| 1 | // required for old g++ to compile PRId64 macros, see |
|---|---|
| 2 | // https://github.com/pytorch/pytorch/issues/3571 |
| 3 | // for context |
| 4 | #ifndef __STDC_FORMAT_MACROS |
| 5 | #define __STDC_FORMAT_MACROS |
| 6 | #endif |
| 7 | |
| 8 | // an external backend might generate file within its code tree |
| 9 | // and check all the source files within the tree with clang-format. |
| 10 | // so, disable it since the backend might have a different config. |
| 11 | // clang-format off |
| 12 | |
| 13 | // NOTE: This condition is true for all PyTorch internal libraries, it |
| 14 | // just excludes external projects such as torch_xla which |
| 15 | // re-use some of the PyTorch codegen machinery. |
| 16 | #if defined(CAFFE2_BUILD_MAIN_LIB) || \ |
| 17 | defined(TORCH_CUDA_BUILD_MAIN_LIB) || \ |
| 18 | defined(TORCH_HIP_BUILD_MAIN_LIB) || \ |
| 19 | defined(TORCH_CUDA_CU_BUILD_MAIN_LIB) || \ |
| 20 | defined(TORCH_CUDA_CPP_BUILD_MAIN_LIB) |
| 21 | #define TORCH_ASSERT_ONLY_METHOD_OPERATORS |
| 22 | #endif |
| 23 | |
| 24 | // @generated by torchgen/gen.py from RegisterDispatchKey.cpp |
| 25 | |
| 26 | #include <c10/core/TensorImpl.h> |
| 27 | #include <c10/core/Allocator.h> |
| 28 | #include <ATen/DeviceGuard.h> |
| 29 | #include <ATen/NamedTensorUtils.h> |
| 30 | #include <ATen/Utils.h> |
| 31 | #include <ATen/WrapDimUtils.h> |
| 32 | #include <ATen/Dispatch.h> |
| 33 | #include <c10/util/ExclusivelyOwned.h> |
| 34 | #include <c10/util/Half.h> |
| 35 | #include <c10/core/UndefinedTensorImpl.h> |
| 36 | #include <c10/util/Optional.h> |
| 37 | #include <ATen/Tensor.h> |
| 38 | #include <ATen/native/Resize.h> |
| 39 | |
| 40 | #include <cstddef> |
| 41 | #include <functional> |
| 42 | #include <memory> |
| 43 | #include <utility> |
| 44 | |
| 45 | #include <ATen/Config.h> |
| 46 | #include <ATen/core/op_registration/adaption.h> |
| 47 | #include <torch/library.h> |
| 48 | |
| 49 | |
| 50 | #include <ATen/ops/as_strided_native.h> |
| 51 | #include <ATen/ops/empty.h> |
| 52 | #include <ATen/ops/empty_strided.h> |
| 53 | #include <ATen/ops/_copy_from_and_resize.h> |
| 54 | #include <ATen/ops/_copy_from.h> |
| 55 | #include <ATen/ops/_addmm_activation.h> |
| 56 | #include <ATen/ops/_addmm_activation_compositeexplicitautogradnonfunctional_dispatch.h> |
| 57 | #include <ATen/ops/_addmm_activation_native.h> |
| 58 | #include <ATen/ops/_conj_copy.h> |
| 59 | #include <ATen/ops/_conj_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 60 | #include <ATen/ops/_conj_copy_native.h> |
| 61 | #include <ATen/ops/_convert_indices_from_coo_to_csr.h> |
| 62 | #include <ATen/ops/_convert_indices_from_coo_to_csr_compositeexplicitautogradnonfunctional_dispatch.h> |
| 63 | #include <ATen/ops/_convert_indices_from_coo_to_csr_native.h> |
| 64 | #include <ATen/ops/_convert_indices_from_csr_to_coo.h> |
| 65 | #include <ATen/ops/_convert_indices_from_csr_to_coo_compositeexplicitautogradnonfunctional_dispatch.h> |
| 66 | #include <ATen/ops/_convert_indices_from_csr_to_coo_native.h> |
| 67 | #include <ATen/ops/_fw_primal_copy.h> |
| 68 | #include <ATen/ops/_fw_primal_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 69 | #include <ATen/ops/_fw_primal_copy_native.h> |
| 70 | #include <ATen/ops/_indices_copy.h> |
| 71 | #include <ATen/ops/_indices_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 72 | #include <ATen/ops/_indices_copy_native.h> |
| 73 | #include <ATen/ops/_linalg_det.h> |
| 74 | #include <ATen/ops/_linalg_det_compositeexplicitautogradnonfunctional_dispatch.h> |
| 75 | #include <ATen/ops/_linalg_det_native.h> |
| 76 | #include <ATen/ops/_linalg_eigh.h> |
| 77 | #include <ATen/ops/_linalg_eigh_compositeexplicitautogradnonfunctional_dispatch.h> |
| 78 | #include <ATen/ops/_linalg_eigh_native.h> |
| 79 | #include <ATen/ops/_linalg_slogdet.h> |
| 80 | #include <ATen/ops/_linalg_slogdet_compositeexplicitautogradnonfunctional_dispatch.h> |
| 81 | #include <ATen/ops/_linalg_slogdet_native.h> |
| 82 | #include <ATen/ops/_linalg_solve_ex.h> |
| 83 | #include <ATen/ops/_linalg_solve_ex_compositeexplicitautogradnonfunctional_dispatch.h> |
| 84 | #include <ATen/ops/_linalg_solve_ex_native.h> |
| 85 | #include <ATen/ops/_linalg_svd.h> |
| 86 | #include <ATen/ops/_linalg_svd_compositeexplicitautogradnonfunctional_dispatch.h> |
| 87 | #include <ATen/ops/_linalg_svd_native.h> |
| 88 | #include <ATen/ops/_log_softmax.h> |
| 89 | #include <ATen/ops/_log_softmax_backward_data.h> |
| 90 | #include <ATen/ops/_log_softmax_backward_data_compositeexplicitautogradnonfunctional_dispatch.h> |
| 91 | #include <ATen/ops/_log_softmax_backward_data_native.h> |
| 92 | #include <ATen/ops/_log_softmax_compositeexplicitautogradnonfunctional_dispatch.h> |
| 93 | #include <ATen/ops/_log_softmax_native.h> |
| 94 | #include <ATen/ops/_make_dual_copy.h> |
| 95 | #include <ATen/ops/_make_dual_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 96 | #include <ATen/ops/_make_dual_copy_native.h> |
| 97 | #include <ATen/ops/_neg_view_copy.h> |
| 98 | #include <ATen/ops/_neg_view_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 99 | #include <ATen/ops/_neg_view_copy_native.h> |
| 100 | #include <ATen/ops/_nested_view_from_buffer_copy.h> |
| 101 | #include <ATen/ops/_nested_view_from_buffer_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 102 | #include <ATen/ops/_nested_view_from_buffer_copy_native.h> |
| 103 | #include <ATen/ops/_reshape_alias_copy.h> |
| 104 | #include <ATen/ops/_reshape_alias_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 105 | #include <ATen/ops/_reshape_alias_copy_native.h> |
| 106 | #include <ATen/ops/_softmax.h> |
| 107 | #include <ATen/ops/_softmax_backward_data.h> |
| 108 | #include <ATen/ops/_softmax_backward_data_compositeexplicitautogradnonfunctional_dispatch.h> |
| 109 | #include <ATen/ops/_softmax_backward_data_native.h> |
| 110 | #include <ATen/ops/_softmax_compositeexplicitautogradnonfunctional_dispatch.h> |
| 111 | #include <ATen/ops/_softmax_native.h> |
| 112 | #include <ATen/ops/_sparse_broadcast_to_copy.h> |
| 113 | #include <ATen/ops/_sparse_broadcast_to_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 114 | #include <ATen/ops/_sparse_broadcast_to_copy_native.h> |
| 115 | #include <ATen/ops/_test_autograd_multiple_dispatch_view_copy.h> |
| 116 | #include <ATen/ops/_test_autograd_multiple_dispatch_view_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 117 | #include <ATen/ops/_test_autograd_multiple_dispatch_view_copy_native.h> |
| 118 | #include <ATen/ops/_trilinear.h> |
| 119 | #include <ATen/ops/_trilinear_compositeexplicitautogradnonfunctional_dispatch.h> |
| 120 | #include <ATen/ops/_trilinear_native.h> |
| 121 | #include <ATen/ops/_upsample_bicubic2d_aa.h> |
| 122 | #include <ATen/ops/_upsample_bicubic2d_aa_backward.h> |
| 123 | #include <ATen/ops/_upsample_bicubic2d_aa_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 124 | #include <ATen/ops/_upsample_bicubic2d_aa_backward_native.h> |
| 125 | #include <ATen/ops/_upsample_bicubic2d_aa_compositeexplicitautogradnonfunctional_dispatch.h> |
| 126 | #include <ATen/ops/_upsample_bicubic2d_aa_native.h> |
| 127 | #include <ATen/ops/_upsample_bilinear2d_aa.h> |
| 128 | #include <ATen/ops/_upsample_bilinear2d_aa_backward.h> |
| 129 | #include <ATen/ops/_upsample_bilinear2d_aa_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 130 | #include <ATen/ops/_upsample_bilinear2d_aa_backward_native.h> |
| 131 | #include <ATen/ops/_upsample_bilinear2d_aa_compositeexplicitautogradnonfunctional_dispatch.h> |
| 132 | #include <ATen/ops/_upsample_bilinear2d_aa_native.h> |
| 133 | #include <ATen/ops/_upsample_nearest_exact1d.h> |
| 134 | #include <ATen/ops/_upsample_nearest_exact1d_backward.h> |
| 135 | #include <ATen/ops/_upsample_nearest_exact1d_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 136 | #include <ATen/ops/_upsample_nearest_exact1d_backward_native.h> |
| 137 | #include <ATen/ops/_upsample_nearest_exact1d_compositeexplicitautogradnonfunctional_dispatch.h> |
| 138 | #include <ATen/ops/_upsample_nearest_exact1d_native.h> |
| 139 | #include <ATen/ops/_upsample_nearest_exact2d.h> |
| 140 | #include <ATen/ops/_upsample_nearest_exact2d_backward.h> |
| 141 | #include <ATen/ops/_upsample_nearest_exact2d_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 142 | #include <ATen/ops/_upsample_nearest_exact2d_backward_native.h> |
| 143 | #include <ATen/ops/_upsample_nearest_exact2d_compositeexplicitautogradnonfunctional_dispatch.h> |
| 144 | #include <ATen/ops/_upsample_nearest_exact2d_native.h> |
| 145 | #include <ATen/ops/_upsample_nearest_exact3d.h> |
| 146 | #include <ATen/ops/_upsample_nearest_exact3d_backward.h> |
| 147 | #include <ATen/ops/_upsample_nearest_exact3d_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 148 | #include <ATen/ops/_upsample_nearest_exact3d_backward_native.h> |
| 149 | #include <ATen/ops/_upsample_nearest_exact3d_compositeexplicitautogradnonfunctional_dispatch.h> |
| 150 | #include <ATen/ops/_upsample_nearest_exact3d_native.h> |
| 151 | #include <ATen/ops/_values_copy.h> |
| 152 | #include <ATen/ops/_values_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 153 | #include <ATen/ops/_values_copy_native.h> |
| 154 | #include <ATen/ops/acos.h> |
| 155 | #include <ATen/ops/acos_compositeexplicitautogradnonfunctional_dispatch.h> |
| 156 | #include <ATen/ops/acos_native.h> |
| 157 | #include <ATen/ops/acosh.h> |
| 158 | #include <ATen/ops/acosh_compositeexplicitautogradnonfunctional_dispatch.h> |
| 159 | #include <ATen/ops/acosh_native.h> |
| 160 | #include <ATen/ops/adaptive_max_pool2d.h> |
| 161 | #include <ATen/ops/adaptive_max_pool2d_backward.h> |
| 162 | #include <ATen/ops/adaptive_max_pool2d_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 163 | #include <ATen/ops/adaptive_max_pool2d_backward_native.h> |
| 164 | #include <ATen/ops/adaptive_max_pool2d_compositeexplicitautogradnonfunctional_dispatch.h> |
| 165 | #include <ATen/ops/adaptive_max_pool2d_native.h> |
| 166 | #include <ATen/ops/adaptive_max_pool3d.h> |
| 167 | #include <ATen/ops/adaptive_max_pool3d_backward.h> |
| 168 | #include <ATen/ops/adaptive_max_pool3d_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 169 | #include <ATen/ops/adaptive_max_pool3d_backward_native.h> |
| 170 | #include <ATen/ops/adaptive_max_pool3d_compositeexplicitautogradnonfunctional_dispatch.h> |
| 171 | #include <ATen/ops/adaptive_max_pool3d_native.h> |
| 172 | #include <ATen/ops/add.h> |
| 173 | #include <ATen/ops/add_compositeexplicitautogradnonfunctional_dispatch.h> |
| 174 | #include <ATen/ops/add_native.h> |
| 175 | #include <ATen/ops/addcdiv.h> |
| 176 | #include <ATen/ops/addcdiv_compositeexplicitautogradnonfunctional_dispatch.h> |
| 177 | #include <ATen/ops/addcdiv_native.h> |
| 178 | #include <ATen/ops/addcmul.h> |
| 179 | #include <ATen/ops/addcmul_compositeexplicitautogradnonfunctional_dispatch.h> |
| 180 | #include <ATen/ops/addcmul_native.h> |
| 181 | #include <ATen/ops/addmm.h> |
| 182 | #include <ATen/ops/addmm_compositeexplicitautogradnonfunctional_dispatch.h> |
| 183 | #include <ATen/ops/addmm_native.h> |
| 184 | #include <ATen/ops/addmv.h> |
| 185 | #include <ATen/ops/addmv_compositeexplicitautogradnonfunctional_dispatch.h> |
| 186 | #include <ATen/ops/addmv_native.h> |
| 187 | #include <ATen/ops/alias_copy.h> |
| 188 | #include <ATen/ops/alias_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 189 | #include <ATen/ops/alias_copy_native.h> |
| 190 | #include <ATen/ops/all.h> |
| 191 | #include <ATen/ops/all_compositeexplicitautogradnonfunctional_dispatch.h> |
| 192 | #include <ATen/ops/all_native.h> |
| 193 | #include <ATen/ops/amax.h> |
| 194 | #include <ATen/ops/amax_compositeexplicitautogradnonfunctional_dispatch.h> |
| 195 | #include <ATen/ops/amax_native.h> |
| 196 | #include <ATen/ops/amin.h> |
| 197 | #include <ATen/ops/amin_compositeexplicitautogradnonfunctional_dispatch.h> |
| 198 | #include <ATen/ops/amin_native.h> |
| 199 | #include <ATen/ops/aminmax.h> |
| 200 | #include <ATen/ops/aminmax_compositeexplicitautogradnonfunctional_dispatch.h> |
| 201 | #include <ATen/ops/aminmax_native.h> |
| 202 | #include <ATen/ops/any.h> |
| 203 | #include <ATen/ops/any_compositeexplicitautogradnonfunctional_dispatch.h> |
| 204 | #include <ATen/ops/any_native.h> |
| 205 | #include <ATen/ops/argmax.h> |
| 206 | #include <ATen/ops/argmax_compositeexplicitautogradnonfunctional_dispatch.h> |
| 207 | #include <ATen/ops/argmax_native.h> |
| 208 | #include <ATen/ops/argmin.h> |
| 209 | #include <ATen/ops/argmin_compositeexplicitautogradnonfunctional_dispatch.h> |
| 210 | #include <ATen/ops/argmin_native.h> |
| 211 | #include <ATen/ops/as_strided.h> |
| 212 | #include <ATen/ops/as_strided_compositeexplicitautogradnonfunctional_dispatch.h> |
| 213 | #include <ATen/ops/as_strided_copy.h> |
| 214 | #include <ATen/ops/as_strided_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 215 | #include <ATen/ops/as_strided_copy_native.h> |
| 216 | #include <ATen/ops/as_strided_native.h> |
| 217 | #include <ATen/ops/asin.h> |
| 218 | #include <ATen/ops/asin_compositeexplicitautogradnonfunctional_dispatch.h> |
| 219 | #include <ATen/ops/asin_native.h> |
| 220 | #include <ATen/ops/asinh.h> |
| 221 | #include <ATen/ops/asinh_compositeexplicitautogradnonfunctional_dispatch.h> |
| 222 | #include <ATen/ops/asinh_native.h> |
| 223 | #include <ATen/ops/atan.h> |
| 224 | #include <ATen/ops/atan2.h> |
| 225 | #include <ATen/ops/atan2_compositeexplicitautogradnonfunctional_dispatch.h> |
| 226 | #include <ATen/ops/atan2_native.h> |
| 227 | #include <ATen/ops/atan_compositeexplicitautogradnonfunctional_dispatch.h> |
| 228 | #include <ATen/ops/atan_native.h> |
| 229 | #include <ATen/ops/atanh.h> |
| 230 | #include <ATen/ops/atanh_compositeexplicitautogradnonfunctional_dispatch.h> |
| 231 | #include <ATen/ops/atanh_native.h> |
| 232 | #include <ATen/ops/avg_pool2d.h> |
| 233 | #include <ATen/ops/avg_pool2d_backward.h> |
| 234 | #include <ATen/ops/avg_pool2d_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 235 | #include <ATen/ops/avg_pool2d_backward_native.h> |
| 236 | #include <ATen/ops/avg_pool2d_compositeexplicitautogradnonfunctional_dispatch.h> |
| 237 | #include <ATen/ops/avg_pool2d_native.h> |
| 238 | #include <ATen/ops/avg_pool3d.h> |
| 239 | #include <ATen/ops/avg_pool3d_backward.h> |
| 240 | #include <ATen/ops/avg_pool3d_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 241 | #include <ATen/ops/avg_pool3d_backward_native.h> |
| 242 | #include <ATen/ops/avg_pool3d_compositeexplicitautogradnonfunctional_dispatch.h> |
| 243 | #include <ATen/ops/avg_pool3d_native.h> |
| 244 | #include <ATen/ops/baddbmm.h> |
| 245 | #include <ATen/ops/baddbmm_compositeexplicitautogradnonfunctional_dispatch.h> |
| 246 | #include <ATen/ops/baddbmm_native.h> |
| 247 | #include <ATen/ops/bernoulli.h> |
| 248 | #include <ATen/ops/bernoulli_compositeexplicitautogradnonfunctional_dispatch.h> |
| 249 | #include <ATen/ops/bernoulli_native.h> |
| 250 | #include <ATen/ops/bitwise_and.h> |
| 251 | #include <ATen/ops/bitwise_and_compositeexplicitautogradnonfunctional_dispatch.h> |
| 252 | #include <ATen/ops/bitwise_and_native.h> |
| 253 | #include <ATen/ops/bitwise_left_shift.h> |
| 254 | #include <ATen/ops/bitwise_left_shift_compositeexplicitautogradnonfunctional_dispatch.h> |
| 255 | #include <ATen/ops/bitwise_left_shift_native.h> |
| 256 | #include <ATen/ops/bitwise_not.h> |
| 257 | #include <ATen/ops/bitwise_not_compositeexplicitautogradnonfunctional_dispatch.h> |
| 258 | #include <ATen/ops/bitwise_not_native.h> |
| 259 | #include <ATen/ops/bitwise_or.h> |
| 260 | #include <ATen/ops/bitwise_or_compositeexplicitautogradnonfunctional_dispatch.h> |
| 261 | #include <ATen/ops/bitwise_or_native.h> |
| 262 | #include <ATen/ops/bitwise_right_shift.h> |
| 263 | #include <ATen/ops/bitwise_right_shift_compositeexplicitautogradnonfunctional_dispatch.h> |
| 264 | #include <ATen/ops/bitwise_right_shift_native.h> |
| 265 | #include <ATen/ops/bitwise_xor.h> |
| 266 | #include <ATen/ops/bitwise_xor_compositeexplicitautogradnonfunctional_dispatch.h> |
| 267 | #include <ATen/ops/bitwise_xor_native.h> |
| 268 | #include <ATen/ops/bmm.h> |
| 269 | #include <ATen/ops/bmm_compositeexplicitautogradnonfunctional_dispatch.h> |
| 270 | #include <ATen/ops/bmm_native.h> |
| 271 | #include <ATen/ops/cat.h> |
| 272 | #include <ATen/ops/cat_compositeexplicitautogradnonfunctional_dispatch.h> |
| 273 | #include <ATen/ops/cat_native.h> |
| 274 | #include <ATen/ops/ccol_indices_copy.h> |
| 275 | #include <ATen/ops/ccol_indices_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 276 | #include <ATen/ops/ccol_indices_copy_native.h> |
| 277 | #include <ATen/ops/ceil.h> |
| 278 | #include <ATen/ops/ceil_compositeexplicitautogradnonfunctional_dispatch.h> |
| 279 | #include <ATen/ops/ceil_native.h> |
| 280 | #include <ATen/ops/clamp.h> |
| 281 | #include <ATen/ops/clamp_compositeexplicitautogradnonfunctional_dispatch.h> |
| 282 | #include <ATen/ops/clamp_max.h> |
| 283 | #include <ATen/ops/clamp_max_compositeexplicitautogradnonfunctional_dispatch.h> |
| 284 | #include <ATen/ops/clamp_max_native.h> |
| 285 | #include <ATen/ops/clamp_min.h> |
| 286 | #include <ATen/ops/clamp_min_compositeexplicitautogradnonfunctional_dispatch.h> |
| 287 | #include <ATen/ops/clamp_min_native.h> |
| 288 | #include <ATen/ops/clamp_native.h> |
| 289 | #include <ATen/ops/col_indices_copy.h> |
| 290 | #include <ATen/ops/col_indices_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 291 | #include <ATen/ops/col_indices_copy_native.h> |
| 292 | #include <ATen/ops/copy.h> |
| 293 | #include <ATen/ops/copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 294 | #include <ATen/ops/copy_native.h> |
| 295 | #include <ATen/ops/copysign.h> |
| 296 | #include <ATen/ops/copysign_compositeexplicitautogradnonfunctional_dispatch.h> |
| 297 | #include <ATen/ops/copysign_native.h> |
| 298 | #include <ATen/ops/cos.h> |
| 299 | #include <ATen/ops/cos_compositeexplicitautogradnonfunctional_dispatch.h> |
| 300 | #include <ATen/ops/cos_native.h> |
| 301 | #include <ATen/ops/cosh.h> |
| 302 | #include <ATen/ops/cosh_compositeexplicitautogradnonfunctional_dispatch.h> |
| 303 | #include <ATen/ops/cosh_native.h> |
| 304 | #include <ATen/ops/crow_indices_copy.h> |
| 305 | #include <ATen/ops/crow_indices_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 306 | #include <ATen/ops/crow_indices_copy_native.h> |
| 307 | #include <ATen/ops/cumprod.h> |
| 308 | #include <ATen/ops/cumprod_compositeexplicitautogradnonfunctional_dispatch.h> |
| 309 | #include <ATen/ops/cumprod_native.h> |
| 310 | #include <ATen/ops/cumsum.h> |
| 311 | #include <ATen/ops/cumsum_compositeexplicitautogradnonfunctional_dispatch.h> |
| 312 | #include <ATen/ops/cumsum_native.h> |
| 313 | #include <ATen/ops/detach_copy.h> |
| 314 | #include <ATen/ops/detach_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 315 | #include <ATen/ops/detach_copy_native.h> |
| 316 | #include <ATen/ops/diag_embed.h> |
| 317 | #include <ATen/ops/diag_embed_compositeexplicitautogradnonfunctional_dispatch.h> |
| 318 | #include <ATen/ops/diag_embed_native.h> |
| 319 | #include <ATen/ops/diagonal_copy.h> |
| 320 | #include <ATen/ops/diagonal_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 321 | #include <ATen/ops/diagonal_copy_native.h> |
| 322 | #include <ATen/ops/digamma.h> |
| 323 | #include <ATen/ops/digamma_compositeexplicitautogradnonfunctional_dispatch.h> |
| 324 | #include <ATen/ops/digamma_native.h> |
| 325 | #include <ATen/ops/div.h> |
| 326 | #include <ATen/ops/div_compositeexplicitautogradnonfunctional_dispatch.h> |
| 327 | #include <ATen/ops/div_native.h> |
| 328 | #include <ATen/ops/elu.h> |
| 329 | #include <ATen/ops/elu_backward.h> |
| 330 | #include <ATen/ops/elu_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 331 | #include <ATen/ops/elu_backward_native.h> |
| 332 | #include <ATen/ops/elu_compositeexplicitautogradnonfunctional_dispatch.h> |
| 333 | #include <ATen/ops/elu_native.h> |
| 334 | #include <ATen/ops/eq.h> |
| 335 | #include <ATen/ops/eq_compositeexplicitautogradnonfunctional_dispatch.h> |
| 336 | #include <ATen/ops/eq_native.h> |
| 337 | #include <ATen/ops/erf.h> |
| 338 | #include <ATen/ops/erf_compositeexplicitautogradnonfunctional_dispatch.h> |
| 339 | #include <ATen/ops/erf_native.h> |
| 340 | #include <ATen/ops/erfc.h> |
| 341 | #include <ATen/ops/erfc_compositeexplicitautogradnonfunctional_dispatch.h> |
| 342 | #include <ATen/ops/erfc_native.h> |
| 343 | #include <ATen/ops/erfinv.h> |
| 344 | #include <ATen/ops/erfinv_compositeexplicitautogradnonfunctional_dispatch.h> |
| 345 | #include <ATen/ops/erfinv_native.h> |
| 346 | #include <ATen/ops/exp.h> |
| 347 | #include <ATen/ops/exp2.h> |
| 348 | #include <ATen/ops/exp2_compositeexplicitautogradnonfunctional_dispatch.h> |
| 349 | #include <ATen/ops/exp2_native.h> |
| 350 | #include <ATen/ops/exp_compositeexplicitautogradnonfunctional_dispatch.h> |
| 351 | #include <ATen/ops/exp_native.h> |
| 352 | #include <ATen/ops/expand_copy.h> |
| 353 | #include <ATen/ops/expand_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 354 | #include <ATen/ops/expand_copy_native.h> |
| 355 | #include <ATen/ops/expm1.h> |
| 356 | #include <ATen/ops/expm1_compositeexplicitautogradnonfunctional_dispatch.h> |
| 357 | #include <ATen/ops/expm1_native.h> |
| 358 | #include <ATen/ops/floor.h> |
| 359 | #include <ATen/ops/floor_compositeexplicitautogradnonfunctional_dispatch.h> |
| 360 | #include <ATen/ops/floor_native.h> |
| 361 | #include <ATen/ops/fmax.h> |
| 362 | #include <ATen/ops/fmax_compositeexplicitautogradnonfunctional_dispatch.h> |
| 363 | #include <ATen/ops/fmax_native.h> |
| 364 | #include <ATen/ops/fmin.h> |
| 365 | #include <ATen/ops/fmin_compositeexplicitautogradnonfunctional_dispatch.h> |
| 366 | #include <ATen/ops/fmin_native.h> |
| 367 | #include <ATen/ops/fmod.h> |
| 368 | #include <ATen/ops/fmod_compositeexplicitautogradnonfunctional_dispatch.h> |
| 369 | #include <ATen/ops/fmod_native.h> |
| 370 | #include <ATen/ops/frac.h> |
| 371 | #include <ATen/ops/frac_compositeexplicitautogradnonfunctional_dispatch.h> |
| 372 | #include <ATen/ops/frac_native.h> |
| 373 | #include <ATen/ops/fractional_max_pool2d.h> |
| 374 | #include <ATen/ops/fractional_max_pool2d_backward.h> |
| 375 | #include <ATen/ops/fractional_max_pool2d_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 376 | #include <ATen/ops/fractional_max_pool2d_backward_native.h> |
| 377 | #include <ATen/ops/fractional_max_pool2d_compositeexplicitautogradnonfunctional_dispatch.h> |
| 378 | #include <ATen/ops/fractional_max_pool2d_native.h> |
| 379 | #include <ATen/ops/fractional_max_pool3d.h> |
| 380 | #include <ATen/ops/fractional_max_pool3d_compositeexplicitautogradnonfunctional_dispatch.h> |
| 381 | #include <ATen/ops/fractional_max_pool3d_native.h> |
| 382 | #include <ATen/ops/gather.h> |
| 383 | #include <ATen/ops/gather_compositeexplicitautogradnonfunctional_dispatch.h> |
| 384 | #include <ATen/ops/gather_native.h> |
| 385 | #include <ATen/ops/gcd.h> |
| 386 | #include <ATen/ops/gcd_compositeexplicitautogradnonfunctional_dispatch.h> |
| 387 | #include <ATen/ops/gcd_native.h> |
| 388 | #include <ATen/ops/ge.h> |
| 389 | #include <ATen/ops/ge_compositeexplicitautogradnonfunctional_dispatch.h> |
| 390 | #include <ATen/ops/ge_native.h> |
| 391 | #include <ATen/ops/gelu.h> |
| 392 | #include <ATen/ops/gelu_backward.h> |
| 393 | #include <ATen/ops/gelu_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 394 | #include <ATen/ops/gelu_backward_native.h> |
| 395 | #include <ATen/ops/gelu_compositeexplicitautogradnonfunctional_dispatch.h> |
| 396 | #include <ATen/ops/gelu_native.h> |
| 397 | #include <ATen/ops/glu.h> |
| 398 | #include <ATen/ops/glu_compositeexplicitautogradnonfunctional_dispatch.h> |
| 399 | #include <ATen/ops/glu_native.h> |
| 400 | #include <ATen/ops/gt.h> |
| 401 | #include <ATen/ops/gt_compositeexplicitautogradnonfunctional_dispatch.h> |
| 402 | #include <ATen/ops/gt_native.h> |
| 403 | #include <ATen/ops/hardshrink.h> |
| 404 | #include <ATen/ops/hardshrink_backward.h> |
| 405 | #include <ATen/ops/hardshrink_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 406 | #include <ATen/ops/hardshrink_backward_native.h> |
| 407 | #include <ATen/ops/hardshrink_compositeexplicitautogradnonfunctional_dispatch.h> |
| 408 | #include <ATen/ops/hardshrink_native.h> |
| 409 | #include <ATen/ops/hardsigmoid.h> |
| 410 | #include <ATen/ops/hardsigmoid_backward.h> |
| 411 | #include <ATen/ops/hardsigmoid_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 412 | #include <ATen/ops/hardsigmoid_backward_native.h> |
| 413 | #include <ATen/ops/hardsigmoid_compositeexplicitautogradnonfunctional_dispatch.h> |
| 414 | #include <ATen/ops/hardsigmoid_native.h> |
| 415 | #include <ATen/ops/heaviside.h> |
| 416 | #include <ATen/ops/heaviside_compositeexplicitautogradnonfunctional_dispatch.h> |
| 417 | #include <ATen/ops/heaviside_native.h> |
| 418 | #include <ATen/ops/hypot.h> |
| 419 | #include <ATen/ops/hypot_compositeexplicitautogradnonfunctional_dispatch.h> |
| 420 | #include <ATen/ops/hypot_native.h> |
| 421 | #include <ATen/ops/i0.h> |
| 422 | #include <ATen/ops/i0_compositeexplicitautogradnonfunctional_dispatch.h> |
| 423 | #include <ATen/ops/i0_native.h> |
| 424 | #include <ATen/ops/igamma.h> |
| 425 | #include <ATen/ops/igamma_compositeexplicitautogradnonfunctional_dispatch.h> |
| 426 | #include <ATen/ops/igamma_native.h> |
| 427 | #include <ATen/ops/igammac.h> |
| 428 | #include <ATen/ops/igammac_compositeexplicitautogradnonfunctional_dispatch.h> |
| 429 | #include <ATen/ops/igammac_native.h> |
| 430 | #include <ATen/ops/index.h> |
| 431 | #include <ATen/ops/index_add.h> |
| 432 | #include <ATen/ops/index_add_compositeexplicitautogradnonfunctional_dispatch.h> |
| 433 | #include <ATen/ops/index_add_native.h> |
| 434 | #include <ATen/ops/index_compositeexplicitautogradnonfunctional_dispatch.h> |
| 435 | #include <ATen/ops/index_copy.h> |
| 436 | #include <ATen/ops/index_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 437 | #include <ATen/ops/index_copy_native.h> |
| 438 | #include <ATen/ops/index_native.h> |
| 439 | #include <ATen/ops/index_reduce.h> |
| 440 | #include <ATen/ops/index_reduce_compositeexplicitautogradnonfunctional_dispatch.h> |
| 441 | #include <ATen/ops/index_reduce_native.h> |
| 442 | #include <ATen/ops/indices_copy.h> |
| 443 | #include <ATen/ops/indices_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 444 | #include <ATen/ops/indices_copy_native.h> |
| 445 | #include <ATen/ops/isin.h> |
| 446 | #include <ATen/ops/isin_compositeexplicitautogradnonfunctional_dispatch.h> |
| 447 | #include <ATen/ops/isin_native.h> |
| 448 | #include <ATen/ops/isneginf.h> |
| 449 | #include <ATen/ops/isneginf_compositeexplicitautogradnonfunctional_dispatch.h> |
| 450 | #include <ATen/ops/isneginf_native.h> |
| 451 | #include <ATen/ops/isposinf.h> |
| 452 | #include <ATen/ops/isposinf_compositeexplicitautogradnonfunctional_dispatch.h> |
| 453 | #include <ATen/ops/isposinf_native.h> |
| 454 | #include <ATen/ops/lcm.h> |
| 455 | #include <ATen/ops/lcm_compositeexplicitautogradnonfunctional_dispatch.h> |
| 456 | #include <ATen/ops/lcm_native.h> |
| 457 | #include <ATen/ops/le.h> |
| 458 | #include <ATen/ops/le_compositeexplicitautogradnonfunctional_dispatch.h> |
| 459 | #include <ATen/ops/le_native.h> |
| 460 | #include <ATen/ops/leaky_relu.h> |
| 461 | #include <ATen/ops/leaky_relu_backward.h> |
| 462 | #include <ATen/ops/leaky_relu_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 463 | #include <ATen/ops/leaky_relu_backward_native.h> |
| 464 | #include <ATen/ops/leaky_relu_compositeexplicitautogradnonfunctional_dispatch.h> |
| 465 | #include <ATen/ops/leaky_relu_native.h> |
| 466 | #include <ATen/ops/lerp.h> |
| 467 | #include <ATen/ops/lerp_compositeexplicitautogradnonfunctional_dispatch.h> |
| 468 | #include <ATen/ops/lerp_native.h> |
| 469 | #include <ATen/ops/lgamma.h> |
| 470 | #include <ATen/ops/lgamma_compositeexplicitautogradnonfunctional_dispatch.h> |
| 471 | #include <ATen/ops/lgamma_native.h> |
| 472 | #include <ATen/ops/lift_fresh_copy.h> |
| 473 | #include <ATen/ops/lift_fresh_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 474 | #include <ATen/ops/lift_fresh_copy_native.h> |
| 475 | #include <ATen/ops/linalg_cholesky_ex.h> |
| 476 | #include <ATen/ops/linalg_cholesky_ex_compositeexplicitautogradnonfunctional_dispatch.h> |
| 477 | #include <ATen/ops/linalg_cholesky_ex_native.h> |
| 478 | #include <ATen/ops/linalg_cross.h> |
| 479 | #include <ATen/ops/linalg_cross_compositeexplicitautogradnonfunctional_dispatch.h> |
| 480 | #include <ATen/ops/linalg_cross_native.h> |
| 481 | #include <ATen/ops/linalg_inv_ex.h> |
| 482 | #include <ATen/ops/linalg_inv_ex_compositeexplicitautogradnonfunctional_dispatch.h> |
| 483 | #include <ATen/ops/linalg_inv_ex_native.h> |
| 484 | #include <ATen/ops/linalg_ldl_factor_ex.h> |
| 485 | #include <ATen/ops/linalg_ldl_factor_ex_compositeexplicitautogradnonfunctional_dispatch.h> |
| 486 | #include <ATen/ops/linalg_ldl_factor_ex_native.h> |
| 487 | #include <ATen/ops/linalg_ldl_solve.h> |
| 488 | #include <ATen/ops/linalg_ldl_solve_compositeexplicitautogradnonfunctional_dispatch.h> |
| 489 | #include <ATen/ops/linalg_ldl_solve_native.h> |
| 490 | #include <ATen/ops/linalg_lu.h> |
| 491 | #include <ATen/ops/linalg_lu_compositeexplicitautogradnonfunctional_dispatch.h> |
| 492 | #include <ATen/ops/linalg_lu_factor_ex.h> |
| 493 | #include <ATen/ops/linalg_lu_factor_ex_compositeexplicitautogradnonfunctional_dispatch.h> |
| 494 | #include <ATen/ops/linalg_lu_factor_ex_native.h> |
| 495 | #include <ATen/ops/linalg_lu_native.h> |
| 496 | #include <ATen/ops/linalg_lu_solve.h> |
| 497 | #include <ATen/ops/linalg_lu_solve_compositeexplicitautogradnonfunctional_dispatch.h> |
| 498 | #include <ATen/ops/linalg_lu_solve_native.h> |
| 499 | #include <ATen/ops/linalg_pinv.h> |
| 500 | #include <ATen/ops/linalg_pinv_compositeexplicitautogradnonfunctional_dispatch.h> |
| 501 | #include <ATen/ops/linalg_pinv_native.h> |
| 502 | #include <ATen/ops/linalg_qr.h> |
| 503 | #include <ATen/ops/linalg_qr_compositeexplicitautogradnonfunctional_dispatch.h> |
| 504 | #include <ATen/ops/linalg_qr_native.h> |
| 505 | #include <ATen/ops/linalg_vector_norm.h> |
| 506 | #include <ATen/ops/linalg_vector_norm_compositeexplicitautogradnonfunctional_dispatch.h> |
| 507 | #include <ATen/ops/linalg_vector_norm_native.h> |
| 508 | #include <ATen/ops/log.h> |
| 509 | #include <ATen/ops/log10.h> |
| 510 | #include <ATen/ops/log10_compositeexplicitautogradnonfunctional_dispatch.h> |
| 511 | #include <ATen/ops/log10_native.h> |
| 512 | #include <ATen/ops/log1p.h> |
| 513 | #include <ATen/ops/log1p_compositeexplicitautogradnonfunctional_dispatch.h> |
| 514 | #include <ATen/ops/log1p_native.h> |
| 515 | #include <ATen/ops/log2.h> |
| 516 | #include <ATen/ops/log2_compositeexplicitautogradnonfunctional_dispatch.h> |
| 517 | #include <ATen/ops/log2_native.h> |
| 518 | #include <ATen/ops/log_compositeexplicitautogradnonfunctional_dispatch.h> |
| 519 | #include <ATen/ops/log_native.h> |
| 520 | #include <ATen/ops/logaddexp.h> |
| 521 | #include <ATen/ops/logaddexp2.h> |
| 522 | #include <ATen/ops/logaddexp2_compositeexplicitautogradnonfunctional_dispatch.h> |
| 523 | #include <ATen/ops/logaddexp2_native.h> |
| 524 | #include <ATen/ops/logaddexp_compositeexplicitautogradnonfunctional_dispatch.h> |
| 525 | #include <ATen/ops/logaddexp_native.h> |
| 526 | #include <ATen/ops/logit_backward.h> |
| 527 | #include <ATen/ops/logit_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 528 | #include <ATen/ops/logit_backward_native.h> |
| 529 | #include <ATen/ops/logsumexp.h> |
| 530 | #include <ATen/ops/logsumexp_compositeexplicitautogradnonfunctional_dispatch.h> |
| 531 | #include <ATen/ops/logsumexp_native.h> |
| 532 | #include <ATen/ops/lt.h> |
| 533 | #include <ATen/ops/lt_compositeexplicitautogradnonfunctional_dispatch.h> |
| 534 | #include <ATen/ops/lt_native.h> |
| 535 | #include <ATen/ops/lu_unpack.h> |
| 536 | #include <ATen/ops/lu_unpack_compositeexplicitautogradnonfunctional_dispatch.h> |
| 537 | #include <ATen/ops/lu_unpack_native.h> |
| 538 | #include <ATen/ops/max.h> |
| 539 | #include <ATen/ops/max_compositeexplicitautogradnonfunctional_dispatch.h> |
| 540 | #include <ATen/ops/max_native.h> |
| 541 | #include <ATen/ops/max_pool2d_with_indices.h> |
| 542 | #include <ATen/ops/max_pool2d_with_indices_backward.h> |
| 543 | #include <ATen/ops/max_pool2d_with_indices_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 544 | #include <ATen/ops/max_pool2d_with_indices_backward_native.h> |
| 545 | #include <ATen/ops/max_pool2d_with_indices_compositeexplicitautogradnonfunctional_dispatch.h> |
| 546 | #include <ATen/ops/max_pool2d_with_indices_native.h> |
| 547 | #include <ATen/ops/maximum.h> |
| 548 | #include <ATen/ops/maximum_compositeexplicitautogradnonfunctional_dispatch.h> |
| 549 | #include <ATen/ops/maximum_native.h> |
| 550 | #include <ATen/ops/mean.h> |
| 551 | #include <ATen/ops/mean_compositeexplicitautogradnonfunctional_dispatch.h> |
| 552 | #include <ATen/ops/mean_native.h> |
| 553 | #include <ATen/ops/min.h> |
| 554 | #include <ATen/ops/min_compositeexplicitautogradnonfunctional_dispatch.h> |
| 555 | #include <ATen/ops/min_native.h> |
| 556 | #include <ATen/ops/minimum.h> |
| 557 | #include <ATen/ops/minimum_compositeexplicitautogradnonfunctional_dispatch.h> |
| 558 | #include <ATen/ops/minimum_native.h> |
| 559 | #include <ATen/ops/mish.h> |
| 560 | #include <ATen/ops/mish_compositeexplicitautogradnonfunctional_dispatch.h> |
| 561 | #include <ATen/ops/mish_native.h> |
| 562 | #include <ATen/ops/mm.h> |
| 563 | #include <ATen/ops/mm_compositeexplicitautogradnonfunctional_dispatch.h> |
| 564 | #include <ATen/ops/mm_native.h> |
| 565 | #include <ATen/ops/mse_loss.h> |
| 566 | #include <ATen/ops/mse_loss_compositeexplicitautogradnonfunctional_dispatch.h> |
| 567 | #include <ATen/ops/mse_loss_native.h> |
| 568 | #include <ATen/ops/mul.h> |
| 569 | #include <ATen/ops/mul_compositeexplicitautogradnonfunctional_dispatch.h> |
| 570 | #include <ATen/ops/mul_native.h> |
| 571 | #include <ATen/ops/narrow_copy.h> |
| 572 | #include <ATen/ops/narrow_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 573 | #include <ATen/ops/narrow_copy_native.h> |
| 574 | #include <ATen/ops/ne.h> |
| 575 | #include <ATen/ops/ne_compositeexplicitautogradnonfunctional_dispatch.h> |
| 576 | #include <ATen/ops/ne_native.h> |
| 577 | #include <ATen/ops/neg.h> |
| 578 | #include <ATen/ops/neg_compositeexplicitautogradnonfunctional_dispatch.h> |
| 579 | #include <ATen/ops/neg_native.h> |
| 580 | #include <ATen/ops/new_empty_strided.h> |
| 581 | #include <ATen/ops/new_empty_strided_compositeexplicitautogradnonfunctional_dispatch.h> |
| 582 | #include <ATen/ops/new_empty_strided_native.h> |
| 583 | #include <ATen/ops/nextafter.h> |
| 584 | #include <ATen/ops/nextafter_compositeexplicitautogradnonfunctional_dispatch.h> |
| 585 | #include <ATen/ops/nextafter_native.h> |
| 586 | #include <ATen/ops/nll_loss_backward.h> |
| 587 | #include <ATen/ops/nll_loss_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 588 | #include <ATen/ops/nll_loss_backward_native.h> |
| 589 | #include <ATen/ops/nll_loss_forward.h> |
| 590 | #include <ATen/ops/nll_loss_forward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 591 | #include <ATen/ops/nll_loss_forward_native.h> |
| 592 | #include <ATen/ops/norm.h> |
| 593 | #include <ATen/ops/norm_compositeexplicitautogradnonfunctional_dispatch.h> |
| 594 | #include <ATen/ops/norm_native.h> |
| 595 | #include <ATen/ops/permute_copy.h> |
| 596 | #include <ATen/ops/permute_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 597 | #include <ATen/ops/permute_copy_native.h> |
| 598 | #include <ATen/ops/pixel_shuffle.h> |
| 599 | #include <ATen/ops/pixel_shuffle_compositeexplicitautogradnonfunctional_dispatch.h> |
| 600 | #include <ATen/ops/pixel_shuffle_native.h> |
| 601 | #include <ATen/ops/pixel_unshuffle.h> |
| 602 | #include <ATen/ops/pixel_unshuffle_compositeexplicitautogradnonfunctional_dispatch.h> |
| 603 | #include <ATen/ops/pixel_unshuffle_native.h> |
| 604 | #include <ATen/ops/polygamma.h> |
| 605 | #include <ATen/ops/polygamma_compositeexplicitautogradnonfunctional_dispatch.h> |
| 606 | #include <ATen/ops/polygamma_native.h> |
| 607 | #include <ATen/ops/pow.h> |
| 608 | #include <ATen/ops/pow_compositeexplicitautogradnonfunctional_dispatch.h> |
| 609 | #include <ATen/ops/pow_native.h> |
| 610 | #include <ATen/ops/prod.h> |
| 611 | #include <ATen/ops/prod_compositeexplicitautogradnonfunctional_dispatch.h> |
| 612 | #include <ATen/ops/prod_native.h> |
| 613 | #include <ATen/ops/reciprocal.h> |
| 614 | #include <ATen/ops/reciprocal_compositeexplicitautogradnonfunctional_dispatch.h> |
| 615 | #include <ATen/ops/reciprocal_native.h> |
| 616 | #include <ATen/ops/reflection_pad1d.h> |
| 617 | #include <ATen/ops/reflection_pad1d_backward.h> |
| 618 | #include <ATen/ops/reflection_pad1d_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 619 | #include <ATen/ops/reflection_pad1d_backward_native.h> |
| 620 | #include <ATen/ops/reflection_pad1d_compositeexplicitautogradnonfunctional_dispatch.h> |
| 621 | #include <ATen/ops/reflection_pad1d_native.h> |
| 622 | #include <ATen/ops/reflection_pad3d.h> |
| 623 | #include <ATen/ops/reflection_pad3d_backward.h> |
| 624 | #include <ATen/ops/reflection_pad3d_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 625 | #include <ATen/ops/reflection_pad3d_backward_native.h> |
| 626 | #include <ATen/ops/reflection_pad3d_compositeexplicitautogradnonfunctional_dispatch.h> |
| 627 | #include <ATen/ops/reflection_pad3d_native.h> |
| 628 | #include <ATen/ops/remainder.h> |
| 629 | #include <ATen/ops/remainder_compositeexplicitautogradnonfunctional_dispatch.h> |
| 630 | #include <ATen/ops/remainder_native.h> |
| 631 | #include <ATen/ops/renorm.h> |
| 632 | #include <ATen/ops/renorm_compositeexplicitautogradnonfunctional_dispatch.h> |
| 633 | #include <ATen/ops/renorm_native.h> |
| 634 | #include <ATen/ops/replication_pad1d.h> |
| 635 | #include <ATen/ops/replication_pad1d_backward.h> |
| 636 | #include <ATen/ops/replication_pad1d_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 637 | #include <ATen/ops/replication_pad1d_backward_native.h> |
| 638 | #include <ATen/ops/replication_pad1d_compositeexplicitautogradnonfunctional_dispatch.h> |
| 639 | #include <ATen/ops/replication_pad1d_native.h> |
| 640 | #include <ATen/ops/replication_pad2d.h> |
| 641 | #include <ATen/ops/replication_pad2d_compositeexplicitautogradnonfunctional_dispatch.h> |
| 642 | #include <ATen/ops/replication_pad2d_native.h> |
| 643 | #include <ATen/ops/replication_pad3d.h> |
| 644 | #include <ATen/ops/replication_pad3d_compositeexplicitautogradnonfunctional_dispatch.h> |
| 645 | #include <ATen/ops/replication_pad3d_native.h> |
| 646 | #include <ATen/ops/round.h> |
| 647 | #include <ATen/ops/round_compositeexplicitautogradnonfunctional_dispatch.h> |
| 648 | #include <ATen/ops/round_native.h> |
| 649 | #include <ATen/ops/row_indices_copy.h> |
| 650 | #include <ATen/ops/row_indices_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 651 | #include <ATen/ops/row_indices_copy_native.h> |
| 652 | #include <ATen/ops/rsqrt.h> |
| 653 | #include <ATen/ops/rsqrt_compositeexplicitautogradnonfunctional_dispatch.h> |
| 654 | #include <ATen/ops/rsqrt_native.h> |
| 655 | #include <ATen/ops/scatter.h> |
| 656 | #include <ATen/ops/scatter_add.h> |
| 657 | #include <ATen/ops/scatter_add_compositeexplicitautogradnonfunctional_dispatch.h> |
| 658 | #include <ATen/ops/scatter_add_native.h> |
| 659 | #include <ATen/ops/scatter_compositeexplicitautogradnonfunctional_dispatch.h> |
| 660 | #include <ATen/ops/scatter_native.h> |
| 661 | #include <ATen/ops/scatter_reduce.h> |
| 662 | #include <ATen/ops/scatter_reduce_compositeexplicitautogradnonfunctional_dispatch.h> |
| 663 | #include <ATen/ops/scatter_reduce_native.h> |
| 664 | #include <ATen/ops/select_backward.h> |
| 665 | #include <ATen/ops/select_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 666 | #include <ATen/ops/select_backward_native.h> |
| 667 | #include <ATen/ops/select_copy.h> |
| 668 | #include <ATen/ops/select_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 669 | #include <ATen/ops/select_copy_native.h> |
| 670 | #include <ATen/ops/sgn.h> |
| 671 | #include <ATen/ops/sgn_compositeexplicitautogradnonfunctional_dispatch.h> |
| 672 | #include <ATen/ops/sgn_native.h> |
| 673 | #include <ATen/ops/sigmoid.h> |
| 674 | #include <ATen/ops/sigmoid_backward.h> |
| 675 | #include <ATen/ops/sigmoid_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 676 | #include <ATen/ops/sigmoid_backward_native.h> |
| 677 | #include <ATen/ops/sigmoid_compositeexplicitautogradnonfunctional_dispatch.h> |
| 678 | #include <ATen/ops/sigmoid_native.h> |
| 679 | #include <ATen/ops/sign.h> |
| 680 | #include <ATen/ops/sign_compositeexplicitautogradnonfunctional_dispatch.h> |
| 681 | #include <ATen/ops/sign_native.h> |
| 682 | #include <ATen/ops/signbit.h> |
| 683 | #include <ATen/ops/signbit_compositeexplicitautogradnonfunctional_dispatch.h> |
| 684 | #include <ATen/ops/signbit_native.h> |
| 685 | #include <ATen/ops/silu.h> |
| 686 | #include <ATen/ops/silu_backward.h> |
| 687 | #include <ATen/ops/silu_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 688 | #include <ATen/ops/silu_backward_native.h> |
| 689 | #include <ATen/ops/silu_compositeexplicitautogradnonfunctional_dispatch.h> |
| 690 | #include <ATen/ops/silu_native.h> |
| 691 | #include <ATen/ops/sin.h> |
| 692 | #include <ATen/ops/sin_compositeexplicitautogradnonfunctional_dispatch.h> |
| 693 | #include <ATen/ops/sin_native.h> |
| 694 | #include <ATen/ops/sinc.h> |
| 695 | #include <ATen/ops/sinc_compositeexplicitautogradnonfunctional_dispatch.h> |
| 696 | #include <ATen/ops/sinc_native.h> |
| 697 | #include <ATen/ops/sinh.h> |
| 698 | #include <ATen/ops/sinh_compositeexplicitautogradnonfunctional_dispatch.h> |
| 699 | #include <ATen/ops/sinh_native.h> |
| 700 | #include <ATen/ops/slice_copy.h> |
| 701 | #include <ATen/ops/slice_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 702 | #include <ATen/ops/slice_copy_native.h> |
| 703 | #include <ATen/ops/slow_conv_transpose2d.h> |
| 704 | #include <ATen/ops/slow_conv_transpose2d_compositeexplicitautogradnonfunctional_dispatch.h> |
| 705 | #include <ATen/ops/slow_conv_transpose2d_native.h> |
| 706 | #include <ATen/ops/smooth_l1_loss.h> |
| 707 | #include <ATen/ops/smooth_l1_loss_compositeexplicitautogradnonfunctional_dispatch.h> |
| 708 | #include <ATen/ops/smooth_l1_loss_native.h> |
| 709 | #include <ATen/ops/softplus.h> |
| 710 | #include <ATen/ops/softplus_backward.h> |
| 711 | #include <ATen/ops/softplus_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 712 | #include <ATen/ops/softplus_backward_native.h> |
| 713 | #include <ATen/ops/softplus_compositeexplicitautogradnonfunctional_dispatch.h> |
| 714 | #include <ATen/ops/softplus_native.h> |
| 715 | #include <ATen/ops/softshrink.h> |
| 716 | #include <ATen/ops/softshrink_backward.h> |
| 717 | #include <ATen/ops/softshrink_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 718 | #include <ATen/ops/softshrink_backward_native.h> |
| 719 | #include <ATen/ops/softshrink_compositeexplicitautogradnonfunctional_dispatch.h> |
| 720 | #include <ATen/ops/softshrink_native.h> |
| 721 | #include <ATen/ops/sort.h> |
| 722 | #include <ATen/ops/sort_compositeexplicitautogradnonfunctional_dispatch.h> |
| 723 | #include <ATen/ops/sort_native.h> |
| 724 | #include <ATen/ops/special_airy_ai.h> |
| 725 | #include <ATen/ops/special_airy_ai_compositeexplicitautogradnonfunctional_dispatch.h> |
| 726 | #include <ATen/ops/special_airy_ai_native.h> |
| 727 | #include <ATen/ops/special_bessel_j0.h> |
| 728 | #include <ATen/ops/special_bessel_j0_compositeexplicitautogradnonfunctional_dispatch.h> |
| 729 | #include <ATen/ops/special_bessel_j0_native.h> |
| 730 | #include <ATen/ops/special_bessel_j1.h> |
| 731 | #include <ATen/ops/special_bessel_j1_compositeexplicitautogradnonfunctional_dispatch.h> |
| 732 | #include <ATen/ops/special_bessel_j1_native.h> |
| 733 | #include <ATen/ops/special_bessel_y0.h> |
| 734 | #include <ATen/ops/special_bessel_y0_compositeexplicitautogradnonfunctional_dispatch.h> |
| 735 | #include <ATen/ops/special_bessel_y0_native.h> |
| 736 | #include <ATen/ops/special_bessel_y1.h> |
| 737 | #include <ATen/ops/special_bessel_y1_compositeexplicitautogradnonfunctional_dispatch.h> |
| 738 | #include <ATen/ops/special_bessel_y1_native.h> |
| 739 | #include <ATen/ops/special_chebyshev_polynomial_t.h> |
| 740 | #include <ATen/ops/special_chebyshev_polynomial_t_compositeexplicitautogradnonfunctional_dispatch.h> |
| 741 | #include <ATen/ops/special_chebyshev_polynomial_t_native.h> |
| 742 | #include <ATen/ops/special_chebyshev_polynomial_u.h> |
| 743 | #include <ATen/ops/special_chebyshev_polynomial_u_compositeexplicitautogradnonfunctional_dispatch.h> |
| 744 | #include <ATen/ops/special_chebyshev_polynomial_u_native.h> |
| 745 | #include <ATen/ops/special_chebyshev_polynomial_v.h> |
| 746 | #include <ATen/ops/special_chebyshev_polynomial_v_compositeexplicitautogradnonfunctional_dispatch.h> |
| 747 | #include <ATen/ops/special_chebyshev_polynomial_v_native.h> |
| 748 | #include <ATen/ops/special_chebyshev_polynomial_w.h> |
| 749 | #include <ATen/ops/special_chebyshev_polynomial_w_compositeexplicitautogradnonfunctional_dispatch.h> |
| 750 | #include <ATen/ops/special_chebyshev_polynomial_w_native.h> |
| 751 | #include <ATen/ops/special_entr.h> |
| 752 | #include <ATen/ops/special_entr_compositeexplicitautogradnonfunctional_dispatch.h> |
| 753 | #include <ATen/ops/special_entr_native.h> |
| 754 | #include <ATen/ops/special_erfcx.h> |
| 755 | #include <ATen/ops/special_erfcx_compositeexplicitautogradnonfunctional_dispatch.h> |
| 756 | #include <ATen/ops/special_erfcx_native.h> |
| 757 | #include <ATen/ops/special_hermite_polynomial_h.h> |
| 758 | #include <ATen/ops/special_hermite_polynomial_h_compositeexplicitautogradnonfunctional_dispatch.h> |
| 759 | #include <ATen/ops/special_hermite_polynomial_h_native.h> |
| 760 | #include <ATen/ops/special_hermite_polynomial_he.h> |
| 761 | #include <ATen/ops/special_hermite_polynomial_he_compositeexplicitautogradnonfunctional_dispatch.h> |
| 762 | #include <ATen/ops/special_hermite_polynomial_he_native.h> |
| 763 | #include <ATen/ops/special_i0e.h> |
| 764 | #include <ATen/ops/special_i0e_compositeexplicitautogradnonfunctional_dispatch.h> |
| 765 | #include <ATen/ops/special_i0e_native.h> |
| 766 | #include <ATen/ops/special_i1.h> |
| 767 | #include <ATen/ops/special_i1_compositeexplicitautogradnonfunctional_dispatch.h> |
| 768 | #include <ATen/ops/special_i1_native.h> |
| 769 | #include <ATen/ops/special_i1e.h> |
| 770 | #include <ATen/ops/special_i1e_compositeexplicitautogradnonfunctional_dispatch.h> |
| 771 | #include <ATen/ops/special_i1e_native.h> |
| 772 | #include <ATen/ops/special_laguerre_polynomial_l.h> |
| 773 | #include <ATen/ops/special_laguerre_polynomial_l_compositeexplicitautogradnonfunctional_dispatch.h> |
| 774 | #include <ATen/ops/special_laguerre_polynomial_l_native.h> |
| 775 | #include <ATen/ops/special_legendre_polynomial_p.h> |
| 776 | #include <ATen/ops/special_legendre_polynomial_p_compositeexplicitautogradnonfunctional_dispatch.h> |
| 777 | #include <ATen/ops/special_legendre_polynomial_p_native.h> |
| 778 | #include <ATen/ops/special_log_ndtr.h> |
| 779 | #include <ATen/ops/special_log_ndtr_compositeexplicitautogradnonfunctional_dispatch.h> |
| 780 | #include <ATen/ops/special_log_ndtr_native.h> |
| 781 | #include <ATen/ops/special_modified_bessel_i0.h> |
| 782 | #include <ATen/ops/special_modified_bessel_i0_compositeexplicitautogradnonfunctional_dispatch.h> |
| 783 | #include <ATen/ops/special_modified_bessel_i0_native.h> |
| 784 | #include <ATen/ops/special_modified_bessel_i1.h> |
| 785 | #include <ATen/ops/special_modified_bessel_i1_compositeexplicitautogradnonfunctional_dispatch.h> |
| 786 | #include <ATen/ops/special_modified_bessel_i1_native.h> |
| 787 | #include <ATen/ops/special_modified_bessel_k0.h> |
| 788 | #include <ATen/ops/special_modified_bessel_k0_compositeexplicitautogradnonfunctional_dispatch.h> |
| 789 | #include <ATen/ops/special_modified_bessel_k0_native.h> |
| 790 | #include <ATen/ops/special_modified_bessel_k1.h> |
| 791 | #include <ATen/ops/special_modified_bessel_k1_compositeexplicitautogradnonfunctional_dispatch.h> |
| 792 | #include <ATen/ops/special_modified_bessel_k1_native.h> |
| 793 | #include <ATen/ops/special_ndtri.h> |
| 794 | #include <ATen/ops/special_ndtri_compositeexplicitautogradnonfunctional_dispatch.h> |
| 795 | #include <ATen/ops/special_ndtri_native.h> |
| 796 | #include <ATen/ops/special_scaled_modified_bessel_k0.h> |
| 797 | #include <ATen/ops/special_scaled_modified_bessel_k0_compositeexplicitautogradnonfunctional_dispatch.h> |
| 798 | #include <ATen/ops/special_scaled_modified_bessel_k0_native.h> |
| 799 | #include <ATen/ops/special_scaled_modified_bessel_k1.h> |
| 800 | #include <ATen/ops/special_scaled_modified_bessel_k1_compositeexplicitautogradnonfunctional_dispatch.h> |
| 801 | #include <ATen/ops/special_scaled_modified_bessel_k1_native.h> |
| 802 | #include <ATen/ops/special_shifted_chebyshev_polynomial_t.h> |
| 803 | #include <ATen/ops/special_shifted_chebyshev_polynomial_t_compositeexplicitautogradnonfunctional_dispatch.h> |
| 804 | #include <ATen/ops/special_shifted_chebyshev_polynomial_t_native.h> |
| 805 | #include <ATen/ops/special_shifted_chebyshev_polynomial_u.h> |
| 806 | #include <ATen/ops/special_shifted_chebyshev_polynomial_u_compositeexplicitautogradnonfunctional_dispatch.h> |
| 807 | #include <ATen/ops/special_shifted_chebyshev_polynomial_u_native.h> |
| 808 | #include <ATen/ops/special_shifted_chebyshev_polynomial_v.h> |
| 809 | #include <ATen/ops/special_shifted_chebyshev_polynomial_v_compositeexplicitautogradnonfunctional_dispatch.h> |
| 810 | #include <ATen/ops/special_shifted_chebyshev_polynomial_v_native.h> |
| 811 | #include <ATen/ops/special_shifted_chebyshev_polynomial_w.h> |
| 812 | #include <ATen/ops/special_shifted_chebyshev_polynomial_w_compositeexplicitautogradnonfunctional_dispatch.h> |
| 813 | #include <ATen/ops/special_shifted_chebyshev_polynomial_w_native.h> |
| 814 | #include <ATen/ops/special_spherical_bessel_j0.h> |
| 815 | #include <ATen/ops/special_spherical_bessel_j0_compositeexplicitautogradnonfunctional_dispatch.h> |
| 816 | #include <ATen/ops/special_spherical_bessel_j0_native.h> |
| 817 | #include <ATen/ops/special_xlog1py.h> |
| 818 | #include <ATen/ops/special_xlog1py_compositeexplicitautogradnonfunctional_dispatch.h> |
| 819 | #include <ATen/ops/special_xlog1py_native.h> |
| 820 | #include <ATen/ops/special_zeta.h> |
| 821 | #include <ATen/ops/special_zeta_compositeexplicitautogradnonfunctional_dispatch.h> |
| 822 | #include <ATen/ops/special_zeta_native.h> |
| 823 | #include <ATen/ops/split_copy.h> |
| 824 | #include <ATen/ops/split_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 825 | #include <ATen/ops/split_copy_native.h> |
| 826 | #include <ATen/ops/split_with_sizes_copy.h> |
| 827 | #include <ATen/ops/split_with_sizes_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 828 | #include <ATen/ops/split_with_sizes_copy_native.h> |
| 829 | #include <ATen/ops/sqrt.h> |
| 830 | #include <ATen/ops/sqrt_compositeexplicitautogradnonfunctional_dispatch.h> |
| 831 | #include <ATen/ops/sqrt_native.h> |
| 832 | #include <ATen/ops/squeeze_copy.h> |
| 833 | #include <ATen/ops/squeeze_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 834 | #include <ATen/ops/squeeze_copy_native.h> |
| 835 | #include <ATen/ops/sub.h> |
| 836 | #include <ATen/ops/sub_compositeexplicitautogradnonfunctional_dispatch.h> |
| 837 | #include <ATen/ops/sub_native.h> |
| 838 | #include <ATen/ops/sum.h> |
| 839 | #include <ATen/ops/sum_compositeexplicitautogradnonfunctional_dispatch.h> |
| 840 | #include <ATen/ops/sum_native.h> |
| 841 | #include <ATen/ops/t_copy.h> |
| 842 | #include <ATen/ops/t_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 843 | #include <ATen/ops/t_copy_native.h> |
| 844 | #include <ATen/ops/tan.h> |
| 845 | #include <ATen/ops/tan_compositeexplicitautogradnonfunctional_dispatch.h> |
| 846 | #include <ATen/ops/tan_native.h> |
| 847 | #include <ATen/ops/tanh.h> |
| 848 | #include <ATen/ops/tanh_backward.h> |
| 849 | #include <ATen/ops/tanh_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 850 | #include <ATen/ops/tanh_backward_native.h> |
| 851 | #include <ATen/ops/tanh_compositeexplicitautogradnonfunctional_dispatch.h> |
| 852 | #include <ATen/ops/tanh_native.h> |
| 853 | #include <ATen/ops/threshold.h> |
| 854 | #include <ATen/ops/threshold_backward.h> |
| 855 | #include <ATen/ops/threshold_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 856 | #include <ATen/ops/threshold_backward_native.h> |
| 857 | #include <ATen/ops/threshold_compositeexplicitautogradnonfunctional_dispatch.h> |
| 858 | #include <ATen/ops/threshold_native.h> |
| 859 | #include <ATen/ops/topk.h> |
| 860 | #include <ATen/ops/topk_compositeexplicitautogradnonfunctional_dispatch.h> |
| 861 | #include <ATen/ops/topk_native.h> |
| 862 | #include <ATen/ops/transpose_copy.h> |
| 863 | #include <ATen/ops/transpose_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 864 | #include <ATen/ops/transpose_copy_native.h> |
| 865 | #include <ATen/ops/triangular_solve.h> |
| 866 | #include <ATen/ops/triangular_solve_compositeexplicitautogradnonfunctional_dispatch.h> |
| 867 | #include <ATen/ops/triangular_solve_native.h> |
| 868 | #include <ATen/ops/tril.h> |
| 869 | #include <ATen/ops/tril_compositeexplicitautogradnonfunctional_dispatch.h> |
| 870 | #include <ATen/ops/tril_native.h> |
| 871 | #include <ATen/ops/triu.h> |
| 872 | #include <ATen/ops/triu_compositeexplicitautogradnonfunctional_dispatch.h> |
| 873 | #include <ATen/ops/triu_native.h> |
| 874 | #include <ATen/ops/trunc.h> |
| 875 | #include <ATen/ops/trunc_compositeexplicitautogradnonfunctional_dispatch.h> |
| 876 | #include <ATen/ops/trunc_native.h> |
| 877 | #include <ATen/ops/unbind_copy.h> |
| 878 | #include <ATen/ops/unbind_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 879 | #include <ATen/ops/unbind_copy_native.h> |
| 880 | #include <ATen/ops/unfold_copy.h> |
| 881 | #include <ATen/ops/unfold_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 882 | #include <ATen/ops/unfold_copy_native.h> |
| 883 | #include <ATen/ops/unsqueeze_copy.h> |
| 884 | #include <ATen/ops/unsqueeze_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 885 | #include <ATen/ops/unsqueeze_copy_native.h> |
| 886 | #include <ATen/ops/upsample_bicubic2d.h> |
| 887 | #include <ATen/ops/upsample_bicubic2d_backward.h> |
| 888 | #include <ATen/ops/upsample_bicubic2d_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 889 | #include <ATen/ops/upsample_bicubic2d_backward_native.h> |
| 890 | #include <ATen/ops/upsample_bicubic2d_compositeexplicitautogradnonfunctional_dispatch.h> |
| 891 | #include <ATen/ops/upsample_bicubic2d_native.h> |
| 892 | #include <ATen/ops/upsample_bilinear2d.h> |
| 893 | #include <ATen/ops/upsample_bilinear2d_backward.h> |
| 894 | #include <ATen/ops/upsample_bilinear2d_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 895 | #include <ATen/ops/upsample_bilinear2d_backward_native.h> |
| 896 | #include <ATen/ops/upsample_bilinear2d_compositeexplicitautogradnonfunctional_dispatch.h> |
| 897 | #include <ATen/ops/upsample_bilinear2d_native.h> |
| 898 | #include <ATen/ops/upsample_linear1d.h> |
| 899 | #include <ATen/ops/upsample_linear1d_backward.h> |
| 900 | #include <ATen/ops/upsample_linear1d_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 901 | #include <ATen/ops/upsample_linear1d_backward_native.h> |
| 902 | #include <ATen/ops/upsample_linear1d_compositeexplicitautogradnonfunctional_dispatch.h> |
| 903 | #include <ATen/ops/upsample_linear1d_native.h> |
| 904 | #include <ATen/ops/upsample_nearest1d.h> |
| 905 | #include <ATen/ops/upsample_nearest1d_backward.h> |
| 906 | #include <ATen/ops/upsample_nearest1d_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 907 | #include <ATen/ops/upsample_nearest1d_backward_native.h> |
| 908 | #include <ATen/ops/upsample_nearest1d_compositeexplicitautogradnonfunctional_dispatch.h> |
| 909 | #include <ATen/ops/upsample_nearest1d_native.h> |
| 910 | #include <ATen/ops/upsample_nearest2d.h> |
| 911 | #include <ATen/ops/upsample_nearest2d_backward.h> |
| 912 | #include <ATen/ops/upsample_nearest2d_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 913 | #include <ATen/ops/upsample_nearest2d_backward_native.h> |
| 914 | #include <ATen/ops/upsample_nearest2d_compositeexplicitautogradnonfunctional_dispatch.h> |
| 915 | #include <ATen/ops/upsample_nearest2d_native.h> |
| 916 | #include <ATen/ops/upsample_nearest3d.h> |
| 917 | #include <ATen/ops/upsample_nearest3d_backward.h> |
| 918 | #include <ATen/ops/upsample_nearest3d_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 919 | #include <ATen/ops/upsample_nearest3d_backward_native.h> |
| 920 | #include <ATen/ops/upsample_nearest3d_compositeexplicitautogradnonfunctional_dispatch.h> |
| 921 | #include <ATen/ops/upsample_nearest3d_native.h> |
| 922 | #include <ATen/ops/upsample_trilinear3d.h> |
| 923 | #include <ATen/ops/upsample_trilinear3d_backward.h> |
| 924 | #include <ATen/ops/upsample_trilinear3d_backward_compositeexplicitautogradnonfunctional_dispatch.h> |
| 925 | #include <ATen/ops/upsample_trilinear3d_backward_native.h> |
| 926 | #include <ATen/ops/upsample_trilinear3d_compositeexplicitautogradnonfunctional_dispatch.h> |
| 927 | #include <ATen/ops/upsample_trilinear3d_native.h> |
| 928 | #include <ATen/ops/values_copy.h> |
| 929 | #include <ATen/ops/values_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 930 | #include <ATen/ops/values_copy_native.h> |
| 931 | #include <ATen/ops/view_as_complex_copy.h> |
| 932 | #include <ATen/ops/view_as_complex_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 933 | #include <ATen/ops/view_as_complex_copy_native.h> |
| 934 | #include <ATen/ops/view_as_real_copy.h> |
| 935 | #include <ATen/ops/view_as_real_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 936 | #include <ATen/ops/view_as_real_copy_native.h> |
| 937 | #include <ATen/ops/view_copy.h> |
| 938 | #include <ATen/ops/view_copy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 939 | #include <ATen/ops/view_copy_native.h> |
| 940 | #include <ATen/ops/xlogy.h> |
| 941 | #include <ATen/ops/xlogy_compositeexplicitautogradnonfunctional_dispatch.h> |
| 942 | #include <ATen/ops/xlogy_native.h> |
| 943 | |
| 944 | // See template file RegisterDispatchDefinitions.ini |
| 945 | namespace at { |
| 946 | // NB: TORCH_LIBRARY_IMPL must be in an anonymous namespace to avoid |
| 947 | // ambiguity with conflicting identifiers that may have been defined in |
| 948 | // at namespace already. |
| 949 | namespace { |
| 950 | Tensor create_out(IntArrayRef sizes, IntArrayRef strides, const TensorOptions &options) { |
| 951 | if (strides.empty()) { |
| 952 | return at::empty(sizes, options); |
| 953 | } else { |
| 954 | return at::empty_strided(sizes, strides, options); |
| 955 | } |
| 956 | } |
| 957 | void check_inplace(const Tensor &self, IntArrayRef sizes, const TensorOptions &options) { |
| 958 | // These checks are needed on those operators that: |
| 959 | // 1) don't use 'TensorIterator' (e.g. 'addmm' and 'baddbmm') |
| 960 | // 2) have particular typing rules (e.g. 'cumsum' and 'cumprod') |
| 961 | // For other operators (e.g. 'add'), 'TensorIterator' already checks |
| 962 | // these things separately. |
| 963 | TORCH_CHECK(options.dtype() == self.dtype(), |
| 964 | "Bad in-place call: ", |
| 965 | "input tensor dtype ", self.dtype(), " and output tensor dtype ", options.dtype(), " should match"); |
| 966 | TORCH_CHECK(options.device() == self.device(), |
| 967 | "Bad in-place call: ", |
| 968 | "input tensor device ", self.device(), " and output tensor device ", options.device(), " should match"); |
| 969 | TORCH_CHECK(sizes == self.sizes(), |
| 970 | "Bad in-place call: ", |
| 971 | "input tensor size ", self.sizes(), " and output tensor size ", sizes, " should match"); |
| 972 | } |
| 973 | c10::optional<Tensor> maybe_create_proxy(const Tensor &out, IntArrayRef sizes, IntArrayRef strides, const TensorOptions &options) { |
| 974 | if (out.strides() != strides) { |
| 975 | return at::empty_strided(sizes, strides, options); |
| 976 | } |
| 977 | return c10::nullopt; |
| 978 | } |
| 979 | struct structured_sgn_default_backend_functional final : public at::meta::structured_sgn { |
| 980 | void set_output_strided( |
| 981 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 982 | TensorOptions options, DimnameList names |
| 983 | ) override { |
| 984 | auto current_device = guard_.current_device(); |
| 985 | if (C10_UNLIKELY(current_device.has_value())) { |
| 986 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 987 | "structured kernels don't support multi-device outputs"); |
| 988 | } else { |
| 989 | guard_.reset_device(options.device()); |
| 990 | } |
| 991 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 992 | if (!names.empty()) { |
| 993 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 994 | } |
| 995 | // super must happen after, so that downstream can use maybe_get_output |
| 996 | // to retrieve the output |
| 997 | at::meta::structured_sgn::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 998 | } |
| 999 | void set_output_raw_strided( |
| 1000 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1001 | TensorOptions options, DimnameList names |
| 1002 | ) override { |
| 1003 | auto current_device = guard_.current_device(); |
| 1004 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1005 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1006 | "structured kernels don't support multi-device outputs"); |
| 1007 | } else { |
| 1008 | guard_.reset_device(options.device()); |
| 1009 | } |
| 1010 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1011 | if (!names.empty()) { |
| 1012 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1013 | } |
| 1014 | // super must happen after, so that downstream can use maybe_get_output |
| 1015 | // to retrieve the output |
| 1016 | at::meta::structured_sgn::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1017 | } |
| 1018 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1019 | return *outputs_[output_idx]; |
| 1020 | } |
| 1021 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 1022 | c10::OptionalDeviceGuard guard_; |
| 1023 | }; |
| 1024 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_sgn(const at::Tensor & self) { |
| 1025 | structured_sgn_default_backend_functional op; |
| 1026 | op.meta(self); |
| 1027 | at::sgn_outf(self, *op.outputs_[0]); |
| 1028 | return std::move(op.outputs_[0]).take(); |
| 1029 | } |
| 1030 | struct structured_sgn_default_backend_inplace final : public at::meta::structured_sgn { |
| 1031 | structured_sgn_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 1032 | void set_output_strided( |
| 1033 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1034 | TensorOptions options, DimnameList names |
| 1035 | ) override { |
| 1036 | auto current_device = guard_.current_device(); |
| 1037 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1038 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1039 | "structured kernels don't support multi-device outputs"); |
| 1040 | } else { |
| 1041 | guard_.reset_device(options.device()); |
| 1042 | } |
| 1043 | const auto& out = outputs_[output_idx].get(); |
| 1044 | check_inplace(out, sizes, options); |
| 1045 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 1046 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 1047 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 1048 | } |
| 1049 | if (!names.empty()) { |
| 1050 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1051 | } |
| 1052 | // super must happen after, so that downstream can use maybe_get_output |
| 1053 | // to retrieve the output |
| 1054 | at::meta::structured_sgn::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1055 | } |
| 1056 | void set_output_raw_strided( |
| 1057 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1058 | TensorOptions options, DimnameList names |
| 1059 | ) override { |
| 1060 | auto current_device = guard_.current_device(); |
| 1061 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1062 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1063 | "structured kernels don't support multi-device outputs"); |
| 1064 | } else { |
| 1065 | guard_.reset_device(options.device()); |
| 1066 | } |
| 1067 | const auto& out = outputs_[output_idx].get(); |
| 1068 | check_inplace(out, sizes, options); |
| 1069 | if (!names.empty()) { |
| 1070 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1071 | } |
| 1072 | // super must happen after, so that downstream can use maybe_get_output |
| 1073 | // to retrieve the output |
| 1074 | at::meta::structured_sgn::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1075 | } |
| 1076 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1077 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 1078 | } |
| 1079 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 1080 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 1081 | c10::OptionalDeviceGuard guard_; |
| 1082 | }; |
| 1083 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_sgn_(at::Tensor & self) { |
| 1084 | structured_sgn_default_backend_inplace op(self); |
| 1085 | op.meta(self); |
| 1086 | at::sgn_outf(self, op.outputs_[0]); |
| 1087 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 1088 | return self; |
| 1089 | } |
| 1090 | struct structured_acos_default_backend_functional final : public at::meta::structured_acos { |
| 1091 | void set_output_strided( |
| 1092 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1093 | TensorOptions options, DimnameList names |
| 1094 | ) override { |
| 1095 | auto current_device = guard_.current_device(); |
| 1096 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1097 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1098 | "structured kernels don't support multi-device outputs"); |
| 1099 | } else { |
| 1100 | guard_.reset_device(options.device()); |
| 1101 | } |
| 1102 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1103 | if (!names.empty()) { |
| 1104 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1105 | } |
| 1106 | // super must happen after, so that downstream can use maybe_get_output |
| 1107 | // to retrieve the output |
| 1108 | at::meta::structured_acos::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1109 | } |
| 1110 | void set_output_raw_strided( |
| 1111 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1112 | TensorOptions options, DimnameList names |
| 1113 | ) override { |
| 1114 | auto current_device = guard_.current_device(); |
| 1115 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1116 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1117 | "structured kernels don't support multi-device outputs"); |
| 1118 | } else { |
| 1119 | guard_.reset_device(options.device()); |
| 1120 | } |
| 1121 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1122 | if (!names.empty()) { |
| 1123 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1124 | } |
| 1125 | // super must happen after, so that downstream can use maybe_get_output |
| 1126 | // to retrieve the output |
| 1127 | at::meta::structured_acos::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1128 | } |
| 1129 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1130 | return *outputs_[output_idx]; |
| 1131 | } |
| 1132 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 1133 | c10::OptionalDeviceGuard guard_; |
| 1134 | }; |
| 1135 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_acos(const at::Tensor & self) { |
| 1136 | structured_acos_default_backend_functional op; |
| 1137 | op.meta(self); |
| 1138 | at::acos_outf(self, *op.outputs_[0]); |
| 1139 | return std::move(op.outputs_[0]).take(); |
| 1140 | } |
| 1141 | struct structured_acos_default_backend_inplace final : public at::meta::structured_acos { |
| 1142 | structured_acos_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 1143 | void set_output_strided( |
| 1144 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1145 | TensorOptions options, DimnameList names |
| 1146 | ) override { |
| 1147 | auto current_device = guard_.current_device(); |
| 1148 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1149 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1150 | "structured kernels don't support multi-device outputs"); |
| 1151 | } else { |
| 1152 | guard_.reset_device(options.device()); |
| 1153 | } |
| 1154 | const auto& out = outputs_[output_idx].get(); |
| 1155 | check_inplace(out, sizes, options); |
| 1156 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 1157 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 1158 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 1159 | } |
| 1160 | if (!names.empty()) { |
| 1161 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1162 | } |
| 1163 | // super must happen after, so that downstream can use maybe_get_output |
| 1164 | // to retrieve the output |
| 1165 | at::meta::structured_acos::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1166 | } |
| 1167 | void set_output_raw_strided( |
| 1168 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1169 | TensorOptions options, DimnameList names |
| 1170 | ) override { |
| 1171 | auto current_device = guard_.current_device(); |
| 1172 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1173 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1174 | "structured kernels don't support multi-device outputs"); |
| 1175 | } else { |
| 1176 | guard_.reset_device(options.device()); |
| 1177 | } |
| 1178 | const auto& out = outputs_[output_idx].get(); |
| 1179 | check_inplace(out, sizes, options); |
| 1180 | if (!names.empty()) { |
| 1181 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1182 | } |
| 1183 | // super must happen after, so that downstream can use maybe_get_output |
| 1184 | // to retrieve the output |
| 1185 | at::meta::structured_acos::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1186 | } |
| 1187 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1188 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 1189 | } |
| 1190 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 1191 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 1192 | c10::OptionalDeviceGuard guard_; |
| 1193 | }; |
| 1194 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_acos_(at::Tensor & self) { |
| 1195 | structured_acos_default_backend_inplace op(self); |
| 1196 | op.meta(self); |
| 1197 | at::acos_outf(self, op.outputs_[0]); |
| 1198 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 1199 | return self; |
| 1200 | } |
| 1201 | struct structured_add_Tensor_default_backend_functional final : public at::meta::structured_add_Tensor { |
| 1202 | void set_output_strided( |
| 1203 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1204 | TensorOptions options, DimnameList names |
| 1205 | ) override { |
| 1206 | auto current_device = guard_.current_device(); |
| 1207 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1208 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1209 | "structured kernels don't support multi-device outputs"); |
| 1210 | } else { |
| 1211 | guard_.reset_device(options.device()); |
| 1212 | } |
| 1213 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1214 | if (!names.empty()) { |
| 1215 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1216 | } |
| 1217 | // super must happen after, so that downstream can use maybe_get_output |
| 1218 | // to retrieve the output |
| 1219 | at::meta::structured_add_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1220 | } |
| 1221 | void set_output_raw_strided( |
| 1222 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1223 | TensorOptions options, DimnameList names |
| 1224 | ) override { |
| 1225 | auto current_device = guard_.current_device(); |
| 1226 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1227 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1228 | "structured kernels don't support multi-device outputs"); |
| 1229 | } else { |
| 1230 | guard_.reset_device(options.device()); |
| 1231 | } |
| 1232 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1233 | if (!names.empty()) { |
| 1234 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1235 | } |
| 1236 | // super must happen after, so that downstream can use maybe_get_output |
| 1237 | // to retrieve the output |
| 1238 | at::meta::structured_add_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1239 | } |
| 1240 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1241 | return *outputs_[output_idx]; |
| 1242 | } |
| 1243 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 1244 | c10::OptionalDeviceGuard guard_; |
| 1245 | }; |
| 1246 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_add_Tensor(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 1247 | structured_add_Tensor_default_backend_functional op; |
| 1248 | op.meta(self, other, alpha); |
| 1249 | at::add_outf(self, other, alpha, *op.outputs_[0]); |
| 1250 | return std::move(op.outputs_[0]).take(); |
| 1251 | } |
| 1252 | struct structured_add_Tensor_default_backend_inplace final : public at::meta::structured_add_Tensor { |
| 1253 | structured_add_Tensor_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 1254 | void set_output_strided( |
| 1255 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1256 | TensorOptions options, DimnameList names |
| 1257 | ) override { |
| 1258 | auto current_device = guard_.current_device(); |
| 1259 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1260 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1261 | "structured kernels don't support multi-device outputs"); |
| 1262 | } else { |
| 1263 | guard_.reset_device(options.device()); |
| 1264 | } |
| 1265 | const auto& out = outputs_[output_idx].get(); |
| 1266 | check_inplace(out, sizes, options); |
| 1267 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 1268 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 1269 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 1270 | } |
| 1271 | if (!names.empty()) { |
| 1272 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1273 | } |
| 1274 | // super must happen after, so that downstream can use maybe_get_output |
| 1275 | // to retrieve the output |
| 1276 | at::meta::structured_add_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1277 | } |
| 1278 | void set_output_raw_strided( |
| 1279 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1280 | TensorOptions options, DimnameList names |
| 1281 | ) override { |
| 1282 | auto current_device = guard_.current_device(); |
| 1283 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1284 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1285 | "structured kernels don't support multi-device outputs"); |
| 1286 | } else { |
| 1287 | guard_.reset_device(options.device()); |
| 1288 | } |
| 1289 | const auto& out = outputs_[output_idx].get(); |
| 1290 | check_inplace(out, sizes, options); |
| 1291 | if (!names.empty()) { |
| 1292 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1293 | } |
| 1294 | // super must happen after, so that downstream can use maybe_get_output |
| 1295 | // to retrieve the output |
| 1296 | at::meta::structured_add_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1297 | } |
| 1298 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1299 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 1300 | } |
| 1301 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 1302 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 1303 | c10::OptionalDeviceGuard guard_; |
| 1304 | }; |
| 1305 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_add__Tensor(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 1306 | structured_add_Tensor_default_backend_inplace op(self); |
| 1307 | op.meta(self, other, alpha); |
| 1308 | at::add_outf(self, other, alpha, op.outputs_[0]); |
| 1309 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 1310 | return self; |
| 1311 | } |
| 1312 | struct structured_addmv_default_backend_functional final : public at::meta::structured_addmv { |
| 1313 | void set_output_strided( |
| 1314 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1315 | TensorOptions options, DimnameList names |
| 1316 | ) override { |
| 1317 | auto current_device = guard_.current_device(); |
| 1318 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1319 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1320 | "structured kernels don't support multi-device outputs"); |
| 1321 | } else { |
| 1322 | guard_.reset_device(options.device()); |
| 1323 | } |
| 1324 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1325 | if (!names.empty()) { |
| 1326 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1327 | } |
| 1328 | // super must happen after, so that downstream can use maybe_get_output |
| 1329 | // to retrieve the output |
| 1330 | } |
| 1331 | void set_output_raw_strided( |
| 1332 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1333 | TensorOptions options, DimnameList names |
| 1334 | ) override { |
| 1335 | auto current_device = guard_.current_device(); |
| 1336 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1337 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1338 | "structured kernels don't support multi-device outputs"); |
| 1339 | } else { |
| 1340 | guard_.reset_device(options.device()); |
| 1341 | } |
| 1342 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1343 | if (!names.empty()) { |
| 1344 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1345 | } |
| 1346 | // super must happen after, so that downstream can use maybe_get_output |
| 1347 | // to retrieve the output |
| 1348 | } |
| 1349 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1350 | return *outputs_[output_idx]; |
| 1351 | } |
| 1352 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 1353 | c10::OptionalDeviceGuard guard_; |
| 1354 | }; |
| 1355 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_addmv(const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha) { |
| 1356 | structured_addmv_default_backend_functional op; |
| 1357 | op.meta(self, mat, vec, beta, alpha); |
| 1358 | at::addmv_outf(self, mat, vec, beta, alpha, *op.outputs_[0]); |
| 1359 | return std::move(op.outputs_[0]).take(); |
| 1360 | } |
| 1361 | struct structured_addmv_default_backend_inplace final : public at::meta::structured_addmv { |
| 1362 | structured_addmv_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 1363 | void set_output_strided( |
| 1364 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1365 | TensorOptions options, DimnameList names |
| 1366 | ) override { |
| 1367 | auto current_device = guard_.current_device(); |
| 1368 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1369 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1370 | "structured kernels don't support multi-device outputs"); |
| 1371 | } else { |
| 1372 | guard_.reset_device(options.device()); |
| 1373 | } |
| 1374 | const auto& out = outputs_[output_idx].get(); |
| 1375 | check_inplace(out, sizes, options); |
| 1376 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 1377 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 1378 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 1379 | } |
| 1380 | if (!names.empty()) { |
| 1381 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1382 | } |
| 1383 | // super must happen after, so that downstream can use maybe_get_output |
| 1384 | // to retrieve the output |
| 1385 | } |
| 1386 | void set_output_raw_strided( |
| 1387 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1388 | TensorOptions options, DimnameList names |
| 1389 | ) override { |
| 1390 | auto current_device = guard_.current_device(); |
| 1391 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1392 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1393 | "structured kernels don't support multi-device outputs"); |
| 1394 | } else { |
| 1395 | guard_.reset_device(options.device()); |
| 1396 | } |
| 1397 | const auto& out = outputs_[output_idx].get(); |
| 1398 | check_inplace(out, sizes, options); |
| 1399 | if (!names.empty()) { |
| 1400 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1401 | } |
| 1402 | // super must happen after, so that downstream can use maybe_get_output |
| 1403 | // to retrieve the output |
| 1404 | } |
| 1405 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1406 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 1407 | } |
| 1408 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 1409 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 1410 | c10::OptionalDeviceGuard guard_; |
| 1411 | }; |
| 1412 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_addmv_(at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha) { |
| 1413 | structured_addmv_default_backend_inplace op(self); |
| 1414 | op.meta(self, mat, vec, beta, alpha); |
| 1415 | at::addmv_outf(self, mat, vec, beta, alpha, op.outputs_[0]); |
| 1416 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 1417 | return self; |
| 1418 | } |
| 1419 | struct structured_all_dim_default_backend_functional final : public at::meta::structured_all_dim { |
| 1420 | void set_output_strided( |
| 1421 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1422 | TensorOptions options, DimnameList names |
| 1423 | ) override { |
| 1424 | auto current_device = guard_.current_device(); |
| 1425 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1426 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1427 | "structured kernels don't support multi-device outputs"); |
| 1428 | } else { |
| 1429 | guard_.reset_device(options.device()); |
| 1430 | } |
| 1431 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1432 | if (!names.empty()) { |
| 1433 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1434 | } |
| 1435 | // super must happen after, so that downstream can use maybe_get_output |
| 1436 | // to retrieve the output |
| 1437 | } |
| 1438 | void set_output_raw_strided( |
| 1439 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1440 | TensorOptions options, DimnameList names |
| 1441 | ) override { |
| 1442 | auto current_device = guard_.current_device(); |
| 1443 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1444 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1445 | "structured kernels don't support multi-device outputs"); |
| 1446 | } else { |
| 1447 | guard_.reset_device(options.device()); |
| 1448 | } |
| 1449 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1450 | if (!names.empty()) { |
| 1451 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1452 | } |
| 1453 | // super must happen after, so that downstream can use maybe_get_output |
| 1454 | // to retrieve the output |
| 1455 | } |
| 1456 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1457 | return *outputs_[output_idx]; |
| 1458 | } |
| 1459 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 1460 | c10::OptionalDeviceGuard guard_; |
| 1461 | }; |
| 1462 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_all_dim(const at::Tensor & self, int64_t dim, bool keepdim) { |
| 1463 | structured_all_dim_default_backend_functional op; |
| 1464 | auto precompute = op.meta(self, dim, keepdim); |
| 1465 | (void)precompute; |
| 1466 | at::all_outf(self, precompute.dim, keepdim, *op.outputs_[0]); |
| 1467 | return std::move(op.outputs_[0]).take(); |
| 1468 | } |
| 1469 | struct structured_any_dim_default_backend_functional final : public at::meta::structured_any_dim { |
| 1470 | void set_output_strided( |
| 1471 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1472 | TensorOptions options, DimnameList names |
| 1473 | ) override { |
| 1474 | auto current_device = guard_.current_device(); |
| 1475 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1476 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1477 | "structured kernels don't support multi-device outputs"); |
| 1478 | } else { |
| 1479 | guard_.reset_device(options.device()); |
| 1480 | } |
| 1481 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1482 | if (!names.empty()) { |
| 1483 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1484 | } |
| 1485 | // super must happen after, so that downstream can use maybe_get_output |
| 1486 | // to retrieve the output |
| 1487 | } |
| 1488 | void set_output_raw_strided( |
| 1489 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1490 | TensorOptions options, DimnameList names |
| 1491 | ) override { |
| 1492 | auto current_device = guard_.current_device(); |
| 1493 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1494 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1495 | "structured kernels don't support multi-device outputs"); |
| 1496 | } else { |
| 1497 | guard_.reset_device(options.device()); |
| 1498 | } |
| 1499 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1500 | if (!names.empty()) { |
| 1501 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1502 | } |
| 1503 | // super must happen after, so that downstream can use maybe_get_output |
| 1504 | // to retrieve the output |
| 1505 | } |
| 1506 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1507 | return *outputs_[output_idx]; |
| 1508 | } |
| 1509 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 1510 | c10::OptionalDeviceGuard guard_; |
| 1511 | }; |
| 1512 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_any_dim(const at::Tensor & self, int64_t dim, bool keepdim) { |
| 1513 | structured_any_dim_default_backend_functional op; |
| 1514 | auto precompute = op.meta(self, dim, keepdim); |
| 1515 | (void)precompute; |
| 1516 | at::any_outf(self, precompute.dim, keepdim, *op.outputs_[0]); |
| 1517 | return std::move(op.outputs_[0]).take(); |
| 1518 | } |
| 1519 | struct structured_argmax_default_backend_functional final : public at::meta::structured_argmax { |
| 1520 | void set_output_strided( |
| 1521 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1522 | TensorOptions options, DimnameList names |
| 1523 | ) override { |
| 1524 | auto current_device = guard_.current_device(); |
| 1525 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1526 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1527 | "structured kernels don't support multi-device outputs"); |
| 1528 | } else { |
| 1529 | guard_.reset_device(options.device()); |
| 1530 | } |
| 1531 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1532 | if (!names.empty()) { |
| 1533 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1534 | } |
| 1535 | // super must happen after, so that downstream can use maybe_get_output |
| 1536 | // to retrieve the output |
| 1537 | } |
| 1538 | void set_output_raw_strided( |
| 1539 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1540 | TensorOptions options, DimnameList names |
| 1541 | ) override { |
| 1542 | auto current_device = guard_.current_device(); |
| 1543 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1544 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1545 | "structured kernels don't support multi-device outputs"); |
| 1546 | } else { |
| 1547 | guard_.reset_device(options.device()); |
| 1548 | } |
| 1549 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1550 | if (!names.empty()) { |
| 1551 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1552 | } |
| 1553 | // super must happen after, so that downstream can use maybe_get_output |
| 1554 | // to retrieve the output |
| 1555 | } |
| 1556 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1557 | return *outputs_[output_idx]; |
| 1558 | } |
| 1559 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 1560 | c10::OptionalDeviceGuard guard_; |
| 1561 | }; |
| 1562 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_argmax(const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim) { |
| 1563 | structured_argmax_default_backend_functional op; |
| 1564 | op.meta(self, dim, keepdim); |
| 1565 | at::argmax_outf(self, dim, keepdim, *op.outputs_[0]); |
| 1566 | return std::move(op.outputs_[0]).take(); |
| 1567 | } |
| 1568 | struct structured_argmin_default_backend_functional final : public at::meta::structured_argmin { |
| 1569 | void set_output_strided( |
| 1570 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1571 | TensorOptions options, DimnameList names |
| 1572 | ) override { |
| 1573 | auto current_device = guard_.current_device(); |
| 1574 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1575 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1576 | "structured kernels don't support multi-device outputs"); |
| 1577 | } else { |
| 1578 | guard_.reset_device(options.device()); |
| 1579 | } |
| 1580 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1581 | if (!names.empty()) { |
| 1582 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1583 | } |
| 1584 | // super must happen after, so that downstream can use maybe_get_output |
| 1585 | // to retrieve the output |
| 1586 | } |
| 1587 | void set_output_raw_strided( |
| 1588 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1589 | TensorOptions options, DimnameList names |
| 1590 | ) override { |
| 1591 | auto current_device = guard_.current_device(); |
| 1592 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1593 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1594 | "structured kernels don't support multi-device outputs"); |
| 1595 | } else { |
| 1596 | guard_.reset_device(options.device()); |
| 1597 | } |
| 1598 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1599 | if (!names.empty()) { |
| 1600 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1601 | } |
| 1602 | // super must happen after, so that downstream can use maybe_get_output |
| 1603 | // to retrieve the output |
| 1604 | } |
| 1605 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1606 | return *outputs_[output_idx]; |
| 1607 | } |
| 1608 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 1609 | c10::OptionalDeviceGuard guard_; |
| 1610 | }; |
| 1611 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_argmin(const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim) { |
| 1612 | structured_argmin_default_backend_functional op; |
| 1613 | op.meta(self, dim, keepdim); |
| 1614 | at::argmin_outf(self, dim, keepdim, *op.outputs_[0]); |
| 1615 | return std::move(op.outputs_[0]).take(); |
| 1616 | } |
| 1617 | struct structured_acosh_default_backend_functional final : public at::meta::structured_acosh { |
| 1618 | void set_output_strided( |
| 1619 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1620 | TensorOptions options, DimnameList names |
| 1621 | ) override { |
| 1622 | auto current_device = guard_.current_device(); |
| 1623 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1624 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1625 | "structured kernels don't support multi-device outputs"); |
| 1626 | } else { |
| 1627 | guard_.reset_device(options.device()); |
| 1628 | } |
| 1629 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1630 | if (!names.empty()) { |
| 1631 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1632 | } |
| 1633 | // super must happen after, so that downstream can use maybe_get_output |
| 1634 | // to retrieve the output |
| 1635 | at::meta::structured_acosh::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1636 | } |
| 1637 | void set_output_raw_strided( |
| 1638 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1639 | TensorOptions options, DimnameList names |
| 1640 | ) override { |
| 1641 | auto current_device = guard_.current_device(); |
| 1642 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1643 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1644 | "structured kernels don't support multi-device outputs"); |
| 1645 | } else { |
| 1646 | guard_.reset_device(options.device()); |
| 1647 | } |
| 1648 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1649 | if (!names.empty()) { |
| 1650 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1651 | } |
| 1652 | // super must happen after, so that downstream can use maybe_get_output |
| 1653 | // to retrieve the output |
| 1654 | at::meta::structured_acosh::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1655 | } |
| 1656 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1657 | return *outputs_[output_idx]; |
| 1658 | } |
| 1659 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 1660 | c10::OptionalDeviceGuard guard_; |
| 1661 | }; |
| 1662 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_acosh(const at::Tensor & self) { |
| 1663 | structured_acosh_default_backend_functional op; |
| 1664 | op.meta(self); |
| 1665 | at::acosh_outf(self, *op.outputs_[0]); |
| 1666 | return std::move(op.outputs_[0]).take(); |
| 1667 | } |
| 1668 | struct structured_acosh_default_backend_inplace final : public at::meta::structured_acosh { |
| 1669 | structured_acosh_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 1670 | void set_output_strided( |
| 1671 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1672 | TensorOptions options, DimnameList names |
| 1673 | ) override { |
| 1674 | auto current_device = guard_.current_device(); |
| 1675 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1676 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1677 | "structured kernels don't support multi-device outputs"); |
| 1678 | } else { |
| 1679 | guard_.reset_device(options.device()); |
| 1680 | } |
| 1681 | const auto& out = outputs_[output_idx].get(); |
| 1682 | check_inplace(out, sizes, options); |
| 1683 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 1684 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 1685 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 1686 | } |
| 1687 | if (!names.empty()) { |
| 1688 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1689 | } |
| 1690 | // super must happen after, so that downstream can use maybe_get_output |
| 1691 | // to retrieve the output |
| 1692 | at::meta::structured_acosh::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1693 | } |
| 1694 | void set_output_raw_strided( |
| 1695 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1696 | TensorOptions options, DimnameList names |
| 1697 | ) override { |
| 1698 | auto current_device = guard_.current_device(); |
| 1699 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1700 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1701 | "structured kernels don't support multi-device outputs"); |
| 1702 | } else { |
| 1703 | guard_.reset_device(options.device()); |
| 1704 | } |
| 1705 | const auto& out = outputs_[output_idx].get(); |
| 1706 | check_inplace(out, sizes, options); |
| 1707 | if (!names.empty()) { |
| 1708 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1709 | } |
| 1710 | // super must happen after, so that downstream can use maybe_get_output |
| 1711 | // to retrieve the output |
| 1712 | at::meta::structured_acosh::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1713 | } |
| 1714 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1715 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 1716 | } |
| 1717 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 1718 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 1719 | c10::OptionalDeviceGuard guard_; |
| 1720 | }; |
| 1721 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_acosh_(at::Tensor & self) { |
| 1722 | structured_acosh_default_backend_inplace op(self); |
| 1723 | op.meta(self); |
| 1724 | at::acosh_outf(self, op.outputs_[0]); |
| 1725 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 1726 | return self; |
| 1727 | } |
| 1728 | struct structured_asinh_default_backend_functional final : public at::meta::structured_asinh { |
| 1729 | void set_output_strided( |
| 1730 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1731 | TensorOptions options, DimnameList names |
| 1732 | ) override { |
| 1733 | auto current_device = guard_.current_device(); |
| 1734 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1735 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1736 | "structured kernels don't support multi-device outputs"); |
| 1737 | } else { |
| 1738 | guard_.reset_device(options.device()); |
| 1739 | } |
| 1740 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1741 | if (!names.empty()) { |
| 1742 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1743 | } |
| 1744 | // super must happen after, so that downstream can use maybe_get_output |
| 1745 | // to retrieve the output |
| 1746 | at::meta::structured_asinh::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1747 | } |
| 1748 | void set_output_raw_strided( |
| 1749 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1750 | TensorOptions options, DimnameList names |
| 1751 | ) override { |
| 1752 | auto current_device = guard_.current_device(); |
| 1753 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1754 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1755 | "structured kernels don't support multi-device outputs"); |
| 1756 | } else { |
| 1757 | guard_.reset_device(options.device()); |
| 1758 | } |
| 1759 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1760 | if (!names.empty()) { |
| 1761 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1762 | } |
| 1763 | // super must happen after, so that downstream can use maybe_get_output |
| 1764 | // to retrieve the output |
| 1765 | at::meta::structured_asinh::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1766 | } |
| 1767 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1768 | return *outputs_[output_idx]; |
| 1769 | } |
| 1770 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 1771 | c10::OptionalDeviceGuard guard_; |
| 1772 | }; |
| 1773 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_asinh(const at::Tensor & self) { |
| 1774 | structured_asinh_default_backend_functional op; |
| 1775 | op.meta(self); |
| 1776 | at::asinh_outf(self, *op.outputs_[0]); |
| 1777 | return std::move(op.outputs_[0]).take(); |
| 1778 | } |
| 1779 | struct structured_asinh_default_backend_inplace final : public at::meta::structured_asinh { |
| 1780 | structured_asinh_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 1781 | void set_output_strided( |
| 1782 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1783 | TensorOptions options, DimnameList names |
| 1784 | ) override { |
| 1785 | auto current_device = guard_.current_device(); |
| 1786 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1787 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1788 | "structured kernels don't support multi-device outputs"); |
| 1789 | } else { |
| 1790 | guard_.reset_device(options.device()); |
| 1791 | } |
| 1792 | const auto& out = outputs_[output_idx].get(); |
| 1793 | check_inplace(out, sizes, options); |
| 1794 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 1795 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 1796 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 1797 | } |
| 1798 | if (!names.empty()) { |
| 1799 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1800 | } |
| 1801 | // super must happen after, so that downstream can use maybe_get_output |
| 1802 | // to retrieve the output |
| 1803 | at::meta::structured_asinh::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1804 | } |
| 1805 | void set_output_raw_strided( |
| 1806 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1807 | TensorOptions options, DimnameList names |
| 1808 | ) override { |
| 1809 | auto current_device = guard_.current_device(); |
| 1810 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1811 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1812 | "structured kernels don't support multi-device outputs"); |
| 1813 | } else { |
| 1814 | guard_.reset_device(options.device()); |
| 1815 | } |
| 1816 | const auto& out = outputs_[output_idx].get(); |
| 1817 | check_inplace(out, sizes, options); |
| 1818 | if (!names.empty()) { |
| 1819 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1820 | } |
| 1821 | // super must happen after, so that downstream can use maybe_get_output |
| 1822 | // to retrieve the output |
| 1823 | at::meta::structured_asinh::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1824 | } |
| 1825 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1826 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 1827 | } |
| 1828 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 1829 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 1830 | c10::OptionalDeviceGuard guard_; |
| 1831 | }; |
| 1832 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_asinh_(at::Tensor & self) { |
| 1833 | structured_asinh_default_backend_inplace op(self); |
| 1834 | op.meta(self); |
| 1835 | at::asinh_outf(self, op.outputs_[0]); |
| 1836 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 1837 | return self; |
| 1838 | } |
| 1839 | struct structured_atanh_default_backend_functional final : public at::meta::structured_atanh { |
| 1840 | void set_output_strided( |
| 1841 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1842 | TensorOptions options, DimnameList names |
| 1843 | ) override { |
| 1844 | auto current_device = guard_.current_device(); |
| 1845 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1846 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1847 | "structured kernels don't support multi-device outputs"); |
| 1848 | } else { |
| 1849 | guard_.reset_device(options.device()); |
| 1850 | } |
| 1851 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1852 | if (!names.empty()) { |
| 1853 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1854 | } |
| 1855 | // super must happen after, so that downstream can use maybe_get_output |
| 1856 | // to retrieve the output |
| 1857 | at::meta::structured_atanh::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1858 | } |
| 1859 | void set_output_raw_strided( |
| 1860 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1861 | TensorOptions options, DimnameList names |
| 1862 | ) override { |
| 1863 | auto current_device = guard_.current_device(); |
| 1864 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1865 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1866 | "structured kernels don't support multi-device outputs"); |
| 1867 | } else { |
| 1868 | guard_.reset_device(options.device()); |
| 1869 | } |
| 1870 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1871 | if (!names.empty()) { |
| 1872 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1873 | } |
| 1874 | // super must happen after, so that downstream can use maybe_get_output |
| 1875 | // to retrieve the output |
| 1876 | at::meta::structured_atanh::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1877 | } |
| 1878 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1879 | return *outputs_[output_idx]; |
| 1880 | } |
| 1881 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 1882 | c10::OptionalDeviceGuard guard_; |
| 1883 | }; |
| 1884 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_atanh(const at::Tensor & self) { |
| 1885 | structured_atanh_default_backend_functional op; |
| 1886 | op.meta(self); |
| 1887 | at::atanh_outf(self, *op.outputs_[0]); |
| 1888 | return std::move(op.outputs_[0]).take(); |
| 1889 | } |
| 1890 | struct structured_atanh_default_backend_inplace final : public at::meta::structured_atanh { |
| 1891 | structured_atanh_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 1892 | void set_output_strided( |
| 1893 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1894 | TensorOptions options, DimnameList names |
| 1895 | ) override { |
| 1896 | auto current_device = guard_.current_device(); |
| 1897 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1898 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1899 | "structured kernels don't support multi-device outputs"); |
| 1900 | } else { |
| 1901 | guard_.reset_device(options.device()); |
| 1902 | } |
| 1903 | const auto& out = outputs_[output_idx].get(); |
| 1904 | check_inplace(out, sizes, options); |
| 1905 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 1906 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 1907 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 1908 | } |
| 1909 | if (!names.empty()) { |
| 1910 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1911 | } |
| 1912 | // super must happen after, so that downstream can use maybe_get_output |
| 1913 | // to retrieve the output |
| 1914 | at::meta::structured_atanh::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1915 | } |
| 1916 | void set_output_raw_strided( |
| 1917 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1918 | TensorOptions options, DimnameList names |
| 1919 | ) override { |
| 1920 | auto current_device = guard_.current_device(); |
| 1921 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1922 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1923 | "structured kernels don't support multi-device outputs"); |
| 1924 | } else { |
| 1925 | guard_.reset_device(options.device()); |
| 1926 | } |
| 1927 | const auto& out = outputs_[output_idx].get(); |
| 1928 | check_inplace(out, sizes, options); |
| 1929 | if (!names.empty()) { |
| 1930 | namedinference::propagate_names(outputs_[output_idx], names); |
| 1931 | } |
| 1932 | // super must happen after, so that downstream can use maybe_get_output |
| 1933 | // to retrieve the output |
| 1934 | at::meta::structured_atanh::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1935 | } |
| 1936 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1937 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 1938 | } |
| 1939 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 1940 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 1941 | c10::OptionalDeviceGuard guard_; |
| 1942 | }; |
| 1943 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_atanh_(at::Tensor & self) { |
| 1944 | structured_atanh_default_backend_inplace op(self); |
| 1945 | op.meta(self); |
| 1946 | at::atanh_outf(self, op.outputs_[0]); |
| 1947 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 1948 | return self; |
| 1949 | } |
| 1950 | namespace { |
| 1951 | const at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional__as_strided_(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional<c10::SymInt> storage_offset) { |
| 1952 | // No device check |
| 1953 | // DeviceGuard omitted |
| 1954 | return at::native::as_strided__symint(self, size, stride, storage_offset); |
| 1955 | } |
| 1956 | } // anonymous namespace |
| 1957 | struct structured_asin_default_backend_functional final : public at::meta::structured_asin { |
| 1958 | void set_output_strided( |
| 1959 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1960 | TensorOptions options, DimnameList names |
| 1961 | ) override { |
| 1962 | auto current_device = guard_.current_device(); |
| 1963 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1964 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1965 | "structured kernels don't support multi-device outputs"); |
| 1966 | } else { |
| 1967 | guard_.reset_device(options.device()); |
| 1968 | } |
| 1969 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1970 | if (!names.empty()) { |
| 1971 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1972 | } |
| 1973 | // super must happen after, so that downstream can use maybe_get_output |
| 1974 | // to retrieve the output |
| 1975 | at::meta::structured_asin::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1976 | } |
| 1977 | void set_output_raw_strided( |
| 1978 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 1979 | TensorOptions options, DimnameList names |
| 1980 | ) override { |
| 1981 | auto current_device = guard_.current_device(); |
| 1982 | if (C10_UNLIKELY(current_device.has_value())) { |
| 1983 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 1984 | "structured kernels don't support multi-device outputs"); |
| 1985 | } else { |
| 1986 | guard_.reset_device(options.device()); |
| 1987 | } |
| 1988 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 1989 | if (!names.empty()) { |
| 1990 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 1991 | } |
| 1992 | // super must happen after, so that downstream can use maybe_get_output |
| 1993 | // to retrieve the output |
| 1994 | at::meta::structured_asin::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 1995 | } |
| 1996 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 1997 | return *outputs_[output_idx]; |
| 1998 | } |
| 1999 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 2000 | c10::OptionalDeviceGuard guard_; |
| 2001 | }; |
| 2002 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_asin(const at::Tensor & self) { |
| 2003 | structured_asin_default_backend_functional op; |
| 2004 | op.meta(self); |
| 2005 | at::asin_outf(self, *op.outputs_[0]); |
| 2006 | return std::move(op.outputs_[0]).take(); |
| 2007 | } |
| 2008 | struct structured_asin_default_backend_inplace final : public at::meta::structured_asin { |
| 2009 | structured_asin_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 2010 | void set_output_strided( |
| 2011 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2012 | TensorOptions options, DimnameList names |
| 2013 | ) override { |
| 2014 | auto current_device = guard_.current_device(); |
| 2015 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2016 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2017 | "structured kernels don't support multi-device outputs"); |
| 2018 | } else { |
| 2019 | guard_.reset_device(options.device()); |
| 2020 | } |
| 2021 | const auto& out = outputs_[output_idx].get(); |
| 2022 | check_inplace(out, sizes, options); |
| 2023 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 2024 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 2025 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 2026 | } |
| 2027 | if (!names.empty()) { |
| 2028 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2029 | } |
| 2030 | // super must happen after, so that downstream can use maybe_get_output |
| 2031 | // to retrieve the output |
| 2032 | at::meta::structured_asin::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2033 | } |
| 2034 | void set_output_raw_strided( |
| 2035 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2036 | TensorOptions options, DimnameList names |
| 2037 | ) override { |
| 2038 | auto current_device = guard_.current_device(); |
| 2039 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2040 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2041 | "structured kernels don't support multi-device outputs"); |
| 2042 | } else { |
| 2043 | guard_.reset_device(options.device()); |
| 2044 | } |
| 2045 | const auto& out = outputs_[output_idx].get(); |
| 2046 | check_inplace(out, sizes, options); |
| 2047 | if (!names.empty()) { |
| 2048 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2049 | } |
| 2050 | // super must happen after, so that downstream can use maybe_get_output |
| 2051 | // to retrieve the output |
| 2052 | at::meta::structured_asin::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2053 | } |
| 2054 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2055 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 2056 | } |
| 2057 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 2058 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 2059 | c10::OptionalDeviceGuard guard_; |
| 2060 | }; |
| 2061 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_asin_(at::Tensor & self) { |
| 2062 | structured_asin_default_backend_inplace op(self); |
| 2063 | op.meta(self); |
| 2064 | at::asin_outf(self, op.outputs_[0]); |
| 2065 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 2066 | return self; |
| 2067 | } |
| 2068 | struct structured_atan_default_backend_functional final : public at::meta::structured_atan { |
| 2069 | void set_output_strided( |
| 2070 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2071 | TensorOptions options, DimnameList names |
| 2072 | ) override { |
| 2073 | auto current_device = guard_.current_device(); |
| 2074 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2075 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2076 | "structured kernels don't support multi-device outputs"); |
| 2077 | } else { |
| 2078 | guard_.reset_device(options.device()); |
| 2079 | } |
| 2080 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2081 | if (!names.empty()) { |
| 2082 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2083 | } |
| 2084 | // super must happen after, so that downstream can use maybe_get_output |
| 2085 | // to retrieve the output |
| 2086 | at::meta::structured_atan::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2087 | } |
| 2088 | void set_output_raw_strided( |
| 2089 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2090 | TensorOptions options, DimnameList names |
| 2091 | ) override { |
| 2092 | auto current_device = guard_.current_device(); |
| 2093 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2094 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2095 | "structured kernels don't support multi-device outputs"); |
| 2096 | } else { |
| 2097 | guard_.reset_device(options.device()); |
| 2098 | } |
| 2099 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2100 | if (!names.empty()) { |
| 2101 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2102 | } |
| 2103 | // super must happen after, so that downstream can use maybe_get_output |
| 2104 | // to retrieve the output |
| 2105 | at::meta::structured_atan::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2106 | } |
| 2107 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2108 | return *outputs_[output_idx]; |
| 2109 | } |
| 2110 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 2111 | c10::OptionalDeviceGuard guard_; |
| 2112 | }; |
| 2113 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_atan(const at::Tensor & self) { |
| 2114 | structured_atan_default_backend_functional op; |
| 2115 | op.meta(self); |
| 2116 | at::atan_outf(self, *op.outputs_[0]); |
| 2117 | return std::move(op.outputs_[0]).take(); |
| 2118 | } |
| 2119 | struct structured_atan_default_backend_inplace final : public at::meta::structured_atan { |
| 2120 | structured_atan_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 2121 | void set_output_strided( |
| 2122 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2123 | TensorOptions options, DimnameList names |
| 2124 | ) override { |
| 2125 | auto current_device = guard_.current_device(); |
| 2126 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2127 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2128 | "structured kernels don't support multi-device outputs"); |
| 2129 | } else { |
| 2130 | guard_.reset_device(options.device()); |
| 2131 | } |
| 2132 | const auto& out = outputs_[output_idx].get(); |
| 2133 | check_inplace(out, sizes, options); |
| 2134 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 2135 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 2136 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 2137 | } |
| 2138 | if (!names.empty()) { |
| 2139 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2140 | } |
| 2141 | // super must happen after, so that downstream can use maybe_get_output |
| 2142 | // to retrieve the output |
| 2143 | at::meta::structured_atan::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2144 | } |
| 2145 | void set_output_raw_strided( |
| 2146 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2147 | TensorOptions options, DimnameList names |
| 2148 | ) override { |
| 2149 | auto current_device = guard_.current_device(); |
| 2150 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2151 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2152 | "structured kernels don't support multi-device outputs"); |
| 2153 | } else { |
| 2154 | guard_.reset_device(options.device()); |
| 2155 | } |
| 2156 | const auto& out = outputs_[output_idx].get(); |
| 2157 | check_inplace(out, sizes, options); |
| 2158 | if (!names.empty()) { |
| 2159 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2160 | } |
| 2161 | // super must happen after, so that downstream can use maybe_get_output |
| 2162 | // to retrieve the output |
| 2163 | at::meta::structured_atan::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2164 | } |
| 2165 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2166 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 2167 | } |
| 2168 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 2169 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 2170 | c10::OptionalDeviceGuard guard_; |
| 2171 | }; |
| 2172 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_atan_(at::Tensor & self) { |
| 2173 | structured_atan_default_backend_inplace op(self); |
| 2174 | op.meta(self); |
| 2175 | at::atan_outf(self, op.outputs_[0]); |
| 2176 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 2177 | return self; |
| 2178 | } |
| 2179 | struct structured_baddbmm_default_backend_functional final : public at::meta::structured_baddbmm { |
| 2180 | void set_output_strided( |
| 2181 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2182 | TensorOptions options, DimnameList names |
| 2183 | ) override { |
| 2184 | auto current_device = guard_.current_device(); |
| 2185 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2186 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2187 | "structured kernels don't support multi-device outputs"); |
| 2188 | } else { |
| 2189 | guard_.reset_device(options.device()); |
| 2190 | } |
| 2191 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2192 | if (!names.empty()) { |
| 2193 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2194 | } |
| 2195 | // super must happen after, so that downstream can use maybe_get_output |
| 2196 | // to retrieve the output |
| 2197 | } |
| 2198 | void set_output_raw_strided( |
| 2199 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2200 | TensorOptions options, DimnameList names |
| 2201 | ) override { |
| 2202 | auto current_device = guard_.current_device(); |
| 2203 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2204 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2205 | "structured kernels don't support multi-device outputs"); |
| 2206 | } else { |
| 2207 | guard_.reset_device(options.device()); |
| 2208 | } |
| 2209 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2210 | if (!names.empty()) { |
| 2211 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2212 | } |
| 2213 | // super must happen after, so that downstream can use maybe_get_output |
| 2214 | // to retrieve the output |
| 2215 | } |
| 2216 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2217 | return *outputs_[output_idx]; |
| 2218 | } |
| 2219 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 2220 | c10::OptionalDeviceGuard guard_; |
| 2221 | }; |
| 2222 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_baddbmm(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 2223 | structured_baddbmm_default_backend_functional op; |
| 2224 | op.meta(self, batch1, batch2, beta, alpha); |
| 2225 | at::baddbmm_outf(self, batch1, batch2, beta, alpha, *op.outputs_[0]); |
| 2226 | return std::move(op.outputs_[0]).take(); |
| 2227 | } |
| 2228 | struct structured_baddbmm_default_backend_inplace final : public at::meta::structured_baddbmm { |
| 2229 | structured_baddbmm_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 2230 | void set_output_strided( |
| 2231 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2232 | TensorOptions options, DimnameList names |
| 2233 | ) override { |
| 2234 | auto current_device = guard_.current_device(); |
| 2235 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2236 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2237 | "structured kernels don't support multi-device outputs"); |
| 2238 | } else { |
| 2239 | guard_.reset_device(options.device()); |
| 2240 | } |
| 2241 | const auto& out = outputs_[output_idx].get(); |
| 2242 | check_inplace(out, sizes, options); |
| 2243 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 2244 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 2245 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 2246 | } |
| 2247 | if (!names.empty()) { |
| 2248 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2249 | } |
| 2250 | // super must happen after, so that downstream can use maybe_get_output |
| 2251 | // to retrieve the output |
| 2252 | } |
| 2253 | void set_output_raw_strided( |
| 2254 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2255 | TensorOptions options, DimnameList names |
| 2256 | ) override { |
| 2257 | auto current_device = guard_.current_device(); |
| 2258 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2259 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2260 | "structured kernels don't support multi-device outputs"); |
| 2261 | } else { |
| 2262 | guard_.reset_device(options.device()); |
| 2263 | } |
| 2264 | const auto& out = outputs_[output_idx].get(); |
| 2265 | check_inplace(out, sizes, options); |
| 2266 | if (!names.empty()) { |
| 2267 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2268 | } |
| 2269 | // super must happen after, so that downstream can use maybe_get_output |
| 2270 | // to retrieve the output |
| 2271 | } |
| 2272 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2273 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 2274 | } |
| 2275 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 2276 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 2277 | c10::OptionalDeviceGuard guard_; |
| 2278 | }; |
| 2279 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_baddbmm_(at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 2280 | structured_baddbmm_default_backend_inplace op(self); |
| 2281 | op.meta(self, batch1, batch2, beta, alpha); |
| 2282 | at::baddbmm_outf(self, batch1, batch2, beta, alpha, op.outputs_[0]); |
| 2283 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 2284 | return self; |
| 2285 | } |
| 2286 | namespace { |
| 2287 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_p_bernoulli(const at::Tensor & self, double p, c10::optional<at::Generator> generator) { |
| 2288 | // No device check |
| 2289 | // DeviceGuard omitted |
| 2290 | return at::native::bernoulli(self, p, generator); |
| 2291 | } |
| 2292 | } // anonymous namespace |
| 2293 | struct structured_bitwise_not_default_backend_functional final : public at::meta::structured_bitwise_not { |
| 2294 | void set_output_strided( |
| 2295 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2296 | TensorOptions options, DimnameList names |
| 2297 | ) override { |
| 2298 | auto current_device = guard_.current_device(); |
| 2299 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2300 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2301 | "structured kernels don't support multi-device outputs"); |
| 2302 | } else { |
| 2303 | guard_.reset_device(options.device()); |
| 2304 | } |
| 2305 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2306 | if (!names.empty()) { |
| 2307 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2308 | } |
| 2309 | // super must happen after, so that downstream can use maybe_get_output |
| 2310 | // to retrieve the output |
| 2311 | at::meta::structured_bitwise_not::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2312 | } |
| 2313 | void set_output_raw_strided( |
| 2314 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2315 | TensorOptions options, DimnameList names |
| 2316 | ) override { |
| 2317 | auto current_device = guard_.current_device(); |
| 2318 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2319 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2320 | "structured kernels don't support multi-device outputs"); |
| 2321 | } else { |
| 2322 | guard_.reset_device(options.device()); |
| 2323 | } |
| 2324 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2325 | if (!names.empty()) { |
| 2326 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2327 | } |
| 2328 | // super must happen after, so that downstream can use maybe_get_output |
| 2329 | // to retrieve the output |
| 2330 | at::meta::structured_bitwise_not::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2331 | } |
| 2332 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2333 | return *outputs_[output_idx]; |
| 2334 | } |
| 2335 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 2336 | c10::OptionalDeviceGuard guard_; |
| 2337 | }; |
| 2338 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_bitwise_not(const at::Tensor & self) { |
| 2339 | structured_bitwise_not_default_backend_functional op; |
| 2340 | op.meta(self); |
| 2341 | at::bitwise_not_outf(self, *op.outputs_[0]); |
| 2342 | return std::move(op.outputs_[0]).take(); |
| 2343 | } |
| 2344 | struct structured_bitwise_not_default_backend_inplace final : public at::meta::structured_bitwise_not { |
| 2345 | structured_bitwise_not_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 2346 | void set_output_strided( |
| 2347 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2348 | TensorOptions options, DimnameList names |
| 2349 | ) override { |
| 2350 | auto current_device = guard_.current_device(); |
| 2351 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2352 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2353 | "structured kernels don't support multi-device outputs"); |
| 2354 | } else { |
| 2355 | guard_.reset_device(options.device()); |
| 2356 | } |
| 2357 | const auto& out = outputs_[output_idx].get(); |
| 2358 | check_inplace(out, sizes, options); |
| 2359 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 2360 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 2361 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 2362 | } |
| 2363 | if (!names.empty()) { |
| 2364 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2365 | } |
| 2366 | // super must happen after, so that downstream can use maybe_get_output |
| 2367 | // to retrieve the output |
| 2368 | at::meta::structured_bitwise_not::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2369 | } |
| 2370 | void set_output_raw_strided( |
| 2371 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2372 | TensorOptions options, DimnameList names |
| 2373 | ) override { |
| 2374 | auto current_device = guard_.current_device(); |
| 2375 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2376 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2377 | "structured kernels don't support multi-device outputs"); |
| 2378 | } else { |
| 2379 | guard_.reset_device(options.device()); |
| 2380 | } |
| 2381 | const auto& out = outputs_[output_idx].get(); |
| 2382 | check_inplace(out, sizes, options); |
| 2383 | if (!names.empty()) { |
| 2384 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2385 | } |
| 2386 | // super must happen after, so that downstream can use maybe_get_output |
| 2387 | // to retrieve the output |
| 2388 | at::meta::structured_bitwise_not::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2389 | } |
| 2390 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2391 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 2392 | } |
| 2393 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 2394 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 2395 | c10::OptionalDeviceGuard guard_; |
| 2396 | }; |
| 2397 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_bitwise_not_(at::Tensor & self) { |
| 2398 | structured_bitwise_not_default_backend_inplace op(self); |
| 2399 | op.meta(self); |
| 2400 | at::bitwise_not_outf(self, op.outputs_[0]); |
| 2401 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 2402 | return self; |
| 2403 | } |
| 2404 | struct structured_copysign_Tensor_default_backend_functional final : public at::meta::structured_copysign_Tensor { |
| 2405 | void set_output_strided( |
| 2406 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2407 | TensorOptions options, DimnameList names |
| 2408 | ) override { |
| 2409 | auto current_device = guard_.current_device(); |
| 2410 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2411 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2412 | "structured kernels don't support multi-device outputs"); |
| 2413 | } else { |
| 2414 | guard_.reset_device(options.device()); |
| 2415 | } |
| 2416 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2417 | if (!names.empty()) { |
| 2418 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2419 | } |
| 2420 | // super must happen after, so that downstream can use maybe_get_output |
| 2421 | // to retrieve the output |
| 2422 | at::meta::structured_copysign_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2423 | } |
| 2424 | void set_output_raw_strided( |
| 2425 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2426 | TensorOptions options, DimnameList names |
| 2427 | ) override { |
| 2428 | auto current_device = guard_.current_device(); |
| 2429 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2430 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2431 | "structured kernels don't support multi-device outputs"); |
| 2432 | } else { |
| 2433 | guard_.reset_device(options.device()); |
| 2434 | } |
| 2435 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2436 | if (!names.empty()) { |
| 2437 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2438 | } |
| 2439 | // super must happen after, so that downstream can use maybe_get_output |
| 2440 | // to retrieve the output |
| 2441 | at::meta::structured_copysign_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2442 | } |
| 2443 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2444 | return *outputs_[output_idx]; |
| 2445 | } |
| 2446 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 2447 | c10::OptionalDeviceGuard guard_; |
| 2448 | }; |
| 2449 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_copysign_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 2450 | structured_copysign_Tensor_default_backend_functional op; |
| 2451 | op.meta(self, other); |
| 2452 | at::copysign_outf(self, other, *op.outputs_[0]); |
| 2453 | return std::move(op.outputs_[0]).take(); |
| 2454 | } |
| 2455 | struct structured_copysign_Tensor_default_backend_inplace final : public at::meta::structured_copysign_Tensor { |
| 2456 | structured_copysign_Tensor_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 2457 | void set_output_strided( |
| 2458 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2459 | TensorOptions options, DimnameList names |
| 2460 | ) override { |
| 2461 | auto current_device = guard_.current_device(); |
| 2462 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2463 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2464 | "structured kernels don't support multi-device outputs"); |
| 2465 | } else { |
| 2466 | guard_.reset_device(options.device()); |
| 2467 | } |
| 2468 | const auto& out = outputs_[output_idx].get(); |
| 2469 | check_inplace(out, sizes, options); |
| 2470 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 2471 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 2472 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 2473 | } |
| 2474 | if (!names.empty()) { |
| 2475 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2476 | } |
| 2477 | // super must happen after, so that downstream can use maybe_get_output |
| 2478 | // to retrieve the output |
| 2479 | at::meta::structured_copysign_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2480 | } |
| 2481 | void set_output_raw_strided( |
| 2482 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2483 | TensorOptions options, DimnameList names |
| 2484 | ) override { |
| 2485 | auto current_device = guard_.current_device(); |
| 2486 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2487 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2488 | "structured kernels don't support multi-device outputs"); |
| 2489 | } else { |
| 2490 | guard_.reset_device(options.device()); |
| 2491 | } |
| 2492 | const auto& out = outputs_[output_idx].get(); |
| 2493 | check_inplace(out, sizes, options); |
| 2494 | if (!names.empty()) { |
| 2495 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2496 | } |
| 2497 | // super must happen after, so that downstream can use maybe_get_output |
| 2498 | // to retrieve the output |
| 2499 | at::meta::structured_copysign_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2500 | } |
| 2501 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2502 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 2503 | } |
| 2504 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 2505 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 2506 | c10::OptionalDeviceGuard guard_; |
| 2507 | }; |
| 2508 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_copysign__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 2509 | structured_copysign_Tensor_default_backend_inplace op(self); |
| 2510 | op.meta(self, other); |
| 2511 | at::copysign_outf(self, other, op.outputs_[0]); |
| 2512 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 2513 | return self; |
| 2514 | } |
| 2515 | struct structured_bmm_default_backend_functional final : public at::meta::structured_bmm { |
| 2516 | void set_output_strided( |
| 2517 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2518 | TensorOptions options, DimnameList names |
| 2519 | ) override { |
| 2520 | auto current_device = guard_.current_device(); |
| 2521 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2522 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2523 | "structured kernels don't support multi-device outputs"); |
| 2524 | } else { |
| 2525 | guard_.reset_device(options.device()); |
| 2526 | } |
| 2527 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2528 | if (!names.empty()) { |
| 2529 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2530 | } |
| 2531 | // super must happen after, so that downstream can use maybe_get_output |
| 2532 | // to retrieve the output |
| 2533 | } |
| 2534 | void set_output_raw_strided( |
| 2535 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2536 | TensorOptions options, DimnameList names |
| 2537 | ) override { |
| 2538 | auto current_device = guard_.current_device(); |
| 2539 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2540 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2541 | "structured kernels don't support multi-device outputs"); |
| 2542 | } else { |
| 2543 | guard_.reset_device(options.device()); |
| 2544 | } |
| 2545 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2546 | if (!names.empty()) { |
| 2547 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2548 | } |
| 2549 | // super must happen after, so that downstream can use maybe_get_output |
| 2550 | // to retrieve the output |
| 2551 | } |
| 2552 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2553 | return *outputs_[output_idx]; |
| 2554 | } |
| 2555 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 2556 | c10::OptionalDeviceGuard guard_; |
| 2557 | }; |
| 2558 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_bmm(const at::Tensor & self, const at::Tensor & mat2) { |
| 2559 | structured_bmm_default_backend_functional op; |
| 2560 | op.meta(self, mat2); |
| 2561 | at::bmm_outf(self, mat2, *op.outputs_[0]); |
| 2562 | return std::move(op.outputs_[0]).take(); |
| 2563 | } |
| 2564 | struct structured_cat_default_backend_functional final : public at::meta::structured_cat { |
| 2565 | void set_output_strided( |
| 2566 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2567 | TensorOptions options, DimnameList names |
| 2568 | ) override { |
| 2569 | auto current_device = guard_.current_device(); |
| 2570 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2571 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2572 | "structured kernels don't support multi-device outputs"); |
| 2573 | } else { |
| 2574 | guard_.reset_device(options.device()); |
| 2575 | } |
| 2576 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2577 | if (!names.empty()) { |
| 2578 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2579 | } |
| 2580 | // super must happen after, so that downstream can use maybe_get_output |
| 2581 | // to retrieve the output |
| 2582 | } |
| 2583 | void set_output_raw_strided( |
| 2584 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2585 | TensorOptions options, DimnameList names |
| 2586 | ) override { |
| 2587 | auto current_device = guard_.current_device(); |
| 2588 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2589 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2590 | "structured kernels don't support multi-device outputs"); |
| 2591 | } else { |
| 2592 | guard_.reset_device(options.device()); |
| 2593 | } |
| 2594 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2595 | if (!names.empty()) { |
| 2596 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2597 | } |
| 2598 | // super must happen after, so that downstream can use maybe_get_output |
| 2599 | // to retrieve the output |
| 2600 | } |
| 2601 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2602 | return *outputs_[output_idx]; |
| 2603 | } |
| 2604 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 2605 | c10::OptionalDeviceGuard guard_; |
| 2606 | }; |
| 2607 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_cat(const at::ITensorListRef & tensors, int64_t dim) { |
| 2608 | structured_cat_default_backend_functional op; |
| 2609 | auto precompute = op.meta(tensors, dim); |
| 2610 | (void)precompute; |
| 2611 | at::cat_outf(tensors, precompute.dim, *op.outputs_[0]); |
| 2612 | return std::move(op.outputs_[0]).take(); |
| 2613 | } |
| 2614 | struct structured_ceil_default_backend_functional final : public at::meta::structured_ceil { |
| 2615 | void set_output_strided( |
| 2616 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2617 | TensorOptions options, DimnameList names |
| 2618 | ) override { |
| 2619 | auto current_device = guard_.current_device(); |
| 2620 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2621 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2622 | "structured kernels don't support multi-device outputs"); |
| 2623 | } else { |
| 2624 | guard_.reset_device(options.device()); |
| 2625 | } |
| 2626 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2627 | if (!names.empty()) { |
| 2628 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2629 | } |
| 2630 | // super must happen after, so that downstream can use maybe_get_output |
| 2631 | // to retrieve the output |
| 2632 | at::meta::structured_ceil::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2633 | } |
| 2634 | void set_output_raw_strided( |
| 2635 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2636 | TensorOptions options, DimnameList names |
| 2637 | ) override { |
| 2638 | auto current_device = guard_.current_device(); |
| 2639 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2640 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2641 | "structured kernels don't support multi-device outputs"); |
| 2642 | } else { |
| 2643 | guard_.reset_device(options.device()); |
| 2644 | } |
| 2645 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2646 | if (!names.empty()) { |
| 2647 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2648 | } |
| 2649 | // super must happen after, so that downstream can use maybe_get_output |
| 2650 | // to retrieve the output |
| 2651 | at::meta::structured_ceil::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2652 | } |
| 2653 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2654 | return *outputs_[output_idx]; |
| 2655 | } |
| 2656 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 2657 | c10::OptionalDeviceGuard guard_; |
| 2658 | }; |
| 2659 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_ceil(const at::Tensor & self) { |
| 2660 | structured_ceil_default_backend_functional op; |
| 2661 | op.meta(self); |
| 2662 | at::ceil_outf(self, *op.outputs_[0]); |
| 2663 | return std::move(op.outputs_[0]).take(); |
| 2664 | } |
| 2665 | struct structured_ceil_default_backend_inplace final : public at::meta::structured_ceil { |
| 2666 | structured_ceil_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 2667 | void set_output_strided( |
| 2668 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2669 | TensorOptions options, DimnameList names |
| 2670 | ) override { |
| 2671 | auto current_device = guard_.current_device(); |
| 2672 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2673 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2674 | "structured kernels don't support multi-device outputs"); |
| 2675 | } else { |
| 2676 | guard_.reset_device(options.device()); |
| 2677 | } |
| 2678 | const auto& out = outputs_[output_idx].get(); |
| 2679 | check_inplace(out, sizes, options); |
| 2680 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 2681 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 2682 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 2683 | } |
| 2684 | if (!names.empty()) { |
| 2685 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2686 | } |
| 2687 | // super must happen after, so that downstream can use maybe_get_output |
| 2688 | // to retrieve the output |
| 2689 | at::meta::structured_ceil::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2690 | } |
| 2691 | void set_output_raw_strided( |
| 2692 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2693 | TensorOptions options, DimnameList names |
| 2694 | ) override { |
| 2695 | auto current_device = guard_.current_device(); |
| 2696 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2697 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2698 | "structured kernels don't support multi-device outputs"); |
| 2699 | } else { |
| 2700 | guard_.reset_device(options.device()); |
| 2701 | } |
| 2702 | const auto& out = outputs_[output_idx].get(); |
| 2703 | check_inplace(out, sizes, options); |
| 2704 | if (!names.empty()) { |
| 2705 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2706 | } |
| 2707 | // super must happen after, so that downstream can use maybe_get_output |
| 2708 | // to retrieve the output |
| 2709 | at::meta::structured_ceil::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2710 | } |
| 2711 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2712 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 2713 | } |
| 2714 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 2715 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 2716 | c10::OptionalDeviceGuard guard_; |
| 2717 | }; |
| 2718 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_ceil_(at::Tensor & self) { |
| 2719 | structured_ceil_default_backend_inplace op(self); |
| 2720 | op.meta(self); |
| 2721 | at::ceil_outf(self, op.outputs_[0]); |
| 2722 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 2723 | return self; |
| 2724 | } |
| 2725 | struct structured_clamp_default_backend_functional final : public at::meta::structured_clamp { |
| 2726 | void set_output_strided( |
| 2727 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2728 | TensorOptions options, DimnameList names |
| 2729 | ) override { |
| 2730 | auto current_device = guard_.current_device(); |
| 2731 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2732 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2733 | "structured kernels don't support multi-device outputs"); |
| 2734 | } else { |
| 2735 | guard_.reset_device(options.device()); |
| 2736 | } |
| 2737 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2738 | if (!names.empty()) { |
| 2739 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2740 | } |
| 2741 | // super must happen after, so that downstream can use maybe_get_output |
| 2742 | // to retrieve the output |
| 2743 | at::meta::structured_clamp::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2744 | } |
| 2745 | void set_output_raw_strided( |
| 2746 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2747 | TensorOptions options, DimnameList names |
| 2748 | ) override { |
| 2749 | auto current_device = guard_.current_device(); |
| 2750 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2751 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2752 | "structured kernels don't support multi-device outputs"); |
| 2753 | } else { |
| 2754 | guard_.reset_device(options.device()); |
| 2755 | } |
| 2756 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2757 | if (!names.empty()) { |
| 2758 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2759 | } |
| 2760 | // super must happen after, so that downstream can use maybe_get_output |
| 2761 | // to retrieve the output |
| 2762 | at::meta::structured_clamp::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2763 | } |
| 2764 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2765 | return *outputs_[output_idx]; |
| 2766 | } |
| 2767 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 2768 | c10::OptionalDeviceGuard guard_; |
| 2769 | }; |
| 2770 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_clamp(const at::Tensor & self, const c10::optional<at::Scalar> & min, const c10::optional<at::Scalar> & max) { |
| 2771 | structured_clamp_default_backend_functional op; |
| 2772 | op.meta(self, (min.has_value() ? at::OptionalScalarRef(&(min.value())) : at::OptionalScalarRef()), (max.has_value() ? at::OptionalScalarRef(&(max.value())) : at::OptionalScalarRef())); |
| 2773 | at::clamp_outf(self, min, max, *op.outputs_[0]); |
| 2774 | return std::move(op.outputs_[0]).take(); |
| 2775 | } |
| 2776 | struct structured_clamp_default_backend_inplace final : public at::meta::structured_clamp { |
| 2777 | structured_clamp_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 2778 | void set_output_strided( |
| 2779 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2780 | TensorOptions options, DimnameList names |
| 2781 | ) override { |
| 2782 | auto current_device = guard_.current_device(); |
| 2783 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2784 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2785 | "structured kernels don't support multi-device outputs"); |
| 2786 | } else { |
| 2787 | guard_.reset_device(options.device()); |
| 2788 | } |
| 2789 | const auto& out = outputs_[output_idx].get(); |
| 2790 | check_inplace(out, sizes, options); |
| 2791 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 2792 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 2793 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 2794 | } |
| 2795 | if (!names.empty()) { |
| 2796 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2797 | } |
| 2798 | // super must happen after, so that downstream can use maybe_get_output |
| 2799 | // to retrieve the output |
| 2800 | at::meta::structured_clamp::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2801 | } |
| 2802 | void set_output_raw_strided( |
| 2803 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2804 | TensorOptions options, DimnameList names |
| 2805 | ) override { |
| 2806 | auto current_device = guard_.current_device(); |
| 2807 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2808 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2809 | "structured kernels don't support multi-device outputs"); |
| 2810 | } else { |
| 2811 | guard_.reset_device(options.device()); |
| 2812 | } |
| 2813 | const auto& out = outputs_[output_idx].get(); |
| 2814 | check_inplace(out, sizes, options); |
| 2815 | if (!names.empty()) { |
| 2816 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2817 | } |
| 2818 | // super must happen after, so that downstream can use maybe_get_output |
| 2819 | // to retrieve the output |
| 2820 | at::meta::structured_clamp::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2821 | } |
| 2822 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2823 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 2824 | } |
| 2825 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 2826 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 2827 | c10::OptionalDeviceGuard guard_; |
| 2828 | }; |
| 2829 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_clamp_(at::Tensor & self, const c10::optional<at::Scalar> & min, const c10::optional<at::Scalar> & max) { |
| 2830 | structured_clamp_default_backend_inplace op(self); |
| 2831 | op.meta(self, (min.has_value() ? at::OptionalScalarRef(&(min.value())) : at::OptionalScalarRef()), (max.has_value() ? at::OptionalScalarRef(&(max.value())) : at::OptionalScalarRef())); |
| 2832 | at::clamp_outf(self, min, max, op.outputs_[0]); |
| 2833 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 2834 | return self; |
| 2835 | } |
| 2836 | struct structured_clamp_Tensor_default_backend_functional final : public at::meta::structured_clamp_Tensor { |
| 2837 | void set_output_strided( |
| 2838 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2839 | TensorOptions options, DimnameList names |
| 2840 | ) override { |
| 2841 | auto current_device = guard_.current_device(); |
| 2842 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2843 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2844 | "structured kernels don't support multi-device outputs"); |
| 2845 | } else { |
| 2846 | guard_.reset_device(options.device()); |
| 2847 | } |
| 2848 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2849 | if (!names.empty()) { |
| 2850 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2851 | } |
| 2852 | // super must happen after, so that downstream can use maybe_get_output |
| 2853 | // to retrieve the output |
| 2854 | at::meta::structured_clamp_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2855 | } |
| 2856 | void set_output_raw_strided( |
| 2857 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2858 | TensorOptions options, DimnameList names |
| 2859 | ) override { |
| 2860 | auto current_device = guard_.current_device(); |
| 2861 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2862 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2863 | "structured kernels don't support multi-device outputs"); |
| 2864 | } else { |
| 2865 | guard_.reset_device(options.device()); |
| 2866 | } |
| 2867 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2868 | if (!names.empty()) { |
| 2869 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2870 | } |
| 2871 | // super must happen after, so that downstream can use maybe_get_output |
| 2872 | // to retrieve the output |
| 2873 | at::meta::structured_clamp_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2874 | } |
| 2875 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2876 | return *outputs_[output_idx]; |
| 2877 | } |
| 2878 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 2879 | c10::OptionalDeviceGuard guard_; |
| 2880 | }; |
| 2881 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_clamp_Tensor(const at::Tensor & self, const c10::optional<at::Tensor> & min, const c10::optional<at::Tensor> & max) { |
| 2882 | structured_clamp_Tensor_default_backend_functional op; |
| 2883 | op.meta(self, ((min.has_value() && (*min).defined()) ? at::OptionalTensorRef(*min) : at::OptionalTensorRef()), ((max.has_value() && (*max).defined()) ? at::OptionalTensorRef(*max) : at::OptionalTensorRef())); |
| 2884 | at::clamp_outf(self, min, max, *op.outputs_[0]); |
| 2885 | return std::move(op.outputs_[0]).take(); |
| 2886 | } |
| 2887 | struct structured_clamp_Tensor_default_backend_inplace final : public at::meta::structured_clamp_Tensor { |
| 2888 | structured_clamp_Tensor_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 2889 | void set_output_strided( |
| 2890 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2891 | TensorOptions options, DimnameList names |
| 2892 | ) override { |
| 2893 | auto current_device = guard_.current_device(); |
| 2894 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2895 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2896 | "structured kernels don't support multi-device outputs"); |
| 2897 | } else { |
| 2898 | guard_.reset_device(options.device()); |
| 2899 | } |
| 2900 | const auto& out = outputs_[output_idx].get(); |
| 2901 | check_inplace(out, sizes, options); |
| 2902 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 2903 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 2904 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 2905 | } |
| 2906 | if (!names.empty()) { |
| 2907 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2908 | } |
| 2909 | // super must happen after, so that downstream can use maybe_get_output |
| 2910 | // to retrieve the output |
| 2911 | at::meta::structured_clamp_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2912 | } |
| 2913 | void set_output_raw_strided( |
| 2914 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2915 | TensorOptions options, DimnameList names |
| 2916 | ) override { |
| 2917 | auto current_device = guard_.current_device(); |
| 2918 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2919 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2920 | "structured kernels don't support multi-device outputs"); |
| 2921 | } else { |
| 2922 | guard_.reset_device(options.device()); |
| 2923 | } |
| 2924 | const auto& out = outputs_[output_idx].get(); |
| 2925 | check_inplace(out, sizes, options); |
| 2926 | if (!names.empty()) { |
| 2927 | namedinference::propagate_names(outputs_[output_idx], names); |
| 2928 | } |
| 2929 | // super must happen after, so that downstream can use maybe_get_output |
| 2930 | // to retrieve the output |
| 2931 | at::meta::structured_clamp_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2932 | } |
| 2933 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2934 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 2935 | } |
| 2936 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 2937 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 2938 | c10::OptionalDeviceGuard guard_; |
| 2939 | }; |
| 2940 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_clamp__Tensor(at::Tensor & self, const c10::optional<at::Tensor> & min, const c10::optional<at::Tensor> & max) { |
| 2941 | structured_clamp_Tensor_default_backend_inplace op(self); |
| 2942 | op.meta(self, ((min.has_value() && (*min).defined()) ? at::OptionalTensorRef(*min) : at::OptionalTensorRef()), ((max.has_value() && (*max).defined()) ? at::OptionalTensorRef(*max) : at::OptionalTensorRef())); |
| 2943 | at::clamp_outf(self, min, max, op.outputs_[0]); |
| 2944 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 2945 | return self; |
| 2946 | } |
| 2947 | struct structured_clamp_max_default_backend_functional final : public at::meta::structured_clamp_max { |
| 2948 | void set_output_strided( |
| 2949 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2950 | TensorOptions options, DimnameList names |
| 2951 | ) override { |
| 2952 | auto current_device = guard_.current_device(); |
| 2953 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2954 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2955 | "structured kernels don't support multi-device outputs"); |
| 2956 | } else { |
| 2957 | guard_.reset_device(options.device()); |
| 2958 | } |
| 2959 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2960 | if (!names.empty()) { |
| 2961 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2962 | } |
| 2963 | // super must happen after, so that downstream can use maybe_get_output |
| 2964 | // to retrieve the output |
| 2965 | at::meta::structured_clamp_max::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2966 | } |
| 2967 | void set_output_raw_strided( |
| 2968 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 2969 | TensorOptions options, DimnameList names |
| 2970 | ) override { |
| 2971 | auto current_device = guard_.current_device(); |
| 2972 | if (C10_UNLIKELY(current_device.has_value())) { |
| 2973 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 2974 | "structured kernels don't support multi-device outputs"); |
| 2975 | } else { |
| 2976 | guard_.reset_device(options.device()); |
| 2977 | } |
| 2978 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 2979 | if (!names.empty()) { |
| 2980 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 2981 | } |
| 2982 | // super must happen after, so that downstream can use maybe_get_output |
| 2983 | // to retrieve the output |
| 2984 | at::meta::structured_clamp_max::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 2985 | } |
| 2986 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 2987 | return *outputs_[output_idx]; |
| 2988 | } |
| 2989 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 2990 | c10::OptionalDeviceGuard guard_; |
| 2991 | }; |
| 2992 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_clamp_max(const at::Tensor & self, const at::Scalar & max) { |
| 2993 | structured_clamp_max_default_backend_functional op; |
| 2994 | op.meta(self, max); |
| 2995 | at::clamp_max_outf(self, max, *op.outputs_[0]); |
| 2996 | return std::move(op.outputs_[0]).take(); |
| 2997 | } |
| 2998 | struct structured_clamp_max_default_backend_inplace final : public at::meta::structured_clamp_max { |
| 2999 | structured_clamp_max_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 3000 | void set_output_strided( |
| 3001 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3002 | TensorOptions options, DimnameList names |
| 3003 | ) override { |
| 3004 | auto current_device = guard_.current_device(); |
| 3005 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3006 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3007 | "structured kernels don't support multi-device outputs"); |
| 3008 | } else { |
| 3009 | guard_.reset_device(options.device()); |
| 3010 | } |
| 3011 | const auto& out = outputs_[output_idx].get(); |
| 3012 | check_inplace(out, sizes, options); |
| 3013 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 3014 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 3015 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 3016 | } |
| 3017 | if (!names.empty()) { |
| 3018 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3019 | } |
| 3020 | // super must happen after, so that downstream can use maybe_get_output |
| 3021 | // to retrieve the output |
| 3022 | at::meta::structured_clamp_max::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3023 | } |
| 3024 | void set_output_raw_strided( |
| 3025 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3026 | TensorOptions options, DimnameList names |
| 3027 | ) override { |
| 3028 | auto current_device = guard_.current_device(); |
| 3029 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3030 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3031 | "structured kernels don't support multi-device outputs"); |
| 3032 | } else { |
| 3033 | guard_.reset_device(options.device()); |
| 3034 | } |
| 3035 | const auto& out = outputs_[output_idx].get(); |
| 3036 | check_inplace(out, sizes, options); |
| 3037 | if (!names.empty()) { |
| 3038 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3039 | } |
| 3040 | // super must happen after, so that downstream can use maybe_get_output |
| 3041 | // to retrieve the output |
| 3042 | at::meta::structured_clamp_max::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3043 | } |
| 3044 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3045 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 3046 | } |
| 3047 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 3048 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 3049 | c10::OptionalDeviceGuard guard_; |
| 3050 | }; |
| 3051 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_clamp_max_(at::Tensor & self, const at::Scalar & max) { |
| 3052 | structured_clamp_max_default_backend_inplace op(self); |
| 3053 | op.meta(self, max); |
| 3054 | at::clamp_max_outf(self, max, op.outputs_[0]); |
| 3055 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 3056 | return self; |
| 3057 | } |
| 3058 | struct structured_clamp_max_Tensor_default_backend_functional final : public at::meta::structured_clamp_max_Tensor { |
| 3059 | void set_output_strided( |
| 3060 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3061 | TensorOptions options, DimnameList names |
| 3062 | ) override { |
| 3063 | auto current_device = guard_.current_device(); |
| 3064 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3065 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3066 | "structured kernels don't support multi-device outputs"); |
| 3067 | } else { |
| 3068 | guard_.reset_device(options.device()); |
| 3069 | } |
| 3070 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3071 | if (!names.empty()) { |
| 3072 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3073 | } |
| 3074 | // super must happen after, so that downstream can use maybe_get_output |
| 3075 | // to retrieve the output |
| 3076 | at::meta::structured_clamp_max_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3077 | } |
| 3078 | void set_output_raw_strided( |
| 3079 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3080 | TensorOptions options, DimnameList names |
| 3081 | ) override { |
| 3082 | auto current_device = guard_.current_device(); |
| 3083 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3084 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3085 | "structured kernels don't support multi-device outputs"); |
| 3086 | } else { |
| 3087 | guard_.reset_device(options.device()); |
| 3088 | } |
| 3089 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3090 | if (!names.empty()) { |
| 3091 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3092 | } |
| 3093 | // super must happen after, so that downstream can use maybe_get_output |
| 3094 | // to retrieve the output |
| 3095 | at::meta::structured_clamp_max_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3096 | } |
| 3097 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3098 | return *outputs_[output_idx]; |
| 3099 | } |
| 3100 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 3101 | c10::OptionalDeviceGuard guard_; |
| 3102 | }; |
| 3103 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_clamp_max_Tensor(const at::Tensor & self, const at::Tensor & max) { |
| 3104 | structured_clamp_max_Tensor_default_backend_functional op; |
| 3105 | op.meta(self, max); |
| 3106 | at::clamp_max_outf(self, max, *op.outputs_[0]); |
| 3107 | return std::move(op.outputs_[0]).take(); |
| 3108 | } |
| 3109 | struct structured_clamp_max_Tensor_default_backend_inplace final : public at::meta::structured_clamp_max_Tensor { |
| 3110 | structured_clamp_max_Tensor_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 3111 | void set_output_strided( |
| 3112 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3113 | TensorOptions options, DimnameList names |
| 3114 | ) override { |
| 3115 | auto current_device = guard_.current_device(); |
| 3116 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3117 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3118 | "structured kernels don't support multi-device outputs"); |
| 3119 | } else { |
| 3120 | guard_.reset_device(options.device()); |
| 3121 | } |
| 3122 | const auto& out = outputs_[output_idx].get(); |
| 3123 | check_inplace(out, sizes, options); |
| 3124 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 3125 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 3126 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 3127 | } |
| 3128 | if (!names.empty()) { |
| 3129 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3130 | } |
| 3131 | // super must happen after, so that downstream can use maybe_get_output |
| 3132 | // to retrieve the output |
| 3133 | at::meta::structured_clamp_max_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3134 | } |
| 3135 | void set_output_raw_strided( |
| 3136 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3137 | TensorOptions options, DimnameList names |
| 3138 | ) override { |
| 3139 | auto current_device = guard_.current_device(); |
| 3140 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3141 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3142 | "structured kernels don't support multi-device outputs"); |
| 3143 | } else { |
| 3144 | guard_.reset_device(options.device()); |
| 3145 | } |
| 3146 | const auto& out = outputs_[output_idx].get(); |
| 3147 | check_inplace(out, sizes, options); |
| 3148 | if (!names.empty()) { |
| 3149 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3150 | } |
| 3151 | // super must happen after, so that downstream can use maybe_get_output |
| 3152 | // to retrieve the output |
| 3153 | at::meta::structured_clamp_max_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3154 | } |
| 3155 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3156 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 3157 | } |
| 3158 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 3159 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 3160 | c10::OptionalDeviceGuard guard_; |
| 3161 | }; |
| 3162 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_clamp_max__Tensor(at::Tensor & self, const at::Tensor & max) { |
| 3163 | structured_clamp_max_Tensor_default_backend_inplace op(self); |
| 3164 | op.meta(self, max); |
| 3165 | at::clamp_max_outf(self, max, op.outputs_[0]); |
| 3166 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 3167 | return self; |
| 3168 | } |
| 3169 | struct structured_clamp_min_default_backend_functional final : public at::meta::structured_clamp_min { |
| 3170 | void set_output_strided( |
| 3171 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3172 | TensorOptions options, DimnameList names |
| 3173 | ) override { |
| 3174 | auto current_device = guard_.current_device(); |
| 3175 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3176 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3177 | "structured kernels don't support multi-device outputs"); |
| 3178 | } else { |
| 3179 | guard_.reset_device(options.device()); |
| 3180 | } |
| 3181 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3182 | if (!names.empty()) { |
| 3183 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3184 | } |
| 3185 | // super must happen after, so that downstream can use maybe_get_output |
| 3186 | // to retrieve the output |
| 3187 | at::meta::structured_clamp_min::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3188 | } |
| 3189 | void set_output_raw_strided( |
| 3190 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3191 | TensorOptions options, DimnameList names |
| 3192 | ) override { |
| 3193 | auto current_device = guard_.current_device(); |
| 3194 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3195 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3196 | "structured kernels don't support multi-device outputs"); |
| 3197 | } else { |
| 3198 | guard_.reset_device(options.device()); |
| 3199 | } |
| 3200 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3201 | if (!names.empty()) { |
| 3202 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3203 | } |
| 3204 | // super must happen after, so that downstream can use maybe_get_output |
| 3205 | // to retrieve the output |
| 3206 | at::meta::structured_clamp_min::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3207 | } |
| 3208 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3209 | return *outputs_[output_idx]; |
| 3210 | } |
| 3211 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 3212 | c10::OptionalDeviceGuard guard_; |
| 3213 | }; |
| 3214 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_clamp_min(const at::Tensor & self, const at::Scalar & min) { |
| 3215 | structured_clamp_min_default_backend_functional op; |
| 3216 | op.meta(self, min); |
| 3217 | at::clamp_min_outf(self, min, *op.outputs_[0]); |
| 3218 | return std::move(op.outputs_[0]).take(); |
| 3219 | } |
| 3220 | struct structured_clamp_min_default_backend_inplace final : public at::meta::structured_clamp_min { |
| 3221 | structured_clamp_min_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 3222 | void set_output_strided( |
| 3223 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3224 | TensorOptions options, DimnameList names |
| 3225 | ) override { |
| 3226 | auto current_device = guard_.current_device(); |
| 3227 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3228 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3229 | "structured kernels don't support multi-device outputs"); |
| 3230 | } else { |
| 3231 | guard_.reset_device(options.device()); |
| 3232 | } |
| 3233 | const auto& out = outputs_[output_idx].get(); |
| 3234 | check_inplace(out, sizes, options); |
| 3235 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 3236 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 3237 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 3238 | } |
| 3239 | if (!names.empty()) { |
| 3240 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3241 | } |
| 3242 | // super must happen after, so that downstream can use maybe_get_output |
| 3243 | // to retrieve the output |
| 3244 | at::meta::structured_clamp_min::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3245 | } |
| 3246 | void set_output_raw_strided( |
| 3247 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3248 | TensorOptions options, DimnameList names |
| 3249 | ) override { |
| 3250 | auto current_device = guard_.current_device(); |
| 3251 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3252 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3253 | "structured kernels don't support multi-device outputs"); |
| 3254 | } else { |
| 3255 | guard_.reset_device(options.device()); |
| 3256 | } |
| 3257 | const auto& out = outputs_[output_idx].get(); |
| 3258 | check_inplace(out, sizes, options); |
| 3259 | if (!names.empty()) { |
| 3260 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3261 | } |
| 3262 | // super must happen after, so that downstream can use maybe_get_output |
| 3263 | // to retrieve the output |
| 3264 | at::meta::structured_clamp_min::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3265 | } |
| 3266 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3267 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 3268 | } |
| 3269 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 3270 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 3271 | c10::OptionalDeviceGuard guard_; |
| 3272 | }; |
| 3273 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_clamp_min_(at::Tensor & self, const at::Scalar & min) { |
| 3274 | structured_clamp_min_default_backend_inplace op(self); |
| 3275 | op.meta(self, min); |
| 3276 | at::clamp_min_outf(self, min, op.outputs_[0]); |
| 3277 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 3278 | return self; |
| 3279 | } |
| 3280 | struct structured_clamp_min_Tensor_default_backend_functional final : public at::meta::structured_clamp_min_Tensor { |
| 3281 | void set_output_strided( |
| 3282 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3283 | TensorOptions options, DimnameList names |
| 3284 | ) override { |
| 3285 | auto current_device = guard_.current_device(); |
| 3286 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3287 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3288 | "structured kernels don't support multi-device outputs"); |
| 3289 | } else { |
| 3290 | guard_.reset_device(options.device()); |
| 3291 | } |
| 3292 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3293 | if (!names.empty()) { |
| 3294 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3295 | } |
| 3296 | // super must happen after, so that downstream can use maybe_get_output |
| 3297 | // to retrieve the output |
| 3298 | at::meta::structured_clamp_min_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3299 | } |
| 3300 | void set_output_raw_strided( |
| 3301 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3302 | TensorOptions options, DimnameList names |
| 3303 | ) override { |
| 3304 | auto current_device = guard_.current_device(); |
| 3305 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3306 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3307 | "structured kernels don't support multi-device outputs"); |
| 3308 | } else { |
| 3309 | guard_.reset_device(options.device()); |
| 3310 | } |
| 3311 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3312 | if (!names.empty()) { |
| 3313 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3314 | } |
| 3315 | // super must happen after, so that downstream can use maybe_get_output |
| 3316 | // to retrieve the output |
| 3317 | at::meta::structured_clamp_min_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3318 | } |
| 3319 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3320 | return *outputs_[output_idx]; |
| 3321 | } |
| 3322 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 3323 | c10::OptionalDeviceGuard guard_; |
| 3324 | }; |
| 3325 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_clamp_min_Tensor(const at::Tensor & self, const at::Tensor & min) { |
| 3326 | structured_clamp_min_Tensor_default_backend_functional op; |
| 3327 | op.meta(self, min); |
| 3328 | at::clamp_min_outf(self, min, *op.outputs_[0]); |
| 3329 | return std::move(op.outputs_[0]).take(); |
| 3330 | } |
| 3331 | struct structured_clamp_min_Tensor_default_backend_inplace final : public at::meta::structured_clamp_min_Tensor { |
| 3332 | structured_clamp_min_Tensor_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 3333 | void set_output_strided( |
| 3334 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3335 | TensorOptions options, DimnameList names |
| 3336 | ) override { |
| 3337 | auto current_device = guard_.current_device(); |
| 3338 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3339 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3340 | "structured kernels don't support multi-device outputs"); |
| 3341 | } else { |
| 3342 | guard_.reset_device(options.device()); |
| 3343 | } |
| 3344 | const auto& out = outputs_[output_idx].get(); |
| 3345 | check_inplace(out, sizes, options); |
| 3346 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 3347 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 3348 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 3349 | } |
| 3350 | if (!names.empty()) { |
| 3351 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3352 | } |
| 3353 | // super must happen after, so that downstream can use maybe_get_output |
| 3354 | // to retrieve the output |
| 3355 | at::meta::structured_clamp_min_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3356 | } |
| 3357 | void set_output_raw_strided( |
| 3358 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3359 | TensorOptions options, DimnameList names |
| 3360 | ) override { |
| 3361 | auto current_device = guard_.current_device(); |
| 3362 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3363 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3364 | "structured kernels don't support multi-device outputs"); |
| 3365 | } else { |
| 3366 | guard_.reset_device(options.device()); |
| 3367 | } |
| 3368 | const auto& out = outputs_[output_idx].get(); |
| 3369 | check_inplace(out, sizes, options); |
| 3370 | if (!names.empty()) { |
| 3371 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3372 | } |
| 3373 | // super must happen after, so that downstream can use maybe_get_output |
| 3374 | // to retrieve the output |
| 3375 | at::meta::structured_clamp_min_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3376 | } |
| 3377 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3378 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 3379 | } |
| 3380 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 3381 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 3382 | c10::OptionalDeviceGuard guard_; |
| 3383 | }; |
| 3384 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_clamp_min__Tensor(at::Tensor & self, const at::Tensor & min) { |
| 3385 | structured_clamp_min_Tensor_default_backend_inplace op(self); |
| 3386 | op.meta(self, min); |
| 3387 | at::clamp_min_outf(self, min, op.outputs_[0]); |
| 3388 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 3389 | return self; |
| 3390 | } |
| 3391 | namespace { |
| 3392 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__copy(const at::Tensor & self, const at::Tensor & src, bool non_blocking) { |
| 3393 | // No device check |
| 3394 | // DeviceGuard omitted |
| 3395 | return at::native::copy(self, src, non_blocking); |
| 3396 | } |
| 3397 | } // anonymous namespace |
| 3398 | struct structured_cos_default_backend_functional final : public at::meta::structured_cos { |
| 3399 | void set_output_strided( |
| 3400 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3401 | TensorOptions options, DimnameList names |
| 3402 | ) override { |
| 3403 | auto current_device = guard_.current_device(); |
| 3404 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3405 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3406 | "structured kernels don't support multi-device outputs"); |
| 3407 | } else { |
| 3408 | guard_.reset_device(options.device()); |
| 3409 | } |
| 3410 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3411 | if (!names.empty()) { |
| 3412 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3413 | } |
| 3414 | // super must happen after, so that downstream can use maybe_get_output |
| 3415 | // to retrieve the output |
| 3416 | at::meta::structured_cos::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3417 | } |
| 3418 | void set_output_raw_strided( |
| 3419 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3420 | TensorOptions options, DimnameList names |
| 3421 | ) override { |
| 3422 | auto current_device = guard_.current_device(); |
| 3423 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3424 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3425 | "structured kernels don't support multi-device outputs"); |
| 3426 | } else { |
| 3427 | guard_.reset_device(options.device()); |
| 3428 | } |
| 3429 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3430 | if (!names.empty()) { |
| 3431 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3432 | } |
| 3433 | // super must happen after, so that downstream can use maybe_get_output |
| 3434 | // to retrieve the output |
| 3435 | at::meta::structured_cos::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3436 | } |
| 3437 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3438 | return *outputs_[output_idx]; |
| 3439 | } |
| 3440 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 3441 | c10::OptionalDeviceGuard guard_; |
| 3442 | }; |
| 3443 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_cos(const at::Tensor & self) { |
| 3444 | structured_cos_default_backend_functional op; |
| 3445 | op.meta(self); |
| 3446 | at::cos_outf(self, *op.outputs_[0]); |
| 3447 | return std::move(op.outputs_[0]).take(); |
| 3448 | } |
| 3449 | struct structured_cos_default_backend_inplace final : public at::meta::structured_cos { |
| 3450 | structured_cos_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 3451 | void set_output_strided( |
| 3452 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3453 | TensorOptions options, DimnameList names |
| 3454 | ) override { |
| 3455 | auto current_device = guard_.current_device(); |
| 3456 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3457 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3458 | "structured kernels don't support multi-device outputs"); |
| 3459 | } else { |
| 3460 | guard_.reset_device(options.device()); |
| 3461 | } |
| 3462 | const auto& out = outputs_[output_idx].get(); |
| 3463 | check_inplace(out, sizes, options); |
| 3464 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 3465 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 3466 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 3467 | } |
| 3468 | if (!names.empty()) { |
| 3469 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3470 | } |
| 3471 | // super must happen after, so that downstream can use maybe_get_output |
| 3472 | // to retrieve the output |
| 3473 | at::meta::structured_cos::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3474 | } |
| 3475 | void set_output_raw_strided( |
| 3476 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3477 | TensorOptions options, DimnameList names |
| 3478 | ) override { |
| 3479 | auto current_device = guard_.current_device(); |
| 3480 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3481 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3482 | "structured kernels don't support multi-device outputs"); |
| 3483 | } else { |
| 3484 | guard_.reset_device(options.device()); |
| 3485 | } |
| 3486 | const auto& out = outputs_[output_idx].get(); |
| 3487 | check_inplace(out, sizes, options); |
| 3488 | if (!names.empty()) { |
| 3489 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3490 | } |
| 3491 | // super must happen after, so that downstream can use maybe_get_output |
| 3492 | // to retrieve the output |
| 3493 | at::meta::structured_cos::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3494 | } |
| 3495 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3496 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 3497 | } |
| 3498 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 3499 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 3500 | c10::OptionalDeviceGuard guard_; |
| 3501 | }; |
| 3502 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_cos_(at::Tensor & self) { |
| 3503 | structured_cos_default_backend_inplace op(self); |
| 3504 | op.meta(self); |
| 3505 | at::cos_outf(self, op.outputs_[0]); |
| 3506 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 3507 | return self; |
| 3508 | } |
| 3509 | struct structured_cosh_default_backend_functional final : public at::meta::structured_cosh { |
| 3510 | void set_output_strided( |
| 3511 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3512 | TensorOptions options, DimnameList names |
| 3513 | ) override { |
| 3514 | auto current_device = guard_.current_device(); |
| 3515 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3516 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3517 | "structured kernels don't support multi-device outputs"); |
| 3518 | } else { |
| 3519 | guard_.reset_device(options.device()); |
| 3520 | } |
| 3521 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3522 | if (!names.empty()) { |
| 3523 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3524 | } |
| 3525 | // super must happen after, so that downstream can use maybe_get_output |
| 3526 | // to retrieve the output |
| 3527 | at::meta::structured_cosh::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3528 | } |
| 3529 | void set_output_raw_strided( |
| 3530 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3531 | TensorOptions options, DimnameList names |
| 3532 | ) override { |
| 3533 | auto current_device = guard_.current_device(); |
| 3534 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3535 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3536 | "structured kernels don't support multi-device outputs"); |
| 3537 | } else { |
| 3538 | guard_.reset_device(options.device()); |
| 3539 | } |
| 3540 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3541 | if (!names.empty()) { |
| 3542 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3543 | } |
| 3544 | // super must happen after, so that downstream can use maybe_get_output |
| 3545 | // to retrieve the output |
| 3546 | at::meta::structured_cosh::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3547 | } |
| 3548 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3549 | return *outputs_[output_idx]; |
| 3550 | } |
| 3551 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 3552 | c10::OptionalDeviceGuard guard_; |
| 3553 | }; |
| 3554 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_cosh(const at::Tensor & self) { |
| 3555 | structured_cosh_default_backend_functional op; |
| 3556 | op.meta(self); |
| 3557 | at::cosh_outf(self, *op.outputs_[0]); |
| 3558 | return std::move(op.outputs_[0]).take(); |
| 3559 | } |
| 3560 | struct structured_cosh_default_backend_inplace final : public at::meta::structured_cosh { |
| 3561 | structured_cosh_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 3562 | void set_output_strided( |
| 3563 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3564 | TensorOptions options, DimnameList names |
| 3565 | ) override { |
| 3566 | auto current_device = guard_.current_device(); |
| 3567 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3568 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3569 | "structured kernels don't support multi-device outputs"); |
| 3570 | } else { |
| 3571 | guard_.reset_device(options.device()); |
| 3572 | } |
| 3573 | const auto& out = outputs_[output_idx].get(); |
| 3574 | check_inplace(out, sizes, options); |
| 3575 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 3576 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 3577 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 3578 | } |
| 3579 | if (!names.empty()) { |
| 3580 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3581 | } |
| 3582 | // super must happen after, so that downstream can use maybe_get_output |
| 3583 | // to retrieve the output |
| 3584 | at::meta::structured_cosh::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3585 | } |
| 3586 | void set_output_raw_strided( |
| 3587 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3588 | TensorOptions options, DimnameList names |
| 3589 | ) override { |
| 3590 | auto current_device = guard_.current_device(); |
| 3591 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3592 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3593 | "structured kernels don't support multi-device outputs"); |
| 3594 | } else { |
| 3595 | guard_.reset_device(options.device()); |
| 3596 | } |
| 3597 | const auto& out = outputs_[output_idx].get(); |
| 3598 | check_inplace(out, sizes, options); |
| 3599 | if (!names.empty()) { |
| 3600 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3601 | } |
| 3602 | // super must happen after, so that downstream can use maybe_get_output |
| 3603 | // to retrieve the output |
| 3604 | at::meta::structured_cosh::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3605 | } |
| 3606 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3607 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 3608 | } |
| 3609 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 3610 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 3611 | c10::OptionalDeviceGuard guard_; |
| 3612 | }; |
| 3613 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_cosh_(at::Tensor & self) { |
| 3614 | structured_cosh_default_backend_inplace op(self); |
| 3615 | op.meta(self); |
| 3616 | at::cosh_outf(self, op.outputs_[0]); |
| 3617 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 3618 | return self; |
| 3619 | } |
| 3620 | struct structured_cumprod_default_backend_functional final : public at::meta::structured_cumprod { |
| 3621 | void set_output_strided( |
| 3622 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3623 | TensorOptions options, DimnameList names |
| 3624 | ) override { |
| 3625 | auto current_device = guard_.current_device(); |
| 3626 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3627 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3628 | "structured kernels don't support multi-device outputs"); |
| 3629 | } else { |
| 3630 | guard_.reset_device(options.device()); |
| 3631 | } |
| 3632 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3633 | if (!names.empty()) { |
| 3634 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3635 | } |
| 3636 | // super must happen after, so that downstream can use maybe_get_output |
| 3637 | // to retrieve the output |
| 3638 | } |
| 3639 | void set_output_raw_strided( |
| 3640 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3641 | TensorOptions options, DimnameList names |
| 3642 | ) override { |
| 3643 | auto current_device = guard_.current_device(); |
| 3644 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3645 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3646 | "structured kernels don't support multi-device outputs"); |
| 3647 | } else { |
| 3648 | guard_.reset_device(options.device()); |
| 3649 | } |
| 3650 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3651 | if (!names.empty()) { |
| 3652 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3653 | } |
| 3654 | // super must happen after, so that downstream can use maybe_get_output |
| 3655 | // to retrieve the output |
| 3656 | } |
| 3657 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3658 | return *outputs_[output_idx]; |
| 3659 | } |
| 3660 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 3661 | c10::OptionalDeviceGuard guard_; |
| 3662 | }; |
| 3663 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_cumprod(const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 3664 | structured_cumprod_default_backend_functional op; |
| 3665 | op.meta(self, dim, dtype); |
| 3666 | at::cumprod_outf(self, dim, dtype, *op.outputs_[0]); |
| 3667 | return std::move(op.outputs_[0]).take(); |
| 3668 | } |
| 3669 | struct structured_cumprod_default_backend_inplace final : public at::meta::structured_cumprod { |
| 3670 | structured_cumprod_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 3671 | void set_output_strided( |
| 3672 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3673 | TensorOptions options, DimnameList names |
| 3674 | ) override { |
| 3675 | auto current_device = guard_.current_device(); |
| 3676 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3677 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3678 | "structured kernels don't support multi-device outputs"); |
| 3679 | } else { |
| 3680 | guard_.reset_device(options.device()); |
| 3681 | } |
| 3682 | const auto& out = outputs_[output_idx].get(); |
| 3683 | check_inplace(out, sizes, options); |
| 3684 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 3685 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 3686 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 3687 | } |
| 3688 | if (!names.empty()) { |
| 3689 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3690 | } |
| 3691 | // super must happen after, so that downstream can use maybe_get_output |
| 3692 | // to retrieve the output |
| 3693 | } |
| 3694 | void set_output_raw_strided( |
| 3695 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3696 | TensorOptions options, DimnameList names |
| 3697 | ) override { |
| 3698 | auto current_device = guard_.current_device(); |
| 3699 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3700 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3701 | "structured kernels don't support multi-device outputs"); |
| 3702 | } else { |
| 3703 | guard_.reset_device(options.device()); |
| 3704 | } |
| 3705 | const auto& out = outputs_[output_idx].get(); |
| 3706 | check_inplace(out, sizes, options); |
| 3707 | if (!names.empty()) { |
| 3708 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3709 | } |
| 3710 | // super must happen after, so that downstream can use maybe_get_output |
| 3711 | // to retrieve the output |
| 3712 | } |
| 3713 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3714 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 3715 | } |
| 3716 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 3717 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 3718 | c10::OptionalDeviceGuard guard_; |
| 3719 | }; |
| 3720 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_cumprod_(at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 3721 | structured_cumprod_default_backend_inplace op(self); |
| 3722 | op.meta(self, dim, dtype); |
| 3723 | at::cumprod_outf(self, dim, dtype, op.outputs_[0]); |
| 3724 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 3725 | return self; |
| 3726 | } |
| 3727 | struct structured_cumsum_default_backend_functional final : public at::meta::structured_cumsum { |
| 3728 | void set_output_strided( |
| 3729 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3730 | TensorOptions options, DimnameList names |
| 3731 | ) override { |
| 3732 | auto current_device = guard_.current_device(); |
| 3733 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3734 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3735 | "structured kernels don't support multi-device outputs"); |
| 3736 | } else { |
| 3737 | guard_.reset_device(options.device()); |
| 3738 | } |
| 3739 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3740 | if (!names.empty()) { |
| 3741 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3742 | } |
| 3743 | // super must happen after, so that downstream can use maybe_get_output |
| 3744 | // to retrieve the output |
| 3745 | } |
| 3746 | void set_output_raw_strided( |
| 3747 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3748 | TensorOptions options, DimnameList names |
| 3749 | ) override { |
| 3750 | auto current_device = guard_.current_device(); |
| 3751 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3752 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3753 | "structured kernels don't support multi-device outputs"); |
| 3754 | } else { |
| 3755 | guard_.reset_device(options.device()); |
| 3756 | } |
| 3757 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3758 | if (!names.empty()) { |
| 3759 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3760 | } |
| 3761 | // super must happen after, so that downstream can use maybe_get_output |
| 3762 | // to retrieve the output |
| 3763 | } |
| 3764 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3765 | return *outputs_[output_idx]; |
| 3766 | } |
| 3767 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 3768 | c10::OptionalDeviceGuard guard_; |
| 3769 | }; |
| 3770 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_cumsum(const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 3771 | structured_cumsum_default_backend_functional op; |
| 3772 | op.meta(self, dim, dtype); |
| 3773 | at::cumsum_outf(self, dim, dtype, *op.outputs_[0]); |
| 3774 | return std::move(op.outputs_[0]).take(); |
| 3775 | } |
| 3776 | struct structured_cumsum_default_backend_inplace final : public at::meta::structured_cumsum { |
| 3777 | structured_cumsum_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 3778 | void set_output_strided( |
| 3779 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3780 | TensorOptions options, DimnameList names |
| 3781 | ) override { |
| 3782 | auto current_device = guard_.current_device(); |
| 3783 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3784 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3785 | "structured kernels don't support multi-device outputs"); |
| 3786 | } else { |
| 3787 | guard_.reset_device(options.device()); |
| 3788 | } |
| 3789 | const auto& out = outputs_[output_idx].get(); |
| 3790 | check_inplace(out, sizes, options); |
| 3791 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 3792 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 3793 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 3794 | } |
| 3795 | if (!names.empty()) { |
| 3796 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3797 | } |
| 3798 | // super must happen after, so that downstream can use maybe_get_output |
| 3799 | // to retrieve the output |
| 3800 | } |
| 3801 | void set_output_raw_strided( |
| 3802 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3803 | TensorOptions options, DimnameList names |
| 3804 | ) override { |
| 3805 | auto current_device = guard_.current_device(); |
| 3806 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3807 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3808 | "structured kernels don't support multi-device outputs"); |
| 3809 | } else { |
| 3810 | guard_.reset_device(options.device()); |
| 3811 | } |
| 3812 | const auto& out = outputs_[output_idx].get(); |
| 3813 | check_inplace(out, sizes, options); |
| 3814 | if (!names.empty()) { |
| 3815 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3816 | } |
| 3817 | // super must happen after, so that downstream can use maybe_get_output |
| 3818 | // to retrieve the output |
| 3819 | } |
| 3820 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3821 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 3822 | } |
| 3823 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 3824 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 3825 | c10::OptionalDeviceGuard guard_; |
| 3826 | }; |
| 3827 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_cumsum_(at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 3828 | structured_cumsum_default_backend_inplace op(self); |
| 3829 | op.meta(self, dim, dtype); |
| 3830 | at::cumsum_outf(self, dim, dtype, op.outputs_[0]); |
| 3831 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 3832 | return self; |
| 3833 | } |
| 3834 | namespace { |
| 3835 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__diag_embed(const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2) { |
| 3836 | // No device check |
| 3837 | // DeviceGuard omitted |
| 3838 | return at::native::diag_embed(self, offset, dim1, dim2); |
| 3839 | } |
| 3840 | } // anonymous namespace |
| 3841 | struct structured_div_Tensor_default_backend_functional final : public at::meta::structured_div_Tensor { |
| 3842 | void set_output_strided( |
| 3843 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3844 | TensorOptions options, DimnameList names |
| 3845 | ) override { |
| 3846 | auto current_device = guard_.current_device(); |
| 3847 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3848 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3849 | "structured kernels don't support multi-device outputs"); |
| 3850 | } else { |
| 3851 | guard_.reset_device(options.device()); |
| 3852 | } |
| 3853 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3854 | if (!names.empty()) { |
| 3855 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3856 | } |
| 3857 | // super must happen after, so that downstream can use maybe_get_output |
| 3858 | // to retrieve the output |
| 3859 | at::meta::structured_div_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3860 | } |
| 3861 | void set_output_raw_strided( |
| 3862 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3863 | TensorOptions options, DimnameList names |
| 3864 | ) override { |
| 3865 | auto current_device = guard_.current_device(); |
| 3866 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3867 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3868 | "structured kernels don't support multi-device outputs"); |
| 3869 | } else { |
| 3870 | guard_.reset_device(options.device()); |
| 3871 | } |
| 3872 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3873 | if (!names.empty()) { |
| 3874 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3875 | } |
| 3876 | // super must happen after, so that downstream can use maybe_get_output |
| 3877 | // to retrieve the output |
| 3878 | at::meta::structured_div_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3879 | } |
| 3880 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3881 | return *outputs_[output_idx]; |
| 3882 | } |
| 3883 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 3884 | c10::OptionalDeviceGuard guard_; |
| 3885 | }; |
| 3886 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_div_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 3887 | structured_div_Tensor_default_backend_functional op; |
| 3888 | op.meta(self, other); |
| 3889 | at::div_outf(self, other, *op.outputs_[0]); |
| 3890 | return std::move(op.outputs_[0]).take(); |
| 3891 | } |
| 3892 | struct structured_div_Tensor_default_backend_inplace final : public at::meta::structured_div_Tensor { |
| 3893 | structured_div_Tensor_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 3894 | void set_output_strided( |
| 3895 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3896 | TensorOptions options, DimnameList names |
| 3897 | ) override { |
| 3898 | auto current_device = guard_.current_device(); |
| 3899 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3900 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3901 | "structured kernels don't support multi-device outputs"); |
| 3902 | } else { |
| 3903 | guard_.reset_device(options.device()); |
| 3904 | } |
| 3905 | const auto& out = outputs_[output_idx].get(); |
| 3906 | check_inplace(out, sizes, options); |
| 3907 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 3908 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 3909 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 3910 | } |
| 3911 | if (!names.empty()) { |
| 3912 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3913 | } |
| 3914 | // super must happen after, so that downstream can use maybe_get_output |
| 3915 | // to retrieve the output |
| 3916 | at::meta::structured_div_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3917 | } |
| 3918 | void set_output_raw_strided( |
| 3919 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3920 | TensorOptions options, DimnameList names |
| 3921 | ) override { |
| 3922 | auto current_device = guard_.current_device(); |
| 3923 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3924 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3925 | "structured kernels don't support multi-device outputs"); |
| 3926 | } else { |
| 3927 | guard_.reset_device(options.device()); |
| 3928 | } |
| 3929 | const auto& out = outputs_[output_idx].get(); |
| 3930 | check_inplace(out, sizes, options); |
| 3931 | if (!names.empty()) { |
| 3932 | namedinference::propagate_names(outputs_[output_idx], names); |
| 3933 | } |
| 3934 | // super must happen after, so that downstream can use maybe_get_output |
| 3935 | // to retrieve the output |
| 3936 | at::meta::structured_div_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3937 | } |
| 3938 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3939 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 3940 | } |
| 3941 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 3942 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 3943 | c10::OptionalDeviceGuard guard_; |
| 3944 | }; |
| 3945 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_div__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 3946 | structured_div_Tensor_default_backend_inplace op(self); |
| 3947 | op.meta(self, other); |
| 3948 | at::div_outf(self, other, op.outputs_[0]); |
| 3949 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 3950 | return self; |
| 3951 | } |
| 3952 | struct structured_div_Tensor_mode_default_backend_functional final : public at::meta::structured_div_Tensor_mode { |
| 3953 | void set_output_strided( |
| 3954 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3955 | TensorOptions options, DimnameList names |
| 3956 | ) override { |
| 3957 | auto current_device = guard_.current_device(); |
| 3958 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3959 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3960 | "structured kernels don't support multi-device outputs"); |
| 3961 | } else { |
| 3962 | guard_.reset_device(options.device()); |
| 3963 | } |
| 3964 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3965 | if (!names.empty()) { |
| 3966 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3967 | } |
| 3968 | // super must happen after, so that downstream can use maybe_get_output |
| 3969 | // to retrieve the output |
| 3970 | at::meta::structured_div_Tensor_mode::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3971 | } |
| 3972 | void set_output_raw_strided( |
| 3973 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 3974 | TensorOptions options, DimnameList names |
| 3975 | ) override { |
| 3976 | auto current_device = guard_.current_device(); |
| 3977 | if (C10_UNLIKELY(current_device.has_value())) { |
| 3978 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 3979 | "structured kernels don't support multi-device outputs"); |
| 3980 | } else { |
| 3981 | guard_.reset_device(options.device()); |
| 3982 | } |
| 3983 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 3984 | if (!names.empty()) { |
| 3985 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 3986 | } |
| 3987 | // super must happen after, so that downstream can use maybe_get_output |
| 3988 | // to retrieve the output |
| 3989 | at::meta::structured_div_Tensor_mode::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 3990 | } |
| 3991 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 3992 | return *outputs_[output_idx]; |
| 3993 | } |
| 3994 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 3995 | c10::OptionalDeviceGuard guard_; |
| 3996 | }; |
| 3997 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_div_Tensor_mode(const at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode) { |
| 3998 | structured_div_Tensor_mode_default_backend_functional op; |
| 3999 | op.meta(self, other, rounding_mode); |
| 4000 | at::div_outf(self, other, rounding_mode, *op.outputs_[0]); |
| 4001 | return std::move(op.outputs_[0]).take(); |
| 4002 | } |
| 4003 | struct structured_div_Tensor_mode_default_backend_inplace final : public at::meta::structured_div_Tensor_mode { |
| 4004 | structured_div_Tensor_mode_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 4005 | void set_output_strided( |
| 4006 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4007 | TensorOptions options, DimnameList names |
| 4008 | ) override { |
| 4009 | auto current_device = guard_.current_device(); |
| 4010 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4011 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4012 | "structured kernels don't support multi-device outputs"); |
| 4013 | } else { |
| 4014 | guard_.reset_device(options.device()); |
| 4015 | } |
| 4016 | const auto& out = outputs_[output_idx].get(); |
| 4017 | check_inplace(out, sizes, options); |
| 4018 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 4019 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 4020 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 4021 | } |
| 4022 | if (!names.empty()) { |
| 4023 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4024 | } |
| 4025 | // super must happen after, so that downstream can use maybe_get_output |
| 4026 | // to retrieve the output |
| 4027 | at::meta::structured_div_Tensor_mode::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4028 | } |
| 4029 | void set_output_raw_strided( |
| 4030 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4031 | TensorOptions options, DimnameList names |
| 4032 | ) override { |
| 4033 | auto current_device = guard_.current_device(); |
| 4034 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4035 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4036 | "structured kernels don't support multi-device outputs"); |
| 4037 | } else { |
| 4038 | guard_.reset_device(options.device()); |
| 4039 | } |
| 4040 | const auto& out = outputs_[output_idx].get(); |
| 4041 | check_inplace(out, sizes, options); |
| 4042 | if (!names.empty()) { |
| 4043 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4044 | } |
| 4045 | // super must happen after, so that downstream can use maybe_get_output |
| 4046 | // to retrieve the output |
| 4047 | at::meta::structured_div_Tensor_mode::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4048 | } |
| 4049 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4050 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 4051 | } |
| 4052 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 4053 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 4054 | c10::OptionalDeviceGuard guard_; |
| 4055 | }; |
| 4056 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_div__Tensor_mode(at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode) { |
| 4057 | structured_div_Tensor_mode_default_backend_inplace op(self); |
| 4058 | op.meta(self, other, rounding_mode); |
| 4059 | at::div_outf(self, other, rounding_mode, op.outputs_[0]); |
| 4060 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 4061 | return self; |
| 4062 | } |
| 4063 | namespace { |
| 4064 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__new_empty_strided(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 4065 | // No device check |
| 4066 | // DeviceGuard omitted |
| 4067 | return at::native::new_empty_strided_symint(self, size, stride, dtype, layout, device, pin_memory); |
| 4068 | } |
| 4069 | } // anonymous namespace |
| 4070 | struct structured_erf_default_backend_functional final : public at::meta::structured_erf { |
| 4071 | void set_output_strided( |
| 4072 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4073 | TensorOptions options, DimnameList names |
| 4074 | ) override { |
| 4075 | auto current_device = guard_.current_device(); |
| 4076 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4077 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4078 | "structured kernels don't support multi-device outputs"); |
| 4079 | } else { |
| 4080 | guard_.reset_device(options.device()); |
| 4081 | } |
| 4082 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4083 | if (!names.empty()) { |
| 4084 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4085 | } |
| 4086 | // super must happen after, so that downstream can use maybe_get_output |
| 4087 | // to retrieve the output |
| 4088 | at::meta::structured_erf::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4089 | } |
| 4090 | void set_output_raw_strided( |
| 4091 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4092 | TensorOptions options, DimnameList names |
| 4093 | ) override { |
| 4094 | auto current_device = guard_.current_device(); |
| 4095 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4096 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4097 | "structured kernels don't support multi-device outputs"); |
| 4098 | } else { |
| 4099 | guard_.reset_device(options.device()); |
| 4100 | } |
| 4101 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4102 | if (!names.empty()) { |
| 4103 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4104 | } |
| 4105 | // super must happen after, so that downstream can use maybe_get_output |
| 4106 | // to retrieve the output |
| 4107 | at::meta::structured_erf::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4108 | } |
| 4109 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4110 | return *outputs_[output_idx]; |
| 4111 | } |
| 4112 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 4113 | c10::OptionalDeviceGuard guard_; |
| 4114 | }; |
| 4115 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_erf(const at::Tensor & self) { |
| 4116 | structured_erf_default_backend_functional op; |
| 4117 | op.meta(self); |
| 4118 | at::erf_outf(self, *op.outputs_[0]); |
| 4119 | return std::move(op.outputs_[0]).take(); |
| 4120 | } |
| 4121 | struct structured_erf_default_backend_inplace final : public at::meta::structured_erf { |
| 4122 | structured_erf_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 4123 | void set_output_strided( |
| 4124 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4125 | TensorOptions options, DimnameList names |
| 4126 | ) override { |
| 4127 | auto current_device = guard_.current_device(); |
| 4128 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4129 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4130 | "structured kernels don't support multi-device outputs"); |
| 4131 | } else { |
| 4132 | guard_.reset_device(options.device()); |
| 4133 | } |
| 4134 | const auto& out = outputs_[output_idx].get(); |
| 4135 | check_inplace(out, sizes, options); |
| 4136 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 4137 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 4138 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 4139 | } |
| 4140 | if (!names.empty()) { |
| 4141 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4142 | } |
| 4143 | // super must happen after, so that downstream can use maybe_get_output |
| 4144 | // to retrieve the output |
| 4145 | at::meta::structured_erf::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4146 | } |
| 4147 | void set_output_raw_strided( |
| 4148 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4149 | TensorOptions options, DimnameList names |
| 4150 | ) override { |
| 4151 | auto current_device = guard_.current_device(); |
| 4152 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4153 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4154 | "structured kernels don't support multi-device outputs"); |
| 4155 | } else { |
| 4156 | guard_.reset_device(options.device()); |
| 4157 | } |
| 4158 | const auto& out = outputs_[output_idx].get(); |
| 4159 | check_inplace(out, sizes, options); |
| 4160 | if (!names.empty()) { |
| 4161 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4162 | } |
| 4163 | // super must happen after, so that downstream can use maybe_get_output |
| 4164 | // to retrieve the output |
| 4165 | at::meta::structured_erf::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4166 | } |
| 4167 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4168 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 4169 | } |
| 4170 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 4171 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 4172 | c10::OptionalDeviceGuard guard_; |
| 4173 | }; |
| 4174 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_erf_(at::Tensor & self) { |
| 4175 | structured_erf_default_backend_inplace op(self); |
| 4176 | op.meta(self); |
| 4177 | at::erf_outf(self, op.outputs_[0]); |
| 4178 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 4179 | return self; |
| 4180 | } |
| 4181 | struct structured_erfc_default_backend_functional final : public at::meta::structured_erfc { |
| 4182 | void set_output_strided( |
| 4183 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4184 | TensorOptions options, DimnameList names |
| 4185 | ) override { |
| 4186 | auto current_device = guard_.current_device(); |
| 4187 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4188 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4189 | "structured kernels don't support multi-device outputs"); |
| 4190 | } else { |
| 4191 | guard_.reset_device(options.device()); |
| 4192 | } |
| 4193 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4194 | if (!names.empty()) { |
| 4195 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4196 | } |
| 4197 | // super must happen after, so that downstream can use maybe_get_output |
| 4198 | // to retrieve the output |
| 4199 | at::meta::structured_erfc::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4200 | } |
| 4201 | void set_output_raw_strided( |
| 4202 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4203 | TensorOptions options, DimnameList names |
| 4204 | ) override { |
| 4205 | auto current_device = guard_.current_device(); |
| 4206 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4207 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4208 | "structured kernels don't support multi-device outputs"); |
| 4209 | } else { |
| 4210 | guard_.reset_device(options.device()); |
| 4211 | } |
| 4212 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4213 | if (!names.empty()) { |
| 4214 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4215 | } |
| 4216 | // super must happen after, so that downstream can use maybe_get_output |
| 4217 | // to retrieve the output |
| 4218 | at::meta::structured_erfc::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4219 | } |
| 4220 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4221 | return *outputs_[output_idx]; |
| 4222 | } |
| 4223 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 4224 | c10::OptionalDeviceGuard guard_; |
| 4225 | }; |
| 4226 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_erfc(const at::Tensor & self) { |
| 4227 | structured_erfc_default_backend_functional op; |
| 4228 | op.meta(self); |
| 4229 | at::erfc_outf(self, *op.outputs_[0]); |
| 4230 | return std::move(op.outputs_[0]).take(); |
| 4231 | } |
| 4232 | struct structured_erfc_default_backend_inplace final : public at::meta::structured_erfc { |
| 4233 | structured_erfc_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 4234 | void set_output_strided( |
| 4235 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4236 | TensorOptions options, DimnameList names |
| 4237 | ) override { |
| 4238 | auto current_device = guard_.current_device(); |
| 4239 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4240 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4241 | "structured kernels don't support multi-device outputs"); |
| 4242 | } else { |
| 4243 | guard_.reset_device(options.device()); |
| 4244 | } |
| 4245 | const auto& out = outputs_[output_idx].get(); |
| 4246 | check_inplace(out, sizes, options); |
| 4247 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 4248 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 4249 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 4250 | } |
| 4251 | if (!names.empty()) { |
| 4252 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4253 | } |
| 4254 | // super must happen after, so that downstream can use maybe_get_output |
| 4255 | // to retrieve the output |
| 4256 | at::meta::structured_erfc::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4257 | } |
| 4258 | void set_output_raw_strided( |
| 4259 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4260 | TensorOptions options, DimnameList names |
| 4261 | ) override { |
| 4262 | auto current_device = guard_.current_device(); |
| 4263 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4264 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4265 | "structured kernels don't support multi-device outputs"); |
| 4266 | } else { |
| 4267 | guard_.reset_device(options.device()); |
| 4268 | } |
| 4269 | const auto& out = outputs_[output_idx].get(); |
| 4270 | check_inplace(out, sizes, options); |
| 4271 | if (!names.empty()) { |
| 4272 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4273 | } |
| 4274 | // super must happen after, so that downstream can use maybe_get_output |
| 4275 | // to retrieve the output |
| 4276 | at::meta::structured_erfc::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4277 | } |
| 4278 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4279 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 4280 | } |
| 4281 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 4282 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 4283 | c10::OptionalDeviceGuard guard_; |
| 4284 | }; |
| 4285 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_erfc_(at::Tensor & self) { |
| 4286 | structured_erfc_default_backend_inplace op(self); |
| 4287 | op.meta(self); |
| 4288 | at::erfc_outf(self, op.outputs_[0]); |
| 4289 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 4290 | return self; |
| 4291 | } |
| 4292 | struct structured_exp_default_backend_functional final : public at::meta::structured_exp { |
| 4293 | void set_output_strided( |
| 4294 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4295 | TensorOptions options, DimnameList names |
| 4296 | ) override { |
| 4297 | auto current_device = guard_.current_device(); |
| 4298 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4299 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4300 | "structured kernels don't support multi-device outputs"); |
| 4301 | } else { |
| 4302 | guard_.reset_device(options.device()); |
| 4303 | } |
| 4304 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4305 | if (!names.empty()) { |
| 4306 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4307 | } |
| 4308 | // super must happen after, so that downstream can use maybe_get_output |
| 4309 | // to retrieve the output |
| 4310 | at::meta::structured_exp::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4311 | } |
| 4312 | void set_output_raw_strided( |
| 4313 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4314 | TensorOptions options, DimnameList names |
| 4315 | ) override { |
| 4316 | auto current_device = guard_.current_device(); |
| 4317 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4318 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4319 | "structured kernels don't support multi-device outputs"); |
| 4320 | } else { |
| 4321 | guard_.reset_device(options.device()); |
| 4322 | } |
| 4323 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4324 | if (!names.empty()) { |
| 4325 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4326 | } |
| 4327 | // super must happen after, so that downstream can use maybe_get_output |
| 4328 | // to retrieve the output |
| 4329 | at::meta::structured_exp::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4330 | } |
| 4331 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4332 | return *outputs_[output_idx]; |
| 4333 | } |
| 4334 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 4335 | c10::OptionalDeviceGuard guard_; |
| 4336 | }; |
| 4337 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_exp(const at::Tensor & self) { |
| 4338 | structured_exp_default_backend_functional op; |
| 4339 | op.meta(self); |
| 4340 | at::exp_outf(self, *op.outputs_[0]); |
| 4341 | return std::move(op.outputs_[0]).take(); |
| 4342 | } |
| 4343 | struct structured_exp_default_backend_inplace final : public at::meta::structured_exp { |
| 4344 | structured_exp_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 4345 | void set_output_strided( |
| 4346 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4347 | TensorOptions options, DimnameList names |
| 4348 | ) override { |
| 4349 | auto current_device = guard_.current_device(); |
| 4350 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4351 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4352 | "structured kernels don't support multi-device outputs"); |
| 4353 | } else { |
| 4354 | guard_.reset_device(options.device()); |
| 4355 | } |
| 4356 | const auto& out = outputs_[output_idx].get(); |
| 4357 | check_inplace(out, sizes, options); |
| 4358 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 4359 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 4360 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 4361 | } |
| 4362 | if (!names.empty()) { |
| 4363 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4364 | } |
| 4365 | // super must happen after, so that downstream can use maybe_get_output |
| 4366 | // to retrieve the output |
| 4367 | at::meta::structured_exp::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4368 | } |
| 4369 | void set_output_raw_strided( |
| 4370 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4371 | TensorOptions options, DimnameList names |
| 4372 | ) override { |
| 4373 | auto current_device = guard_.current_device(); |
| 4374 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4375 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4376 | "structured kernels don't support multi-device outputs"); |
| 4377 | } else { |
| 4378 | guard_.reset_device(options.device()); |
| 4379 | } |
| 4380 | const auto& out = outputs_[output_idx].get(); |
| 4381 | check_inplace(out, sizes, options); |
| 4382 | if (!names.empty()) { |
| 4383 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4384 | } |
| 4385 | // super must happen after, so that downstream can use maybe_get_output |
| 4386 | // to retrieve the output |
| 4387 | at::meta::structured_exp::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4388 | } |
| 4389 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4390 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 4391 | } |
| 4392 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 4393 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 4394 | c10::OptionalDeviceGuard guard_; |
| 4395 | }; |
| 4396 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_exp_(at::Tensor & self) { |
| 4397 | structured_exp_default_backend_inplace op(self); |
| 4398 | op.meta(self); |
| 4399 | at::exp_outf(self, op.outputs_[0]); |
| 4400 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 4401 | return self; |
| 4402 | } |
| 4403 | struct structured_exp2_default_backend_functional final : public at::meta::structured_exp2 { |
| 4404 | void set_output_strided( |
| 4405 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4406 | TensorOptions options, DimnameList names |
| 4407 | ) override { |
| 4408 | auto current_device = guard_.current_device(); |
| 4409 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4410 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4411 | "structured kernels don't support multi-device outputs"); |
| 4412 | } else { |
| 4413 | guard_.reset_device(options.device()); |
| 4414 | } |
| 4415 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4416 | if (!names.empty()) { |
| 4417 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4418 | } |
| 4419 | // super must happen after, so that downstream can use maybe_get_output |
| 4420 | // to retrieve the output |
| 4421 | at::meta::structured_exp2::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4422 | } |
| 4423 | void set_output_raw_strided( |
| 4424 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4425 | TensorOptions options, DimnameList names |
| 4426 | ) override { |
| 4427 | auto current_device = guard_.current_device(); |
| 4428 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4429 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4430 | "structured kernels don't support multi-device outputs"); |
| 4431 | } else { |
| 4432 | guard_.reset_device(options.device()); |
| 4433 | } |
| 4434 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4435 | if (!names.empty()) { |
| 4436 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4437 | } |
| 4438 | // super must happen after, so that downstream can use maybe_get_output |
| 4439 | // to retrieve the output |
| 4440 | at::meta::structured_exp2::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4441 | } |
| 4442 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4443 | return *outputs_[output_idx]; |
| 4444 | } |
| 4445 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 4446 | c10::OptionalDeviceGuard guard_; |
| 4447 | }; |
| 4448 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_exp2(const at::Tensor & self) { |
| 4449 | structured_exp2_default_backend_functional op; |
| 4450 | op.meta(self); |
| 4451 | at::exp2_outf(self, *op.outputs_[0]); |
| 4452 | return std::move(op.outputs_[0]).take(); |
| 4453 | } |
| 4454 | struct structured_exp2_default_backend_inplace final : public at::meta::structured_exp2 { |
| 4455 | structured_exp2_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 4456 | void set_output_strided( |
| 4457 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4458 | TensorOptions options, DimnameList names |
| 4459 | ) override { |
| 4460 | auto current_device = guard_.current_device(); |
| 4461 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4462 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4463 | "structured kernels don't support multi-device outputs"); |
| 4464 | } else { |
| 4465 | guard_.reset_device(options.device()); |
| 4466 | } |
| 4467 | const auto& out = outputs_[output_idx].get(); |
| 4468 | check_inplace(out, sizes, options); |
| 4469 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 4470 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 4471 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 4472 | } |
| 4473 | if (!names.empty()) { |
| 4474 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4475 | } |
| 4476 | // super must happen after, so that downstream can use maybe_get_output |
| 4477 | // to retrieve the output |
| 4478 | at::meta::structured_exp2::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4479 | } |
| 4480 | void set_output_raw_strided( |
| 4481 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4482 | TensorOptions options, DimnameList names |
| 4483 | ) override { |
| 4484 | auto current_device = guard_.current_device(); |
| 4485 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4486 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4487 | "structured kernels don't support multi-device outputs"); |
| 4488 | } else { |
| 4489 | guard_.reset_device(options.device()); |
| 4490 | } |
| 4491 | const auto& out = outputs_[output_idx].get(); |
| 4492 | check_inplace(out, sizes, options); |
| 4493 | if (!names.empty()) { |
| 4494 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4495 | } |
| 4496 | // super must happen after, so that downstream can use maybe_get_output |
| 4497 | // to retrieve the output |
| 4498 | at::meta::structured_exp2::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4499 | } |
| 4500 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4501 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 4502 | } |
| 4503 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 4504 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 4505 | c10::OptionalDeviceGuard guard_; |
| 4506 | }; |
| 4507 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_exp2_(at::Tensor & self) { |
| 4508 | structured_exp2_default_backend_inplace op(self); |
| 4509 | op.meta(self); |
| 4510 | at::exp2_outf(self, op.outputs_[0]); |
| 4511 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 4512 | return self; |
| 4513 | } |
| 4514 | struct structured_expm1_default_backend_functional final : public at::meta::structured_expm1 { |
| 4515 | void set_output_strided( |
| 4516 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4517 | TensorOptions options, DimnameList names |
| 4518 | ) override { |
| 4519 | auto current_device = guard_.current_device(); |
| 4520 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4521 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4522 | "structured kernels don't support multi-device outputs"); |
| 4523 | } else { |
| 4524 | guard_.reset_device(options.device()); |
| 4525 | } |
| 4526 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4527 | if (!names.empty()) { |
| 4528 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4529 | } |
| 4530 | // super must happen after, so that downstream can use maybe_get_output |
| 4531 | // to retrieve the output |
| 4532 | at::meta::structured_expm1::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4533 | } |
| 4534 | void set_output_raw_strided( |
| 4535 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4536 | TensorOptions options, DimnameList names |
| 4537 | ) override { |
| 4538 | auto current_device = guard_.current_device(); |
| 4539 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4540 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4541 | "structured kernels don't support multi-device outputs"); |
| 4542 | } else { |
| 4543 | guard_.reset_device(options.device()); |
| 4544 | } |
| 4545 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4546 | if (!names.empty()) { |
| 4547 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4548 | } |
| 4549 | // super must happen after, so that downstream can use maybe_get_output |
| 4550 | // to retrieve the output |
| 4551 | at::meta::structured_expm1::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4552 | } |
| 4553 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4554 | return *outputs_[output_idx]; |
| 4555 | } |
| 4556 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 4557 | c10::OptionalDeviceGuard guard_; |
| 4558 | }; |
| 4559 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_expm1(const at::Tensor & self) { |
| 4560 | structured_expm1_default_backend_functional op; |
| 4561 | op.meta(self); |
| 4562 | at::expm1_outf(self, *op.outputs_[0]); |
| 4563 | return std::move(op.outputs_[0]).take(); |
| 4564 | } |
| 4565 | struct structured_expm1_default_backend_inplace final : public at::meta::structured_expm1 { |
| 4566 | structured_expm1_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 4567 | void set_output_strided( |
| 4568 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4569 | TensorOptions options, DimnameList names |
| 4570 | ) override { |
| 4571 | auto current_device = guard_.current_device(); |
| 4572 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4573 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4574 | "structured kernels don't support multi-device outputs"); |
| 4575 | } else { |
| 4576 | guard_.reset_device(options.device()); |
| 4577 | } |
| 4578 | const auto& out = outputs_[output_idx].get(); |
| 4579 | check_inplace(out, sizes, options); |
| 4580 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 4581 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 4582 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 4583 | } |
| 4584 | if (!names.empty()) { |
| 4585 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4586 | } |
| 4587 | // super must happen after, so that downstream can use maybe_get_output |
| 4588 | // to retrieve the output |
| 4589 | at::meta::structured_expm1::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4590 | } |
| 4591 | void set_output_raw_strided( |
| 4592 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4593 | TensorOptions options, DimnameList names |
| 4594 | ) override { |
| 4595 | auto current_device = guard_.current_device(); |
| 4596 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4597 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4598 | "structured kernels don't support multi-device outputs"); |
| 4599 | } else { |
| 4600 | guard_.reset_device(options.device()); |
| 4601 | } |
| 4602 | const auto& out = outputs_[output_idx].get(); |
| 4603 | check_inplace(out, sizes, options); |
| 4604 | if (!names.empty()) { |
| 4605 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4606 | } |
| 4607 | // super must happen after, so that downstream can use maybe_get_output |
| 4608 | // to retrieve the output |
| 4609 | at::meta::structured_expm1::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4610 | } |
| 4611 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4612 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 4613 | } |
| 4614 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 4615 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 4616 | c10::OptionalDeviceGuard guard_; |
| 4617 | }; |
| 4618 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_expm1_(at::Tensor & self) { |
| 4619 | structured_expm1_default_backend_inplace op(self); |
| 4620 | op.meta(self); |
| 4621 | at::expm1_outf(self, op.outputs_[0]); |
| 4622 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 4623 | return self; |
| 4624 | } |
| 4625 | struct structured_floor_default_backend_functional final : public at::meta::structured_floor { |
| 4626 | void set_output_strided( |
| 4627 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4628 | TensorOptions options, DimnameList names |
| 4629 | ) override { |
| 4630 | auto current_device = guard_.current_device(); |
| 4631 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4632 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4633 | "structured kernels don't support multi-device outputs"); |
| 4634 | } else { |
| 4635 | guard_.reset_device(options.device()); |
| 4636 | } |
| 4637 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4638 | if (!names.empty()) { |
| 4639 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4640 | } |
| 4641 | // super must happen after, so that downstream can use maybe_get_output |
| 4642 | // to retrieve the output |
| 4643 | at::meta::structured_floor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4644 | } |
| 4645 | void set_output_raw_strided( |
| 4646 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4647 | TensorOptions options, DimnameList names |
| 4648 | ) override { |
| 4649 | auto current_device = guard_.current_device(); |
| 4650 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4651 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4652 | "structured kernels don't support multi-device outputs"); |
| 4653 | } else { |
| 4654 | guard_.reset_device(options.device()); |
| 4655 | } |
| 4656 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4657 | if (!names.empty()) { |
| 4658 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4659 | } |
| 4660 | // super must happen after, so that downstream can use maybe_get_output |
| 4661 | // to retrieve the output |
| 4662 | at::meta::structured_floor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4663 | } |
| 4664 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4665 | return *outputs_[output_idx]; |
| 4666 | } |
| 4667 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 4668 | c10::OptionalDeviceGuard guard_; |
| 4669 | }; |
| 4670 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_floor(const at::Tensor & self) { |
| 4671 | structured_floor_default_backend_functional op; |
| 4672 | op.meta(self); |
| 4673 | at::floor_outf(self, *op.outputs_[0]); |
| 4674 | return std::move(op.outputs_[0]).take(); |
| 4675 | } |
| 4676 | struct structured_floor_default_backend_inplace final : public at::meta::structured_floor { |
| 4677 | structured_floor_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 4678 | void set_output_strided( |
| 4679 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4680 | TensorOptions options, DimnameList names |
| 4681 | ) override { |
| 4682 | auto current_device = guard_.current_device(); |
| 4683 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4684 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4685 | "structured kernels don't support multi-device outputs"); |
| 4686 | } else { |
| 4687 | guard_.reset_device(options.device()); |
| 4688 | } |
| 4689 | const auto& out = outputs_[output_idx].get(); |
| 4690 | check_inplace(out, sizes, options); |
| 4691 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 4692 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 4693 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 4694 | } |
| 4695 | if (!names.empty()) { |
| 4696 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4697 | } |
| 4698 | // super must happen after, so that downstream can use maybe_get_output |
| 4699 | // to retrieve the output |
| 4700 | at::meta::structured_floor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4701 | } |
| 4702 | void set_output_raw_strided( |
| 4703 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4704 | TensorOptions options, DimnameList names |
| 4705 | ) override { |
| 4706 | auto current_device = guard_.current_device(); |
| 4707 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4708 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4709 | "structured kernels don't support multi-device outputs"); |
| 4710 | } else { |
| 4711 | guard_.reset_device(options.device()); |
| 4712 | } |
| 4713 | const auto& out = outputs_[output_idx].get(); |
| 4714 | check_inplace(out, sizes, options); |
| 4715 | if (!names.empty()) { |
| 4716 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4717 | } |
| 4718 | // super must happen after, so that downstream can use maybe_get_output |
| 4719 | // to retrieve the output |
| 4720 | at::meta::structured_floor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4721 | } |
| 4722 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4723 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 4724 | } |
| 4725 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 4726 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 4727 | c10::OptionalDeviceGuard guard_; |
| 4728 | }; |
| 4729 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_floor_(at::Tensor & self) { |
| 4730 | structured_floor_default_backend_inplace op(self); |
| 4731 | op.meta(self); |
| 4732 | at::floor_outf(self, op.outputs_[0]); |
| 4733 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 4734 | return self; |
| 4735 | } |
| 4736 | struct structured_frac_default_backend_functional final : public at::meta::structured_frac { |
| 4737 | void set_output_strided( |
| 4738 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4739 | TensorOptions options, DimnameList names |
| 4740 | ) override { |
| 4741 | auto current_device = guard_.current_device(); |
| 4742 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4743 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4744 | "structured kernels don't support multi-device outputs"); |
| 4745 | } else { |
| 4746 | guard_.reset_device(options.device()); |
| 4747 | } |
| 4748 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4749 | if (!names.empty()) { |
| 4750 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4751 | } |
| 4752 | // super must happen after, so that downstream can use maybe_get_output |
| 4753 | // to retrieve the output |
| 4754 | at::meta::structured_frac::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4755 | } |
| 4756 | void set_output_raw_strided( |
| 4757 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4758 | TensorOptions options, DimnameList names |
| 4759 | ) override { |
| 4760 | auto current_device = guard_.current_device(); |
| 4761 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4762 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4763 | "structured kernels don't support multi-device outputs"); |
| 4764 | } else { |
| 4765 | guard_.reset_device(options.device()); |
| 4766 | } |
| 4767 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4768 | if (!names.empty()) { |
| 4769 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4770 | } |
| 4771 | // super must happen after, so that downstream can use maybe_get_output |
| 4772 | // to retrieve the output |
| 4773 | at::meta::structured_frac::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4774 | } |
| 4775 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4776 | return *outputs_[output_idx]; |
| 4777 | } |
| 4778 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 4779 | c10::OptionalDeviceGuard guard_; |
| 4780 | }; |
| 4781 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_frac(const at::Tensor & self) { |
| 4782 | structured_frac_default_backend_functional op; |
| 4783 | op.meta(self); |
| 4784 | at::frac_outf(self, *op.outputs_[0]); |
| 4785 | return std::move(op.outputs_[0]).take(); |
| 4786 | } |
| 4787 | struct structured_frac_default_backend_inplace final : public at::meta::structured_frac { |
| 4788 | structured_frac_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 4789 | void set_output_strided( |
| 4790 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4791 | TensorOptions options, DimnameList names |
| 4792 | ) override { |
| 4793 | auto current_device = guard_.current_device(); |
| 4794 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4795 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4796 | "structured kernels don't support multi-device outputs"); |
| 4797 | } else { |
| 4798 | guard_.reset_device(options.device()); |
| 4799 | } |
| 4800 | const auto& out = outputs_[output_idx].get(); |
| 4801 | check_inplace(out, sizes, options); |
| 4802 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 4803 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 4804 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 4805 | } |
| 4806 | if (!names.empty()) { |
| 4807 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4808 | } |
| 4809 | // super must happen after, so that downstream can use maybe_get_output |
| 4810 | // to retrieve the output |
| 4811 | at::meta::structured_frac::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4812 | } |
| 4813 | void set_output_raw_strided( |
| 4814 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4815 | TensorOptions options, DimnameList names |
| 4816 | ) override { |
| 4817 | auto current_device = guard_.current_device(); |
| 4818 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4819 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4820 | "structured kernels don't support multi-device outputs"); |
| 4821 | } else { |
| 4822 | guard_.reset_device(options.device()); |
| 4823 | } |
| 4824 | const auto& out = outputs_[output_idx].get(); |
| 4825 | check_inplace(out, sizes, options); |
| 4826 | if (!names.empty()) { |
| 4827 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4828 | } |
| 4829 | // super must happen after, so that downstream can use maybe_get_output |
| 4830 | // to retrieve the output |
| 4831 | at::meta::structured_frac::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4832 | } |
| 4833 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4834 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 4835 | } |
| 4836 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 4837 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 4838 | c10::OptionalDeviceGuard guard_; |
| 4839 | }; |
| 4840 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_frac_(at::Tensor & self) { |
| 4841 | structured_frac_default_backend_inplace op(self); |
| 4842 | op.meta(self); |
| 4843 | at::frac_outf(self, op.outputs_[0]); |
| 4844 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 4845 | return self; |
| 4846 | } |
| 4847 | struct structured_gcd_default_backend_functional final : public at::meta::structured_gcd { |
| 4848 | void set_output_strided( |
| 4849 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4850 | TensorOptions options, DimnameList names |
| 4851 | ) override { |
| 4852 | auto current_device = guard_.current_device(); |
| 4853 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4854 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4855 | "structured kernels don't support multi-device outputs"); |
| 4856 | } else { |
| 4857 | guard_.reset_device(options.device()); |
| 4858 | } |
| 4859 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4860 | if (!names.empty()) { |
| 4861 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4862 | } |
| 4863 | // super must happen after, so that downstream can use maybe_get_output |
| 4864 | // to retrieve the output |
| 4865 | at::meta::structured_gcd::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4866 | } |
| 4867 | void set_output_raw_strided( |
| 4868 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4869 | TensorOptions options, DimnameList names |
| 4870 | ) override { |
| 4871 | auto current_device = guard_.current_device(); |
| 4872 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4873 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4874 | "structured kernels don't support multi-device outputs"); |
| 4875 | } else { |
| 4876 | guard_.reset_device(options.device()); |
| 4877 | } |
| 4878 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4879 | if (!names.empty()) { |
| 4880 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4881 | } |
| 4882 | // super must happen after, so that downstream can use maybe_get_output |
| 4883 | // to retrieve the output |
| 4884 | at::meta::structured_gcd::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4885 | } |
| 4886 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4887 | return *outputs_[output_idx]; |
| 4888 | } |
| 4889 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 4890 | c10::OptionalDeviceGuard guard_; |
| 4891 | }; |
| 4892 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_gcd(const at::Tensor & self, const at::Tensor & other) { |
| 4893 | structured_gcd_default_backend_functional op; |
| 4894 | op.meta(self, other); |
| 4895 | at::gcd_outf(self, other, *op.outputs_[0]); |
| 4896 | return std::move(op.outputs_[0]).take(); |
| 4897 | } |
| 4898 | struct structured_gcd_default_backend_inplace final : public at::meta::structured_gcd { |
| 4899 | structured_gcd_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 4900 | void set_output_strided( |
| 4901 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4902 | TensorOptions options, DimnameList names |
| 4903 | ) override { |
| 4904 | auto current_device = guard_.current_device(); |
| 4905 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4906 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4907 | "structured kernels don't support multi-device outputs"); |
| 4908 | } else { |
| 4909 | guard_.reset_device(options.device()); |
| 4910 | } |
| 4911 | const auto& out = outputs_[output_idx].get(); |
| 4912 | check_inplace(out, sizes, options); |
| 4913 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 4914 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 4915 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 4916 | } |
| 4917 | if (!names.empty()) { |
| 4918 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4919 | } |
| 4920 | // super must happen after, so that downstream can use maybe_get_output |
| 4921 | // to retrieve the output |
| 4922 | at::meta::structured_gcd::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4923 | } |
| 4924 | void set_output_raw_strided( |
| 4925 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4926 | TensorOptions options, DimnameList names |
| 4927 | ) override { |
| 4928 | auto current_device = guard_.current_device(); |
| 4929 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4930 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4931 | "structured kernels don't support multi-device outputs"); |
| 4932 | } else { |
| 4933 | guard_.reset_device(options.device()); |
| 4934 | } |
| 4935 | const auto& out = outputs_[output_idx].get(); |
| 4936 | check_inplace(out, sizes, options); |
| 4937 | if (!names.empty()) { |
| 4938 | namedinference::propagate_names(outputs_[output_idx], names); |
| 4939 | } |
| 4940 | // super must happen after, so that downstream can use maybe_get_output |
| 4941 | // to retrieve the output |
| 4942 | at::meta::structured_gcd::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4943 | } |
| 4944 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4945 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 4946 | } |
| 4947 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 4948 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 4949 | c10::OptionalDeviceGuard guard_; |
| 4950 | }; |
| 4951 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_gcd_(at::Tensor & self, const at::Tensor & other) { |
| 4952 | structured_gcd_default_backend_inplace op(self); |
| 4953 | op.meta(self, other); |
| 4954 | at::gcd_outf(self, other, op.outputs_[0]); |
| 4955 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 4956 | return self; |
| 4957 | } |
| 4958 | struct structured_lcm_default_backend_functional final : public at::meta::structured_lcm { |
| 4959 | void set_output_strided( |
| 4960 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4961 | TensorOptions options, DimnameList names |
| 4962 | ) override { |
| 4963 | auto current_device = guard_.current_device(); |
| 4964 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4965 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4966 | "structured kernels don't support multi-device outputs"); |
| 4967 | } else { |
| 4968 | guard_.reset_device(options.device()); |
| 4969 | } |
| 4970 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4971 | if (!names.empty()) { |
| 4972 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4973 | } |
| 4974 | // super must happen after, so that downstream can use maybe_get_output |
| 4975 | // to retrieve the output |
| 4976 | at::meta::structured_lcm::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4977 | } |
| 4978 | void set_output_raw_strided( |
| 4979 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 4980 | TensorOptions options, DimnameList names |
| 4981 | ) override { |
| 4982 | auto current_device = guard_.current_device(); |
| 4983 | if (C10_UNLIKELY(current_device.has_value())) { |
| 4984 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 4985 | "structured kernels don't support multi-device outputs"); |
| 4986 | } else { |
| 4987 | guard_.reset_device(options.device()); |
| 4988 | } |
| 4989 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 4990 | if (!names.empty()) { |
| 4991 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 4992 | } |
| 4993 | // super must happen after, so that downstream can use maybe_get_output |
| 4994 | // to retrieve the output |
| 4995 | at::meta::structured_lcm::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 4996 | } |
| 4997 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 4998 | return *outputs_[output_idx]; |
| 4999 | } |
| 5000 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 5001 | c10::OptionalDeviceGuard guard_; |
| 5002 | }; |
| 5003 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_lcm(const at::Tensor & self, const at::Tensor & other) { |
| 5004 | structured_lcm_default_backend_functional op; |
| 5005 | op.meta(self, other); |
| 5006 | at::lcm_outf(self, other, *op.outputs_[0]); |
| 5007 | return std::move(op.outputs_[0]).take(); |
| 5008 | } |
| 5009 | struct structured_lcm_default_backend_inplace final : public at::meta::structured_lcm { |
| 5010 | structured_lcm_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 5011 | void set_output_strided( |
| 5012 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5013 | TensorOptions options, DimnameList names |
| 5014 | ) override { |
| 5015 | auto current_device = guard_.current_device(); |
| 5016 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5017 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5018 | "structured kernels don't support multi-device outputs"); |
| 5019 | } else { |
| 5020 | guard_.reset_device(options.device()); |
| 5021 | } |
| 5022 | const auto& out = outputs_[output_idx].get(); |
| 5023 | check_inplace(out, sizes, options); |
| 5024 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 5025 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 5026 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 5027 | } |
| 5028 | if (!names.empty()) { |
| 5029 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5030 | } |
| 5031 | // super must happen after, so that downstream can use maybe_get_output |
| 5032 | // to retrieve the output |
| 5033 | at::meta::structured_lcm::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5034 | } |
| 5035 | void set_output_raw_strided( |
| 5036 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5037 | TensorOptions options, DimnameList names |
| 5038 | ) override { |
| 5039 | auto current_device = guard_.current_device(); |
| 5040 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5041 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5042 | "structured kernels don't support multi-device outputs"); |
| 5043 | } else { |
| 5044 | guard_.reset_device(options.device()); |
| 5045 | } |
| 5046 | const auto& out = outputs_[output_idx].get(); |
| 5047 | check_inplace(out, sizes, options); |
| 5048 | if (!names.empty()) { |
| 5049 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5050 | } |
| 5051 | // super must happen after, so that downstream can use maybe_get_output |
| 5052 | // to retrieve the output |
| 5053 | at::meta::structured_lcm::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5054 | } |
| 5055 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5056 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 5057 | } |
| 5058 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 5059 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 5060 | c10::OptionalDeviceGuard guard_; |
| 5061 | }; |
| 5062 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_lcm_(at::Tensor & self, const at::Tensor & other) { |
| 5063 | structured_lcm_default_backend_inplace op(self); |
| 5064 | op.meta(self, other); |
| 5065 | at::lcm_outf(self, other, op.outputs_[0]); |
| 5066 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 5067 | return self; |
| 5068 | } |
| 5069 | struct structured_index_Tensor_default_backend_functional final : public at::meta::structured_index_Tensor { |
| 5070 | void set_output_strided( |
| 5071 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5072 | TensorOptions options, DimnameList names |
| 5073 | ) override { |
| 5074 | auto current_device = guard_.current_device(); |
| 5075 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5076 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5077 | "structured kernels don't support multi-device outputs"); |
| 5078 | } else { |
| 5079 | guard_.reset_device(options.device()); |
| 5080 | } |
| 5081 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5082 | if (!names.empty()) { |
| 5083 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5084 | } |
| 5085 | // super must happen after, so that downstream can use maybe_get_output |
| 5086 | // to retrieve the output |
| 5087 | at::meta::structured_index_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5088 | } |
| 5089 | void set_output_raw_strided( |
| 5090 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5091 | TensorOptions options, DimnameList names |
| 5092 | ) override { |
| 5093 | auto current_device = guard_.current_device(); |
| 5094 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5095 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5096 | "structured kernels don't support multi-device outputs"); |
| 5097 | } else { |
| 5098 | guard_.reset_device(options.device()); |
| 5099 | } |
| 5100 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5101 | if (!names.empty()) { |
| 5102 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5103 | } |
| 5104 | // super must happen after, so that downstream can use maybe_get_output |
| 5105 | // to retrieve the output |
| 5106 | at::meta::structured_index_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5107 | } |
| 5108 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5109 | return *outputs_[output_idx]; |
| 5110 | } |
| 5111 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 5112 | c10::OptionalDeviceGuard guard_; |
| 5113 | }; |
| 5114 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_index_Tensor(const at::Tensor & self, const c10::List<c10::optional<at::Tensor>> & indices) { |
| 5115 | structured_index_Tensor_default_backend_functional op; |
| 5116 | auto precompute = op.meta(self, at::IOptTensorListRef(indices)); |
| 5117 | (void)precompute; |
| 5118 | at::index_outf(self, indices, *op.outputs_[0]); |
| 5119 | return std::move(op.outputs_[0]).take(); |
| 5120 | } |
| 5121 | struct structured_index_copy_default_backend_functional final : public at::meta::structured_index_copy { |
| 5122 | void set_output_strided( |
| 5123 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5124 | TensorOptions options, DimnameList names |
| 5125 | ) override { |
| 5126 | auto current_device = guard_.current_device(); |
| 5127 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5128 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5129 | "structured kernels don't support multi-device outputs"); |
| 5130 | } else { |
| 5131 | guard_.reset_device(options.device()); |
| 5132 | } |
| 5133 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5134 | if (!names.empty()) { |
| 5135 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5136 | } |
| 5137 | // super must happen after, so that downstream can use maybe_get_output |
| 5138 | // to retrieve the output |
| 5139 | } |
| 5140 | void set_output_raw_strided( |
| 5141 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5142 | TensorOptions options, DimnameList names |
| 5143 | ) override { |
| 5144 | auto current_device = guard_.current_device(); |
| 5145 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5146 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5147 | "structured kernels don't support multi-device outputs"); |
| 5148 | } else { |
| 5149 | guard_.reset_device(options.device()); |
| 5150 | } |
| 5151 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5152 | if (!names.empty()) { |
| 5153 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5154 | } |
| 5155 | // super must happen after, so that downstream can use maybe_get_output |
| 5156 | // to retrieve the output |
| 5157 | } |
| 5158 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5159 | return *outputs_[output_idx]; |
| 5160 | } |
| 5161 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 5162 | c10::OptionalDeviceGuard guard_; |
| 5163 | }; |
| 5164 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_index_copy(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source) { |
| 5165 | structured_index_copy_default_backend_functional op; |
| 5166 | auto precompute = op.meta(self, dim, index, source); |
| 5167 | (void)precompute; |
| 5168 | at::index_copy_outf(self, precompute.dim, index, source, *op.outputs_[0]); |
| 5169 | return std::move(op.outputs_[0]).take(); |
| 5170 | } |
| 5171 | struct structured_index_copy_default_backend_inplace final : public at::meta::structured_index_copy { |
| 5172 | structured_index_copy_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 5173 | void set_output_strided( |
| 5174 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5175 | TensorOptions options, DimnameList names |
| 5176 | ) override { |
| 5177 | auto current_device = guard_.current_device(); |
| 5178 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5179 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5180 | "structured kernels don't support multi-device outputs"); |
| 5181 | } else { |
| 5182 | guard_.reset_device(options.device()); |
| 5183 | } |
| 5184 | const auto& out = outputs_[output_idx].get(); |
| 5185 | check_inplace(out, sizes, options); |
| 5186 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 5187 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 5188 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 5189 | } |
| 5190 | if (!names.empty()) { |
| 5191 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5192 | } |
| 5193 | // super must happen after, so that downstream can use maybe_get_output |
| 5194 | // to retrieve the output |
| 5195 | } |
| 5196 | void set_output_raw_strided( |
| 5197 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5198 | TensorOptions options, DimnameList names |
| 5199 | ) override { |
| 5200 | auto current_device = guard_.current_device(); |
| 5201 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5202 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5203 | "structured kernels don't support multi-device outputs"); |
| 5204 | } else { |
| 5205 | guard_.reset_device(options.device()); |
| 5206 | } |
| 5207 | const auto& out = outputs_[output_idx].get(); |
| 5208 | check_inplace(out, sizes, options); |
| 5209 | if (!names.empty()) { |
| 5210 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5211 | } |
| 5212 | // super must happen after, so that downstream can use maybe_get_output |
| 5213 | // to retrieve the output |
| 5214 | } |
| 5215 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5216 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 5217 | } |
| 5218 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 5219 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 5220 | c10::OptionalDeviceGuard guard_; |
| 5221 | }; |
| 5222 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_index_copy_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source) { |
| 5223 | structured_index_copy_default_backend_inplace op(self); |
| 5224 | auto precompute = op.meta(self, dim, index, source); |
| 5225 | (void)precompute; |
| 5226 | at::index_copy_outf(self, precompute.dim, index, source, op.outputs_[0]); |
| 5227 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 5228 | return self; |
| 5229 | } |
| 5230 | struct structured_isin_Tensor_Tensor_default_backend_functional final : public at::meta::structured_isin_Tensor_Tensor { |
| 5231 | void set_output_strided( |
| 5232 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5233 | TensorOptions options, DimnameList names |
| 5234 | ) override { |
| 5235 | auto current_device = guard_.current_device(); |
| 5236 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5237 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5238 | "structured kernels don't support multi-device outputs"); |
| 5239 | } else { |
| 5240 | guard_.reset_device(options.device()); |
| 5241 | } |
| 5242 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5243 | if (!names.empty()) { |
| 5244 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5245 | } |
| 5246 | // super must happen after, so that downstream can use maybe_get_output |
| 5247 | // to retrieve the output |
| 5248 | } |
| 5249 | void set_output_raw_strided( |
| 5250 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5251 | TensorOptions options, DimnameList names |
| 5252 | ) override { |
| 5253 | auto current_device = guard_.current_device(); |
| 5254 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5255 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5256 | "structured kernels don't support multi-device outputs"); |
| 5257 | } else { |
| 5258 | guard_.reset_device(options.device()); |
| 5259 | } |
| 5260 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5261 | if (!names.empty()) { |
| 5262 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5263 | } |
| 5264 | // super must happen after, so that downstream can use maybe_get_output |
| 5265 | // to retrieve the output |
| 5266 | } |
| 5267 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5268 | return *outputs_[output_idx]; |
| 5269 | } |
| 5270 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 5271 | c10::OptionalDeviceGuard guard_; |
| 5272 | }; |
| 5273 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_isin_Tensor_Tensor(const at::Tensor & elements, const at::Tensor & test_elements, bool assume_unique, bool invert) { |
| 5274 | structured_isin_Tensor_Tensor_default_backend_functional op; |
| 5275 | op.meta(elements, test_elements, assume_unique, invert); |
| 5276 | at::isin_outf(elements, test_elements, assume_unique, invert, *op.outputs_[0]); |
| 5277 | return std::move(op.outputs_[0]).take(); |
| 5278 | } |
| 5279 | struct structured_isin_Tensor_Scalar_default_backend_functional final : public at::meta::structured_isin_Tensor_Scalar { |
| 5280 | void set_output_strided( |
| 5281 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5282 | TensorOptions options, DimnameList names |
| 5283 | ) override { |
| 5284 | auto current_device = guard_.current_device(); |
| 5285 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5286 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5287 | "structured kernels don't support multi-device outputs"); |
| 5288 | } else { |
| 5289 | guard_.reset_device(options.device()); |
| 5290 | } |
| 5291 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5292 | if (!names.empty()) { |
| 5293 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5294 | } |
| 5295 | // super must happen after, so that downstream can use maybe_get_output |
| 5296 | // to retrieve the output |
| 5297 | } |
| 5298 | void set_output_raw_strided( |
| 5299 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5300 | TensorOptions options, DimnameList names |
| 5301 | ) override { |
| 5302 | auto current_device = guard_.current_device(); |
| 5303 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5304 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5305 | "structured kernels don't support multi-device outputs"); |
| 5306 | } else { |
| 5307 | guard_.reset_device(options.device()); |
| 5308 | } |
| 5309 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5310 | if (!names.empty()) { |
| 5311 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5312 | } |
| 5313 | // super must happen after, so that downstream can use maybe_get_output |
| 5314 | // to retrieve the output |
| 5315 | } |
| 5316 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5317 | return *outputs_[output_idx]; |
| 5318 | } |
| 5319 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 5320 | c10::OptionalDeviceGuard guard_; |
| 5321 | }; |
| 5322 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_isin_Tensor_Scalar(const at::Tensor & elements, const at::Scalar & test_element, bool assume_unique, bool invert) { |
| 5323 | structured_isin_Tensor_Scalar_default_backend_functional op; |
| 5324 | op.meta(elements, test_element, assume_unique, invert); |
| 5325 | at::isin_outf(elements, test_element, assume_unique, invert, *op.outputs_[0]); |
| 5326 | return std::move(op.outputs_[0]).take(); |
| 5327 | } |
| 5328 | struct structured_isin_Scalar_Tensor_default_backend_functional final : public at::meta::structured_isin_Scalar_Tensor { |
| 5329 | void set_output_strided( |
| 5330 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5331 | TensorOptions options, DimnameList names |
| 5332 | ) override { |
| 5333 | auto current_device = guard_.current_device(); |
| 5334 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5335 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5336 | "structured kernels don't support multi-device outputs"); |
| 5337 | } else { |
| 5338 | guard_.reset_device(options.device()); |
| 5339 | } |
| 5340 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5341 | if (!names.empty()) { |
| 5342 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5343 | } |
| 5344 | // super must happen after, so that downstream can use maybe_get_output |
| 5345 | // to retrieve the output |
| 5346 | } |
| 5347 | void set_output_raw_strided( |
| 5348 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5349 | TensorOptions options, DimnameList names |
| 5350 | ) override { |
| 5351 | auto current_device = guard_.current_device(); |
| 5352 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5353 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5354 | "structured kernels don't support multi-device outputs"); |
| 5355 | } else { |
| 5356 | guard_.reset_device(options.device()); |
| 5357 | } |
| 5358 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5359 | if (!names.empty()) { |
| 5360 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5361 | } |
| 5362 | // super must happen after, so that downstream can use maybe_get_output |
| 5363 | // to retrieve the output |
| 5364 | } |
| 5365 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5366 | return *outputs_[output_idx]; |
| 5367 | } |
| 5368 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 5369 | c10::OptionalDeviceGuard guard_; |
| 5370 | }; |
| 5371 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_isin_Scalar_Tensor(const at::Scalar & element, const at::Tensor & test_elements, bool assume_unique, bool invert) { |
| 5372 | structured_isin_Scalar_Tensor_default_backend_functional op; |
| 5373 | op.meta(element, test_elements, assume_unique, invert); |
| 5374 | at::isin_outf(element, test_elements, assume_unique, invert, *op.outputs_[0]); |
| 5375 | return std::move(op.outputs_[0]).take(); |
| 5376 | } |
| 5377 | struct structured_log_default_backend_functional final : public at::meta::structured_log { |
| 5378 | void set_output_strided( |
| 5379 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5380 | TensorOptions options, DimnameList names |
| 5381 | ) override { |
| 5382 | auto current_device = guard_.current_device(); |
| 5383 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5384 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5385 | "structured kernels don't support multi-device outputs"); |
| 5386 | } else { |
| 5387 | guard_.reset_device(options.device()); |
| 5388 | } |
| 5389 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5390 | if (!names.empty()) { |
| 5391 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5392 | } |
| 5393 | // super must happen after, so that downstream can use maybe_get_output |
| 5394 | // to retrieve the output |
| 5395 | at::meta::structured_log::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5396 | } |
| 5397 | void set_output_raw_strided( |
| 5398 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5399 | TensorOptions options, DimnameList names |
| 5400 | ) override { |
| 5401 | auto current_device = guard_.current_device(); |
| 5402 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5403 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5404 | "structured kernels don't support multi-device outputs"); |
| 5405 | } else { |
| 5406 | guard_.reset_device(options.device()); |
| 5407 | } |
| 5408 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5409 | if (!names.empty()) { |
| 5410 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5411 | } |
| 5412 | // super must happen after, so that downstream can use maybe_get_output |
| 5413 | // to retrieve the output |
| 5414 | at::meta::structured_log::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5415 | } |
| 5416 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5417 | return *outputs_[output_idx]; |
| 5418 | } |
| 5419 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 5420 | c10::OptionalDeviceGuard guard_; |
| 5421 | }; |
| 5422 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_log(const at::Tensor & self) { |
| 5423 | structured_log_default_backend_functional op; |
| 5424 | op.meta(self); |
| 5425 | at::log_outf(self, *op.outputs_[0]); |
| 5426 | return std::move(op.outputs_[0]).take(); |
| 5427 | } |
| 5428 | struct structured_log_default_backend_inplace final : public at::meta::structured_log { |
| 5429 | structured_log_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 5430 | void set_output_strided( |
| 5431 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5432 | TensorOptions options, DimnameList names |
| 5433 | ) override { |
| 5434 | auto current_device = guard_.current_device(); |
| 5435 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5436 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5437 | "structured kernels don't support multi-device outputs"); |
| 5438 | } else { |
| 5439 | guard_.reset_device(options.device()); |
| 5440 | } |
| 5441 | const auto& out = outputs_[output_idx].get(); |
| 5442 | check_inplace(out, sizes, options); |
| 5443 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 5444 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 5445 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 5446 | } |
| 5447 | if (!names.empty()) { |
| 5448 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5449 | } |
| 5450 | // super must happen after, so that downstream can use maybe_get_output |
| 5451 | // to retrieve the output |
| 5452 | at::meta::structured_log::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5453 | } |
| 5454 | void set_output_raw_strided( |
| 5455 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5456 | TensorOptions options, DimnameList names |
| 5457 | ) override { |
| 5458 | auto current_device = guard_.current_device(); |
| 5459 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5460 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5461 | "structured kernels don't support multi-device outputs"); |
| 5462 | } else { |
| 5463 | guard_.reset_device(options.device()); |
| 5464 | } |
| 5465 | const auto& out = outputs_[output_idx].get(); |
| 5466 | check_inplace(out, sizes, options); |
| 5467 | if (!names.empty()) { |
| 5468 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5469 | } |
| 5470 | // super must happen after, so that downstream can use maybe_get_output |
| 5471 | // to retrieve the output |
| 5472 | at::meta::structured_log::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5473 | } |
| 5474 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5475 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 5476 | } |
| 5477 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 5478 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 5479 | c10::OptionalDeviceGuard guard_; |
| 5480 | }; |
| 5481 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_log_(at::Tensor & self) { |
| 5482 | structured_log_default_backend_inplace op(self); |
| 5483 | op.meta(self); |
| 5484 | at::log_outf(self, op.outputs_[0]); |
| 5485 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 5486 | return self; |
| 5487 | } |
| 5488 | struct structured_log10_default_backend_functional final : public at::meta::structured_log10 { |
| 5489 | void set_output_strided( |
| 5490 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5491 | TensorOptions options, DimnameList names |
| 5492 | ) override { |
| 5493 | auto current_device = guard_.current_device(); |
| 5494 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5495 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5496 | "structured kernels don't support multi-device outputs"); |
| 5497 | } else { |
| 5498 | guard_.reset_device(options.device()); |
| 5499 | } |
| 5500 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5501 | if (!names.empty()) { |
| 5502 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5503 | } |
| 5504 | // super must happen after, so that downstream can use maybe_get_output |
| 5505 | // to retrieve the output |
| 5506 | at::meta::structured_log10::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5507 | } |
| 5508 | void set_output_raw_strided( |
| 5509 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5510 | TensorOptions options, DimnameList names |
| 5511 | ) override { |
| 5512 | auto current_device = guard_.current_device(); |
| 5513 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5514 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5515 | "structured kernels don't support multi-device outputs"); |
| 5516 | } else { |
| 5517 | guard_.reset_device(options.device()); |
| 5518 | } |
| 5519 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5520 | if (!names.empty()) { |
| 5521 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5522 | } |
| 5523 | // super must happen after, so that downstream can use maybe_get_output |
| 5524 | // to retrieve the output |
| 5525 | at::meta::structured_log10::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5526 | } |
| 5527 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5528 | return *outputs_[output_idx]; |
| 5529 | } |
| 5530 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 5531 | c10::OptionalDeviceGuard guard_; |
| 5532 | }; |
| 5533 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_log10(const at::Tensor & self) { |
| 5534 | structured_log10_default_backend_functional op; |
| 5535 | op.meta(self); |
| 5536 | at::log10_outf(self, *op.outputs_[0]); |
| 5537 | return std::move(op.outputs_[0]).take(); |
| 5538 | } |
| 5539 | struct structured_log10_default_backend_inplace final : public at::meta::structured_log10 { |
| 5540 | structured_log10_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 5541 | void set_output_strided( |
| 5542 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5543 | TensorOptions options, DimnameList names |
| 5544 | ) override { |
| 5545 | auto current_device = guard_.current_device(); |
| 5546 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5547 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5548 | "structured kernels don't support multi-device outputs"); |
| 5549 | } else { |
| 5550 | guard_.reset_device(options.device()); |
| 5551 | } |
| 5552 | const auto& out = outputs_[output_idx].get(); |
| 5553 | check_inplace(out, sizes, options); |
| 5554 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 5555 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 5556 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 5557 | } |
| 5558 | if (!names.empty()) { |
| 5559 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5560 | } |
| 5561 | // super must happen after, so that downstream can use maybe_get_output |
| 5562 | // to retrieve the output |
| 5563 | at::meta::structured_log10::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5564 | } |
| 5565 | void set_output_raw_strided( |
| 5566 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5567 | TensorOptions options, DimnameList names |
| 5568 | ) override { |
| 5569 | auto current_device = guard_.current_device(); |
| 5570 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5571 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5572 | "structured kernels don't support multi-device outputs"); |
| 5573 | } else { |
| 5574 | guard_.reset_device(options.device()); |
| 5575 | } |
| 5576 | const auto& out = outputs_[output_idx].get(); |
| 5577 | check_inplace(out, sizes, options); |
| 5578 | if (!names.empty()) { |
| 5579 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5580 | } |
| 5581 | // super must happen after, so that downstream can use maybe_get_output |
| 5582 | // to retrieve the output |
| 5583 | at::meta::structured_log10::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5584 | } |
| 5585 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5586 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 5587 | } |
| 5588 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 5589 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 5590 | c10::OptionalDeviceGuard guard_; |
| 5591 | }; |
| 5592 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_log10_(at::Tensor & self) { |
| 5593 | structured_log10_default_backend_inplace op(self); |
| 5594 | op.meta(self); |
| 5595 | at::log10_outf(self, op.outputs_[0]); |
| 5596 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 5597 | return self; |
| 5598 | } |
| 5599 | struct structured_log1p_default_backend_functional final : public at::meta::structured_log1p { |
| 5600 | void set_output_strided( |
| 5601 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5602 | TensorOptions options, DimnameList names |
| 5603 | ) override { |
| 5604 | auto current_device = guard_.current_device(); |
| 5605 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5606 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5607 | "structured kernels don't support multi-device outputs"); |
| 5608 | } else { |
| 5609 | guard_.reset_device(options.device()); |
| 5610 | } |
| 5611 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5612 | if (!names.empty()) { |
| 5613 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5614 | } |
| 5615 | // super must happen after, so that downstream can use maybe_get_output |
| 5616 | // to retrieve the output |
| 5617 | at::meta::structured_log1p::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5618 | } |
| 5619 | void set_output_raw_strided( |
| 5620 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5621 | TensorOptions options, DimnameList names |
| 5622 | ) override { |
| 5623 | auto current_device = guard_.current_device(); |
| 5624 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5625 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5626 | "structured kernels don't support multi-device outputs"); |
| 5627 | } else { |
| 5628 | guard_.reset_device(options.device()); |
| 5629 | } |
| 5630 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5631 | if (!names.empty()) { |
| 5632 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5633 | } |
| 5634 | // super must happen after, so that downstream can use maybe_get_output |
| 5635 | // to retrieve the output |
| 5636 | at::meta::structured_log1p::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5637 | } |
| 5638 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5639 | return *outputs_[output_idx]; |
| 5640 | } |
| 5641 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 5642 | c10::OptionalDeviceGuard guard_; |
| 5643 | }; |
| 5644 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_log1p(const at::Tensor & self) { |
| 5645 | structured_log1p_default_backend_functional op; |
| 5646 | op.meta(self); |
| 5647 | at::log1p_outf(self, *op.outputs_[0]); |
| 5648 | return std::move(op.outputs_[0]).take(); |
| 5649 | } |
| 5650 | struct structured_log1p_default_backend_inplace final : public at::meta::structured_log1p { |
| 5651 | structured_log1p_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 5652 | void set_output_strided( |
| 5653 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5654 | TensorOptions options, DimnameList names |
| 5655 | ) override { |
| 5656 | auto current_device = guard_.current_device(); |
| 5657 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5658 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5659 | "structured kernels don't support multi-device outputs"); |
| 5660 | } else { |
| 5661 | guard_.reset_device(options.device()); |
| 5662 | } |
| 5663 | const auto& out = outputs_[output_idx].get(); |
| 5664 | check_inplace(out, sizes, options); |
| 5665 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 5666 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 5667 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 5668 | } |
| 5669 | if (!names.empty()) { |
| 5670 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5671 | } |
| 5672 | // super must happen after, so that downstream can use maybe_get_output |
| 5673 | // to retrieve the output |
| 5674 | at::meta::structured_log1p::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5675 | } |
| 5676 | void set_output_raw_strided( |
| 5677 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5678 | TensorOptions options, DimnameList names |
| 5679 | ) override { |
| 5680 | auto current_device = guard_.current_device(); |
| 5681 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5682 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5683 | "structured kernels don't support multi-device outputs"); |
| 5684 | } else { |
| 5685 | guard_.reset_device(options.device()); |
| 5686 | } |
| 5687 | const auto& out = outputs_[output_idx].get(); |
| 5688 | check_inplace(out, sizes, options); |
| 5689 | if (!names.empty()) { |
| 5690 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5691 | } |
| 5692 | // super must happen after, so that downstream can use maybe_get_output |
| 5693 | // to retrieve the output |
| 5694 | at::meta::structured_log1p::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5695 | } |
| 5696 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5697 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 5698 | } |
| 5699 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 5700 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 5701 | c10::OptionalDeviceGuard guard_; |
| 5702 | }; |
| 5703 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_log1p_(at::Tensor & self) { |
| 5704 | structured_log1p_default_backend_inplace op(self); |
| 5705 | op.meta(self); |
| 5706 | at::log1p_outf(self, op.outputs_[0]); |
| 5707 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 5708 | return self; |
| 5709 | } |
| 5710 | struct structured_log2_default_backend_functional final : public at::meta::structured_log2 { |
| 5711 | void set_output_strided( |
| 5712 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5713 | TensorOptions options, DimnameList names |
| 5714 | ) override { |
| 5715 | auto current_device = guard_.current_device(); |
| 5716 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5717 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5718 | "structured kernels don't support multi-device outputs"); |
| 5719 | } else { |
| 5720 | guard_.reset_device(options.device()); |
| 5721 | } |
| 5722 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5723 | if (!names.empty()) { |
| 5724 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5725 | } |
| 5726 | // super must happen after, so that downstream can use maybe_get_output |
| 5727 | // to retrieve the output |
| 5728 | at::meta::structured_log2::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5729 | } |
| 5730 | void set_output_raw_strided( |
| 5731 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5732 | TensorOptions options, DimnameList names |
| 5733 | ) override { |
| 5734 | auto current_device = guard_.current_device(); |
| 5735 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5736 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5737 | "structured kernels don't support multi-device outputs"); |
| 5738 | } else { |
| 5739 | guard_.reset_device(options.device()); |
| 5740 | } |
| 5741 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5742 | if (!names.empty()) { |
| 5743 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5744 | } |
| 5745 | // super must happen after, so that downstream can use maybe_get_output |
| 5746 | // to retrieve the output |
| 5747 | at::meta::structured_log2::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5748 | } |
| 5749 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5750 | return *outputs_[output_idx]; |
| 5751 | } |
| 5752 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 5753 | c10::OptionalDeviceGuard guard_; |
| 5754 | }; |
| 5755 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_log2(const at::Tensor & self) { |
| 5756 | structured_log2_default_backend_functional op; |
| 5757 | op.meta(self); |
| 5758 | at::log2_outf(self, *op.outputs_[0]); |
| 5759 | return std::move(op.outputs_[0]).take(); |
| 5760 | } |
| 5761 | struct structured_log2_default_backend_inplace final : public at::meta::structured_log2 { |
| 5762 | structured_log2_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 5763 | void set_output_strided( |
| 5764 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5765 | TensorOptions options, DimnameList names |
| 5766 | ) override { |
| 5767 | auto current_device = guard_.current_device(); |
| 5768 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5769 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5770 | "structured kernels don't support multi-device outputs"); |
| 5771 | } else { |
| 5772 | guard_.reset_device(options.device()); |
| 5773 | } |
| 5774 | const auto& out = outputs_[output_idx].get(); |
| 5775 | check_inplace(out, sizes, options); |
| 5776 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 5777 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 5778 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 5779 | } |
| 5780 | if (!names.empty()) { |
| 5781 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5782 | } |
| 5783 | // super must happen after, so that downstream can use maybe_get_output |
| 5784 | // to retrieve the output |
| 5785 | at::meta::structured_log2::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5786 | } |
| 5787 | void set_output_raw_strided( |
| 5788 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5789 | TensorOptions options, DimnameList names |
| 5790 | ) override { |
| 5791 | auto current_device = guard_.current_device(); |
| 5792 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5793 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5794 | "structured kernels don't support multi-device outputs"); |
| 5795 | } else { |
| 5796 | guard_.reset_device(options.device()); |
| 5797 | } |
| 5798 | const auto& out = outputs_[output_idx].get(); |
| 5799 | check_inplace(out, sizes, options); |
| 5800 | if (!names.empty()) { |
| 5801 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5802 | } |
| 5803 | // super must happen after, so that downstream can use maybe_get_output |
| 5804 | // to retrieve the output |
| 5805 | at::meta::structured_log2::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5806 | } |
| 5807 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5808 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 5809 | } |
| 5810 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 5811 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 5812 | c10::OptionalDeviceGuard guard_; |
| 5813 | }; |
| 5814 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_log2_(at::Tensor & self) { |
| 5815 | structured_log2_default_backend_inplace op(self); |
| 5816 | op.meta(self); |
| 5817 | at::log2_outf(self, op.outputs_[0]); |
| 5818 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 5819 | return self; |
| 5820 | } |
| 5821 | struct structured_logaddexp_default_backend_functional final : public at::meta::structured_logaddexp { |
| 5822 | void set_output_strided( |
| 5823 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5824 | TensorOptions options, DimnameList names |
| 5825 | ) override { |
| 5826 | auto current_device = guard_.current_device(); |
| 5827 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5828 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5829 | "structured kernels don't support multi-device outputs"); |
| 5830 | } else { |
| 5831 | guard_.reset_device(options.device()); |
| 5832 | } |
| 5833 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5834 | if (!names.empty()) { |
| 5835 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5836 | } |
| 5837 | // super must happen after, so that downstream can use maybe_get_output |
| 5838 | // to retrieve the output |
| 5839 | at::meta::structured_logaddexp::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5840 | } |
| 5841 | void set_output_raw_strided( |
| 5842 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5843 | TensorOptions options, DimnameList names |
| 5844 | ) override { |
| 5845 | auto current_device = guard_.current_device(); |
| 5846 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5847 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5848 | "structured kernels don't support multi-device outputs"); |
| 5849 | } else { |
| 5850 | guard_.reset_device(options.device()); |
| 5851 | } |
| 5852 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5853 | if (!names.empty()) { |
| 5854 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5855 | } |
| 5856 | // super must happen after, so that downstream can use maybe_get_output |
| 5857 | // to retrieve the output |
| 5858 | at::meta::structured_logaddexp::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5859 | } |
| 5860 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5861 | return *outputs_[output_idx]; |
| 5862 | } |
| 5863 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 5864 | c10::OptionalDeviceGuard guard_; |
| 5865 | }; |
| 5866 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_logaddexp(const at::Tensor & self, const at::Tensor & other) { |
| 5867 | structured_logaddexp_default_backend_functional op; |
| 5868 | op.meta(self, other); |
| 5869 | at::logaddexp_outf(self, other, *op.outputs_[0]); |
| 5870 | return std::move(op.outputs_[0]).take(); |
| 5871 | } |
| 5872 | struct structured_logaddexp2_default_backend_functional final : public at::meta::structured_logaddexp2 { |
| 5873 | void set_output_strided( |
| 5874 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5875 | TensorOptions options, DimnameList names |
| 5876 | ) override { |
| 5877 | auto current_device = guard_.current_device(); |
| 5878 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5879 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5880 | "structured kernels don't support multi-device outputs"); |
| 5881 | } else { |
| 5882 | guard_.reset_device(options.device()); |
| 5883 | } |
| 5884 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5885 | if (!names.empty()) { |
| 5886 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5887 | } |
| 5888 | // super must happen after, so that downstream can use maybe_get_output |
| 5889 | // to retrieve the output |
| 5890 | at::meta::structured_logaddexp2::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5891 | } |
| 5892 | void set_output_raw_strided( |
| 5893 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5894 | TensorOptions options, DimnameList names |
| 5895 | ) override { |
| 5896 | auto current_device = guard_.current_device(); |
| 5897 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5898 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5899 | "structured kernels don't support multi-device outputs"); |
| 5900 | } else { |
| 5901 | guard_.reset_device(options.device()); |
| 5902 | } |
| 5903 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5904 | if (!names.empty()) { |
| 5905 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5906 | } |
| 5907 | // super must happen after, so that downstream can use maybe_get_output |
| 5908 | // to retrieve the output |
| 5909 | at::meta::structured_logaddexp2::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5910 | } |
| 5911 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5912 | return *outputs_[output_idx]; |
| 5913 | } |
| 5914 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 5915 | c10::OptionalDeviceGuard guard_; |
| 5916 | }; |
| 5917 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_logaddexp2(const at::Tensor & self, const at::Tensor & other) { |
| 5918 | structured_logaddexp2_default_backend_functional op; |
| 5919 | op.meta(self, other); |
| 5920 | at::logaddexp2_outf(self, other, *op.outputs_[0]); |
| 5921 | return std::move(op.outputs_[0]).take(); |
| 5922 | } |
| 5923 | struct structured_xlogy_Tensor_default_backend_functional final : public at::meta::structured_xlogy_Tensor { |
| 5924 | void set_output_strided( |
| 5925 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5926 | TensorOptions options, DimnameList names |
| 5927 | ) override { |
| 5928 | auto current_device = guard_.current_device(); |
| 5929 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5930 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5931 | "structured kernels don't support multi-device outputs"); |
| 5932 | } else { |
| 5933 | guard_.reset_device(options.device()); |
| 5934 | } |
| 5935 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5936 | if (!names.empty()) { |
| 5937 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5938 | } |
| 5939 | // super must happen after, so that downstream can use maybe_get_output |
| 5940 | // to retrieve the output |
| 5941 | at::meta::structured_xlogy_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5942 | } |
| 5943 | void set_output_raw_strided( |
| 5944 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5945 | TensorOptions options, DimnameList names |
| 5946 | ) override { |
| 5947 | auto current_device = guard_.current_device(); |
| 5948 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5949 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5950 | "structured kernels don't support multi-device outputs"); |
| 5951 | } else { |
| 5952 | guard_.reset_device(options.device()); |
| 5953 | } |
| 5954 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 5955 | if (!names.empty()) { |
| 5956 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 5957 | } |
| 5958 | // super must happen after, so that downstream can use maybe_get_output |
| 5959 | // to retrieve the output |
| 5960 | at::meta::structured_xlogy_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5961 | } |
| 5962 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 5963 | return *outputs_[output_idx]; |
| 5964 | } |
| 5965 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 5966 | c10::OptionalDeviceGuard guard_; |
| 5967 | }; |
| 5968 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_xlogy_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 5969 | structured_xlogy_Tensor_default_backend_functional op; |
| 5970 | op.meta(self, other); |
| 5971 | at::xlogy_outf(self, other, *op.outputs_[0]); |
| 5972 | return std::move(op.outputs_[0]).take(); |
| 5973 | } |
| 5974 | struct structured_xlogy_Tensor_default_backend_inplace final : public at::meta::structured_xlogy_Tensor { |
| 5975 | structured_xlogy_Tensor_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 5976 | void set_output_strided( |
| 5977 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 5978 | TensorOptions options, DimnameList names |
| 5979 | ) override { |
| 5980 | auto current_device = guard_.current_device(); |
| 5981 | if (C10_UNLIKELY(current_device.has_value())) { |
| 5982 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 5983 | "structured kernels don't support multi-device outputs"); |
| 5984 | } else { |
| 5985 | guard_.reset_device(options.device()); |
| 5986 | } |
| 5987 | const auto& out = outputs_[output_idx].get(); |
| 5988 | check_inplace(out, sizes, options); |
| 5989 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 5990 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 5991 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 5992 | } |
| 5993 | if (!names.empty()) { |
| 5994 | namedinference::propagate_names(outputs_[output_idx], names); |
| 5995 | } |
| 5996 | // super must happen after, so that downstream can use maybe_get_output |
| 5997 | // to retrieve the output |
| 5998 | at::meta::structured_xlogy_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 5999 | } |
| 6000 | void set_output_raw_strided( |
| 6001 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6002 | TensorOptions options, DimnameList names |
| 6003 | ) override { |
| 6004 | auto current_device = guard_.current_device(); |
| 6005 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6006 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6007 | "structured kernels don't support multi-device outputs"); |
| 6008 | } else { |
| 6009 | guard_.reset_device(options.device()); |
| 6010 | } |
| 6011 | const auto& out = outputs_[output_idx].get(); |
| 6012 | check_inplace(out, sizes, options); |
| 6013 | if (!names.empty()) { |
| 6014 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6015 | } |
| 6016 | // super must happen after, so that downstream can use maybe_get_output |
| 6017 | // to retrieve the output |
| 6018 | at::meta::structured_xlogy_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6019 | } |
| 6020 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6021 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 6022 | } |
| 6023 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 6024 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 6025 | c10::OptionalDeviceGuard guard_; |
| 6026 | }; |
| 6027 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_xlogy__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 6028 | structured_xlogy_Tensor_default_backend_inplace op(self); |
| 6029 | op.meta(self, other); |
| 6030 | at::xlogy_outf(self, other, op.outputs_[0]); |
| 6031 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 6032 | return self; |
| 6033 | } |
| 6034 | struct structured__log_softmax_default_backend_functional final : public at::meta::structured__log_softmax { |
| 6035 | void set_output_strided( |
| 6036 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6037 | TensorOptions options, DimnameList names |
| 6038 | ) override { |
| 6039 | auto current_device = guard_.current_device(); |
| 6040 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6041 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6042 | "structured kernels don't support multi-device outputs"); |
| 6043 | } else { |
| 6044 | guard_.reset_device(options.device()); |
| 6045 | } |
| 6046 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6047 | if (!names.empty()) { |
| 6048 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6049 | } |
| 6050 | // super must happen after, so that downstream can use maybe_get_output |
| 6051 | // to retrieve the output |
| 6052 | } |
| 6053 | void set_output_raw_strided( |
| 6054 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6055 | TensorOptions options, DimnameList names |
| 6056 | ) override { |
| 6057 | auto current_device = guard_.current_device(); |
| 6058 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6059 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6060 | "structured kernels don't support multi-device outputs"); |
| 6061 | } else { |
| 6062 | guard_.reset_device(options.device()); |
| 6063 | } |
| 6064 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6065 | if (!names.empty()) { |
| 6066 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6067 | } |
| 6068 | // super must happen after, so that downstream can use maybe_get_output |
| 6069 | // to retrieve the output |
| 6070 | } |
| 6071 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6072 | return *outputs_[output_idx]; |
| 6073 | } |
| 6074 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 6075 | c10::OptionalDeviceGuard guard_; |
| 6076 | }; |
| 6077 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__log_softmax(const at::Tensor & self, int64_t dim, bool half_to_float) { |
| 6078 | structured__log_softmax_default_backend_functional op; |
| 6079 | op.meta(self, dim, half_to_float); |
| 6080 | at::_log_softmax_outf(self, dim, half_to_float, *op.outputs_[0]); |
| 6081 | return std::move(op.outputs_[0]).take(); |
| 6082 | } |
| 6083 | struct structured__log_softmax_backward_data_default_backend_functional final : public at::meta::structured__log_softmax_backward_data { |
| 6084 | void set_output_strided( |
| 6085 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6086 | TensorOptions options, DimnameList names |
| 6087 | ) override { |
| 6088 | auto current_device = guard_.current_device(); |
| 6089 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6090 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6091 | "structured kernels don't support multi-device outputs"); |
| 6092 | } else { |
| 6093 | guard_.reset_device(options.device()); |
| 6094 | } |
| 6095 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6096 | if (!names.empty()) { |
| 6097 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6098 | } |
| 6099 | // super must happen after, so that downstream can use maybe_get_output |
| 6100 | // to retrieve the output |
| 6101 | } |
| 6102 | void set_output_raw_strided( |
| 6103 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6104 | TensorOptions options, DimnameList names |
| 6105 | ) override { |
| 6106 | auto current_device = guard_.current_device(); |
| 6107 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6108 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6109 | "structured kernels don't support multi-device outputs"); |
| 6110 | } else { |
| 6111 | guard_.reset_device(options.device()); |
| 6112 | } |
| 6113 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6114 | if (!names.empty()) { |
| 6115 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6116 | } |
| 6117 | // super must happen after, so that downstream can use maybe_get_output |
| 6118 | // to retrieve the output |
| 6119 | } |
| 6120 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6121 | return *outputs_[output_idx]; |
| 6122 | } |
| 6123 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 6124 | c10::OptionalDeviceGuard guard_; |
| 6125 | }; |
| 6126 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__log_softmax_backward_data(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) { |
| 6127 | structured__log_softmax_backward_data_default_backend_functional op; |
| 6128 | op.meta(grad_output, output, dim, input_dtype); |
| 6129 | at::_log_softmax_backward_data_outf(grad_output, output, dim, input_dtype, *op.outputs_[0]); |
| 6130 | return std::move(op.outputs_[0]).take(); |
| 6131 | } |
| 6132 | namespace { |
| 6133 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_out_logsumexp_out(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { |
| 6134 | // No device check |
| 6135 | // DeviceGuard omitted |
| 6136 | return at::native::logsumexp_out(self, dim, keepdim, out); |
| 6137 | } |
| 6138 | } // anonymous namespace |
| 6139 | struct structured_aminmax_default_backend_functional final : public at::meta::structured_aminmax { |
| 6140 | void set_output_strided( |
| 6141 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6142 | TensorOptions options, DimnameList names |
| 6143 | ) override { |
| 6144 | auto current_device = guard_.current_device(); |
| 6145 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6146 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6147 | "structured kernels don't support multi-device outputs"); |
| 6148 | } else { |
| 6149 | guard_.reset_device(options.device()); |
| 6150 | } |
| 6151 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6152 | if (!names.empty()) { |
| 6153 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6154 | } |
| 6155 | // super must happen after, so that downstream can use maybe_get_output |
| 6156 | // to retrieve the output |
| 6157 | } |
| 6158 | void set_output_raw_strided( |
| 6159 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6160 | TensorOptions options, DimnameList names |
| 6161 | ) override { |
| 6162 | auto current_device = guard_.current_device(); |
| 6163 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6164 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6165 | "structured kernels don't support multi-device outputs"); |
| 6166 | } else { |
| 6167 | guard_.reset_device(options.device()); |
| 6168 | } |
| 6169 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6170 | if (!names.empty()) { |
| 6171 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6172 | } |
| 6173 | // super must happen after, so that downstream can use maybe_get_output |
| 6174 | // to retrieve the output |
| 6175 | } |
| 6176 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6177 | return *outputs_[output_idx]; |
| 6178 | } |
| 6179 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 6180 | c10::OptionalDeviceGuard guard_; |
| 6181 | }; |
| 6182 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CompositeExplicitAutogradNonFunctional_aminmax(const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim) { |
| 6183 | structured_aminmax_default_backend_functional op; |
| 6184 | op.meta(self, dim, keepdim); |
| 6185 | at::aminmax_outf(self, dim, keepdim, *op.outputs_[0], *op.outputs_[1]); |
| 6186 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 6187 | } |
| 6188 | struct structured_max_dim_default_backend_functional final : public at::meta::structured_max_dim { |
| 6189 | void set_output_strided( |
| 6190 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6191 | TensorOptions options, DimnameList names |
| 6192 | ) override { |
| 6193 | auto current_device = guard_.current_device(); |
| 6194 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6195 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6196 | "structured kernels don't support multi-device outputs"); |
| 6197 | } else { |
| 6198 | guard_.reset_device(options.device()); |
| 6199 | } |
| 6200 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6201 | if (!names.empty()) { |
| 6202 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6203 | } |
| 6204 | // super must happen after, so that downstream can use maybe_get_output |
| 6205 | // to retrieve the output |
| 6206 | } |
| 6207 | void set_output_raw_strided( |
| 6208 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6209 | TensorOptions options, DimnameList names |
| 6210 | ) override { |
| 6211 | auto current_device = guard_.current_device(); |
| 6212 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6213 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6214 | "structured kernels don't support multi-device outputs"); |
| 6215 | } else { |
| 6216 | guard_.reset_device(options.device()); |
| 6217 | } |
| 6218 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6219 | if (!names.empty()) { |
| 6220 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6221 | } |
| 6222 | // super must happen after, so that downstream can use maybe_get_output |
| 6223 | // to retrieve the output |
| 6224 | } |
| 6225 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6226 | return *outputs_[output_idx]; |
| 6227 | } |
| 6228 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 6229 | c10::OptionalDeviceGuard guard_; |
| 6230 | }; |
| 6231 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CompositeExplicitAutogradNonFunctional_max_dim(const at::Tensor & self, int64_t dim, bool keepdim) { |
| 6232 | structured_max_dim_default_backend_functional op; |
| 6233 | auto precompute = op.meta(self, dim, keepdim); |
| 6234 | (void)precompute; |
| 6235 | at::max_outf(self, precompute.dim, keepdim, *op.outputs_[0], *op.outputs_[1]); |
| 6236 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 6237 | } |
| 6238 | struct structured_amax_default_backend_functional final : public at::meta::structured_amax { |
| 6239 | void set_output_strided( |
| 6240 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6241 | TensorOptions options, DimnameList names |
| 6242 | ) override { |
| 6243 | auto current_device = guard_.current_device(); |
| 6244 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6245 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6246 | "structured kernels don't support multi-device outputs"); |
| 6247 | } else { |
| 6248 | guard_.reset_device(options.device()); |
| 6249 | } |
| 6250 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6251 | if (!names.empty()) { |
| 6252 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6253 | } |
| 6254 | // super must happen after, so that downstream can use maybe_get_output |
| 6255 | // to retrieve the output |
| 6256 | } |
| 6257 | void set_output_raw_strided( |
| 6258 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6259 | TensorOptions options, DimnameList names |
| 6260 | ) override { |
| 6261 | auto current_device = guard_.current_device(); |
| 6262 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6263 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6264 | "structured kernels don't support multi-device outputs"); |
| 6265 | } else { |
| 6266 | guard_.reset_device(options.device()); |
| 6267 | } |
| 6268 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6269 | if (!names.empty()) { |
| 6270 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6271 | } |
| 6272 | // super must happen after, so that downstream can use maybe_get_output |
| 6273 | // to retrieve the output |
| 6274 | } |
| 6275 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6276 | return *outputs_[output_idx]; |
| 6277 | } |
| 6278 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 6279 | c10::OptionalDeviceGuard guard_; |
| 6280 | }; |
| 6281 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_amax(const at::Tensor & self, at::IntArrayRef dim, bool keepdim) { |
| 6282 | structured_amax_default_backend_functional op; |
| 6283 | op.meta(self, dim, keepdim); |
| 6284 | at::amax_outf(self, dim, keepdim, *op.outputs_[0]); |
| 6285 | return std::move(op.outputs_[0]).take(); |
| 6286 | } |
| 6287 | struct structured_mean_dim_default_backend_functional final : public at::meta::structured_mean_dim { |
| 6288 | void set_output_strided( |
| 6289 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6290 | TensorOptions options, DimnameList names |
| 6291 | ) override { |
| 6292 | auto current_device = guard_.current_device(); |
| 6293 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6294 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6295 | "structured kernels don't support multi-device outputs"); |
| 6296 | } else { |
| 6297 | guard_.reset_device(options.device()); |
| 6298 | } |
| 6299 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6300 | if (!names.empty()) { |
| 6301 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6302 | } |
| 6303 | // super must happen after, so that downstream can use maybe_get_output |
| 6304 | // to retrieve the output |
| 6305 | } |
| 6306 | void set_output_raw_strided( |
| 6307 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6308 | TensorOptions options, DimnameList names |
| 6309 | ) override { |
| 6310 | auto current_device = guard_.current_device(); |
| 6311 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6312 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6313 | "structured kernels don't support multi-device outputs"); |
| 6314 | } else { |
| 6315 | guard_.reset_device(options.device()); |
| 6316 | } |
| 6317 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6318 | if (!names.empty()) { |
| 6319 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6320 | } |
| 6321 | // super must happen after, so that downstream can use maybe_get_output |
| 6322 | // to retrieve the output |
| 6323 | } |
| 6324 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6325 | return *outputs_[output_idx]; |
| 6326 | } |
| 6327 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 6328 | c10::OptionalDeviceGuard guard_; |
| 6329 | }; |
| 6330 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_mean_dim(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 6331 | structured_mean_dim_default_backend_functional op; |
| 6332 | op.meta(self, dim, keepdim, dtype); |
| 6333 | at::mean_outf(self, dim, keepdim, dtype, *op.outputs_[0]); |
| 6334 | return std::move(op.outputs_[0]).take(); |
| 6335 | } |
| 6336 | struct structured_min_dim_default_backend_functional final : public at::meta::structured_min_dim { |
| 6337 | void set_output_strided( |
| 6338 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6339 | TensorOptions options, DimnameList names |
| 6340 | ) override { |
| 6341 | auto current_device = guard_.current_device(); |
| 6342 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6343 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6344 | "structured kernels don't support multi-device outputs"); |
| 6345 | } else { |
| 6346 | guard_.reset_device(options.device()); |
| 6347 | } |
| 6348 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6349 | if (!names.empty()) { |
| 6350 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6351 | } |
| 6352 | // super must happen after, so that downstream can use maybe_get_output |
| 6353 | // to retrieve the output |
| 6354 | } |
| 6355 | void set_output_raw_strided( |
| 6356 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6357 | TensorOptions options, DimnameList names |
| 6358 | ) override { |
| 6359 | auto current_device = guard_.current_device(); |
| 6360 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6361 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6362 | "structured kernels don't support multi-device outputs"); |
| 6363 | } else { |
| 6364 | guard_.reset_device(options.device()); |
| 6365 | } |
| 6366 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6367 | if (!names.empty()) { |
| 6368 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6369 | } |
| 6370 | // super must happen after, so that downstream can use maybe_get_output |
| 6371 | // to retrieve the output |
| 6372 | } |
| 6373 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6374 | return *outputs_[output_idx]; |
| 6375 | } |
| 6376 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 6377 | c10::OptionalDeviceGuard guard_; |
| 6378 | }; |
| 6379 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CompositeExplicitAutogradNonFunctional_min_dim(const at::Tensor & self, int64_t dim, bool keepdim) { |
| 6380 | structured_min_dim_default_backend_functional op; |
| 6381 | auto precompute = op.meta(self, dim, keepdim); |
| 6382 | (void)precompute; |
| 6383 | at::min_outf(self, precompute.dim, keepdim, *op.outputs_[0], *op.outputs_[1]); |
| 6384 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 6385 | } |
| 6386 | struct structured_amin_default_backend_functional final : public at::meta::structured_amin { |
| 6387 | void set_output_strided( |
| 6388 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6389 | TensorOptions options, DimnameList names |
| 6390 | ) override { |
| 6391 | auto current_device = guard_.current_device(); |
| 6392 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6393 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6394 | "structured kernels don't support multi-device outputs"); |
| 6395 | } else { |
| 6396 | guard_.reset_device(options.device()); |
| 6397 | } |
| 6398 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6399 | if (!names.empty()) { |
| 6400 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6401 | } |
| 6402 | // super must happen after, so that downstream can use maybe_get_output |
| 6403 | // to retrieve the output |
| 6404 | } |
| 6405 | void set_output_raw_strided( |
| 6406 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6407 | TensorOptions options, DimnameList names |
| 6408 | ) override { |
| 6409 | auto current_device = guard_.current_device(); |
| 6410 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6411 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6412 | "structured kernels don't support multi-device outputs"); |
| 6413 | } else { |
| 6414 | guard_.reset_device(options.device()); |
| 6415 | } |
| 6416 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6417 | if (!names.empty()) { |
| 6418 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6419 | } |
| 6420 | // super must happen after, so that downstream can use maybe_get_output |
| 6421 | // to retrieve the output |
| 6422 | } |
| 6423 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6424 | return *outputs_[output_idx]; |
| 6425 | } |
| 6426 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 6427 | c10::OptionalDeviceGuard guard_; |
| 6428 | }; |
| 6429 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_amin(const at::Tensor & self, at::IntArrayRef dim, bool keepdim) { |
| 6430 | structured_amin_default_backend_functional op; |
| 6431 | op.meta(self, dim, keepdim); |
| 6432 | at::amin_outf(self, dim, keepdim, *op.outputs_[0]); |
| 6433 | return std::move(op.outputs_[0]).take(); |
| 6434 | } |
| 6435 | struct structured_mm_default_backend_functional final : public at::meta::structured_mm { |
| 6436 | void set_output_strided( |
| 6437 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6438 | TensorOptions options, DimnameList names |
| 6439 | ) override { |
| 6440 | auto current_device = guard_.current_device(); |
| 6441 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6442 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6443 | "structured kernels don't support multi-device outputs"); |
| 6444 | } else { |
| 6445 | guard_.reset_device(options.device()); |
| 6446 | } |
| 6447 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6448 | if (!names.empty()) { |
| 6449 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6450 | } |
| 6451 | // super must happen after, so that downstream can use maybe_get_output |
| 6452 | // to retrieve the output |
| 6453 | } |
| 6454 | void set_output_raw_strided( |
| 6455 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6456 | TensorOptions options, DimnameList names |
| 6457 | ) override { |
| 6458 | auto current_device = guard_.current_device(); |
| 6459 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6460 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6461 | "structured kernels don't support multi-device outputs"); |
| 6462 | } else { |
| 6463 | guard_.reset_device(options.device()); |
| 6464 | } |
| 6465 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6466 | if (!names.empty()) { |
| 6467 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6468 | } |
| 6469 | // super must happen after, so that downstream can use maybe_get_output |
| 6470 | // to retrieve the output |
| 6471 | } |
| 6472 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6473 | return *outputs_[output_idx]; |
| 6474 | } |
| 6475 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 6476 | c10::OptionalDeviceGuard guard_; |
| 6477 | }; |
| 6478 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_mm(const at::Tensor & self, const at::Tensor & mat2) { |
| 6479 | structured_mm_default_backend_functional op; |
| 6480 | op.meta(self, mat2); |
| 6481 | at::mm_outf(self, mat2, *op.outputs_[0]); |
| 6482 | return std::move(op.outputs_[0]).take(); |
| 6483 | } |
| 6484 | struct structured_mul_Tensor_default_backend_functional final : public at::meta::structured_mul_Tensor { |
| 6485 | void set_output_strided( |
| 6486 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6487 | TensorOptions options, DimnameList names |
| 6488 | ) override { |
| 6489 | auto current_device = guard_.current_device(); |
| 6490 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6491 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6492 | "structured kernels don't support multi-device outputs"); |
| 6493 | } else { |
| 6494 | guard_.reset_device(options.device()); |
| 6495 | } |
| 6496 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6497 | if (!names.empty()) { |
| 6498 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6499 | } |
| 6500 | // super must happen after, so that downstream can use maybe_get_output |
| 6501 | // to retrieve the output |
| 6502 | at::meta::structured_mul_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6503 | } |
| 6504 | void set_output_raw_strided( |
| 6505 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6506 | TensorOptions options, DimnameList names |
| 6507 | ) override { |
| 6508 | auto current_device = guard_.current_device(); |
| 6509 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6510 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6511 | "structured kernels don't support multi-device outputs"); |
| 6512 | } else { |
| 6513 | guard_.reset_device(options.device()); |
| 6514 | } |
| 6515 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6516 | if (!names.empty()) { |
| 6517 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6518 | } |
| 6519 | // super must happen after, so that downstream can use maybe_get_output |
| 6520 | // to retrieve the output |
| 6521 | at::meta::structured_mul_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6522 | } |
| 6523 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6524 | return *outputs_[output_idx]; |
| 6525 | } |
| 6526 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 6527 | c10::OptionalDeviceGuard guard_; |
| 6528 | }; |
| 6529 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_mul_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 6530 | structured_mul_Tensor_default_backend_functional op; |
| 6531 | op.meta(self, other); |
| 6532 | at::mul_outf(self, other, *op.outputs_[0]); |
| 6533 | return std::move(op.outputs_[0]).take(); |
| 6534 | } |
| 6535 | struct structured_mul_Tensor_default_backend_inplace final : public at::meta::structured_mul_Tensor { |
| 6536 | structured_mul_Tensor_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 6537 | void set_output_strided( |
| 6538 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6539 | TensorOptions options, DimnameList names |
| 6540 | ) override { |
| 6541 | auto current_device = guard_.current_device(); |
| 6542 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6543 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6544 | "structured kernels don't support multi-device outputs"); |
| 6545 | } else { |
| 6546 | guard_.reset_device(options.device()); |
| 6547 | } |
| 6548 | const auto& out = outputs_[output_idx].get(); |
| 6549 | check_inplace(out, sizes, options); |
| 6550 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 6551 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 6552 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 6553 | } |
| 6554 | if (!names.empty()) { |
| 6555 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6556 | } |
| 6557 | // super must happen after, so that downstream can use maybe_get_output |
| 6558 | // to retrieve the output |
| 6559 | at::meta::structured_mul_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6560 | } |
| 6561 | void set_output_raw_strided( |
| 6562 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6563 | TensorOptions options, DimnameList names |
| 6564 | ) override { |
| 6565 | auto current_device = guard_.current_device(); |
| 6566 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6567 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6568 | "structured kernels don't support multi-device outputs"); |
| 6569 | } else { |
| 6570 | guard_.reset_device(options.device()); |
| 6571 | } |
| 6572 | const auto& out = outputs_[output_idx].get(); |
| 6573 | check_inplace(out, sizes, options); |
| 6574 | if (!names.empty()) { |
| 6575 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6576 | } |
| 6577 | // super must happen after, so that downstream can use maybe_get_output |
| 6578 | // to retrieve the output |
| 6579 | at::meta::structured_mul_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6580 | } |
| 6581 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6582 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 6583 | } |
| 6584 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 6585 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 6586 | c10::OptionalDeviceGuard guard_; |
| 6587 | }; |
| 6588 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_mul__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 6589 | structured_mul_Tensor_default_backend_inplace op(self); |
| 6590 | op.meta(self, other); |
| 6591 | at::mul_outf(self, other, op.outputs_[0]); |
| 6592 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 6593 | return self; |
| 6594 | } |
| 6595 | namespace { |
| 6596 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__narrow_copy(const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length) { |
| 6597 | // No device check |
| 6598 | // DeviceGuard omitted |
| 6599 | return at::native::narrow_copy_dense_symint(self, dim, start, length); |
| 6600 | } |
| 6601 | } // anonymous namespace |
| 6602 | namespace { |
| 6603 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__pixel_shuffle(const at::Tensor & self, int64_t upscale_factor) { |
| 6604 | // No device check |
| 6605 | // DeviceGuard omitted |
| 6606 | return at::native::math_pixel_shuffle(self, upscale_factor); |
| 6607 | } |
| 6608 | } // anonymous namespace |
| 6609 | namespace { |
| 6610 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__pixel_unshuffle(const at::Tensor & self, int64_t downscale_factor) { |
| 6611 | // No device check |
| 6612 | // DeviceGuard omitted |
| 6613 | return at::native::math_pixel_unshuffle(self, downscale_factor); |
| 6614 | } |
| 6615 | } // anonymous namespace |
| 6616 | struct structured_reciprocal_default_backend_functional final : public at::meta::structured_reciprocal { |
| 6617 | void set_output_strided( |
| 6618 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6619 | TensorOptions options, DimnameList names |
| 6620 | ) override { |
| 6621 | auto current_device = guard_.current_device(); |
| 6622 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6623 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6624 | "structured kernels don't support multi-device outputs"); |
| 6625 | } else { |
| 6626 | guard_.reset_device(options.device()); |
| 6627 | } |
| 6628 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6629 | if (!names.empty()) { |
| 6630 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6631 | } |
| 6632 | // super must happen after, so that downstream can use maybe_get_output |
| 6633 | // to retrieve the output |
| 6634 | at::meta::structured_reciprocal::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6635 | } |
| 6636 | void set_output_raw_strided( |
| 6637 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6638 | TensorOptions options, DimnameList names |
| 6639 | ) override { |
| 6640 | auto current_device = guard_.current_device(); |
| 6641 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6642 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6643 | "structured kernels don't support multi-device outputs"); |
| 6644 | } else { |
| 6645 | guard_.reset_device(options.device()); |
| 6646 | } |
| 6647 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6648 | if (!names.empty()) { |
| 6649 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6650 | } |
| 6651 | // super must happen after, so that downstream can use maybe_get_output |
| 6652 | // to retrieve the output |
| 6653 | at::meta::structured_reciprocal::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6654 | } |
| 6655 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6656 | return *outputs_[output_idx]; |
| 6657 | } |
| 6658 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 6659 | c10::OptionalDeviceGuard guard_; |
| 6660 | }; |
| 6661 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_reciprocal(const at::Tensor & self) { |
| 6662 | structured_reciprocal_default_backend_functional op; |
| 6663 | op.meta(self); |
| 6664 | at::reciprocal_outf(self, *op.outputs_[0]); |
| 6665 | return std::move(op.outputs_[0]).take(); |
| 6666 | } |
| 6667 | struct structured_reciprocal_default_backend_inplace final : public at::meta::structured_reciprocal { |
| 6668 | structured_reciprocal_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 6669 | void set_output_strided( |
| 6670 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6671 | TensorOptions options, DimnameList names |
| 6672 | ) override { |
| 6673 | auto current_device = guard_.current_device(); |
| 6674 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6675 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6676 | "structured kernels don't support multi-device outputs"); |
| 6677 | } else { |
| 6678 | guard_.reset_device(options.device()); |
| 6679 | } |
| 6680 | const auto& out = outputs_[output_idx].get(); |
| 6681 | check_inplace(out, sizes, options); |
| 6682 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 6683 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 6684 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 6685 | } |
| 6686 | if (!names.empty()) { |
| 6687 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6688 | } |
| 6689 | // super must happen after, so that downstream can use maybe_get_output |
| 6690 | // to retrieve the output |
| 6691 | at::meta::structured_reciprocal::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6692 | } |
| 6693 | void set_output_raw_strided( |
| 6694 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6695 | TensorOptions options, DimnameList names |
| 6696 | ) override { |
| 6697 | auto current_device = guard_.current_device(); |
| 6698 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6699 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6700 | "structured kernels don't support multi-device outputs"); |
| 6701 | } else { |
| 6702 | guard_.reset_device(options.device()); |
| 6703 | } |
| 6704 | const auto& out = outputs_[output_idx].get(); |
| 6705 | check_inplace(out, sizes, options); |
| 6706 | if (!names.empty()) { |
| 6707 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6708 | } |
| 6709 | // super must happen after, so that downstream can use maybe_get_output |
| 6710 | // to retrieve the output |
| 6711 | at::meta::structured_reciprocal::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6712 | } |
| 6713 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6714 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 6715 | } |
| 6716 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 6717 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 6718 | c10::OptionalDeviceGuard guard_; |
| 6719 | }; |
| 6720 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_reciprocal_(at::Tensor & self) { |
| 6721 | structured_reciprocal_default_backend_inplace op(self); |
| 6722 | op.meta(self); |
| 6723 | at::reciprocal_outf(self, op.outputs_[0]); |
| 6724 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 6725 | return self; |
| 6726 | } |
| 6727 | struct structured_neg_default_backend_functional final : public at::meta::structured_neg { |
| 6728 | void set_output_strided( |
| 6729 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6730 | TensorOptions options, DimnameList names |
| 6731 | ) override { |
| 6732 | auto current_device = guard_.current_device(); |
| 6733 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6734 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6735 | "structured kernels don't support multi-device outputs"); |
| 6736 | } else { |
| 6737 | guard_.reset_device(options.device()); |
| 6738 | } |
| 6739 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6740 | if (!names.empty()) { |
| 6741 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6742 | } |
| 6743 | // super must happen after, so that downstream can use maybe_get_output |
| 6744 | // to retrieve the output |
| 6745 | at::meta::structured_neg::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6746 | } |
| 6747 | void set_output_raw_strided( |
| 6748 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6749 | TensorOptions options, DimnameList names |
| 6750 | ) override { |
| 6751 | auto current_device = guard_.current_device(); |
| 6752 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6753 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6754 | "structured kernels don't support multi-device outputs"); |
| 6755 | } else { |
| 6756 | guard_.reset_device(options.device()); |
| 6757 | } |
| 6758 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6759 | if (!names.empty()) { |
| 6760 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6761 | } |
| 6762 | // super must happen after, so that downstream can use maybe_get_output |
| 6763 | // to retrieve the output |
| 6764 | at::meta::structured_neg::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6765 | } |
| 6766 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6767 | return *outputs_[output_idx]; |
| 6768 | } |
| 6769 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 6770 | c10::OptionalDeviceGuard guard_; |
| 6771 | }; |
| 6772 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_neg(const at::Tensor & self) { |
| 6773 | structured_neg_default_backend_functional op; |
| 6774 | op.meta(self); |
| 6775 | at::neg_outf(self, *op.outputs_[0]); |
| 6776 | return std::move(op.outputs_[0]).take(); |
| 6777 | } |
| 6778 | struct structured_neg_default_backend_inplace final : public at::meta::structured_neg { |
| 6779 | structured_neg_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 6780 | void set_output_strided( |
| 6781 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6782 | TensorOptions options, DimnameList names |
| 6783 | ) override { |
| 6784 | auto current_device = guard_.current_device(); |
| 6785 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6786 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6787 | "structured kernels don't support multi-device outputs"); |
| 6788 | } else { |
| 6789 | guard_.reset_device(options.device()); |
| 6790 | } |
| 6791 | const auto& out = outputs_[output_idx].get(); |
| 6792 | check_inplace(out, sizes, options); |
| 6793 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 6794 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 6795 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 6796 | } |
| 6797 | if (!names.empty()) { |
| 6798 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6799 | } |
| 6800 | // super must happen after, so that downstream can use maybe_get_output |
| 6801 | // to retrieve the output |
| 6802 | at::meta::structured_neg::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6803 | } |
| 6804 | void set_output_raw_strided( |
| 6805 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6806 | TensorOptions options, DimnameList names |
| 6807 | ) override { |
| 6808 | auto current_device = guard_.current_device(); |
| 6809 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6810 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6811 | "structured kernels don't support multi-device outputs"); |
| 6812 | } else { |
| 6813 | guard_.reset_device(options.device()); |
| 6814 | } |
| 6815 | const auto& out = outputs_[output_idx].get(); |
| 6816 | check_inplace(out, sizes, options); |
| 6817 | if (!names.empty()) { |
| 6818 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6819 | } |
| 6820 | // super must happen after, so that downstream can use maybe_get_output |
| 6821 | // to retrieve the output |
| 6822 | at::meta::structured_neg::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6823 | } |
| 6824 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6825 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 6826 | } |
| 6827 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 6828 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 6829 | c10::OptionalDeviceGuard guard_; |
| 6830 | }; |
| 6831 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_neg_(at::Tensor & self) { |
| 6832 | structured_neg_default_backend_inplace op(self); |
| 6833 | op.meta(self); |
| 6834 | at::neg_outf(self, op.outputs_[0]); |
| 6835 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 6836 | return self; |
| 6837 | } |
| 6838 | struct structured_round_default_backend_functional final : public at::meta::structured_round { |
| 6839 | void set_output_strided( |
| 6840 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6841 | TensorOptions options, DimnameList names |
| 6842 | ) override { |
| 6843 | auto current_device = guard_.current_device(); |
| 6844 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6845 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6846 | "structured kernels don't support multi-device outputs"); |
| 6847 | } else { |
| 6848 | guard_.reset_device(options.device()); |
| 6849 | } |
| 6850 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6851 | if (!names.empty()) { |
| 6852 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6853 | } |
| 6854 | // super must happen after, so that downstream can use maybe_get_output |
| 6855 | // to retrieve the output |
| 6856 | at::meta::structured_round::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6857 | } |
| 6858 | void set_output_raw_strided( |
| 6859 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6860 | TensorOptions options, DimnameList names |
| 6861 | ) override { |
| 6862 | auto current_device = guard_.current_device(); |
| 6863 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6864 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6865 | "structured kernels don't support multi-device outputs"); |
| 6866 | } else { |
| 6867 | guard_.reset_device(options.device()); |
| 6868 | } |
| 6869 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6870 | if (!names.empty()) { |
| 6871 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6872 | } |
| 6873 | // super must happen after, so that downstream can use maybe_get_output |
| 6874 | // to retrieve the output |
| 6875 | at::meta::structured_round::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6876 | } |
| 6877 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6878 | return *outputs_[output_idx]; |
| 6879 | } |
| 6880 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 6881 | c10::OptionalDeviceGuard guard_; |
| 6882 | }; |
| 6883 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_round(const at::Tensor & self) { |
| 6884 | structured_round_default_backend_functional op; |
| 6885 | op.meta(self); |
| 6886 | at::round_outf(self, *op.outputs_[0]); |
| 6887 | return std::move(op.outputs_[0]).take(); |
| 6888 | } |
| 6889 | struct structured_round_default_backend_inplace final : public at::meta::structured_round { |
| 6890 | structured_round_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 6891 | void set_output_strided( |
| 6892 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6893 | TensorOptions options, DimnameList names |
| 6894 | ) override { |
| 6895 | auto current_device = guard_.current_device(); |
| 6896 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6897 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6898 | "structured kernels don't support multi-device outputs"); |
| 6899 | } else { |
| 6900 | guard_.reset_device(options.device()); |
| 6901 | } |
| 6902 | const auto& out = outputs_[output_idx].get(); |
| 6903 | check_inplace(out, sizes, options); |
| 6904 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 6905 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 6906 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 6907 | } |
| 6908 | if (!names.empty()) { |
| 6909 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6910 | } |
| 6911 | // super must happen after, so that downstream can use maybe_get_output |
| 6912 | // to retrieve the output |
| 6913 | at::meta::structured_round::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6914 | } |
| 6915 | void set_output_raw_strided( |
| 6916 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6917 | TensorOptions options, DimnameList names |
| 6918 | ) override { |
| 6919 | auto current_device = guard_.current_device(); |
| 6920 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6921 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6922 | "structured kernels don't support multi-device outputs"); |
| 6923 | } else { |
| 6924 | guard_.reset_device(options.device()); |
| 6925 | } |
| 6926 | const auto& out = outputs_[output_idx].get(); |
| 6927 | check_inplace(out, sizes, options); |
| 6928 | if (!names.empty()) { |
| 6929 | namedinference::propagate_names(outputs_[output_idx], names); |
| 6930 | } |
| 6931 | // super must happen after, so that downstream can use maybe_get_output |
| 6932 | // to retrieve the output |
| 6933 | at::meta::structured_round::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6934 | } |
| 6935 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6936 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 6937 | } |
| 6938 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 6939 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 6940 | c10::OptionalDeviceGuard guard_; |
| 6941 | }; |
| 6942 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_round_(at::Tensor & self) { |
| 6943 | structured_round_default_backend_inplace op(self); |
| 6944 | op.meta(self); |
| 6945 | at::round_outf(self, op.outputs_[0]); |
| 6946 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 6947 | return self; |
| 6948 | } |
| 6949 | struct structured_round_decimals_default_backend_functional final : public at::meta::structured_round_decimals { |
| 6950 | void set_output_strided( |
| 6951 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6952 | TensorOptions options, DimnameList names |
| 6953 | ) override { |
| 6954 | auto current_device = guard_.current_device(); |
| 6955 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6956 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6957 | "structured kernels don't support multi-device outputs"); |
| 6958 | } else { |
| 6959 | guard_.reset_device(options.device()); |
| 6960 | } |
| 6961 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6962 | if (!names.empty()) { |
| 6963 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6964 | } |
| 6965 | // super must happen after, so that downstream can use maybe_get_output |
| 6966 | // to retrieve the output |
| 6967 | at::meta::structured_round_decimals::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6968 | } |
| 6969 | void set_output_raw_strided( |
| 6970 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 6971 | TensorOptions options, DimnameList names |
| 6972 | ) override { |
| 6973 | auto current_device = guard_.current_device(); |
| 6974 | if (C10_UNLIKELY(current_device.has_value())) { |
| 6975 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 6976 | "structured kernels don't support multi-device outputs"); |
| 6977 | } else { |
| 6978 | guard_.reset_device(options.device()); |
| 6979 | } |
| 6980 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 6981 | if (!names.empty()) { |
| 6982 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 6983 | } |
| 6984 | // super must happen after, so that downstream can use maybe_get_output |
| 6985 | // to retrieve the output |
| 6986 | at::meta::structured_round_decimals::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 6987 | } |
| 6988 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 6989 | return *outputs_[output_idx]; |
| 6990 | } |
| 6991 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 6992 | c10::OptionalDeviceGuard guard_; |
| 6993 | }; |
| 6994 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_round_decimals(const at::Tensor & self, int64_t decimals) { |
| 6995 | structured_round_decimals_default_backend_functional op; |
| 6996 | op.meta(self, decimals); |
| 6997 | at::round_outf(self, decimals, *op.outputs_[0]); |
| 6998 | return std::move(op.outputs_[0]).take(); |
| 6999 | } |
| 7000 | struct structured_round_decimals_default_backend_inplace final : public at::meta::structured_round_decimals { |
| 7001 | structured_round_decimals_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 7002 | void set_output_strided( |
| 7003 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7004 | TensorOptions options, DimnameList names |
| 7005 | ) override { |
| 7006 | auto current_device = guard_.current_device(); |
| 7007 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7008 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7009 | "structured kernels don't support multi-device outputs"); |
| 7010 | } else { |
| 7011 | guard_.reset_device(options.device()); |
| 7012 | } |
| 7013 | const auto& out = outputs_[output_idx].get(); |
| 7014 | check_inplace(out, sizes, options); |
| 7015 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 7016 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 7017 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 7018 | } |
| 7019 | if (!names.empty()) { |
| 7020 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7021 | } |
| 7022 | // super must happen after, so that downstream can use maybe_get_output |
| 7023 | // to retrieve the output |
| 7024 | at::meta::structured_round_decimals::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7025 | } |
| 7026 | void set_output_raw_strided( |
| 7027 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7028 | TensorOptions options, DimnameList names |
| 7029 | ) override { |
| 7030 | auto current_device = guard_.current_device(); |
| 7031 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7032 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7033 | "structured kernels don't support multi-device outputs"); |
| 7034 | } else { |
| 7035 | guard_.reset_device(options.device()); |
| 7036 | } |
| 7037 | const auto& out = outputs_[output_idx].get(); |
| 7038 | check_inplace(out, sizes, options); |
| 7039 | if (!names.empty()) { |
| 7040 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7041 | } |
| 7042 | // super must happen after, so that downstream can use maybe_get_output |
| 7043 | // to retrieve the output |
| 7044 | at::meta::structured_round_decimals::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7045 | } |
| 7046 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7047 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 7048 | } |
| 7049 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 7050 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 7051 | c10::OptionalDeviceGuard guard_; |
| 7052 | }; |
| 7053 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_round__decimals(at::Tensor & self, int64_t decimals) { |
| 7054 | structured_round_decimals_default_backend_inplace op(self); |
| 7055 | op.meta(self, decimals); |
| 7056 | at::round_outf(self, decimals, op.outputs_[0]); |
| 7057 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 7058 | return self; |
| 7059 | } |
| 7060 | struct structured_gelu_default_backend_functional final : public at::meta::structured_gelu { |
| 7061 | void set_output_strided( |
| 7062 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7063 | TensorOptions options, DimnameList names |
| 7064 | ) override { |
| 7065 | auto current_device = guard_.current_device(); |
| 7066 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7067 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7068 | "structured kernels don't support multi-device outputs"); |
| 7069 | } else { |
| 7070 | guard_.reset_device(options.device()); |
| 7071 | } |
| 7072 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7073 | if (!names.empty()) { |
| 7074 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7075 | } |
| 7076 | // super must happen after, so that downstream can use maybe_get_output |
| 7077 | // to retrieve the output |
| 7078 | at::meta::structured_gelu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7079 | } |
| 7080 | void set_output_raw_strided( |
| 7081 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7082 | TensorOptions options, DimnameList names |
| 7083 | ) override { |
| 7084 | auto current_device = guard_.current_device(); |
| 7085 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7086 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7087 | "structured kernels don't support multi-device outputs"); |
| 7088 | } else { |
| 7089 | guard_.reset_device(options.device()); |
| 7090 | } |
| 7091 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7092 | if (!names.empty()) { |
| 7093 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7094 | } |
| 7095 | // super must happen after, so that downstream can use maybe_get_output |
| 7096 | // to retrieve the output |
| 7097 | at::meta::structured_gelu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7098 | } |
| 7099 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7100 | return *outputs_[output_idx]; |
| 7101 | } |
| 7102 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 7103 | c10::OptionalDeviceGuard guard_; |
| 7104 | }; |
| 7105 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_gelu(const at::Tensor & self, c10::string_view approximate) { |
| 7106 | structured_gelu_default_backend_functional op; |
| 7107 | op.meta(self, approximate); |
| 7108 | at::gelu_outf(self, approximate, *op.outputs_[0]); |
| 7109 | return std::move(op.outputs_[0]).take(); |
| 7110 | } |
| 7111 | struct structured_gelu_default_backend_inplace final : public at::meta::structured_gelu { |
| 7112 | structured_gelu_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 7113 | void set_output_strided( |
| 7114 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7115 | TensorOptions options, DimnameList names |
| 7116 | ) override { |
| 7117 | auto current_device = guard_.current_device(); |
| 7118 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7119 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7120 | "structured kernels don't support multi-device outputs"); |
| 7121 | } else { |
| 7122 | guard_.reset_device(options.device()); |
| 7123 | } |
| 7124 | const auto& out = outputs_[output_idx].get(); |
| 7125 | check_inplace(out, sizes, options); |
| 7126 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 7127 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 7128 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 7129 | } |
| 7130 | if (!names.empty()) { |
| 7131 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7132 | } |
| 7133 | // super must happen after, so that downstream can use maybe_get_output |
| 7134 | // to retrieve the output |
| 7135 | at::meta::structured_gelu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7136 | } |
| 7137 | void set_output_raw_strided( |
| 7138 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7139 | TensorOptions options, DimnameList names |
| 7140 | ) override { |
| 7141 | auto current_device = guard_.current_device(); |
| 7142 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7143 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7144 | "structured kernels don't support multi-device outputs"); |
| 7145 | } else { |
| 7146 | guard_.reset_device(options.device()); |
| 7147 | } |
| 7148 | const auto& out = outputs_[output_idx].get(); |
| 7149 | check_inplace(out, sizes, options); |
| 7150 | if (!names.empty()) { |
| 7151 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7152 | } |
| 7153 | // super must happen after, so that downstream can use maybe_get_output |
| 7154 | // to retrieve the output |
| 7155 | at::meta::structured_gelu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7156 | } |
| 7157 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7158 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 7159 | } |
| 7160 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 7161 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 7162 | c10::OptionalDeviceGuard guard_; |
| 7163 | }; |
| 7164 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_gelu_(at::Tensor & self, c10::string_view approximate) { |
| 7165 | structured_gelu_default_backend_inplace op(self); |
| 7166 | op.meta(self, approximate); |
| 7167 | at::gelu_outf(self, approximate, op.outputs_[0]); |
| 7168 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 7169 | return self; |
| 7170 | } |
| 7171 | struct structured_gelu_backward_default_backend_functional final : public at::meta::structured_gelu_backward { |
| 7172 | void set_output_strided( |
| 7173 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7174 | TensorOptions options, DimnameList names |
| 7175 | ) override { |
| 7176 | auto current_device = guard_.current_device(); |
| 7177 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7178 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7179 | "structured kernels don't support multi-device outputs"); |
| 7180 | } else { |
| 7181 | guard_.reset_device(options.device()); |
| 7182 | } |
| 7183 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7184 | if (!names.empty()) { |
| 7185 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7186 | } |
| 7187 | // super must happen after, so that downstream can use maybe_get_output |
| 7188 | // to retrieve the output |
| 7189 | at::meta::structured_gelu_backward::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7190 | } |
| 7191 | void set_output_raw_strided( |
| 7192 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7193 | TensorOptions options, DimnameList names |
| 7194 | ) override { |
| 7195 | auto current_device = guard_.current_device(); |
| 7196 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7197 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7198 | "structured kernels don't support multi-device outputs"); |
| 7199 | } else { |
| 7200 | guard_.reset_device(options.device()); |
| 7201 | } |
| 7202 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7203 | if (!names.empty()) { |
| 7204 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7205 | } |
| 7206 | // super must happen after, so that downstream can use maybe_get_output |
| 7207 | // to retrieve the output |
| 7208 | at::meta::structured_gelu_backward::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7209 | } |
| 7210 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7211 | return *outputs_[output_idx]; |
| 7212 | } |
| 7213 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 7214 | c10::OptionalDeviceGuard guard_; |
| 7215 | }; |
| 7216 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_gelu_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::string_view approximate) { |
| 7217 | structured_gelu_backward_default_backend_functional op; |
| 7218 | op.meta(grad_output, self, approximate); |
| 7219 | at::gelu_backward_outf(grad_output, self, approximate, *op.outputs_[0]); |
| 7220 | return std::move(op.outputs_[0]).take(); |
| 7221 | } |
| 7222 | struct structured_hardshrink_default_backend_functional final : public at::meta::structured_hardshrink { |
| 7223 | void set_output_strided( |
| 7224 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7225 | TensorOptions options, DimnameList names |
| 7226 | ) override { |
| 7227 | auto current_device = guard_.current_device(); |
| 7228 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7229 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7230 | "structured kernels don't support multi-device outputs"); |
| 7231 | } else { |
| 7232 | guard_.reset_device(options.device()); |
| 7233 | } |
| 7234 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7235 | if (!names.empty()) { |
| 7236 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7237 | } |
| 7238 | // super must happen after, so that downstream can use maybe_get_output |
| 7239 | // to retrieve the output |
| 7240 | at::meta::structured_hardshrink::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7241 | } |
| 7242 | void set_output_raw_strided( |
| 7243 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7244 | TensorOptions options, DimnameList names |
| 7245 | ) override { |
| 7246 | auto current_device = guard_.current_device(); |
| 7247 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7248 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7249 | "structured kernels don't support multi-device outputs"); |
| 7250 | } else { |
| 7251 | guard_.reset_device(options.device()); |
| 7252 | } |
| 7253 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7254 | if (!names.empty()) { |
| 7255 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7256 | } |
| 7257 | // super must happen after, so that downstream can use maybe_get_output |
| 7258 | // to retrieve the output |
| 7259 | at::meta::structured_hardshrink::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7260 | } |
| 7261 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7262 | return *outputs_[output_idx]; |
| 7263 | } |
| 7264 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 7265 | c10::OptionalDeviceGuard guard_; |
| 7266 | }; |
| 7267 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_hardshrink(const at::Tensor & self, const at::Scalar & lambd) { |
| 7268 | structured_hardshrink_default_backend_functional op; |
| 7269 | op.meta(self, lambd); |
| 7270 | at::hardshrink_outf(self, lambd, *op.outputs_[0]); |
| 7271 | return std::move(op.outputs_[0]).take(); |
| 7272 | } |
| 7273 | struct structured_hardshrink_backward_default_backend_functional final : public at::meta::structured_hardshrink_backward { |
| 7274 | void set_output_strided( |
| 7275 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7276 | TensorOptions options, DimnameList names |
| 7277 | ) override { |
| 7278 | auto current_device = guard_.current_device(); |
| 7279 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7280 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7281 | "structured kernels don't support multi-device outputs"); |
| 7282 | } else { |
| 7283 | guard_.reset_device(options.device()); |
| 7284 | } |
| 7285 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7286 | if (!names.empty()) { |
| 7287 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7288 | } |
| 7289 | // super must happen after, so that downstream can use maybe_get_output |
| 7290 | // to retrieve the output |
| 7291 | at::meta::structured_hardshrink_backward::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7292 | } |
| 7293 | void set_output_raw_strided( |
| 7294 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7295 | TensorOptions options, DimnameList names |
| 7296 | ) override { |
| 7297 | auto current_device = guard_.current_device(); |
| 7298 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7299 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7300 | "structured kernels don't support multi-device outputs"); |
| 7301 | } else { |
| 7302 | guard_.reset_device(options.device()); |
| 7303 | } |
| 7304 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7305 | if (!names.empty()) { |
| 7306 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7307 | } |
| 7308 | // super must happen after, so that downstream can use maybe_get_output |
| 7309 | // to retrieve the output |
| 7310 | at::meta::structured_hardshrink_backward::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7311 | } |
| 7312 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7313 | return *outputs_[output_idx]; |
| 7314 | } |
| 7315 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 7316 | c10::OptionalDeviceGuard guard_; |
| 7317 | }; |
| 7318 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_hardshrink_backward(const at::Tensor & grad_out, const at::Tensor & self, const at::Scalar & lambd) { |
| 7319 | structured_hardshrink_backward_default_backend_functional op; |
| 7320 | op.meta(grad_out, self, lambd); |
| 7321 | at::hardshrink_backward_outf(grad_out, self, lambd, *op.outputs_[0]); |
| 7322 | return std::move(op.outputs_[0]).take(); |
| 7323 | } |
| 7324 | struct structured_rsqrt_default_backend_functional final : public at::meta::structured_rsqrt { |
| 7325 | void set_output_strided( |
| 7326 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7327 | TensorOptions options, DimnameList names |
| 7328 | ) override { |
| 7329 | auto current_device = guard_.current_device(); |
| 7330 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7331 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7332 | "structured kernels don't support multi-device outputs"); |
| 7333 | } else { |
| 7334 | guard_.reset_device(options.device()); |
| 7335 | } |
| 7336 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7337 | if (!names.empty()) { |
| 7338 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7339 | } |
| 7340 | // super must happen after, so that downstream can use maybe_get_output |
| 7341 | // to retrieve the output |
| 7342 | at::meta::structured_rsqrt::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7343 | } |
| 7344 | void set_output_raw_strided( |
| 7345 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7346 | TensorOptions options, DimnameList names |
| 7347 | ) override { |
| 7348 | auto current_device = guard_.current_device(); |
| 7349 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7350 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7351 | "structured kernels don't support multi-device outputs"); |
| 7352 | } else { |
| 7353 | guard_.reset_device(options.device()); |
| 7354 | } |
| 7355 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7356 | if (!names.empty()) { |
| 7357 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7358 | } |
| 7359 | // super must happen after, so that downstream can use maybe_get_output |
| 7360 | // to retrieve the output |
| 7361 | at::meta::structured_rsqrt::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7362 | } |
| 7363 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7364 | return *outputs_[output_idx]; |
| 7365 | } |
| 7366 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 7367 | c10::OptionalDeviceGuard guard_; |
| 7368 | }; |
| 7369 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_rsqrt(const at::Tensor & self) { |
| 7370 | structured_rsqrt_default_backend_functional op; |
| 7371 | op.meta(self); |
| 7372 | at::rsqrt_outf(self, *op.outputs_[0]); |
| 7373 | return std::move(op.outputs_[0]).take(); |
| 7374 | } |
| 7375 | struct structured_rsqrt_default_backend_inplace final : public at::meta::structured_rsqrt { |
| 7376 | structured_rsqrt_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 7377 | void set_output_strided( |
| 7378 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7379 | TensorOptions options, DimnameList names |
| 7380 | ) override { |
| 7381 | auto current_device = guard_.current_device(); |
| 7382 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7383 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7384 | "structured kernels don't support multi-device outputs"); |
| 7385 | } else { |
| 7386 | guard_.reset_device(options.device()); |
| 7387 | } |
| 7388 | const auto& out = outputs_[output_idx].get(); |
| 7389 | check_inplace(out, sizes, options); |
| 7390 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 7391 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 7392 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 7393 | } |
| 7394 | if (!names.empty()) { |
| 7395 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7396 | } |
| 7397 | // super must happen after, so that downstream can use maybe_get_output |
| 7398 | // to retrieve the output |
| 7399 | at::meta::structured_rsqrt::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7400 | } |
| 7401 | void set_output_raw_strided( |
| 7402 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7403 | TensorOptions options, DimnameList names |
| 7404 | ) override { |
| 7405 | auto current_device = guard_.current_device(); |
| 7406 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7407 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7408 | "structured kernels don't support multi-device outputs"); |
| 7409 | } else { |
| 7410 | guard_.reset_device(options.device()); |
| 7411 | } |
| 7412 | const auto& out = outputs_[output_idx].get(); |
| 7413 | check_inplace(out, sizes, options); |
| 7414 | if (!names.empty()) { |
| 7415 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7416 | } |
| 7417 | // super must happen after, so that downstream can use maybe_get_output |
| 7418 | // to retrieve the output |
| 7419 | at::meta::structured_rsqrt::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7420 | } |
| 7421 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7422 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 7423 | } |
| 7424 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 7425 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 7426 | c10::OptionalDeviceGuard guard_; |
| 7427 | }; |
| 7428 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_rsqrt_(at::Tensor & self) { |
| 7429 | structured_rsqrt_default_backend_inplace op(self); |
| 7430 | op.meta(self); |
| 7431 | at::rsqrt_outf(self, op.outputs_[0]); |
| 7432 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 7433 | return self; |
| 7434 | } |
| 7435 | namespace { |
| 7436 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__select_backward(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt index) { |
| 7437 | // No device check |
| 7438 | // DeviceGuard omitted |
| 7439 | return at::native::select_backward_symint(grad_output, input_sizes, dim, index); |
| 7440 | } |
| 7441 | } // anonymous namespace |
| 7442 | struct structured_silu_default_backend_functional final : public at::meta::structured_silu { |
| 7443 | void set_output_strided( |
| 7444 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7445 | TensorOptions options, DimnameList names |
| 7446 | ) override { |
| 7447 | auto current_device = guard_.current_device(); |
| 7448 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7449 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7450 | "structured kernels don't support multi-device outputs"); |
| 7451 | } else { |
| 7452 | guard_.reset_device(options.device()); |
| 7453 | } |
| 7454 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7455 | if (!names.empty()) { |
| 7456 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7457 | } |
| 7458 | // super must happen after, so that downstream can use maybe_get_output |
| 7459 | // to retrieve the output |
| 7460 | at::meta::structured_silu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7461 | } |
| 7462 | void set_output_raw_strided( |
| 7463 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7464 | TensorOptions options, DimnameList names |
| 7465 | ) override { |
| 7466 | auto current_device = guard_.current_device(); |
| 7467 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7468 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7469 | "structured kernels don't support multi-device outputs"); |
| 7470 | } else { |
| 7471 | guard_.reset_device(options.device()); |
| 7472 | } |
| 7473 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7474 | if (!names.empty()) { |
| 7475 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7476 | } |
| 7477 | // super must happen after, so that downstream can use maybe_get_output |
| 7478 | // to retrieve the output |
| 7479 | at::meta::structured_silu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7480 | } |
| 7481 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7482 | return *outputs_[output_idx]; |
| 7483 | } |
| 7484 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 7485 | c10::OptionalDeviceGuard guard_; |
| 7486 | }; |
| 7487 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_silu(const at::Tensor & self) { |
| 7488 | structured_silu_default_backend_functional op; |
| 7489 | op.meta(self); |
| 7490 | at::silu_outf(self, *op.outputs_[0]); |
| 7491 | return std::move(op.outputs_[0]).take(); |
| 7492 | } |
| 7493 | struct structured_silu_default_backend_inplace final : public at::meta::structured_silu { |
| 7494 | structured_silu_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 7495 | void set_output_strided( |
| 7496 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7497 | TensorOptions options, DimnameList names |
| 7498 | ) override { |
| 7499 | auto current_device = guard_.current_device(); |
| 7500 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7501 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7502 | "structured kernels don't support multi-device outputs"); |
| 7503 | } else { |
| 7504 | guard_.reset_device(options.device()); |
| 7505 | } |
| 7506 | const auto& out = outputs_[output_idx].get(); |
| 7507 | check_inplace(out, sizes, options); |
| 7508 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 7509 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 7510 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 7511 | } |
| 7512 | if (!names.empty()) { |
| 7513 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7514 | } |
| 7515 | // super must happen after, so that downstream can use maybe_get_output |
| 7516 | // to retrieve the output |
| 7517 | at::meta::structured_silu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7518 | } |
| 7519 | void set_output_raw_strided( |
| 7520 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7521 | TensorOptions options, DimnameList names |
| 7522 | ) override { |
| 7523 | auto current_device = guard_.current_device(); |
| 7524 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7525 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7526 | "structured kernels don't support multi-device outputs"); |
| 7527 | } else { |
| 7528 | guard_.reset_device(options.device()); |
| 7529 | } |
| 7530 | const auto& out = outputs_[output_idx].get(); |
| 7531 | check_inplace(out, sizes, options); |
| 7532 | if (!names.empty()) { |
| 7533 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7534 | } |
| 7535 | // super must happen after, so that downstream can use maybe_get_output |
| 7536 | // to retrieve the output |
| 7537 | at::meta::structured_silu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7538 | } |
| 7539 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7540 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 7541 | } |
| 7542 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 7543 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 7544 | c10::OptionalDeviceGuard guard_; |
| 7545 | }; |
| 7546 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_silu_(at::Tensor & self) { |
| 7547 | structured_silu_default_backend_inplace op(self); |
| 7548 | op.meta(self); |
| 7549 | at::silu_outf(self, op.outputs_[0]); |
| 7550 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 7551 | return self; |
| 7552 | } |
| 7553 | struct structured_silu_backward_default_backend_functional final : public at::meta::structured_silu_backward { |
| 7554 | void set_output_strided( |
| 7555 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7556 | TensorOptions options, DimnameList names |
| 7557 | ) override { |
| 7558 | auto current_device = guard_.current_device(); |
| 7559 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7560 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7561 | "structured kernels don't support multi-device outputs"); |
| 7562 | } else { |
| 7563 | guard_.reset_device(options.device()); |
| 7564 | } |
| 7565 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7566 | if (!names.empty()) { |
| 7567 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7568 | } |
| 7569 | // super must happen after, so that downstream can use maybe_get_output |
| 7570 | // to retrieve the output |
| 7571 | at::meta::structured_silu_backward::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7572 | } |
| 7573 | void set_output_raw_strided( |
| 7574 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7575 | TensorOptions options, DimnameList names |
| 7576 | ) override { |
| 7577 | auto current_device = guard_.current_device(); |
| 7578 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7579 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7580 | "structured kernels don't support multi-device outputs"); |
| 7581 | } else { |
| 7582 | guard_.reset_device(options.device()); |
| 7583 | } |
| 7584 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7585 | if (!names.empty()) { |
| 7586 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7587 | } |
| 7588 | // super must happen after, so that downstream can use maybe_get_output |
| 7589 | // to retrieve the output |
| 7590 | at::meta::structured_silu_backward::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7591 | } |
| 7592 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7593 | return *outputs_[output_idx]; |
| 7594 | } |
| 7595 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 7596 | c10::OptionalDeviceGuard guard_; |
| 7597 | }; |
| 7598 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_silu_backward(const at::Tensor & grad_output, const at::Tensor & self) { |
| 7599 | structured_silu_backward_default_backend_functional op; |
| 7600 | op.meta(grad_output, self); |
| 7601 | at::silu_backward_outf(grad_output, self, *op.outputs_[0]); |
| 7602 | return std::move(op.outputs_[0]).take(); |
| 7603 | } |
| 7604 | struct structured_mish_default_backend_functional final : public at::meta::structured_mish { |
| 7605 | void set_output_strided( |
| 7606 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7607 | TensorOptions options, DimnameList names |
| 7608 | ) override { |
| 7609 | auto current_device = guard_.current_device(); |
| 7610 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7611 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7612 | "structured kernels don't support multi-device outputs"); |
| 7613 | } else { |
| 7614 | guard_.reset_device(options.device()); |
| 7615 | } |
| 7616 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7617 | if (!names.empty()) { |
| 7618 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7619 | } |
| 7620 | // super must happen after, so that downstream can use maybe_get_output |
| 7621 | // to retrieve the output |
| 7622 | at::meta::structured_mish::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7623 | } |
| 7624 | void set_output_raw_strided( |
| 7625 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7626 | TensorOptions options, DimnameList names |
| 7627 | ) override { |
| 7628 | auto current_device = guard_.current_device(); |
| 7629 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7630 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7631 | "structured kernels don't support multi-device outputs"); |
| 7632 | } else { |
| 7633 | guard_.reset_device(options.device()); |
| 7634 | } |
| 7635 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7636 | if (!names.empty()) { |
| 7637 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7638 | } |
| 7639 | // super must happen after, so that downstream can use maybe_get_output |
| 7640 | // to retrieve the output |
| 7641 | at::meta::structured_mish::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7642 | } |
| 7643 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7644 | return *outputs_[output_idx]; |
| 7645 | } |
| 7646 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 7647 | c10::OptionalDeviceGuard guard_; |
| 7648 | }; |
| 7649 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_mish(const at::Tensor & self) { |
| 7650 | structured_mish_default_backend_functional op; |
| 7651 | op.meta(self); |
| 7652 | at::mish_outf(self, *op.outputs_[0]); |
| 7653 | return std::move(op.outputs_[0]).take(); |
| 7654 | } |
| 7655 | struct structured_mish_default_backend_inplace final : public at::meta::structured_mish { |
| 7656 | structured_mish_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 7657 | void set_output_strided( |
| 7658 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7659 | TensorOptions options, DimnameList names |
| 7660 | ) override { |
| 7661 | auto current_device = guard_.current_device(); |
| 7662 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7663 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7664 | "structured kernels don't support multi-device outputs"); |
| 7665 | } else { |
| 7666 | guard_.reset_device(options.device()); |
| 7667 | } |
| 7668 | const auto& out = outputs_[output_idx].get(); |
| 7669 | check_inplace(out, sizes, options); |
| 7670 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 7671 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 7672 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 7673 | } |
| 7674 | if (!names.empty()) { |
| 7675 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7676 | } |
| 7677 | // super must happen after, so that downstream can use maybe_get_output |
| 7678 | // to retrieve the output |
| 7679 | at::meta::structured_mish::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7680 | } |
| 7681 | void set_output_raw_strided( |
| 7682 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7683 | TensorOptions options, DimnameList names |
| 7684 | ) override { |
| 7685 | auto current_device = guard_.current_device(); |
| 7686 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7687 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7688 | "structured kernels don't support multi-device outputs"); |
| 7689 | } else { |
| 7690 | guard_.reset_device(options.device()); |
| 7691 | } |
| 7692 | const auto& out = outputs_[output_idx].get(); |
| 7693 | check_inplace(out, sizes, options); |
| 7694 | if (!names.empty()) { |
| 7695 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7696 | } |
| 7697 | // super must happen after, so that downstream can use maybe_get_output |
| 7698 | // to retrieve the output |
| 7699 | at::meta::structured_mish::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7700 | } |
| 7701 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7702 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 7703 | } |
| 7704 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 7705 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 7706 | c10::OptionalDeviceGuard guard_; |
| 7707 | }; |
| 7708 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_mish_(at::Tensor & self) { |
| 7709 | structured_mish_default_backend_inplace op(self); |
| 7710 | op.meta(self); |
| 7711 | at::mish_outf(self, op.outputs_[0]); |
| 7712 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 7713 | return self; |
| 7714 | } |
| 7715 | struct structured_sigmoid_default_backend_functional final : public at::meta::structured_sigmoid { |
| 7716 | void set_output_strided( |
| 7717 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7718 | TensorOptions options, DimnameList names |
| 7719 | ) override { |
| 7720 | auto current_device = guard_.current_device(); |
| 7721 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7722 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7723 | "structured kernels don't support multi-device outputs"); |
| 7724 | } else { |
| 7725 | guard_.reset_device(options.device()); |
| 7726 | } |
| 7727 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7728 | if (!names.empty()) { |
| 7729 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7730 | } |
| 7731 | // super must happen after, so that downstream can use maybe_get_output |
| 7732 | // to retrieve the output |
| 7733 | at::meta::structured_sigmoid::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7734 | } |
| 7735 | void set_output_raw_strided( |
| 7736 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7737 | TensorOptions options, DimnameList names |
| 7738 | ) override { |
| 7739 | auto current_device = guard_.current_device(); |
| 7740 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7741 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7742 | "structured kernels don't support multi-device outputs"); |
| 7743 | } else { |
| 7744 | guard_.reset_device(options.device()); |
| 7745 | } |
| 7746 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7747 | if (!names.empty()) { |
| 7748 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7749 | } |
| 7750 | // super must happen after, so that downstream can use maybe_get_output |
| 7751 | // to retrieve the output |
| 7752 | at::meta::structured_sigmoid::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7753 | } |
| 7754 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7755 | return *outputs_[output_idx]; |
| 7756 | } |
| 7757 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 7758 | c10::OptionalDeviceGuard guard_; |
| 7759 | }; |
| 7760 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_sigmoid(const at::Tensor & self) { |
| 7761 | structured_sigmoid_default_backend_functional op; |
| 7762 | op.meta(self); |
| 7763 | at::sigmoid_outf(self, *op.outputs_[0]); |
| 7764 | return std::move(op.outputs_[0]).take(); |
| 7765 | } |
| 7766 | struct structured_sigmoid_default_backend_inplace final : public at::meta::structured_sigmoid { |
| 7767 | structured_sigmoid_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 7768 | void set_output_strided( |
| 7769 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7770 | TensorOptions options, DimnameList names |
| 7771 | ) override { |
| 7772 | auto current_device = guard_.current_device(); |
| 7773 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7774 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7775 | "structured kernels don't support multi-device outputs"); |
| 7776 | } else { |
| 7777 | guard_.reset_device(options.device()); |
| 7778 | } |
| 7779 | const auto& out = outputs_[output_idx].get(); |
| 7780 | check_inplace(out, sizes, options); |
| 7781 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 7782 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 7783 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 7784 | } |
| 7785 | if (!names.empty()) { |
| 7786 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7787 | } |
| 7788 | // super must happen after, so that downstream can use maybe_get_output |
| 7789 | // to retrieve the output |
| 7790 | at::meta::structured_sigmoid::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7791 | } |
| 7792 | void set_output_raw_strided( |
| 7793 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7794 | TensorOptions options, DimnameList names |
| 7795 | ) override { |
| 7796 | auto current_device = guard_.current_device(); |
| 7797 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7798 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7799 | "structured kernels don't support multi-device outputs"); |
| 7800 | } else { |
| 7801 | guard_.reset_device(options.device()); |
| 7802 | } |
| 7803 | const auto& out = outputs_[output_idx].get(); |
| 7804 | check_inplace(out, sizes, options); |
| 7805 | if (!names.empty()) { |
| 7806 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7807 | } |
| 7808 | // super must happen after, so that downstream can use maybe_get_output |
| 7809 | // to retrieve the output |
| 7810 | at::meta::structured_sigmoid::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7811 | } |
| 7812 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7813 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 7814 | } |
| 7815 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 7816 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 7817 | c10::OptionalDeviceGuard guard_; |
| 7818 | }; |
| 7819 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_sigmoid_(at::Tensor & self) { |
| 7820 | structured_sigmoid_default_backend_inplace op(self); |
| 7821 | op.meta(self); |
| 7822 | at::sigmoid_outf(self, op.outputs_[0]); |
| 7823 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 7824 | return self; |
| 7825 | } |
| 7826 | struct structured_sin_default_backend_functional final : public at::meta::structured_sin { |
| 7827 | void set_output_strided( |
| 7828 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7829 | TensorOptions options, DimnameList names |
| 7830 | ) override { |
| 7831 | auto current_device = guard_.current_device(); |
| 7832 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7833 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7834 | "structured kernels don't support multi-device outputs"); |
| 7835 | } else { |
| 7836 | guard_.reset_device(options.device()); |
| 7837 | } |
| 7838 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7839 | if (!names.empty()) { |
| 7840 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7841 | } |
| 7842 | // super must happen after, so that downstream can use maybe_get_output |
| 7843 | // to retrieve the output |
| 7844 | at::meta::structured_sin::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7845 | } |
| 7846 | void set_output_raw_strided( |
| 7847 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7848 | TensorOptions options, DimnameList names |
| 7849 | ) override { |
| 7850 | auto current_device = guard_.current_device(); |
| 7851 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7852 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7853 | "structured kernels don't support multi-device outputs"); |
| 7854 | } else { |
| 7855 | guard_.reset_device(options.device()); |
| 7856 | } |
| 7857 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7858 | if (!names.empty()) { |
| 7859 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7860 | } |
| 7861 | // super must happen after, so that downstream can use maybe_get_output |
| 7862 | // to retrieve the output |
| 7863 | at::meta::structured_sin::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7864 | } |
| 7865 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7866 | return *outputs_[output_idx]; |
| 7867 | } |
| 7868 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 7869 | c10::OptionalDeviceGuard guard_; |
| 7870 | }; |
| 7871 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_sin(const at::Tensor & self) { |
| 7872 | structured_sin_default_backend_functional op; |
| 7873 | op.meta(self); |
| 7874 | at::sin_outf(self, *op.outputs_[0]); |
| 7875 | return std::move(op.outputs_[0]).take(); |
| 7876 | } |
| 7877 | struct structured_sin_default_backend_inplace final : public at::meta::structured_sin { |
| 7878 | structured_sin_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 7879 | void set_output_strided( |
| 7880 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7881 | TensorOptions options, DimnameList names |
| 7882 | ) override { |
| 7883 | auto current_device = guard_.current_device(); |
| 7884 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7885 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7886 | "structured kernels don't support multi-device outputs"); |
| 7887 | } else { |
| 7888 | guard_.reset_device(options.device()); |
| 7889 | } |
| 7890 | const auto& out = outputs_[output_idx].get(); |
| 7891 | check_inplace(out, sizes, options); |
| 7892 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 7893 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 7894 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 7895 | } |
| 7896 | if (!names.empty()) { |
| 7897 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7898 | } |
| 7899 | // super must happen after, so that downstream can use maybe_get_output |
| 7900 | // to retrieve the output |
| 7901 | at::meta::structured_sin::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7902 | } |
| 7903 | void set_output_raw_strided( |
| 7904 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7905 | TensorOptions options, DimnameList names |
| 7906 | ) override { |
| 7907 | auto current_device = guard_.current_device(); |
| 7908 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7909 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7910 | "structured kernels don't support multi-device outputs"); |
| 7911 | } else { |
| 7912 | guard_.reset_device(options.device()); |
| 7913 | } |
| 7914 | const auto& out = outputs_[output_idx].get(); |
| 7915 | check_inplace(out, sizes, options); |
| 7916 | if (!names.empty()) { |
| 7917 | namedinference::propagate_names(outputs_[output_idx], names); |
| 7918 | } |
| 7919 | // super must happen after, so that downstream can use maybe_get_output |
| 7920 | // to retrieve the output |
| 7921 | at::meta::structured_sin::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7922 | } |
| 7923 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7924 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 7925 | } |
| 7926 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 7927 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 7928 | c10::OptionalDeviceGuard guard_; |
| 7929 | }; |
| 7930 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_sin_(at::Tensor & self) { |
| 7931 | structured_sin_default_backend_inplace op(self); |
| 7932 | op.meta(self); |
| 7933 | at::sin_outf(self, op.outputs_[0]); |
| 7934 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 7935 | return self; |
| 7936 | } |
| 7937 | struct structured_sinc_default_backend_functional final : public at::meta::structured_sinc { |
| 7938 | void set_output_strided( |
| 7939 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7940 | TensorOptions options, DimnameList names |
| 7941 | ) override { |
| 7942 | auto current_device = guard_.current_device(); |
| 7943 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7944 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7945 | "structured kernels don't support multi-device outputs"); |
| 7946 | } else { |
| 7947 | guard_.reset_device(options.device()); |
| 7948 | } |
| 7949 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7950 | if (!names.empty()) { |
| 7951 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7952 | } |
| 7953 | // super must happen after, so that downstream can use maybe_get_output |
| 7954 | // to retrieve the output |
| 7955 | at::meta::structured_sinc::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7956 | } |
| 7957 | void set_output_raw_strided( |
| 7958 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7959 | TensorOptions options, DimnameList names |
| 7960 | ) override { |
| 7961 | auto current_device = guard_.current_device(); |
| 7962 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7963 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7964 | "structured kernels don't support multi-device outputs"); |
| 7965 | } else { |
| 7966 | guard_.reset_device(options.device()); |
| 7967 | } |
| 7968 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 7969 | if (!names.empty()) { |
| 7970 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 7971 | } |
| 7972 | // super must happen after, so that downstream can use maybe_get_output |
| 7973 | // to retrieve the output |
| 7974 | at::meta::structured_sinc::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 7975 | } |
| 7976 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 7977 | return *outputs_[output_idx]; |
| 7978 | } |
| 7979 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 7980 | c10::OptionalDeviceGuard guard_; |
| 7981 | }; |
| 7982 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_sinc(const at::Tensor & self) { |
| 7983 | structured_sinc_default_backend_functional op; |
| 7984 | op.meta(self); |
| 7985 | at::sinc_outf(self, *op.outputs_[0]); |
| 7986 | return std::move(op.outputs_[0]).take(); |
| 7987 | } |
| 7988 | struct structured_sinc_default_backend_inplace final : public at::meta::structured_sinc { |
| 7989 | structured_sinc_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 7990 | void set_output_strided( |
| 7991 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 7992 | TensorOptions options, DimnameList names |
| 7993 | ) override { |
| 7994 | auto current_device = guard_.current_device(); |
| 7995 | if (C10_UNLIKELY(current_device.has_value())) { |
| 7996 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 7997 | "structured kernels don't support multi-device outputs"); |
| 7998 | } else { |
| 7999 | guard_.reset_device(options.device()); |
| 8000 | } |
| 8001 | const auto& out = outputs_[output_idx].get(); |
| 8002 | check_inplace(out, sizes, options); |
| 8003 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 8004 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 8005 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 8006 | } |
| 8007 | if (!names.empty()) { |
| 8008 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8009 | } |
| 8010 | // super must happen after, so that downstream can use maybe_get_output |
| 8011 | // to retrieve the output |
| 8012 | at::meta::structured_sinc::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8013 | } |
| 8014 | void set_output_raw_strided( |
| 8015 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8016 | TensorOptions options, DimnameList names |
| 8017 | ) override { |
| 8018 | auto current_device = guard_.current_device(); |
| 8019 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8020 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8021 | "structured kernels don't support multi-device outputs"); |
| 8022 | } else { |
| 8023 | guard_.reset_device(options.device()); |
| 8024 | } |
| 8025 | const auto& out = outputs_[output_idx].get(); |
| 8026 | check_inplace(out, sizes, options); |
| 8027 | if (!names.empty()) { |
| 8028 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8029 | } |
| 8030 | // super must happen after, so that downstream can use maybe_get_output |
| 8031 | // to retrieve the output |
| 8032 | at::meta::structured_sinc::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8033 | } |
| 8034 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8035 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 8036 | } |
| 8037 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 8038 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 8039 | c10::OptionalDeviceGuard guard_; |
| 8040 | }; |
| 8041 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_sinc_(at::Tensor & self) { |
| 8042 | structured_sinc_default_backend_inplace op(self); |
| 8043 | op.meta(self); |
| 8044 | at::sinc_outf(self, op.outputs_[0]); |
| 8045 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 8046 | return self; |
| 8047 | } |
| 8048 | struct structured_sinh_default_backend_functional final : public at::meta::structured_sinh { |
| 8049 | void set_output_strided( |
| 8050 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8051 | TensorOptions options, DimnameList names |
| 8052 | ) override { |
| 8053 | auto current_device = guard_.current_device(); |
| 8054 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8055 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8056 | "structured kernels don't support multi-device outputs"); |
| 8057 | } else { |
| 8058 | guard_.reset_device(options.device()); |
| 8059 | } |
| 8060 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8061 | if (!names.empty()) { |
| 8062 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8063 | } |
| 8064 | // super must happen after, so that downstream can use maybe_get_output |
| 8065 | // to retrieve the output |
| 8066 | at::meta::structured_sinh::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8067 | } |
| 8068 | void set_output_raw_strided( |
| 8069 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8070 | TensorOptions options, DimnameList names |
| 8071 | ) override { |
| 8072 | auto current_device = guard_.current_device(); |
| 8073 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8074 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8075 | "structured kernels don't support multi-device outputs"); |
| 8076 | } else { |
| 8077 | guard_.reset_device(options.device()); |
| 8078 | } |
| 8079 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8080 | if (!names.empty()) { |
| 8081 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8082 | } |
| 8083 | // super must happen after, so that downstream can use maybe_get_output |
| 8084 | // to retrieve the output |
| 8085 | at::meta::structured_sinh::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8086 | } |
| 8087 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8088 | return *outputs_[output_idx]; |
| 8089 | } |
| 8090 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 8091 | c10::OptionalDeviceGuard guard_; |
| 8092 | }; |
| 8093 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_sinh(const at::Tensor & self) { |
| 8094 | structured_sinh_default_backend_functional op; |
| 8095 | op.meta(self); |
| 8096 | at::sinh_outf(self, *op.outputs_[0]); |
| 8097 | return std::move(op.outputs_[0]).take(); |
| 8098 | } |
| 8099 | struct structured_sinh_default_backend_inplace final : public at::meta::structured_sinh { |
| 8100 | structured_sinh_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 8101 | void set_output_strided( |
| 8102 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8103 | TensorOptions options, DimnameList names |
| 8104 | ) override { |
| 8105 | auto current_device = guard_.current_device(); |
| 8106 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8107 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8108 | "structured kernels don't support multi-device outputs"); |
| 8109 | } else { |
| 8110 | guard_.reset_device(options.device()); |
| 8111 | } |
| 8112 | const auto& out = outputs_[output_idx].get(); |
| 8113 | check_inplace(out, sizes, options); |
| 8114 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 8115 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 8116 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 8117 | } |
| 8118 | if (!names.empty()) { |
| 8119 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8120 | } |
| 8121 | // super must happen after, so that downstream can use maybe_get_output |
| 8122 | // to retrieve the output |
| 8123 | at::meta::structured_sinh::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8124 | } |
| 8125 | void set_output_raw_strided( |
| 8126 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8127 | TensorOptions options, DimnameList names |
| 8128 | ) override { |
| 8129 | auto current_device = guard_.current_device(); |
| 8130 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8131 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8132 | "structured kernels don't support multi-device outputs"); |
| 8133 | } else { |
| 8134 | guard_.reset_device(options.device()); |
| 8135 | } |
| 8136 | const auto& out = outputs_[output_idx].get(); |
| 8137 | check_inplace(out, sizes, options); |
| 8138 | if (!names.empty()) { |
| 8139 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8140 | } |
| 8141 | // super must happen after, so that downstream can use maybe_get_output |
| 8142 | // to retrieve the output |
| 8143 | at::meta::structured_sinh::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8144 | } |
| 8145 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8146 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 8147 | } |
| 8148 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 8149 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 8150 | c10::OptionalDeviceGuard guard_; |
| 8151 | }; |
| 8152 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_sinh_(at::Tensor & self) { |
| 8153 | structured_sinh_default_backend_inplace op(self); |
| 8154 | op.meta(self); |
| 8155 | at::sinh_outf(self, op.outputs_[0]); |
| 8156 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 8157 | return self; |
| 8158 | } |
| 8159 | struct structured__softmax_default_backend_functional final : public at::meta::structured__softmax { |
| 8160 | void set_output_strided( |
| 8161 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8162 | TensorOptions options, DimnameList names |
| 8163 | ) override { |
| 8164 | auto current_device = guard_.current_device(); |
| 8165 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8166 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8167 | "structured kernels don't support multi-device outputs"); |
| 8168 | } else { |
| 8169 | guard_.reset_device(options.device()); |
| 8170 | } |
| 8171 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8172 | if (!names.empty()) { |
| 8173 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8174 | } |
| 8175 | // super must happen after, so that downstream can use maybe_get_output |
| 8176 | // to retrieve the output |
| 8177 | } |
| 8178 | void set_output_raw_strided( |
| 8179 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8180 | TensorOptions options, DimnameList names |
| 8181 | ) override { |
| 8182 | auto current_device = guard_.current_device(); |
| 8183 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8184 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8185 | "structured kernels don't support multi-device outputs"); |
| 8186 | } else { |
| 8187 | guard_.reset_device(options.device()); |
| 8188 | } |
| 8189 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8190 | if (!names.empty()) { |
| 8191 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8192 | } |
| 8193 | // super must happen after, so that downstream can use maybe_get_output |
| 8194 | // to retrieve the output |
| 8195 | } |
| 8196 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8197 | return *outputs_[output_idx]; |
| 8198 | } |
| 8199 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 8200 | c10::OptionalDeviceGuard guard_; |
| 8201 | }; |
| 8202 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__softmax(const at::Tensor & self, int64_t dim, bool half_to_float) { |
| 8203 | structured__softmax_default_backend_functional op; |
| 8204 | op.meta(self, dim, half_to_float); |
| 8205 | at::_softmax_outf(self, dim, half_to_float, *op.outputs_[0]); |
| 8206 | return std::move(op.outputs_[0]).take(); |
| 8207 | } |
| 8208 | struct structured__softmax_backward_data_default_backend_functional final : public at::meta::structured__softmax_backward_data { |
| 8209 | void set_output_strided( |
| 8210 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8211 | TensorOptions options, DimnameList names |
| 8212 | ) override { |
| 8213 | auto current_device = guard_.current_device(); |
| 8214 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8215 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8216 | "structured kernels don't support multi-device outputs"); |
| 8217 | } else { |
| 8218 | guard_.reset_device(options.device()); |
| 8219 | } |
| 8220 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8221 | if (!names.empty()) { |
| 8222 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8223 | } |
| 8224 | // super must happen after, so that downstream can use maybe_get_output |
| 8225 | // to retrieve the output |
| 8226 | } |
| 8227 | void set_output_raw_strided( |
| 8228 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8229 | TensorOptions options, DimnameList names |
| 8230 | ) override { |
| 8231 | auto current_device = guard_.current_device(); |
| 8232 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8233 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8234 | "structured kernels don't support multi-device outputs"); |
| 8235 | } else { |
| 8236 | guard_.reset_device(options.device()); |
| 8237 | } |
| 8238 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8239 | if (!names.empty()) { |
| 8240 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8241 | } |
| 8242 | // super must happen after, so that downstream can use maybe_get_output |
| 8243 | // to retrieve the output |
| 8244 | } |
| 8245 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8246 | return *outputs_[output_idx]; |
| 8247 | } |
| 8248 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 8249 | c10::OptionalDeviceGuard guard_; |
| 8250 | }; |
| 8251 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__softmax_backward_data(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) { |
| 8252 | structured__softmax_backward_data_default_backend_functional op; |
| 8253 | op.meta(grad_output, output, dim, input_dtype); |
| 8254 | at::_softmax_backward_data_outf(grad_output, output, dim, input_dtype, *op.outputs_[0]); |
| 8255 | return std::move(op.outputs_[0]).take(); |
| 8256 | } |
| 8257 | struct structured_sum_dim_IntList_default_backend_functional final : public at::meta::structured_sum_dim_IntList { |
| 8258 | void set_output_strided( |
| 8259 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8260 | TensorOptions options, DimnameList names |
| 8261 | ) override { |
| 8262 | auto current_device = guard_.current_device(); |
| 8263 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8264 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8265 | "structured kernels don't support multi-device outputs"); |
| 8266 | } else { |
| 8267 | guard_.reset_device(options.device()); |
| 8268 | } |
| 8269 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8270 | if (!names.empty()) { |
| 8271 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8272 | } |
| 8273 | // super must happen after, so that downstream can use maybe_get_output |
| 8274 | // to retrieve the output |
| 8275 | } |
| 8276 | void set_output_raw_strided( |
| 8277 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8278 | TensorOptions options, DimnameList names |
| 8279 | ) override { |
| 8280 | auto current_device = guard_.current_device(); |
| 8281 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8282 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8283 | "structured kernels don't support multi-device outputs"); |
| 8284 | } else { |
| 8285 | guard_.reset_device(options.device()); |
| 8286 | } |
| 8287 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8288 | if (!names.empty()) { |
| 8289 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8290 | } |
| 8291 | // super must happen after, so that downstream can use maybe_get_output |
| 8292 | // to retrieve the output |
| 8293 | } |
| 8294 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8295 | return *outputs_[output_idx]; |
| 8296 | } |
| 8297 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 8298 | c10::OptionalDeviceGuard guard_; |
| 8299 | }; |
| 8300 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_sum_dim_IntList(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 8301 | structured_sum_dim_IntList_default_backend_functional op; |
| 8302 | op.meta(self, dim, keepdim, dtype); |
| 8303 | at::sum_outf(self, dim, keepdim, dtype, *op.outputs_[0]); |
| 8304 | return std::move(op.outputs_[0]).take(); |
| 8305 | } |
| 8306 | struct structured_sqrt_default_backend_functional final : public at::meta::structured_sqrt { |
| 8307 | void set_output_strided( |
| 8308 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8309 | TensorOptions options, DimnameList names |
| 8310 | ) override { |
| 8311 | auto current_device = guard_.current_device(); |
| 8312 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8313 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8314 | "structured kernels don't support multi-device outputs"); |
| 8315 | } else { |
| 8316 | guard_.reset_device(options.device()); |
| 8317 | } |
| 8318 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8319 | if (!names.empty()) { |
| 8320 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8321 | } |
| 8322 | // super must happen after, so that downstream can use maybe_get_output |
| 8323 | // to retrieve the output |
| 8324 | at::meta::structured_sqrt::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8325 | } |
| 8326 | void set_output_raw_strided( |
| 8327 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8328 | TensorOptions options, DimnameList names |
| 8329 | ) override { |
| 8330 | auto current_device = guard_.current_device(); |
| 8331 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8332 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8333 | "structured kernels don't support multi-device outputs"); |
| 8334 | } else { |
| 8335 | guard_.reset_device(options.device()); |
| 8336 | } |
| 8337 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8338 | if (!names.empty()) { |
| 8339 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8340 | } |
| 8341 | // super must happen after, so that downstream can use maybe_get_output |
| 8342 | // to retrieve the output |
| 8343 | at::meta::structured_sqrt::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8344 | } |
| 8345 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8346 | return *outputs_[output_idx]; |
| 8347 | } |
| 8348 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 8349 | c10::OptionalDeviceGuard guard_; |
| 8350 | }; |
| 8351 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_sqrt(const at::Tensor & self) { |
| 8352 | structured_sqrt_default_backend_functional op; |
| 8353 | op.meta(self); |
| 8354 | at::sqrt_outf(self, *op.outputs_[0]); |
| 8355 | return std::move(op.outputs_[0]).take(); |
| 8356 | } |
| 8357 | struct structured_sqrt_default_backend_inplace final : public at::meta::structured_sqrt { |
| 8358 | structured_sqrt_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 8359 | void set_output_strided( |
| 8360 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8361 | TensorOptions options, DimnameList names |
| 8362 | ) override { |
| 8363 | auto current_device = guard_.current_device(); |
| 8364 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8365 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8366 | "structured kernels don't support multi-device outputs"); |
| 8367 | } else { |
| 8368 | guard_.reset_device(options.device()); |
| 8369 | } |
| 8370 | const auto& out = outputs_[output_idx].get(); |
| 8371 | check_inplace(out, sizes, options); |
| 8372 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 8373 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 8374 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 8375 | } |
| 8376 | if (!names.empty()) { |
| 8377 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8378 | } |
| 8379 | // super must happen after, so that downstream can use maybe_get_output |
| 8380 | // to retrieve the output |
| 8381 | at::meta::structured_sqrt::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8382 | } |
| 8383 | void set_output_raw_strided( |
| 8384 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8385 | TensorOptions options, DimnameList names |
| 8386 | ) override { |
| 8387 | auto current_device = guard_.current_device(); |
| 8388 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8389 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8390 | "structured kernels don't support multi-device outputs"); |
| 8391 | } else { |
| 8392 | guard_.reset_device(options.device()); |
| 8393 | } |
| 8394 | const auto& out = outputs_[output_idx].get(); |
| 8395 | check_inplace(out, sizes, options); |
| 8396 | if (!names.empty()) { |
| 8397 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8398 | } |
| 8399 | // super must happen after, so that downstream can use maybe_get_output |
| 8400 | // to retrieve the output |
| 8401 | at::meta::structured_sqrt::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8402 | } |
| 8403 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8404 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 8405 | } |
| 8406 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 8407 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 8408 | c10::OptionalDeviceGuard guard_; |
| 8409 | }; |
| 8410 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_sqrt_(at::Tensor & self) { |
| 8411 | structured_sqrt_default_backend_inplace op(self); |
| 8412 | op.meta(self); |
| 8413 | at::sqrt_outf(self, op.outputs_[0]); |
| 8414 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 8415 | return self; |
| 8416 | } |
| 8417 | struct structured_prod_dim_int_default_backend_functional final : public at::meta::structured_prod_dim_int { |
| 8418 | void set_output_strided( |
| 8419 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8420 | TensorOptions options, DimnameList names |
| 8421 | ) override { |
| 8422 | auto current_device = guard_.current_device(); |
| 8423 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8424 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8425 | "structured kernels don't support multi-device outputs"); |
| 8426 | } else { |
| 8427 | guard_.reset_device(options.device()); |
| 8428 | } |
| 8429 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8430 | if (!names.empty()) { |
| 8431 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8432 | } |
| 8433 | // super must happen after, so that downstream can use maybe_get_output |
| 8434 | // to retrieve the output |
| 8435 | } |
| 8436 | void set_output_raw_strided( |
| 8437 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8438 | TensorOptions options, DimnameList names |
| 8439 | ) override { |
| 8440 | auto current_device = guard_.current_device(); |
| 8441 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8442 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8443 | "structured kernels don't support multi-device outputs"); |
| 8444 | } else { |
| 8445 | guard_.reset_device(options.device()); |
| 8446 | } |
| 8447 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8448 | if (!names.empty()) { |
| 8449 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8450 | } |
| 8451 | // super must happen after, so that downstream can use maybe_get_output |
| 8452 | // to retrieve the output |
| 8453 | } |
| 8454 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8455 | return *outputs_[output_idx]; |
| 8456 | } |
| 8457 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 8458 | c10::OptionalDeviceGuard guard_; |
| 8459 | }; |
| 8460 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_prod_dim_int(const at::Tensor & self, int64_t dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 8461 | structured_prod_dim_int_default_backend_functional op; |
| 8462 | op.meta(self, dim, keepdim, dtype); |
| 8463 | at::prod_outf(self, dim, keepdim, dtype, *op.outputs_[0]); |
| 8464 | return std::move(op.outputs_[0]).take(); |
| 8465 | } |
| 8466 | struct structured_tan_default_backend_functional final : public at::meta::structured_tan { |
| 8467 | void set_output_strided( |
| 8468 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8469 | TensorOptions options, DimnameList names |
| 8470 | ) override { |
| 8471 | auto current_device = guard_.current_device(); |
| 8472 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8473 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8474 | "structured kernels don't support multi-device outputs"); |
| 8475 | } else { |
| 8476 | guard_.reset_device(options.device()); |
| 8477 | } |
| 8478 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8479 | if (!names.empty()) { |
| 8480 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8481 | } |
| 8482 | // super must happen after, so that downstream can use maybe_get_output |
| 8483 | // to retrieve the output |
| 8484 | at::meta::structured_tan::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8485 | } |
| 8486 | void set_output_raw_strided( |
| 8487 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8488 | TensorOptions options, DimnameList names |
| 8489 | ) override { |
| 8490 | auto current_device = guard_.current_device(); |
| 8491 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8492 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8493 | "structured kernels don't support multi-device outputs"); |
| 8494 | } else { |
| 8495 | guard_.reset_device(options.device()); |
| 8496 | } |
| 8497 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8498 | if (!names.empty()) { |
| 8499 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8500 | } |
| 8501 | // super must happen after, so that downstream can use maybe_get_output |
| 8502 | // to retrieve the output |
| 8503 | at::meta::structured_tan::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8504 | } |
| 8505 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8506 | return *outputs_[output_idx]; |
| 8507 | } |
| 8508 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 8509 | c10::OptionalDeviceGuard guard_; |
| 8510 | }; |
| 8511 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_tan(const at::Tensor & self) { |
| 8512 | structured_tan_default_backend_functional op; |
| 8513 | op.meta(self); |
| 8514 | at::tan_outf(self, *op.outputs_[0]); |
| 8515 | return std::move(op.outputs_[0]).take(); |
| 8516 | } |
| 8517 | struct structured_tan_default_backend_inplace final : public at::meta::structured_tan { |
| 8518 | structured_tan_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 8519 | void set_output_strided( |
| 8520 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8521 | TensorOptions options, DimnameList names |
| 8522 | ) override { |
| 8523 | auto current_device = guard_.current_device(); |
| 8524 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8525 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8526 | "structured kernels don't support multi-device outputs"); |
| 8527 | } else { |
| 8528 | guard_.reset_device(options.device()); |
| 8529 | } |
| 8530 | const auto& out = outputs_[output_idx].get(); |
| 8531 | check_inplace(out, sizes, options); |
| 8532 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 8533 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 8534 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 8535 | } |
| 8536 | if (!names.empty()) { |
| 8537 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8538 | } |
| 8539 | // super must happen after, so that downstream can use maybe_get_output |
| 8540 | // to retrieve the output |
| 8541 | at::meta::structured_tan::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8542 | } |
| 8543 | void set_output_raw_strided( |
| 8544 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8545 | TensorOptions options, DimnameList names |
| 8546 | ) override { |
| 8547 | auto current_device = guard_.current_device(); |
| 8548 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8549 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8550 | "structured kernels don't support multi-device outputs"); |
| 8551 | } else { |
| 8552 | guard_.reset_device(options.device()); |
| 8553 | } |
| 8554 | const auto& out = outputs_[output_idx].get(); |
| 8555 | check_inplace(out, sizes, options); |
| 8556 | if (!names.empty()) { |
| 8557 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8558 | } |
| 8559 | // super must happen after, so that downstream can use maybe_get_output |
| 8560 | // to retrieve the output |
| 8561 | at::meta::structured_tan::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8562 | } |
| 8563 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8564 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 8565 | } |
| 8566 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 8567 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 8568 | c10::OptionalDeviceGuard guard_; |
| 8569 | }; |
| 8570 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_tan_(at::Tensor & self) { |
| 8571 | structured_tan_default_backend_inplace op(self); |
| 8572 | op.meta(self); |
| 8573 | at::tan_outf(self, op.outputs_[0]); |
| 8574 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 8575 | return self; |
| 8576 | } |
| 8577 | struct structured_tanh_default_backend_functional final : public at::meta::structured_tanh { |
| 8578 | void set_output_strided( |
| 8579 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8580 | TensorOptions options, DimnameList names |
| 8581 | ) override { |
| 8582 | auto current_device = guard_.current_device(); |
| 8583 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8584 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8585 | "structured kernels don't support multi-device outputs"); |
| 8586 | } else { |
| 8587 | guard_.reset_device(options.device()); |
| 8588 | } |
| 8589 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8590 | if (!names.empty()) { |
| 8591 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8592 | } |
| 8593 | // super must happen after, so that downstream can use maybe_get_output |
| 8594 | // to retrieve the output |
| 8595 | at::meta::structured_tanh::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8596 | } |
| 8597 | void set_output_raw_strided( |
| 8598 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8599 | TensorOptions options, DimnameList names |
| 8600 | ) override { |
| 8601 | auto current_device = guard_.current_device(); |
| 8602 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8603 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8604 | "structured kernels don't support multi-device outputs"); |
| 8605 | } else { |
| 8606 | guard_.reset_device(options.device()); |
| 8607 | } |
| 8608 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8609 | if (!names.empty()) { |
| 8610 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8611 | } |
| 8612 | // super must happen after, so that downstream can use maybe_get_output |
| 8613 | // to retrieve the output |
| 8614 | at::meta::structured_tanh::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8615 | } |
| 8616 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8617 | return *outputs_[output_idx]; |
| 8618 | } |
| 8619 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 8620 | c10::OptionalDeviceGuard guard_; |
| 8621 | }; |
| 8622 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_tanh(const at::Tensor & self) { |
| 8623 | structured_tanh_default_backend_functional op; |
| 8624 | op.meta(self); |
| 8625 | at::tanh_outf(self, *op.outputs_[0]); |
| 8626 | return std::move(op.outputs_[0]).take(); |
| 8627 | } |
| 8628 | struct structured_tanh_default_backend_inplace final : public at::meta::structured_tanh { |
| 8629 | structured_tanh_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 8630 | void set_output_strided( |
| 8631 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8632 | TensorOptions options, DimnameList names |
| 8633 | ) override { |
| 8634 | auto current_device = guard_.current_device(); |
| 8635 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8636 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8637 | "structured kernels don't support multi-device outputs"); |
| 8638 | } else { |
| 8639 | guard_.reset_device(options.device()); |
| 8640 | } |
| 8641 | const auto& out = outputs_[output_idx].get(); |
| 8642 | check_inplace(out, sizes, options); |
| 8643 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 8644 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 8645 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 8646 | } |
| 8647 | if (!names.empty()) { |
| 8648 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8649 | } |
| 8650 | // super must happen after, so that downstream can use maybe_get_output |
| 8651 | // to retrieve the output |
| 8652 | at::meta::structured_tanh::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8653 | } |
| 8654 | void set_output_raw_strided( |
| 8655 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8656 | TensorOptions options, DimnameList names |
| 8657 | ) override { |
| 8658 | auto current_device = guard_.current_device(); |
| 8659 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8660 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8661 | "structured kernels don't support multi-device outputs"); |
| 8662 | } else { |
| 8663 | guard_.reset_device(options.device()); |
| 8664 | } |
| 8665 | const auto& out = outputs_[output_idx].get(); |
| 8666 | check_inplace(out, sizes, options); |
| 8667 | if (!names.empty()) { |
| 8668 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8669 | } |
| 8670 | // super must happen after, so that downstream can use maybe_get_output |
| 8671 | // to retrieve the output |
| 8672 | at::meta::structured_tanh::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8673 | } |
| 8674 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8675 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 8676 | } |
| 8677 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 8678 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 8679 | c10::OptionalDeviceGuard guard_; |
| 8680 | }; |
| 8681 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_tanh_(at::Tensor & self) { |
| 8682 | structured_tanh_default_backend_inplace op(self); |
| 8683 | op.meta(self); |
| 8684 | at::tanh_outf(self, op.outputs_[0]); |
| 8685 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 8686 | return self; |
| 8687 | } |
| 8688 | struct structured_threshold_default_backend_functional final : public at::meta::structured_threshold { |
| 8689 | void set_output_strided( |
| 8690 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8691 | TensorOptions options, DimnameList names |
| 8692 | ) override { |
| 8693 | auto current_device = guard_.current_device(); |
| 8694 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8695 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8696 | "structured kernels don't support multi-device outputs"); |
| 8697 | } else { |
| 8698 | guard_.reset_device(options.device()); |
| 8699 | } |
| 8700 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8701 | if (!names.empty()) { |
| 8702 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8703 | } |
| 8704 | // super must happen after, so that downstream can use maybe_get_output |
| 8705 | // to retrieve the output |
| 8706 | at::meta::structured_threshold::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8707 | } |
| 8708 | void set_output_raw_strided( |
| 8709 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8710 | TensorOptions options, DimnameList names |
| 8711 | ) override { |
| 8712 | auto current_device = guard_.current_device(); |
| 8713 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8714 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8715 | "structured kernels don't support multi-device outputs"); |
| 8716 | } else { |
| 8717 | guard_.reset_device(options.device()); |
| 8718 | } |
| 8719 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8720 | if (!names.empty()) { |
| 8721 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8722 | } |
| 8723 | // super must happen after, so that downstream can use maybe_get_output |
| 8724 | // to retrieve the output |
| 8725 | at::meta::structured_threshold::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8726 | } |
| 8727 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8728 | return *outputs_[output_idx]; |
| 8729 | } |
| 8730 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 8731 | c10::OptionalDeviceGuard guard_; |
| 8732 | }; |
| 8733 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_threshold(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value) { |
| 8734 | structured_threshold_default_backend_functional op; |
| 8735 | op.meta(self, threshold, value); |
| 8736 | at::threshold_outf(self, threshold, value, *op.outputs_[0]); |
| 8737 | return std::move(op.outputs_[0]).take(); |
| 8738 | } |
| 8739 | struct structured_threshold_default_backend_inplace final : public at::meta::structured_threshold { |
| 8740 | structured_threshold_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 8741 | void set_output_strided( |
| 8742 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8743 | TensorOptions options, DimnameList names |
| 8744 | ) override { |
| 8745 | auto current_device = guard_.current_device(); |
| 8746 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8747 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8748 | "structured kernels don't support multi-device outputs"); |
| 8749 | } else { |
| 8750 | guard_.reset_device(options.device()); |
| 8751 | } |
| 8752 | const auto& out = outputs_[output_idx].get(); |
| 8753 | check_inplace(out, sizes, options); |
| 8754 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 8755 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 8756 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 8757 | } |
| 8758 | if (!names.empty()) { |
| 8759 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8760 | } |
| 8761 | // super must happen after, so that downstream can use maybe_get_output |
| 8762 | // to retrieve the output |
| 8763 | at::meta::structured_threshold::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8764 | } |
| 8765 | void set_output_raw_strided( |
| 8766 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8767 | TensorOptions options, DimnameList names |
| 8768 | ) override { |
| 8769 | auto current_device = guard_.current_device(); |
| 8770 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8771 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8772 | "structured kernels don't support multi-device outputs"); |
| 8773 | } else { |
| 8774 | guard_.reset_device(options.device()); |
| 8775 | } |
| 8776 | const auto& out = outputs_[output_idx].get(); |
| 8777 | check_inplace(out, sizes, options); |
| 8778 | if (!names.empty()) { |
| 8779 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8780 | } |
| 8781 | // super must happen after, so that downstream can use maybe_get_output |
| 8782 | // to retrieve the output |
| 8783 | at::meta::structured_threshold::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8784 | } |
| 8785 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8786 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 8787 | } |
| 8788 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 8789 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 8790 | c10::OptionalDeviceGuard guard_; |
| 8791 | }; |
| 8792 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_threshold_(at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value) { |
| 8793 | structured_threshold_default_backend_inplace op(self); |
| 8794 | op.meta(self, threshold, value); |
| 8795 | at::threshold_outf(self, threshold, value, op.outputs_[0]); |
| 8796 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 8797 | return self; |
| 8798 | } |
| 8799 | struct structured_threshold_backward_default_backend_functional final : public at::meta::structured_threshold_backward { |
| 8800 | void set_output_strided( |
| 8801 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8802 | TensorOptions options, DimnameList names |
| 8803 | ) override { |
| 8804 | auto current_device = guard_.current_device(); |
| 8805 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8806 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8807 | "structured kernels don't support multi-device outputs"); |
| 8808 | } else { |
| 8809 | guard_.reset_device(options.device()); |
| 8810 | } |
| 8811 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8812 | if (!names.empty()) { |
| 8813 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8814 | } |
| 8815 | // super must happen after, so that downstream can use maybe_get_output |
| 8816 | // to retrieve the output |
| 8817 | at::meta::structured_threshold_backward::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8818 | } |
| 8819 | void set_output_raw_strided( |
| 8820 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8821 | TensorOptions options, DimnameList names |
| 8822 | ) override { |
| 8823 | auto current_device = guard_.current_device(); |
| 8824 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8825 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8826 | "structured kernels don't support multi-device outputs"); |
| 8827 | } else { |
| 8828 | guard_.reset_device(options.device()); |
| 8829 | } |
| 8830 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8831 | if (!names.empty()) { |
| 8832 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8833 | } |
| 8834 | // super must happen after, so that downstream can use maybe_get_output |
| 8835 | // to retrieve the output |
| 8836 | at::meta::structured_threshold_backward::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8837 | } |
| 8838 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8839 | return *outputs_[output_idx]; |
| 8840 | } |
| 8841 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 8842 | c10::OptionalDeviceGuard guard_; |
| 8843 | }; |
| 8844 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_threshold_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold) { |
| 8845 | structured_threshold_backward_default_backend_functional op; |
| 8846 | op.meta(grad_output, self, threshold); |
| 8847 | at::threshold_backward_outf(grad_output, self, threshold, *op.outputs_[0]); |
| 8848 | return std::move(op.outputs_[0]).take(); |
| 8849 | } |
| 8850 | namespace { |
| 8851 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional___nested_view_from_buffer_copy(const at::Tensor & self, const at::Tensor & nested_size, const at::Tensor & nested_strides, at::IntArrayRef offsets) { |
| 8852 | // No device check |
| 8853 | // DeviceGuard omitted |
| 8854 | return at::native::_nested_view_from_buffer_copy(self, nested_size, nested_strides, offsets); |
| 8855 | } |
| 8856 | } // anonymous namespace |
| 8857 | namespace { |
| 8858 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional___trilinear(const at::Tensor & i1, const at::Tensor & i2, const at::Tensor & i3, at::IntArrayRef expand1, at::IntArrayRef expand2, at::IntArrayRef expand3, at::IntArrayRef sumdim, int64_t unroll_dim) { |
| 8859 | // No device check |
| 8860 | // DeviceGuard omitted |
| 8861 | return at::native::_trilinear(i1, i2, i3, expand1, expand2, expand3, sumdim, unroll_dim); |
| 8862 | } |
| 8863 | } // anonymous namespace |
| 8864 | struct structured_trunc_default_backend_functional final : public at::meta::structured_trunc { |
| 8865 | void set_output_strided( |
| 8866 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8867 | TensorOptions options, DimnameList names |
| 8868 | ) override { |
| 8869 | auto current_device = guard_.current_device(); |
| 8870 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8871 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8872 | "structured kernels don't support multi-device outputs"); |
| 8873 | } else { |
| 8874 | guard_.reset_device(options.device()); |
| 8875 | } |
| 8876 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8877 | if (!names.empty()) { |
| 8878 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8879 | } |
| 8880 | // super must happen after, so that downstream can use maybe_get_output |
| 8881 | // to retrieve the output |
| 8882 | at::meta::structured_trunc::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8883 | } |
| 8884 | void set_output_raw_strided( |
| 8885 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8886 | TensorOptions options, DimnameList names |
| 8887 | ) override { |
| 8888 | auto current_device = guard_.current_device(); |
| 8889 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8890 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8891 | "structured kernels don't support multi-device outputs"); |
| 8892 | } else { |
| 8893 | guard_.reset_device(options.device()); |
| 8894 | } |
| 8895 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8896 | if (!names.empty()) { |
| 8897 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8898 | } |
| 8899 | // super must happen after, so that downstream can use maybe_get_output |
| 8900 | // to retrieve the output |
| 8901 | at::meta::structured_trunc::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8902 | } |
| 8903 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8904 | return *outputs_[output_idx]; |
| 8905 | } |
| 8906 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 8907 | c10::OptionalDeviceGuard guard_; |
| 8908 | }; |
| 8909 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_trunc(const at::Tensor & self) { |
| 8910 | structured_trunc_default_backend_functional op; |
| 8911 | op.meta(self); |
| 8912 | at::trunc_outf(self, *op.outputs_[0]); |
| 8913 | return std::move(op.outputs_[0]).take(); |
| 8914 | } |
| 8915 | struct structured_trunc_default_backend_inplace final : public at::meta::structured_trunc { |
| 8916 | structured_trunc_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 8917 | void set_output_strided( |
| 8918 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8919 | TensorOptions options, DimnameList names |
| 8920 | ) override { |
| 8921 | auto current_device = guard_.current_device(); |
| 8922 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8923 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8924 | "structured kernels don't support multi-device outputs"); |
| 8925 | } else { |
| 8926 | guard_.reset_device(options.device()); |
| 8927 | } |
| 8928 | const auto& out = outputs_[output_idx].get(); |
| 8929 | check_inplace(out, sizes, options); |
| 8930 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 8931 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 8932 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 8933 | } |
| 8934 | if (!names.empty()) { |
| 8935 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8936 | } |
| 8937 | // super must happen after, so that downstream can use maybe_get_output |
| 8938 | // to retrieve the output |
| 8939 | at::meta::structured_trunc::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8940 | } |
| 8941 | void set_output_raw_strided( |
| 8942 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8943 | TensorOptions options, DimnameList names |
| 8944 | ) override { |
| 8945 | auto current_device = guard_.current_device(); |
| 8946 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8947 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8948 | "structured kernels don't support multi-device outputs"); |
| 8949 | } else { |
| 8950 | guard_.reset_device(options.device()); |
| 8951 | } |
| 8952 | const auto& out = outputs_[output_idx].get(); |
| 8953 | check_inplace(out, sizes, options); |
| 8954 | if (!names.empty()) { |
| 8955 | namedinference::propagate_names(outputs_[output_idx], names); |
| 8956 | } |
| 8957 | // super must happen after, so that downstream can use maybe_get_output |
| 8958 | // to retrieve the output |
| 8959 | at::meta::structured_trunc::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 8960 | } |
| 8961 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 8962 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 8963 | } |
| 8964 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 8965 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 8966 | c10::OptionalDeviceGuard guard_; |
| 8967 | }; |
| 8968 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_trunc_(at::Tensor & self) { |
| 8969 | structured_trunc_default_backend_inplace op(self); |
| 8970 | op.meta(self); |
| 8971 | at::trunc_outf(self, op.outputs_[0]); |
| 8972 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 8973 | return self; |
| 8974 | } |
| 8975 | struct structured_norm_ScalarOpt_dim_dtype_default_backend_functional final : public at::meta::structured_norm_ScalarOpt_dim_dtype { |
| 8976 | void set_output_strided( |
| 8977 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8978 | TensorOptions options, DimnameList names |
| 8979 | ) override { |
| 8980 | auto current_device = guard_.current_device(); |
| 8981 | if (C10_UNLIKELY(current_device.has_value())) { |
| 8982 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 8983 | "structured kernels don't support multi-device outputs"); |
| 8984 | } else { |
| 8985 | guard_.reset_device(options.device()); |
| 8986 | } |
| 8987 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 8988 | if (!names.empty()) { |
| 8989 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 8990 | } |
| 8991 | // super must happen after, so that downstream can use maybe_get_output |
| 8992 | // to retrieve the output |
| 8993 | } |
| 8994 | void set_output_raw_strided( |
| 8995 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 8996 | TensorOptions options, DimnameList names |
| 8997 | ) override { |
| 8998 | auto current_device = guard_.current_device(); |
| 8999 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9000 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9001 | "structured kernels don't support multi-device outputs"); |
| 9002 | } else { |
| 9003 | guard_.reset_device(options.device()); |
| 9004 | } |
| 9005 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9006 | if (!names.empty()) { |
| 9007 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9008 | } |
| 9009 | // super must happen after, so that downstream can use maybe_get_output |
| 9010 | // to retrieve the output |
| 9011 | } |
| 9012 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9013 | return *outputs_[output_idx]; |
| 9014 | } |
| 9015 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 9016 | c10::OptionalDeviceGuard guard_; |
| 9017 | }; |
| 9018 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_norm_ScalarOpt_dim_dtype(const at::Tensor & self, const c10::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim, at::ScalarType dtype) { |
| 9019 | structured_norm_ScalarOpt_dim_dtype_default_backend_functional op; |
| 9020 | op.meta(self, (p.has_value() ? at::OptionalScalarRef(&(p.value())) : at::OptionalScalarRef()), dim, keepdim, dtype); |
| 9021 | at::norm_outf(self, p, dim, keepdim, dtype, *op.outputs_[0]); |
| 9022 | return std::move(op.outputs_[0]).take(); |
| 9023 | } |
| 9024 | struct structured_norm_ScalarOpt_dim_default_backend_functional final : public at::meta::structured_norm_ScalarOpt_dim { |
| 9025 | void set_output_strided( |
| 9026 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9027 | TensorOptions options, DimnameList names |
| 9028 | ) override { |
| 9029 | auto current_device = guard_.current_device(); |
| 9030 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9031 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9032 | "structured kernels don't support multi-device outputs"); |
| 9033 | } else { |
| 9034 | guard_.reset_device(options.device()); |
| 9035 | } |
| 9036 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9037 | if (!names.empty()) { |
| 9038 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9039 | } |
| 9040 | // super must happen after, so that downstream can use maybe_get_output |
| 9041 | // to retrieve the output |
| 9042 | } |
| 9043 | void set_output_raw_strided( |
| 9044 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9045 | TensorOptions options, DimnameList names |
| 9046 | ) override { |
| 9047 | auto current_device = guard_.current_device(); |
| 9048 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9049 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9050 | "structured kernels don't support multi-device outputs"); |
| 9051 | } else { |
| 9052 | guard_.reset_device(options.device()); |
| 9053 | } |
| 9054 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9055 | if (!names.empty()) { |
| 9056 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9057 | } |
| 9058 | // super must happen after, so that downstream can use maybe_get_output |
| 9059 | // to retrieve the output |
| 9060 | } |
| 9061 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9062 | return *outputs_[output_idx]; |
| 9063 | } |
| 9064 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 9065 | c10::OptionalDeviceGuard guard_; |
| 9066 | }; |
| 9067 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_norm_ScalarOpt_dim(const at::Tensor & self, const c10::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim) { |
| 9068 | structured_norm_ScalarOpt_dim_default_backend_functional op; |
| 9069 | op.meta(self, (p.has_value() ? at::OptionalScalarRef(&(p.value())) : at::OptionalScalarRef()), dim, keepdim); |
| 9070 | at::norm_outf(self, p, dim, keepdim, *op.outputs_[0]); |
| 9071 | return std::move(op.outputs_[0]).take(); |
| 9072 | } |
| 9073 | struct structured_sub_Tensor_default_backend_functional final : public at::meta::structured_sub_Tensor { |
| 9074 | void set_output_strided( |
| 9075 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9076 | TensorOptions options, DimnameList names |
| 9077 | ) override { |
| 9078 | auto current_device = guard_.current_device(); |
| 9079 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9080 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9081 | "structured kernels don't support multi-device outputs"); |
| 9082 | } else { |
| 9083 | guard_.reset_device(options.device()); |
| 9084 | } |
| 9085 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9086 | if (!names.empty()) { |
| 9087 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9088 | } |
| 9089 | // super must happen after, so that downstream can use maybe_get_output |
| 9090 | // to retrieve the output |
| 9091 | at::meta::structured_sub_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9092 | } |
| 9093 | void set_output_raw_strided( |
| 9094 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9095 | TensorOptions options, DimnameList names |
| 9096 | ) override { |
| 9097 | auto current_device = guard_.current_device(); |
| 9098 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9099 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9100 | "structured kernels don't support multi-device outputs"); |
| 9101 | } else { |
| 9102 | guard_.reset_device(options.device()); |
| 9103 | } |
| 9104 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9105 | if (!names.empty()) { |
| 9106 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9107 | } |
| 9108 | // super must happen after, so that downstream can use maybe_get_output |
| 9109 | // to retrieve the output |
| 9110 | at::meta::structured_sub_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9111 | } |
| 9112 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9113 | return *outputs_[output_idx]; |
| 9114 | } |
| 9115 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 9116 | c10::OptionalDeviceGuard guard_; |
| 9117 | }; |
| 9118 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_sub_Tensor(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 9119 | structured_sub_Tensor_default_backend_functional op; |
| 9120 | op.meta(self, other, alpha); |
| 9121 | at::sub_outf(self, other, alpha, *op.outputs_[0]); |
| 9122 | return std::move(op.outputs_[0]).take(); |
| 9123 | } |
| 9124 | struct structured_sub_Tensor_default_backend_inplace final : public at::meta::structured_sub_Tensor { |
| 9125 | structured_sub_Tensor_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 9126 | void set_output_strided( |
| 9127 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9128 | TensorOptions options, DimnameList names |
| 9129 | ) override { |
| 9130 | auto current_device = guard_.current_device(); |
| 9131 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9132 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9133 | "structured kernels don't support multi-device outputs"); |
| 9134 | } else { |
| 9135 | guard_.reset_device(options.device()); |
| 9136 | } |
| 9137 | const auto& out = outputs_[output_idx].get(); |
| 9138 | check_inplace(out, sizes, options); |
| 9139 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 9140 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 9141 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 9142 | } |
| 9143 | if (!names.empty()) { |
| 9144 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9145 | } |
| 9146 | // super must happen after, so that downstream can use maybe_get_output |
| 9147 | // to retrieve the output |
| 9148 | at::meta::structured_sub_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9149 | } |
| 9150 | void set_output_raw_strided( |
| 9151 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9152 | TensorOptions options, DimnameList names |
| 9153 | ) override { |
| 9154 | auto current_device = guard_.current_device(); |
| 9155 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9156 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9157 | "structured kernels don't support multi-device outputs"); |
| 9158 | } else { |
| 9159 | guard_.reset_device(options.device()); |
| 9160 | } |
| 9161 | const auto& out = outputs_[output_idx].get(); |
| 9162 | check_inplace(out, sizes, options); |
| 9163 | if (!names.empty()) { |
| 9164 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9165 | } |
| 9166 | // super must happen after, so that downstream can use maybe_get_output |
| 9167 | // to retrieve the output |
| 9168 | at::meta::structured_sub_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9169 | } |
| 9170 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9171 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 9172 | } |
| 9173 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 9174 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 9175 | c10::OptionalDeviceGuard guard_; |
| 9176 | }; |
| 9177 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_sub__Tensor(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 9178 | structured_sub_Tensor_default_backend_inplace op(self); |
| 9179 | op.meta(self, other, alpha); |
| 9180 | at::sub_outf(self, other, alpha, op.outputs_[0]); |
| 9181 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 9182 | return self; |
| 9183 | } |
| 9184 | struct structured_heaviside_default_backend_functional final : public at::meta::structured_heaviside { |
| 9185 | void set_output_strided( |
| 9186 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9187 | TensorOptions options, DimnameList names |
| 9188 | ) override { |
| 9189 | auto current_device = guard_.current_device(); |
| 9190 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9191 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9192 | "structured kernels don't support multi-device outputs"); |
| 9193 | } else { |
| 9194 | guard_.reset_device(options.device()); |
| 9195 | } |
| 9196 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9197 | if (!names.empty()) { |
| 9198 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9199 | } |
| 9200 | // super must happen after, so that downstream can use maybe_get_output |
| 9201 | // to retrieve the output |
| 9202 | at::meta::structured_heaviside::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9203 | } |
| 9204 | void set_output_raw_strided( |
| 9205 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9206 | TensorOptions options, DimnameList names |
| 9207 | ) override { |
| 9208 | auto current_device = guard_.current_device(); |
| 9209 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9210 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9211 | "structured kernels don't support multi-device outputs"); |
| 9212 | } else { |
| 9213 | guard_.reset_device(options.device()); |
| 9214 | } |
| 9215 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9216 | if (!names.empty()) { |
| 9217 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9218 | } |
| 9219 | // super must happen after, so that downstream can use maybe_get_output |
| 9220 | // to retrieve the output |
| 9221 | at::meta::structured_heaviside::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9222 | } |
| 9223 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9224 | return *outputs_[output_idx]; |
| 9225 | } |
| 9226 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 9227 | c10::OptionalDeviceGuard guard_; |
| 9228 | }; |
| 9229 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_heaviside(const at::Tensor & self, const at::Tensor & values) { |
| 9230 | structured_heaviside_default_backend_functional op; |
| 9231 | op.meta(self, values); |
| 9232 | at::heaviside_outf(self, values, *op.outputs_[0]); |
| 9233 | return std::move(op.outputs_[0]).take(); |
| 9234 | } |
| 9235 | struct structured_heaviside_default_backend_inplace final : public at::meta::structured_heaviside { |
| 9236 | structured_heaviside_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 9237 | void set_output_strided( |
| 9238 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9239 | TensorOptions options, DimnameList names |
| 9240 | ) override { |
| 9241 | auto current_device = guard_.current_device(); |
| 9242 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9243 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9244 | "structured kernels don't support multi-device outputs"); |
| 9245 | } else { |
| 9246 | guard_.reset_device(options.device()); |
| 9247 | } |
| 9248 | const auto& out = outputs_[output_idx].get(); |
| 9249 | check_inplace(out, sizes, options); |
| 9250 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 9251 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 9252 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 9253 | } |
| 9254 | if (!names.empty()) { |
| 9255 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9256 | } |
| 9257 | // super must happen after, so that downstream can use maybe_get_output |
| 9258 | // to retrieve the output |
| 9259 | at::meta::structured_heaviside::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9260 | } |
| 9261 | void set_output_raw_strided( |
| 9262 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9263 | TensorOptions options, DimnameList names |
| 9264 | ) override { |
| 9265 | auto current_device = guard_.current_device(); |
| 9266 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9267 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9268 | "structured kernels don't support multi-device outputs"); |
| 9269 | } else { |
| 9270 | guard_.reset_device(options.device()); |
| 9271 | } |
| 9272 | const auto& out = outputs_[output_idx].get(); |
| 9273 | check_inplace(out, sizes, options); |
| 9274 | if (!names.empty()) { |
| 9275 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9276 | } |
| 9277 | // super must happen after, so that downstream can use maybe_get_output |
| 9278 | // to retrieve the output |
| 9279 | at::meta::structured_heaviside::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 9280 | } |
| 9281 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9282 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 9283 | } |
| 9284 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 9285 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 9286 | c10::OptionalDeviceGuard guard_; |
| 9287 | }; |
| 9288 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_heaviside_(at::Tensor & self, const at::Tensor & values) { |
| 9289 | structured_heaviside_default_backend_inplace op(self); |
| 9290 | op.meta(self, values); |
| 9291 | at::heaviside_outf(self, values, op.outputs_[0]); |
| 9292 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 9293 | return self; |
| 9294 | } |
| 9295 | struct structured_addmm_default_backend_functional final : public at::meta::structured_addmm { |
| 9296 | void set_output_strided( |
| 9297 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9298 | TensorOptions options, DimnameList names |
| 9299 | ) override { |
| 9300 | auto current_device = guard_.current_device(); |
| 9301 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9302 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9303 | "structured kernels don't support multi-device outputs"); |
| 9304 | } else { |
| 9305 | guard_.reset_device(options.device()); |
| 9306 | } |
| 9307 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9308 | if (!names.empty()) { |
| 9309 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9310 | } |
| 9311 | // super must happen after, so that downstream can use maybe_get_output |
| 9312 | // to retrieve the output |
| 9313 | } |
| 9314 | void set_output_raw_strided( |
| 9315 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9316 | TensorOptions options, DimnameList names |
| 9317 | ) override { |
| 9318 | auto current_device = guard_.current_device(); |
| 9319 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9320 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9321 | "structured kernels don't support multi-device outputs"); |
| 9322 | } else { |
| 9323 | guard_.reset_device(options.device()); |
| 9324 | } |
| 9325 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9326 | if (!names.empty()) { |
| 9327 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9328 | } |
| 9329 | // super must happen after, so that downstream can use maybe_get_output |
| 9330 | // to retrieve the output |
| 9331 | } |
| 9332 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9333 | return *outputs_[output_idx]; |
| 9334 | } |
| 9335 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 9336 | c10::OptionalDeviceGuard guard_; |
| 9337 | }; |
| 9338 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_addmm(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 9339 | structured_addmm_default_backend_functional op; |
| 9340 | op.meta(self, mat1, mat2, beta, alpha); |
| 9341 | at::addmm_outf(self, mat1, mat2, beta, alpha, *op.outputs_[0]); |
| 9342 | return std::move(op.outputs_[0]).take(); |
| 9343 | } |
| 9344 | struct structured_addmm_default_backend_inplace final : public at::meta::structured_addmm { |
| 9345 | structured_addmm_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 9346 | void set_output_strided( |
| 9347 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9348 | TensorOptions options, DimnameList names |
| 9349 | ) override { |
| 9350 | auto current_device = guard_.current_device(); |
| 9351 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9352 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9353 | "structured kernels don't support multi-device outputs"); |
| 9354 | } else { |
| 9355 | guard_.reset_device(options.device()); |
| 9356 | } |
| 9357 | const auto& out = outputs_[output_idx].get(); |
| 9358 | check_inplace(out, sizes, options); |
| 9359 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 9360 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 9361 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 9362 | } |
| 9363 | if (!names.empty()) { |
| 9364 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9365 | } |
| 9366 | // super must happen after, so that downstream can use maybe_get_output |
| 9367 | // to retrieve the output |
| 9368 | } |
| 9369 | void set_output_raw_strided( |
| 9370 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9371 | TensorOptions options, DimnameList names |
| 9372 | ) override { |
| 9373 | auto current_device = guard_.current_device(); |
| 9374 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9375 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9376 | "structured kernels don't support multi-device outputs"); |
| 9377 | } else { |
| 9378 | guard_.reset_device(options.device()); |
| 9379 | } |
| 9380 | const auto& out = outputs_[output_idx].get(); |
| 9381 | check_inplace(out, sizes, options); |
| 9382 | if (!names.empty()) { |
| 9383 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9384 | } |
| 9385 | // super must happen after, so that downstream can use maybe_get_output |
| 9386 | // to retrieve the output |
| 9387 | } |
| 9388 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9389 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 9390 | } |
| 9391 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 9392 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 9393 | c10::OptionalDeviceGuard guard_; |
| 9394 | }; |
| 9395 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_addmm_(at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 9396 | structured_addmm_default_backend_inplace op(self); |
| 9397 | op.meta(self, mat1, mat2, beta, alpha); |
| 9398 | at::addmm_outf(self, mat1, mat2, beta, alpha, op.outputs_[0]); |
| 9399 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 9400 | return self; |
| 9401 | } |
| 9402 | struct structured__addmm_activation_default_backend_functional final : public at::meta::structured__addmm_activation { |
| 9403 | void set_output_strided( |
| 9404 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9405 | TensorOptions options, DimnameList names |
| 9406 | ) override { |
| 9407 | auto current_device = guard_.current_device(); |
| 9408 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9409 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9410 | "structured kernels don't support multi-device outputs"); |
| 9411 | } else { |
| 9412 | guard_.reset_device(options.device()); |
| 9413 | } |
| 9414 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9415 | if (!names.empty()) { |
| 9416 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9417 | } |
| 9418 | // super must happen after, so that downstream can use maybe_get_output |
| 9419 | // to retrieve the output |
| 9420 | } |
| 9421 | void set_output_raw_strided( |
| 9422 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9423 | TensorOptions options, DimnameList names |
| 9424 | ) override { |
| 9425 | auto current_device = guard_.current_device(); |
| 9426 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9427 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9428 | "structured kernels don't support multi-device outputs"); |
| 9429 | } else { |
| 9430 | guard_.reset_device(options.device()); |
| 9431 | } |
| 9432 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9433 | if (!names.empty()) { |
| 9434 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9435 | } |
| 9436 | // super must happen after, so that downstream can use maybe_get_output |
| 9437 | // to retrieve the output |
| 9438 | } |
| 9439 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9440 | return *outputs_[output_idx]; |
| 9441 | } |
| 9442 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 9443 | c10::OptionalDeviceGuard guard_; |
| 9444 | }; |
| 9445 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__addmm_activation(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, bool use_gelu) { |
| 9446 | structured__addmm_activation_default_backend_functional op; |
| 9447 | op.meta(self, mat1, mat2, beta, alpha, use_gelu); |
| 9448 | at::_addmm_activation_outf(self, mat1, mat2, beta, alpha, use_gelu, *op.outputs_[0]); |
| 9449 | return std::move(op.outputs_[0]).take(); |
| 9450 | } |
| 9451 | namespace { |
| 9452 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__lift_fresh_copy(const at::Tensor & self) { |
| 9453 | // No device check |
| 9454 | // DeviceGuard omitted |
| 9455 | return at::native::lift_fresh_copy(self); |
| 9456 | } |
| 9457 | } // anonymous namespace |
| 9458 | struct structured_index_add_default_backend_functional final : public at::meta::structured_index_add { |
| 9459 | void set_output_strided( |
| 9460 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9461 | TensorOptions options, DimnameList names |
| 9462 | ) override { |
| 9463 | auto current_device = guard_.current_device(); |
| 9464 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9465 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9466 | "structured kernels don't support multi-device outputs"); |
| 9467 | } else { |
| 9468 | guard_.reset_device(options.device()); |
| 9469 | } |
| 9470 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9471 | if (!names.empty()) { |
| 9472 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9473 | } |
| 9474 | // super must happen after, so that downstream can use maybe_get_output |
| 9475 | // to retrieve the output |
| 9476 | } |
| 9477 | void set_output_raw_strided( |
| 9478 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9479 | TensorOptions options, DimnameList names |
| 9480 | ) override { |
| 9481 | auto current_device = guard_.current_device(); |
| 9482 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9483 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9484 | "structured kernels don't support multi-device outputs"); |
| 9485 | } else { |
| 9486 | guard_.reset_device(options.device()); |
| 9487 | } |
| 9488 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9489 | if (!names.empty()) { |
| 9490 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9491 | } |
| 9492 | // super must happen after, so that downstream can use maybe_get_output |
| 9493 | // to retrieve the output |
| 9494 | } |
| 9495 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9496 | return *outputs_[output_idx]; |
| 9497 | } |
| 9498 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 9499 | c10::OptionalDeviceGuard guard_; |
| 9500 | }; |
| 9501 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_index_add(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha) { |
| 9502 | structured_index_add_default_backend_functional op; |
| 9503 | auto precompute = op.meta(self, dim, index, source, alpha); |
| 9504 | (void)precompute; |
| 9505 | at::index_add_outf(self, precompute.dim, index, source, alpha, *op.outputs_[0]); |
| 9506 | return std::move(op.outputs_[0]).take(); |
| 9507 | } |
| 9508 | struct structured_index_add_default_backend_inplace final : public at::meta::structured_index_add { |
| 9509 | structured_index_add_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 9510 | void set_output_strided( |
| 9511 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9512 | TensorOptions options, DimnameList names |
| 9513 | ) override { |
| 9514 | auto current_device = guard_.current_device(); |
| 9515 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9516 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9517 | "structured kernels don't support multi-device outputs"); |
| 9518 | } else { |
| 9519 | guard_.reset_device(options.device()); |
| 9520 | } |
| 9521 | const auto& out = outputs_[output_idx].get(); |
| 9522 | check_inplace(out, sizes, options); |
| 9523 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 9524 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 9525 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 9526 | } |
| 9527 | if (!names.empty()) { |
| 9528 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9529 | } |
| 9530 | // super must happen after, so that downstream can use maybe_get_output |
| 9531 | // to retrieve the output |
| 9532 | } |
| 9533 | void set_output_raw_strided( |
| 9534 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9535 | TensorOptions options, DimnameList names |
| 9536 | ) override { |
| 9537 | auto current_device = guard_.current_device(); |
| 9538 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9539 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9540 | "structured kernels don't support multi-device outputs"); |
| 9541 | } else { |
| 9542 | guard_.reset_device(options.device()); |
| 9543 | } |
| 9544 | const auto& out = outputs_[output_idx].get(); |
| 9545 | check_inplace(out, sizes, options); |
| 9546 | if (!names.empty()) { |
| 9547 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9548 | } |
| 9549 | // super must happen after, so that downstream can use maybe_get_output |
| 9550 | // to retrieve the output |
| 9551 | } |
| 9552 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9553 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 9554 | } |
| 9555 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 9556 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 9557 | c10::OptionalDeviceGuard guard_; |
| 9558 | }; |
| 9559 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_index_add_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha) { |
| 9560 | structured_index_add_default_backend_inplace op(self); |
| 9561 | auto precompute = op.meta(self, dim, index, source, alpha); |
| 9562 | (void)precompute; |
| 9563 | at::index_add_outf(self, precompute.dim, index, source, alpha, op.outputs_[0]); |
| 9564 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 9565 | return self; |
| 9566 | } |
| 9567 | struct structured_index_reduce_default_backend_functional final : public at::meta::structured_index_reduce { |
| 9568 | void set_output_strided( |
| 9569 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9570 | TensorOptions options, DimnameList names |
| 9571 | ) override { |
| 9572 | auto current_device = guard_.current_device(); |
| 9573 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9574 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9575 | "structured kernels don't support multi-device outputs"); |
| 9576 | } else { |
| 9577 | guard_.reset_device(options.device()); |
| 9578 | } |
| 9579 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9580 | if (!names.empty()) { |
| 9581 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9582 | } |
| 9583 | // super must happen after, so that downstream can use maybe_get_output |
| 9584 | // to retrieve the output |
| 9585 | } |
| 9586 | void set_output_raw_strided( |
| 9587 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9588 | TensorOptions options, DimnameList names |
| 9589 | ) override { |
| 9590 | auto current_device = guard_.current_device(); |
| 9591 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9592 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9593 | "structured kernels don't support multi-device outputs"); |
| 9594 | } else { |
| 9595 | guard_.reset_device(options.device()); |
| 9596 | } |
| 9597 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9598 | if (!names.empty()) { |
| 9599 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9600 | } |
| 9601 | // super must happen after, so that downstream can use maybe_get_output |
| 9602 | // to retrieve the output |
| 9603 | } |
| 9604 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9605 | return *outputs_[output_idx]; |
| 9606 | } |
| 9607 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 9608 | c10::OptionalDeviceGuard guard_; |
| 9609 | }; |
| 9610 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_index_reduce(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self) { |
| 9611 | structured_index_reduce_default_backend_functional op; |
| 9612 | auto precompute = op.meta(self, dim, index, source, reduce, include_self); |
| 9613 | (void)precompute; |
| 9614 | at::index_reduce_outf(self, precompute.dim, index, source, reduce, include_self, *op.outputs_[0]); |
| 9615 | return std::move(op.outputs_[0]).take(); |
| 9616 | } |
| 9617 | struct structured_index_reduce_default_backend_inplace final : public at::meta::structured_index_reduce { |
| 9618 | structured_index_reduce_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 9619 | void set_output_strided( |
| 9620 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9621 | TensorOptions options, DimnameList names |
| 9622 | ) override { |
| 9623 | auto current_device = guard_.current_device(); |
| 9624 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9625 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9626 | "structured kernels don't support multi-device outputs"); |
| 9627 | } else { |
| 9628 | guard_.reset_device(options.device()); |
| 9629 | } |
| 9630 | const auto& out = outputs_[output_idx].get(); |
| 9631 | check_inplace(out, sizes, options); |
| 9632 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 9633 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 9634 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 9635 | } |
| 9636 | if (!names.empty()) { |
| 9637 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9638 | } |
| 9639 | // super must happen after, so that downstream can use maybe_get_output |
| 9640 | // to retrieve the output |
| 9641 | } |
| 9642 | void set_output_raw_strided( |
| 9643 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9644 | TensorOptions options, DimnameList names |
| 9645 | ) override { |
| 9646 | auto current_device = guard_.current_device(); |
| 9647 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9648 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9649 | "structured kernels don't support multi-device outputs"); |
| 9650 | } else { |
| 9651 | guard_.reset_device(options.device()); |
| 9652 | } |
| 9653 | const auto& out = outputs_[output_idx].get(); |
| 9654 | check_inplace(out, sizes, options); |
| 9655 | if (!names.empty()) { |
| 9656 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9657 | } |
| 9658 | // super must happen after, so that downstream can use maybe_get_output |
| 9659 | // to retrieve the output |
| 9660 | } |
| 9661 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9662 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 9663 | } |
| 9664 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 9665 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 9666 | c10::OptionalDeviceGuard guard_; |
| 9667 | }; |
| 9668 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_index_reduce_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self) { |
| 9669 | structured_index_reduce_default_backend_inplace op(self); |
| 9670 | auto precompute = op.meta(self, dim, index, source, reduce, include_self); |
| 9671 | (void)precompute; |
| 9672 | at::index_reduce_outf(self, precompute.dim, index, source, reduce, include_self, op.outputs_[0]); |
| 9673 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 9674 | return self; |
| 9675 | } |
| 9676 | struct structured_scatter_src_default_backend_functional final : public at::meta::structured_scatter_src { |
| 9677 | void set_output_strided( |
| 9678 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9679 | TensorOptions options, DimnameList names |
| 9680 | ) override { |
| 9681 | auto current_device = guard_.current_device(); |
| 9682 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9683 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9684 | "structured kernels don't support multi-device outputs"); |
| 9685 | } else { |
| 9686 | guard_.reset_device(options.device()); |
| 9687 | } |
| 9688 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9689 | if (!names.empty()) { |
| 9690 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9691 | } |
| 9692 | // super must happen after, so that downstream can use maybe_get_output |
| 9693 | // to retrieve the output |
| 9694 | } |
| 9695 | void set_output_raw_strided( |
| 9696 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9697 | TensorOptions options, DimnameList names |
| 9698 | ) override { |
| 9699 | auto current_device = guard_.current_device(); |
| 9700 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9701 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9702 | "structured kernels don't support multi-device outputs"); |
| 9703 | } else { |
| 9704 | guard_.reset_device(options.device()); |
| 9705 | } |
| 9706 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9707 | if (!names.empty()) { |
| 9708 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9709 | } |
| 9710 | // super must happen after, so that downstream can use maybe_get_output |
| 9711 | // to retrieve the output |
| 9712 | } |
| 9713 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9714 | return *outputs_[output_idx]; |
| 9715 | } |
| 9716 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 9717 | c10::OptionalDeviceGuard guard_; |
| 9718 | }; |
| 9719 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_scatter_src(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { |
| 9720 | structured_scatter_src_default_backend_functional op; |
| 9721 | op.meta(self, dim, index, src); |
| 9722 | at::scatter_outf(self, dim, index, src, *op.outputs_[0]); |
| 9723 | return std::move(op.outputs_[0]).take(); |
| 9724 | } |
| 9725 | struct structured_scatter_src_default_backend_inplace final : public at::meta::structured_scatter_src { |
| 9726 | structured_scatter_src_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 9727 | void set_output_strided( |
| 9728 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9729 | TensorOptions options, DimnameList names |
| 9730 | ) override { |
| 9731 | auto current_device = guard_.current_device(); |
| 9732 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9733 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9734 | "structured kernels don't support multi-device outputs"); |
| 9735 | } else { |
| 9736 | guard_.reset_device(options.device()); |
| 9737 | } |
| 9738 | const auto& out = outputs_[output_idx].get(); |
| 9739 | check_inplace(out, sizes, options); |
| 9740 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 9741 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 9742 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 9743 | } |
| 9744 | if (!names.empty()) { |
| 9745 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9746 | } |
| 9747 | // super must happen after, so that downstream can use maybe_get_output |
| 9748 | // to retrieve the output |
| 9749 | } |
| 9750 | void set_output_raw_strided( |
| 9751 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9752 | TensorOptions options, DimnameList names |
| 9753 | ) override { |
| 9754 | auto current_device = guard_.current_device(); |
| 9755 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9756 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9757 | "structured kernels don't support multi-device outputs"); |
| 9758 | } else { |
| 9759 | guard_.reset_device(options.device()); |
| 9760 | } |
| 9761 | const auto& out = outputs_[output_idx].get(); |
| 9762 | check_inplace(out, sizes, options); |
| 9763 | if (!names.empty()) { |
| 9764 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9765 | } |
| 9766 | // super must happen after, so that downstream can use maybe_get_output |
| 9767 | // to retrieve the output |
| 9768 | } |
| 9769 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9770 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 9771 | } |
| 9772 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 9773 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 9774 | c10::OptionalDeviceGuard guard_; |
| 9775 | }; |
| 9776 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_scatter__src(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { |
| 9777 | structured_scatter_src_default_backend_inplace op(self); |
| 9778 | op.meta(self, dim, index, src); |
| 9779 | at::scatter_outf(self, dim, index, src, op.outputs_[0]); |
| 9780 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 9781 | return self; |
| 9782 | } |
| 9783 | struct structured_scatter_value_default_backend_functional final : public at::meta::structured_scatter_value { |
| 9784 | void set_output_strided( |
| 9785 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9786 | TensorOptions options, DimnameList names |
| 9787 | ) override { |
| 9788 | auto current_device = guard_.current_device(); |
| 9789 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9790 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9791 | "structured kernels don't support multi-device outputs"); |
| 9792 | } else { |
| 9793 | guard_.reset_device(options.device()); |
| 9794 | } |
| 9795 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9796 | if (!names.empty()) { |
| 9797 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9798 | } |
| 9799 | // super must happen after, so that downstream can use maybe_get_output |
| 9800 | // to retrieve the output |
| 9801 | } |
| 9802 | void set_output_raw_strided( |
| 9803 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9804 | TensorOptions options, DimnameList names |
| 9805 | ) override { |
| 9806 | auto current_device = guard_.current_device(); |
| 9807 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9808 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9809 | "structured kernels don't support multi-device outputs"); |
| 9810 | } else { |
| 9811 | guard_.reset_device(options.device()); |
| 9812 | } |
| 9813 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9814 | if (!names.empty()) { |
| 9815 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9816 | } |
| 9817 | // super must happen after, so that downstream can use maybe_get_output |
| 9818 | // to retrieve the output |
| 9819 | } |
| 9820 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9821 | return *outputs_[output_idx]; |
| 9822 | } |
| 9823 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 9824 | c10::OptionalDeviceGuard guard_; |
| 9825 | }; |
| 9826 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_scatter_value(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) { |
| 9827 | structured_scatter_value_default_backend_functional op; |
| 9828 | op.meta(self, dim, index, value); |
| 9829 | at::scatter_outf(self, dim, index, value, *op.outputs_[0]); |
| 9830 | return std::move(op.outputs_[0]).take(); |
| 9831 | } |
| 9832 | struct structured_scatter_value_default_backend_inplace final : public at::meta::structured_scatter_value { |
| 9833 | structured_scatter_value_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 9834 | void set_output_strided( |
| 9835 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9836 | TensorOptions options, DimnameList names |
| 9837 | ) override { |
| 9838 | auto current_device = guard_.current_device(); |
| 9839 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9840 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9841 | "structured kernels don't support multi-device outputs"); |
| 9842 | } else { |
| 9843 | guard_.reset_device(options.device()); |
| 9844 | } |
| 9845 | const auto& out = outputs_[output_idx].get(); |
| 9846 | check_inplace(out, sizes, options); |
| 9847 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 9848 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 9849 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 9850 | } |
| 9851 | if (!names.empty()) { |
| 9852 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9853 | } |
| 9854 | // super must happen after, so that downstream can use maybe_get_output |
| 9855 | // to retrieve the output |
| 9856 | } |
| 9857 | void set_output_raw_strided( |
| 9858 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9859 | TensorOptions options, DimnameList names |
| 9860 | ) override { |
| 9861 | auto current_device = guard_.current_device(); |
| 9862 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9863 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9864 | "structured kernels don't support multi-device outputs"); |
| 9865 | } else { |
| 9866 | guard_.reset_device(options.device()); |
| 9867 | } |
| 9868 | const auto& out = outputs_[output_idx].get(); |
| 9869 | check_inplace(out, sizes, options); |
| 9870 | if (!names.empty()) { |
| 9871 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9872 | } |
| 9873 | // super must happen after, so that downstream can use maybe_get_output |
| 9874 | // to retrieve the output |
| 9875 | } |
| 9876 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9877 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 9878 | } |
| 9879 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 9880 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 9881 | c10::OptionalDeviceGuard guard_; |
| 9882 | }; |
| 9883 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_scatter__value(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) { |
| 9884 | structured_scatter_value_default_backend_inplace op(self); |
| 9885 | op.meta(self, dim, index, value); |
| 9886 | at::scatter_outf(self, dim, index, value, op.outputs_[0]); |
| 9887 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 9888 | return self; |
| 9889 | } |
| 9890 | struct structured_scatter_reduce_default_backend_functional final : public at::meta::structured_scatter_reduce { |
| 9891 | void set_output_strided( |
| 9892 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9893 | TensorOptions options, DimnameList names |
| 9894 | ) override { |
| 9895 | auto current_device = guard_.current_device(); |
| 9896 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9897 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9898 | "structured kernels don't support multi-device outputs"); |
| 9899 | } else { |
| 9900 | guard_.reset_device(options.device()); |
| 9901 | } |
| 9902 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9903 | if (!names.empty()) { |
| 9904 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9905 | } |
| 9906 | // super must happen after, so that downstream can use maybe_get_output |
| 9907 | // to retrieve the output |
| 9908 | } |
| 9909 | void set_output_raw_strided( |
| 9910 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9911 | TensorOptions options, DimnameList names |
| 9912 | ) override { |
| 9913 | auto current_device = guard_.current_device(); |
| 9914 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9915 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9916 | "structured kernels don't support multi-device outputs"); |
| 9917 | } else { |
| 9918 | guard_.reset_device(options.device()); |
| 9919 | } |
| 9920 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 9921 | if (!names.empty()) { |
| 9922 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 9923 | } |
| 9924 | // super must happen after, so that downstream can use maybe_get_output |
| 9925 | // to retrieve the output |
| 9926 | } |
| 9927 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9928 | return *outputs_[output_idx]; |
| 9929 | } |
| 9930 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 9931 | c10::OptionalDeviceGuard guard_; |
| 9932 | }; |
| 9933 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_scatter_reduce(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce) { |
| 9934 | structured_scatter_reduce_default_backend_functional op; |
| 9935 | op.meta(self, dim, index, src, reduce); |
| 9936 | at::scatter_outf(self, dim, index, src, reduce, *op.outputs_[0]); |
| 9937 | return std::move(op.outputs_[0]).take(); |
| 9938 | } |
| 9939 | struct structured_scatter_reduce_default_backend_inplace final : public at::meta::structured_scatter_reduce { |
| 9940 | structured_scatter_reduce_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 9941 | void set_output_strided( |
| 9942 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9943 | TensorOptions options, DimnameList names |
| 9944 | ) override { |
| 9945 | auto current_device = guard_.current_device(); |
| 9946 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9947 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9948 | "structured kernels don't support multi-device outputs"); |
| 9949 | } else { |
| 9950 | guard_.reset_device(options.device()); |
| 9951 | } |
| 9952 | const auto& out = outputs_[output_idx].get(); |
| 9953 | check_inplace(out, sizes, options); |
| 9954 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 9955 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 9956 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 9957 | } |
| 9958 | if (!names.empty()) { |
| 9959 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9960 | } |
| 9961 | // super must happen after, so that downstream can use maybe_get_output |
| 9962 | // to retrieve the output |
| 9963 | } |
| 9964 | void set_output_raw_strided( |
| 9965 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 9966 | TensorOptions options, DimnameList names |
| 9967 | ) override { |
| 9968 | auto current_device = guard_.current_device(); |
| 9969 | if (C10_UNLIKELY(current_device.has_value())) { |
| 9970 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 9971 | "structured kernels don't support multi-device outputs"); |
| 9972 | } else { |
| 9973 | guard_.reset_device(options.device()); |
| 9974 | } |
| 9975 | const auto& out = outputs_[output_idx].get(); |
| 9976 | check_inplace(out, sizes, options); |
| 9977 | if (!names.empty()) { |
| 9978 | namedinference::propagate_names(outputs_[output_idx], names); |
| 9979 | } |
| 9980 | // super must happen after, so that downstream can use maybe_get_output |
| 9981 | // to retrieve the output |
| 9982 | } |
| 9983 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 9984 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 9985 | } |
| 9986 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 9987 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 9988 | c10::OptionalDeviceGuard guard_; |
| 9989 | }; |
| 9990 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_scatter__reduce(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce) { |
| 9991 | structured_scatter_reduce_default_backend_inplace op(self); |
| 9992 | op.meta(self, dim, index, src, reduce); |
| 9993 | at::scatter_outf(self, dim, index, src, reduce, op.outputs_[0]); |
| 9994 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 9995 | return self; |
| 9996 | } |
| 9997 | struct structured_scatter_value_reduce_default_backend_functional final : public at::meta::structured_scatter_value_reduce { |
| 9998 | void set_output_strided( |
| 9999 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10000 | TensorOptions options, DimnameList names |
| 10001 | ) override { |
| 10002 | auto current_device = guard_.current_device(); |
| 10003 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10004 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10005 | "structured kernels don't support multi-device outputs"); |
| 10006 | } else { |
| 10007 | guard_.reset_device(options.device()); |
| 10008 | } |
| 10009 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10010 | if (!names.empty()) { |
| 10011 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10012 | } |
| 10013 | // super must happen after, so that downstream can use maybe_get_output |
| 10014 | // to retrieve the output |
| 10015 | } |
| 10016 | void set_output_raw_strided( |
| 10017 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10018 | TensorOptions options, DimnameList names |
| 10019 | ) override { |
| 10020 | auto current_device = guard_.current_device(); |
| 10021 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10022 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10023 | "structured kernels don't support multi-device outputs"); |
| 10024 | } else { |
| 10025 | guard_.reset_device(options.device()); |
| 10026 | } |
| 10027 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10028 | if (!names.empty()) { |
| 10029 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10030 | } |
| 10031 | // super must happen after, so that downstream can use maybe_get_output |
| 10032 | // to retrieve the output |
| 10033 | } |
| 10034 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10035 | return *outputs_[output_idx]; |
| 10036 | } |
| 10037 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 10038 | c10::OptionalDeviceGuard guard_; |
| 10039 | }; |
| 10040 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_scatter_value_reduce(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce) { |
| 10041 | structured_scatter_value_reduce_default_backend_functional op; |
| 10042 | op.meta(self, dim, index, value, reduce); |
| 10043 | at::scatter_outf(self, dim, index, value, reduce, *op.outputs_[0]); |
| 10044 | return std::move(op.outputs_[0]).take(); |
| 10045 | } |
| 10046 | struct structured_scatter_value_reduce_default_backend_inplace final : public at::meta::structured_scatter_value_reduce { |
| 10047 | structured_scatter_value_reduce_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 10048 | void set_output_strided( |
| 10049 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10050 | TensorOptions options, DimnameList names |
| 10051 | ) override { |
| 10052 | auto current_device = guard_.current_device(); |
| 10053 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10054 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10055 | "structured kernels don't support multi-device outputs"); |
| 10056 | } else { |
| 10057 | guard_.reset_device(options.device()); |
| 10058 | } |
| 10059 | const auto& out = outputs_[output_idx].get(); |
| 10060 | check_inplace(out, sizes, options); |
| 10061 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 10062 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 10063 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 10064 | } |
| 10065 | if (!names.empty()) { |
| 10066 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10067 | } |
| 10068 | // super must happen after, so that downstream can use maybe_get_output |
| 10069 | // to retrieve the output |
| 10070 | } |
| 10071 | void set_output_raw_strided( |
| 10072 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10073 | TensorOptions options, DimnameList names |
| 10074 | ) override { |
| 10075 | auto current_device = guard_.current_device(); |
| 10076 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10077 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10078 | "structured kernels don't support multi-device outputs"); |
| 10079 | } else { |
| 10080 | guard_.reset_device(options.device()); |
| 10081 | } |
| 10082 | const auto& out = outputs_[output_idx].get(); |
| 10083 | check_inplace(out, sizes, options); |
| 10084 | if (!names.empty()) { |
| 10085 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10086 | } |
| 10087 | // super must happen after, so that downstream can use maybe_get_output |
| 10088 | // to retrieve the output |
| 10089 | } |
| 10090 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10091 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 10092 | } |
| 10093 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 10094 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 10095 | c10::OptionalDeviceGuard guard_; |
| 10096 | }; |
| 10097 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_scatter__value_reduce(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce) { |
| 10098 | structured_scatter_value_reduce_default_backend_inplace op(self); |
| 10099 | op.meta(self, dim, index, value, reduce); |
| 10100 | at::scatter_outf(self, dim, index, value, reduce, op.outputs_[0]); |
| 10101 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 10102 | return self; |
| 10103 | } |
| 10104 | struct structured_scatter_add_default_backend_functional final : public at::meta::structured_scatter_add { |
| 10105 | void set_output_strided( |
| 10106 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10107 | TensorOptions options, DimnameList names |
| 10108 | ) override { |
| 10109 | auto current_device = guard_.current_device(); |
| 10110 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10111 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10112 | "structured kernels don't support multi-device outputs"); |
| 10113 | } else { |
| 10114 | guard_.reset_device(options.device()); |
| 10115 | } |
| 10116 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10117 | if (!names.empty()) { |
| 10118 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10119 | } |
| 10120 | // super must happen after, so that downstream can use maybe_get_output |
| 10121 | // to retrieve the output |
| 10122 | } |
| 10123 | void set_output_raw_strided( |
| 10124 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10125 | TensorOptions options, DimnameList names |
| 10126 | ) override { |
| 10127 | auto current_device = guard_.current_device(); |
| 10128 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10129 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10130 | "structured kernels don't support multi-device outputs"); |
| 10131 | } else { |
| 10132 | guard_.reset_device(options.device()); |
| 10133 | } |
| 10134 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10135 | if (!names.empty()) { |
| 10136 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10137 | } |
| 10138 | // super must happen after, so that downstream can use maybe_get_output |
| 10139 | // to retrieve the output |
| 10140 | } |
| 10141 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10142 | return *outputs_[output_idx]; |
| 10143 | } |
| 10144 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 10145 | c10::OptionalDeviceGuard guard_; |
| 10146 | }; |
| 10147 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_scatter_add(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { |
| 10148 | structured_scatter_add_default_backend_functional op; |
| 10149 | op.meta(self, dim, index, src); |
| 10150 | at::scatter_add_outf(self, dim, index, src, *op.outputs_[0]); |
| 10151 | return std::move(op.outputs_[0]).take(); |
| 10152 | } |
| 10153 | struct structured_scatter_add_default_backend_inplace final : public at::meta::structured_scatter_add { |
| 10154 | structured_scatter_add_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 10155 | void set_output_strided( |
| 10156 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10157 | TensorOptions options, DimnameList names |
| 10158 | ) override { |
| 10159 | auto current_device = guard_.current_device(); |
| 10160 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10161 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10162 | "structured kernels don't support multi-device outputs"); |
| 10163 | } else { |
| 10164 | guard_.reset_device(options.device()); |
| 10165 | } |
| 10166 | const auto& out = outputs_[output_idx].get(); |
| 10167 | check_inplace(out, sizes, options); |
| 10168 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 10169 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 10170 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 10171 | } |
| 10172 | if (!names.empty()) { |
| 10173 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10174 | } |
| 10175 | // super must happen after, so that downstream can use maybe_get_output |
| 10176 | // to retrieve the output |
| 10177 | } |
| 10178 | void set_output_raw_strided( |
| 10179 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10180 | TensorOptions options, DimnameList names |
| 10181 | ) override { |
| 10182 | auto current_device = guard_.current_device(); |
| 10183 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10184 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10185 | "structured kernels don't support multi-device outputs"); |
| 10186 | } else { |
| 10187 | guard_.reset_device(options.device()); |
| 10188 | } |
| 10189 | const auto& out = outputs_[output_idx].get(); |
| 10190 | check_inplace(out, sizes, options); |
| 10191 | if (!names.empty()) { |
| 10192 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10193 | } |
| 10194 | // super must happen after, so that downstream can use maybe_get_output |
| 10195 | // to retrieve the output |
| 10196 | } |
| 10197 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10198 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 10199 | } |
| 10200 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 10201 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 10202 | c10::OptionalDeviceGuard guard_; |
| 10203 | }; |
| 10204 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_scatter_add_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { |
| 10205 | structured_scatter_add_default_backend_inplace op(self); |
| 10206 | op.meta(self, dim, index, src); |
| 10207 | at::scatter_add_outf(self, dim, index, src, op.outputs_[0]); |
| 10208 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 10209 | return self; |
| 10210 | } |
| 10211 | struct structured_scatter_reduce_two_default_backend_functional final : public at::meta::structured_scatter_reduce_two { |
| 10212 | void set_output_strided( |
| 10213 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10214 | TensorOptions options, DimnameList names |
| 10215 | ) override { |
| 10216 | auto current_device = guard_.current_device(); |
| 10217 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10218 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10219 | "structured kernels don't support multi-device outputs"); |
| 10220 | } else { |
| 10221 | guard_.reset_device(options.device()); |
| 10222 | } |
| 10223 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10224 | if (!names.empty()) { |
| 10225 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10226 | } |
| 10227 | // super must happen after, so that downstream can use maybe_get_output |
| 10228 | // to retrieve the output |
| 10229 | } |
| 10230 | void set_output_raw_strided( |
| 10231 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10232 | TensorOptions options, DimnameList names |
| 10233 | ) override { |
| 10234 | auto current_device = guard_.current_device(); |
| 10235 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10236 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10237 | "structured kernels don't support multi-device outputs"); |
| 10238 | } else { |
| 10239 | guard_.reset_device(options.device()); |
| 10240 | } |
| 10241 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10242 | if (!names.empty()) { |
| 10243 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10244 | } |
| 10245 | // super must happen after, so that downstream can use maybe_get_output |
| 10246 | // to retrieve the output |
| 10247 | } |
| 10248 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10249 | return *outputs_[output_idx]; |
| 10250 | } |
| 10251 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 10252 | c10::OptionalDeviceGuard guard_; |
| 10253 | }; |
| 10254 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_scatter_reduce_two(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self) { |
| 10255 | structured_scatter_reduce_two_default_backend_functional op; |
| 10256 | op.meta(self, dim, index, src, reduce, include_self); |
| 10257 | at::scatter_reduce_outf(self, dim, index, src, reduce, include_self, *op.outputs_[0]); |
| 10258 | return std::move(op.outputs_[0]).take(); |
| 10259 | } |
| 10260 | struct structured_scatter_reduce_two_default_backend_inplace final : public at::meta::structured_scatter_reduce_two { |
| 10261 | structured_scatter_reduce_two_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 10262 | void set_output_strided( |
| 10263 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10264 | TensorOptions options, DimnameList names |
| 10265 | ) override { |
| 10266 | auto current_device = guard_.current_device(); |
| 10267 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10268 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10269 | "structured kernels don't support multi-device outputs"); |
| 10270 | } else { |
| 10271 | guard_.reset_device(options.device()); |
| 10272 | } |
| 10273 | const auto& out = outputs_[output_idx].get(); |
| 10274 | check_inplace(out, sizes, options); |
| 10275 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 10276 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 10277 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 10278 | } |
| 10279 | if (!names.empty()) { |
| 10280 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10281 | } |
| 10282 | // super must happen after, so that downstream can use maybe_get_output |
| 10283 | // to retrieve the output |
| 10284 | } |
| 10285 | void set_output_raw_strided( |
| 10286 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10287 | TensorOptions options, DimnameList names |
| 10288 | ) override { |
| 10289 | auto current_device = guard_.current_device(); |
| 10290 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10291 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10292 | "structured kernels don't support multi-device outputs"); |
| 10293 | } else { |
| 10294 | guard_.reset_device(options.device()); |
| 10295 | } |
| 10296 | const auto& out = outputs_[output_idx].get(); |
| 10297 | check_inplace(out, sizes, options); |
| 10298 | if (!names.empty()) { |
| 10299 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10300 | } |
| 10301 | // super must happen after, so that downstream can use maybe_get_output |
| 10302 | // to retrieve the output |
| 10303 | } |
| 10304 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10305 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 10306 | } |
| 10307 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 10308 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 10309 | c10::OptionalDeviceGuard guard_; |
| 10310 | }; |
| 10311 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_scatter_reduce__two(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self) { |
| 10312 | structured_scatter_reduce_two_default_backend_inplace op(self); |
| 10313 | op.meta(self, dim, index, src, reduce, include_self); |
| 10314 | at::scatter_reduce_outf(self, dim, index, src, reduce, include_self, op.outputs_[0]); |
| 10315 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 10316 | return self; |
| 10317 | } |
| 10318 | struct structured_eq_Scalar_default_backend_functional final : public at::meta::structured_eq_Scalar { |
| 10319 | void set_output_strided( |
| 10320 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10321 | TensorOptions options, DimnameList names |
| 10322 | ) override { |
| 10323 | auto current_device = guard_.current_device(); |
| 10324 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10325 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10326 | "structured kernels don't support multi-device outputs"); |
| 10327 | } else { |
| 10328 | guard_.reset_device(options.device()); |
| 10329 | } |
| 10330 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10331 | if (!names.empty()) { |
| 10332 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10333 | } |
| 10334 | // super must happen after, so that downstream can use maybe_get_output |
| 10335 | // to retrieve the output |
| 10336 | at::meta::structured_eq_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10337 | } |
| 10338 | void set_output_raw_strided( |
| 10339 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10340 | TensorOptions options, DimnameList names |
| 10341 | ) override { |
| 10342 | auto current_device = guard_.current_device(); |
| 10343 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10344 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10345 | "structured kernels don't support multi-device outputs"); |
| 10346 | } else { |
| 10347 | guard_.reset_device(options.device()); |
| 10348 | } |
| 10349 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10350 | if (!names.empty()) { |
| 10351 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10352 | } |
| 10353 | // super must happen after, so that downstream can use maybe_get_output |
| 10354 | // to retrieve the output |
| 10355 | at::meta::structured_eq_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10356 | } |
| 10357 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10358 | return *outputs_[output_idx]; |
| 10359 | } |
| 10360 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 10361 | c10::OptionalDeviceGuard guard_; |
| 10362 | }; |
| 10363 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_eq_Scalar(const at::Tensor & self, const at::Scalar & other) { |
| 10364 | structured_eq_Scalar_default_backend_functional op; |
| 10365 | op.meta(self, other); |
| 10366 | at::eq_outf(self, other, *op.outputs_[0]); |
| 10367 | return std::move(op.outputs_[0]).take(); |
| 10368 | } |
| 10369 | struct structured_eq_Scalar_default_backend_inplace final : public at::meta::structured_eq_Scalar { |
| 10370 | structured_eq_Scalar_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 10371 | void set_output_strided( |
| 10372 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10373 | TensorOptions options, DimnameList names |
| 10374 | ) override { |
| 10375 | auto current_device = guard_.current_device(); |
| 10376 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10377 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10378 | "structured kernels don't support multi-device outputs"); |
| 10379 | } else { |
| 10380 | guard_.reset_device(options.device()); |
| 10381 | } |
| 10382 | const auto& out = outputs_[output_idx].get(); |
| 10383 | check_inplace(out, sizes, options); |
| 10384 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 10385 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 10386 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 10387 | } |
| 10388 | if (!names.empty()) { |
| 10389 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10390 | } |
| 10391 | // super must happen after, so that downstream can use maybe_get_output |
| 10392 | // to retrieve the output |
| 10393 | at::meta::structured_eq_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10394 | } |
| 10395 | void set_output_raw_strided( |
| 10396 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10397 | TensorOptions options, DimnameList names |
| 10398 | ) override { |
| 10399 | auto current_device = guard_.current_device(); |
| 10400 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10401 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10402 | "structured kernels don't support multi-device outputs"); |
| 10403 | } else { |
| 10404 | guard_.reset_device(options.device()); |
| 10405 | } |
| 10406 | const auto& out = outputs_[output_idx].get(); |
| 10407 | check_inplace(out, sizes, options); |
| 10408 | if (!names.empty()) { |
| 10409 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10410 | } |
| 10411 | // super must happen after, so that downstream can use maybe_get_output |
| 10412 | // to retrieve the output |
| 10413 | at::meta::structured_eq_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10414 | } |
| 10415 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10416 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 10417 | } |
| 10418 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 10419 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 10420 | c10::OptionalDeviceGuard guard_; |
| 10421 | }; |
| 10422 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_eq__Scalar(at::Tensor & self, const at::Scalar & other) { |
| 10423 | structured_eq_Scalar_default_backend_inplace op(self); |
| 10424 | op.meta(self, other); |
| 10425 | at::eq_outf(self, other, op.outputs_[0]); |
| 10426 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 10427 | return self; |
| 10428 | } |
| 10429 | struct structured_eq_Tensor_default_backend_functional final : public at::meta::structured_eq_Tensor { |
| 10430 | void set_output_strided( |
| 10431 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10432 | TensorOptions options, DimnameList names |
| 10433 | ) override { |
| 10434 | auto current_device = guard_.current_device(); |
| 10435 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10436 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10437 | "structured kernels don't support multi-device outputs"); |
| 10438 | } else { |
| 10439 | guard_.reset_device(options.device()); |
| 10440 | } |
| 10441 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10442 | if (!names.empty()) { |
| 10443 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10444 | } |
| 10445 | // super must happen after, so that downstream can use maybe_get_output |
| 10446 | // to retrieve the output |
| 10447 | at::meta::structured_eq_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10448 | } |
| 10449 | void set_output_raw_strided( |
| 10450 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10451 | TensorOptions options, DimnameList names |
| 10452 | ) override { |
| 10453 | auto current_device = guard_.current_device(); |
| 10454 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10455 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10456 | "structured kernels don't support multi-device outputs"); |
| 10457 | } else { |
| 10458 | guard_.reset_device(options.device()); |
| 10459 | } |
| 10460 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10461 | if (!names.empty()) { |
| 10462 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10463 | } |
| 10464 | // super must happen after, so that downstream can use maybe_get_output |
| 10465 | // to retrieve the output |
| 10466 | at::meta::structured_eq_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10467 | } |
| 10468 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10469 | return *outputs_[output_idx]; |
| 10470 | } |
| 10471 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 10472 | c10::OptionalDeviceGuard guard_; |
| 10473 | }; |
| 10474 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_eq_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 10475 | structured_eq_Tensor_default_backend_functional op; |
| 10476 | op.meta(self, other); |
| 10477 | at::eq_outf(self, other, *op.outputs_[0]); |
| 10478 | return std::move(op.outputs_[0]).take(); |
| 10479 | } |
| 10480 | struct structured_eq_Tensor_default_backend_inplace final : public at::meta::structured_eq_Tensor { |
| 10481 | structured_eq_Tensor_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 10482 | void set_output_strided( |
| 10483 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10484 | TensorOptions options, DimnameList names |
| 10485 | ) override { |
| 10486 | auto current_device = guard_.current_device(); |
| 10487 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10488 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10489 | "structured kernels don't support multi-device outputs"); |
| 10490 | } else { |
| 10491 | guard_.reset_device(options.device()); |
| 10492 | } |
| 10493 | const auto& out = outputs_[output_idx].get(); |
| 10494 | check_inplace(out, sizes, options); |
| 10495 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 10496 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 10497 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 10498 | } |
| 10499 | if (!names.empty()) { |
| 10500 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10501 | } |
| 10502 | // super must happen after, so that downstream can use maybe_get_output |
| 10503 | // to retrieve the output |
| 10504 | at::meta::structured_eq_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10505 | } |
| 10506 | void set_output_raw_strided( |
| 10507 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10508 | TensorOptions options, DimnameList names |
| 10509 | ) override { |
| 10510 | auto current_device = guard_.current_device(); |
| 10511 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10512 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10513 | "structured kernels don't support multi-device outputs"); |
| 10514 | } else { |
| 10515 | guard_.reset_device(options.device()); |
| 10516 | } |
| 10517 | const auto& out = outputs_[output_idx].get(); |
| 10518 | check_inplace(out, sizes, options); |
| 10519 | if (!names.empty()) { |
| 10520 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10521 | } |
| 10522 | // super must happen after, so that downstream can use maybe_get_output |
| 10523 | // to retrieve the output |
| 10524 | at::meta::structured_eq_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10525 | } |
| 10526 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10527 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 10528 | } |
| 10529 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 10530 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 10531 | c10::OptionalDeviceGuard guard_; |
| 10532 | }; |
| 10533 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_eq__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 10534 | structured_eq_Tensor_default_backend_inplace op(self); |
| 10535 | op.meta(self, other); |
| 10536 | at::eq_outf(self, other, op.outputs_[0]); |
| 10537 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 10538 | return self; |
| 10539 | } |
| 10540 | struct structured_bitwise_and_Tensor_default_backend_functional final : public at::meta::structured_bitwise_and_Tensor { |
| 10541 | void set_output_strided( |
| 10542 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10543 | TensorOptions options, DimnameList names |
| 10544 | ) override { |
| 10545 | auto current_device = guard_.current_device(); |
| 10546 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10547 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10548 | "structured kernels don't support multi-device outputs"); |
| 10549 | } else { |
| 10550 | guard_.reset_device(options.device()); |
| 10551 | } |
| 10552 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10553 | if (!names.empty()) { |
| 10554 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10555 | } |
| 10556 | // super must happen after, so that downstream can use maybe_get_output |
| 10557 | // to retrieve the output |
| 10558 | at::meta::structured_bitwise_and_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10559 | } |
| 10560 | void set_output_raw_strided( |
| 10561 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10562 | TensorOptions options, DimnameList names |
| 10563 | ) override { |
| 10564 | auto current_device = guard_.current_device(); |
| 10565 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10566 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10567 | "structured kernels don't support multi-device outputs"); |
| 10568 | } else { |
| 10569 | guard_.reset_device(options.device()); |
| 10570 | } |
| 10571 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10572 | if (!names.empty()) { |
| 10573 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10574 | } |
| 10575 | // super must happen after, so that downstream can use maybe_get_output |
| 10576 | // to retrieve the output |
| 10577 | at::meta::structured_bitwise_and_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10578 | } |
| 10579 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10580 | return *outputs_[output_idx]; |
| 10581 | } |
| 10582 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 10583 | c10::OptionalDeviceGuard guard_; |
| 10584 | }; |
| 10585 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_bitwise_and_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 10586 | structured_bitwise_and_Tensor_default_backend_functional op; |
| 10587 | op.meta(self, other); |
| 10588 | at::bitwise_and_outf(self, other, *op.outputs_[0]); |
| 10589 | return std::move(op.outputs_[0]).take(); |
| 10590 | } |
| 10591 | struct structured_bitwise_and_Tensor_default_backend_inplace final : public at::meta::structured_bitwise_and_Tensor { |
| 10592 | structured_bitwise_and_Tensor_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 10593 | void set_output_strided( |
| 10594 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10595 | TensorOptions options, DimnameList names |
| 10596 | ) override { |
| 10597 | auto current_device = guard_.current_device(); |
| 10598 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10599 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10600 | "structured kernels don't support multi-device outputs"); |
| 10601 | } else { |
| 10602 | guard_.reset_device(options.device()); |
| 10603 | } |
| 10604 | const auto& out = outputs_[output_idx].get(); |
| 10605 | check_inplace(out, sizes, options); |
| 10606 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 10607 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 10608 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 10609 | } |
| 10610 | if (!names.empty()) { |
| 10611 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10612 | } |
| 10613 | // super must happen after, so that downstream can use maybe_get_output |
| 10614 | // to retrieve the output |
| 10615 | at::meta::structured_bitwise_and_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10616 | } |
| 10617 | void set_output_raw_strided( |
| 10618 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10619 | TensorOptions options, DimnameList names |
| 10620 | ) override { |
| 10621 | auto current_device = guard_.current_device(); |
| 10622 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10623 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10624 | "structured kernels don't support multi-device outputs"); |
| 10625 | } else { |
| 10626 | guard_.reset_device(options.device()); |
| 10627 | } |
| 10628 | const auto& out = outputs_[output_idx].get(); |
| 10629 | check_inplace(out, sizes, options); |
| 10630 | if (!names.empty()) { |
| 10631 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10632 | } |
| 10633 | // super must happen after, so that downstream can use maybe_get_output |
| 10634 | // to retrieve the output |
| 10635 | at::meta::structured_bitwise_and_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10636 | } |
| 10637 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10638 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 10639 | } |
| 10640 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 10641 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 10642 | c10::OptionalDeviceGuard guard_; |
| 10643 | }; |
| 10644 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_bitwise_and__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 10645 | structured_bitwise_and_Tensor_default_backend_inplace op(self); |
| 10646 | op.meta(self, other); |
| 10647 | at::bitwise_and_outf(self, other, op.outputs_[0]); |
| 10648 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 10649 | return self; |
| 10650 | } |
| 10651 | struct structured_bitwise_or_Tensor_default_backend_functional final : public at::meta::structured_bitwise_or_Tensor { |
| 10652 | void set_output_strided( |
| 10653 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10654 | TensorOptions options, DimnameList names |
| 10655 | ) override { |
| 10656 | auto current_device = guard_.current_device(); |
| 10657 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10658 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10659 | "structured kernels don't support multi-device outputs"); |
| 10660 | } else { |
| 10661 | guard_.reset_device(options.device()); |
| 10662 | } |
| 10663 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10664 | if (!names.empty()) { |
| 10665 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10666 | } |
| 10667 | // super must happen after, so that downstream can use maybe_get_output |
| 10668 | // to retrieve the output |
| 10669 | at::meta::structured_bitwise_or_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10670 | } |
| 10671 | void set_output_raw_strided( |
| 10672 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10673 | TensorOptions options, DimnameList names |
| 10674 | ) override { |
| 10675 | auto current_device = guard_.current_device(); |
| 10676 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10677 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10678 | "structured kernels don't support multi-device outputs"); |
| 10679 | } else { |
| 10680 | guard_.reset_device(options.device()); |
| 10681 | } |
| 10682 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10683 | if (!names.empty()) { |
| 10684 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10685 | } |
| 10686 | // super must happen after, so that downstream can use maybe_get_output |
| 10687 | // to retrieve the output |
| 10688 | at::meta::structured_bitwise_or_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10689 | } |
| 10690 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10691 | return *outputs_[output_idx]; |
| 10692 | } |
| 10693 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 10694 | c10::OptionalDeviceGuard guard_; |
| 10695 | }; |
| 10696 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_bitwise_or_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 10697 | structured_bitwise_or_Tensor_default_backend_functional op; |
| 10698 | op.meta(self, other); |
| 10699 | at::bitwise_or_outf(self, other, *op.outputs_[0]); |
| 10700 | return std::move(op.outputs_[0]).take(); |
| 10701 | } |
| 10702 | struct structured_bitwise_or_Tensor_default_backend_inplace final : public at::meta::structured_bitwise_or_Tensor { |
| 10703 | structured_bitwise_or_Tensor_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 10704 | void set_output_strided( |
| 10705 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10706 | TensorOptions options, DimnameList names |
| 10707 | ) override { |
| 10708 | auto current_device = guard_.current_device(); |
| 10709 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10710 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10711 | "structured kernels don't support multi-device outputs"); |
| 10712 | } else { |
| 10713 | guard_.reset_device(options.device()); |
| 10714 | } |
| 10715 | const auto& out = outputs_[output_idx].get(); |
| 10716 | check_inplace(out, sizes, options); |
| 10717 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 10718 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 10719 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 10720 | } |
| 10721 | if (!names.empty()) { |
| 10722 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10723 | } |
| 10724 | // super must happen after, so that downstream can use maybe_get_output |
| 10725 | // to retrieve the output |
| 10726 | at::meta::structured_bitwise_or_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10727 | } |
| 10728 | void set_output_raw_strided( |
| 10729 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10730 | TensorOptions options, DimnameList names |
| 10731 | ) override { |
| 10732 | auto current_device = guard_.current_device(); |
| 10733 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10734 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10735 | "structured kernels don't support multi-device outputs"); |
| 10736 | } else { |
| 10737 | guard_.reset_device(options.device()); |
| 10738 | } |
| 10739 | const auto& out = outputs_[output_idx].get(); |
| 10740 | check_inplace(out, sizes, options); |
| 10741 | if (!names.empty()) { |
| 10742 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10743 | } |
| 10744 | // super must happen after, so that downstream can use maybe_get_output |
| 10745 | // to retrieve the output |
| 10746 | at::meta::structured_bitwise_or_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10747 | } |
| 10748 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10749 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 10750 | } |
| 10751 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 10752 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 10753 | c10::OptionalDeviceGuard guard_; |
| 10754 | }; |
| 10755 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_bitwise_or__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 10756 | structured_bitwise_or_Tensor_default_backend_inplace op(self); |
| 10757 | op.meta(self, other); |
| 10758 | at::bitwise_or_outf(self, other, op.outputs_[0]); |
| 10759 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 10760 | return self; |
| 10761 | } |
| 10762 | struct structured_bitwise_xor_Tensor_default_backend_functional final : public at::meta::structured_bitwise_xor_Tensor { |
| 10763 | void set_output_strided( |
| 10764 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10765 | TensorOptions options, DimnameList names |
| 10766 | ) override { |
| 10767 | auto current_device = guard_.current_device(); |
| 10768 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10769 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10770 | "structured kernels don't support multi-device outputs"); |
| 10771 | } else { |
| 10772 | guard_.reset_device(options.device()); |
| 10773 | } |
| 10774 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10775 | if (!names.empty()) { |
| 10776 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10777 | } |
| 10778 | // super must happen after, so that downstream can use maybe_get_output |
| 10779 | // to retrieve the output |
| 10780 | at::meta::structured_bitwise_xor_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10781 | } |
| 10782 | void set_output_raw_strided( |
| 10783 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10784 | TensorOptions options, DimnameList names |
| 10785 | ) override { |
| 10786 | auto current_device = guard_.current_device(); |
| 10787 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10788 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10789 | "structured kernels don't support multi-device outputs"); |
| 10790 | } else { |
| 10791 | guard_.reset_device(options.device()); |
| 10792 | } |
| 10793 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10794 | if (!names.empty()) { |
| 10795 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10796 | } |
| 10797 | // super must happen after, so that downstream can use maybe_get_output |
| 10798 | // to retrieve the output |
| 10799 | at::meta::structured_bitwise_xor_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10800 | } |
| 10801 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10802 | return *outputs_[output_idx]; |
| 10803 | } |
| 10804 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 10805 | c10::OptionalDeviceGuard guard_; |
| 10806 | }; |
| 10807 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_bitwise_xor_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 10808 | structured_bitwise_xor_Tensor_default_backend_functional op; |
| 10809 | op.meta(self, other); |
| 10810 | at::bitwise_xor_outf(self, other, *op.outputs_[0]); |
| 10811 | return std::move(op.outputs_[0]).take(); |
| 10812 | } |
| 10813 | struct structured_bitwise_xor_Tensor_default_backend_inplace final : public at::meta::structured_bitwise_xor_Tensor { |
| 10814 | structured_bitwise_xor_Tensor_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 10815 | void set_output_strided( |
| 10816 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10817 | TensorOptions options, DimnameList names |
| 10818 | ) override { |
| 10819 | auto current_device = guard_.current_device(); |
| 10820 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10821 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10822 | "structured kernels don't support multi-device outputs"); |
| 10823 | } else { |
| 10824 | guard_.reset_device(options.device()); |
| 10825 | } |
| 10826 | const auto& out = outputs_[output_idx].get(); |
| 10827 | check_inplace(out, sizes, options); |
| 10828 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 10829 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 10830 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 10831 | } |
| 10832 | if (!names.empty()) { |
| 10833 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10834 | } |
| 10835 | // super must happen after, so that downstream can use maybe_get_output |
| 10836 | // to retrieve the output |
| 10837 | at::meta::structured_bitwise_xor_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10838 | } |
| 10839 | void set_output_raw_strided( |
| 10840 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10841 | TensorOptions options, DimnameList names |
| 10842 | ) override { |
| 10843 | auto current_device = guard_.current_device(); |
| 10844 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10845 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10846 | "structured kernels don't support multi-device outputs"); |
| 10847 | } else { |
| 10848 | guard_.reset_device(options.device()); |
| 10849 | } |
| 10850 | const auto& out = outputs_[output_idx].get(); |
| 10851 | check_inplace(out, sizes, options); |
| 10852 | if (!names.empty()) { |
| 10853 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10854 | } |
| 10855 | // super must happen after, so that downstream can use maybe_get_output |
| 10856 | // to retrieve the output |
| 10857 | at::meta::structured_bitwise_xor_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10858 | } |
| 10859 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10860 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 10861 | } |
| 10862 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 10863 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 10864 | c10::OptionalDeviceGuard guard_; |
| 10865 | }; |
| 10866 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_bitwise_xor__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 10867 | structured_bitwise_xor_Tensor_default_backend_inplace op(self); |
| 10868 | op.meta(self, other); |
| 10869 | at::bitwise_xor_outf(self, other, op.outputs_[0]); |
| 10870 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 10871 | return self; |
| 10872 | } |
| 10873 | struct structured_bitwise_left_shift_Tensor_default_backend_functional final : public at::meta::structured_bitwise_left_shift_Tensor { |
| 10874 | void set_output_strided( |
| 10875 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10876 | TensorOptions options, DimnameList names |
| 10877 | ) override { |
| 10878 | auto current_device = guard_.current_device(); |
| 10879 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10880 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10881 | "structured kernels don't support multi-device outputs"); |
| 10882 | } else { |
| 10883 | guard_.reset_device(options.device()); |
| 10884 | } |
| 10885 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10886 | if (!names.empty()) { |
| 10887 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10888 | } |
| 10889 | // super must happen after, so that downstream can use maybe_get_output |
| 10890 | // to retrieve the output |
| 10891 | at::meta::structured_bitwise_left_shift_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10892 | } |
| 10893 | void set_output_raw_strided( |
| 10894 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10895 | TensorOptions options, DimnameList names |
| 10896 | ) override { |
| 10897 | auto current_device = guard_.current_device(); |
| 10898 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10899 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10900 | "structured kernels don't support multi-device outputs"); |
| 10901 | } else { |
| 10902 | guard_.reset_device(options.device()); |
| 10903 | } |
| 10904 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10905 | if (!names.empty()) { |
| 10906 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10907 | } |
| 10908 | // super must happen after, so that downstream can use maybe_get_output |
| 10909 | // to retrieve the output |
| 10910 | at::meta::structured_bitwise_left_shift_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10911 | } |
| 10912 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10913 | return *outputs_[output_idx]; |
| 10914 | } |
| 10915 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 10916 | c10::OptionalDeviceGuard guard_; |
| 10917 | }; |
| 10918 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_bitwise_left_shift_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 10919 | structured_bitwise_left_shift_Tensor_default_backend_functional op; |
| 10920 | op.meta(self, other); |
| 10921 | at::bitwise_left_shift_outf(self, other, *op.outputs_[0]); |
| 10922 | return std::move(op.outputs_[0]).take(); |
| 10923 | } |
| 10924 | struct structured_bitwise_left_shift_Tensor_default_backend_inplace final : public at::meta::structured_bitwise_left_shift_Tensor { |
| 10925 | structured_bitwise_left_shift_Tensor_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 10926 | void set_output_strided( |
| 10927 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10928 | TensorOptions options, DimnameList names |
| 10929 | ) override { |
| 10930 | auto current_device = guard_.current_device(); |
| 10931 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10932 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10933 | "structured kernels don't support multi-device outputs"); |
| 10934 | } else { |
| 10935 | guard_.reset_device(options.device()); |
| 10936 | } |
| 10937 | const auto& out = outputs_[output_idx].get(); |
| 10938 | check_inplace(out, sizes, options); |
| 10939 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 10940 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 10941 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 10942 | } |
| 10943 | if (!names.empty()) { |
| 10944 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10945 | } |
| 10946 | // super must happen after, so that downstream can use maybe_get_output |
| 10947 | // to retrieve the output |
| 10948 | at::meta::structured_bitwise_left_shift_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10949 | } |
| 10950 | void set_output_raw_strided( |
| 10951 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10952 | TensorOptions options, DimnameList names |
| 10953 | ) override { |
| 10954 | auto current_device = guard_.current_device(); |
| 10955 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10956 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10957 | "structured kernels don't support multi-device outputs"); |
| 10958 | } else { |
| 10959 | guard_.reset_device(options.device()); |
| 10960 | } |
| 10961 | const auto& out = outputs_[output_idx].get(); |
| 10962 | check_inplace(out, sizes, options); |
| 10963 | if (!names.empty()) { |
| 10964 | namedinference::propagate_names(outputs_[output_idx], names); |
| 10965 | } |
| 10966 | // super must happen after, so that downstream can use maybe_get_output |
| 10967 | // to retrieve the output |
| 10968 | at::meta::structured_bitwise_left_shift_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 10969 | } |
| 10970 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 10971 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 10972 | } |
| 10973 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 10974 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 10975 | c10::OptionalDeviceGuard guard_; |
| 10976 | }; |
| 10977 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_bitwise_left_shift__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 10978 | structured_bitwise_left_shift_Tensor_default_backend_inplace op(self); |
| 10979 | op.meta(self, other); |
| 10980 | at::bitwise_left_shift_outf(self, other, op.outputs_[0]); |
| 10981 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 10982 | return self; |
| 10983 | } |
| 10984 | struct structured_bitwise_right_shift_Tensor_default_backend_functional final : public at::meta::structured_bitwise_right_shift_Tensor { |
| 10985 | void set_output_strided( |
| 10986 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 10987 | TensorOptions options, DimnameList names |
| 10988 | ) override { |
| 10989 | auto current_device = guard_.current_device(); |
| 10990 | if (C10_UNLIKELY(current_device.has_value())) { |
| 10991 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 10992 | "structured kernels don't support multi-device outputs"); |
| 10993 | } else { |
| 10994 | guard_.reset_device(options.device()); |
| 10995 | } |
| 10996 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 10997 | if (!names.empty()) { |
| 10998 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 10999 | } |
| 11000 | // super must happen after, so that downstream can use maybe_get_output |
| 11001 | // to retrieve the output |
| 11002 | at::meta::structured_bitwise_right_shift_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11003 | } |
| 11004 | void set_output_raw_strided( |
| 11005 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11006 | TensorOptions options, DimnameList names |
| 11007 | ) override { |
| 11008 | auto current_device = guard_.current_device(); |
| 11009 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11010 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11011 | "structured kernels don't support multi-device outputs"); |
| 11012 | } else { |
| 11013 | guard_.reset_device(options.device()); |
| 11014 | } |
| 11015 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11016 | if (!names.empty()) { |
| 11017 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11018 | } |
| 11019 | // super must happen after, so that downstream can use maybe_get_output |
| 11020 | // to retrieve the output |
| 11021 | at::meta::structured_bitwise_right_shift_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11022 | } |
| 11023 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11024 | return *outputs_[output_idx]; |
| 11025 | } |
| 11026 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 11027 | c10::OptionalDeviceGuard guard_; |
| 11028 | }; |
| 11029 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_bitwise_right_shift_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 11030 | structured_bitwise_right_shift_Tensor_default_backend_functional op; |
| 11031 | op.meta(self, other); |
| 11032 | at::bitwise_right_shift_outf(self, other, *op.outputs_[0]); |
| 11033 | return std::move(op.outputs_[0]).take(); |
| 11034 | } |
| 11035 | struct structured_bitwise_right_shift_Tensor_default_backend_inplace final : public at::meta::structured_bitwise_right_shift_Tensor { |
| 11036 | structured_bitwise_right_shift_Tensor_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 11037 | void set_output_strided( |
| 11038 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11039 | TensorOptions options, DimnameList names |
| 11040 | ) override { |
| 11041 | auto current_device = guard_.current_device(); |
| 11042 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11043 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11044 | "structured kernels don't support multi-device outputs"); |
| 11045 | } else { |
| 11046 | guard_.reset_device(options.device()); |
| 11047 | } |
| 11048 | const auto& out = outputs_[output_idx].get(); |
| 11049 | check_inplace(out, sizes, options); |
| 11050 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 11051 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 11052 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 11053 | } |
| 11054 | if (!names.empty()) { |
| 11055 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11056 | } |
| 11057 | // super must happen after, so that downstream can use maybe_get_output |
| 11058 | // to retrieve the output |
| 11059 | at::meta::structured_bitwise_right_shift_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11060 | } |
| 11061 | void set_output_raw_strided( |
| 11062 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11063 | TensorOptions options, DimnameList names |
| 11064 | ) override { |
| 11065 | auto current_device = guard_.current_device(); |
| 11066 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11067 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11068 | "structured kernels don't support multi-device outputs"); |
| 11069 | } else { |
| 11070 | guard_.reset_device(options.device()); |
| 11071 | } |
| 11072 | const auto& out = outputs_[output_idx].get(); |
| 11073 | check_inplace(out, sizes, options); |
| 11074 | if (!names.empty()) { |
| 11075 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11076 | } |
| 11077 | // super must happen after, so that downstream can use maybe_get_output |
| 11078 | // to retrieve the output |
| 11079 | at::meta::structured_bitwise_right_shift_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11080 | } |
| 11081 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11082 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 11083 | } |
| 11084 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 11085 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 11086 | c10::OptionalDeviceGuard guard_; |
| 11087 | }; |
| 11088 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_bitwise_right_shift__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 11089 | structured_bitwise_right_shift_Tensor_default_backend_inplace op(self); |
| 11090 | op.meta(self, other); |
| 11091 | at::bitwise_right_shift_outf(self, other, op.outputs_[0]); |
| 11092 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 11093 | return self; |
| 11094 | } |
| 11095 | struct structured_tril_default_backend_functional final : public at::meta::structured_tril { |
| 11096 | void set_output_strided( |
| 11097 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11098 | TensorOptions options, DimnameList names |
| 11099 | ) override { |
| 11100 | auto current_device = guard_.current_device(); |
| 11101 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11102 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11103 | "structured kernels don't support multi-device outputs"); |
| 11104 | } else { |
| 11105 | guard_.reset_device(options.device()); |
| 11106 | } |
| 11107 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11108 | if (!names.empty()) { |
| 11109 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11110 | } |
| 11111 | // super must happen after, so that downstream can use maybe_get_output |
| 11112 | // to retrieve the output |
| 11113 | } |
| 11114 | void set_output_raw_strided( |
| 11115 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11116 | TensorOptions options, DimnameList names |
| 11117 | ) override { |
| 11118 | auto current_device = guard_.current_device(); |
| 11119 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11120 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11121 | "structured kernels don't support multi-device outputs"); |
| 11122 | } else { |
| 11123 | guard_.reset_device(options.device()); |
| 11124 | } |
| 11125 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11126 | if (!names.empty()) { |
| 11127 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11128 | } |
| 11129 | // super must happen after, so that downstream can use maybe_get_output |
| 11130 | // to retrieve the output |
| 11131 | } |
| 11132 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11133 | return *outputs_[output_idx]; |
| 11134 | } |
| 11135 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 11136 | c10::OptionalDeviceGuard guard_; |
| 11137 | }; |
| 11138 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_tril(const at::Tensor & self, int64_t diagonal) { |
| 11139 | structured_tril_default_backend_functional op; |
| 11140 | op.meta(self, diagonal); |
| 11141 | at::tril_outf(self, diagonal, *op.outputs_[0]); |
| 11142 | return std::move(op.outputs_[0]).take(); |
| 11143 | } |
| 11144 | struct structured_tril_default_backend_inplace final : public at::meta::structured_tril { |
| 11145 | structured_tril_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 11146 | void set_output_strided( |
| 11147 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11148 | TensorOptions options, DimnameList names |
| 11149 | ) override { |
| 11150 | auto current_device = guard_.current_device(); |
| 11151 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11152 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11153 | "structured kernels don't support multi-device outputs"); |
| 11154 | } else { |
| 11155 | guard_.reset_device(options.device()); |
| 11156 | } |
| 11157 | const auto& out = outputs_[output_idx].get(); |
| 11158 | check_inplace(out, sizes, options); |
| 11159 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 11160 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 11161 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 11162 | } |
| 11163 | if (!names.empty()) { |
| 11164 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11165 | } |
| 11166 | // super must happen after, so that downstream can use maybe_get_output |
| 11167 | // to retrieve the output |
| 11168 | } |
| 11169 | void set_output_raw_strided( |
| 11170 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11171 | TensorOptions options, DimnameList names |
| 11172 | ) override { |
| 11173 | auto current_device = guard_.current_device(); |
| 11174 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11175 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11176 | "structured kernels don't support multi-device outputs"); |
| 11177 | } else { |
| 11178 | guard_.reset_device(options.device()); |
| 11179 | } |
| 11180 | const auto& out = outputs_[output_idx].get(); |
| 11181 | check_inplace(out, sizes, options); |
| 11182 | if (!names.empty()) { |
| 11183 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11184 | } |
| 11185 | // super must happen after, so that downstream can use maybe_get_output |
| 11186 | // to retrieve the output |
| 11187 | } |
| 11188 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11189 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 11190 | } |
| 11191 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 11192 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 11193 | c10::OptionalDeviceGuard guard_; |
| 11194 | }; |
| 11195 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_tril_(at::Tensor & self, int64_t diagonal) { |
| 11196 | structured_tril_default_backend_inplace op(self); |
| 11197 | op.meta(self, diagonal); |
| 11198 | at::tril_outf(self, diagonal, op.outputs_[0]); |
| 11199 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 11200 | return self; |
| 11201 | } |
| 11202 | struct structured_triu_default_backend_functional final : public at::meta::structured_triu { |
| 11203 | void set_output_strided( |
| 11204 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11205 | TensorOptions options, DimnameList names |
| 11206 | ) override { |
| 11207 | auto current_device = guard_.current_device(); |
| 11208 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11209 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11210 | "structured kernels don't support multi-device outputs"); |
| 11211 | } else { |
| 11212 | guard_.reset_device(options.device()); |
| 11213 | } |
| 11214 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11215 | if (!names.empty()) { |
| 11216 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11217 | } |
| 11218 | // super must happen after, so that downstream can use maybe_get_output |
| 11219 | // to retrieve the output |
| 11220 | } |
| 11221 | void set_output_raw_strided( |
| 11222 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11223 | TensorOptions options, DimnameList names |
| 11224 | ) override { |
| 11225 | auto current_device = guard_.current_device(); |
| 11226 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11227 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11228 | "structured kernels don't support multi-device outputs"); |
| 11229 | } else { |
| 11230 | guard_.reset_device(options.device()); |
| 11231 | } |
| 11232 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11233 | if (!names.empty()) { |
| 11234 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11235 | } |
| 11236 | // super must happen after, so that downstream can use maybe_get_output |
| 11237 | // to retrieve the output |
| 11238 | } |
| 11239 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11240 | return *outputs_[output_idx]; |
| 11241 | } |
| 11242 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 11243 | c10::OptionalDeviceGuard guard_; |
| 11244 | }; |
| 11245 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_triu(const at::Tensor & self, int64_t diagonal) { |
| 11246 | structured_triu_default_backend_functional op; |
| 11247 | op.meta(self, diagonal); |
| 11248 | at::triu_outf(self, diagonal, *op.outputs_[0]); |
| 11249 | return std::move(op.outputs_[0]).take(); |
| 11250 | } |
| 11251 | struct structured_triu_default_backend_inplace final : public at::meta::structured_triu { |
| 11252 | structured_triu_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 11253 | void set_output_strided( |
| 11254 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11255 | TensorOptions options, DimnameList names |
| 11256 | ) override { |
| 11257 | auto current_device = guard_.current_device(); |
| 11258 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11259 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11260 | "structured kernels don't support multi-device outputs"); |
| 11261 | } else { |
| 11262 | guard_.reset_device(options.device()); |
| 11263 | } |
| 11264 | const auto& out = outputs_[output_idx].get(); |
| 11265 | check_inplace(out, sizes, options); |
| 11266 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 11267 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 11268 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 11269 | } |
| 11270 | if (!names.empty()) { |
| 11271 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11272 | } |
| 11273 | // super must happen after, so that downstream can use maybe_get_output |
| 11274 | // to retrieve the output |
| 11275 | } |
| 11276 | void set_output_raw_strided( |
| 11277 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11278 | TensorOptions options, DimnameList names |
| 11279 | ) override { |
| 11280 | auto current_device = guard_.current_device(); |
| 11281 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11282 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11283 | "structured kernels don't support multi-device outputs"); |
| 11284 | } else { |
| 11285 | guard_.reset_device(options.device()); |
| 11286 | } |
| 11287 | const auto& out = outputs_[output_idx].get(); |
| 11288 | check_inplace(out, sizes, options); |
| 11289 | if (!names.empty()) { |
| 11290 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11291 | } |
| 11292 | // super must happen after, so that downstream can use maybe_get_output |
| 11293 | // to retrieve the output |
| 11294 | } |
| 11295 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11296 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 11297 | } |
| 11298 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 11299 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 11300 | c10::OptionalDeviceGuard guard_; |
| 11301 | }; |
| 11302 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_triu_(at::Tensor & self, int64_t diagonal) { |
| 11303 | structured_triu_default_backend_inplace op(self); |
| 11304 | op.meta(self, diagonal); |
| 11305 | at::triu_outf(self, diagonal, op.outputs_[0]); |
| 11306 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 11307 | return self; |
| 11308 | } |
| 11309 | struct structured_digamma_default_backend_functional final : public at::meta::structured_digamma { |
| 11310 | void set_output_strided( |
| 11311 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11312 | TensorOptions options, DimnameList names |
| 11313 | ) override { |
| 11314 | auto current_device = guard_.current_device(); |
| 11315 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11316 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11317 | "structured kernels don't support multi-device outputs"); |
| 11318 | } else { |
| 11319 | guard_.reset_device(options.device()); |
| 11320 | } |
| 11321 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11322 | if (!names.empty()) { |
| 11323 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11324 | } |
| 11325 | // super must happen after, so that downstream can use maybe_get_output |
| 11326 | // to retrieve the output |
| 11327 | at::meta::structured_digamma::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11328 | } |
| 11329 | void set_output_raw_strided( |
| 11330 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11331 | TensorOptions options, DimnameList names |
| 11332 | ) override { |
| 11333 | auto current_device = guard_.current_device(); |
| 11334 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11335 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11336 | "structured kernels don't support multi-device outputs"); |
| 11337 | } else { |
| 11338 | guard_.reset_device(options.device()); |
| 11339 | } |
| 11340 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11341 | if (!names.empty()) { |
| 11342 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11343 | } |
| 11344 | // super must happen after, so that downstream can use maybe_get_output |
| 11345 | // to retrieve the output |
| 11346 | at::meta::structured_digamma::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11347 | } |
| 11348 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11349 | return *outputs_[output_idx]; |
| 11350 | } |
| 11351 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 11352 | c10::OptionalDeviceGuard guard_; |
| 11353 | }; |
| 11354 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_digamma(const at::Tensor & self) { |
| 11355 | structured_digamma_default_backend_functional op; |
| 11356 | op.meta(self); |
| 11357 | at::digamma_outf(self, *op.outputs_[0]); |
| 11358 | return std::move(op.outputs_[0]).take(); |
| 11359 | } |
| 11360 | struct structured_digamma_default_backend_inplace final : public at::meta::structured_digamma { |
| 11361 | structured_digamma_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 11362 | void set_output_strided( |
| 11363 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11364 | TensorOptions options, DimnameList names |
| 11365 | ) override { |
| 11366 | auto current_device = guard_.current_device(); |
| 11367 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11368 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11369 | "structured kernels don't support multi-device outputs"); |
| 11370 | } else { |
| 11371 | guard_.reset_device(options.device()); |
| 11372 | } |
| 11373 | const auto& out = outputs_[output_idx].get(); |
| 11374 | check_inplace(out, sizes, options); |
| 11375 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 11376 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 11377 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 11378 | } |
| 11379 | if (!names.empty()) { |
| 11380 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11381 | } |
| 11382 | // super must happen after, so that downstream can use maybe_get_output |
| 11383 | // to retrieve the output |
| 11384 | at::meta::structured_digamma::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11385 | } |
| 11386 | void set_output_raw_strided( |
| 11387 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11388 | TensorOptions options, DimnameList names |
| 11389 | ) override { |
| 11390 | auto current_device = guard_.current_device(); |
| 11391 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11392 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11393 | "structured kernels don't support multi-device outputs"); |
| 11394 | } else { |
| 11395 | guard_.reset_device(options.device()); |
| 11396 | } |
| 11397 | const auto& out = outputs_[output_idx].get(); |
| 11398 | check_inplace(out, sizes, options); |
| 11399 | if (!names.empty()) { |
| 11400 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11401 | } |
| 11402 | // super must happen after, so that downstream can use maybe_get_output |
| 11403 | // to retrieve the output |
| 11404 | at::meta::structured_digamma::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11405 | } |
| 11406 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11407 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 11408 | } |
| 11409 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 11410 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 11411 | c10::OptionalDeviceGuard guard_; |
| 11412 | }; |
| 11413 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_digamma_(at::Tensor & self) { |
| 11414 | structured_digamma_default_backend_inplace op(self); |
| 11415 | op.meta(self); |
| 11416 | at::digamma_outf(self, op.outputs_[0]); |
| 11417 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 11418 | return self; |
| 11419 | } |
| 11420 | struct structured_lerp_Scalar_default_backend_functional final : public at::meta::structured_lerp_Scalar { |
| 11421 | void set_output_strided( |
| 11422 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11423 | TensorOptions options, DimnameList names |
| 11424 | ) override { |
| 11425 | auto current_device = guard_.current_device(); |
| 11426 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11427 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11428 | "structured kernels don't support multi-device outputs"); |
| 11429 | } else { |
| 11430 | guard_.reset_device(options.device()); |
| 11431 | } |
| 11432 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11433 | if (!names.empty()) { |
| 11434 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11435 | } |
| 11436 | // super must happen after, so that downstream can use maybe_get_output |
| 11437 | // to retrieve the output |
| 11438 | at::meta::structured_lerp_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11439 | } |
| 11440 | void set_output_raw_strided( |
| 11441 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11442 | TensorOptions options, DimnameList names |
| 11443 | ) override { |
| 11444 | auto current_device = guard_.current_device(); |
| 11445 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11446 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11447 | "structured kernels don't support multi-device outputs"); |
| 11448 | } else { |
| 11449 | guard_.reset_device(options.device()); |
| 11450 | } |
| 11451 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11452 | if (!names.empty()) { |
| 11453 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11454 | } |
| 11455 | // super must happen after, so that downstream can use maybe_get_output |
| 11456 | // to retrieve the output |
| 11457 | at::meta::structured_lerp_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11458 | } |
| 11459 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11460 | return *outputs_[output_idx]; |
| 11461 | } |
| 11462 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 11463 | c10::OptionalDeviceGuard guard_; |
| 11464 | }; |
| 11465 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_lerp_Scalar(const at::Tensor & self, const at::Tensor & end, const at::Scalar & weight) { |
| 11466 | structured_lerp_Scalar_default_backend_functional op; |
| 11467 | op.meta(self, end, weight); |
| 11468 | at::lerp_outf(self, end, weight, *op.outputs_[0]); |
| 11469 | return std::move(op.outputs_[0]).take(); |
| 11470 | } |
| 11471 | struct structured_lerp_Scalar_default_backend_inplace final : public at::meta::structured_lerp_Scalar { |
| 11472 | structured_lerp_Scalar_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 11473 | void set_output_strided( |
| 11474 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11475 | TensorOptions options, DimnameList names |
| 11476 | ) override { |
| 11477 | auto current_device = guard_.current_device(); |
| 11478 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11479 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11480 | "structured kernels don't support multi-device outputs"); |
| 11481 | } else { |
| 11482 | guard_.reset_device(options.device()); |
| 11483 | } |
| 11484 | const auto& out = outputs_[output_idx].get(); |
| 11485 | check_inplace(out, sizes, options); |
| 11486 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 11487 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 11488 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 11489 | } |
| 11490 | if (!names.empty()) { |
| 11491 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11492 | } |
| 11493 | // super must happen after, so that downstream can use maybe_get_output |
| 11494 | // to retrieve the output |
| 11495 | at::meta::structured_lerp_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11496 | } |
| 11497 | void set_output_raw_strided( |
| 11498 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11499 | TensorOptions options, DimnameList names |
| 11500 | ) override { |
| 11501 | auto current_device = guard_.current_device(); |
| 11502 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11503 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11504 | "structured kernels don't support multi-device outputs"); |
| 11505 | } else { |
| 11506 | guard_.reset_device(options.device()); |
| 11507 | } |
| 11508 | const auto& out = outputs_[output_idx].get(); |
| 11509 | check_inplace(out, sizes, options); |
| 11510 | if (!names.empty()) { |
| 11511 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11512 | } |
| 11513 | // super must happen after, so that downstream can use maybe_get_output |
| 11514 | // to retrieve the output |
| 11515 | at::meta::structured_lerp_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11516 | } |
| 11517 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11518 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 11519 | } |
| 11520 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 11521 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 11522 | c10::OptionalDeviceGuard guard_; |
| 11523 | }; |
| 11524 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_lerp__Scalar(at::Tensor & self, const at::Tensor & end, const at::Scalar & weight) { |
| 11525 | structured_lerp_Scalar_default_backend_inplace op(self); |
| 11526 | op.meta(self, end, weight); |
| 11527 | at::lerp_outf(self, end, weight, op.outputs_[0]); |
| 11528 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 11529 | return self; |
| 11530 | } |
| 11531 | struct structured_lerp_Tensor_default_backend_functional final : public at::meta::structured_lerp_Tensor { |
| 11532 | void set_output_strided( |
| 11533 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11534 | TensorOptions options, DimnameList names |
| 11535 | ) override { |
| 11536 | auto current_device = guard_.current_device(); |
| 11537 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11538 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11539 | "structured kernels don't support multi-device outputs"); |
| 11540 | } else { |
| 11541 | guard_.reset_device(options.device()); |
| 11542 | } |
| 11543 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11544 | if (!names.empty()) { |
| 11545 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11546 | } |
| 11547 | // super must happen after, so that downstream can use maybe_get_output |
| 11548 | // to retrieve the output |
| 11549 | at::meta::structured_lerp_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11550 | } |
| 11551 | void set_output_raw_strided( |
| 11552 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11553 | TensorOptions options, DimnameList names |
| 11554 | ) override { |
| 11555 | auto current_device = guard_.current_device(); |
| 11556 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11557 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11558 | "structured kernels don't support multi-device outputs"); |
| 11559 | } else { |
| 11560 | guard_.reset_device(options.device()); |
| 11561 | } |
| 11562 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11563 | if (!names.empty()) { |
| 11564 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11565 | } |
| 11566 | // super must happen after, so that downstream can use maybe_get_output |
| 11567 | // to retrieve the output |
| 11568 | at::meta::structured_lerp_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11569 | } |
| 11570 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11571 | return *outputs_[output_idx]; |
| 11572 | } |
| 11573 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 11574 | c10::OptionalDeviceGuard guard_; |
| 11575 | }; |
| 11576 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_lerp_Tensor(const at::Tensor & self, const at::Tensor & end, const at::Tensor & weight) { |
| 11577 | structured_lerp_Tensor_default_backend_functional op; |
| 11578 | op.meta(self, end, weight); |
| 11579 | at::lerp_outf(self, end, weight, *op.outputs_[0]); |
| 11580 | return std::move(op.outputs_[0]).take(); |
| 11581 | } |
| 11582 | struct structured_lerp_Tensor_default_backend_inplace final : public at::meta::structured_lerp_Tensor { |
| 11583 | structured_lerp_Tensor_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 11584 | void set_output_strided( |
| 11585 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11586 | TensorOptions options, DimnameList names |
| 11587 | ) override { |
| 11588 | auto current_device = guard_.current_device(); |
| 11589 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11590 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11591 | "structured kernels don't support multi-device outputs"); |
| 11592 | } else { |
| 11593 | guard_.reset_device(options.device()); |
| 11594 | } |
| 11595 | const auto& out = outputs_[output_idx].get(); |
| 11596 | check_inplace(out, sizes, options); |
| 11597 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 11598 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 11599 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 11600 | } |
| 11601 | if (!names.empty()) { |
| 11602 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11603 | } |
| 11604 | // super must happen after, so that downstream can use maybe_get_output |
| 11605 | // to retrieve the output |
| 11606 | at::meta::structured_lerp_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11607 | } |
| 11608 | void set_output_raw_strided( |
| 11609 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11610 | TensorOptions options, DimnameList names |
| 11611 | ) override { |
| 11612 | auto current_device = guard_.current_device(); |
| 11613 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11614 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11615 | "structured kernels don't support multi-device outputs"); |
| 11616 | } else { |
| 11617 | guard_.reset_device(options.device()); |
| 11618 | } |
| 11619 | const auto& out = outputs_[output_idx].get(); |
| 11620 | check_inplace(out, sizes, options); |
| 11621 | if (!names.empty()) { |
| 11622 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11623 | } |
| 11624 | // super must happen after, so that downstream can use maybe_get_output |
| 11625 | // to retrieve the output |
| 11626 | at::meta::structured_lerp_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11627 | } |
| 11628 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11629 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 11630 | } |
| 11631 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 11632 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 11633 | c10::OptionalDeviceGuard guard_; |
| 11634 | }; |
| 11635 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_lerp__Tensor(at::Tensor & self, const at::Tensor & end, const at::Tensor & weight) { |
| 11636 | structured_lerp_Tensor_default_backend_inplace op(self); |
| 11637 | op.meta(self, end, weight); |
| 11638 | at::lerp_outf(self, end, weight, op.outputs_[0]); |
| 11639 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 11640 | return self; |
| 11641 | } |
| 11642 | struct structured_ne_Scalar_default_backend_functional final : public at::meta::structured_ne_Scalar { |
| 11643 | void set_output_strided( |
| 11644 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11645 | TensorOptions options, DimnameList names |
| 11646 | ) override { |
| 11647 | auto current_device = guard_.current_device(); |
| 11648 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11649 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11650 | "structured kernels don't support multi-device outputs"); |
| 11651 | } else { |
| 11652 | guard_.reset_device(options.device()); |
| 11653 | } |
| 11654 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11655 | if (!names.empty()) { |
| 11656 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11657 | } |
| 11658 | // super must happen after, so that downstream can use maybe_get_output |
| 11659 | // to retrieve the output |
| 11660 | at::meta::structured_ne_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11661 | } |
| 11662 | void set_output_raw_strided( |
| 11663 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11664 | TensorOptions options, DimnameList names |
| 11665 | ) override { |
| 11666 | auto current_device = guard_.current_device(); |
| 11667 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11668 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11669 | "structured kernels don't support multi-device outputs"); |
| 11670 | } else { |
| 11671 | guard_.reset_device(options.device()); |
| 11672 | } |
| 11673 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11674 | if (!names.empty()) { |
| 11675 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11676 | } |
| 11677 | // super must happen after, so that downstream can use maybe_get_output |
| 11678 | // to retrieve the output |
| 11679 | at::meta::structured_ne_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11680 | } |
| 11681 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11682 | return *outputs_[output_idx]; |
| 11683 | } |
| 11684 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 11685 | c10::OptionalDeviceGuard guard_; |
| 11686 | }; |
| 11687 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_ne_Scalar(const at::Tensor & self, const at::Scalar & other) { |
| 11688 | structured_ne_Scalar_default_backend_functional op; |
| 11689 | op.meta(self, other); |
| 11690 | at::ne_outf(self, other, *op.outputs_[0]); |
| 11691 | return std::move(op.outputs_[0]).take(); |
| 11692 | } |
| 11693 | struct structured_ne_Scalar_default_backend_inplace final : public at::meta::structured_ne_Scalar { |
| 11694 | structured_ne_Scalar_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 11695 | void set_output_strided( |
| 11696 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11697 | TensorOptions options, DimnameList names |
| 11698 | ) override { |
| 11699 | auto current_device = guard_.current_device(); |
| 11700 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11701 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11702 | "structured kernels don't support multi-device outputs"); |
| 11703 | } else { |
| 11704 | guard_.reset_device(options.device()); |
| 11705 | } |
| 11706 | const auto& out = outputs_[output_idx].get(); |
| 11707 | check_inplace(out, sizes, options); |
| 11708 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 11709 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 11710 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 11711 | } |
| 11712 | if (!names.empty()) { |
| 11713 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11714 | } |
| 11715 | // super must happen after, so that downstream can use maybe_get_output |
| 11716 | // to retrieve the output |
| 11717 | at::meta::structured_ne_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11718 | } |
| 11719 | void set_output_raw_strided( |
| 11720 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11721 | TensorOptions options, DimnameList names |
| 11722 | ) override { |
| 11723 | auto current_device = guard_.current_device(); |
| 11724 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11725 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11726 | "structured kernels don't support multi-device outputs"); |
| 11727 | } else { |
| 11728 | guard_.reset_device(options.device()); |
| 11729 | } |
| 11730 | const auto& out = outputs_[output_idx].get(); |
| 11731 | check_inplace(out, sizes, options); |
| 11732 | if (!names.empty()) { |
| 11733 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11734 | } |
| 11735 | // super must happen after, so that downstream can use maybe_get_output |
| 11736 | // to retrieve the output |
| 11737 | at::meta::structured_ne_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11738 | } |
| 11739 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11740 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 11741 | } |
| 11742 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 11743 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 11744 | c10::OptionalDeviceGuard guard_; |
| 11745 | }; |
| 11746 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_ne__Scalar(at::Tensor & self, const at::Scalar & other) { |
| 11747 | structured_ne_Scalar_default_backend_inplace op(self); |
| 11748 | op.meta(self, other); |
| 11749 | at::ne_outf(self, other, op.outputs_[0]); |
| 11750 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 11751 | return self; |
| 11752 | } |
| 11753 | struct structured_ne_Tensor_default_backend_functional final : public at::meta::structured_ne_Tensor { |
| 11754 | void set_output_strided( |
| 11755 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11756 | TensorOptions options, DimnameList names |
| 11757 | ) override { |
| 11758 | auto current_device = guard_.current_device(); |
| 11759 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11760 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11761 | "structured kernels don't support multi-device outputs"); |
| 11762 | } else { |
| 11763 | guard_.reset_device(options.device()); |
| 11764 | } |
| 11765 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11766 | if (!names.empty()) { |
| 11767 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11768 | } |
| 11769 | // super must happen after, so that downstream can use maybe_get_output |
| 11770 | // to retrieve the output |
| 11771 | at::meta::structured_ne_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11772 | } |
| 11773 | void set_output_raw_strided( |
| 11774 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11775 | TensorOptions options, DimnameList names |
| 11776 | ) override { |
| 11777 | auto current_device = guard_.current_device(); |
| 11778 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11779 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11780 | "structured kernels don't support multi-device outputs"); |
| 11781 | } else { |
| 11782 | guard_.reset_device(options.device()); |
| 11783 | } |
| 11784 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11785 | if (!names.empty()) { |
| 11786 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11787 | } |
| 11788 | // super must happen after, so that downstream can use maybe_get_output |
| 11789 | // to retrieve the output |
| 11790 | at::meta::structured_ne_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11791 | } |
| 11792 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11793 | return *outputs_[output_idx]; |
| 11794 | } |
| 11795 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 11796 | c10::OptionalDeviceGuard guard_; |
| 11797 | }; |
| 11798 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_ne_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 11799 | structured_ne_Tensor_default_backend_functional op; |
| 11800 | op.meta(self, other); |
| 11801 | at::ne_outf(self, other, *op.outputs_[0]); |
| 11802 | return std::move(op.outputs_[0]).take(); |
| 11803 | } |
| 11804 | struct structured_ne_Tensor_default_backend_inplace final : public at::meta::structured_ne_Tensor { |
| 11805 | structured_ne_Tensor_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 11806 | void set_output_strided( |
| 11807 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11808 | TensorOptions options, DimnameList names |
| 11809 | ) override { |
| 11810 | auto current_device = guard_.current_device(); |
| 11811 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11812 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11813 | "structured kernels don't support multi-device outputs"); |
| 11814 | } else { |
| 11815 | guard_.reset_device(options.device()); |
| 11816 | } |
| 11817 | const auto& out = outputs_[output_idx].get(); |
| 11818 | check_inplace(out, sizes, options); |
| 11819 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 11820 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 11821 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 11822 | } |
| 11823 | if (!names.empty()) { |
| 11824 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11825 | } |
| 11826 | // super must happen after, so that downstream can use maybe_get_output |
| 11827 | // to retrieve the output |
| 11828 | at::meta::structured_ne_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11829 | } |
| 11830 | void set_output_raw_strided( |
| 11831 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11832 | TensorOptions options, DimnameList names |
| 11833 | ) override { |
| 11834 | auto current_device = guard_.current_device(); |
| 11835 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11836 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11837 | "structured kernels don't support multi-device outputs"); |
| 11838 | } else { |
| 11839 | guard_.reset_device(options.device()); |
| 11840 | } |
| 11841 | const auto& out = outputs_[output_idx].get(); |
| 11842 | check_inplace(out, sizes, options); |
| 11843 | if (!names.empty()) { |
| 11844 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11845 | } |
| 11846 | // super must happen after, so that downstream can use maybe_get_output |
| 11847 | // to retrieve the output |
| 11848 | at::meta::structured_ne_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11849 | } |
| 11850 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11851 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 11852 | } |
| 11853 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 11854 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 11855 | c10::OptionalDeviceGuard guard_; |
| 11856 | }; |
| 11857 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_ne__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 11858 | structured_ne_Tensor_default_backend_inplace op(self); |
| 11859 | op.meta(self, other); |
| 11860 | at::ne_outf(self, other, op.outputs_[0]); |
| 11861 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 11862 | return self; |
| 11863 | } |
| 11864 | struct structured_ge_Scalar_default_backend_functional final : public at::meta::structured_ge_Scalar { |
| 11865 | void set_output_strided( |
| 11866 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11867 | TensorOptions options, DimnameList names |
| 11868 | ) override { |
| 11869 | auto current_device = guard_.current_device(); |
| 11870 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11871 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11872 | "structured kernels don't support multi-device outputs"); |
| 11873 | } else { |
| 11874 | guard_.reset_device(options.device()); |
| 11875 | } |
| 11876 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11877 | if (!names.empty()) { |
| 11878 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11879 | } |
| 11880 | // super must happen after, so that downstream can use maybe_get_output |
| 11881 | // to retrieve the output |
| 11882 | at::meta::structured_ge_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11883 | } |
| 11884 | void set_output_raw_strided( |
| 11885 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11886 | TensorOptions options, DimnameList names |
| 11887 | ) override { |
| 11888 | auto current_device = guard_.current_device(); |
| 11889 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11890 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11891 | "structured kernels don't support multi-device outputs"); |
| 11892 | } else { |
| 11893 | guard_.reset_device(options.device()); |
| 11894 | } |
| 11895 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11896 | if (!names.empty()) { |
| 11897 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11898 | } |
| 11899 | // super must happen after, so that downstream can use maybe_get_output |
| 11900 | // to retrieve the output |
| 11901 | at::meta::structured_ge_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11902 | } |
| 11903 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11904 | return *outputs_[output_idx]; |
| 11905 | } |
| 11906 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 11907 | c10::OptionalDeviceGuard guard_; |
| 11908 | }; |
| 11909 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_ge_Scalar(const at::Tensor & self, const at::Scalar & other) { |
| 11910 | structured_ge_Scalar_default_backend_functional op; |
| 11911 | op.meta(self, other); |
| 11912 | at::ge_outf(self, other, *op.outputs_[0]); |
| 11913 | return std::move(op.outputs_[0]).take(); |
| 11914 | } |
| 11915 | struct structured_ge_Scalar_default_backend_inplace final : public at::meta::structured_ge_Scalar { |
| 11916 | structured_ge_Scalar_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 11917 | void set_output_strided( |
| 11918 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11919 | TensorOptions options, DimnameList names |
| 11920 | ) override { |
| 11921 | auto current_device = guard_.current_device(); |
| 11922 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11923 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11924 | "structured kernels don't support multi-device outputs"); |
| 11925 | } else { |
| 11926 | guard_.reset_device(options.device()); |
| 11927 | } |
| 11928 | const auto& out = outputs_[output_idx].get(); |
| 11929 | check_inplace(out, sizes, options); |
| 11930 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 11931 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 11932 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 11933 | } |
| 11934 | if (!names.empty()) { |
| 11935 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11936 | } |
| 11937 | // super must happen after, so that downstream can use maybe_get_output |
| 11938 | // to retrieve the output |
| 11939 | at::meta::structured_ge_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11940 | } |
| 11941 | void set_output_raw_strided( |
| 11942 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11943 | TensorOptions options, DimnameList names |
| 11944 | ) override { |
| 11945 | auto current_device = guard_.current_device(); |
| 11946 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11947 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11948 | "structured kernels don't support multi-device outputs"); |
| 11949 | } else { |
| 11950 | guard_.reset_device(options.device()); |
| 11951 | } |
| 11952 | const auto& out = outputs_[output_idx].get(); |
| 11953 | check_inplace(out, sizes, options); |
| 11954 | if (!names.empty()) { |
| 11955 | namedinference::propagate_names(outputs_[output_idx], names); |
| 11956 | } |
| 11957 | // super must happen after, so that downstream can use maybe_get_output |
| 11958 | // to retrieve the output |
| 11959 | at::meta::structured_ge_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11960 | } |
| 11961 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 11962 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 11963 | } |
| 11964 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 11965 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 11966 | c10::OptionalDeviceGuard guard_; |
| 11967 | }; |
| 11968 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_ge__Scalar(at::Tensor & self, const at::Scalar & other) { |
| 11969 | structured_ge_Scalar_default_backend_inplace op(self); |
| 11970 | op.meta(self, other); |
| 11971 | at::ge_outf(self, other, op.outputs_[0]); |
| 11972 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 11973 | return self; |
| 11974 | } |
| 11975 | struct structured_ge_Tensor_default_backend_functional final : public at::meta::structured_ge_Tensor { |
| 11976 | void set_output_strided( |
| 11977 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11978 | TensorOptions options, DimnameList names |
| 11979 | ) override { |
| 11980 | auto current_device = guard_.current_device(); |
| 11981 | if (C10_UNLIKELY(current_device.has_value())) { |
| 11982 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 11983 | "structured kernels don't support multi-device outputs"); |
| 11984 | } else { |
| 11985 | guard_.reset_device(options.device()); |
| 11986 | } |
| 11987 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 11988 | if (!names.empty()) { |
| 11989 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 11990 | } |
| 11991 | // super must happen after, so that downstream can use maybe_get_output |
| 11992 | // to retrieve the output |
| 11993 | at::meta::structured_ge_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 11994 | } |
| 11995 | void set_output_raw_strided( |
| 11996 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 11997 | TensorOptions options, DimnameList names |
| 11998 | ) override { |
| 11999 | auto current_device = guard_.current_device(); |
| 12000 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12001 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12002 | "structured kernels don't support multi-device outputs"); |
| 12003 | } else { |
| 12004 | guard_.reset_device(options.device()); |
| 12005 | } |
| 12006 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12007 | if (!names.empty()) { |
| 12008 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12009 | } |
| 12010 | // super must happen after, so that downstream can use maybe_get_output |
| 12011 | // to retrieve the output |
| 12012 | at::meta::structured_ge_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12013 | } |
| 12014 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12015 | return *outputs_[output_idx]; |
| 12016 | } |
| 12017 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 12018 | c10::OptionalDeviceGuard guard_; |
| 12019 | }; |
| 12020 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_ge_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 12021 | structured_ge_Tensor_default_backend_functional op; |
| 12022 | op.meta(self, other); |
| 12023 | at::ge_outf(self, other, *op.outputs_[0]); |
| 12024 | return std::move(op.outputs_[0]).take(); |
| 12025 | } |
| 12026 | struct structured_ge_Tensor_default_backend_inplace final : public at::meta::structured_ge_Tensor { |
| 12027 | structured_ge_Tensor_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 12028 | void set_output_strided( |
| 12029 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12030 | TensorOptions options, DimnameList names |
| 12031 | ) override { |
| 12032 | auto current_device = guard_.current_device(); |
| 12033 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12034 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12035 | "structured kernels don't support multi-device outputs"); |
| 12036 | } else { |
| 12037 | guard_.reset_device(options.device()); |
| 12038 | } |
| 12039 | const auto& out = outputs_[output_idx].get(); |
| 12040 | check_inplace(out, sizes, options); |
| 12041 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 12042 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 12043 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 12044 | } |
| 12045 | if (!names.empty()) { |
| 12046 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12047 | } |
| 12048 | // super must happen after, so that downstream can use maybe_get_output |
| 12049 | // to retrieve the output |
| 12050 | at::meta::structured_ge_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12051 | } |
| 12052 | void set_output_raw_strided( |
| 12053 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12054 | TensorOptions options, DimnameList names |
| 12055 | ) override { |
| 12056 | auto current_device = guard_.current_device(); |
| 12057 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12058 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12059 | "structured kernels don't support multi-device outputs"); |
| 12060 | } else { |
| 12061 | guard_.reset_device(options.device()); |
| 12062 | } |
| 12063 | const auto& out = outputs_[output_idx].get(); |
| 12064 | check_inplace(out, sizes, options); |
| 12065 | if (!names.empty()) { |
| 12066 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12067 | } |
| 12068 | // super must happen after, so that downstream can use maybe_get_output |
| 12069 | // to retrieve the output |
| 12070 | at::meta::structured_ge_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12071 | } |
| 12072 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12073 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 12074 | } |
| 12075 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 12076 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 12077 | c10::OptionalDeviceGuard guard_; |
| 12078 | }; |
| 12079 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_ge__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 12080 | structured_ge_Tensor_default_backend_inplace op(self); |
| 12081 | op.meta(self, other); |
| 12082 | at::ge_outf(self, other, op.outputs_[0]); |
| 12083 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 12084 | return self; |
| 12085 | } |
| 12086 | struct structured_le_Scalar_default_backend_functional final : public at::meta::structured_le_Scalar { |
| 12087 | void set_output_strided( |
| 12088 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12089 | TensorOptions options, DimnameList names |
| 12090 | ) override { |
| 12091 | auto current_device = guard_.current_device(); |
| 12092 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12093 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12094 | "structured kernels don't support multi-device outputs"); |
| 12095 | } else { |
| 12096 | guard_.reset_device(options.device()); |
| 12097 | } |
| 12098 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12099 | if (!names.empty()) { |
| 12100 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12101 | } |
| 12102 | // super must happen after, so that downstream can use maybe_get_output |
| 12103 | // to retrieve the output |
| 12104 | at::meta::structured_le_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12105 | } |
| 12106 | void set_output_raw_strided( |
| 12107 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12108 | TensorOptions options, DimnameList names |
| 12109 | ) override { |
| 12110 | auto current_device = guard_.current_device(); |
| 12111 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12112 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12113 | "structured kernels don't support multi-device outputs"); |
| 12114 | } else { |
| 12115 | guard_.reset_device(options.device()); |
| 12116 | } |
| 12117 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12118 | if (!names.empty()) { |
| 12119 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12120 | } |
| 12121 | // super must happen after, so that downstream can use maybe_get_output |
| 12122 | // to retrieve the output |
| 12123 | at::meta::structured_le_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12124 | } |
| 12125 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12126 | return *outputs_[output_idx]; |
| 12127 | } |
| 12128 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 12129 | c10::OptionalDeviceGuard guard_; |
| 12130 | }; |
| 12131 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_le_Scalar(const at::Tensor & self, const at::Scalar & other) { |
| 12132 | structured_le_Scalar_default_backend_functional op; |
| 12133 | op.meta(self, other); |
| 12134 | at::le_outf(self, other, *op.outputs_[0]); |
| 12135 | return std::move(op.outputs_[0]).take(); |
| 12136 | } |
| 12137 | struct structured_le_Scalar_default_backend_inplace final : public at::meta::structured_le_Scalar { |
| 12138 | structured_le_Scalar_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 12139 | void set_output_strided( |
| 12140 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12141 | TensorOptions options, DimnameList names |
| 12142 | ) override { |
| 12143 | auto current_device = guard_.current_device(); |
| 12144 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12145 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12146 | "structured kernels don't support multi-device outputs"); |
| 12147 | } else { |
| 12148 | guard_.reset_device(options.device()); |
| 12149 | } |
| 12150 | const auto& out = outputs_[output_idx].get(); |
| 12151 | check_inplace(out, sizes, options); |
| 12152 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 12153 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 12154 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 12155 | } |
| 12156 | if (!names.empty()) { |
| 12157 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12158 | } |
| 12159 | // super must happen after, so that downstream can use maybe_get_output |
| 12160 | // to retrieve the output |
| 12161 | at::meta::structured_le_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12162 | } |
| 12163 | void set_output_raw_strided( |
| 12164 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12165 | TensorOptions options, DimnameList names |
| 12166 | ) override { |
| 12167 | auto current_device = guard_.current_device(); |
| 12168 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12169 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12170 | "structured kernels don't support multi-device outputs"); |
| 12171 | } else { |
| 12172 | guard_.reset_device(options.device()); |
| 12173 | } |
| 12174 | const auto& out = outputs_[output_idx].get(); |
| 12175 | check_inplace(out, sizes, options); |
| 12176 | if (!names.empty()) { |
| 12177 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12178 | } |
| 12179 | // super must happen after, so that downstream can use maybe_get_output |
| 12180 | // to retrieve the output |
| 12181 | at::meta::structured_le_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12182 | } |
| 12183 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12184 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 12185 | } |
| 12186 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 12187 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 12188 | c10::OptionalDeviceGuard guard_; |
| 12189 | }; |
| 12190 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_le__Scalar(at::Tensor & self, const at::Scalar & other) { |
| 12191 | structured_le_Scalar_default_backend_inplace op(self); |
| 12192 | op.meta(self, other); |
| 12193 | at::le_outf(self, other, op.outputs_[0]); |
| 12194 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 12195 | return self; |
| 12196 | } |
| 12197 | struct structured_le_Tensor_default_backend_functional final : public at::meta::structured_le_Tensor { |
| 12198 | void set_output_strided( |
| 12199 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12200 | TensorOptions options, DimnameList names |
| 12201 | ) override { |
| 12202 | auto current_device = guard_.current_device(); |
| 12203 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12204 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12205 | "structured kernels don't support multi-device outputs"); |
| 12206 | } else { |
| 12207 | guard_.reset_device(options.device()); |
| 12208 | } |
| 12209 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12210 | if (!names.empty()) { |
| 12211 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12212 | } |
| 12213 | // super must happen after, so that downstream can use maybe_get_output |
| 12214 | // to retrieve the output |
| 12215 | at::meta::structured_le_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12216 | } |
| 12217 | void set_output_raw_strided( |
| 12218 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12219 | TensorOptions options, DimnameList names |
| 12220 | ) override { |
| 12221 | auto current_device = guard_.current_device(); |
| 12222 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12223 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12224 | "structured kernels don't support multi-device outputs"); |
| 12225 | } else { |
| 12226 | guard_.reset_device(options.device()); |
| 12227 | } |
| 12228 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12229 | if (!names.empty()) { |
| 12230 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12231 | } |
| 12232 | // super must happen after, so that downstream can use maybe_get_output |
| 12233 | // to retrieve the output |
| 12234 | at::meta::structured_le_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12235 | } |
| 12236 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12237 | return *outputs_[output_idx]; |
| 12238 | } |
| 12239 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 12240 | c10::OptionalDeviceGuard guard_; |
| 12241 | }; |
| 12242 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_le_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 12243 | structured_le_Tensor_default_backend_functional op; |
| 12244 | op.meta(self, other); |
| 12245 | at::le_outf(self, other, *op.outputs_[0]); |
| 12246 | return std::move(op.outputs_[0]).take(); |
| 12247 | } |
| 12248 | struct structured_le_Tensor_default_backend_inplace final : public at::meta::structured_le_Tensor { |
| 12249 | structured_le_Tensor_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 12250 | void set_output_strided( |
| 12251 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12252 | TensorOptions options, DimnameList names |
| 12253 | ) override { |
| 12254 | auto current_device = guard_.current_device(); |
| 12255 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12256 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12257 | "structured kernels don't support multi-device outputs"); |
| 12258 | } else { |
| 12259 | guard_.reset_device(options.device()); |
| 12260 | } |
| 12261 | const auto& out = outputs_[output_idx].get(); |
| 12262 | check_inplace(out, sizes, options); |
| 12263 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 12264 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 12265 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 12266 | } |
| 12267 | if (!names.empty()) { |
| 12268 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12269 | } |
| 12270 | // super must happen after, so that downstream can use maybe_get_output |
| 12271 | // to retrieve the output |
| 12272 | at::meta::structured_le_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12273 | } |
| 12274 | void set_output_raw_strided( |
| 12275 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12276 | TensorOptions options, DimnameList names |
| 12277 | ) override { |
| 12278 | auto current_device = guard_.current_device(); |
| 12279 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12280 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12281 | "structured kernels don't support multi-device outputs"); |
| 12282 | } else { |
| 12283 | guard_.reset_device(options.device()); |
| 12284 | } |
| 12285 | const auto& out = outputs_[output_idx].get(); |
| 12286 | check_inplace(out, sizes, options); |
| 12287 | if (!names.empty()) { |
| 12288 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12289 | } |
| 12290 | // super must happen after, so that downstream can use maybe_get_output |
| 12291 | // to retrieve the output |
| 12292 | at::meta::structured_le_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12293 | } |
| 12294 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12295 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 12296 | } |
| 12297 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 12298 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 12299 | c10::OptionalDeviceGuard guard_; |
| 12300 | }; |
| 12301 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_le__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 12302 | structured_le_Tensor_default_backend_inplace op(self); |
| 12303 | op.meta(self, other); |
| 12304 | at::le_outf(self, other, op.outputs_[0]); |
| 12305 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 12306 | return self; |
| 12307 | } |
| 12308 | struct structured_gt_Scalar_default_backend_functional final : public at::meta::structured_gt_Scalar { |
| 12309 | void set_output_strided( |
| 12310 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12311 | TensorOptions options, DimnameList names |
| 12312 | ) override { |
| 12313 | auto current_device = guard_.current_device(); |
| 12314 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12315 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12316 | "structured kernels don't support multi-device outputs"); |
| 12317 | } else { |
| 12318 | guard_.reset_device(options.device()); |
| 12319 | } |
| 12320 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12321 | if (!names.empty()) { |
| 12322 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12323 | } |
| 12324 | // super must happen after, so that downstream can use maybe_get_output |
| 12325 | // to retrieve the output |
| 12326 | at::meta::structured_gt_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12327 | } |
| 12328 | void set_output_raw_strided( |
| 12329 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12330 | TensorOptions options, DimnameList names |
| 12331 | ) override { |
| 12332 | auto current_device = guard_.current_device(); |
| 12333 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12334 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12335 | "structured kernels don't support multi-device outputs"); |
| 12336 | } else { |
| 12337 | guard_.reset_device(options.device()); |
| 12338 | } |
| 12339 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12340 | if (!names.empty()) { |
| 12341 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12342 | } |
| 12343 | // super must happen after, so that downstream can use maybe_get_output |
| 12344 | // to retrieve the output |
| 12345 | at::meta::structured_gt_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12346 | } |
| 12347 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12348 | return *outputs_[output_idx]; |
| 12349 | } |
| 12350 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 12351 | c10::OptionalDeviceGuard guard_; |
| 12352 | }; |
| 12353 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_gt_Scalar(const at::Tensor & self, const at::Scalar & other) { |
| 12354 | structured_gt_Scalar_default_backend_functional op; |
| 12355 | op.meta(self, other); |
| 12356 | at::gt_outf(self, other, *op.outputs_[0]); |
| 12357 | return std::move(op.outputs_[0]).take(); |
| 12358 | } |
| 12359 | struct structured_gt_Scalar_default_backend_inplace final : public at::meta::structured_gt_Scalar { |
| 12360 | structured_gt_Scalar_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 12361 | void set_output_strided( |
| 12362 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12363 | TensorOptions options, DimnameList names |
| 12364 | ) override { |
| 12365 | auto current_device = guard_.current_device(); |
| 12366 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12367 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12368 | "structured kernels don't support multi-device outputs"); |
| 12369 | } else { |
| 12370 | guard_.reset_device(options.device()); |
| 12371 | } |
| 12372 | const auto& out = outputs_[output_idx].get(); |
| 12373 | check_inplace(out, sizes, options); |
| 12374 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 12375 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 12376 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 12377 | } |
| 12378 | if (!names.empty()) { |
| 12379 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12380 | } |
| 12381 | // super must happen after, so that downstream can use maybe_get_output |
| 12382 | // to retrieve the output |
| 12383 | at::meta::structured_gt_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12384 | } |
| 12385 | void set_output_raw_strided( |
| 12386 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12387 | TensorOptions options, DimnameList names |
| 12388 | ) override { |
| 12389 | auto current_device = guard_.current_device(); |
| 12390 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12391 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12392 | "structured kernels don't support multi-device outputs"); |
| 12393 | } else { |
| 12394 | guard_.reset_device(options.device()); |
| 12395 | } |
| 12396 | const auto& out = outputs_[output_idx].get(); |
| 12397 | check_inplace(out, sizes, options); |
| 12398 | if (!names.empty()) { |
| 12399 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12400 | } |
| 12401 | // super must happen after, so that downstream can use maybe_get_output |
| 12402 | // to retrieve the output |
| 12403 | at::meta::structured_gt_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12404 | } |
| 12405 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12406 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 12407 | } |
| 12408 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 12409 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 12410 | c10::OptionalDeviceGuard guard_; |
| 12411 | }; |
| 12412 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_gt__Scalar(at::Tensor & self, const at::Scalar & other) { |
| 12413 | structured_gt_Scalar_default_backend_inplace op(self); |
| 12414 | op.meta(self, other); |
| 12415 | at::gt_outf(self, other, op.outputs_[0]); |
| 12416 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 12417 | return self; |
| 12418 | } |
| 12419 | struct structured_gt_Tensor_default_backend_functional final : public at::meta::structured_gt_Tensor { |
| 12420 | void set_output_strided( |
| 12421 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12422 | TensorOptions options, DimnameList names |
| 12423 | ) override { |
| 12424 | auto current_device = guard_.current_device(); |
| 12425 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12426 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12427 | "structured kernels don't support multi-device outputs"); |
| 12428 | } else { |
| 12429 | guard_.reset_device(options.device()); |
| 12430 | } |
| 12431 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12432 | if (!names.empty()) { |
| 12433 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12434 | } |
| 12435 | // super must happen after, so that downstream can use maybe_get_output |
| 12436 | // to retrieve the output |
| 12437 | at::meta::structured_gt_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12438 | } |
| 12439 | void set_output_raw_strided( |
| 12440 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12441 | TensorOptions options, DimnameList names |
| 12442 | ) override { |
| 12443 | auto current_device = guard_.current_device(); |
| 12444 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12445 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12446 | "structured kernels don't support multi-device outputs"); |
| 12447 | } else { |
| 12448 | guard_.reset_device(options.device()); |
| 12449 | } |
| 12450 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12451 | if (!names.empty()) { |
| 12452 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12453 | } |
| 12454 | // super must happen after, so that downstream can use maybe_get_output |
| 12455 | // to retrieve the output |
| 12456 | at::meta::structured_gt_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12457 | } |
| 12458 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12459 | return *outputs_[output_idx]; |
| 12460 | } |
| 12461 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 12462 | c10::OptionalDeviceGuard guard_; |
| 12463 | }; |
| 12464 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_gt_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 12465 | structured_gt_Tensor_default_backend_functional op; |
| 12466 | op.meta(self, other); |
| 12467 | at::gt_outf(self, other, *op.outputs_[0]); |
| 12468 | return std::move(op.outputs_[0]).take(); |
| 12469 | } |
| 12470 | struct structured_gt_Tensor_default_backend_inplace final : public at::meta::structured_gt_Tensor { |
| 12471 | structured_gt_Tensor_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 12472 | void set_output_strided( |
| 12473 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12474 | TensorOptions options, DimnameList names |
| 12475 | ) override { |
| 12476 | auto current_device = guard_.current_device(); |
| 12477 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12478 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12479 | "structured kernels don't support multi-device outputs"); |
| 12480 | } else { |
| 12481 | guard_.reset_device(options.device()); |
| 12482 | } |
| 12483 | const auto& out = outputs_[output_idx].get(); |
| 12484 | check_inplace(out, sizes, options); |
| 12485 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 12486 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 12487 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 12488 | } |
| 12489 | if (!names.empty()) { |
| 12490 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12491 | } |
| 12492 | // super must happen after, so that downstream can use maybe_get_output |
| 12493 | // to retrieve the output |
| 12494 | at::meta::structured_gt_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12495 | } |
| 12496 | void set_output_raw_strided( |
| 12497 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12498 | TensorOptions options, DimnameList names |
| 12499 | ) override { |
| 12500 | auto current_device = guard_.current_device(); |
| 12501 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12502 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12503 | "structured kernels don't support multi-device outputs"); |
| 12504 | } else { |
| 12505 | guard_.reset_device(options.device()); |
| 12506 | } |
| 12507 | const auto& out = outputs_[output_idx].get(); |
| 12508 | check_inplace(out, sizes, options); |
| 12509 | if (!names.empty()) { |
| 12510 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12511 | } |
| 12512 | // super must happen after, so that downstream can use maybe_get_output |
| 12513 | // to retrieve the output |
| 12514 | at::meta::structured_gt_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12515 | } |
| 12516 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12517 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 12518 | } |
| 12519 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 12520 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 12521 | c10::OptionalDeviceGuard guard_; |
| 12522 | }; |
| 12523 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_gt__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 12524 | structured_gt_Tensor_default_backend_inplace op(self); |
| 12525 | op.meta(self, other); |
| 12526 | at::gt_outf(self, other, op.outputs_[0]); |
| 12527 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 12528 | return self; |
| 12529 | } |
| 12530 | struct structured_lt_Scalar_default_backend_functional final : public at::meta::structured_lt_Scalar { |
| 12531 | void set_output_strided( |
| 12532 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12533 | TensorOptions options, DimnameList names |
| 12534 | ) override { |
| 12535 | auto current_device = guard_.current_device(); |
| 12536 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12537 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12538 | "structured kernels don't support multi-device outputs"); |
| 12539 | } else { |
| 12540 | guard_.reset_device(options.device()); |
| 12541 | } |
| 12542 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12543 | if (!names.empty()) { |
| 12544 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12545 | } |
| 12546 | // super must happen after, so that downstream can use maybe_get_output |
| 12547 | // to retrieve the output |
| 12548 | at::meta::structured_lt_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12549 | } |
| 12550 | void set_output_raw_strided( |
| 12551 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12552 | TensorOptions options, DimnameList names |
| 12553 | ) override { |
| 12554 | auto current_device = guard_.current_device(); |
| 12555 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12556 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12557 | "structured kernels don't support multi-device outputs"); |
| 12558 | } else { |
| 12559 | guard_.reset_device(options.device()); |
| 12560 | } |
| 12561 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12562 | if (!names.empty()) { |
| 12563 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12564 | } |
| 12565 | // super must happen after, so that downstream can use maybe_get_output |
| 12566 | // to retrieve the output |
| 12567 | at::meta::structured_lt_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12568 | } |
| 12569 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12570 | return *outputs_[output_idx]; |
| 12571 | } |
| 12572 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 12573 | c10::OptionalDeviceGuard guard_; |
| 12574 | }; |
| 12575 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_lt_Scalar(const at::Tensor & self, const at::Scalar & other) { |
| 12576 | structured_lt_Scalar_default_backend_functional op; |
| 12577 | op.meta(self, other); |
| 12578 | at::lt_outf(self, other, *op.outputs_[0]); |
| 12579 | return std::move(op.outputs_[0]).take(); |
| 12580 | } |
| 12581 | struct structured_lt_Scalar_default_backend_inplace final : public at::meta::structured_lt_Scalar { |
| 12582 | structured_lt_Scalar_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 12583 | void set_output_strided( |
| 12584 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12585 | TensorOptions options, DimnameList names |
| 12586 | ) override { |
| 12587 | auto current_device = guard_.current_device(); |
| 12588 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12589 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12590 | "structured kernels don't support multi-device outputs"); |
| 12591 | } else { |
| 12592 | guard_.reset_device(options.device()); |
| 12593 | } |
| 12594 | const auto& out = outputs_[output_idx].get(); |
| 12595 | check_inplace(out, sizes, options); |
| 12596 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 12597 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 12598 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 12599 | } |
| 12600 | if (!names.empty()) { |
| 12601 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12602 | } |
| 12603 | // super must happen after, so that downstream can use maybe_get_output |
| 12604 | // to retrieve the output |
| 12605 | at::meta::structured_lt_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12606 | } |
| 12607 | void set_output_raw_strided( |
| 12608 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12609 | TensorOptions options, DimnameList names |
| 12610 | ) override { |
| 12611 | auto current_device = guard_.current_device(); |
| 12612 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12613 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12614 | "structured kernels don't support multi-device outputs"); |
| 12615 | } else { |
| 12616 | guard_.reset_device(options.device()); |
| 12617 | } |
| 12618 | const auto& out = outputs_[output_idx].get(); |
| 12619 | check_inplace(out, sizes, options); |
| 12620 | if (!names.empty()) { |
| 12621 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12622 | } |
| 12623 | // super must happen after, so that downstream can use maybe_get_output |
| 12624 | // to retrieve the output |
| 12625 | at::meta::structured_lt_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12626 | } |
| 12627 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12628 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 12629 | } |
| 12630 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 12631 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 12632 | c10::OptionalDeviceGuard guard_; |
| 12633 | }; |
| 12634 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_lt__Scalar(at::Tensor & self, const at::Scalar & other) { |
| 12635 | structured_lt_Scalar_default_backend_inplace op(self); |
| 12636 | op.meta(self, other); |
| 12637 | at::lt_outf(self, other, op.outputs_[0]); |
| 12638 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 12639 | return self; |
| 12640 | } |
| 12641 | struct structured_lt_Tensor_default_backend_functional final : public at::meta::structured_lt_Tensor { |
| 12642 | void set_output_strided( |
| 12643 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12644 | TensorOptions options, DimnameList names |
| 12645 | ) override { |
| 12646 | auto current_device = guard_.current_device(); |
| 12647 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12648 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12649 | "structured kernels don't support multi-device outputs"); |
| 12650 | } else { |
| 12651 | guard_.reset_device(options.device()); |
| 12652 | } |
| 12653 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12654 | if (!names.empty()) { |
| 12655 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12656 | } |
| 12657 | // super must happen after, so that downstream can use maybe_get_output |
| 12658 | // to retrieve the output |
| 12659 | at::meta::structured_lt_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12660 | } |
| 12661 | void set_output_raw_strided( |
| 12662 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12663 | TensorOptions options, DimnameList names |
| 12664 | ) override { |
| 12665 | auto current_device = guard_.current_device(); |
| 12666 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12667 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12668 | "structured kernels don't support multi-device outputs"); |
| 12669 | } else { |
| 12670 | guard_.reset_device(options.device()); |
| 12671 | } |
| 12672 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12673 | if (!names.empty()) { |
| 12674 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12675 | } |
| 12676 | // super must happen after, so that downstream can use maybe_get_output |
| 12677 | // to retrieve the output |
| 12678 | at::meta::structured_lt_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12679 | } |
| 12680 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12681 | return *outputs_[output_idx]; |
| 12682 | } |
| 12683 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 12684 | c10::OptionalDeviceGuard guard_; |
| 12685 | }; |
| 12686 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_lt_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 12687 | structured_lt_Tensor_default_backend_functional op; |
| 12688 | op.meta(self, other); |
| 12689 | at::lt_outf(self, other, *op.outputs_[0]); |
| 12690 | return std::move(op.outputs_[0]).take(); |
| 12691 | } |
| 12692 | struct structured_lt_Tensor_default_backend_inplace final : public at::meta::structured_lt_Tensor { |
| 12693 | structured_lt_Tensor_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 12694 | void set_output_strided( |
| 12695 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12696 | TensorOptions options, DimnameList names |
| 12697 | ) override { |
| 12698 | auto current_device = guard_.current_device(); |
| 12699 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12700 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12701 | "structured kernels don't support multi-device outputs"); |
| 12702 | } else { |
| 12703 | guard_.reset_device(options.device()); |
| 12704 | } |
| 12705 | const auto& out = outputs_[output_idx].get(); |
| 12706 | check_inplace(out, sizes, options); |
| 12707 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 12708 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 12709 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 12710 | } |
| 12711 | if (!names.empty()) { |
| 12712 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12713 | } |
| 12714 | // super must happen after, so that downstream can use maybe_get_output |
| 12715 | // to retrieve the output |
| 12716 | at::meta::structured_lt_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12717 | } |
| 12718 | void set_output_raw_strided( |
| 12719 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12720 | TensorOptions options, DimnameList names |
| 12721 | ) override { |
| 12722 | auto current_device = guard_.current_device(); |
| 12723 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12724 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12725 | "structured kernels don't support multi-device outputs"); |
| 12726 | } else { |
| 12727 | guard_.reset_device(options.device()); |
| 12728 | } |
| 12729 | const auto& out = outputs_[output_idx].get(); |
| 12730 | check_inplace(out, sizes, options); |
| 12731 | if (!names.empty()) { |
| 12732 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12733 | } |
| 12734 | // super must happen after, so that downstream can use maybe_get_output |
| 12735 | // to retrieve the output |
| 12736 | at::meta::structured_lt_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12737 | } |
| 12738 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12739 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 12740 | } |
| 12741 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 12742 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 12743 | c10::OptionalDeviceGuard guard_; |
| 12744 | }; |
| 12745 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_lt__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 12746 | structured_lt_Tensor_default_backend_inplace op(self); |
| 12747 | op.meta(self, other); |
| 12748 | at::lt_outf(self, other, op.outputs_[0]); |
| 12749 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 12750 | return self; |
| 12751 | } |
| 12752 | struct structured_gather_default_backend_functional final : public at::meta::structured_gather { |
| 12753 | void set_output_strided( |
| 12754 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12755 | TensorOptions options, DimnameList names |
| 12756 | ) override { |
| 12757 | auto current_device = guard_.current_device(); |
| 12758 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12759 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12760 | "structured kernels don't support multi-device outputs"); |
| 12761 | } else { |
| 12762 | guard_.reset_device(options.device()); |
| 12763 | } |
| 12764 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12765 | if (!names.empty()) { |
| 12766 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12767 | } |
| 12768 | // super must happen after, so that downstream can use maybe_get_output |
| 12769 | // to retrieve the output |
| 12770 | } |
| 12771 | void set_output_raw_strided( |
| 12772 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12773 | TensorOptions options, DimnameList names |
| 12774 | ) override { |
| 12775 | auto current_device = guard_.current_device(); |
| 12776 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12777 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12778 | "structured kernels don't support multi-device outputs"); |
| 12779 | } else { |
| 12780 | guard_.reset_device(options.device()); |
| 12781 | } |
| 12782 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12783 | if (!names.empty()) { |
| 12784 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12785 | } |
| 12786 | // super must happen after, so that downstream can use maybe_get_output |
| 12787 | // to retrieve the output |
| 12788 | } |
| 12789 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12790 | return *outputs_[output_idx]; |
| 12791 | } |
| 12792 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 12793 | c10::OptionalDeviceGuard guard_; |
| 12794 | }; |
| 12795 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_gather(const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad) { |
| 12796 | structured_gather_default_backend_functional op; |
| 12797 | op.meta(self, dim, index, sparse_grad); |
| 12798 | at::gather_outf(self, dim, index, sparse_grad, *op.outputs_[0]); |
| 12799 | return std::move(op.outputs_[0]).take(); |
| 12800 | } |
| 12801 | struct structured_addcmul_default_backend_functional final : public at::meta::structured_addcmul { |
| 12802 | void set_output_strided( |
| 12803 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12804 | TensorOptions options, DimnameList names |
| 12805 | ) override { |
| 12806 | auto current_device = guard_.current_device(); |
| 12807 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12808 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12809 | "structured kernels don't support multi-device outputs"); |
| 12810 | } else { |
| 12811 | guard_.reset_device(options.device()); |
| 12812 | } |
| 12813 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12814 | if (!names.empty()) { |
| 12815 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12816 | } |
| 12817 | // super must happen after, so that downstream can use maybe_get_output |
| 12818 | // to retrieve the output |
| 12819 | at::meta::structured_addcmul::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12820 | } |
| 12821 | void set_output_raw_strided( |
| 12822 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12823 | TensorOptions options, DimnameList names |
| 12824 | ) override { |
| 12825 | auto current_device = guard_.current_device(); |
| 12826 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12827 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12828 | "structured kernels don't support multi-device outputs"); |
| 12829 | } else { |
| 12830 | guard_.reset_device(options.device()); |
| 12831 | } |
| 12832 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12833 | if (!names.empty()) { |
| 12834 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12835 | } |
| 12836 | // super must happen after, so that downstream can use maybe_get_output |
| 12837 | // to retrieve the output |
| 12838 | at::meta::structured_addcmul::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12839 | } |
| 12840 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12841 | return *outputs_[output_idx]; |
| 12842 | } |
| 12843 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 12844 | c10::OptionalDeviceGuard guard_; |
| 12845 | }; |
| 12846 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_addcmul(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value) { |
| 12847 | structured_addcmul_default_backend_functional op; |
| 12848 | op.meta(self, tensor1, tensor2, value); |
| 12849 | at::addcmul_outf(self, tensor1, tensor2, value, *op.outputs_[0]); |
| 12850 | return std::move(op.outputs_[0]).take(); |
| 12851 | } |
| 12852 | struct structured_addcmul_default_backend_inplace final : public at::meta::structured_addcmul { |
| 12853 | structured_addcmul_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 12854 | void set_output_strided( |
| 12855 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12856 | TensorOptions options, DimnameList names |
| 12857 | ) override { |
| 12858 | auto current_device = guard_.current_device(); |
| 12859 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12860 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12861 | "structured kernels don't support multi-device outputs"); |
| 12862 | } else { |
| 12863 | guard_.reset_device(options.device()); |
| 12864 | } |
| 12865 | const auto& out = outputs_[output_idx].get(); |
| 12866 | check_inplace(out, sizes, options); |
| 12867 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 12868 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 12869 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 12870 | } |
| 12871 | if (!names.empty()) { |
| 12872 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12873 | } |
| 12874 | // super must happen after, so that downstream can use maybe_get_output |
| 12875 | // to retrieve the output |
| 12876 | at::meta::structured_addcmul::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12877 | } |
| 12878 | void set_output_raw_strided( |
| 12879 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12880 | TensorOptions options, DimnameList names |
| 12881 | ) override { |
| 12882 | auto current_device = guard_.current_device(); |
| 12883 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12884 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12885 | "structured kernels don't support multi-device outputs"); |
| 12886 | } else { |
| 12887 | guard_.reset_device(options.device()); |
| 12888 | } |
| 12889 | const auto& out = outputs_[output_idx].get(); |
| 12890 | check_inplace(out, sizes, options); |
| 12891 | if (!names.empty()) { |
| 12892 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12893 | } |
| 12894 | // super must happen after, so that downstream can use maybe_get_output |
| 12895 | // to retrieve the output |
| 12896 | at::meta::structured_addcmul::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12897 | } |
| 12898 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12899 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 12900 | } |
| 12901 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 12902 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 12903 | c10::OptionalDeviceGuard guard_; |
| 12904 | }; |
| 12905 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_addcmul_(at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value) { |
| 12906 | structured_addcmul_default_backend_inplace op(self); |
| 12907 | op.meta(self, tensor1, tensor2, value); |
| 12908 | at::addcmul_outf(self, tensor1, tensor2, value, op.outputs_[0]); |
| 12909 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 12910 | return self; |
| 12911 | } |
| 12912 | struct structured_addcdiv_default_backend_functional final : public at::meta::structured_addcdiv { |
| 12913 | void set_output_strided( |
| 12914 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12915 | TensorOptions options, DimnameList names |
| 12916 | ) override { |
| 12917 | auto current_device = guard_.current_device(); |
| 12918 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12919 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12920 | "structured kernels don't support multi-device outputs"); |
| 12921 | } else { |
| 12922 | guard_.reset_device(options.device()); |
| 12923 | } |
| 12924 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12925 | if (!names.empty()) { |
| 12926 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12927 | } |
| 12928 | // super must happen after, so that downstream can use maybe_get_output |
| 12929 | // to retrieve the output |
| 12930 | at::meta::structured_addcdiv::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12931 | } |
| 12932 | void set_output_raw_strided( |
| 12933 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12934 | TensorOptions options, DimnameList names |
| 12935 | ) override { |
| 12936 | auto current_device = guard_.current_device(); |
| 12937 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12938 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12939 | "structured kernels don't support multi-device outputs"); |
| 12940 | } else { |
| 12941 | guard_.reset_device(options.device()); |
| 12942 | } |
| 12943 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 12944 | if (!names.empty()) { |
| 12945 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 12946 | } |
| 12947 | // super must happen after, so that downstream can use maybe_get_output |
| 12948 | // to retrieve the output |
| 12949 | at::meta::structured_addcdiv::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12950 | } |
| 12951 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 12952 | return *outputs_[output_idx]; |
| 12953 | } |
| 12954 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 12955 | c10::OptionalDeviceGuard guard_; |
| 12956 | }; |
| 12957 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_addcdiv(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value) { |
| 12958 | structured_addcdiv_default_backend_functional op; |
| 12959 | op.meta(self, tensor1, tensor2, value); |
| 12960 | at::addcdiv_outf(self, tensor1, tensor2, value, *op.outputs_[0]); |
| 12961 | return std::move(op.outputs_[0]).take(); |
| 12962 | } |
| 12963 | struct structured_addcdiv_default_backend_inplace final : public at::meta::structured_addcdiv { |
| 12964 | structured_addcdiv_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 12965 | void set_output_strided( |
| 12966 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12967 | TensorOptions options, DimnameList names |
| 12968 | ) override { |
| 12969 | auto current_device = guard_.current_device(); |
| 12970 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12971 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12972 | "structured kernels don't support multi-device outputs"); |
| 12973 | } else { |
| 12974 | guard_.reset_device(options.device()); |
| 12975 | } |
| 12976 | const auto& out = outputs_[output_idx].get(); |
| 12977 | check_inplace(out, sizes, options); |
| 12978 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 12979 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 12980 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 12981 | } |
| 12982 | if (!names.empty()) { |
| 12983 | namedinference::propagate_names(outputs_[output_idx], names); |
| 12984 | } |
| 12985 | // super must happen after, so that downstream can use maybe_get_output |
| 12986 | // to retrieve the output |
| 12987 | at::meta::structured_addcdiv::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 12988 | } |
| 12989 | void set_output_raw_strided( |
| 12990 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 12991 | TensorOptions options, DimnameList names |
| 12992 | ) override { |
| 12993 | auto current_device = guard_.current_device(); |
| 12994 | if (C10_UNLIKELY(current_device.has_value())) { |
| 12995 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 12996 | "structured kernels don't support multi-device outputs"); |
| 12997 | } else { |
| 12998 | guard_.reset_device(options.device()); |
| 12999 | } |
| 13000 | const auto& out = outputs_[output_idx].get(); |
| 13001 | check_inplace(out, sizes, options); |
| 13002 | if (!names.empty()) { |
| 13003 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13004 | } |
| 13005 | // super must happen after, so that downstream can use maybe_get_output |
| 13006 | // to retrieve the output |
| 13007 | at::meta::structured_addcdiv::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13008 | } |
| 13009 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13010 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 13011 | } |
| 13012 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 13013 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 13014 | c10::OptionalDeviceGuard guard_; |
| 13015 | }; |
| 13016 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_addcdiv_(at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value) { |
| 13017 | structured_addcdiv_default_backend_inplace op(self); |
| 13018 | op.meta(self, tensor1, tensor2, value); |
| 13019 | at::addcdiv_outf(self, tensor1, tensor2, value, op.outputs_[0]); |
| 13020 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 13021 | return self; |
| 13022 | } |
| 13023 | struct structured_triangular_solve_default_backend_functional final : public at::meta::structured_triangular_solve { |
| 13024 | void set_output_strided( |
| 13025 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13026 | TensorOptions options, DimnameList names |
| 13027 | ) override { |
| 13028 | auto current_device = guard_.current_device(); |
| 13029 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13030 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13031 | "structured kernels don't support multi-device outputs"); |
| 13032 | } else { |
| 13033 | guard_.reset_device(options.device()); |
| 13034 | } |
| 13035 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13036 | if (!names.empty()) { |
| 13037 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13038 | } |
| 13039 | // super must happen after, so that downstream can use maybe_get_output |
| 13040 | // to retrieve the output |
| 13041 | } |
| 13042 | void set_output_raw_strided( |
| 13043 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13044 | TensorOptions options, DimnameList names |
| 13045 | ) override { |
| 13046 | auto current_device = guard_.current_device(); |
| 13047 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13048 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13049 | "structured kernels don't support multi-device outputs"); |
| 13050 | } else { |
| 13051 | guard_.reset_device(options.device()); |
| 13052 | } |
| 13053 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13054 | if (!names.empty()) { |
| 13055 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13056 | } |
| 13057 | // super must happen after, so that downstream can use maybe_get_output |
| 13058 | // to retrieve the output |
| 13059 | } |
| 13060 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13061 | return *outputs_[output_idx]; |
| 13062 | } |
| 13063 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 13064 | c10::OptionalDeviceGuard guard_; |
| 13065 | }; |
| 13066 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CompositeExplicitAutogradNonFunctional_triangular_solve(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular) { |
| 13067 | structured_triangular_solve_default_backend_functional op; |
| 13068 | op.meta(self, A, upper, transpose, unitriangular); |
| 13069 | at::triangular_solve_outf(self, A, upper, transpose, unitriangular, *op.outputs_[0], *op.outputs_[1]); |
| 13070 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 13071 | } |
| 13072 | struct structured_lu_unpack_default_backend_functional final : public at::meta::structured_lu_unpack { |
| 13073 | void set_output_strided( |
| 13074 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13075 | TensorOptions options, DimnameList names |
| 13076 | ) override { |
| 13077 | auto current_device = guard_.current_device(); |
| 13078 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13079 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13080 | "structured kernels don't support multi-device outputs"); |
| 13081 | } else { |
| 13082 | guard_.reset_device(options.device()); |
| 13083 | } |
| 13084 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13085 | if (!names.empty()) { |
| 13086 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13087 | } |
| 13088 | // super must happen after, so that downstream can use maybe_get_output |
| 13089 | // to retrieve the output |
| 13090 | } |
| 13091 | void set_output_raw_strided( |
| 13092 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13093 | TensorOptions options, DimnameList names |
| 13094 | ) override { |
| 13095 | auto current_device = guard_.current_device(); |
| 13096 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13097 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13098 | "structured kernels don't support multi-device outputs"); |
| 13099 | } else { |
| 13100 | guard_.reset_device(options.device()); |
| 13101 | } |
| 13102 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13103 | if (!names.empty()) { |
| 13104 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13105 | } |
| 13106 | // super must happen after, so that downstream can use maybe_get_output |
| 13107 | // to retrieve the output |
| 13108 | } |
| 13109 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13110 | return *outputs_[output_idx]; |
| 13111 | } |
| 13112 | std::array<c10::ExclusivelyOwned<Tensor>, 3> outputs_; |
| 13113 | c10::OptionalDeviceGuard guard_; |
| 13114 | }; |
| 13115 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CompositeExplicitAutogradNonFunctional_lu_unpack(const at::Tensor & LU_data, const at::Tensor & LU_pivots, bool unpack_data, bool unpack_pivots) { |
| 13116 | structured_lu_unpack_default_backend_functional op; |
| 13117 | op.meta(LU_data, LU_pivots, unpack_data, unpack_pivots); |
| 13118 | at::lu_unpack_outf(LU_data, LU_pivots, unpack_data, unpack_pivots, *op.outputs_[0], *op.outputs_[1], *op.outputs_[2]); |
| 13119 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take(), std::move(op.outputs_[2]).take()); |
| 13120 | } |
| 13121 | struct structured_lgamma_default_backend_functional final : public at::meta::structured_lgamma { |
| 13122 | void set_output_strided( |
| 13123 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13124 | TensorOptions options, DimnameList names |
| 13125 | ) override { |
| 13126 | auto current_device = guard_.current_device(); |
| 13127 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13128 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13129 | "structured kernels don't support multi-device outputs"); |
| 13130 | } else { |
| 13131 | guard_.reset_device(options.device()); |
| 13132 | } |
| 13133 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13134 | if (!names.empty()) { |
| 13135 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13136 | } |
| 13137 | // super must happen after, so that downstream can use maybe_get_output |
| 13138 | // to retrieve the output |
| 13139 | at::meta::structured_lgamma::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13140 | } |
| 13141 | void set_output_raw_strided( |
| 13142 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13143 | TensorOptions options, DimnameList names |
| 13144 | ) override { |
| 13145 | auto current_device = guard_.current_device(); |
| 13146 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13147 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13148 | "structured kernels don't support multi-device outputs"); |
| 13149 | } else { |
| 13150 | guard_.reset_device(options.device()); |
| 13151 | } |
| 13152 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13153 | if (!names.empty()) { |
| 13154 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13155 | } |
| 13156 | // super must happen after, so that downstream can use maybe_get_output |
| 13157 | // to retrieve the output |
| 13158 | at::meta::structured_lgamma::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13159 | } |
| 13160 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13161 | return *outputs_[output_idx]; |
| 13162 | } |
| 13163 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 13164 | c10::OptionalDeviceGuard guard_; |
| 13165 | }; |
| 13166 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_lgamma(const at::Tensor & self) { |
| 13167 | structured_lgamma_default_backend_functional op; |
| 13168 | op.meta(self); |
| 13169 | at::lgamma_outf(self, *op.outputs_[0]); |
| 13170 | return std::move(op.outputs_[0]).take(); |
| 13171 | } |
| 13172 | struct structured_lgamma_default_backend_inplace final : public at::meta::structured_lgamma { |
| 13173 | structured_lgamma_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 13174 | void set_output_strided( |
| 13175 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13176 | TensorOptions options, DimnameList names |
| 13177 | ) override { |
| 13178 | auto current_device = guard_.current_device(); |
| 13179 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13180 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13181 | "structured kernels don't support multi-device outputs"); |
| 13182 | } else { |
| 13183 | guard_.reset_device(options.device()); |
| 13184 | } |
| 13185 | const auto& out = outputs_[output_idx].get(); |
| 13186 | check_inplace(out, sizes, options); |
| 13187 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 13188 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 13189 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 13190 | } |
| 13191 | if (!names.empty()) { |
| 13192 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13193 | } |
| 13194 | // super must happen after, so that downstream can use maybe_get_output |
| 13195 | // to retrieve the output |
| 13196 | at::meta::structured_lgamma::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13197 | } |
| 13198 | void set_output_raw_strided( |
| 13199 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13200 | TensorOptions options, DimnameList names |
| 13201 | ) override { |
| 13202 | auto current_device = guard_.current_device(); |
| 13203 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13204 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13205 | "structured kernels don't support multi-device outputs"); |
| 13206 | } else { |
| 13207 | guard_.reset_device(options.device()); |
| 13208 | } |
| 13209 | const auto& out = outputs_[output_idx].get(); |
| 13210 | check_inplace(out, sizes, options); |
| 13211 | if (!names.empty()) { |
| 13212 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13213 | } |
| 13214 | // super must happen after, so that downstream can use maybe_get_output |
| 13215 | // to retrieve the output |
| 13216 | at::meta::structured_lgamma::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13217 | } |
| 13218 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13219 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 13220 | } |
| 13221 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 13222 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 13223 | c10::OptionalDeviceGuard guard_; |
| 13224 | }; |
| 13225 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_lgamma_(at::Tensor & self) { |
| 13226 | structured_lgamma_default_backend_inplace op(self); |
| 13227 | op.meta(self); |
| 13228 | at::lgamma_outf(self, op.outputs_[0]); |
| 13229 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 13230 | return self; |
| 13231 | } |
| 13232 | struct structured_polygamma_default_backend_functional final : public at::meta::structured_polygamma { |
| 13233 | void set_output_strided( |
| 13234 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13235 | TensorOptions options, DimnameList names |
| 13236 | ) override { |
| 13237 | auto current_device = guard_.current_device(); |
| 13238 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13239 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13240 | "structured kernels don't support multi-device outputs"); |
| 13241 | } else { |
| 13242 | guard_.reset_device(options.device()); |
| 13243 | } |
| 13244 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13245 | if (!names.empty()) { |
| 13246 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13247 | } |
| 13248 | // super must happen after, so that downstream can use maybe_get_output |
| 13249 | // to retrieve the output |
| 13250 | at::meta::structured_polygamma::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13251 | } |
| 13252 | void set_output_raw_strided( |
| 13253 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13254 | TensorOptions options, DimnameList names |
| 13255 | ) override { |
| 13256 | auto current_device = guard_.current_device(); |
| 13257 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13258 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13259 | "structured kernels don't support multi-device outputs"); |
| 13260 | } else { |
| 13261 | guard_.reset_device(options.device()); |
| 13262 | } |
| 13263 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13264 | if (!names.empty()) { |
| 13265 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13266 | } |
| 13267 | // super must happen after, so that downstream can use maybe_get_output |
| 13268 | // to retrieve the output |
| 13269 | at::meta::structured_polygamma::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13270 | } |
| 13271 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13272 | return *outputs_[output_idx]; |
| 13273 | } |
| 13274 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 13275 | c10::OptionalDeviceGuard guard_; |
| 13276 | }; |
| 13277 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_polygamma(int64_t n, const at::Tensor & self) { |
| 13278 | structured_polygamma_default_backend_functional op; |
| 13279 | op.meta(n, self); |
| 13280 | at::polygamma_outf(n, self, *op.outputs_[0]); |
| 13281 | return std::move(op.outputs_[0]).take(); |
| 13282 | } |
| 13283 | struct structured_erfinv_default_backend_functional final : public at::meta::structured_erfinv { |
| 13284 | void set_output_strided( |
| 13285 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13286 | TensorOptions options, DimnameList names |
| 13287 | ) override { |
| 13288 | auto current_device = guard_.current_device(); |
| 13289 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13290 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13291 | "structured kernels don't support multi-device outputs"); |
| 13292 | } else { |
| 13293 | guard_.reset_device(options.device()); |
| 13294 | } |
| 13295 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13296 | if (!names.empty()) { |
| 13297 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13298 | } |
| 13299 | // super must happen after, so that downstream can use maybe_get_output |
| 13300 | // to retrieve the output |
| 13301 | at::meta::structured_erfinv::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13302 | } |
| 13303 | void set_output_raw_strided( |
| 13304 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13305 | TensorOptions options, DimnameList names |
| 13306 | ) override { |
| 13307 | auto current_device = guard_.current_device(); |
| 13308 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13309 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13310 | "structured kernels don't support multi-device outputs"); |
| 13311 | } else { |
| 13312 | guard_.reset_device(options.device()); |
| 13313 | } |
| 13314 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13315 | if (!names.empty()) { |
| 13316 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13317 | } |
| 13318 | // super must happen after, so that downstream can use maybe_get_output |
| 13319 | // to retrieve the output |
| 13320 | at::meta::structured_erfinv::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13321 | } |
| 13322 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13323 | return *outputs_[output_idx]; |
| 13324 | } |
| 13325 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 13326 | c10::OptionalDeviceGuard guard_; |
| 13327 | }; |
| 13328 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_erfinv(const at::Tensor & self) { |
| 13329 | structured_erfinv_default_backend_functional op; |
| 13330 | op.meta(self); |
| 13331 | at::erfinv_outf(self, *op.outputs_[0]); |
| 13332 | return std::move(op.outputs_[0]).take(); |
| 13333 | } |
| 13334 | struct structured_erfinv_default_backend_inplace final : public at::meta::structured_erfinv { |
| 13335 | structured_erfinv_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 13336 | void set_output_strided( |
| 13337 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13338 | TensorOptions options, DimnameList names |
| 13339 | ) override { |
| 13340 | auto current_device = guard_.current_device(); |
| 13341 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13342 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13343 | "structured kernels don't support multi-device outputs"); |
| 13344 | } else { |
| 13345 | guard_.reset_device(options.device()); |
| 13346 | } |
| 13347 | const auto& out = outputs_[output_idx].get(); |
| 13348 | check_inplace(out, sizes, options); |
| 13349 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 13350 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 13351 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 13352 | } |
| 13353 | if (!names.empty()) { |
| 13354 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13355 | } |
| 13356 | // super must happen after, so that downstream can use maybe_get_output |
| 13357 | // to retrieve the output |
| 13358 | at::meta::structured_erfinv::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13359 | } |
| 13360 | void set_output_raw_strided( |
| 13361 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13362 | TensorOptions options, DimnameList names |
| 13363 | ) override { |
| 13364 | auto current_device = guard_.current_device(); |
| 13365 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13366 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13367 | "structured kernels don't support multi-device outputs"); |
| 13368 | } else { |
| 13369 | guard_.reset_device(options.device()); |
| 13370 | } |
| 13371 | const auto& out = outputs_[output_idx].get(); |
| 13372 | check_inplace(out, sizes, options); |
| 13373 | if (!names.empty()) { |
| 13374 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13375 | } |
| 13376 | // super must happen after, so that downstream can use maybe_get_output |
| 13377 | // to retrieve the output |
| 13378 | at::meta::structured_erfinv::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13379 | } |
| 13380 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13381 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 13382 | } |
| 13383 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 13384 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 13385 | c10::OptionalDeviceGuard guard_; |
| 13386 | }; |
| 13387 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_erfinv_(at::Tensor & self) { |
| 13388 | structured_erfinv_default_backend_inplace op(self); |
| 13389 | op.meta(self); |
| 13390 | at::erfinv_outf(self, op.outputs_[0]); |
| 13391 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 13392 | return self; |
| 13393 | } |
| 13394 | struct structured_i0_default_backend_functional final : public at::meta::structured_i0 { |
| 13395 | void set_output_strided( |
| 13396 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13397 | TensorOptions options, DimnameList names |
| 13398 | ) override { |
| 13399 | auto current_device = guard_.current_device(); |
| 13400 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13401 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13402 | "structured kernels don't support multi-device outputs"); |
| 13403 | } else { |
| 13404 | guard_.reset_device(options.device()); |
| 13405 | } |
| 13406 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13407 | if (!names.empty()) { |
| 13408 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13409 | } |
| 13410 | // super must happen after, so that downstream can use maybe_get_output |
| 13411 | // to retrieve the output |
| 13412 | at::meta::structured_i0::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13413 | } |
| 13414 | void set_output_raw_strided( |
| 13415 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13416 | TensorOptions options, DimnameList names |
| 13417 | ) override { |
| 13418 | auto current_device = guard_.current_device(); |
| 13419 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13420 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13421 | "structured kernels don't support multi-device outputs"); |
| 13422 | } else { |
| 13423 | guard_.reset_device(options.device()); |
| 13424 | } |
| 13425 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13426 | if (!names.empty()) { |
| 13427 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13428 | } |
| 13429 | // super must happen after, so that downstream can use maybe_get_output |
| 13430 | // to retrieve the output |
| 13431 | at::meta::structured_i0::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13432 | } |
| 13433 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13434 | return *outputs_[output_idx]; |
| 13435 | } |
| 13436 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 13437 | c10::OptionalDeviceGuard guard_; |
| 13438 | }; |
| 13439 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_i0(const at::Tensor & self) { |
| 13440 | structured_i0_default_backend_functional op; |
| 13441 | op.meta(self); |
| 13442 | at::i0_outf(self, *op.outputs_[0]); |
| 13443 | return std::move(op.outputs_[0]).take(); |
| 13444 | } |
| 13445 | struct structured_i0_default_backend_inplace final : public at::meta::structured_i0 { |
| 13446 | structured_i0_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 13447 | void set_output_strided( |
| 13448 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13449 | TensorOptions options, DimnameList names |
| 13450 | ) override { |
| 13451 | auto current_device = guard_.current_device(); |
| 13452 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13453 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13454 | "structured kernels don't support multi-device outputs"); |
| 13455 | } else { |
| 13456 | guard_.reset_device(options.device()); |
| 13457 | } |
| 13458 | const auto& out = outputs_[output_idx].get(); |
| 13459 | check_inplace(out, sizes, options); |
| 13460 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 13461 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 13462 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 13463 | } |
| 13464 | if (!names.empty()) { |
| 13465 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13466 | } |
| 13467 | // super must happen after, so that downstream can use maybe_get_output |
| 13468 | // to retrieve the output |
| 13469 | at::meta::structured_i0::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13470 | } |
| 13471 | void set_output_raw_strided( |
| 13472 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13473 | TensorOptions options, DimnameList names |
| 13474 | ) override { |
| 13475 | auto current_device = guard_.current_device(); |
| 13476 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13477 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13478 | "structured kernels don't support multi-device outputs"); |
| 13479 | } else { |
| 13480 | guard_.reset_device(options.device()); |
| 13481 | } |
| 13482 | const auto& out = outputs_[output_idx].get(); |
| 13483 | check_inplace(out, sizes, options); |
| 13484 | if (!names.empty()) { |
| 13485 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13486 | } |
| 13487 | // super must happen after, so that downstream can use maybe_get_output |
| 13488 | // to retrieve the output |
| 13489 | at::meta::structured_i0::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13490 | } |
| 13491 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13492 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 13493 | } |
| 13494 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 13495 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 13496 | c10::OptionalDeviceGuard guard_; |
| 13497 | }; |
| 13498 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_i0_(at::Tensor & self) { |
| 13499 | structured_i0_default_backend_inplace op(self); |
| 13500 | op.meta(self); |
| 13501 | at::i0_outf(self, op.outputs_[0]); |
| 13502 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 13503 | return self; |
| 13504 | } |
| 13505 | struct structured_sign_default_backend_functional final : public at::meta::structured_sign { |
| 13506 | void set_output_strided( |
| 13507 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13508 | TensorOptions options, DimnameList names |
| 13509 | ) override { |
| 13510 | auto current_device = guard_.current_device(); |
| 13511 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13512 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13513 | "structured kernels don't support multi-device outputs"); |
| 13514 | } else { |
| 13515 | guard_.reset_device(options.device()); |
| 13516 | } |
| 13517 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13518 | if (!names.empty()) { |
| 13519 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13520 | } |
| 13521 | // super must happen after, so that downstream can use maybe_get_output |
| 13522 | // to retrieve the output |
| 13523 | at::meta::structured_sign::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13524 | } |
| 13525 | void set_output_raw_strided( |
| 13526 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13527 | TensorOptions options, DimnameList names |
| 13528 | ) override { |
| 13529 | auto current_device = guard_.current_device(); |
| 13530 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13531 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13532 | "structured kernels don't support multi-device outputs"); |
| 13533 | } else { |
| 13534 | guard_.reset_device(options.device()); |
| 13535 | } |
| 13536 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13537 | if (!names.empty()) { |
| 13538 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13539 | } |
| 13540 | // super must happen after, so that downstream can use maybe_get_output |
| 13541 | // to retrieve the output |
| 13542 | at::meta::structured_sign::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13543 | } |
| 13544 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13545 | return *outputs_[output_idx]; |
| 13546 | } |
| 13547 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 13548 | c10::OptionalDeviceGuard guard_; |
| 13549 | }; |
| 13550 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_sign(const at::Tensor & self) { |
| 13551 | structured_sign_default_backend_functional op; |
| 13552 | op.meta(self); |
| 13553 | at::sign_outf(self, *op.outputs_[0]); |
| 13554 | return std::move(op.outputs_[0]).take(); |
| 13555 | } |
| 13556 | struct structured_sign_default_backend_inplace final : public at::meta::structured_sign { |
| 13557 | structured_sign_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 13558 | void set_output_strided( |
| 13559 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13560 | TensorOptions options, DimnameList names |
| 13561 | ) override { |
| 13562 | auto current_device = guard_.current_device(); |
| 13563 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13564 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13565 | "structured kernels don't support multi-device outputs"); |
| 13566 | } else { |
| 13567 | guard_.reset_device(options.device()); |
| 13568 | } |
| 13569 | const auto& out = outputs_[output_idx].get(); |
| 13570 | check_inplace(out, sizes, options); |
| 13571 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 13572 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 13573 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 13574 | } |
| 13575 | if (!names.empty()) { |
| 13576 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13577 | } |
| 13578 | // super must happen after, so that downstream can use maybe_get_output |
| 13579 | // to retrieve the output |
| 13580 | at::meta::structured_sign::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13581 | } |
| 13582 | void set_output_raw_strided( |
| 13583 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13584 | TensorOptions options, DimnameList names |
| 13585 | ) override { |
| 13586 | auto current_device = guard_.current_device(); |
| 13587 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13588 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13589 | "structured kernels don't support multi-device outputs"); |
| 13590 | } else { |
| 13591 | guard_.reset_device(options.device()); |
| 13592 | } |
| 13593 | const auto& out = outputs_[output_idx].get(); |
| 13594 | check_inplace(out, sizes, options); |
| 13595 | if (!names.empty()) { |
| 13596 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13597 | } |
| 13598 | // super must happen after, so that downstream can use maybe_get_output |
| 13599 | // to retrieve the output |
| 13600 | at::meta::structured_sign::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13601 | } |
| 13602 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13603 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 13604 | } |
| 13605 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 13606 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 13607 | c10::OptionalDeviceGuard guard_; |
| 13608 | }; |
| 13609 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_sign_(at::Tensor & self) { |
| 13610 | structured_sign_default_backend_inplace op(self); |
| 13611 | op.meta(self); |
| 13612 | at::sign_outf(self, op.outputs_[0]); |
| 13613 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 13614 | return self; |
| 13615 | } |
| 13616 | struct structured_signbit_default_backend_functional final : public at::meta::structured_signbit { |
| 13617 | void set_output_strided( |
| 13618 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13619 | TensorOptions options, DimnameList names |
| 13620 | ) override { |
| 13621 | auto current_device = guard_.current_device(); |
| 13622 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13623 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13624 | "structured kernels don't support multi-device outputs"); |
| 13625 | } else { |
| 13626 | guard_.reset_device(options.device()); |
| 13627 | } |
| 13628 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13629 | if (!names.empty()) { |
| 13630 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13631 | } |
| 13632 | // super must happen after, so that downstream can use maybe_get_output |
| 13633 | // to retrieve the output |
| 13634 | at::meta::structured_signbit::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13635 | } |
| 13636 | void set_output_raw_strided( |
| 13637 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13638 | TensorOptions options, DimnameList names |
| 13639 | ) override { |
| 13640 | auto current_device = guard_.current_device(); |
| 13641 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13642 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13643 | "structured kernels don't support multi-device outputs"); |
| 13644 | } else { |
| 13645 | guard_.reset_device(options.device()); |
| 13646 | } |
| 13647 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13648 | if (!names.empty()) { |
| 13649 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13650 | } |
| 13651 | // super must happen after, so that downstream can use maybe_get_output |
| 13652 | // to retrieve the output |
| 13653 | at::meta::structured_signbit::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13654 | } |
| 13655 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13656 | return *outputs_[output_idx]; |
| 13657 | } |
| 13658 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 13659 | c10::OptionalDeviceGuard guard_; |
| 13660 | }; |
| 13661 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_signbit(const at::Tensor & self) { |
| 13662 | structured_signbit_default_backend_functional op; |
| 13663 | op.meta(self); |
| 13664 | at::signbit_outf(self, *op.outputs_[0]); |
| 13665 | return std::move(op.outputs_[0]).take(); |
| 13666 | } |
| 13667 | struct structured_atan2_default_backend_functional final : public at::meta::structured_atan2 { |
| 13668 | void set_output_strided( |
| 13669 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13670 | TensorOptions options, DimnameList names |
| 13671 | ) override { |
| 13672 | auto current_device = guard_.current_device(); |
| 13673 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13674 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13675 | "structured kernels don't support multi-device outputs"); |
| 13676 | } else { |
| 13677 | guard_.reset_device(options.device()); |
| 13678 | } |
| 13679 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13680 | if (!names.empty()) { |
| 13681 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13682 | } |
| 13683 | // super must happen after, so that downstream can use maybe_get_output |
| 13684 | // to retrieve the output |
| 13685 | at::meta::structured_atan2::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13686 | } |
| 13687 | void set_output_raw_strided( |
| 13688 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13689 | TensorOptions options, DimnameList names |
| 13690 | ) override { |
| 13691 | auto current_device = guard_.current_device(); |
| 13692 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13693 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13694 | "structured kernels don't support multi-device outputs"); |
| 13695 | } else { |
| 13696 | guard_.reset_device(options.device()); |
| 13697 | } |
| 13698 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13699 | if (!names.empty()) { |
| 13700 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13701 | } |
| 13702 | // super must happen after, so that downstream can use maybe_get_output |
| 13703 | // to retrieve the output |
| 13704 | at::meta::structured_atan2::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13705 | } |
| 13706 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13707 | return *outputs_[output_idx]; |
| 13708 | } |
| 13709 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 13710 | c10::OptionalDeviceGuard guard_; |
| 13711 | }; |
| 13712 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_atan2(const at::Tensor & self, const at::Tensor & other) { |
| 13713 | structured_atan2_default_backend_functional op; |
| 13714 | op.meta(self, other); |
| 13715 | at::atan2_outf(self, other, *op.outputs_[0]); |
| 13716 | return std::move(op.outputs_[0]).take(); |
| 13717 | } |
| 13718 | struct structured_atan2_default_backend_inplace final : public at::meta::structured_atan2 { |
| 13719 | structured_atan2_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 13720 | void set_output_strided( |
| 13721 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13722 | TensorOptions options, DimnameList names |
| 13723 | ) override { |
| 13724 | auto current_device = guard_.current_device(); |
| 13725 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13726 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13727 | "structured kernels don't support multi-device outputs"); |
| 13728 | } else { |
| 13729 | guard_.reset_device(options.device()); |
| 13730 | } |
| 13731 | const auto& out = outputs_[output_idx].get(); |
| 13732 | check_inplace(out, sizes, options); |
| 13733 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 13734 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 13735 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 13736 | } |
| 13737 | if (!names.empty()) { |
| 13738 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13739 | } |
| 13740 | // super must happen after, so that downstream can use maybe_get_output |
| 13741 | // to retrieve the output |
| 13742 | at::meta::structured_atan2::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13743 | } |
| 13744 | void set_output_raw_strided( |
| 13745 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13746 | TensorOptions options, DimnameList names |
| 13747 | ) override { |
| 13748 | auto current_device = guard_.current_device(); |
| 13749 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13750 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13751 | "structured kernels don't support multi-device outputs"); |
| 13752 | } else { |
| 13753 | guard_.reset_device(options.device()); |
| 13754 | } |
| 13755 | const auto& out = outputs_[output_idx].get(); |
| 13756 | check_inplace(out, sizes, options); |
| 13757 | if (!names.empty()) { |
| 13758 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13759 | } |
| 13760 | // super must happen after, so that downstream can use maybe_get_output |
| 13761 | // to retrieve the output |
| 13762 | at::meta::structured_atan2::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13763 | } |
| 13764 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13765 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 13766 | } |
| 13767 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 13768 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 13769 | c10::OptionalDeviceGuard guard_; |
| 13770 | }; |
| 13771 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_atan2_(at::Tensor & self, const at::Tensor & other) { |
| 13772 | structured_atan2_default_backend_inplace op(self); |
| 13773 | op.meta(self, other); |
| 13774 | at::atan2_outf(self, other, op.outputs_[0]); |
| 13775 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 13776 | return self; |
| 13777 | } |
| 13778 | struct structured_fmod_Tensor_default_backend_functional final : public at::meta::structured_fmod_Tensor { |
| 13779 | void set_output_strided( |
| 13780 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13781 | TensorOptions options, DimnameList names |
| 13782 | ) override { |
| 13783 | auto current_device = guard_.current_device(); |
| 13784 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13785 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13786 | "structured kernels don't support multi-device outputs"); |
| 13787 | } else { |
| 13788 | guard_.reset_device(options.device()); |
| 13789 | } |
| 13790 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13791 | if (!names.empty()) { |
| 13792 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13793 | } |
| 13794 | // super must happen after, so that downstream can use maybe_get_output |
| 13795 | // to retrieve the output |
| 13796 | at::meta::structured_fmod_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13797 | } |
| 13798 | void set_output_raw_strided( |
| 13799 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13800 | TensorOptions options, DimnameList names |
| 13801 | ) override { |
| 13802 | auto current_device = guard_.current_device(); |
| 13803 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13804 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13805 | "structured kernels don't support multi-device outputs"); |
| 13806 | } else { |
| 13807 | guard_.reset_device(options.device()); |
| 13808 | } |
| 13809 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13810 | if (!names.empty()) { |
| 13811 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13812 | } |
| 13813 | // super must happen after, so that downstream can use maybe_get_output |
| 13814 | // to retrieve the output |
| 13815 | at::meta::structured_fmod_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13816 | } |
| 13817 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13818 | return *outputs_[output_idx]; |
| 13819 | } |
| 13820 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 13821 | c10::OptionalDeviceGuard guard_; |
| 13822 | }; |
| 13823 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_fmod_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 13824 | structured_fmod_Tensor_default_backend_functional op; |
| 13825 | op.meta(self, other); |
| 13826 | at::fmod_outf(self, other, *op.outputs_[0]); |
| 13827 | return std::move(op.outputs_[0]).take(); |
| 13828 | } |
| 13829 | struct structured_fmod_Tensor_default_backend_inplace final : public at::meta::structured_fmod_Tensor { |
| 13830 | structured_fmod_Tensor_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 13831 | void set_output_strided( |
| 13832 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13833 | TensorOptions options, DimnameList names |
| 13834 | ) override { |
| 13835 | auto current_device = guard_.current_device(); |
| 13836 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13837 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13838 | "structured kernels don't support multi-device outputs"); |
| 13839 | } else { |
| 13840 | guard_.reset_device(options.device()); |
| 13841 | } |
| 13842 | const auto& out = outputs_[output_idx].get(); |
| 13843 | check_inplace(out, sizes, options); |
| 13844 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 13845 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 13846 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 13847 | } |
| 13848 | if (!names.empty()) { |
| 13849 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13850 | } |
| 13851 | // super must happen after, so that downstream can use maybe_get_output |
| 13852 | // to retrieve the output |
| 13853 | at::meta::structured_fmod_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13854 | } |
| 13855 | void set_output_raw_strided( |
| 13856 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13857 | TensorOptions options, DimnameList names |
| 13858 | ) override { |
| 13859 | auto current_device = guard_.current_device(); |
| 13860 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13861 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13862 | "structured kernels don't support multi-device outputs"); |
| 13863 | } else { |
| 13864 | guard_.reset_device(options.device()); |
| 13865 | } |
| 13866 | const auto& out = outputs_[output_idx].get(); |
| 13867 | check_inplace(out, sizes, options); |
| 13868 | if (!names.empty()) { |
| 13869 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13870 | } |
| 13871 | // super must happen after, so that downstream can use maybe_get_output |
| 13872 | // to retrieve the output |
| 13873 | at::meta::structured_fmod_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13874 | } |
| 13875 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13876 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 13877 | } |
| 13878 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 13879 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 13880 | c10::OptionalDeviceGuard guard_; |
| 13881 | }; |
| 13882 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_fmod__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 13883 | structured_fmod_Tensor_default_backend_inplace op(self); |
| 13884 | op.meta(self, other); |
| 13885 | at::fmod_outf(self, other, op.outputs_[0]); |
| 13886 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 13887 | return self; |
| 13888 | } |
| 13889 | struct structured_hypot_default_backend_functional final : public at::meta::structured_hypot { |
| 13890 | void set_output_strided( |
| 13891 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13892 | TensorOptions options, DimnameList names |
| 13893 | ) override { |
| 13894 | auto current_device = guard_.current_device(); |
| 13895 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13896 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13897 | "structured kernels don't support multi-device outputs"); |
| 13898 | } else { |
| 13899 | guard_.reset_device(options.device()); |
| 13900 | } |
| 13901 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13902 | if (!names.empty()) { |
| 13903 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13904 | } |
| 13905 | // super must happen after, so that downstream can use maybe_get_output |
| 13906 | // to retrieve the output |
| 13907 | at::meta::structured_hypot::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13908 | } |
| 13909 | void set_output_raw_strided( |
| 13910 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13911 | TensorOptions options, DimnameList names |
| 13912 | ) override { |
| 13913 | auto current_device = guard_.current_device(); |
| 13914 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13915 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13916 | "structured kernels don't support multi-device outputs"); |
| 13917 | } else { |
| 13918 | guard_.reset_device(options.device()); |
| 13919 | } |
| 13920 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 13921 | if (!names.empty()) { |
| 13922 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 13923 | } |
| 13924 | // super must happen after, so that downstream can use maybe_get_output |
| 13925 | // to retrieve the output |
| 13926 | at::meta::structured_hypot::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13927 | } |
| 13928 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13929 | return *outputs_[output_idx]; |
| 13930 | } |
| 13931 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 13932 | c10::OptionalDeviceGuard guard_; |
| 13933 | }; |
| 13934 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_hypot(const at::Tensor & self, const at::Tensor & other) { |
| 13935 | structured_hypot_default_backend_functional op; |
| 13936 | op.meta(self, other); |
| 13937 | at::hypot_outf(self, other, *op.outputs_[0]); |
| 13938 | return std::move(op.outputs_[0]).take(); |
| 13939 | } |
| 13940 | struct structured_hypot_default_backend_inplace final : public at::meta::structured_hypot { |
| 13941 | structured_hypot_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 13942 | void set_output_strided( |
| 13943 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13944 | TensorOptions options, DimnameList names |
| 13945 | ) override { |
| 13946 | auto current_device = guard_.current_device(); |
| 13947 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13948 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13949 | "structured kernels don't support multi-device outputs"); |
| 13950 | } else { |
| 13951 | guard_.reset_device(options.device()); |
| 13952 | } |
| 13953 | const auto& out = outputs_[output_idx].get(); |
| 13954 | check_inplace(out, sizes, options); |
| 13955 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 13956 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 13957 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 13958 | } |
| 13959 | if (!names.empty()) { |
| 13960 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13961 | } |
| 13962 | // super must happen after, so that downstream can use maybe_get_output |
| 13963 | // to retrieve the output |
| 13964 | at::meta::structured_hypot::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13965 | } |
| 13966 | void set_output_raw_strided( |
| 13967 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 13968 | TensorOptions options, DimnameList names |
| 13969 | ) override { |
| 13970 | auto current_device = guard_.current_device(); |
| 13971 | if (C10_UNLIKELY(current_device.has_value())) { |
| 13972 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 13973 | "structured kernels don't support multi-device outputs"); |
| 13974 | } else { |
| 13975 | guard_.reset_device(options.device()); |
| 13976 | } |
| 13977 | const auto& out = outputs_[output_idx].get(); |
| 13978 | check_inplace(out, sizes, options); |
| 13979 | if (!names.empty()) { |
| 13980 | namedinference::propagate_names(outputs_[output_idx], names); |
| 13981 | } |
| 13982 | // super must happen after, so that downstream can use maybe_get_output |
| 13983 | // to retrieve the output |
| 13984 | at::meta::structured_hypot::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 13985 | } |
| 13986 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 13987 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 13988 | } |
| 13989 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 13990 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 13991 | c10::OptionalDeviceGuard guard_; |
| 13992 | }; |
| 13993 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_hypot_(at::Tensor & self, const at::Tensor & other) { |
| 13994 | structured_hypot_default_backend_inplace op(self); |
| 13995 | op.meta(self, other); |
| 13996 | at::hypot_outf(self, other, op.outputs_[0]); |
| 13997 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 13998 | return self; |
| 13999 | } |
| 14000 | struct structured_igamma_default_backend_functional final : public at::meta::structured_igamma { |
| 14001 | void set_output_strided( |
| 14002 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14003 | TensorOptions options, DimnameList names |
| 14004 | ) override { |
| 14005 | auto current_device = guard_.current_device(); |
| 14006 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14007 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14008 | "structured kernels don't support multi-device outputs"); |
| 14009 | } else { |
| 14010 | guard_.reset_device(options.device()); |
| 14011 | } |
| 14012 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14013 | if (!names.empty()) { |
| 14014 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14015 | } |
| 14016 | // super must happen after, so that downstream can use maybe_get_output |
| 14017 | // to retrieve the output |
| 14018 | at::meta::structured_igamma::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14019 | } |
| 14020 | void set_output_raw_strided( |
| 14021 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14022 | TensorOptions options, DimnameList names |
| 14023 | ) override { |
| 14024 | auto current_device = guard_.current_device(); |
| 14025 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14026 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14027 | "structured kernels don't support multi-device outputs"); |
| 14028 | } else { |
| 14029 | guard_.reset_device(options.device()); |
| 14030 | } |
| 14031 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14032 | if (!names.empty()) { |
| 14033 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14034 | } |
| 14035 | // super must happen after, so that downstream can use maybe_get_output |
| 14036 | // to retrieve the output |
| 14037 | at::meta::structured_igamma::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14038 | } |
| 14039 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14040 | return *outputs_[output_idx]; |
| 14041 | } |
| 14042 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 14043 | c10::OptionalDeviceGuard guard_; |
| 14044 | }; |
| 14045 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_igamma(const at::Tensor & self, const at::Tensor & other) { |
| 14046 | structured_igamma_default_backend_functional op; |
| 14047 | op.meta(self, other); |
| 14048 | at::igamma_outf(self, other, *op.outputs_[0]); |
| 14049 | return std::move(op.outputs_[0]).take(); |
| 14050 | } |
| 14051 | struct structured_igamma_default_backend_inplace final : public at::meta::structured_igamma { |
| 14052 | structured_igamma_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 14053 | void set_output_strided( |
| 14054 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14055 | TensorOptions options, DimnameList names |
| 14056 | ) override { |
| 14057 | auto current_device = guard_.current_device(); |
| 14058 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14059 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14060 | "structured kernels don't support multi-device outputs"); |
| 14061 | } else { |
| 14062 | guard_.reset_device(options.device()); |
| 14063 | } |
| 14064 | const auto& out = outputs_[output_idx].get(); |
| 14065 | check_inplace(out, sizes, options); |
| 14066 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 14067 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 14068 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 14069 | } |
| 14070 | if (!names.empty()) { |
| 14071 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14072 | } |
| 14073 | // super must happen after, so that downstream can use maybe_get_output |
| 14074 | // to retrieve the output |
| 14075 | at::meta::structured_igamma::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14076 | } |
| 14077 | void set_output_raw_strided( |
| 14078 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14079 | TensorOptions options, DimnameList names |
| 14080 | ) override { |
| 14081 | auto current_device = guard_.current_device(); |
| 14082 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14083 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14084 | "structured kernels don't support multi-device outputs"); |
| 14085 | } else { |
| 14086 | guard_.reset_device(options.device()); |
| 14087 | } |
| 14088 | const auto& out = outputs_[output_idx].get(); |
| 14089 | check_inplace(out, sizes, options); |
| 14090 | if (!names.empty()) { |
| 14091 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14092 | } |
| 14093 | // super must happen after, so that downstream can use maybe_get_output |
| 14094 | // to retrieve the output |
| 14095 | at::meta::structured_igamma::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14096 | } |
| 14097 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14098 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 14099 | } |
| 14100 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 14101 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 14102 | c10::OptionalDeviceGuard guard_; |
| 14103 | }; |
| 14104 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_igamma_(at::Tensor & self, const at::Tensor & other) { |
| 14105 | structured_igamma_default_backend_inplace op(self); |
| 14106 | op.meta(self, other); |
| 14107 | at::igamma_outf(self, other, op.outputs_[0]); |
| 14108 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 14109 | return self; |
| 14110 | } |
| 14111 | struct structured_igammac_default_backend_functional final : public at::meta::structured_igammac { |
| 14112 | void set_output_strided( |
| 14113 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14114 | TensorOptions options, DimnameList names |
| 14115 | ) override { |
| 14116 | auto current_device = guard_.current_device(); |
| 14117 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14118 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14119 | "structured kernels don't support multi-device outputs"); |
| 14120 | } else { |
| 14121 | guard_.reset_device(options.device()); |
| 14122 | } |
| 14123 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14124 | if (!names.empty()) { |
| 14125 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14126 | } |
| 14127 | // super must happen after, so that downstream can use maybe_get_output |
| 14128 | // to retrieve the output |
| 14129 | at::meta::structured_igammac::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14130 | } |
| 14131 | void set_output_raw_strided( |
| 14132 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14133 | TensorOptions options, DimnameList names |
| 14134 | ) override { |
| 14135 | auto current_device = guard_.current_device(); |
| 14136 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14137 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14138 | "structured kernels don't support multi-device outputs"); |
| 14139 | } else { |
| 14140 | guard_.reset_device(options.device()); |
| 14141 | } |
| 14142 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14143 | if (!names.empty()) { |
| 14144 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14145 | } |
| 14146 | // super must happen after, so that downstream can use maybe_get_output |
| 14147 | // to retrieve the output |
| 14148 | at::meta::structured_igammac::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14149 | } |
| 14150 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14151 | return *outputs_[output_idx]; |
| 14152 | } |
| 14153 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 14154 | c10::OptionalDeviceGuard guard_; |
| 14155 | }; |
| 14156 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_igammac(const at::Tensor & self, const at::Tensor & other) { |
| 14157 | structured_igammac_default_backend_functional op; |
| 14158 | op.meta(self, other); |
| 14159 | at::igammac_outf(self, other, *op.outputs_[0]); |
| 14160 | return std::move(op.outputs_[0]).take(); |
| 14161 | } |
| 14162 | struct structured_igammac_default_backend_inplace final : public at::meta::structured_igammac { |
| 14163 | structured_igammac_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 14164 | void set_output_strided( |
| 14165 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14166 | TensorOptions options, DimnameList names |
| 14167 | ) override { |
| 14168 | auto current_device = guard_.current_device(); |
| 14169 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14170 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14171 | "structured kernels don't support multi-device outputs"); |
| 14172 | } else { |
| 14173 | guard_.reset_device(options.device()); |
| 14174 | } |
| 14175 | const auto& out = outputs_[output_idx].get(); |
| 14176 | check_inplace(out, sizes, options); |
| 14177 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 14178 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 14179 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 14180 | } |
| 14181 | if (!names.empty()) { |
| 14182 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14183 | } |
| 14184 | // super must happen after, so that downstream can use maybe_get_output |
| 14185 | // to retrieve the output |
| 14186 | at::meta::structured_igammac::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14187 | } |
| 14188 | void set_output_raw_strided( |
| 14189 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14190 | TensorOptions options, DimnameList names |
| 14191 | ) override { |
| 14192 | auto current_device = guard_.current_device(); |
| 14193 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14194 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14195 | "structured kernels don't support multi-device outputs"); |
| 14196 | } else { |
| 14197 | guard_.reset_device(options.device()); |
| 14198 | } |
| 14199 | const auto& out = outputs_[output_idx].get(); |
| 14200 | check_inplace(out, sizes, options); |
| 14201 | if (!names.empty()) { |
| 14202 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14203 | } |
| 14204 | // super must happen after, so that downstream can use maybe_get_output |
| 14205 | // to retrieve the output |
| 14206 | at::meta::structured_igammac::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14207 | } |
| 14208 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14209 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 14210 | } |
| 14211 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 14212 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 14213 | c10::OptionalDeviceGuard guard_; |
| 14214 | }; |
| 14215 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_igammac_(at::Tensor & self, const at::Tensor & other) { |
| 14216 | structured_igammac_default_backend_inplace op(self); |
| 14217 | op.meta(self, other); |
| 14218 | at::igammac_outf(self, other, op.outputs_[0]); |
| 14219 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 14220 | return self; |
| 14221 | } |
| 14222 | struct structured_nextafter_default_backend_functional final : public at::meta::structured_nextafter { |
| 14223 | void set_output_strided( |
| 14224 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14225 | TensorOptions options, DimnameList names |
| 14226 | ) override { |
| 14227 | auto current_device = guard_.current_device(); |
| 14228 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14229 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14230 | "structured kernels don't support multi-device outputs"); |
| 14231 | } else { |
| 14232 | guard_.reset_device(options.device()); |
| 14233 | } |
| 14234 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14235 | if (!names.empty()) { |
| 14236 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14237 | } |
| 14238 | // super must happen after, so that downstream can use maybe_get_output |
| 14239 | // to retrieve the output |
| 14240 | at::meta::structured_nextafter::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14241 | } |
| 14242 | void set_output_raw_strided( |
| 14243 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14244 | TensorOptions options, DimnameList names |
| 14245 | ) override { |
| 14246 | auto current_device = guard_.current_device(); |
| 14247 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14248 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14249 | "structured kernels don't support multi-device outputs"); |
| 14250 | } else { |
| 14251 | guard_.reset_device(options.device()); |
| 14252 | } |
| 14253 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14254 | if (!names.empty()) { |
| 14255 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14256 | } |
| 14257 | // super must happen after, so that downstream can use maybe_get_output |
| 14258 | // to retrieve the output |
| 14259 | at::meta::structured_nextafter::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14260 | } |
| 14261 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14262 | return *outputs_[output_idx]; |
| 14263 | } |
| 14264 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 14265 | c10::OptionalDeviceGuard guard_; |
| 14266 | }; |
| 14267 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_nextafter(const at::Tensor & self, const at::Tensor & other) { |
| 14268 | structured_nextafter_default_backend_functional op; |
| 14269 | op.meta(self, other); |
| 14270 | at::nextafter_outf(self, other, *op.outputs_[0]); |
| 14271 | return std::move(op.outputs_[0]).take(); |
| 14272 | } |
| 14273 | struct structured_nextafter_default_backend_inplace final : public at::meta::structured_nextafter { |
| 14274 | structured_nextafter_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 14275 | void set_output_strided( |
| 14276 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14277 | TensorOptions options, DimnameList names |
| 14278 | ) override { |
| 14279 | auto current_device = guard_.current_device(); |
| 14280 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14281 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14282 | "structured kernels don't support multi-device outputs"); |
| 14283 | } else { |
| 14284 | guard_.reset_device(options.device()); |
| 14285 | } |
| 14286 | const auto& out = outputs_[output_idx].get(); |
| 14287 | check_inplace(out, sizes, options); |
| 14288 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 14289 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 14290 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 14291 | } |
| 14292 | if (!names.empty()) { |
| 14293 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14294 | } |
| 14295 | // super must happen after, so that downstream can use maybe_get_output |
| 14296 | // to retrieve the output |
| 14297 | at::meta::structured_nextafter::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14298 | } |
| 14299 | void set_output_raw_strided( |
| 14300 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14301 | TensorOptions options, DimnameList names |
| 14302 | ) override { |
| 14303 | auto current_device = guard_.current_device(); |
| 14304 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14305 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14306 | "structured kernels don't support multi-device outputs"); |
| 14307 | } else { |
| 14308 | guard_.reset_device(options.device()); |
| 14309 | } |
| 14310 | const auto& out = outputs_[output_idx].get(); |
| 14311 | check_inplace(out, sizes, options); |
| 14312 | if (!names.empty()) { |
| 14313 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14314 | } |
| 14315 | // super must happen after, so that downstream can use maybe_get_output |
| 14316 | // to retrieve the output |
| 14317 | at::meta::structured_nextafter::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14318 | } |
| 14319 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14320 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 14321 | } |
| 14322 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 14323 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 14324 | c10::OptionalDeviceGuard guard_; |
| 14325 | }; |
| 14326 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_nextafter_(at::Tensor & self, const at::Tensor & other) { |
| 14327 | structured_nextafter_default_backend_inplace op(self); |
| 14328 | op.meta(self, other); |
| 14329 | at::nextafter_outf(self, other, op.outputs_[0]); |
| 14330 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 14331 | return self; |
| 14332 | } |
| 14333 | struct structured_remainder_Tensor_default_backend_functional final : public at::meta::structured_remainder_Tensor { |
| 14334 | void set_output_strided( |
| 14335 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14336 | TensorOptions options, DimnameList names |
| 14337 | ) override { |
| 14338 | auto current_device = guard_.current_device(); |
| 14339 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14340 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14341 | "structured kernels don't support multi-device outputs"); |
| 14342 | } else { |
| 14343 | guard_.reset_device(options.device()); |
| 14344 | } |
| 14345 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14346 | if (!names.empty()) { |
| 14347 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14348 | } |
| 14349 | // super must happen after, so that downstream can use maybe_get_output |
| 14350 | // to retrieve the output |
| 14351 | at::meta::structured_remainder_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14352 | } |
| 14353 | void set_output_raw_strided( |
| 14354 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14355 | TensorOptions options, DimnameList names |
| 14356 | ) override { |
| 14357 | auto current_device = guard_.current_device(); |
| 14358 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14359 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14360 | "structured kernels don't support multi-device outputs"); |
| 14361 | } else { |
| 14362 | guard_.reset_device(options.device()); |
| 14363 | } |
| 14364 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14365 | if (!names.empty()) { |
| 14366 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14367 | } |
| 14368 | // super must happen after, so that downstream can use maybe_get_output |
| 14369 | // to retrieve the output |
| 14370 | at::meta::structured_remainder_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14371 | } |
| 14372 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14373 | return *outputs_[output_idx]; |
| 14374 | } |
| 14375 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 14376 | c10::OptionalDeviceGuard guard_; |
| 14377 | }; |
| 14378 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_remainder_Tensor(const at::Tensor & self, const at::Tensor & other) { |
| 14379 | structured_remainder_Tensor_default_backend_functional op; |
| 14380 | op.meta(self, other); |
| 14381 | at::remainder_outf(self, other, *op.outputs_[0]); |
| 14382 | return std::move(op.outputs_[0]).take(); |
| 14383 | } |
| 14384 | struct structured_remainder_Tensor_default_backend_inplace final : public at::meta::structured_remainder_Tensor { |
| 14385 | structured_remainder_Tensor_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 14386 | void set_output_strided( |
| 14387 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14388 | TensorOptions options, DimnameList names |
| 14389 | ) override { |
| 14390 | auto current_device = guard_.current_device(); |
| 14391 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14392 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14393 | "structured kernels don't support multi-device outputs"); |
| 14394 | } else { |
| 14395 | guard_.reset_device(options.device()); |
| 14396 | } |
| 14397 | const auto& out = outputs_[output_idx].get(); |
| 14398 | check_inplace(out, sizes, options); |
| 14399 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 14400 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 14401 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 14402 | } |
| 14403 | if (!names.empty()) { |
| 14404 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14405 | } |
| 14406 | // super must happen after, so that downstream can use maybe_get_output |
| 14407 | // to retrieve the output |
| 14408 | at::meta::structured_remainder_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14409 | } |
| 14410 | void set_output_raw_strided( |
| 14411 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14412 | TensorOptions options, DimnameList names |
| 14413 | ) override { |
| 14414 | auto current_device = guard_.current_device(); |
| 14415 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14416 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14417 | "structured kernels don't support multi-device outputs"); |
| 14418 | } else { |
| 14419 | guard_.reset_device(options.device()); |
| 14420 | } |
| 14421 | const auto& out = outputs_[output_idx].get(); |
| 14422 | check_inplace(out, sizes, options); |
| 14423 | if (!names.empty()) { |
| 14424 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14425 | } |
| 14426 | // super must happen after, so that downstream can use maybe_get_output |
| 14427 | // to retrieve the output |
| 14428 | at::meta::structured_remainder_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14429 | } |
| 14430 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14431 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 14432 | } |
| 14433 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 14434 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 14435 | c10::OptionalDeviceGuard guard_; |
| 14436 | }; |
| 14437 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_remainder__Tensor(at::Tensor & self, const at::Tensor & other) { |
| 14438 | structured_remainder_Tensor_default_backend_inplace op(self); |
| 14439 | op.meta(self, other); |
| 14440 | at::remainder_outf(self, other, op.outputs_[0]); |
| 14441 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 14442 | return self; |
| 14443 | } |
| 14444 | struct structured_fmin_default_backend_functional final : public at::meta::structured_fmin { |
| 14445 | void set_output_strided( |
| 14446 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14447 | TensorOptions options, DimnameList names |
| 14448 | ) override { |
| 14449 | auto current_device = guard_.current_device(); |
| 14450 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14451 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14452 | "structured kernels don't support multi-device outputs"); |
| 14453 | } else { |
| 14454 | guard_.reset_device(options.device()); |
| 14455 | } |
| 14456 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14457 | if (!names.empty()) { |
| 14458 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14459 | } |
| 14460 | // super must happen after, so that downstream can use maybe_get_output |
| 14461 | // to retrieve the output |
| 14462 | at::meta::structured_fmin::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14463 | } |
| 14464 | void set_output_raw_strided( |
| 14465 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14466 | TensorOptions options, DimnameList names |
| 14467 | ) override { |
| 14468 | auto current_device = guard_.current_device(); |
| 14469 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14470 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14471 | "structured kernels don't support multi-device outputs"); |
| 14472 | } else { |
| 14473 | guard_.reset_device(options.device()); |
| 14474 | } |
| 14475 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14476 | if (!names.empty()) { |
| 14477 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14478 | } |
| 14479 | // super must happen after, so that downstream can use maybe_get_output |
| 14480 | // to retrieve the output |
| 14481 | at::meta::structured_fmin::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14482 | } |
| 14483 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14484 | return *outputs_[output_idx]; |
| 14485 | } |
| 14486 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 14487 | c10::OptionalDeviceGuard guard_; |
| 14488 | }; |
| 14489 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_fmin(const at::Tensor & self, const at::Tensor & other) { |
| 14490 | structured_fmin_default_backend_functional op; |
| 14491 | op.meta(self, other); |
| 14492 | at::fmin_outf(self, other, *op.outputs_[0]); |
| 14493 | return std::move(op.outputs_[0]).take(); |
| 14494 | } |
| 14495 | struct structured_fmax_default_backend_functional final : public at::meta::structured_fmax { |
| 14496 | void set_output_strided( |
| 14497 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14498 | TensorOptions options, DimnameList names |
| 14499 | ) override { |
| 14500 | auto current_device = guard_.current_device(); |
| 14501 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14502 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14503 | "structured kernels don't support multi-device outputs"); |
| 14504 | } else { |
| 14505 | guard_.reset_device(options.device()); |
| 14506 | } |
| 14507 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14508 | if (!names.empty()) { |
| 14509 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14510 | } |
| 14511 | // super must happen after, so that downstream can use maybe_get_output |
| 14512 | // to retrieve the output |
| 14513 | at::meta::structured_fmax::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14514 | } |
| 14515 | void set_output_raw_strided( |
| 14516 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14517 | TensorOptions options, DimnameList names |
| 14518 | ) override { |
| 14519 | auto current_device = guard_.current_device(); |
| 14520 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14521 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14522 | "structured kernels don't support multi-device outputs"); |
| 14523 | } else { |
| 14524 | guard_.reset_device(options.device()); |
| 14525 | } |
| 14526 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14527 | if (!names.empty()) { |
| 14528 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14529 | } |
| 14530 | // super must happen after, so that downstream can use maybe_get_output |
| 14531 | // to retrieve the output |
| 14532 | at::meta::structured_fmax::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14533 | } |
| 14534 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14535 | return *outputs_[output_idx]; |
| 14536 | } |
| 14537 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 14538 | c10::OptionalDeviceGuard guard_; |
| 14539 | }; |
| 14540 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_fmax(const at::Tensor & self, const at::Tensor & other) { |
| 14541 | structured_fmax_default_backend_functional op; |
| 14542 | op.meta(self, other); |
| 14543 | at::fmax_outf(self, other, *op.outputs_[0]); |
| 14544 | return std::move(op.outputs_[0]).take(); |
| 14545 | } |
| 14546 | struct structured_maximum_default_backend_functional final : public at::meta::structured_maximum { |
| 14547 | void set_output_strided( |
| 14548 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14549 | TensorOptions options, DimnameList names |
| 14550 | ) override { |
| 14551 | auto current_device = guard_.current_device(); |
| 14552 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14553 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14554 | "structured kernels don't support multi-device outputs"); |
| 14555 | } else { |
| 14556 | guard_.reset_device(options.device()); |
| 14557 | } |
| 14558 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14559 | if (!names.empty()) { |
| 14560 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14561 | } |
| 14562 | // super must happen after, so that downstream can use maybe_get_output |
| 14563 | // to retrieve the output |
| 14564 | at::meta::structured_maximum::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14565 | } |
| 14566 | void set_output_raw_strided( |
| 14567 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14568 | TensorOptions options, DimnameList names |
| 14569 | ) override { |
| 14570 | auto current_device = guard_.current_device(); |
| 14571 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14572 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14573 | "structured kernels don't support multi-device outputs"); |
| 14574 | } else { |
| 14575 | guard_.reset_device(options.device()); |
| 14576 | } |
| 14577 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14578 | if (!names.empty()) { |
| 14579 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14580 | } |
| 14581 | // super must happen after, so that downstream can use maybe_get_output |
| 14582 | // to retrieve the output |
| 14583 | at::meta::structured_maximum::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14584 | } |
| 14585 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14586 | return *outputs_[output_idx]; |
| 14587 | } |
| 14588 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 14589 | c10::OptionalDeviceGuard guard_; |
| 14590 | }; |
| 14591 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_maximum(const at::Tensor & self, const at::Tensor & other) { |
| 14592 | structured_maximum_default_backend_functional op; |
| 14593 | op.meta(self, other); |
| 14594 | at::maximum_outf(self, other, *op.outputs_[0]); |
| 14595 | return std::move(op.outputs_[0]).take(); |
| 14596 | } |
| 14597 | struct structured_minimum_default_backend_functional final : public at::meta::structured_minimum { |
| 14598 | void set_output_strided( |
| 14599 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14600 | TensorOptions options, DimnameList names |
| 14601 | ) override { |
| 14602 | auto current_device = guard_.current_device(); |
| 14603 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14604 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14605 | "structured kernels don't support multi-device outputs"); |
| 14606 | } else { |
| 14607 | guard_.reset_device(options.device()); |
| 14608 | } |
| 14609 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14610 | if (!names.empty()) { |
| 14611 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14612 | } |
| 14613 | // super must happen after, so that downstream can use maybe_get_output |
| 14614 | // to retrieve the output |
| 14615 | at::meta::structured_minimum::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14616 | } |
| 14617 | void set_output_raw_strided( |
| 14618 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14619 | TensorOptions options, DimnameList names |
| 14620 | ) override { |
| 14621 | auto current_device = guard_.current_device(); |
| 14622 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14623 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14624 | "structured kernels don't support multi-device outputs"); |
| 14625 | } else { |
| 14626 | guard_.reset_device(options.device()); |
| 14627 | } |
| 14628 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14629 | if (!names.empty()) { |
| 14630 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14631 | } |
| 14632 | // super must happen after, so that downstream can use maybe_get_output |
| 14633 | // to retrieve the output |
| 14634 | at::meta::structured_minimum::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14635 | } |
| 14636 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14637 | return *outputs_[output_idx]; |
| 14638 | } |
| 14639 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 14640 | c10::OptionalDeviceGuard guard_; |
| 14641 | }; |
| 14642 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_minimum(const at::Tensor & self, const at::Tensor & other) { |
| 14643 | structured_minimum_default_backend_functional op; |
| 14644 | op.meta(self, other); |
| 14645 | at::minimum_outf(self, other, *op.outputs_[0]); |
| 14646 | return std::move(op.outputs_[0]).take(); |
| 14647 | } |
| 14648 | struct structured_sort_stable_default_backend_functional final : public at::meta::structured_sort_stable { |
| 14649 | void set_output_strided( |
| 14650 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14651 | TensorOptions options, DimnameList names |
| 14652 | ) override { |
| 14653 | auto current_device = guard_.current_device(); |
| 14654 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14655 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14656 | "structured kernels don't support multi-device outputs"); |
| 14657 | } else { |
| 14658 | guard_.reset_device(options.device()); |
| 14659 | } |
| 14660 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14661 | if (!names.empty()) { |
| 14662 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14663 | } |
| 14664 | // super must happen after, so that downstream can use maybe_get_output |
| 14665 | // to retrieve the output |
| 14666 | } |
| 14667 | void set_output_raw_strided( |
| 14668 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14669 | TensorOptions options, DimnameList names |
| 14670 | ) override { |
| 14671 | auto current_device = guard_.current_device(); |
| 14672 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14673 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14674 | "structured kernels don't support multi-device outputs"); |
| 14675 | } else { |
| 14676 | guard_.reset_device(options.device()); |
| 14677 | } |
| 14678 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14679 | if (!names.empty()) { |
| 14680 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14681 | } |
| 14682 | // super must happen after, so that downstream can use maybe_get_output |
| 14683 | // to retrieve the output |
| 14684 | } |
| 14685 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14686 | return *outputs_[output_idx]; |
| 14687 | } |
| 14688 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 14689 | c10::OptionalDeviceGuard guard_; |
| 14690 | }; |
| 14691 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CompositeExplicitAutogradNonFunctional_sort_stable(const at::Tensor & self, c10::optional<bool> stable, int64_t dim, bool descending) { |
| 14692 | structured_sort_stable_default_backend_functional op; |
| 14693 | op.meta(self, stable, dim, descending); |
| 14694 | at::sort_outf(self, stable, dim, descending, *op.outputs_[0], *op.outputs_[1]); |
| 14695 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 14696 | } |
| 14697 | struct structured_topk_default_backend_functional final : public at::meta::structured_topk { |
| 14698 | void set_output_strided( |
| 14699 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14700 | TensorOptions options, DimnameList names |
| 14701 | ) override { |
| 14702 | auto current_device = guard_.current_device(); |
| 14703 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14704 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14705 | "structured kernels don't support multi-device outputs"); |
| 14706 | } else { |
| 14707 | guard_.reset_device(options.device()); |
| 14708 | } |
| 14709 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14710 | if (!names.empty()) { |
| 14711 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14712 | } |
| 14713 | // super must happen after, so that downstream can use maybe_get_output |
| 14714 | // to retrieve the output |
| 14715 | } |
| 14716 | void set_output_raw_strided( |
| 14717 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14718 | TensorOptions options, DimnameList names |
| 14719 | ) override { |
| 14720 | auto current_device = guard_.current_device(); |
| 14721 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14722 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14723 | "structured kernels don't support multi-device outputs"); |
| 14724 | } else { |
| 14725 | guard_.reset_device(options.device()); |
| 14726 | } |
| 14727 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14728 | if (!names.empty()) { |
| 14729 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14730 | } |
| 14731 | // super must happen after, so that downstream can use maybe_get_output |
| 14732 | // to retrieve the output |
| 14733 | } |
| 14734 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14735 | return *outputs_[output_idx]; |
| 14736 | } |
| 14737 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 14738 | c10::OptionalDeviceGuard guard_; |
| 14739 | }; |
| 14740 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CompositeExplicitAutogradNonFunctional_topk(const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted) { |
| 14741 | structured_topk_default_backend_functional op; |
| 14742 | op.meta(self, k, dim, largest, sorted); |
| 14743 | at::topk_outf(self, k, dim, largest, sorted, *op.outputs_[0], *op.outputs_[1]); |
| 14744 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 14745 | } |
| 14746 | struct structured_all_default_backend_functional final : public at::meta::structured_all { |
| 14747 | void set_output_strided( |
| 14748 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14749 | TensorOptions options, DimnameList names |
| 14750 | ) override { |
| 14751 | auto current_device = guard_.current_device(); |
| 14752 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14753 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14754 | "structured kernels don't support multi-device outputs"); |
| 14755 | } else { |
| 14756 | guard_.reset_device(options.device()); |
| 14757 | } |
| 14758 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14759 | if (!names.empty()) { |
| 14760 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14761 | } |
| 14762 | // super must happen after, so that downstream can use maybe_get_output |
| 14763 | // to retrieve the output |
| 14764 | } |
| 14765 | void set_output_raw_strided( |
| 14766 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14767 | TensorOptions options, DimnameList names |
| 14768 | ) override { |
| 14769 | auto current_device = guard_.current_device(); |
| 14770 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14771 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14772 | "structured kernels don't support multi-device outputs"); |
| 14773 | } else { |
| 14774 | guard_.reset_device(options.device()); |
| 14775 | } |
| 14776 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14777 | if (!names.empty()) { |
| 14778 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14779 | } |
| 14780 | // super must happen after, so that downstream can use maybe_get_output |
| 14781 | // to retrieve the output |
| 14782 | } |
| 14783 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14784 | return *outputs_[output_idx]; |
| 14785 | } |
| 14786 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 14787 | c10::OptionalDeviceGuard guard_; |
| 14788 | }; |
| 14789 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_all(const at::Tensor & self) { |
| 14790 | structured_all_default_backend_functional op; |
| 14791 | op.meta(self); |
| 14792 | at::all_outf(self, *op.outputs_[0]); |
| 14793 | return std::move(op.outputs_[0]).take(); |
| 14794 | } |
| 14795 | struct structured_any_default_backend_functional final : public at::meta::structured_any { |
| 14796 | void set_output_strided( |
| 14797 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14798 | TensorOptions options, DimnameList names |
| 14799 | ) override { |
| 14800 | auto current_device = guard_.current_device(); |
| 14801 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14802 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14803 | "structured kernels don't support multi-device outputs"); |
| 14804 | } else { |
| 14805 | guard_.reset_device(options.device()); |
| 14806 | } |
| 14807 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14808 | if (!names.empty()) { |
| 14809 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14810 | } |
| 14811 | // super must happen after, so that downstream can use maybe_get_output |
| 14812 | // to retrieve the output |
| 14813 | } |
| 14814 | void set_output_raw_strided( |
| 14815 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14816 | TensorOptions options, DimnameList names |
| 14817 | ) override { |
| 14818 | auto current_device = guard_.current_device(); |
| 14819 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14820 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14821 | "structured kernels don't support multi-device outputs"); |
| 14822 | } else { |
| 14823 | guard_.reset_device(options.device()); |
| 14824 | } |
| 14825 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14826 | if (!names.empty()) { |
| 14827 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14828 | } |
| 14829 | // super must happen after, so that downstream can use maybe_get_output |
| 14830 | // to retrieve the output |
| 14831 | } |
| 14832 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14833 | return *outputs_[output_idx]; |
| 14834 | } |
| 14835 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 14836 | c10::OptionalDeviceGuard guard_; |
| 14837 | }; |
| 14838 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_any(const at::Tensor & self) { |
| 14839 | structured_any_default_backend_functional op; |
| 14840 | op.meta(self); |
| 14841 | at::any_outf(self, *op.outputs_[0]); |
| 14842 | return std::move(op.outputs_[0]).take(); |
| 14843 | } |
| 14844 | struct structured_renorm_default_backend_functional final : public at::meta::structured_renorm { |
| 14845 | void set_output_strided( |
| 14846 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14847 | TensorOptions options, DimnameList names |
| 14848 | ) override { |
| 14849 | auto current_device = guard_.current_device(); |
| 14850 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14851 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14852 | "structured kernels don't support multi-device outputs"); |
| 14853 | } else { |
| 14854 | guard_.reset_device(options.device()); |
| 14855 | } |
| 14856 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14857 | if (!names.empty()) { |
| 14858 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14859 | } |
| 14860 | // super must happen after, so that downstream can use maybe_get_output |
| 14861 | // to retrieve the output |
| 14862 | } |
| 14863 | void set_output_raw_strided( |
| 14864 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14865 | TensorOptions options, DimnameList names |
| 14866 | ) override { |
| 14867 | auto current_device = guard_.current_device(); |
| 14868 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14869 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14870 | "structured kernels don't support multi-device outputs"); |
| 14871 | } else { |
| 14872 | guard_.reset_device(options.device()); |
| 14873 | } |
| 14874 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14875 | if (!names.empty()) { |
| 14876 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14877 | } |
| 14878 | // super must happen after, so that downstream can use maybe_get_output |
| 14879 | // to retrieve the output |
| 14880 | } |
| 14881 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14882 | return *outputs_[output_idx]; |
| 14883 | } |
| 14884 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 14885 | c10::OptionalDeviceGuard guard_; |
| 14886 | }; |
| 14887 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_renorm(const at::Tensor & self, const at::Scalar & p, int64_t dim, const at::Scalar & maxnorm) { |
| 14888 | structured_renorm_default_backend_functional op; |
| 14889 | op.meta(self, p, dim, maxnorm); |
| 14890 | at::renorm_outf(self, p, dim, maxnorm, *op.outputs_[0]); |
| 14891 | return std::move(op.outputs_[0]).take(); |
| 14892 | } |
| 14893 | struct structured_renorm_default_backend_inplace final : public at::meta::structured_renorm { |
| 14894 | structured_renorm_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 14895 | void set_output_strided( |
| 14896 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14897 | TensorOptions options, DimnameList names |
| 14898 | ) override { |
| 14899 | auto current_device = guard_.current_device(); |
| 14900 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14901 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14902 | "structured kernels don't support multi-device outputs"); |
| 14903 | } else { |
| 14904 | guard_.reset_device(options.device()); |
| 14905 | } |
| 14906 | const auto& out = outputs_[output_idx].get(); |
| 14907 | check_inplace(out, sizes, options); |
| 14908 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 14909 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 14910 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 14911 | } |
| 14912 | if (!names.empty()) { |
| 14913 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14914 | } |
| 14915 | // super must happen after, so that downstream can use maybe_get_output |
| 14916 | // to retrieve the output |
| 14917 | } |
| 14918 | void set_output_raw_strided( |
| 14919 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14920 | TensorOptions options, DimnameList names |
| 14921 | ) override { |
| 14922 | auto current_device = guard_.current_device(); |
| 14923 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14924 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14925 | "structured kernels don't support multi-device outputs"); |
| 14926 | } else { |
| 14927 | guard_.reset_device(options.device()); |
| 14928 | } |
| 14929 | const auto& out = outputs_[output_idx].get(); |
| 14930 | check_inplace(out, sizes, options); |
| 14931 | if (!names.empty()) { |
| 14932 | namedinference::propagate_names(outputs_[output_idx], names); |
| 14933 | } |
| 14934 | // super must happen after, so that downstream can use maybe_get_output |
| 14935 | // to retrieve the output |
| 14936 | } |
| 14937 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14938 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 14939 | } |
| 14940 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 14941 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 14942 | c10::OptionalDeviceGuard guard_; |
| 14943 | }; |
| 14944 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_renorm_(at::Tensor & self, const at::Scalar & p, int64_t dim, const at::Scalar & maxnorm) { |
| 14945 | structured_renorm_default_backend_inplace op(self); |
| 14946 | op.meta(self, p, dim, maxnorm); |
| 14947 | at::renorm_outf(self, p, dim, maxnorm, op.outputs_[0]); |
| 14948 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 14949 | return self; |
| 14950 | } |
| 14951 | struct structured_pow_Tensor_Tensor_default_backend_functional final : public at::meta::structured_pow_Tensor_Tensor { |
| 14952 | void set_output_strided( |
| 14953 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14954 | TensorOptions options, DimnameList names |
| 14955 | ) override { |
| 14956 | auto current_device = guard_.current_device(); |
| 14957 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14958 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14959 | "structured kernels don't support multi-device outputs"); |
| 14960 | } else { |
| 14961 | guard_.reset_device(options.device()); |
| 14962 | } |
| 14963 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14964 | if (!names.empty()) { |
| 14965 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14966 | } |
| 14967 | // super must happen after, so that downstream can use maybe_get_output |
| 14968 | // to retrieve the output |
| 14969 | at::meta::structured_pow_Tensor_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14970 | } |
| 14971 | void set_output_raw_strided( |
| 14972 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 14973 | TensorOptions options, DimnameList names |
| 14974 | ) override { |
| 14975 | auto current_device = guard_.current_device(); |
| 14976 | if (C10_UNLIKELY(current_device.has_value())) { |
| 14977 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 14978 | "structured kernels don't support multi-device outputs"); |
| 14979 | } else { |
| 14980 | guard_.reset_device(options.device()); |
| 14981 | } |
| 14982 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 14983 | if (!names.empty()) { |
| 14984 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 14985 | } |
| 14986 | // super must happen after, so that downstream can use maybe_get_output |
| 14987 | // to retrieve the output |
| 14988 | at::meta::structured_pow_Tensor_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 14989 | } |
| 14990 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 14991 | return *outputs_[output_idx]; |
| 14992 | } |
| 14993 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 14994 | c10::OptionalDeviceGuard guard_; |
| 14995 | }; |
| 14996 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_pow_Tensor_Tensor(const at::Tensor & self, const at::Tensor & exponent) { |
| 14997 | structured_pow_Tensor_Tensor_default_backend_functional op; |
| 14998 | op.meta(self, exponent); |
| 14999 | at::pow_outf(self, exponent, *op.outputs_[0]); |
| 15000 | return std::move(op.outputs_[0]).take(); |
| 15001 | } |
| 15002 | struct structured_pow_Tensor_Tensor_default_backend_inplace final : public at::meta::structured_pow_Tensor_Tensor { |
| 15003 | structured_pow_Tensor_Tensor_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 15004 | void set_output_strided( |
| 15005 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15006 | TensorOptions options, DimnameList names |
| 15007 | ) override { |
| 15008 | auto current_device = guard_.current_device(); |
| 15009 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15010 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15011 | "structured kernels don't support multi-device outputs"); |
| 15012 | } else { |
| 15013 | guard_.reset_device(options.device()); |
| 15014 | } |
| 15015 | const auto& out = outputs_[output_idx].get(); |
| 15016 | check_inplace(out, sizes, options); |
| 15017 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 15018 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 15019 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 15020 | } |
| 15021 | if (!names.empty()) { |
| 15022 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15023 | } |
| 15024 | // super must happen after, so that downstream can use maybe_get_output |
| 15025 | // to retrieve the output |
| 15026 | at::meta::structured_pow_Tensor_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15027 | } |
| 15028 | void set_output_raw_strided( |
| 15029 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15030 | TensorOptions options, DimnameList names |
| 15031 | ) override { |
| 15032 | auto current_device = guard_.current_device(); |
| 15033 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15034 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15035 | "structured kernels don't support multi-device outputs"); |
| 15036 | } else { |
| 15037 | guard_.reset_device(options.device()); |
| 15038 | } |
| 15039 | const auto& out = outputs_[output_idx].get(); |
| 15040 | check_inplace(out, sizes, options); |
| 15041 | if (!names.empty()) { |
| 15042 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15043 | } |
| 15044 | // super must happen after, so that downstream can use maybe_get_output |
| 15045 | // to retrieve the output |
| 15046 | at::meta::structured_pow_Tensor_Tensor::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15047 | } |
| 15048 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15049 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 15050 | } |
| 15051 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 15052 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 15053 | c10::OptionalDeviceGuard guard_; |
| 15054 | }; |
| 15055 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_pow__Tensor(at::Tensor & self, const at::Tensor & exponent) { |
| 15056 | structured_pow_Tensor_Tensor_default_backend_inplace op(self); |
| 15057 | op.meta(self, exponent); |
| 15058 | at::pow_outf(self, exponent, op.outputs_[0]); |
| 15059 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 15060 | return self; |
| 15061 | } |
| 15062 | struct structured_pow_Scalar_default_backend_functional final : public at::meta::structured_pow_Scalar { |
| 15063 | void set_output_strided( |
| 15064 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15065 | TensorOptions options, DimnameList names |
| 15066 | ) override { |
| 15067 | auto current_device = guard_.current_device(); |
| 15068 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15069 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15070 | "structured kernels don't support multi-device outputs"); |
| 15071 | } else { |
| 15072 | guard_.reset_device(options.device()); |
| 15073 | } |
| 15074 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15075 | if (!names.empty()) { |
| 15076 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15077 | } |
| 15078 | // super must happen after, so that downstream can use maybe_get_output |
| 15079 | // to retrieve the output |
| 15080 | } |
| 15081 | void set_output_raw_strided( |
| 15082 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15083 | TensorOptions options, DimnameList names |
| 15084 | ) override { |
| 15085 | auto current_device = guard_.current_device(); |
| 15086 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15087 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15088 | "structured kernels don't support multi-device outputs"); |
| 15089 | } else { |
| 15090 | guard_.reset_device(options.device()); |
| 15091 | } |
| 15092 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15093 | if (!names.empty()) { |
| 15094 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15095 | } |
| 15096 | // super must happen after, so that downstream can use maybe_get_output |
| 15097 | // to retrieve the output |
| 15098 | } |
| 15099 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15100 | return *outputs_[output_idx]; |
| 15101 | } |
| 15102 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 15103 | c10::OptionalDeviceGuard guard_; |
| 15104 | }; |
| 15105 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_pow_Scalar(const at::Scalar & self, const at::Tensor & exponent) { |
| 15106 | structured_pow_Scalar_default_backend_functional op; |
| 15107 | op.meta(self, exponent); |
| 15108 | at::pow_outf(self, exponent, *op.outputs_[0]); |
| 15109 | return std::move(op.outputs_[0]).take(); |
| 15110 | } |
| 15111 | struct structured_pow_Tensor_Scalar_default_backend_functional final : public at::meta::structured_pow_Tensor_Scalar { |
| 15112 | void set_output_strided( |
| 15113 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15114 | TensorOptions options, DimnameList names |
| 15115 | ) override { |
| 15116 | auto current_device = guard_.current_device(); |
| 15117 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15118 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15119 | "structured kernels don't support multi-device outputs"); |
| 15120 | } else { |
| 15121 | guard_.reset_device(options.device()); |
| 15122 | } |
| 15123 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15124 | if (!names.empty()) { |
| 15125 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15126 | } |
| 15127 | // super must happen after, so that downstream can use maybe_get_output |
| 15128 | // to retrieve the output |
| 15129 | at::meta::structured_pow_Tensor_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15130 | } |
| 15131 | void set_output_raw_strided( |
| 15132 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15133 | TensorOptions options, DimnameList names |
| 15134 | ) override { |
| 15135 | auto current_device = guard_.current_device(); |
| 15136 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15137 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15138 | "structured kernels don't support multi-device outputs"); |
| 15139 | } else { |
| 15140 | guard_.reset_device(options.device()); |
| 15141 | } |
| 15142 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15143 | if (!names.empty()) { |
| 15144 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15145 | } |
| 15146 | // super must happen after, so that downstream can use maybe_get_output |
| 15147 | // to retrieve the output |
| 15148 | at::meta::structured_pow_Tensor_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15149 | } |
| 15150 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15151 | return *outputs_[output_idx]; |
| 15152 | } |
| 15153 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 15154 | c10::OptionalDeviceGuard guard_; |
| 15155 | }; |
| 15156 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_pow_Tensor_Scalar(const at::Tensor & self, const at::Scalar & exponent) { |
| 15157 | structured_pow_Tensor_Scalar_default_backend_functional op; |
| 15158 | op.meta(self, exponent); |
| 15159 | at::pow_outf(self, exponent, *op.outputs_[0]); |
| 15160 | return std::move(op.outputs_[0]).take(); |
| 15161 | } |
| 15162 | struct structured_pow_Tensor_Scalar_default_backend_inplace final : public at::meta::structured_pow_Tensor_Scalar { |
| 15163 | structured_pow_Tensor_Scalar_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 15164 | void set_output_strided( |
| 15165 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15166 | TensorOptions options, DimnameList names |
| 15167 | ) override { |
| 15168 | auto current_device = guard_.current_device(); |
| 15169 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15170 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15171 | "structured kernels don't support multi-device outputs"); |
| 15172 | } else { |
| 15173 | guard_.reset_device(options.device()); |
| 15174 | } |
| 15175 | const auto& out = outputs_[output_idx].get(); |
| 15176 | check_inplace(out, sizes, options); |
| 15177 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 15178 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 15179 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 15180 | } |
| 15181 | if (!names.empty()) { |
| 15182 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15183 | } |
| 15184 | // super must happen after, so that downstream can use maybe_get_output |
| 15185 | // to retrieve the output |
| 15186 | at::meta::structured_pow_Tensor_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15187 | } |
| 15188 | void set_output_raw_strided( |
| 15189 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15190 | TensorOptions options, DimnameList names |
| 15191 | ) override { |
| 15192 | auto current_device = guard_.current_device(); |
| 15193 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15194 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15195 | "structured kernels don't support multi-device outputs"); |
| 15196 | } else { |
| 15197 | guard_.reset_device(options.device()); |
| 15198 | } |
| 15199 | const auto& out = outputs_[output_idx].get(); |
| 15200 | check_inplace(out, sizes, options); |
| 15201 | if (!names.empty()) { |
| 15202 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15203 | } |
| 15204 | // super must happen after, so that downstream can use maybe_get_output |
| 15205 | // to retrieve the output |
| 15206 | at::meta::structured_pow_Tensor_Scalar::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15207 | } |
| 15208 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15209 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 15210 | } |
| 15211 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 15212 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 15213 | c10::OptionalDeviceGuard guard_; |
| 15214 | }; |
| 15215 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_pow__Scalar(at::Tensor & self, const at::Scalar & exponent) { |
| 15216 | structured_pow_Tensor_Scalar_default_backend_inplace op(self); |
| 15217 | op.meta(self, exponent); |
| 15218 | at::pow_outf(self, exponent, op.outputs_[0]); |
| 15219 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 15220 | return self; |
| 15221 | } |
| 15222 | struct structured__convert_indices_from_coo_to_csr_default_backend_functional final : public at::meta::structured__convert_indices_from_coo_to_csr { |
| 15223 | void set_output_strided( |
| 15224 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15225 | TensorOptions options, DimnameList names |
| 15226 | ) override { |
| 15227 | auto current_device = guard_.current_device(); |
| 15228 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15229 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15230 | "structured kernels don't support multi-device outputs"); |
| 15231 | } else { |
| 15232 | guard_.reset_device(options.device()); |
| 15233 | } |
| 15234 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15235 | if (!names.empty()) { |
| 15236 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15237 | } |
| 15238 | // super must happen after, so that downstream can use maybe_get_output |
| 15239 | // to retrieve the output |
| 15240 | } |
| 15241 | void set_output_raw_strided( |
| 15242 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15243 | TensorOptions options, DimnameList names |
| 15244 | ) override { |
| 15245 | auto current_device = guard_.current_device(); |
| 15246 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15247 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15248 | "structured kernels don't support multi-device outputs"); |
| 15249 | } else { |
| 15250 | guard_.reset_device(options.device()); |
| 15251 | } |
| 15252 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15253 | if (!names.empty()) { |
| 15254 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15255 | } |
| 15256 | // super must happen after, so that downstream can use maybe_get_output |
| 15257 | // to retrieve the output |
| 15258 | } |
| 15259 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15260 | return *outputs_[output_idx]; |
| 15261 | } |
| 15262 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 15263 | c10::OptionalDeviceGuard guard_; |
| 15264 | }; |
| 15265 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__convert_indices_from_coo_to_csr(const at::Tensor & self, int64_t size, bool out_int32) { |
| 15266 | structured__convert_indices_from_coo_to_csr_default_backend_functional op; |
| 15267 | op.meta(self, size, out_int32); |
| 15268 | at::_convert_indices_from_coo_to_csr_outf(self, size, out_int32, *op.outputs_[0]); |
| 15269 | return std::move(op.outputs_[0]).take(); |
| 15270 | } |
| 15271 | struct structured__convert_indices_from_csr_to_coo_default_backend_functional final : public at::meta::structured__convert_indices_from_csr_to_coo { |
| 15272 | void set_output_strided( |
| 15273 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15274 | TensorOptions options, DimnameList names |
| 15275 | ) override { |
| 15276 | auto current_device = guard_.current_device(); |
| 15277 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15278 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15279 | "structured kernels don't support multi-device outputs"); |
| 15280 | } else { |
| 15281 | guard_.reset_device(options.device()); |
| 15282 | } |
| 15283 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15284 | if (!names.empty()) { |
| 15285 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15286 | } |
| 15287 | // super must happen after, so that downstream can use maybe_get_output |
| 15288 | // to retrieve the output |
| 15289 | } |
| 15290 | void set_output_raw_strided( |
| 15291 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15292 | TensorOptions options, DimnameList names |
| 15293 | ) override { |
| 15294 | auto current_device = guard_.current_device(); |
| 15295 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15296 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15297 | "structured kernels don't support multi-device outputs"); |
| 15298 | } else { |
| 15299 | guard_.reset_device(options.device()); |
| 15300 | } |
| 15301 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15302 | if (!names.empty()) { |
| 15303 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15304 | } |
| 15305 | // super must happen after, so that downstream can use maybe_get_output |
| 15306 | // to retrieve the output |
| 15307 | } |
| 15308 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15309 | return *outputs_[output_idx]; |
| 15310 | } |
| 15311 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 15312 | c10::OptionalDeviceGuard guard_; |
| 15313 | }; |
| 15314 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__convert_indices_from_csr_to_coo(const at::Tensor & crow_indices, const at::Tensor & col_indices, bool out_int32, bool transpose) { |
| 15315 | structured__convert_indices_from_csr_to_coo_default_backend_functional op; |
| 15316 | op.meta(crow_indices, col_indices, out_int32, transpose); |
| 15317 | at::_convert_indices_from_csr_to_coo_outf(crow_indices, col_indices, out_int32, transpose, *op.outputs_[0]); |
| 15318 | return std::move(op.outputs_[0]).take(); |
| 15319 | } |
| 15320 | struct structured_mse_loss_default_backend_functional final : public at::meta::structured_mse_loss { |
| 15321 | void set_output_strided( |
| 15322 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15323 | TensorOptions options, DimnameList names |
| 15324 | ) override { |
| 15325 | auto current_device = guard_.current_device(); |
| 15326 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15327 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15328 | "structured kernels don't support multi-device outputs"); |
| 15329 | } else { |
| 15330 | guard_.reset_device(options.device()); |
| 15331 | } |
| 15332 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15333 | if (!names.empty()) { |
| 15334 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15335 | } |
| 15336 | // super must happen after, so that downstream can use maybe_get_output |
| 15337 | // to retrieve the output |
| 15338 | at::meta::structured_mse_loss::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15339 | } |
| 15340 | void set_output_raw_strided( |
| 15341 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15342 | TensorOptions options, DimnameList names |
| 15343 | ) override { |
| 15344 | auto current_device = guard_.current_device(); |
| 15345 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15346 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15347 | "structured kernels don't support multi-device outputs"); |
| 15348 | } else { |
| 15349 | guard_.reset_device(options.device()); |
| 15350 | } |
| 15351 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15352 | if (!names.empty()) { |
| 15353 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15354 | } |
| 15355 | // super must happen after, so that downstream can use maybe_get_output |
| 15356 | // to retrieve the output |
| 15357 | at::meta::structured_mse_loss::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15358 | } |
| 15359 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15360 | return *outputs_[output_idx]; |
| 15361 | } |
| 15362 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 15363 | c10::OptionalDeviceGuard guard_; |
| 15364 | }; |
| 15365 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_mse_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction) { |
| 15366 | structured_mse_loss_default_backend_functional op; |
| 15367 | op.meta(self, target, reduction); |
| 15368 | at::mse_loss_outf(self, target, reduction, *op.outputs_[0]); |
| 15369 | return std::move(op.outputs_[0]).take(); |
| 15370 | } |
| 15371 | struct structured_nll_loss_forward_default_backend_functional final : public at::meta::structured_nll_loss_forward { |
| 15372 | void set_output_strided( |
| 15373 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15374 | TensorOptions options, DimnameList names |
| 15375 | ) override { |
| 15376 | auto current_device = guard_.current_device(); |
| 15377 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15378 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15379 | "structured kernels don't support multi-device outputs"); |
| 15380 | } else { |
| 15381 | guard_.reset_device(options.device()); |
| 15382 | } |
| 15383 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15384 | if (!names.empty()) { |
| 15385 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15386 | } |
| 15387 | // super must happen after, so that downstream can use maybe_get_output |
| 15388 | // to retrieve the output |
| 15389 | } |
| 15390 | void set_output_raw_strided( |
| 15391 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15392 | TensorOptions options, DimnameList names |
| 15393 | ) override { |
| 15394 | auto current_device = guard_.current_device(); |
| 15395 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15396 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15397 | "structured kernels don't support multi-device outputs"); |
| 15398 | } else { |
| 15399 | guard_.reset_device(options.device()); |
| 15400 | } |
| 15401 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15402 | if (!names.empty()) { |
| 15403 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15404 | } |
| 15405 | // super must happen after, so that downstream can use maybe_get_output |
| 15406 | // to retrieve the output |
| 15407 | } |
| 15408 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15409 | return *outputs_[output_idx]; |
| 15410 | } |
| 15411 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 15412 | c10::OptionalDeviceGuard guard_; |
| 15413 | }; |
| 15414 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CompositeExplicitAutogradNonFunctional_nll_loss_forward(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index) { |
| 15415 | structured_nll_loss_forward_default_backend_functional op; |
| 15416 | op.meta(self, target, ((weight.has_value() && (*weight).defined()) ? at::OptionalTensorRef(*weight) : at::OptionalTensorRef()), reduction, ignore_index); |
| 15417 | at::nll_loss_forward_outf(self, target, weight, reduction, ignore_index, *op.outputs_[0], *op.outputs_[1]); |
| 15418 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 15419 | } |
| 15420 | struct structured_nll_loss_backward_default_backend_functional final : public at::meta::structured_nll_loss_backward { |
| 15421 | void set_output_strided( |
| 15422 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15423 | TensorOptions options, DimnameList names |
| 15424 | ) override { |
| 15425 | auto current_device = guard_.current_device(); |
| 15426 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15427 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15428 | "structured kernels don't support multi-device outputs"); |
| 15429 | } else { |
| 15430 | guard_.reset_device(options.device()); |
| 15431 | } |
| 15432 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15433 | if (!names.empty()) { |
| 15434 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15435 | } |
| 15436 | // super must happen after, so that downstream can use maybe_get_output |
| 15437 | // to retrieve the output |
| 15438 | } |
| 15439 | void set_output_raw_strided( |
| 15440 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15441 | TensorOptions options, DimnameList names |
| 15442 | ) override { |
| 15443 | auto current_device = guard_.current_device(); |
| 15444 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15445 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15446 | "structured kernels don't support multi-device outputs"); |
| 15447 | } else { |
| 15448 | guard_.reset_device(options.device()); |
| 15449 | } |
| 15450 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15451 | if (!names.empty()) { |
| 15452 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15453 | } |
| 15454 | // super must happen after, so that downstream can use maybe_get_output |
| 15455 | // to retrieve the output |
| 15456 | } |
| 15457 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15458 | return *outputs_[output_idx]; |
| 15459 | } |
| 15460 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 15461 | c10::OptionalDeviceGuard guard_; |
| 15462 | }; |
| 15463 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_nll_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) { |
| 15464 | structured_nll_loss_backward_default_backend_functional op; |
| 15465 | op.meta(grad_output, self, target, ((weight.has_value() && (*weight).defined()) ? at::OptionalTensorRef(*weight) : at::OptionalTensorRef()), reduction, ignore_index, total_weight); |
| 15466 | at::nll_loss_backward_outf(grad_output, self, target, weight, reduction, ignore_index, total_weight, *op.outputs_[0]); |
| 15467 | return std::move(op.outputs_[0]).take(); |
| 15468 | } |
| 15469 | struct structured_smooth_l1_loss_default_backend_functional final : public at::meta::structured_smooth_l1_loss { |
| 15470 | void set_output_strided( |
| 15471 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15472 | TensorOptions options, DimnameList names |
| 15473 | ) override { |
| 15474 | auto current_device = guard_.current_device(); |
| 15475 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15476 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15477 | "structured kernels don't support multi-device outputs"); |
| 15478 | } else { |
| 15479 | guard_.reset_device(options.device()); |
| 15480 | } |
| 15481 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15482 | if (!names.empty()) { |
| 15483 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15484 | } |
| 15485 | // super must happen after, so that downstream can use maybe_get_output |
| 15486 | // to retrieve the output |
| 15487 | at::meta::structured_smooth_l1_loss::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15488 | } |
| 15489 | void set_output_raw_strided( |
| 15490 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15491 | TensorOptions options, DimnameList names |
| 15492 | ) override { |
| 15493 | auto current_device = guard_.current_device(); |
| 15494 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15495 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15496 | "structured kernels don't support multi-device outputs"); |
| 15497 | } else { |
| 15498 | guard_.reset_device(options.device()); |
| 15499 | } |
| 15500 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15501 | if (!names.empty()) { |
| 15502 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15503 | } |
| 15504 | // super must happen after, so that downstream can use maybe_get_output |
| 15505 | // to retrieve the output |
| 15506 | at::meta::structured_smooth_l1_loss::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15507 | } |
| 15508 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15509 | return *outputs_[output_idx]; |
| 15510 | } |
| 15511 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 15512 | c10::OptionalDeviceGuard guard_; |
| 15513 | }; |
| 15514 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_smooth_l1_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta) { |
| 15515 | structured_smooth_l1_loss_default_backend_functional op; |
| 15516 | op.meta(self, target, reduction, beta); |
| 15517 | at::smooth_l1_loss_outf(self, target, reduction, beta, *op.outputs_[0]); |
| 15518 | return std::move(op.outputs_[0]).take(); |
| 15519 | } |
| 15520 | struct structured_elu_default_backend_functional final : public at::meta::structured_elu { |
| 15521 | void set_output_strided( |
| 15522 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15523 | TensorOptions options, DimnameList names |
| 15524 | ) override { |
| 15525 | auto current_device = guard_.current_device(); |
| 15526 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15527 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15528 | "structured kernels don't support multi-device outputs"); |
| 15529 | } else { |
| 15530 | guard_.reset_device(options.device()); |
| 15531 | } |
| 15532 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15533 | if (!names.empty()) { |
| 15534 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15535 | } |
| 15536 | // super must happen after, so that downstream can use maybe_get_output |
| 15537 | // to retrieve the output |
| 15538 | at::meta::structured_elu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15539 | } |
| 15540 | void set_output_raw_strided( |
| 15541 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15542 | TensorOptions options, DimnameList names |
| 15543 | ) override { |
| 15544 | auto current_device = guard_.current_device(); |
| 15545 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15546 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15547 | "structured kernels don't support multi-device outputs"); |
| 15548 | } else { |
| 15549 | guard_.reset_device(options.device()); |
| 15550 | } |
| 15551 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15552 | if (!names.empty()) { |
| 15553 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15554 | } |
| 15555 | // super must happen after, so that downstream can use maybe_get_output |
| 15556 | // to retrieve the output |
| 15557 | at::meta::structured_elu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15558 | } |
| 15559 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15560 | return *outputs_[output_idx]; |
| 15561 | } |
| 15562 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 15563 | c10::OptionalDeviceGuard guard_; |
| 15564 | }; |
| 15565 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_elu(const at::Tensor & self, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale) { |
| 15566 | structured_elu_default_backend_functional op; |
| 15567 | op.meta(self, alpha, scale, input_scale); |
| 15568 | at::elu_outf(self, alpha, scale, input_scale, *op.outputs_[0]); |
| 15569 | return std::move(op.outputs_[0]).take(); |
| 15570 | } |
| 15571 | struct structured_elu_default_backend_inplace final : public at::meta::structured_elu { |
| 15572 | structured_elu_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 15573 | void set_output_strided( |
| 15574 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15575 | TensorOptions options, DimnameList names |
| 15576 | ) override { |
| 15577 | auto current_device = guard_.current_device(); |
| 15578 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15579 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15580 | "structured kernels don't support multi-device outputs"); |
| 15581 | } else { |
| 15582 | guard_.reset_device(options.device()); |
| 15583 | } |
| 15584 | const auto& out = outputs_[output_idx].get(); |
| 15585 | check_inplace(out, sizes, options); |
| 15586 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 15587 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 15588 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 15589 | } |
| 15590 | if (!names.empty()) { |
| 15591 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15592 | } |
| 15593 | // super must happen after, so that downstream can use maybe_get_output |
| 15594 | // to retrieve the output |
| 15595 | at::meta::structured_elu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15596 | } |
| 15597 | void set_output_raw_strided( |
| 15598 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15599 | TensorOptions options, DimnameList names |
| 15600 | ) override { |
| 15601 | auto current_device = guard_.current_device(); |
| 15602 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15603 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15604 | "structured kernels don't support multi-device outputs"); |
| 15605 | } else { |
| 15606 | guard_.reset_device(options.device()); |
| 15607 | } |
| 15608 | const auto& out = outputs_[output_idx].get(); |
| 15609 | check_inplace(out, sizes, options); |
| 15610 | if (!names.empty()) { |
| 15611 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15612 | } |
| 15613 | // super must happen after, so that downstream can use maybe_get_output |
| 15614 | // to retrieve the output |
| 15615 | at::meta::structured_elu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15616 | } |
| 15617 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15618 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 15619 | } |
| 15620 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 15621 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 15622 | c10::OptionalDeviceGuard guard_; |
| 15623 | }; |
| 15624 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_elu_(at::Tensor & self, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale) { |
| 15625 | structured_elu_default_backend_inplace op(self); |
| 15626 | op.meta(self, alpha, scale, input_scale); |
| 15627 | at::elu_outf(self, alpha, scale, input_scale, op.outputs_[0]); |
| 15628 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 15629 | return self; |
| 15630 | } |
| 15631 | struct structured_elu_backward_default_backend_functional final : public at::meta::structured_elu_backward { |
| 15632 | void set_output_strided( |
| 15633 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15634 | TensorOptions options, DimnameList names |
| 15635 | ) override { |
| 15636 | auto current_device = guard_.current_device(); |
| 15637 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15638 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15639 | "structured kernels don't support multi-device outputs"); |
| 15640 | } else { |
| 15641 | guard_.reset_device(options.device()); |
| 15642 | } |
| 15643 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15644 | if (!names.empty()) { |
| 15645 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15646 | } |
| 15647 | // super must happen after, so that downstream can use maybe_get_output |
| 15648 | // to retrieve the output |
| 15649 | at::meta::structured_elu_backward::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15650 | } |
| 15651 | void set_output_raw_strided( |
| 15652 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15653 | TensorOptions options, DimnameList names |
| 15654 | ) override { |
| 15655 | auto current_device = guard_.current_device(); |
| 15656 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15657 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15658 | "structured kernels don't support multi-device outputs"); |
| 15659 | } else { |
| 15660 | guard_.reset_device(options.device()); |
| 15661 | } |
| 15662 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15663 | if (!names.empty()) { |
| 15664 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15665 | } |
| 15666 | // super must happen after, so that downstream can use maybe_get_output |
| 15667 | // to retrieve the output |
| 15668 | at::meta::structured_elu_backward::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15669 | } |
| 15670 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15671 | return *outputs_[output_idx]; |
| 15672 | } |
| 15673 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 15674 | c10::OptionalDeviceGuard guard_; |
| 15675 | }; |
| 15676 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_elu_backward(const at::Tensor & grad_output, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale, bool is_result, const at::Tensor & self_or_result) { |
| 15677 | structured_elu_backward_default_backend_functional op; |
| 15678 | op.meta(grad_output, alpha, scale, input_scale, is_result, self_or_result); |
| 15679 | at::elu_backward_outf(grad_output, alpha, scale, input_scale, is_result, self_or_result, *op.outputs_[0]); |
| 15680 | return std::move(op.outputs_[0]).take(); |
| 15681 | } |
| 15682 | struct structured_glu_default_backend_functional final : public at::meta::structured_glu { |
| 15683 | void set_output_strided( |
| 15684 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15685 | TensorOptions options, DimnameList names |
| 15686 | ) override { |
| 15687 | auto current_device = guard_.current_device(); |
| 15688 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15689 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15690 | "structured kernels don't support multi-device outputs"); |
| 15691 | } else { |
| 15692 | guard_.reset_device(options.device()); |
| 15693 | } |
| 15694 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15695 | if (!names.empty()) { |
| 15696 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15697 | } |
| 15698 | // super must happen after, so that downstream can use maybe_get_output |
| 15699 | // to retrieve the output |
| 15700 | at::meta::structured_glu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15701 | } |
| 15702 | void set_output_raw_strided( |
| 15703 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15704 | TensorOptions options, DimnameList names |
| 15705 | ) override { |
| 15706 | auto current_device = guard_.current_device(); |
| 15707 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15708 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15709 | "structured kernels don't support multi-device outputs"); |
| 15710 | } else { |
| 15711 | guard_.reset_device(options.device()); |
| 15712 | } |
| 15713 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15714 | if (!names.empty()) { |
| 15715 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15716 | } |
| 15717 | // super must happen after, so that downstream can use maybe_get_output |
| 15718 | // to retrieve the output |
| 15719 | at::meta::structured_glu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15720 | } |
| 15721 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15722 | return *outputs_[output_idx]; |
| 15723 | } |
| 15724 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 15725 | c10::OptionalDeviceGuard guard_; |
| 15726 | }; |
| 15727 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_glu(const at::Tensor & self, int64_t dim) { |
| 15728 | structured_glu_default_backend_functional op; |
| 15729 | op.meta(self, dim); |
| 15730 | at::glu_outf(self, dim, *op.outputs_[0]); |
| 15731 | return std::move(op.outputs_[0]).take(); |
| 15732 | } |
| 15733 | struct structured_hardsigmoid_default_backend_functional final : public at::meta::structured_hardsigmoid { |
| 15734 | void set_output_strided( |
| 15735 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15736 | TensorOptions options, DimnameList names |
| 15737 | ) override { |
| 15738 | auto current_device = guard_.current_device(); |
| 15739 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15740 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15741 | "structured kernels don't support multi-device outputs"); |
| 15742 | } else { |
| 15743 | guard_.reset_device(options.device()); |
| 15744 | } |
| 15745 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15746 | if (!names.empty()) { |
| 15747 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15748 | } |
| 15749 | // super must happen after, so that downstream can use maybe_get_output |
| 15750 | // to retrieve the output |
| 15751 | at::meta::structured_hardsigmoid::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15752 | } |
| 15753 | void set_output_raw_strided( |
| 15754 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15755 | TensorOptions options, DimnameList names |
| 15756 | ) override { |
| 15757 | auto current_device = guard_.current_device(); |
| 15758 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15759 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15760 | "structured kernels don't support multi-device outputs"); |
| 15761 | } else { |
| 15762 | guard_.reset_device(options.device()); |
| 15763 | } |
| 15764 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15765 | if (!names.empty()) { |
| 15766 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15767 | } |
| 15768 | // super must happen after, so that downstream can use maybe_get_output |
| 15769 | // to retrieve the output |
| 15770 | at::meta::structured_hardsigmoid::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15771 | } |
| 15772 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15773 | return *outputs_[output_idx]; |
| 15774 | } |
| 15775 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 15776 | c10::OptionalDeviceGuard guard_; |
| 15777 | }; |
| 15778 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_hardsigmoid(const at::Tensor & self) { |
| 15779 | structured_hardsigmoid_default_backend_functional op; |
| 15780 | op.meta(self); |
| 15781 | at::hardsigmoid_outf(self, *op.outputs_[0]); |
| 15782 | return std::move(op.outputs_[0]).take(); |
| 15783 | } |
| 15784 | struct structured_hardsigmoid_default_backend_inplace final : public at::meta::structured_hardsigmoid { |
| 15785 | structured_hardsigmoid_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 15786 | void set_output_strided( |
| 15787 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15788 | TensorOptions options, DimnameList names |
| 15789 | ) override { |
| 15790 | auto current_device = guard_.current_device(); |
| 15791 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15792 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15793 | "structured kernels don't support multi-device outputs"); |
| 15794 | } else { |
| 15795 | guard_.reset_device(options.device()); |
| 15796 | } |
| 15797 | const auto& out = outputs_[output_idx].get(); |
| 15798 | check_inplace(out, sizes, options); |
| 15799 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 15800 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 15801 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 15802 | } |
| 15803 | if (!names.empty()) { |
| 15804 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15805 | } |
| 15806 | // super must happen after, so that downstream can use maybe_get_output |
| 15807 | // to retrieve the output |
| 15808 | at::meta::structured_hardsigmoid::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15809 | } |
| 15810 | void set_output_raw_strided( |
| 15811 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15812 | TensorOptions options, DimnameList names |
| 15813 | ) override { |
| 15814 | auto current_device = guard_.current_device(); |
| 15815 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15816 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15817 | "structured kernels don't support multi-device outputs"); |
| 15818 | } else { |
| 15819 | guard_.reset_device(options.device()); |
| 15820 | } |
| 15821 | const auto& out = outputs_[output_idx].get(); |
| 15822 | check_inplace(out, sizes, options); |
| 15823 | if (!names.empty()) { |
| 15824 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15825 | } |
| 15826 | // super must happen after, so that downstream can use maybe_get_output |
| 15827 | // to retrieve the output |
| 15828 | at::meta::structured_hardsigmoid::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15829 | } |
| 15830 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15831 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 15832 | } |
| 15833 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 15834 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 15835 | c10::OptionalDeviceGuard guard_; |
| 15836 | }; |
| 15837 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_hardsigmoid_(at::Tensor & self) { |
| 15838 | structured_hardsigmoid_default_backend_inplace op(self); |
| 15839 | op.meta(self); |
| 15840 | at::hardsigmoid_outf(self, op.outputs_[0]); |
| 15841 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 15842 | return self; |
| 15843 | } |
| 15844 | struct structured_hardsigmoid_backward_default_backend_functional final : public at::meta::structured_hardsigmoid_backward { |
| 15845 | void set_output_strided( |
| 15846 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15847 | TensorOptions options, DimnameList names |
| 15848 | ) override { |
| 15849 | auto current_device = guard_.current_device(); |
| 15850 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15851 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15852 | "structured kernels don't support multi-device outputs"); |
| 15853 | } else { |
| 15854 | guard_.reset_device(options.device()); |
| 15855 | } |
| 15856 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15857 | if (!names.empty()) { |
| 15858 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15859 | } |
| 15860 | // super must happen after, so that downstream can use maybe_get_output |
| 15861 | // to retrieve the output |
| 15862 | at::meta::structured_hardsigmoid_backward::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15863 | } |
| 15864 | void set_output_raw_strided( |
| 15865 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15866 | TensorOptions options, DimnameList names |
| 15867 | ) override { |
| 15868 | auto current_device = guard_.current_device(); |
| 15869 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15870 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15871 | "structured kernels don't support multi-device outputs"); |
| 15872 | } else { |
| 15873 | guard_.reset_device(options.device()); |
| 15874 | } |
| 15875 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15876 | if (!names.empty()) { |
| 15877 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15878 | } |
| 15879 | // super must happen after, so that downstream can use maybe_get_output |
| 15880 | // to retrieve the output |
| 15881 | at::meta::structured_hardsigmoid_backward::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15882 | } |
| 15883 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15884 | return *outputs_[output_idx]; |
| 15885 | } |
| 15886 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 15887 | c10::OptionalDeviceGuard guard_; |
| 15888 | }; |
| 15889 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_hardsigmoid_backward(const at::Tensor & grad_output, const at::Tensor & self) { |
| 15890 | structured_hardsigmoid_backward_default_backend_functional op; |
| 15891 | op.meta(grad_output, self); |
| 15892 | at::hardsigmoid_backward_outf(grad_output, self, *op.outputs_[0]); |
| 15893 | return std::move(op.outputs_[0]).take(); |
| 15894 | } |
| 15895 | struct structured_leaky_relu_default_backend_functional final : public at::meta::structured_leaky_relu { |
| 15896 | void set_output_strided( |
| 15897 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15898 | TensorOptions options, DimnameList names |
| 15899 | ) override { |
| 15900 | auto current_device = guard_.current_device(); |
| 15901 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15902 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15903 | "structured kernels don't support multi-device outputs"); |
| 15904 | } else { |
| 15905 | guard_.reset_device(options.device()); |
| 15906 | } |
| 15907 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15908 | if (!names.empty()) { |
| 15909 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15910 | } |
| 15911 | // super must happen after, so that downstream can use maybe_get_output |
| 15912 | // to retrieve the output |
| 15913 | at::meta::structured_leaky_relu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15914 | } |
| 15915 | void set_output_raw_strided( |
| 15916 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15917 | TensorOptions options, DimnameList names |
| 15918 | ) override { |
| 15919 | auto current_device = guard_.current_device(); |
| 15920 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15921 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15922 | "structured kernels don't support multi-device outputs"); |
| 15923 | } else { |
| 15924 | guard_.reset_device(options.device()); |
| 15925 | } |
| 15926 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 15927 | if (!names.empty()) { |
| 15928 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 15929 | } |
| 15930 | // super must happen after, so that downstream can use maybe_get_output |
| 15931 | // to retrieve the output |
| 15932 | at::meta::structured_leaky_relu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15933 | } |
| 15934 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15935 | return *outputs_[output_idx]; |
| 15936 | } |
| 15937 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 15938 | c10::OptionalDeviceGuard guard_; |
| 15939 | }; |
| 15940 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_leaky_relu(const at::Tensor & self, const at::Scalar & negative_slope) { |
| 15941 | structured_leaky_relu_default_backend_functional op; |
| 15942 | op.meta(self, negative_slope); |
| 15943 | at::leaky_relu_outf(self, negative_slope, *op.outputs_[0]); |
| 15944 | return std::move(op.outputs_[0]).take(); |
| 15945 | } |
| 15946 | struct structured_leaky_relu_default_backend_inplace final : public at::meta::structured_leaky_relu { |
| 15947 | structured_leaky_relu_default_backend_inplace(Tensor& self) : outputs_{std::ref(self)} {} |
| 15948 | void set_output_strided( |
| 15949 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15950 | TensorOptions options, DimnameList names |
| 15951 | ) override { |
| 15952 | auto current_device = guard_.current_device(); |
| 15953 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15954 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15955 | "structured kernels don't support multi-device outputs"); |
| 15956 | } else { |
| 15957 | guard_.reset_device(options.device()); |
| 15958 | } |
| 15959 | const auto& out = outputs_[output_idx].get(); |
| 15960 | check_inplace(out, sizes, options); |
| 15961 | auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options); |
| 15962 | if (C10_UNLIKELY(maybe_proxy.has_value())) { |
| 15963 | proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value()); |
| 15964 | } |
| 15965 | if (!names.empty()) { |
| 15966 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15967 | } |
| 15968 | // super must happen after, so that downstream can use maybe_get_output |
| 15969 | // to retrieve the output |
| 15970 | at::meta::structured_leaky_relu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15971 | } |
| 15972 | void set_output_raw_strided( |
| 15973 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 15974 | TensorOptions options, DimnameList names |
| 15975 | ) override { |
| 15976 | auto current_device = guard_.current_device(); |
| 15977 | if (C10_UNLIKELY(current_device.has_value())) { |
| 15978 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 15979 | "structured kernels don't support multi-device outputs"); |
| 15980 | } else { |
| 15981 | guard_.reset_device(options.device()); |
| 15982 | } |
| 15983 | const auto& out = outputs_[output_idx].get(); |
| 15984 | check_inplace(out, sizes, options); |
| 15985 | if (!names.empty()) { |
| 15986 | namedinference::propagate_names(outputs_[output_idx], names); |
| 15987 | } |
| 15988 | // super must happen after, so that downstream can use maybe_get_output |
| 15989 | // to retrieve the output |
| 15990 | at::meta::structured_leaky_relu::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 15991 | } |
| 15992 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 15993 | return proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get(); |
| 15994 | } |
| 15995 | std::array<std::reference_wrapper<Tensor>, 1> outputs_; |
| 15996 | std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, 1> proxy_outputs_; |
| 15997 | c10::OptionalDeviceGuard guard_; |
| 15998 | }; |
| 15999 | at::Tensor & wrapper_CompositeExplicitAutogradNonFunctional_leaky_relu_(at::Tensor & self, const at::Scalar & negative_slope) { |
| 16000 | structured_leaky_relu_default_backend_inplace op(self); |
| 16001 | op.meta(self, negative_slope); |
| 16002 | at::leaky_relu_outf(self, negative_slope, op.outputs_[0]); |
| 16003 | if (op.proxy_outputs_[0].has_value()) op.outputs_[0].get().copy_(**op.proxy_outputs_[0]); |
| 16004 | return self; |
| 16005 | } |
| 16006 | struct structured_leaky_relu_backward_default_backend_functional final : public at::meta::structured_leaky_relu_backward { |
| 16007 | void set_output_strided( |
| 16008 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16009 | TensorOptions options, DimnameList names |
| 16010 | ) override { |
| 16011 | auto current_device = guard_.current_device(); |
| 16012 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16013 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16014 | "structured kernels don't support multi-device outputs"); |
| 16015 | } else { |
| 16016 | guard_.reset_device(options.device()); |
| 16017 | } |
| 16018 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16019 | if (!names.empty()) { |
| 16020 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16021 | } |
| 16022 | // super must happen after, so that downstream can use maybe_get_output |
| 16023 | // to retrieve the output |
| 16024 | at::meta::structured_leaky_relu_backward::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16025 | } |
| 16026 | void set_output_raw_strided( |
| 16027 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16028 | TensorOptions options, DimnameList names |
| 16029 | ) override { |
| 16030 | auto current_device = guard_.current_device(); |
| 16031 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16032 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16033 | "structured kernels don't support multi-device outputs"); |
| 16034 | } else { |
| 16035 | guard_.reset_device(options.device()); |
| 16036 | } |
| 16037 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16038 | if (!names.empty()) { |
| 16039 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16040 | } |
| 16041 | // super must happen after, so that downstream can use maybe_get_output |
| 16042 | // to retrieve the output |
| 16043 | at::meta::structured_leaky_relu_backward::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16044 | } |
| 16045 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16046 | return *outputs_[output_idx]; |
| 16047 | } |
| 16048 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 16049 | c10::OptionalDeviceGuard guard_; |
| 16050 | }; |
| 16051 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_leaky_relu_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & negative_slope, bool self_is_result) { |
| 16052 | structured_leaky_relu_backward_default_backend_functional op; |
| 16053 | op.meta(grad_output, self, negative_slope, self_is_result); |
| 16054 | at::leaky_relu_backward_outf(grad_output, self, negative_slope, self_is_result, *op.outputs_[0]); |
| 16055 | return std::move(op.outputs_[0]).take(); |
| 16056 | } |
| 16057 | struct structured_softplus_default_backend_functional final : public at::meta::structured_softplus { |
| 16058 | void set_output_strided( |
| 16059 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16060 | TensorOptions options, DimnameList names |
| 16061 | ) override { |
| 16062 | auto current_device = guard_.current_device(); |
| 16063 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16064 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16065 | "structured kernels don't support multi-device outputs"); |
| 16066 | } else { |
| 16067 | guard_.reset_device(options.device()); |
| 16068 | } |
| 16069 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16070 | if (!names.empty()) { |
| 16071 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16072 | } |
| 16073 | // super must happen after, so that downstream can use maybe_get_output |
| 16074 | // to retrieve the output |
| 16075 | at::meta::structured_softplus::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16076 | } |
| 16077 | void set_output_raw_strided( |
| 16078 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16079 | TensorOptions options, DimnameList names |
| 16080 | ) override { |
| 16081 | auto current_device = guard_.current_device(); |
| 16082 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16083 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16084 | "structured kernels don't support multi-device outputs"); |
| 16085 | } else { |
| 16086 | guard_.reset_device(options.device()); |
| 16087 | } |
| 16088 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16089 | if (!names.empty()) { |
| 16090 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16091 | } |
| 16092 | // super must happen after, so that downstream can use maybe_get_output |
| 16093 | // to retrieve the output |
| 16094 | at::meta::structured_softplus::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16095 | } |
| 16096 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16097 | return *outputs_[output_idx]; |
| 16098 | } |
| 16099 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 16100 | c10::OptionalDeviceGuard guard_; |
| 16101 | }; |
| 16102 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_softplus(const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold) { |
| 16103 | structured_softplus_default_backend_functional op; |
| 16104 | op.meta(self, beta, threshold); |
| 16105 | at::softplus_outf(self, beta, threshold, *op.outputs_[0]); |
| 16106 | return std::move(op.outputs_[0]).take(); |
| 16107 | } |
| 16108 | struct structured_softplus_backward_default_backend_functional final : public at::meta::structured_softplus_backward { |
| 16109 | void set_output_strided( |
| 16110 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16111 | TensorOptions options, DimnameList names |
| 16112 | ) override { |
| 16113 | auto current_device = guard_.current_device(); |
| 16114 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16115 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16116 | "structured kernels don't support multi-device outputs"); |
| 16117 | } else { |
| 16118 | guard_.reset_device(options.device()); |
| 16119 | } |
| 16120 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16121 | if (!names.empty()) { |
| 16122 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16123 | } |
| 16124 | // super must happen after, so that downstream can use maybe_get_output |
| 16125 | // to retrieve the output |
| 16126 | at::meta::structured_softplus_backward::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16127 | } |
| 16128 | void set_output_raw_strided( |
| 16129 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16130 | TensorOptions options, DimnameList names |
| 16131 | ) override { |
| 16132 | auto current_device = guard_.current_device(); |
| 16133 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16134 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16135 | "structured kernels don't support multi-device outputs"); |
| 16136 | } else { |
| 16137 | guard_.reset_device(options.device()); |
| 16138 | } |
| 16139 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16140 | if (!names.empty()) { |
| 16141 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16142 | } |
| 16143 | // super must happen after, so that downstream can use maybe_get_output |
| 16144 | // to retrieve the output |
| 16145 | at::meta::structured_softplus_backward::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16146 | } |
| 16147 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16148 | return *outputs_[output_idx]; |
| 16149 | } |
| 16150 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 16151 | c10::OptionalDeviceGuard guard_; |
| 16152 | }; |
| 16153 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_softplus_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold) { |
| 16154 | structured_softplus_backward_default_backend_functional op; |
| 16155 | op.meta(grad_output, self, beta, threshold); |
| 16156 | at::softplus_backward_outf(grad_output, self, beta, threshold, *op.outputs_[0]); |
| 16157 | return std::move(op.outputs_[0]).take(); |
| 16158 | } |
| 16159 | struct structured_softshrink_default_backend_functional final : public at::meta::structured_softshrink { |
| 16160 | void set_output_strided( |
| 16161 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16162 | TensorOptions options, DimnameList names |
| 16163 | ) override { |
| 16164 | auto current_device = guard_.current_device(); |
| 16165 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16166 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16167 | "structured kernels don't support multi-device outputs"); |
| 16168 | } else { |
| 16169 | guard_.reset_device(options.device()); |
| 16170 | } |
| 16171 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16172 | if (!names.empty()) { |
| 16173 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16174 | } |
| 16175 | // super must happen after, so that downstream can use maybe_get_output |
| 16176 | // to retrieve the output |
| 16177 | at::meta::structured_softshrink::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16178 | } |
| 16179 | void set_output_raw_strided( |
| 16180 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16181 | TensorOptions options, DimnameList names |
| 16182 | ) override { |
| 16183 | auto current_device = guard_.current_device(); |
| 16184 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16185 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16186 | "structured kernels don't support multi-device outputs"); |
| 16187 | } else { |
| 16188 | guard_.reset_device(options.device()); |
| 16189 | } |
| 16190 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16191 | if (!names.empty()) { |
| 16192 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16193 | } |
| 16194 | // super must happen after, so that downstream can use maybe_get_output |
| 16195 | // to retrieve the output |
| 16196 | at::meta::structured_softshrink::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16197 | } |
| 16198 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16199 | return *outputs_[output_idx]; |
| 16200 | } |
| 16201 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 16202 | c10::OptionalDeviceGuard guard_; |
| 16203 | }; |
| 16204 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_softshrink(const at::Tensor & self, const at::Scalar & lambd) { |
| 16205 | structured_softshrink_default_backend_functional op; |
| 16206 | op.meta(self, lambd); |
| 16207 | at::softshrink_outf(self, lambd, *op.outputs_[0]); |
| 16208 | return std::move(op.outputs_[0]).take(); |
| 16209 | } |
| 16210 | struct structured_softshrink_backward_default_backend_functional final : public at::meta::structured_softshrink_backward { |
| 16211 | void set_output_strided( |
| 16212 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16213 | TensorOptions options, DimnameList names |
| 16214 | ) override { |
| 16215 | auto current_device = guard_.current_device(); |
| 16216 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16217 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16218 | "structured kernels don't support multi-device outputs"); |
| 16219 | } else { |
| 16220 | guard_.reset_device(options.device()); |
| 16221 | } |
| 16222 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16223 | if (!names.empty()) { |
| 16224 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16225 | } |
| 16226 | // super must happen after, so that downstream can use maybe_get_output |
| 16227 | // to retrieve the output |
| 16228 | at::meta::structured_softshrink_backward::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16229 | } |
| 16230 | void set_output_raw_strided( |
| 16231 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16232 | TensorOptions options, DimnameList names |
| 16233 | ) override { |
| 16234 | auto current_device = guard_.current_device(); |
| 16235 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16236 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16237 | "structured kernels don't support multi-device outputs"); |
| 16238 | } else { |
| 16239 | guard_.reset_device(options.device()); |
| 16240 | } |
| 16241 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16242 | if (!names.empty()) { |
| 16243 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16244 | } |
| 16245 | // super must happen after, so that downstream can use maybe_get_output |
| 16246 | // to retrieve the output |
| 16247 | at::meta::structured_softshrink_backward::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 16248 | } |
| 16249 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16250 | return *outputs_[output_idx]; |
| 16251 | } |
| 16252 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 16253 | c10::OptionalDeviceGuard guard_; |
| 16254 | }; |
| 16255 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_softshrink_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & lambd) { |
| 16256 | structured_softshrink_backward_default_backend_functional op; |
| 16257 | op.meta(grad_output, self, lambd); |
| 16258 | at::softshrink_backward_outf(grad_output, self, lambd, *op.outputs_[0]); |
| 16259 | return std::move(op.outputs_[0]).take(); |
| 16260 | } |
| 16261 | struct structured_adaptive_max_pool2d_default_backend_functional final : public at::meta::structured_adaptive_max_pool2d { |
| 16262 | void set_output_strided( |
| 16263 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16264 | TensorOptions options, DimnameList names |
| 16265 | ) override { |
| 16266 | auto current_device = guard_.current_device(); |
| 16267 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16268 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16269 | "structured kernels don't support multi-device outputs"); |
| 16270 | } else { |
| 16271 | guard_.reset_device(options.device()); |
| 16272 | } |
| 16273 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16274 | if (!names.empty()) { |
| 16275 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16276 | } |
| 16277 | // super must happen after, so that downstream can use maybe_get_output |
| 16278 | // to retrieve the output |
| 16279 | } |
| 16280 | void set_output_raw_strided( |
| 16281 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16282 | TensorOptions options, DimnameList names |
| 16283 | ) override { |
| 16284 | auto current_device = guard_.current_device(); |
| 16285 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16286 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16287 | "structured kernels don't support multi-device outputs"); |
| 16288 | } else { |
| 16289 | guard_.reset_device(options.device()); |
| 16290 | } |
| 16291 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16292 | if (!names.empty()) { |
| 16293 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16294 | } |
| 16295 | // super must happen after, so that downstream can use maybe_get_output |
| 16296 | // to retrieve the output |
| 16297 | } |
| 16298 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16299 | return *outputs_[output_idx]; |
| 16300 | } |
| 16301 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 16302 | c10::OptionalDeviceGuard guard_; |
| 16303 | }; |
| 16304 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CompositeExplicitAutogradNonFunctional_adaptive_max_pool2d(const at::Tensor & self, at::IntArrayRef output_size) { |
| 16305 | structured_adaptive_max_pool2d_default_backend_functional op; |
| 16306 | op.meta(self, output_size); |
| 16307 | at::adaptive_max_pool2d_outf(self, output_size, *op.outputs_[0], *op.outputs_[1]); |
| 16308 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 16309 | } |
| 16310 | struct structured_adaptive_max_pool2d_backward_default_backend_functional final : public at::meta::structured_adaptive_max_pool2d_backward { |
| 16311 | void set_output_strided( |
| 16312 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16313 | TensorOptions options, DimnameList names |
| 16314 | ) override { |
| 16315 | auto current_device = guard_.current_device(); |
| 16316 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16317 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16318 | "structured kernels don't support multi-device outputs"); |
| 16319 | } else { |
| 16320 | guard_.reset_device(options.device()); |
| 16321 | } |
| 16322 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16323 | if (!names.empty()) { |
| 16324 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16325 | } |
| 16326 | // super must happen after, so that downstream can use maybe_get_output |
| 16327 | // to retrieve the output |
| 16328 | } |
| 16329 | void set_output_raw_strided( |
| 16330 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16331 | TensorOptions options, DimnameList names |
| 16332 | ) override { |
| 16333 | auto current_device = guard_.current_device(); |
| 16334 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16335 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16336 | "structured kernels don't support multi-device outputs"); |
| 16337 | } else { |
| 16338 | guard_.reset_device(options.device()); |
| 16339 | } |
| 16340 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16341 | if (!names.empty()) { |
| 16342 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16343 | } |
| 16344 | // super must happen after, so that downstream can use maybe_get_output |
| 16345 | // to retrieve the output |
| 16346 | } |
| 16347 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16348 | return *outputs_[output_idx]; |
| 16349 | } |
| 16350 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 16351 | c10::OptionalDeviceGuard guard_; |
| 16352 | }; |
| 16353 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_adaptive_max_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) { |
| 16354 | structured_adaptive_max_pool2d_backward_default_backend_functional op; |
| 16355 | op.meta(grad_output, self, indices); |
| 16356 | at::adaptive_max_pool2d_backward_outf(grad_output, self, indices, *op.outputs_[0]); |
| 16357 | return std::move(op.outputs_[0]).take(); |
| 16358 | } |
| 16359 | struct structured_adaptive_max_pool3d_default_backend_functional final : public at::meta::structured_adaptive_max_pool3d { |
| 16360 | void set_output_strided( |
| 16361 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16362 | TensorOptions options, DimnameList names |
| 16363 | ) override { |
| 16364 | auto current_device = guard_.current_device(); |
| 16365 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16366 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16367 | "structured kernels don't support multi-device outputs"); |
| 16368 | } else { |
| 16369 | guard_.reset_device(options.device()); |
| 16370 | } |
| 16371 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16372 | if (!names.empty()) { |
| 16373 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16374 | } |
| 16375 | // super must happen after, so that downstream can use maybe_get_output |
| 16376 | // to retrieve the output |
| 16377 | } |
| 16378 | void set_output_raw_strided( |
| 16379 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16380 | TensorOptions options, DimnameList names |
| 16381 | ) override { |
| 16382 | auto current_device = guard_.current_device(); |
| 16383 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16384 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16385 | "structured kernels don't support multi-device outputs"); |
| 16386 | } else { |
| 16387 | guard_.reset_device(options.device()); |
| 16388 | } |
| 16389 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16390 | if (!names.empty()) { |
| 16391 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16392 | } |
| 16393 | // super must happen after, so that downstream can use maybe_get_output |
| 16394 | // to retrieve the output |
| 16395 | } |
| 16396 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16397 | return *outputs_[output_idx]; |
| 16398 | } |
| 16399 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 16400 | c10::OptionalDeviceGuard guard_; |
| 16401 | }; |
| 16402 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CompositeExplicitAutogradNonFunctional_adaptive_max_pool3d(const at::Tensor & self, at::IntArrayRef output_size) { |
| 16403 | structured_adaptive_max_pool3d_default_backend_functional op; |
| 16404 | op.meta(self, output_size); |
| 16405 | at::adaptive_max_pool3d_outf(self, output_size, *op.outputs_[0], *op.outputs_[1]); |
| 16406 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 16407 | } |
| 16408 | struct structured_adaptive_max_pool3d_backward_default_backend_functional final : public at::meta::structured_adaptive_max_pool3d_backward { |
| 16409 | void set_output_strided( |
| 16410 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16411 | TensorOptions options, DimnameList names |
| 16412 | ) override { |
| 16413 | auto current_device = guard_.current_device(); |
| 16414 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16415 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16416 | "structured kernels don't support multi-device outputs"); |
| 16417 | } else { |
| 16418 | guard_.reset_device(options.device()); |
| 16419 | } |
| 16420 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16421 | if (!names.empty()) { |
| 16422 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16423 | } |
| 16424 | // super must happen after, so that downstream can use maybe_get_output |
| 16425 | // to retrieve the output |
| 16426 | } |
| 16427 | void set_output_raw_strided( |
| 16428 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16429 | TensorOptions options, DimnameList names |
| 16430 | ) override { |
| 16431 | auto current_device = guard_.current_device(); |
| 16432 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16433 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16434 | "structured kernels don't support multi-device outputs"); |
| 16435 | } else { |
| 16436 | guard_.reset_device(options.device()); |
| 16437 | } |
| 16438 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16439 | if (!names.empty()) { |
| 16440 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16441 | } |
| 16442 | // super must happen after, so that downstream can use maybe_get_output |
| 16443 | // to retrieve the output |
| 16444 | } |
| 16445 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16446 | return *outputs_[output_idx]; |
| 16447 | } |
| 16448 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 16449 | c10::OptionalDeviceGuard guard_; |
| 16450 | }; |
| 16451 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_adaptive_max_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) { |
| 16452 | structured_adaptive_max_pool3d_backward_default_backend_functional op; |
| 16453 | op.meta(grad_output, self, indices); |
| 16454 | at::adaptive_max_pool3d_backward_outf(grad_output, self, indices, *op.outputs_[0]); |
| 16455 | return std::move(op.outputs_[0]).take(); |
| 16456 | } |
| 16457 | struct structured_avg_pool2d_default_backend_functional final : public at::meta::structured_avg_pool2d { |
| 16458 | void set_output_strided( |
| 16459 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16460 | TensorOptions options, DimnameList names |
| 16461 | ) override { |
| 16462 | auto current_device = guard_.current_device(); |
| 16463 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16464 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16465 | "structured kernels don't support multi-device outputs"); |
| 16466 | } else { |
| 16467 | guard_.reset_device(options.device()); |
| 16468 | } |
| 16469 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16470 | if (!names.empty()) { |
| 16471 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16472 | } |
| 16473 | // super must happen after, so that downstream can use maybe_get_output |
| 16474 | // to retrieve the output |
| 16475 | } |
| 16476 | void set_output_raw_strided( |
| 16477 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16478 | TensorOptions options, DimnameList names |
| 16479 | ) override { |
| 16480 | auto current_device = guard_.current_device(); |
| 16481 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16482 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16483 | "structured kernels don't support multi-device outputs"); |
| 16484 | } else { |
| 16485 | guard_.reset_device(options.device()); |
| 16486 | } |
| 16487 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16488 | if (!names.empty()) { |
| 16489 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16490 | } |
| 16491 | // super must happen after, so that downstream can use maybe_get_output |
| 16492 | // to retrieve the output |
| 16493 | } |
| 16494 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16495 | return *outputs_[output_idx]; |
| 16496 | } |
| 16497 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 16498 | c10::OptionalDeviceGuard guard_; |
| 16499 | }; |
| 16500 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_avg_pool2d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional<int64_t> divisor_override) { |
| 16501 | structured_avg_pool2d_default_backend_functional op; |
| 16502 | auto precompute = op.meta(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); |
| 16503 | (void)precompute; |
| 16504 | at::avg_pool2d_outf(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, *op.outputs_[0]); |
| 16505 | return std::move(op.outputs_[0]).take(); |
| 16506 | } |
| 16507 | struct structured_avg_pool2d_backward_default_backend_functional final : public at::meta::structured_avg_pool2d_backward { |
| 16508 | void set_output_strided( |
| 16509 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16510 | TensorOptions options, DimnameList names |
| 16511 | ) override { |
| 16512 | auto current_device = guard_.current_device(); |
| 16513 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16514 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16515 | "structured kernels don't support multi-device outputs"); |
| 16516 | } else { |
| 16517 | guard_.reset_device(options.device()); |
| 16518 | } |
| 16519 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16520 | if (!names.empty()) { |
| 16521 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16522 | } |
| 16523 | // super must happen after, so that downstream can use maybe_get_output |
| 16524 | // to retrieve the output |
| 16525 | } |
| 16526 | void set_output_raw_strided( |
| 16527 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16528 | TensorOptions options, DimnameList names |
| 16529 | ) override { |
| 16530 | auto current_device = guard_.current_device(); |
| 16531 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16532 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16533 | "structured kernels don't support multi-device outputs"); |
| 16534 | } else { |
| 16535 | guard_.reset_device(options.device()); |
| 16536 | } |
| 16537 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16538 | if (!names.empty()) { |
| 16539 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16540 | } |
| 16541 | // super must happen after, so that downstream can use maybe_get_output |
| 16542 | // to retrieve the output |
| 16543 | } |
| 16544 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16545 | return *outputs_[output_idx]; |
| 16546 | } |
| 16547 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 16548 | c10::OptionalDeviceGuard guard_; |
| 16549 | }; |
| 16550 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_avg_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional<int64_t> divisor_override) { |
| 16551 | structured_avg_pool2d_backward_default_backend_functional op; |
| 16552 | op.meta(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); |
| 16553 | at::avg_pool2d_backward_outf(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, *op.outputs_[0]); |
| 16554 | return std::move(op.outputs_[0]).take(); |
| 16555 | } |
| 16556 | struct structured_avg_pool3d_default_backend_functional final : public at::meta::structured_avg_pool3d { |
| 16557 | void set_output_strided( |
| 16558 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16559 | TensorOptions options, DimnameList names |
| 16560 | ) override { |
| 16561 | auto current_device = guard_.current_device(); |
| 16562 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16563 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16564 | "structured kernels don't support multi-device outputs"); |
| 16565 | } else { |
| 16566 | guard_.reset_device(options.device()); |
| 16567 | } |
| 16568 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16569 | if (!names.empty()) { |
| 16570 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16571 | } |
| 16572 | // super must happen after, so that downstream can use maybe_get_output |
| 16573 | // to retrieve the output |
| 16574 | } |
| 16575 | void set_output_raw_strided( |
| 16576 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16577 | TensorOptions options, DimnameList names |
| 16578 | ) override { |
| 16579 | auto current_device = guard_.current_device(); |
| 16580 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16581 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16582 | "structured kernels don't support multi-device outputs"); |
| 16583 | } else { |
| 16584 | guard_.reset_device(options.device()); |
| 16585 | } |
| 16586 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16587 | if (!names.empty()) { |
| 16588 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16589 | } |
| 16590 | // super must happen after, so that downstream can use maybe_get_output |
| 16591 | // to retrieve the output |
| 16592 | } |
| 16593 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16594 | return *outputs_[output_idx]; |
| 16595 | } |
| 16596 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 16597 | c10::OptionalDeviceGuard guard_; |
| 16598 | }; |
| 16599 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_avg_pool3d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional<int64_t> divisor_override) { |
| 16600 | structured_avg_pool3d_default_backend_functional op; |
| 16601 | op.meta(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); |
| 16602 | at::avg_pool3d_outf(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, *op.outputs_[0]); |
| 16603 | return std::move(op.outputs_[0]).take(); |
| 16604 | } |
| 16605 | struct structured_avg_pool3d_backward_default_backend_functional final : public at::meta::structured_avg_pool3d_backward { |
| 16606 | void set_output_strided( |
| 16607 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16608 | TensorOptions options, DimnameList names |
| 16609 | ) override { |
| 16610 | auto current_device = guard_.current_device(); |
| 16611 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16612 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16613 | "structured kernels don't support multi-device outputs"); |
| 16614 | } else { |
| 16615 | guard_.reset_device(options.device()); |
| 16616 | } |
| 16617 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16618 | if (!names.empty()) { |
| 16619 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16620 | } |
| 16621 | // super must happen after, so that downstream can use maybe_get_output |
| 16622 | // to retrieve the output |
| 16623 | } |
| 16624 | void set_output_raw_strided( |
| 16625 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16626 | TensorOptions options, DimnameList names |
| 16627 | ) override { |
| 16628 | auto current_device = guard_.current_device(); |
| 16629 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16630 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16631 | "structured kernels don't support multi-device outputs"); |
| 16632 | } else { |
| 16633 | guard_.reset_device(options.device()); |
| 16634 | } |
| 16635 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16636 | if (!names.empty()) { |
| 16637 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16638 | } |
| 16639 | // super must happen after, so that downstream can use maybe_get_output |
| 16640 | // to retrieve the output |
| 16641 | } |
| 16642 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16643 | return *outputs_[output_idx]; |
| 16644 | } |
| 16645 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 16646 | c10::OptionalDeviceGuard guard_; |
| 16647 | }; |
| 16648 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_avg_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional<int64_t> divisor_override) { |
| 16649 | structured_avg_pool3d_backward_default_backend_functional op; |
| 16650 | op.meta(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); |
| 16651 | at::avg_pool3d_backward_outf(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, *op.outputs_[0]); |
| 16652 | return std::move(op.outputs_[0]).take(); |
| 16653 | } |
| 16654 | struct structured_fractional_max_pool2d_default_backend_functional final : public at::meta::structured_fractional_max_pool2d { |
| 16655 | void set_output_strided( |
| 16656 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16657 | TensorOptions options, DimnameList names |
| 16658 | ) override { |
| 16659 | auto current_device = guard_.current_device(); |
| 16660 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16661 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16662 | "structured kernels don't support multi-device outputs"); |
| 16663 | } else { |
| 16664 | guard_.reset_device(options.device()); |
| 16665 | } |
| 16666 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16667 | if (!names.empty()) { |
| 16668 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16669 | } |
| 16670 | // super must happen after, so that downstream can use maybe_get_output |
| 16671 | // to retrieve the output |
| 16672 | } |
| 16673 | void set_output_raw_strided( |
| 16674 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16675 | TensorOptions options, DimnameList names |
| 16676 | ) override { |
| 16677 | auto current_device = guard_.current_device(); |
| 16678 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16679 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16680 | "structured kernels don't support multi-device outputs"); |
| 16681 | } else { |
| 16682 | guard_.reset_device(options.device()); |
| 16683 | } |
| 16684 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16685 | if (!names.empty()) { |
| 16686 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16687 | } |
| 16688 | // super must happen after, so that downstream can use maybe_get_output |
| 16689 | // to retrieve the output |
| 16690 | } |
| 16691 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16692 | return *outputs_[output_idx]; |
| 16693 | } |
| 16694 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 16695 | c10::OptionalDeviceGuard guard_; |
| 16696 | }; |
| 16697 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CompositeExplicitAutogradNonFunctional_fractional_max_pool2d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples) { |
| 16698 | structured_fractional_max_pool2d_default_backend_functional op; |
| 16699 | op.meta(self, kernel_size, output_size, random_samples); |
| 16700 | at::fractional_max_pool2d_outf(self, kernel_size, output_size, random_samples, *op.outputs_[0], *op.outputs_[1]); |
| 16701 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 16702 | } |
| 16703 | struct structured_fractional_max_pool2d_backward_default_backend_functional final : public at::meta::structured_fractional_max_pool2d_backward { |
| 16704 | void set_output_strided( |
| 16705 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16706 | TensorOptions options, DimnameList names |
| 16707 | ) override { |
| 16708 | auto current_device = guard_.current_device(); |
| 16709 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16710 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16711 | "structured kernels don't support multi-device outputs"); |
| 16712 | } else { |
| 16713 | guard_.reset_device(options.device()); |
| 16714 | } |
| 16715 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16716 | if (!names.empty()) { |
| 16717 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16718 | } |
| 16719 | // super must happen after, so that downstream can use maybe_get_output |
| 16720 | // to retrieve the output |
| 16721 | } |
| 16722 | void set_output_raw_strided( |
| 16723 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16724 | TensorOptions options, DimnameList names |
| 16725 | ) override { |
| 16726 | auto current_device = guard_.current_device(); |
| 16727 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16728 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16729 | "structured kernels don't support multi-device outputs"); |
| 16730 | } else { |
| 16731 | guard_.reset_device(options.device()); |
| 16732 | } |
| 16733 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16734 | if (!names.empty()) { |
| 16735 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16736 | } |
| 16737 | // super must happen after, so that downstream can use maybe_get_output |
| 16738 | // to retrieve the output |
| 16739 | } |
| 16740 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16741 | return *outputs_[output_idx]; |
| 16742 | } |
| 16743 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 16744 | c10::OptionalDeviceGuard guard_; |
| 16745 | }; |
| 16746 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_fractional_max_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices) { |
| 16747 | structured_fractional_max_pool2d_backward_default_backend_functional op; |
| 16748 | op.meta(grad_output, self, kernel_size, output_size, indices); |
| 16749 | at::fractional_max_pool2d_backward_outf(grad_output, self, kernel_size, output_size, indices, *op.outputs_[0]); |
| 16750 | return std::move(op.outputs_[0]).take(); |
| 16751 | } |
| 16752 | struct structured_fractional_max_pool3d_default_backend_functional final : public at::meta::structured_fractional_max_pool3d { |
| 16753 | void set_output_strided( |
| 16754 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16755 | TensorOptions options, DimnameList names |
| 16756 | ) override { |
| 16757 | auto current_device = guard_.current_device(); |
| 16758 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16759 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16760 | "structured kernels don't support multi-device outputs"); |
| 16761 | } else { |
| 16762 | guard_.reset_device(options.device()); |
| 16763 | } |
| 16764 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16765 | if (!names.empty()) { |
| 16766 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16767 | } |
| 16768 | // super must happen after, so that downstream can use maybe_get_output |
| 16769 | // to retrieve the output |
| 16770 | } |
| 16771 | void set_output_raw_strided( |
| 16772 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16773 | TensorOptions options, DimnameList names |
| 16774 | ) override { |
| 16775 | auto current_device = guard_.current_device(); |
| 16776 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16777 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16778 | "structured kernels don't support multi-device outputs"); |
| 16779 | } else { |
| 16780 | guard_.reset_device(options.device()); |
| 16781 | } |
| 16782 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16783 | if (!names.empty()) { |
| 16784 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16785 | } |
| 16786 | // super must happen after, so that downstream can use maybe_get_output |
| 16787 | // to retrieve the output |
| 16788 | } |
| 16789 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16790 | return *outputs_[output_idx]; |
| 16791 | } |
| 16792 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 16793 | c10::OptionalDeviceGuard guard_; |
| 16794 | }; |
| 16795 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CompositeExplicitAutogradNonFunctional_fractional_max_pool3d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples) { |
| 16796 | structured_fractional_max_pool3d_default_backend_functional op; |
| 16797 | auto precompute = op.meta(self, kernel_size, output_size, random_samples); |
| 16798 | (void)precompute; |
| 16799 | at::fractional_max_pool3d_outf(self, kernel_size, output_size, random_samples, *op.outputs_[0], *op.outputs_[1]); |
| 16800 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 16801 | } |
| 16802 | struct structured_max_pool2d_with_indices_default_backend_functional final : public at::meta::structured_max_pool2d_with_indices { |
| 16803 | void set_output_strided( |
| 16804 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16805 | TensorOptions options, DimnameList names |
| 16806 | ) override { |
| 16807 | auto current_device = guard_.current_device(); |
| 16808 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16809 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16810 | "structured kernels don't support multi-device outputs"); |
| 16811 | } else { |
| 16812 | guard_.reset_device(options.device()); |
| 16813 | } |
| 16814 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16815 | if (!names.empty()) { |
| 16816 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16817 | } |
| 16818 | // super must happen after, so that downstream can use maybe_get_output |
| 16819 | // to retrieve the output |
| 16820 | } |
| 16821 | void set_output_raw_strided( |
| 16822 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16823 | TensorOptions options, DimnameList names |
| 16824 | ) override { |
| 16825 | auto current_device = guard_.current_device(); |
| 16826 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16827 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16828 | "structured kernels don't support multi-device outputs"); |
| 16829 | } else { |
| 16830 | guard_.reset_device(options.device()); |
| 16831 | } |
| 16832 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16833 | if (!names.empty()) { |
| 16834 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16835 | } |
| 16836 | // super must happen after, so that downstream can use maybe_get_output |
| 16837 | // to retrieve the output |
| 16838 | } |
| 16839 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16840 | return *outputs_[output_idx]; |
| 16841 | } |
| 16842 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 16843 | c10::OptionalDeviceGuard guard_; |
| 16844 | }; |
| 16845 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CompositeExplicitAutogradNonFunctional_max_pool2d_with_indices(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { |
| 16846 | structured_max_pool2d_with_indices_default_backend_functional op; |
| 16847 | op.meta(self, kernel_size, stride, padding, dilation, ceil_mode); |
| 16848 | at::max_pool2d_with_indices_outf(self, kernel_size, stride, padding, dilation, ceil_mode, *op.outputs_[0], *op.outputs_[1]); |
| 16849 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 16850 | } |
| 16851 | struct structured_max_pool2d_with_indices_backward_default_backend_functional final : public at::meta::structured_max_pool2d_with_indices_backward { |
| 16852 | void set_output_strided( |
| 16853 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16854 | TensorOptions options, DimnameList names |
| 16855 | ) override { |
| 16856 | auto current_device = guard_.current_device(); |
| 16857 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16858 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16859 | "structured kernels don't support multi-device outputs"); |
| 16860 | } else { |
| 16861 | guard_.reset_device(options.device()); |
| 16862 | } |
| 16863 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16864 | if (!names.empty()) { |
| 16865 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16866 | } |
| 16867 | // super must happen after, so that downstream can use maybe_get_output |
| 16868 | // to retrieve the output |
| 16869 | } |
| 16870 | void set_output_raw_strided( |
| 16871 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16872 | TensorOptions options, DimnameList names |
| 16873 | ) override { |
| 16874 | auto current_device = guard_.current_device(); |
| 16875 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16876 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16877 | "structured kernels don't support multi-device outputs"); |
| 16878 | } else { |
| 16879 | guard_.reset_device(options.device()); |
| 16880 | } |
| 16881 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16882 | if (!names.empty()) { |
| 16883 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16884 | } |
| 16885 | // super must happen after, so that downstream can use maybe_get_output |
| 16886 | // to retrieve the output |
| 16887 | } |
| 16888 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16889 | return *outputs_[output_idx]; |
| 16890 | } |
| 16891 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 16892 | c10::OptionalDeviceGuard guard_; |
| 16893 | }; |
| 16894 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_max_pool2d_with_indices_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, const at::Tensor & indices) { |
| 16895 | structured_max_pool2d_with_indices_backward_default_backend_functional op; |
| 16896 | op.meta(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices); |
| 16897 | at::max_pool2d_with_indices_backward_outf(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices, *op.outputs_[0]); |
| 16898 | return std::move(op.outputs_[0]).take(); |
| 16899 | } |
| 16900 | struct structured_reflection_pad1d_default_backend_functional final : public at::meta::structured_reflection_pad1d { |
| 16901 | void set_output_strided( |
| 16902 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16903 | TensorOptions options, DimnameList names |
| 16904 | ) override { |
| 16905 | auto current_device = guard_.current_device(); |
| 16906 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16907 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16908 | "structured kernels don't support multi-device outputs"); |
| 16909 | } else { |
| 16910 | guard_.reset_device(options.device()); |
| 16911 | } |
| 16912 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16913 | if (!names.empty()) { |
| 16914 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16915 | } |
| 16916 | // super must happen after, so that downstream can use maybe_get_output |
| 16917 | // to retrieve the output |
| 16918 | } |
| 16919 | void set_output_raw_strided( |
| 16920 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16921 | TensorOptions options, DimnameList names |
| 16922 | ) override { |
| 16923 | auto current_device = guard_.current_device(); |
| 16924 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16925 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16926 | "structured kernels don't support multi-device outputs"); |
| 16927 | } else { |
| 16928 | guard_.reset_device(options.device()); |
| 16929 | } |
| 16930 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16931 | if (!names.empty()) { |
| 16932 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16933 | } |
| 16934 | // super must happen after, so that downstream can use maybe_get_output |
| 16935 | // to retrieve the output |
| 16936 | } |
| 16937 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16938 | return *outputs_[output_idx]; |
| 16939 | } |
| 16940 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 16941 | c10::OptionalDeviceGuard guard_; |
| 16942 | }; |
| 16943 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_reflection_pad1d(const at::Tensor & self, at::IntArrayRef padding) { |
| 16944 | structured_reflection_pad1d_default_backend_functional op; |
| 16945 | op.meta(self, padding); |
| 16946 | at::reflection_pad1d_outf(self, padding, *op.outputs_[0]); |
| 16947 | return std::move(op.outputs_[0]).take(); |
| 16948 | } |
| 16949 | struct structured_reflection_pad1d_backward_default_backend_functional final : public at::meta::structured_reflection_pad1d_backward { |
| 16950 | void set_output_strided( |
| 16951 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16952 | TensorOptions options, DimnameList names |
| 16953 | ) override { |
| 16954 | auto current_device = guard_.current_device(); |
| 16955 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16956 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16957 | "structured kernels don't support multi-device outputs"); |
| 16958 | } else { |
| 16959 | guard_.reset_device(options.device()); |
| 16960 | } |
| 16961 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16962 | if (!names.empty()) { |
| 16963 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16964 | } |
| 16965 | // super must happen after, so that downstream can use maybe_get_output |
| 16966 | // to retrieve the output |
| 16967 | } |
| 16968 | void set_output_raw_strided( |
| 16969 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 16970 | TensorOptions options, DimnameList names |
| 16971 | ) override { |
| 16972 | auto current_device = guard_.current_device(); |
| 16973 | if (C10_UNLIKELY(current_device.has_value())) { |
| 16974 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 16975 | "structured kernels don't support multi-device outputs"); |
| 16976 | } else { |
| 16977 | guard_.reset_device(options.device()); |
| 16978 | } |
| 16979 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 16980 | if (!names.empty()) { |
| 16981 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 16982 | } |
| 16983 | // super must happen after, so that downstream can use maybe_get_output |
| 16984 | // to retrieve the output |
| 16985 | } |
| 16986 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 16987 | return *outputs_[output_idx]; |
| 16988 | } |
| 16989 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 16990 | c10::OptionalDeviceGuard guard_; |
| 16991 | }; |
| 16992 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_reflection_pad1d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { |
| 16993 | structured_reflection_pad1d_backward_default_backend_functional op; |
| 16994 | op.meta(grad_output, self, padding); |
| 16995 | at::reflection_pad1d_backward_outf(grad_output, self, padding, *op.outputs_[0]); |
| 16996 | return std::move(op.outputs_[0]).take(); |
| 16997 | } |
| 16998 | struct structured_reflection_pad3d_default_backend_functional final : public at::meta::structured_reflection_pad3d { |
| 16999 | void set_output_strided( |
| 17000 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17001 | TensorOptions options, DimnameList names |
| 17002 | ) override { |
| 17003 | auto current_device = guard_.current_device(); |
| 17004 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17005 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17006 | "structured kernels don't support multi-device outputs"); |
| 17007 | } else { |
| 17008 | guard_.reset_device(options.device()); |
| 17009 | } |
| 17010 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17011 | if (!names.empty()) { |
| 17012 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17013 | } |
| 17014 | // super must happen after, so that downstream can use maybe_get_output |
| 17015 | // to retrieve the output |
| 17016 | } |
| 17017 | void set_output_raw_strided( |
| 17018 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17019 | TensorOptions options, DimnameList names |
| 17020 | ) override { |
| 17021 | auto current_device = guard_.current_device(); |
| 17022 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17023 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17024 | "structured kernels don't support multi-device outputs"); |
| 17025 | } else { |
| 17026 | guard_.reset_device(options.device()); |
| 17027 | } |
| 17028 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17029 | if (!names.empty()) { |
| 17030 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17031 | } |
| 17032 | // super must happen after, so that downstream can use maybe_get_output |
| 17033 | // to retrieve the output |
| 17034 | } |
| 17035 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17036 | return *outputs_[output_idx]; |
| 17037 | } |
| 17038 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 17039 | c10::OptionalDeviceGuard guard_; |
| 17040 | }; |
| 17041 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_reflection_pad3d(const at::Tensor & self, at::IntArrayRef padding) { |
| 17042 | structured_reflection_pad3d_default_backend_functional op; |
| 17043 | op.meta(self, padding); |
| 17044 | at::reflection_pad3d_outf(self, padding, *op.outputs_[0]); |
| 17045 | return std::move(op.outputs_[0]).take(); |
| 17046 | } |
| 17047 | struct structured_reflection_pad3d_backward_default_backend_functional final : public at::meta::structured_reflection_pad3d_backward { |
| 17048 | void set_output_strided( |
| 17049 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17050 | TensorOptions options, DimnameList names |
| 17051 | ) override { |
| 17052 | auto current_device = guard_.current_device(); |
| 17053 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17054 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17055 | "structured kernels don't support multi-device outputs"); |
| 17056 | } else { |
| 17057 | guard_.reset_device(options.device()); |
| 17058 | } |
| 17059 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17060 | if (!names.empty()) { |
| 17061 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17062 | } |
| 17063 | // super must happen after, so that downstream can use maybe_get_output |
| 17064 | // to retrieve the output |
| 17065 | } |
| 17066 | void set_output_raw_strided( |
| 17067 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17068 | TensorOptions options, DimnameList names |
| 17069 | ) override { |
| 17070 | auto current_device = guard_.current_device(); |
| 17071 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17072 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17073 | "structured kernels don't support multi-device outputs"); |
| 17074 | } else { |
| 17075 | guard_.reset_device(options.device()); |
| 17076 | } |
| 17077 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17078 | if (!names.empty()) { |
| 17079 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17080 | } |
| 17081 | // super must happen after, so that downstream can use maybe_get_output |
| 17082 | // to retrieve the output |
| 17083 | } |
| 17084 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17085 | return *outputs_[output_idx]; |
| 17086 | } |
| 17087 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 17088 | c10::OptionalDeviceGuard guard_; |
| 17089 | }; |
| 17090 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_reflection_pad3d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { |
| 17091 | structured_reflection_pad3d_backward_default_backend_functional op; |
| 17092 | op.meta(grad_output, self, padding); |
| 17093 | at::reflection_pad3d_backward_outf(grad_output, self, padding, *op.outputs_[0]); |
| 17094 | return std::move(op.outputs_[0]).take(); |
| 17095 | } |
| 17096 | struct structured_replication_pad1d_default_backend_functional final : public at::meta::structured_replication_pad1d { |
| 17097 | void set_output_strided( |
| 17098 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17099 | TensorOptions options, DimnameList names |
| 17100 | ) override { |
| 17101 | auto current_device = guard_.current_device(); |
| 17102 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17103 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17104 | "structured kernels don't support multi-device outputs"); |
| 17105 | } else { |
| 17106 | guard_.reset_device(options.device()); |
| 17107 | } |
| 17108 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17109 | if (!names.empty()) { |
| 17110 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17111 | } |
| 17112 | // super must happen after, so that downstream can use maybe_get_output |
| 17113 | // to retrieve the output |
| 17114 | } |
| 17115 | void set_output_raw_strided( |
| 17116 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17117 | TensorOptions options, DimnameList names |
| 17118 | ) override { |
| 17119 | auto current_device = guard_.current_device(); |
| 17120 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17121 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17122 | "structured kernels don't support multi-device outputs"); |
| 17123 | } else { |
| 17124 | guard_.reset_device(options.device()); |
| 17125 | } |
| 17126 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17127 | if (!names.empty()) { |
| 17128 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17129 | } |
| 17130 | // super must happen after, so that downstream can use maybe_get_output |
| 17131 | // to retrieve the output |
| 17132 | } |
| 17133 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17134 | return *outputs_[output_idx]; |
| 17135 | } |
| 17136 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 17137 | c10::OptionalDeviceGuard guard_; |
| 17138 | }; |
| 17139 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_replication_pad1d(const at::Tensor & self, at::IntArrayRef padding) { |
| 17140 | structured_replication_pad1d_default_backend_functional op; |
| 17141 | op.meta(self, padding); |
| 17142 | at::replication_pad1d_outf(self, padding, *op.outputs_[0]); |
| 17143 | return std::move(op.outputs_[0]).take(); |
| 17144 | } |
| 17145 | struct structured_replication_pad1d_backward_default_backend_functional final : public at::meta::structured_replication_pad1d_backward { |
| 17146 | void set_output_strided( |
| 17147 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17148 | TensorOptions options, DimnameList names |
| 17149 | ) override { |
| 17150 | auto current_device = guard_.current_device(); |
| 17151 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17152 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17153 | "structured kernels don't support multi-device outputs"); |
| 17154 | } else { |
| 17155 | guard_.reset_device(options.device()); |
| 17156 | } |
| 17157 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17158 | if (!names.empty()) { |
| 17159 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17160 | } |
| 17161 | // super must happen after, so that downstream can use maybe_get_output |
| 17162 | // to retrieve the output |
| 17163 | } |
| 17164 | void set_output_raw_strided( |
| 17165 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17166 | TensorOptions options, DimnameList names |
| 17167 | ) override { |
| 17168 | auto current_device = guard_.current_device(); |
| 17169 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17170 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17171 | "structured kernels don't support multi-device outputs"); |
| 17172 | } else { |
| 17173 | guard_.reset_device(options.device()); |
| 17174 | } |
| 17175 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17176 | if (!names.empty()) { |
| 17177 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17178 | } |
| 17179 | // super must happen after, so that downstream can use maybe_get_output |
| 17180 | // to retrieve the output |
| 17181 | } |
| 17182 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17183 | return *outputs_[output_idx]; |
| 17184 | } |
| 17185 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 17186 | c10::OptionalDeviceGuard guard_; |
| 17187 | }; |
| 17188 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_replication_pad1d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { |
| 17189 | structured_replication_pad1d_backward_default_backend_functional op; |
| 17190 | op.meta(grad_output, self, padding); |
| 17191 | at::replication_pad1d_backward_outf(grad_output, self, padding, *op.outputs_[0]); |
| 17192 | return std::move(op.outputs_[0]).take(); |
| 17193 | } |
| 17194 | struct structured_replication_pad2d_default_backend_functional final : public at::meta::structured_replication_pad2d { |
| 17195 | void set_output_strided( |
| 17196 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17197 | TensorOptions options, DimnameList names |
| 17198 | ) override { |
| 17199 | auto current_device = guard_.current_device(); |
| 17200 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17201 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17202 | "structured kernels don't support multi-device outputs"); |
| 17203 | } else { |
| 17204 | guard_.reset_device(options.device()); |
| 17205 | } |
| 17206 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17207 | if (!names.empty()) { |
| 17208 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17209 | } |
| 17210 | // super must happen after, so that downstream can use maybe_get_output |
| 17211 | // to retrieve the output |
| 17212 | } |
| 17213 | void set_output_raw_strided( |
| 17214 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17215 | TensorOptions options, DimnameList names |
| 17216 | ) override { |
| 17217 | auto current_device = guard_.current_device(); |
| 17218 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17219 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17220 | "structured kernels don't support multi-device outputs"); |
| 17221 | } else { |
| 17222 | guard_.reset_device(options.device()); |
| 17223 | } |
| 17224 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17225 | if (!names.empty()) { |
| 17226 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17227 | } |
| 17228 | // super must happen after, so that downstream can use maybe_get_output |
| 17229 | // to retrieve the output |
| 17230 | } |
| 17231 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17232 | return *outputs_[output_idx]; |
| 17233 | } |
| 17234 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 17235 | c10::OptionalDeviceGuard guard_; |
| 17236 | }; |
| 17237 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_replication_pad2d(const at::Tensor & self, at::IntArrayRef padding) { |
| 17238 | structured_replication_pad2d_default_backend_functional op; |
| 17239 | op.meta(self, padding); |
| 17240 | at::replication_pad2d_outf(self, padding, *op.outputs_[0]); |
| 17241 | return std::move(op.outputs_[0]).take(); |
| 17242 | } |
| 17243 | struct structured_replication_pad3d_default_backend_functional final : public at::meta::structured_replication_pad3d { |
| 17244 | void set_output_strided( |
| 17245 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17246 | TensorOptions options, DimnameList names |
| 17247 | ) override { |
| 17248 | auto current_device = guard_.current_device(); |
| 17249 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17250 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17251 | "structured kernels don't support multi-device outputs"); |
| 17252 | } else { |
| 17253 | guard_.reset_device(options.device()); |
| 17254 | } |
| 17255 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17256 | if (!names.empty()) { |
| 17257 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17258 | } |
| 17259 | // super must happen after, so that downstream can use maybe_get_output |
| 17260 | // to retrieve the output |
| 17261 | } |
| 17262 | void set_output_raw_strided( |
| 17263 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17264 | TensorOptions options, DimnameList names |
| 17265 | ) override { |
| 17266 | auto current_device = guard_.current_device(); |
| 17267 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17268 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17269 | "structured kernels don't support multi-device outputs"); |
| 17270 | } else { |
| 17271 | guard_.reset_device(options.device()); |
| 17272 | } |
| 17273 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17274 | if (!names.empty()) { |
| 17275 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17276 | } |
| 17277 | // super must happen after, so that downstream can use maybe_get_output |
| 17278 | // to retrieve the output |
| 17279 | } |
| 17280 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17281 | return *outputs_[output_idx]; |
| 17282 | } |
| 17283 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 17284 | c10::OptionalDeviceGuard guard_; |
| 17285 | }; |
| 17286 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_replication_pad3d(const at::Tensor & self, at::IntArrayRef padding) { |
| 17287 | structured_replication_pad3d_default_backend_functional op; |
| 17288 | op.meta(self, padding); |
| 17289 | at::replication_pad3d_outf(self, padding, *op.outputs_[0]); |
| 17290 | return std::move(op.outputs_[0]).take(); |
| 17291 | } |
| 17292 | struct structured_upsample_linear1d_default_backend_functional final : public at::meta::structured_upsample_linear1d { |
| 17293 | void set_output_strided( |
| 17294 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17295 | TensorOptions options, DimnameList names |
| 17296 | ) override { |
| 17297 | auto current_device = guard_.current_device(); |
| 17298 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17299 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17300 | "structured kernels don't support multi-device outputs"); |
| 17301 | } else { |
| 17302 | guard_.reset_device(options.device()); |
| 17303 | } |
| 17304 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17305 | if (!names.empty()) { |
| 17306 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17307 | } |
| 17308 | // super must happen after, so that downstream can use maybe_get_output |
| 17309 | // to retrieve the output |
| 17310 | } |
| 17311 | void set_output_raw_strided( |
| 17312 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17313 | TensorOptions options, DimnameList names |
| 17314 | ) override { |
| 17315 | auto current_device = guard_.current_device(); |
| 17316 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17317 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17318 | "structured kernels don't support multi-device outputs"); |
| 17319 | } else { |
| 17320 | guard_.reset_device(options.device()); |
| 17321 | } |
| 17322 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17323 | if (!names.empty()) { |
| 17324 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17325 | } |
| 17326 | // super must happen after, so that downstream can use maybe_get_output |
| 17327 | // to retrieve the output |
| 17328 | } |
| 17329 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17330 | return *outputs_[output_idx]; |
| 17331 | } |
| 17332 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 17333 | c10::OptionalDeviceGuard guard_; |
| 17334 | }; |
| 17335 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_upsample_linear1d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales) { |
| 17336 | structured_upsample_linear1d_default_backend_functional op; |
| 17337 | op.meta(self, output_size, align_corners, scales); |
| 17338 | at::upsample_linear1d_outf(self, output_size, align_corners, scales, *op.outputs_[0]); |
| 17339 | return std::move(op.outputs_[0]).take(); |
| 17340 | } |
| 17341 | struct structured_upsample_linear1d_backward_default_backend_functional final : public at::meta::structured_upsample_linear1d_backward { |
| 17342 | void set_output_strided( |
| 17343 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17344 | TensorOptions options, DimnameList names |
| 17345 | ) override { |
| 17346 | auto current_device = guard_.current_device(); |
| 17347 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17348 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17349 | "structured kernels don't support multi-device outputs"); |
| 17350 | } else { |
| 17351 | guard_.reset_device(options.device()); |
| 17352 | } |
| 17353 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17354 | if (!names.empty()) { |
| 17355 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17356 | } |
| 17357 | // super must happen after, so that downstream can use maybe_get_output |
| 17358 | // to retrieve the output |
| 17359 | } |
| 17360 | void set_output_raw_strided( |
| 17361 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17362 | TensorOptions options, DimnameList names |
| 17363 | ) override { |
| 17364 | auto current_device = guard_.current_device(); |
| 17365 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17366 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17367 | "structured kernels don't support multi-device outputs"); |
| 17368 | } else { |
| 17369 | guard_.reset_device(options.device()); |
| 17370 | } |
| 17371 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17372 | if (!names.empty()) { |
| 17373 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17374 | } |
| 17375 | // super must happen after, so that downstream can use maybe_get_output |
| 17376 | // to retrieve the output |
| 17377 | } |
| 17378 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17379 | return *outputs_[output_idx]; |
| 17380 | } |
| 17381 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 17382 | c10::OptionalDeviceGuard guard_; |
| 17383 | }; |
| 17384 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_upsample_linear1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional<double> scales) { |
| 17385 | structured_upsample_linear1d_backward_default_backend_functional op; |
| 17386 | op.meta(grad_output, output_size, input_size, align_corners, scales); |
| 17387 | at::upsample_linear1d_backward_outf(grad_output, output_size, input_size, align_corners, scales, *op.outputs_[0]); |
| 17388 | return std::move(op.outputs_[0]).take(); |
| 17389 | } |
| 17390 | struct structured_upsample_bilinear2d_default_backend_functional final : public at::meta::structured_upsample_bilinear2d { |
| 17391 | void set_output_strided( |
| 17392 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17393 | TensorOptions options, DimnameList names |
| 17394 | ) override { |
| 17395 | auto current_device = guard_.current_device(); |
| 17396 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17397 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17398 | "structured kernels don't support multi-device outputs"); |
| 17399 | } else { |
| 17400 | guard_.reset_device(options.device()); |
| 17401 | } |
| 17402 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17403 | if (!names.empty()) { |
| 17404 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17405 | } |
| 17406 | // super must happen after, so that downstream can use maybe_get_output |
| 17407 | // to retrieve the output |
| 17408 | } |
| 17409 | void set_output_raw_strided( |
| 17410 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17411 | TensorOptions options, DimnameList names |
| 17412 | ) override { |
| 17413 | auto current_device = guard_.current_device(); |
| 17414 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17415 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17416 | "structured kernels don't support multi-device outputs"); |
| 17417 | } else { |
| 17418 | guard_.reset_device(options.device()); |
| 17419 | } |
| 17420 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17421 | if (!names.empty()) { |
| 17422 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17423 | } |
| 17424 | // super must happen after, so that downstream can use maybe_get_output |
| 17425 | // to retrieve the output |
| 17426 | } |
| 17427 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17428 | return *outputs_[output_idx]; |
| 17429 | } |
| 17430 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 17431 | c10::OptionalDeviceGuard guard_; |
| 17432 | }; |
| 17433 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_upsample_bilinear2d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 17434 | structured_upsample_bilinear2d_default_backend_functional op; |
| 17435 | op.meta(self, output_size, align_corners, scales_h, scales_w); |
| 17436 | at::upsample_bilinear2d_outf(self, output_size, align_corners, scales_h, scales_w, *op.outputs_[0]); |
| 17437 | return std::move(op.outputs_[0]).take(); |
| 17438 | } |
| 17439 | struct structured_upsample_bilinear2d_backward_default_backend_functional final : public at::meta::structured_upsample_bilinear2d_backward { |
| 17440 | void set_output_strided( |
| 17441 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17442 | TensorOptions options, DimnameList names |
| 17443 | ) override { |
| 17444 | auto current_device = guard_.current_device(); |
| 17445 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17446 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17447 | "structured kernels don't support multi-device outputs"); |
| 17448 | } else { |
| 17449 | guard_.reset_device(options.device()); |
| 17450 | } |
| 17451 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17452 | if (!names.empty()) { |
| 17453 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17454 | } |
| 17455 | // super must happen after, so that downstream can use maybe_get_output |
| 17456 | // to retrieve the output |
| 17457 | } |
| 17458 | void set_output_raw_strided( |
| 17459 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17460 | TensorOptions options, DimnameList names |
| 17461 | ) override { |
| 17462 | auto current_device = guard_.current_device(); |
| 17463 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17464 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17465 | "structured kernels don't support multi-device outputs"); |
| 17466 | } else { |
| 17467 | guard_.reset_device(options.device()); |
| 17468 | } |
| 17469 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17470 | if (!names.empty()) { |
| 17471 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17472 | } |
| 17473 | // super must happen after, so that downstream can use maybe_get_output |
| 17474 | // to retrieve the output |
| 17475 | } |
| 17476 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17477 | return *outputs_[output_idx]; |
| 17478 | } |
| 17479 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 17480 | c10::OptionalDeviceGuard guard_; |
| 17481 | }; |
| 17482 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_upsample_bilinear2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 17483 | structured_upsample_bilinear2d_backward_default_backend_functional op; |
| 17484 | op.meta(grad_output, output_size, input_size, align_corners, scales_h, scales_w); |
| 17485 | at::upsample_bilinear2d_backward_outf(grad_output, output_size, input_size, align_corners, scales_h, scales_w, *op.outputs_[0]); |
| 17486 | return std::move(op.outputs_[0]).take(); |
| 17487 | } |
| 17488 | struct structured__upsample_bilinear2d_aa_default_backend_functional final : public at::meta::structured__upsample_bilinear2d_aa { |
| 17489 | void set_output_strided( |
| 17490 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17491 | TensorOptions options, DimnameList names |
| 17492 | ) override { |
| 17493 | auto current_device = guard_.current_device(); |
| 17494 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17495 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17496 | "structured kernels don't support multi-device outputs"); |
| 17497 | } else { |
| 17498 | guard_.reset_device(options.device()); |
| 17499 | } |
| 17500 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17501 | if (!names.empty()) { |
| 17502 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17503 | } |
| 17504 | // super must happen after, so that downstream can use maybe_get_output |
| 17505 | // to retrieve the output |
| 17506 | } |
| 17507 | void set_output_raw_strided( |
| 17508 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17509 | TensorOptions options, DimnameList names |
| 17510 | ) override { |
| 17511 | auto current_device = guard_.current_device(); |
| 17512 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17513 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17514 | "structured kernels don't support multi-device outputs"); |
| 17515 | } else { |
| 17516 | guard_.reset_device(options.device()); |
| 17517 | } |
| 17518 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17519 | if (!names.empty()) { |
| 17520 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17521 | } |
| 17522 | // super must happen after, so that downstream can use maybe_get_output |
| 17523 | // to retrieve the output |
| 17524 | } |
| 17525 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17526 | return *outputs_[output_idx]; |
| 17527 | } |
| 17528 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 17529 | c10::OptionalDeviceGuard guard_; |
| 17530 | }; |
| 17531 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__upsample_bilinear2d_aa(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 17532 | structured__upsample_bilinear2d_aa_default_backend_functional op; |
| 17533 | op.meta(self, output_size, align_corners, scales_h, scales_w); |
| 17534 | at::_upsample_bilinear2d_aa_outf(self, output_size, align_corners, scales_h, scales_w, *op.outputs_[0]); |
| 17535 | return std::move(op.outputs_[0]).take(); |
| 17536 | } |
| 17537 | struct structured__upsample_bilinear2d_aa_backward_default_backend_functional final : public at::meta::structured__upsample_bilinear2d_aa_backward { |
| 17538 | void set_output_strided( |
| 17539 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17540 | TensorOptions options, DimnameList names |
| 17541 | ) override { |
| 17542 | auto current_device = guard_.current_device(); |
| 17543 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17544 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17545 | "structured kernels don't support multi-device outputs"); |
| 17546 | } else { |
| 17547 | guard_.reset_device(options.device()); |
| 17548 | } |
| 17549 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17550 | if (!names.empty()) { |
| 17551 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17552 | } |
| 17553 | // super must happen after, so that downstream can use maybe_get_output |
| 17554 | // to retrieve the output |
| 17555 | } |
| 17556 | void set_output_raw_strided( |
| 17557 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17558 | TensorOptions options, DimnameList names |
| 17559 | ) override { |
| 17560 | auto current_device = guard_.current_device(); |
| 17561 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17562 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17563 | "structured kernels don't support multi-device outputs"); |
| 17564 | } else { |
| 17565 | guard_.reset_device(options.device()); |
| 17566 | } |
| 17567 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17568 | if (!names.empty()) { |
| 17569 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17570 | } |
| 17571 | // super must happen after, so that downstream can use maybe_get_output |
| 17572 | // to retrieve the output |
| 17573 | } |
| 17574 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17575 | return *outputs_[output_idx]; |
| 17576 | } |
| 17577 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 17578 | c10::OptionalDeviceGuard guard_; |
| 17579 | }; |
| 17580 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__upsample_bilinear2d_aa_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 17581 | structured__upsample_bilinear2d_aa_backward_default_backend_functional op; |
| 17582 | op.meta(grad_output, output_size, input_size, align_corners, scales_h, scales_w); |
| 17583 | at::_upsample_bilinear2d_aa_backward_outf(grad_output, output_size, input_size, align_corners, scales_h, scales_w, *op.outputs_[0]); |
| 17584 | return std::move(op.outputs_[0]).take(); |
| 17585 | } |
| 17586 | struct structured_upsample_bicubic2d_default_backend_functional final : public at::meta::structured_upsample_bicubic2d { |
| 17587 | void set_output_strided( |
| 17588 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17589 | TensorOptions options, DimnameList names |
| 17590 | ) override { |
| 17591 | auto current_device = guard_.current_device(); |
| 17592 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17593 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17594 | "structured kernels don't support multi-device outputs"); |
| 17595 | } else { |
| 17596 | guard_.reset_device(options.device()); |
| 17597 | } |
| 17598 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17599 | if (!names.empty()) { |
| 17600 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17601 | } |
| 17602 | // super must happen after, so that downstream can use maybe_get_output |
| 17603 | // to retrieve the output |
| 17604 | } |
| 17605 | void set_output_raw_strided( |
| 17606 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17607 | TensorOptions options, DimnameList names |
| 17608 | ) override { |
| 17609 | auto current_device = guard_.current_device(); |
| 17610 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17611 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17612 | "structured kernels don't support multi-device outputs"); |
| 17613 | } else { |
| 17614 | guard_.reset_device(options.device()); |
| 17615 | } |
| 17616 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17617 | if (!names.empty()) { |
| 17618 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17619 | } |
| 17620 | // super must happen after, so that downstream can use maybe_get_output |
| 17621 | // to retrieve the output |
| 17622 | } |
| 17623 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17624 | return *outputs_[output_idx]; |
| 17625 | } |
| 17626 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 17627 | c10::OptionalDeviceGuard guard_; |
| 17628 | }; |
| 17629 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_upsample_bicubic2d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 17630 | structured_upsample_bicubic2d_default_backend_functional op; |
| 17631 | op.meta(self, output_size, align_corners, scales_h, scales_w); |
| 17632 | at::upsample_bicubic2d_outf(self, output_size, align_corners, scales_h, scales_w, *op.outputs_[0]); |
| 17633 | return std::move(op.outputs_[0]).take(); |
| 17634 | } |
| 17635 | struct structured_upsample_bicubic2d_backward_default_backend_functional final : public at::meta::structured_upsample_bicubic2d_backward { |
| 17636 | void set_output_strided( |
| 17637 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17638 | TensorOptions options, DimnameList names |
| 17639 | ) override { |
| 17640 | auto current_device = guard_.current_device(); |
| 17641 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17642 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17643 | "structured kernels don't support multi-device outputs"); |
| 17644 | } else { |
| 17645 | guard_.reset_device(options.device()); |
| 17646 | } |
| 17647 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17648 | if (!names.empty()) { |
| 17649 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17650 | } |
| 17651 | // super must happen after, so that downstream can use maybe_get_output |
| 17652 | // to retrieve the output |
| 17653 | } |
| 17654 | void set_output_raw_strided( |
| 17655 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17656 | TensorOptions options, DimnameList names |
| 17657 | ) override { |
| 17658 | auto current_device = guard_.current_device(); |
| 17659 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17660 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17661 | "structured kernels don't support multi-device outputs"); |
| 17662 | } else { |
| 17663 | guard_.reset_device(options.device()); |
| 17664 | } |
| 17665 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17666 | if (!names.empty()) { |
| 17667 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17668 | } |
| 17669 | // super must happen after, so that downstream can use maybe_get_output |
| 17670 | // to retrieve the output |
| 17671 | } |
| 17672 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17673 | return *outputs_[output_idx]; |
| 17674 | } |
| 17675 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 17676 | c10::OptionalDeviceGuard guard_; |
| 17677 | }; |
| 17678 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_upsample_bicubic2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 17679 | structured_upsample_bicubic2d_backward_default_backend_functional op; |
| 17680 | op.meta(grad_output, output_size, input_size, align_corners, scales_h, scales_w); |
| 17681 | at::upsample_bicubic2d_backward_outf(grad_output, output_size, input_size, align_corners, scales_h, scales_w, *op.outputs_[0]); |
| 17682 | return std::move(op.outputs_[0]).take(); |
| 17683 | } |
| 17684 | struct structured__upsample_bicubic2d_aa_default_backend_functional final : public at::meta::structured__upsample_bicubic2d_aa { |
| 17685 | void set_output_strided( |
| 17686 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17687 | TensorOptions options, DimnameList names |
| 17688 | ) override { |
| 17689 | auto current_device = guard_.current_device(); |
| 17690 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17691 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17692 | "structured kernels don't support multi-device outputs"); |
| 17693 | } else { |
| 17694 | guard_.reset_device(options.device()); |
| 17695 | } |
| 17696 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17697 | if (!names.empty()) { |
| 17698 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17699 | } |
| 17700 | // super must happen after, so that downstream can use maybe_get_output |
| 17701 | // to retrieve the output |
| 17702 | } |
| 17703 | void set_output_raw_strided( |
| 17704 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17705 | TensorOptions options, DimnameList names |
| 17706 | ) override { |
| 17707 | auto current_device = guard_.current_device(); |
| 17708 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17709 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17710 | "structured kernels don't support multi-device outputs"); |
| 17711 | } else { |
| 17712 | guard_.reset_device(options.device()); |
| 17713 | } |
| 17714 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17715 | if (!names.empty()) { |
| 17716 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17717 | } |
| 17718 | // super must happen after, so that downstream can use maybe_get_output |
| 17719 | // to retrieve the output |
| 17720 | } |
| 17721 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17722 | return *outputs_[output_idx]; |
| 17723 | } |
| 17724 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 17725 | c10::OptionalDeviceGuard guard_; |
| 17726 | }; |
| 17727 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__upsample_bicubic2d_aa(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 17728 | structured__upsample_bicubic2d_aa_default_backend_functional op; |
| 17729 | op.meta(self, output_size, align_corners, scales_h, scales_w); |
| 17730 | at::_upsample_bicubic2d_aa_outf(self, output_size, align_corners, scales_h, scales_w, *op.outputs_[0]); |
| 17731 | return std::move(op.outputs_[0]).take(); |
| 17732 | } |
| 17733 | struct structured__upsample_bicubic2d_aa_backward_default_backend_functional final : public at::meta::structured__upsample_bicubic2d_aa_backward { |
| 17734 | void set_output_strided( |
| 17735 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17736 | TensorOptions options, DimnameList names |
| 17737 | ) override { |
| 17738 | auto current_device = guard_.current_device(); |
| 17739 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17740 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17741 | "structured kernels don't support multi-device outputs"); |
| 17742 | } else { |
| 17743 | guard_.reset_device(options.device()); |
| 17744 | } |
| 17745 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17746 | if (!names.empty()) { |
| 17747 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17748 | } |
| 17749 | // super must happen after, so that downstream can use maybe_get_output |
| 17750 | // to retrieve the output |
| 17751 | } |
| 17752 | void set_output_raw_strided( |
| 17753 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17754 | TensorOptions options, DimnameList names |
| 17755 | ) override { |
| 17756 | auto current_device = guard_.current_device(); |
| 17757 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17758 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17759 | "structured kernels don't support multi-device outputs"); |
| 17760 | } else { |
| 17761 | guard_.reset_device(options.device()); |
| 17762 | } |
| 17763 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17764 | if (!names.empty()) { |
| 17765 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17766 | } |
| 17767 | // super must happen after, so that downstream can use maybe_get_output |
| 17768 | // to retrieve the output |
| 17769 | } |
| 17770 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17771 | return *outputs_[output_idx]; |
| 17772 | } |
| 17773 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 17774 | c10::OptionalDeviceGuard guard_; |
| 17775 | }; |
| 17776 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__upsample_bicubic2d_aa_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 17777 | structured__upsample_bicubic2d_aa_backward_default_backend_functional op; |
| 17778 | op.meta(grad_output, output_size, input_size, align_corners, scales_h, scales_w); |
| 17779 | at::_upsample_bicubic2d_aa_backward_outf(grad_output, output_size, input_size, align_corners, scales_h, scales_w, *op.outputs_[0]); |
| 17780 | return std::move(op.outputs_[0]).take(); |
| 17781 | } |
| 17782 | struct structured_upsample_trilinear3d_default_backend_functional final : public at::meta::structured_upsample_trilinear3d { |
| 17783 | void set_output_strided( |
| 17784 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17785 | TensorOptions options, DimnameList names |
| 17786 | ) override { |
| 17787 | auto current_device = guard_.current_device(); |
| 17788 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17789 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17790 | "structured kernels don't support multi-device outputs"); |
| 17791 | } else { |
| 17792 | guard_.reset_device(options.device()); |
| 17793 | } |
| 17794 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17795 | if (!names.empty()) { |
| 17796 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17797 | } |
| 17798 | // super must happen after, so that downstream can use maybe_get_output |
| 17799 | // to retrieve the output |
| 17800 | } |
| 17801 | void set_output_raw_strided( |
| 17802 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17803 | TensorOptions options, DimnameList names |
| 17804 | ) override { |
| 17805 | auto current_device = guard_.current_device(); |
| 17806 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17807 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17808 | "structured kernels don't support multi-device outputs"); |
| 17809 | } else { |
| 17810 | guard_.reset_device(options.device()); |
| 17811 | } |
| 17812 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17813 | if (!names.empty()) { |
| 17814 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17815 | } |
| 17816 | // super must happen after, so that downstream can use maybe_get_output |
| 17817 | // to retrieve the output |
| 17818 | } |
| 17819 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17820 | return *outputs_[output_idx]; |
| 17821 | } |
| 17822 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 17823 | c10::OptionalDeviceGuard guard_; |
| 17824 | }; |
| 17825 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_upsample_trilinear3d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 17826 | structured_upsample_trilinear3d_default_backend_functional op; |
| 17827 | op.meta(self, output_size, align_corners, scales_d, scales_h, scales_w); |
| 17828 | at::upsample_trilinear3d_outf(self, output_size, align_corners, scales_d, scales_h, scales_w, *op.outputs_[0]); |
| 17829 | return std::move(op.outputs_[0]).take(); |
| 17830 | } |
| 17831 | struct structured_upsample_trilinear3d_backward_default_backend_functional final : public at::meta::structured_upsample_trilinear3d_backward { |
| 17832 | void set_output_strided( |
| 17833 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17834 | TensorOptions options, DimnameList names |
| 17835 | ) override { |
| 17836 | auto current_device = guard_.current_device(); |
| 17837 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17838 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17839 | "structured kernels don't support multi-device outputs"); |
| 17840 | } else { |
| 17841 | guard_.reset_device(options.device()); |
| 17842 | } |
| 17843 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17844 | if (!names.empty()) { |
| 17845 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17846 | } |
| 17847 | // super must happen after, so that downstream can use maybe_get_output |
| 17848 | // to retrieve the output |
| 17849 | } |
| 17850 | void set_output_raw_strided( |
| 17851 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17852 | TensorOptions options, DimnameList names |
| 17853 | ) override { |
| 17854 | auto current_device = guard_.current_device(); |
| 17855 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17856 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17857 | "structured kernels don't support multi-device outputs"); |
| 17858 | } else { |
| 17859 | guard_.reset_device(options.device()); |
| 17860 | } |
| 17861 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17862 | if (!names.empty()) { |
| 17863 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17864 | } |
| 17865 | // super must happen after, so that downstream can use maybe_get_output |
| 17866 | // to retrieve the output |
| 17867 | } |
| 17868 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17869 | return *outputs_[output_idx]; |
| 17870 | } |
| 17871 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 17872 | c10::OptionalDeviceGuard guard_; |
| 17873 | }; |
| 17874 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_upsample_trilinear3d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 17875 | structured_upsample_trilinear3d_backward_default_backend_functional op; |
| 17876 | op.meta(grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w); |
| 17877 | at::upsample_trilinear3d_backward_outf(grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w, *op.outputs_[0]); |
| 17878 | return std::move(op.outputs_[0]).take(); |
| 17879 | } |
| 17880 | struct structured_upsample_nearest1d_default_backend_functional final : public at::meta::structured_upsample_nearest1d { |
| 17881 | void set_output_strided( |
| 17882 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17883 | TensorOptions options, DimnameList names |
| 17884 | ) override { |
| 17885 | auto current_device = guard_.current_device(); |
| 17886 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17887 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17888 | "structured kernels don't support multi-device outputs"); |
| 17889 | } else { |
| 17890 | guard_.reset_device(options.device()); |
| 17891 | } |
| 17892 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17893 | if (!names.empty()) { |
| 17894 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17895 | } |
| 17896 | // super must happen after, so that downstream can use maybe_get_output |
| 17897 | // to retrieve the output |
| 17898 | } |
| 17899 | void set_output_raw_strided( |
| 17900 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17901 | TensorOptions options, DimnameList names |
| 17902 | ) override { |
| 17903 | auto current_device = guard_.current_device(); |
| 17904 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17905 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17906 | "structured kernels don't support multi-device outputs"); |
| 17907 | } else { |
| 17908 | guard_.reset_device(options.device()); |
| 17909 | } |
| 17910 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17911 | if (!names.empty()) { |
| 17912 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17913 | } |
| 17914 | // super must happen after, so that downstream can use maybe_get_output |
| 17915 | // to retrieve the output |
| 17916 | } |
| 17917 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17918 | return *outputs_[output_idx]; |
| 17919 | } |
| 17920 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 17921 | c10::OptionalDeviceGuard guard_; |
| 17922 | }; |
| 17923 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_upsample_nearest1d(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales) { |
| 17924 | structured_upsample_nearest1d_default_backend_functional op; |
| 17925 | op.meta(self, output_size, scales); |
| 17926 | at::upsample_nearest1d_outf(self, output_size, scales, *op.outputs_[0]); |
| 17927 | return std::move(op.outputs_[0]).take(); |
| 17928 | } |
| 17929 | struct structured__upsample_nearest_exact1d_default_backend_functional final : public at::meta::structured__upsample_nearest_exact1d { |
| 17930 | void set_output_strided( |
| 17931 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17932 | TensorOptions options, DimnameList names |
| 17933 | ) override { |
| 17934 | auto current_device = guard_.current_device(); |
| 17935 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17936 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17937 | "structured kernels don't support multi-device outputs"); |
| 17938 | } else { |
| 17939 | guard_.reset_device(options.device()); |
| 17940 | } |
| 17941 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17942 | if (!names.empty()) { |
| 17943 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17944 | } |
| 17945 | // super must happen after, so that downstream can use maybe_get_output |
| 17946 | // to retrieve the output |
| 17947 | } |
| 17948 | void set_output_raw_strided( |
| 17949 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17950 | TensorOptions options, DimnameList names |
| 17951 | ) override { |
| 17952 | auto current_device = guard_.current_device(); |
| 17953 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17954 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17955 | "structured kernels don't support multi-device outputs"); |
| 17956 | } else { |
| 17957 | guard_.reset_device(options.device()); |
| 17958 | } |
| 17959 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17960 | if (!names.empty()) { |
| 17961 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17962 | } |
| 17963 | // super must happen after, so that downstream can use maybe_get_output |
| 17964 | // to retrieve the output |
| 17965 | } |
| 17966 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 17967 | return *outputs_[output_idx]; |
| 17968 | } |
| 17969 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 17970 | c10::OptionalDeviceGuard guard_; |
| 17971 | }; |
| 17972 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__upsample_nearest_exact1d(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales) { |
| 17973 | structured__upsample_nearest_exact1d_default_backend_functional op; |
| 17974 | op.meta(self, output_size, scales); |
| 17975 | at::_upsample_nearest_exact1d_outf(self, output_size, scales, *op.outputs_[0]); |
| 17976 | return std::move(op.outputs_[0]).take(); |
| 17977 | } |
| 17978 | struct structured_upsample_nearest1d_backward_default_backend_functional final : public at::meta::structured_upsample_nearest1d_backward { |
| 17979 | void set_output_strided( |
| 17980 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17981 | TensorOptions options, DimnameList names |
| 17982 | ) override { |
| 17983 | auto current_device = guard_.current_device(); |
| 17984 | if (C10_UNLIKELY(current_device.has_value())) { |
| 17985 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 17986 | "structured kernels don't support multi-device outputs"); |
| 17987 | } else { |
| 17988 | guard_.reset_device(options.device()); |
| 17989 | } |
| 17990 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 17991 | if (!names.empty()) { |
| 17992 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 17993 | } |
| 17994 | // super must happen after, so that downstream can use maybe_get_output |
| 17995 | // to retrieve the output |
| 17996 | } |
| 17997 | void set_output_raw_strided( |
| 17998 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 17999 | TensorOptions options, DimnameList names |
| 18000 | ) override { |
| 18001 | auto current_device = guard_.current_device(); |
| 18002 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18003 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18004 | "structured kernels don't support multi-device outputs"); |
| 18005 | } else { |
| 18006 | guard_.reset_device(options.device()); |
| 18007 | } |
| 18008 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18009 | if (!names.empty()) { |
| 18010 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18011 | } |
| 18012 | // super must happen after, so that downstream can use maybe_get_output |
| 18013 | // to retrieve the output |
| 18014 | } |
| 18015 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18016 | return *outputs_[output_idx]; |
| 18017 | } |
| 18018 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 18019 | c10::OptionalDeviceGuard guard_; |
| 18020 | }; |
| 18021 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_upsample_nearest1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales) { |
| 18022 | structured_upsample_nearest1d_backward_default_backend_functional op; |
| 18023 | op.meta(grad_output, output_size, input_size, scales); |
| 18024 | at::upsample_nearest1d_backward_outf(grad_output, output_size, input_size, scales, *op.outputs_[0]); |
| 18025 | return std::move(op.outputs_[0]).take(); |
| 18026 | } |
| 18027 | struct structured__upsample_nearest_exact1d_backward_default_backend_functional final : public at::meta::structured__upsample_nearest_exact1d_backward { |
| 18028 | void set_output_strided( |
| 18029 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18030 | TensorOptions options, DimnameList names |
| 18031 | ) override { |
| 18032 | auto current_device = guard_.current_device(); |
| 18033 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18034 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18035 | "structured kernels don't support multi-device outputs"); |
| 18036 | } else { |
| 18037 | guard_.reset_device(options.device()); |
| 18038 | } |
| 18039 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18040 | if (!names.empty()) { |
| 18041 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18042 | } |
| 18043 | // super must happen after, so that downstream can use maybe_get_output |
| 18044 | // to retrieve the output |
| 18045 | } |
| 18046 | void set_output_raw_strided( |
| 18047 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18048 | TensorOptions options, DimnameList names |
| 18049 | ) override { |
| 18050 | auto current_device = guard_.current_device(); |
| 18051 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18052 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18053 | "structured kernels don't support multi-device outputs"); |
| 18054 | } else { |
| 18055 | guard_.reset_device(options.device()); |
| 18056 | } |
| 18057 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18058 | if (!names.empty()) { |
| 18059 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18060 | } |
| 18061 | // super must happen after, so that downstream can use maybe_get_output |
| 18062 | // to retrieve the output |
| 18063 | } |
| 18064 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18065 | return *outputs_[output_idx]; |
| 18066 | } |
| 18067 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 18068 | c10::OptionalDeviceGuard guard_; |
| 18069 | }; |
| 18070 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__upsample_nearest_exact1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales) { |
| 18071 | structured__upsample_nearest_exact1d_backward_default_backend_functional op; |
| 18072 | op.meta(grad_output, output_size, input_size, scales); |
| 18073 | at::_upsample_nearest_exact1d_backward_outf(grad_output, output_size, input_size, scales, *op.outputs_[0]); |
| 18074 | return std::move(op.outputs_[0]).take(); |
| 18075 | } |
| 18076 | struct structured_upsample_nearest2d_default_backend_functional final : public at::meta::structured_upsample_nearest2d { |
| 18077 | void set_output_strided( |
| 18078 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18079 | TensorOptions options, DimnameList names |
| 18080 | ) override { |
| 18081 | auto current_device = guard_.current_device(); |
| 18082 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18083 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18084 | "structured kernels don't support multi-device outputs"); |
| 18085 | } else { |
| 18086 | guard_.reset_device(options.device()); |
| 18087 | } |
| 18088 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18089 | if (!names.empty()) { |
| 18090 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18091 | } |
| 18092 | // super must happen after, so that downstream can use maybe_get_output |
| 18093 | // to retrieve the output |
| 18094 | } |
| 18095 | void set_output_raw_strided( |
| 18096 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18097 | TensorOptions options, DimnameList names |
| 18098 | ) override { |
| 18099 | auto current_device = guard_.current_device(); |
| 18100 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18101 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18102 | "structured kernels don't support multi-device outputs"); |
| 18103 | } else { |
| 18104 | guard_.reset_device(options.device()); |
| 18105 | } |
| 18106 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18107 | if (!names.empty()) { |
| 18108 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18109 | } |
| 18110 | // super must happen after, so that downstream can use maybe_get_output |
| 18111 | // to retrieve the output |
| 18112 | } |
| 18113 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18114 | return *outputs_[output_idx]; |
| 18115 | } |
| 18116 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 18117 | c10::OptionalDeviceGuard guard_; |
| 18118 | }; |
| 18119 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_upsample_nearest2d(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 18120 | structured_upsample_nearest2d_default_backend_functional op; |
| 18121 | op.meta(self, output_size, scales_h, scales_w); |
| 18122 | at::upsample_nearest2d_outf(self, output_size, scales_h, scales_w, *op.outputs_[0]); |
| 18123 | return std::move(op.outputs_[0]).take(); |
| 18124 | } |
| 18125 | struct structured__upsample_nearest_exact2d_default_backend_functional final : public at::meta::structured__upsample_nearest_exact2d { |
| 18126 | void set_output_strided( |
| 18127 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18128 | TensorOptions options, DimnameList names |
| 18129 | ) override { |
| 18130 | auto current_device = guard_.current_device(); |
| 18131 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18132 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18133 | "structured kernels don't support multi-device outputs"); |
| 18134 | } else { |
| 18135 | guard_.reset_device(options.device()); |
| 18136 | } |
| 18137 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18138 | if (!names.empty()) { |
| 18139 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18140 | } |
| 18141 | // super must happen after, so that downstream can use maybe_get_output |
| 18142 | // to retrieve the output |
| 18143 | } |
| 18144 | void set_output_raw_strided( |
| 18145 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18146 | TensorOptions options, DimnameList names |
| 18147 | ) override { |
| 18148 | auto current_device = guard_.current_device(); |
| 18149 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18150 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18151 | "structured kernels don't support multi-device outputs"); |
| 18152 | } else { |
| 18153 | guard_.reset_device(options.device()); |
| 18154 | } |
| 18155 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18156 | if (!names.empty()) { |
| 18157 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18158 | } |
| 18159 | // super must happen after, so that downstream can use maybe_get_output |
| 18160 | // to retrieve the output |
| 18161 | } |
| 18162 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18163 | return *outputs_[output_idx]; |
| 18164 | } |
| 18165 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 18166 | c10::OptionalDeviceGuard guard_; |
| 18167 | }; |
| 18168 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__upsample_nearest_exact2d(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 18169 | structured__upsample_nearest_exact2d_default_backend_functional op; |
| 18170 | op.meta(self, output_size, scales_h, scales_w); |
| 18171 | at::_upsample_nearest_exact2d_outf(self, output_size, scales_h, scales_w, *op.outputs_[0]); |
| 18172 | return std::move(op.outputs_[0]).take(); |
| 18173 | } |
| 18174 | struct structured_upsample_nearest2d_backward_default_backend_functional final : public at::meta::structured_upsample_nearest2d_backward { |
| 18175 | void set_output_strided( |
| 18176 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18177 | TensorOptions options, DimnameList names |
| 18178 | ) override { |
| 18179 | auto current_device = guard_.current_device(); |
| 18180 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18181 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18182 | "structured kernels don't support multi-device outputs"); |
| 18183 | } else { |
| 18184 | guard_.reset_device(options.device()); |
| 18185 | } |
| 18186 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18187 | if (!names.empty()) { |
| 18188 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18189 | } |
| 18190 | // super must happen after, so that downstream can use maybe_get_output |
| 18191 | // to retrieve the output |
| 18192 | } |
| 18193 | void set_output_raw_strided( |
| 18194 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18195 | TensorOptions options, DimnameList names |
| 18196 | ) override { |
| 18197 | auto current_device = guard_.current_device(); |
| 18198 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18199 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18200 | "structured kernels don't support multi-device outputs"); |
| 18201 | } else { |
| 18202 | guard_.reset_device(options.device()); |
| 18203 | } |
| 18204 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18205 | if (!names.empty()) { |
| 18206 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18207 | } |
| 18208 | // super must happen after, so that downstream can use maybe_get_output |
| 18209 | // to retrieve the output |
| 18210 | } |
| 18211 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18212 | return *outputs_[output_idx]; |
| 18213 | } |
| 18214 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 18215 | c10::OptionalDeviceGuard guard_; |
| 18216 | }; |
| 18217 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_upsample_nearest2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 18218 | structured_upsample_nearest2d_backward_default_backend_functional op; |
| 18219 | op.meta(grad_output, output_size, input_size, scales_h, scales_w); |
| 18220 | at::upsample_nearest2d_backward_outf(grad_output, output_size, input_size, scales_h, scales_w, *op.outputs_[0]); |
| 18221 | return std::move(op.outputs_[0]).take(); |
| 18222 | } |
| 18223 | struct structured__upsample_nearest_exact2d_backward_default_backend_functional final : public at::meta::structured__upsample_nearest_exact2d_backward { |
| 18224 | void set_output_strided( |
| 18225 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18226 | TensorOptions options, DimnameList names |
| 18227 | ) override { |
| 18228 | auto current_device = guard_.current_device(); |
| 18229 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18230 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18231 | "structured kernels don't support multi-device outputs"); |
| 18232 | } else { |
| 18233 | guard_.reset_device(options.device()); |
| 18234 | } |
| 18235 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18236 | if (!names.empty()) { |
| 18237 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18238 | } |
| 18239 | // super must happen after, so that downstream can use maybe_get_output |
| 18240 | // to retrieve the output |
| 18241 | } |
| 18242 | void set_output_raw_strided( |
| 18243 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18244 | TensorOptions options, DimnameList names |
| 18245 | ) override { |
| 18246 | auto current_device = guard_.current_device(); |
| 18247 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18248 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18249 | "structured kernels don't support multi-device outputs"); |
| 18250 | } else { |
| 18251 | guard_.reset_device(options.device()); |
| 18252 | } |
| 18253 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18254 | if (!names.empty()) { |
| 18255 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18256 | } |
| 18257 | // super must happen after, so that downstream can use maybe_get_output |
| 18258 | // to retrieve the output |
| 18259 | } |
| 18260 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18261 | return *outputs_[output_idx]; |
| 18262 | } |
| 18263 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 18264 | c10::OptionalDeviceGuard guard_; |
| 18265 | }; |
| 18266 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__upsample_nearest_exact2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 18267 | structured__upsample_nearest_exact2d_backward_default_backend_functional op; |
| 18268 | op.meta(grad_output, output_size, input_size, scales_h, scales_w); |
| 18269 | at::_upsample_nearest_exact2d_backward_outf(grad_output, output_size, input_size, scales_h, scales_w, *op.outputs_[0]); |
| 18270 | return std::move(op.outputs_[0]).take(); |
| 18271 | } |
| 18272 | struct structured_upsample_nearest3d_default_backend_functional final : public at::meta::structured_upsample_nearest3d { |
| 18273 | void set_output_strided( |
| 18274 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18275 | TensorOptions options, DimnameList names |
| 18276 | ) override { |
| 18277 | auto current_device = guard_.current_device(); |
| 18278 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18279 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18280 | "structured kernels don't support multi-device outputs"); |
| 18281 | } else { |
| 18282 | guard_.reset_device(options.device()); |
| 18283 | } |
| 18284 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18285 | if (!names.empty()) { |
| 18286 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18287 | } |
| 18288 | // super must happen after, so that downstream can use maybe_get_output |
| 18289 | // to retrieve the output |
| 18290 | } |
| 18291 | void set_output_raw_strided( |
| 18292 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18293 | TensorOptions options, DimnameList names |
| 18294 | ) override { |
| 18295 | auto current_device = guard_.current_device(); |
| 18296 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18297 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18298 | "structured kernels don't support multi-device outputs"); |
| 18299 | } else { |
| 18300 | guard_.reset_device(options.device()); |
| 18301 | } |
| 18302 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18303 | if (!names.empty()) { |
| 18304 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18305 | } |
| 18306 | // super must happen after, so that downstream can use maybe_get_output |
| 18307 | // to retrieve the output |
| 18308 | } |
| 18309 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18310 | return *outputs_[output_idx]; |
| 18311 | } |
| 18312 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 18313 | c10::OptionalDeviceGuard guard_; |
| 18314 | }; |
| 18315 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_upsample_nearest3d(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 18316 | structured_upsample_nearest3d_default_backend_functional op; |
| 18317 | op.meta(self, output_size, scales_d, scales_h, scales_w); |
| 18318 | at::upsample_nearest3d_outf(self, output_size, scales_d, scales_h, scales_w, *op.outputs_[0]); |
| 18319 | return std::move(op.outputs_[0]).take(); |
| 18320 | } |
| 18321 | struct structured__upsample_nearest_exact3d_default_backend_functional final : public at::meta::structured__upsample_nearest_exact3d { |
| 18322 | void set_output_strided( |
| 18323 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18324 | TensorOptions options, DimnameList names |
| 18325 | ) override { |
| 18326 | auto current_device = guard_.current_device(); |
| 18327 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18328 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18329 | "structured kernels don't support multi-device outputs"); |
| 18330 | } else { |
| 18331 | guard_.reset_device(options.device()); |
| 18332 | } |
| 18333 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18334 | if (!names.empty()) { |
| 18335 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18336 | } |
| 18337 | // super must happen after, so that downstream can use maybe_get_output |
| 18338 | // to retrieve the output |
| 18339 | } |
| 18340 | void set_output_raw_strided( |
| 18341 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18342 | TensorOptions options, DimnameList names |
| 18343 | ) override { |
| 18344 | auto current_device = guard_.current_device(); |
| 18345 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18346 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18347 | "structured kernels don't support multi-device outputs"); |
| 18348 | } else { |
| 18349 | guard_.reset_device(options.device()); |
| 18350 | } |
| 18351 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18352 | if (!names.empty()) { |
| 18353 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18354 | } |
| 18355 | // super must happen after, so that downstream can use maybe_get_output |
| 18356 | // to retrieve the output |
| 18357 | } |
| 18358 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18359 | return *outputs_[output_idx]; |
| 18360 | } |
| 18361 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 18362 | c10::OptionalDeviceGuard guard_; |
| 18363 | }; |
| 18364 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__upsample_nearest_exact3d(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 18365 | structured__upsample_nearest_exact3d_default_backend_functional op; |
| 18366 | op.meta(self, output_size, scales_d, scales_h, scales_w); |
| 18367 | at::_upsample_nearest_exact3d_outf(self, output_size, scales_d, scales_h, scales_w, *op.outputs_[0]); |
| 18368 | return std::move(op.outputs_[0]).take(); |
| 18369 | } |
| 18370 | struct structured_upsample_nearest3d_backward_default_backend_functional final : public at::meta::structured_upsample_nearest3d_backward { |
| 18371 | void set_output_strided( |
| 18372 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18373 | TensorOptions options, DimnameList names |
| 18374 | ) override { |
| 18375 | auto current_device = guard_.current_device(); |
| 18376 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18377 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18378 | "structured kernels don't support multi-device outputs"); |
| 18379 | } else { |
| 18380 | guard_.reset_device(options.device()); |
| 18381 | } |
| 18382 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18383 | if (!names.empty()) { |
| 18384 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18385 | } |
| 18386 | // super must happen after, so that downstream can use maybe_get_output |
| 18387 | // to retrieve the output |
| 18388 | } |
| 18389 | void set_output_raw_strided( |
| 18390 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18391 | TensorOptions options, DimnameList names |
| 18392 | ) override { |
| 18393 | auto current_device = guard_.current_device(); |
| 18394 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18395 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18396 | "structured kernels don't support multi-device outputs"); |
| 18397 | } else { |
| 18398 | guard_.reset_device(options.device()); |
| 18399 | } |
| 18400 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18401 | if (!names.empty()) { |
| 18402 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18403 | } |
| 18404 | // super must happen after, so that downstream can use maybe_get_output |
| 18405 | // to retrieve the output |
| 18406 | } |
| 18407 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18408 | return *outputs_[output_idx]; |
| 18409 | } |
| 18410 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 18411 | c10::OptionalDeviceGuard guard_; |
| 18412 | }; |
| 18413 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_upsample_nearest3d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 18414 | structured_upsample_nearest3d_backward_default_backend_functional op; |
| 18415 | op.meta(grad_output, output_size, input_size, scales_d, scales_h, scales_w); |
| 18416 | at::upsample_nearest3d_backward_outf(grad_output, output_size, input_size, scales_d, scales_h, scales_w, *op.outputs_[0]); |
| 18417 | return std::move(op.outputs_[0]).take(); |
| 18418 | } |
| 18419 | struct structured__upsample_nearest_exact3d_backward_default_backend_functional final : public at::meta::structured__upsample_nearest_exact3d_backward { |
| 18420 | void set_output_strided( |
| 18421 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18422 | TensorOptions options, DimnameList names |
| 18423 | ) override { |
| 18424 | auto current_device = guard_.current_device(); |
| 18425 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18426 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18427 | "structured kernels don't support multi-device outputs"); |
| 18428 | } else { |
| 18429 | guard_.reset_device(options.device()); |
| 18430 | } |
| 18431 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18432 | if (!names.empty()) { |
| 18433 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18434 | } |
| 18435 | // super must happen after, so that downstream can use maybe_get_output |
| 18436 | // to retrieve the output |
| 18437 | } |
| 18438 | void set_output_raw_strided( |
| 18439 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18440 | TensorOptions options, DimnameList names |
| 18441 | ) override { |
| 18442 | auto current_device = guard_.current_device(); |
| 18443 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18444 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18445 | "structured kernels don't support multi-device outputs"); |
| 18446 | } else { |
| 18447 | guard_.reset_device(options.device()); |
| 18448 | } |
| 18449 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18450 | if (!names.empty()) { |
| 18451 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18452 | } |
| 18453 | // super must happen after, so that downstream can use maybe_get_output |
| 18454 | // to retrieve the output |
| 18455 | } |
| 18456 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18457 | return *outputs_[output_idx]; |
| 18458 | } |
| 18459 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 18460 | c10::OptionalDeviceGuard guard_; |
| 18461 | }; |
| 18462 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__upsample_nearest_exact3d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 18463 | structured__upsample_nearest_exact3d_backward_default_backend_functional op; |
| 18464 | op.meta(grad_output, output_size, input_size, scales_d, scales_h, scales_w); |
| 18465 | at::_upsample_nearest_exact3d_backward_outf(grad_output, output_size, input_size, scales_d, scales_h, scales_w, *op.outputs_[0]); |
| 18466 | return std::move(op.outputs_[0]).take(); |
| 18467 | } |
| 18468 | struct structured_sigmoid_backward_default_backend_functional final : public at::meta::structured_sigmoid_backward { |
| 18469 | void set_output_strided( |
| 18470 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18471 | TensorOptions options, DimnameList names |
| 18472 | ) override { |
| 18473 | auto current_device = guard_.current_device(); |
| 18474 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18475 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18476 | "structured kernels don't support multi-device outputs"); |
| 18477 | } else { |
| 18478 | guard_.reset_device(options.device()); |
| 18479 | } |
| 18480 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18481 | if (!names.empty()) { |
| 18482 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18483 | } |
| 18484 | // super must happen after, so that downstream can use maybe_get_output |
| 18485 | // to retrieve the output |
| 18486 | at::meta::structured_sigmoid_backward::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18487 | } |
| 18488 | void set_output_raw_strided( |
| 18489 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18490 | TensorOptions options, DimnameList names |
| 18491 | ) override { |
| 18492 | auto current_device = guard_.current_device(); |
| 18493 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18494 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18495 | "structured kernels don't support multi-device outputs"); |
| 18496 | } else { |
| 18497 | guard_.reset_device(options.device()); |
| 18498 | } |
| 18499 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18500 | if (!names.empty()) { |
| 18501 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18502 | } |
| 18503 | // super must happen after, so that downstream can use maybe_get_output |
| 18504 | // to retrieve the output |
| 18505 | at::meta::structured_sigmoid_backward::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18506 | } |
| 18507 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18508 | return *outputs_[output_idx]; |
| 18509 | } |
| 18510 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 18511 | c10::OptionalDeviceGuard guard_; |
| 18512 | }; |
| 18513 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_sigmoid_backward(const at::Tensor & grad_output, const at::Tensor & output) { |
| 18514 | structured_sigmoid_backward_default_backend_functional op; |
| 18515 | op.meta(grad_output, output); |
| 18516 | at::sigmoid_backward_outf(grad_output, output, *op.outputs_[0]); |
| 18517 | return std::move(op.outputs_[0]).take(); |
| 18518 | } |
| 18519 | struct structured_logit_backward_default_backend_functional final : public at::meta::structured_logit_backward { |
| 18520 | void set_output_strided( |
| 18521 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18522 | TensorOptions options, DimnameList names |
| 18523 | ) override { |
| 18524 | auto current_device = guard_.current_device(); |
| 18525 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18526 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18527 | "structured kernels don't support multi-device outputs"); |
| 18528 | } else { |
| 18529 | guard_.reset_device(options.device()); |
| 18530 | } |
| 18531 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18532 | if (!names.empty()) { |
| 18533 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18534 | } |
| 18535 | // super must happen after, so that downstream can use maybe_get_output |
| 18536 | // to retrieve the output |
| 18537 | at::meta::structured_logit_backward::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18538 | } |
| 18539 | void set_output_raw_strided( |
| 18540 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18541 | TensorOptions options, DimnameList names |
| 18542 | ) override { |
| 18543 | auto current_device = guard_.current_device(); |
| 18544 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18545 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18546 | "structured kernels don't support multi-device outputs"); |
| 18547 | } else { |
| 18548 | guard_.reset_device(options.device()); |
| 18549 | } |
| 18550 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18551 | if (!names.empty()) { |
| 18552 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18553 | } |
| 18554 | // super must happen after, so that downstream can use maybe_get_output |
| 18555 | // to retrieve the output |
| 18556 | at::meta::structured_logit_backward::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18557 | } |
| 18558 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18559 | return *outputs_[output_idx]; |
| 18560 | } |
| 18561 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 18562 | c10::OptionalDeviceGuard guard_; |
| 18563 | }; |
| 18564 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_logit_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::optional<double> eps) { |
| 18565 | structured_logit_backward_default_backend_functional op; |
| 18566 | op.meta(grad_output, self, eps); |
| 18567 | at::logit_backward_outf(grad_output, self, eps, *op.outputs_[0]); |
| 18568 | return std::move(op.outputs_[0]).take(); |
| 18569 | } |
| 18570 | struct structured_tanh_backward_default_backend_functional final : public at::meta::structured_tanh_backward { |
| 18571 | void set_output_strided( |
| 18572 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18573 | TensorOptions options, DimnameList names |
| 18574 | ) override { |
| 18575 | auto current_device = guard_.current_device(); |
| 18576 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18577 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18578 | "structured kernels don't support multi-device outputs"); |
| 18579 | } else { |
| 18580 | guard_.reset_device(options.device()); |
| 18581 | } |
| 18582 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18583 | if (!names.empty()) { |
| 18584 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18585 | } |
| 18586 | // super must happen after, so that downstream can use maybe_get_output |
| 18587 | // to retrieve the output |
| 18588 | at::meta::structured_tanh_backward::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18589 | } |
| 18590 | void set_output_raw_strided( |
| 18591 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18592 | TensorOptions options, DimnameList names |
| 18593 | ) override { |
| 18594 | auto current_device = guard_.current_device(); |
| 18595 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18596 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18597 | "structured kernels don't support multi-device outputs"); |
| 18598 | } else { |
| 18599 | guard_.reset_device(options.device()); |
| 18600 | } |
| 18601 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18602 | if (!names.empty()) { |
| 18603 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18604 | } |
| 18605 | // super must happen after, so that downstream can use maybe_get_output |
| 18606 | // to retrieve the output |
| 18607 | at::meta::structured_tanh_backward::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18608 | } |
| 18609 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18610 | return *outputs_[output_idx]; |
| 18611 | } |
| 18612 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 18613 | c10::OptionalDeviceGuard guard_; |
| 18614 | }; |
| 18615 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_tanh_backward(const at::Tensor & grad_output, const at::Tensor & output) { |
| 18616 | structured_tanh_backward_default_backend_functional op; |
| 18617 | op.meta(grad_output, output); |
| 18618 | at::tanh_backward_outf(grad_output, output, *op.outputs_[0]); |
| 18619 | return std::move(op.outputs_[0]).take(); |
| 18620 | } |
| 18621 | struct structured_slow_conv_transpose2d_default_backend_functional final : public at::meta::structured_slow_conv_transpose2d { |
| 18622 | void set_output_strided( |
| 18623 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18624 | TensorOptions options, DimnameList names |
| 18625 | ) override { |
| 18626 | auto current_device = guard_.current_device(); |
| 18627 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18628 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18629 | "structured kernels don't support multi-device outputs"); |
| 18630 | } else { |
| 18631 | guard_.reset_device(options.device()); |
| 18632 | } |
| 18633 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18634 | if (!names.empty()) { |
| 18635 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18636 | } |
| 18637 | // super must happen after, so that downstream can use maybe_get_output |
| 18638 | // to retrieve the output |
| 18639 | } |
| 18640 | void set_output_raw_strided( |
| 18641 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18642 | TensorOptions options, DimnameList names |
| 18643 | ) override { |
| 18644 | auto current_device = guard_.current_device(); |
| 18645 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18646 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18647 | "structured kernels don't support multi-device outputs"); |
| 18648 | } else { |
| 18649 | guard_.reset_device(options.device()); |
| 18650 | } |
| 18651 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18652 | if (!names.empty()) { |
| 18653 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18654 | } |
| 18655 | // super must happen after, so that downstream can use maybe_get_output |
| 18656 | // to retrieve the output |
| 18657 | } |
| 18658 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18659 | return *outputs_[output_idx]; |
| 18660 | } |
| 18661 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 18662 | c10::OptionalDeviceGuard guard_; |
| 18663 | }; |
| 18664 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_slow_conv_transpose2d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef dilation) { |
| 18665 | structured_slow_conv_transpose2d_default_backend_functional op; |
| 18666 | op.meta(self, weight, kernel_size, ((bias.has_value() && (*bias).defined()) ? at::OptionalTensorRef(*bias) : at::OptionalTensorRef()), stride, padding, output_padding, dilation); |
| 18667 | at::slow_conv_transpose2d_outf(self, weight, kernel_size, bias, stride, padding, output_padding, dilation, *op.outputs_[0]); |
| 18668 | return std::move(op.outputs_[0]).take(); |
| 18669 | } |
| 18670 | struct structured_isposinf_default_backend_functional final : public at::meta::structured_isposinf { |
| 18671 | void set_output_strided( |
| 18672 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18673 | TensorOptions options, DimnameList names |
| 18674 | ) override { |
| 18675 | auto current_device = guard_.current_device(); |
| 18676 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18677 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18678 | "structured kernels don't support multi-device outputs"); |
| 18679 | } else { |
| 18680 | guard_.reset_device(options.device()); |
| 18681 | } |
| 18682 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18683 | if (!names.empty()) { |
| 18684 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18685 | } |
| 18686 | // super must happen after, so that downstream can use maybe_get_output |
| 18687 | // to retrieve the output |
| 18688 | at::meta::structured_isposinf::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18689 | } |
| 18690 | void set_output_raw_strided( |
| 18691 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18692 | TensorOptions options, DimnameList names |
| 18693 | ) override { |
| 18694 | auto current_device = guard_.current_device(); |
| 18695 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18696 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18697 | "structured kernels don't support multi-device outputs"); |
| 18698 | } else { |
| 18699 | guard_.reset_device(options.device()); |
| 18700 | } |
| 18701 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18702 | if (!names.empty()) { |
| 18703 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18704 | } |
| 18705 | // super must happen after, so that downstream can use maybe_get_output |
| 18706 | // to retrieve the output |
| 18707 | at::meta::structured_isposinf::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18708 | } |
| 18709 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18710 | return *outputs_[output_idx]; |
| 18711 | } |
| 18712 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 18713 | c10::OptionalDeviceGuard guard_; |
| 18714 | }; |
| 18715 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_isposinf(const at::Tensor & self) { |
| 18716 | structured_isposinf_default_backend_functional op; |
| 18717 | op.meta(self); |
| 18718 | at::isposinf_outf(self, *op.outputs_[0]); |
| 18719 | return std::move(op.outputs_[0]).take(); |
| 18720 | } |
| 18721 | struct structured_isneginf_default_backend_functional final : public at::meta::structured_isneginf { |
| 18722 | void set_output_strided( |
| 18723 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18724 | TensorOptions options, DimnameList names |
| 18725 | ) override { |
| 18726 | auto current_device = guard_.current_device(); |
| 18727 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18728 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18729 | "structured kernels don't support multi-device outputs"); |
| 18730 | } else { |
| 18731 | guard_.reset_device(options.device()); |
| 18732 | } |
| 18733 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18734 | if (!names.empty()) { |
| 18735 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18736 | } |
| 18737 | // super must happen after, so that downstream can use maybe_get_output |
| 18738 | // to retrieve the output |
| 18739 | at::meta::structured_isneginf::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18740 | } |
| 18741 | void set_output_raw_strided( |
| 18742 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18743 | TensorOptions options, DimnameList names |
| 18744 | ) override { |
| 18745 | auto current_device = guard_.current_device(); |
| 18746 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18747 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18748 | "structured kernels don't support multi-device outputs"); |
| 18749 | } else { |
| 18750 | guard_.reset_device(options.device()); |
| 18751 | } |
| 18752 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18753 | if (!names.empty()) { |
| 18754 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18755 | } |
| 18756 | // super must happen after, so that downstream can use maybe_get_output |
| 18757 | // to retrieve the output |
| 18758 | at::meta::structured_isneginf::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18759 | } |
| 18760 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18761 | return *outputs_[output_idx]; |
| 18762 | } |
| 18763 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 18764 | c10::OptionalDeviceGuard guard_; |
| 18765 | }; |
| 18766 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_isneginf(const at::Tensor & self) { |
| 18767 | structured_isneginf_default_backend_functional op; |
| 18768 | op.meta(self); |
| 18769 | at::isneginf_outf(self, *op.outputs_[0]); |
| 18770 | return std::move(op.outputs_[0]).take(); |
| 18771 | } |
| 18772 | struct structured_special_entr_default_backend_functional final : public at::meta::structured_special_entr { |
| 18773 | void set_output_strided( |
| 18774 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18775 | TensorOptions options, DimnameList names |
| 18776 | ) override { |
| 18777 | auto current_device = guard_.current_device(); |
| 18778 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18779 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18780 | "structured kernels don't support multi-device outputs"); |
| 18781 | } else { |
| 18782 | guard_.reset_device(options.device()); |
| 18783 | } |
| 18784 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18785 | if (!names.empty()) { |
| 18786 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18787 | } |
| 18788 | // super must happen after, so that downstream can use maybe_get_output |
| 18789 | // to retrieve the output |
| 18790 | at::meta::structured_special_entr::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18791 | } |
| 18792 | void set_output_raw_strided( |
| 18793 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18794 | TensorOptions options, DimnameList names |
| 18795 | ) override { |
| 18796 | auto current_device = guard_.current_device(); |
| 18797 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18798 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18799 | "structured kernels don't support multi-device outputs"); |
| 18800 | } else { |
| 18801 | guard_.reset_device(options.device()); |
| 18802 | } |
| 18803 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18804 | if (!names.empty()) { |
| 18805 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18806 | } |
| 18807 | // super must happen after, so that downstream can use maybe_get_output |
| 18808 | // to retrieve the output |
| 18809 | at::meta::structured_special_entr::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18810 | } |
| 18811 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18812 | return *outputs_[output_idx]; |
| 18813 | } |
| 18814 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 18815 | c10::OptionalDeviceGuard guard_; |
| 18816 | }; |
| 18817 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_entr(const at::Tensor & self) { |
| 18818 | structured_special_entr_default_backend_functional op; |
| 18819 | op.meta(self); |
| 18820 | at::special_entr_outf(self, *op.outputs_[0]); |
| 18821 | return std::move(op.outputs_[0]).take(); |
| 18822 | } |
| 18823 | struct structured_special_ndtri_default_backend_functional final : public at::meta::structured_special_ndtri { |
| 18824 | void set_output_strided( |
| 18825 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18826 | TensorOptions options, DimnameList names |
| 18827 | ) override { |
| 18828 | auto current_device = guard_.current_device(); |
| 18829 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18830 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18831 | "structured kernels don't support multi-device outputs"); |
| 18832 | } else { |
| 18833 | guard_.reset_device(options.device()); |
| 18834 | } |
| 18835 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18836 | if (!names.empty()) { |
| 18837 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18838 | } |
| 18839 | // super must happen after, so that downstream can use maybe_get_output |
| 18840 | // to retrieve the output |
| 18841 | at::meta::structured_special_ndtri::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18842 | } |
| 18843 | void set_output_raw_strided( |
| 18844 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18845 | TensorOptions options, DimnameList names |
| 18846 | ) override { |
| 18847 | auto current_device = guard_.current_device(); |
| 18848 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18849 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18850 | "structured kernels don't support multi-device outputs"); |
| 18851 | } else { |
| 18852 | guard_.reset_device(options.device()); |
| 18853 | } |
| 18854 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18855 | if (!names.empty()) { |
| 18856 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18857 | } |
| 18858 | // super must happen after, so that downstream can use maybe_get_output |
| 18859 | // to retrieve the output |
| 18860 | at::meta::structured_special_ndtri::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18861 | } |
| 18862 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18863 | return *outputs_[output_idx]; |
| 18864 | } |
| 18865 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 18866 | c10::OptionalDeviceGuard guard_; |
| 18867 | }; |
| 18868 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_ndtri(const at::Tensor & self) { |
| 18869 | structured_special_ndtri_default_backend_functional op; |
| 18870 | op.meta(self); |
| 18871 | at::special_ndtri_outf(self, *op.outputs_[0]); |
| 18872 | return std::move(op.outputs_[0]).take(); |
| 18873 | } |
| 18874 | struct structured_special_log_ndtr_default_backend_functional final : public at::meta::structured_special_log_ndtr { |
| 18875 | void set_output_strided( |
| 18876 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18877 | TensorOptions options, DimnameList names |
| 18878 | ) override { |
| 18879 | auto current_device = guard_.current_device(); |
| 18880 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18881 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18882 | "structured kernels don't support multi-device outputs"); |
| 18883 | } else { |
| 18884 | guard_.reset_device(options.device()); |
| 18885 | } |
| 18886 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18887 | if (!names.empty()) { |
| 18888 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18889 | } |
| 18890 | // super must happen after, so that downstream can use maybe_get_output |
| 18891 | // to retrieve the output |
| 18892 | at::meta::structured_special_log_ndtr::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18893 | } |
| 18894 | void set_output_raw_strided( |
| 18895 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18896 | TensorOptions options, DimnameList names |
| 18897 | ) override { |
| 18898 | auto current_device = guard_.current_device(); |
| 18899 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18900 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18901 | "structured kernels don't support multi-device outputs"); |
| 18902 | } else { |
| 18903 | guard_.reset_device(options.device()); |
| 18904 | } |
| 18905 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18906 | if (!names.empty()) { |
| 18907 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18908 | } |
| 18909 | // super must happen after, so that downstream can use maybe_get_output |
| 18910 | // to retrieve the output |
| 18911 | at::meta::structured_special_log_ndtr::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18912 | } |
| 18913 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18914 | return *outputs_[output_idx]; |
| 18915 | } |
| 18916 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 18917 | c10::OptionalDeviceGuard guard_; |
| 18918 | }; |
| 18919 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_log_ndtr(const at::Tensor & self) { |
| 18920 | structured_special_log_ndtr_default_backend_functional op; |
| 18921 | op.meta(self); |
| 18922 | at::special_log_ndtr_outf(self, *op.outputs_[0]); |
| 18923 | return std::move(op.outputs_[0]).take(); |
| 18924 | } |
| 18925 | struct structured_special_erfcx_default_backend_functional final : public at::meta::structured_special_erfcx { |
| 18926 | void set_output_strided( |
| 18927 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18928 | TensorOptions options, DimnameList names |
| 18929 | ) override { |
| 18930 | auto current_device = guard_.current_device(); |
| 18931 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18932 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18933 | "structured kernels don't support multi-device outputs"); |
| 18934 | } else { |
| 18935 | guard_.reset_device(options.device()); |
| 18936 | } |
| 18937 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18938 | if (!names.empty()) { |
| 18939 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18940 | } |
| 18941 | // super must happen after, so that downstream can use maybe_get_output |
| 18942 | // to retrieve the output |
| 18943 | at::meta::structured_special_erfcx::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18944 | } |
| 18945 | void set_output_raw_strided( |
| 18946 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18947 | TensorOptions options, DimnameList names |
| 18948 | ) override { |
| 18949 | auto current_device = guard_.current_device(); |
| 18950 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18951 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18952 | "structured kernels don't support multi-device outputs"); |
| 18953 | } else { |
| 18954 | guard_.reset_device(options.device()); |
| 18955 | } |
| 18956 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18957 | if (!names.empty()) { |
| 18958 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18959 | } |
| 18960 | // super must happen after, so that downstream can use maybe_get_output |
| 18961 | // to retrieve the output |
| 18962 | at::meta::structured_special_erfcx::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18963 | } |
| 18964 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 18965 | return *outputs_[output_idx]; |
| 18966 | } |
| 18967 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 18968 | c10::OptionalDeviceGuard guard_; |
| 18969 | }; |
| 18970 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_erfcx(const at::Tensor & self) { |
| 18971 | structured_special_erfcx_default_backend_functional op; |
| 18972 | op.meta(self); |
| 18973 | at::special_erfcx_outf(self, *op.outputs_[0]); |
| 18974 | return std::move(op.outputs_[0]).take(); |
| 18975 | } |
| 18976 | struct structured_special_xlog1py_default_backend_functional final : public at::meta::structured_special_xlog1py { |
| 18977 | void set_output_strided( |
| 18978 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18979 | TensorOptions options, DimnameList names |
| 18980 | ) override { |
| 18981 | auto current_device = guard_.current_device(); |
| 18982 | if (C10_UNLIKELY(current_device.has_value())) { |
| 18983 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 18984 | "structured kernels don't support multi-device outputs"); |
| 18985 | } else { |
| 18986 | guard_.reset_device(options.device()); |
| 18987 | } |
| 18988 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 18989 | if (!names.empty()) { |
| 18990 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 18991 | } |
| 18992 | // super must happen after, so that downstream can use maybe_get_output |
| 18993 | // to retrieve the output |
| 18994 | at::meta::structured_special_xlog1py::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 18995 | } |
| 18996 | void set_output_raw_strided( |
| 18997 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 18998 | TensorOptions options, DimnameList names |
| 18999 | ) override { |
| 19000 | auto current_device = guard_.current_device(); |
| 19001 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19002 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19003 | "structured kernels don't support multi-device outputs"); |
| 19004 | } else { |
| 19005 | guard_.reset_device(options.device()); |
| 19006 | } |
| 19007 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19008 | if (!names.empty()) { |
| 19009 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19010 | } |
| 19011 | // super must happen after, so that downstream can use maybe_get_output |
| 19012 | // to retrieve the output |
| 19013 | at::meta::structured_special_xlog1py::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19014 | } |
| 19015 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19016 | return *outputs_[output_idx]; |
| 19017 | } |
| 19018 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 19019 | c10::OptionalDeviceGuard guard_; |
| 19020 | }; |
| 19021 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_xlog1py(const at::Tensor & self, const at::Tensor & other) { |
| 19022 | structured_special_xlog1py_default_backend_functional op; |
| 19023 | op.meta(self, other); |
| 19024 | at::special_xlog1py_outf(self, other, *op.outputs_[0]); |
| 19025 | return std::move(op.outputs_[0]).take(); |
| 19026 | } |
| 19027 | struct structured_special_zeta_default_backend_functional final : public at::meta::structured_special_zeta { |
| 19028 | void set_output_strided( |
| 19029 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19030 | TensorOptions options, DimnameList names |
| 19031 | ) override { |
| 19032 | auto current_device = guard_.current_device(); |
| 19033 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19034 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19035 | "structured kernels don't support multi-device outputs"); |
| 19036 | } else { |
| 19037 | guard_.reset_device(options.device()); |
| 19038 | } |
| 19039 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19040 | if (!names.empty()) { |
| 19041 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19042 | } |
| 19043 | // super must happen after, so that downstream can use maybe_get_output |
| 19044 | // to retrieve the output |
| 19045 | at::meta::structured_special_zeta::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19046 | } |
| 19047 | void set_output_raw_strided( |
| 19048 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19049 | TensorOptions options, DimnameList names |
| 19050 | ) override { |
| 19051 | auto current_device = guard_.current_device(); |
| 19052 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19053 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19054 | "structured kernels don't support multi-device outputs"); |
| 19055 | } else { |
| 19056 | guard_.reset_device(options.device()); |
| 19057 | } |
| 19058 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19059 | if (!names.empty()) { |
| 19060 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19061 | } |
| 19062 | // super must happen after, so that downstream can use maybe_get_output |
| 19063 | // to retrieve the output |
| 19064 | at::meta::structured_special_zeta::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19065 | } |
| 19066 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19067 | return *outputs_[output_idx]; |
| 19068 | } |
| 19069 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 19070 | c10::OptionalDeviceGuard guard_; |
| 19071 | }; |
| 19072 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_zeta(const at::Tensor & self, const at::Tensor & other) { |
| 19073 | structured_special_zeta_default_backend_functional op; |
| 19074 | op.meta(self, other); |
| 19075 | at::special_zeta_outf(self, other, *op.outputs_[0]); |
| 19076 | return std::move(op.outputs_[0]).take(); |
| 19077 | } |
| 19078 | struct structured_special_i0e_default_backend_functional final : public at::meta::structured_special_i0e { |
| 19079 | void set_output_strided( |
| 19080 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19081 | TensorOptions options, DimnameList names |
| 19082 | ) override { |
| 19083 | auto current_device = guard_.current_device(); |
| 19084 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19085 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19086 | "structured kernels don't support multi-device outputs"); |
| 19087 | } else { |
| 19088 | guard_.reset_device(options.device()); |
| 19089 | } |
| 19090 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19091 | if (!names.empty()) { |
| 19092 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19093 | } |
| 19094 | // super must happen after, so that downstream can use maybe_get_output |
| 19095 | // to retrieve the output |
| 19096 | at::meta::structured_special_i0e::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19097 | } |
| 19098 | void set_output_raw_strided( |
| 19099 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19100 | TensorOptions options, DimnameList names |
| 19101 | ) override { |
| 19102 | auto current_device = guard_.current_device(); |
| 19103 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19104 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19105 | "structured kernels don't support multi-device outputs"); |
| 19106 | } else { |
| 19107 | guard_.reset_device(options.device()); |
| 19108 | } |
| 19109 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19110 | if (!names.empty()) { |
| 19111 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19112 | } |
| 19113 | // super must happen after, so that downstream can use maybe_get_output |
| 19114 | // to retrieve the output |
| 19115 | at::meta::structured_special_i0e::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19116 | } |
| 19117 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19118 | return *outputs_[output_idx]; |
| 19119 | } |
| 19120 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 19121 | c10::OptionalDeviceGuard guard_; |
| 19122 | }; |
| 19123 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_i0e(const at::Tensor & self) { |
| 19124 | structured_special_i0e_default_backend_functional op; |
| 19125 | op.meta(self); |
| 19126 | at::special_i0e_outf(self, *op.outputs_[0]); |
| 19127 | return std::move(op.outputs_[0]).take(); |
| 19128 | } |
| 19129 | struct structured_special_i1_default_backend_functional final : public at::meta::structured_special_i1 { |
| 19130 | void set_output_strided( |
| 19131 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19132 | TensorOptions options, DimnameList names |
| 19133 | ) override { |
| 19134 | auto current_device = guard_.current_device(); |
| 19135 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19136 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19137 | "structured kernels don't support multi-device outputs"); |
| 19138 | } else { |
| 19139 | guard_.reset_device(options.device()); |
| 19140 | } |
| 19141 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19142 | if (!names.empty()) { |
| 19143 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19144 | } |
| 19145 | // super must happen after, so that downstream can use maybe_get_output |
| 19146 | // to retrieve the output |
| 19147 | at::meta::structured_special_i1::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19148 | } |
| 19149 | void set_output_raw_strided( |
| 19150 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19151 | TensorOptions options, DimnameList names |
| 19152 | ) override { |
| 19153 | auto current_device = guard_.current_device(); |
| 19154 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19155 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19156 | "structured kernels don't support multi-device outputs"); |
| 19157 | } else { |
| 19158 | guard_.reset_device(options.device()); |
| 19159 | } |
| 19160 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19161 | if (!names.empty()) { |
| 19162 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19163 | } |
| 19164 | // super must happen after, so that downstream can use maybe_get_output |
| 19165 | // to retrieve the output |
| 19166 | at::meta::structured_special_i1::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19167 | } |
| 19168 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19169 | return *outputs_[output_idx]; |
| 19170 | } |
| 19171 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 19172 | c10::OptionalDeviceGuard guard_; |
| 19173 | }; |
| 19174 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_i1(const at::Tensor & self) { |
| 19175 | structured_special_i1_default_backend_functional op; |
| 19176 | op.meta(self); |
| 19177 | at::special_i1_outf(self, *op.outputs_[0]); |
| 19178 | return std::move(op.outputs_[0]).take(); |
| 19179 | } |
| 19180 | struct structured_special_i1e_default_backend_functional final : public at::meta::structured_special_i1e { |
| 19181 | void set_output_strided( |
| 19182 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19183 | TensorOptions options, DimnameList names |
| 19184 | ) override { |
| 19185 | auto current_device = guard_.current_device(); |
| 19186 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19187 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19188 | "structured kernels don't support multi-device outputs"); |
| 19189 | } else { |
| 19190 | guard_.reset_device(options.device()); |
| 19191 | } |
| 19192 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19193 | if (!names.empty()) { |
| 19194 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19195 | } |
| 19196 | // super must happen after, so that downstream can use maybe_get_output |
| 19197 | // to retrieve the output |
| 19198 | at::meta::structured_special_i1e::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19199 | } |
| 19200 | void set_output_raw_strided( |
| 19201 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19202 | TensorOptions options, DimnameList names |
| 19203 | ) override { |
| 19204 | auto current_device = guard_.current_device(); |
| 19205 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19206 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19207 | "structured kernels don't support multi-device outputs"); |
| 19208 | } else { |
| 19209 | guard_.reset_device(options.device()); |
| 19210 | } |
| 19211 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19212 | if (!names.empty()) { |
| 19213 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19214 | } |
| 19215 | // super must happen after, so that downstream can use maybe_get_output |
| 19216 | // to retrieve the output |
| 19217 | at::meta::structured_special_i1e::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 19218 | } |
| 19219 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19220 | return *outputs_[output_idx]; |
| 19221 | } |
| 19222 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 19223 | c10::OptionalDeviceGuard guard_; |
| 19224 | }; |
| 19225 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_i1e(const at::Tensor & self) { |
| 19226 | structured_special_i1e_default_backend_functional op; |
| 19227 | op.meta(self); |
| 19228 | at::special_i1e_outf(self, *op.outputs_[0]); |
| 19229 | return std::move(op.outputs_[0]).take(); |
| 19230 | } |
| 19231 | struct structured_linalg_cholesky_ex_default_backend_functional final : public at::meta::structured_linalg_cholesky_ex { |
| 19232 | void set_output_strided( |
| 19233 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19234 | TensorOptions options, DimnameList names |
| 19235 | ) override { |
| 19236 | auto current_device = guard_.current_device(); |
| 19237 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19238 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19239 | "structured kernels don't support multi-device outputs"); |
| 19240 | } else { |
| 19241 | guard_.reset_device(options.device()); |
| 19242 | } |
| 19243 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19244 | if (!names.empty()) { |
| 19245 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19246 | } |
| 19247 | // super must happen after, so that downstream can use maybe_get_output |
| 19248 | // to retrieve the output |
| 19249 | } |
| 19250 | void set_output_raw_strided( |
| 19251 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19252 | TensorOptions options, DimnameList names |
| 19253 | ) override { |
| 19254 | auto current_device = guard_.current_device(); |
| 19255 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19256 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19257 | "structured kernels don't support multi-device outputs"); |
| 19258 | } else { |
| 19259 | guard_.reset_device(options.device()); |
| 19260 | } |
| 19261 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19262 | if (!names.empty()) { |
| 19263 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19264 | } |
| 19265 | // super must happen after, so that downstream can use maybe_get_output |
| 19266 | // to retrieve the output |
| 19267 | } |
| 19268 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19269 | return *outputs_[output_idx]; |
| 19270 | } |
| 19271 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 19272 | c10::OptionalDeviceGuard guard_; |
| 19273 | }; |
| 19274 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CompositeExplicitAutogradNonFunctional_linalg_cholesky_ex(const at::Tensor & self, bool upper, bool check_errors) { |
| 19275 | structured_linalg_cholesky_ex_default_backend_functional op; |
| 19276 | op.meta(self, upper, check_errors); |
| 19277 | at::linalg_cholesky_ex_outf(self, upper, check_errors, *op.outputs_[0], *op.outputs_[1]); |
| 19278 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 19279 | } |
| 19280 | struct structured_linalg_cross_default_backend_functional final : public at::meta::structured_linalg_cross { |
| 19281 | void set_output_strided( |
| 19282 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19283 | TensorOptions options, DimnameList names |
| 19284 | ) override { |
| 19285 | auto current_device = guard_.current_device(); |
| 19286 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19287 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19288 | "structured kernels don't support multi-device outputs"); |
| 19289 | } else { |
| 19290 | guard_.reset_device(options.device()); |
| 19291 | } |
| 19292 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19293 | if (!names.empty()) { |
| 19294 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19295 | } |
| 19296 | // super must happen after, so that downstream can use maybe_get_output |
| 19297 | // to retrieve the output |
| 19298 | } |
| 19299 | void set_output_raw_strided( |
| 19300 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19301 | TensorOptions options, DimnameList names |
| 19302 | ) override { |
| 19303 | auto current_device = guard_.current_device(); |
| 19304 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19305 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19306 | "structured kernels don't support multi-device outputs"); |
| 19307 | } else { |
| 19308 | guard_.reset_device(options.device()); |
| 19309 | } |
| 19310 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19311 | if (!names.empty()) { |
| 19312 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19313 | } |
| 19314 | // super must happen after, so that downstream can use maybe_get_output |
| 19315 | // to retrieve the output |
| 19316 | } |
| 19317 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19318 | return *outputs_[output_idx]; |
| 19319 | } |
| 19320 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 19321 | c10::OptionalDeviceGuard guard_; |
| 19322 | }; |
| 19323 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_linalg_cross(const at::Tensor & self, const at::Tensor & other, int64_t dim) { |
| 19324 | structured_linalg_cross_default_backend_functional op; |
| 19325 | op.meta(self, other, dim); |
| 19326 | at::linalg_cross_outf(self, other, dim, *op.outputs_[0]); |
| 19327 | return std::move(op.outputs_[0]).take(); |
| 19328 | } |
| 19329 | struct structured_linalg_lu_factor_ex_default_backend_functional final : public at::meta::structured_linalg_lu_factor_ex { |
| 19330 | void set_output_strided( |
| 19331 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19332 | TensorOptions options, DimnameList names |
| 19333 | ) override { |
| 19334 | auto current_device = guard_.current_device(); |
| 19335 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19336 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19337 | "structured kernels don't support multi-device outputs"); |
| 19338 | } else { |
| 19339 | guard_.reset_device(options.device()); |
| 19340 | } |
| 19341 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19342 | if (!names.empty()) { |
| 19343 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19344 | } |
| 19345 | // super must happen after, so that downstream can use maybe_get_output |
| 19346 | // to retrieve the output |
| 19347 | } |
| 19348 | void set_output_raw_strided( |
| 19349 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19350 | TensorOptions options, DimnameList names |
| 19351 | ) override { |
| 19352 | auto current_device = guard_.current_device(); |
| 19353 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19354 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19355 | "structured kernels don't support multi-device outputs"); |
| 19356 | } else { |
| 19357 | guard_.reset_device(options.device()); |
| 19358 | } |
| 19359 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19360 | if (!names.empty()) { |
| 19361 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19362 | } |
| 19363 | // super must happen after, so that downstream can use maybe_get_output |
| 19364 | // to retrieve the output |
| 19365 | } |
| 19366 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19367 | return *outputs_[output_idx]; |
| 19368 | } |
| 19369 | std::array<c10::ExclusivelyOwned<Tensor>, 3> outputs_; |
| 19370 | c10::OptionalDeviceGuard guard_; |
| 19371 | }; |
| 19372 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CompositeExplicitAutogradNonFunctional_linalg_lu_factor_ex(const at::Tensor & A, bool pivot, bool check_errors) { |
| 19373 | structured_linalg_lu_factor_ex_default_backend_functional op; |
| 19374 | op.meta(A, pivot, check_errors); |
| 19375 | at::linalg_lu_factor_ex_outf(A, pivot, check_errors, *op.outputs_[0], *op.outputs_[1], *op.outputs_[2]); |
| 19376 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take(), std::move(op.outputs_[2]).take()); |
| 19377 | } |
| 19378 | struct structured_linalg_lu_default_backend_functional final : public at::meta::structured_linalg_lu { |
| 19379 | void set_output_strided( |
| 19380 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19381 | TensorOptions options, DimnameList names |
| 19382 | ) override { |
| 19383 | auto current_device = guard_.current_device(); |
| 19384 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19385 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19386 | "structured kernels don't support multi-device outputs"); |
| 19387 | } else { |
| 19388 | guard_.reset_device(options.device()); |
| 19389 | } |
| 19390 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19391 | if (!names.empty()) { |
| 19392 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19393 | } |
| 19394 | // super must happen after, so that downstream can use maybe_get_output |
| 19395 | // to retrieve the output |
| 19396 | } |
| 19397 | void set_output_raw_strided( |
| 19398 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19399 | TensorOptions options, DimnameList names |
| 19400 | ) override { |
| 19401 | auto current_device = guard_.current_device(); |
| 19402 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19403 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19404 | "structured kernels don't support multi-device outputs"); |
| 19405 | } else { |
| 19406 | guard_.reset_device(options.device()); |
| 19407 | } |
| 19408 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19409 | if (!names.empty()) { |
| 19410 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19411 | } |
| 19412 | // super must happen after, so that downstream can use maybe_get_output |
| 19413 | // to retrieve the output |
| 19414 | } |
| 19415 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19416 | return *outputs_[output_idx]; |
| 19417 | } |
| 19418 | std::array<c10::ExclusivelyOwned<Tensor>, 3> outputs_; |
| 19419 | c10::OptionalDeviceGuard guard_; |
| 19420 | }; |
| 19421 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CompositeExplicitAutogradNonFunctional_linalg_lu(const at::Tensor & A, bool pivot) { |
| 19422 | structured_linalg_lu_default_backend_functional op; |
| 19423 | op.meta(A, pivot); |
| 19424 | at::linalg_lu_outf(A, pivot, *op.outputs_[0], *op.outputs_[1], *op.outputs_[2]); |
| 19425 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take(), std::move(op.outputs_[2]).take()); |
| 19426 | } |
| 19427 | struct structured_linalg_lu_solve_default_backend_functional final : public at::meta::structured_linalg_lu_solve { |
| 19428 | void set_output_strided( |
| 19429 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19430 | TensorOptions options, DimnameList names |
| 19431 | ) override { |
| 19432 | auto current_device = guard_.current_device(); |
| 19433 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19434 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19435 | "structured kernels don't support multi-device outputs"); |
| 19436 | } else { |
| 19437 | guard_.reset_device(options.device()); |
| 19438 | } |
| 19439 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19440 | if (!names.empty()) { |
| 19441 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19442 | } |
| 19443 | // super must happen after, so that downstream can use maybe_get_output |
| 19444 | // to retrieve the output |
| 19445 | } |
| 19446 | void set_output_raw_strided( |
| 19447 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19448 | TensorOptions options, DimnameList names |
| 19449 | ) override { |
| 19450 | auto current_device = guard_.current_device(); |
| 19451 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19452 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19453 | "structured kernels don't support multi-device outputs"); |
| 19454 | } else { |
| 19455 | guard_.reset_device(options.device()); |
| 19456 | } |
| 19457 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19458 | if (!names.empty()) { |
| 19459 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19460 | } |
| 19461 | // super must happen after, so that downstream can use maybe_get_output |
| 19462 | // to retrieve the output |
| 19463 | } |
| 19464 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19465 | return *outputs_[output_idx]; |
| 19466 | } |
| 19467 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 19468 | c10::OptionalDeviceGuard guard_; |
| 19469 | }; |
| 19470 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_linalg_lu_solve(const at::Tensor & LU, const at::Tensor & pivots, const at::Tensor & B, bool left, bool adjoint) { |
| 19471 | structured_linalg_lu_solve_default_backend_functional op; |
| 19472 | op.meta(LU, pivots, B, left, adjoint); |
| 19473 | at::linalg_lu_solve_outf(LU, pivots, B, left, adjoint, *op.outputs_[0]); |
| 19474 | return std::move(op.outputs_[0]).take(); |
| 19475 | } |
| 19476 | struct structured__linalg_det_default_backend_functional final : public at::meta::structured__linalg_det { |
| 19477 | void set_output_strided( |
| 19478 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19479 | TensorOptions options, DimnameList names |
| 19480 | ) override { |
| 19481 | auto current_device = guard_.current_device(); |
| 19482 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19483 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19484 | "structured kernels don't support multi-device outputs"); |
| 19485 | } else { |
| 19486 | guard_.reset_device(options.device()); |
| 19487 | } |
| 19488 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19489 | if (!names.empty()) { |
| 19490 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19491 | } |
| 19492 | // super must happen after, so that downstream can use maybe_get_output |
| 19493 | // to retrieve the output |
| 19494 | } |
| 19495 | void set_output_raw_strided( |
| 19496 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19497 | TensorOptions options, DimnameList names |
| 19498 | ) override { |
| 19499 | auto current_device = guard_.current_device(); |
| 19500 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19501 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19502 | "structured kernels don't support multi-device outputs"); |
| 19503 | } else { |
| 19504 | guard_.reset_device(options.device()); |
| 19505 | } |
| 19506 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19507 | if (!names.empty()) { |
| 19508 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19509 | } |
| 19510 | // super must happen after, so that downstream can use maybe_get_output |
| 19511 | // to retrieve the output |
| 19512 | } |
| 19513 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19514 | return *outputs_[output_idx]; |
| 19515 | } |
| 19516 | std::array<c10::ExclusivelyOwned<Tensor>, 3> outputs_; |
| 19517 | c10::OptionalDeviceGuard guard_; |
| 19518 | }; |
| 19519 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CompositeExplicitAutogradNonFunctional__linalg_det(const at::Tensor & A) { |
| 19520 | structured__linalg_det_default_backend_functional op; |
| 19521 | op.meta(A); |
| 19522 | at::_linalg_det_outf(A, *op.outputs_[0], *op.outputs_[1], *op.outputs_[2]); |
| 19523 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take(), std::move(op.outputs_[2]).take()); |
| 19524 | } |
| 19525 | struct structured_linalg_ldl_factor_ex_default_backend_functional final : public at::meta::structured_linalg_ldl_factor_ex { |
| 19526 | void set_output_strided( |
| 19527 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19528 | TensorOptions options, DimnameList names |
| 19529 | ) override { |
| 19530 | auto current_device = guard_.current_device(); |
| 19531 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19532 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19533 | "structured kernels don't support multi-device outputs"); |
| 19534 | } else { |
| 19535 | guard_.reset_device(options.device()); |
| 19536 | } |
| 19537 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19538 | if (!names.empty()) { |
| 19539 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19540 | } |
| 19541 | // super must happen after, so that downstream can use maybe_get_output |
| 19542 | // to retrieve the output |
| 19543 | } |
| 19544 | void set_output_raw_strided( |
| 19545 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19546 | TensorOptions options, DimnameList names |
| 19547 | ) override { |
| 19548 | auto current_device = guard_.current_device(); |
| 19549 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19550 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19551 | "structured kernels don't support multi-device outputs"); |
| 19552 | } else { |
| 19553 | guard_.reset_device(options.device()); |
| 19554 | } |
| 19555 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19556 | if (!names.empty()) { |
| 19557 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19558 | } |
| 19559 | // super must happen after, so that downstream can use maybe_get_output |
| 19560 | // to retrieve the output |
| 19561 | } |
| 19562 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19563 | return *outputs_[output_idx]; |
| 19564 | } |
| 19565 | std::array<c10::ExclusivelyOwned<Tensor>, 3> outputs_; |
| 19566 | c10::OptionalDeviceGuard guard_; |
| 19567 | }; |
| 19568 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CompositeExplicitAutogradNonFunctional_linalg_ldl_factor_ex(const at::Tensor & self, bool hermitian, bool check_errors) { |
| 19569 | structured_linalg_ldl_factor_ex_default_backend_functional op; |
| 19570 | op.meta(self, hermitian, check_errors); |
| 19571 | at::linalg_ldl_factor_ex_outf(self, hermitian, check_errors, *op.outputs_[0], *op.outputs_[1], *op.outputs_[2]); |
| 19572 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take(), std::move(op.outputs_[2]).take()); |
| 19573 | } |
| 19574 | struct structured_linalg_ldl_solve_default_backend_functional final : public at::meta::structured_linalg_ldl_solve { |
| 19575 | void set_output_strided( |
| 19576 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19577 | TensorOptions options, DimnameList names |
| 19578 | ) override { |
| 19579 | auto current_device = guard_.current_device(); |
| 19580 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19581 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19582 | "structured kernels don't support multi-device outputs"); |
| 19583 | } else { |
| 19584 | guard_.reset_device(options.device()); |
| 19585 | } |
| 19586 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19587 | if (!names.empty()) { |
| 19588 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19589 | } |
| 19590 | // super must happen after, so that downstream can use maybe_get_output |
| 19591 | // to retrieve the output |
| 19592 | } |
| 19593 | void set_output_raw_strided( |
| 19594 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19595 | TensorOptions options, DimnameList names |
| 19596 | ) override { |
| 19597 | auto current_device = guard_.current_device(); |
| 19598 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19599 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19600 | "structured kernels don't support multi-device outputs"); |
| 19601 | } else { |
| 19602 | guard_.reset_device(options.device()); |
| 19603 | } |
| 19604 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19605 | if (!names.empty()) { |
| 19606 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19607 | } |
| 19608 | // super must happen after, so that downstream can use maybe_get_output |
| 19609 | // to retrieve the output |
| 19610 | } |
| 19611 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19612 | return *outputs_[output_idx]; |
| 19613 | } |
| 19614 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 19615 | c10::OptionalDeviceGuard guard_; |
| 19616 | }; |
| 19617 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_linalg_ldl_solve(const at::Tensor & LD, const at::Tensor & pivots, const at::Tensor & B, bool hermitian) { |
| 19618 | structured_linalg_ldl_solve_default_backend_functional op; |
| 19619 | op.meta(LD, pivots, B, hermitian); |
| 19620 | at::linalg_ldl_solve_outf(LD, pivots, B, hermitian, *op.outputs_[0]); |
| 19621 | return std::move(op.outputs_[0]).take(); |
| 19622 | } |
| 19623 | struct structured__linalg_slogdet_default_backend_functional final : public at::meta::structured__linalg_slogdet { |
| 19624 | void set_output_strided( |
| 19625 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19626 | TensorOptions options, DimnameList names |
| 19627 | ) override { |
| 19628 | auto current_device = guard_.current_device(); |
| 19629 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19630 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19631 | "structured kernels don't support multi-device outputs"); |
| 19632 | } else { |
| 19633 | guard_.reset_device(options.device()); |
| 19634 | } |
| 19635 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19636 | if (!names.empty()) { |
| 19637 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19638 | } |
| 19639 | // super must happen after, so that downstream can use maybe_get_output |
| 19640 | // to retrieve the output |
| 19641 | } |
| 19642 | void set_output_raw_strided( |
| 19643 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19644 | TensorOptions options, DimnameList names |
| 19645 | ) override { |
| 19646 | auto current_device = guard_.current_device(); |
| 19647 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19648 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19649 | "structured kernels don't support multi-device outputs"); |
| 19650 | } else { |
| 19651 | guard_.reset_device(options.device()); |
| 19652 | } |
| 19653 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19654 | if (!names.empty()) { |
| 19655 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19656 | } |
| 19657 | // super must happen after, so that downstream can use maybe_get_output |
| 19658 | // to retrieve the output |
| 19659 | } |
| 19660 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19661 | return *outputs_[output_idx]; |
| 19662 | } |
| 19663 | std::array<c10::ExclusivelyOwned<Tensor>, 4> outputs_; |
| 19664 | c10::OptionalDeviceGuard guard_; |
| 19665 | }; |
| 19666 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> wrapper_CompositeExplicitAutogradNonFunctional__linalg_slogdet(const at::Tensor & A) { |
| 19667 | structured__linalg_slogdet_default_backend_functional op; |
| 19668 | op.meta(A); |
| 19669 | at::_linalg_slogdet_outf(A, *op.outputs_[0], *op.outputs_[1], *op.outputs_[2], *op.outputs_[3]); |
| 19670 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take(), std::move(op.outputs_[2]).take(), std::move(op.outputs_[3]).take()); |
| 19671 | } |
| 19672 | struct structured__linalg_eigh_default_backend_functional final : public at::meta::structured__linalg_eigh { |
| 19673 | void set_output_strided( |
| 19674 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19675 | TensorOptions options, DimnameList names |
| 19676 | ) override { |
| 19677 | auto current_device = guard_.current_device(); |
| 19678 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19679 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19680 | "structured kernels don't support multi-device outputs"); |
| 19681 | } else { |
| 19682 | guard_.reset_device(options.device()); |
| 19683 | } |
| 19684 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19685 | if (!names.empty()) { |
| 19686 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19687 | } |
| 19688 | // super must happen after, so that downstream can use maybe_get_output |
| 19689 | // to retrieve the output |
| 19690 | } |
| 19691 | void set_output_raw_strided( |
| 19692 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19693 | TensorOptions options, DimnameList names |
| 19694 | ) override { |
| 19695 | auto current_device = guard_.current_device(); |
| 19696 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19697 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19698 | "structured kernels don't support multi-device outputs"); |
| 19699 | } else { |
| 19700 | guard_.reset_device(options.device()); |
| 19701 | } |
| 19702 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19703 | if (!names.empty()) { |
| 19704 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19705 | } |
| 19706 | // super must happen after, so that downstream can use maybe_get_output |
| 19707 | // to retrieve the output |
| 19708 | } |
| 19709 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19710 | return *outputs_[output_idx]; |
| 19711 | } |
| 19712 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 19713 | c10::OptionalDeviceGuard guard_; |
| 19714 | }; |
| 19715 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CompositeExplicitAutogradNonFunctional__linalg_eigh(const at::Tensor & A, c10::string_view UPLO, bool compute_v) { |
| 19716 | structured__linalg_eigh_default_backend_functional op; |
| 19717 | op.meta(A, UPLO, compute_v); |
| 19718 | at::_linalg_eigh_outf(A, UPLO, compute_v, *op.outputs_[0], *op.outputs_[1]); |
| 19719 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 19720 | } |
| 19721 | struct structured_linalg_inv_ex_default_backend_functional final : public at::meta::structured_linalg_inv_ex { |
| 19722 | void set_output_strided( |
| 19723 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19724 | TensorOptions options, DimnameList names |
| 19725 | ) override { |
| 19726 | auto current_device = guard_.current_device(); |
| 19727 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19728 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19729 | "structured kernels don't support multi-device outputs"); |
| 19730 | } else { |
| 19731 | guard_.reset_device(options.device()); |
| 19732 | } |
| 19733 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19734 | if (!names.empty()) { |
| 19735 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19736 | } |
| 19737 | // super must happen after, so that downstream can use maybe_get_output |
| 19738 | // to retrieve the output |
| 19739 | } |
| 19740 | void set_output_raw_strided( |
| 19741 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19742 | TensorOptions options, DimnameList names |
| 19743 | ) override { |
| 19744 | auto current_device = guard_.current_device(); |
| 19745 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19746 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19747 | "structured kernels don't support multi-device outputs"); |
| 19748 | } else { |
| 19749 | guard_.reset_device(options.device()); |
| 19750 | } |
| 19751 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19752 | if (!names.empty()) { |
| 19753 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19754 | } |
| 19755 | // super must happen after, so that downstream can use maybe_get_output |
| 19756 | // to retrieve the output |
| 19757 | } |
| 19758 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19759 | return *outputs_[output_idx]; |
| 19760 | } |
| 19761 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 19762 | c10::OptionalDeviceGuard guard_; |
| 19763 | }; |
| 19764 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CompositeExplicitAutogradNonFunctional_linalg_inv_ex(const at::Tensor & A, bool check_errors) { |
| 19765 | structured_linalg_inv_ex_default_backend_functional op; |
| 19766 | op.meta(A, check_errors); |
| 19767 | at::linalg_inv_ex_outf(A, check_errors, *op.outputs_[0], *op.outputs_[1]); |
| 19768 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 19769 | } |
| 19770 | struct structured_linalg_vector_norm_default_backend_functional final : public at::meta::structured_linalg_vector_norm { |
| 19771 | void set_output_strided( |
| 19772 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19773 | TensorOptions options, DimnameList names |
| 19774 | ) override { |
| 19775 | auto current_device = guard_.current_device(); |
| 19776 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19777 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19778 | "structured kernels don't support multi-device outputs"); |
| 19779 | } else { |
| 19780 | guard_.reset_device(options.device()); |
| 19781 | } |
| 19782 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19783 | if (!names.empty()) { |
| 19784 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19785 | } |
| 19786 | // super must happen after, so that downstream can use maybe_get_output |
| 19787 | // to retrieve the output |
| 19788 | } |
| 19789 | void set_output_raw_strided( |
| 19790 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19791 | TensorOptions options, DimnameList names |
| 19792 | ) override { |
| 19793 | auto current_device = guard_.current_device(); |
| 19794 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19795 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19796 | "structured kernels don't support multi-device outputs"); |
| 19797 | } else { |
| 19798 | guard_.reset_device(options.device()); |
| 19799 | } |
| 19800 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19801 | if (!names.empty()) { |
| 19802 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19803 | } |
| 19804 | // super must happen after, so that downstream can use maybe_get_output |
| 19805 | // to retrieve the output |
| 19806 | } |
| 19807 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19808 | return *outputs_[output_idx]; |
| 19809 | } |
| 19810 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 19811 | c10::OptionalDeviceGuard guard_; |
| 19812 | }; |
| 19813 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_linalg_vector_norm(const at::Tensor & self, const at::Scalar & ord, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 19814 | structured_linalg_vector_norm_default_backend_functional op; |
| 19815 | op.meta(self, ord, dim, keepdim, dtype); |
| 19816 | at::linalg_vector_norm_outf(self, ord, dim, keepdim, dtype, *op.outputs_[0]); |
| 19817 | return std::move(op.outputs_[0]).take(); |
| 19818 | } |
| 19819 | struct structured__linalg_svd_default_backend_functional final : public at::meta::structured__linalg_svd { |
| 19820 | void set_output_strided( |
| 19821 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19822 | TensorOptions options, DimnameList names |
| 19823 | ) override { |
| 19824 | auto current_device = guard_.current_device(); |
| 19825 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19826 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19827 | "structured kernels don't support multi-device outputs"); |
| 19828 | } else { |
| 19829 | guard_.reset_device(options.device()); |
| 19830 | } |
| 19831 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19832 | if (!names.empty()) { |
| 19833 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19834 | } |
| 19835 | // super must happen after, so that downstream can use maybe_get_output |
| 19836 | // to retrieve the output |
| 19837 | } |
| 19838 | void set_output_raw_strided( |
| 19839 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19840 | TensorOptions options, DimnameList names |
| 19841 | ) override { |
| 19842 | auto current_device = guard_.current_device(); |
| 19843 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19844 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19845 | "structured kernels don't support multi-device outputs"); |
| 19846 | } else { |
| 19847 | guard_.reset_device(options.device()); |
| 19848 | } |
| 19849 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19850 | if (!names.empty()) { |
| 19851 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19852 | } |
| 19853 | // super must happen after, so that downstream can use maybe_get_output |
| 19854 | // to retrieve the output |
| 19855 | } |
| 19856 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19857 | return *outputs_[output_idx]; |
| 19858 | } |
| 19859 | std::array<c10::ExclusivelyOwned<Tensor>, 3> outputs_; |
| 19860 | c10::OptionalDeviceGuard guard_; |
| 19861 | }; |
| 19862 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> wrapper_CompositeExplicitAutogradNonFunctional__linalg_svd(const at::Tensor & A, bool full_matrices, bool compute_uv, c10::optional<c10::string_view> driver) { |
| 19863 | structured__linalg_svd_default_backend_functional op; |
| 19864 | op.meta(A, full_matrices, compute_uv, driver); |
| 19865 | at::_linalg_svd_outf(A, full_matrices, compute_uv, driver, *op.outputs_[0], *op.outputs_[1], *op.outputs_[2]); |
| 19866 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take(), std::move(op.outputs_[2]).take()); |
| 19867 | } |
| 19868 | namespace { |
| 19869 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_atol_rtol_tensor_linalg_pinv(const at::Tensor & self, const c10::optional<at::Tensor> & atol, const c10::optional<at::Tensor> & rtol, bool hermitian) { |
| 19870 | // No device check |
| 19871 | // DeviceGuard omitted |
| 19872 | return at::native::linalg_pinv(self, atol, rtol, hermitian); |
| 19873 | } |
| 19874 | } // anonymous namespace |
| 19875 | struct structured__linalg_solve_ex_default_backend_functional final : public at::meta::structured__linalg_solve_ex { |
| 19876 | void set_output_strided( |
| 19877 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19878 | TensorOptions options, DimnameList names |
| 19879 | ) override { |
| 19880 | auto current_device = guard_.current_device(); |
| 19881 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19882 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19883 | "structured kernels don't support multi-device outputs"); |
| 19884 | } else { |
| 19885 | guard_.reset_device(options.device()); |
| 19886 | } |
| 19887 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19888 | if (!names.empty()) { |
| 19889 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19890 | } |
| 19891 | // super must happen after, so that downstream can use maybe_get_output |
| 19892 | // to retrieve the output |
| 19893 | } |
| 19894 | void set_output_raw_strided( |
| 19895 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19896 | TensorOptions options, DimnameList names |
| 19897 | ) override { |
| 19898 | auto current_device = guard_.current_device(); |
| 19899 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19900 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19901 | "structured kernels don't support multi-device outputs"); |
| 19902 | } else { |
| 19903 | guard_.reset_device(options.device()); |
| 19904 | } |
| 19905 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19906 | if (!names.empty()) { |
| 19907 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19908 | } |
| 19909 | // super must happen after, so that downstream can use maybe_get_output |
| 19910 | // to retrieve the output |
| 19911 | } |
| 19912 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19913 | return *outputs_[output_idx]; |
| 19914 | } |
| 19915 | std::array<c10::ExclusivelyOwned<Tensor>, 4> outputs_; |
| 19916 | c10::OptionalDeviceGuard guard_; |
| 19917 | }; |
| 19918 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> wrapper_CompositeExplicitAutogradNonFunctional__linalg_solve_ex(const at::Tensor & A, const at::Tensor & B, bool left, bool check_errors) { |
| 19919 | structured__linalg_solve_ex_default_backend_functional op; |
| 19920 | op.meta(A, B, left, check_errors); |
| 19921 | at::_linalg_solve_ex_outf(A, B, left, check_errors, *op.outputs_[0], *op.outputs_[1], *op.outputs_[2], *op.outputs_[3]); |
| 19922 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take(), std::move(op.outputs_[2]).take(), std::move(op.outputs_[3]).take()); |
| 19923 | } |
| 19924 | struct structured_linalg_qr_default_backend_functional final : public at::meta::structured_linalg_qr { |
| 19925 | void set_output_strided( |
| 19926 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19927 | TensorOptions options, DimnameList names |
| 19928 | ) override { |
| 19929 | auto current_device = guard_.current_device(); |
| 19930 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19931 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19932 | "structured kernels don't support multi-device outputs"); |
| 19933 | } else { |
| 19934 | guard_.reset_device(options.device()); |
| 19935 | } |
| 19936 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19937 | if (!names.empty()) { |
| 19938 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19939 | } |
| 19940 | // super must happen after, so that downstream can use maybe_get_output |
| 19941 | // to retrieve the output |
| 19942 | } |
| 19943 | void set_output_raw_strided( |
| 19944 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 19945 | TensorOptions options, DimnameList names |
| 19946 | ) override { |
| 19947 | auto current_device = guard_.current_device(); |
| 19948 | if (C10_UNLIKELY(current_device.has_value())) { |
| 19949 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 19950 | "structured kernels don't support multi-device outputs"); |
| 19951 | } else { |
| 19952 | guard_.reset_device(options.device()); |
| 19953 | } |
| 19954 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 19955 | if (!names.empty()) { |
| 19956 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 19957 | } |
| 19958 | // super must happen after, so that downstream can use maybe_get_output |
| 19959 | // to retrieve the output |
| 19960 | } |
| 19961 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 19962 | return *outputs_[output_idx]; |
| 19963 | } |
| 19964 | std::array<c10::ExclusivelyOwned<Tensor>, 2> outputs_; |
| 19965 | c10::OptionalDeviceGuard guard_; |
| 19966 | }; |
| 19967 | ::std::tuple<at::Tensor,at::Tensor> wrapper_CompositeExplicitAutogradNonFunctional_linalg_qr(const at::Tensor & A, c10::string_view mode) { |
| 19968 | structured_linalg_qr_default_backend_functional op; |
| 19969 | op.meta(A, mode); |
| 19970 | at::linalg_qr_outf(A, mode, *op.outputs_[0], *op.outputs_[1]); |
| 19971 | return std::make_tuple(std::move(op.outputs_[0]).take(), std::move(op.outputs_[1]).take()); |
| 19972 | } |
| 19973 | namespace { |
| 19974 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional___test_autograd_multiple_dispatch_view_copy(const at::Tensor & self) { |
| 19975 | // No device check |
| 19976 | // DeviceGuard omitted |
| 19977 | return at::native::_test_autograd_multiple_dispatch_view_copy(self); |
| 19978 | } |
| 19979 | } // anonymous namespace |
| 19980 | namespace { |
| 19981 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional___fw_primal_copy(const at::Tensor & self, int64_t level) { |
| 19982 | // No device check |
| 19983 | // DeviceGuard omitted |
| 19984 | return at::native::_fw_primal_copy(self, level); |
| 19985 | } |
| 19986 | } // anonymous namespace |
| 19987 | namespace { |
| 19988 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional___make_dual_copy(const at::Tensor & primal, const at::Tensor & tangent, int64_t level) { |
| 19989 | // No device check |
| 19990 | // DeviceGuard omitted |
| 19991 | return at::native::_make_dual_copy(primal, tangent, level); |
| 19992 | } |
| 19993 | } // anonymous namespace |
| 19994 | namespace { |
| 19995 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__view_as_real_copy(const at::Tensor & self) { |
| 19996 | // No device check |
| 19997 | // DeviceGuard omitted |
| 19998 | return at::native::view_as_real_copy(self); |
| 19999 | } |
| 20000 | } // anonymous namespace |
| 20001 | namespace { |
| 20002 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__view_as_complex_copy(const at::Tensor & self) { |
| 20003 | // No device check |
| 20004 | // DeviceGuard omitted |
| 20005 | return at::native::view_as_complex_copy(self); |
| 20006 | } |
| 20007 | } // anonymous namespace |
| 20008 | namespace { |
| 20009 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional___conj_copy(const at::Tensor & self) { |
| 20010 | // No device check |
| 20011 | // DeviceGuard omitted |
| 20012 | return at::native::_conj_copy(self); |
| 20013 | } |
| 20014 | } // anonymous namespace |
| 20015 | namespace { |
| 20016 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional___neg_view_copy(const at::Tensor & self) { |
| 20017 | // No device check |
| 20018 | // DeviceGuard omitted |
| 20019 | return at::native::_neg_view_copy(self); |
| 20020 | } |
| 20021 | } // anonymous namespace |
| 20022 | namespace { |
| 20023 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__as_strided_copy(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional<c10::SymInt> storage_offset) { |
| 20024 | // No device check |
| 20025 | // DeviceGuard omitted |
| 20026 | return at::native::as_strided_copy_symint(self, size, stride, storage_offset); |
| 20027 | } |
| 20028 | } // anonymous namespace |
| 20029 | namespace { |
| 20030 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional___sparse_broadcast_to_copy(const at::Tensor & self, at::IntArrayRef size) { |
| 20031 | // No device check |
| 20032 | // DeviceGuard omitted |
| 20033 | return at::native::_sparse_broadcast_to_copy(self, size); |
| 20034 | } |
| 20035 | } // anonymous namespace |
| 20036 | namespace { |
| 20037 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__diagonal_copy(const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2) { |
| 20038 | // No device check |
| 20039 | // DeviceGuard omitted |
| 20040 | return at::native::diagonal_copy(self, offset, dim1, dim2); |
| 20041 | } |
| 20042 | } // anonymous namespace |
| 20043 | namespace { |
| 20044 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__expand_copy(const at::Tensor & self, c10::SymIntArrayRef size, bool implicit) { |
| 20045 | // No device check |
| 20046 | // DeviceGuard omitted |
| 20047 | return at::native::expand_copy_symint(self, size, implicit); |
| 20048 | } |
| 20049 | } // anonymous namespace |
| 20050 | namespace { |
| 20051 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__permute_copy(const at::Tensor & self, at::IntArrayRef dims) { |
| 20052 | // No device check |
| 20053 | // DeviceGuard omitted |
| 20054 | return at::native::permute_copy(self, dims); |
| 20055 | } |
| 20056 | } // anonymous namespace |
| 20057 | namespace { |
| 20058 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional___reshape_alias_copy(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) { |
| 20059 | // No device check |
| 20060 | // DeviceGuard omitted |
| 20061 | return at::native::_reshape_alias_copy_symint(self, size, stride); |
| 20062 | } |
| 20063 | } // anonymous namespace |
| 20064 | namespace { |
| 20065 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_int_select_copy(const at::Tensor & self, int64_t dim, c10::SymInt index) { |
| 20066 | // No device check |
| 20067 | // DeviceGuard omitted |
| 20068 | return at::native::select_copy_symint(self, dim, index); |
| 20069 | } |
| 20070 | } // anonymous namespace |
| 20071 | namespace { |
| 20072 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__detach_copy(const at::Tensor & self) { |
| 20073 | // No device check |
| 20074 | // DeviceGuard omitted |
| 20075 | return at::native::detach_copy(self); |
| 20076 | } |
| 20077 | } // anonymous namespace |
| 20078 | namespace { |
| 20079 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_Tensor_slice_copy(const at::Tensor & self, int64_t dim, c10::optional<c10::SymInt> start, c10::optional<c10::SymInt> end, c10::SymInt step) { |
| 20080 | // No device check |
| 20081 | // DeviceGuard omitted |
| 20082 | return at::native::slice_copy_Tensor_symint(self, dim, start, end, step); |
| 20083 | } |
| 20084 | } // anonymous namespace |
| 20085 | namespace { |
| 20086 | ::std::vector<at::Tensor> wrapper_CompositeExplicitAutogradNonFunctional_Tensor_split_copy(const at::Tensor & self, c10::SymInt split_size, int64_t dim) { |
| 20087 | // No device check |
| 20088 | // DeviceGuard omitted |
| 20089 | return at::native::split_copy_Tensor_symint(self, split_size, dim); |
| 20090 | } |
| 20091 | } // anonymous namespace |
| 20092 | namespace { |
| 20093 | ::std::vector<at::Tensor> wrapper_CompositeExplicitAutogradNonFunctional__split_with_sizes_copy(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim) { |
| 20094 | // No device check |
| 20095 | // DeviceGuard omitted |
| 20096 | return at::native::split_with_sizes_copy_symint(self, split_sizes, dim); |
| 20097 | } |
| 20098 | } // anonymous namespace |
| 20099 | namespace { |
| 20100 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__squeeze_copy(const at::Tensor & self) { |
| 20101 | // No device check |
| 20102 | // DeviceGuard omitted |
| 20103 | return at::native::squeeze_copy(self); |
| 20104 | } |
| 20105 | } // anonymous namespace |
| 20106 | namespace { |
| 20107 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_dim_squeeze_copy(const at::Tensor & self, int64_t dim) { |
| 20108 | // No device check |
| 20109 | // DeviceGuard omitted |
| 20110 | return at::native::squeeze_copy_dim(self, dim); |
| 20111 | } |
| 20112 | } // anonymous namespace |
| 20113 | namespace { |
| 20114 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_dims_squeeze_copy(const at::Tensor & self, at::IntArrayRef dim) { |
| 20115 | // No device check |
| 20116 | // DeviceGuard omitted |
| 20117 | return at::native::squeeze_copy_dims(self, dim); |
| 20118 | } |
| 20119 | } // anonymous namespace |
| 20120 | namespace { |
| 20121 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__t_copy(const at::Tensor & self) { |
| 20122 | // No device check |
| 20123 | // DeviceGuard omitted |
| 20124 | return at::native::t_copy(self); |
| 20125 | } |
| 20126 | } // anonymous namespace |
| 20127 | namespace { |
| 20128 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_int_transpose_copy(const at::Tensor & self, int64_t dim0, int64_t dim1) { |
| 20129 | // No device check |
| 20130 | // DeviceGuard omitted |
| 20131 | return at::native::transpose_copy_int(self, dim0, dim1); |
| 20132 | } |
| 20133 | } // anonymous namespace |
| 20134 | namespace { |
| 20135 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__unsqueeze_copy(const at::Tensor & self, int64_t dim) { |
| 20136 | // No device check |
| 20137 | // DeviceGuard omitted |
| 20138 | return at::native::unsqueeze_copy(self, dim); |
| 20139 | } |
| 20140 | } // anonymous namespace |
| 20141 | namespace { |
| 20142 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional___indices_copy(const at::Tensor & self) { |
| 20143 | // No device check |
| 20144 | // DeviceGuard omitted |
| 20145 | return at::native::_indices_copy(self); |
| 20146 | } |
| 20147 | } // anonymous namespace |
| 20148 | namespace { |
| 20149 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional___values_copy(const at::Tensor & self) { |
| 20150 | // No device check |
| 20151 | // DeviceGuard omitted |
| 20152 | return at::native::_values_copy(self); |
| 20153 | } |
| 20154 | } // anonymous namespace |
| 20155 | namespace { |
| 20156 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__indices_copy(const at::Tensor & self) { |
| 20157 | // No device check |
| 20158 | // DeviceGuard omitted |
| 20159 | return at::native::indices_copy(self); |
| 20160 | } |
| 20161 | } // anonymous namespace |
| 20162 | namespace { |
| 20163 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__values_copy(const at::Tensor & self) { |
| 20164 | // No device check |
| 20165 | // DeviceGuard omitted |
| 20166 | return at::native::values_copy(self); |
| 20167 | } |
| 20168 | } // anonymous namespace |
| 20169 | namespace { |
| 20170 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__crow_indices_copy(const at::Tensor & self) { |
| 20171 | // No device check |
| 20172 | // DeviceGuard omitted |
| 20173 | return at::native::crow_indices_copy(self); |
| 20174 | } |
| 20175 | } // anonymous namespace |
| 20176 | namespace { |
| 20177 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__col_indices_copy(const at::Tensor & self) { |
| 20178 | // No device check |
| 20179 | // DeviceGuard omitted |
| 20180 | return at::native::col_indices_copy(self); |
| 20181 | } |
| 20182 | } // anonymous namespace |
| 20183 | namespace { |
| 20184 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__ccol_indices_copy(const at::Tensor & self) { |
| 20185 | // No device check |
| 20186 | // DeviceGuard omitted |
| 20187 | return at::native::ccol_indices_copy(self); |
| 20188 | } |
| 20189 | } // anonymous namespace |
| 20190 | namespace { |
| 20191 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__row_indices_copy(const at::Tensor & self) { |
| 20192 | // No device check |
| 20193 | // DeviceGuard omitted |
| 20194 | return at::native::row_indices_copy(self); |
| 20195 | } |
| 20196 | } // anonymous namespace |
| 20197 | namespace { |
| 20198 | ::std::vector<at::Tensor> wrapper_CompositeExplicitAutogradNonFunctional_int_unbind_copy(const at::Tensor & self, int64_t dim) { |
| 20199 | // No device check |
| 20200 | // DeviceGuard omitted |
| 20201 | return at::native::unbind_copy_int(self, dim); |
| 20202 | } |
| 20203 | } // anonymous namespace |
| 20204 | namespace { |
| 20205 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__view_copy(const at::Tensor & self, c10::SymIntArrayRef size) { |
| 20206 | // No device check |
| 20207 | // DeviceGuard omitted |
| 20208 | return at::native::view_copy_symint(self, size); |
| 20209 | } |
| 20210 | } // anonymous namespace |
| 20211 | namespace { |
| 20212 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_dtype_view_copy(const at::Tensor & self, at::ScalarType dtype) { |
| 20213 | // No device check |
| 20214 | // DeviceGuard omitted |
| 20215 | return at::native::view_copy_dtype(self, dtype); |
| 20216 | } |
| 20217 | } // anonymous namespace |
| 20218 | namespace { |
| 20219 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__unfold_copy(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step) { |
| 20220 | // No device check |
| 20221 | // DeviceGuard omitted |
| 20222 | return at::native::unfold_copy(self, dimension, size, step); |
| 20223 | } |
| 20224 | } // anonymous namespace |
| 20225 | namespace { |
| 20226 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional__alias_copy(const at::Tensor & self) { |
| 20227 | // No device check |
| 20228 | // DeviceGuard omitted |
| 20229 | return at::native::alias_copy(self); |
| 20230 | } |
| 20231 | } // anonymous namespace |
| 20232 | struct structured_special_airy_ai_default_backend_functional final : public at::meta::structured_special_airy_ai { |
| 20233 | void set_output_strided( |
| 20234 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20235 | TensorOptions options, DimnameList names |
| 20236 | ) override { |
| 20237 | auto current_device = guard_.current_device(); |
| 20238 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20239 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20240 | "structured kernels don't support multi-device outputs"); |
| 20241 | } else { |
| 20242 | guard_.reset_device(options.device()); |
| 20243 | } |
| 20244 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20245 | if (!names.empty()) { |
| 20246 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20247 | } |
| 20248 | // super must happen after, so that downstream can use maybe_get_output |
| 20249 | // to retrieve the output |
| 20250 | at::meta::structured_special_airy_ai::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20251 | } |
| 20252 | void set_output_raw_strided( |
| 20253 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20254 | TensorOptions options, DimnameList names |
| 20255 | ) override { |
| 20256 | auto current_device = guard_.current_device(); |
| 20257 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20258 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20259 | "structured kernels don't support multi-device outputs"); |
| 20260 | } else { |
| 20261 | guard_.reset_device(options.device()); |
| 20262 | } |
| 20263 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20264 | if (!names.empty()) { |
| 20265 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20266 | } |
| 20267 | // super must happen after, so that downstream can use maybe_get_output |
| 20268 | // to retrieve the output |
| 20269 | at::meta::structured_special_airy_ai::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20270 | } |
| 20271 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 20272 | return *outputs_[output_idx]; |
| 20273 | } |
| 20274 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 20275 | c10::OptionalDeviceGuard guard_; |
| 20276 | }; |
| 20277 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_airy_ai(const at::Tensor & x) { |
| 20278 | structured_special_airy_ai_default_backend_functional op; |
| 20279 | op.meta(x); |
| 20280 | at::special_airy_ai_outf(x, *op.outputs_[0]); |
| 20281 | return std::move(op.outputs_[0]).take(); |
| 20282 | } |
| 20283 | struct structured_special_bessel_j0_default_backend_functional final : public at::meta::structured_special_bessel_j0 { |
| 20284 | void set_output_strided( |
| 20285 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20286 | TensorOptions options, DimnameList names |
| 20287 | ) override { |
| 20288 | auto current_device = guard_.current_device(); |
| 20289 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20290 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20291 | "structured kernels don't support multi-device outputs"); |
| 20292 | } else { |
| 20293 | guard_.reset_device(options.device()); |
| 20294 | } |
| 20295 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20296 | if (!names.empty()) { |
| 20297 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20298 | } |
| 20299 | // super must happen after, so that downstream can use maybe_get_output |
| 20300 | // to retrieve the output |
| 20301 | at::meta::structured_special_bessel_j0::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20302 | } |
| 20303 | void set_output_raw_strided( |
| 20304 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20305 | TensorOptions options, DimnameList names |
| 20306 | ) override { |
| 20307 | auto current_device = guard_.current_device(); |
| 20308 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20309 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20310 | "structured kernels don't support multi-device outputs"); |
| 20311 | } else { |
| 20312 | guard_.reset_device(options.device()); |
| 20313 | } |
| 20314 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20315 | if (!names.empty()) { |
| 20316 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20317 | } |
| 20318 | // super must happen after, so that downstream can use maybe_get_output |
| 20319 | // to retrieve the output |
| 20320 | at::meta::structured_special_bessel_j0::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20321 | } |
| 20322 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 20323 | return *outputs_[output_idx]; |
| 20324 | } |
| 20325 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 20326 | c10::OptionalDeviceGuard guard_; |
| 20327 | }; |
| 20328 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_bessel_j0(const at::Tensor & self) { |
| 20329 | structured_special_bessel_j0_default_backend_functional op; |
| 20330 | op.meta(self); |
| 20331 | at::special_bessel_j0_outf(self, *op.outputs_[0]); |
| 20332 | return std::move(op.outputs_[0]).take(); |
| 20333 | } |
| 20334 | struct structured_special_bessel_j1_default_backend_functional final : public at::meta::structured_special_bessel_j1 { |
| 20335 | void set_output_strided( |
| 20336 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20337 | TensorOptions options, DimnameList names |
| 20338 | ) override { |
| 20339 | auto current_device = guard_.current_device(); |
| 20340 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20341 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20342 | "structured kernels don't support multi-device outputs"); |
| 20343 | } else { |
| 20344 | guard_.reset_device(options.device()); |
| 20345 | } |
| 20346 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20347 | if (!names.empty()) { |
| 20348 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20349 | } |
| 20350 | // super must happen after, so that downstream can use maybe_get_output |
| 20351 | // to retrieve the output |
| 20352 | at::meta::structured_special_bessel_j1::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20353 | } |
| 20354 | void set_output_raw_strided( |
| 20355 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20356 | TensorOptions options, DimnameList names |
| 20357 | ) override { |
| 20358 | auto current_device = guard_.current_device(); |
| 20359 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20360 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20361 | "structured kernels don't support multi-device outputs"); |
| 20362 | } else { |
| 20363 | guard_.reset_device(options.device()); |
| 20364 | } |
| 20365 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20366 | if (!names.empty()) { |
| 20367 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20368 | } |
| 20369 | // super must happen after, so that downstream can use maybe_get_output |
| 20370 | // to retrieve the output |
| 20371 | at::meta::structured_special_bessel_j1::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20372 | } |
| 20373 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 20374 | return *outputs_[output_idx]; |
| 20375 | } |
| 20376 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 20377 | c10::OptionalDeviceGuard guard_; |
| 20378 | }; |
| 20379 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_bessel_j1(const at::Tensor & self) { |
| 20380 | structured_special_bessel_j1_default_backend_functional op; |
| 20381 | op.meta(self); |
| 20382 | at::special_bessel_j1_outf(self, *op.outputs_[0]); |
| 20383 | return std::move(op.outputs_[0]).take(); |
| 20384 | } |
| 20385 | struct structured_special_bessel_y0_default_backend_functional final : public at::meta::structured_special_bessel_y0 { |
| 20386 | void set_output_strided( |
| 20387 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20388 | TensorOptions options, DimnameList names |
| 20389 | ) override { |
| 20390 | auto current_device = guard_.current_device(); |
| 20391 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20392 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20393 | "structured kernels don't support multi-device outputs"); |
| 20394 | } else { |
| 20395 | guard_.reset_device(options.device()); |
| 20396 | } |
| 20397 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20398 | if (!names.empty()) { |
| 20399 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20400 | } |
| 20401 | // super must happen after, so that downstream can use maybe_get_output |
| 20402 | // to retrieve the output |
| 20403 | at::meta::structured_special_bessel_y0::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20404 | } |
| 20405 | void set_output_raw_strided( |
| 20406 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20407 | TensorOptions options, DimnameList names |
| 20408 | ) override { |
| 20409 | auto current_device = guard_.current_device(); |
| 20410 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20411 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20412 | "structured kernels don't support multi-device outputs"); |
| 20413 | } else { |
| 20414 | guard_.reset_device(options.device()); |
| 20415 | } |
| 20416 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20417 | if (!names.empty()) { |
| 20418 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20419 | } |
| 20420 | // super must happen after, so that downstream can use maybe_get_output |
| 20421 | // to retrieve the output |
| 20422 | at::meta::structured_special_bessel_y0::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20423 | } |
| 20424 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 20425 | return *outputs_[output_idx]; |
| 20426 | } |
| 20427 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 20428 | c10::OptionalDeviceGuard guard_; |
| 20429 | }; |
| 20430 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_bessel_y0(const at::Tensor & self) { |
| 20431 | structured_special_bessel_y0_default_backend_functional op; |
| 20432 | op.meta(self); |
| 20433 | at::special_bessel_y0_outf(self, *op.outputs_[0]); |
| 20434 | return std::move(op.outputs_[0]).take(); |
| 20435 | } |
| 20436 | struct structured_special_bessel_y1_default_backend_functional final : public at::meta::structured_special_bessel_y1 { |
| 20437 | void set_output_strided( |
| 20438 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20439 | TensorOptions options, DimnameList names |
| 20440 | ) override { |
| 20441 | auto current_device = guard_.current_device(); |
| 20442 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20443 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20444 | "structured kernels don't support multi-device outputs"); |
| 20445 | } else { |
| 20446 | guard_.reset_device(options.device()); |
| 20447 | } |
| 20448 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20449 | if (!names.empty()) { |
| 20450 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20451 | } |
| 20452 | // super must happen after, so that downstream can use maybe_get_output |
| 20453 | // to retrieve the output |
| 20454 | at::meta::structured_special_bessel_y1::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20455 | } |
| 20456 | void set_output_raw_strided( |
| 20457 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20458 | TensorOptions options, DimnameList names |
| 20459 | ) override { |
| 20460 | auto current_device = guard_.current_device(); |
| 20461 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20462 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20463 | "structured kernels don't support multi-device outputs"); |
| 20464 | } else { |
| 20465 | guard_.reset_device(options.device()); |
| 20466 | } |
| 20467 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20468 | if (!names.empty()) { |
| 20469 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20470 | } |
| 20471 | // super must happen after, so that downstream can use maybe_get_output |
| 20472 | // to retrieve the output |
| 20473 | at::meta::structured_special_bessel_y1::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20474 | } |
| 20475 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 20476 | return *outputs_[output_idx]; |
| 20477 | } |
| 20478 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 20479 | c10::OptionalDeviceGuard guard_; |
| 20480 | }; |
| 20481 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_bessel_y1(const at::Tensor & self) { |
| 20482 | structured_special_bessel_y1_default_backend_functional op; |
| 20483 | op.meta(self); |
| 20484 | at::special_bessel_y1_outf(self, *op.outputs_[0]); |
| 20485 | return std::move(op.outputs_[0]).take(); |
| 20486 | } |
| 20487 | struct structured_special_chebyshev_polynomial_t_default_backend_functional final : public at::meta::structured_special_chebyshev_polynomial_t { |
| 20488 | void set_output_strided( |
| 20489 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20490 | TensorOptions options, DimnameList names |
| 20491 | ) override { |
| 20492 | auto current_device = guard_.current_device(); |
| 20493 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20494 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20495 | "structured kernels don't support multi-device outputs"); |
| 20496 | } else { |
| 20497 | guard_.reset_device(options.device()); |
| 20498 | } |
| 20499 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20500 | if (!names.empty()) { |
| 20501 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20502 | } |
| 20503 | // super must happen after, so that downstream can use maybe_get_output |
| 20504 | // to retrieve the output |
| 20505 | at::meta::structured_special_chebyshev_polynomial_t::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20506 | } |
| 20507 | void set_output_raw_strided( |
| 20508 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20509 | TensorOptions options, DimnameList names |
| 20510 | ) override { |
| 20511 | auto current_device = guard_.current_device(); |
| 20512 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20513 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20514 | "structured kernels don't support multi-device outputs"); |
| 20515 | } else { |
| 20516 | guard_.reset_device(options.device()); |
| 20517 | } |
| 20518 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20519 | if (!names.empty()) { |
| 20520 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20521 | } |
| 20522 | // super must happen after, so that downstream can use maybe_get_output |
| 20523 | // to retrieve the output |
| 20524 | at::meta::structured_special_chebyshev_polynomial_t::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20525 | } |
| 20526 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 20527 | return *outputs_[output_idx]; |
| 20528 | } |
| 20529 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 20530 | c10::OptionalDeviceGuard guard_; |
| 20531 | }; |
| 20532 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_chebyshev_polynomial_t(const at::Tensor & x, const at::Tensor & n) { |
| 20533 | structured_special_chebyshev_polynomial_t_default_backend_functional op; |
| 20534 | op.meta(x, n); |
| 20535 | at::special_chebyshev_polynomial_t_outf(x, n, *op.outputs_[0]); |
| 20536 | return std::move(op.outputs_[0]).take(); |
| 20537 | } |
| 20538 | struct structured_special_chebyshev_polynomial_u_default_backend_functional final : public at::meta::structured_special_chebyshev_polynomial_u { |
| 20539 | void set_output_strided( |
| 20540 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20541 | TensorOptions options, DimnameList names |
| 20542 | ) override { |
| 20543 | auto current_device = guard_.current_device(); |
| 20544 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20545 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20546 | "structured kernels don't support multi-device outputs"); |
| 20547 | } else { |
| 20548 | guard_.reset_device(options.device()); |
| 20549 | } |
| 20550 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20551 | if (!names.empty()) { |
| 20552 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20553 | } |
| 20554 | // super must happen after, so that downstream can use maybe_get_output |
| 20555 | // to retrieve the output |
| 20556 | at::meta::structured_special_chebyshev_polynomial_u::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20557 | } |
| 20558 | void set_output_raw_strided( |
| 20559 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20560 | TensorOptions options, DimnameList names |
| 20561 | ) override { |
| 20562 | auto current_device = guard_.current_device(); |
| 20563 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20564 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20565 | "structured kernels don't support multi-device outputs"); |
| 20566 | } else { |
| 20567 | guard_.reset_device(options.device()); |
| 20568 | } |
| 20569 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20570 | if (!names.empty()) { |
| 20571 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20572 | } |
| 20573 | // super must happen after, so that downstream can use maybe_get_output |
| 20574 | // to retrieve the output |
| 20575 | at::meta::structured_special_chebyshev_polynomial_u::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20576 | } |
| 20577 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 20578 | return *outputs_[output_idx]; |
| 20579 | } |
| 20580 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 20581 | c10::OptionalDeviceGuard guard_; |
| 20582 | }; |
| 20583 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_chebyshev_polynomial_u(const at::Tensor & x, const at::Tensor & n) { |
| 20584 | structured_special_chebyshev_polynomial_u_default_backend_functional op; |
| 20585 | op.meta(x, n); |
| 20586 | at::special_chebyshev_polynomial_u_outf(x, n, *op.outputs_[0]); |
| 20587 | return std::move(op.outputs_[0]).take(); |
| 20588 | } |
| 20589 | struct structured_special_chebyshev_polynomial_v_default_backend_functional final : public at::meta::structured_special_chebyshev_polynomial_v { |
| 20590 | void set_output_strided( |
| 20591 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20592 | TensorOptions options, DimnameList names |
| 20593 | ) override { |
| 20594 | auto current_device = guard_.current_device(); |
| 20595 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20596 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20597 | "structured kernels don't support multi-device outputs"); |
| 20598 | } else { |
| 20599 | guard_.reset_device(options.device()); |
| 20600 | } |
| 20601 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20602 | if (!names.empty()) { |
| 20603 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20604 | } |
| 20605 | // super must happen after, so that downstream can use maybe_get_output |
| 20606 | // to retrieve the output |
| 20607 | at::meta::structured_special_chebyshev_polynomial_v::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20608 | } |
| 20609 | void set_output_raw_strided( |
| 20610 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20611 | TensorOptions options, DimnameList names |
| 20612 | ) override { |
| 20613 | auto current_device = guard_.current_device(); |
| 20614 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20615 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20616 | "structured kernels don't support multi-device outputs"); |
| 20617 | } else { |
| 20618 | guard_.reset_device(options.device()); |
| 20619 | } |
| 20620 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20621 | if (!names.empty()) { |
| 20622 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20623 | } |
| 20624 | // super must happen after, so that downstream can use maybe_get_output |
| 20625 | // to retrieve the output |
| 20626 | at::meta::structured_special_chebyshev_polynomial_v::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20627 | } |
| 20628 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 20629 | return *outputs_[output_idx]; |
| 20630 | } |
| 20631 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 20632 | c10::OptionalDeviceGuard guard_; |
| 20633 | }; |
| 20634 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_chebyshev_polynomial_v(const at::Tensor & x, const at::Tensor & n) { |
| 20635 | structured_special_chebyshev_polynomial_v_default_backend_functional op; |
| 20636 | op.meta(x, n); |
| 20637 | at::special_chebyshev_polynomial_v_outf(x, n, *op.outputs_[0]); |
| 20638 | return std::move(op.outputs_[0]).take(); |
| 20639 | } |
| 20640 | struct structured_special_chebyshev_polynomial_w_default_backend_functional final : public at::meta::structured_special_chebyshev_polynomial_w { |
| 20641 | void set_output_strided( |
| 20642 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20643 | TensorOptions options, DimnameList names |
| 20644 | ) override { |
| 20645 | auto current_device = guard_.current_device(); |
| 20646 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20647 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20648 | "structured kernels don't support multi-device outputs"); |
| 20649 | } else { |
| 20650 | guard_.reset_device(options.device()); |
| 20651 | } |
| 20652 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20653 | if (!names.empty()) { |
| 20654 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20655 | } |
| 20656 | // super must happen after, so that downstream can use maybe_get_output |
| 20657 | // to retrieve the output |
| 20658 | at::meta::structured_special_chebyshev_polynomial_w::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20659 | } |
| 20660 | void set_output_raw_strided( |
| 20661 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20662 | TensorOptions options, DimnameList names |
| 20663 | ) override { |
| 20664 | auto current_device = guard_.current_device(); |
| 20665 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20666 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20667 | "structured kernels don't support multi-device outputs"); |
| 20668 | } else { |
| 20669 | guard_.reset_device(options.device()); |
| 20670 | } |
| 20671 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20672 | if (!names.empty()) { |
| 20673 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20674 | } |
| 20675 | // super must happen after, so that downstream can use maybe_get_output |
| 20676 | // to retrieve the output |
| 20677 | at::meta::structured_special_chebyshev_polynomial_w::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20678 | } |
| 20679 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 20680 | return *outputs_[output_idx]; |
| 20681 | } |
| 20682 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 20683 | c10::OptionalDeviceGuard guard_; |
| 20684 | }; |
| 20685 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_chebyshev_polynomial_w(const at::Tensor & x, const at::Tensor & n) { |
| 20686 | structured_special_chebyshev_polynomial_w_default_backend_functional op; |
| 20687 | op.meta(x, n); |
| 20688 | at::special_chebyshev_polynomial_w_outf(x, n, *op.outputs_[0]); |
| 20689 | return std::move(op.outputs_[0]).take(); |
| 20690 | } |
| 20691 | struct structured_special_hermite_polynomial_h_default_backend_functional final : public at::meta::structured_special_hermite_polynomial_h { |
| 20692 | void set_output_strided( |
| 20693 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20694 | TensorOptions options, DimnameList names |
| 20695 | ) override { |
| 20696 | auto current_device = guard_.current_device(); |
| 20697 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20698 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20699 | "structured kernels don't support multi-device outputs"); |
| 20700 | } else { |
| 20701 | guard_.reset_device(options.device()); |
| 20702 | } |
| 20703 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20704 | if (!names.empty()) { |
| 20705 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20706 | } |
| 20707 | // super must happen after, so that downstream can use maybe_get_output |
| 20708 | // to retrieve the output |
| 20709 | at::meta::structured_special_hermite_polynomial_h::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20710 | } |
| 20711 | void set_output_raw_strided( |
| 20712 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20713 | TensorOptions options, DimnameList names |
| 20714 | ) override { |
| 20715 | auto current_device = guard_.current_device(); |
| 20716 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20717 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20718 | "structured kernels don't support multi-device outputs"); |
| 20719 | } else { |
| 20720 | guard_.reset_device(options.device()); |
| 20721 | } |
| 20722 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20723 | if (!names.empty()) { |
| 20724 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20725 | } |
| 20726 | // super must happen after, so that downstream can use maybe_get_output |
| 20727 | // to retrieve the output |
| 20728 | at::meta::structured_special_hermite_polynomial_h::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20729 | } |
| 20730 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 20731 | return *outputs_[output_idx]; |
| 20732 | } |
| 20733 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 20734 | c10::OptionalDeviceGuard guard_; |
| 20735 | }; |
| 20736 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_hermite_polynomial_h(const at::Tensor & x, const at::Tensor & n) { |
| 20737 | structured_special_hermite_polynomial_h_default_backend_functional op; |
| 20738 | op.meta(x, n); |
| 20739 | at::special_hermite_polynomial_h_outf(x, n, *op.outputs_[0]); |
| 20740 | return std::move(op.outputs_[0]).take(); |
| 20741 | } |
| 20742 | struct structured_special_hermite_polynomial_he_default_backend_functional final : public at::meta::structured_special_hermite_polynomial_he { |
| 20743 | void set_output_strided( |
| 20744 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20745 | TensorOptions options, DimnameList names |
| 20746 | ) override { |
| 20747 | auto current_device = guard_.current_device(); |
| 20748 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20749 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20750 | "structured kernels don't support multi-device outputs"); |
| 20751 | } else { |
| 20752 | guard_.reset_device(options.device()); |
| 20753 | } |
| 20754 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20755 | if (!names.empty()) { |
| 20756 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20757 | } |
| 20758 | // super must happen after, so that downstream can use maybe_get_output |
| 20759 | // to retrieve the output |
| 20760 | at::meta::structured_special_hermite_polynomial_he::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20761 | } |
| 20762 | void set_output_raw_strided( |
| 20763 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20764 | TensorOptions options, DimnameList names |
| 20765 | ) override { |
| 20766 | auto current_device = guard_.current_device(); |
| 20767 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20768 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20769 | "structured kernels don't support multi-device outputs"); |
| 20770 | } else { |
| 20771 | guard_.reset_device(options.device()); |
| 20772 | } |
| 20773 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20774 | if (!names.empty()) { |
| 20775 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20776 | } |
| 20777 | // super must happen after, so that downstream can use maybe_get_output |
| 20778 | // to retrieve the output |
| 20779 | at::meta::structured_special_hermite_polynomial_he::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20780 | } |
| 20781 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 20782 | return *outputs_[output_idx]; |
| 20783 | } |
| 20784 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 20785 | c10::OptionalDeviceGuard guard_; |
| 20786 | }; |
| 20787 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_hermite_polynomial_he(const at::Tensor & x, const at::Tensor & n) { |
| 20788 | structured_special_hermite_polynomial_he_default_backend_functional op; |
| 20789 | op.meta(x, n); |
| 20790 | at::special_hermite_polynomial_he_outf(x, n, *op.outputs_[0]); |
| 20791 | return std::move(op.outputs_[0]).take(); |
| 20792 | } |
| 20793 | struct structured_special_laguerre_polynomial_l_default_backend_functional final : public at::meta::structured_special_laguerre_polynomial_l { |
| 20794 | void set_output_strided( |
| 20795 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20796 | TensorOptions options, DimnameList names |
| 20797 | ) override { |
| 20798 | auto current_device = guard_.current_device(); |
| 20799 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20800 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20801 | "structured kernels don't support multi-device outputs"); |
| 20802 | } else { |
| 20803 | guard_.reset_device(options.device()); |
| 20804 | } |
| 20805 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20806 | if (!names.empty()) { |
| 20807 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20808 | } |
| 20809 | // super must happen after, so that downstream can use maybe_get_output |
| 20810 | // to retrieve the output |
| 20811 | at::meta::structured_special_laguerre_polynomial_l::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20812 | } |
| 20813 | void set_output_raw_strided( |
| 20814 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20815 | TensorOptions options, DimnameList names |
| 20816 | ) override { |
| 20817 | auto current_device = guard_.current_device(); |
| 20818 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20819 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20820 | "structured kernels don't support multi-device outputs"); |
| 20821 | } else { |
| 20822 | guard_.reset_device(options.device()); |
| 20823 | } |
| 20824 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20825 | if (!names.empty()) { |
| 20826 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20827 | } |
| 20828 | // super must happen after, so that downstream can use maybe_get_output |
| 20829 | // to retrieve the output |
| 20830 | at::meta::structured_special_laguerre_polynomial_l::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20831 | } |
| 20832 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 20833 | return *outputs_[output_idx]; |
| 20834 | } |
| 20835 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 20836 | c10::OptionalDeviceGuard guard_; |
| 20837 | }; |
| 20838 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_laguerre_polynomial_l(const at::Tensor & x, const at::Tensor & n) { |
| 20839 | structured_special_laguerre_polynomial_l_default_backend_functional op; |
| 20840 | op.meta(x, n); |
| 20841 | at::special_laguerre_polynomial_l_outf(x, n, *op.outputs_[0]); |
| 20842 | return std::move(op.outputs_[0]).take(); |
| 20843 | } |
| 20844 | struct structured_special_legendre_polynomial_p_default_backend_functional final : public at::meta::structured_special_legendre_polynomial_p { |
| 20845 | void set_output_strided( |
| 20846 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20847 | TensorOptions options, DimnameList names |
| 20848 | ) override { |
| 20849 | auto current_device = guard_.current_device(); |
| 20850 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20851 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20852 | "structured kernels don't support multi-device outputs"); |
| 20853 | } else { |
| 20854 | guard_.reset_device(options.device()); |
| 20855 | } |
| 20856 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20857 | if (!names.empty()) { |
| 20858 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20859 | } |
| 20860 | // super must happen after, so that downstream can use maybe_get_output |
| 20861 | // to retrieve the output |
| 20862 | at::meta::structured_special_legendre_polynomial_p::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20863 | } |
| 20864 | void set_output_raw_strided( |
| 20865 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20866 | TensorOptions options, DimnameList names |
| 20867 | ) override { |
| 20868 | auto current_device = guard_.current_device(); |
| 20869 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20870 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20871 | "structured kernels don't support multi-device outputs"); |
| 20872 | } else { |
| 20873 | guard_.reset_device(options.device()); |
| 20874 | } |
| 20875 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20876 | if (!names.empty()) { |
| 20877 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20878 | } |
| 20879 | // super must happen after, so that downstream can use maybe_get_output |
| 20880 | // to retrieve the output |
| 20881 | at::meta::structured_special_legendre_polynomial_p::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20882 | } |
| 20883 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 20884 | return *outputs_[output_idx]; |
| 20885 | } |
| 20886 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 20887 | c10::OptionalDeviceGuard guard_; |
| 20888 | }; |
| 20889 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_legendre_polynomial_p(const at::Tensor & x, const at::Tensor & n) { |
| 20890 | structured_special_legendre_polynomial_p_default_backend_functional op; |
| 20891 | op.meta(x, n); |
| 20892 | at::special_legendre_polynomial_p_outf(x, n, *op.outputs_[0]); |
| 20893 | return std::move(op.outputs_[0]).take(); |
| 20894 | } |
| 20895 | struct structured_special_modified_bessel_i0_default_backend_functional final : public at::meta::structured_special_modified_bessel_i0 { |
| 20896 | void set_output_strided( |
| 20897 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20898 | TensorOptions options, DimnameList names |
| 20899 | ) override { |
| 20900 | auto current_device = guard_.current_device(); |
| 20901 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20902 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20903 | "structured kernels don't support multi-device outputs"); |
| 20904 | } else { |
| 20905 | guard_.reset_device(options.device()); |
| 20906 | } |
| 20907 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20908 | if (!names.empty()) { |
| 20909 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20910 | } |
| 20911 | // super must happen after, so that downstream can use maybe_get_output |
| 20912 | // to retrieve the output |
| 20913 | at::meta::structured_special_modified_bessel_i0::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20914 | } |
| 20915 | void set_output_raw_strided( |
| 20916 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20917 | TensorOptions options, DimnameList names |
| 20918 | ) override { |
| 20919 | auto current_device = guard_.current_device(); |
| 20920 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20921 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20922 | "structured kernels don't support multi-device outputs"); |
| 20923 | } else { |
| 20924 | guard_.reset_device(options.device()); |
| 20925 | } |
| 20926 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20927 | if (!names.empty()) { |
| 20928 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20929 | } |
| 20930 | // super must happen after, so that downstream can use maybe_get_output |
| 20931 | // to retrieve the output |
| 20932 | at::meta::structured_special_modified_bessel_i0::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20933 | } |
| 20934 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 20935 | return *outputs_[output_idx]; |
| 20936 | } |
| 20937 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 20938 | c10::OptionalDeviceGuard guard_; |
| 20939 | }; |
| 20940 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_modified_bessel_i0(const at::Tensor & self) { |
| 20941 | structured_special_modified_bessel_i0_default_backend_functional op; |
| 20942 | op.meta(self); |
| 20943 | at::special_modified_bessel_i0_outf(self, *op.outputs_[0]); |
| 20944 | return std::move(op.outputs_[0]).take(); |
| 20945 | } |
| 20946 | struct structured_special_modified_bessel_i1_default_backend_functional final : public at::meta::structured_special_modified_bessel_i1 { |
| 20947 | void set_output_strided( |
| 20948 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20949 | TensorOptions options, DimnameList names |
| 20950 | ) override { |
| 20951 | auto current_device = guard_.current_device(); |
| 20952 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20953 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20954 | "structured kernels don't support multi-device outputs"); |
| 20955 | } else { |
| 20956 | guard_.reset_device(options.device()); |
| 20957 | } |
| 20958 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20959 | if (!names.empty()) { |
| 20960 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20961 | } |
| 20962 | // super must happen after, so that downstream can use maybe_get_output |
| 20963 | // to retrieve the output |
| 20964 | at::meta::structured_special_modified_bessel_i1::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20965 | } |
| 20966 | void set_output_raw_strided( |
| 20967 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 20968 | TensorOptions options, DimnameList names |
| 20969 | ) override { |
| 20970 | auto current_device = guard_.current_device(); |
| 20971 | if (C10_UNLIKELY(current_device.has_value())) { |
| 20972 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 20973 | "structured kernels don't support multi-device outputs"); |
| 20974 | } else { |
| 20975 | guard_.reset_device(options.device()); |
| 20976 | } |
| 20977 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 20978 | if (!names.empty()) { |
| 20979 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 20980 | } |
| 20981 | // super must happen after, so that downstream can use maybe_get_output |
| 20982 | // to retrieve the output |
| 20983 | at::meta::structured_special_modified_bessel_i1::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 20984 | } |
| 20985 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 20986 | return *outputs_[output_idx]; |
| 20987 | } |
| 20988 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 20989 | c10::OptionalDeviceGuard guard_; |
| 20990 | }; |
| 20991 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_modified_bessel_i1(const at::Tensor & self) { |
| 20992 | structured_special_modified_bessel_i1_default_backend_functional op; |
| 20993 | op.meta(self); |
| 20994 | at::special_modified_bessel_i1_outf(self, *op.outputs_[0]); |
| 20995 | return std::move(op.outputs_[0]).take(); |
| 20996 | } |
| 20997 | struct structured_special_modified_bessel_k0_default_backend_functional final : public at::meta::structured_special_modified_bessel_k0 { |
| 20998 | void set_output_strided( |
| 20999 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21000 | TensorOptions options, DimnameList names |
| 21001 | ) override { |
| 21002 | auto current_device = guard_.current_device(); |
| 21003 | if (C10_UNLIKELY(current_device.has_value())) { |
| 21004 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 21005 | "structured kernels don't support multi-device outputs"); |
| 21006 | } else { |
| 21007 | guard_.reset_device(options.device()); |
| 21008 | } |
| 21009 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21010 | if (!names.empty()) { |
| 21011 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21012 | } |
| 21013 | // super must happen after, so that downstream can use maybe_get_output |
| 21014 | // to retrieve the output |
| 21015 | at::meta::structured_special_modified_bessel_k0::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21016 | } |
| 21017 | void set_output_raw_strided( |
| 21018 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21019 | TensorOptions options, DimnameList names |
| 21020 | ) override { |
| 21021 | auto current_device = guard_.current_device(); |
| 21022 | if (C10_UNLIKELY(current_device.has_value())) { |
| 21023 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 21024 | "structured kernels don't support multi-device outputs"); |
| 21025 | } else { |
| 21026 | guard_.reset_device(options.device()); |
| 21027 | } |
| 21028 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21029 | if (!names.empty()) { |
| 21030 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21031 | } |
| 21032 | // super must happen after, so that downstream can use maybe_get_output |
| 21033 | // to retrieve the output |
| 21034 | at::meta::structured_special_modified_bessel_k0::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21035 | } |
| 21036 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21037 | return *outputs_[output_idx]; |
| 21038 | } |
| 21039 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 21040 | c10::OptionalDeviceGuard guard_; |
| 21041 | }; |
| 21042 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_modified_bessel_k0(const at::Tensor & self) { |
| 21043 | structured_special_modified_bessel_k0_default_backend_functional op; |
| 21044 | op.meta(self); |
| 21045 | at::special_modified_bessel_k0_outf(self, *op.outputs_[0]); |
| 21046 | return std::move(op.outputs_[0]).take(); |
| 21047 | } |
| 21048 | struct structured_special_modified_bessel_k1_default_backend_functional final : public at::meta::structured_special_modified_bessel_k1 { |
| 21049 | void set_output_strided( |
| 21050 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21051 | TensorOptions options, DimnameList names |
| 21052 | ) override { |
| 21053 | auto current_device = guard_.current_device(); |
| 21054 | if (C10_UNLIKELY(current_device.has_value())) { |
| 21055 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 21056 | "structured kernels don't support multi-device outputs"); |
| 21057 | } else { |
| 21058 | guard_.reset_device(options.device()); |
| 21059 | } |
| 21060 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21061 | if (!names.empty()) { |
| 21062 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21063 | } |
| 21064 | // super must happen after, so that downstream can use maybe_get_output |
| 21065 | // to retrieve the output |
| 21066 | at::meta::structured_special_modified_bessel_k1::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21067 | } |
| 21068 | void set_output_raw_strided( |
| 21069 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21070 | TensorOptions options, DimnameList names |
| 21071 | ) override { |
| 21072 | auto current_device = guard_.current_device(); |
| 21073 | if (C10_UNLIKELY(current_device.has_value())) { |
| 21074 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 21075 | "structured kernels don't support multi-device outputs"); |
| 21076 | } else { |
| 21077 | guard_.reset_device(options.device()); |
| 21078 | } |
| 21079 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21080 | if (!names.empty()) { |
| 21081 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21082 | } |
| 21083 | // super must happen after, so that downstream can use maybe_get_output |
| 21084 | // to retrieve the output |
| 21085 | at::meta::structured_special_modified_bessel_k1::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21086 | } |
| 21087 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21088 | return *outputs_[output_idx]; |
| 21089 | } |
| 21090 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 21091 | c10::OptionalDeviceGuard guard_; |
| 21092 | }; |
| 21093 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_modified_bessel_k1(const at::Tensor & self) { |
| 21094 | structured_special_modified_bessel_k1_default_backend_functional op; |
| 21095 | op.meta(self); |
| 21096 | at::special_modified_bessel_k1_outf(self, *op.outputs_[0]); |
| 21097 | return std::move(op.outputs_[0]).take(); |
| 21098 | } |
| 21099 | struct structured_special_scaled_modified_bessel_k0_default_backend_functional final : public at::meta::structured_special_scaled_modified_bessel_k0 { |
| 21100 | void set_output_strided( |
| 21101 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21102 | TensorOptions options, DimnameList names |
| 21103 | ) override { |
| 21104 | auto current_device = guard_.current_device(); |
| 21105 | if (C10_UNLIKELY(current_device.has_value())) { |
| 21106 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 21107 | "structured kernels don't support multi-device outputs"); |
| 21108 | } else { |
| 21109 | guard_.reset_device(options.device()); |
| 21110 | } |
| 21111 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21112 | if (!names.empty()) { |
| 21113 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21114 | } |
| 21115 | // super must happen after, so that downstream can use maybe_get_output |
| 21116 | // to retrieve the output |
| 21117 | at::meta::structured_special_scaled_modified_bessel_k0::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21118 | } |
| 21119 | void set_output_raw_strided( |
| 21120 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21121 | TensorOptions options, DimnameList names |
| 21122 | ) override { |
| 21123 | auto current_device = guard_.current_device(); |
| 21124 | if (C10_UNLIKELY(current_device.has_value())) { |
| 21125 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 21126 | "structured kernels don't support multi-device outputs"); |
| 21127 | } else { |
| 21128 | guard_.reset_device(options.device()); |
| 21129 | } |
| 21130 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21131 | if (!names.empty()) { |
| 21132 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21133 | } |
| 21134 | // super must happen after, so that downstream can use maybe_get_output |
| 21135 | // to retrieve the output |
| 21136 | at::meta::structured_special_scaled_modified_bessel_k0::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21137 | } |
| 21138 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21139 | return *outputs_[output_idx]; |
| 21140 | } |
| 21141 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 21142 | c10::OptionalDeviceGuard guard_; |
| 21143 | }; |
| 21144 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_scaled_modified_bessel_k0(const at::Tensor & x) { |
| 21145 | structured_special_scaled_modified_bessel_k0_default_backend_functional op; |
| 21146 | op.meta(x); |
| 21147 | at::special_scaled_modified_bessel_k0_outf(x, *op.outputs_[0]); |
| 21148 | return std::move(op.outputs_[0]).take(); |
| 21149 | } |
| 21150 | struct structured_special_scaled_modified_bessel_k1_default_backend_functional final : public at::meta::structured_special_scaled_modified_bessel_k1 { |
| 21151 | void set_output_strided( |
| 21152 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21153 | TensorOptions options, DimnameList names |
| 21154 | ) override { |
| 21155 | auto current_device = guard_.current_device(); |
| 21156 | if (C10_UNLIKELY(current_device.has_value())) { |
| 21157 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 21158 | "structured kernels don't support multi-device outputs"); |
| 21159 | } else { |
| 21160 | guard_.reset_device(options.device()); |
| 21161 | } |
| 21162 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21163 | if (!names.empty()) { |
| 21164 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21165 | } |
| 21166 | // super must happen after, so that downstream can use maybe_get_output |
| 21167 | // to retrieve the output |
| 21168 | at::meta::structured_special_scaled_modified_bessel_k1::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21169 | } |
| 21170 | void set_output_raw_strided( |
| 21171 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21172 | TensorOptions options, DimnameList names |
| 21173 | ) override { |
| 21174 | auto current_device = guard_.current_device(); |
| 21175 | if (C10_UNLIKELY(current_device.has_value())) { |
| 21176 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 21177 | "structured kernels don't support multi-device outputs"); |
| 21178 | } else { |
| 21179 | guard_.reset_device(options.device()); |
| 21180 | } |
| 21181 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21182 | if (!names.empty()) { |
| 21183 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21184 | } |
| 21185 | // super must happen after, so that downstream can use maybe_get_output |
| 21186 | // to retrieve the output |
| 21187 | at::meta::structured_special_scaled_modified_bessel_k1::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21188 | } |
| 21189 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21190 | return *outputs_[output_idx]; |
| 21191 | } |
| 21192 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 21193 | c10::OptionalDeviceGuard guard_; |
| 21194 | }; |
| 21195 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_scaled_modified_bessel_k1(const at::Tensor & x) { |
| 21196 | structured_special_scaled_modified_bessel_k1_default_backend_functional op; |
| 21197 | op.meta(x); |
| 21198 | at::special_scaled_modified_bessel_k1_outf(x, *op.outputs_[0]); |
| 21199 | return std::move(op.outputs_[0]).take(); |
| 21200 | } |
| 21201 | struct structured_special_shifted_chebyshev_polynomial_t_default_backend_functional final : public at::meta::structured_special_shifted_chebyshev_polynomial_t { |
| 21202 | void set_output_strided( |
| 21203 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21204 | TensorOptions options, DimnameList names |
| 21205 | ) override { |
| 21206 | auto current_device = guard_.current_device(); |
| 21207 | if (C10_UNLIKELY(current_device.has_value())) { |
| 21208 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 21209 | "structured kernels don't support multi-device outputs"); |
| 21210 | } else { |
| 21211 | guard_.reset_device(options.device()); |
| 21212 | } |
| 21213 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21214 | if (!names.empty()) { |
| 21215 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21216 | } |
| 21217 | // super must happen after, so that downstream can use maybe_get_output |
| 21218 | // to retrieve the output |
| 21219 | at::meta::structured_special_shifted_chebyshev_polynomial_t::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21220 | } |
| 21221 | void set_output_raw_strided( |
| 21222 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21223 | TensorOptions options, DimnameList names |
| 21224 | ) override { |
| 21225 | auto current_device = guard_.current_device(); |
| 21226 | if (C10_UNLIKELY(current_device.has_value())) { |
| 21227 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 21228 | "structured kernels don't support multi-device outputs"); |
| 21229 | } else { |
| 21230 | guard_.reset_device(options.device()); |
| 21231 | } |
| 21232 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21233 | if (!names.empty()) { |
| 21234 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21235 | } |
| 21236 | // super must happen after, so that downstream can use maybe_get_output |
| 21237 | // to retrieve the output |
| 21238 | at::meta::structured_special_shifted_chebyshev_polynomial_t::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21239 | } |
| 21240 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21241 | return *outputs_[output_idx]; |
| 21242 | } |
| 21243 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 21244 | c10::OptionalDeviceGuard guard_; |
| 21245 | }; |
| 21246 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_shifted_chebyshev_polynomial_t(const at::Tensor & x, const at::Tensor & n) { |
| 21247 | structured_special_shifted_chebyshev_polynomial_t_default_backend_functional op; |
| 21248 | op.meta(x, n); |
| 21249 | at::special_shifted_chebyshev_polynomial_t_outf(x, n, *op.outputs_[0]); |
| 21250 | return std::move(op.outputs_[0]).take(); |
| 21251 | } |
| 21252 | struct structured_special_shifted_chebyshev_polynomial_u_default_backend_functional final : public at::meta::structured_special_shifted_chebyshev_polynomial_u { |
| 21253 | void set_output_strided( |
| 21254 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21255 | TensorOptions options, DimnameList names |
| 21256 | ) override { |
| 21257 | auto current_device = guard_.current_device(); |
| 21258 | if (C10_UNLIKELY(current_device.has_value())) { |
| 21259 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 21260 | "structured kernels don't support multi-device outputs"); |
| 21261 | } else { |
| 21262 | guard_.reset_device(options.device()); |
| 21263 | } |
| 21264 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21265 | if (!names.empty()) { |
| 21266 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21267 | } |
| 21268 | // super must happen after, so that downstream can use maybe_get_output |
| 21269 | // to retrieve the output |
| 21270 | at::meta::structured_special_shifted_chebyshev_polynomial_u::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21271 | } |
| 21272 | void set_output_raw_strided( |
| 21273 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21274 | TensorOptions options, DimnameList names |
| 21275 | ) override { |
| 21276 | auto current_device = guard_.current_device(); |
| 21277 | if (C10_UNLIKELY(current_device.has_value())) { |
| 21278 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 21279 | "structured kernels don't support multi-device outputs"); |
| 21280 | } else { |
| 21281 | guard_.reset_device(options.device()); |
| 21282 | } |
| 21283 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21284 | if (!names.empty()) { |
| 21285 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21286 | } |
| 21287 | // super must happen after, so that downstream can use maybe_get_output |
| 21288 | // to retrieve the output |
| 21289 | at::meta::structured_special_shifted_chebyshev_polynomial_u::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21290 | } |
| 21291 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21292 | return *outputs_[output_idx]; |
| 21293 | } |
| 21294 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 21295 | c10::OptionalDeviceGuard guard_; |
| 21296 | }; |
| 21297 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_shifted_chebyshev_polynomial_u(const at::Tensor & x, const at::Tensor & n) { |
| 21298 | structured_special_shifted_chebyshev_polynomial_u_default_backend_functional op; |
| 21299 | op.meta(x, n); |
| 21300 | at::special_shifted_chebyshev_polynomial_u_outf(x, n, *op.outputs_[0]); |
| 21301 | return std::move(op.outputs_[0]).take(); |
| 21302 | } |
| 21303 | struct structured_special_shifted_chebyshev_polynomial_v_default_backend_functional final : public at::meta::structured_special_shifted_chebyshev_polynomial_v { |
| 21304 | void set_output_strided( |
| 21305 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21306 | TensorOptions options, DimnameList names |
| 21307 | ) override { |
| 21308 | auto current_device = guard_.current_device(); |
| 21309 | if (C10_UNLIKELY(current_device.has_value())) { |
| 21310 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 21311 | "structured kernels don't support multi-device outputs"); |
| 21312 | } else { |
| 21313 | guard_.reset_device(options.device()); |
| 21314 | } |
| 21315 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21316 | if (!names.empty()) { |
| 21317 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21318 | } |
| 21319 | // super must happen after, so that downstream can use maybe_get_output |
| 21320 | // to retrieve the output |
| 21321 | at::meta::structured_special_shifted_chebyshev_polynomial_v::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21322 | } |
| 21323 | void set_output_raw_strided( |
| 21324 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21325 | TensorOptions options, DimnameList names |
| 21326 | ) override { |
| 21327 | auto current_device = guard_.current_device(); |
| 21328 | if (C10_UNLIKELY(current_device.has_value())) { |
| 21329 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 21330 | "structured kernels don't support multi-device outputs"); |
| 21331 | } else { |
| 21332 | guard_.reset_device(options.device()); |
| 21333 | } |
| 21334 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21335 | if (!names.empty()) { |
| 21336 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21337 | } |
| 21338 | // super must happen after, so that downstream can use maybe_get_output |
| 21339 | // to retrieve the output |
| 21340 | at::meta::structured_special_shifted_chebyshev_polynomial_v::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21341 | } |
| 21342 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21343 | return *outputs_[output_idx]; |
| 21344 | } |
| 21345 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 21346 | c10::OptionalDeviceGuard guard_; |
| 21347 | }; |
| 21348 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_shifted_chebyshev_polynomial_v(const at::Tensor & x, const at::Tensor & n) { |
| 21349 | structured_special_shifted_chebyshev_polynomial_v_default_backend_functional op; |
| 21350 | op.meta(x, n); |
| 21351 | at::special_shifted_chebyshev_polynomial_v_outf(x, n, *op.outputs_[0]); |
| 21352 | return std::move(op.outputs_[0]).take(); |
| 21353 | } |
| 21354 | struct structured_special_shifted_chebyshev_polynomial_w_default_backend_functional final : public at::meta::structured_special_shifted_chebyshev_polynomial_w { |
| 21355 | void set_output_strided( |
| 21356 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21357 | TensorOptions options, DimnameList names |
| 21358 | ) override { |
| 21359 | auto current_device = guard_.current_device(); |
| 21360 | if (C10_UNLIKELY(current_device.has_value())) { |
| 21361 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 21362 | "structured kernels don't support multi-device outputs"); |
| 21363 | } else { |
| 21364 | guard_.reset_device(options.device()); |
| 21365 | } |
| 21366 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21367 | if (!names.empty()) { |
| 21368 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21369 | } |
| 21370 | // super must happen after, so that downstream can use maybe_get_output |
| 21371 | // to retrieve the output |
| 21372 | at::meta::structured_special_shifted_chebyshev_polynomial_w::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21373 | } |
| 21374 | void set_output_raw_strided( |
| 21375 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21376 | TensorOptions options, DimnameList names |
| 21377 | ) override { |
| 21378 | auto current_device = guard_.current_device(); |
| 21379 | if (C10_UNLIKELY(current_device.has_value())) { |
| 21380 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 21381 | "structured kernels don't support multi-device outputs"); |
| 21382 | } else { |
| 21383 | guard_.reset_device(options.device()); |
| 21384 | } |
| 21385 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21386 | if (!names.empty()) { |
| 21387 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21388 | } |
| 21389 | // super must happen after, so that downstream can use maybe_get_output |
| 21390 | // to retrieve the output |
| 21391 | at::meta::structured_special_shifted_chebyshev_polynomial_w::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21392 | } |
| 21393 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21394 | return *outputs_[output_idx]; |
| 21395 | } |
| 21396 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 21397 | c10::OptionalDeviceGuard guard_; |
| 21398 | }; |
| 21399 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_shifted_chebyshev_polynomial_w(const at::Tensor & x, const at::Tensor & n) { |
| 21400 | structured_special_shifted_chebyshev_polynomial_w_default_backend_functional op; |
| 21401 | op.meta(x, n); |
| 21402 | at::special_shifted_chebyshev_polynomial_w_outf(x, n, *op.outputs_[0]); |
| 21403 | return std::move(op.outputs_[0]).take(); |
| 21404 | } |
| 21405 | struct structured_special_spherical_bessel_j0_default_backend_functional final : public at::meta::structured_special_spherical_bessel_j0 { |
| 21406 | void set_output_strided( |
| 21407 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21408 | TensorOptions options, DimnameList names |
| 21409 | ) override { |
| 21410 | auto current_device = guard_.current_device(); |
| 21411 | if (C10_UNLIKELY(current_device.has_value())) { |
| 21412 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 21413 | "structured kernels don't support multi-device outputs"); |
| 21414 | } else { |
| 21415 | guard_.reset_device(options.device()); |
| 21416 | } |
| 21417 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21418 | if (!names.empty()) { |
| 21419 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21420 | } |
| 21421 | // super must happen after, so that downstream can use maybe_get_output |
| 21422 | // to retrieve the output |
| 21423 | at::meta::structured_special_spherical_bessel_j0::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21424 | } |
| 21425 | void set_output_raw_strided( |
| 21426 | int64_t output_idx, IntArrayRef sizes, IntArrayRef strides, |
| 21427 | TensorOptions options, DimnameList names |
| 21428 | ) override { |
| 21429 | auto current_device = guard_.current_device(); |
| 21430 | if (C10_UNLIKELY(current_device.has_value())) { |
| 21431 | TORCH_INTERNAL_ASSERT(*current_device == options.device(), |
| 21432 | "structured kernels don't support multi-device outputs"); |
| 21433 | } else { |
| 21434 | guard_.reset_device(options.device()); |
| 21435 | } |
| 21436 | outputs_[output_idx] = create_out(sizes, strides, options); |
| 21437 | if (!names.empty()) { |
| 21438 | namedinference::propagate_names(*outputs_[output_idx], names); |
| 21439 | } |
| 21440 | // super must happen after, so that downstream can use maybe_get_output |
| 21441 | // to retrieve the output |
| 21442 | at::meta::structured_special_spherical_bessel_j0::set_output_raw_strided(output_idx, sizes, strides, options, names); |
| 21443 | } |
| 21444 | const Tensor& maybe_get_output(int64_t output_idx) override { |
| 21445 | return *outputs_[output_idx]; |
| 21446 | } |
| 21447 | std::array<c10::ExclusivelyOwned<Tensor>, 1> outputs_; |
| 21448 | c10::OptionalDeviceGuard guard_; |
| 21449 | }; |
| 21450 | at::Tensor wrapper_CompositeExplicitAutogradNonFunctional_special_spherical_bessel_j0(const at::Tensor & x) { |
| 21451 | structured_special_spherical_bessel_j0_default_backend_functional op; |
| 21452 | op.meta(x); |
| 21453 | at::special_spherical_bessel_j0_outf(x, *op.outputs_[0]); |
| 21454 | return std::move(op.outputs_[0]).take(); |
| 21455 | } |
| 21456 | TORCH_LIBRARY_IMPL(aten, CompositeExplicitAutogradNonFunctional, m) { |
| 21457 | m.impl("sgn", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_sgn)); |
| 21458 | m.impl("sgn_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_sgn_)); |
| 21459 | m.impl("acos", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_acos)); |
| 21460 | m.impl("acos_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_acos_)); |
| 21461 | m.impl("add.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_add_Tensor)); |
| 21462 | m.impl("add_.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_add__Tensor)); |
| 21463 | m.impl("addmv", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_addmv)); |
| 21464 | m.impl("addmv_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_addmv_)); |
| 21465 | m.impl("all.dim", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_all_dim)); |
| 21466 | m.impl("any.dim", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_any_dim)); |
| 21467 | m.impl("argmax", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_argmax)); |
| 21468 | m.impl("argmin", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_argmin)); |
| 21469 | m.impl("acosh", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_acosh)); |
| 21470 | m.impl("acosh_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_acosh_)); |
| 21471 | m.impl("asinh", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_asinh)); |
| 21472 | m.impl("asinh_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_asinh_)); |
| 21473 | m.impl("atanh", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_atanh)); |
| 21474 | m.impl("atanh_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_atanh_)); |
| 21475 | m.impl("as_strided_", |
| 21476 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__as_strided_)); |
| 21477 | m.impl("asin", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_asin)); |
| 21478 | m.impl("asin_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_asin_)); |
| 21479 | m.impl("atan", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_atan)); |
| 21480 | m.impl("atan_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_atan_)); |
| 21481 | m.impl("baddbmm", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_baddbmm)); |
| 21482 | m.impl("baddbmm_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_baddbmm_)); |
| 21483 | m.impl("bernoulli.p", |
| 21484 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_p_bernoulli)); |
| 21485 | m.impl("bitwise_not", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_bitwise_not)); |
| 21486 | m.impl("bitwise_not_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_bitwise_not_)); |
| 21487 | m.impl("copysign.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_copysign_Tensor)); |
| 21488 | m.impl("copysign_.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_copysign__Tensor)); |
| 21489 | m.impl("bmm", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_bmm)); |
| 21490 | m.impl("cat", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_cat)); |
| 21491 | m.impl("ceil", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_ceil)); |
| 21492 | m.impl("ceil_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_ceil_)); |
| 21493 | m.impl("clamp", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_clamp)); |
| 21494 | m.impl("clamp_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_clamp_)); |
| 21495 | m.impl("clamp.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_clamp_Tensor)); |
| 21496 | m.impl("clamp_.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_clamp__Tensor)); |
| 21497 | m.impl("clamp_max", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_clamp_max)); |
| 21498 | m.impl("clamp_max_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_clamp_max_)); |
| 21499 | m.impl("clamp_max.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_clamp_max_Tensor)); |
| 21500 | m.impl("clamp_max_.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_clamp_max__Tensor)); |
| 21501 | m.impl("clamp_min", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_clamp_min)); |
| 21502 | m.impl("clamp_min_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_clamp_min_)); |
| 21503 | m.impl("clamp_min.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_clamp_min_Tensor)); |
| 21504 | m.impl("clamp_min_.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_clamp_min__Tensor)); |
| 21505 | m.impl("copy", |
| 21506 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__copy)); |
| 21507 | m.impl("cos", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_cos)); |
| 21508 | m.impl("cos_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_cos_)); |
| 21509 | m.impl("cosh", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_cosh)); |
| 21510 | m.impl("cosh_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_cosh_)); |
| 21511 | m.impl("cumprod", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_cumprod)); |
| 21512 | m.impl("cumprod_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_cumprod_)); |
| 21513 | m.impl("cumsum", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_cumsum)); |
| 21514 | m.impl("cumsum_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_cumsum_)); |
| 21515 | m.impl("diag_embed", |
| 21516 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__diag_embed)); |
| 21517 | m.impl("div.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_div_Tensor)); |
| 21518 | m.impl("div_.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_div__Tensor)); |
| 21519 | m.impl("div.Tensor_mode", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_div_Tensor_mode)); |
| 21520 | m.impl("div_.Tensor_mode", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_div__Tensor_mode)); |
| 21521 | m.impl("new_empty_strided", |
| 21522 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__new_empty_strided)); |
| 21523 | m.impl("erf", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_erf)); |
| 21524 | m.impl("erf_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_erf_)); |
| 21525 | m.impl("erfc", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_erfc)); |
| 21526 | m.impl("erfc_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_erfc_)); |
| 21527 | m.impl("exp", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_exp)); |
| 21528 | m.impl("exp_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_exp_)); |
| 21529 | m.impl("exp2", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_exp2)); |
| 21530 | m.impl("exp2_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_exp2_)); |
| 21531 | m.impl("expm1", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_expm1)); |
| 21532 | m.impl("expm1_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_expm1_)); |
| 21533 | m.impl("floor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_floor)); |
| 21534 | m.impl("floor_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_floor_)); |
| 21535 | m.impl("frac", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_frac)); |
| 21536 | m.impl("frac_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_frac_)); |
| 21537 | m.impl("gcd", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_gcd)); |
| 21538 | m.impl("gcd_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_gcd_)); |
| 21539 | m.impl("lcm", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_lcm)); |
| 21540 | m.impl("lcm_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_lcm_)); |
| 21541 | m.impl("index.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_index_Tensor)); |
| 21542 | m.impl("index_copy", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_index_copy)); |
| 21543 | m.impl("index_copy_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_index_copy_)); |
| 21544 | m.impl("isin.Tensor_Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_isin_Tensor_Tensor)); |
| 21545 | m.impl("isin.Tensor_Scalar", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_isin_Tensor_Scalar)); |
| 21546 | m.impl("isin.Scalar_Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_isin_Scalar_Tensor)); |
| 21547 | m.impl("log", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_log)); |
| 21548 | m.impl("log_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_log_)); |
| 21549 | m.impl("log10", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_log10)); |
| 21550 | m.impl("log10_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_log10_)); |
| 21551 | m.impl("log1p", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_log1p)); |
| 21552 | m.impl("log1p_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_log1p_)); |
| 21553 | m.impl("log2", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_log2)); |
| 21554 | m.impl("log2_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_log2_)); |
| 21555 | m.impl("logaddexp", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_logaddexp)); |
| 21556 | m.impl("logaddexp2", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_logaddexp2)); |
| 21557 | m.impl("xlogy.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_xlogy_Tensor)); |
| 21558 | m.impl("xlogy_.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_xlogy__Tensor)); |
| 21559 | m.impl("_log_softmax", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__log_softmax)); |
| 21560 | m.impl("_log_softmax_backward_data", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__log_softmax_backward_data)); |
| 21561 | m.impl("logsumexp.out", |
| 21562 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_out_logsumexp_out)); |
| 21563 | m.impl("aminmax", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_aminmax)); |
| 21564 | m.impl("max.dim", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_max_dim)); |
| 21565 | m.impl("amax", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_amax)); |
| 21566 | m.impl("mean.dim", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_mean_dim)); |
| 21567 | m.impl("min.dim", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_min_dim)); |
| 21568 | m.impl("amin", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_amin)); |
| 21569 | m.impl("mm", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_mm)); |
| 21570 | m.impl("mul.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_mul_Tensor)); |
| 21571 | m.impl("mul_.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_mul__Tensor)); |
| 21572 | m.impl("narrow_copy", |
| 21573 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__narrow_copy)); |
| 21574 | m.impl("pixel_shuffle", |
| 21575 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__pixel_shuffle)); |
| 21576 | m.impl("pixel_unshuffle", |
| 21577 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__pixel_unshuffle)); |
| 21578 | m.impl("reciprocal", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_reciprocal)); |
| 21579 | m.impl("reciprocal_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_reciprocal_)); |
| 21580 | m.impl("neg", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_neg)); |
| 21581 | m.impl("neg_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_neg_)); |
| 21582 | m.impl("round", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_round)); |
| 21583 | m.impl("round_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_round_)); |
| 21584 | m.impl("round.decimals", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_round_decimals)); |
| 21585 | m.impl("round_.decimals", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_round__decimals)); |
| 21586 | m.impl("gelu", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_gelu)); |
| 21587 | m.impl("gelu_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_gelu_)); |
| 21588 | m.impl("gelu_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_gelu_backward)); |
| 21589 | m.impl("hardshrink", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_hardshrink)); |
| 21590 | m.impl("hardshrink_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_hardshrink_backward)); |
| 21591 | m.impl("rsqrt", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_rsqrt)); |
| 21592 | m.impl("rsqrt_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_rsqrt_)); |
| 21593 | m.impl("select_backward", |
| 21594 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__select_backward)); |
| 21595 | m.impl("silu", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_silu)); |
| 21596 | m.impl("silu_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_silu_)); |
| 21597 | m.impl("silu_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_silu_backward)); |
| 21598 | m.impl("mish", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_mish)); |
| 21599 | m.impl("mish_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_mish_)); |
| 21600 | m.impl("sigmoid", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_sigmoid)); |
| 21601 | m.impl("sigmoid_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_sigmoid_)); |
| 21602 | m.impl("sin", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_sin)); |
| 21603 | m.impl("sin_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_sin_)); |
| 21604 | m.impl("sinc", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_sinc)); |
| 21605 | m.impl("sinc_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_sinc_)); |
| 21606 | m.impl("sinh", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_sinh)); |
| 21607 | m.impl("sinh_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_sinh_)); |
| 21608 | m.impl("_softmax", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__softmax)); |
| 21609 | m.impl("_softmax_backward_data", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__softmax_backward_data)); |
| 21610 | m.impl("sum.dim_IntList", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_sum_dim_IntList)); |
| 21611 | m.impl("sqrt", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_sqrt)); |
| 21612 | m.impl("sqrt_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_sqrt_)); |
| 21613 | m.impl("prod.dim_int", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_prod_dim_int)); |
| 21614 | m.impl("tan", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_tan)); |
| 21615 | m.impl("tan_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_tan_)); |
| 21616 | m.impl("tanh", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_tanh)); |
| 21617 | m.impl("tanh_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_tanh_)); |
| 21618 | m.impl("threshold", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_threshold)); |
| 21619 | m.impl("threshold_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_threshold_)); |
| 21620 | m.impl("threshold_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_threshold_backward)); |
| 21621 | m.impl("_nested_view_from_buffer_copy", |
| 21622 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional___nested_view_from_buffer_copy)); |
| 21623 | m.impl("_trilinear", |
| 21624 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional___trilinear)); |
| 21625 | m.impl("trunc", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_trunc)); |
| 21626 | m.impl("trunc_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_trunc_)); |
| 21627 | m.impl("norm.ScalarOpt_dim_dtype", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_norm_ScalarOpt_dim_dtype)); |
| 21628 | m.impl("norm.ScalarOpt_dim", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_norm_ScalarOpt_dim)); |
| 21629 | m.impl("sub.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_sub_Tensor)); |
| 21630 | m.impl("sub_.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_sub__Tensor)); |
| 21631 | m.impl("heaviside", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_heaviside)); |
| 21632 | m.impl("heaviside_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_heaviside_)); |
| 21633 | m.impl("addmm", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_addmm)); |
| 21634 | m.impl("addmm_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_addmm_)); |
| 21635 | m.impl("_addmm_activation", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__addmm_activation)); |
| 21636 | m.impl("lift_fresh_copy", |
| 21637 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__lift_fresh_copy)); |
| 21638 | m.impl("index_add", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_index_add)); |
| 21639 | m.impl("index_add_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_index_add_)); |
| 21640 | m.impl("index_reduce", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_index_reduce)); |
| 21641 | m.impl("index_reduce_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_index_reduce_)); |
| 21642 | m.impl("scatter.src", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_scatter_src)); |
| 21643 | m.impl("scatter_.src", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_scatter__src)); |
| 21644 | m.impl("scatter.value", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_scatter_value)); |
| 21645 | m.impl("scatter_.value", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_scatter__value)); |
| 21646 | m.impl("scatter.reduce", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_scatter_reduce)); |
| 21647 | m.impl("scatter_.reduce", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_scatter__reduce)); |
| 21648 | m.impl("scatter.value_reduce", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_scatter_value_reduce)); |
| 21649 | m.impl("scatter_.value_reduce", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_scatter__value_reduce)); |
| 21650 | m.impl("scatter_add", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_scatter_add)); |
| 21651 | m.impl("scatter_add_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_scatter_add_)); |
| 21652 | m.impl("scatter_reduce.two", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_scatter_reduce_two)); |
| 21653 | m.impl("scatter_reduce_.two", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_scatter_reduce__two)); |
| 21654 | m.impl("eq.Scalar", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_eq_Scalar)); |
| 21655 | m.impl("eq_.Scalar", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_eq__Scalar)); |
| 21656 | m.impl("eq.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_eq_Tensor)); |
| 21657 | m.impl("eq_.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_eq__Tensor)); |
| 21658 | m.impl("bitwise_and.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_bitwise_and_Tensor)); |
| 21659 | m.impl("bitwise_and_.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_bitwise_and__Tensor)); |
| 21660 | m.impl("bitwise_or.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_bitwise_or_Tensor)); |
| 21661 | m.impl("bitwise_or_.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_bitwise_or__Tensor)); |
| 21662 | m.impl("bitwise_xor.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_bitwise_xor_Tensor)); |
| 21663 | m.impl("bitwise_xor_.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_bitwise_xor__Tensor)); |
| 21664 | m.impl("bitwise_left_shift.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_bitwise_left_shift_Tensor)); |
| 21665 | m.impl("bitwise_left_shift_.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_bitwise_left_shift__Tensor)); |
| 21666 | m.impl("bitwise_right_shift.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_bitwise_right_shift_Tensor)); |
| 21667 | m.impl("bitwise_right_shift_.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_bitwise_right_shift__Tensor)); |
| 21668 | m.impl("tril", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_tril)); |
| 21669 | m.impl("tril_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_tril_)); |
| 21670 | m.impl("triu", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_triu)); |
| 21671 | m.impl("triu_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_triu_)); |
| 21672 | m.impl("digamma", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_digamma)); |
| 21673 | m.impl("digamma_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_digamma_)); |
| 21674 | m.impl("lerp.Scalar", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_lerp_Scalar)); |
| 21675 | m.impl("lerp_.Scalar", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_lerp__Scalar)); |
| 21676 | m.impl("lerp.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_lerp_Tensor)); |
| 21677 | m.impl("lerp_.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_lerp__Tensor)); |
| 21678 | m.impl("ne.Scalar", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_ne_Scalar)); |
| 21679 | m.impl("ne_.Scalar", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_ne__Scalar)); |
| 21680 | m.impl("ne.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_ne_Tensor)); |
| 21681 | m.impl("ne_.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_ne__Tensor)); |
| 21682 | m.impl("ge.Scalar", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_ge_Scalar)); |
| 21683 | m.impl("ge_.Scalar", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_ge__Scalar)); |
| 21684 | m.impl("ge.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_ge_Tensor)); |
| 21685 | m.impl("ge_.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_ge__Tensor)); |
| 21686 | m.impl("le.Scalar", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_le_Scalar)); |
| 21687 | m.impl("le_.Scalar", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_le__Scalar)); |
| 21688 | m.impl("le.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_le_Tensor)); |
| 21689 | m.impl("le_.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_le__Tensor)); |
| 21690 | m.impl("gt.Scalar", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_gt_Scalar)); |
| 21691 | m.impl("gt_.Scalar", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_gt__Scalar)); |
| 21692 | m.impl("gt.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_gt_Tensor)); |
| 21693 | m.impl("gt_.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_gt__Tensor)); |
| 21694 | m.impl("lt.Scalar", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_lt_Scalar)); |
| 21695 | m.impl("lt_.Scalar", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_lt__Scalar)); |
| 21696 | m.impl("lt.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_lt_Tensor)); |
| 21697 | m.impl("lt_.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_lt__Tensor)); |
| 21698 | m.impl("gather", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_gather)); |
| 21699 | m.impl("addcmul", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_addcmul)); |
| 21700 | m.impl("addcmul_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_addcmul_)); |
| 21701 | m.impl("addcdiv", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_addcdiv)); |
| 21702 | m.impl("addcdiv_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_addcdiv_)); |
| 21703 | m.impl("triangular_solve", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_triangular_solve)); |
| 21704 | m.impl("lu_unpack", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_lu_unpack)); |
| 21705 | m.impl("lgamma", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_lgamma)); |
| 21706 | m.impl("lgamma_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_lgamma_)); |
| 21707 | m.impl("polygamma", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_polygamma)); |
| 21708 | m.impl("erfinv", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_erfinv)); |
| 21709 | m.impl("erfinv_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_erfinv_)); |
| 21710 | m.impl("i0", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_i0)); |
| 21711 | m.impl("i0_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_i0_)); |
| 21712 | m.impl("sign", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_sign)); |
| 21713 | m.impl("sign_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_sign_)); |
| 21714 | m.impl("signbit", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_signbit)); |
| 21715 | m.impl("atan2", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_atan2)); |
| 21716 | m.impl("atan2_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_atan2_)); |
| 21717 | m.impl("fmod.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_fmod_Tensor)); |
| 21718 | m.impl("fmod_.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_fmod__Tensor)); |
| 21719 | m.impl("hypot", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_hypot)); |
| 21720 | m.impl("hypot_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_hypot_)); |
| 21721 | m.impl("igamma", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_igamma)); |
| 21722 | m.impl("igamma_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_igamma_)); |
| 21723 | m.impl("igammac", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_igammac)); |
| 21724 | m.impl("igammac_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_igammac_)); |
| 21725 | m.impl("nextafter", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_nextafter)); |
| 21726 | m.impl("nextafter_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_nextafter_)); |
| 21727 | m.impl("remainder.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_remainder_Tensor)); |
| 21728 | m.impl("remainder_.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_remainder__Tensor)); |
| 21729 | m.impl("fmin", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_fmin)); |
| 21730 | m.impl("fmax", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_fmax)); |
| 21731 | m.impl("maximum", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_maximum)); |
| 21732 | m.impl("minimum", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_minimum)); |
| 21733 | m.impl("sort.stable", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_sort_stable)); |
| 21734 | m.impl("topk", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_topk)); |
| 21735 | m.impl("all", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_all)); |
| 21736 | m.impl("any", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_any)); |
| 21737 | m.impl("renorm", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_renorm)); |
| 21738 | m.impl("renorm_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_renorm_)); |
| 21739 | m.impl("pow.Tensor_Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_pow_Tensor_Tensor)); |
| 21740 | m.impl("pow_.Tensor", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_pow__Tensor)); |
| 21741 | m.impl("pow.Scalar", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_pow_Scalar)); |
| 21742 | m.impl("pow.Tensor_Scalar", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_pow_Tensor_Scalar)); |
| 21743 | m.impl("pow_.Scalar", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_pow__Scalar)); |
| 21744 | m.impl("_convert_indices_from_coo_to_csr", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__convert_indices_from_coo_to_csr)); |
| 21745 | m.impl("_convert_indices_from_csr_to_coo", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__convert_indices_from_csr_to_coo)); |
| 21746 | m.impl("mse_loss", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_mse_loss)); |
| 21747 | m.impl("nll_loss_forward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_nll_loss_forward)); |
| 21748 | m.impl("nll_loss_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_nll_loss_backward)); |
| 21749 | m.impl("smooth_l1_loss", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_smooth_l1_loss)); |
| 21750 | m.impl("elu", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_elu)); |
| 21751 | m.impl("elu_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_elu_)); |
| 21752 | m.impl("elu_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_elu_backward)); |
| 21753 | m.impl("glu", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_glu)); |
| 21754 | m.impl("hardsigmoid", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_hardsigmoid)); |
| 21755 | m.impl("hardsigmoid_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_hardsigmoid_)); |
| 21756 | m.impl("hardsigmoid_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_hardsigmoid_backward)); |
| 21757 | m.impl("leaky_relu", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_leaky_relu)); |
| 21758 | m.impl("leaky_relu_", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_leaky_relu_)); |
| 21759 | m.impl("leaky_relu_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_leaky_relu_backward)); |
| 21760 | m.impl("softplus", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_softplus)); |
| 21761 | m.impl("softplus_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_softplus_backward)); |
| 21762 | m.impl("softshrink", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_softshrink)); |
| 21763 | m.impl("softshrink_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_softshrink_backward)); |
| 21764 | m.impl("adaptive_max_pool2d", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_adaptive_max_pool2d)); |
| 21765 | m.impl("adaptive_max_pool2d_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_adaptive_max_pool2d_backward)); |
| 21766 | m.impl("adaptive_max_pool3d", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_adaptive_max_pool3d)); |
| 21767 | m.impl("adaptive_max_pool3d_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_adaptive_max_pool3d_backward)); |
| 21768 | m.impl("avg_pool2d", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_avg_pool2d)); |
| 21769 | m.impl("avg_pool2d_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_avg_pool2d_backward)); |
| 21770 | m.impl("avg_pool3d", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_avg_pool3d)); |
| 21771 | m.impl("avg_pool3d_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_avg_pool3d_backward)); |
| 21772 | m.impl("fractional_max_pool2d", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_fractional_max_pool2d)); |
| 21773 | m.impl("fractional_max_pool2d_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_fractional_max_pool2d_backward)); |
| 21774 | m.impl("fractional_max_pool3d", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_fractional_max_pool3d)); |
| 21775 | m.impl("max_pool2d_with_indices", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_max_pool2d_with_indices)); |
| 21776 | m.impl("max_pool2d_with_indices_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_max_pool2d_with_indices_backward)); |
| 21777 | m.impl("reflection_pad1d", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_reflection_pad1d)); |
| 21778 | m.impl("reflection_pad1d_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_reflection_pad1d_backward)); |
| 21779 | m.impl("reflection_pad3d", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_reflection_pad3d)); |
| 21780 | m.impl("reflection_pad3d_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_reflection_pad3d_backward)); |
| 21781 | m.impl("replication_pad1d", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_replication_pad1d)); |
| 21782 | m.impl("replication_pad1d_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_replication_pad1d_backward)); |
| 21783 | m.impl("replication_pad2d", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_replication_pad2d)); |
| 21784 | m.impl("replication_pad3d", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_replication_pad3d)); |
| 21785 | m.impl("upsample_linear1d", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_upsample_linear1d)); |
| 21786 | m.impl("upsample_linear1d_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_upsample_linear1d_backward)); |
| 21787 | m.impl("upsample_bilinear2d", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_upsample_bilinear2d)); |
| 21788 | m.impl("upsample_bilinear2d_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_upsample_bilinear2d_backward)); |
| 21789 | m.impl("_upsample_bilinear2d_aa", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__upsample_bilinear2d_aa)); |
| 21790 | m.impl("_upsample_bilinear2d_aa_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__upsample_bilinear2d_aa_backward)); |
| 21791 | m.impl("upsample_bicubic2d", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_upsample_bicubic2d)); |
| 21792 | m.impl("upsample_bicubic2d_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_upsample_bicubic2d_backward)); |
| 21793 | m.impl("_upsample_bicubic2d_aa", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__upsample_bicubic2d_aa)); |
| 21794 | m.impl("_upsample_bicubic2d_aa_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__upsample_bicubic2d_aa_backward)); |
| 21795 | m.impl("upsample_trilinear3d", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_upsample_trilinear3d)); |
| 21796 | m.impl("upsample_trilinear3d_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_upsample_trilinear3d_backward)); |
| 21797 | m.impl("upsample_nearest1d", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_upsample_nearest1d)); |
| 21798 | m.impl("_upsample_nearest_exact1d", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__upsample_nearest_exact1d)); |
| 21799 | m.impl("upsample_nearest1d_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_upsample_nearest1d_backward)); |
| 21800 | m.impl("_upsample_nearest_exact1d_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__upsample_nearest_exact1d_backward)); |
| 21801 | m.impl("upsample_nearest2d", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_upsample_nearest2d)); |
| 21802 | m.impl("_upsample_nearest_exact2d", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__upsample_nearest_exact2d)); |
| 21803 | m.impl("upsample_nearest2d_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_upsample_nearest2d_backward)); |
| 21804 | m.impl("_upsample_nearest_exact2d_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__upsample_nearest_exact2d_backward)); |
| 21805 | m.impl("upsample_nearest3d", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_upsample_nearest3d)); |
| 21806 | m.impl("_upsample_nearest_exact3d", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__upsample_nearest_exact3d)); |
| 21807 | m.impl("upsample_nearest3d_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_upsample_nearest3d_backward)); |
| 21808 | m.impl("_upsample_nearest_exact3d_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__upsample_nearest_exact3d_backward)); |
| 21809 | m.impl("sigmoid_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_sigmoid_backward)); |
| 21810 | m.impl("logit_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_logit_backward)); |
| 21811 | m.impl("tanh_backward", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_tanh_backward)); |
| 21812 | m.impl("slow_conv_transpose2d", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_slow_conv_transpose2d)); |
| 21813 | m.impl("isposinf", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_isposinf)); |
| 21814 | m.impl("isneginf", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_isneginf)); |
| 21815 | m.impl("special_entr", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_entr)); |
| 21816 | m.impl("special_ndtri", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_ndtri)); |
| 21817 | m.impl("special_log_ndtr", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_log_ndtr)); |
| 21818 | m.impl("special_erfcx", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_erfcx)); |
| 21819 | m.impl("special_xlog1py", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_xlog1py)); |
| 21820 | m.impl("special_zeta", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_zeta)); |
| 21821 | m.impl("special_i0e", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_i0e)); |
| 21822 | m.impl("special_i1", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_i1)); |
| 21823 | m.impl("special_i1e", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_i1e)); |
| 21824 | m.impl("linalg_cholesky_ex", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_linalg_cholesky_ex)); |
| 21825 | m.impl("linalg_cross", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_linalg_cross)); |
| 21826 | m.impl("linalg_lu_factor_ex", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_linalg_lu_factor_ex)); |
| 21827 | m.impl("linalg_lu", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_linalg_lu)); |
| 21828 | m.impl("linalg_lu_solve", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_linalg_lu_solve)); |
| 21829 | m.impl("_linalg_det", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__linalg_det)); |
| 21830 | m.impl("linalg_ldl_factor_ex", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_linalg_ldl_factor_ex)); |
| 21831 | m.impl("linalg_ldl_solve", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_linalg_ldl_solve)); |
| 21832 | m.impl("_linalg_slogdet", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__linalg_slogdet)); |
| 21833 | m.impl("_linalg_eigh", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__linalg_eigh)); |
| 21834 | m.impl("linalg_inv_ex", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_linalg_inv_ex)); |
| 21835 | m.impl("linalg_vector_norm", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_linalg_vector_norm)); |
| 21836 | m.impl("_linalg_svd", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__linalg_svd)); |
| 21837 | m.impl("linalg_pinv.atol_rtol_tensor", |
| 21838 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_atol_rtol_tensor_linalg_pinv)); |
| 21839 | m.impl("_linalg_solve_ex", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__linalg_solve_ex)); |
| 21840 | m.impl("linalg_qr", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_linalg_qr)); |
| 21841 | m.impl("_test_autograd_multiple_dispatch_view_copy", |
| 21842 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional___test_autograd_multiple_dispatch_view_copy)); |
| 21843 | m.impl("_fw_primal_copy", |
| 21844 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional___fw_primal_copy)); |
| 21845 | m.impl("_make_dual_copy", |
| 21846 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional___make_dual_copy)); |
| 21847 | m.impl("view_as_real_copy", |
| 21848 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__view_as_real_copy)); |
| 21849 | m.impl("view_as_complex_copy", |
| 21850 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__view_as_complex_copy)); |
| 21851 | m.impl("_conj_copy", |
| 21852 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional___conj_copy)); |
| 21853 | m.impl("_neg_view_copy", |
| 21854 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional___neg_view_copy)); |
| 21855 | m.impl("as_strided_copy", |
| 21856 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__as_strided_copy)); |
| 21857 | m.impl("_sparse_broadcast_to_copy", |
| 21858 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional___sparse_broadcast_to_copy)); |
| 21859 | m.impl("diagonal_copy", |
| 21860 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__diagonal_copy)); |
| 21861 | m.impl("expand_copy", |
| 21862 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__expand_copy)); |
| 21863 | m.impl("permute_copy", |
| 21864 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__permute_copy)); |
| 21865 | m.impl("_reshape_alias_copy", |
| 21866 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional___reshape_alias_copy)); |
| 21867 | m.impl("select_copy.int", |
| 21868 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_int_select_copy)); |
| 21869 | m.impl("detach_copy", |
| 21870 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__detach_copy)); |
| 21871 | m.impl("slice_copy.Tensor", |
| 21872 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_Tensor_slice_copy)); |
| 21873 | m.impl("split_copy.Tensor", |
| 21874 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_Tensor_split_copy)); |
| 21875 | m.impl("split_with_sizes_copy", |
| 21876 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__split_with_sizes_copy)); |
| 21877 | m.impl("squeeze_copy", |
| 21878 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__squeeze_copy)); |
| 21879 | m.impl("squeeze_copy.dim", |
| 21880 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_dim_squeeze_copy)); |
| 21881 | m.impl("squeeze_copy.dims", |
| 21882 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_dims_squeeze_copy)); |
| 21883 | m.impl("t_copy", |
| 21884 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__t_copy)); |
| 21885 | m.impl("transpose_copy.int", |
| 21886 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_int_transpose_copy)); |
| 21887 | m.impl("unsqueeze_copy", |
| 21888 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__unsqueeze_copy)); |
| 21889 | m.impl("_indices_copy", |
| 21890 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional___indices_copy)); |
| 21891 | m.impl("_values_copy", |
| 21892 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional___values_copy)); |
| 21893 | m.impl("indices_copy", |
| 21894 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__indices_copy)); |
| 21895 | m.impl("values_copy", |
| 21896 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__values_copy)); |
| 21897 | m.impl("crow_indices_copy", |
| 21898 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__crow_indices_copy)); |
| 21899 | m.impl("col_indices_copy", |
| 21900 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__col_indices_copy)); |
| 21901 | m.impl("ccol_indices_copy", |
| 21902 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__ccol_indices_copy)); |
| 21903 | m.impl("row_indices_copy", |
| 21904 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__row_indices_copy)); |
| 21905 | m.impl("unbind_copy.int", |
| 21906 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_int_unbind_copy)); |
| 21907 | m.impl("view_copy", |
| 21908 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__view_copy)); |
| 21909 | m.impl("view_copy.dtype", |
| 21910 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_dtype_view_copy)); |
| 21911 | m.impl("unfold_copy", |
| 21912 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__unfold_copy)); |
| 21913 | m.impl("alias_copy", |
| 21914 | TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional__alias_copy)); |
| 21915 | m.impl("special_airy_ai", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_airy_ai)); |
| 21916 | m.impl("special_bessel_j0", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_bessel_j0)); |
| 21917 | m.impl("special_bessel_j1", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_bessel_j1)); |
| 21918 | m.impl("special_bessel_y0", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_bessel_y0)); |
| 21919 | m.impl("special_bessel_y1", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_bessel_y1)); |
| 21920 | m.impl("special_chebyshev_polynomial_t", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_chebyshev_polynomial_t)); |
| 21921 | m.impl("special_chebyshev_polynomial_u", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_chebyshev_polynomial_u)); |
| 21922 | m.impl("special_chebyshev_polynomial_v", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_chebyshev_polynomial_v)); |
| 21923 | m.impl("special_chebyshev_polynomial_w", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_chebyshev_polynomial_w)); |
| 21924 | m.impl("special_hermite_polynomial_h", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_hermite_polynomial_h)); |
| 21925 | m.impl("special_hermite_polynomial_he", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_hermite_polynomial_he)); |
| 21926 | m.impl("special_laguerre_polynomial_l", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_laguerre_polynomial_l)); |
| 21927 | m.impl("special_legendre_polynomial_p", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_legendre_polynomial_p)); |
| 21928 | m.impl("special_modified_bessel_i0", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_modified_bessel_i0)); |
| 21929 | m.impl("special_modified_bessel_i1", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_modified_bessel_i1)); |
| 21930 | m.impl("special_modified_bessel_k0", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_modified_bessel_k0)); |
| 21931 | m.impl("special_modified_bessel_k1", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_modified_bessel_k1)); |
| 21932 | m.impl("special_scaled_modified_bessel_k0", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_scaled_modified_bessel_k0)); |
| 21933 | m.impl("special_scaled_modified_bessel_k1", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_scaled_modified_bessel_k1)); |
| 21934 | m.impl("special_shifted_chebyshev_polynomial_t", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_shifted_chebyshev_polynomial_t)); |
| 21935 | m.impl("special_shifted_chebyshev_polynomial_u", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_shifted_chebyshev_polynomial_u)); |
| 21936 | m.impl("special_shifted_chebyshev_polynomial_v", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_shifted_chebyshev_polynomial_v)); |
| 21937 | m.impl("special_shifted_chebyshev_polynomial_w", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_shifted_chebyshev_polynomial_w)); |
| 21938 | m.impl("special_spherical_bessel_j0", TORCH_FN(wrapper_CompositeExplicitAutogradNonFunctional_special_spherical_bessel_j0)); |
| 21939 | }; |
| 21940 | } // anonymous namespace |
| 21941 | namespace compositeexplicitautogradnonfunctional { |
| 21942 | at::Tensor sgn(const at::Tensor & self) { |
| 21943 | return wrapper_CompositeExplicitAutogradNonFunctional_sgn(self); |
| 21944 | } |
| 21945 | at::Tensor & sgn_(at::Tensor & self) { |
| 21946 | return wrapper_CompositeExplicitAutogradNonFunctional_sgn_(self); |
| 21947 | } |
| 21948 | at::Tensor acos(const at::Tensor & self) { |
| 21949 | return wrapper_CompositeExplicitAutogradNonFunctional_acos(self); |
| 21950 | } |
| 21951 | at::Tensor & acos_(at::Tensor & self) { |
| 21952 | return wrapper_CompositeExplicitAutogradNonFunctional_acos_(self); |
| 21953 | } |
| 21954 | at::Tensor add(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 21955 | return wrapper_CompositeExplicitAutogradNonFunctional_add_Tensor(self, other, alpha); |
| 21956 | } |
| 21957 | at::Tensor & add_(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 21958 | return wrapper_CompositeExplicitAutogradNonFunctional_add__Tensor(self, other, alpha); |
| 21959 | } |
| 21960 | at::Tensor addmv(const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha) { |
| 21961 | return wrapper_CompositeExplicitAutogradNonFunctional_addmv(self, mat, vec, beta, alpha); |
| 21962 | } |
| 21963 | at::Tensor & addmv_(at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha) { |
| 21964 | return wrapper_CompositeExplicitAutogradNonFunctional_addmv_(self, mat, vec, beta, alpha); |
| 21965 | } |
| 21966 | at::Tensor all(const at::Tensor & self, int64_t dim, bool keepdim) { |
| 21967 | return wrapper_CompositeExplicitAutogradNonFunctional_all_dim(self, dim, keepdim); |
| 21968 | } |
| 21969 | at::Tensor any(const at::Tensor & self, int64_t dim, bool keepdim) { |
| 21970 | return wrapper_CompositeExplicitAutogradNonFunctional_any_dim(self, dim, keepdim); |
| 21971 | } |
| 21972 | at::Tensor argmax(const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim) { |
| 21973 | return wrapper_CompositeExplicitAutogradNonFunctional_argmax(self, dim, keepdim); |
| 21974 | } |
| 21975 | at::Tensor argmin(const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim) { |
| 21976 | return wrapper_CompositeExplicitAutogradNonFunctional_argmin(self, dim, keepdim); |
| 21977 | } |
| 21978 | at::Tensor acosh(const at::Tensor & self) { |
| 21979 | return wrapper_CompositeExplicitAutogradNonFunctional_acosh(self); |
| 21980 | } |
| 21981 | at::Tensor & acosh_(at::Tensor & self) { |
| 21982 | return wrapper_CompositeExplicitAutogradNonFunctional_acosh_(self); |
| 21983 | } |
| 21984 | at::Tensor asinh(const at::Tensor & self) { |
| 21985 | return wrapper_CompositeExplicitAutogradNonFunctional_asinh(self); |
| 21986 | } |
| 21987 | at::Tensor & asinh_(at::Tensor & self) { |
| 21988 | return wrapper_CompositeExplicitAutogradNonFunctional_asinh_(self); |
| 21989 | } |
| 21990 | at::Tensor atanh(const at::Tensor & self) { |
| 21991 | return wrapper_CompositeExplicitAutogradNonFunctional_atanh(self); |
| 21992 | } |
| 21993 | at::Tensor & atanh_(at::Tensor & self) { |
| 21994 | return wrapper_CompositeExplicitAutogradNonFunctional_atanh_(self); |
| 21995 | } |
| 21996 | const at::Tensor & as_strided_(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, c10::optional<int64_t> storage_offset) { |
| 21997 | return wrapper_CompositeExplicitAutogradNonFunctional__as_strided_(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), storage_offset.has_value() ? c10::make_optional(c10::SymInt(*storage_offset)) : c10::nullopt); |
| 21998 | } |
| 21999 | const at::Tensor & as_strided__symint(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional<c10::SymInt> storage_offset) { |
| 22000 | return wrapper_CompositeExplicitAutogradNonFunctional__as_strided_(self, size, stride, storage_offset); |
| 22001 | } |
| 22002 | at::Tensor asin(const at::Tensor & self) { |
| 22003 | return wrapper_CompositeExplicitAutogradNonFunctional_asin(self); |
| 22004 | } |
| 22005 | at::Tensor & asin_(at::Tensor & self) { |
| 22006 | return wrapper_CompositeExplicitAutogradNonFunctional_asin_(self); |
| 22007 | } |
| 22008 | at::Tensor atan(const at::Tensor & self) { |
| 22009 | return wrapper_CompositeExplicitAutogradNonFunctional_atan(self); |
| 22010 | } |
| 22011 | at::Tensor & atan_(at::Tensor & self) { |
| 22012 | return wrapper_CompositeExplicitAutogradNonFunctional_atan_(self); |
| 22013 | } |
| 22014 | at::Tensor baddbmm(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 22015 | return wrapper_CompositeExplicitAutogradNonFunctional_baddbmm(self, batch1, batch2, beta, alpha); |
| 22016 | } |
| 22017 | at::Tensor & baddbmm_(at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 22018 | return wrapper_CompositeExplicitAutogradNonFunctional_baddbmm_(self, batch1, batch2, beta, alpha); |
| 22019 | } |
| 22020 | at::Tensor bernoulli(const at::Tensor & self, double p, c10::optional<at::Generator> generator) { |
| 22021 | return wrapper_CompositeExplicitAutogradNonFunctional_p_bernoulli(self, p, generator); |
| 22022 | } |
| 22023 | at::Tensor bitwise_not(const at::Tensor & self) { |
| 22024 | return wrapper_CompositeExplicitAutogradNonFunctional_bitwise_not(self); |
| 22025 | } |
| 22026 | at::Tensor & bitwise_not_(at::Tensor & self) { |
| 22027 | return wrapper_CompositeExplicitAutogradNonFunctional_bitwise_not_(self); |
| 22028 | } |
| 22029 | at::Tensor copysign(const at::Tensor & self, const at::Tensor & other) { |
| 22030 | return wrapper_CompositeExplicitAutogradNonFunctional_copysign_Tensor(self, other); |
| 22031 | } |
| 22032 | at::Tensor & copysign_(at::Tensor & self, const at::Tensor & other) { |
| 22033 | return wrapper_CompositeExplicitAutogradNonFunctional_copysign__Tensor(self, other); |
| 22034 | } |
| 22035 | at::Tensor bmm(const at::Tensor & self, const at::Tensor & mat2) { |
| 22036 | return wrapper_CompositeExplicitAutogradNonFunctional_bmm(self, mat2); |
| 22037 | } |
| 22038 | at::Tensor cat(const at::ITensorListRef & tensors, int64_t dim) { |
| 22039 | return wrapper_CompositeExplicitAutogradNonFunctional_cat(tensors, dim); |
| 22040 | } |
| 22041 | at::Tensor ceil(const at::Tensor & self) { |
| 22042 | return wrapper_CompositeExplicitAutogradNonFunctional_ceil(self); |
| 22043 | } |
| 22044 | at::Tensor & ceil_(at::Tensor & self) { |
| 22045 | return wrapper_CompositeExplicitAutogradNonFunctional_ceil_(self); |
| 22046 | } |
| 22047 | at::Tensor clamp(const at::Tensor & self, const c10::optional<at::Scalar> & min, const c10::optional<at::Scalar> & max) { |
| 22048 | return wrapper_CompositeExplicitAutogradNonFunctional_clamp(self, min, max); |
| 22049 | } |
| 22050 | at::Tensor & clamp_(at::Tensor & self, const c10::optional<at::Scalar> & min, const c10::optional<at::Scalar> & max) { |
| 22051 | return wrapper_CompositeExplicitAutogradNonFunctional_clamp_(self, min, max); |
| 22052 | } |
| 22053 | at::Tensor clamp(const at::Tensor & self, const c10::optional<at::Tensor> & min, const c10::optional<at::Tensor> & max) { |
| 22054 | return wrapper_CompositeExplicitAutogradNonFunctional_clamp_Tensor(self, min, max); |
| 22055 | } |
| 22056 | at::Tensor & clamp_(at::Tensor & self, const c10::optional<at::Tensor> & min, const c10::optional<at::Tensor> & max) { |
| 22057 | return wrapper_CompositeExplicitAutogradNonFunctional_clamp__Tensor(self, min, max); |
| 22058 | } |
| 22059 | at::Tensor clamp_max(const at::Tensor & self, const at::Scalar & max) { |
| 22060 | return wrapper_CompositeExplicitAutogradNonFunctional_clamp_max(self, max); |
| 22061 | } |
| 22062 | at::Tensor & clamp_max_(at::Tensor & self, const at::Scalar & max) { |
| 22063 | return wrapper_CompositeExplicitAutogradNonFunctional_clamp_max_(self, max); |
| 22064 | } |
| 22065 | at::Tensor clamp_max(const at::Tensor & self, const at::Tensor & max) { |
| 22066 | return wrapper_CompositeExplicitAutogradNonFunctional_clamp_max_Tensor(self, max); |
| 22067 | } |
| 22068 | at::Tensor & clamp_max_(at::Tensor & self, const at::Tensor & max) { |
| 22069 | return wrapper_CompositeExplicitAutogradNonFunctional_clamp_max__Tensor(self, max); |
| 22070 | } |
| 22071 | at::Tensor clamp_min(const at::Tensor & self, const at::Scalar & min) { |
| 22072 | return wrapper_CompositeExplicitAutogradNonFunctional_clamp_min(self, min); |
| 22073 | } |
| 22074 | at::Tensor & clamp_min_(at::Tensor & self, const at::Scalar & min) { |
| 22075 | return wrapper_CompositeExplicitAutogradNonFunctional_clamp_min_(self, min); |
| 22076 | } |
| 22077 | at::Tensor clamp_min(const at::Tensor & self, const at::Tensor & min) { |
| 22078 | return wrapper_CompositeExplicitAutogradNonFunctional_clamp_min_Tensor(self, min); |
| 22079 | } |
| 22080 | at::Tensor & clamp_min_(at::Tensor & self, const at::Tensor & min) { |
| 22081 | return wrapper_CompositeExplicitAutogradNonFunctional_clamp_min__Tensor(self, min); |
| 22082 | } |
| 22083 | at::Tensor copy(const at::Tensor & self, const at::Tensor & src, bool non_blocking) { |
| 22084 | return wrapper_CompositeExplicitAutogradNonFunctional__copy(self, src, non_blocking); |
| 22085 | } |
| 22086 | at::Tensor cos(const at::Tensor & self) { |
| 22087 | return wrapper_CompositeExplicitAutogradNonFunctional_cos(self); |
| 22088 | } |
| 22089 | at::Tensor & cos_(at::Tensor & self) { |
| 22090 | return wrapper_CompositeExplicitAutogradNonFunctional_cos_(self); |
| 22091 | } |
| 22092 | at::Tensor cosh(const at::Tensor & self) { |
| 22093 | return wrapper_CompositeExplicitAutogradNonFunctional_cosh(self); |
| 22094 | } |
| 22095 | at::Tensor & cosh_(at::Tensor & self) { |
| 22096 | return wrapper_CompositeExplicitAutogradNonFunctional_cosh_(self); |
| 22097 | } |
| 22098 | at::Tensor cumprod(const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 22099 | return wrapper_CompositeExplicitAutogradNonFunctional_cumprod(self, dim, dtype); |
| 22100 | } |
| 22101 | at::Tensor & cumprod_(at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 22102 | return wrapper_CompositeExplicitAutogradNonFunctional_cumprod_(self, dim, dtype); |
| 22103 | } |
| 22104 | at::Tensor cumsum(const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 22105 | return wrapper_CompositeExplicitAutogradNonFunctional_cumsum(self, dim, dtype); |
| 22106 | } |
| 22107 | at::Tensor & cumsum_(at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype) { |
| 22108 | return wrapper_CompositeExplicitAutogradNonFunctional_cumsum_(self, dim, dtype); |
| 22109 | } |
| 22110 | at::Tensor diag_embed(const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2) { |
| 22111 | return wrapper_CompositeExplicitAutogradNonFunctional__diag_embed(self, offset, dim1, dim2); |
| 22112 | } |
| 22113 | at::Tensor div(const at::Tensor & self, const at::Tensor & other) { |
| 22114 | return wrapper_CompositeExplicitAutogradNonFunctional_div_Tensor(self, other); |
| 22115 | } |
| 22116 | at::Tensor & div_(at::Tensor & self, const at::Tensor & other) { |
| 22117 | return wrapper_CompositeExplicitAutogradNonFunctional_div__Tensor(self, other); |
| 22118 | } |
| 22119 | at::Tensor div(const at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode) { |
| 22120 | return wrapper_CompositeExplicitAutogradNonFunctional_div_Tensor_mode(self, other, rounding_mode); |
| 22121 | } |
| 22122 | at::Tensor & div_(at::Tensor & self, const at::Tensor & other, c10::optional<c10::string_view> rounding_mode) { |
| 22123 | return wrapper_CompositeExplicitAutogradNonFunctional_div__Tensor_mode(self, other, rounding_mode); |
| 22124 | } |
| 22125 | at::Tensor new_empty_strided(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, at::TensorOptions options) { |
| 22126 | return wrapper_CompositeExplicitAutogradNonFunctional__new_empty_strided(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); |
| 22127 | } |
| 22128 | at::Tensor new_empty_strided(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 22129 | return wrapper_CompositeExplicitAutogradNonFunctional__new_empty_strided(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), dtype, layout, device, pin_memory); |
| 22130 | } |
| 22131 | at::Tensor new_empty_strided_symint(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::TensorOptions options) { |
| 22132 | return wrapper_CompositeExplicitAutogradNonFunctional__new_empty_strided(self, size, stride, optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); |
| 22133 | } |
| 22134 | at::Tensor new_empty_strided_symint(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) { |
| 22135 | return wrapper_CompositeExplicitAutogradNonFunctional__new_empty_strided(self, size, stride, dtype, layout, device, pin_memory); |
| 22136 | } |
| 22137 | at::Tensor erf(const at::Tensor & self) { |
| 22138 | return wrapper_CompositeExplicitAutogradNonFunctional_erf(self); |
| 22139 | } |
| 22140 | at::Tensor & erf_(at::Tensor & self) { |
| 22141 | return wrapper_CompositeExplicitAutogradNonFunctional_erf_(self); |
| 22142 | } |
| 22143 | at::Tensor erfc(const at::Tensor & self) { |
| 22144 | return wrapper_CompositeExplicitAutogradNonFunctional_erfc(self); |
| 22145 | } |
| 22146 | at::Tensor & erfc_(at::Tensor & self) { |
| 22147 | return wrapper_CompositeExplicitAutogradNonFunctional_erfc_(self); |
| 22148 | } |
| 22149 | at::Tensor exp(const at::Tensor & self) { |
| 22150 | return wrapper_CompositeExplicitAutogradNonFunctional_exp(self); |
| 22151 | } |
| 22152 | at::Tensor & exp_(at::Tensor & self) { |
| 22153 | return wrapper_CompositeExplicitAutogradNonFunctional_exp_(self); |
| 22154 | } |
| 22155 | at::Tensor exp2(const at::Tensor & self) { |
| 22156 | return wrapper_CompositeExplicitAutogradNonFunctional_exp2(self); |
| 22157 | } |
| 22158 | at::Tensor & exp2_(at::Tensor & self) { |
| 22159 | return wrapper_CompositeExplicitAutogradNonFunctional_exp2_(self); |
| 22160 | } |
| 22161 | at::Tensor expm1(const at::Tensor & self) { |
| 22162 | return wrapper_CompositeExplicitAutogradNonFunctional_expm1(self); |
| 22163 | } |
| 22164 | at::Tensor & expm1_(at::Tensor & self) { |
| 22165 | return wrapper_CompositeExplicitAutogradNonFunctional_expm1_(self); |
| 22166 | } |
| 22167 | at::Tensor floor(const at::Tensor & self) { |
| 22168 | return wrapper_CompositeExplicitAutogradNonFunctional_floor(self); |
| 22169 | } |
| 22170 | at::Tensor & floor_(at::Tensor & self) { |
| 22171 | return wrapper_CompositeExplicitAutogradNonFunctional_floor_(self); |
| 22172 | } |
| 22173 | at::Tensor frac(const at::Tensor & self) { |
| 22174 | return wrapper_CompositeExplicitAutogradNonFunctional_frac(self); |
| 22175 | } |
| 22176 | at::Tensor & frac_(at::Tensor & self) { |
| 22177 | return wrapper_CompositeExplicitAutogradNonFunctional_frac_(self); |
| 22178 | } |
| 22179 | at::Tensor gcd(const at::Tensor & self, const at::Tensor & other) { |
| 22180 | return wrapper_CompositeExplicitAutogradNonFunctional_gcd(self, other); |
| 22181 | } |
| 22182 | at::Tensor & gcd_(at::Tensor & self, const at::Tensor & other) { |
| 22183 | return wrapper_CompositeExplicitAutogradNonFunctional_gcd_(self, other); |
| 22184 | } |
| 22185 | at::Tensor lcm(const at::Tensor & self, const at::Tensor & other) { |
| 22186 | return wrapper_CompositeExplicitAutogradNonFunctional_lcm(self, other); |
| 22187 | } |
| 22188 | at::Tensor & lcm_(at::Tensor & self, const at::Tensor & other) { |
| 22189 | return wrapper_CompositeExplicitAutogradNonFunctional_lcm_(self, other); |
| 22190 | } |
| 22191 | at::Tensor index(const at::Tensor & self, const c10::List<c10::optional<at::Tensor>> & indices) { |
| 22192 | return wrapper_CompositeExplicitAutogradNonFunctional_index_Tensor(self, indices); |
| 22193 | } |
| 22194 | at::Tensor index_copy(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source) { |
| 22195 | return wrapper_CompositeExplicitAutogradNonFunctional_index_copy(self, dim, index, source); |
| 22196 | } |
| 22197 | at::Tensor & index_copy_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source) { |
| 22198 | return wrapper_CompositeExplicitAutogradNonFunctional_index_copy_(self, dim, index, source); |
| 22199 | } |
| 22200 | at::Tensor isin(const at::Tensor & elements, const at::Tensor & test_elements, bool assume_unique, bool invert) { |
| 22201 | return wrapper_CompositeExplicitAutogradNonFunctional_isin_Tensor_Tensor(elements, test_elements, assume_unique, invert); |
| 22202 | } |
| 22203 | at::Tensor isin(const at::Tensor & elements, const at::Scalar & test_element, bool assume_unique, bool invert) { |
| 22204 | return wrapper_CompositeExplicitAutogradNonFunctional_isin_Tensor_Scalar(elements, test_element, assume_unique, invert); |
| 22205 | } |
| 22206 | at::Tensor isin(const at::Scalar & element, const at::Tensor & test_elements, bool assume_unique, bool invert) { |
| 22207 | return wrapper_CompositeExplicitAutogradNonFunctional_isin_Scalar_Tensor(element, test_elements, assume_unique, invert); |
| 22208 | } |
| 22209 | at::Tensor log(const at::Tensor & self) { |
| 22210 | return wrapper_CompositeExplicitAutogradNonFunctional_log(self); |
| 22211 | } |
| 22212 | at::Tensor & log_(at::Tensor & self) { |
| 22213 | return wrapper_CompositeExplicitAutogradNonFunctional_log_(self); |
| 22214 | } |
| 22215 | at::Tensor log10(const at::Tensor & self) { |
| 22216 | return wrapper_CompositeExplicitAutogradNonFunctional_log10(self); |
| 22217 | } |
| 22218 | at::Tensor & log10_(at::Tensor & self) { |
| 22219 | return wrapper_CompositeExplicitAutogradNonFunctional_log10_(self); |
| 22220 | } |
| 22221 | at::Tensor log1p(const at::Tensor & self) { |
| 22222 | return wrapper_CompositeExplicitAutogradNonFunctional_log1p(self); |
| 22223 | } |
| 22224 | at::Tensor & log1p_(at::Tensor & self) { |
| 22225 | return wrapper_CompositeExplicitAutogradNonFunctional_log1p_(self); |
| 22226 | } |
| 22227 | at::Tensor log2(const at::Tensor & self) { |
| 22228 | return wrapper_CompositeExplicitAutogradNonFunctional_log2(self); |
| 22229 | } |
| 22230 | at::Tensor & log2_(at::Tensor & self) { |
| 22231 | return wrapper_CompositeExplicitAutogradNonFunctional_log2_(self); |
| 22232 | } |
| 22233 | at::Tensor logaddexp(const at::Tensor & self, const at::Tensor & other) { |
| 22234 | return wrapper_CompositeExplicitAutogradNonFunctional_logaddexp(self, other); |
| 22235 | } |
| 22236 | at::Tensor logaddexp2(const at::Tensor & self, const at::Tensor & other) { |
| 22237 | return wrapper_CompositeExplicitAutogradNonFunctional_logaddexp2(self, other); |
| 22238 | } |
| 22239 | at::Tensor xlogy(const at::Tensor & self, const at::Tensor & other) { |
| 22240 | return wrapper_CompositeExplicitAutogradNonFunctional_xlogy_Tensor(self, other); |
| 22241 | } |
| 22242 | at::Tensor & xlogy_(at::Tensor & self, const at::Tensor & other) { |
| 22243 | return wrapper_CompositeExplicitAutogradNonFunctional_xlogy__Tensor(self, other); |
| 22244 | } |
| 22245 | at::Tensor _log_softmax(const at::Tensor & self, int64_t dim, bool half_to_float) { |
| 22246 | return wrapper_CompositeExplicitAutogradNonFunctional__log_softmax(self, dim, half_to_float); |
| 22247 | } |
| 22248 | at::Tensor _log_softmax_backward_data(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) { |
| 22249 | return wrapper_CompositeExplicitAutogradNonFunctional__log_softmax_backward_data(grad_output, output, dim, input_dtype); |
| 22250 | } |
| 22251 | at::Tensor & logsumexp_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, bool keepdim) { |
| 22252 | return wrapper_CompositeExplicitAutogradNonFunctional_out_logsumexp_out(self, dim, keepdim, out); |
| 22253 | } |
| 22254 | at::Tensor & logsumexp_outf(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { |
| 22255 | return wrapper_CompositeExplicitAutogradNonFunctional_out_logsumexp_out(self, dim, keepdim, out); |
| 22256 | } |
| 22257 | ::std::tuple<at::Tensor,at::Tensor> aminmax(const at::Tensor & self, c10::optional<int64_t> dim, bool keepdim) { |
| 22258 | return wrapper_CompositeExplicitAutogradNonFunctional_aminmax(self, dim, keepdim); |
| 22259 | } |
| 22260 | ::std::tuple<at::Tensor,at::Tensor> max(const at::Tensor & self, int64_t dim, bool keepdim) { |
| 22261 | return wrapper_CompositeExplicitAutogradNonFunctional_max_dim(self, dim, keepdim); |
| 22262 | } |
| 22263 | at::Tensor amax(const at::Tensor & self, at::IntArrayRef dim, bool keepdim) { |
| 22264 | return wrapper_CompositeExplicitAutogradNonFunctional_amax(self, dim, keepdim); |
| 22265 | } |
| 22266 | at::Tensor mean(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 22267 | return wrapper_CompositeExplicitAutogradNonFunctional_mean_dim(self, dim, keepdim, dtype); |
| 22268 | } |
| 22269 | ::std::tuple<at::Tensor,at::Tensor> min(const at::Tensor & self, int64_t dim, bool keepdim) { |
| 22270 | return wrapper_CompositeExplicitAutogradNonFunctional_min_dim(self, dim, keepdim); |
| 22271 | } |
| 22272 | at::Tensor amin(const at::Tensor & self, at::IntArrayRef dim, bool keepdim) { |
| 22273 | return wrapper_CompositeExplicitAutogradNonFunctional_amin(self, dim, keepdim); |
| 22274 | } |
| 22275 | at::Tensor mm(const at::Tensor & self, const at::Tensor & mat2) { |
| 22276 | return wrapper_CompositeExplicitAutogradNonFunctional_mm(self, mat2); |
| 22277 | } |
| 22278 | at::Tensor mul(const at::Tensor & self, const at::Tensor & other) { |
| 22279 | return wrapper_CompositeExplicitAutogradNonFunctional_mul_Tensor(self, other); |
| 22280 | } |
| 22281 | at::Tensor & mul_(at::Tensor & self, const at::Tensor & other) { |
| 22282 | return wrapper_CompositeExplicitAutogradNonFunctional_mul__Tensor(self, other); |
| 22283 | } |
| 22284 | at::Tensor narrow_copy(const at::Tensor & self, int64_t dim, int64_t start, int64_t length) { |
| 22285 | return wrapper_CompositeExplicitAutogradNonFunctional__narrow_copy(self, dim, start, length); |
| 22286 | } |
| 22287 | at::Tensor narrow_copy_symint(const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length) { |
| 22288 | return wrapper_CompositeExplicitAutogradNonFunctional__narrow_copy(self, dim, start, length); |
| 22289 | } |
| 22290 | at::Tensor pixel_shuffle(const at::Tensor & self, int64_t upscale_factor) { |
| 22291 | return wrapper_CompositeExplicitAutogradNonFunctional__pixel_shuffle(self, upscale_factor); |
| 22292 | } |
| 22293 | at::Tensor pixel_unshuffle(const at::Tensor & self, int64_t downscale_factor) { |
| 22294 | return wrapper_CompositeExplicitAutogradNonFunctional__pixel_unshuffle(self, downscale_factor); |
| 22295 | } |
| 22296 | at::Tensor reciprocal(const at::Tensor & self) { |
| 22297 | return wrapper_CompositeExplicitAutogradNonFunctional_reciprocal(self); |
| 22298 | } |
| 22299 | at::Tensor & reciprocal_(at::Tensor & self) { |
| 22300 | return wrapper_CompositeExplicitAutogradNonFunctional_reciprocal_(self); |
| 22301 | } |
| 22302 | at::Tensor neg(const at::Tensor & self) { |
| 22303 | return wrapper_CompositeExplicitAutogradNonFunctional_neg(self); |
| 22304 | } |
| 22305 | at::Tensor & neg_(at::Tensor & self) { |
| 22306 | return wrapper_CompositeExplicitAutogradNonFunctional_neg_(self); |
| 22307 | } |
| 22308 | at::Tensor round(const at::Tensor & self) { |
| 22309 | return wrapper_CompositeExplicitAutogradNonFunctional_round(self); |
| 22310 | } |
| 22311 | at::Tensor & round_(at::Tensor & self) { |
| 22312 | return wrapper_CompositeExplicitAutogradNonFunctional_round_(self); |
| 22313 | } |
| 22314 | at::Tensor round(const at::Tensor & self, int64_t decimals) { |
| 22315 | return wrapper_CompositeExplicitAutogradNonFunctional_round_decimals(self, decimals); |
| 22316 | } |
| 22317 | at::Tensor & round_(at::Tensor & self, int64_t decimals) { |
| 22318 | return wrapper_CompositeExplicitAutogradNonFunctional_round__decimals(self, decimals); |
| 22319 | } |
| 22320 | at::Tensor gelu(const at::Tensor & self, c10::string_view approximate) { |
| 22321 | return wrapper_CompositeExplicitAutogradNonFunctional_gelu(self, approximate); |
| 22322 | } |
| 22323 | at::Tensor & gelu_(at::Tensor & self, c10::string_view approximate) { |
| 22324 | return wrapper_CompositeExplicitAutogradNonFunctional_gelu_(self, approximate); |
| 22325 | } |
| 22326 | at::Tensor gelu_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::string_view approximate) { |
| 22327 | return wrapper_CompositeExplicitAutogradNonFunctional_gelu_backward(grad_output, self, approximate); |
| 22328 | } |
| 22329 | at::Tensor hardshrink(const at::Tensor & self, const at::Scalar & lambd) { |
| 22330 | return wrapper_CompositeExplicitAutogradNonFunctional_hardshrink(self, lambd); |
| 22331 | } |
| 22332 | at::Tensor hardshrink_backward(const at::Tensor & grad_out, const at::Tensor & self, const at::Scalar & lambd) { |
| 22333 | return wrapper_CompositeExplicitAutogradNonFunctional_hardshrink_backward(grad_out, self, lambd); |
| 22334 | } |
| 22335 | at::Tensor rsqrt(const at::Tensor & self) { |
| 22336 | return wrapper_CompositeExplicitAutogradNonFunctional_rsqrt(self); |
| 22337 | } |
| 22338 | at::Tensor & rsqrt_(at::Tensor & self) { |
| 22339 | return wrapper_CompositeExplicitAutogradNonFunctional_rsqrt_(self); |
| 22340 | } |
| 22341 | at::Tensor select_backward(const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t index) { |
| 22342 | return wrapper_CompositeExplicitAutogradNonFunctional__select_backward(grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, index); |
| 22343 | } |
| 22344 | at::Tensor select_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt index) { |
| 22345 | return wrapper_CompositeExplicitAutogradNonFunctional__select_backward(grad_output, input_sizes, dim, index); |
| 22346 | } |
| 22347 | at::Tensor silu(const at::Tensor & self) { |
| 22348 | return wrapper_CompositeExplicitAutogradNonFunctional_silu(self); |
| 22349 | } |
| 22350 | at::Tensor & silu_(at::Tensor & self) { |
| 22351 | return wrapper_CompositeExplicitAutogradNonFunctional_silu_(self); |
| 22352 | } |
| 22353 | at::Tensor silu_backward(const at::Tensor & grad_output, const at::Tensor & self) { |
| 22354 | return wrapper_CompositeExplicitAutogradNonFunctional_silu_backward(grad_output, self); |
| 22355 | } |
| 22356 | at::Tensor mish(const at::Tensor & self) { |
| 22357 | return wrapper_CompositeExplicitAutogradNonFunctional_mish(self); |
| 22358 | } |
| 22359 | at::Tensor & mish_(at::Tensor & self) { |
| 22360 | return wrapper_CompositeExplicitAutogradNonFunctional_mish_(self); |
| 22361 | } |
| 22362 | at::Tensor sigmoid(const at::Tensor & self) { |
| 22363 | return wrapper_CompositeExplicitAutogradNonFunctional_sigmoid(self); |
| 22364 | } |
| 22365 | at::Tensor & sigmoid_(at::Tensor & self) { |
| 22366 | return wrapper_CompositeExplicitAutogradNonFunctional_sigmoid_(self); |
| 22367 | } |
| 22368 | at::Tensor sin(const at::Tensor & self) { |
| 22369 | return wrapper_CompositeExplicitAutogradNonFunctional_sin(self); |
| 22370 | } |
| 22371 | at::Tensor & sin_(at::Tensor & self) { |
| 22372 | return wrapper_CompositeExplicitAutogradNonFunctional_sin_(self); |
| 22373 | } |
| 22374 | at::Tensor sinc(const at::Tensor & self) { |
| 22375 | return wrapper_CompositeExplicitAutogradNonFunctional_sinc(self); |
| 22376 | } |
| 22377 | at::Tensor & sinc_(at::Tensor & self) { |
| 22378 | return wrapper_CompositeExplicitAutogradNonFunctional_sinc_(self); |
| 22379 | } |
| 22380 | at::Tensor sinh(const at::Tensor & self) { |
| 22381 | return wrapper_CompositeExplicitAutogradNonFunctional_sinh(self); |
| 22382 | } |
| 22383 | at::Tensor & sinh_(at::Tensor & self) { |
| 22384 | return wrapper_CompositeExplicitAutogradNonFunctional_sinh_(self); |
| 22385 | } |
| 22386 | at::Tensor _softmax(const at::Tensor & self, int64_t dim, bool half_to_float) { |
| 22387 | return wrapper_CompositeExplicitAutogradNonFunctional__softmax(self, dim, half_to_float); |
| 22388 | } |
| 22389 | at::Tensor _softmax_backward_data(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) { |
| 22390 | return wrapper_CompositeExplicitAutogradNonFunctional__softmax_backward_data(grad_output, output, dim, input_dtype); |
| 22391 | } |
| 22392 | at::Tensor sum(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 22393 | return wrapper_CompositeExplicitAutogradNonFunctional_sum_dim_IntList(self, dim, keepdim, dtype); |
| 22394 | } |
| 22395 | at::Tensor sqrt(const at::Tensor & self) { |
| 22396 | return wrapper_CompositeExplicitAutogradNonFunctional_sqrt(self); |
| 22397 | } |
| 22398 | at::Tensor & sqrt_(at::Tensor & self) { |
| 22399 | return wrapper_CompositeExplicitAutogradNonFunctional_sqrt_(self); |
| 22400 | } |
| 22401 | at::Tensor prod(const at::Tensor & self, int64_t dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 22402 | return wrapper_CompositeExplicitAutogradNonFunctional_prod_dim_int(self, dim, keepdim, dtype); |
| 22403 | } |
| 22404 | at::Tensor tan(const at::Tensor & self) { |
| 22405 | return wrapper_CompositeExplicitAutogradNonFunctional_tan(self); |
| 22406 | } |
| 22407 | at::Tensor & tan_(at::Tensor & self) { |
| 22408 | return wrapper_CompositeExplicitAutogradNonFunctional_tan_(self); |
| 22409 | } |
| 22410 | at::Tensor tanh(const at::Tensor & self) { |
| 22411 | return wrapper_CompositeExplicitAutogradNonFunctional_tanh(self); |
| 22412 | } |
| 22413 | at::Tensor & tanh_(at::Tensor & self) { |
| 22414 | return wrapper_CompositeExplicitAutogradNonFunctional_tanh_(self); |
| 22415 | } |
| 22416 | at::Tensor threshold(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value) { |
| 22417 | return wrapper_CompositeExplicitAutogradNonFunctional_threshold(self, threshold, value); |
| 22418 | } |
| 22419 | at::Tensor & threshold_(at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value) { |
| 22420 | return wrapper_CompositeExplicitAutogradNonFunctional_threshold_(self, threshold, value); |
| 22421 | } |
| 22422 | at::Tensor threshold_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold) { |
| 22423 | return wrapper_CompositeExplicitAutogradNonFunctional_threshold_backward(grad_output, self, threshold); |
| 22424 | } |
| 22425 | at::Tensor _nested_view_from_buffer_copy(const at::Tensor & self, const at::Tensor & nested_size, const at::Tensor & nested_strides, at::IntArrayRef offsets) { |
| 22426 | return wrapper_CompositeExplicitAutogradNonFunctional___nested_view_from_buffer_copy(self, nested_size, nested_strides, offsets); |
| 22427 | } |
| 22428 | at::Tensor _trilinear(const at::Tensor & i1, const at::Tensor & i2, const at::Tensor & i3, at::IntArrayRef expand1, at::IntArrayRef expand2, at::IntArrayRef expand3, at::IntArrayRef sumdim, int64_t unroll_dim) { |
| 22429 | return wrapper_CompositeExplicitAutogradNonFunctional___trilinear(i1, i2, i3, expand1, expand2, expand3, sumdim, unroll_dim); |
| 22430 | } |
| 22431 | at::Tensor trunc(const at::Tensor & self) { |
| 22432 | return wrapper_CompositeExplicitAutogradNonFunctional_trunc(self); |
| 22433 | } |
| 22434 | at::Tensor & trunc_(at::Tensor & self) { |
| 22435 | return wrapper_CompositeExplicitAutogradNonFunctional_trunc_(self); |
| 22436 | } |
| 22437 | at::Tensor norm(const at::Tensor & self, const c10::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim, at::ScalarType dtype) { |
| 22438 | return wrapper_CompositeExplicitAutogradNonFunctional_norm_ScalarOpt_dim_dtype(self, p, dim, keepdim, dtype); |
| 22439 | } |
| 22440 | at::Tensor norm(const at::Tensor & self, const c10::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim) { |
| 22441 | return wrapper_CompositeExplicitAutogradNonFunctional_norm_ScalarOpt_dim(self, p, dim, keepdim); |
| 22442 | } |
| 22443 | at::Tensor sub(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 22444 | return wrapper_CompositeExplicitAutogradNonFunctional_sub_Tensor(self, other, alpha); |
| 22445 | } |
| 22446 | at::Tensor & sub_(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { |
| 22447 | return wrapper_CompositeExplicitAutogradNonFunctional_sub__Tensor(self, other, alpha); |
| 22448 | } |
| 22449 | at::Tensor heaviside(const at::Tensor & self, const at::Tensor & values) { |
| 22450 | return wrapper_CompositeExplicitAutogradNonFunctional_heaviside(self, values); |
| 22451 | } |
| 22452 | at::Tensor & heaviside_(at::Tensor & self, const at::Tensor & values) { |
| 22453 | return wrapper_CompositeExplicitAutogradNonFunctional_heaviside_(self, values); |
| 22454 | } |
| 22455 | at::Tensor addmm(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 22456 | return wrapper_CompositeExplicitAutogradNonFunctional_addmm(self, mat1, mat2, beta, alpha); |
| 22457 | } |
| 22458 | at::Tensor & addmm_(at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha) { |
| 22459 | return wrapper_CompositeExplicitAutogradNonFunctional_addmm_(self, mat1, mat2, beta, alpha); |
| 22460 | } |
| 22461 | at::Tensor _addmm_activation(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, bool use_gelu) { |
| 22462 | return wrapper_CompositeExplicitAutogradNonFunctional__addmm_activation(self, mat1, mat2, beta, alpha, use_gelu); |
| 22463 | } |
| 22464 | at::Tensor lift_fresh_copy(const at::Tensor & self) { |
| 22465 | return wrapper_CompositeExplicitAutogradNonFunctional__lift_fresh_copy(self); |
| 22466 | } |
| 22467 | at::Tensor index_add(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha) { |
| 22468 | return wrapper_CompositeExplicitAutogradNonFunctional_index_add(self, dim, index, source, alpha); |
| 22469 | } |
| 22470 | at::Tensor & index_add_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha) { |
| 22471 | return wrapper_CompositeExplicitAutogradNonFunctional_index_add_(self, dim, index, source, alpha); |
| 22472 | } |
| 22473 | at::Tensor index_reduce(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self) { |
| 22474 | return wrapper_CompositeExplicitAutogradNonFunctional_index_reduce(self, dim, index, source, reduce, include_self); |
| 22475 | } |
| 22476 | at::Tensor & index_reduce_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self) { |
| 22477 | return wrapper_CompositeExplicitAutogradNonFunctional_index_reduce_(self, dim, index, source, reduce, include_self); |
| 22478 | } |
| 22479 | at::Tensor scatter(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { |
| 22480 | return wrapper_CompositeExplicitAutogradNonFunctional_scatter_src(self, dim, index, src); |
| 22481 | } |
| 22482 | at::Tensor & scatter_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { |
| 22483 | return wrapper_CompositeExplicitAutogradNonFunctional_scatter__src(self, dim, index, src); |
| 22484 | } |
| 22485 | at::Tensor scatter(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) { |
| 22486 | return wrapper_CompositeExplicitAutogradNonFunctional_scatter_value(self, dim, index, value); |
| 22487 | } |
| 22488 | at::Tensor & scatter_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) { |
| 22489 | return wrapper_CompositeExplicitAutogradNonFunctional_scatter__value(self, dim, index, value); |
| 22490 | } |
| 22491 | at::Tensor scatter(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce) { |
| 22492 | return wrapper_CompositeExplicitAutogradNonFunctional_scatter_reduce(self, dim, index, src, reduce); |
| 22493 | } |
| 22494 | at::Tensor & scatter_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce) { |
| 22495 | return wrapper_CompositeExplicitAutogradNonFunctional_scatter__reduce(self, dim, index, src, reduce); |
| 22496 | } |
| 22497 | at::Tensor scatter(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce) { |
| 22498 | return wrapper_CompositeExplicitAutogradNonFunctional_scatter_value_reduce(self, dim, index, value, reduce); |
| 22499 | } |
| 22500 | at::Tensor & scatter_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce) { |
| 22501 | return wrapper_CompositeExplicitAutogradNonFunctional_scatter__value_reduce(self, dim, index, value, reduce); |
| 22502 | } |
| 22503 | at::Tensor scatter_add(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { |
| 22504 | return wrapper_CompositeExplicitAutogradNonFunctional_scatter_add(self, dim, index, src); |
| 22505 | } |
| 22506 | at::Tensor & scatter_add_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { |
| 22507 | return wrapper_CompositeExplicitAutogradNonFunctional_scatter_add_(self, dim, index, src); |
| 22508 | } |
| 22509 | at::Tensor scatter_reduce(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self) { |
| 22510 | return wrapper_CompositeExplicitAutogradNonFunctional_scatter_reduce_two(self, dim, index, src, reduce, include_self); |
| 22511 | } |
| 22512 | at::Tensor & scatter_reduce_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self) { |
| 22513 | return wrapper_CompositeExplicitAutogradNonFunctional_scatter_reduce__two(self, dim, index, src, reduce, include_self); |
| 22514 | } |
| 22515 | at::Tensor eq(const at::Tensor & self, const at::Scalar & other) { |
| 22516 | return wrapper_CompositeExplicitAutogradNonFunctional_eq_Scalar(self, other); |
| 22517 | } |
| 22518 | at::Tensor & eq_(at::Tensor & self, const at::Scalar & other) { |
| 22519 | return wrapper_CompositeExplicitAutogradNonFunctional_eq__Scalar(self, other); |
| 22520 | } |
| 22521 | at::Tensor eq(const at::Tensor & self, const at::Tensor & other) { |
| 22522 | return wrapper_CompositeExplicitAutogradNonFunctional_eq_Tensor(self, other); |
| 22523 | } |
| 22524 | at::Tensor & eq_(at::Tensor & self, const at::Tensor & other) { |
| 22525 | return wrapper_CompositeExplicitAutogradNonFunctional_eq__Tensor(self, other); |
| 22526 | } |
| 22527 | at::Tensor bitwise_and(const at::Tensor & self, const at::Tensor & other) { |
| 22528 | return wrapper_CompositeExplicitAutogradNonFunctional_bitwise_and_Tensor(self, other); |
| 22529 | } |
| 22530 | at::Tensor & bitwise_and_(at::Tensor & self, const at::Tensor & other) { |
| 22531 | return wrapper_CompositeExplicitAutogradNonFunctional_bitwise_and__Tensor(self, other); |
| 22532 | } |
| 22533 | at::Tensor bitwise_or(const at::Tensor & self, const at::Tensor & other) { |
| 22534 | return wrapper_CompositeExplicitAutogradNonFunctional_bitwise_or_Tensor(self, other); |
| 22535 | } |
| 22536 | at::Tensor & bitwise_or_(at::Tensor & self, const at::Tensor & other) { |
| 22537 | return wrapper_CompositeExplicitAutogradNonFunctional_bitwise_or__Tensor(self, other); |
| 22538 | } |
| 22539 | at::Tensor bitwise_xor(const at::Tensor & self, const at::Tensor & other) { |
| 22540 | return wrapper_CompositeExplicitAutogradNonFunctional_bitwise_xor_Tensor(self, other); |
| 22541 | } |
| 22542 | at::Tensor & bitwise_xor_(at::Tensor & self, const at::Tensor & other) { |
| 22543 | return wrapper_CompositeExplicitAutogradNonFunctional_bitwise_xor__Tensor(self, other); |
| 22544 | } |
| 22545 | at::Tensor bitwise_left_shift(const at::Tensor & self, const at::Tensor & other) { |
| 22546 | return wrapper_CompositeExplicitAutogradNonFunctional_bitwise_left_shift_Tensor(self, other); |
| 22547 | } |
| 22548 | at::Tensor & bitwise_left_shift_(at::Tensor & self, const at::Tensor & other) { |
| 22549 | return wrapper_CompositeExplicitAutogradNonFunctional_bitwise_left_shift__Tensor(self, other); |
| 22550 | } |
| 22551 | at::Tensor bitwise_right_shift(const at::Tensor & self, const at::Tensor & other) { |
| 22552 | return wrapper_CompositeExplicitAutogradNonFunctional_bitwise_right_shift_Tensor(self, other); |
| 22553 | } |
| 22554 | at::Tensor & bitwise_right_shift_(at::Tensor & self, const at::Tensor & other) { |
| 22555 | return wrapper_CompositeExplicitAutogradNonFunctional_bitwise_right_shift__Tensor(self, other); |
| 22556 | } |
| 22557 | at::Tensor tril(const at::Tensor & self, int64_t diagonal) { |
| 22558 | return wrapper_CompositeExplicitAutogradNonFunctional_tril(self, diagonal); |
| 22559 | } |
| 22560 | at::Tensor & tril_(at::Tensor & self, int64_t diagonal) { |
| 22561 | return wrapper_CompositeExplicitAutogradNonFunctional_tril_(self, diagonal); |
| 22562 | } |
| 22563 | at::Tensor triu(const at::Tensor & self, int64_t diagonal) { |
| 22564 | return wrapper_CompositeExplicitAutogradNonFunctional_triu(self, diagonal); |
| 22565 | } |
| 22566 | at::Tensor & triu_(at::Tensor & self, int64_t diagonal) { |
| 22567 | return wrapper_CompositeExplicitAutogradNonFunctional_triu_(self, diagonal); |
| 22568 | } |
| 22569 | at::Tensor digamma(const at::Tensor & self) { |
| 22570 | return wrapper_CompositeExplicitAutogradNonFunctional_digamma(self); |
| 22571 | } |
| 22572 | at::Tensor & digamma_(at::Tensor & self) { |
| 22573 | return wrapper_CompositeExplicitAutogradNonFunctional_digamma_(self); |
| 22574 | } |
| 22575 | at::Tensor lerp(const at::Tensor & self, const at::Tensor & end, const at::Scalar & weight) { |
| 22576 | return wrapper_CompositeExplicitAutogradNonFunctional_lerp_Scalar(self, end, weight); |
| 22577 | } |
| 22578 | at::Tensor & lerp_(at::Tensor & self, const at::Tensor & end, const at::Scalar & weight) { |
| 22579 | return wrapper_CompositeExplicitAutogradNonFunctional_lerp__Scalar(self, end, weight); |
| 22580 | } |
| 22581 | at::Tensor lerp(const at::Tensor & self, const at::Tensor & end, const at::Tensor & weight) { |
| 22582 | return wrapper_CompositeExplicitAutogradNonFunctional_lerp_Tensor(self, end, weight); |
| 22583 | } |
| 22584 | at::Tensor & lerp_(at::Tensor & self, const at::Tensor & end, const at::Tensor & weight) { |
| 22585 | return wrapper_CompositeExplicitAutogradNonFunctional_lerp__Tensor(self, end, weight); |
| 22586 | } |
| 22587 | at::Tensor ne(const at::Tensor & self, const at::Scalar & other) { |
| 22588 | return wrapper_CompositeExplicitAutogradNonFunctional_ne_Scalar(self, other); |
| 22589 | } |
| 22590 | at::Tensor & ne_(at::Tensor & self, const at::Scalar & other) { |
| 22591 | return wrapper_CompositeExplicitAutogradNonFunctional_ne__Scalar(self, other); |
| 22592 | } |
| 22593 | at::Tensor ne(const at::Tensor & self, const at::Tensor & other) { |
| 22594 | return wrapper_CompositeExplicitAutogradNonFunctional_ne_Tensor(self, other); |
| 22595 | } |
| 22596 | at::Tensor & ne_(at::Tensor & self, const at::Tensor & other) { |
| 22597 | return wrapper_CompositeExplicitAutogradNonFunctional_ne__Tensor(self, other); |
| 22598 | } |
| 22599 | at::Tensor ge(const at::Tensor & self, const at::Scalar & other) { |
| 22600 | return wrapper_CompositeExplicitAutogradNonFunctional_ge_Scalar(self, other); |
| 22601 | } |
| 22602 | at::Tensor & ge_(at::Tensor & self, const at::Scalar & other) { |
| 22603 | return wrapper_CompositeExplicitAutogradNonFunctional_ge__Scalar(self, other); |
| 22604 | } |
| 22605 | at::Tensor ge(const at::Tensor & self, const at::Tensor & other) { |
| 22606 | return wrapper_CompositeExplicitAutogradNonFunctional_ge_Tensor(self, other); |
| 22607 | } |
| 22608 | at::Tensor & ge_(at::Tensor & self, const at::Tensor & other) { |
| 22609 | return wrapper_CompositeExplicitAutogradNonFunctional_ge__Tensor(self, other); |
| 22610 | } |
| 22611 | at::Tensor le(const at::Tensor & self, const at::Scalar & other) { |
| 22612 | return wrapper_CompositeExplicitAutogradNonFunctional_le_Scalar(self, other); |
| 22613 | } |
| 22614 | at::Tensor & le_(at::Tensor & self, const at::Scalar & other) { |
| 22615 | return wrapper_CompositeExplicitAutogradNonFunctional_le__Scalar(self, other); |
| 22616 | } |
| 22617 | at::Tensor le(const at::Tensor & self, const at::Tensor & other) { |
| 22618 | return wrapper_CompositeExplicitAutogradNonFunctional_le_Tensor(self, other); |
| 22619 | } |
| 22620 | at::Tensor & le_(at::Tensor & self, const at::Tensor & other) { |
| 22621 | return wrapper_CompositeExplicitAutogradNonFunctional_le__Tensor(self, other); |
| 22622 | } |
| 22623 | at::Tensor gt(const at::Tensor & self, const at::Scalar & other) { |
| 22624 | return wrapper_CompositeExplicitAutogradNonFunctional_gt_Scalar(self, other); |
| 22625 | } |
| 22626 | at::Tensor & gt_(at::Tensor & self, const at::Scalar & other) { |
| 22627 | return wrapper_CompositeExplicitAutogradNonFunctional_gt__Scalar(self, other); |
| 22628 | } |
| 22629 | at::Tensor gt(const at::Tensor & self, const at::Tensor & other) { |
| 22630 | return wrapper_CompositeExplicitAutogradNonFunctional_gt_Tensor(self, other); |
| 22631 | } |
| 22632 | at::Tensor & gt_(at::Tensor & self, const at::Tensor & other) { |
| 22633 | return wrapper_CompositeExplicitAutogradNonFunctional_gt__Tensor(self, other); |
| 22634 | } |
| 22635 | at::Tensor lt(const at::Tensor & self, const at::Scalar & other) { |
| 22636 | return wrapper_CompositeExplicitAutogradNonFunctional_lt_Scalar(self, other); |
| 22637 | } |
| 22638 | at::Tensor & lt_(at::Tensor & self, const at::Scalar & other) { |
| 22639 | return wrapper_CompositeExplicitAutogradNonFunctional_lt__Scalar(self, other); |
| 22640 | } |
| 22641 | at::Tensor lt(const at::Tensor & self, const at::Tensor & other) { |
| 22642 | return wrapper_CompositeExplicitAutogradNonFunctional_lt_Tensor(self, other); |
| 22643 | } |
| 22644 | at::Tensor & lt_(at::Tensor & self, const at::Tensor & other) { |
| 22645 | return wrapper_CompositeExplicitAutogradNonFunctional_lt__Tensor(self, other); |
| 22646 | } |
| 22647 | at::Tensor gather(const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad) { |
| 22648 | return wrapper_CompositeExplicitAutogradNonFunctional_gather(self, dim, index, sparse_grad); |
| 22649 | } |
| 22650 | at::Tensor addcmul(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value) { |
| 22651 | return wrapper_CompositeExplicitAutogradNonFunctional_addcmul(self, tensor1, tensor2, value); |
| 22652 | } |
| 22653 | at::Tensor & addcmul_(at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value) { |
| 22654 | return wrapper_CompositeExplicitAutogradNonFunctional_addcmul_(self, tensor1, tensor2, value); |
| 22655 | } |
| 22656 | at::Tensor addcdiv(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value) { |
| 22657 | return wrapper_CompositeExplicitAutogradNonFunctional_addcdiv(self, tensor1, tensor2, value); |
| 22658 | } |
| 22659 | at::Tensor & addcdiv_(at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value) { |
| 22660 | return wrapper_CompositeExplicitAutogradNonFunctional_addcdiv_(self, tensor1, tensor2, value); |
| 22661 | } |
| 22662 | ::std::tuple<at::Tensor,at::Tensor> triangular_solve(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular) { |
| 22663 | return wrapper_CompositeExplicitAutogradNonFunctional_triangular_solve(self, A, upper, transpose, unitriangular); |
| 22664 | } |
| 22665 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> lu_unpack(const at::Tensor & LU_data, const at::Tensor & LU_pivots, bool unpack_data, bool unpack_pivots) { |
| 22666 | return wrapper_CompositeExplicitAutogradNonFunctional_lu_unpack(LU_data, LU_pivots, unpack_data, unpack_pivots); |
| 22667 | } |
| 22668 | at::Tensor lgamma(const at::Tensor & self) { |
| 22669 | return wrapper_CompositeExplicitAutogradNonFunctional_lgamma(self); |
| 22670 | } |
| 22671 | at::Tensor & lgamma_(at::Tensor & self) { |
| 22672 | return wrapper_CompositeExplicitAutogradNonFunctional_lgamma_(self); |
| 22673 | } |
| 22674 | at::Tensor polygamma(int64_t n, const at::Tensor & self) { |
| 22675 | return wrapper_CompositeExplicitAutogradNonFunctional_polygamma(n, self); |
| 22676 | } |
| 22677 | at::Tensor erfinv(const at::Tensor & self) { |
| 22678 | return wrapper_CompositeExplicitAutogradNonFunctional_erfinv(self); |
| 22679 | } |
| 22680 | at::Tensor & erfinv_(at::Tensor & self) { |
| 22681 | return wrapper_CompositeExplicitAutogradNonFunctional_erfinv_(self); |
| 22682 | } |
| 22683 | at::Tensor i0(const at::Tensor & self) { |
| 22684 | return wrapper_CompositeExplicitAutogradNonFunctional_i0(self); |
| 22685 | } |
| 22686 | at::Tensor & i0_(at::Tensor & self) { |
| 22687 | return wrapper_CompositeExplicitAutogradNonFunctional_i0_(self); |
| 22688 | } |
| 22689 | at::Tensor sign(const at::Tensor & self) { |
| 22690 | return wrapper_CompositeExplicitAutogradNonFunctional_sign(self); |
| 22691 | } |
| 22692 | at::Tensor & sign_(at::Tensor & self) { |
| 22693 | return wrapper_CompositeExplicitAutogradNonFunctional_sign_(self); |
| 22694 | } |
| 22695 | at::Tensor signbit(const at::Tensor & self) { |
| 22696 | return wrapper_CompositeExplicitAutogradNonFunctional_signbit(self); |
| 22697 | } |
| 22698 | at::Tensor atan2(const at::Tensor & self, const at::Tensor & other) { |
| 22699 | return wrapper_CompositeExplicitAutogradNonFunctional_atan2(self, other); |
| 22700 | } |
| 22701 | at::Tensor & atan2_(at::Tensor & self, const at::Tensor & other) { |
| 22702 | return wrapper_CompositeExplicitAutogradNonFunctional_atan2_(self, other); |
| 22703 | } |
| 22704 | at::Tensor fmod(const at::Tensor & self, const at::Tensor & other) { |
| 22705 | return wrapper_CompositeExplicitAutogradNonFunctional_fmod_Tensor(self, other); |
| 22706 | } |
| 22707 | at::Tensor & fmod_(at::Tensor & self, const at::Tensor & other) { |
| 22708 | return wrapper_CompositeExplicitAutogradNonFunctional_fmod__Tensor(self, other); |
| 22709 | } |
| 22710 | at::Tensor hypot(const at::Tensor & self, const at::Tensor & other) { |
| 22711 | return wrapper_CompositeExplicitAutogradNonFunctional_hypot(self, other); |
| 22712 | } |
| 22713 | at::Tensor & hypot_(at::Tensor & self, const at::Tensor & other) { |
| 22714 | return wrapper_CompositeExplicitAutogradNonFunctional_hypot_(self, other); |
| 22715 | } |
| 22716 | at::Tensor igamma(const at::Tensor & self, const at::Tensor & other) { |
| 22717 | return wrapper_CompositeExplicitAutogradNonFunctional_igamma(self, other); |
| 22718 | } |
| 22719 | at::Tensor & igamma_(at::Tensor & self, const at::Tensor & other) { |
| 22720 | return wrapper_CompositeExplicitAutogradNonFunctional_igamma_(self, other); |
| 22721 | } |
| 22722 | at::Tensor igammac(const at::Tensor & self, const at::Tensor & other) { |
| 22723 | return wrapper_CompositeExplicitAutogradNonFunctional_igammac(self, other); |
| 22724 | } |
| 22725 | at::Tensor & igammac_(at::Tensor & self, const at::Tensor & other) { |
| 22726 | return wrapper_CompositeExplicitAutogradNonFunctional_igammac_(self, other); |
| 22727 | } |
| 22728 | at::Tensor nextafter(const at::Tensor & self, const at::Tensor & other) { |
| 22729 | return wrapper_CompositeExplicitAutogradNonFunctional_nextafter(self, other); |
| 22730 | } |
| 22731 | at::Tensor & nextafter_(at::Tensor & self, const at::Tensor & other) { |
| 22732 | return wrapper_CompositeExplicitAutogradNonFunctional_nextafter_(self, other); |
| 22733 | } |
| 22734 | at::Tensor remainder(const at::Tensor & self, const at::Tensor & other) { |
| 22735 | return wrapper_CompositeExplicitAutogradNonFunctional_remainder_Tensor(self, other); |
| 22736 | } |
| 22737 | at::Tensor & remainder_(at::Tensor & self, const at::Tensor & other) { |
| 22738 | return wrapper_CompositeExplicitAutogradNonFunctional_remainder__Tensor(self, other); |
| 22739 | } |
| 22740 | at::Tensor fmin(const at::Tensor & self, const at::Tensor & other) { |
| 22741 | return wrapper_CompositeExplicitAutogradNonFunctional_fmin(self, other); |
| 22742 | } |
| 22743 | at::Tensor fmax(const at::Tensor & self, const at::Tensor & other) { |
| 22744 | return wrapper_CompositeExplicitAutogradNonFunctional_fmax(self, other); |
| 22745 | } |
| 22746 | at::Tensor maximum(const at::Tensor & self, const at::Tensor & other) { |
| 22747 | return wrapper_CompositeExplicitAutogradNonFunctional_maximum(self, other); |
| 22748 | } |
| 22749 | at::Tensor minimum(const at::Tensor & self, const at::Tensor & other) { |
| 22750 | return wrapper_CompositeExplicitAutogradNonFunctional_minimum(self, other); |
| 22751 | } |
| 22752 | ::std::tuple<at::Tensor,at::Tensor> sort(const at::Tensor & self, c10::optional<bool> stable, int64_t dim, bool descending) { |
| 22753 | return wrapper_CompositeExplicitAutogradNonFunctional_sort_stable(self, stable, dim, descending); |
| 22754 | } |
| 22755 | ::std::tuple<at::Tensor,at::Tensor> topk(const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted) { |
| 22756 | return wrapper_CompositeExplicitAutogradNonFunctional_topk(self, k, dim, largest, sorted); |
| 22757 | } |
| 22758 | at::Tensor all(const at::Tensor & self) { |
| 22759 | return wrapper_CompositeExplicitAutogradNonFunctional_all(self); |
| 22760 | } |
| 22761 | at::Tensor any(const at::Tensor & self) { |
| 22762 | return wrapper_CompositeExplicitAutogradNonFunctional_any(self); |
| 22763 | } |
| 22764 | at::Tensor renorm(const at::Tensor & self, const at::Scalar & p, int64_t dim, const at::Scalar & maxnorm) { |
| 22765 | return wrapper_CompositeExplicitAutogradNonFunctional_renorm(self, p, dim, maxnorm); |
| 22766 | } |
| 22767 | at::Tensor & renorm_(at::Tensor & self, const at::Scalar & p, int64_t dim, const at::Scalar & maxnorm) { |
| 22768 | return wrapper_CompositeExplicitAutogradNonFunctional_renorm_(self, p, dim, maxnorm); |
| 22769 | } |
| 22770 | at::Tensor pow(const at::Tensor & self, const at::Tensor & exponent) { |
| 22771 | return wrapper_CompositeExplicitAutogradNonFunctional_pow_Tensor_Tensor(self, exponent); |
| 22772 | } |
| 22773 | at::Tensor & pow_(at::Tensor & self, const at::Tensor & exponent) { |
| 22774 | return wrapper_CompositeExplicitAutogradNonFunctional_pow__Tensor(self, exponent); |
| 22775 | } |
| 22776 | at::Tensor pow(const at::Scalar & self, const at::Tensor & exponent) { |
| 22777 | return wrapper_CompositeExplicitAutogradNonFunctional_pow_Scalar(self, exponent); |
| 22778 | } |
| 22779 | at::Tensor pow(const at::Tensor & self, const at::Scalar & exponent) { |
| 22780 | return wrapper_CompositeExplicitAutogradNonFunctional_pow_Tensor_Scalar(self, exponent); |
| 22781 | } |
| 22782 | at::Tensor & pow_(at::Tensor & self, const at::Scalar & exponent) { |
| 22783 | return wrapper_CompositeExplicitAutogradNonFunctional_pow__Scalar(self, exponent); |
| 22784 | } |
| 22785 | at::Tensor _convert_indices_from_coo_to_csr(const at::Tensor & self, int64_t size, bool out_int32) { |
| 22786 | return wrapper_CompositeExplicitAutogradNonFunctional__convert_indices_from_coo_to_csr(self, size, out_int32); |
| 22787 | } |
| 22788 | at::Tensor _convert_indices_from_csr_to_coo(const at::Tensor & crow_indices, const at::Tensor & col_indices, bool out_int32, bool transpose) { |
| 22789 | return wrapper_CompositeExplicitAutogradNonFunctional__convert_indices_from_csr_to_coo(crow_indices, col_indices, out_int32, transpose); |
| 22790 | } |
| 22791 | at::Tensor mse_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction) { |
| 22792 | return wrapper_CompositeExplicitAutogradNonFunctional_mse_loss(self, target, reduction); |
| 22793 | } |
| 22794 | ::std::tuple<at::Tensor,at::Tensor> nll_loss_forward(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index) { |
| 22795 | return wrapper_CompositeExplicitAutogradNonFunctional_nll_loss_forward(self, target, weight, reduction, ignore_index); |
| 22796 | } |
| 22797 | ::std::tuple<at::Tensor,at::Tensor> nll_loss_forward_symint(const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index) { |
| 22798 | return wrapper_CompositeExplicitAutogradNonFunctional_nll_loss_forward(self, target, weight, reduction, ignore_index.expect_int()); |
| 22799 | } |
| 22800 | at::Tensor nll_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) { |
| 22801 | return wrapper_CompositeExplicitAutogradNonFunctional_nll_loss_backward(grad_output, self, target, weight, reduction, ignore_index, total_weight); |
| 22802 | } |
| 22803 | at::Tensor nll_loss_backward_symint(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) { |
| 22804 | return wrapper_CompositeExplicitAutogradNonFunctional_nll_loss_backward(grad_output, self, target, weight, reduction, ignore_index.expect_int(), total_weight); |
| 22805 | } |
| 22806 | at::Tensor smooth_l1_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta) { |
| 22807 | return wrapper_CompositeExplicitAutogradNonFunctional_smooth_l1_loss(self, target, reduction, beta); |
| 22808 | } |
| 22809 | at::Tensor elu(const at::Tensor & self, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale) { |
| 22810 | return wrapper_CompositeExplicitAutogradNonFunctional_elu(self, alpha, scale, input_scale); |
| 22811 | } |
| 22812 | at::Tensor & elu_(at::Tensor & self, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale) { |
| 22813 | return wrapper_CompositeExplicitAutogradNonFunctional_elu_(self, alpha, scale, input_scale); |
| 22814 | } |
| 22815 | at::Tensor elu_backward(const at::Tensor & grad_output, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale, bool is_result, const at::Tensor & self_or_result) { |
| 22816 | return wrapper_CompositeExplicitAutogradNonFunctional_elu_backward(grad_output, alpha, scale, input_scale, is_result, self_or_result); |
| 22817 | } |
| 22818 | at::Tensor glu(const at::Tensor & self, int64_t dim) { |
| 22819 | return wrapper_CompositeExplicitAutogradNonFunctional_glu(self, dim); |
| 22820 | } |
| 22821 | at::Tensor hardsigmoid(const at::Tensor & self) { |
| 22822 | return wrapper_CompositeExplicitAutogradNonFunctional_hardsigmoid(self); |
| 22823 | } |
| 22824 | at::Tensor & hardsigmoid_(at::Tensor & self) { |
| 22825 | return wrapper_CompositeExplicitAutogradNonFunctional_hardsigmoid_(self); |
| 22826 | } |
| 22827 | at::Tensor hardsigmoid_backward(const at::Tensor & grad_output, const at::Tensor & self) { |
| 22828 | return wrapper_CompositeExplicitAutogradNonFunctional_hardsigmoid_backward(grad_output, self); |
| 22829 | } |
| 22830 | at::Tensor leaky_relu(const at::Tensor & self, const at::Scalar & negative_slope) { |
| 22831 | return wrapper_CompositeExplicitAutogradNonFunctional_leaky_relu(self, negative_slope); |
| 22832 | } |
| 22833 | at::Tensor & leaky_relu_(at::Tensor & self, const at::Scalar & negative_slope) { |
| 22834 | return wrapper_CompositeExplicitAutogradNonFunctional_leaky_relu_(self, negative_slope); |
| 22835 | } |
| 22836 | at::Tensor leaky_relu_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & negative_slope, bool self_is_result) { |
| 22837 | return wrapper_CompositeExplicitAutogradNonFunctional_leaky_relu_backward(grad_output, self, negative_slope, self_is_result); |
| 22838 | } |
| 22839 | at::Tensor softplus(const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold) { |
| 22840 | return wrapper_CompositeExplicitAutogradNonFunctional_softplus(self, beta, threshold); |
| 22841 | } |
| 22842 | at::Tensor softplus_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold) { |
| 22843 | return wrapper_CompositeExplicitAutogradNonFunctional_softplus_backward(grad_output, self, beta, threshold); |
| 22844 | } |
| 22845 | at::Tensor softshrink(const at::Tensor & self, const at::Scalar & lambd) { |
| 22846 | return wrapper_CompositeExplicitAutogradNonFunctional_softshrink(self, lambd); |
| 22847 | } |
| 22848 | at::Tensor softshrink_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & lambd) { |
| 22849 | return wrapper_CompositeExplicitAutogradNonFunctional_softshrink_backward(grad_output, self, lambd); |
| 22850 | } |
| 22851 | ::std::tuple<at::Tensor,at::Tensor> adaptive_max_pool2d(const at::Tensor & self, at::IntArrayRef output_size) { |
| 22852 | return wrapper_CompositeExplicitAutogradNonFunctional_adaptive_max_pool2d(self, output_size); |
| 22853 | } |
| 22854 | at::Tensor adaptive_max_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) { |
| 22855 | return wrapper_CompositeExplicitAutogradNonFunctional_adaptive_max_pool2d_backward(grad_output, self, indices); |
| 22856 | } |
| 22857 | ::std::tuple<at::Tensor,at::Tensor> adaptive_max_pool3d(const at::Tensor & self, at::IntArrayRef output_size) { |
| 22858 | return wrapper_CompositeExplicitAutogradNonFunctional_adaptive_max_pool3d(self, output_size); |
| 22859 | } |
| 22860 | at::Tensor adaptive_max_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) { |
| 22861 | return wrapper_CompositeExplicitAutogradNonFunctional_adaptive_max_pool3d_backward(grad_output, self, indices); |
| 22862 | } |
| 22863 | at::Tensor avg_pool2d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional<int64_t> divisor_override) { |
| 22864 | return wrapper_CompositeExplicitAutogradNonFunctional_avg_pool2d(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); |
| 22865 | } |
| 22866 | at::Tensor avg_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional<int64_t> divisor_override) { |
| 22867 | return wrapper_CompositeExplicitAutogradNonFunctional_avg_pool2d_backward(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); |
| 22868 | } |
| 22869 | at::Tensor avg_pool3d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional<int64_t> divisor_override) { |
| 22870 | return wrapper_CompositeExplicitAutogradNonFunctional_avg_pool3d(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); |
| 22871 | } |
| 22872 | at::Tensor avg_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional<int64_t> divisor_override) { |
| 22873 | return wrapper_CompositeExplicitAutogradNonFunctional_avg_pool3d_backward(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); |
| 22874 | } |
| 22875 | ::std::tuple<at::Tensor,at::Tensor> fractional_max_pool2d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples) { |
| 22876 | return wrapper_CompositeExplicitAutogradNonFunctional_fractional_max_pool2d(self, kernel_size, output_size, random_samples); |
| 22877 | } |
| 22878 | at::Tensor fractional_max_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices) { |
| 22879 | return wrapper_CompositeExplicitAutogradNonFunctional_fractional_max_pool2d_backward(grad_output, self, kernel_size, output_size, indices); |
| 22880 | } |
| 22881 | ::std::tuple<at::Tensor,at::Tensor> fractional_max_pool3d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples) { |
| 22882 | return wrapper_CompositeExplicitAutogradNonFunctional_fractional_max_pool3d(self, kernel_size, output_size, random_samples); |
| 22883 | } |
| 22884 | ::std::tuple<at::Tensor,at::Tensor> max_pool2d_with_indices(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { |
| 22885 | return wrapper_CompositeExplicitAutogradNonFunctional_max_pool2d_with_indices(self, kernel_size, stride, padding, dilation, ceil_mode); |
| 22886 | } |
| 22887 | at::Tensor max_pool2d_with_indices_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, const at::Tensor & indices) { |
| 22888 | return wrapper_CompositeExplicitAutogradNonFunctional_max_pool2d_with_indices_backward(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices); |
| 22889 | } |
| 22890 | at::Tensor reflection_pad1d(const at::Tensor & self, at::IntArrayRef padding) { |
| 22891 | return wrapper_CompositeExplicitAutogradNonFunctional_reflection_pad1d(self, padding); |
| 22892 | } |
| 22893 | at::Tensor reflection_pad1d_symint(const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 22894 | return wrapper_CompositeExplicitAutogradNonFunctional_reflection_pad1d(self, C10_AS_INTARRAYREF_SLOW(padding)); |
| 22895 | } |
| 22896 | at::Tensor reflection_pad1d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { |
| 22897 | return wrapper_CompositeExplicitAutogradNonFunctional_reflection_pad1d_backward(grad_output, self, padding); |
| 22898 | } |
| 22899 | at::Tensor reflection_pad1d_backward_symint(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 22900 | return wrapper_CompositeExplicitAutogradNonFunctional_reflection_pad1d_backward(grad_output, self, C10_AS_INTARRAYREF_SLOW(padding)); |
| 22901 | } |
| 22902 | at::Tensor reflection_pad3d(const at::Tensor & self, at::IntArrayRef padding) { |
| 22903 | return wrapper_CompositeExplicitAutogradNonFunctional_reflection_pad3d(self, padding); |
| 22904 | } |
| 22905 | at::Tensor reflection_pad3d_symint(const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 22906 | return wrapper_CompositeExplicitAutogradNonFunctional_reflection_pad3d(self, C10_AS_INTARRAYREF_SLOW(padding)); |
| 22907 | } |
| 22908 | at::Tensor reflection_pad3d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { |
| 22909 | return wrapper_CompositeExplicitAutogradNonFunctional_reflection_pad3d_backward(grad_output, self, padding); |
| 22910 | } |
| 22911 | at::Tensor reflection_pad3d_backward_symint(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 22912 | return wrapper_CompositeExplicitAutogradNonFunctional_reflection_pad3d_backward(grad_output, self, C10_AS_INTARRAYREF_SLOW(padding)); |
| 22913 | } |
| 22914 | at::Tensor replication_pad1d(const at::Tensor & self, at::IntArrayRef padding) { |
| 22915 | return wrapper_CompositeExplicitAutogradNonFunctional_replication_pad1d(self, padding); |
| 22916 | } |
| 22917 | at::Tensor replication_pad1d_symint(const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 22918 | return wrapper_CompositeExplicitAutogradNonFunctional_replication_pad1d(self, C10_AS_INTARRAYREF_SLOW(padding)); |
| 22919 | } |
| 22920 | at::Tensor replication_pad1d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { |
| 22921 | return wrapper_CompositeExplicitAutogradNonFunctional_replication_pad1d_backward(grad_output, self, padding); |
| 22922 | } |
| 22923 | at::Tensor replication_pad1d_backward_symint(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 22924 | return wrapper_CompositeExplicitAutogradNonFunctional_replication_pad1d_backward(grad_output, self, C10_AS_INTARRAYREF_SLOW(padding)); |
| 22925 | } |
| 22926 | at::Tensor replication_pad2d(const at::Tensor & self, at::IntArrayRef padding) { |
| 22927 | return wrapper_CompositeExplicitAutogradNonFunctional_replication_pad2d(self, padding); |
| 22928 | } |
| 22929 | at::Tensor replication_pad2d_symint(const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 22930 | return wrapper_CompositeExplicitAutogradNonFunctional_replication_pad2d(self, C10_AS_INTARRAYREF_SLOW(padding)); |
| 22931 | } |
| 22932 | at::Tensor replication_pad3d(const at::Tensor & self, at::IntArrayRef padding) { |
| 22933 | return wrapper_CompositeExplicitAutogradNonFunctional_replication_pad3d(self, padding); |
| 22934 | } |
| 22935 | at::Tensor replication_pad3d_symint(const at::Tensor & self, c10::SymIntArrayRef padding) { |
| 22936 | return wrapper_CompositeExplicitAutogradNonFunctional_replication_pad3d(self, C10_AS_INTARRAYREF_SLOW(padding)); |
| 22937 | } |
| 22938 | at::Tensor upsample_linear1d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales) { |
| 22939 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_linear1d(self, output_size, align_corners, scales); |
| 22940 | } |
| 22941 | at::Tensor upsample_linear1d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales) { |
| 22942 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_linear1d(self, C10_AS_INTARRAYREF_SLOW(output_size), align_corners, scales); |
| 22943 | } |
| 22944 | at::Tensor upsample_linear1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional<double> scales) { |
| 22945 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_linear1d_backward(grad_output, output_size, input_size, align_corners, scales); |
| 22946 | } |
| 22947 | at::Tensor upsample_linear1d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional<double> scales) { |
| 22948 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_linear1d_backward(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), align_corners, scales); |
| 22949 | } |
| 22950 | at::Tensor upsample_bilinear2d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 22951 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_bilinear2d(self, output_size, align_corners, scales_h, scales_w); |
| 22952 | } |
| 22953 | at::Tensor upsample_bilinear2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 22954 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_bilinear2d(self, C10_AS_INTARRAYREF_SLOW(output_size), align_corners, scales_h, scales_w); |
| 22955 | } |
| 22956 | at::Tensor upsample_bilinear2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 22957 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_bilinear2d_backward(grad_output, output_size, input_size, align_corners, scales_h, scales_w); |
| 22958 | } |
| 22959 | at::Tensor upsample_bilinear2d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 22960 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_bilinear2d_backward(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), align_corners, scales_h, scales_w); |
| 22961 | } |
| 22962 | at::Tensor _upsample_bilinear2d_aa(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 22963 | return wrapper_CompositeExplicitAutogradNonFunctional__upsample_bilinear2d_aa(self, output_size, align_corners, scales_h, scales_w); |
| 22964 | } |
| 22965 | at::Tensor _upsample_bilinear2d_aa_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 22966 | return wrapper_CompositeExplicitAutogradNonFunctional__upsample_bilinear2d_aa(self, C10_AS_INTARRAYREF_SLOW(output_size), align_corners, scales_h, scales_w); |
| 22967 | } |
| 22968 | at::Tensor _upsample_bilinear2d_aa_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 22969 | return wrapper_CompositeExplicitAutogradNonFunctional__upsample_bilinear2d_aa_backward(grad_output, output_size, input_size, align_corners, scales_h, scales_w); |
| 22970 | } |
| 22971 | at::Tensor _upsample_bilinear2d_aa_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 22972 | return wrapper_CompositeExplicitAutogradNonFunctional__upsample_bilinear2d_aa_backward(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), align_corners, scales_h, scales_w); |
| 22973 | } |
| 22974 | at::Tensor upsample_bicubic2d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 22975 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_bicubic2d(self, output_size, align_corners, scales_h, scales_w); |
| 22976 | } |
| 22977 | at::Tensor upsample_bicubic2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 22978 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_bicubic2d(self, C10_AS_INTARRAYREF_SLOW(output_size), align_corners, scales_h, scales_w); |
| 22979 | } |
| 22980 | at::Tensor upsample_bicubic2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 22981 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_bicubic2d_backward(grad_output, output_size, input_size, align_corners, scales_h, scales_w); |
| 22982 | } |
| 22983 | at::Tensor upsample_bicubic2d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 22984 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_bicubic2d_backward(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), align_corners, scales_h, scales_w); |
| 22985 | } |
| 22986 | at::Tensor _upsample_bicubic2d_aa(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 22987 | return wrapper_CompositeExplicitAutogradNonFunctional__upsample_bicubic2d_aa(self, output_size, align_corners, scales_h, scales_w); |
| 22988 | } |
| 22989 | at::Tensor _upsample_bicubic2d_aa_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 22990 | return wrapper_CompositeExplicitAutogradNonFunctional__upsample_bicubic2d_aa(self, C10_AS_INTARRAYREF_SLOW(output_size), align_corners, scales_h, scales_w); |
| 22991 | } |
| 22992 | at::Tensor _upsample_bicubic2d_aa_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 22993 | return wrapper_CompositeExplicitAutogradNonFunctional__upsample_bicubic2d_aa_backward(grad_output, output_size, input_size, align_corners, scales_h, scales_w); |
| 22994 | } |
| 22995 | at::Tensor _upsample_bicubic2d_aa_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 22996 | return wrapper_CompositeExplicitAutogradNonFunctional__upsample_bicubic2d_aa_backward(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), align_corners, scales_h, scales_w); |
| 22997 | } |
| 22998 | at::Tensor upsample_trilinear3d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 22999 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_trilinear3d(self, output_size, align_corners, scales_d, scales_h, scales_w); |
| 23000 | } |
| 23001 | at::Tensor upsample_trilinear3d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 23002 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_trilinear3d(self, C10_AS_INTARRAYREF_SLOW(output_size), align_corners, scales_d, scales_h, scales_w); |
| 23003 | } |
| 23004 | at::Tensor upsample_trilinear3d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 23005 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_trilinear3d_backward(grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w); |
| 23006 | } |
| 23007 | at::Tensor upsample_trilinear3d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 23008 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_trilinear3d_backward(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), align_corners, scales_d, scales_h, scales_w); |
| 23009 | } |
| 23010 | at::Tensor upsample_nearest1d(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales) { |
| 23011 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_nearest1d(self, output_size, scales); |
| 23012 | } |
| 23013 | at::Tensor upsample_nearest1d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales) { |
| 23014 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_nearest1d(self, C10_AS_INTARRAYREF_SLOW(output_size), scales); |
| 23015 | } |
| 23016 | at::Tensor _upsample_nearest_exact1d(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales) { |
| 23017 | return wrapper_CompositeExplicitAutogradNonFunctional__upsample_nearest_exact1d(self, output_size, scales); |
| 23018 | } |
| 23019 | at::Tensor _upsample_nearest_exact1d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales) { |
| 23020 | return wrapper_CompositeExplicitAutogradNonFunctional__upsample_nearest_exact1d(self, C10_AS_INTARRAYREF_SLOW(output_size), scales); |
| 23021 | } |
| 23022 | at::Tensor upsample_nearest1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales) { |
| 23023 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_nearest1d_backward(grad_output, output_size, input_size, scales); |
| 23024 | } |
| 23025 | at::Tensor upsample_nearest1d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales) { |
| 23026 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_nearest1d_backward(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), scales); |
| 23027 | } |
| 23028 | at::Tensor _upsample_nearest_exact1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales) { |
| 23029 | return wrapper_CompositeExplicitAutogradNonFunctional__upsample_nearest_exact1d_backward(grad_output, output_size, input_size, scales); |
| 23030 | } |
| 23031 | at::Tensor _upsample_nearest_exact1d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales) { |
| 23032 | return wrapper_CompositeExplicitAutogradNonFunctional__upsample_nearest_exact1d_backward(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), scales); |
| 23033 | } |
| 23034 | at::Tensor upsample_nearest2d(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 23035 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_nearest2d(self, output_size, scales_h, scales_w); |
| 23036 | } |
| 23037 | at::Tensor upsample_nearest2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 23038 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_nearest2d(self, C10_AS_INTARRAYREF_SLOW(output_size), scales_h, scales_w); |
| 23039 | } |
| 23040 | at::Tensor _upsample_nearest_exact2d(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 23041 | return wrapper_CompositeExplicitAutogradNonFunctional__upsample_nearest_exact2d(self, output_size, scales_h, scales_w); |
| 23042 | } |
| 23043 | at::Tensor _upsample_nearest_exact2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 23044 | return wrapper_CompositeExplicitAutogradNonFunctional__upsample_nearest_exact2d(self, C10_AS_INTARRAYREF_SLOW(output_size), scales_h, scales_w); |
| 23045 | } |
| 23046 | at::Tensor upsample_nearest2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 23047 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_nearest2d_backward(grad_output, output_size, input_size, scales_h, scales_w); |
| 23048 | } |
| 23049 | at::Tensor upsample_nearest2d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 23050 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_nearest2d_backward(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), scales_h, scales_w); |
| 23051 | } |
| 23052 | at::Tensor _upsample_nearest_exact2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 23053 | return wrapper_CompositeExplicitAutogradNonFunctional__upsample_nearest_exact2d_backward(grad_output, output_size, input_size, scales_h, scales_w); |
| 23054 | } |
| 23055 | at::Tensor _upsample_nearest_exact2d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 23056 | return wrapper_CompositeExplicitAutogradNonFunctional__upsample_nearest_exact2d_backward(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), scales_h, scales_w); |
| 23057 | } |
| 23058 | at::Tensor upsample_nearest3d(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 23059 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_nearest3d(self, output_size, scales_d, scales_h, scales_w); |
| 23060 | } |
| 23061 | at::Tensor upsample_nearest3d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 23062 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_nearest3d(self, C10_AS_INTARRAYREF_SLOW(output_size), scales_d, scales_h, scales_w); |
| 23063 | } |
| 23064 | at::Tensor _upsample_nearest_exact3d(const at::Tensor & self, at::IntArrayRef output_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 23065 | return wrapper_CompositeExplicitAutogradNonFunctional__upsample_nearest_exact3d(self, output_size, scales_d, scales_h, scales_w); |
| 23066 | } |
| 23067 | at::Tensor _upsample_nearest_exact3d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 23068 | return wrapper_CompositeExplicitAutogradNonFunctional__upsample_nearest_exact3d(self, C10_AS_INTARRAYREF_SLOW(output_size), scales_d, scales_h, scales_w); |
| 23069 | } |
| 23070 | at::Tensor upsample_nearest3d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 23071 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_nearest3d_backward(grad_output, output_size, input_size, scales_d, scales_h, scales_w); |
| 23072 | } |
| 23073 | at::Tensor upsample_nearest3d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 23074 | return wrapper_CompositeExplicitAutogradNonFunctional_upsample_nearest3d_backward(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), scales_d, scales_h, scales_w); |
| 23075 | } |
| 23076 | at::Tensor _upsample_nearest_exact3d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 23077 | return wrapper_CompositeExplicitAutogradNonFunctional__upsample_nearest_exact3d_backward(grad_output, output_size, input_size, scales_d, scales_h, scales_w); |
| 23078 | } |
| 23079 | at::Tensor _upsample_nearest_exact3d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional<double> scales_d, c10::optional<double> scales_h, c10::optional<double> scales_w) { |
| 23080 | return wrapper_CompositeExplicitAutogradNonFunctional__upsample_nearest_exact3d_backward(grad_output, C10_AS_INTARRAYREF_SLOW(output_size), C10_AS_INTARRAYREF_SLOW(input_size), scales_d, scales_h, scales_w); |
| 23081 | } |
| 23082 | at::Tensor sigmoid_backward(const at::Tensor & grad_output, const at::Tensor & output) { |
| 23083 | return wrapper_CompositeExplicitAutogradNonFunctional_sigmoid_backward(grad_output, output); |
| 23084 | } |
| 23085 | at::Tensor logit_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::optional<double> eps) { |
| 23086 | return wrapper_CompositeExplicitAutogradNonFunctional_logit_backward(grad_output, self, eps); |
| 23087 | } |
| 23088 | at::Tensor tanh_backward(const at::Tensor & grad_output, const at::Tensor & output) { |
| 23089 | return wrapper_CompositeExplicitAutogradNonFunctional_tanh_backward(grad_output, output); |
| 23090 | } |
| 23091 | at::Tensor slow_conv_transpose2d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef dilation) { |
| 23092 | return wrapper_CompositeExplicitAutogradNonFunctional_slow_conv_transpose2d(self, weight, kernel_size, bias, stride, padding, output_padding, dilation); |
| 23093 | } |
| 23094 | at::Tensor slow_conv_transpose2d_symint(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, at::IntArrayRef dilation) { |
| 23095 | return wrapper_CompositeExplicitAutogradNonFunctional_slow_conv_transpose2d(self, weight, kernel_size, bias, stride, C10_AS_INTARRAYREF_SLOW(padding), C10_AS_INTARRAYREF_SLOW(output_padding), dilation); |
| 23096 | } |
| 23097 | at::Tensor isposinf(const at::Tensor & self) { |
| 23098 | return wrapper_CompositeExplicitAutogradNonFunctional_isposinf(self); |
| 23099 | } |
| 23100 | at::Tensor isneginf(const at::Tensor & self) { |
| 23101 | return wrapper_CompositeExplicitAutogradNonFunctional_isneginf(self); |
| 23102 | } |
| 23103 | at::Tensor special_entr(const at::Tensor & self) { |
| 23104 | return wrapper_CompositeExplicitAutogradNonFunctional_special_entr(self); |
| 23105 | } |
| 23106 | at::Tensor special_ndtri(const at::Tensor & self) { |
| 23107 | return wrapper_CompositeExplicitAutogradNonFunctional_special_ndtri(self); |
| 23108 | } |
| 23109 | at::Tensor special_log_ndtr(const at::Tensor & self) { |
| 23110 | return wrapper_CompositeExplicitAutogradNonFunctional_special_log_ndtr(self); |
| 23111 | } |
| 23112 | at::Tensor special_erfcx(const at::Tensor & self) { |
| 23113 | return wrapper_CompositeExplicitAutogradNonFunctional_special_erfcx(self); |
| 23114 | } |
| 23115 | at::Tensor special_xlog1py(const at::Tensor & self, const at::Tensor & other) { |
| 23116 | return wrapper_CompositeExplicitAutogradNonFunctional_special_xlog1py(self, other); |
| 23117 | } |
| 23118 | at::Tensor special_zeta(const at::Tensor & self, const at::Tensor & other) { |
| 23119 | return wrapper_CompositeExplicitAutogradNonFunctional_special_zeta(self, other); |
| 23120 | } |
| 23121 | at::Tensor special_i0e(const at::Tensor & self) { |
| 23122 | return wrapper_CompositeExplicitAutogradNonFunctional_special_i0e(self); |
| 23123 | } |
| 23124 | at::Tensor special_i1(const at::Tensor & self) { |
| 23125 | return wrapper_CompositeExplicitAutogradNonFunctional_special_i1(self); |
| 23126 | } |
| 23127 | at::Tensor special_i1e(const at::Tensor & self) { |
| 23128 | return wrapper_CompositeExplicitAutogradNonFunctional_special_i1e(self); |
| 23129 | } |
| 23130 | ::std::tuple<at::Tensor,at::Tensor> linalg_cholesky_ex(const at::Tensor & self, bool upper, bool check_errors) { |
| 23131 | return wrapper_CompositeExplicitAutogradNonFunctional_linalg_cholesky_ex(self, upper, check_errors); |
| 23132 | } |
| 23133 | at::Tensor linalg_cross(const at::Tensor & self, const at::Tensor & other, int64_t dim) { |
| 23134 | return wrapper_CompositeExplicitAutogradNonFunctional_linalg_cross(self, other, dim); |
| 23135 | } |
| 23136 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> linalg_lu_factor_ex(const at::Tensor & A, bool pivot, bool check_errors) { |
| 23137 | return wrapper_CompositeExplicitAutogradNonFunctional_linalg_lu_factor_ex(A, pivot, check_errors); |
| 23138 | } |
| 23139 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> linalg_lu(const at::Tensor & A, bool pivot) { |
| 23140 | return wrapper_CompositeExplicitAutogradNonFunctional_linalg_lu(A, pivot); |
| 23141 | } |
| 23142 | at::Tensor linalg_lu_solve(const at::Tensor & LU, const at::Tensor & pivots, const at::Tensor & B, bool left, bool adjoint) { |
| 23143 | return wrapper_CompositeExplicitAutogradNonFunctional_linalg_lu_solve(LU, pivots, B, left, adjoint); |
| 23144 | } |
| 23145 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _linalg_det(const at::Tensor & A) { |
| 23146 | return wrapper_CompositeExplicitAutogradNonFunctional__linalg_det(A); |
| 23147 | } |
| 23148 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> linalg_ldl_factor_ex(const at::Tensor & self, bool hermitian, bool check_errors) { |
| 23149 | return wrapper_CompositeExplicitAutogradNonFunctional_linalg_ldl_factor_ex(self, hermitian, check_errors); |
| 23150 | } |
| 23151 | at::Tensor linalg_ldl_solve(const at::Tensor & LD, const at::Tensor & pivots, const at::Tensor & B, bool hermitian) { |
| 23152 | return wrapper_CompositeExplicitAutogradNonFunctional_linalg_ldl_solve(LD, pivots, B, hermitian); |
| 23153 | } |
| 23154 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _linalg_slogdet(const at::Tensor & A) { |
| 23155 | return wrapper_CompositeExplicitAutogradNonFunctional__linalg_slogdet(A); |
| 23156 | } |
| 23157 | ::std::tuple<at::Tensor,at::Tensor> _linalg_eigh(const at::Tensor & A, c10::string_view UPLO, bool compute_v) { |
| 23158 | return wrapper_CompositeExplicitAutogradNonFunctional__linalg_eigh(A, UPLO, compute_v); |
| 23159 | } |
| 23160 | ::std::tuple<at::Tensor,at::Tensor> linalg_inv_ex(const at::Tensor & A, bool check_errors) { |
| 23161 | return wrapper_CompositeExplicitAutogradNonFunctional_linalg_inv_ex(A, check_errors); |
| 23162 | } |
| 23163 | at::Tensor linalg_vector_norm(const at::Tensor & self, const at::Scalar & ord, at::OptionalIntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype) { |
| 23164 | return wrapper_CompositeExplicitAutogradNonFunctional_linalg_vector_norm(self, ord, dim, keepdim, dtype); |
| 23165 | } |
| 23166 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _linalg_svd(const at::Tensor & A, bool full_matrices, bool compute_uv, c10::optional<c10::string_view> driver) { |
| 23167 | return wrapper_CompositeExplicitAutogradNonFunctional__linalg_svd(A, full_matrices, compute_uv, driver); |
| 23168 | } |
| 23169 | at::Tensor linalg_pinv(const at::Tensor & self, const c10::optional<at::Tensor> & atol, const c10::optional<at::Tensor> & rtol, bool hermitian) { |
| 23170 | return wrapper_CompositeExplicitAutogradNonFunctional_atol_rtol_tensor_linalg_pinv(self, atol, rtol, hermitian); |
| 23171 | } |
| 23172 | ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _linalg_solve_ex(const at::Tensor & A, const at::Tensor & B, bool left, bool check_errors) { |
| 23173 | return wrapper_CompositeExplicitAutogradNonFunctional__linalg_solve_ex(A, B, left, check_errors); |
| 23174 | } |
| 23175 | ::std::tuple<at::Tensor,at::Tensor> linalg_qr(const at::Tensor & A, c10::string_view mode) { |
| 23176 | return wrapper_CompositeExplicitAutogradNonFunctional_linalg_qr(A, mode); |
| 23177 | } |
| 23178 | at::Tensor _test_autograd_multiple_dispatch_view_copy(const at::Tensor & self) { |
| 23179 | return wrapper_CompositeExplicitAutogradNonFunctional___test_autograd_multiple_dispatch_view_copy(self); |
| 23180 | } |
| 23181 | at::Tensor _fw_primal_copy(const at::Tensor & self, int64_t level) { |
| 23182 | return wrapper_CompositeExplicitAutogradNonFunctional___fw_primal_copy(self, level); |
| 23183 | } |
| 23184 | at::Tensor _make_dual_copy(const at::Tensor & primal, const at::Tensor & tangent, int64_t level) { |
| 23185 | return wrapper_CompositeExplicitAutogradNonFunctional___make_dual_copy(primal, tangent, level); |
| 23186 | } |
| 23187 | at::Tensor view_as_real_copy(const at::Tensor & self) { |
| 23188 | return wrapper_CompositeExplicitAutogradNonFunctional__view_as_real_copy(self); |
| 23189 | } |
| 23190 | at::Tensor view_as_complex_copy(const at::Tensor & self) { |
| 23191 | return wrapper_CompositeExplicitAutogradNonFunctional__view_as_complex_copy(self); |
| 23192 | } |
| 23193 | at::Tensor _conj_copy(const at::Tensor & self) { |
| 23194 | return wrapper_CompositeExplicitAutogradNonFunctional___conj_copy(self); |
| 23195 | } |
| 23196 | at::Tensor _neg_view_copy(const at::Tensor & self) { |
| 23197 | return wrapper_CompositeExplicitAutogradNonFunctional___neg_view_copy(self); |
| 23198 | } |
| 23199 | at::Tensor as_strided_copy(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, c10::optional<int64_t> storage_offset) { |
| 23200 | return wrapper_CompositeExplicitAutogradNonFunctional__as_strided_copy(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), storage_offset.has_value() ? c10::make_optional(c10::SymInt(*storage_offset)) : c10::nullopt); |
| 23201 | } |
| 23202 | at::Tensor as_strided_copy_symint(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional<c10::SymInt> storage_offset) { |
| 23203 | return wrapper_CompositeExplicitAutogradNonFunctional__as_strided_copy(self, size, stride, storage_offset); |
| 23204 | } |
| 23205 | at::Tensor _sparse_broadcast_to_copy(const at::Tensor & self, at::IntArrayRef size) { |
| 23206 | return wrapper_CompositeExplicitAutogradNonFunctional___sparse_broadcast_to_copy(self, size); |
| 23207 | } |
| 23208 | at::Tensor diagonal_copy(const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2) { |
| 23209 | return wrapper_CompositeExplicitAutogradNonFunctional__diagonal_copy(self, offset, dim1, dim2); |
| 23210 | } |
| 23211 | at::Tensor expand_copy(const at::Tensor & self, at::IntArrayRef size, bool implicit) { |
| 23212 | return wrapper_CompositeExplicitAutogradNonFunctional__expand_copy(self, c10::fromIntArrayRefSlow(size), implicit); |
| 23213 | } |
| 23214 | at::Tensor expand_copy_symint(const at::Tensor & self, c10::SymIntArrayRef size, bool implicit) { |
| 23215 | return wrapper_CompositeExplicitAutogradNonFunctional__expand_copy(self, size, implicit); |
| 23216 | } |
| 23217 | at::Tensor permute_copy(const at::Tensor & self, at::IntArrayRef dims) { |
| 23218 | return wrapper_CompositeExplicitAutogradNonFunctional__permute_copy(self, dims); |
| 23219 | } |
| 23220 | at::Tensor _reshape_alias_copy(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride) { |
| 23221 | return wrapper_CompositeExplicitAutogradNonFunctional___reshape_alias_copy(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride)); |
| 23222 | } |
| 23223 | at::Tensor _reshape_alias_copy_symint(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) { |
| 23224 | return wrapper_CompositeExplicitAutogradNonFunctional___reshape_alias_copy(self, size, stride); |
| 23225 | } |
| 23226 | at::Tensor select_copy(const at::Tensor & self, int64_t dim, int64_t index) { |
| 23227 | return wrapper_CompositeExplicitAutogradNonFunctional_int_select_copy(self, dim, index); |
| 23228 | } |
| 23229 | at::Tensor select_copy_symint(const at::Tensor & self, int64_t dim, c10::SymInt index) { |
| 23230 | return wrapper_CompositeExplicitAutogradNonFunctional_int_select_copy(self, dim, index); |
| 23231 | } |
| 23232 | at::Tensor detach_copy(const at::Tensor & self) { |
| 23233 | return wrapper_CompositeExplicitAutogradNonFunctional__detach_copy(self); |
| 23234 | } |
| 23235 | at::Tensor slice_copy(const at::Tensor & self, int64_t dim, c10::optional<int64_t> start, c10::optional<int64_t> end, int64_t step) { |
| 23236 | return wrapper_CompositeExplicitAutogradNonFunctional_Tensor_slice_copy(self, dim, start.has_value() ? c10::make_optional(c10::SymInt(*start)) : c10::nullopt, end.has_value() ? c10::make_optional(c10::SymInt(*end)) : c10::nullopt, step); |
| 23237 | } |
| 23238 | at::Tensor slice_copy_symint(const at::Tensor & self, int64_t dim, c10::optional<c10::SymInt> start, c10::optional<c10::SymInt> end, c10::SymInt step) { |
| 23239 | return wrapper_CompositeExplicitAutogradNonFunctional_Tensor_slice_copy(self, dim, start, end, step); |
| 23240 | } |
| 23241 | ::std::vector<at::Tensor> split_copy(const at::Tensor & self, int64_t split_size, int64_t dim) { |
| 23242 | return wrapper_CompositeExplicitAutogradNonFunctional_Tensor_split_copy(self, split_size, dim); |
| 23243 | } |
| 23244 | ::std::vector<at::Tensor> split_copy_symint(const at::Tensor & self, c10::SymInt split_size, int64_t dim) { |
| 23245 | return wrapper_CompositeExplicitAutogradNonFunctional_Tensor_split_copy(self, split_size, dim); |
| 23246 | } |
| 23247 | ::std::vector<at::Tensor> split_with_sizes_copy(const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim) { |
| 23248 | return wrapper_CompositeExplicitAutogradNonFunctional__split_with_sizes_copy(self, c10::fromIntArrayRefSlow(split_sizes), dim); |
| 23249 | } |
| 23250 | ::std::vector<at::Tensor> split_with_sizes_copy_symint(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim) { |
| 23251 | return wrapper_CompositeExplicitAutogradNonFunctional__split_with_sizes_copy(self, split_sizes, dim); |
| 23252 | } |
| 23253 | at::Tensor squeeze_copy(const at::Tensor & self) { |
| 23254 | return wrapper_CompositeExplicitAutogradNonFunctional__squeeze_copy(self); |
| 23255 | } |
| 23256 | at::Tensor squeeze_copy(const at::Tensor & self, int64_t dim) { |
| 23257 | return wrapper_CompositeExplicitAutogradNonFunctional_dim_squeeze_copy(self, dim); |
| 23258 | } |
| 23259 | at::Tensor squeeze_copy(const at::Tensor & self, at::IntArrayRef dim) { |
| 23260 | return wrapper_CompositeExplicitAutogradNonFunctional_dims_squeeze_copy(self, dim); |
| 23261 | } |
| 23262 | at::Tensor t_copy(const at::Tensor & self) { |
| 23263 | return wrapper_CompositeExplicitAutogradNonFunctional__t_copy(self); |
| 23264 | } |
| 23265 | at::Tensor transpose_copy(const at::Tensor & self, int64_t dim0, int64_t dim1) { |
| 23266 | return wrapper_CompositeExplicitAutogradNonFunctional_int_transpose_copy(self, dim0, dim1); |
| 23267 | } |
| 23268 | at::Tensor unsqueeze_copy(const at::Tensor & self, int64_t dim) { |
| 23269 | return wrapper_CompositeExplicitAutogradNonFunctional__unsqueeze_copy(self, dim); |
| 23270 | } |
| 23271 | at::Tensor _indices_copy(const at::Tensor & self) { |
| 23272 | return wrapper_CompositeExplicitAutogradNonFunctional___indices_copy(self); |
| 23273 | } |
| 23274 | at::Tensor _values_copy(const at::Tensor & self) { |
| 23275 | return wrapper_CompositeExplicitAutogradNonFunctional___values_copy(self); |
| 23276 | } |
| 23277 | at::Tensor indices_copy(const at::Tensor & self) { |
| 23278 | return wrapper_CompositeExplicitAutogradNonFunctional__indices_copy(self); |
| 23279 | } |
| 23280 | at::Tensor values_copy(const at::Tensor & self) { |
| 23281 | return wrapper_CompositeExplicitAutogradNonFunctional__values_copy(self); |
| 23282 | } |
| 23283 | at::Tensor crow_indices_copy(const at::Tensor & self) { |
| 23284 | return wrapper_CompositeExplicitAutogradNonFunctional__crow_indices_copy(self); |
| 23285 | } |
| 23286 | at::Tensor col_indices_copy(const at::Tensor & self) { |
| 23287 | return wrapper_CompositeExplicitAutogradNonFunctional__col_indices_copy(self); |
| 23288 | } |
| 23289 | at::Tensor ccol_indices_copy(const at::Tensor & self) { |
| 23290 | return wrapper_CompositeExplicitAutogradNonFunctional__ccol_indices_copy(self); |
| 23291 | } |
| 23292 | at::Tensor row_indices_copy(const at::Tensor & self) { |
| 23293 | return wrapper_CompositeExplicitAutogradNonFunctional__row_indices_copy(self); |
| 23294 | } |
| 23295 | ::std::vector<at::Tensor> unbind_copy(const at::Tensor & self, int64_t dim) { |
| 23296 | return wrapper_CompositeExplicitAutogradNonFunctional_int_unbind_copy(self, dim); |
| 23297 | } |
| 23298 | at::Tensor view_copy(const at::Tensor & self, at::IntArrayRef size) { |
| 23299 | return wrapper_CompositeExplicitAutogradNonFunctional__view_copy(self, c10::fromIntArrayRefSlow(size)); |
| 23300 | } |
| 23301 | at::Tensor view_copy_symint(const at::Tensor & self, c10::SymIntArrayRef size) { |
| 23302 | return wrapper_CompositeExplicitAutogradNonFunctional__view_copy(self, size); |
| 23303 | } |
| 23304 | at::Tensor view_copy(const at::Tensor & self, at::ScalarType dtype) { |
| 23305 | return wrapper_CompositeExplicitAutogradNonFunctional_dtype_view_copy(self, dtype); |
| 23306 | } |
| 23307 | at::Tensor unfold_copy(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step) { |
| 23308 | return wrapper_CompositeExplicitAutogradNonFunctional__unfold_copy(self, dimension, size, step); |
| 23309 | } |
| 23310 | at::Tensor alias_copy(const at::Tensor & self) { |
| 23311 | return wrapper_CompositeExplicitAutogradNonFunctional__alias_copy(self); |
| 23312 | } |
| 23313 | at::Tensor special_airy_ai(const at::Tensor & x) { |
| 23314 | return wrapper_CompositeExplicitAutogradNonFunctional_special_airy_ai(x); |
| 23315 | } |
| 23316 | at::Tensor special_bessel_j0(const at::Tensor & self) { |
| 23317 | return wrapper_CompositeExplicitAutogradNonFunctional_special_bessel_j0(self); |
| 23318 | } |
| 23319 | at::Tensor special_bessel_j1(const at::Tensor & self) { |
| 23320 | return wrapper_CompositeExplicitAutogradNonFunctional_special_bessel_j1(self); |
| 23321 | } |
| 23322 | at::Tensor special_bessel_y0(const at::Tensor & self) { |
| 23323 | return wrapper_CompositeExplicitAutogradNonFunctional_special_bessel_y0(self); |
| 23324 | } |
| 23325 | at::Tensor special_bessel_y1(const at::Tensor & self) { |
| 23326 | return wrapper_CompositeExplicitAutogradNonFunctional_special_bessel_y1(self); |
| 23327 | } |
| 23328 | at::Tensor special_chebyshev_polynomial_t(const at::Tensor & x, const at::Tensor & n) { |
| 23329 | return wrapper_CompositeExplicitAutogradNonFunctional_special_chebyshev_polynomial_t(x, n); |
| 23330 | } |
| 23331 | at::Tensor special_chebyshev_polynomial_u(const at::Tensor & x, const at::Tensor & n) { |
| 23332 | return wrapper_CompositeExplicitAutogradNonFunctional_special_chebyshev_polynomial_u(x, n); |
| 23333 | } |
| 23334 | at::Tensor special_chebyshev_polynomial_v(const at::Tensor & x, const at::Tensor & n) { |
| 23335 | return wrapper_CompositeExplicitAutogradNonFunctional_special_chebyshev_polynomial_v(x, n); |
| 23336 | } |
| 23337 | at::Tensor special_chebyshev_polynomial_w(const at::Tensor & x, const at::Tensor & n) { |
| 23338 | return wrapper_CompositeExplicitAutogradNonFunctional_special_chebyshev_polynomial_w(x, n); |
| 23339 | } |
| 23340 | at::Tensor special_hermite_polynomial_h(const at::Tensor & x, const at::Tensor & n) { |
| 23341 | return wrapper_CompositeExplicitAutogradNonFunctional_special_hermite_polynomial_h(x, n); |
| 23342 | } |
| 23343 | at::Tensor special_hermite_polynomial_he(const at::Tensor & x, const at::Tensor & n) { |
| 23344 | return wrapper_CompositeExplicitAutogradNonFunctional_special_hermite_polynomial_he(x, n); |
| 23345 | } |
| 23346 | at::Tensor special_laguerre_polynomial_l(const at::Tensor & x, const at::Tensor & n) { |
| 23347 | return wrapper_CompositeExplicitAutogradNonFunctional_special_laguerre_polynomial_l(x, n); |
| 23348 | } |
| 23349 | at::Tensor special_legendre_polynomial_p(const at::Tensor & x, const at::Tensor & n) { |
| 23350 | return wrapper_CompositeExplicitAutogradNonFunctional_special_legendre_polynomial_p(x, n); |
| 23351 | } |
| 23352 | at::Tensor special_modified_bessel_i0(const at::Tensor & self) { |
| 23353 | return wrapper_CompositeExplicitAutogradNonFunctional_special_modified_bessel_i0(self); |
| 23354 | } |
| 23355 | at::Tensor special_modified_bessel_i1(const at::Tensor & self) { |
| 23356 | return wrapper_CompositeExplicitAutogradNonFunctional_special_modified_bessel_i1(self); |
| 23357 | } |
| 23358 | at::Tensor special_modified_bessel_k0(const at::Tensor & self) { |
| 23359 | return wrapper_CompositeExplicitAutogradNonFunctional_special_modified_bessel_k0(self); |
| 23360 | } |
| 23361 | at::Tensor special_modified_bessel_k1(const at::Tensor & self) { |
| 23362 | return wrapper_CompositeExplicitAutogradNonFunctional_special_modified_bessel_k1(self); |
| 23363 | } |
| 23364 | at::Tensor special_scaled_modified_bessel_k0(const at::Tensor & x) { |
| 23365 | return wrapper_CompositeExplicitAutogradNonFunctional_special_scaled_modified_bessel_k0(x); |
| 23366 | } |
| 23367 | at::Tensor special_scaled_modified_bessel_k1(const at::Tensor & x) { |
| 23368 | return wrapper_CompositeExplicitAutogradNonFunctional_special_scaled_modified_bessel_k1(x); |
| 23369 | } |
| 23370 | at::Tensor special_shifted_chebyshev_polynomial_t(const at::Tensor & x, const at::Tensor & n) { |
| 23371 | return wrapper_CompositeExplicitAutogradNonFunctional_special_shifted_chebyshev_polynomial_t(x, n); |
| 23372 | } |
| 23373 | at::Tensor special_shifted_chebyshev_polynomial_u(const at::Tensor & x, const at::Tensor & n) { |
| 23374 | return wrapper_CompositeExplicitAutogradNonFunctional_special_shifted_chebyshev_polynomial_u(x, n); |
| 23375 | } |
| 23376 | at::Tensor special_shifted_chebyshev_polynomial_v(const at::Tensor & x, const at::Tensor & n) { |
| 23377 | return wrapper_CompositeExplicitAutogradNonFunctional_special_shifted_chebyshev_polynomial_v(x, n); |
| 23378 | } |
| 23379 | at::Tensor special_shifted_chebyshev_polynomial_w(const at::Tensor & x, const at::Tensor & n) { |
| 23380 | return wrapper_CompositeExplicitAutogradNonFunctional_special_shifted_chebyshev_polynomial_w(x, n); |
| 23381 | } |
| 23382 | at::Tensor special_spherical_bessel_j0(const at::Tensor & x) { |
| 23383 | return wrapper_CompositeExplicitAutogradNonFunctional_special_spherical_bessel_j0(x); |
| 23384 | } |
| 23385 | } // namespace compositeexplicitautogradnonfunctional |
| 23386 | } // namespace at |
| 23387 |
Generated on 2023-Feb-12 from project pytorch revision 2c76838d7ff
Powered by 
Code Browser 2.1
Generator usage only permitted with license.