grid_sampler_out, name, "aten::cudnn_grid_sampler") |
| 13450 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cudnn_grid_sampler_out, overload_name, "out") |
| 13451 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cudnn_grid_sampler_out, schema_str, "cudnn_grid_sampler.out(Tensor self, Tensor grid, *, Tensor(a!) out) -> Tensor(a!)") |
| 13452 | |
| 13453 | // aten::cudnn_grid_sampler.out(Tensor self, Tensor grid, *, Tensor(a!) out) -> Tensor(a!) |
| 13454 | static C10_NOINLINE c10::TypedOperatorHandle<cudnn_grid_sampler_out::schema> create_cudnn_grid_sampler_out_typed_handle() { |
| 13455 | return c10::Dispatcher::singleton() |
| 13456 | .findSchemaOrThrow(cudnn_grid_sampler_out::name, cudnn_grid_sampler_out::overload_name) |
| 13457 | .typed<cudnn_grid_sampler_out::schema>(); |
| 13458 | } |
| 13459 | |
| 13460 | // aten::cudnn_grid_sampler.out(Tensor self, Tensor grid, *, Tensor(a!) out) -> Tensor(a!) |
| 13461 | at::Tensor & cudnn_grid_sampler_out::call(const at::Tensor & self, const at::Tensor & grid, at::Tensor & out) { |
| 13462 | |
| 13463 | static auto op = create_cudnn_grid_sampler_out_typed_handle(); |
| 13464 | return op.call(self, grid, out); |
| 13465 | } |
| 13466 | |
| 13467 | // aten::cudnn_grid_sampler.out(Tensor self, Tensor grid, *, Tensor(a!) out) -> Tensor(a!) |
| 13468 | at::Tensor & cudnn_grid_sampler_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & grid, at::Tensor & out) { |
| 13469 | |
| 13470 | static auto op = create_cudnn_grid_sampler_out_typed_handle(); |
| 13471 | return op.redispatch(dispatchKeySet, self, grid, out); |
| 13472 | } |
| 13473 | |
| 13474 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div_Scalar_out, name, "aten::div") |
| 13475 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div_Scalar_out, overload_name, "Scalar_out") |
| 13476 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div_Scalar_out, schema_str, "div.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)") |
| 13477 | |
| 13478 | // aten::div.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) |
| 13479 | static C10_NOINLINE c10::TypedOperatorHandle<div_Scalar_out::schema> create_div_Scalar_out_typed_handle() { |
| 13480 | return c10::Dispatcher::singleton() |
| 13481 | .findSchemaOrThrow(div_Scalar_out::name, div_Scalar_out::overload_name) |
| 13482 | .typed<div_Scalar_out::schema>(); |
| 13483 | } |
| 13484 | |
| 13485 | // aten::div.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) |
| 13486 | at::Tensor & div_Scalar_out::call(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 13487 | |
| 13488 | static auto op = create_div_Scalar_out_typed_handle(); |
| 13489 | return op.call(self, other, out); |
| 13490 | } |
| 13491 | |
| 13492 | // aten::div.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) |
| 13493 | at::Tensor & div_Scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { |
| 13494 | |
| 13495 | static auto op = create_div_Scalar_out_typed_handle(); |
| 13496 | return op.redispatch(dispatchKeySet, self, other, out); |
| 13497 | } |
| 13498 | |
| 13499 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div_Scalar_mode_out, name, "aten::div") |
| 13500 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div_Scalar_mode_out, overload_name, "Scalar_mode_out") |
| 13501 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(div_Scalar_mode_out, schema_str, "div.Scalar_mode_out(Tensor self, Scalar other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!)") |
| 13502 | |
| 13503 | // aten::div.Scalar_mode_out(Tensor self, Scalar other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!) |
| 13504 | static C10_NOINLINE c10::TypedOperatorHandle<div_Scalar_mode_out::schema> create_div_Scalar_mode_out_typed_handle() { |
| 13505 | return c10::Dispatcher::singleton() |
| 13506 | .findSchemaOrThrow(div_Scalar_mode_out::name, div_Scalar_mode_out::overload_name) |
| 13507 | .typed<div_Scalar_mode_out::schema>(); |
| 13508 | } |
| 13509 | |
| 13510 | // aten::div.Scalar_mode_out(Tensor self, Scalar other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!) |
| 13511 | at::Tensor & div_Scalar_mode_out::call(const at::Tensor & self, const at::Scalar & other, c10::optional<c10::string_view> rounding_mode, at::Tensor & out) { |
| 13512 | |
| 13513 | static auto op = create_div_Scalar_mode_out_typed_handle(); |
| 13514 | return op.call(self, other, rounding_mode, out); |
| 13515 | } |
| 13516 | |
| 13517 | // aten::div.Scalar_mode_out(Tensor self, Scalar other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!) |
| 13518 | at::Tensor & div_Scalar_mode_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, c10::optional<c10::string_view> rounding_mode, at::Tensor & out) { |
| 13519 | |
| 13520 | static auto op = create_div_Scalar_mode_out_typed_handle(); |
| 13521 | return op.redispatch(dispatchKeySet, self, other, rounding_mode, out); |
| 13522 | } |
| 13523 | |
| 13524 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_embedding_bag_forward_only_out, name, "aten::_embedding_bag_forward_only") |
| 13525 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_embedding_bag_forward_only_out, overload_name, "out") |
| 13526 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_embedding_bag_forward_only_out, schema_str, "_embedding_bag_forward_only.out(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!))") |
| 13527 | |
| 13528 | // aten::_embedding_bag_forward_only.out(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!)) |
| 13529 | static C10_NOINLINE c10::TypedOperatorHandle<_embedding_bag_forward_only_out::schema> create__embedding_bag_forward_only_out_typed_handle() { |
| 13530 | return c10::Dispatcher::singleton() |
| 13531 | .findSchemaOrThrow(_embedding_bag_forward_only_out::name, _embedding_bag_forward_only_out::overload_name) |
| 13532 | .typed<_embedding_bag_forward_only_out::schema>(); |
| 13533 | } |
| 13534 | |
| 13535 | // aten::_embedding_bag_forward_only.out(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!)) |
| 13536 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _embedding_bag_forward_only_out::call(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional<at::Tensor> & per_sample_weights, bool include_last_offset, int64_t padding_idx, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3) { |
| 13537 | |
| 13538 | static auto op = create__embedding_bag_forward_only_out_typed_handle(); |
| 13539 | return op.call(weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx, out0, out1, out2, out3); |
| 13540 | } |
| 13541 | |
| 13542 | // aten::_embedding_bag_forward_only.out(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!)) |
| 13543 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _embedding_bag_forward_only_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional<at::Tensor> & per_sample_weights, bool include_last_offset, int64_t padding_idx, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3) { |
| 13544 | |
| 13545 | static auto op = create__embedding_bag_forward_only_out_typed_handle(); |
| 13546 | return op.redispatch(dispatchKeySet, weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx, out0, out1, out2, out3); |
| 13547 | } |
| 13548 | |
| 13549 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(new_zeros_out, name, "aten::new_zeros") |
| 13550 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(new_zeros_out, overload_name, "out") |
| 13551 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(new_zeros_out, schema_str, "new_zeros.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)") |
| 13552 | |
| 13553 | // aten::new_zeros.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) |
| 13554 | static C10_NOINLINE c10::TypedOperatorHandle<new_zeros_out::schema> create_new_zeros_out_typed_handle() { |
| 13555 | return c10::Dispatcher::singleton() |
| 13556 | .findSchemaOrThrow(new_zeros_out::name, new_zeros_out::overload_name) |
| 13557 | .typed<new_zeros_out::schema>(); |
| 13558 | } |
| 13559 | |
| 13560 | // aten::new_zeros.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) |
| 13561 | at::Tensor & new_zeros_out::call(const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out) { |
| 13562 | |
| 13563 | static auto op = create_new_zeros_out_typed_handle(); |
| 13564 | return op.call(self, size, out); |
| 13565 | } |
| 13566 | |
| 13567 | // aten::new_zeros.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) |
| 13568 | at::Tensor & new_zeros_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out) { |
| 13569 | |
| 13570 | static auto op = create_new_zeros_out_typed_handle(); |
| 13571 | return op.redispatch(dispatchKeySet, self, size, out); |
| 13572 | } |
| 13573 | |
| 13574 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_grid_sampler_2d_cpu_fallback_out, name, "aten::_grid_sampler_2d_cpu_fallback") |
| 13575 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_grid_sampler_2d_cpu_fallback_out, overload_name, "out") |
| 13576 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_grid_sampler_2d_cpu_fallback_out, schema_str, "_grid_sampler_2d_cpu_fallback.out(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)") |
| 13577 | |
| 13578 | // aten::_grid_sampler_2d_cpu_fallback.out(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, *, Tensor(a!) out) -> Tensor(a!) |
| 13579 | static C10_NOINLINE c10::TypedOperatorHandle<_grid_sampler_2d_cpu_fallback_out::schema> create__grid_sampler_2d_cpu_fallback_out_typed_handle() { |
| 13580 | return c10::Dispatcher::singleton() |
| 13581 | .findSchemaOrThrow(_grid_sampler_2d_cpu_fallback_out::name, _grid_sampler_2d_cpu_fallback_out::overload_name) |
| 13582 | .typed<_grid_sampler_2d_cpu_fallback_out::schema>(); |
| 13583 | } |
| 13584 | |
| 13585 | // aten::_grid_sampler_2d_cpu_fallback.out(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, *, Tensor(a!) out) -> Tensor(a!) |
| 13586 | at::Tensor & _grid_sampler_2d_cpu_fallback_out::call(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, at::Tensor & out) { |
| 13587 | |
| 13588 | static auto op = create__grid_sampler_2d_cpu_fallback_out_typed_handle(); |
| 13589 | return op.call(input, grid, interpolation_mode, padding_mode, align_corners, out); |
| 13590 | } |
| 13591 | |
| 13592 | // aten::_grid_sampler_2d_cpu_fallback.out(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, *, Tensor(a!) out) -> Tensor(a!) |
| 13593 | at::Tensor & _grid_sampler_2d_cpu_fallback_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, at::Tensor & out) { |
| 13594 | |
| 13595 | static auto op = create__grid_sampler_2d_cpu_fallback_out_typed_handle(); |
| 13596 | return op.redispatch(dispatchKeySet, input, grid, interpolation_mode, padding_mode, align_corners, out); |
| 13597 | } |
| 13598 | |
| 13599 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(grid_sampler_3d_out, name, "aten::grid_sampler_3d") |
| 13600 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(grid_sampler_3d_out, overload_name, "out") |
| 13601 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(grid_sampler_3d_out, schema_str, "grid_sampler_3d.out(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)") |
| 13602 | |
| 13603 | // aten::grid_sampler_3d.out(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, *, Tensor(a!) out) -> Tensor(a!) |
| 13604 | static C10_NOINLINE c10::TypedOperatorHandle<grid_sampler_3d_out::schema> create_grid_sampler_3d_out_typed_handle() { |
| 13605 | return c10::Dispatcher::singleton() |
| 13606 | .findSchemaOrThrow(grid_sampler_3d_out::name, grid_sampler_3d_out::overload_name) |
| 13607 | .typed<grid_sampler_3d_out::schema>(); |
| 13608 | } |
| 13609 | |
| 13610 | // aten::grid_sampler_3d.out(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, *, Tensor(a!) out) -> Tensor(a!) |
| 13611 | at::Tensor & grid_sampler_3d_out::call(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, at::Tensor & out) { |
| 13612 | |
| 13613 | static auto op = create_grid_sampler_3d_out_typed_handle(); |
| 13614 | return op.call(input, grid, interpolation_mode, padding_mode, align_corners, out); |
| 13615 | } |
| 13616 | |
| 13617 | // aten::grid_sampler_3d.out(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, *, Tensor(a!) out) -> Tensor(a!) |
| 13618 | at::Tensor & grid_sampler_3d_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, at::Tensor & out) { |
| 13619 | |
| 13620 | static auto op = create_grid_sampler_3d_out_typed_handle(); |
| 13621 | return op.redispatch(dispatchKeySet, input, grid, interpolation_mode, padding_mode, align_corners, out); |
| 13622 | } |
| 13623 | |
| 13624 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hann_window_out, name, "aten::hann_window") |
| 13625 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hann_window_out, overload_name, "out") |
| 13626 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hann_window_out, schema_str, "hann_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)") |
| 13627 | |
| 13628 | // aten::hann_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!) |
| 13629 | static C10_NOINLINE c10::TypedOperatorHandle<hann_window_out::schema> create_hann_window_out_typed_handle() { |
| 13630 | return c10::Dispatcher::singleton() |
| 13631 | .findSchemaOrThrow(hann_window_out::name, hann_window_out::overload_name) |
| 13632 | .typed<hann_window_out::schema>(); |
| 13633 | } |
| 13634 | |
| 13635 | // aten::hann_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!) |
| 13636 | at::Tensor & hann_window_out::call(int64_t window_length, at::Tensor & out) { |
| 13637 | |
| 13638 | static auto op = create_hann_window_out_typed_handle(); |
| 13639 | return op.call(window_length, out); |
| 13640 | } |
| 13641 | |
| 13642 | // aten::hann_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!) |
| 13643 | at::Tensor & hann_window_out::redispatch(c10::DispatchKeySet dispatchKeySet, int64_t window_length, at::Tensor & out) { |
| 13644 | |
| 13645 | static auto op = create_hann_window_out_typed_handle(); |
| 13646 | return op.redispatch(dispatchKeySet, window_length, out); |
| 13647 | } |
| 13648 | |
| 13649 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hann_window_periodic_out, name, "aten::hann_window") |
| 13650 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hann_window_periodic_out, overload_name, "periodic_out") |
| 13651 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hann_window_periodic_out, schema_str, "hann_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)") |
| 13652 | |
| 13653 | // aten::hann_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!) |
| 13654 | static C10_NOINLINE c10::TypedOperatorHandle<hann_window_periodic_out::schema> create_hann_window_periodic_out_typed_handle() { |
| 13655 | return c10::Dispatcher::singleton() |
| 13656 | .findSchemaOrThrow(hann_window_periodic_out::name, hann_window_periodic_out::overload_name) |
| 13657 | .typed<hann_window_periodic_out::schema>(); |
| 13658 | } |
| 13659 | |
| 13660 | // aten::hann_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!) |
| 13661 | at::Tensor & hann_window_periodic_out::call(int64_t window_length, bool periodic, at::Tensor & out) { |
| 13662 | |
| 13663 | static auto op = create_hann_window_periodic_out_typed_handle(); |
| 13664 | return op.call(window_length, periodic, out); |
| 13665 | } |
| 13666 | |
| 13667 | // aten::hann_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!) |
| 13668 | at::Tensor & hann_window_periodic_out::redispatch(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, at::Tensor & out) { |
| 13669 | |
| 13670 | static auto op = create_hann_window_periodic_out_typed_handle(); |
| 13671 | return op.redispatch(dispatchKeySet, window_length, periodic, out); |
| 13672 | } |
| 13673 | |
| 13674 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hamming_window_out, name, "aten::hamming_window") |
| 13675 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hamming_window_out, overload_name, "out") |
| 13676 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hamming_window_out, schema_str, "hamming_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)") |
| 13677 | |
| 13678 | // aten::hamming_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!) |
| 13679 | static C10_NOINLINE c10::TypedOperatorHandle<hamming_window_out::schema> create_hamming_window_out_typed_handle() { |
| 13680 | return c10::Dispatcher::singleton() |
| 13681 | .findSchemaOrThrow(hamming_window_out::name, hamming_window_out::overload_name) |
| 13682 | .typed<hamming_window_out::schema>(); |
| 13683 | } |
| 13684 | |
| 13685 | // aten::hamming_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!) |
| 13686 | at::Tensor & hamming_window_out::call(int64_t window_length, at::Tensor & out) { |
| 13687 | |
| 13688 | static auto op = create_hamming_window_out_typed_handle(); |
| 13689 | return op.call(window_length, out); |
| 13690 | } |
| 13691 | |
| 13692 | // aten::hamming_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!) |
| 13693 | at::Tensor & hamming_window_out::redispatch(c10::DispatchKeySet dispatchKeySet, int64_t window_length, at::Tensor & out) { |
| 13694 | |
| 13695 | static auto op = create_hamming_window_out_typed_handle(); |
| 13696 | return op.redispatch(dispatchKeySet, window_length, out); |
| 13697 | } |
| 13698 | |
| 13699 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hamming_window_periodic_out, name, "aten::hamming_window") |
| 13700 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hamming_window_periodic_out, overload_name, "periodic_out") |
| 13701 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hamming_window_periodic_out, schema_str, "hamming_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)") |
| 13702 | |
| 13703 | // aten::hamming_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!) |
| 13704 | static C10_NOINLINE c10::TypedOperatorHandle<hamming_window_periodic_out::schema> create_hamming_window_periodic_out_typed_handle() { |
| 13705 | return c10::Dispatcher::singleton() |
| 13706 | .findSchemaOrThrow(hamming_window_periodic_out::name, hamming_window_periodic_out::overload_name) |
| 13707 | .typed<hamming_window_periodic_out::schema>(); |
| 13708 | } |
| 13709 | |
| 13710 | // aten::hamming_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!) |
| 13711 | at::Tensor & hamming_window_periodic_out::call(int64_t window_length, bool periodic, at::Tensor & out) { |
| 13712 | |
| 13713 | static auto op = create_hamming_window_periodic_out_typed_handle(); |
| 13714 | return op.call(window_length, periodic, out); |
| 13715 | } |
| 13716 | |
| 13717 | // aten::hamming_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!) |
| 13718 | at::Tensor & hamming_window_periodic_out::redispatch(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, at::Tensor & out) { |
| 13719 | |
| 13720 | static auto op = create_hamming_window_periodic_out_typed_handle(); |
| 13721 | return op.redispatch(dispatchKeySet, window_length, periodic, out); |
| 13722 | } |
| 13723 | |
| 13724 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hamming_window_periodic_alpha_out, name, "aten::hamming_window") |
| 13725 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hamming_window_periodic_alpha_out, overload_name, "periodic_alpha_out") |
| 13726 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hamming_window_periodic_alpha_out, schema_str, "hamming_window.periodic_alpha_out(int window_length, bool periodic, float alpha, *, Tensor(a!) out) -> Tensor(a!)") |
| 13727 | |
| 13728 | // aten::hamming_window.periodic_alpha_out(int window_length, bool periodic, float alpha, *, Tensor(a!) out) -> Tensor(a!) |
| 13729 | static C10_NOINLINE c10::TypedOperatorHandle<hamming_window_periodic_alpha_out::schema> create_hamming_window_periodic_alpha_out_typed_handle() { |
| 13730 | return c10::Dispatcher::singleton() |
| 13731 | .findSchemaOrThrow(hamming_window_periodic_alpha_out::name, hamming_window_periodic_alpha_out::overload_name) |
| 13732 | .typed<hamming_window_periodic_alpha_out::schema>(); |
| 13733 | } |
| 13734 | |
| 13735 | // aten::hamming_window.periodic_alpha_out(int window_length, bool periodic, float alpha, *, Tensor(a!) out) -> Tensor(a!) |
| 13736 | at::Tensor & hamming_window_periodic_alpha_out::call(int64_t window_length, bool periodic, double alpha, at::Tensor & out) { |
| 13737 | |
| 13738 | static auto op = create_hamming_window_periodic_alpha_out_typed_handle(); |
| 13739 | return op.call(window_length, periodic, alpha, out); |
| 13740 | } |
| 13741 | |
| 13742 | // aten::hamming_window.periodic_alpha_out(int window_length, bool periodic, float alpha, *, Tensor(a!) out) -> Tensor(a!) |
| 13743 | at::Tensor & hamming_window_periodic_alpha_out::redispatch(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, double alpha, at::Tensor & out) { |
| 13744 | |
| 13745 | static auto op = create_hamming_window_periodic_alpha_out_typed_handle(); |
| 13746 | return op.redispatch(dispatchKeySet, window_length, periodic, alpha, out); |
| 13747 | } |
| 13748 | |
| 13749 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hamming_window_periodic_alpha_beta_out, name, "aten::hamming_window") |
| 13750 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hamming_window_periodic_alpha_beta_out, overload_name, "periodic_alpha_beta_out") |
| 13751 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hamming_window_periodic_alpha_beta_out, schema_str, "hamming_window.periodic_alpha_beta_out(int window_length, bool periodic, float alpha, float beta, *, Tensor(a!) out) -> Tensor(a!)") |
| 13752 | |
| 13753 | // aten::hamming_window.periodic_alpha_beta_out(int window_length, bool periodic, float alpha, float beta, *, Tensor(a!) out) -> Tensor(a!) |
| 13754 | static C10_NOINLINE c10::TypedOperatorHandle<hamming_window_periodic_alpha_beta_out::schema> create_hamming_window_periodic_alpha_beta_out_typed_handle() { |
| 13755 | return c10::Dispatcher::singleton() |
| 13756 | .findSchemaOrThrow(hamming_window_periodic_alpha_beta_out::name, hamming_window_periodic_alpha_beta_out::overload_name) |
| 13757 | .typed<hamming_window_periodic_alpha_beta_out::schema>(); |
| 13758 | } |
| 13759 | |
| 13760 | // aten::hamming_window.periodic_alpha_beta_out(int window_length, bool periodic, float alpha, float beta, *, Tensor(a!) out) -> Tensor(a!) |
| 13761 | at::Tensor & hamming_window_periodic_alpha_beta_out::call(int64_t window_length, bool periodic, double alpha, double beta, at::Tensor & out) { |
| 13762 | |
| 13763 | static auto op = create_hamming_window_periodic_alpha_beta_out_typed_handle(); |
| 13764 | return op.call(window_length, periodic, alpha, beta, out); |
| 13765 | } |
| 13766 | |
| 13767 | // aten::hamming_window.periodic_alpha_beta_out(int window_length, bool periodic, float alpha, float beta, *, Tensor(a!) out) -> Tensor(a!) |
| 13768 | at::Tensor & hamming_window_periodic_alpha_beta_out::redispatch(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, double alpha, double beta, at::Tensor & out) { |
| 13769 | |
| 13770 | static auto op = create_hamming_window_periodic_alpha_beta_out_typed_handle(); |
| 13771 | return op.redispatch(dispatchKeySet, window_length, periodic, alpha, beta, out); |
| 13772 | } |
| 13773 | |
| 13774 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_group_norm_backward_out, name, "aten::native_group_norm_backward") |
| 13775 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_group_norm_backward_out, overload_name, "out") |
| 13776 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_group_norm_backward_out, schema_str, "native_group_norm_backward.out(Tensor grad_out, Tensor input, Tensor mean, Tensor rstd, Tensor? weight, SymInt N, SymInt C, SymInt HxW, int group, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))") |
| 13777 | |
| 13778 | // aten::native_group_norm_backward.out(Tensor grad_out, Tensor input, Tensor mean, Tensor rstd, Tensor? weight, SymInt N, SymInt C, SymInt HxW, int group, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 13779 | static C10_NOINLINE c10::TypedOperatorHandle<native_group_norm_backward_out::schema> create_native_group_norm_backward_out_typed_handle() { |
| 13780 | return c10::Dispatcher::singleton() |
| 13781 | .findSchemaOrThrow(native_group_norm_backward_out::name, native_group_norm_backward_out::overload_name) |
| 13782 | .typed<native_group_norm_backward_out::schema>(); |
| 13783 | } |
| 13784 | |
| 13785 | // aten::native_group_norm_backward.out(Tensor grad_out, Tensor input, Tensor mean, Tensor rstd, Tensor? weight, SymInt N, SymInt C, SymInt HxW, int group, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 13786 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_group_norm_backward_out::call(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const c10::optional<at::Tensor> & weight, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 13787 | |
| 13788 | static auto op = create_native_group_norm_backward_out_typed_handle(); |
| 13789 | return op.call(grad_out, input, mean, rstd, weight, N, C, HxW, group, output_mask, out0, out1, out2); |
| 13790 | } |
| 13791 | |
| 13792 | // aten::native_group_norm_backward.out(Tensor grad_out, Tensor input, Tensor mean, Tensor rstd, Tensor? weight, SymInt N, SymInt C, SymInt HxW, int group, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 13793 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_group_norm_backward_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const c10::optional<at::Tensor> & weight, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 13794 | |
| 13795 | static auto op = create_native_group_norm_backward_out_typed_handle(); |
| 13796 | return op.redispatch(dispatchKeySet, grad_out, input, mean, rstd, weight, N, C, HxW, group, output_mask, out0, out1, out2); |
| 13797 | } |
| 13798 | |
| 13799 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(isnan_out, name, "aten::isnan") |
| 13800 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(isnan_out, overload_name, "out") |
| 13801 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(isnan_out, schema_str, "isnan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 13802 | |
| 13803 | // aten::isnan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 13804 | static C10_NOINLINE c10::TypedOperatorHandle<isnan_out::schema> create_isnan_out_typed_handle() { |
| 13805 | return c10::Dispatcher::singleton() |
| 13806 | .findSchemaOrThrow(isnan_out::name, isnan_out::overload_name) |
| 13807 | .typed<isnan_out::schema>(); |
| 13808 | } |
| 13809 | |
| 13810 | // aten::isnan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 13811 | at::Tensor & isnan_out::call(const at::Tensor & self, at::Tensor & out) { |
| 13812 | |
| 13813 | static auto op = create_isnan_out_typed_handle(); |
| 13814 | return op.call(self, out); |
| 13815 | } |
| 13816 | |
| 13817 | // aten::isnan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 13818 | at::Tensor & isnan_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 13819 | |
| 13820 | static auto op = create_isnan_out_typed_handle(); |
| 13821 | return op.redispatch(dispatchKeySet, self, out); |
| 13822 | } |
| 13823 | |
| 13824 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_layer_norm_out, name, "aten::native_layer_norm") |
| 13825 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_layer_norm_out, overload_name, "out") |
| 13826 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_layer_norm_out, schema_str, "native_layer_norm.out(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))") |
| 13827 | |
| 13828 | // aten::native_layer_norm.out(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 13829 | static C10_NOINLINE c10::TypedOperatorHandle<native_layer_norm_out::schema> create_native_layer_norm_out_typed_handle() { |
| 13830 | return c10::Dispatcher::singleton() |
| 13831 | .findSchemaOrThrow(native_layer_norm_out::name, native_layer_norm_out::overload_name) |
| 13832 | .typed<native_layer_norm_out::schema>(); |
| 13833 | } |
| 13834 | |
| 13835 | // aten::native_layer_norm.out(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 13836 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_layer_norm_out::call(const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, double eps, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 13837 | |
| 13838 | static auto op = create_native_layer_norm_out_typed_handle(); |
| 13839 | return op.call(input, normalized_shape, weight, bias, eps, out0, out1, out2); |
| 13840 | } |
| 13841 | |
| 13842 | // aten::native_layer_norm.out(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 13843 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_layer_norm_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & bias, double eps, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 13844 | |
| 13845 | static auto op = create_native_layer_norm_out_typed_handle(); |
| 13846 | return op.redispatch(dispatchKeySet, input, normalized_shape, weight, bias, eps, out0, out1, out2); |
| 13847 | } |
| 13848 | |
| 13849 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_mps_max_pool2d_out, name, "aten::_mps_max_pool2d") |
| 13850 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_mps_max_pool2d_out, overload_name, "out") |
| 13851 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_mps_max_pool2d_out, schema_str, "_mps_max_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)") |
| 13852 | |
| 13853 | // aten::_mps_max_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!) |
| 13854 | static C10_NOINLINE c10::TypedOperatorHandle<_mps_max_pool2d_out::schema> create__mps_max_pool2d_out_typed_handle() { |
| 13855 | return c10::Dispatcher::singleton() |
| 13856 | .findSchemaOrThrow(_mps_max_pool2d_out::name, _mps_max_pool2d_out::overload_name) |
| 13857 | .typed<_mps_max_pool2d_out::schema>(); |
| 13858 | } |
| 13859 | |
| 13860 | // aten::_mps_max_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!) |
| 13861 | at::Tensor & _mps_max_pool2d_out::call(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out) { |
| 13862 | |
| 13863 | static auto op = create__mps_max_pool2d_out_typed_handle(); |
| 13864 | return op.call(self, kernel_size, stride, padding, dilation, ceil_mode, out); |
| 13865 | } |
| 13866 | |
| 13867 | // aten::_mps_max_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!) |
| 13868 | at::Tensor & _mps_max_pool2d_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out) { |
| 13869 | |
| 13870 | static auto op = create__mps_max_pool2d_out_typed_handle(); |
| 13871 | return op.redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode, out); |
| 13872 | } |
| 13873 | |
| 13874 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_max_pool2d_out, name, "aten::mkldnn_max_pool2d") |
| 13875 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_max_pool2d_out, overload_name, "out") |
| 13876 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_max_pool2d_out, schema_str, "mkldnn_max_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)") |
| 13877 | |
| 13878 | // aten::mkldnn_max_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!) |
| 13879 | static C10_NOINLINE c10::TypedOperatorHandle<mkldnn_max_pool2d_out::schema> create_mkldnn_max_pool2d_out_typed_handle() { |
| 13880 | return c10::Dispatcher::singleton() |
| 13881 | .findSchemaOrThrow(mkldnn_max_pool2d_out::name, mkldnn_max_pool2d_out::overload_name) |
| 13882 | .typed<mkldnn_max_pool2d_out::schema>(); |
| 13883 | } |
| 13884 | |
| 13885 | // aten::mkldnn_max_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!) |
| 13886 | at::Tensor & mkldnn_max_pool2d_out::call(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out) { |
| 13887 | |
| 13888 | static auto op = create_mkldnn_max_pool2d_out_typed_handle(); |
| 13889 | return op.call(self, kernel_size, stride, padding, dilation, ceil_mode, out); |
| 13890 | } |
| 13891 | |
| 13892 | // aten::mkldnn_max_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!) |
| 13893 | at::Tensor & mkldnn_max_pool2d_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out) { |
| 13894 | |
| 13895 | static auto op = create_mkldnn_max_pool2d_out_typed_handle(); |
| 13896 | return op.redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode, out); |
| 13897 | } |
| 13898 | |
| 13899 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantized_max_pool2d_out, name, "aten::quantized_max_pool2d") |
| 13900 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantized_max_pool2d_out, overload_name, "out") |
| 13901 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantized_max_pool2d_out, schema_str, "quantized_max_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)") |
| 13902 | |
| 13903 | // aten::quantized_max_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!) |
| 13904 | static C10_NOINLINE c10::TypedOperatorHandle<quantized_max_pool2d_out::schema> create_quantized_max_pool2d_out_typed_handle() { |
| 13905 | return c10::Dispatcher::singleton() |
| 13906 | .findSchemaOrThrow(quantized_max_pool2d_out::name, quantized_max_pool2d_out::overload_name) |
| 13907 | .typed<quantized_max_pool2d_out::schema>(); |
| 13908 | } |
| 13909 | |
| 13910 | // aten::quantized_max_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!) |
| 13911 | at::Tensor & quantized_max_pool2d_out::call(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out) { |
| 13912 | |
| 13913 | static auto op = create_quantized_max_pool2d_out_typed_handle(); |
| 13914 | return op.call(self, kernel_size, stride, padding, dilation, ceil_mode, out); |
| 13915 | } |
| 13916 | |
| 13917 | // aten::quantized_max_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!) |
| 13918 | at::Tensor & quantized_max_pool2d_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out) { |
| 13919 | |
| 13920 | static auto op = create_quantized_max_pool2d_out_typed_handle(); |
| 13921 | return op.redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode, out); |
| 13922 | } |
| 13923 | |
| 13924 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_mps_convolution_out, name, "aten::_mps_convolution") |
| 13925 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_mps_convolution_out, overload_name, "out") |
| 13926 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_mps_convolution_out, schema_str, "_mps_convolution.out(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups, *, Tensor(a!) out) -> Tensor(a!)") |
| 13927 | |
| 13928 | // aten::_mps_convolution.out(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups, *, Tensor(a!) out) -> Tensor(a!) |
| 13929 | static C10_NOINLINE c10::TypedOperatorHandle<_mps_convolution_out::schema> create__mps_convolution_out_typed_handle() { |
| 13930 | return c10::Dispatcher::singleton() |
| 13931 | .findSchemaOrThrow(_mps_convolution_out::name, _mps_convolution_out::overload_name) |
| 13932 | .typed<_mps_convolution_out::schema>(); |
| 13933 | } |
| 13934 | |
| 13935 | // aten::_mps_convolution.out(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups, *, Tensor(a!) out) -> Tensor(a!) |
| 13936 | at::Tensor & _mps_convolution_out::call(const at::Tensor & self, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, at::Tensor & out) { |
| 13937 | |
| 13938 | static auto op = create__mps_convolution_out_typed_handle(); |
| 13939 | return op.call(self, weight, bias, padding, stride, dilation, groups, out); |
| 13940 | } |
| 13941 | |
| 13942 | // aten::_mps_convolution.out(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups, *, Tensor(a!) out) -> Tensor(a!) |
| 13943 | at::Tensor & _mps_convolution_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, at::Tensor & out) { |
| 13944 | |
| 13945 | static auto op = create__mps_convolution_out_typed_handle(); |
| 13946 | return op.redispatch(dispatchKeySet, self, weight, bias, padding, stride, dilation, groups, out); |
| 13947 | } |
| 13948 | |
| 13949 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_rnn_layer_backward_out, name, "aten::mkldnn_rnn_layer_backward") |
| 13950 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_rnn_layer_backward_out, overload_name, "out") |
| 13951 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_rnn_layer_backward_out, schema_str, "mkldnn_rnn_layer_backward.out(Tensor input, Tensor weight1, Tensor weight2, Tensor weight3, Tensor weight4, Tensor hx_, Tensor cx_tmp, Tensor output, Tensor hy_, Tensor cy_, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, bool reverse, int mode, int hidden_size, int num_layers, bool has_biases, bool train, bool bidirectional, int[] batch_sizes, bool batch_first, Tensor workspace, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4, Tensor(f!) out5, Tensor(g!) out6) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!), Tensor(f!), Tensor(g!))") |
| 13952 | |
| 13953 | // aten::mkldnn_rnn_layer_backward.out(Tensor input, Tensor weight1, Tensor weight2, Tensor weight3, Tensor weight4, Tensor hx_, Tensor cx_tmp, Tensor output, Tensor hy_, Tensor cy_, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, bool reverse, int mode, int hidden_size, int num_layers, bool has_biases, bool train, bool bidirectional, int[] batch_sizes, bool batch_first, Tensor workspace, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4, Tensor(f!) out5, Tensor(g!) out6) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!), Tensor(f!), Tensor(g!)) |
| 13954 | static C10_NOINLINE c10::TypedOperatorHandle<mkldnn_rnn_layer_backward_out::schema> create_mkldnn_rnn_layer_backward_out_typed_handle() { |
| 13955 | return c10::Dispatcher::singleton() |
| 13956 | .findSchemaOrThrow(mkldnn_rnn_layer_backward_out::name, mkldnn_rnn_layer_backward_out::overload_name) |
| 13957 | .typed<mkldnn_rnn_layer_backward_out::schema>(); |
| 13958 | } |
| 13959 | |
| 13960 | // aten::mkldnn_rnn_layer_backward.out(Tensor input, Tensor weight1, Tensor weight2, Tensor weight3, Tensor weight4, Tensor hx_, Tensor cx_tmp, Tensor output, Tensor hy_, Tensor cy_, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, bool reverse, int mode, int hidden_size, int num_layers, bool has_biases, bool train, bool bidirectional, int[] batch_sizes, bool batch_first, Tensor workspace, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4, Tensor(f!) out5, Tensor(g!) out6) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!), Tensor(f!), Tensor(g!)) |
| 13961 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> mkldnn_rnn_layer_backward_out::call(const at::Tensor & input, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & weight4, const at::Tensor & hx_, const at::Tensor & cx_tmp, const at::Tensor & output, const at::Tensor & hy_, const at::Tensor & cy_, const c10::optional<at::Tensor> & grad_output, const c10::optional<at::Tensor> & grad_hy, const c10::optional<at::Tensor> & grad_cy, bool reverse, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool train, bool bidirectional, at::IntArrayRef batch_sizes, bool batch_first, const at::Tensor & workspace, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, at::Tensor & out5, at::Tensor & out6) { |
| 13962 | |
| 13963 | static auto op = create_mkldnn_rnn_layer_backward_out_typed_handle(); |
| 13964 | return op.call(input, weight1, weight2, weight3, weight4, hx_, cx_tmp, output, hy_, cy_, grad_output, grad_hy, grad_cy, reverse, mode, hidden_size, num_layers, has_biases, train, bidirectional, batch_sizes, batch_first, workspace, out0, out1, out2, out3, out4, out5, out6); |
| 13965 | } |
| 13966 | |
| 13967 | // aten::mkldnn_rnn_layer_backward.out(Tensor input, Tensor weight1, Tensor weight2, Tensor weight3, Tensor weight4, Tensor hx_, Tensor cx_tmp, Tensor output, Tensor hy_, Tensor cy_, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, bool reverse, int mode, int hidden_size, int num_layers, bool has_biases, bool train, bool bidirectional, int[] batch_sizes, bool batch_first, Tensor workspace, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4, Tensor(f!) out5, Tensor(g!) out6) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!), Tensor(f!), Tensor(g!)) |
| 13968 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> mkldnn_rnn_layer_backward_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & weight4, const at::Tensor & hx_, const at::Tensor & cx_tmp, const at::Tensor & output, const at::Tensor & hy_, const at::Tensor & cy_, const c10::optional<at::Tensor> & grad_output, const c10::optional<at::Tensor> & grad_hy, const c10::optional<at::Tensor> & grad_cy, bool reverse, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool train, bool bidirectional, at::IntArrayRef batch_sizes, bool batch_first, const at::Tensor & workspace, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, at::Tensor & out5, at::Tensor & out6) { |
| 13969 | |
| 13970 | static auto op = create_mkldnn_rnn_layer_backward_out_typed_handle(); |
| 13971 | return op.redispatch(dispatchKeySet, input, weight1, weight2, weight3, weight4, hx_, cx_tmp, output, hy_, cy_, grad_output, grad_hy, grad_cy, reverse, mode, hidden_size, num_layers, has_biases, train, bidirectional, batch_sizes, batch_first, workspace, out0, out1, out2, out3, out4, out5, out6); |
| 13972 | } |
| 13973 | |
| 13974 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(miopen_depthwise_convolution_out, name, "aten::miopen_depthwise_convolution") |
| 13975 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(miopen_depthwise_convolution_out, overload_name, "out") |
| 13976 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(miopen_depthwise_convolution_out, schema_str, "miopen_depthwise_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!)") |
| 13977 | |
| 13978 | // aten::miopen_depthwise_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!) |
| 13979 | static C10_NOINLINE c10::TypedOperatorHandle<miopen_depthwise_convolution_out::schema> create_miopen_depthwise_convolution_out_typed_handle() { |
| 13980 | return c10::Dispatcher::singleton() |
| 13981 | .findSchemaOrThrow(miopen_depthwise_convolution_out::name, miopen_depthwise_convolution_out::overload_name) |
| 13982 | .typed<miopen_depthwise_convolution_out::schema>(); |
| 13983 | } |
| 13984 | |
| 13985 | // aten::miopen_depthwise_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!) |
| 13986 | at::Tensor & miopen_depthwise_convolution_out::call(const at::Tensor & self, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, at::Tensor & out) { |
| 13987 | |
| 13988 | static auto op = create_miopen_depthwise_convolution_out_typed_handle(); |
| 13989 | return op.call(self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic, out); |
| 13990 | } |
| 13991 | |
| 13992 | // aten::miopen_depthwise_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!) |
| 13993 | at::Tensor & miopen_depthwise_convolution_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, at::Tensor & out) { |
| 13994 | |
| 13995 | static auto op = create_miopen_depthwise_convolution_out_typed_handle(); |
| 13996 | return op.redispatch(dispatchKeySet, self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic, out); |
| 13997 | } |
| 13998 | |
| 13999 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(batch_norm_stats_out, name, "aten::batch_norm_stats") |
| 14000 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(batch_norm_stats_out, overload_name, "out") |
| 14001 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(batch_norm_stats_out, schema_str, "batch_norm_stats.out(Tensor input, float eps, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))") |
| 14002 | |
| 14003 | // aten::batch_norm_stats.out(Tensor input, float eps, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) |
| 14004 | static C10_NOINLINE c10::TypedOperatorHandle<batch_norm_stats_out::schema> create_batch_norm_stats_out_typed_handle() { |
| 14005 | return c10::Dispatcher::singleton() |
| 14006 | .findSchemaOrThrow(batch_norm_stats_out::name, batch_norm_stats_out::overload_name) |
| 14007 | .typed<batch_norm_stats_out::schema>(); |
| 14008 | } |
| 14009 | |
| 14010 | // aten::batch_norm_stats.out(Tensor input, float eps, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) |
| 14011 | ::std::tuple<at::Tensor &,at::Tensor &> batch_norm_stats_out::call(const at::Tensor & input, double eps, at::Tensor & out0, at::Tensor & out1) { |
| 14012 | |
| 14013 | static auto op = create_batch_norm_stats_out_typed_handle(); |
| 14014 | return op.call(input, eps, out0, out1); |
| 14015 | } |
| 14016 | |
| 14017 | // aten::batch_norm_stats.out(Tensor input, float eps, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) |
| 14018 | ::std::tuple<at::Tensor &,at::Tensor &> batch_norm_stats_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, double eps, at::Tensor & out0, at::Tensor & out1) { |
| 14019 | |
| 14020 | static auto op = create_batch_norm_stats_out_typed_handle(); |
| 14021 | return op.redispatch(dispatchKeySet, input, eps, out0, out1); |
| 14022 | } |
| 14023 | |
| 14024 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(batch_norm_gather_stats_out, name, "aten::batch_norm_gather_stats") |
| 14025 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(batch_norm_gather_stats_out, overload_name, "out") |
| 14026 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(batch_norm_gather_stats_out, schema_str, "batch_norm_gather_stats.out(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int count, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))") |
| 14027 | |
| 14028 | // aten::batch_norm_gather_stats.out(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int count, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) |
| 14029 | static C10_NOINLINE c10::TypedOperatorHandle<batch_norm_gather_stats_out::schema> create_batch_norm_gather_stats_out_typed_handle() { |
| 14030 | return c10::Dispatcher::singleton() |
| 14031 | .findSchemaOrThrow(batch_norm_gather_stats_out::name, batch_norm_gather_stats_out::overload_name) |
| 14032 | .typed<batch_norm_gather_stats_out::schema>(); |
| 14033 | } |
| 14034 | |
| 14035 | // aten::batch_norm_gather_stats.out(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int count, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) |
| 14036 | ::std::tuple<at::Tensor &,at::Tensor &> batch_norm_gather_stats_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, int64_t count, at::Tensor & out0, at::Tensor & out1) { |
| 14037 | |
| 14038 | static auto op = create_batch_norm_gather_stats_out_typed_handle(); |
| 14039 | return op.call(input, mean, invstd, running_mean, running_var, momentum, eps, count, out0, out1); |
| 14040 | } |
| 14041 | |
| 14042 | // aten::batch_norm_gather_stats.out(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int count, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) |
| 14043 | ::std::tuple<at::Tensor &,at::Tensor &> batch_norm_gather_stats_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, int64_t count, at::Tensor & out0, at::Tensor & out1) { |
| 14044 | |
| 14045 | static auto op = create_batch_norm_gather_stats_out_typed_handle(); |
| 14046 | return op.redispatch(dispatchKeySet, input, mean, invstd, running_mean, running_var, momentum, eps, count, out0, out1); |
| 14047 | } |
| 14048 | |
| 14049 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_batch_norm_backward_out, name, "aten::native_batch_norm_backward") |
| 14050 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_batch_norm_backward_out, overload_name, "out") |
| 14051 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(native_batch_norm_backward_out, schema_str, "native_batch_norm_backward.out(Tensor grad_out, Tensor input, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_invstd, bool train, float eps, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))") |
| 14052 | |
| 14053 | // aten::native_batch_norm_backward.out(Tensor grad_out, Tensor input, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_invstd, bool train, float eps, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 14054 | static C10_NOINLINE c10::TypedOperatorHandle<native_batch_norm_backward_out::schema> create_native_batch_norm_backward_out_typed_handle() { |
| 14055 | return c10::Dispatcher::singleton() |
| 14056 | .findSchemaOrThrow(native_batch_norm_backward_out::name, native_batch_norm_backward_out::overload_name) |
| 14057 | .typed<native_batch_norm_backward_out::schema>(); |
| 14058 | } |
| 14059 | |
| 14060 | // aten::native_batch_norm_backward.out(Tensor grad_out, Tensor input, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_invstd, bool train, float eps, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 14061 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_batch_norm_backward_out::call(const at::Tensor & grad_out, const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, const c10::optional<at::Tensor> & save_mean, const c10::optional<at::Tensor> & save_invstd, bool train, double eps, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 14062 | |
| 14063 | static auto op = create_native_batch_norm_backward_out_typed_handle(); |
| 14064 | return op.call(grad_out, input, weight, running_mean, running_var, save_mean, save_invstd, train, eps, output_mask, out0, out1, out2); |
| 14065 | } |
| 14066 | |
| 14067 | // aten::native_batch_norm_backward.out(Tensor grad_out, Tensor input, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_invstd, bool train, float eps, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 14068 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_batch_norm_backward_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & input, const c10::optional<at::Tensor> & weight, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, const c10::optional<at::Tensor> & save_mean, const c10::optional<at::Tensor> & save_invstd, bool train, double eps, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 14069 | |
| 14070 | static auto op = create_native_batch_norm_backward_out_typed_handle(); |
| 14071 | return op.redispatch(dispatchKeySet, grad_out, input, weight, running_mean, running_var, save_mean, save_invstd, train, eps, output_mask, out0, out1, out2); |
| 14072 | } |
| 14073 | |
| 14074 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(batch_norm_backward_reduce_out, name, "aten::batch_norm_backward_reduce") |
| 14075 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(batch_norm_backward_reduce_out, overload_name, "out") |
| 14076 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(batch_norm_backward_reduce_out, schema_str, "batch_norm_backward_reduce.out(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, bool input_g, bool weight_g, bool bias_g, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!))") |
| 14077 | |
| 14078 | // aten::batch_norm_backward_reduce.out(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, bool input_g, bool weight_g, bool bias_g, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!)) |
| 14079 | static C10_NOINLINE c10::TypedOperatorHandle<batch_norm_backward_reduce_out::schema> create_batch_norm_backward_reduce_out_typed_handle() { |
| 14080 | return c10::Dispatcher::singleton() |
| 14081 | .findSchemaOrThrow(batch_norm_backward_reduce_out::name, batch_norm_backward_reduce_out::overload_name) |
| 14082 | .typed<batch_norm_backward_reduce_out::schema>(); |
| 14083 | } |
| 14084 | |
| 14085 | // aten::batch_norm_backward_reduce.out(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, bool input_g, bool weight_g, bool bias_g, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!)) |
| 14086 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> batch_norm_backward_reduce_out::call(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional<at::Tensor> & weight, bool input_g, bool weight_g, bool bias_g, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3) { |
| 14087 | |
| 14088 | static auto op = create_batch_norm_backward_reduce_out_typed_handle(); |
| 14089 | return op.call(grad_out, input, mean, invstd, weight, input_g, weight_g, bias_g, out0, out1, out2, out3); |
| 14090 | } |
| 14091 | |
| 14092 | // aten::batch_norm_backward_reduce.out(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, bool input_g, bool weight_g, bool bias_g, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!)) |
| 14093 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> batch_norm_backward_reduce_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional<at::Tensor> & weight, bool input_g, bool weight_g, bool bias_g, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3) { |
| 14094 | |
| 14095 | static auto op = create_batch_norm_backward_reduce_out_typed_handle(); |
| 14096 | return op.redispatch(dispatchKeySet, grad_out, input, mean, invstd, weight, input_g, weight_g, bias_g, out0, out1, out2, out3); |
| 14097 | } |
| 14098 | |
| 14099 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_nnpack_spatial_convolution_out, name, "aten::_nnpack_spatial_convolution") |
| 14100 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_nnpack_spatial_convolution_out, overload_name, "out") |
| 14101 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_nnpack_spatial_convolution_out, schema_str, "_nnpack_spatial_convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[2] padding, int[2] stride=1, *, Tensor(a!) out) -> Tensor(a!)") |
| 14102 | |
| 14103 | // aten::_nnpack_spatial_convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[2] padding, int[2] stride=1, *, Tensor(a!) out) -> Tensor(a!) |
| 14104 | static C10_NOINLINE c10::TypedOperatorHandle<_nnpack_spatial_convolution_out::schema> create__nnpack_spatial_convolution_out_typed_handle() { |
| 14105 | return c10::Dispatcher::singleton() |
| 14106 | .findSchemaOrThrow(_nnpack_spatial_convolution_out::name, _nnpack_spatial_convolution_out::overload_name) |
| 14107 | .typed<_nnpack_spatial_convolution_out::schema>(); |
| 14108 | } |
| 14109 | |
| 14110 | // aten::_nnpack_spatial_convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[2] padding, int[2] stride=1, *, Tensor(a!) out) -> Tensor(a!) |
| 14111 | at::Tensor & _nnpack_spatial_convolution_out::call(const at::Tensor & input, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, at::IntArrayRef stride, at::Tensor & out) { |
| 14112 | |
| 14113 | static auto op = create__nnpack_spatial_convolution_out_typed_handle(); |
| 14114 | return op.call(input, weight, bias, padding, stride, out); |
| 14115 | } |
| 14116 | |
| 14117 | // aten::_nnpack_spatial_convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[2] padding, int[2] stride=1, *, Tensor(a!) out) -> Tensor(a!) |
| 14118 | at::Tensor & _nnpack_spatial_convolution_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, at::IntArrayRef stride, at::Tensor & out) { |
| 14119 | |
| 14120 | static auto op = create__nnpack_spatial_convolution_out_typed_handle(); |
| 14121 | return op.redispatch(dispatchKeySet, input, weight, bias, padding, stride, out); |
| 14122 | } |
| 14123 | |
| 14124 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ones_names_out, name, "aten::ones") |
| 14125 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ones_names_out, overload_name, "names_out") |
| 14126 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(ones_names_out, schema_str, "ones.names_out(int[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)") |
| 14127 | |
| 14128 | // aten::ones.names_out(int[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!) |
| 14129 | static C10_NOINLINE c10::TypedOperatorHandle<ones_names_out::schema> create_ones_names_out_typed_handle() { |
| 14130 | return c10::Dispatcher::singleton() |
| 14131 | .findSchemaOrThrow(ones_names_out::name, ones_names_out::overload_name) |
| 14132 | .typed<ones_names_out::schema>(); |
| 14133 | } |
| 14134 | |
| 14135 | // aten::ones.names_out(int[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!) |
| 14136 | at::Tensor & ones_names_out::call(at::IntArrayRef size, c10::optional<at::DimnameList> names, at::Tensor & out) { |
| 14137 | |
| 14138 | static auto op = create_ones_names_out_typed_handle(); |
| 14139 | return op.call(size, names, out); |
| 14140 | } |
| 14141 | |
| 14142 | // aten::ones.names_out(int[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!) |
| 14143 | at::Tensor & ones_names_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional<at::DimnameList> names, at::Tensor & out) { |
| 14144 | |
| 14145 | static auto op = create_ones_names_out_typed_handle(); |
| 14146 | return op.redispatch(dispatchKeySet, size, names, out); |
| 14147 | } |
| 14148 | |
| 14149 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cdist_forward_out, name, "aten::_cdist_forward") |
| 14150 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cdist_forward_out, overload_name, "out") |
| 14151 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_cdist_forward_out, schema_str, "_cdist_forward.out(Tensor x1, Tensor x2, float p, int? compute_mode, *, Tensor(a!) out) -> Tensor(a!)") |
| 14152 | |
| 14153 | // aten::_cdist_forward.out(Tensor x1, Tensor x2, float p, int? compute_mode, *, Tensor(a!) out) -> Tensor(a!) |
| 14154 | static C10_NOINLINE c10::TypedOperatorHandle<_cdist_forward_out::schema> create__cdist_forward_out_typed_handle() { |
| 14155 | return c10::Dispatcher::singleton() |
| 14156 | .findSchemaOrThrow(_cdist_forward_out::name, _cdist_forward_out::overload_name) |
| 14157 | .typed<_cdist_forward_out::schema>(); |
| 14158 | } |
| 14159 | |
| 14160 | // aten::_cdist_forward.out(Tensor x1, Tensor x2, float p, int? compute_mode, *, Tensor(a!) out) -> Tensor(a!) |
| 14161 | at::Tensor & _cdist_forward_out::call(const at::Tensor & x1, const at::Tensor & x2, double p, c10::optional<int64_t> compute_mode, at::Tensor & out) { |
| 14162 | |
| 14163 | static auto op = create__cdist_forward_out_typed_handle(); |
| 14164 | return op.call(x1, x2, p, compute_mode, out); |
| 14165 | } |
| 14166 | |
| 14167 | // aten::_cdist_forward.out(Tensor x1, Tensor x2, float p, int? compute_mode, *, Tensor(a!) out) -> Tensor(a!) |
| 14168 | at::Tensor & _cdist_forward_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x1, const at::Tensor & x2, double p, c10::optional<int64_t> compute_mode, at::Tensor & out) { |
| 14169 | |
| 14170 | static auto op = create__cdist_forward_out_typed_handle(); |
| 14171 | return op.redispatch(dispatchKeySet, x1, x2, p, compute_mode, out); |
| 14172 | } |
| 14173 | |
| 14174 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rand_like_out, name, "aten::rand_like") |
| 14175 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rand_like_out, overload_name, "out") |
| 14176 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(rand_like_out, schema_str, "rand_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)") |
| 14177 | |
| 14178 | // aten::rand_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) |
| 14179 | static C10_NOINLINE c10::TypedOperatorHandle<rand_like_out::schema> create_rand_like_out_typed_handle() { |
| 14180 | return c10::Dispatcher::singleton() |
| 14181 | .findSchemaOrThrow(rand_like_out::name, rand_like_out::overload_name) |
| 14182 | .typed<rand_like_out::schema>(); |
| 14183 | } |
| 14184 | |
| 14185 | // aten::rand_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) |
| 14186 | at::Tensor & rand_like_out::call(const at::Tensor & self, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out) { |
| 14187 | |
| 14188 | static auto op = create_rand_like_out_typed_handle(); |
| 14189 | return op.call(self, memory_format, out); |
| 14190 | } |
| 14191 | |
| 14192 | // aten::rand_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) |
| 14193 | at::Tensor & rand_like_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out) { |
| 14194 | |
| 14195 | static auto op = create_rand_like_out_typed_handle(); |
| 14196 | return op.redispatch(dispatchKeySet, self, memory_format, out); |
| 14197 | } |
| 14198 | |
| 14199 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(randint_like_out, name, "aten::randint_like") |
| 14200 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(randint_like_out, overload_name, "out") |
| 14201 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(randint_like_out, schema_str, "randint_like.out(Tensor self, int high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)") |
| 14202 | |
| 14203 | // aten::randint_like.out(Tensor self, int high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) |
| 14204 | static C10_NOINLINE c10::TypedOperatorHandle<randint_like_out::schema> create_randint_like_out_typed_handle() { |
| 14205 | return c10::Dispatcher::singleton() |
| 14206 | .findSchemaOrThrow(randint_like_out::name, randint_like_out::overload_name) |
| 14207 | .typed<randint_like_out::schema>(); |
| 14208 | } |
| 14209 | |
| 14210 | // aten::randint_like.out(Tensor self, int high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) |
| 14211 | at::Tensor & randint_like_out::call(const at::Tensor & self, int64_t high, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out) { |
| 14212 | |
| 14213 | static auto op = create_randint_like_out_typed_handle(); |
| 14214 | return op.call(self, high, memory_format, out); |
| 14215 | } |
| 14216 | |
| 14217 | // aten::randint_like.out(Tensor self, int high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) |
| 14218 | at::Tensor & randint_like_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t high, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out) { |
| 14219 | |
| 14220 | static auto op = create_randint_like_out_typed_handle(); |
| 14221 | return op.redispatch(dispatchKeySet, self, high, memory_format, out); |
| 14222 | } |
| 14223 | |
| 14224 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(randint_like_low_dtype_out, name, "aten::randint_like") |
| 14225 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(randint_like_low_dtype_out, overload_name, "low_dtype_out") |
| 14226 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(randint_like_low_dtype_out, schema_str, "randint_like.low_dtype_out(Tensor self, int low, int high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)") |
| 14227 | |
| 14228 | // aten::randint_like.low_dtype_out(Tensor self, int low, int high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) |
| 14229 | static C10_NOINLINE c10::TypedOperatorHandle<randint_like_low_dtype_out::schema> create_randint_like_low_dtype_out_typed_handle() { |
| 14230 | return c10::Dispatcher::singleton() |
| 14231 | .findSchemaOrThrow(randint_like_low_dtype_out::name, randint_like_low_dtype_out::overload_name) |
| 14232 | .typed<randint_like_low_dtype_out::schema>(); |
| 14233 | } |
| 14234 | |
| 14235 | // aten::randint_like.low_dtype_out(Tensor self, int low, int high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) |
| 14236 | at::Tensor & randint_like_low_dtype_out::call(const at::Tensor & self, int64_t low, int64_t high, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out) { |
| 14237 | |
| 14238 | static auto op = create_randint_like_low_dtype_out_typed_handle(); |
| 14239 | return op.call(self, low, high, memory_format, out); |
| 14240 | } |
| 14241 | |
| 14242 | // aten::randint_like.low_dtype_out(Tensor self, int low, int high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) |
| 14243 | at::Tensor & randint_like_low_dtype_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t low, int64_t high, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out) { |
| 14244 | |
| 14245 | static auto op = create_randint_like_low_dtype_out_typed_handle(); |
| 14246 | return op.redispatch(dispatchKeySet, self, low, high, memory_format, out); |
| 14247 | } |
| 14248 | |
| 14249 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(select_backward_out, name, "aten::select_backward") |
| 14250 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(select_backward_out, overload_name, "out") |
| 14251 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(select_backward_out, schema_str, "select_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!)") |
| 14252 | |
| 14253 | // aten::select_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!) |
| 14254 | static C10_NOINLINE c10::TypedOperatorHandle<select_backward_out::schema> create_select_backward_out_typed_handle() { |
| 14255 | return c10::Dispatcher::singleton() |
| 14256 | .findSchemaOrThrow(select_backward_out::name, select_backward_out::overload_name) |
| 14257 | .typed<select_backward_out::schema>(); |
| 14258 | } |
| 14259 | |
| 14260 | // aten::select_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!) |
| 14261 | at::Tensor & select_backward_out::call(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt index, at::Tensor & out) { |
| 14262 | |
| 14263 | static auto op = create_select_backward_out_typed_handle(); |
| 14264 | return op.call(grad_output, input_sizes, dim, index, out); |
| 14265 | } |
| 14266 | |
| 14267 | // aten::select_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!) |
| 14268 | at::Tensor & select_backward_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt index, at::Tensor & out) { |
| 14269 | |
| 14270 | static auto op = create_select_backward_out_typed_handle(); |
| 14271 | return op.redispatch(dispatchKeySet, grad_output, input_sizes, dim, index, out); |
| 14272 | } |
| 14273 | |
| 14274 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slice_scatter_out, name, "aten::slice_scatter") |
| 14275 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slice_scatter_out, overload_name, "out") |
| 14276 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(slice_scatter_out, schema_str, "slice_scatter.out(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!)") |
| 14277 | |
| 14278 | // aten::slice_scatter.out(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!) |
| 14279 | static C10_NOINLINE c10::TypedOperatorHandle<slice_scatter_out::schema> create_slice_scatter_out_typed_handle() { |
| 14280 | return c10::Dispatcher::singleton() |
| 14281 | .findSchemaOrThrow(slice_scatter_out::name, slice_scatter_out::overload_name) |
| 14282 | .typed<slice_scatter_out::schema>(); |
| 14283 | } |
| 14284 | |
| 14285 | // aten::slice_scatter.out(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!) |
| 14286 | at::Tensor & slice_scatter_out::call(const at::Tensor & self, const at::Tensor & src, int64_t dim, c10::optional<c10::SymInt> start, c10::optional<c10::SymInt> end, c10::SymInt step, at::Tensor & out) { |
| 14287 | |
| 14288 | static auto op = create_slice_scatter_out_typed_handle(); |
| 14289 | return op.call(self, src, dim, start, end, step, out); |
| 14290 | } |
| 14291 | |
| 14292 | // aten::slice_scatter.out(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!) |
| 14293 | at::Tensor & slice_scatter_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, int64_t dim, c10::optional<c10::SymInt> start, c10::optional<c10::SymInt> end, c10::SymInt step, at::Tensor & out) { |
| 14294 | |
| 14295 | static auto op = create_slice_scatter_out_typed_handle(); |
| 14296 | return op.redispatch(dispatchKeySet, self, src, dim, start, end, step, out); |
| 14297 | } |
| 14298 | |
| 14299 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_mkldnn_transpose_out, name, "aten::_mkldnn_transpose") |
| 14300 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_mkldnn_transpose_out, overload_name, "out") |
| 14301 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_mkldnn_transpose_out, schema_str, "_mkldnn_transpose.out(Tensor self, int dim0, int dim1, *, Tensor(a!) out) -> Tensor(a!)") |
| 14302 | |
| 14303 | // aten::_mkldnn_transpose.out(Tensor self, int dim0, int dim1, *, Tensor(a!) out) -> Tensor(a!) |
| 14304 | static C10_NOINLINE c10::TypedOperatorHandle<_mkldnn_transpose_out::schema> create__mkldnn_transpose_out_typed_handle() { |
| 14305 | return c10::Dispatcher::singleton() |
| 14306 | .findSchemaOrThrow(_mkldnn_transpose_out::name, _mkldnn_transpose_out::overload_name) |
| 14307 | .typed<_mkldnn_transpose_out::schema>(); |
| 14308 | } |
| 14309 | |
| 14310 | // aten::_mkldnn_transpose.out(Tensor self, int dim0, int dim1, *, Tensor(a!) out) -> Tensor(a!) |
| 14311 | at::Tensor & _mkldnn_transpose_out::call(const at::Tensor & self, int64_t dim0, int64_t dim1, at::Tensor & out) { |
| 14312 | |
| 14313 | static auto op = create__mkldnn_transpose_out_typed_handle(); |
| 14314 | return op.call(self, dim0, dim1, out); |
| 14315 | } |
| 14316 | |
| 14317 | // aten::_mkldnn_transpose.out(Tensor self, int dim0, int dim1, *, Tensor(a!) out) -> Tensor(a!) |
| 14318 | at::Tensor & _mkldnn_transpose_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim0, int64_t dim1, at::Tensor & out) { |
| 14319 | |
| 14320 | static auto op = create__mkldnn_transpose_out_typed_handle(); |
| 14321 | return op.redispatch(dispatchKeySet, self, dim0, dim1, out); |
| 14322 | } |
| 14323 | |
| 14324 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_nested_from_padded_and_nested_example_out, name, "aten::_nested_from_padded_and_nested_example") |
| 14325 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_nested_from_padded_and_nested_example_out, overload_name, "out") |
| 14326 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_nested_from_padded_and_nested_example_out, schema_str, "_nested_from_padded_and_nested_example.out(Tensor padded, Tensor nt_example, *, Tensor(a!) out) -> Tensor(a!)") |
| 14327 | |
| 14328 | // aten::_nested_from_padded_and_nested_example.out(Tensor padded, Tensor nt_example, *, Tensor(a!) out) -> Tensor(a!) |
| 14329 | static C10_NOINLINE c10::TypedOperatorHandle<_nested_from_padded_and_nested_example_out::schema> create__nested_from_padded_and_nested_example_out_typed_handle() { |
| 14330 | return c10::Dispatcher::singleton() |
| 14331 | .findSchemaOrThrow(_nested_from_padded_and_nested_example_out::name, _nested_from_padded_and_nested_example_out::overload_name) |
| 14332 | .typed<_nested_from_padded_and_nested_example_out::schema>(); |
| 14333 | } |
| 14334 | |
| 14335 | // aten::_nested_from_padded_and_nested_example.out(Tensor padded, Tensor nt_example, *, Tensor(a!) out) -> Tensor(a!) |
| 14336 | at::Tensor & _nested_from_padded_and_nested_example_out::call(const at::Tensor & padded, const at::Tensor & nt_example, at::Tensor & out) { |
| 14337 | |
| 14338 | static auto op = create__nested_from_padded_and_nested_example_out_typed_handle(); |
| 14339 | return op.call(padded, nt_example, out); |
| 14340 | } |
| 14341 | |
| 14342 | // aten::_nested_from_padded_and_nested_example.out(Tensor padded, Tensor nt_example, *, Tensor(a!) out) -> Tensor(a!) |
| 14343 | at::Tensor & _nested_from_padded_and_nested_example_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & padded, const at::Tensor & nt_example, at::Tensor & out) { |
| 14344 | |
| 14345 | static auto op = create__nested_from_padded_and_nested_example_out_typed_handle(); |
| 14346 | return op.redispatch(dispatchKeySet, padded, nt_example, out); |
| 14347 | } |
| 14348 | |
| 14349 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(unique_dim_out, name, "aten::unique_dim") |
| 14350 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(unique_dim_out, overload_name, "out") |
| 14351 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(unique_dim_out, schema_str, "unique_dim.out(Tensor self, int dim, 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!))") |
| 14352 | |
| 14353 | // aten::unique_dim.out(Tensor self, int dim, 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!)) |
| 14354 | static C10_NOINLINE c10::TypedOperatorHandle<unique_dim_out::schema> create_unique_dim_out_typed_handle() { |
| 14355 | return c10::Dispatcher::singleton() |
| 14356 | .findSchemaOrThrow(unique_dim_out::name, unique_dim_out::overload_name) |
| 14357 | .typed<unique_dim_out::schema>(); |
| 14358 | } |
| 14359 | |
| 14360 | // aten::unique_dim.out(Tensor self, int dim, 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!)) |
| 14361 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_dim_out::call(const at::Tensor & self, int64_t dim, bool sorted, bool return_inverse, bool return_counts, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 14362 | |
| 14363 | static auto op = create_unique_dim_out_typed_handle(); |
| 14364 | return op.call(self, dim, sorted, return_inverse, return_counts, out0, out1, out2); |
| 14365 | } |
| 14366 | |
| 14367 | // aten::unique_dim.out(Tensor self, int dim, 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!)) |
| 14368 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_dim_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool sorted, bool return_inverse, bool return_counts, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 14369 | |
| 14370 | static auto op = create_unique_dim_out_typed_handle(); |
| 14371 | return op.redispatch(dispatchKeySet, self, dim, sorted, return_inverse, return_counts, out0, out1, out2); |
| 14372 | } |
| 14373 | |
| 14374 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(unique_consecutive_out, name, "aten::unique_consecutive") |
| 14375 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(unique_consecutive_out, overload_name, "out") |
| 14376 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(unique_consecutive_out, schema_str, "unique_consecutive.out(Tensor self, bool return_inverse=False, bool return_counts=False, int? dim=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))") |
| 14377 | |
| 14378 | // aten::unique_consecutive.out(Tensor self, bool return_inverse=False, bool return_counts=False, int? dim=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 14379 | static C10_NOINLINE c10::TypedOperatorHandle<unique_consecutive_out::schema> create_unique_consecutive_out_typed_handle() { |
| 14380 | return c10::Dispatcher::singleton() |
| 14381 | .findSchemaOrThrow(unique_consecutive_out::name, unique_consecutive_out::overload_name) |
| 14382 | .typed<unique_consecutive_out::schema>(); |
| 14383 | } |
| 14384 | |
| 14385 | // aten::unique_consecutive.out(Tensor self, bool return_inverse=False, bool return_counts=False, int? dim=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 14386 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_consecutive_out::call(const at::Tensor & self, bool return_inverse, bool return_counts, c10::optional<int64_t> dim, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 14387 | |
| 14388 | static auto op = create_unique_consecutive_out_typed_handle(); |
| 14389 | return op.call(self, return_inverse, return_counts, dim, out0, out1, out2); |
| 14390 | } |
| 14391 | |
| 14392 | // aten::unique_consecutive.out(Tensor self, bool return_inverse=False, bool return_counts=False, int? dim=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 14393 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_consecutive_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool return_inverse, bool return_counts, c10::optional<int64_t> dim, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 14394 | |
| 14395 | static auto op = create_unique_consecutive_out_typed_handle(); |
| 14396 | return op.redispatch(dispatchKeySet, self, return_inverse, return_counts, dim, out0, out1, out2); |
| 14397 | } |
| 14398 | |
| 14399 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_dirichlet_grad_out, name, "aten::_dirichlet_grad") |
| 14400 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_dirichlet_grad_out, overload_name, "out") |
| 14401 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_dirichlet_grad_out, schema_str, "_dirichlet_grad.out(Tensor x, Tensor alpha, Tensor total, *, Tensor(a!) out) -> Tensor(a!)") |
| 14402 | |
| 14403 | // aten::_dirichlet_grad.out(Tensor x, Tensor alpha, Tensor total, *, Tensor(a!) out) -> Tensor(a!) |
| 14404 | static C10_NOINLINE c10::TypedOperatorHandle<_dirichlet_grad_out::schema> create__dirichlet_grad_out_typed_handle() { |
| 14405 | return c10::Dispatcher::singleton() |
| 14406 | .findSchemaOrThrow(_dirichlet_grad_out::name, _dirichlet_grad_out::overload_name) |
| 14407 | .typed<_dirichlet_grad_out::schema>(); |
| 14408 | } |
| 14409 | |
| 14410 | // aten::_dirichlet_grad.out(Tensor x, Tensor alpha, Tensor total, *, Tensor(a!) out) -> Tensor(a!) |
| 14411 | at::Tensor & _dirichlet_grad_out::call(const at::Tensor & x, const at::Tensor & alpha, const at::Tensor & total, at::Tensor & out) { |
| 14412 | |
| 14413 | static auto op = create__dirichlet_grad_out_typed_handle(); |
| 14414 | return op.call(x, alpha, total, out); |
| 14415 | } |
| 14416 | |
| 14417 | // aten::_dirichlet_grad.out(Tensor x, Tensor alpha, Tensor total, *, Tensor(a!) out) -> Tensor(a!) |
| 14418 | at::Tensor & _dirichlet_grad_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & alpha, const at::Tensor & total, at::Tensor & out) { |
| 14419 | |
| 14420 | static auto op = create__dirichlet_grad_out_typed_handle(); |
| 14421 | return op.redispatch(dispatchKeySet, x, alpha, total, out); |
| 14422 | } |
| 14423 | |
| 14424 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(clone_out, name, "aten::clone") |
| 14425 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(clone_out, overload_name, "out") |
| 14426 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(clone_out, schema_str, "clone.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)") |
| 14427 | |
| 14428 | // aten::clone.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) |
| 14429 | static C10_NOINLINE c10::TypedOperatorHandle<clone_out::schema> create_clone_out_typed_handle() { |
| 14430 | return c10::Dispatcher::singleton() |
| 14431 | .findSchemaOrThrow(clone_out::name, clone_out::overload_name) |
| 14432 | .typed<clone_out::schema>(); |
| 14433 | } |
| 14434 | |
| 14435 | // aten::clone.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) |
| 14436 | at::Tensor & clone_out::call(const at::Tensor & self, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out) { |
| 14437 | |
| 14438 | static auto op = create_clone_out_typed_handle(); |
| 14439 | return op.call(self, memory_format, out); |
| 14440 | } |
| 14441 | |
| 14442 | // aten::clone.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) |
| 14443 | at::Tensor & clone_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out) { |
| 14444 | |
| 14445 | static auto op = create_clone_out_typed_handle(); |
| 14446 | return op.redispatch(dispatchKeySet, self, memory_format, out); |
| 14447 | } |
| 14448 | |
| 14449 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(resize_as_out, name, "aten::resize_as") |
| 14450 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(resize_as_out, overload_name, "out") |
| 14451 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(resize_as_out, schema_str, "resize_as.out(Tensor self, Tensor the_template, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)") |
| 14452 | |
| 14453 | // aten::resize_as.out(Tensor self, Tensor the_template, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) |
| 14454 | static C10_NOINLINE c10::TypedOperatorHandle<resize_as_out::schema> create_resize_as_out_typed_handle() { |
| 14455 | return c10::Dispatcher::singleton() |
| 14456 | .findSchemaOrThrow(resize_as_out::name, resize_as_out::overload_name) |
| 14457 | .typed<resize_as_out::schema>(); |
| 14458 | } |
| 14459 | |
| 14460 | // aten::resize_as.out(Tensor self, Tensor the_template, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) |
| 14461 | const at::Tensor & resize_as_out::call(const at::Tensor & self, const at::Tensor & the_template, c10::optional<at::MemoryFormat> memory_format, const at::Tensor & out) { |
| 14462 | |
| 14463 | static auto op = create_resize_as_out_typed_handle(); |
| 14464 | return op.call(self, the_template, memory_format, out); |
| 14465 | } |
| 14466 | |
| 14467 | // aten::resize_as.out(Tensor self, Tensor the_template, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) |
| 14468 | const at::Tensor & resize_as_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & the_template, c10::optional<at::MemoryFormat> memory_format, const at::Tensor & out) { |
| 14469 | |
| 14470 | static auto op = create_resize_as_out_typed_handle(); |
| 14471 | return op.redispatch(dispatchKeySet, self, the_template, memory_format, out); |
| 14472 | } |
| 14473 | |
| 14474 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(resize_as, name, "aten::resize_as") |
| 14475 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(resize_as, overload_name, "") |
| 14476 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(resize_as, schema_str, "resize_as(Tensor self, Tensor the_template, *, MemoryFormat? memory_format=None) -> Tensor") |
| 14477 | |
| 14478 | // aten::resize_as(Tensor self, Tensor the_template, *, MemoryFormat? memory_format=None) -> Tensor |
| 14479 | static C10_NOINLINE c10::TypedOperatorHandle<resize_as::schema> create_resize_as_typed_handle() { |
| 14480 | return c10::Dispatcher::singleton() |
| 14481 | .findSchemaOrThrow(resize_as::name, resize_as::overload_name) |
| 14482 | .typed<resize_as::schema>(); |
| 14483 | } |
| 14484 | |
| 14485 | // aten::resize_as(Tensor self, Tensor the_template, *, MemoryFormat? memory_format=None) -> Tensor |
| 14486 | at::Tensor resize_as::call(const at::Tensor & self, const at::Tensor & the_template, c10::optional<at::MemoryFormat> memory_format) { |
| 14487 | |
| 14488 | static auto op = create_resize_as_typed_handle(); |
| 14489 | return op.call(self, the_template, memory_format); |
| 14490 | } |
| 14491 | |
| 14492 | // aten::resize_as(Tensor self, Tensor the_template, *, MemoryFormat? memory_format=None) -> Tensor |
| 14493 | at::Tensor resize_as::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & the_template, c10::optional<at::MemoryFormat> memory_format) { |
| 14494 | |
| 14495 | static auto op = create_resize_as_typed_handle(); |
| 14496 | return op.redispatch(dispatchKeySet, self, the_template, memory_format); |
| 14497 | } |
| 14498 | |
| 14499 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(resize_as_sparse_out, name, "aten::resize_as_sparse") |
| 14500 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(resize_as_sparse_out, overload_name, "out") |
| 14501 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(resize_as_sparse_out, schema_str, "resize_as_sparse.out(Tensor self, Tensor the_template, *, Tensor(a!) out) -> Tensor(a!)") |
| 14502 | |
| 14503 | // aten::resize_as_sparse.out(Tensor self, Tensor the_template, *, Tensor(a!) out) -> Tensor(a!) |
| 14504 | static C10_NOINLINE c10::TypedOperatorHandle<resize_as_sparse_out::schema> create_resize_as_sparse_out_typed_handle() { |
| 14505 | return c10::Dispatcher::singleton() |
| 14506 | .findSchemaOrThrow(resize_as_sparse_out::name, resize_as_sparse_out::overload_name) |
| 14507 | .typed<resize_as_sparse_out::schema>(); |
| 14508 | } |
| 14509 | |
| 14510 | // aten::resize_as_sparse.out(Tensor self, Tensor the_template, *, Tensor(a!) out) -> Tensor(a!) |
| 14511 | const at::Tensor & resize_as_sparse_out::call(const at::Tensor & self, const at::Tensor & the_template, const at::Tensor & out) { |
| 14512 | |
| 14513 | static auto op = create_resize_as_sparse_out_typed_handle(); |
| 14514 | return op.call(self, the_template, out); |
| 14515 | } |
| 14516 | |
| 14517 | // aten::resize_as_sparse.out(Tensor self, Tensor the_template, *, Tensor(a!) out) -> Tensor(a!) |
| 14518 | const at::Tensor & resize_as_sparse_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & the_template, const at::Tensor & out) { |
| 14519 | |
| 14520 | static auto op = create_resize_as_sparse_out_typed_handle(); |
| 14521 | return op.redispatch(dispatchKeySet, self, the_template, out); |
| 14522 | } |
| 14523 | |
| 14524 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(resize_as_sparse, name, "aten::resize_as_sparse") |
| 14525 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(resize_as_sparse, overload_name, "") |
| 14526 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(resize_as_sparse, schema_str, "resize_as_sparse(Tensor self, Tensor the_template) -> Tensor") |
| 14527 | |
| 14528 | // aten::resize_as_sparse(Tensor self, Tensor the_template) -> Tensor |
| 14529 | static C10_NOINLINE c10::TypedOperatorHandle<resize_as_sparse::schema> create_resize_as_sparse_typed_handle() { |
| 14530 | return c10::Dispatcher::singleton() |
| 14531 | .findSchemaOrThrow(resize_as_sparse::name, resize_as_sparse::overload_name) |
| 14532 | .typed<resize_as_sparse::schema>(); |
| 14533 | } |
| 14534 | |
| 14535 | // aten::resize_as_sparse(Tensor self, Tensor the_template) -> Tensor |
| 14536 | at::Tensor resize_as_sparse::call(const at::Tensor & self, const at::Tensor & the_template) { |
| 14537 | |
| 14538 | static auto op = create_resize_as_sparse_typed_handle(); |
| 14539 | return op.call(self, the_template); |
| 14540 | } |
| 14541 | |
| 14542 | // aten::resize_as_sparse(Tensor self, Tensor the_template) -> Tensor |
| 14543 | at::Tensor resize_as_sparse::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & the_template) { |
| 14544 | |
| 14545 | static auto op = create_resize_as_sparse_typed_handle(); |
| 14546 | return op.redispatch(dispatchKeySet, self, the_template); |
| 14547 | } |
| 14548 | |
| 14549 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(to_sparse_csc_out, name, "aten::to_sparse_csc") |
| 14550 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(to_sparse_csc_out, overload_name, "out") |
| 14551 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(to_sparse_csc_out, schema_str, "to_sparse_csc.out(Tensor self, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 14552 | |
| 14553 | // aten::to_sparse_csc.out(Tensor self, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!) |
| 14554 | static C10_NOINLINE c10::TypedOperatorHandle<to_sparse_csc_out::schema> create_to_sparse_csc_out_typed_handle() { |
| 14555 | return c10::Dispatcher::singleton() |
| 14556 | .findSchemaOrThrow(to_sparse_csc_out::name, to_sparse_csc_out::overload_name) |
| 14557 | .typed<to_sparse_csc_out::schema>(); |
| 14558 | } |
| 14559 | |
| 14560 | // aten::to_sparse_csc.out(Tensor self, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!) |
| 14561 | at::Tensor & to_sparse_csc_out::call(const at::Tensor & self, c10::optional<int64_t> dense_dim, at::Tensor & out) { |
| 14562 | |
| 14563 | static auto op = create_to_sparse_csc_out_typed_handle(); |
| 14564 | return op.call(self, dense_dim, out); |
| 14565 | } |
| 14566 | |
| 14567 | // aten::to_sparse_csc.out(Tensor self, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!) |
| 14568 | at::Tensor & to_sparse_csc_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<int64_t> dense_dim, at::Tensor & out) { |
| 14569 | |
| 14570 | static auto op = create_to_sparse_csc_out_typed_handle(); |
| 14571 | return op.redispatch(dispatchKeySet, self, dense_dim, out); |
| 14572 | } |
| 14573 | |
| 14574 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_reorder_conv2d_weight_out, name, "aten::mkldnn_reorder_conv2d_weight") |
| 14575 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_reorder_conv2d_weight_out, overload_name, "out") |
| 14576 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(mkldnn_reorder_conv2d_weight_out, schema_str, "mkldnn_reorder_conv2d_weight.out(Tensor self, int[2] padding=0, int[2] stride=1, int[2] dilation=1, int groups=1, int[]? input_size=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 14577 | |
| 14578 | // aten::mkldnn_reorder_conv2d_weight.out(Tensor self, int[2] padding=0, int[2] stride=1, int[2] dilation=1, int groups=1, int[]? input_size=None, *, Tensor(a!) out) -> Tensor(a!) |
| 14579 | static C10_NOINLINE c10::TypedOperatorHandle<mkldnn_reorder_conv2d_weight_out::schema> create_mkldnn_reorder_conv2d_weight_out_typed_handle() { |
| 14580 | return c10::Dispatcher::singleton() |
| 14581 | .findSchemaOrThrow(mkldnn_reorder_conv2d_weight_out::name, mkldnn_reorder_conv2d_weight_out::overload_name) |
| 14582 | .typed<mkldnn_reorder_conv2d_weight_out::schema>(); |
| 14583 | } |
| 14584 | |
| 14585 | // aten::mkldnn_reorder_conv2d_weight.out(Tensor self, int[2] padding=0, int[2] stride=1, int[2] dilation=1, int groups=1, int[]? input_size=None, *, Tensor(a!) out) -> Tensor(a!) |
| 14586 | at::Tensor & mkldnn_reorder_conv2d_weight_out::call(const at::Tensor & self, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, at::OptionalIntArrayRef input_size, at::Tensor & out) { |
| 14587 | |
| 14588 | static auto op = create_mkldnn_reorder_conv2d_weight_out_typed_handle(); |
| 14589 | return op.call(self, padding, stride, dilation, groups, input_size, out); |
| 14590 | } |
| 14591 | |
| 14592 | // aten::mkldnn_reorder_conv2d_weight.out(Tensor self, int[2] padding=0, int[2] stride=1, int[2] dilation=1, int groups=1, int[]? input_size=None, *, Tensor(a!) out) -> Tensor(a!) |
| 14593 | at::Tensor & mkldnn_reorder_conv2d_weight_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, at::OptionalIntArrayRef input_size, at::Tensor & out) { |
| 14594 | |
| 14595 | static auto op = create_mkldnn_reorder_conv2d_weight_out_typed_handle(); |
| 14596 | return op.redispatch(dispatchKeySet, self, padding, stride, dilation, groups, input_size, out); |
| 14597 | } |
| 14598 | |
| 14599 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantize_per_channel_out, name, "aten::quantize_per_channel") |
| 14600 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantize_per_channel_out, overload_name, "out") |
| 14601 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(quantize_per_channel_out, schema_str, "quantize_per_channel.out(Tensor self, Tensor scales, Tensor zero_points, int axis, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)") |
| 14602 | |
| 14603 | // aten::quantize_per_channel.out(Tensor self, Tensor scales, Tensor zero_points, int axis, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!) |
| 14604 | static C10_NOINLINE c10::TypedOperatorHandle<quantize_per_channel_out::schema> create_quantize_per_channel_out_typed_handle() { |
| 14605 | return c10::Dispatcher::singleton() |
| 14606 | .findSchemaOrThrow(quantize_per_channel_out::name, quantize_per_channel_out::overload_name) |
| 14607 | .typed<quantize_per_channel_out::schema>(); |
| 14608 | } |
| 14609 | |
| 14610 | // aten::quantize_per_channel.out(Tensor self, Tensor scales, Tensor zero_points, int axis, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!) |
| 14611 | at::Tensor & quantize_per_channel_out::call(const at::Tensor & self, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::ScalarType dtype, at::Tensor & out) { |
| 14612 | |
| 14613 | static auto op = create_quantize_per_channel_out_typed_handle(); |
| 14614 | return op.call(self, scales, zero_points, axis, dtype, out); |
| 14615 | } |
| 14616 | |
| 14617 | // aten::quantize_per_channel.out(Tensor self, Tensor scales, Tensor zero_points, int axis, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!) |
| 14618 | at::Tensor & quantize_per_channel_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::ScalarType dtype, at::Tensor & out) { |
| 14619 | |
| 14620 | static auto op = create_quantize_per_channel_out_typed_handle(); |
| 14621 | return op.redispatch(dispatchKeySet, self, scales, zero_points, axis, dtype, out); |
| 14622 | } |
| 14623 | |
| 14624 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(dequantize_self_out, name, "aten::dequantize") |
| 14625 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(dequantize_self_out, overload_name, "self_out") |
| 14626 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(dequantize_self_out, schema_str, "dequantize.self_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 14627 | |
| 14628 | // aten::dequantize.self_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 14629 | static C10_NOINLINE c10::TypedOperatorHandle<dequantize_self_out::schema> create_dequantize_self_out_typed_handle() { |
| 14630 | return c10::Dispatcher::singleton() |
| 14631 | .findSchemaOrThrow(dequantize_self_out::name, dequantize_self_out::overload_name) |
| 14632 | .typed<dequantize_self_out::schema>(); |
| 14633 | } |
| 14634 | |
| 14635 | // aten::dequantize.self_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 14636 | at::Tensor & dequantize_self_out::call(const at::Tensor & self, at::Tensor & out) { |
| 14637 | |
| 14638 | static auto op = create_dequantize_self_out_typed_handle(); |
| 14639 | return op.call(self, out); |
| 14640 | } |
| 14641 | |
| 14642 | // aten::dequantize.self_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 14643 | at::Tensor & dequantize_self_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 14644 | |
| 14645 | static auto op = create_dequantize_self_out_typed_handle(); |
| 14646 | return op.redispatch(dispatchKeySet, self, out); |
| 14647 | } |
| 14648 | |
| 14649 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(dequantize_tensors_out, name, "aten::dequantize") |
| 14650 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(dequantize_tensors_out, overload_name, "tensors_out") |
| 14651 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(dequantize_tensors_out, schema_str, "dequantize.tensors_out(Tensor[] tensors, *, Tensor(a!)[] out) -> ()") |
| 14652 | |
| 14653 | // aten::dequantize.tensors_out(Tensor[] tensors, *, Tensor(a!)[] out) -> () |
| 14654 | static C10_NOINLINE c10::TypedOperatorHandle<dequantize_tensors_out::schema> create_dequantize_tensors_out_typed_handle() { |
| 14655 | return c10::Dispatcher::singleton() |
| 14656 | .findSchemaOrThrow(dequantize_tensors_out::name, dequantize_tensors_out::overload_name) |
| 14657 | .typed<dequantize_tensors_out::schema>(); |
| 14658 | } |
| 14659 | |
| 14660 | // aten::dequantize.tensors_out(Tensor[] tensors, *, Tensor(a!)[] out) -> () |
| 14661 | void dequantize_tensors_out::call(at::TensorList tensors, at::TensorList out) { |
| 14662 | |
| 14663 | static auto op = create_dequantize_tensors_out_typed_handle(); |
| 14664 | return op.call(tensors, out); |
| 14665 | } |
| 14666 | |
| 14667 | // aten::dequantize.tensors_out(Tensor[] tensors, *, Tensor(a!)[] out) -> () |
| 14668 | void dequantize_tensors_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::TensorList out) { |
| 14669 | |
| 14670 | static auto op = create_dequantize_tensors_out_typed_handle(); |
| 14671 | return op.redispatch(dispatchKeySet, tensors, out); |
| 14672 | } |
| 14673 | |
| 14674 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(q_per_channel_zero_points_out, name, "aten::q_per_channel_zero_points") |
| 14675 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(q_per_channel_zero_points_out, overload_name, "out") |
| 14676 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(q_per_channel_zero_points_out, schema_str, "q_per_channel_zero_points.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 14677 | |
| 14678 | // aten::q_per_channel_zero_points.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 14679 | static C10_NOINLINE c10::TypedOperatorHandle<q_per_channel_zero_points_out::schema> create_q_per_channel_zero_points_out_typed_handle() { |
| 14680 | return c10::Dispatcher::singleton() |
| 14681 | .findSchemaOrThrow(q_per_channel_zero_points_out::name, q_per_channel_zero_points_out::overload_name) |
| 14682 | .typed<q_per_channel_zero_points_out::schema>(); |
| 14683 | } |
| 14684 | |
| 14685 | // aten::q_per_channel_zero_points.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 14686 | at::Tensor & q_per_channel_zero_points_out::call(const at::Tensor & self, at::Tensor & out) { |
| 14687 | |
| 14688 | static auto op = create_q_per_channel_zero_points_out_typed_handle(); |
| 14689 | return op.call(self, out); |
| 14690 | } |
| 14691 | |
| 14692 | // aten::q_per_channel_zero_points.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 14693 | at::Tensor & q_per_channel_zero_points_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 14694 | |
| 14695 | static auto op = create_q_per_channel_zero_points_out_typed_handle(); |
| 14696 | return op.redispatch(dispatchKeySet, self, out); |
| 14697 | } |
| 14698 | |
| 14699 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fake_quantize_learnable_per_channel_affine_out, name, "aten::_fake_quantize_learnable_per_channel_affine") |
| 14700 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fake_quantize_learnable_per_channel_affine_out, overload_name, "out") |
| 14701 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fake_quantize_learnable_per_channel_affine_out, schema_str, "_fake_quantize_learnable_per_channel_affine.out(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0, *, Tensor(a!) out) -> Tensor(a!)") |
| 14702 | |
| 14703 | // aten::_fake_quantize_learnable_per_channel_affine.out(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0, *, Tensor(a!) out) -> Tensor(a!) |
| 14704 | static C10_NOINLINE c10::TypedOperatorHandle<_fake_quantize_learnable_per_channel_affine_out::schema> create__fake_quantize_learnable_per_channel_affine_out_typed_handle() { |
| 14705 | return c10::Dispatcher::singleton() |
| 14706 | .findSchemaOrThrow(_fake_quantize_learnable_per_channel_affine_out::name, _fake_quantize_learnable_per_channel_affine_out::overload_name) |
| 14707 | .typed<_fake_quantize_learnable_per_channel_affine_out::schema>(); |
| 14708 | } |
| 14709 | |
| 14710 | // aten::_fake_quantize_learnable_per_channel_affine.out(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0, *, Tensor(a!) out) -> Tensor(a!) |
| 14711 | at::Tensor & _fake_quantize_learnable_per_channel_affine_out::call(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max, double grad_factor, at::Tensor & out) { |
| 14712 | |
| 14713 | static auto op = create__fake_quantize_learnable_per_channel_affine_out_typed_handle(); |
| 14714 | return op.call(self, scale, zero_point, axis, quant_min, quant_max, grad_factor, out); |
| 14715 | } |
| 14716 | |
| 14717 | // aten::_fake_quantize_learnable_per_channel_affine.out(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0, *, Tensor(a!) out) -> Tensor(a!) |
| 14718 | at::Tensor & _fake_quantize_learnable_per_channel_affine_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max, double grad_factor, at::Tensor & out) { |
| 14719 | |
| 14720 | static auto op = create__fake_quantize_learnable_per_channel_affine_out_typed_handle(); |
| 14721 | return op.redispatch(dispatchKeySet, self, scale, zero_point, axis, quant_min, quant_max, grad_factor, out); |
| 14722 | } |
| 14723 | |
| 14724 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_lstm_mps_out, name, "aten::_lstm_mps") |
| 14725 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_lstm_mps_out, overload_name, "out") |
| 14726 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_lstm_mps_out, schema_str, "_lstm_mps.out(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, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!))") |
| 14727 | |
| 14728 | // aten::_lstm_mps.out(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, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!)) |
| 14729 | static C10_NOINLINE c10::TypedOperatorHandle<_lstm_mps_out::schema> create__lstm_mps_out_typed_handle() { |
| 14730 | return c10::Dispatcher::singleton() |
| 14731 | .findSchemaOrThrow(_lstm_mps_out::name, _lstm_mps_out::overload_name) |
| 14732 | .typed<_lstm_mps_out::schema>(); |
| 14733 | } |
| 14734 | |
| 14735 | // aten::_lstm_mps.out(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, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!)) |
| 14736 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _lstm_mps_out::call(const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4) { |
| 14737 | |
| 14738 | static auto op = create__lstm_mps_out_typed_handle(); |
| 14739 | return op.call(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first, out0, out1, out2, out3, out4); |
| 14740 | } |
| 14741 | |
| 14742 | // aten::_lstm_mps.out(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, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!)) |
| 14743 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _lstm_mps_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4) { |
| 14744 | |
| 14745 | static auto op = create__lstm_mps_out_typed_handle(); |
| 14746 | return op.redispatch(dispatchKeySet, input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first, out0, out1, out2, out3, out4); |
| 14747 | } |
| 14748 | |
| 14749 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_thnn_fused_lstm_cell_out, name, "aten::_thnn_fused_lstm_cell") |
| 14750 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_thnn_fused_lstm_cell_out, overload_name, "out") |
| 14751 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_thnn_fused_lstm_cell_out, schema_str, "_thnn_fused_lstm_cell.out(Tensor input_gates, Tensor hidden_gates, Tensor cx, Tensor? input_bias=None, Tensor? hidden_bias=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))") |
| 14752 | |
| 14753 | // aten::_thnn_fused_lstm_cell.out(Tensor input_gates, Tensor hidden_gates, Tensor cx, Tensor? input_bias=None, Tensor? hidden_bias=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 14754 | static C10_NOINLINE c10::TypedOperatorHandle<_thnn_fused_lstm_cell_out::schema> create__thnn_fused_lstm_cell_out_typed_handle() { |
| 14755 | return c10::Dispatcher::singleton() |
| 14756 | .findSchemaOrThrow(_thnn_fused_lstm_cell_out::name, _thnn_fused_lstm_cell_out::overload_name) |
| 14757 | .typed<_thnn_fused_lstm_cell_out::schema>(); |
| 14758 | } |
| 14759 | |
| 14760 | // aten::_thnn_fused_lstm_cell.out(Tensor input_gates, Tensor hidden_gates, Tensor cx, Tensor? input_bias=None, Tensor? hidden_bias=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 14761 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _thnn_fused_lstm_cell_out::call(const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & cx, const c10::optional<at::Tensor> & input_bias, const c10::optional<at::Tensor> & hidden_bias, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 14762 | |
| 14763 | static auto op = create__thnn_fused_lstm_cell_out_typed_handle(); |
| 14764 | return op.call(input_gates, hidden_gates, cx, input_bias, hidden_bias, out0, out1, out2); |
| 14765 | } |
| 14766 | |
| 14767 | // aten::_thnn_fused_lstm_cell.out(Tensor input_gates, Tensor hidden_gates, Tensor cx, Tensor? input_bias=None, Tensor? hidden_bias=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) |
| 14768 | ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _thnn_fused_lstm_cell_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & cx, const c10::optional<at::Tensor> & input_bias, const c10::optional<at::Tensor> & hidden_bias, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { |
| 14769 | |
| 14770 | static auto op = create__thnn_fused_lstm_cell_out_typed_handle(); |
| 14771 | return op.redispatch(dispatchKeySet, input_gates, hidden_gates, cx, input_bias, hidden_bias, out0, out1, out2); |
| 14772 | } |
| 14773 | |
| 14774 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lift_fresh_copy_out, name, "aten::lift_fresh_copy") |
| 14775 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lift_fresh_copy_out, overload_name, "out") |
| 14776 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(lift_fresh_copy_out, schema_str, "lift_fresh_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 14777 | |
| 14778 | // aten::lift_fresh_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 14779 | static C10_NOINLINE c10::TypedOperatorHandle<lift_fresh_copy_out::schema> create_lift_fresh_copy_out_typed_handle() { |
| 14780 | return c10::Dispatcher::singleton() |
| 14781 | .findSchemaOrThrow(lift_fresh_copy_out::name, lift_fresh_copy_out::overload_name) |
| 14782 | .typed<lift_fresh_copy_out::schema>(); |
| 14783 | } |
| 14784 | |
| 14785 | // aten::lift_fresh_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 14786 | at::Tensor & lift_fresh_copy_out::call(const at::Tensor & self, at::Tensor & out) { |
| 14787 | |
| 14788 | static auto op = create_lift_fresh_copy_out_typed_handle(); |
| 14789 | return op.call(self, out); |
| 14790 | } |
| 14791 | |
| 14792 | // aten::lift_fresh_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 14793 | at::Tensor & lift_fresh_copy_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 14794 | |
| 14795 | static auto op = create_lift_fresh_copy_out_typed_handle(); |
| 14796 | return op.redispatch(dispatchKeySet, self, out); |
| 14797 | } |
| 14798 | |
| 14799 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill_int_Scalar_out, name, "aten::index_fill") |
| 14800 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill_int_Scalar_out, overload_name, "int_Scalar_out") |
| 14801 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill_int_Scalar_out, schema_str, "index_fill.int_Scalar_out(Tensor self, int dim, Tensor index, Scalar value, *, Tensor(a!) out) -> Tensor(a!)") |
| 14802 | |
| 14803 | // aten::index_fill.int_Scalar_out(Tensor self, int dim, Tensor index, Scalar value, *, Tensor(a!) out) -> Tensor(a!) |
| 14804 | static C10_NOINLINE c10::TypedOperatorHandle<index_fill_int_Scalar_out::schema> create_index_fill_int_Scalar_out_typed_handle() { |
| 14805 | return c10::Dispatcher::singleton() |
| 14806 | .findSchemaOrThrow(index_fill_int_Scalar_out::name, index_fill_int_Scalar_out::overload_name) |
| 14807 | .typed<index_fill_int_Scalar_out::schema>(); |
| 14808 | } |
| 14809 | |
| 14810 | // aten::index_fill.int_Scalar_out(Tensor self, int dim, Tensor index, Scalar value, *, Tensor(a!) out) -> Tensor(a!) |
| 14811 | at::Tensor & index_fill_int_Scalar_out::call(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, at::Tensor & out) { |
| 14812 | |
| 14813 | static auto op = create_index_fill_int_Scalar_out_typed_handle(); |
| 14814 | return op.call(self, dim, index, value, out); |
| 14815 | } |
| 14816 | |
| 14817 | // aten::index_fill.int_Scalar_out(Tensor self, int dim, Tensor index, Scalar value, *, Tensor(a!) out) -> Tensor(a!) |
| 14818 | at::Tensor & index_fill_int_Scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, at::Tensor & out) { |
| 14819 | |
| 14820 | static auto op = create_index_fill_int_Scalar_out_typed_handle(); |
| 14821 | return op.redispatch(dispatchKeySet, self, dim, index, value, out); |
| 14822 | } |
| 14823 | |
| 14824 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill_int_Tensor_out, name, "aten::index_fill") |
| 14825 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill_int_Tensor_out, overload_name, "int_Tensor_out") |
| 14826 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(index_fill_int_Tensor_out, schema_str, "index_fill.int_Tensor_out(Tensor self, int dim, Tensor index, Tensor value, *, Tensor(a!) out) -> Tensor(a!)") |
| 14827 | |
| 14828 | // aten::index_fill.int_Tensor_out(Tensor self, int dim, Tensor index, Tensor value, *, Tensor(a!) out) -> Tensor(a!) |
| 14829 | static C10_NOINLINE c10::TypedOperatorHandle<index_fill_int_Tensor_out::schema> create_index_fill_int_Tensor_out_typed_handle() { |
| 14830 | return c10::Dispatcher::singleton() |
| 14831 | .findSchemaOrThrow(index_fill_int_Tensor_out::name, index_fill_int_Tensor_out::overload_name) |
| 14832 | .typed<index_fill_int_Tensor_out::schema>(); |
| 14833 | } |
| 14834 | |
| 14835 | // aten::index_fill.int_Tensor_out(Tensor self, int dim, Tensor index, Tensor value, *, Tensor(a!) out) -> Tensor(a!) |
| 14836 | at::Tensor & index_fill_int_Tensor_out::call(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & value, at::Tensor & out) { |
| 14837 | |
| 14838 | static auto op = create_index_fill_int_Tensor_out_typed_handle(); |
| 14839 | return op.call(self, dim, index, value, out); |
| 14840 | } |
| 14841 | |
| 14842 | // aten::index_fill.int_Tensor_out(Tensor self, int dim, Tensor index, Tensor value, *, Tensor(a!) out) -> Tensor(a!) |
| 14843 | at::Tensor & index_fill_int_Tensor_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & value, at::Tensor & out) { |
| 14844 | |
| 14845 | static auto op = create_index_fill_int_Tensor_out_typed_handle(); |
| 14846 | return op.redispatch(dispatchKeySet, self, dim, index, value, out); |
| 14847 | } |
| 14848 | |
| 14849 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(random_from_out, name, "aten::random") |
| 14850 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(random_from_out, overload_name, "from_out") |
| 14851 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(random_from_out, schema_str, "random.from_out(Tensor self, int from, int? to, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)") |
| 14852 | |
| 14853 | // aten::random.from_out(Tensor self, int from, int? to, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 14854 | static C10_NOINLINE c10::TypedOperatorHandle<random_from_out::schema> create_random_from_out_typed_handle() { |
| 14855 | return c10::Dispatcher::singleton() |
| 14856 | .findSchemaOrThrow(random_from_out::name, random_from_out::overload_name) |
| 14857 | .typed<random_from_out::schema>(); |
| 14858 | } |
| 14859 | |
| 14860 | // aten::random.from_out(Tensor self, int from, int? to, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 14861 | at::Tensor & random_from_out::call(const at::Tensor & self, int64_t from, c10::optional<int64_t> to, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 14862 | |
| 14863 | static auto op = create_random_from_out_typed_handle(); |
| 14864 | return op.call(self, from, to, generator, out); |
| 14865 | } |
| 14866 | |
| 14867 | // aten::random.from_out(Tensor self, int from, int? to, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 14868 | at::Tensor & random_from_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t from, c10::optional<int64_t> to, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 14869 | |
| 14870 | static auto op = create_random_from_out_typed_handle(); |
| 14871 | return op.redispatch(dispatchKeySet, self, from, to, generator, out); |
| 14872 | } |
| 14873 | |
| 14874 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(random_from, name, "aten::random") |
| 14875 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(random_from, overload_name, "from") |
| 14876 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(random_from, schema_str, "random.from(Tensor self, int from, int? to, *, Generator? generator=None) -> Tensor") |
| 14877 | |
| 14878 | // aten::random.from(Tensor self, int from, int? to, *, Generator? generator=None) -> Tensor |
| 14879 | static C10_NOINLINE c10::TypedOperatorHandle<random_from::schema> create_random_from_typed_handle() { |
| 14880 | return c10::Dispatcher::singleton() |
| 14881 | .findSchemaOrThrow(random_from::name, random_from::overload_name) |
| 14882 | .typed<random_from::schema>(); |
| 14883 | } |
| 14884 | |
| 14885 | // aten::random.from(Tensor self, int from, int? to, *, Generator? generator=None) -> Tensor |
| 14886 | at::Tensor random_from::call(const at::Tensor & self, int64_t from, c10::optional<int64_t> to, c10::optional<at::Generator> generator) { |
| 14887 | |
| 14888 | static auto op = create_random_from_typed_handle(); |
| 14889 | return op.call(self, from, to, generator); |
| 14890 | } |
| 14891 | |
| 14892 | // aten::random.from(Tensor self, int from, int? to, *, Generator? generator=None) -> Tensor |
| 14893 | at::Tensor random_from::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t from, c10::optional<int64_t> to, c10::optional<at::Generator> generator) { |
| 14894 | |
| 14895 | static auto op = create_random_from_typed_handle(); |
| 14896 | return op.redispatch(dispatchKeySet, self, from, to, generator); |
| 14897 | } |
| 14898 | |
| 14899 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(random_to_out, name, "aten::random") |
| 14900 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(random_to_out, overload_name, "to_out") |
| 14901 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(random_to_out, schema_str, "random.to_out(Tensor self, int to, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)") |
| 14902 | |
| 14903 | // aten::random.to_out(Tensor self, int to, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 14904 | static C10_NOINLINE c10::TypedOperatorHandle<random_to_out::schema> create_random_to_out_typed_handle() { |
| 14905 | return c10::Dispatcher::singleton() |
| 14906 | .findSchemaOrThrow(random_to_out::name, random_to_out::overload_name) |
| 14907 | .typed<random_to_out::schema>(); |
| 14908 | } |
| 14909 | |
| 14910 | // aten::random.to_out(Tensor self, int to, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 14911 | at::Tensor & random_to_out::call(const at::Tensor & self, int64_t to, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 14912 | |
| 14913 | static auto op = create_random_to_out_typed_handle(); |
| 14914 | return op.call(self, to, generator, out); |
| 14915 | } |
| 14916 | |
| 14917 | // aten::random.to_out(Tensor self, int to, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 14918 | at::Tensor & random_to_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t to, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 14919 | |
| 14920 | static auto op = create_random_to_out_typed_handle(); |
| 14921 | return op.redispatch(dispatchKeySet, self, to, generator, out); |
| 14922 | } |
| 14923 | |
| 14924 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(random_to, name, "aten::random") |
| 14925 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(random_to, overload_name, "to") |
| 14926 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(random_to, schema_str, "random.to(Tensor self, int to, *, Generator? generator=None) -> Tensor") |
| 14927 | |
| 14928 | // aten::random.to(Tensor self, int to, *, Generator? generator=None) -> Tensor |
| 14929 | static C10_NOINLINE c10::TypedOperatorHandle<random_to::schema> create_random_to_typed_handle() { |
| 14930 | return c10::Dispatcher::singleton() |
| 14931 | .findSchemaOrThrow(random_to::name, random_to::overload_name) |
| 14932 | .typed<random_to::schema>(); |
| 14933 | } |
| 14934 | |
| 14935 | // aten::random.to(Tensor self, int to, *, Generator? generator=None) -> Tensor |
| 14936 | at::Tensor random_to::call(const at::Tensor & self, int64_t to, c10::optional<at::Generator> generator) { |
| 14937 | |
| 14938 | static auto op = create_random_to_typed_handle(); |
| 14939 | return op.call(self, to, generator); |
| 14940 | } |
| 14941 | |
| 14942 | // aten::random.to(Tensor self, int to, *, Generator? generator=None) -> Tensor |
| 14943 | at::Tensor random_to::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t to, c10::optional<at::Generator> generator) { |
| 14944 | |
| 14945 | static auto op = create_random_to_typed_handle(); |
| 14946 | return op.redispatch(dispatchKeySet, self, to, generator); |
| 14947 | } |
| 14948 | |
| 14949 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(random_out, name, "aten::random") |
| 14950 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(random_out, overload_name, "out") |
| 14951 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(random_out, schema_str, "random.out(Tensor self, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)") |
| 14952 | |
| 14953 | // aten::random.out(Tensor self, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 14954 | static C10_NOINLINE c10::TypedOperatorHandle<random_out::schema> create_random_out_typed_handle() { |
| 14955 | return c10::Dispatcher::singleton() |
| 14956 | .findSchemaOrThrow(random_out::name, random_out::overload_name) |
| 14957 | .typed<random_out::schema>(); |
| 14958 | } |
| 14959 | |
| 14960 | // aten::random.out(Tensor self, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 14961 | at::Tensor & random_out::call(const at::Tensor & self, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 14962 | |
| 14963 | static auto op = create_random_out_typed_handle(); |
| 14964 | return op.call(self, generator, out); |
| 14965 | } |
| 14966 | |
| 14967 | // aten::random.out(Tensor self, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 14968 | at::Tensor & random_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 14969 | |
| 14970 | static auto op = create_random_out_typed_handle(); |
| 14971 | return op.redispatch(dispatchKeySet, self, generator, out); |
| 14972 | } |
| 14973 | |
| 14974 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(random, name, "aten::random") |
| 14975 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(random, overload_name, "") |
| 14976 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(random, schema_str, "random(Tensor self, *, Generator? generator=None) -> Tensor") |
| 14977 | |
| 14978 | // aten::random(Tensor self, *, Generator? generator=None) -> Tensor |
| 14979 | static C10_NOINLINE c10::TypedOperatorHandle<random::schema> create_random_typed_handle() { |
| 14980 | return c10::Dispatcher::singleton() |
| 14981 | .findSchemaOrThrow(random::name, random::overload_name) |
| 14982 | .typed<random::schema>(); |
| 14983 | } |
| 14984 | |
| 14985 | // aten::random(Tensor self, *, Generator? generator=None) -> Tensor |
| 14986 | at::Tensor random::call(const at::Tensor & self, c10::optional<at::Generator> generator) { |
| 14987 | |
| 14988 | static auto op = create_random_typed_handle(); |
| 14989 | return op.call(self, generator); |
| 14990 | } |
| 14991 | |
| 14992 | // aten::random(Tensor self, *, Generator? generator=None) -> Tensor |
| 14993 | at::Tensor random::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::Generator> generator) { |
| 14994 | |
| 14995 | static auto op = create_random_typed_handle(); |
| 14996 | return op.redispatch(dispatchKeySet, self, generator); |
| 14997 | } |
| 14998 | |
| 14999 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cauchy_out, name, "aten::cauchy") |
| 15000 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cauchy_out, overload_name, "out") |
| 15001 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cauchy_out, schema_str, "cauchy.out(Tensor self, float median=0, float sigma=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)") |
| 15002 | |
| 15003 | // aten::cauchy.out(Tensor self, float median=0, float sigma=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 15004 | static C10_NOINLINE c10::TypedOperatorHandle<cauchy_out::schema> create_cauchy_out_typed_handle() { |
| 15005 | return c10::Dispatcher::singleton() |
| 15006 | .findSchemaOrThrow(cauchy_out::name, cauchy_out::overload_name) |
| 15007 | .typed<cauchy_out::schema>(); |
| 15008 | } |
| 15009 | |
| 15010 | // aten::cauchy.out(Tensor self, float median=0, float sigma=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 15011 | at::Tensor & cauchy_out::call(const at::Tensor & self, double median, double sigma, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 15012 | |
| 15013 | static auto op = create_cauchy_out_typed_handle(); |
| 15014 | return op.call(self, median, sigma, generator, out); |
| 15015 | } |
| 15016 | |
| 15017 | // aten::cauchy.out(Tensor self, float median=0, float sigma=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 15018 | at::Tensor & cauchy_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double median, double sigma, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 15019 | |
| 15020 | static auto op = create_cauchy_out_typed_handle(); |
| 15021 | return op.redispatch(dispatchKeySet, self, median, sigma, generator, out); |
| 15022 | } |
| 15023 | |
| 15024 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cauchy, name, "aten::cauchy") |
| 15025 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cauchy, overload_name, "") |
| 15026 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cauchy, schema_str, "cauchy(Tensor self, float median=0, float sigma=1, *, Generator? generator=None) -> Tensor") |
| 15027 | |
| 15028 | // aten::cauchy(Tensor self, float median=0, float sigma=1, *, Generator? generator=None) -> Tensor |
| 15029 | static C10_NOINLINE c10::TypedOperatorHandle<cauchy::schema> create_cauchy_typed_handle() { |
| 15030 | return c10::Dispatcher::singleton() |
| 15031 | .findSchemaOrThrow(cauchy::name, cauchy::overload_name) |
| 15032 | .typed<cauchy::schema>(); |
| 15033 | } |
| 15034 | |
| 15035 | // aten::cauchy(Tensor self, float median=0, float sigma=1, *, Generator? generator=None) -> Tensor |
| 15036 | at::Tensor cauchy::call(const at::Tensor & self, double median, double sigma, c10::optional<at::Generator> generator) { |
| 15037 | |
| 15038 | static auto op = create_cauchy_typed_handle(); |
| 15039 | return op.call(self, median, sigma, generator); |
| 15040 | } |
| 15041 | |
| 15042 | // aten::cauchy(Tensor self, float median=0, float sigma=1, *, Generator? generator=None) -> Tensor |
| 15043 | at::Tensor cauchy::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double median, double sigma, c10::optional<at::Generator> generator) { |
| 15044 | |
| 15045 | static auto op = create_cauchy_typed_handle(); |
| 15046 | return op.redispatch(dispatchKeySet, self, median, sigma, generator); |
| 15047 | } |
| 15048 | |
| 15049 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(log_normal_out, name, "aten::log_normal") |
| 15050 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(log_normal_out, overload_name, "out") |
| 15051 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(log_normal_out, schema_str, "log_normal.out(Tensor self, float mean=1, float std=2, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)") |
| 15052 | |
| 15053 | // aten::log_normal.out(Tensor self, float mean=1, float std=2, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 15054 | static C10_NOINLINE c10::TypedOperatorHandle<log_normal_out::schema> create_log_normal_out_typed_handle() { |
| 15055 | return c10::Dispatcher::singleton() |
| 15056 | .findSchemaOrThrow(log_normal_out::name, log_normal_out::overload_name) |
| 15057 | .typed<log_normal_out::schema>(); |
| 15058 | } |
| 15059 | |
| 15060 | // aten::log_normal.out(Tensor self, float mean=1, float std=2, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 15061 | at::Tensor & log_normal_out::call(const at::Tensor & self, double mean, double std, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 15062 | |
| 15063 | static auto op = create_log_normal_out_typed_handle(); |
| 15064 | return op.call(self, mean, std, generator, out); |
| 15065 | } |
| 15066 | |
| 15067 | // aten::log_normal.out(Tensor self, float mean=1, float std=2, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 15068 | at::Tensor & log_normal_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double mean, double std, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 15069 | |
| 15070 | static auto op = create_log_normal_out_typed_handle(); |
| 15071 | return op.redispatch(dispatchKeySet, self, mean, std, generator, out); |
| 15072 | } |
| 15073 | |
| 15074 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(log_normal, name, "aten::log_normal") |
| 15075 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(log_normal, overload_name, "") |
| 15076 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(log_normal, schema_str, "log_normal(Tensor self, float mean=1, float std=2, *, Generator? generator=None) -> Tensor") |
| 15077 | |
| 15078 | // aten::log_normal(Tensor self, float mean=1, float std=2, *, Generator? generator=None) -> Tensor |
| 15079 | static C10_NOINLINE c10::TypedOperatorHandle<log_normal::schema> create_log_normal_typed_handle() { |
| 15080 | return c10::Dispatcher::singleton() |
| 15081 | .findSchemaOrThrow(log_normal::name, log_normal::overload_name) |
| 15082 | .typed<log_normal::schema>(); |
| 15083 | } |
| 15084 | |
| 15085 | // aten::log_normal(Tensor self, float mean=1, float std=2, *, Generator? generator=None) -> Tensor |
| 15086 | at::Tensor log_normal::call(const at::Tensor & self, double mean, double std, c10::optional<at::Generator> generator) { |
| 15087 | |
| 15088 | static auto op = create_log_normal_typed_handle(); |
| 15089 | return op.call(self, mean, std, generator); |
| 15090 | } |
| 15091 | |
| 15092 | // aten::log_normal(Tensor self, float mean=1, float std=2, *, Generator? generator=None) -> Tensor |
| 15093 | at::Tensor log_normal::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double mean, double std, c10::optional<at::Generator> generator) { |
| 15094 | |
| 15095 | static auto op = create_log_normal_typed_handle(); |
| 15096 | return op.redispatch(dispatchKeySet, self, mean, std, generator); |
| 15097 | } |
| 15098 | |
| 15099 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_histogramdd_bin_edges_out, name, "aten::_histogramdd_bin_edges") |
| 15100 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_histogramdd_bin_edges_out, overload_name, "out") |
| 15101 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_histogramdd_bin_edges_out, schema_str, "_histogramdd_bin_edges.out(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False, Tensor(a!)[] out) -> ()") |
| 15102 | |
| 15103 | // aten::_histogramdd_bin_edges.out(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False, Tensor(a!)[] out) -> () |
| 15104 | static C10_NOINLINE c10::TypedOperatorHandle<_histogramdd_bin_edges_out::schema> create__histogramdd_bin_edges_out_typed_handle() { |
| 15105 | return c10::Dispatcher::singleton() |
| 15106 | .findSchemaOrThrow(_histogramdd_bin_edges_out::name, _histogramdd_bin_edges_out::overload_name) |
| 15107 | .typed<_histogramdd_bin_edges_out::schema>(); |
| 15108 | } |
| 15109 | |
| 15110 | // aten::_histogramdd_bin_edges.out(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False, Tensor(a!)[] out) -> () |
| 15111 | void _histogramdd_bin_edges_out::call(const at::Tensor & self, at::IntArrayRef bins, c10::optional<at::ArrayRef<double>> range, const c10::optional<at::Tensor> & weight, bool density, at::TensorList out) { |
| 15112 | |
| 15113 | static auto op = create__histogramdd_bin_edges_out_typed_handle(); |
| 15114 | return op.call(self, bins, range, weight, density, out); |
| 15115 | } |
| 15116 | |
| 15117 | // aten::_histogramdd_bin_edges.out(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False, Tensor(a!)[] out) -> () |
| 15118 | void _histogramdd_bin_edges_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef bins, c10::optional<at::ArrayRef<double>> range, const c10::optional<at::Tensor> & weight, bool density, at::TensorList out) { |
| 15119 | |
| 15120 | static auto op = create__histogramdd_bin_edges_out_typed_handle(); |
| 15121 | return op.redispatch(dispatchKeySet, self, bins, range, weight, density, out); |
| 15122 | } |
| 15123 | |
| 15124 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_histogramdd_from_bin_tensors_out, name, "aten::_histogramdd_from_bin_tensors") |
| 15125 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_histogramdd_from_bin_tensors_out, overload_name, "out") |
| 15126 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_histogramdd_from_bin_tensors_out, schema_str, "_histogramdd_from_bin_tensors.out(Tensor self, Tensor[] bins, *, Tensor? weight=None, bool density=False, Tensor(a!) out) -> Tensor(a!)") |
| 15127 | |
| 15128 | // aten::_histogramdd_from_bin_tensors.out(Tensor self, Tensor[] bins, *, Tensor? weight=None, bool density=False, Tensor(a!) out) -> Tensor(a!) |
| 15129 | static C10_NOINLINE c10::TypedOperatorHandle<_histogramdd_from_bin_tensors_out::schema> create__histogramdd_from_bin_tensors_out_typed_handle() { |
| 15130 | return c10::Dispatcher::singleton() |
| 15131 | .findSchemaOrThrow(_histogramdd_from_bin_tensors_out::name, _histogramdd_from_bin_tensors_out::overload_name) |
| 15132 | .typed<_histogramdd_from_bin_tensors_out::schema>(); |
| 15133 | } |
| 15134 | |
| 15135 | // aten::_histogramdd_from_bin_tensors.out(Tensor self, Tensor[] bins, *, Tensor? weight=None, bool density=False, Tensor(a!) out) -> Tensor(a!) |
| 15136 | at::Tensor & _histogramdd_from_bin_tensors_out::call(const at::Tensor & self, at::TensorList bins, const c10::optional<at::Tensor> & weight, bool density, at::Tensor & out) { |
| 15137 | |
| 15138 | static auto op = create__histogramdd_from_bin_tensors_out_typed_handle(); |
| 15139 | return op.call(self, bins, weight, density, out); |
| 15140 | } |
| 15141 | |
| 15142 | // aten::_histogramdd_from_bin_tensors.out(Tensor self, Tensor[] bins, *, Tensor? weight=None, bool density=False, Tensor(a!) out) -> Tensor(a!) |
| 15143 | at::Tensor & _histogramdd_from_bin_tensors_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::TensorList bins, const c10::optional<at::Tensor> & weight, bool density, at::Tensor & out) { |
| 15144 | |
| 15145 | static auto op = create__histogramdd_from_bin_tensors_out_typed_handle(); |
| 15146 | return op.redispatch(dispatchKeySet, self, bins, weight, density, out); |
| 15147 | } |
| 15148 | |
| 15149 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(argsort_stable_out, name, "aten::argsort") |
| 15150 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(argsort_stable_out, overload_name, "stable_out") |
| 15151 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(argsort_stable_out, schema_str, "argsort.stable_out(Tensor self, *, bool stable, int dim=-1, bool descending=False, Tensor(a!) out) -> Tensor(a!)") |
| 15152 | |
| 15153 | // aten::argsort.stable_out(Tensor self, *, bool stable, int dim=-1, bool descending=False, Tensor(a!) out) -> Tensor(a!) |
| 15154 | static C10_NOINLINE c10::TypedOperatorHandle<argsort_stable_out::schema> create_argsort_stable_out_typed_handle() { |
| 15155 | return c10::Dispatcher::singleton() |
| 15156 | .findSchemaOrThrow(argsort_stable_out::name, argsort_stable_out::overload_name) |
| 15157 | .typed<argsort_stable_out::schema>(); |
| 15158 | } |
| 15159 | |
| 15160 | // aten::argsort.stable_out(Tensor self, *, bool stable, int dim=-1, bool descending=False, Tensor(a!) out) -> Tensor(a!) |
| 15161 | at::Tensor & argsort_stable_out::call(const at::Tensor & self, bool stable, int64_t dim, bool descending, at::Tensor & out) { |
| 15162 | |
| 15163 | static auto op = create_argsort_stable_out_typed_handle(); |
| 15164 | return op.call(self, stable, dim, descending, out); |
| 15165 | } |
| 15166 | |
| 15167 | // aten::argsort.stable_out(Tensor self, *, bool stable, int dim=-1, bool descending=False, Tensor(a!) out) -> Tensor(a!) |
| 15168 | at::Tensor & argsort_stable_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool stable, int64_t dim, bool descending, at::Tensor & out) { |
| 15169 | |
| 15170 | static auto op = create_argsort_stable_out_typed_handle(); |
| 15171 | return op.redispatch(dispatchKeySet, self, stable, dim, descending, out); |
| 15172 | } |
| 15173 | |
| 15174 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(unfold_backward_out, name, "aten::unfold_backward") |
| 15175 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(unfold_backward_out, overload_name, "out") |
| 15176 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(unfold_backward_out, schema_str, "unfold_backward.out(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step, *, Tensor(a!) out) -> Tensor(a!)") |
| 15177 | |
| 15178 | // aten::unfold_backward.out(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step, *, Tensor(a!) out) -> Tensor(a!) |
| 15179 | static C10_NOINLINE c10::TypedOperatorHandle<unfold_backward_out::schema> create_unfold_backward_out_typed_handle() { |
| 15180 | return c10::Dispatcher::singleton() |
| 15181 | .findSchemaOrThrow(unfold_backward_out::name, unfold_backward_out::overload_name) |
| 15182 | .typed<unfold_backward_out::schema>(); |
| 15183 | } |
| 15184 | |
| 15185 | // aten::unfold_backward.out(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step, *, Tensor(a!) out) -> Tensor(a!) |
| 15186 | at::Tensor & unfold_backward_out::call(const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step, at::Tensor & out) { |
| 15187 | |
| 15188 | static auto op = create_unfold_backward_out_typed_handle(); |
| 15189 | return op.call(grad_in, input_sizes, dim, size, step, out); |
| 15190 | } |
| 15191 | |
| 15192 | // aten::unfold_backward.out(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step, *, Tensor(a!) out) -> Tensor(a!) |
| 15193 | at::Tensor & unfold_backward_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step, at::Tensor & out) { |
| 15194 | |
| 15195 | static auto op = create_unfold_backward_out_typed_handle(); |
| 15196 | return op.redispatch(dispatchKeySet, grad_in, input_sizes, dim, size, step, out); |
| 15197 | } |
| 15198 | |
| 15199 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_out, name, "aten::normal") |
| 15200 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_out, overload_name, "out") |
| 15201 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(normal_out, schema_str, "normal.out(Tensor self, float mean=0, float std=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)") |
| 15202 | |
| 15203 | // aten::normal.out(Tensor self, float mean=0, float std=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 15204 | static C10_NOINLINE c10::TypedOperatorHandle<normal_out::schema> create_normal_out_typed_handle() { |
| 15205 | return c10::Dispatcher::singleton() |
| 15206 | .findSchemaOrThrow(normal_out::name, normal_out::overload_name) |
| 15207 | .typed<normal_out::schema>(); |
| 15208 | } |
| 15209 | |
| 15210 | // aten::normal.out(Tensor self, float mean=0, float std=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 15211 | at::Tensor & normal_out::call(const at::Tensor & self, double mean, double std, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 15212 | |
| 15213 | static auto op = create_normal_out_typed_handle(); |
| 15214 | return op.call(self, mean, std, generator, out); |
| 15215 | } |
| 15216 | |
| 15217 | // aten::normal.out(Tensor self, float mean=0, float std=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) |
| 15218 | at::Tensor & normal_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double mean, double std, c10::optional<at::Generator> generator, at::Tensor & out) { |
| 15219 | |
| 15220 | static auto op = create_normal_out_typed_handle(); |
| 15221 | return op.redispatch(dispatchKeySet, self, mean, std, generator, out); |
| 15222 | } |
| 15223 | |
| 15224 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_sub_Scalar_out, name, "aten::_foreach_sub") |
| 15225 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_sub_Scalar_out, overload_name, "Scalar_out") |
| 15226 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_sub_Scalar_out, schema_str, "_foreach_sub.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()") |
| 15227 | |
| 15228 | // aten::_foreach_sub.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () |
| 15229 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_sub_Scalar_out::schema> create__foreach_sub_Scalar_out_typed_handle() { |
| 15230 | return c10::Dispatcher::singleton() |
| 15231 | .findSchemaOrThrow(_foreach_sub_Scalar_out::name, _foreach_sub_Scalar_out::overload_name) |
| 15232 | .typed<_foreach_sub_Scalar_out::schema>(); |
| 15233 | } |
| 15234 | |
| 15235 | // aten::_foreach_sub.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () |
| 15236 | void _foreach_sub_Scalar_out::call(at::TensorList self, const at::Scalar & scalar, at::TensorList out) { |
| 15237 | |
| 15238 | static auto op = create__foreach_sub_Scalar_out_typed_handle(); |
| 15239 | return op.call(self, scalar, out); |
| 15240 | } |
| 15241 | |
| 15242 | // aten::_foreach_sub.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () |
| 15243 | void _foreach_sub_Scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar, at::TensorList out) { |
| 15244 | |
| 15245 | static auto op = create__foreach_sub_Scalar_out_typed_handle(); |
| 15246 | return op.redispatch(dispatchKeySet, self, scalar, out); |
| 15247 | } |
| 15248 | |
| 15249 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_maximum_Scalar_out, name, "aten::_foreach_maximum") |
| 15250 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_maximum_Scalar_out, overload_name, "Scalar_out") |
| 15251 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_maximum_Scalar_out, schema_str, "_foreach_maximum.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()") |
| 15252 | |
| 15253 | // aten::_foreach_maximum.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () |
| 15254 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_maximum_Scalar_out::schema> create__foreach_maximum_Scalar_out_typed_handle() { |
| 15255 | return c10::Dispatcher::singleton() |
| 15256 | .findSchemaOrThrow(_foreach_maximum_Scalar_out::name, _foreach_maximum_Scalar_out::overload_name) |
| 15257 | .typed<_foreach_maximum_Scalar_out::schema>(); |
| 15258 | } |
| 15259 | |
| 15260 | // aten::_foreach_maximum.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () |
| 15261 | void _foreach_maximum_Scalar_out::call(at::TensorList self, const at::Scalar & scalar, at::TensorList out) { |
| 15262 | |
| 15263 | static auto op = create__foreach_maximum_Scalar_out_typed_handle(); |
| 15264 | return op.call(self, scalar, out); |
| 15265 | } |
| 15266 | |
| 15267 | // aten::_foreach_maximum.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () |
| 15268 | void _foreach_maximum_Scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar, at::TensorList out) { |
| 15269 | |
| 15270 | static auto op = create__foreach_maximum_Scalar_out_typed_handle(); |
| 15271 | return op.redispatch(dispatchKeySet, self, scalar, out); |
| 15272 | } |
| 15273 | |
| 15274 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_sub_List_out, name, "aten::_foreach_sub") |
| 15275 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_sub_List_out, overload_name, "List_out") |
| 15276 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_sub_List_out, schema_str, "_foreach_sub.List_out(Tensor[] self, Tensor[] other, *, Scalar alpha=1, Tensor(a!)[] out) -> ()") |
| 15277 | |
| 15278 | // aten::_foreach_sub.List_out(Tensor[] self, Tensor[] other, *, Scalar alpha=1, Tensor(a!)[] out) -> () |
| 15279 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_sub_List_out::schema> create__foreach_sub_List_out_typed_handle() { |
| 15280 | return c10::Dispatcher::singleton() |
| 15281 | .findSchemaOrThrow(_foreach_sub_List_out::name, _foreach_sub_List_out::overload_name) |
| 15282 | .typed<_foreach_sub_List_out::schema>(); |
| 15283 | } |
| 15284 | |
| 15285 | // aten::_foreach_sub.List_out(Tensor[] self, Tensor[] other, *, Scalar alpha=1, Tensor(a!)[] out) -> () |
| 15286 | void _foreach_sub_List_out::call(at::TensorList self, at::TensorList other, const at::Scalar & alpha, at::TensorList out) { |
| 15287 | |
| 15288 | static auto op = create__foreach_sub_List_out_typed_handle(); |
| 15289 | return op.call(self, other, alpha, out); |
| 15290 | } |
| 15291 | |
| 15292 | // aten::_foreach_sub.List_out(Tensor[] self, Tensor[] other, *, Scalar alpha=1, Tensor(a!)[] out) -> () |
| 15293 | void _foreach_sub_List_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other, const at::Scalar & alpha, at::TensorList out) { |
| 15294 | |
| 15295 | static auto op = create__foreach_sub_List_out_typed_handle(); |
| 15296 | return op.redispatch(dispatchKeySet, self, other, alpha, out); |
| 15297 | } |
| 15298 | |
| 15299 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_maximum_List_out, name, "aten::_foreach_maximum") |
| 15300 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_maximum_List_out, overload_name, "List_out") |
| 15301 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_maximum_List_out, schema_str, "_foreach_maximum.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()") |
| 15302 | |
| 15303 | // aten::_foreach_maximum.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> () |
| 15304 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_maximum_List_out::schema> create__foreach_maximum_List_out_typed_handle() { |
| 15305 | return c10::Dispatcher::singleton() |
| 15306 | .findSchemaOrThrow(_foreach_maximum_List_out::name, _foreach_maximum_List_out::overload_name) |
| 15307 | .typed<_foreach_maximum_List_out::schema>(); |
| 15308 | } |
| 15309 | |
| 15310 | // aten::_foreach_maximum.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> () |
| 15311 | void _foreach_maximum_List_out::call(at::TensorList self, at::TensorList other, at::TensorList out) { |
| 15312 | |
| 15313 | static auto op = create__foreach_maximum_List_out_typed_handle(); |
| 15314 | return op.call(self, other, out); |
| 15315 | } |
| 15316 | |
| 15317 | // aten::_foreach_maximum.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> () |
| 15318 | void _foreach_maximum_List_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other, at::TensorList out) { |
| 15319 | |
| 15320 | static auto op = create__foreach_maximum_List_out_typed_handle(); |
| 15321 | return op.redispatch(dispatchKeySet, self, other, out); |
| 15322 | } |
| 15323 | |
| 15324 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_sub_ScalarList_out, name, "aten::_foreach_sub") |
| 15325 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_sub_ScalarList_out, overload_name, "ScalarList_out") |
| 15326 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_sub_ScalarList_out, schema_str, "_foreach_sub.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()") |
| 15327 | |
| 15328 | // aten::_foreach_sub.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () |
| 15329 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_sub_ScalarList_out::schema> create__foreach_sub_ScalarList_out_typed_handle() { |
| 15330 | return c10::Dispatcher::singleton() |
| 15331 | .findSchemaOrThrow(_foreach_sub_ScalarList_out::name, _foreach_sub_ScalarList_out::overload_name) |
| 15332 | .typed<_foreach_sub_ScalarList_out::schema>(); |
| 15333 | } |
| 15334 | |
| 15335 | // aten::_foreach_sub.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () |
| 15336 | void _foreach_sub_ScalarList_out::call(at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out) { |
| 15337 | |
| 15338 | static auto op = create__foreach_sub_ScalarList_out_typed_handle(); |
| 15339 | return op.call(self, scalars, out); |
| 15340 | } |
| 15341 | |
| 15342 | // aten::_foreach_sub.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () |
| 15343 | void _foreach_sub_ScalarList_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out) { |
| 15344 | |
| 15345 | static auto op = create__foreach_sub_ScalarList_out_typed_handle(); |
| 15346 | return op.redispatch(dispatchKeySet, self, scalars, out); |
| 15347 | } |
| 15348 | |
| 15349 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_maximum_ScalarList_out, name, "aten::_foreach_maximum") |
| 15350 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_maximum_ScalarList_out, overload_name, "ScalarList_out") |
| 15351 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_maximum_ScalarList_out, schema_str, "_foreach_maximum.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()") |
| 15352 | |
| 15353 | // aten::_foreach_maximum.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () |
| 15354 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_maximum_ScalarList_out::schema> create__foreach_maximum_ScalarList_out_typed_handle() { |
| 15355 | return c10::Dispatcher::singleton() |
| 15356 | .findSchemaOrThrow(_foreach_maximum_ScalarList_out::name, _foreach_maximum_ScalarList_out::overload_name) |
| 15357 | .typed<_foreach_maximum_ScalarList_out::schema>(); |
| 15358 | } |
| 15359 | |
| 15360 | // aten::_foreach_maximum.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () |
| 15361 | void _foreach_maximum_ScalarList_out::call(at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out) { |
| 15362 | |
| 15363 | static auto op = create__foreach_maximum_ScalarList_out_typed_handle(); |
| 15364 | return op.call(self, scalars, out); |
| 15365 | } |
| 15366 | |
| 15367 | // aten::_foreach_maximum.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () |
| 15368 | void _foreach_maximum_ScalarList_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out) { |
| 15369 | |
| 15370 | static auto op = create__foreach_maximum_ScalarList_out_typed_handle(); |
| 15371 | return op.redispatch(dispatchKeySet, self, scalars, out); |
| 15372 | } |
| 15373 | |
| 15374 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_acos_out, name, "aten::_foreach_acos") |
| 15375 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_acos_out, overload_name, "out") |
| 15376 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_acos_out, schema_str, "_foreach_acos.out(Tensor[] self, *, Tensor(a!)[] out) -> ()") |
| 15377 | |
| 15378 | // aten::_foreach_acos.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 15379 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_acos_out::schema> create__foreach_acos_out_typed_handle() { |
| 15380 | return c10::Dispatcher::singleton() |
| 15381 | .findSchemaOrThrow(_foreach_acos_out::name, _foreach_acos_out::overload_name) |
| 15382 | .typed<_foreach_acos_out::schema>(); |
| 15383 | } |
| 15384 | |
| 15385 | // aten::_foreach_acos.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 15386 | void _foreach_acos_out::call(at::TensorList self, at::TensorList out) { |
| 15387 | |
| 15388 | static auto op = create__foreach_acos_out_typed_handle(); |
| 15389 | return op.call(self, out); |
| 15390 | } |
| 15391 | |
| 15392 | // aten::_foreach_acos.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 15393 | void _foreach_acos_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 15394 | |
| 15395 | static auto op = create__foreach_acos_out_typed_handle(); |
| 15396 | return op.redispatch(dispatchKeySet, self, out); |
| 15397 | } |
| 15398 | |
| 15399 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_atan_out, name, "aten::_foreach_atan") |
| 15400 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_atan_out, overload_name, "out") |
| 15401 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_atan_out, schema_str, "_foreach_atan.out(Tensor[] self, *, Tensor(a!)[] out) -> ()") |
| 15402 | |
| 15403 | // aten::_foreach_atan.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 15404 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_atan_out::schema> create__foreach_atan_out_typed_handle() { |
| 15405 | return c10::Dispatcher::singleton() |
| 15406 | .findSchemaOrThrow(_foreach_atan_out::name, _foreach_atan_out::overload_name) |
| 15407 | .typed<_foreach_atan_out::schema>(); |
| 15408 | } |
| 15409 | |
| 15410 | // aten::_foreach_atan.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 15411 | void _foreach_atan_out::call(at::TensorList self, at::TensorList out) { |
| 15412 | |
| 15413 | static auto op = create__foreach_atan_out_typed_handle(); |
| 15414 | return op.call(self, out); |
| 15415 | } |
| 15416 | |
| 15417 | // aten::_foreach_atan.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 15418 | void _foreach_atan_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 15419 | |
| 15420 | static auto op = create__foreach_atan_out_typed_handle(); |
| 15421 | return op.redispatch(dispatchKeySet, self, out); |
| 15422 | } |
| 15423 | |
| 15424 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_ceil_out, name, "aten::_foreach_ceil") |
| 15425 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_ceil_out, overload_name, "out") |
| 15426 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_ceil_out, schema_str, "_foreach_ceil.out(Tensor[] self, *, Tensor(a!)[] out) -> ()") |
| 15427 | |
| 15428 | // aten::_foreach_ceil.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 15429 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_ceil_out::schema> create__foreach_ceil_out_typed_handle() { |
| 15430 | return c10::Dispatcher::singleton() |
| 15431 | .findSchemaOrThrow(_foreach_ceil_out::name, _foreach_ceil_out::overload_name) |
| 15432 | .typed<_foreach_ceil_out::schema>(); |
| 15433 | } |
| 15434 | |
| 15435 | // aten::_foreach_ceil.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 15436 | void _foreach_ceil_out::call(at::TensorList self, at::TensorList out) { |
| 15437 | |
| 15438 | static auto op = create__foreach_ceil_out_typed_handle(); |
| 15439 | return op.call(self, out); |
| 15440 | } |
| 15441 | |
| 15442 | // aten::_foreach_ceil.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 15443 | void _foreach_ceil_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 15444 | |
| 15445 | static auto op = create__foreach_ceil_out_typed_handle(); |
| 15446 | return op.redispatch(dispatchKeySet, self, out); |
| 15447 | } |
| 15448 | |
| 15449 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_erf_out, name, "aten::_foreach_erf") |
| 15450 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_erf_out, overload_name, "out") |
| 15451 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_erf_out, schema_str, "_foreach_erf.out(Tensor[] self, *, Tensor(a!)[] out) -> ()") |
| 15452 | |
| 15453 | // aten::_foreach_erf.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 15454 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_erf_out::schema> create__foreach_erf_out_typed_handle() { |
| 15455 | return c10::Dispatcher::singleton() |
| 15456 | .findSchemaOrThrow(_foreach_erf_out::name, _foreach_erf_out::overload_name) |
| 15457 | .typed<_foreach_erf_out::schema>(); |
| 15458 | } |
| 15459 | |
| 15460 | // aten::_foreach_erf.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 15461 | void _foreach_erf_out::call(at::TensorList self, at::TensorList out) { |
| 15462 | |
| 15463 | static auto op = create__foreach_erf_out_typed_handle(); |
| 15464 | return op.call(self, out); |
| 15465 | } |
| 15466 | |
| 15467 | // aten::_foreach_erf.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 15468 | void _foreach_erf_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 15469 | |
| 15470 | static auto op = create__foreach_erf_out_typed_handle(); |
| 15471 | return op.redispatch(dispatchKeySet, self, out); |
| 15472 | } |
| 15473 | |
| 15474 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_log2_out, name, "aten::_foreach_log2") |
| 15475 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_log2_out, overload_name, "out") |
| 15476 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_foreach_log2_out, schema_str, "_foreach_log2.out(Tensor[] self, *, Tensor(a!)[] out) -> ()") |
| 15477 | |
| 15478 | // aten::_foreach_log2.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 15479 | static C10_NOINLINE c10::TypedOperatorHandle<_foreach_log2_out::schema> create__foreach_log2_out_typed_handle() { |
| 15480 | return c10::Dispatcher::singleton() |
| 15481 | .findSchemaOrThrow(_foreach_log2_out::name, _foreach_log2_out::overload_name) |
| 15482 | .typed<_foreach_log2_out::schema>(); |
| 15483 | } |
| 15484 | |
| 15485 | // aten::_foreach_log2.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 15486 | void _foreach_log2_out::call(at::TensorList self, at::TensorList out) { |
| 15487 | |
| 15488 | static auto op = create__foreach_log2_out_typed_handle(); |
| 15489 | return op.call(self, out); |
| 15490 | } |
| 15491 | |
| 15492 | // aten::_foreach_log2.out(Tensor[] self, *, Tensor(a!)[] out) -> () |
| 15493 | void _foreach_log2_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { |
| 15494 | |
| 15495 | static auto op = create__foreach_log2_out_typed_handle(); |
| 15496 | return op.redispatch(dispatchKeySet, self, out); |
| 15497 | } |
| 15498 | |
| 15499 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bucketize_Scalar_out, name, "aten::bucketize") |
| 15500 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bucketize_Scalar_out, overload_name, "Scalar_out") |
| 15501 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(bucketize_Scalar_out, schema_str, "bucketize.Scalar_out(Scalar self, Tensor boundaries, *, bool out_int32=False, bool right=False, Tensor(a!) out) -> Tensor(a!)") |
| 15502 | |
| 15503 | // aten::bucketize.Scalar_out(Scalar self, Tensor boundaries, *, bool out_int32=False, bool right=False, Tensor(a!) out) -> Tensor(a!) |
| 15504 | static C10_NOINLINE c10::TypedOperatorHandle<bucketize_Scalar_out::schema> create_bucketize_Scalar_out_typed_handle() { |
| 15505 | return c10::Dispatcher::singleton() |
| 15506 | .findSchemaOrThrow(bucketize_Scalar_out::name, bucketize_Scalar_out::overload_name) |
| 15507 | .typed<bucketize_Scalar_out::schema>(); |
| 15508 | } |
| 15509 | |
| 15510 | // aten::bucketize.Scalar_out(Scalar self, Tensor boundaries, *, bool out_int32=False, bool right=False, Tensor(a!) out) -> Tensor(a!) |
| 15511 | at::Tensor & bucketize_Scalar_out::call(const at::Scalar & self, const at::Tensor & boundaries, bool out_int32, bool right, at::Tensor & out) { |
| 15512 | |
| 15513 | static auto op = create_bucketize_Scalar_out_typed_handle(); |
| 15514 | return op.call(self, boundaries, out_int32, right, out); |
| 15515 | } |
| 15516 | |
| 15517 | // aten::bucketize.Scalar_out(Scalar self, Tensor boundaries, *, bool out_int32=False, bool right=False, Tensor(a!) out) -> Tensor(a!) |
| 15518 | at::Tensor & bucketize_Scalar_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & boundaries, bool out_int32, bool right, at::Tensor & out) { |
| 15519 | |
| 15520 | static auto op = create_bucketize_Scalar_out_typed_handle(); |
| 15521 | return op.redispatch(dispatchKeySet, self, boundaries, out_int32, right, out); |
| 15522 | } |
| 15523 | |
| 15524 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(glu_backward_jvp_out, name, "aten::glu_backward_jvp") |
| 15525 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(glu_backward_jvp_out, overload_name, "out") |
| 15526 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(glu_backward_jvp_out, schema_str, "glu_backward_jvp.out(Tensor grad_x, Tensor grad_glu, Tensor x, Tensor dgrad_glu, Tensor dx, int dim, *, Tensor(a!) out) -> Tensor(a!)") |
| 15527 | |
| 15528 | // aten::glu_backward_jvp.out(Tensor grad_x, Tensor grad_glu, Tensor x, Tensor dgrad_glu, Tensor dx, int dim, *, Tensor(a!) out) -> Tensor(a!) |
| 15529 | static C10_NOINLINE c10::TypedOperatorHandle<glu_backward_jvp_out::schema> create_glu_backward_jvp_out_typed_handle() { |
| 15530 | return c10::Dispatcher::singleton() |
| 15531 | .findSchemaOrThrow(glu_backward_jvp_out::name, glu_backward_jvp_out::overload_name) |
| 15532 | .typed<glu_backward_jvp_out::schema>(); |
| 15533 | } |
| 15534 | |
| 15535 | // aten::glu_backward_jvp.out(Tensor grad_x, Tensor grad_glu, Tensor x, Tensor dgrad_glu, Tensor dx, int dim, *, Tensor(a!) out) -> Tensor(a!) |
| 15536 | at::Tensor & glu_backward_jvp_out::call(const at::Tensor & grad_x, const at::Tensor & grad_glu, const at::Tensor & x, const at::Tensor & dgrad_glu, const at::Tensor & dx, int64_t dim, at::Tensor & out) { |
| 15537 | |
| 15538 | static auto op = create_glu_backward_jvp_out_typed_handle(); |
| 15539 | return op.call(grad_x, grad_glu, x, dgrad_glu, dx, dim, out); |
| 15540 | } |
| 15541 | |
| 15542 | // aten::glu_backward_jvp.out(Tensor grad_x, Tensor grad_glu, Tensor x, Tensor dgrad_glu, Tensor dx, int dim, *, Tensor(a!) out) -> Tensor(a!) |
| 15543 | at::Tensor & glu_backward_jvp_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_x, const at::Tensor & grad_glu, const at::Tensor & x, const at::Tensor & dgrad_glu, const at::Tensor & dx, int64_t dim, at::Tensor & out) { |
| 15544 | |
| 15545 | static auto op = create_glu_backward_jvp_out_typed_handle(); |
| 15546 | return op.redispatch(dispatchKeySet, grad_x, grad_glu, x, dgrad_glu, dx, dim, out); |
| 15547 | } |
| 15548 | |
| 15549 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardswish_backward_out, name, "aten::hardswish_backward") |
| 15550 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardswish_backward_out, overload_name, "out") |
| 15551 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(hardswish_backward_out, schema_str, "hardswish_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 15552 | |
| 15553 | // aten::hardswish_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 15554 | static C10_NOINLINE c10::TypedOperatorHandle<hardswish_backward_out::schema> create_hardswish_backward_out_typed_handle() { |
| 15555 | return c10::Dispatcher::singleton() |
| 15556 | .findSchemaOrThrow(hardswish_backward_out::name, hardswish_backward_out::overload_name) |
| 15557 | .typed<hardswish_backward_out::schema>(); |
| 15558 | } |
| 15559 | |
| 15560 | // aten::hardswish_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 15561 | at::Tensor & hardswish_backward_out::call(const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & out) { |
| 15562 | |
| 15563 | static auto op = create_hardswish_backward_out_typed_handle(); |
| 15564 | return op.call(grad_output, self, out); |
| 15565 | } |
| 15566 | |
| 15567 | // aten::hardswish_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 15568 | at::Tensor & hardswish_backward_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & out) { |
| 15569 | |
| 15570 | static auto op = create_hardswish_backward_out_typed_handle(); |
| 15571 | return op.redispatch(dispatchKeySet, grad_output, self, out); |
| 15572 | } |
| 15573 | |
| 15574 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_adaptive_avg_pool3d_backward_out, name, "aten::_adaptive_avg_pool3d_backward") |
| 15575 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_adaptive_avg_pool3d_backward_out, overload_name, "out") |
| 15576 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_adaptive_avg_pool3d_backward_out, schema_str, "_adaptive_avg_pool3d_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 15577 | |
| 15578 | // aten::_adaptive_avg_pool3d_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 15579 | static C10_NOINLINE c10::TypedOperatorHandle<_adaptive_avg_pool3d_backward_out::schema> create__adaptive_avg_pool3d_backward_out_typed_handle() { |
| 15580 | return c10::Dispatcher::singleton() |
| 15581 | .findSchemaOrThrow(_adaptive_avg_pool3d_backward_out::name, _adaptive_avg_pool3d_backward_out::overload_name) |
| 15582 | .typed<_adaptive_avg_pool3d_backward_out::schema>(); |
| 15583 | } |
| 15584 | |
| 15585 | // aten::_adaptive_avg_pool3d_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 15586 | at::Tensor & _adaptive_avg_pool3d_backward_out::call(const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & out) { |
| 15587 | |
| 15588 | static auto op = create__adaptive_avg_pool3d_backward_out_typed_handle(); |
| 15589 | return op.call(grad_output, self, out); |
| 15590 | } |
| 15591 | |
| 15592 | // aten::_adaptive_avg_pool3d_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 15593 | at::Tensor & _adaptive_avg_pool3d_backward_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & out) { |
| 15594 | |
| 15595 | static auto op = create__adaptive_avg_pool3d_backward_out_typed_handle(); |
| 15596 | return op.redispatch(dispatchKeySet, grad_output, self, out); |
| 15597 | } |
| 15598 | |
| 15599 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_conj_copy_out, name, "aten::_conj_copy") |
| 15600 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_conj_copy_out, overload_name, "out") |
| 15601 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_conj_copy_out, schema_str, "_conj_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 15602 | |
| 15603 | // aten::_conj_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 15604 | static C10_NOINLINE c10::TypedOperatorHandle<_conj_copy_out::schema> create__conj_copy_out_typed_handle() { |
| 15605 | return c10::Dispatcher::singleton() |
| 15606 | .findSchemaOrThrow(_conj_copy_out::name, _conj_copy_out::overload_name) |
| 15607 | .typed<_conj_copy_out::schema>(); |
| 15608 | } |
| 15609 | |
| 15610 | // aten::_conj_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 15611 | at::Tensor & _conj_copy_out::call(const at::Tensor & self, at::Tensor & out) { |
| 15612 | |
| 15613 | static auto op = create__conj_copy_out_typed_handle(); |
| 15614 | return op.call(self, out); |
| 15615 | } |
| 15616 | |
| 15617 | // aten::_conj_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 15618 | at::Tensor & _conj_copy_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 15619 | |
| 15620 | static auto op = create__conj_copy_out_typed_handle(); |
| 15621 | return op.redispatch(dispatchKeySet, self, out); |
| 15622 | } |
| 15623 | |
| 15624 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(detach_copy_out, name, "aten::detach_copy") |
| 15625 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(detach_copy_out, overload_name, "out") |
| 15626 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(detach_copy_out, schema_str, "detach_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 15627 | |
| 15628 | // aten::detach_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 15629 | static C10_NOINLINE c10::TypedOperatorHandle<detach_copy_out::schema> create_detach_copy_out_typed_handle() { |
| 15630 | return c10::Dispatcher::singleton() |
| 15631 | .findSchemaOrThrow(detach_copy_out::name, detach_copy_out::overload_name) |
| 15632 | .typed<detach_copy_out::schema>(); |
| 15633 | } |
| 15634 | |
| 15635 | // aten::detach_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 15636 | at::Tensor & detach_copy_out::call(const at::Tensor & self, at::Tensor & out) { |
| 15637 | |
| 15638 | static auto op = create_detach_copy_out_typed_handle(); |
| 15639 | return op.call(self, out); |
| 15640 | } |
| 15641 | |
| 15642 | // aten::detach_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 15643 | at::Tensor & detach_copy_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 15644 | |
| 15645 | static auto op = create_detach_copy_out_typed_handle(); |
| 15646 | return op.redispatch(dispatchKeySet, self, out); |
| 15647 | } |
| 15648 | |
| 15649 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(row_indices_copy_out, name, "aten::row_indices_copy") |
| 15650 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(row_indices_copy_out, overload_name, "out") |
| 15651 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(row_indices_copy_out, schema_str, "row_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") |
| 15652 | |
| 15653 | // aten::row_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 15654 | static C10_NOINLINE c10::TypedOperatorHandle<row_indices_copy_out::schema> create_row_indices_copy_out_typed_handle() { |
| 15655 | return c10::Dispatcher::singleton() |
| 15656 | .findSchemaOrThrow(row_indices_copy_out::name, row_indices_copy_out::overload_name) |
| 15657 | .typed<row_indices_copy_out::schema>(); |
| 15658 | } |
| 15659 | |
| 15660 | // aten::row_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 15661 | at::Tensor & row_indices_copy_out::call(const at::Tensor & self, at::Tensor & out) { |
| 15662 | |
| 15663 | static auto op = create_row_indices_copy_out_typed_handle(); |
| 15664 | return op.call(self, out); |
| 15665 | } |
| 15666 | |
| 15667 | // aten::row_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) |
| 15668 | at::Tensor & row_indices_copy_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { |
| 15669 | |
| 15670 | static auto op = create_row_indices_copy_out_typed_handle(); |
| 15671 | return op.redispatch(dispatchKeySet, self, out); |
| 15672 | } |
| 15673 | |
| 15674 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_transformer_encoder_layer_fwd_out, name, "aten::_transformer_encoder_layer_fwd") |
| 15675 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_transformer_encoder_layer_fwd_out, overload_name, "out") |
| 15676 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_transformer_encoder_layer_fwd_out, schema_str, "_transformer_encoder_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, int? mask_type=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 15677 | |
| 15678 | // aten::_transformer_encoder_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, int? mask_type=None, *, Tensor(a!) out) -> Tensor(a!) |
| 15679 | static C10_NOINLINE c10::TypedOperatorHandle<_transformer_encoder_layer_fwd_out::schema> create__transformer_encoder_layer_fwd_out_typed_handle() { |
| 15680 | return c10::Dispatcher::singleton() |
| 15681 | .findSchemaOrThrow(_transformer_encoder_layer_fwd_out::name, _transformer_encoder_layer_fwd_out::overload_name) |
| 15682 | .typed<_transformer_encoder_layer_fwd_out::schema>(); |
| 15683 | } |
| 15684 | |
| 15685 | // aten::_transformer_encoder_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, int? mask_type=None, *, Tensor(a!) out) -> Tensor(a!) |
| 15686 | at::Tensor & _transformer_encoder_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, c10::optional<int64_t> mask_type, at::Tensor & out) { |
| 15687 | |
| 15688 | static auto op = create__transformer_encoder_layer_fwd_out_typed_handle(); |
| 15689 | 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, mask_type, out); |
| 15690 | } |
| 15691 | |
| 15692 | // aten::_transformer_encoder_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, int? mask_type=None, *, Tensor(a!) out) -> Tensor(a!) |
| 15693 | at::Tensor & _transformer_encoder_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, c10::optional<int64_t> mask_type, at::Tensor & out) { |
| 15694 | |
| 15695 | static auto op = create__transformer_encoder_layer_fwd_out_typed_handle(); |
| 15696 | 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, mask_type, out); |
| 15697 | } |
| 15698 | |
| 15699 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_native_multi_head_attention_out, name, "aten::_native_multi_head_attention") |
| 15700 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_native_multi_head_attention_out, overload_name, "out") |
| 15701 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_native_multi_head_attention_out, schema_str, "_native_multi_head_attention.out(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, bool need_weights=True, bool average_attn_weights=True, int? mask_type=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))") |
| 15702 | |
| 15703 | // aten::_native_multi_head_attention.out(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, bool need_weights=True, bool average_attn_weights=True, int? mask_type=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) |
| 15704 | static C10_NOINLINE c10::TypedOperatorHandle<_native_multi_head_attention_out::schema> create__native_multi_head_attention_out_typed_handle() { |
| 15705 | return c10::Dispatcher::singleton() |
| 15706 | .findSchemaOrThrow(_native_multi_head_attention_out::name, _native_multi_head_attention_out::overload_name) |
| 15707 | .typed<_native_multi_head_attention_out::schema>(); |
| 15708 | } |
| 15709 | |
| 15710 | // aten::_native_multi_head_attention.out(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, bool need_weights=True, bool average_attn_weights=True, int? mask_type=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) |
| 15711 | ::std::tuple<at::Tensor &,at::Tensor &> _native_multi_head_attention_out::call(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const c10::optional<at::Tensor> & mask, bool need_weights, bool average_attn_weights, c10::optional<int64_t> mask_type, at::Tensor & out0, at::Tensor & out1) { |
| 15712 | |
| 15713 | static auto op = create__native_multi_head_attention_out_typed_handle(); |
| 15714 | return op.call(query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask, need_weights, average_attn_weights, mask_type, out0, out1); |
| 15715 | } |
| 15716 | |
| 15717 | // aten::_native_multi_head_attention.out(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, bool need_weights=True, bool average_attn_weights=True, int? mask_type=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) |
| 15718 | ::std::tuple<at::Tensor &,at::Tensor &> _native_multi_head_attention_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const c10::optional<at::Tensor> & mask, bool need_weights, bool average_attn_weights, c10::optional<int64_t> mask_type, at::Tensor & out0, at::Tensor & out1) { |
| 15719 | |
| 15720 | static auto op = create__native_multi_head_attention_out_typed_handle(); |
| 15721 | return op.redispatch(dispatchKeySet, query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask, need_weights, average_attn_weights, mask_type, out0, out1); |
| 15722 | } |
| 15723 | |
| 15724 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_triton_multi_head_attention_out, name, "aten::_triton_multi_head_attention") |
| 15725 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_triton_multi_head_attention_out, overload_name, "out") |
| 15726 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_triton_multi_head_attention_out, schema_str, "_triton_multi_head_attention.out(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, *, Tensor(a!) out) -> Tensor(a!)") |
| 15727 | |
| 15728 | // aten::_triton_multi_head_attention.out(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, *, Tensor(a!) out) -> Tensor(a!) |
| 15729 | static C10_NOINLINE c10::TypedOperatorHandle<_triton_multi_head_attention_out::schema> create__triton_multi_head_attention_out_typed_handle() { |
| 15730 | return c10::Dispatcher::singleton() |
| 15731 | .findSchemaOrThrow(_triton_multi_head_attention_out::name, _triton_multi_head_attention_out::overload_name) |
| 15732 | .typed<_triton_multi_head_attention_out::schema>(); |
| 15733 | } |
| 15734 | |
| 15735 | // aten::_triton_multi_head_attention.out(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, *, Tensor(a!) out) -> Tensor(a!) |
| 15736 | at::Tensor & _triton_multi_head_attention_out::call(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const c10::optional<at::Tensor> & mask, at::Tensor & out) { |
| 15737 | |
| 15738 | static auto op = create__triton_multi_head_attention_out_typed_handle(); |
| 15739 | return op.call(query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask, out); |
| 15740 | } |
| 15741 | |
| 15742 | // aten::_triton_multi_head_attention.out(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, *, Tensor(a!) out) -> Tensor(a!) |
| 15743 | at::Tensor & _triton_multi_head_attention_out::redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const c10::optional<at::Tensor> & mask, at::Tensor & out) { |
| 15744 | |
| 15745 | static auto op = create__triton_multi_head_attention_out_typed_handle(); |
| 15746 | return op.redispatch(dispatchKeySet, query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask, out); |
| 15747 | } |
| 15748 | |
| 15749 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fused_adamw_out, name, "aten::_fused_adamw") |
| 15750 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fused_adamw_out, overload_name, "out") |
| 15751 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fused_adamw_out, schema_str, "_fused_adamw.out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> ()") |
| 15752 | |
| 15753 | // aten::_fused_adamw.out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> () |
| 15754 | static C10_NOINLINE c10::TypedOperatorHandle<_fused_adamw_out::schema> create__fused_adamw_out_typed_handle() { |
| 15755 | return c10::Dispatcher::singleton() |
| 15756 | .findSchemaOrThrow(_fused_adamw_out::name, _fused_adamw_out::overload_name) |
| 15757 | .typed<_fused_adamw_out::schema>(); |
| 15758 | } |
| 15759 | |
| 15760 | // aten::_fused_adamw.out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> () |
| 15761 | void _fused_adamw_out::call(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional<at::Tensor> & grad_scale, const c10::optional<at::Tensor> & found_inf, at::TensorList out) { |
| 15762 | |
| 15763 | static auto op = create__fused_adamw_out_typed_handle(); |
| 15764 | return op.call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf, out); |
| 15765 | } |
| 15766 | |
| 15767 | // aten::_fused_adamw.out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> () |
| 15768 | void _fused_adamw_out::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional<at::Tensor> & grad_scale, const c10::optional<at::Tensor> & found_inf, at::TensorList out) { |
| 15769 | |
| 15770 | static auto op = create__fused_adamw_out_typed_handle(); |
| 15771 | return op.redispatch(dispatchKeySet, self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf, out); |
| 15772 | } |
| 15773 | |
| 15774 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fused_adamw, name, "aten::_fused_adamw") |
| 15775 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fused_adamw, overload_name, "") |
| 15776 | STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(_fused_adamw, schema_str, "_fused_adamw(Tensor[] self, Tensor[] grads, Tensor[] exp_avgs, Tensor[] exp_avg_sqs, Tensor[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] exp_avgs_out, Tensor[] exp_avg_sqs_out, Tensor[] max_exp_avg_sqs_out)") |
| 15777 | |
| 15778 | // aten::_fused_adamw(Tensor[] self, Tensor[] grads, Tensor[] exp_avgs, Tensor[] exp_avg_sqs, Tensor[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] exp_avgs_out, Tensor[] exp_avg_sqs_out, Tensor[] max_exp_avg_sqs_out) |
| 15779 | static C10_NOINLINE c10::TypedOperatorHandle<_fused_adamw::schema> create__fused_adamw_typed_handle() { |
| 15780 | return c10::Dispatcher::singleton() |
| 15781 | .findSchemaOrThrow(_fused_adamw::name, _fused_adamw::overload_name) |
| 15782 | .typed<_fused_adamw::schema>(); |
| 15783 | } |
| 15784 | |
| 15785 | // aten::_fused_adamw(Tensor[] self, Tensor[] grads, Tensor[] exp_avgs, Tensor[] exp_avg_sqs, Tensor[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] exp_avgs_out, Tensor[] exp_avg_sqs_out, Tensor[] max_exp_avg_sqs_out) |
| 15786 | ::std::tuple<::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>> _fused_adamw::call(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional<at::Tensor> & grad_scale, const c10::optional<at::Tensor> & found_inf) { |
| 15787 | |
| 15788 | static auto op = create__fused_adamw_typed_handle(); |
| 15789 | return op.call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf); |
| 15790 | } |
| 15791 | |
| 15792 | // aten::_fused_adamw(Tensor[] self, Tensor[] grads, Tensor[] exp_avgs, Tensor[] exp_avg_sqs, Tensor[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] exp_avgs_out, Tensor[] exp_avg_sqs_out, Tensor[] max_exp_avg_sqs_out) |
| 15793 | ::std::tuple<::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>> _fused_adamw::redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional<at::Tensor> & grad_scale, const c10::optional<at::Tensor> & found_inf) { |
| 15794 | |
| 15795 | static auto op = create__fused_adamw_typed_handle(); |
| 15796 | return op.redispatch(dispatchKeySet, self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf); |
| 15797 | } |
| 15798 | |
| 15799 | }} // namespace at::_ops |
| 15800 |
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